serving_engine.py 49.6 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 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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    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
)

AnyResponse: TypeAlias = (
    CompletionResponse
    | ChatCompletionResponse
    | EmbeddingResponse
    | TranscriptionResponse
    | TokenizeResponse
    | PoolingResponse
    | ClassificationResponse
    | ScoreResponse
)
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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)

        beam_search_params = SamplingParams(
            logprobs=2 * beam_width,
            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 = []
            for i, current_beam in enumerate(all_beams):
                result = output[i]

                if result.outputs[0].logprobs is not None:
                    logprobs = result.outputs[0].logprobs[0]
                    for token_id, logprob_obj in logprobs.items():
                        if token_id == eos_token_id and not ignore_eos:
                            completed.append(
                                BeamSearchSequence(
                                    tokens=current_beam.tokens + [token_id]
                                    if include_stop_str_in_output
                                    else current_beam.tokens,
                                    logprobs=current_beam.logprobs + [logprobs],
                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                    finish_reason="stop",
                                    stop_reason=eos_token_id,
                                )
                            )
                        else:
                            new_beams.append(
                                BeamSearchSequence(
                                    tokens=current_beam.tokens + [token_id],
                                    logprobs=current_beam.logprobs + [logprobs],
                                    lora_request=current_beam.lora_request,
                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                    multi_modal_data=current_beam.multi_modal_data,
                                    mm_processor_kwargs=current_beam.mm_processor_kwargs,
                                )
                            )

            sorted_beams = sorted(new_beams, key=sort_beams_key, reverse=True)
            all_beams = sorted_beams[:beam_width]

        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)

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

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

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    async def _prepare_generators(
        self,
        ctx: ServeContext,
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    ) -> ErrorResponse | None:
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        """Schedule the request and get the result generator."""
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        generators: list[
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            AsyncGenerator[RequestOutput | PoolingRequestOutput, None]
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        ] = []
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        try:
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            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
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645
            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
646
647

            if ctx.engine_prompts is None:
648
                return self.create_error_response("Engine prompts not available")
649
650
651
652

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

653
654
                self._log_inputs(
                    request_id_item,
655
                    engine_prompt,
656
657
658
                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681

                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,
682
    ) -> ErrorResponse | None:
683
684
685
        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
686
                return self.create_error_response("Engine prompts not available")
687
688

            num_prompts = len(ctx.engine_prompts)
689
            final_res_batch: list[RequestOutput | PoolingRequestOutput | None]
690
691
692
            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
693
                return self.create_error_response("Result generator not available")
694
695
696
697
698
699

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

            if None in final_res_batch:
                return self.create_error_response(
700
701
                    "Failed to generate results for all prompts"
                )
702

703
            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
704
705
706
707
708
709

            return None

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

710
    def create_error_response(
711
712
713
714
715
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> ErrorResponse:
716
717
718
719
720
721
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
722
723
724
        return ErrorResponse(
            error=ErrorInfo(message=message, type=err_type, code=status_code.value)
        )
725

726
    def create_streaming_error_response(
727
728
729
730
731
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> str:
732
        json_str = json.dumps(
733
734
735
736
            self.create_error_response(
                message=message, err_type=err_type, status_code=status_code
            ).model_dump()
        )
737
738
        return json_str

739
    async def _check_model(
740
741
        self,
        request: AnyRequest,
742
    ) -> ErrorResponse | None:
743
744
        error_response = None

745
        if self._is_model_supported(request.model):
746
            return None
747
        if request.model in self.models.lora_requests:
748
            return None
749
750
751
752
753
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
754
755
            if isinstance(load_result, LoRARequest):
                return None
756
757
758
759
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
760
761
762
                error_response = load_result

        return error_response or self.create_error_response(
763
764
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
765
766
            status_code=HTTPStatus.NOT_FOUND,
        )
767

768
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
        """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

791
    def _maybe_get_adapters(
792
793
794
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
795
    ) -> LoRARequest | None:
796
        if request.model in self.models.lora_requests:
797
            return self.models.lora_requests[request.model]
798
799
800
801
802
803

        # 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:
804
                return default_mm_lora
805
806

        if self._is_model_supported(request.model):
807
            return None
808

809
        # if _check_model has been called earlier, this will be unreachable
810
        raise ValueError(f"The model `{request.model}` does not exist.")
811

812
813
814
815
816
817
818
819
820
821
    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

822
823
824
825
826
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
827
828
829
830
831
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
832
833
834
835
836
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

837
    async def _normalize_prompt_text_to_input(
838
839
840
        self,
        request: AnyRequest,
        prompt: str,
841
        tokenizer: AnyTokenizer,
842
843
        add_special_tokens: bool,
    ) -> TextTokensPrompt:
844
845
        async_tokenizer = self._get_async_tokenizer(tokenizer)

846
847
848
849
        if (
            self.model_config.encoder_config is not None
            and self.model_config.encoder_config.get("do_lower_case", False)
        ):
850
851
            prompt = prompt.lower()

852
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
853

854
        if truncate_prompt_tokens is None:
855
            encoded = await async_tokenizer(
856
857
                prompt, add_special_tokens=add_special_tokens
            )
858
859
        elif truncate_prompt_tokens < 0:
            # Negative means we cap at the model's max length
860
861
862
863
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
864
865
                max_length=self.max_model_len,
            )
866
        else:
867
868
869
870
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
871
872
                max_length=truncate_prompt_tokens,
            )
873
874
875
876
877
878

        input_ids = encoded.input_ids
        input_text = prompt

        return self._validate_input(request, input_ids, input_text)

879
    async def _normalize_prompt_tokens_to_input(
880
881
        self,
        request: AnyRequest,
882
        prompt_ids: list[int],
883
        tokenizer: AnyTokenizer | None,
884
    ) -> TextTokensPrompt:
885
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
886

887
        if truncate_prompt_tokens is None:
888
            input_ids = prompt_ids
889
        elif truncate_prompt_tokens < 0:
890
            input_ids = prompt_ids[-self.max_model_len :]
891
892
893
        else:
            input_ids = prompt_ids[-truncate_prompt_tokens:]

894
895
896
897
898
        if tokenizer is None:
            input_text = ""
        else:
            async_tokenizer = self._get_async_tokenizer(tokenizer)
            input_text = await async_tokenizer.decode(input_ids)
899

900
901
902
903
904
        return self._validate_input(request, input_ids, input_text)

    def _validate_input(
        self,
        request: AnyRequest,
905
        input_ids: list[int],
906
907
        input_text: str,
    ) -> TextTokensPrompt:
908
909
        token_num = len(input_ids)

910
911
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
912
        if isinstance(
913
            request,
914
915
916
917
918
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
                ScoreRequest,
                RerankRequest,
919
920
                ClassificationCompletionRequest,
                ClassificationChatRequest,
921
922
            ),
        ):
923
924
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
925
            if token_num > self.max_model_len:
926
927
                operations: dict[type[AnyRequest], str] = {
                    ScoreRequest: "score",
928
929
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
930
                }
931
                operation = operations.get(type(request), "embedding generation")
932
933
934
                raise ValueError(
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
935
                    f"{token_num} tokens in the input for {operation}. "
936
937
938
                    f"Please reduce the length of the input."
                )
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
939

940
941
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
942
        if isinstance(
943
944
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
945
        ):
946
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
947

948
949
950
951
952
        # 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:
953
            max_tokens = getattr(request, "max_tokens", None)
954
955
956
957

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
958
            raise ValueError(
959
                f"This model's maximum context length is "
960
961
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
962
963
                "the input messages."
            )
964

965
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
966
967
968
969
970
            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}"
971
972
                f" - {token_num})."
            )
973
974
975

        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

976
    async def _tokenize_prompt_input_async(
977
978
979
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
980
        prompt_input: str | list[int],
981
982
983
        add_special_tokens: bool = True,
    ) -> TextTokensPrompt:
        """
984
        A simpler implementation that tokenizes a single prompt input.
985
        """
986
        async for result in self._tokenize_prompt_inputs_async(
987
988
            request,
            tokenizer,
989
            [prompt_input],
990
            add_special_tokens=add_special_tokens,
991
992
993
        ):
            return result
        raise ValueError("No results yielded from tokenization")
994

995
    async def _tokenize_prompt_inputs_async(
996
997
998
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
999
        prompt_inputs: Iterable[str | list[int]],
1000
        add_special_tokens: bool = True,
1001
    ) -> AsyncGenerator[TextTokensPrompt, None]:
1002
        """
1003
        A simpler implementation that tokenizes multiple prompt inputs.
1004
        """
1005
1006
        for prompt in prompt_inputs:
            if isinstance(prompt, str):
1007
                yield await self._normalize_prompt_text_to_input(
1008
                    request,
1009
1010
                    prompt=prompt,
                    tokenizer=tokenizer,
1011
1012
1013
                    add_special_tokens=add_special_tokens,
                )
            else:
1014
                yield await self._normalize_prompt_tokens_to_input(
1015
                    request,
1016
1017
                    prompt_ids=prompt,
                    tokenizer=tokenizer,
1018
1019
                )

1020
1021
    def _validate_chat_template(
        self,
1022
1023
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
1024
        trust_request_chat_template: bool,
1025
    ) -> ErrorResponse | None:
1026
        if not trust_request_chat_template and (
1027
1028
1029
1030
1031
1032
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
1033
1034
1035
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
1036
1037
                "Refused request with untrusted chat template."
            )
1038
1039
        return None

1040
1041
    async def _preprocess_chat(
        self,
1042
        request: ChatLikeRequest | ResponsesRequest,
1043
        tokenizer: AnyTokenizer,
1044
        messages: list[ChatCompletionMessageParam],
1045
        chat_template: str | None,
1046
        chat_template_content_format: ChatTemplateContentFormatOption,
1047
1048
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
1049
1050
1051
1052
        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,
1053
        add_special_tokens: bool = False,
1054
    ) -> tuple[
1055
1056
1057
        list[ConversationMessage],
        Sequence[RequestPrompt],
        list[EngineTokensPrompt],
1058
    ]:
1059
1060
        model_config = self.model_config

1061
1062
        resolved_content_format = resolve_chat_template_content_format(
            chat_template,
1063
            tool_dicts,
1064
1065
            chat_template_content_format,
            tokenizer,
1066
            model_config=model_config,
1067
        )
1068
        conversation, mm_data_future, mm_uuids = parse_chat_messages_futures(
1069
            messages,
1070
            model_config,
1071
            tokenizer,
1072
            content_format=resolved_content_format,
1073
1074
        )

1075
        _chat_template_kwargs: dict[str, Any] = dict(
1076
1077
1078
1079
1080
1081
1082
1083
            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 {})

1084
        request_prompt: str | list[int]
1085
1086
1087
1088

        if tokenizer is None:
            request_prompt = "placeholder"
        elif isinstance(tokenizer, MistralTokenizer):
1089
            request_prompt = await self._apply_mistral_chat_template_async(
1090
1091
                tokenizer,
                messages=messages,
1092
                **_chat_template_kwargs,
1093
1094
1095
            )
        else:
            request_prompt = apply_hf_chat_template(
1096
                tokenizer=tokenizer,
1097
                conversation=conversation,
1098
                model_config=model_config,
1099
                **_chat_template_kwargs,
1100
1101
1102
1103
            )

        mm_data = await mm_data_future

1104
1105
1106
        # 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
1107
1108
1109
        should_parse_tools = tool_parser is not None and (
            hasattr(request, "tool_choice") and request.tool_choice != "none"
        )
1110
1111

        if should_parse_tools:
1112
1113
1114
1115
1116
            if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                msg = (
                    "Tool usage is only supported for Chat Completions API "
                    "or Responses API requests."
                )
1117
                raise NotImplementedError(msg)
1118
            request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore
1119

1120
1121
        if tokenizer is None:
            assert isinstance(request_prompt, str), (
1122
1123
                "Prompt has to be a string",
                "when the tokenizer is not initialised",
1124
            )
1125
1126
1127
            prompt_inputs = TextTokensPrompt(
                prompt=request_prompt, prompt_token_ids=[1]
            )
1128
        elif isinstance(request_prompt, str):
1129
            prompt_inputs = await self._tokenize_prompt_input_async(
1130
1131
1132
1133
1134
1135
1136
1137
                request,
                tokenizer,
                request_prompt,
                add_special_tokens=add_special_tokens,
            )
        else:
            # For MistralTokenizer
            assert is_list_of(request_prompt, int), (
1138
1139
                "Prompt has to be either a string or a list of token ids"
            )
1140
1141
            prompt_inputs = TextTokensPrompt(
                prompt=tokenizer.decode(request_prompt),
1142
1143
                prompt_token_ids=request_prompt,
            )
1144

1145
        engine_prompt = EngineTokensPrompt(
1146
1147
            prompt_token_ids=prompt_inputs["prompt_token_ids"]
        )
1148
1149
        if mm_data is not None:
            engine_prompt["multi_modal_data"] = mm_data
1150
1151
1152
1153

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

1154
1155
        if request.mm_processor_kwargs is not None:
            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
1156

1157
1158
1159
        if hasattr(request, "cache_salt") and request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

1160
1161
        return conversation, [request_prompt], [engine_prompt]

1162
1163
1164
1165
    async def _process_inputs(
        self,
        request_id: str,
        engine_prompt: PromptType,
1166
        params: SamplingParams | PoolingParams,
1167
        *,
1168
1169
        lora_request: LoRARequest | None,
        trace_headers: Mapping[str, str] | None,
1170
1171
        priority: int,
    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
1172
        """Use the Processor to process inputs for AsyncLLM."""
1173
        tokenization_kwargs: dict[str, Any] = {}
1174
1175
1176
        _validate_truncation_size(
            self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
        )
1177

1178
        engine_request = self.processor.process_inputs(
1179
1180
            request_id,
            engine_prompt,
1181
            params,
1182
1183
1184
1185
1186
1187
1188
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
        )
        return engine_request, tokenization_kwargs

1189
1190
1191
1192
1193
1194
1195
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
        request_prompt: RequestPrompt,
        engine_prompt: EngineTokensPrompt,
        sampling_params: SamplingParams,
        context: ConversationContext,
1196
        lora_request: LoRARequest | None = None,
1197
1198
1199
        priority: int = 0,
        **kwargs,
    ):
1200
        prompt_text, _, _ = self._get_prompt_components(request_prompt)
1201
1202
1203
1204
1205
1206
1207
1208
        orig_priority = priority
        while True:
            self._log_inputs(
                request_id,
                request_prompt,
                params=sampling_params,
                lora_request=lora_request,
            )
1209
            trace_headers = kwargs.get("trace_headers")
1210
            engine_request, tokenization_kwargs = await self._process_inputs(
1211
                request_id,
1212
1213
                engine_prompt,
                sampling_params,
1214
1215
1216
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
1217
            )
1218
1219
1220
1221

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1222
1223
1224
                request_id,
                lora_request=lora_request,
                priority=priority,
1225
1226
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1227
1228
                **kwargs,
            )
1229

1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
            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()
1241
            context.append_tool_output(tool_output)
1242
1243
1244
1245
1246
1247
1248

            # 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()
1249
            engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
1250
1251
            request_prompt = prompt_token_ids
            # Update the sampling params.
1252
            sampling_params.max_tokens = self.max_model_len - len(prompt_token_ids)
1253
1254
1255
            # OPTIMIZATION
            priority = orig_priority - 1

1256
1257
    def _get_prompt_components(
        self,
1258
        prompt: RequestPrompt | PromptType,
1259
    ) -> PromptComponents:
1260
1261
        if isinstance(prompt, list):
            return PromptComponents(token_ids=prompt)
1262

1263
        return get_prompt_components(prompt)  # type: ignore[arg-type]
1264

1265
1266
1267
    def _log_inputs(
        self,
        request_id: str,
1268
1269
1270
        inputs: RequestPrompt | PromptType,
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1271
1272
1273
    ) -> None:
        if self.request_logger is None:
            return
1274

1275
        prompt, prompt_token_ids, prompt_embeds = self._get_prompt_components(inputs)
1276
1277
1278
1279
1280

        self.request_logger.log_inputs(
            request_id,
            prompt,
            prompt_token_ids,
1281
            prompt_embeds,
1282
1283
1284
            params=params,
            lora_request=lora_request,
        )
1285

1286
1287
1288
    async def _get_trace_headers(
        self,
        headers: Headers,
1289
    ) -> Mapping[str, str] | None:
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
        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

1300
    @staticmethod
1301
    def _base_request_id(
1302
1303
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1304
1305
        """Pulls the request id to use from a header, if provided"""
        default = default or random_uuid()
1306
1307
1308
1309
        if raw_request is None:
            return default

        return raw_request.headers.get("X-Request-Id", default)
1310

1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
    @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

1326
1327
1328
1329
1330
1331
1332
1333
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1340
1341
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1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
    @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
1389
1390
                if content and content.strip() == "":
                    content = None
1391
1392
1393
1394
1395
1396
            else:
                # No tool calls.
                return None, content

        return function_calls, content

1397
    @staticmethod
1398
1399
1400
1401
1402
1403
    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
        tokenizer: AnyTokenizer,
        return_as_token_id: bool = False,
    ) -> str:
1404
1405
1406
        if return_as_token_id:
            return f"token_id:{token_id}"

1407
1408
        if logprob.decoded_token is not None:
            return logprob.decoded_token
1409
        return tokenizer.decode(token_id)
1410

1411
    def _is_model_supported(self, model_name: str | None) -> bool:
1412
1413
        if not model_name:
            return True
1414
        return self.models.is_base_model(model_name)
1415

1416
1417

def clamp_prompt_logprobs(
1418
1419
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1420
1421
1422
1423
1424
1425
1426
    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():
1427
            if logprob_values.logprob == float("-inf"):
1428
1429
                logprob_values.logprob = -9999.0
    return prompt_logprobs