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serving_engine.py 51.5 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import json
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import sys
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import time
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import traceback
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from collections.abc import AsyncGenerator, Callable, Iterable, Mapping, Sequence
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass, field
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from http import HTTPStatus
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from typing import Any, ClassVar, Generic, 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 ConfigDict, TypeAdapter
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from starlette.datastructures import Headers
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from typing_extensions import TypeIs

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from vllm.entrypoints.pooling.classify.protocol import (
    ClassificationChatRequest,
    ClassificationCompletionRequest,
    ClassificationRequest,
    ClassificationResponse,
)
from vllm.entrypoints.pooling.embed.protocol import (
    EmbeddingChatRequest,
    EmbeddingCompletionRequest,
    EmbeddingRequest,
    EmbeddingResponse,
)
from vllm.entrypoints.pooling.pooling.protocol import (
    IOProcessorRequest,
    PoolingResponse,
)
from vllm.entrypoints.pooling.score.protocol import (
    RerankRequest,
    ScoreRequest,
    ScoreResponse,
)

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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,
    CompletionRequest,
    CompletionResponse,
    DetokenizeRequest,
    ErrorInfo,
    ErrorResponse,
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    FunctionCall,
    FunctionDefinition,
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    GenerateRequest,
    GenerateResponse,
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    ResponsesRequest,
    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.tokenizers import MistralTokenizer, TokenizerLike
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from vllm.tracing import (
    contains_trace_headers,
    extract_trace_headers,
    log_tracing_disabled_warning,
)
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from vllm.utils import random_uuid
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from vllm.utils.async_utils import (
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    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)


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@dataclass(kw_only=True)
class RequestProcessingMixin:
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    """
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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 = field(default_factory=list)
    engine_prompts: list[EngineTokensPrompt] | None = field(default_factory=list)
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@dataclass(kw_only=True)
class ResponseGenerationMixin:
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    """
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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
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    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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@dataclass(kw_only=True)
class ServeContext(RequestProcessingMixin, ResponseGenerationMixin, Generic[RequestT]):
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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
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    created_time: int = field(default_factory=lambda: int(time.time()))
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    lora_request: LoRARequest | None = None
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    # Shared across most requests
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    tokenizer: TokenizerLike | None = None
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@dataclass(kw_only=True)
class ClassificationServeContext(ServeContext[ClassificationRequest]):
    pass
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@dataclass(kw_only=True)
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class EmbeddingServeContext(ServeContext[EmbeddingRequest]):
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    chat_template: str | None = None
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    chat_template_content_format: ChatTemplateContentFormatOption


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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[TokenizerLike, AsyncMicrobatchTokenizer] = {}
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        self.log_error_stack = log_error_stack
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        self.input_processor = self.models.input_processor
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        self.io_processor = self.models.io_processor
        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
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    ) -> Callable[[TokenizerLike], 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[[TokenizerLike], 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:
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        self.input_processor.clear_mm_cache()
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        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

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

        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: TokenizerLike | 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)

635
    def _validate_request(self, ctx: ServeContext) -> ErrorResponse | None:
636
        truncate_prompt_tokens = getattr(ctx.request, "truncate_prompt_tokens", None)
637

638
639
640
641
        if (
            truncate_prompt_tokens is not None
            and truncate_prompt_tokens > self.max_model_len
        ):
642
643
644
            return self.create_error_response(
                "truncate_prompt_tokens value is "
                "greater than max_model_len."
645
646
                " Please, select a smaller truncation size."
            )
647
648
        return None

649
650
651
    def _create_pooling_params(
        self,
        ctx: ServeContext,
652
    ) -> PoolingParams | ErrorResponse:
653
654
        if not hasattr(ctx.request, "to_pooling_params"):
            return self.create_error_response(
655
656
                "Request type does not support pooling parameters"
            )
657
658
659

        return ctx.request.to_pooling_params()

660
661
662
    async def _prepare_generators(
        self,
        ctx: ServeContext,
663
    ) -> ErrorResponse | None:
664
        """Schedule the request and get the result generator."""
665
        generators: list[
666
            AsyncGenerator[RequestOutput | PoolingRequestOutput, None]
667
        ] = []
668
669

        try:
670
671
672
673
674
            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
675

676
677
678
            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
679
680

            if ctx.engine_prompts is None:
681
                return self.create_error_response("Engine prompts not available")
682
683
684
685

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

686
687
                self._log_inputs(
                    request_id_item,
688
                    engine_prompt,
689
690
691
                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714

                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,
715
    ) -> ErrorResponse | None:
716
717
718
        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
719
                return self.create_error_response("Engine prompts not available")
720
721

            num_prompts = len(ctx.engine_prompts)
722
            final_res_batch: list[RequestOutput | PoolingRequestOutput | None]
723
724
725
            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
726
                return self.create_error_response("Result generator not available")
727
728
729
730
731
732

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

            if None in final_res_batch:
                return self.create_error_response(
733
734
                    "Failed to generate results for all prompts"
                )
735

736
            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
737
738
739
740
741
742

            return None

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

743
    def create_error_response(
744
745
746
747
748
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> ErrorResponse:
749
750
751
752
753
754
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
755
756
757
        return ErrorResponse(
            error=ErrorInfo(message=message, type=err_type, code=status_code.value)
        )
758

759
    def create_streaming_error_response(
760
761
762
763
764
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> str:
765
        json_str = json.dumps(
766
767
768
769
            self.create_error_response(
                message=message, err_type=err_type, status_code=status_code
            ).model_dump()
        )
770
771
        return json_str

772
    async def _check_model(
773
774
        self,
        request: AnyRequest,
775
    ) -> ErrorResponse | None:
776
777
        error_response = None

778
        if self._is_model_supported(request.model):
779
            return None
780
        if request.model in self.models.lora_requests:
781
            return None
782
783
784
785
786
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
787
788
            if isinstance(load_result, LoRARequest):
                return None
789
790
791
792
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
793
794
795
                error_response = load_result

        return error_response or self.create_error_response(
796
797
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
798
799
            status_code=HTTPStatus.NOT_FOUND,
        )
800

801
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
        """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

824
    def _maybe_get_adapters(
825
826
827
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
828
    ) -> LoRARequest | None:
829
        if request.model in self.models.lora_requests:
830
            return self.models.lora_requests[request.model]
831
832
833
834
835
836

        # 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:
837
                return default_mm_lora
838
839

        if self._is_model_supported(request.model):
840
            return None
841

842
        # if _check_model has been called earlier, this will be unreachable
843
        raise ValueError(f"The model `{request.model}` does not exist.")
844

845
846
847
848
849
850
851
852
853
854
    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

855
856
857
858
859
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
860
861
862
863
864
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
865
866
867
868
869
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

870
    async def _normalize_prompt_text_to_input(
871
872
873
        self,
        request: AnyRequest,
        prompt: str,
874
        tokenizer: TokenizerLike,
875
876
        add_special_tokens: bool,
    ) -> TextTokensPrompt:
877
878
        async_tokenizer = self._get_async_tokenizer(tokenizer)

879
880
881
882
        if (
            self.model_config.encoder_config is not None
            and self.model_config.encoder_config.get("do_lower_case", False)
        ):
883
884
            prompt = prompt.lower()

885
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
886

887
        if truncate_prompt_tokens is None:
888
            encoded = await async_tokenizer(
889
890
                prompt, add_special_tokens=add_special_tokens
            )
891
892
        elif truncate_prompt_tokens < 0:
            # Negative means we cap at the model's max length
893
894
895
896
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
897
898
                max_length=self.max_model_len,
            )
899
        else:
900
901
902
903
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
904
905
                max_length=truncate_prompt_tokens,
            )
906
907
908
909
910
911

        input_ids = encoded.input_ids
        input_text = prompt

        return self._validate_input(request, input_ids, input_text)

912
    async def _normalize_prompt_tokens_to_input(
913
914
        self,
        request: AnyRequest,
915
        prompt_ids: list[int],
916
        tokenizer: TokenizerLike | None,
917
    ) -> TextTokensPrompt:
918
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
919

920
        if truncate_prompt_tokens is None:
921
            input_ids = prompt_ids
922
        elif truncate_prompt_tokens < 0:
923
            input_ids = prompt_ids[-self.max_model_len :]
924
925
926
        else:
            input_ids = prompt_ids[-truncate_prompt_tokens:]

927
928
929
930
931
        if tokenizer is None:
            input_text = ""
        else:
            async_tokenizer = self._get_async_tokenizer(tokenizer)
            input_text = await async_tokenizer.decode(input_ids)
932

933
934
935
936
937
        return self._validate_input(request, input_ids, input_text)

    def _validate_input(
        self,
        request: AnyRequest,
938
        input_ids: list[int],
939
940
        input_text: str,
    ) -> TextTokensPrompt:
941
942
        token_num = len(input_ids)

943
944
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
945
        if isinstance(
946
            request,
947
948
949
950
951
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
                ScoreRequest,
                RerankRequest,
952
953
                ClassificationCompletionRequest,
                ClassificationChatRequest,
954
955
            ),
        ):
956
957
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
958
            if token_num > self.max_model_len:
959
960
                operations: dict[type[AnyRequest], str] = {
                    ScoreRequest: "score",
961
962
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
963
                }
964
                operation = operations.get(type(request), "embedding generation")
965
966
967
                raise ValueError(
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
968
                    f"{token_num} tokens in the input for {operation}. "
969
970
971
                    f"Please reduce the length of the input."
                )
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
972

973
974
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
975
        if isinstance(
976
977
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
978
        ):
979
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
980

981
982
983
984
985
        # 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:
986
            max_tokens = getattr(request, "max_tokens", None)
987
988
989
990

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
991
            raise ValueError(
992
                f"This model's maximum context length is "
993
994
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
995
996
                "the input messages."
            )
997

998
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
999
1000
1001
1002
1003
            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}"
1004
1005
                f" - {token_num})."
            )
1006
1007
1008

        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

1009
    async def _tokenize_prompt_input_async(
1010
1011
        self,
        request: AnyRequest,
1012
        tokenizer: TokenizerLike,
1013
        prompt_input: str | list[int],
1014
1015
1016
        add_special_tokens: bool = True,
    ) -> TextTokensPrompt:
        """
1017
        A simpler implementation that tokenizes a single prompt input.
1018
        """
1019
        async for result in self._tokenize_prompt_inputs_async(
1020
1021
            request,
            tokenizer,
1022
            [prompt_input],
1023
            add_special_tokens=add_special_tokens,
1024
1025
1026
        ):
            return result
        raise ValueError("No results yielded from tokenization")
1027

1028
    async def _tokenize_prompt_inputs_async(
1029
1030
        self,
        request: AnyRequest,
1031
        tokenizer: TokenizerLike,
1032
        prompt_inputs: Iterable[str | list[int]],
1033
        add_special_tokens: bool = True,
1034
    ) -> AsyncGenerator[TextTokensPrompt, None]:
1035
        """
1036
        A simpler implementation that tokenizes multiple prompt inputs.
1037
        """
1038
1039
        for prompt in prompt_inputs:
            if isinstance(prompt, str):
1040
                yield await self._normalize_prompt_text_to_input(
1041
                    request,
1042
1043
                    prompt=prompt,
                    tokenizer=tokenizer,
1044
1045
1046
                    add_special_tokens=add_special_tokens,
                )
            else:
1047
                yield await self._normalize_prompt_tokens_to_input(
1048
                    request,
1049
1050
                    prompt_ids=prompt,
                    tokenizer=tokenizer,
1051
1052
                )

1053
1054
    def _validate_chat_template(
        self,
1055
1056
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
1057
        trust_request_chat_template: bool,
1058
    ) -> ErrorResponse | None:
1059
        if not trust_request_chat_template and (
1060
1061
1062
1063
1064
1065
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
1066
1067
1068
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
1069
1070
                "Refused request with untrusted chat template."
            )
1071
1072
        return None

1073
1074
    async def _preprocess_chat(
        self,
1075
        request: ChatLikeRequest | ResponsesRequest,
1076
        tokenizer: TokenizerLike | None,
1077
        messages: list[ChatCompletionMessageParam],
1078
        chat_template: str | None,
1079
        chat_template_content_format: ChatTemplateContentFormatOption,
1080
1081
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
1082
1083
1084
        tool_dicts: list[dict[str, Any]] | None = None,
        documents: list[dict[str, str]] | None = None,
        chat_template_kwargs: dict[str, Any] | None = None,
1085
        tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
1086
        add_special_tokens: bool = False,
1087
    ) -> tuple[
1088
1089
1090
        list[ConversationMessage],
        Sequence[RequestPrompt],
        list[EngineTokensPrompt],
1091
    ]:
1092
1093
1094
1095
1096
        if tokenizer is None:
            raise ValueError(
                "Unable to get tokenizer because `skip_tokenizer_init=True`"
            )

1097
1098
        model_config = self.model_config

1099
1100
        resolved_content_format = resolve_chat_template_content_format(
            chat_template,
1101
            tool_dicts,
1102
1103
            chat_template_content_format,
            tokenizer,
1104
            model_config=model_config,
1105
        )
1106
        conversation, mm_data_future, mm_uuids = parse_chat_messages_futures(
1107
            messages,
1108
            model_config,
1109
            tokenizer,
1110
            content_format=resolved_content_format,
1111
1112
        )

1113
        _chat_template_kwargs: dict[str, Any] = dict(
1114
1115
1116
1117
1118
1119
1120
1121
            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 {})

1122
        request_prompt: str | list[int]
1123
1124
1125
1126

        if tokenizer is None:
            request_prompt = "placeholder"
        elif isinstance(tokenizer, MistralTokenizer):
1127
            request_prompt = await self._apply_mistral_chat_template_async(
1128
1129
                tokenizer,
                messages=messages,
1130
                **_chat_template_kwargs,
1131
1132
1133
            )
        else:
            request_prompt = apply_hf_chat_template(
1134
                tokenizer=tokenizer,
1135
                conversation=conversation,
1136
                model_config=model_config,
1137
                **_chat_template_kwargs,
1138
1139
1140
1141
            )

        mm_data = await mm_data_future

1142
1143
1144
        # 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
1145
1146
1147
        should_parse_tools = tool_parser is not None and (
            hasattr(request, "tool_choice") and request.tool_choice != "none"
        )
1148
1149

        if should_parse_tools:
1150
1151
1152
1153
1154
            if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                msg = (
                    "Tool usage is only supported for Chat Completions API "
                    "or Responses API requests."
                )
1155
                raise NotImplementedError(msg)
1156
            request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore
1157

1158
1159
        if tokenizer is None:
            assert isinstance(request_prompt, str), (
1160
1161
                "Prompt has to be a string",
                "when the tokenizer is not initialised",
1162
            )
1163
1164
1165
            prompt_inputs = TextTokensPrompt(
                prompt=request_prompt, prompt_token_ids=[1]
            )
1166
        elif isinstance(request_prompt, str):
1167
            prompt_inputs = await self._tokenize_prompt_input_async(
1168
1169
1170
1171
1172
1173
1174
1175
                request,
                tokenizer,
                request_prompt,
                add_special_tokens=add_special_tokens,
            )
        else:
            # For MistralTokenizer
            assert is_list_of(request_prompt, int), (
1176
1177
                "Prompt has to be either a string or a list of token ids"
            )
1178
1179
            prompt_inputs = TextTokensPrompt(
                prompt=tokenizer.decode(request_prompt),
1180
1181
                prompt_token_ids=request_prompt,
            )
1182

1183
        engine_prompt = EngineTokensPrompt(
1184
1185
            prompt_token_ids=prompt_inputs["prompt_token_ids"]
        )
1186
1187
        if mm_data is not None:
            engine_prompt["multi_modal_data"] = mm_data
1188
1189
1190
1191

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

1192
1193
        if request.mm_processor_kwargs is not None:
            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
1194

1195
1196
1197
        if hasattr(request, "cache_salt") and request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

1198
1199
        return conversation, [request_prompt], [engine_prompt]

1200
1201
1202
1203
    async def _process_inputs(
        self,
        request_id: str,
        engine_prompt: PromptType,
1204
        params: SamplingParams | PoolingParams,
1205
        *,
1206
1207
        lora_request: LoRARequest | None,
        trace_headers: Mapping[str, str] | None,
1208
1209
        priority: int,
    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
1210
        """Use the Processor to process inputs for AsyncLLM."""
1211
        tokenization_kwargs: dict[str, Any] = {}
1212
1213
1214
        _validate_truncation_size(
            self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
        )
1215

1216
        engine_request = self.input_processor.process_inputs(
1217
1218
            request_id,
            engine_prompt,
1219
            params,
1220
1221
1222
1223
1224
1225
1226
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
        )
        return engine_request, tokenization_kwargs

1227
1228
1229
1230
1231
1232
1233
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
        request_prompt: RequestPrompt,
        engine_prompt: EngineTokensPrompt,
        sampling_params: SamplingParams,
        context: ConversationContext,
1234
        lora_request: LoRARequest | None = None,
1235
1236
1237
        priority: int = 0,
        **kwargs,
    ):
1238
        prompt_text, _, _ = self._get_prompt_components(request_prompt)
1239
        orig_priority = priority
1240
        sub_request = 0
1241
        while True:
1242
1243
            # Ensure that each sub-request has a unique request id.
            sub_request_id = f"{request_id}_{sub_request}"
1244
            self._log_inputs(
1245
                sub_request_id,
1246
1247
1248
1249
                request_prompt,
                params=sampling_params,
                lora_request=lora_request,
            )
1250
            trace_headers = kwargs.get("trace_headers")
1251
            engine_request, tokenization_kwargs = await self._process_inputs(
1252
                sub_request_id,
1253
1254
                engine_prompt,
                sampling_params,
1255
1256
1257
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
1258
            )
1259
1260
1261
1262

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1263
                sub_request_id,
1264
1265
                lora_request=lora_request,
                priority=priority,
1266
1267
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1268
1269
                **kwargs,
            )
1270

1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
            async for res in generator:
                context.append_output(res)
                # NOTE(woosuk): The stop condition is handled by the engine.
                yield context

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

            # Call the tool and update the context with the result.
            tool_output = await context.call_tool()
1282
            context.append_tool_output(tool_output)
1283
1284
1285
1286
1287
1288
1289

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

            # Create inputs for the next turn.
            # Render the next prompt token ids.
            prompt_token_ids = context.render_for_completion()
1290
            engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
1291
1292
            request_prompt = prompt_token_ids
            # Update the sampling params.
1293
            sampling_params.max_tokens = self.max_model_len - len(prompt_token_ids)
1294
1295
            # OPTIMIZATION
            priority = orig_priority - 1
1296
            sub_request += 1
1297

1298
1299
    def _get_prompt_components(
        self,
1300
        prompt: RequestPrompt | PromptType,
1301
    ) -> PromptComponents:
1302
1303
        if isinstance(prompt, list):
            return PromptComponents(token_ids=prompt)
1304

1305
        return get_prompt_components(prompt)  # type: ignore[arg-type]
1306

1307
1308
1309
    def _log_inputs(
        self,
        request_id: str,
1310
1311
1312
        inputs: RequestPrompt | PromptType,
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1313
1314
1315
    ) -> None:
        if self.request_logger is None:
            return
1316

1317
        prompt, prompt_token_ids, prompt_embeds = self._get_prompt_components(inputs)
1318
1319
1320
1321
1322

        self.request_logger.log_inputs(
            request_id,
            prompt,
            prompt_token_ids,
1323
            prompt_embeds,
1324
1325
1326
            params=params,
            lora_request=lora_request,
        )
1327

1328
1329
1330
    async def _get_trace_headers(
        self,
        headers: Headers,
1331
    ) -> Mapping[str, str] | None:
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
        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

1342
    @staticmethod
1343
    def _base_request_id(
1344
1345
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1346
        """Pulls the request id to use from a header, if provided"""
1347
1348
1349
1350
        if raw_request is not None and (
            (req_id := raw_request.headers.get("X-Request-Id")) is not None
        ):
            return req_id
1351

1352
        return random_uuid() if default is None else default
1353

1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
    @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

1369
1370
1371
    @staticmethod
    def _parse_tool_calls_from_content(
        request: ResponsesRequest | ChatCompletionRequest,
1372
        tokenizer: TokenizerLike,
1373
        enable_auto_tools: bool,
1374
        tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None,
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
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
        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
1432
1433
                if content and content.strip() == "":
                    content = None
1434
1435
1436
1437
1438
1439
            else:
                # No tool calls.
                return None, content

        return function_calls, content

1440
    @staticmethod
1441
1442
1443
    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
1444
        tokenizer: TokenizerLike | None,
1445
1446
        return_as_token_id: bool = False,
    ) -> str:
1447
1448
1449
        if return_as_token_id:
            return f"token_id:{token_id}"

1450
1451
        if logprob.decoded_token is not None:
            return logprob.decoded_token
1452
1453
1454
1455
1456
1457

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

1458
        return tokenizer.decode(token_id)
1459

1460
    def _is_model_supported(self, model_name: str | None) -> bool:
1461
1462
        if not model_name:
            return True
1463
        return self.models.is_base_model(model_name)
1464

1465
1466

def clamp_prompt_logprobs(
1467
1468
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1469
1470
1471
1472
1473
1474
1475
    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():
1476
            if logprob_values.logprob == float("-inf"):
1477
1478
                logprob_values.logprob = -9999.0
    return prompt_logprobs