serving_engine.py 51.1 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, Iterable, Mapping, Sequence
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from concurrent.futures import ThreadPoolExecutor
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from http import HTTPStatus
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from typing import Any, ClassVar, Generic, TypeAlias, TypeVar
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import numpy as np
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import torch
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from fastapi import Request
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from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
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from starlette.datastructures import Headers
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from typing_extensions import TypeIs

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if sys.version_info >= (3, 12):
    from typing import TypedDict
else:
    from typing_extensions import TypedDict

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from openai.types.responses import (
    ToolChoiceFunction,
)

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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.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
    ConversationMessage,
    apply_hf_chat_template,
    apply_mistral_chat_template,
    parse_chat_messages_futures,
    resolve_chat_template_content_format,
)
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from vllm.entrypoints.context import ConversationContext
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
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    ChatCompletionNamedToolChoiceParam,
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    ChatCompletionRequest,
    ChatCompletionResponse,
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    ClassificationChatRequest,
    ClassificationCompletionRequest,
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    ClassificationRequest,
    ClassificationResponse,
    CompletionRequest,
    CompletionResponse,
    DetokenizeRequest,
    EmbeddingChatRequest,
    EmbeddingCompletionRequest,
    EmbeddingRequest,
    EmbeddingResponse,
    ErrorInfo,
    ErrorResponse,
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    FunctionCall,
    FunctionDefinition,
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    GenerateRequest,
    GenerateResponse,
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    IOProcessorRequest,
    PoolingResponse,
    RerankRequest,
    ResponsesRequest,
    ScoreRequest,
    ScoreResponse,
    TokenizeChatRequest,
    TokenizeCompletionRequest,
    TokenizeResponse,
    TranscriptionRequest,
    TranscriptionResponse,
    TranslationRequest,
)
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
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from vllm.entrypoints.renderer import BaseRenderer, CompletionRenderer, RenderConfig
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from vllm.entrypoints.utils import _validate_truncation_size
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from vllm.inputs.data import PromptType
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from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
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from vllm.inputs.parse import (
    PromptComponents,
    get_prompt_components,
    is_explicit_encoder_decoder_prompt,
)
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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 (  # noqa: F401 - Required to resolve Pydantic error in RequestProcessingMixin
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    MultiModalDataDict,
    MultiModalUUIDDict,
)
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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.reasoning import ReasoningParser, ReasoningParserManager
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.tracing import (
    contains_trace_headers,
    extract_trace_headers,
    log_tracing_disabled_warning,
)
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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from vllm.utils import random_uuid
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from vllm.utils.async_utils import (
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    AsyncMicrobatchTokenizer,
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    collect_from_async_generator,
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    make_async,
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    merge_async_iterators,
)
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from vllm.utils.collection_utils import is_list_of
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from vllm.v1.engine import EngineCoreRequest
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logger = init_logger(__name__)

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

AnyResponse: TypeAlias = (
    CompletionResponse
    | ChatCompletionResponse
    | EmbeddingResponse
    | TranscriptionResponse
    | TokenizeResponse
    | PoolingResponse
    | ClassificationResponse
    | ScoreResponse
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    | GenerateResponse
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)
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class TextTokensPrompt(TypedDict):
    prompt: str
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    prompt_token_ids: list[int]
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class EmbedsPrompt(TypedDict):
    prompt_embeds: torch.Tensor


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RequestPrompt: TypeAlias = list[int] | str | TextTokensPrompt | EmbedsPrompt
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def is_text_tokens_prompt(prompt: RequestPrompt) -> TypeIs[TextTokensPrompt]:
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    return (
        isinstance(prompt, dict)
        and "prompt_token_ids" in prompt
        and "prompt_embeds" not in prompt
    )
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def is_embeds_prompt(prompt: RequestPrompt) -> TypeIs[EmbedsPrompt]:
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    return (
        isinstance(prompt, dict)
        and "prompt_token_ids" not in prompt
        and "prompt_embeds" in prompt
    )
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RequestT = TypeVar("RequestT", bound=AnyRequest)


class RequestProcessingMixin(BaseModel):
    """
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    Mixin for request processing,
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    handling prompt preparation and engine input.
    """
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    request_prompts: Sequence[RequestPrompt] | None = []
    engine_prompts: list[EngineTokensPrompt] | None = []
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    model_config = ConfigDict(arbitrary_types_allowed=True)


class ResponseGenerationMixin(BaseModel):
    """
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    Mixin for response generation,
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    managing result generators and final batch results.
    """
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    result_generator: (
        AsyncGenerator[tuple[int, RequestOutput | PoolingRequestOutput], None] | None
    ) = None
    final_res_batch: list[RequestOutput | PoolingRequestOutput] = Field(
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        default_factory=list
    )
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    model_config = ConfigDict(arbitrary_types_allowed=True)


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class ServeContext(
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    RequestProcessingMixin,
    ResponseGenerationMixin,
    BaseModel,
    Generic[RequestT],
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):
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    # Shared across all requests
    request: RequestT
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    raw_request: Request | None = None
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    model_name: str
    request_id: str
    created_time: int = Field(default_factory=lambda: int(time.time()))
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    lora_request: LoRARequest | None = None
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    # Shared across most requests
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    tokenizer: AnyTokenizer | None = None
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    # `protected_namespaces` resolves Pydantic v2's warning
    # on conflict with protected namespace "model_"
    model_config = ConfigDict(
        protected_namespaces=(),
        arbitrary_types_allowed=True,
    )


ClassificationServeContext = ServeContext[ClassificationRequest]


class EmbeddingServeContext(ServeContext[EmbeddingRequest]):
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    chat_template: str | None = None
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    chat_template_content_format: ChatTemplateContentFormatOption


# Used to resolve the Pydantic error related to
# forward reference of MultiModalDataDict in TokensPrompt
RequestProcessingMixin.model_rebuild()
ServeContext.model_rebuild()
ClassificationServeContext.model_rebuild()
EmbeddingServeContext.model_rebuild()

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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._tokenizer_executor = ThreadPoolExecutor(max_workers=1)
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        self._apply_mistral_chat_template_async = make_async(
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            apply_mistral_chat_template, executor=self._tokenizer_executor
        )
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        self._async_tokenizer_pool: dict[AnyTokenizer, AsyncMicrobatchTokenizer] = {}
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        self.log_error_stack = log_error_stack
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        self.processor = self.models.processor
        self.io_processor = self.models.io_processor
        self.model_config = self.models.model_config
        self.max_model_len = self.model_config.max_model_len

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    def _get_tool_parser(
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        self, tool_parser_name: str | None = None, enable_auto_tools: bool = False
    ) -> Callable[[AnyTokenizer], ToolParser] | None:
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        """Get the tool parser based on the name."""
        parser = None
        if not enable_auto_tools or tool_parser_name is None:
            return parser
        logger.info(
            '"auto" tool choice has been enabled please note that while'
            " the parallel_tool_calls client option is preset for "
            "compatibility reasons, it will be ignored."
        )

        try:
            if tool_parser_name == "pythonic" and self.model_config.model.startswith(
                "meta-llama/Llama-3.2"
            ):
                logger.warning(
                    "Llama3.2 models may struggle to emit valid pythonic tool calls"
                )
            parser = ToolParserManager.get_tool_parser(tool_parser_name)
        except Exception as e:
            raise TypeError(
                "Error: --enable-auto-tool-choice requires "
                f"tool_parser:'{tool_parser_name}' which has not "
                "been registered"
            ) from e
        return parser

    def _get_reasoning_parser(
        self,
        reasoning_parser_name: str,
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    ) -> Callable[[AnyTokenizer], ReasoningParser] | None:
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        """Get the reasoning parser based on the name."""
        parser = None
        if not reasoning_parser_name:
            return None
        try:
            parser = ReasoningParserManager.get_reasoning_parser(reasoning_parser_name)
            assert parser is not None
        except Exception as e:
            raise TypeError(f"{reasoning_parser_name=} has not been registered") from e
        return parser

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    async def reset_mm_cache(self) -> None:
        self.processor.clear_mm_cache()
        await self.engine_client.reset_mm_cache()

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    async def beam_search(
        self,
        prompt: PromptType,
        request_id: str,
        params: BeamSearchParams,
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        lora_request: LoRARequest | 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

        processor = self.processor
        tokenizer = processor.tokenizer
        if tokenizer is None:
            raise ValueError(
                "You cannot use beam search when `skip_tokenizer_init` is True"
            )

        eos_token_id: int = tokenizer.eos_token_id  # type: ignore

        if is_explicit_encoder_decoder_prompt(prompt):
            raise NotImplementedError

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        prompt_text: str | None
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        prompt_token_ids: list[int]
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        multi_modal_data: MultiModalDataDict | None
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        if isinstance(prompt, str):
            prompt_text = prompt
            prompt_token_ids = []
            multi_modal_data = None
        else:
            prompt_text = prompt.get("prompt")  # type: ignore
            prompt_token_ids = prompt.get("prompt_token_ids", [])  # type: ignore
            multi_modal_data = 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(
                *[
                    (
                        EngineTokensPrompt(
                            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,
                        )
                    )
                )
                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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                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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    def _get_renderer(self, tokenizer: AnyTokenizer | None) -> BaseRenderer:
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        """
        Get a Renderer instance with the provided tokenizer.
        Uses shared async tokenizer pool for efficiency.
        """
        return CompletionRenderer(
            model_config=self.model_config,
            tokenizer=tokenizer,
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            async_tokenizer_pool=self._async_tokenizer_pool,
        )
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    def _build_render_config(
        self,
        request: Any,
    ) -> RenderConfig:
        """
        Build and return a `RenderConfig` for an endpoint.

        Used by the renderer to control how prompts are prepared
        (e.g., tokenization and length handling). Endpoints should
        implement this with logic appropriate to their request type.
        """
        raise NotImplementedError

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    def _get_async_tokenizer(self, tokenizer) -> AsyncMicrobatchTokenizer:
        """
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        Return (and cache) an `AsyncMicrobatchTokenizer` bound to the
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        given tokenizer.
        """
        async_tokenizer = self._async_tokenizer_pool.get(tokenizer)
        if async_tokenizer is None:
            async_tokenizer = AsyncMicrobatchTokenizer(tokenizer)
            self._async_tokenizer_pool[tokenizer] = async_tokenizer
        return async_tokenizer
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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:
        generation: AsyncGenerator[AnyResponse | ErrorResponse, None]
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        generation = self._pipeline(ctx)

        async for response in generation:
            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)

643
    def _validate_request(self, ctx: ServeContext) -> ErrorResponse | None:
644
        truncate_prompt_tokens = getattr(ctx.request, "truncate_prompt_tokens", None)
645

646
647
648
649
        if (
            truncate_prompt_tokens is not None
            and truncate_prompt_tokens > self.max_model_len
        ):
650
651
652
            return self.create_error_response(
                "truncate_prompt_tokens value is "
                "greater than max_model_len."
653
654
                " Please, select a smaller truncation size."
            )
655
656
        return None

657
658
659
    def _create_pooling_params(
        self,
        ctx: ServeContext,
660
    ) -> PoolingParams | ErrorResponse:
661
662
        if not hasattr(ctx.request, "to_pooling_params"):
            return self.create_error_response(
663
664
                "Request type does not support pooling parameters"
            )
665
666
667

        return ctx.request.to_pooling_params()

668
669
670
    async def _prepare_generators(
        self,
        ctx: ServeContext,
671
    ) -> ErrorResponse | None:
672
        """Schedule the request and get the result generator."""
673
        generators: list[
674
            AsyncGenerator[RequestOutput | PoolingRequestOutput, None]
675
        ] = []
676
677

        try:
678
679
680
681
682
            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
683

684
685
686
            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
687
688

            if ctx.engine_prompts is None:
689
                return self.create_error_response("Engine prompts not available")
690
691
692
693

            for i, engine_prompt in enumerate(ctx.engine_prompts):
                request_id_item = f"{ctx.request_id}-{i}"

694
695
                self._log_inputs(
                    request_id_item,
696
                    engine_prompt,
697
698
699
                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722

                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:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

    async def _collect_batch(
        self,
        ctx: ServeContext,
723
    ) -> ErrorResponse | None:
724
725
726
        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
727
                return self.create_error_response("Engine prompts not available")
728
729

            num_prompts = len(ctx.engine_prompts)
730
            final_res_batch: list[RequestOutput | PoolingRequestOutput | None]
731
732
733
            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
734
                return self.create_error_response("Result generator not available")
735
736
737
738
739
740

            async for i, res in ctx.result_generator:
                final_res_batch[i] = res

            if None in final_res_batch:
                return self.create_error_response(
741
742
                    "Failed to generate results for all prompts"
                )
743

744
            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
745
746
747
748
749
750

            return None

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

751
    def create_error_response(
752
753
754
755
756
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> ErrorResponse:
757
758
759
760
761
762
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
763
764
765
        return ErrorResponse(
            error=ErrorInfo(message=message, type=err_type, code=status_code.value)
        )
766

767
    def create_streaming_error_response(
768
769
770
771
772
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> str:
773
        json_str = json.dumps(
774
775
776
777
            self.create_error_response(
                message=message, err_type=err_type, status_code=status_code
            ).model_dump()
        )
778
779
        return json_str

780
    async def _check_model(
781
782
        self,
        request: AnyRequest,
783
    ) -> ErrorResponse | None:
784
785
        error_response = None

786
        if self._is_model_supported(request.model):
787
            return None
788
        if request.model in self.models.lora_requests:
789
            return None
790
791
792
793
794
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
795
796
            if isinstance(load_result, LoRARequest):
                return None
797
798
799
800
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
801
802
803
                error_response = load_result

        return error_response or self.create_error_response(
804
805
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
806
807
            status_code=HTTPStatus.NOT_FOUND,
        )
808

809
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
        """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

832
    def _maybe_get_adapters(
833
834
835
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
836
    ) -> LoRARequest | None:
837
        if request.model in self.models.lora_requests:
838
            return self.models.lora_requests[request.model]
839
840
841
842
843
844

        # 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:
845
                return default_mm_lora
846
847

        if self._is_model_supported(request.model):
848
            return None
849

850
        # if _check_model has been called earlier, this will be unreachable
851
        raise ValueError(f"The model `{request.model}` does not exist.")
852

853
854
855
856
857
858
859
860
861
862
    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

863
864
865
866
867
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
868
869
870
871
872
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
873
874
875
876
877
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

878
    async def _normalize_prompt_text_to_input(
879
880
881
        self,
        request: AnyRequest,
        prompt: str,
882
        tokenizer: AnyTokenizer,
883
884
        add_special_tokens: bool,
    ) -> TextTokensPrompt:
885
886
        async_tokenizer = self._get_async_tokenizer(tokenizer)

887
888
889
890
        if (
            self.model_config.encoder_config is not None
            and self.model_config.encoder_config.get("do_lower_case", False)
        ):
891
892
            prompt = prompt.lower()

893
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
894

895
        if truncate_prompt_tokens is None:
896
            encoded = await async_tokenizer(
897
898
                prompt, add_special_tokens=add_special_tokens
            )
899
900
        elif truncate_prompt_tokens < 0:
            # Negative means we cap at the model's max length
901
902
903
904
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
905
906
                max_length=self.max_model_len,
            )
907
        else:
908
909
910
911
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
912
913
                max_length=truncate_prompt_tokens,
            )
914
915
916
917
918
919

        input_ids = encoded.input_ids
        input_text = prompt

        return self._validate_input(request, input_ids, input_text)

920
    async def _normalize_prompt_tokens_to_input(
921
922
        self,
        request: AnyRequest,
923
        prompt_ids: list[int],
924
        tokenizer: AnyTokenizer | None,
925
    ) -> TextTokensPrompt:
926
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
927

928
        if truncate_prompt_tokens is None:
929
            input_ids = prompt_ids
930
        elif truncate_prompt_tokens < 0:
931
            input_ids = prompt_ids[-self.max_model_len :]
932
933
934
        else:
            input_ids = prompt_ids[-truncate_prompt_tokens:]

935
936
937
938
939
        if tokenizer is None:
            input_text = ""
        else:
            async_tokenizer = self._get_async_tokenizer(tokenizer)
            input_text = await async_tokenizer.decode(input_ids)
940

941
942
943
944
945
        return self._validate_input(request, input_ids, input_text)

    def _validate_input(
        self,
        request: AnyRequest,
946
        input_ids: list[int],
947
948
        input_text: str,
    ) -> TextTokensPrompt:
949
950
        token_num = len(input_ids)

951
952
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
953
        if isinstance(
954
            request,
955
956
957
958
959
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
                ScoreRequest,
                RerankRequest,
960
961
                ClassificationCompletionRequest,
                ClassificationChatRequest,
962
963
            ),
        ):
964
965
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
966
            if token_num > self.max_model_len:
967
968
                operations: dict[type[AnyRequest], str] = {
                    ScoreRequest: "score",
969
970
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
971
                }
972
                operation = operations.get(type(request), "embedding generation")
973
974
975
                raise ValueError(
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
976
                    f"{token_num} tokens in the input for {operation}. "
977
978
979
                    f"Please reduce the length of the input."
                )
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
980

981
982
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
983
        if isinstance(
984
985
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
986
        ):
987
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
988

989
990
991
992
993
        # 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:
994
            max_tokens = getattr(request, "max_tokens", None)
995
996
997
998

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
999
            raise ValueError(
1000
                f"This model's maximum context length is "
1001
1002
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
1003
1004
                "the input messages."
            )
1005

1006
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
1007
1008
1009
1010
1011
            raise ValueError(
                "'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}"
1012
1013
                f" - {token_num})."
            )
1014
1015
1016

        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

1017
    async def _tokenize_prompt_input_async(
1018
1019
1020
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
1021
        prompt_input: str | list[int],
1022
1023
1024
        add_special_tokens: bool = True,
    ) -> TextTokensPrompt:
        """
1025
        A simpler implementation that tokenizes a single prompt input.
1026
        """
1027
        async for result in self._tokenize_prompt_inputs_async(
1028
1029
            request,
            tokenizer,
1030
            [prompt_input],
1031
            add_special_tokens=add_special_tokens,
1032
1033
1034
        ):
            return result
        raise ValueError("No results yielded from tokenization")
1035

1036
    async def _tokenize_prompt_inputs_async(
1037
1038
1039
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
1040
        prompt_inputs: Iterable[str | list[int]],
1041
        add_special_tokens: bool = True,
1042
    ) -> AsyncGenerator[TextTokensPrompt, None]:
1043
        """
1044
        A simpler implementation that tokenizes multiple prompt inputs.
1045
        """
1046
1047
        for prompt in prompt_inputs:
            if isinstance(prompt, str):
1048
                yield await self._normalize_prompt_text_to_input(
1049
                    request,
1050
1051
                    prompt=prompt,
                    tokenizer=tokenizer,
1052
1053
1054
                    add_special_tokens=add_special_tokens,
                )
            else:
1055
                yield await self._normalize_prompt_tokens_to_input(
1056
                    request,
1057
1058
                    prompt_ids=prompt,
                    tokenizer=tokenizer,
1059
1060
                )

1061
1062
    def _validate_chat_template(
        self,
1063
1064
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
1065
        trust_request_chat_template: bool,
1066
    ) -> ErrorResponse | None:
1067
        if not trust_request_chat_template and (
1068
1069
1070
1071
1072
1073
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
1074
1075
1076
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
1077
1078
                "Refused request with untrusted chat template."
            )
1079
1080
        return None

1081
1082
    async def _preprocess_chat(
        self,
1083
        request: ChatLikeRequest | ResponsesRequest,
1084
        tokenizer: AnyTokenizer,
1085
        messages: list[ChatCompletionMessageParam],
1086
        chat_template: str | None,
1087
        chat_template_content_format: ChatTemplateContentFormatOption,
1088
1089
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
1090
1091
1092
1093
        tool_dicts: list[dict[str, Any]] | None = None,
        documents: list[dict[str, str]] | None = None,
        chat_template_kwargs: dict[str, Any] | None = None,
        tool_parser: Callable[[AnyTokenizer], ToolParser] | None = None,
1094
        add_special_tokens: bool = False,
1095
    ) -> tuple[
1096
1097
1098
        list[ConversationMessage],
        Sequence[RequestPrompt],
        list[EngineTokensPrompt],
1099
    ]:
1100
1101
        model_config = self.model_config

1102
1103
        resolved_content_format = resolve_chat_template_content_format(
            chat_template,
1104
            tool_dicts,
1105
1106
            chat_template_content_format,
            tokenizer,
1107
            model_config=model_config,
1108
        )
1109
        conversation, mm_data_future, mm_uuids = parse_chat_messages_futures(
1110
            messages,
1111
            model_config,
1112
            tokenizer,
1113
            content_format=resolved_content_format,
1114
1115
        )

1116
        _chat_template_kwargs: dict[str, Any] = dict(
1117
1118
1119
1120
1121
1122
1123
1124
            chat_template=chat_template,
            add_generation_prompt=add_generation_prompt,
            continue_final_message=continue_final_message,
            tools=tool_dicts,
            documents=documents,
        )
        _chat_template_kwargs.update(chat_template_kwargs or {})

1125
        request_prompt: str | list[int]
1126
1127
1128
1129

        if tokenizer is None:
            request_prompt = "placeholder"
        elif isinstance(tokenizer, MistralTokenizer):
1130
            request_prompt = await self._apply_mistral_chat_template_async(
1131
1132
                tokenizer,
                messages=messages,
1133
                **_chat_template_kwargs,
1134
1135
1136
            )
        else:
            request_prompt = apply_hf_chat_template(
1137
                tokenizer=tokenizer,
1138
                conversation=conversation,
1139
                model_config=model_config,
1140
                **_chat_template_kwargs,
1141
1142
1143
1144
            )

        mm_data = await mm_data_future

1145
1146
1147
        # 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
1148
1149
1150
        should_parse_tools = tool_parser is not None and (
            hasattr(request, "tool_choice") and request.tool_choice != "none"
        )
1151
1152

        if should_parse_tools:
1153
1154
1155
1156
1157
            if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                msg = (
                    "Tool usage is only supported for Chat Completions API "
                    "or Responses API requests."
                )
1158
                raise NotImplementedError(msg)
1159
            request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore
1160

1161
1162
        if tokenizer is None:
            assert isinstance(request_prompt, str), (
1163
1164
                "Prompt has to be a string",
                "when the tokenizer is not initialised",
1165
            )
1166
1167
1168
            prompt_inputs = TextTokensPrompt(
                prompt=request_prompt, prompt_token_ids=[1]
            )
1169
        elif isinstance(request_prompt, str):
1170
            prompt_inputs = await self._tokenize_prompt_input_async(
1171
1172
1173
1174
1175
1176
1177
1178
                request,
                tokenizer,
                request_prompt,
                add_special_tokens=add_special_tokens,
            )
        else:
            # For MistralTokenizer
            assert is_list_of(request_prompt, int), (
1179
1180
                "Prompt has to be either a string or a list of token ids"
            )
1181
1182
            prompt_inputs = TextTokensPrompt(
                prompt=tokenizer.decode(request_prompt),
1183
1184
                prompt_token_ids=request_prompt,
            )
1185

1186
        engine_prompt = EngineTokensPrompt(
1187
1188
            prompt_token_ids=prompt_inputs["prompt_token_ids"]
        )
1189
1190
        if mm_data is not None:
            engine_prompt["multi_modal_data"] = mm_data
1191
1192
1193
1194

        if mm_uuids is not None:
            engine_prompt["multi_modal_uuids"] = mm_uuids

1195
1196
        if request.mm_processor_kwargs is not None:
            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
1197

1198
1199
1200
        if hasattr(request, "cache_salt") and request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

1201
1202
        return conversation, [request_prompt], [engine_prompt]

1203
1204
1205
1206
    async def _process_inputs(
        self,
        request_id: str,
        engine_prompt: PromptType,
1207
        params: SamplingParams | PoolingParams,
1208
        *,
1209
1210
        lora_request: LoRARequest | None,
        trace_headers: Mapping[str, str] | None,
1211
1212
        priority: int,
    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
1213
        """Use the Processor to process inputs for AsyncLLM."""
1214
        tokenization_kwargs: dict[str, Any] = {}
1215
1216
1217
        _validate_truncation_size(
            self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
        )
1218

1219
        engine_request = self.processor.process_inputs(
1220
1221
            request_id,
            engine_prompt,
1222
            params,
1223
1224
1225
1226
1227
1228
1229
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
        )
        return engine_request, tokenization_kwargs

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    async def _generate_with_builtin_tools(
        self,
        request_id: str,
        request_prompt: RequestPrompt,
        engine_prompt: EngineTokensPrompt,
        sampling_params: SamplingParams,
        context: ConversationContext,
1237
        lora_request: LoRARequest | None = None,
1238
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1240
        priority: int = 0,
        **kwargs,
    ):
1241
        prompt_text, _, _ = self._get_prompt_components(request_prompt)
1242
1243
1244
1245
1246
1247
1248
1249
        orig_priority = priority
        while True:
            self._log_inputs(
                request_id,
                request_prompt,
                params=sampling_params,
                lora_request=lora_request,
            )
1250
            trace_headers = kwargs.get("trace_headers")
1251
            engine_request, tokenization_kwargs = await self._process_inputs(
1252
                request_id,
1253
1254
                engine_prompt,
                sampling_params,
1255
1256
1257
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
1258
            )
1259
1260
1261
1262

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1263
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1265
                request_id,
                lora_request=lora_request,
                priority=priority,
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                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1268
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                **kwargs,
            )
1270

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            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()
1282
            context.append_tool_output(tool_output)
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            # TODO: uncomment this and enable tool output streaming
            # yield context

            # Create inputs for the next turn.
            # Render the next prompt token ids.
            prompt_token_ids = context.render_for_completion()
1290
            engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
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            request_prompt = prompt_token_ids
            # Update the sampling params.
1293
            sampling_params.max_tokens = self.max_model_len - len(prompt_token_ids)
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            # OPTIMIZATION
            priority = orig_priority - 1

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    def _get_prompt_components(
        self,
1299
        prompt: RequestPrompt | PromptType,
1300
    ) -> PromptComponents:
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1302
        if isinstance(prompt, list):
            return PromptComponents(token_ids=prompt)
1303

1304
        return get_prompt_components(prompt)  # type: ignore[arg-type]
1305

1306
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    def _log_inputs(
        self,
        request_id: str,
1309
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        inputs: RequestPrompt | PromptType,
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1312
1313
1314
    ) -> None:
        if self.request_logger is None:
            return
1315

1316
        prompt, prompt_token_ids, prompt_embeds = self._get_prompt_components(inputs)
1317
1318
1319
1320
1321

        self.request_logger.log_inputs(
            request_id,
            prompt,
            prompt_token_ids,
1322
            prompt_embeds,
1323
1324
1325
            params=params,
            lora_request=lora_request,
        )
1326

1327
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1329
    async def _get_trace_headers(
        self,
        headers: Headers,
1330
    ) -> Mapping[str, str] | None:
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
        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

1341
    @staticmethod
1342
    def _base_request_id(
1343
1344
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1345
1346
        """Pulls the request id to use from a header, if provided"""
        default = default or random_uuid()
1347
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1349
1350
        if raw_request is None:
            return default

        return raw_request.headers.get("X-Request-Id", default)
1351

1352
1353
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1361
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1363
1364
1365
1366
    @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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1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
    @staticmethod
    def _parse_tool_calls_from_content(
        request: ResponsesRequest | ChatCompletionRequest,
        tokenizer: AnyTokenizer,
        enable_auto_tools: bool,
        tool_parser_cls: Callable[[AnyTokenizer], ToolParser] | None,
        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)
        ):
            # 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(
                        name=tool_call.function.name,
                        arguments=tool_call.function.arguments,
                    )
                    for tool_call in tool_call_info.tool_calls
                )
                content = tool_call_info.content
1430
1431
                if content and content.strip() == "":
                    content = None
1432
1433
1434
1435
1436
1437
            else:
                # No tool calls.
                return None, content

        return function_calls, content

1438
    @staticmethod
1439
1440
1441
1442
1443
1444
    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
        tokenizer: AnyTokenizer,
        return_as_token_id: bool = False,
    ) -> str:
1445
1446
1447
        if return_as_token_id:
            return f"token_id:{token_id}"

1448
1449
        if logprob.decoded_token is not None:
            return logprob.decoded_token
1450
        return tokenizer.decode(token_id)
1451

1452
    def _is_model_supported(self, model_name: str | None) -> bool:
1453
1454
        if not model_name:
            return True
1455
        return self.models.is_base_model(model_name)
1456

1457
1458

def clamp_prompt_logprobs(
1459
1460
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1461
1462
1463
1464
1465
1466
1467
    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():
1468
            if logprob_values.logprob == float("-inf"):
1469
1470
                logprob_values.logprob = -9999.0
    return prompt_logprobs