serving.py 48 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 (
    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)
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    def create_error_response(
641
        self,
642
        message: str | Exception,
643
644
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
645
        param: str | None = None,
646
    ) -> ErrorResponse:
647
648
649
650
651
        exc: Exception | None = None

        if isinstance(message, Exception):
            exc = message

652
            from vllm.exceptions import VLLMValidationError
653
654
655
656
657

            if isinstance(exc, VLLMValidationError):
                err_type = "BadRequestError"
                status_code = HTTPStatus.BAD_REQUEST
                param = exc.parameter
658
            elif isinstance(exc, (ValueError, TypeError, RuntimeError, OverflowError)):
659
660
661
662
                # Common validation errors from user input
                err_type = "BadRequestError"
                status_code = HTTPStatus.BAD_REQUEST
                param = None
663
664
665
666
            elif isinstance(exc, NotImplementedError):
                err_type = "NotImplementedError"
                status_code = HTTPStatus.NOT_IMPLEMENTED
                param = None
667
668
669
670
671
672
673
674
675
676
677
678
            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)

679
680
681
682
683
684
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
685

686
        return ErrorResponse(
687
            error=ErrorInfo(
688
                message=sanitize_message(message),
689
690
691
692
                type=err_type,
                code=status_code.value,
                param=param,
            )
693
        )
694

695
    def create_streaming_error_response(
696
        self,
697
        message: str | Exception,
698
699
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
700
        param: str | None = None,
701
    ) -> str:
702
        json_str = json.dumps(
703
            self.create_error_response(
704
705
706
707
                message=message,
                err_type=err_type,
                status_code=status_code,
                param=param,
708
709
            ).model_dump()
        )
710
711
        return json_str

712
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719
720
721
722
723
724
725
726
727
728
729
730
731
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733
734
735
736
737
738
739
740
    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,
        )

741
    async def _check_model(
742
743
        self,
        request: AnyRequest,
744
    ) -> ErrorResponse | None:
745
746
        error_response = None

747
        if self._is_model_supported(request.model):
748
            return None
749
        if request.model in self.models.lora_requests:
750
            return None
751
752
753
754
755
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
756
757
            if isinstance(load_result, LoRARequest):
                return None
758
759
760
761
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
762
763
764
                error_response = load_result

        return error_response or self.create_error_response(
765
766
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
767
            status_code=HTTPStatus.NOT_FOUND,
768
            param="model",
769
        )
770

771
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
        """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

794
    def _maybe_get_adapters(
795
796
797
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
798
    ) -> LoRARequest | None:
799
        if request.model in self.models.lora_requests:
800
            return self.models.lora_requests[request.model]
801
802
803
804
805
806

        # 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:
807
                return default_mm_lora
808
809

        if self._is_model_supported(request.model):
810
            return None
811

812
        # if _check_model has been called earlier, this will be unreachable
813
        raise ValueError(f"The model `{request.model}` does not exist.")
814

815
816
817
818
819
820
821
822
823
824
    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

825
826
827
828
829
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
830
831
832
833
834
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
835
836
837
838
839
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

840
841
    def _validate_input(
        self,
842
        request: object,
843
        input_ids: list[int],
844
        input_text: str,
845
    ) -> TokensPrompt:
846
847
        token_num = len(input_ids)

848
849
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
850
        if isinstance(
851
            request,
852
853
854
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
855
856
857
                ScoreDataRequest,
                ScoreTextRequest,
                ScoreQueriesDocumentsRequest,
858
                RerankRequest,
859
860
                ClassificationCompletionRequest,
                ClassificationChatRequest,
861
862
            ),
        ):
863
864
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
865
            if token_num > self.max_model_len:
866
                operations: dict[type[AnyRequest], str] = {
867
868
869
                    ScoreDataRequest: "score",
                    ScoreTextRequest: "score",
                    ScoreQueriesDocumentsRequest: "score",
870
871
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
872
                }
873
                operation = operations.get(type(request), "embedding generation")
874
                raise VLLMValidationError(
875
876
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
877
                    f"{token_num} tokens in the input for {operation}. "
878
879
880
                    f"Please reduce the length of the input.",
                    parameter="input_tokens",
                    value=token_num,
881
                )
882
            return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
883

884
885
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
886
        if isinstance(
887
888
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
889
        ):
890
            return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
891

892
893
894
895
896
        # 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:
897
            max_tokens = getattr(request, "max_tokens", None)
898
899
900
901

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
902
            raise VLLMValidationError(
903
                f"This model's maximum context length is "
904
905
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
906
907
908
                "the input messages.",
                parameter="input_tokens",
                value=token_num,
909
            )
910

911
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
912
            raise VLLMValidationError(
913
914
915
916
                "'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}"
917
918
919
                f" - {token_num}).",
                parameter="max_tokens",
                value=max_tokens,
920
            )
921

922
        return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
923

924
925
    def _validate_chat_template(
        self,
926
927
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
928
        trust_request_chat_template: bool,
929
    ) -> ErrorResponse | None:
930
        if not trust_request_chat_template and (
931
932
933
934
935
936
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
937
938
939
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
940
941
                "Refused request with untrusted chat template."
            )
942
943
        return None

944
945
946
947
948
949
950
951
952
953
954
955
    @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

956
957
958
959
960
    async def _preprocess_completion(
        self,
        request: RendererRequest,
        prompt_input: str | list[str] | list[int] | list[list[int]] | None,
        prompt_embeds: bytes | list[bytes] | None,
961
    ) -> list[TokPrompt]:
962
        renderer = self.renderer
963
        model_config = self.model_config
964

965
966
967
968
969
970
971
972
973
974
975
976
977
978
        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
        ]
979
        tok_params = request.build_tok_params(model_config)
980

981
982
983
984
985
986
987
988
989
        return await renderer.render_cmpl_async(
            parsed_prompts,
            tok_params,
            prompt_extras={
                k: v
                for k in ("mm_processor_kwargs", "cache_salt")
                if (v := getattr(request, k, None)) is not None
            },
        )
990

991
992
    async def _preprocess_chat(
        self,
993
        request: RendererChatRequest,
994
        messages: list[ChatCompletionMessageParam],
995
996
997
        default_template: str | None,
        default_template_content_format: ChatTemplateContentFormatOption,
        default_template_kwargs: dict[str, Any] | None,
998
        tool_dicts: list[dict[str, Any]] | None = None,
999
        tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
1000
    ) -> tuple[list[ConversationMessage], list[TokPrompt]]:
1001
        from vllm.tokenizers.mistral import MistralTokenizer
1002

1003
1004
1005
1006
1007
1008
1009
        renderer = self.renderer

        default_template_kwargs = merge_kwargs(
            default_template_kwargs,
            dict(
                tools=tool_dicts,
                tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
1010
1011
1012
            ),
        )

1013
1014
1015
1016
        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)
1017

1018
1019
1020
1021
1022
1023
1024
1025
1026
        (conversation,), (engine_prompt,) = await renderer.render_chat_async(
            [messages],
            chat_params,
            tok_params,
            prompt_extras={
                k: v
                for k in ("mm_processor_kwargs", "cache_salt")
                if (v := getattr(request, k, None)) is not None
            },
1027
        )
1028

1029
1030
1031
        # 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
1032
1033
1034
1035
1036
1037
1038
1039
1040
        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)
1041

1042
1043
1044
                # TODO: Update adjust_request to accept ResponsesRequest
                tokenizer = renderer.get_tokenizer()
                request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore[arg-type]
1045

1046
        return conversation, [engine_prompt]
1047

1048
1049
1050
1051
1052
1053
1054
1055
1056
    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)

1057
1058
1059
1060
1061
    async def _render_next_turn(
        self,
        request: ResponsesRequest,
        messages: list[ResponseInputOutputItem],
        tool_dicts: list[dict[str, Any]] | None,
1062
        tool_parser: Callable[[TokenizerLike], ToolParser] | None,
1063
1064
1065
1066
1067
1068
1069
        chat_template: str | None,
        chat_template_content_format: ChatTemplateContentFormatOption,
    ):
        new_messages = construct_input_messages(
            request_input=messages,
        )

1070
        _, engine_prompts = await self._preprocess_chat(
1071
1072
            request,
            new_messages,
1073
1074
1075
            default_template=chat_template,
            default_template_content_format=chat_template_content_format,
            default_template_kwargs=None,
1076
1077
1078
            tool_dicts=tool_dicts,
            tool_parser=tool_parser,
        )
1079
        return engine_prompts
1080

1081
1082
1083
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
1084
        engine_prompt: TokPrompt,
1085
        sampling_params: SamplingParams,
1086
        tok_params: TokenizeParams,
1087
        context: ConversationContext,
1088
        lora_request: LoRARequest | None = None,
1089
        priority: int = 0,
1090
        trace_headers: Mapping[str, str] | None = None,
1091
    ):
1092
        prompt_text = self._extract_prompt_text(engine_prompt)
1093

1094
        orig_priority = priority
1095
        sub_request = 0
1096
        while True:
1097
1098
            # Ensure that each sub-request has a unique request id.
            sub_request_id = f"{request_id}_{sub_request}"
1099

1100
            self._log_inputs(
1101
                sub_request_id,
1102
                engine_prompt,
1103
1104
1105
                params=sampling_params,
                lora_request=lora_request,
            )
1106
1107
1108

            tokenization_kwargs = tok_params.get_encode_kwargs()
            engine_request = self.input_processor.process_inputs(
1109
                sub_request_id,
1110
1111
                engine_prompt,
                sampling_params,
1112
                lora_request=lora_request,
1113
                tokenization_kwargs=tokenization_kwargs,
1114
1115
                trace_headers=trace_headers,
                priority=priority,
1116
            )
1117
1118
1119
1120

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1121
                sub_request_id,
1122
                lora_request=lora_request,
1123
                trace_headers=trace_headers,
1124
                priority=priority,
1125
1126
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1127
            )
1128

1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
            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()
1140
            context.append_tool_output(tool_output)
1141
1142
1143
1144
1145

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

            # Create inputs for the next turn.
1146
            # Render the next prompt token ids and update sampling_params.
1147
            if isinstance(context, (HarmonyContext, StreamingHarmonyContext)):
1148
1149
1150
1151
                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)
1152
            elif isinstance(context, ParsableContext):
1153
                engine_prompts = await self._render_next_turn(
1154
1155
1156
1157
1158
1159
1160
1161
                    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]
1162
                prompt_text = self._extract_prompt_text(engine_prompt)
1163
1164
1165

                sampling_params.max_tokens = get_max_tokens(
                    self.max_model_len,
1166
                    context.request.max_output_tokens,
1167
                    self._extract_prompt_len(engine_prompt),
1168
1169
                    self.default_sampling_params,  # type: ignore
                )
1170

1171
1172
            # OPTIMIZATION
            priority = orig_priority - 1
1173
            sub_request += 1
1174

1175
1176
1177
    def _log_inputs(
        self,
        request_id: str,
1178
        inputs: PromptType | TokPrompt,
1179
1180
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1181
1182
1183
    ) -> None:
        if self.request_logger is None:
            return
1184

1185
        components = self._extract_prompt_components(inputs)
1186
1187
1188

        self.request_logger.log_inputs(
            request_id,
1189
1190
1191
            components.text,
            components.token_ids,
            components.embeds,
1192
1193
1194
            params=params,
            lora_request=lora_request,
        )
1195

1196
1197
1198
    async def _get_trace_headers(
        self,
        headers: Headers,
1199
    ) -> Mapping[str, str] | None:
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
        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

1210
    @staticmethod
1211
    def _base_request_id(
1212
1213
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1214
        """Pulls the request id to use from a header, if provided"""
1215
1216
1217
1218
        if raw_request is not None and (
            (req_id := raw_request.headers.get("X-Request-Id")) is not None
        ):
            return req_id
1219

1220
        return random_uuid() if default is None else default
1221

1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
    @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

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    @staticmethod
    def _parse_tool_calls_from_content(
        request: ResponsesRequest | ChatCompletionRequest,
1240
        tokenizer: TokenizerLike | None,
1241
        enable_auto_tools: bool,
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        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(
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                        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

1314
    @staticmethod
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    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
1318
        tokenizer: TokenizerLike | None,
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        return_as_token_id: bool = False,
    ) -> str:
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        if return_as_token_id:
            return f"token_id:{token_id}"

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        if logprob.decoded_token is not None:
            return logprob.decoded_token
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        if tokenizer is None:
            raise ValueError(
                "Unable to get tokenizer because `skip_tokenizer_init=True`"
            )

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        return tokenizer.decode([token_id])
1333

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    def _is_model_supported(self, model_name: str | None) -> bool:
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        if not model_name:
            return True
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        return self.models.is_base_model(model_name)
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def clamp_prompt_logprobs(
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    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
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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():
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            if logprob_values.logprob == float("-inf"):
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                logprob_values.logprob = -9999.0
    return prompt_logprobs