mirror of
https://github.com/wassname/vllm.git
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296 lines
12 KiB
Python
296 lines
12 KiB
Python
import time
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from fastapi import Request
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from typing import AsyncGenerator, Optional
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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 .protocol import (CompletionRequest, CompletionResponse,
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CompletionResponseChoice,
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CompletionResponseStreamChoice,
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CompletionStreamResponse, LogProbs, 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 OpenAIServingCompletion(OpenAIServing):
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def __init__(self, engine: AsyncLLMEngine, served_model: str):
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super().__init__(engine=engine, served_model=served_model)
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async def create_completion(self, request: CompletionRequest,
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raw_request: Request):
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"""Completion API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/completions/create
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for the API specification. This API mimics the OpenAI Completion API.
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NOTE: Currently we do not support the following features:
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- suffix (the language models we currently support do not support
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suffix)
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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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# OpenAI API supports echoing the prompt when max_tokens is 0.
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echo_without_generation = request.echo and request.max_tokens == 0
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if request.suffix is not None:
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# The language models we currently support do not support suffix.
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return self.create_error_response(
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"suffix is not currently supported")
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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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model_name = request.model
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request_id = f"cmpl-{random_uuid()}"
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use_token_ids = False
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if isinstance(request.prompt, list):
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if len(request.prompt) == 0:
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return self.create_error_response(
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"please provide at least one prompt")
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first_element = request.prompt[0]
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if isinstance(first_element, int):
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use_token_ids = True
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prompt = request.prompt
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elif isinstance(first_element, (str, list)):
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# TODO: handles multiple prompt case in list[list[int]]
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if len(request.prompt) > 1:
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return self.create_error_response(
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"multiple prompts in a batch is not currently supported"
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)
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use_token_ids = not isinstance(first_element, str)
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prompt = request.prompt[0]
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else:
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prompt = request.prompt
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if use_token_ids:
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_, error_check_ret = await self._check_length(request,
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prompt_ids=prompt)
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else:
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token_ids, error_check_ret = await self._check_length(
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request, 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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created_time = int(time.monotonic())
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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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best_of=request.best_of,
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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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top_k=request.top_k,
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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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ignore_eos=request.ignore_eos,
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max_tokens=request.max_tokens
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if not echo_without_generation else 1,
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logprobs=request.logprobs,
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use_beam_search=request.use_beam_search,
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prompt_logprobs=request.logprobs if request.echo else None,
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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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if use_token_ids:
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result_generator = self.engine.generate(None,
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sampling_params,
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request_id,
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prompt_token_ids=prompt)
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else:
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result_generator = self.engine.generate(prompt, sampling_params,
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request_id, token_ids)
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# Similar to the OpenAI API, when n != best_of, we do not stream the
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# results. In addition, we do not stream the results when use beam search.
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stream = (request.stream
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and (request.best_of is None or request.n == request.best_of)
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and not request.use_beam_search)
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def create_stream_response_json(
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index: int,
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text: str,
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logprobs: Optional[LogProbs] = None,
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finish_reason: Optional[str] = None,
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usage: Optional[UsageInfo] = None,
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) -> str:
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choice_data = CompletionResponseStreamChoice(
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index=index,
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text=text,
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logprobs=logprobs,
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finish_reason=finish_reason,
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)
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response = CompletionStreamResponse(
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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=[choice_data],
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)
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if usage is not None:
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response.usage = usage
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response_json = response.json(exclude_unset=True,
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ensure_ascii=False)
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return response_json
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async def completion_stream_generator() -> AsyncGenerator[str, None]:
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previous_texts = [""] * request.n
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previous_num_tokens = [0] * request.n
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has_echoed = [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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delta_text = output.text[len(previous_texts[i]):]
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token_ids = output.token_ids[previous_num_tokens[i]:]
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if request.logprobs is not None:
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top_logprobs = output.logprobs[previous_num_tokens[i]:]
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else:
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top_logprobs = None
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offsets = len(previous_texts[i])
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if request.echo and not has_echoed[i]:
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if not echo_without_generation:
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delta_text = res.prompt + delta_text
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token_ids = res.prompt_token_ids + token_ids
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if top_logprobs:
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top_logprobs = res.prompt_logprobs + top_logprobs
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else: # only just return the prompt
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delta_text = res.prompt
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token_ids = res.prompt_token_ids
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if top_logprobs:
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top_logprobs = res.prompt_logprobs
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has_echoed[i] = True
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if request.logprobs is not None:
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logprobs = self._create_logprobs(
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token_ids=token_ids,
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top_logprobs=top_logprobs,
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num_output_top_logprobs=request.logprobs,
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initial_text_offset=offsets,
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)
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else:
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logprobs = None
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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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finish_reason = output.finish_reason
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response_json = create_stream_response_json(
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index=i,
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text=delta_text,
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logprobs=logprobs,
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finish_reason=finish_reason,
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)
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yield f"data: {response_json}\n\n"
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if output.finish_reason is not None:
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logprobs = (LogProbs()
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if request.logprobs is not None else None)
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prompt_tokens = len(res.prompt_token_ids)
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completion_tokens = len(output.token_ids)
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final_usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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response_json = create_stream_response_json(
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index=i,
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text="",
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logprobs=logprobs,
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finish_reason=output.finish_reason,
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usage=final_usage,
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)
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yield f"data: {response_json}\n\n"
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yield "data: [DONE]\n\n"
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# Streaming response
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if stream:
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return completion_stream_generator()
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# Non-streaming response
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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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prompt_token_ids = final_res.prompt_token_ids
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prompt_logprobs = final_res.prompt_logprobs
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prompt_text = final_res.prompt
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for output in final_res.outputs:
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if request.logprobs is not None:
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if not echo_without_generation:
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token_ids = output.token_ids
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top_logprobs = output.logprobs
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if request.echo:
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token_ids = prompt_token_ids + token_ids
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top_logprobs = prompt_logprobs + top_logprobs
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else:
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token_ids = prompt_token_ids
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top_logprobs = prompt_logprobs
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logprobs = self._create_logprobs(
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token_ids=token_ids,
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top_logprobs=top_logprobs,
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num_output_top_logprobs=request.logprobs,
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)
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else:
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logprobs = None
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if not echo_without_generation:
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output_text = output.text
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if request.echo:
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output_text = prompt_text + output_text
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else:
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output_text = prompt_text
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choice_data = CompletionResponseChoice(
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index=output.index,
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text=output_text,
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logprobs=logprobs,
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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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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 = CompletionResponse(
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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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if request.stream:
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# When user requests streaming but we don't stream, we still need to
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# return a streaming response with a single event.
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response_json = response.json(ensure_ascii=False)
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async def fake_stream_generator() -> AsyncGenerator[str, None]:
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yield f"data: {response_json}\n\n"
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yield "data: [DONE]\n\n"
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return fake_stream_generator()
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return response
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