555 lines
23 KiB
Python
555 lines
23 KiB
Python
import asyncio
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import time
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from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
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Optional)
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from typing import Sequence as GenericSequence
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from typing import Tuple, Union, cast
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from fastapi import Request
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from vllm.config import ModelConfig
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.engine.multiprocessing.client import MQLLMEngineClient
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.logger import RequestLogger
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
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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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ErrorResponse,
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RequestResponseMetadata,
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UsageInfo)
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# yapf: enable
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from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
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LoRAModulePath,
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OpenAIServing,
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PromptAdapterPath)
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from vllm.logger import init_logger
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.sequence import Logprob
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from vllm.tracing import (contains_trace_headers, extract_trace_headers,
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log_tracing_disabled_warning)
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import merge_async_iterators, random_uuid
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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], CompletionLogProbs]
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class OpenAIServingCompletion(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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base_model_paths: List[BaseModelPath],
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*,
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lora_modules: Optional[List[LoRAModulePath]],
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prompt_adapters: Optional[List[PromptAdapterPath]],
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request_logger: Optional[RequestLogger],
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return_tokens_as_token_ids: bool = False,
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):
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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base_model_paths=base_model_paths,
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lora_modules=lora_modules,
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prompt_adapters=prompt_adapters,
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request_logger=request_logger,
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return_tokens_as_token_ids=return_tokens_as_token_ids)
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async def create_completion(
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self,
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request: CompletionRequest,
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raw_request: Request,
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) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
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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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# If the engine is dead, raise the engine's DEAD_ERROR.
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# This is required for the streaming case, where we return a
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# success status before we actually start generating text :).
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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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 = self.base_model_paths[0].name
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request_id = f"cmpl-{random_uuid()}"
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created_time = int(time.time())
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request_metadata = RequestResponseMetadata(request_id=request_id)
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if raw_request:
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raw_request.state.request_metadata = request_metadata
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# Schedule the request and get the result generator.
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generators: List[AsyncGenerator[RequestOutput, None]] = []
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try:
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(
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lora_request,
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prompt_adapter_request,
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) = self._maybe_get_adapters(request)
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tokenizer = await self.engine_client.get_tokenizer(lora_request)
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prompts = list(
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self._tokenize_prompt_input_or_inputs(
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request,
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tokenizer,
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request.prompt,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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add_special_tokens=request.add_special_tokens,
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))
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for i, prompt_inputs in enumerate(prompts):
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sampling_params: Union[SamplingParams, BeamSearchParams]
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default_max_tokens = self.max_model_len - len(
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prompt_inputs["prompt_token_ids"])
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if request.use_beam_search:
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sampling_params = request.to_beam_search_params(
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default_max_tokens)
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else:
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sampling_params = request.to_sampling_params(
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default_max_tokens)
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(request_id_item,
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prompt_inputs,
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params=sampling_params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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is_tracing_enabled = (await
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self.engine_client.is_tracing_enabled())
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trace_headers = None
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if is_tracing_enabled:
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trace_headers = extract_trace_headers(raw_request.headers)
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if not is_tracing_enabled and contains_trace_headers(
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raw_request.headers):
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log_tracing_disabled_warning()
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if isinstance(sampling_params, BeamSearchParams):
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assert isinstance(self.engine_client,
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(AsyncLLMEngine,
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MQLLMEngineClient)), \
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"Beam search is only supported with" \
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"AsyncLLMEngine and MQLLMEngineClient."
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generator = self.engine_client.beam_search(
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prompt_inputs["prompt_token_ids"],
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request_id_item,
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sampling_params,
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)
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else:
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generator = self.engine_client.generate(
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{
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"prompt_token_ids":
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prompt_inputs["prompt_token_ids"]
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},
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sampling_params,
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request_id_item,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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trace_headers=trace_headers,
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priority=request.priority,
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)
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generators.append(generator)
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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 = merge_async_iterators(
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*generators, is_cancelled=raw_request.is_disconnected)
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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
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# 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(
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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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tokenizer=tokenizer,
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request_metadata=request_metadata)
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# Non-streaming response
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final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
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try:
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async for i, res in result_generator:
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final_res_batch[i] = res
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for i, final_res in enumerate(final_res_batch):
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assert final_res is not None
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# The output should contain the input text
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# We did not pass it into vLLM engine to avoid being redundant
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# with the inputs token IDs
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if final_res.prompt is None:
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final_res.prompt = prompts[i]["prompt"]
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final_res_batch_checked = cast(List[RequestOutput],
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final_res_batch)
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response = self.request_output_to_completion_response(
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final_res_batch_checked,
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request,
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request_id,
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created_time,
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model_name,
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tokenizer,
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request_metadata,
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)
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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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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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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tokenizer: AnyTokenizer,
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request_metadata: RequestResponseMetadata,
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) -> AsyncGenerator[str, None]:
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num_choices = 1 if request.n is None else request.n
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previous_text_lens = [0] * num_choices * num_prompts
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previous_num_tokens = [0] * num_choices * num_prompts
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has_echoed = [False] * num_choices * num_prompts
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num_prompt_tokens = [0] * num_prompts
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try:
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async for prompt_idx, res in result_generator:
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prompt_token_ids = res.prompt_token_ids
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prompt_logprobs = res.prompt_logprobs
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prompt_text = res.prompt
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# Prompt details are excluded from later streamed outputs
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if res.prompt_token_ids is not None:
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num_prompt_tokens[prompt_idx] = len(res.prompt_token_ids)
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delta_token_ids: GenericSequence[int]
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out_logprobs: Optional[GenericSequence[Optional[Dict[
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int, Logprob]]]]
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for output in res.outputs:
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i = output.index + prompt_idx * num_choices
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# TODO(simon): optimize the performance by avoiding full
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# text O(n^2) sending.
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assert request.max_tokens is not None
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if request.echo and request.max_tokens == 0:
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assert prompt_token_ids is not None
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assert prompt_text is not None
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# only return the prompt
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delta_text = prompt_text
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delta_token_ids = prompt_token_ids
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out_logprobs = prompt_logprobs
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has_echoed[i] = True
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elif (request.echo and request.max_tokens > 0
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and not has_echoed[i]):
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assert prompt_token_ids is not None
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assert prompt_text is not None
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assert prompt_logprobs is not None
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# echo the prompt and first token
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delta_text = prompt_text + output.text
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delta_token_ids = [
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*prompt_token_ids, *output.token_ids
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]
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out_logprobs = [
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*prompt_logprobs,
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*(output.logprobs or []),
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]
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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
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delta_token_ids = output.token_ids
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out_logprobs = output.logprobs
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if request.logprobs is not None:
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assert out_logprobs is not None, (
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"Did not output logprobs")
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logprobs = self._create_completion_logprobs(
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token_ids=delta_token_ids,
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top_logprobs=out_logprobs,
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num_output_top_logprobs=request.logprobs,
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tokenizer=tokenizer,
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initial_text_offset=previous_text_lens[i],
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)
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else:
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logprobs = None
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previous_text_lens[i] += len(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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stop_reason = output.stop_reason
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chunk = 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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stop_reason=stop_reason,
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)
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])
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if (request.stream_options
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and request.stream_options.include_usage):
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if (request.stream_options.continuous_usage_stats
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or output.finish_reason is not None):
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prompt_tokens = num_prompt_tokens[prompt_idx]
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completion_tokens = previous_num_tokens[i]
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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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if request.stream_options.continuous_usage_stats:
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chunk.usage = usage
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else:
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chunk.usage = None
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response_json = chunk.model_dump_json(exclude_unset=False)
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yield f"data: {response_json}\n\n"
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if (request.stream_options
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and request.stream_options.include_usage):
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final_usage_chunk = 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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usage=usage,
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)
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final_usage_data = (final_usage_chunk.model_dump_json(
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exclude_unset=False, exclude_none=True))
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yield f"data: {final_usage_data}\n\n"
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# report to FastAPI middleware aggregate usage across all choices
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total_prompt_tokens = sum(num_prompt_tokens)
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total_completion_tokens = sum(previous_num_tokens)
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request_metadata.final_usage_info = UsageInfo(
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prompt_tokens=total_prompt_tokens,
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completion_tokens=total_completion_tokens,
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total_tokens=total_prompt_tokens + total_completion_tokens)
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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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yield f"data: {data}\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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tokenizer: AnyTokenizer,
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request_metadata: RequestResponseMetadata,
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) -> CompletionResponse:
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choices: List[CompletionResponseChoice] = []
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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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prompt_token_ids = final_res.prompt_token_ids
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assert prompt_token_ids is not None
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prompt_logprobs = final_res.prompt_logprobs
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prompt_text = final_res.prompt
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token_ids: GenericSequence[int]
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out_logprobs: Optional[GenericSequence[Optional[Dict[int,
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Logprob]]]]
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for output in final_res.outputs:
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assert request.max_tokens is not None
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if request.echo and request.max_tokens == 0:
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assert prompt_text is not None
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token_ids = prompt_token_ids
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out_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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assert prompt_text is not None
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token_ids = [*prompt_token_ids, *output.token_ids]
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if request.logprobs is None:
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out_logprobs = None
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else:
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assert prompt_logprobs is not None
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assert output.logprobs is not None
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out_logprobs = [
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*prompt_logprobs,
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*output.logprobs,
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]
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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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out_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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assert out_logprobs is not None, "Did not output logprobs"
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logprobs = self._create_completion_logprobs(
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token_ids=token_ids,
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top_logprobs=out_logprobs,
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tokenizer=tokenizer,
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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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stop_reason=output.stop_reason,
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prompt_logprobs=final_res.prompt_logprobs,
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)
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choices.append(choice_data)
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num_generated_tokens += len(output.token_ids)
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num_prompt_tokens += len(prompt_token_ids)
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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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request_metadata.final_usage_info = usage
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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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def _create_completion_logprobs(
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self,
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token_ids: GenericSequence[int],
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top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
|
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num_output_top_logprobs: int,
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|
tokenizer: AnyTokenizer,
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initial_text_offset: int = 0,
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) -> CompletionLogProbs:
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|
"""Create logprobs for OpenAI Completion API."""
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|
out_text_offset: List[int] = []
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out_token_logprobs: List[Optional[float]] = []
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out_tokens: List[str] = []
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out_top_logprobs: List[Optional[Dict[str, float]]] = []
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last_token_len = 0
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for i, token_id in enumerate(token_ids):
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step_top_logprobs = top_logprobs[i]
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if step_top_logprobs is None:
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token = tokenizer.decode(token_id)
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if self.return_tokens_as_token_ids:
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token = f"token_id:{token_id}"
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out_tokens.append(token)
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out_token_logprobs.append(None)
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out_top_logprobs.append(None)
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else:
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step_token = step_top_logprobs[token_id]
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token = self._get_decoded_token(
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step_token,
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token_id,
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tokenizer,
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|
return_as_token_id=self.return_tokens_as_token_ids,
|
|
)
|
|
token_logprob = max(step_token.logprob, -9999.0)
|
|
|
|
out_tokens.append(token)
|
|
out_token_logprobs.append(token_logprob)
|
|
|
|
# makes sure to add the top num_output_top_logprobs + 1
|
|
# logprobs, as defined in the openai API
|
|
# (cf. https://github.com/openai/openai-openapi/blob/
|
|
# 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
|
|
out_top_logprobs.append({
|
|
# Convert float("-inf") to the
|
|
# JSON-serializable float that OpenAI uses
|
|
self._get_decoded_token(
|
|
top_lp[1],
|
|
top_lp[0],
|
|
tokenizer,
|
|
return_as_token_id=self.return_tokens_as_token_ids):
|
|
max(top_lp[1].logprob, -9999.0)
|
|
for i, top_lp in enumerate(step_top_logprobs.items())
|
|
if num_output_top_logprobs >= i
|
|
})
|
|
|
|
if len(out_text_offset) == 0:
|
|
out_text_offset.append(initial_text_offset)
|
|
else:
|
|
out_text_offset.append(out_text_offset[-1] + last_token_len)
|
|
last_token_len = len(token)
|
|
|
|
return CompletionLogProbs(
|
|
text_offset=out_text_offset,
|
|
token_logprobs=out_token_logprobs,
|
|
tokens=out_tokens,
|
|
top_logprobs=out_top_logprobs,
|
|
)
|