forked from phoenix-oss/llama-stack-mirror
fdff24e77a
This PR changes the way model id gets translated to the final model name that gets passed through the provider. Major changes include: 1) Providers are responsible for registering an object and as part of the registration returning the object with the correct provider specific name of the model provider_resource_id 2) To help with the common look ups different names a new ModelLookup class is created. Tested all inference providers including together, fireworks, vllm, ollama, meta reference and bedrock
222 lines
8 KiB
Python
222 lines
8 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 AsyncGenerator, Optional
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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 llama_models.sku_list import resolve_model
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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 as VLLMSamplingParams
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
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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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class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
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"""Inference implementation for vLLM."""
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def __init__(self, config: VLLMConfig):
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self.config = config
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self.engine = None
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self.formatter = ChatFormat(Tokenizer.get_instance())
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async def initialize(self):
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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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model = resolve_model(self.config.model)
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if model is None:
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raise ValueError(f"Unknown model {self.config.model}")
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if model.huggingface_repo is None:
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raise ValueError(f"Model {self.config.model} needs a huggingface repo")
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# TODO -- there are a ton of options supported here ...
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engine_args = AsyncEngineArgs(
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model=model.huggingface_repo,
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tokenizer=model.huggingface_repo,
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tensor_parallel_size=self.config.tensor_parallel_size,
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enforce_eager=self.config.enforce_eager,
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gpu_memory_utilization=self.config.gpu_memory_utilization,
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guided_decoding_backend="lm-format-enforcer",
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)
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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 register_model(self, model: Model) -> None:
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raise ValueError(
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"You cannot dynamically add a model to a running vllm instance"
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)
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def _sampling_params(self, sampling_params: SamplingParams) -> VLLMSamplingParams:
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if sampling_params is None:
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return VLLMSamplingParams(max_tokens=self.config.max_tokens)
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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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"max_tokens": self.config.max_tokens,
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}
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if sampling_params.top_k:
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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:
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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 VLLMSamplingParams(**kwargs)
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async def completion(
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self,
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model_id: str,
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content: InterleavedTextMedia,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = 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_id,
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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_id: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = 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_id,
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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 = self._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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cur = []
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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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output = chunk.outputs[-1]
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new_tokens = output.token_ids[len(cur) :]
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text = self.formatter.tokenizer.decode(new_tokens)
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cur.extend(new_tokens)
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choice = OpenAICompatCompletionChoice(
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finish_reason=output.finish_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_id: 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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