Add vLLM inference provider for OpenAI compatible vLLM server

llama_stack/distribution/templates/build_configs/local-vllm-build.yaml

@@ -7,4 +7,4 @@ distribution_spec:
     safety: meta-reference
     agents: meta-reference
     telemetry: meta-reference
-image_type: conda
+image_type: conda

llama_stack/distribution/templates/remote-vllm-build.yaml

@@ -0,0 +1,10 @@
+name: remote-vllm
+distribution_spec:
+  description: Use remote vLLM for running LLM inference
+  providers:
+    inference: remote::vllm
+    memory: meta-reference
+    safety: meta-reference
+    agents: meta-reference
+    telemetry: meta-reference
+image_type: docker

llama_stack/providers/adapters/inference/vllm/__init__.py

@@ -0,0 +1,15 @@
+# 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 VLLMImplConfig
+from .vllm import VLLMInferenceAdapter
+
+
+async def get_adapter_impl(config: VLLMImplConfig, _deps):
+    assert isinstance(config, VLLMImplConfig), f"Unexpected config type: {type(config)}"
+    impl = VLLMInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/adapters/inference/vllm/config.py

@@ -0,0 +1,22 @@
+# 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 Optional
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class VLLMImplConfig(BaseModel):
+    url: Optional[str] = Field(
+        default=None,
+        description="The URL for the vLLM model serving endpoint",
+    )
+    api_token: Optional[str] = Field(
+        default=None,
+        description="The API token",
+    )

llama_stack/providers/adapters/inference/vllm/vllm.py

@@ -0,0 +1,152 @@
+# 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.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.datatypes import ModelsProtocolPrivate
+
+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 VLLMImplConfig
+
+VLLM_SUPPORTED_MODELS = {
+    "Llama3.1-8B": "meta-llama/Llama-3.1-8B",
+    "Llama3.1-70B": "meta-llama/Llama-3.1-70B",
+    "Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
+    "Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
+    "Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
+    "Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
+    "Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
+    "Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
+    "Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
+    "Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
+    "Llama3.2-1B": "meta-llama/Llama-3.2-1B",
+    "Llama3.2-3B": "meta-llama/Llama-3.2-3B",
+    "Llama3.2-11B-Vision": "meta-llama/Llama-3.2-11B-Vision",
+    "Llama3.2-90B-Vision": "meta-llama/Llama-3.2-90B-Vision",
+    "Llama3.2-1B-Instruct": "meta-llama/Llama-3.2-1B-Instruct",
+    "Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct",
+    "Llama3.2-11B-Vision-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct",
+    "Llama3.2-90B-Vision-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct",
+    "Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision",
+    "Llama-Guard-3-1B:int4-mp1": "meta-llama/Llama-Guard-3-1B-INT4",
+    "Llama-Guard-3-1B": "meta-llama/Llama-Guard-3-1B",
+    "Llama-Guard-3-8B": "meta-llama/Llama-Guard-3-8B",
+    "Llama-Guard-3-8B:int8-mp1": "meta-llama/Llama-Guard-3-8B-INT8",
+    "Prompt-Guard-86M": "meta-llama/Prompt-Guard-86M",
+    "Llama-Guard-2-8B": "meta-llama/Llama-Guard-2-8B",
+}
+
+
+class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
+    def __init__(self, config: VLLMImplConfig) -> None:
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+        self.client = None
+
+    async def initialize(self) -> None:
+        self.client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
+
+    async def register_model(self, model: ModelDef) -> None:
+        raise ValueError("Model registration is not supported for vLLM models")
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def list_models(self) -> List[ModelDef]:
+        return [
+            ModelDef(identifier=model.id, llama_model=model.id)
+            for model in self.client.models.list()
+        ]
+
+    def completion(
+        self,
+        model: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
+        raise NotImplementedError()
+
+    def chat_completion(
+        self,
+        model: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        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,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_chat_completion(request, self.client)
+        else:
+            return self._nonstream_chat_completion(request, self.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(request, r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest, client: OpenAI
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        # TODO: Can we use client.completions.acreate() or maybe there is another way to directly create an async
+        #  generator so this wrapper is not necessary?
+        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(
+            request, stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        return {
+            "model": VLLM_SUPPORTED_MODELS[request.model],
+            "prompt": chat_completion_request_to_prompt(request, self.formatter),
+            "stream": request.stream,
+            **get_sampling_options(request),
+        }
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/registry/inference.py

@@ -60,6 +60,15 @@ def available_providers() -> List[ProviderSpec]:
                 module="llama_stack.providers.adapters.inference.ollama",
             ),
         ),
+#        remote_provider_spec(
+#            api=Api.inference,
+#            adapter=AdapterSpec(
+#                adapter_type="vllm",
+#                pip_packages=["openai"],
+#                module="llama_stack.providers.adapters.inference.vllm",
+#                config_class="llama_stack.providers.adapters.inference.vllm.VLLMImplConfig",
+#            ),
+#        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(