Add Sambanova Inference

distributions/dependencies.json

@@ -311,5 +311,11 @@
     "uvicorn",
     "sentence-transformers --no-deps",
     "torch --index-url https://download.pytorch.org/whl/cpu"
+  ],
+  "sambanova": [
+    "aiohttp",
+    "aiosqlite",
+    "fastapi",
+    "openai"
   ]
 }

distributions/ssambanova/build.yaml

@@ -0,0 +1 @@
+../../llama_stack/templates/fireworks/build.yaml

distributions/ssambanova/compose.yaml

@@ -0,0 +1,16 @@
+services:
+  llamastack:
+    image: llamastack/distribution-sambanova
+    network_mode: "host"
+    volumes:
+      - ~/.llama:/root/.llama
+      - ./run.yaml:/root/llamastack-run-sambanova.yaml
+    ports:
+      - "5000:5000"
+    entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-sambanova.yaml"
+    deploy:
+      restart_policy:
+        condition: on-failure
+        delay: 3s
+        max_attempts: 5
+        window: 60s

distributions/ssambanova/run.yaml

@@ -0,0 +1,71 @@
+version: "2"
+image_name: sambanova
+docker_image: null
+conda_env: sambanova
+apis:
+  - inference
+  - safety
+  - agents
+  - memory
+  - datasetio
+  - scoring
+  - eval
+  - telemetry
+providers:
+  inference:
+    - provider_id: sambanova
+      provider_type: remote::sambanova
+      config:
+        url: https://api.sambanova.ai/v1
+        api_token: ${env.SAMBANOVA_API_KEY}
+  safety:
+    - provider_id: llama-guard
+      provider_type: inline::llama-guard
+      config: {}
+  agents:
+    - provider_id: meta-reference
+      provider_type: inline::meta-reference
+      config:
+        persistence_store:
+          type: sqlite
+          namespace: null
+          db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/agents_store.db
+  memory:
+    - provider_id: faiss
+      provider_type: inline::faiss
+      config:
+        kvstore:
+          type: sqlite
+          namespace: null
+          db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/faiss_store.db
+  datasetio:
+    - provider_id: localfs
+      provider_type: inline::localfs
+      config: {}
+  scoring:
+    - provider_id: basic
+      provider_type: inline::basic
+      config: {}
+  eval:
+    - provider_id: meta-reference
+      provider_type: inline::meta-reference
+      config: {}
+  telemetry:
+    - provider_id: meta-reference
+      provider_type: inline::meta-reference
+      config: {}
+metadata_store: null
+models:
+  - metadata: {}
+    model_id: meta-llama/Llama-3.1-8B-Instruct
+    provider_id: null
+    provider_model_id: Meta-Llama-3.1-8B-Instruct
+shields:
+  - params: null
+    shield_id: meta-llama/Llama-Guard-3-8B
+    provider_id: null
+    provider_shield_id: null
+memory_banks: []
+datasets: []
+scoring_fns: []
+eval_tasks: []

llama_stack/providers/registry/inference.py

@@ -150,4 +150,15 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="sambanova",
+                pip_packages=[
+                    "openai",
+                ],
+                module="llama_stack.providers.remote.inference.sambanova",
+                config_class="llama_stack.providers.remote.inference.sambanova.SambanovaImplConfig",
+            ),
+        ),
     ]

llama_stack/providers/remote/inference/sambanova/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .config import SambanovaImplConfig
+from .sambanova import SambanovaInferenceAdapter
+
+
+async def get_adapter_impl(config: SambanovaImplConfig, _deps):
+    assert isinstance(
+        config, SambanovaImplConfig
+    ), f"Unexpected config type: {type(config)}"
+    impl = SambanovaInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/inference/sambanova/config.py

@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class SambanovaImplConfig(BaseModel):
+    url: str = Field(
+        default="https://api.sambanova.ai/v1",
+        description="The URL for the Sambanova model serving endpoint",
+    )
+    api_token: str = Field(
+        default=None,
+        description="The Sambanova API token",
+    )

llama_stack/providers/remote/inference/sambanova/sambanova.py

@@ -0,0 +1,153 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator
+
+from llama_models.datatypes import CoreModelId
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from openai import OpenAI
+
+from llama_stack.apis.inference import *  # noqa: F403
+
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+)
+
+from .config import SambanovaImplConfig
+
+
+model_aliases = [
+    build_model_alias(
+        "Meta-Llama-3.1-8B-Instruct",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-70B-Instruct",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.1-405B-Instruct",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-1B-Instruct",
+        CoreModelId.llama3_2_1b_instruct.value,
+    ),
+    build_model_alias(
+        "Meta-Llama-3.2-3B-Instruct",
+        CoreModelId.llama3_2_3b_instruct.value,
+    ),
+]
+
+
+class SambanovaInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: SambanovaImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self,
+            model_aliases=model_aliases,
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        raise NotImplementedError()
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        request = ChatCompletionRequest(
+            model=model_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
+        if stream:
+            return self._stream_chat_completion(request, client)
+        else:
+            return await self._nonstream_chat_completion(request, client)
+
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = client.completions.create(**params)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        async def _to_async_generator():
+            s = client.completions.create(**params)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        return {
+            "model": request.model,
+            "prompt": chat_completion_request_to_prompt(
+                request, self.get_llama_model(request.model), self.formatter
+            ),
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/utils/inference/openai_compat.py

@@ -47,7 +47,10 @@ def text_from_choice(choice) -> str:
         return choice.delta.content
 
     if hasattr(choice, "message"):
-        return choice.message.content
+        try:
+            return choice.message.content
+        except:
+            return choice.text
 
     return choice.text
 

llama_stack/templates/sambanova/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .sambanova import get_distribution_template  # noqa: F401

llama_stack/templates/sambanova/build.yaml

@@ -0,0 +1,17 @@
+version: "2"
+name: sambanova
+distribution_spec:
+  description: Use Sambanova for running LLM inference
+  docker_image: null
+  providers:
+    inference:
+      - remote::sambanova
+    memory:
+      - inline::faiss
+    safety:
+      - inline::llama-guard
+    agents:
+      - inline::meta-reference
+    telemetry:
+      - inline::meta-reference
+image_type: conda