chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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 time
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from collections.abc import AsyncGenerator
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from http import HTTPStatus
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from fastapi import Request
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from vllm.entrypoints.chat_utils import ConversationMessage
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from vllm.entrypoints.openai.chat_completion.protocol import (
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BatchChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatMessage,
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)
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from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
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from vllm.entrypoints.openai.engine.protocol import (
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ErrorResponse,
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RequestResponseMetadata,
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UsageInfo,
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)
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from vllm.entrypoints.serve.utils.api_utils import get_max_tokens
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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.outputs import RequestOutput
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from vllm.parser.abstract_parser import Parser
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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 OpenAIServingChatBatch(OpenAIServingChat):
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"""Extends OpenAIServingChat with the /v1/chat/completions/batch endpoint.
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Processes N conversations from a single request concurrently and returns
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one choice per conversation indexed 0, 1, ..., N-1.
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"""
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async def render_batch_chat_request(
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self,
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request: BatchChatCompletionRequest,
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) -> tuple[list[list[ConversationMessage]], list[EngineInput]] | ErrorResponse:
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"""Validate the model and preprocess a batched chat completion request.
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Performs engine-aware checks then delegates per-conversation
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preprocessing to OnlineRenderer, validating the chat template
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once for the whole batch.
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Returns:
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A tuple of (all_conversations, engine_prompts) on success — one
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entry per conversation — 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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logger.error("Error with model %s", error_check_ret)
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return error_check_ret
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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renderer = self.online_renderer
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if not renderer.use_harmony:
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# Common case: validate the chat template once for the whole batch.
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error_check_ret = renderer.validate_chat_template(
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request_chat_template=request.chat_template,
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chat_template_kwargs=request.chat_template_kwargs,
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trust_request_chat_template=renderer.trust_request_chat_template,
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)
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if error_check_ret is not None:
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return error_check_ret
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parser = renderer.parser
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tool_dicts: list[dict] | None = None
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all_conversations: list[list[ConversationMessage]] = []
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all_engine_prompts: list[EngineInput] = []
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for messages in request.messages:
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single_request = request.to_chat_completion_request(messages)
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if renderer.use_harmony:
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conversation, engine_prompts = renderer._make_request_with_harmony(
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single_request, should_include_tools=tool_dicts is not None
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)
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else:
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conversation, engine_prompts = await renderer.preprocess_chat(
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single_request,
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messages,
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default_template=renderer.chat_template,
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default_template_content_format=renderer.chat_template_content_format,
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default_template_kwargs=renderer.default_chat_template_kwargs,
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tool_dicts=tool_dicts,
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parser=parser,
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)
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all_conversations.append(conversation)
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all_engine_prompts.append(engine_prompts[0])
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return all_conversations, all_engine_prompts
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async def create_batch_chat_completion(
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self,
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request: BatchChatCompletionRequest,
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raw_request: Request | None = None,
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) -> ChatCompletionResponse | ErrorResponse:
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"""Batch Chat Completion endpoint (/v1/chat/completions/batch).
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Processes N conversations from a single request concurrently and
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returns one choice per conversation indexed 0, 1, ..., N-1.
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Streaming, tool use, and beam search are not supported.
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"""
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tokenizer = self.renderer.tokenizer
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assert tokenizer is not None
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single_requests = [
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request.to_chat_completion_request(messages)
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for messages in request.messages
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]
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parser: Parser | None = None
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if self.parser_cls is not None:
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chat_template_kwargs = self._effective_chat_template_kwargs(
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single_requests[0]
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)
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parser = self.parser_cls(
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tokenizer,
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None, # tools
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chat_template_kwargs=chat_template_kwargs,
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)
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render_result = await self.render_batch_chat_request(request)
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if isinstance(render_result, ErrorResponse):
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return render_result
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all_conversations, engine_prompts = render_result
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request_id = (
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f"chatcmpl-{self._base_request_id(raw_request, request.request_id)}"
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)
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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, supports_default_mm_loras=True)
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model_name = self.models.model_name(lora_request)
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data_parallel_rank = self._get_data_parallel_rank(raw_request)
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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_prompt in enumerate(engine_prompts):
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sub_request_id = f"{request_id}_{i}"
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max_tokens = get_max_tokens(
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max_model_len,
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request.max_completion_tokens
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if request.max_completion_tokens is not None
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else request.max_tokens,
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self._extract_prompt_len(engine_prompt),
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self.default_sampling_params,
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self.override_max_tokens,
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)
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single_request = single_requests[i]
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sampling_params = single_request.to_sampling_params(
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max_tokens, self.default_sampling_params
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)
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self._log_inputs(
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sub_request_id,
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engine_prompt,
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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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generators.append(
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self.engine_client.generate(
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engine_prompt,
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sampling_params,
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sub_request_id,
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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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reasoning_ended=None,
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)
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)
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return await self.chat_completion_full_generator_batch(
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request, # type: ignore[arg-type]
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generators,
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request_id,
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model_name,
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all_conversations,
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tokenizer,
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request_metadata,
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parser,
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)
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async def chat_completion_full_generator_batch(
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self,
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request: BatchChatCompletionRequest, # type: ignore[override]
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generators: list[AsyncGenerator[RequestOutput, None]],
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request_id: str,
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model_name: str,
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all_conversations: list[list[ConversationMessage]],
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tokenizer: TokenizerLike,
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request_metadata: RequestResponseMetadata,
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parser: Parser | None = None,
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) -> ErrorResponse | ChatCompletionResponse:
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"""Handle batched (non-streaming) chat completions.
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Fans out N generators (one per conversation in the batch), collects
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the final output for each, and assembles a single
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``ChatCompletionResponse`` whose ``choices`` are indexed 0,...,N-1.
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Tool-use and streaming are rejected upstream by the
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``check_batch_mode`` validator, so neither needs to be handled here.
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"""
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created_time = int(time.time())
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final_results: dict[int, RequestOutput] = {}
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try:
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async for prompt_idx, res in merge_async_iterators(*generators):
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final_results[prompt_idx] = res
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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choices: list[ChatCompletionResponseChoice] = []
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total_prompt_tokens = 0
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total_completion_tokens = 0
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for prompt_idx in range(len(generators)):
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final_res = final_results.get(prompt_idx)
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if final_res is None:
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return self.create_error_response(
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f"No output received from the engine for prompt {prompt_idx}.",
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err_type="InternalServerError",
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status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
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)
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assert final_res.prompt_token_ids is not None
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num_prompt_tokens = len(final_res.prompt_token_ids)
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if final_res.encoder_prompt_token_ids is not None:
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num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
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total_prompt_tokens += num_prompt_tokens
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total_completion_tokens += sum(
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len(output.token_ids) for output in final_res.outputs
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)
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for output in final_res.outputs:
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self._raise_if_error(output.finish_reason, request_id)
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if request.logprobs and request.top_logprobs is not None:
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assert output.logprobs is not None, "Did not output logprobs"
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logprobs = self._create_chat_logprobs(
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token_ids=output.token_ids,
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top_logprobs=output.logprobs,
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num_output_top_logprobs=request.top_logprobs,
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tokenizer=tokenizer,
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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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if parser is not None:
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reasoning, content, _ = parser.parse(
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output.text,
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request=request, # type: ignore[arg-type]
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model_output_token_ids=output.token_ids,
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)
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if not request.include_reasoning:
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reasoning = None
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else:
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reasoning = None
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content = output.text
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role = (
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self.response_role
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if request.add_generation_prompt
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else request.messages[prompt_idx][-1]["role"]
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)
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message = ChatMessage(role=role, reasoning=reasoning, content=content)
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if request.echo:
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conversation = all_conversations[prompt_idx]
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last_msg_content: str | list[dict[str, str]] = ""
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if conversation and "content" in conversation[-1]:
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last_msg_content = conversation[-1]["content"] or ""
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if isinstance(last_msg_content, list):
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last_msg_content = "\n".join(
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msg["text"] for msg in last_msg_content
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)
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message.content = last_msg_content + (message.content or "")
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choice_data = ChatCompletionResponseChoice(
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index=prompt_idx,
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message=message,
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logprobs=logprobs,
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finish_reason=output.finish_reason
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if output.finish_reason
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else "stop",
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stop_reason=output.stop_reason,
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token_ids=(
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as_list(output.token_ids) if request.return_token_ids else None
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),
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)
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choices.append(choice_data)
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usage = 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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request_metadata.final_usage_info = usage
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choices.sort(key=lambda c: c.index)
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return ChatCompletionResponse(
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id=request_id,
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created=created_time,
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model=model_name,
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choices=choices,
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usage=usage,
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system_fingerprint=self.system_fingerprint,
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)
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