forked from phoenix-oss/llama-stack-mirror
2089427d60
PR #201 had made several changes while trying to fix issues with getting the stream=False branches of inference and agents API working. As part of this, it made a change which was slightly gratuitous. Namely, making chat_completion() and brethren "def" instead of "async def". The rationale was that this allowed the user (within llama-stack) of this to use it as: ``` async for chunk in api.chat_completion(params) ``` However, it causes unnecessary confusion for several folks. Given that clients (e.g., llama-stack-apps) anyway use the SDK methods (which are completely isolated) this choice was not ideal. Let's revert back so the call now looks like: ``` async for chunk in await api.chat_completion(params) ``` Bonus: Added a completion() implementation for the meta-reference provider. Technically should have been another PR :)
241 lines
9 KiB
Python
241 lines
9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import logging
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import os
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import uuid
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from typing import Any
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_models.llama3.api.tokenizer import Tokenizer
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.sampling_params import SamplingParams
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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OpenAICompatCompletionChoice,
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OpenAICompatCompletionResponse,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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)
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from .config import VLLMConfig
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log = logging.getLogger(__name__)
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def _random_uuid() -> str:
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return str(uuid.uuid4().hex)
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def _vllm_sampling_params(sampling_params: Any) -> SamplingParams:
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"""Convert sampling params to vLLM sampling params."""
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if sampling_params is None:
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return SamplingParams()
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# TODO convert what I saw in my first test ... but surely there's more to do here
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kwargs = {
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"temperature": sampling_params.temperature,
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}
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if sampling_params.top_k >= 1:
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kwargs["top_k"] = sampling_params.top_k
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if sampling_params.top_p:
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kwargs["top_p"] = sampling_params.top_p
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if sampling_params.max_tokens >= 1:
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kwargs["max_tokens"] = sampling_params.max_tokens
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if sampling_params.repetition_penalty > 0:
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kwargs["repetition_penalty"] = sampling_params.repetition_penalty
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return SamplingParams(**kwargs)
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class VLLMInferenceImpl(ModelRegistryHelper, Inference):
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"""Inference implementation for vLLM."""
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HF_MODEL_MAPPINGS = {
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# TODO: seems like we should be able to build this table dynamically ...
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"Llama3.1-8B": "meta-llama/Llama-3.1-8B",
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"Llama3.1-70B": "meta-llama/Llama-3.1-70B",
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"Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
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"Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
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"Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
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"Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
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"Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
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"Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
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"Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
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"Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
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"Llama3.2-1B": "meta-llama/Llama-3.2-1B",
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"Llama3.2-3B": "meta-llama/Llama-3.2-3B",
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"Llama3.2-11B-Vision": "meta-llama/Llama-3.2-11B-Vision",
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"Llama3.2-90B-Vision": "meta-llama/Llama-3.2-90B-Vision",
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"Llama3.2-1B-Instruct": "meta-llama/Llama-3.2-1B-Instruct",
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"Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct",
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"Llama3.2-11B-Vision-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct",
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"Llama3.2-90B-Vision-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct",
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"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision",
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"Llama-Guard-3-1B:int4-mp1": "meta-llama/Llama-Guard-3-1B-INT4",
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"Llama-Guard-3-1B": "meta-llama/Llama-Guard-3-1B",
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"Llama-Guard-3-8B": "meta-llama/Llama-Guard-3-8B",
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"Llama-Guard-3-8B:int8-mp1": "meta-llama/Llama-Guard-3-8B-INT8",
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"Prompt-Guard-86M": "meta-llama/Prompt-Guard-86M",
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"Llama-Guard-2-8B": "meta-llama/Llama-Guard-2-8B",
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}
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def __init__(self, config: VLLMConfig):
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Inference.__init__(self)
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ModelRegistryHelper.__init__(
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self,
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stack_to_provider_models_map=self.HF_MODEL_MAPPINGS,
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)
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self.config = config
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self.engine = None
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tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(tokenizer)
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async def initialize(self):
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"""Initialize the vLLM inference adapter."""
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log.info("Initializing vLLM inference adapter")
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# Disable usage stats reporting. This would be a surprising thing for most
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# people to find out was on by default.
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# https://docs.vllm.ai/en/latest/serving/usage_stats.html
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if "VLLM_NO_USAGE_STATS" not in os.environ:
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os.environ["VLLM_NO_USAGE_STATS"] = "1"
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hf_model = self.HF_MODEL_MAPPINGS.get(self.config.model)
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# TODO -- there are a ton of options supported here ...
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engine_args = AsyncEngineArgs()
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engine_args.model = hf_model
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# We will need a new config item for this in the future if model support is more broad
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# than it is today (llama only)
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engine_args.tokenizer = hf_model
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engine_args.tensor_parallel_size = self.config.tensor_parallel_size
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self.engine = AsyncLLMEngine.from_engine_args(engine_args)
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async def shutdown(self):
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"""Shutdown the vLLM inference adapter."""
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log.info("Shutting down vLLM inference adapter")
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if self.engine:
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self.engine.shutdown_background_loop()
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async def completion(
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self,
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model: str,
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content: InterleavedTextMedia,
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sampling_params: Any | None = ...,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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) -> CompletionResponse | CompletionResponseStreamChunk:
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log.info("vLLM completion")
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messages = [UserMessage(content=content)]
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return self.chat_completion(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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stream=stream,
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logprobs=logprobs,
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)
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async def chat_completion(
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self,
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model: str,
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messages: list[Message],
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sampling_params: Any | None = ...,
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tools: list[ToolDefinition] | None = ...,
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tool_choice: ToolChoice | None = ...,
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tool_prompt_format: ToolPromptFormat | None = ...,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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) -> ChatCompletionResponse | ChatCompletionResponseStreamChunk:
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log.info("vLLM chat completion")
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assert self.engine is not None
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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log.info("Sampling params: %s", sampling_params)
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request_id = _random_uuid()
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prompt = chat_completion_request_to_prompt(request, self.formatter)
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vllm_sampling_params = _vllm_sampling_params(request.sampling_params)
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results_generator = self.engine.generate(
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prompt, vllm_sampling_params, request_id
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)
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if stream:
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return self._stream_chat_completion(request, results_generator)
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else:
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return await self._nonstream_chat_completion(request, results_generator)
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest, results_generator: AsyncGenerator
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) -> ChatCompletionResponse:
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outputs = [o async for o in results_generator]
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final_output = outputs[-1]
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assert final_output is not None
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outputs = final_output.outputs
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finish_reason = outputs[-1].stop_reason
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choice = OpenAICompatCompletionChoice(
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finish_reason=finish_reason,
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text="".join([output.text for output in outputs]),
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)
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response = OpenAICompatCompletionResponse(
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choices=[choice],
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)
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return process_chat_completion_response(response, self.formatter)
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest, results_generator: AsyncGenerator
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) -> AsyncGenerator:
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async def _generate_and_convert_to_openai_compat():
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async for chunk in results_generator:
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if not chunk.outputs:
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log.warning("Empty chunk received")
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continue
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text = "".join([output.text for output in chunk.outputs])
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choice = OpenAICompatCompletionChoice(
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finish_reason=chunk.outputs[-1].stop_reason,
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text=text,
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_chat_completion_stream_response(
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stream, self.formatter
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):
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yield chunk
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async def embeddings(
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self, model: str, contents: list[InterleavedTextMedia]
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) -> EmbeddingsResponse:
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log.info("vLLM embeddings")
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# TODO
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raise NotImplementedError()
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