wehub-resource-sync
736 lines
29 KiB
Python
736 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import io
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import time
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from collections.abc import AsyncGenerator, AsyncIterator
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from collections.abc import Sequence as GenericSequence
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from typing import cast
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import numpy as np
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import pybase64 as base64
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from fastapi import Request
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.generate.base.serving import (
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GenerateBaseServing,
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GenerationError,
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build_per_request_timing_metrics,
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clamp_prompt_logprobs,
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format_token_id_placeholder,
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)
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from vllm.entrypoints.openai.completion.protocol import (
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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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)
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from vllm.entrypoints.openai.engine.protocol import (
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ErrorResponse,
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PerRequestTimingMetrics,
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PromptTokenUsageInfo,
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RequestResponseMetadata,
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UsageInfo,
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)
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from vllm.entrypoints.openai.models.serving import OpenAIServingModels
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from vllm.entrypoints.serve.utils.api_utils import get_max_tokens, should_include_usage
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from vllm.entrypoints.serve.utils.request_logger import RequestLogger
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from vllm.exceptions import VLLMValidationError
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from vllm.inputs import EngineInput
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob
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from vllm.outputs import RequestOutput
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from vllm.renderers.online_renderer import OnlineRenderer
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.async_utils import merge_async_iterators
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from vllm.utils.collection_utils import as_list
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logger = init_logger(__name__)
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class OpenAIServingCompletion(GenerateBaseServing):
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def __init__(
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self,
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engine_client: EngineClient,
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models: OpenAIServingModels,
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*,
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online_renderer: "OnlineRenderer",
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request_logger: RequestLogger | None,
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return_tokens_as_token_ids: bool = False,
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enable_prompt_tokens_details: bool = False,
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enable_force_include_usage: bool = False,
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enable_per_request_metrics: bool = False,
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):
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super().__init__(
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engine_client=engine_client,
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models=models,
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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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)
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self.online_renderer = online_renderer
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self.enable_prompt_tokens_details = enable_prompt_tokens_details
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self.enable_force_include_usage = enable_force_include_usage
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self.enable_per_request_metrics = enable_per_request_metrics
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self.default_sampling_params = self.model_config.get_diff_sampling_param()
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mc = self.model_config
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self.override_max_tokens = (
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self.default_sampling_params.get("max_tokens")
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if mc.generation_config not in ("auto", "vllm")
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else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
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)
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async def render_completion_request(
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self,
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request: CompletionRequest,
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) -> list[EngineInput] | ErrorResponse:
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"""
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Validate the model and preprocess a completion request.
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Delegates preprocessing logic to OnlineRenderer, adding the
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engine-aware checks (LoRA model validation, engine health).
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Returns:
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A list of engine_inputs on success, or an ErrorResponse on failure.
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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 await self.online_renderer.render_completion(request)
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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 | None = None,
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) -> 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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return await self._with_kv_transfer_rejection_cleanup(
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self._create_completion(request, raw_request), request, raw_request
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)
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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 | None = None,
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) -> AsyncGenerator[str, None] | CompletionResponse | ErrorResponse:
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if request.stream and request.use_beam_search:
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return self.create_error_response(
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"Streaming is not currently supported with beam search"
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)
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result = await self.render_completion_request(request)
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if isinstance(result, ErrorResponse):
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return result
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engine_inputs = result
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request_id = f"cmpl-{self._base_request_id(raw_request, request.request_id)}"
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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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lora_request = self._maybe_get_adapters(request)
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# Extract data_parallel_rank from header (router can inject it)
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data_parallel_rank = self._get_data_parallel_rank(raw_request)
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# Schedule the request and get the result generator.
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max_model_len = self.model_config.max_model_len
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generators: list[AsyncGenerator[RequestOutput, None]] = []
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for i, engine_input in enumerate(engine_inputs):
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max_tokens = get_max_tokens(
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max_model_len,
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request.max_tokens,
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self._extract_prompt_len(engine_input),
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self.default_sampling_params,
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self.override_max_tokens,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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)
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sampling_params: SamplingParams | BeamSearchParams
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if request.use_beam_search:
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sampling_params = request.to_beam_search_params(
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max_tokens, self.default_sampling_params
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)
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else:
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sampling_params = request.to_sampling_params(
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max_tokens,
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self.default_sampling_params,
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)
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(
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request_id_item,
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engine_input,
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params=sampling_params,
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lora_request=lora_request,
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)
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trace_headers = (
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None
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if raw_request is None
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else await self._get_trace_headers(raw_request.headers)
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)
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if isinstance(sampling_params, BeamSearchParams):
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generator = self.beam_search(
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prompt=engine_input,
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request_id=request_id,
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params=sampling_params,
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lora_request=lora_request,
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trace_headers=trace_headers,
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)
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else:
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generator = self.engine_client.generate(
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engine_input,
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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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trace_headers=trace_headers,
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priority=request.priority,
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data_parallel_rank=data_parallel_rank,
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)
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generators.append(generator)
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result_generator = merge_async_iterators(*generators)
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model_name = self.models.model_name(lora_request)
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num_prompts = len(engine_inputs)
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# Streaming response
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tokenizer = self.renderer.tokenizer
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if request.stream:
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return self.completion_stream_generator(
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request,
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engine_inputs,
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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=num_prompts,
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tokenizer=tokenizer,
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request_metadata=request_metadata,
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)
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# Non-streaming response
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final_res_batch: list[RequestOutput | None] = [None] * num_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 = self._extract_prompt_text(engine_inputs[i])
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final_res_batch_checked = cast(list[RequestOutput], 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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# 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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engine_inputs: list[EngineInput],
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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: TokenizerLike | None,
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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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num_cached_tokens = None
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first_iteration = True
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stream_options = request.stream_options
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include_usage, include_continuous_usage = should_include_usage(
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stream_options, self.enable_force_include_usage
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)
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last_res: RequestOutput | None = None
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try:
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async for prompt_idx, res in result_generator:
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last_res = res
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prompt_token_ids = res.prompt_token_ids
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prompt_logprobs = res.prompt_logprobs
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if first_iteration:
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num_cached_tokens = res.num_cached_tokens
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first_iteration = False
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prompt_text = res.prompt
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if prompt_text is None:
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engine_input = engine_inputs[prompt_idx]
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prompt_text = self._extract_prompt_text(engine_input)
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# Prompt details are excluded from later streamed outputs
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if prompt_token_ids is not None:
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num_prompt_tokens[prompt_idx] = len(prompt_token_ids)
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delta_token_ids: GenericSequence[int]
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out_logprobs: GenericSequence[dict[int, Logprob] | None] | None
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for output in res.outputs:
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i = output.index + prompt_idx * num_choices
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# Useful when request.return_token_ids is True
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# Returning prompt token IDs shares the same logic
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# with the echo implementation.
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prompt_token_ids_to_return: list[int] | None = None
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assert request.max_tokens is not None
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if request.echo and not has_echoed[i]:
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assert prompt_token_ids is not None
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if request.return_token_ids:
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prompt_text = ""
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assert prompt_text is not None
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if request.max_tokens == 0:
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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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else:
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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,
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*output.token_ids,
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]
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out_logprobs = [
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*(prompt_logprobs or []),
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*(output.logprobs or []),
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]
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prompt_token_ids_to_return = prompt_token_ids
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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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# has_echoed[i] is reused here to indicate whether
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# we have already returned the prompt token IDs.
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if not has_echoed[i] and request.return_token_ids:
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prompt_token_ids_to_return = prompt_token_ids
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has_echoed[i] = True
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if (
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not delta_text
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and not delta_token_ids
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and not previous_num_tokens[i]
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):
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# Chunked prefill case, don't return empty chunks
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continue
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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=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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return_as_token_id=request.return_tokens_as_token_ids,
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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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self._raise_if_error(finish_reason, request_id)
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chunk = CompletionStreamResponse(
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id=request_id,
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object="text_completion",
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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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prompt_token_ids=prompt_token_ids_to_return,
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token_ids=(
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as_list(output.token_ids)
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if request.return_token_ids
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else None
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),
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)
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],
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)
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# Stamp on terminal chunk only when no trailing usage chunk
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# will follow (that one is the true final message).
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if (
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not include_usage
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and self.system_fingerprint is not None
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and finish_reason is not None
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):
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chunk.system_fingerprint = self.system_fingerprint
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if include_continuous_usage:
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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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chunk.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 = chunk.model_dump_json(exclude_unset=True)
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yield f"data: {response_json}\n\n"
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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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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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)
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if self.enable_prompt_tokens_details and num_cached_tokens is not None:
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final_usage_info.prompt_tokens_details = PromptTokenUsageInfo(
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cached_tokens=num_cached_tokens
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)
|
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|
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if include_usage:
|
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# In streaming, metrics ride on this final usage chunk, which is
|
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# only emitted when usage reporting is enabled (i.e.
|
|
# ``stream_options.include_usage=true`` or
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# ``--enable-force-include-usage``).
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stream_per_request_metrics: PerRequestTimingMetrics | None = None
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if (
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self.enable_per_request_metrics
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# See note in request_output_to_completion_response: suppress
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# when not attributable to one stream (multi-prompt or n>1).
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and num_prompts == 1
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and (request.n or 1) == 1
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):
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last_metrics = last_res.metrics if last_res is not None else None
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stream_per_request_metrics = build_per_request_timing_metrics(
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last_metrics, total_completion_tokens
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)
|
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|
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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=final_usage_info,
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system_fingerprint=self.system_fingerprint,
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metrics=stream_per_request_metrics,
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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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)
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yield f"data: {final_usage_data}\n\n"
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|
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# report to FastAPI middleware aggregate usage across all choices
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request_metadata.final_usage_info = final_usage_info
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except GenerationError as e:
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yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
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except Exception as e:
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logger.exception("Error in completion stream generator.")
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data = self.create_streaming_error_response(e)
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yield f"data: {data}\n\n"
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yield "data: [DONE]\n\n"
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|
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def request_output_to_completion_response(
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self,
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final_res_batch: list[RequestOutput],
|
|
request: CompletionRequest,
|
|
request_id: str,
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created_time: int,
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model_name: str,
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tokenizer: TokenizerLike | None,
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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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kv_transfer_params = None
|
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ec_transfer_params = None
|
|
last_final_res = None
|
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for final_res in final_res_batch:
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last_final_res = final_res
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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 = clamp_prompt_logprobs(final_res.prompt_logprobs)
|
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prompt_text = final_res.prompt
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|
|
|
token_ids: GenericSequence[int]
|
|
out_logprobs: GenericSequence[dict[int, Logprob] | None] | None
|
|
|
|
for output in final_res.outputs:
|
|
self._raise_if_error(output.finish_reason, request_id)
|
|
|
|
assert request.max_tokens is not None
|
|
if request.echo:
|
|
if request.return_token_ids:
|
|
prompt_text = ""
|
|
assert prompt_text is not None
|
|
if request.max_tokens == 0:
|
|
token_ids = prompt_token_ids
|
|
out_logprobs = prompt_logprobs
|
|
output_text = prompt_text
|
|
else:
|
|
token_ids = [*prompt_token_ids, *output.token_ids]
|
|
|
|
if request.logprobs is None:
|
|
out_logprobs = None
|
|
else:
|
|
assert prompt_logprobs is not None
|
|
assert output.logprobs is not None
|
|
out_logprobs = [
|
|
*prompt_logprobs,
|
|
*output.logprobs,
|
|
]
|
|
|
|
output_text = prompt_text + output.text
|
|
else:
|
|
token_ids = output.token_ids
|
|
out_logprobs = output.logprobs
|
|
output_text = output.text
|
|
|
|
if request.logprobs is not None:
|
|
assert out_logprobs is not None, "Did not output logprobs"
|
|
logprobs = self._create_completion_logprobs(
|
|
token_ids=token_ids,
|
|
top_logprobs=out_logprobs,
|
|
tokenizer=tokenizer,
|
|
num_output_top_logprobs=request.logprobs,
|
|
return_as_token_id=request.return_tokens_as_token_ids,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
# Encode routed_experts for transport. JSON can't carry raw
|
|
# bytes, so we write the ndarray as a ``.npy`` byte stream
|
|
# and base64-encode it. ``pybase64`` is ~3x faster than the
|
|
# stdlib ``base64`` on large payloads thanks to SIMD.
|
|
routed_experts_b64 = None
|
|
if output.routed_experts is not None:
|
|
buf = io.BytesIO()
|
|
np.save(buf, output.routed_experts)
|
|
routed_experts_b64 = base64.b64encode(buf.getvalue()).decode(
|
|
"ascii"
|
|
)
|
|
|
|
choice_data = CompletionResponseChoice(
|
|
index=len(choices),
|
|
text=output_text,
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason,
|
|
stop_reason=output.stop_reason,
|
|
prompt_logprobs=final_res.prompt_logprobs,
|
|
prompt_token_ids=(
|
|
prompt_token_ids if request.return_token_ids else None
|
|
),
|
|
token_ids=(
|
|
as_list(output.token_ids) if request.return_token_ids else None
|
|
),
|
|
routed_experts=routed_experts_b64,
|
|
)
|
|
choices.append(choice_data)
|
|
|
|
num_generated_tokens += len(output.token_ids)
|
|
|
|
num_prompt_tokens += len(prompt_token_ids)
|
|
|
|
usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_generated_tokens,
|
|
total_tokens=num_prompt_tokens + num_generated_tokens,
|
|
)
|
|
|
|
if (
|
|
self.enable_prompt_tokens_details
|
|
and last_final_res
|
|
and last_final_res.num_cached_tokens is not None
|
|
):
|
|
usage.prompt_tokens_details = PromptTokenUsageInfo(
|
|
cached_tokens=last_final_res.num_cached_tokens
|
|
)
|
|
|
|
request_metadata.final_usage_info = usage
|
|
|
|
per_request_metrics: PerRequestTimingMetrics | None = None
|
|
if (
|
|
self.enable_per_request_metrics
|
|
# Metrics describe a single generation stream, so suppress them when
|
|
# they cannot be attributed to one: multiple prompts (timestamps
|
|
# span prompts) or n>1 (stats belong to one of the n sequences).
|
|
and len(final_res_batch) == 1
|
|
and (request.n or 1) == 1
|
|
):
|
|
last_metrics = (
|
|
last_final_res.metrics if last_final_res is not None else None
|
|
)
|
|
per_request_metrics = build_per_request_timing_metrics(
|
|
last_metrics, num_generated_tokens
|
|
)
|
|
|
|
if final_res_batch:
|
|
kv_transfer_params = final_res_batch[0].kv_transfer_params
|
|
ec_transfer_params = final_res_batch[0].ec_transfer_params
|
|
|
|
return CompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
system_fingerprint=self.system_fingerprint,
|
|
kv_transfer_params=kv_transfer_params,
|
|
ec_transfer_params=ec_transfer_params,
|
|
metrics=per_request_metrics,
|
|
)
|
|
|
|
def _create_completion_logprobs(
|
|
self,
|
|
token_ids: GenericSequence[int],
|
|
top_logprobs: GenericSequence[dict[int, Logprob] | None],
|
|
num_output_top_logprobs: int,
|
|
tokenizer: TokenizerLike | None,
|
|
initial_text_offset: int = 0,
|
|
return_as_token_id: bool | None = None,
|
|
) -> CompletionLogProbs:
|
|
"""Create logprobs for OpenAI Completion API."""
|
|
out_text_offset: list[int] = []
|
|
out_token_logprobs: list[float | None] = []
|
|
out_tokens: list[str] = []
|
|
out_top_logprobs: list[dict[str, float] | None] = []
|
|
|
|
last_token_len = 0
|
|
|
|
should_return_as_token_id = (
|
|
return_as_token_id
|
|
if return_as_token_id is not None
|
|
else self.return_tokens_as_token_ids
|
|
)
|
|
for i, token_id in enumerate(token_ids):
|
|
step_top_logprobs = top_logprobs[i]
|
|
if step_top_logprobs is None:
|
|
if should_return_as_token_id:
|
|
token = format_token_id_placeholder(token_id)
|
|
else:
|
|
if tokenizer is None:
|
|
raise VLLMValidationError(
|
|
"Unable to get tokenizer because "
|
|
"`skip_tokenizer_init=True`",
|
|
parameter="skip_tokenizer_init",
|
|
value=True,
|
|
)
|
|
|
|
token = tokenizer.decode(token_id)
|
|
|
|
out_tokens.append(token)
|
|
out_token_logprobs.append(None)
|
|
out_top_logprobs.append(None)
|
|
else:
|
|
step_token = step_top_logprobs[token_id]
|
|
|
|
token = self._get_decoded_token(
|
|
step_token,
|
|
token_id,
|
|
tokenizer,
|
|
return_as_token_id=should_return_as_token_id,
|
|
)
|
|
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=should_return_as_token_id,
|
|
): 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,
|
|
)
|