289 lines
12 KiB
Python
289 lines
12 KiB
Python
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import time
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import codecs
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from fastapi import Request
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from typing import AsyncGenerator, AsyncIterator, Union
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from vllm.logger import init_logger
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from vllm.utils import random_uuid
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
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UsageInfo)
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import SamplingParams
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
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logger = init_logger(__name__)
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class OpenAIServingChat(OpenAIServing):
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def __init__(self,
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engine: AsyncLLMEngine,
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served_model: str,
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response_role: str,
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chat_template=None):
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super().__init__(engine=engine, served_model=served_model)
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self.response_role = response_role
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self._load_chat_template(chat_template)
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async def create_chat_completion(
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self, request: ChatCompletionRequest, raw_request: Request
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) -> Union[ErrorResponse, AsyncGenerator[str, None],
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ChatCompletionResponse]:
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"""Completion API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/chat/create
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for the API specification. This API mimics the OpenAI ChatCompletion API.
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NOTE: Currently we do not support the following features:
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- function_call (Users should implement this by themselves)
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- logit_bias (to be supported by vLLM engine)
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"""
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error_check_ret = await self._check_model(request)
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if error_check_ret is not None:
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return error_check_ret
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if request.logit_bias is not None and len(request.logit_bias) > 0:
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# TODO: support logit_bias in vLLM engine.
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return self.create_error_response(
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"logit_bias is not currently supported")
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try:
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prompt = self.tokenizer.apply_chat_template(
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conversation=request.messages,
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tokenize=False,
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add_generation_prompt=request.add_generation_prompt)
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except Exception as e:
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logger.error(
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f"Error in applying chat template from request: {str(e)}")
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return self.create_error_response(str(e))
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token_ids, error_check_ret = await self._check_length(request,
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prompt=prompt)
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if error_check_ret is not None:
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return error_check_ret
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request_id = f"cmpl-{random_uuid()}"
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try:
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spaces_between_special_tokens = request.spaces_between_special_tokens
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sampling_params = SamplingParams(
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n=request.n,
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presence_penalty=request.presence_penalty,
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frequency_penalty=request.frequency_penalty,
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repetition_penalty=request.repetition_penalty,
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temperature=request.temperature,
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top_p=request.top_p,
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min_p=request.min_p,
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stop=request.stop,
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stop_token_ids=request.stop_token_ids,
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max_tokens=request.max_tokens,
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best_of=request.best_of,
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top_k=request.top_k,
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ignore_eos=request.ignore_eos,
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use_beam_search=request.use_beam_search,
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skip_special_tokens=request.skip_special_tokens,
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spaces_between_special_tokens=spaces_between_special_tokens,
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)
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except ValueError as e:
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return self.create_error_response(str(e))
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result_generator = self.engine.generate(prompt, sampling_params,
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request_id, token_ids)
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# Streaming response
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if request.stream:
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return self.chat_completion_stream_generator(
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request, result_generator, request_id)
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else:
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return await self.chat_completion_full_generator(
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request, raw_request, result_generator, request_id)
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def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
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if request.add_generation_prompt:
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return self.response_role
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else:
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return request.messages[-1].role
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async def chat_completion_stream_generator(
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self, request: ChatCompletionRequest,
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result_generator: AsyncIterator[RequestOutput], request_id: str
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) -> Union[ErrorResponse, AsyncGenerator[str, None]]:
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model_name = request.model
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created_time = int(time.monotonic())
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chunk_object_type = "chat.completion.chunk"
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# Send first response for each request.n (index) with the role
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role = self.get_chat_request_role(request)
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for i in range(request.n):
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choice_data = ChatCompletionResponseStreamChoice(
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index=i, delta=DeltaMessage(role=role), finish_reason=None)
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chunk = ChatCompletionStreamResponse(id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
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data = chunk.json(exclude_unset=True, ensure_ascii=False)
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yield f"data: {data}\n\n"
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# Send response to echo the input portion of the last message
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if request.echo:
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last_msg_content = ""
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if request.messages and isinstance(
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request.messages, list) and request.messages[-1].get(
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"content") and request.messages[-1].get(
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"role") == role:
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last_msg_content = request.messages[-1]["content"]
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if last_msg_content:
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for i in range(request.n):
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(content=last_msg_content),
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finish_reason=None)
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
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data = chunk.json(exclude_unset=True, ensure_ascii=False)
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yield f"data: {data}\n\n"
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# Send response for each token for each request.n (index)
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previous_texts = [""] * request.n
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previous_num_tokens = [0] * request.n
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finish_reason_sent = [False] * request.n
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async for res in result_generator:
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res: RequestOutput
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for output in res.outputs:
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i = output.index
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if finish_reason_sent[i]:
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continue
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delta_text = output.text[len(previous_texts[i]):]
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previous_texts[i] = output.text
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previous_num_tokens[i] = len(output.token_ids)
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if output.finish_reason is None:
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# Send token-by-token response for each request.n
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(content=delta_text),
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finish_reason=None)
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
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data = chunk.json(exclude_unset=True, ensure_ascii=False)
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yield f"data: {data}\n\n"
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else:
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# Send the finish response for each request.n only once
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prompt_tokens = len(res.prompt_token_ids)
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final_usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=previous_num_tokens[i],
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total_tokens=prompt_tokens + previous_num_tokens[i],
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(content=delta_text),
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finish_reason=output.finish_reason)
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
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if final_usage is not None:
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chunk.usage = final_usage
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data = chunk.json(exclude_unset=True,
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exclude_none=True,
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ensure_ascii=False)
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yield f"data: {data}\n\n"
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finish_reason_sent[i] = True
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# Send the final done message after all response.n are finished
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yield "data: [DONE]\n\n"
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async def chat_completion_full_generator(
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self, request: ChatCompletionRequest, raw_request: Request,
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result_generator: AsyncIterator[RequestOutput],
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request_id: str) -> Union[ErrorResponse, ChatCompletionResponse]:
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model_name = request.model
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created_time = int(time.monotonic())
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final_res: RequestOutput = None
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async for res in result_generator:
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if await raw_request.is_disconnected():
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# Abort the request if the client disconnects.
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await self.engine.abort(request_id)
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return self.create_error_response("Client disconnected")
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final_res = res
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assert final_res is not None
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choices = []
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role = self.get_chat_request_role(request)
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for output in final_res.outputs:
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choice_data = ChatCompletionResponseChoice(
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index=output.index,
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message=ChatMessage(role=role, content=output.text),
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finish_reason=output.finish_reason,
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)
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choices.append(choice_data)
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if request.echo:
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last_msg_content = ""
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if request.messages and isinstance(
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request.messages, list) and request.messages[-1].get(
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"content") and request.messages[-1].get(
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"role") == role:
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last_msg_content = request.messages[-1]["content"]
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for choice in choices:
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full_message = last_msg_content + choice.message.content
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choice.message.content = full_message
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num_prompt_tokens = len(final_res.prompt_token_ids)
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num_generated_tokens = sum(
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len(output.token_ids) for output in final_res.outputs)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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total_tokens=num_prompt_tokens + num_generated_tokens,
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)
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response = ChatCompletionResponse(
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id=request_id,
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created=created_time,
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model=model_name,
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choices=choices,
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usage=usage,
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)
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return response
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def _load_chat_template(self, chat_template):
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if chat_template is not None:
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try:
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with open(chat_template, "r") as f:
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self.tokenizer.chat_template = f.read()
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except OSError:
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# If opening a file fails, set chat template to be args to
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# ensure we decode so our escape are interpreted correctly
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self.tokenizer.chat_template = codecs.decode(
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chat_template, "unicode_escape")
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logger.info(
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f"Using supplied chat template:\n{self.tokenizer.chat_template}"
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)
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elif self.tokenizer.chat_template is not None:
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logger.info(
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f"Using default chat template:\n{self.tokenizer.chat_template}"
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)
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else:
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logger.warning(
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"No chat template provided. Chat API will not work.")
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