serving_engine.py 51.3 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
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        logger.info('"auto" tool choice has been enabled.')
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        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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        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

        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,
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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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                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)

641
    def _validate_request(self, ctx: ServeContext) -> ErrorResponse | None:
642
        truncate_prompt_tokens = getattr(ctx.request, "truncate_prompt_tokens", None)
643

644
645
646
647
        if (
            truncate_prompt_tokens is not None
            and truncate_prompt_tokens > self.max_model_len
        ):
648
649
650
            return self.create_error_response(
                "truncate_prompt_tokens value is "
                "greater than max_model_len."
651
652
                " Please, select a smaller truncation size."
            )
653
654
        return None

655
656
657
    def _create_pooling_params(
        self,
        ctx: ServeContext,
658
    ) -> PoolingParams | ErrorResponse:
659
660
        if not hasattr(ctx.request, "to_pooling_params"):
            return self.create_error_response(
661
662
                "Request type does not support pooling parameters"
            )
663
664
665

        return ctx.request.to_pooling_params()

666
667
668
    async def _prepare_generators(
        self,
        ctx: ServeContext,
669
    ) -> ErrorResponse | None:
670
        """Schedule the request and get the result generator."""
671
        generators: list[
672
            AsyncGenerator[RequestOutput | PoolingRequestOutput, None]
673
        ] = []
674
675

        try:
676
677
678
679
680
            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
681

682
683
684
            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
685
686

            if ctx.engine_prompts is None:
687
                return self.create_error_response("Engine prompts not available")
688
689
690
691

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

692
693
                self._log_inputs(
                    request_id_item,
694
                    engine_prompt,
695
696
697
                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720

                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,
721
    ) -> ErrorResponse | None:
722
723
724
        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
725
                return self.create_error_response("Engine prompts not available")
726
727

            num_prompts = len(ctx.engine_prompts)
728
            final_res_batch: list[RequestOutput | PoolingRequestOutput | None]
729
730
731
            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
732
                return self.create_error_response("Result generator not available")
733
734
735
736
737
738

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

            if None in final_res_batch:
                return self.create_error_response(
739
740
                    "Failed to generate results for all prompts"
                )
741

742
            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
743
744
745
746
747
748

            return None

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

749
    def create_error_response(
750
751
752
753
754
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> ErrorResponse:
755
756
757
758
759
760
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
761
762
763
        return ErrorResponse(
            error=ErrorInfo(message=message, type=err_type, code=status_code.value)
        )
764

765
    def create_streaming_error_response(
766
767
768
769
770
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> str:
771
        json_str = json.dumps(
772
773
774
775
            self.create_error_response(
                message=message, err_type=err_type, status_code=status_code
            ).model_dump()
        )
776
777
        return json_str

778
    async def _check_model(
779
780
        self,
        request: AnyRequest,
781
    ) -> ErrorResponse | None:
782
783
        error_response = None

784
        if self._is_model_supported(request.model):
785
            return None
786
        if request.model in self.models.lora_requests:
787
            return None
788
789
790
791
792
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
793
794
            if isinstance(load_result, LoRARequest):
                return None
795
796
797
798
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
799
800
801
                error_response = load_result

        return error_response or self.create_error_response(
802
803
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
804
805
            status_code=HTTPStatus.NOT_FOUND,
        )
806

807
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
        """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

830
    def _maybe_get_adapters(
831
832
833
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
834
    ) -> LoRARequest | None:
835
        if request.model in self.models.lora_requests:
836
            return self.models.lora_requests[request.model]
837
838
839
840
841
842

        # 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:
843
                return default_mm_lora
844
845

        if self._is_model_supported(request.model):
846
            return None
847

848
        # if _check_model has been called earlier, this will be unreachable
849
        raise ValueError(f"The model `{request.model}` does not exist.")
850

851
852
853
854
855
856
857
858
859
860
    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

861
862
863
864
865
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
866
867
868
869
870
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
871
872
873
874
875
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

876
    async def _normalize_prompt_text_to_input(
877
878
879
        self,
        request: AnyRequest,
        prompt: str,
880
        tokenizer: AnyTokenizer,
881
882
        add_special_tokens: bool,
    ) -> TextTokensPrompt:
883
884
        async_tokenizer = self._get_async_tokenizer(tokenizer)

885
886
887
888
        if (
            self.model_config.encoder_config is not None
            and self.model_config.encoder_config.get("do_lower_case", False)
        ):
889
890
            prompt = prompt.lower()

891
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
892

893
        if truncate_prompt_tokens is None:
894
            encoded = await async_tokenizer(
895
896
                prompt, add_special_tokens=add_special_tokens
            )
897
898
        elif truncate_prompt_tokens < 0:
            # Negative means we cap at the model's max length
899
900
901
902
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
903
904
                max_length=self.max_model_len,
            )
905
        else:
906
907
908
909
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
910
911
                max_length=truncate_prompt_tokens,
            )
912
913
914
915
916
917

        input_ids = encoded.input_ids
        input_text = prompt

        return self._validate_input(request, input_ids, input_text)

918
    async def _normalize_prompt_tokens_to_input(
919
920
        self,
        request: AnyRequest,
921
        prompt_ids: list[int],
922
        tokenizer: AnyTokenizer | None,
923
    ) -> TextTokensPrompt:
924
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
925

926
        if truncate_prompt_tokens is None:
927
            input_ids = prompt_ids
928
        elif truncate_prompt_tokens < 0:
929
            input_ids = prompt_ids[-self.max_model_len :]
930
931
932
        else:
            input_ids = prompt_ids[-truncate_prompt_tokens:]

933
934
935
936
937
        if tokenizer is None:
            input_text = ""
        else:
            async_tokenizer = self._get_async_tokenizer(tokenizer)
            input_text = await async_tokenizer.decode(input_ids)
938

939
940
941
942
943
        return self._validate_input(request, input_ids, input_text)

    def _validate_input(
        self,
        request: AnyRequest,
944
        input_ids: list[int],
945
946
        input_text: str,
    ) -> TextTokensPrompt:
947
948
        token_num = len(input_ids)

949
950
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
951
        if isinstance(
952
            request,
953
954
955
956
957
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
                ScoreRequest,
                RerankRequest,
958
959
                ClassificationCompletionRequest,
                ClassificationChatRequest,
960
961
            ),
        ):
962
963
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
964
            if token_num > self.max_model_len:
965
966
                operations: dict[type[AnyRequest], str] = {
                    ScoreRequest: "score",
967
968
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
969
                }
970
                operation = operations.get(type(request), "embedding generation")
971
972
973
                raise ValueError(
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
974
                    f"{token_num} tokens in the input for {operation}. "
975
976
977
                    f"Please reduce the length of the input."
                )
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
978

979
980
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
981
        if isinstance(
982
983
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
984
        ):
985
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
986

987
988
989
990
991
        # 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:
992
            max_tokens = getattr(request, "max_tokens", None)
993
994
995
996

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
997
            raise ValueError(
998
                f"This model's maximum context length is "
999
1000
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
1001
1002
                "the input messages."
            )
1003

1004
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
1005
1006
1007
1008
1009
            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}"
1010
1011
                f" - {token_num})."
            )
1012
1013
1014

        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

1015
    async def _tokenize_prompt_input_async(
1016
1017
1018
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
1019
        prompt_input: str | list[int],
1020
1021
1022
        add_special_tokens: bool = True,
    ) -> TextTokensPrompt:
        """
1023
        A simpler implementation that tokenizes a single prompt input.
1024
        """
1025
        async for result in self._tokenize_prompt_inputs_async(
1026
1027
            request,
            tokenizer,
1028
            [prompt_input],
1029
            add_special_tokens=add_special_tokens,
1030
1031
1032
        ):
            return result
        raise ValueError("No results yielded from tokenization")
1033

1034
    async def _tokenize_prompt_inputs_async(
1035
1036
1037
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
1038
        prompt_inputs: Iterable[str | list[int]],
1039
        add_special_tokens: bool = True,
1040
    ) -> AsyncGenerator[TextTokensPrompt, None]:
1041
        """
1042
        A simpler implementation that tokenizes multiple prompt inputs.
1043
        """
1044
1045
        for prompt in prompt_inputs:
            if isinstance(prompt, str):
1046
                yield await self._normalize_prompt_text_to_input(
1047
                    request,
1048
1049
                    prompt=prompt,
                    tokenizer=tokenizer,
1050
1051
1052
                    add_special_tokens=add_special_tokens,
                )
            else:
1053
                yield await self._normalize_prompt_tokens_to_input(
1054
                    request,
1055
1056
                    prompt_ids=prompt,
                    tokenizer=tokenizer,
1057
1058
                )

1059
1060
    def _validate_chat_template(
        self,
1061
1062
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
1063
        trust_request_chat_template: bool,
1064
    ) -> ErrorResponse | None:
1065
        if not trust_request_chat_template and (
1066
1067
1068
1069
1070
1071
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
1072
1073
1074
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
1075
1076
                "Refused request with untrusted chat template."
            )
1077
1078
        return None

1079
1080
    async def _preprocess_chat(
        self,
1081
        request: ChatLikeRequest | ResponsesRequest,
1082
        tokenizer: AnyTokenizer,
1083
        messages: list[ChatCompletionMessageParam],
1084
        chat_template: str | None,
1085
        chat_template_content_format: ChatTemplateContentFormatOption,
1086
1087
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
1088
1089
1090
1091
        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,
1092
        add_special_tokens: bool = False,
1093
    ) -> tuple[
1094
1095
1096
        list[ConversationMessage],
        Sequence[RequestPrompt],
        list[EngineTokensPrompt],
1097
    ]:
1098
1099
        model_config = self.model_config

1100
1101
        resolved_content_format = resolve_chat_template_content_format(
            chat_template,
1102
            tool_dicts,
1103
1104
            chat_template_content_format,
            tokenizer,
1105
            model_config=model_config,
1106
        )
1107
        conversation, mm_data_future, mm_uuids = parse_chat_messages_futures(
1108
            messages,
1109
            model_config,
1110
            tokenizer,
1111
            content_format=resolved_content_format,
1112
1113
        )

1114
        _chat_template_kwargs: dict[str, Any] = dict(
1115
1116
1117
1118
1119
1120
1121
1122
            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 {})

1123
        request_prompt: str | list[int]
1124
1125
1126
1127

        if tokenizer is None:
            request_prompt = "placeholder"
        elif isinstance(tokenizer, MistralTokenizer):
1128
            request_prompt = await self._apply_mistral_chat_template_async(
1129
1130
                tokenizer,
                messages=messages,
1131
                **_chat_template_kwargs,
1132
1133
1134
            )
        else:
            request_prompt = apply_hf_chat_template(
1135
                tokenizer=tokenizer,
1136
                conversation=conversation,
1137
                model_config=model_config,
1138
                **_chat_template_kwargs,
1139
1140
1141
1142
            )

        mm_data = await mm_data_future

1143
1144
1145
        # 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
1146
1147
1148
        should_parse_tools = tool_parser is not None and (
            hasattr(request, "tool_choice") and request.tool_choice != "none"
        )
1149
1150

        if should_parse_tools:
1151
1152
1153
1154
1155
            if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                msg = (
                    "Tool usage is only supported for Chat Completions API "
                    "or Responses API requests."
                )
1156
                raise NotImplementedError(msg)
1157
            request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore
1158

1159
1160
        if tokenizer is None:
            assert isinstance(request_prompt, str), (
1161
1162
                "Prompt has to be a string",
                "when the tokenizer is not initialised",
1163
            )
1164
1165
1166
            prompt_inputs = TextTokensPrompt(
                prompt=request_prompt, prompt_token_ids=[1]
            )
1167
        elif isinstance(request_prompt, str):
1168
            prompt_inputs = await self._tokenize_prompt_input_async(
1169
1170
1171
1172
1173
1174
1175
1176
                request,
                tokenizer,
                request_prompt,
                add_special_tokens=add_special_tokens,
            )
        else:
            # For MistralTokenizer
            assert is_list_of(request_prompt, int), (
1177
1178
                "Prompt has to be either a string or a list of token ids"
            )
1179
1180
            prompt_inputs = TextTokensPrompt(
                prompt=tokenizer.decode(request_prompt),
1181
1182
                prompt_token_ids=request_prompt,
            )
1183

1184
        engine_prompt = EngineTokensPrompt(
1185
1186
            prompt_token_ids=prompt_inputs["prompt_token_ids"]
        )
1187
1188
        if mm_data is not None:
            engine_prompt["multi_modal_data"] = mm_data
1189
1190
1191
1192

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

1193
1194
        if request.mm_processor_kwargs is not None:
            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
1195

1196
1197
1198
        if hasattr(request, "cache_salt") and request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

1199
1200
        return conversation, [request_prompt], [engine_prompt]

1201
1202
1203
1204
    async def _process_inputs(
        self,
        request_id: str,
        engine_prompt: PromptType,
1205
        params: SamplingParams | PoolingParams,
1206
        *,
1207
1208
        lora_request: LoRARequest | None,
        trace_headers: Mapping[str, str] | None,
1209
1210
        priority: int,
    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
1211
        """Use the Processor to process inputs for AsyncLLM."""
1212
        tokenization_kwargs: dict[str, Any] = {}
1213
1214
1215
        _validate_truncation_size(
            self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
        )
1216

1217
        engine_request = self.processor.process_inputs(
1218
1219
            request_id,
            engine_prompt,
1220
            params,
1221
1222
1223
1224
1225
1226
1227
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
        )
        return engine_request, tokenization_kwargs

1228
1229
1230
1231
1232
1233
1234
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
        request_prompt: RequestPrompt,
        engine_prompt: EngineTokensPrompt,
        sampling_params: SamplingParams,
        context: ConversationContext,
1235
        lora_request: LoRARequest | None = None,
1236
1237
1238
        priority: int = 0,
        **kwargs,
    ):
1239
        prompt_text, _, _ = self._get_prompt_components(request_prompt)
1240
        orig_priority = priority
1241
        sub_request = 0
1242
        while True:
1243
1244
            # Ensure that each sub-request has a unique request id.
            sub_request_id = f"{request_id}_{sub_request}"
1245
            self._log_inputs(
1246
                sub_request_id,
1247
1248
1249
1250
                request_prompt,
                params=sampling_params,
                lora_request=lora_request,
            )
1251
            trace_headers = kwargs.get("trace_headers")
1252
            engine_request, tokenization_kwargs = await self._process_inputs(
1253
                sub_request_id,
1254
1255
                engine_prompt,
                sampling_params,
1256
1257
1258
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
1259
            )
1260
1261
1262
1263

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1264
                sub_request_id,
1265
1266
                lora_request=lora_request,
                priority=priority,
1267
1268
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1269
1270
                **kwargs,
            )
1271

1272
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1275
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1277
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1280
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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()
1283
            context.append_tool_output(tool_output)
1284
1285
1286
1287
1288
1289
1290

            # 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()
1291
            engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
1292
1293
            request_prompt = prompt_token_ids
            # Update the sampling params.
1294
            sampling_params.max_tokens = self.max_model_len - len(prompt_token_ids)
1295
1296
            # OPTIMIZATION
            priority = orig_priority - 1
1297
            sub_request += 1
1298

1299
1300
    def _get_prompt_components(
        self,
1301
        prompt: RequestPrompt | PromptType,
1302
    ) -> PromptComponents:
1303
1304
        if isinstance(prompt, list):
            return PromptComponents(token_ids=prompt)
1305

1306
        return get_prompt_components(prompt)  # type: ignore[arg-type]
1307

1308
1309
1310
    def _log_inputs(
        self,
        request_id: str,
1311
1312
1313
        inputs: RequestPrompt | PromptType,
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1314
1315
1316
    ) -> None:
        if self.request_logger is None:
            return
1317

1318
        prompt, prompt_token_ids, prompt_embeds = self._get_prompt_components(inputs)
1319
1320
1321
1322
1323

        self.request_logger.log_inputs(
            request_id,
            prompt,
            prompt_token_ids,
1324
            prompt_embeds,
1325
1326
1327
            params=params,
            lora_request=lora_request,
        )
1328

1329
1330
1331
    async def _get_trace_headers(
        self,
        headers: Headers,
1332
    ) -> Mapping[str, str] | None:
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
        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

1343
    @staticmethod
1344
    def _base_request_id(
1345
1346
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1347
        """Pulls the request id to use from a header, if provided"""
1348
1349
1350
1351
        if raw_request is not None and (
            (req_id := raw_request.headers.get("X-Request-Id")) is not None
        ):
            return req_id
1352

1353
        return random_uuid() if default is None else default
1354

1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
    @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

1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
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1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
    @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
1433
1434
                if content and content.strip() == "":
                    content = None
1435
1436
1437
1438
1439
1440
            else:
                # No tool calls.
                return None, content

        return function_calls, content

1441
    @staticmethod
1442
1443
1444
1445
1446
1447
    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
        tokenizer: AnyTokenizer,
        return_as_token_id: bool = False,
    ) -> str:
1448
1449
1450
        if return_as_token_id:
            return f"token_id:{token_id}"

1451
1452
        if logprob.decoded_token is not None:
            return logprob.decoded_token
1453
        return tokenizer.decode(token_id)
1454

1455
    def _is_model_supported(self, model_name: str | None) -> bool:
1456
1457
        if not model_name:
            return True
1458
        return self.models.is_base_model(model_name)
1459

1460
1461

def clamp_prompt_logprobs(
1462
1463
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1464
1465
1466
1467
1468
1469
1470
    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():
1471
            if logprob_values.logprob == float("-inf"):
1472
1473
                logprob_values.logprob = -9999.0
    return prompt_logprobs