serving.py 48.5 KB
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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 json
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import sys
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import time
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import traceback
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from collections.abc import AsyncGenerator, Callable, Mapping
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from dataclasses import dataclass, field
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from http import HTTPStatus
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from typing import Any, ClassVar, Generic, Protocol, TypeAlias, TypeVar
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import numpy as np
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from fastapi import Request
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from openai.types.responses import (
    ToolChoiceFunction,
)
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from pydantic import ConfigDict, TypeAdapter
from starlette.datastructures import Headers
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import vllm.envs as envs
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from vllm.beam_search import BeamSearchSequence, create_sort_beams_key_function
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from vllm.config import ModelConfig
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
    ConversationMessage,
)
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.chat_completion.protocol import (
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    ChatCompletionNamedToolChoiceParam,
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    ChatCompletionRequest,
    ChatCompletionResponse,
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)
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from vllm.entrypoints.openai.completion.protocol import (
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    CompletionRequest,
    CompletionResponse,
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)
from vllm.entrypoints.openai.engine.protocol import (
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    ErrorInfo,
    ErrorResponse,
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    FunctionCall,
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    FunctionDefinition,
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)
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from vllm.entrypoints.openai.models.serving import OpenAIServingModels
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from vllm.entrypoints.openai.responses.context import (
    ConversationContext,
    HarmonyContext,
    ParsableContext,
    StreamingHarmonyContext,
)
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from vllm.entrypoints.openai.responses.protocol import (
    ResponseInputOutputItem,
    ResponsesRequest,
)
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from vllm.entrypoints.openai.responses.utils import (
    construct_input_messages,
)
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from vllm.entrypoints.openai.speech_to_text.protocol import (
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    TranscriptionRequest,
    TranscriptionResponse,
    TranslationRequest,
)
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from vllm.entrypoints.pooling.classify.protocol import (
    ClassificationChatRequest,
    ClassificationCompletionRequest,
    ClassificationResponse,
)
from vllm.entrypoints.pooling.embed.protocol import (
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    EmbeddingBytesResponse,
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    EmbeddingChatRequest,
    EmbeddingCompletionRequest,
    EmbeddingResponse,
)
from vllm.entrypoints.pooling.pooling.protocol import (
    IOProcessorRequest,
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    PoolingChatRequest,
    PoolingCompletionRequest,
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    PoolingResponse,
)
from vllm.entrypoints.pooling.score.protocol import (
    RerankRequest,
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    ScoreDataRequest,
    ScoreQueriesDocumentsRequest,
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    ScoreRequest,
    ScoreResponse,
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    ScoreTextRequest,
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)
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from vllm.entrypoints.serve.disagg.protocol import GenerateRequest, GenerateResponse
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from vllm.entrypoints.serve.tokenize.protocol import (
    DetokenizeRequest,
    TokenizeChatRequest,
    TokenizeCompletionRequest,
    TokenizeResponse,
)
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from vllm.entrypoints.utils import get_max_tokens, sanitize_message
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from vllm.exceptions import VLLMValidationError
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from vllm.inputs.data import PromptType, SingletonPrompt, TokensPrompt
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob, PromptLogprobs
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from vllm.lora.request import LoRARequest
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from vllm.multimodal import MultiModalDataDict
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from vllm.outputs import CompletionOutput, PoolingRequestOutput, RequestOutput
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from vllm.pooling_params import PoolingParams
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from vllm.renderers import ChatParams, TokenizeParams, merge_kwargs
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from vllm.renderers.inputs import TokPrompt
from vllm.renderers.inputs.preprocess import (
    SingletonDictPrompt,
    extract_prompt_components,
    extract_prompt_len,
    parse_model_prompt,
    prompt_to_seq,
)
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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.tool_parsers import ToolParser
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from vllm.tracing import (
    contains_trace_headers,
    extract_trace_headers,
    log_tracing_disabled_warning,
)
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from vllm.utils import random_uuid
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from vllm.utils.async_utils import (
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    collect_from_async_generator,
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    merge_async_iterators,
)
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class GenerationError(Exception):
    """raised when finish_reason indicates internal server error (500)"""

    def __init__(self, message: str = "Internal server error"):
        super().__init__(message)
        self.status_code = HTTPStatus.INTERNAL_SERVER_ERROR


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logger = init_logger(__name__)

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class RendererRequest(Protocol):
    def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
        raise NotImplementedError


class RendererChatRequest(RendererRequest, Protocol):
    def build_chat_params(
        self,
        default_template: str | None,
        default_template_content_format: ChatTemplateContentFormatOption,
    ) -> ChatParams:
        raise NotImplementedError


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CompletionLikeRequest: TypeAlias = (
    CompletionRequest
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    | TokenizeCompletionRequest
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    | DetokenizeRequest
    | EmbeddingCompletionRequest
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    | ClassificationCompletionRequest
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    | RerankRequest
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    | ScoreRequest
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    | PoolingCompletionRequest
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)
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ChatLikeRequest: TypeAlias = (
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    ChatCompletionRequest
    | TokenizeChatRequest
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    | EmbeddingChatRequest
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    | ClassificationChatRequest
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    | PoolingChatRequest
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)
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SpeechToTextRequest: TypeAlias = TranscriptionRequest | TranslationRequest
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AnyRequest: TypeAlias = (
    CompletionLikeRequest
    | ChatLikeRequest
    | SpeechToTextRequest
    | ResponsesRequest
    | IOProcessorRequest
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    | GenerateRequest
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)

AnyResponse: TypeAlias = (
    CompletionResponse
    | ChatCompletionResponse
    | EmbeddingResponse
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    | EmbeddingBytesResponse
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    | TranscriptionResponse
    | TokenizeResponse
    | PoolingResponse
    | ClassificationResponse
    | ScoreResponse
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    | GenerateResponse
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)
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RequestT = TypeVar("RequestT", bound=AnyRequest)


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@dataclass(kw_only=True)
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class ServeContext(Generic[RequestT]):
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    request: RequestT
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    raw_request: Request | None = None
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    model_name: str
    request_id: str
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    created_time: int = field(default_factory=lambda: int(time.time()))
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    lora_request: LoRARequest | None = None
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    engine_prompts: list[TokPrompt] | None = None
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    result_generator: AsyncGenerator[tuple[int, PoolingRequestOutput], None] | None = (
        None
    )
    final_res_batch: list[PoolingRequestOutput] = field(default_factory=list)
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    model_config = ConfigDict(arbitrary_types_allowed=True)
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class OpenAIServing:
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    request_id_prefix: ClassVar[str] = """
    A short string prepended to every request’s ID (e.g. "embd", "classify")
    so you can easily tell “this ID came from Embedding vs Classification.”
    """
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    def __init__(
        self,
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        engine_client: EngineClient,
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        models: OpenAIServingModels,
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        *,
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        request_logger: RequestLogger | None,
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        return_tokens_as_token_ids: bool = False,
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        log_error_stack: bool = False,
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    ):
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        super().__init__()

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        self.engine_client = engine_client
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        self.models = models
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        self.request_logger = request_logger
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        self.return_tokens_as_token_ids = return_tokens_as_token_ids
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        self.log_error_stack = log_error_stack
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        self.input_processor = self.models.input_processor
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        self.io_processor = self.models.io_processor
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        self.renderer = self.models.renderer
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        self.model_config = self.models.model_config
        self.max_model_len = self.model_config.max_model_len

    async def beam_search(
        self,
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        prompt: TokPrompt,
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        request_id: str,
        params: BeamSearchParams,
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        lora_request: LoRARequest | None = None,
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        trace_headers: Mapping[str, str] | None = None,
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    ) -> AsyncGenerator[RequestOutput, None]:
        beam_width = params.beam_width
        max_tokens = params.max_tokens
        ignore_eos = params.ignore_eos
        temperature = params.temperature
        length_penalty = params.length_penalty
        include_stop_str_in_output = params.include_stop_str_in_output

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        input_processor = self.input_processor
        tokenizer = input_processor.tokenizer
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        if tokenizer is None:
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            raise VLLMValidationError(
                "You cannot use beam search when `skip_tokenizer_init=True`",
                parameter="skip_tokenizer_init",
                value=True,
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            )

        eos_token_id: int = tokenizer.eos_token_id  # type: ignore

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        if isinstance(prompt, dict) and "encoder_prompt" in prompt:
            raise NotImplementedError("Encoder-decoder prompt not supported")
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        prompt_text: str | None = prompt.get("prompt")  # type: ignore
        prompt_token_ids: list[int] = prompt.get("prompt_token_ids", [])  # type: ignore
        multi_modal_data: MultiModalDataDict | None = prompt.get("multi_modal_data")  # type: ignore
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        mm_processor_kwargs: dict[str, Any] | None = None

        # This is a workaround to fix multimodal beam search; this is a
        # bandaid fix for 2 small problems:
        # 1. Multi_modal_data on the processed_inputs currently resolves to
        #    `None`.
        # 2. preprocessing above expands the multimodal placeholders. However,
        #    this happens again in generation, so the double expansion causes
        #    a mismatch.
        # TODO - would be ideal to handle this more gracefully.
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        tokenized_length = len(prompt_token_ids)

        sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)

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        logprobs_num = 2 * beam_width
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        beam_search_params = SamplingParams(
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            logprobs=logprobs_num,
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            max_tokens=1,
            temperature=temperature,
        )
        all_beams = [
            BeamSearchSequence(
                tokens=prompt_token_ids,
                cum_logprob=0,
                logprobs=[],
                multi_modal_data=multi_modal_data,
                mm_processor_kwargs=mm_processor_kwargs,
                lora_request=lora_request,
            )
        ]
        completed = []

        for _ in range(max_tokens):
            prompts_batch, lora_req_batch = zip(
                *[
                    (
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                        TokensPrompt(
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                            prompt_token_ids=beam.tokens,
                            multi_modal_data=beam.multi_modal_data,
                            mm_processor_kwargs=beam.mm_processor_kwargs,
                        ),
                        beam.lora_request,
                    )
                    for beam in all_beams
                ]
            )

            tasks = []
            request_id_batch = f"{request_id}-{random_uuid()}"

            for i, (individual_prompt, lora_req) in enumerate(
                zip(prompts_batch, lora_req_batch)
            ):
                request_id_item = f"{request_id_batch}-beam-{i}"
                task = asyncio.create_task(
                    collect_from_async_generator(
                        self.engine_client.generate(
                            individual_prompt,
                            beam_search_params,
                            request_id_item,
                            lora_request=lora_req,
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                            trace_headers=trace_headers,
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                        )
                    )
                )
                tasks.append(task)

            output = [x[0] for x in await asyncio.gather(*tasks)]

            new_beams = []
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            # Store all new tokens generated by beam
            all_beams_token_id = []
            # Store the cumulative probability of all tokens
            # generated by beam search
            all_beams_logprob = []
            # Iterate through all beam inference results
            for i, result in enumerate(output):
                current_beam = all_beams[i]
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                # check for error finish reason and abort beam search
                if result.outputs[0].finish_reason == "error":
                    # yield error output and terminate beam search
                    yield RequestOutput(
                        request_id=request_id,
                        prompt=prompt_text,
                        outputs=[
                            CompletionOutput(
                                index=0,
                                text="",
                                token_ids=[],
                                cumulative_logprob=None,
                                logprobs=None,
                                finish_reason="error",
                            )
                        ],
                        finished=True,
                        prompt_token_ids=prompt_token_ids,
                        prompt_logprobs=None,
                    )
                    return

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                if result.outputs[0].logprobs is not None:
                    logprobs = result.outputs[0].logprobs[0]
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                    all_beams_token_id.extend(list(logprobs.keys()))
                    all_beams_logprob.extend(
                        [
                            current_beam.cum_logprob + obj.logprob
                            for obj in logprobs.values()
                        ]
                    )

            # Handle the token for the end of sentence (EOS)
            all_beams_token_id = np.array(all_beams_token_id)
            all_beams_logprob = np.array(all_beams_logprob)

            if not ignore_eos:
                # Get the index position of eos token in all generated results
                eos_idx = np.where(all_beams_token_id == eos_token_id)[0]
                for idx in eos_idx:
                    current_beam = all_beams[idx // logprobs_num]
                    result = output[idx // logprobs_num]
                    assert result.outputs[0].logprobs is not None
                    logprobs_entry = result.outputs[0].logprobs[0]
                    completed.append(
                        BeamSearchSequence(
                            tokens=current_beam.tokens + [eos_token_id]
                            if include_stop_str_in_output
                            else current_beam.tokens,
                            logprobs=current_beam.logprobs + [logprobs_entry],
                            cum_logprob=float(all_beams_logprob[idx]),
                            finish_reason="stop",
                            stop_reason=eos_token_id,
                        )
                    )
                # After processing, set the log probability of the eos condition
                # to negative infinity.
                all_beams_logprob[eos_idx] = -np.inf

            # Processing non-EOS tokens
            # Get indices of the top beam_width probabilities
            topn_idx = np.argpartition(np.negative(all_beams_logprob), beam_width)[
                :beam_width
            ]

            for idx in topn_idx:
                current_beam = all_beams[idx // logprobs_num]
                result = output[idx // logprobs_num]
                token_id = int(all_beams_token_id[idx])
                assert result.outputs[0].logprobs is not None
                logprobs_entry = result.outputs[0].logprobs[0]
                new_beams.append(
                    BeamSearchSequence(
                        tokens=current_beam.tokens + [token_id],
                        logprobs=current_beam.logprobs + [logprobs_entry],
                        lora_request=current_beam.lora_request,
                        cum_logprob=float(all_beams_logprob[idx]),
                        multi_modal_data=current_beam.multi_modal_data,
                        mm_processor_kwargs=current_beam.mm_processor_kwargs,
                    )
                )

            all_beams = new_beams
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        completed.extend(all_beams)
        sorted_completed = sorted(completed, key=sort_beams_key, reverse=True)
        best_beams = sorted_completed[:beam_width]

        for beam in best_beams:
            if beam.tokens[-1] == eos_token_id and not ignore_eos:
                # Skip the eos token in the text.
                tokens = beam.tokens[tokenized_length:-1]
            else:
                tokens = beam.tokens[tokenized_length:]
            beam.text = tokenizer.decode(tokens)

        yield RequestOutput(
            request_id=request_id,
            prompt=prompt_text,
            outputs=[
                CompletionOutput(
                    text=beam.text,  # type: ignore
                    cumulative_logprob=beam.cum_logprob,
                    token_ids=beam.tokens[tokenized_length:],
                    index=i,
                    logprobs=beam.logprobs,
                    finish_reason=beam.finish_reason
                    if beam.finish_reason is not None
                    else "length",
                    stop_reason=beam.stop_reason,
                )
                for (i, beam) in enumerate(best_beams)
            ],
            finished=True,
            prompt_token_ids=prompt_token_ids,
            prompt_logprobs=None,
        )
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    async def _preprocess(
        self,
        ctx: ServeContext,
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    ) -> ErrorResponse | None:
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        """
        Default preprocessing hook. Subclasses may override
        to prepare `ctx` (classification, embedding, etc.).
        """
        return None

    def _build_response(
        self,
        ctx: ServeContext,
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    ) -> AnyResponse | ErrorResponse:
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        """
        Default response builder. Subclass may override this method
        to return the appropriate response object.
        """
        return self.create_error_response("unimplemented endpoint")

    async def handle(
        self,
        ctx: ServeContext,
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    ) -> AnyResponse | ErrorResponse:
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        async for response in self._pipeline(ctx):
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            return response

        return self.create_error_response("No response yielded from pipeline")

    async def _pipeline(
        self,
        ctx: ServeContext,
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    ) -> AsyncGenerator[AnyResponse | ErrorResponse, None]:
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        """Execute the request processing pipeline yielding responses."""
        if error := await self._check_model(ctx.request):
            yield error
        if error := self._validate_request(ctx):
            yield error

        preprocess_ret = await self._preprocess(ctx)
        if isinstance(preprocess_ret, ErrorResponse):
            yield preprocess_ret

        generators_ret = await self._prepare_generators(ctx)
        if isinstance(generators_ret, ErrorResponse):
            yield generators_ret

        collect_ret = await self._collect_batch(ctx)
        if isinstance(collect_ret, ErrorResponse):
            yield collect_ret

        yield self._build_response(ctx)

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    def _validate_request(self, ctx: ServeContext) -> ErrorResponse | None:
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        truncate_prompt_tokens = getattr(ctx.request, "truncate_prompt_tokens", None)
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        if (
            truncate_prompt_tokens is not None
            and truncate_prompt_tokens > self.max_model_len
        ):
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            return self.create_error_response(
                "truncate_prompt_tokens value is "
                "greater than max_model_len."
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                " Please, select a smaller truncation size."
            )
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        return None

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    def _create_pooling_params(
        self,
        ctx: ServeContext,
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    ) -> PoolingParams | ErrorResponse:
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        if not hasattr(ctx.request, "to_pooling_params"):
            return self.create_error_response(
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                "Request type does not support pooling parameters"
            )
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        return ctx.request.to_pooling_params()

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    async def _prepare_generators(
        self,
        ctx: ServeContext,
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    ) -> ErrorResponse | None:
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        """Schedule the request and get the result generator."""
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        generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
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        try:
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            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
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            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
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            if ctx.engine_prompts is None:
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                return self.create_error_response("Engine prompts not available")
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            for i, engine_prompt in enumerate(ctx.engine_prompts):
                request_id_item = f"{ctx.request_id}-{i}"

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                self._log_inputs(
                    request_id_item,
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                    engine_prompt,
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                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
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                generator = self.engine_client.encode(
                    engine_prompt,
                    pooling_params,
                    request_id_item,
                    lora_request=ctx.lora_request,
                    trace_headers=trace_headers,
                    priority=getattr(ctx.request, "priority", 0),
                )

                generators.append(generator)

            ctx.result_generator = merge_async_iterators(*generators)

            return None

        except Exception as e:
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            return self.create_error_response(e)
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    async def _collect_batch(
        self,
        ctx: ServeContext,
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    ) -> ErrorResponse | None:
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        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
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                return self.create_error_response("Engine prompts not available")
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            num_prompts = len(ctx.engine_prompts)
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            final_res_batch: list[PoolingRequestOutput | None]
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            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
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                return self.create_error_response("Result generator not available")
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            async for i, res in ctx.result_generator:
                final_res_batch[i] = res

            if None in final_res_batch:
                return self.create_error_response(
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                    "Failed to generate results for all prompts"
                )
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            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
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            return None

        except Exception as e:
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            return self.create_error_response(e)
640

641
    def create_error_response(
642
        self,
643
        message: str | Exception,
644
645
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
646
        param: str | None = None,
647
    ) -> ErrorResponse:
648
649
650
651
652
        exc: Exception | None = None

        if isinstance(message, Exception):
            exc = message

653
            from vllm.exceptions import VLLMValidationError
654
655
656
657
658

            if isinstance(exc, VLLMValidationError):
                err_type = "BadRequestError"
                status_code = HTTPStatus.BAD_REQUEST
                param = exc.parameter
659
            elif isinstance(exc, (ValueError, TypeError, RuntimeError, OverflowError)):
660
661
662
663
                # Common validation errors from user input
                err_type = "BadRequestError"
                status_code = HTTPStatus.BAD_REQUEST
                param = None
664
665
666
667
            elif isinstance(exc, NotImplementedError):
                err_type = "NotImplementedError"
                status_code = HTTPStatus.NOT_IMPLEMENTED
                param = None
668
669
670
671
672
673
674
675
676
677
678
679
            elif exc.__class__.__name__ == "TemplateError":
                # jinja2.TemplateError (avoid importing jinja2)
                err_type = "BadRequestError"
                status_code = HTTPStatus.BAD_REQUEST
                param = None
            else:
                err_type = "InternalServerError"
                status_code = HTTPStatus.INTERNAL_SERVER_ERROR
                param = None

            message = str(exc)

680
681
682
683
684
685
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
686

687
        return ErrorResponse(
688
            error=ErrorInfo(
689
                message=sanitize_message(message),
690
691
692
693
                type=err_type,
                code=status_code.value,
                param=param,
            )
694
        )
695

696
    def create_streaming_error_response(
697
        self,
698
        message: str | Exception,
699
700
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
701
        param: str | None = None,
702
    ) -> str:
703
        json_str = json.dumps(
704
            self.create_error_response(
705
706
707
708
                message=message,
                err_type=err_type,
                status_code=status_code,
                param=param,
709
710
            ).model_dump()
        )
711
712
        return json_str

713
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719
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721
722
723
724
725
726
727
728
729
730
731
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733
734
735
736
737
738
739
740
741
    def _raise_if_error(self, finish_reason: str | None, request_id: str) -> None:
        """Raise GenerationError if finish_reason indicates an error."""
        if finish_reason == "error":
            logger.error(
                "Request %s failed with an internal error during generation",
                request_id,
            )
            raise GenerationError("Internal server error")

    def _convert_generation_error_to_response(
        self, e: GenerationError
    ) -> ErrorResponse:
        """Convert GenerationError to ErrorResponse."""
        return self.create_error_response(
            str(e),
            err_type="InternalServerError",
            status_code=e.status_code,
        )

    def _convert_generation_error_to_streaming_response(
        self, e: GenerationError
    ) -> str:
        """Convert GenerationError to streaming error response."""
        return self.create_streaming_error_response(
            str(e),
            err_type="InternalServerError",
            status_code=e.status_code,
        )

742
    async def _check_model(
743
744
        self,
        request: AnyRequest,
745
    ) -> ErrorResponse | None:
746
747
        error_response = None

748
        if self._is_model_supported(request.model):
749
            return None
750
        if request.model in self.models.lora_requests:
751
            return None
752
753
754
755
756
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
757
758
            if isinstance(load_result, LoRARequest):
                return None
759
760
761
762
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
763
764
765
                error_response = load_result

        return error_response or self.create_error_response(
766
767
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
768
            status_code=HTTPStatus.NOT_FOUND,
769
            param="model",
770
        )
771

772
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
        """Determine if there are any active default multimodal loras."""
        # TODO: Currently this is only enabled for chat completions
        # to be better aligned with only being enabled for .generate
        # when run offline. It would be nice to support additional
        # tasks types in the future.
        message_types = self._get_message_types(request)
        default_mm_loras = set()

        for lora in self.models.lora_requests.values():
            # Best effort match for default multimodal lora adapters;
            # There is probably a better way to do this, but currently
            # this matches against the set of 'types' in any content lists
            # up until '_', e.g., to match audio_url -> audio
            if lora.lora_name in message_types:
                default_mm_loras.add(lora)

        # Currently only support default modality specific loras if
        # we have exactly one lora matched on the request.
        if len(default_mm_loras) == 1:
            return default_mm_loras.pop()
        return None

795
    def _maybe_get_adapters(
796
797
798
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
799
    ) -> LoRARequest | None:
800
        if request.model in self.models.lora_requests:
801
            return self.models.lora_requests[request.model]
802
803
804
805
806
807

        # Currently only support default modality specific loras
        # if we have exactly one lora matched on the request.
        if supports_default_mm_loras:
            default_mm_lora = self._get_active_default_mm_loras(request)
            if default_mm_lora is not None:
808
                return default_mm_lora
809
810

        if self._is_model_supported(request.model):
811
            return None
812

813
        # if _check_model has been called earlier, this will be unreachable
814
        raise ValueError(f"The model `{request.model}` does not exist.")
815

816
817
818
819
820
821
822
823
824
825
    def _get_message_types(self, request: AnyRequest) -> set[str]:
        """Retrieve the set of types from message content dicts up
        until `_`; we use this to match potential multimodal data
        with default per modality loras.
        """
        message_types: set[str] = set()

        if not hasattr(request, "messages"):
            return message_types

826
827
828
829
830
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
831
832
833
834
835
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
836
837
838
839
840
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

841
842
    def _validate_input(
        self,
843
        request: object,
844
        input_ids: list[int],
845
        input_text: str,
846
    ) -> TokensPrompt:
847
848
        token_num = len(input_ids)

849
850
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
851
        if isinstance(
852
            request,
853
854
855
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
856
857
858
                ScoreDataRequest,
                ScoreTextRequest,
                ScoreQueriesDocumentsRequest,
859
                RerankRequest,
860
861
                ClassificationCompletionRequest,
                ClassificationChatRequest,
862
863
            ),
        ):
864
865
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
866
            if token_num > self.max_model_len:
867
                operations: dict[type[AnyRequest], str] = {
868
869
870
                    ScoreDataRequest: "score",
                    ScoreTextRequest: "score",
                    ScoreQueriesDocumentsRequest: "score",
871
872
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
873
                }
874
                operation = operations.get(type(request), "embedding generation")
875
                raise VLLMValidationError(
876
877
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
878
                    f"{token_num} tokens in the input for {operation}. "
879
880
881
                    f"Please reduce the length of the input.",
                    parameter="input_tokens",
                    value=token_num,
882
                )
883
            return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
884

885
886
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
887
        if isinstance(
888
889
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
890
        ):
891
            return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
892

893
894
895
896
897
        # chat completion endpoint supports max_completion_tokens
        if isinstance(request, ChatCompletionRequest):
            # TODO(#9845): remove max_tokens when field dropped from OpenAI API
            max_tokens = request.max_completion_tokens or request.max_tokens
        else:
898
            max_tokens = getattr(request, "max_tokens", None)
899
900
901
902

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
903
            raise VLLMValidationError(
904
                f"This model's maximum context length is "
905
906
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
907
908
909
                "the input messages.",
                parameter="input_tokens",
                value=token_num,
910
            )
911

912
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
913
            raise VLLMValidationError(
914
915
916
917
                "'max_tokens' or 'max_completion_tokens' is too large: "
                f"{max_tokens}. This model's maximum context length is "
                f"{self.max_model_len} tokens and your request has "
                f"{token_num} input tokens ({max_tokens} > {self.max_model_len}"
918
919
920
                f" - {token_num}).",
                parameter="max_tokens",
                value=max_tokens,
921
            )
922

923
        return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
924

925
926
    def _validate_chat_template(
        self,
927
928
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
929
        trust_request_chat_template: bool,
930
    ) -> ErrorResponse | None:
931
        if not trust_request_chat_template and (
932
933
934
935
936
937
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
938
939
940
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
941
942
                "Refused request with untrusted chat template."
            )
943
944
        return None

945
946
947
948
949
950
951
952
953
954
955
956
    @staticmethod
    def _prepare_extra_chat_template_kwargs(
        request_chat_template_kwargs: dict[str, Any] | None = None,
        default_chat_template_kwargs: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        """Helper to merge server-default and request-specific chat template kwargs."""
        request_chat_template_kwargs = request_chat_template_kwargs or {}
        if default_chat_template_kwargs is None:
            return request_chat_template_kwargs
        # Apply server defaults first, then request kwargs override.
        return default_chat_template_kwargs | request_chat_template_kwargs

957
958
959
960
961
    async def _preprocess_completion(
        self,
        request: RendererRequest,
        prompt_input: str | list[str] | list[int] | list[list[int]] | None,
        prompt_embeds: bytes | list[bytes] | None,
962
    ) -> list[TokPrompt]:
963
        renderer = self.renderer
964
        model_config = self.model_config
965

966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
        tok_params = request.build_tok_params(model_config)

        prompts = list[SingletonPrompt | bytes]()
        if prompt_embeds is not None:  # embeds take higher priority
            prompts.extend(prompt_to_seq(prompt_embeds))
        if prompt_input is not None:
            prompts.extend(prompt_to_seq(prompt_input))

        parsed_prompts = [
            (
                prompt
                if isinstance(prompt, bytes)
                else parse_model_prompt(model_config, prompt)
            )
            for prompt in prompts
        ]
        in_prompts = await renderer.render_prompts_async(parsed_prompts)
983
984
985
986
987
988

        extra_items = {
            k: v
            for k in ("mm_processor_kwargs", "cache_salt")
            if (v := getattr(request, k, None)) is not None
        }
989
990
991
992
993
994
995
        for in_prompt in in_prompts:
            target_prompt: SingletonDictPrompt = in_prompt.get(  # type: ignore
                "encoder_prompt", in_prompt
            )
            target_prompt.update(extra_items)  # type: ignore

        engine_prompts = await renderer.tokenize_prompts_async(in_prompts, tok_params)
996
997
998

        return engine_prompts

999
1000
    async def _preprocess_chat(
        self,
1001
        request: RendererChatRequest,
1002
        messages: list[ChatCompletionMessageParam],
1003
1004
1005
        default_template: str | None,
        default_template_content_format: ChatTemplateContentFormatOption,
        default_template_kwargs: dict[str, Any] | None,
1006
        tool_dicts: list[dict[str, Any]] | None = None,
1007
        tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
1008
    ) -> tuple[list[ConversationMessage], list[TokPrompt]]:
1009
        from vllm.tokenizers.mistral import MistralTokenizer
1010

1011
1012
1013
1014
1015
1016
1017
        renderer = self.renderer

        default_template_kwargs = merge_kwargs(
            default_template_kwargs,
            dict(
                tools=tool_dicts,
                tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
1018
1019
1020
            ),
        )

1021
1022
1023
1024
        tok_params = request.build_tok_params(self.model_config)
        chat_params = request.build_chat_params(
            default_template, default_template_content_format
        ).with_defaults(default_template_kwargs)
1025

1026
        conversation, in_prompt = await renderer.render_messages_async(
1027
1028
            messages, chat_params
        )
1029
1030
1031
        target_prompt: SingletonDictPrompt = in_prompt.get(  # type: ignore
            "encoder_prompt", in_prompt
        )
1032

1033
1034
1035
1036
1037
        extra_items = {
            k: v
            for k in ("mm_processor_kwargs", "cache_salt")
            if (v := getattr(request, k, None)) is not None
        }
1038
1039
1040
        target_prompt.update(extra_items)  # type: ignore

        engine_prompt = await renderer.tokenize_prompt_async(target_prompt, tok_params)
1041

1042
1043
1044
        # tool parsing is done only if a tool_parser has been set and if
        # tool_choice is not "none" (if tool_choice is "none" but a tool_parser
        # is set, we want to prevent parsing a tool_call hallucinated by the LLM
1045
1046
1047
1048
1049
1050
1051
1052
1053
        if tool_parser is not None:
            tool_choice = getattr(request, "tool_choice", "none")
            if tool_choice != "none":
                if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                    msg = (
                        "Tool usage is only supported for Chat Completions API "
                        "or Responses API requests."
                    )
                    raise NotImplementedError(msg)
1054

1055
1056
1057
                # TODO: Update adjust_request to accept ResponsesRequest
                tokenizer = renderer.get_tokenizer()
                request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore[arg-type]
1058

1059
        return conversation, [engine_prompt]
1060

1061
1062
1063
1064
1065
1066
1067
1068
1069
    def _extract_prompt_components(self, prompt: object):
        return extract_prompt_components(self.model_config, prompt)

    def _extract_prompt_text(self, prompt: object):
        return self._extract_prompt_components(prompt).text

    def _extract_prompt_len(self, prompt: object):
        return extract_prompt_len(self.model_config, prompt)

1070
1071
1072
1073
1074
    async def _render_next_turn(
        self,
        request: ResponsesRequest,
        messages: list[ResponseInputOutputItem],
        tool_dicts: list[dict[str, Any]] | None,
1075
        tool_parser: Callable[[TokenizerLike], ToolParser] | None,
1076
1077
1078
1079
1080
1081
1082
        chat_template: str | None,
        chat_template_content_format: ChatTemplateContentFormatOption,
    ):
        new_messages = construct_input_messages(
            request_input=messages,
        )

1083
        _, engine_prompts = await self._preprocess_chat(
1084
1085
            request,
            new_messages,
1086
1087
1088
            default_template=chat_template,
            default_template_content_format=chat_template_content_format,
            default_template_kwargs=None,
1089
1090
1091
            tool_dicts=tool_dicts,
            tool_parser=tool_parser,
        )
1092
        return engine_prompts
1093

1094
1095
1096
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
1097
        engine_prompt: TokPrompt,
1098
        sampling_params: SamplingParams,
1099
        tok_params: TokenizeParams,
1100
        context: ConversationContext,
1101
        lora_request: LoRARequest | None = None,
1102
        priority: int = 0,
1103
        trace_headers: Mapping[str, str] | None = None,
1104
    ):
1105
        prompt_text = self._extract_prompt_text(engine_prompt)
1106

1107
        orig_priority = priority
1108
        sub_request = 0
1109
        while True:
1110
1111
            # Ensure that each sub-request has a unique request id.
            sub_request_id = f"{request_id}_{sub_request}"
1112

1113
            self._log_inputs(
1114
                sub_request_id,
1115
                engine_prompt,
1116
1117
1118
                params=sampling_params,
                lora_request=lora_request,
            )
1119
1120
1121

            tokenization_kwargs = tok_params.get_encode_kwargs()
            engine_request = self.input_processor.process_inputs(
1122
                sub_request_id,
1123
1124
                engine_prompt,
                sampling_params,
1125
                lora_request=lora_request,
1126
                tokenization_kwargs=tokenization_kwargs,
1127
1128
                trace_headers=trace_headers,
                priority=priority,
1129
            )
1130
1131
1132
1133

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1134
                sub_request_id,
1135
                lora_request=lora_request,
1136
                trace_headers=trace_headers,
1137
                priority=priority,
1138
1139
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1140
            )
1141

1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
            async for res in generator:
                context.append_output(res)
                # NOTE(woosuk): The stop condition is handled by the engine.
                yield context

            if not context.need_builtin_tool_call():
                # The model did not ask for a tool call, so we're done.
                break

            # Call the tool and update the context with the result.
            tool_output = await context.call_tool()
1153
            context.append_tool_output(tool_output)
1154
1155
1156
1157
1158

            # TODO: uncomment this and enable tool output streaming
            # yield context

            # Create inputs for the next turn.
1159
            # Render the next prompt token ids and update sampling_params.
1160
            if isinstance(context, (HarmonyContext, StreamingHarmonyContext)):
1161
1162
1163
1164
                token_ids = context.render_for_completion()
                engine_prompt = TokensPrompt(prompt_token_ids=token_ids)

                sampling_params.max_tokens = self.max_model_len - len(token_ids)
1165
            elif isinstance(context, ParsableContext):
1166
                engine_prompts = await self._render_next_turn(
1167
1168
1169
1170
1171
1172
1173
1174
                    context.request,
                    context.parser.response_messages,
                    context.tool_dicts,
                    context.tool_parser_cls,
                    context.chat_template,
                    context.chat_template_content_format,
                )
                engine_prompt = engine_prompts[0]
1175
                prompt_text = self._extract_prompt_text(engine_prompt)
1176
1177
1178
1179

                sampling_params.max_tokens = get_max_tokens(
                    self.max_model_len,
                    context.request,
1180
                    self._extract_prompt_len(engine_prompt),
1181
1182
                    self.default_sampling_params,  # type: ignore
                )
1183

1184
1185
            # OPTIMIZATION
            priority = orig_priority - 1
1186
            sub_request += 1
1187

1188
1189
1190
    def _log_inputs(
        self,
        request_id: str,
1191
        inputs: PromptType | TokPrompt,
1192
1193
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1194
1195
1196
    ) -> None:
        if self.request_logger is None:
            return
1197

1198
        components = self._extract_prompt_components(inputs)
1199
1200
1201

        self.request_logger.log_inputs(
            request_id,
1202
1203
1204
            components.text,
            components.token_ids,
            components.embeds,
1205
1206
1207
            params=params,
            lora_request=lora_request,
        )
1208

1209
1210
1211
    async def _get_trace_headers(
        self,
        headers: Headers,
1212
    ) -> Mapping[str, str] | None:
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
        is_tracing_enabled = await self.engine_client.is_tracing_enabled()

        if is_tracing_enabled:
            return extract_trace_headers(headers)

        if contains_trace_headers(headers):
            log_tracing_disabled_warning()

        return None

1223
    @staticmethod
1224
    def _base_request_id(
1225
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        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1227
        """Pulls the request id to use from a header, if provided"""
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        if raw_request is not None and (
            (req_id := raw_request.headers.get("X-Request-Id")) is not None
        ):
            return req_id
1232

1233
        return random_uuid() if default is None else default
1234

1235
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    @staticmethod
    def _get_data_parallel_rank(raw_request: Request | None) -> int | None:
        """Pulls the data parallel rank from a header, if provided"""
        if raw_request is None:
            return None

        rank_str = raw_request.headers.get("X-data-parallel-rank")
        if rank_str is None:
            return None

        try:
            return int(rank_str)
        except ValueError:
            return None

1250
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    @staticmethod
    def _parse_tool_calls_from_content(
        request: ResponsesRequest | ChatCompletionRequest,
1253
        tokenizer: TokenizerLike | None,
1254
        enable_auto_tools: bool,
1255
        tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None,
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        content: str | None = None,
    ) -> tuple[list[FunctionCall] | None, str | None]:
        function_calls = list[FunctionCall]()
        if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
            assert content is not None
            # Forced Function Call
            function_calls.append(
                FunctionCall(name=request.tool_choice.name, arguments=content)
            )
            content = None  # Clear content since tool is called.
        elif request.tool_choice and isinstance(
            request.tool_choice, ChatCompletionNamedToolChoiceParam
        ):
            assert content is not None
            # Forced Function Call
            function_calls.append(
                FunctionCall(name=request.tool_choice.function.name, arguments=content)
            )
            content = None  # Clear content since tool is called.
        elif request.tool_choice == "required":
            assert content is not None
            tool_calls = TypeAdapter(list[FunctionDefinition]).validate_json(content)
            function_calls.extend(
                [
                    FunctionCall(
                        name=tool_call.name,
                        arguments=json.dumps(tool_call.parameters, ensure_ascii=False),
                    )
                    for tool_call in tool_calls
                ]
            )
            content = None  # Clear content since tool is called.
        elif (
            tool_parser_cls
            and enable_auto_tools
            and (request.tool_choice == "auto" or request.tool_choice is None)
        ):
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            if tokenizer is None:
                raise ValueError(
                    "Tokenizer not available when `skip_tokenizer_init=True`"
                )

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            # Automatic Tool Call Parsing
            try:
                tool_parser = tool_parser_cls(tokenizer)
            except RuntimeError as e:
                logger.exception("Error in tool parser creation.")
                raise e
            tool_call_info = tool_parser.extract_tool_calls(
                content if content is not None else "",
                request=request,  # type: ignore
            )
            if tool_call_info is not None and tool_call_info.tools_called:
                # extract_tool_calls() returns a list of tool calls.
                function_calls.extend(
                    FunctionCall(
1312
                        id=tool_call.id,
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                        name=tool_call.function.name,
                        arguments=tool_call.function.arguments,
                    )
                    for tool_call in tool_call_info.tool_calls
                )
                content = tool_call_info.content
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                if content and content.strip() == "":
                    content = None
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            else:
                # No tool calls.
                return None, content

        return function_calls, content

1327
    @staticmethod
1328
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    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
1331
        tokenizer: TokenizerLike | None,
1332
1333
        return_as_token_id: bool = False,
    ) -> str:
1334
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        if return_as_token_id:
            return f"token_id:{token_id}"

1337
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        if logprob.decoded_token is not None:
            return logprob.decoded_token
1339
1340
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1342
1343
1344

        if tokenizer is None:
            raise ValueError(
                "Unable to get tokenizer because `skip_tokenizer_init=True`"
            )

1345
        return tokenizer.decode([token_id])
1346

1347
    def _is_model_supported(self, model_name: str | None) -> bool:
1348
1349
        if not model_name:
            return True
1350
        return self.models.is_base_model(model_name)
1351

1352
1353

def clamp_prompt_logprobs(
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1355
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1356
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    if prompt_logprobs is None:
        return prompt_logprobs

    for logprob_dict in prompt_logprobs:
        if logprob_dict is None:
            continue
        for logprob_values in logprob_dict.values():
1363
            if logprob_values.logprob == float("-inf"):
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                logprob_values.logprob = -9999.0
    return prompt_logprobs