377 lines
15 KiB
Python
377 lines
15 KiB
Python
import asyncio
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import time
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from fastapi import Request
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from typing import AsyncGenerator, AsyncIterator, Callable, List, Optional, Dict, Tuple
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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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CompletionRequest,
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CompletionResponse,
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CompletionResponseChoice,
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CompletionResponseStreamChoice,
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CompletionStreamResponse,
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LogProbs,
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UsageInfo,
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)
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from vllm.outputs import RequestOutput
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from vllm.entrypoints.openai.serving_engine import OpenAIServing, LoRA
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from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
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logger = init_logger(__name__)
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TypeTokenIDs = List[int]
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TypeTopLogProbs = List[Optional[Dict[int, float]]]
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TypeCreateLogProbsFn = Callable[
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[TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs]
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def parse_prompt_format(prompt) -> Tuple[bool, list]:
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# get the prompt, openai supports the following
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# "a string, array of strings, array of tokens, or array of token arrays."
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prompt_is_tokens = False
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prompts = [prompt] # case 1: a string
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if isinstance(prompt, list):
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if len(prompt) == 0:
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raise ValueError("please provide at least one prompt")
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elif isinstance(prompt[0], str):
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prompt_is_tokens = False
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prompts = prompt # case 2: array of strings
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elif isinstance(prompt[0], int):
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prompt_is_tokens = True
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prompts = [prompt] # case 3: array of tokens
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elif isinstance(prompt[0], list) and isinstance(prompt[0][0], int):
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prompt_is_tokens = True
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prompts = prompt # case 4: array of token arrays
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else:
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raise ValueError(
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"prompt must be a string, array of strings, array of tokens, or array of token arrays"
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)
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return prompt_is_tokens, prompts
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def merge_async_iterators(*iterators):
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"""Merge multiple asynchronous iterators into a single iterator.
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This method handle the case where some iterators finish before others.
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When it yields, it yields a tuple (i, item) where i is the index of the
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iterator that yields the item.
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"""
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queue = asyncio.Queue()
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finished = [False] * len(iterators)
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async def producer(i, iterator):
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try:
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async for item in iterator:
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await queue.put((i, item))
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except Exception as e:
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await queue.put(e)
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finished[i] = True
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_tasks = [
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asyncio.create_task(producer(i, iterator))
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for i, iterator in enumerate(iterators)
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]
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async def consumer():
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while not all(finished) or not queue.empty():
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item = await queue.get()
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if isinstance(item, Exception):
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raise item
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yield item
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await asyncio.gather(*_tasks)
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return consumer()
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class OpenAIServingCompletion(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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lora_modules: Optional[List[LoRA]] = None):
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super().__init__(engine=engine,
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served_model=served_model,
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lora_modules=lora_modules)
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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 feature:
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- suffix (the language models we currently support do not support
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suffix)
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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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# Return error for unsupported features.
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if request.suffix is not None:
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return self.create_error_response(
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"suffix 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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created_time = int(time.monotonic())
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# Schedule the request and get the result generator.
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generators = []
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try:
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sampling_params = request.to_sampling_params()
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lora_request = self._maybe_get_lora(request)
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guided_decode_logit_processor = (
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await get_guided_decoding_logits_processor(
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request, await self.engine.get_tokenizer()))
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if guided_decode_logit_processor is not None:
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if sampling_params.logits_processors is None:
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sampling_params.logits_processors = []
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sampling_params.logits_processors.append(
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guided_decode_logit_processor)
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prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
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for i, prompt in enumerate(prompts):
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if prompt_is_tokens:
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input_ids = self._validate_prompt_and_tokenize(
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request, prompt_ids=prompt)
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else:
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input_ids = self._validate_prompt_and_tokenize(
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request, prompt=prompt)
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generators.append(
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self.engine.generate(prompt,
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sampling_params,
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f"{request_id}-{i}",
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prompt_token_ids=input_ids,
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lora_request=lora_request))
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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result_generator: AsyncIterator[Tuple[
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int, RequestOutput]] = merge_async_iterators(*generators)
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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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# Streaming response
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if stream:
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return self.completion_stream_generator(request,
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raw_request,
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result_generator,
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request_id,
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created_time,
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model_name,
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num_prompts=len(prompts))
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# Non-streaming response
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final_res_batch: RequestOutput = [None] * len(prompts)
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try:
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async for i, 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(f"{request_id}-{i}")
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return self.create_error_response("Client disconnected")
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final_res_batch[i] = res
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response = self.request_output_to_completion_response(
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final_res_batch, request, request_id, created_time, model_name)
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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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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if request.stream:
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response_json = response.model_dump_json()
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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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async def completion_stream_generator(
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self,
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request: CompletionRequest,
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raw_request: Request,
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result_generator: AsyncIterator[Tuple[int, RequestOutput]],
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request_id: str,
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created_time: int,
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model_name: str,
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num_prompts: int,
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) -> AsyncGenerator[str, None]:
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previous_texts = [""] * request.n * num_prompts
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previous_num_tokens = [0] * request.n * num_prompts
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has_echoed = [False] * request.n * num_prompts
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try:
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async for prompt_idx, res in result_generator:
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# Abort the request if the client disconnects.
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if await raw_request.is_disconnected():
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await self.engine.abort(f"{request_id}-{prompt_idx}")
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raise StopAsyncIteration()
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for output in res.outputs:
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i = output.index + prompt_idx * request.n
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# TODO(simon): optimize the performance by avoiding full text O(n^2) sending.
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if request.echo and request.max_tokens == 0:
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# only return the prompt
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delta_text = res.prompt
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delta_token_ids = res.prompt_token_ids
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top_logprobs = res.prompt_logprobs
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has_echoed[i] = True
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elif request.echo and request.max_tokens > 0 and not has_echoed[
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i]:
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# echo the prompt and first token
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delta_text = res.prompt + output.text
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delta_token_ids = res.prompt_token_ids + output.token_ids
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top_logprobs = res.prompt_logprobs + (output.logprobs
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or [])
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has_echoed[i] = True
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else:
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# return just the delta
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delta_text = output.text[len(previous_texts[i]):]
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delta_token_ids = output.token_ids[
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previous_num_tokens[i]:]
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top_logprobs = output.logprobs[previous_num_tokens[
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i]:] if output.logprobs else None
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if request.logprobs is not None:
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assert top_logprobs is not None, "top_logprobs must be provided when logprobs is requested"
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logprobs = self._create_logprobs(
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token_ids=delta_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=len(previous_texts[i]),
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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 = 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=[
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CompletionResponseStreamChoice(
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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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]).model_dump_json()
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yield f"data: {response_json}\n\n"
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if output.finish_reason is not None: # return final usage
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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 = 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=[
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CompletionResponseStreamChoice(
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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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)
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],
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usage=final_usage,
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).model_dump_json()
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yield f"data: {response_json}\n\n"
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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data = self.create_streaming_error_response(str(e))
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print("yield", f"data: {data}\n\n")
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yield f"data: {data}\n\n"
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print("yield", "data: [DONE]\n\n")
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yield "data: [DONE]\n\n"
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def request_output_to_completion_response(
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self,
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final_res_batch: List[RequestOutput],
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request: CompletionRequest,
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request_id: str,
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created_time: int,
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model_name: str,
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) -> CompletionResponse:
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choices = []
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num_prompt_tokens = 0
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num_generated_tokens = 0
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for final_res in final_res_batch:
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assert final_res is not None
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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.echo and request.max_tokens == 0:
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token_ids = prompt_token_ids
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top_logprobs = prompt_logprobs
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output_text = prompt_text
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elif request.echo and request.max_tokens > 0:
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token_ids = prompt_token_ids + output.token_ids
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top_logprobs = prompt_logprobs + output.logprobs
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output_text = prompt_text + output.text
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else:
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token_ids = output.token_ids
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top_logprobs = output.logprobs
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output_text = output.text
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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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)
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else:
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logprobs = None
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choice_data = CompletionResponseChoice(
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index=len(choices),
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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(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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return 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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