Add Allegro API server implementation with Prometheus metrics support

api/allegro_serve.py

@@ -0,0 +1,204 @@
+import os
+import sys
+import time
+import tempfile
+from typing import Dict, Any, List, Union, Optional
+from pathlib import Path
+from pydantic import BaseModel, Field
+from litserve import LitAPI, LitServer, Logger
+from loguru import logger
+from prometheus_client import (
+    CollectorRegistry,
+    Histogram,
+    make_asgi_app,
+    multiprocess
+)
+
+from configs.allegro_settings import AllegroSettings
+from scripts.allegro_diffusers import AllegroInference
+from scripts.mp4_to_s3_json import mp4_to_s3_json
+import torch
+
+# Set up prometheus multiprocess mode
+os.environ["PROMETHEUS_MULTIPROC_DIR"] = "/tmp/prometheus_multiproc_dir"
+if not os.path.exists("/tmp/prometheus_multiproc_dir"):
+    os.makedirs("/tmp/prometheus_multiproc_dir")
+
+# Initialize prometheus registry
+registry = CollectorRegistry()
+multiprocess.MultiProcessCollector(registry)
+
+class PrometheusLogger(Logger):
+    """Custom logger for Prometheus metrics.
+
+    Implements metric collection for request processing times
+    using Prometheus Histograms.
+    Metrics are stored in a multi-process compatible registry.
+
+    Attributes:
+        function_duration (Histogram): Prometheus histogram for tracking processing times
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.function_duration = Histogram(
+            "allegro_request_processing_seconds",
+            "Time spent processing Allegro request",
+            ["function_name"],
+            registry=registry
+        )
+
+    def process(self, key: str, value: float) -> None:
+        """Process and record a metric value.
+
+        Args:
+            key (str): The name of the function or operation being measured
+            value (float): The duration or metric value to record
+        """
+        self.function_duration.labels(function_name=key).observe(value)
+
+class AllegroRequest(BaseModel):
+    """Model representing a request for the Allegro model.
+
+    Validates input parameters for Allegro model inference.
+
+    Attributes:
+        prompt (str): Text prompt for inference
+        negative_prompt (Optional[str]): Text prompt for elements to avoid
+        num_inference_steps (int): Number of denoising steps (1-100)
+        guidance_scale (float): Controls adherence to prompt (1.0-20.0)
+        height (int): Image height (256-720, multiple of 32)
+        width (int): Image width (256-1280, multiple of 32)
+        seed (Optional[int]): Random seed for reproducibility
+    """
+    prompt: str = Field(..., description="Main text prompt for generation")
+    negative_prompt: Optional[str] = Field(
+        "worst quality, blurry, distorted",
+        description="Text description of what to avoid"
+    )
+    num_inference_steps: int = Field(50, ge=1, le=100, description="Number of inference steps")
+    guidance_scale: float = Field(7.5, ge=1.0, le=20.0, description="Guidance scale")
+    height: int = Field(512, ge=256, le=720, multiple_of=32, description="Image height")
+    width: int = Field(512, ge=256, le=1280, multiple_of=32, description="Image width")
+    seed: Optional[int] = Field(None, description="Random seed for reproducibility")
+
+class AllegroAPI(LitAPI):
+    """API implementation for Allegro model inference using LitServe.
+
+    Attributes:
+        settings (AllegroSettings): Configuration for Allegro model
+        engine (AllegroInference): Inference engine for Allegro
+    """
+
+    def setup(self, device: str) -> None:
+        """Initialize the Allegro inference engine.
+
+        Args:
+            device (str): Target device for inference ('cuda', 'cpu', etc.)
+        """
+        try:
+            logger.info(f"Initializing Allegro model on device: {device}")
+            self.settings = AllegroSettings(device=device)
+            self.engine = AllegroInference(self.settings)
+            logger.info("Allegro setup completed successfully")
+        except Exception as e:
+            logger.error(f"Error during Allegro setup: {e}")
+            raise
+
+    def decode_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
+        """Decode and validate the incoming request.
+
+        Args:
+            request (dict): Input request dictionary
+
+        Returns:
+            dict: Validated request
+        """
+        try:
+            return AllegroRequest(**request).dict()
+        except Exception as e:
+            logger.error(f"Request validation error: {e}")
+            raise
+
+    def predict(self, inputs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Perform inference using the Allegro model.
+
+        Args:
+            inputs (list): List of validated requests
+
+        Returns:
+            list: Results with URLs and metadata
+        """
+        results = []
+        for request in inputs:
+            start_time = time.time()
+            try:
+                self.settings.update(request)
+                with tempfile.TemporaryDirectory() as temp_dir:
+                    output_path = Path(temp_dir) / "output.mp4"
+                    self.settings.output_path = output_path
+                    self.engine.generate()
+
+                    if not output_path.exists():
+                        raise FileNotFoundError(f"Output not found at {output_path}")
+
+                    with open(output_path, 'rb') as video_file:
+                        s3_response = mp4_to_s3_json(video_file, output_path.name)
+
+                    generation_time = time.time() - start_time
+
+                    results.append({
+                        "status": "success",
+                        "video_url": s3_response["url"],
+                        "prompt": request["prompt"],
+                        "time_taken": generation_time
+                    })
+
+            except Exception as e:
+                logger.error(f"Error during prediction: {e}")
+                results.append({"status": "error", "error": str(e)})
+        return results
+
+    def encode_response(self, output: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Encode the results into a response format.
+
+        Args:
+            output (list): Results list
+
+        Returns:
+            dict: Encoded response
+        """
+        return {"results": output}
+
+def main():
+    prometheus_logger = PrometheusLogger()
+    prometheus_logger.mount(
+        path="/api/v1/metrics",
+        app=make_asgi_app(registry=registry)
+    )
+
+    logger.remove()
+    logger.add(sys.stdout, format="<green>{time}</green> | <level>{message}</level>", level="INFO")
+    logger.add("logs/error.log", format="<red>{time}</red> | <level>{message}</level>", level="ERROR")
+
+    try:
+        api = AllegroAPI()
+        server = LitServer(
+            api,
+            api_path='/api/v1/allegro',
+            accelerator="auto",
+            devices="auto",
+            max_batch_size=4,
+            loggers=prometheus_logger,
+        )
+
+        logger.info("Starting Allegro API server on port 8000")
+        server.run(port=8000)
+    except Exception as e:
+        logger.error(f"Failed to start server: {e}")
+        sys.exit(1)
+
+
+
+if __name__ == "__main__":
+    main()

scripts/allegro_diffusers.py

@@ -95,7 +95,7 @@ def generate_video(self, prompt: str, positive_prompt: str, negative_prompt: str
             logger.error(f"Error during video generation: {e}")
             raise
 
-# Example usage (to be executed in a main script or testing environment)
+
 if __name__ == "__main__":
     settings = AllegroSettings()
     inference = AllegroInference(settings)