api_server.py 72.3 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import gc
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import importlib
import inspect
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import json
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import multiprocessing
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import multiprocessing.forkserver as forkserver
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import os
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import signal
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import socket
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import tempfile
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import uuid
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from argparse import Namespace
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from collections.abc import AsyncGenerator, AsyncIterator, Awaitable
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from contextlib import asynccontextmanager
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from http import HTTPStatus
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from typing import Annotated, Any, Callable, Optional
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import prometheus_client
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import pydantic
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import regex as re
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import uvloop
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from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Request
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from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse, Response, StreamingResponse
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from openai import BaseModel
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from prometheus_client import make_asgi_app
from prometheus_fastapi_instrumentator import Instrumentator
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from starlette.concurrency import iterate_in_threadpool
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from starlette.datastructures import URL, Headers, MutableHeaders, State
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from starlette.routing import Mount
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from starlette.types import ASGIApp, Message, Receive, Scope, Send
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from typing_extensions import assert_never
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import vllm.envs as envs
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from vllm.config import VllmConfig
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (load_chat_template,
                                         resolve_hf_chat_template,
                                         resolve_mistral_chat_template)
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from vllm.entrypoints.launcher import serve_http
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.cli_args import (make_arg_parser,
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                                              validate_parsed_serve_args)
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# yapf conflicts with isort for this block
# yapf: disable
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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                                              ChatCompletionResponse,
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                                              ClassificationRequest,
                                              ClassificationResponse,
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                                              CompletionRequest,
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                                              CompletionResponse,
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                                              DetokenizeRequest,
                                              DetokenizeResponse,
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                                              EmbeddingRequest,
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                                              EmbeddingResponse, ErrorInfo,
                                              ErrorResponse,
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                                              IOProcessorResponse,
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                                              LoadLoRAAdapterRequest,
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                                              PoolingRequest, PoolingResponse,
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                                              RerankRequest, RerankResponse,
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                                              ResponsesRequest,
                                              ResponsesResponse, ScoreRequest,
                                              ScoreResponse, TokenizeRequest,
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                                              TokenizeResponse,
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                                              TranscriptionRequest,
                                              TranscriptionResponse,
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                                              TranslationRequest,
                                              TranslationResponse,
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                                              UnloadLoRAAdapterRequest)
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# yapf: enable
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from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
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from vllm.entrypoints.openai.serving_classification import (
    ServingClassification)
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from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
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from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
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                                                    LoRAModulePath,
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                                                    OpenAIServingModels)
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from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
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from vllm.entrypoints.openai.serving_responses import OpenAIServingResponses
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from vllm.entrypoints.openai.serving_score import ServingScores
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from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
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from vllm.entrypoints.openai.serving_transcription import (
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    OpenAIServingTranscription, OpenAIServingTranslation)
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from vllm.entrypoints.openai.tool_parsers import ToolParserManager
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from vllm.entrypoints.tool_server import (DemoToolServer, MCPToolServer,
                                          ToolServer)
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from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
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                                    log_non_default_args, with_cancellation)
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from vllm.logger import init_logger
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from vllm.reasoning import ReasoningParserManager
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from vllm.transformers_utils.tokenizer import MistralTokenizer
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import (Device, FlexibleArgumentParser, decorate_logs,
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                        is_valid_ipv6_address, set_ulimit)
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from vllm.v1.metrics.prometheus import get_prometheus_registry
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from vllm.version import __version__ as VLLM_VERSION
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prometheus_multiproc_dir: tempfile.TemporaryDirectory
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# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
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logger = init_logger('vllm.entrypoints.openai.api_server')
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_running_tasks: set[asyncio.Task] = set()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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    try:
        if app.state.log_stats:
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            engine_client: EngineClient = app.state.engine_client
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            async def _force_log():
                while True:
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                    await asyncio.sleep(envs.VLLM_LOG_STATS_INTERVAL)
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                    await engine_client.do_log_stats()
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            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
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        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
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        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
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@asynccontextmanager
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async def build_async_engine_client(
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    args: Namespace,
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    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: Optional[bool] = None,
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    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:
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    if os.getenv("VLLM_WORKER_MULTIPROC_METHOD") == "forkserver":
        # The executor is expected to be mp.
        # Pre-import heavy modules in the forkserver process
        logger.debug("Setup forkserver with pre-imports")
        multiprocessing.set_start_method('forkserver')
        multiprocessing.set_forkserver_preload(["vllm.v1.engine.async_llm"])
        forkserver.ensure_running()
        logger.debug("Forkserver setup complete!")

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    # Context manager to handle engine_client lifecycle
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    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

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    if disable_frontend_multiprocessing is None:
        disable_frontend_multiprocessing = bool(
            args.disable_frontend_multiprocessing)

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    async with build_async_engine_client_from_engine_args(
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            engine_args,
            usage_context=usage_context,
            disable_frontend_multiprocessing=disable_frontend_multiprocessing,
            client_config=client_config,
    ) as engine:
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        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
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    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
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    disable_frontend_multiprocessing: bool = False,
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    client_config: Optional[dict[str, Any]] = None,
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) -> AsyncIterator[EngineClient]:
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    """
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    Create EngineClient, either:
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        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

    Returns the Client or None if the creation failed.
    """

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    # Create the EngineConfig (determines if we can use V1).
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    # V1 AsyncLLM.
    if envs.VLLM_USE_V1:
        if disable_frontend_multiprocessing:
            logger.warning(
                "V1 is enabled, but got --disable-frontend-multiprocessing. "
                "To disable frontend multiprocessing, set VLLM_USE_V1=0.")

        from vllm.v1.engine.async_llm import AsyncLLM
        async_llm: Optional[AsyncLLM] = None
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        client_count = client_config.pop(
            "client_count") if client_config else 1
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        client_index = client_config.pop(
            "client_index") if client_config else 0
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        try:
            async_llm = AsyncLLM.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
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                enable_log_requests=engine_args.enable_log_requests,
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                disable_log_stats=engine_args.disable_log_stats,
                client_addresses=client_config,
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                client_count=client_count,
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                client_index=client_index)
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            # Don't keep the dummy data in memory
            await async_llm.reset_mm_cache()

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            yield async_llm
        finally:
            if async_llm:
                async_llm.shutdown()

    # V0 AsyncLLM.
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    else:
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        engine_client: Optional[EngineClient] = None
        try:
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            engine_client = AsyncLLMEngine.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
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                enable_log_requests=engine_args.enable_log_requests,
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                disable_log_stats=engine_args.disable_log_stats)
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            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()
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async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
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    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
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        raise RequestValidationError(errors=[
            "Unsupported Media Type: Only 'application/json' is allowed"
        ])
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router = APIRouter()
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class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


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def mount_metrics(app: FastAPI):
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    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()
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    # `response_class=PrometheusResponse` is needed to return an HTTP response
    # with header "Content-Type: text/plain; version=0.0.4; charset=utf-8"
    # instead of the default "application/json" which is incorrect.
    # See https://github.com/trallnag/prometheus-fastapi-instrumentator/issues/163#issue-1296092364
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    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
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    ).add().instrument(app).expose(app, response_class=PrometheusResponse)
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    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
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    # Workaround for 307 Redirect for /metrics
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    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
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    app.routes.append(metrics_route)
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def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


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def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


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def responses(request: Request) -> Optional[OpenAIServingResponses]:
    return request.app.state.openai_serving_responses


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def chat(request: Request) -> Optional[OpenAIServingChat]:
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    return request.app.state.openai_serving_chat


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def completion(request: Request) -> Optional[OpenAIServingCompletion]:
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    return request.app.state.openai_serving_completion


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def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


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def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
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def score(request: Request) -> Optional[ServingScores]:
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    return request.app.state.openai_serving_scores


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def classify(request: Request) -> Optional[ServingClassification]:
    return request.app.state.openai_serving_classification


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def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores
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def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
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def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


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def translation(request: Request) -> OpenAIServingTranslation:
    return request.app.state.openai_serving_translation


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def engine_client(request: Request) -> EngineClient:
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    return request.app.state.engine_client


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@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
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    """Health check."""
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    await engine_client(raw_request).check_health()
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    return Response(status_code=200)
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@router.get("/load")
async def get_server_load_metrics(request: Request):
    # This endpoint returns the current server load metrics.
    # It tracks requests utilizing the GPU from the following routes:
    # - /v1/chat/completions
    # - /v1/completions
    # - /v1/audio/transcriptions
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    # - /v1/audio/translations
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    # - /v1/embeddings
    # - /pooling
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    # - /classify
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    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
    return JSONResponse(
        content={'server_load': request.app.state.server_load_metrics})


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@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
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    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


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@router.post("/tokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_IMPLEMENTED.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
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async def tokenize(request: TokenizeRequest, raw_request: Request):
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    handler = tokenization(raw_request)

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    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
        raise HTTPException(status_code=HTTPStatus.NOT_IMPLEMENTED.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, TokenizeResponse):
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        return JSONResponse(content=generator.model_dump())

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    assert_never(generator)

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@router.post("/detokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
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async def detokenize(request: DetokenizeRequest, raw_request: Request):
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    handler = tokenization(raw_request)

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    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, DetokenizeResponse):
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        return JSONResponse(content=generator.model_dump())

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    assert_never(generator)

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def maybe_register_tokenizer_info_endpoint(args):
    """Conditionally register the tokenizer info endpoint if enabled."""
    if getattr(args, 'enable_tokenizer_info_endpoint', False):

        @router.get("/tokenizer_info")
        async def get_tokenizer_info(raw_request: Request):
            """Get comprehensive tokenizer information."""
            result = await tokenization(raw_request).get_tokenizer_info()
            return JSONResponse(content=result.model_dump(),
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                                status_code=result.error.code if isinstance(
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                                    result, ErrorResponse) else 200)


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@router.get("/v1/models")
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async def show_available_models(raw_request: Request):
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    handler = models(raw_request)
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    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
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@router.get("/version")
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async def show_version():
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    ver = {"version": VLLM_VERSION}
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    return JSONResponse(content=ver)


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async def _convert_stream_to_sse_events(
        generator: AsyncGenerator[BaseModel,
                                  None]) -> AsyncGenerator[str, None]:
    """Convert the generator to a stream of events in SSE format"""
    async for event in generator:
        event_type = getattr(event, 'type', 'unknown')
        # https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format
        event_data = (f"event: {event_type}\n"
                      f"data: {event.model_dump_json(indent=None)}\n\n")
        yield event_data


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@router.post("/v1/responses",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
async def create_responses(request: ResponsesRequest, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")
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    try:
        generator = await handler.create_responses(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, ResponsesResponse):
        return JSONResponse(content=generator.model_dump())
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    return StreamingResponse(content=_convert_stream_to_sse_events(generator),
                             media_type="text/event-stream")
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@router.get("/v1/responses/{response_id}")
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async def retrieve_responses(
    response_id: str,
    raw_request: Request,
    starting_after: Optional[int] = None,
    stream: Optional[bool] = False,
):
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    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")

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    try:
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        response = await handler.retrieve_responses(
            response_id,
            starting_after=starting_after,
            stream=stream,
        )
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    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
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                            status_code=response.error.code)
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    elif isinstance(response, ResponsesResponse):
        return JSONResponse(content=response.model_dump())
    return StreamingResponse(content=_convert_stream_to_sse_events(response),
                             media_type="text/event-stream")
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@router.post("/v1/responses/{response_id}/cancel")
async def cancel_responses(response_id: str, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API")

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    try:
        response = await handler.cancel_responses(response_id)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
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                            status_code=response.error.code)
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    return JSONResponse(content=response.model_dump())


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@router.post("/v1/chat/completions",
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             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 }
             })
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@with_cancellation
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@load_aware_call
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async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
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    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")
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    try:
        generator = await handler.create_chat_completion(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, ChatCompletionResponse):
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        return JSONResponse(content=generator.model_dump())
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    return StreamingResponse(content=generator, media_type="text/event-stream")

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@router.post("/v1/completions",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
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@load_aware_call
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async def create_completion(request: CompletionRequest, raw_request: Request):
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    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Completions API")

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    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, CompletionResponse):
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        return JSONResponse(content=generator.model_dump())
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    return StreamingResponse(content=generator, media_type="text/event-stream")

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@router.post("/v1/embeddings",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
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@load_aware_call
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async def create_embedding(request: EmbeddingRequest, raw_request: Request):
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    handler = embedding(raw_request)
    if handler is None:
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        return base(raw_request).create_error_response(
            message="The model does not support Embeddings API")

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    try:
        generator = await handler.create_embedding(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, EmbeddingResponse):
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        return JSONResponse(content=generator.model_dump())

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    assert_never(generator)

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@router.post("/pooling",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@load_aware_call
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async def create_pooling(request: PoolingRequest, raw_request: Request):
    handler = pooling(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Pooling API")
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    try:
        generator = await handler.create_pooling(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, (PoolingResponse, IOProcessorResponse)):
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        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


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@router.post("/classify", dependencies=[Depends(validate_json_request)])
@with_cancellation
@load_aware_call
async def create_classify(request: ClassificationRequest,
                          raw_request: Request):
    handler = classify(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Classification API")

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    try:
        generator = await handler.create_classify(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, ClassificationResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


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@router.post("/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@load_aware_call
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async def create_score(request: ScoreRequest, raw_request: Request):
    handler = score(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Score API")

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    try:
        generator = await handler.create_score(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, ScoreResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


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@router.post("/v1/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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async def create_score_v1(request: ScoreRequest, raw_request: Request):
    logger.warning(
        "To indicate that Score API is not part of standard OpenAI API, we "
        "have moved it to `/score`. Please update your client accordingly.")

    return await create_score(request, raw_request)


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@router.post("/v1/audio/transcriptions",
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNPROCESSABLE_ENTITY.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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async def create_transcriptions(raw_request: Request,
                                request: Annotated[TranscriptionRequest,
                                                   Form()]):
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    handler = transcription(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Transcriptions API")

    audio_data = await request.file.read()
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    try:
        generator = await handler.create_transcription(audio_data, request,
                                                       raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, TranscriptionResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


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@router.post("/v1/audio/translations",
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNPROCESSABLE_ENTITY.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
@with_cancellation
@load_aware_call
async def create_translations(request: Annotated[TranslationRequest,
                                                 Form()],
                              raw_request: Request):
    handler = translation(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Translations API")

    audio_data = await request.file.read()
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    try:
        generator = await handler.create_translation(audio_data, request,
                                                     raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, TranslationResponse):
        return JSONResponse(content=generator.model_dump())

    return StreamingResponse(content=generator, media_type="text/event-stream")


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@router.post("/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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async def do_rerank(request: RerankRequest, raw_request: Request):
    handler = rerank(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Rerank (Score) API")
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    try:
        generator = await handler.do_rerank(request, raw_request)
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
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                            status_code=generator.error.code)
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    elif isinstance(generator, RerankResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


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@router.post("/v1/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
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    logger.warning_once(
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        "To indicate that the rerank API is not part of the standard OpenAI"
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        " API, we have located it at `/rerank`. Please update your client "
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        "accordingly. (Note: Conforms to JinaAI rerank API)")

    return await do_rerank(request, raw_request)


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@router.post("/v2/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


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if envs.VLLM_SERVER_DEV_MODE:
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    logger.warning("SECURITY WARNING: Development endpoints are enabled! "
                   "This should NOT be used in production!")
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    @router.get("/server_info")
    async def show_server_info(raw_request: Request):
        server_info = {"vllm_config": str(raw_request.app.state.vllm_config)}
        return JSONResponse(content=server_info)

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    @router.post("/reset_prefix_cache")
    async def reset_prefix_cache(raw_request: Request):
        """
        Reset the prefix cache. Note that we currently do not check if the
        prefix cache is successfully reset in the API server.
        """
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        device = None
        device_str = raw_request.query_params.get("device")
        if device_str is not None:
            device = Device[device_str.upper()]
        logger.info("Resetting prefix cache with specific %s...", str(device))
        await engine_client(raw_request).reset_prefix_cache(device)
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        return Response(status_code=200)

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    @router.post("/sleep")
    async def sleep(raw_request: Request):
        # get POST params
        level = raw_request.query_params.get("level", "1")
        await engine_client(raw_request).sleep(int(level))
        # FIXME: in v0 with frontend multiprocessing, the sleep command
        # is sent but does not finish yet when we return a response.
        return Response(status_code=200)

    @router.post("/wake_up")
    async def wake_up(raw_request: Request):
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        tags = raw_request.query_params.getlist("tags")
        if tags == []:
            # set to None to wake up all tags if no tags are provided
            tags = None
        logger.info("wake up the engine with tags: %s", tags)
        await engine_client(raw_request).wake_up(tags)
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        # FIXME: in v0 with frontend multiprocessing, the wake-up command
        # is sent but does not finish yet when we return a response.
        return Response(status_code=200)

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    @router.get("/is_sleeping")
    async def is_sleeping(raw_request: Request):
        logger.info("check whether the engine is sleeping")
        is_sleeping = await engine_client(raw_request).is_sleeping()
        return JSONResponse(content={"is_sleeping": is_sleeping})

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    @router.post("/collective_rpc")
    async def collective_rpc(raw_request: Request):
        try:
            body = await raw_request.json()
        except json.JSONDecodeError as e:
            raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                                detail=f"JSON decode error: {e}") from e
        method = body.get("method")
        if method is None:
            raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                                detail="Missing 'method' in request body")
        # For security reason, only serialized string args/kwargs are passed.
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        # User-defined `method` is responsible for deserialization if needed.
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        args: list[str] = body.get("args", [])
        kwargs: dict[str, str] = body.get("kwargs", {})
        timeout: Optional[float] = body.get("timeout")
        results = await engine_client(raw_request).collective_rpc(
            method=method, timeout=timeout, args=tuple(args), kwargs=kwargs)
        if results is None:
            return Response(status_code=200)
        response: list[Any] = []
        for result in results:
            if result is None or isinstance(result, (dict, list)):
                response.append(result)
            else:
                response.append(str(result))
        return JSONResponse(content={"results": response})

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@router.post("/scale_elastic_ep",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "model": dict
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.REQUEST_TIMEOUT.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
async def scale_elastic_ep(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
        raise HTTPException(status_code=400,
                            detail="Invalid JSON format") from e  # noqa: B904

    new_data_parallel_size = body.get("new_data_parallel_size")
    drain_timeout = body.get("drain_timeout", 120)  # Default 2 minutes

    if new_data_parallel_size is None:
        raise HTTPException(status_code=400,
                            detail="new_data_parallel_size is required")

    if not isinstance(new_data_parallel_size,
                      int) or new_data_parallel_size <= 0:
        raise HTTPException(
            status_code=400,
            detail="new_data_parallel_size must be a positive integer")

    if not isinstance(drain_timeout, int) or drain_timeout <= 0:
        raise HTTPException(status_code=400,
                            detail="drain_timeout must be a positive integer")

    # Set scaling flag to prevent new requests
    global _scaling_elastic_ep
    _scaling_elastic_ep = True
    client = engine_client(raw_request)
    try:
        await client.scale_elastic_ep(new_data_parallel_size, drain_timeout)
        return JSONResponse({
            "message":
            f"Scaled to {new_data_parallel_size} "
            "data parallel engines",
        })
    except TimeoutError as e:
        raise HTTPException(status_code=408,
                            detail="Scale failed due to request drain timeout "
                            f"after {drain_timeout} seconds") from e
    except Exception as e:
        logger.error("Scale failed: %s", e)
        raise HTTPException(status_code=500, detail="Scale failed") from e
    finally:
        _scaling_elastic_ep = False


@router.post("/is_scaling_elastic_ep")
async def is_scaling_elastic_ep(raw_request: Request):
    return JSONResponse({"is_scaling_elastic_ep": _scaling_elastic_ep})


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# TODO: RequestType = TypeForm[BaseModel] when recognized by type checkers
# (requires typing_extensions >= 4.13)
RequestType = Any
GetHandlerFn = Callable[[Request], Optional[OpenAIServing]]
EndpointFn = Callable[[RequestType, Request], Awaitable[Any]]

# NOTE: Items defined earlier take higher priority
INVOCATION_TYPES: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = [
    (ChatCompletionRequest, (chat, create_chat_completion)),
    (CompletionRequest, (completion, create_completion)),
    (EmbeddingRequest, (embedding, create_embedding)),
    (ClassificationRequest, (classify, create_classify)),
    (ScoreRequest, (score, create_score)),
    (RerankRequest, (rerank, do_rerank)),
    (PoolingRequest, (pooling, create_pooling)),
]

# NOTE: Construct the TypeAdapters only once
INVOCATION_VALIDATORS = [
    (pydantic.TypeAdapter(request_type), (get_handler, endpoint))
    for request_type, (get_handler, endpoint) in INVOCATION_TYPES
]


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@router.post("/invocations",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNSUPPORTED_MEDIA_TYPE.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
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async def invocations(raw_request: Request):
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    """For SageMaker, routes requests based on the request type."""
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    try:
        body = await raw_request.json()
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    except json.JSONDecodeError as e:
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        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=f"JSON decode error: {e}") from e

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    valid_endpoints = [(validator, endpoint)
                       for validator, (get_handler,
                                       endpoint) in INVOCATION_VALIDATORS
                       if get_handler(raw_request) is not None]
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    for request_validator, endpoint in valid_endpoints:
        try:
            request = request_validator.validate_python(body)
        except pydantic.ValidationError:
            continue

        return await endpoint(request, raw_request)

    type_names = [
        t.__name__ if isinstance(t := validator._type, type) else str(t)
        for validator, _ in valid_endpoints
    ]
    msg = ("Cannot find suitable handler for request. "
           f"Expected one of: {type_names}")
    res = base(raw_request).create_error_response(message=msg)
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    return JSONResponse(content=res.model_dump(), status_code=res.error.code)
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if envs.VLLM_TORCH_PROFILER_DIR:
    logger.warning(
        "Torch Profiler is enabled in the API server. This should ONLY be "
        "used for local development!")

    @router.post("/start_profile")
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    async def start_profile(raw_request: Request):
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        logger.info("Starting profiler...")
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        await engine_client(raw_request).start_profile()
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        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
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    async def stop_profile(raw_request: Request):
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        logger.info("Stopping profiler...")
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        await engine_client(raw_request).stop_profile()
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        logger.info("Profiler stopped.")
        return Response(status_code=200)


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    logger.warning(
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        "LoRA dynamic loading & unloading is enabled in the API server. "
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        "This should ONLY be used for local development!")

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    @router.post("/v1/load_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
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    async def load_lora_adapter(request: LoadLoRAAdapterRequest,
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                                raw_request: Request):
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        handler = models(raw_request)
        response = await handler.load_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
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                                status_code=response.error.code)
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        return Response(status_code=200, content=response)

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    @router.post("/v1/unload_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
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    async def unload_lora_adapter(request: UnloadLoRAAdapterRequest,
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                                  raw_request: Request):
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        handler = models(raw_request)
        response = await handler.unload_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
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                                status_code=response.error.code)
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        return Response(status_code=200, content=response)


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def load_log_config(log_config_file: Optional[str]) -> Optional[dict]:
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
        logger.warning("Failed to load log config from file %s: error %s",
                       log_config_file, e)
        return None


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class AuthenticationMiddleware:
    """
    Pure ASGI middleware that authenticates each request by checking
    if the Authorization header exists and equals "Bearer {api_key}".

    Notes
    -----
    There are two cases in which authentication is skipped:
        1. The HTTP method is OPTIONS.
        2. The request path doesn't start with /v1 (e.g. /health).
    """

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    def __init__(self, app: ASGIApp, tokens: list[str]) -> None:
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        self.app = app
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        self.api_tokens = {f"Bearer {token}" for token in tokens}
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    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http",
                                 "websocket") or scope["method"] == "OPTIONS":
            # scope["type"] can be "lifespan" or "startup" for example,
            # in which case we don't need to do anything
            return self.app(scope, receive, send)
        root_path = scope.get("root_path", "")
        url_path = URL(scope=scope).path.removeprefix(root_path)
        headers = Headers(scope=scope)
        # Type narrow to satisfy mypy.
        if url_path.startswith("/v1") and headers.get(
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                "Authorization") not in self.api_tokens:
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            response = JSONResponse(content={"error": "Unauthorized"},
                                    status_code=401)
            return response(scope, receive, send)
        return self.app(scope, receive, send)


class XRequestIdMiddleware:
    """
    Middleware the set's the X-Request-Id header for each response
    to a random uuid4 (hex) value if the header isn't already
    present in the request, otherwise use the provided request id.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http", "websocket"):
            return self.app(scope, receive, send)

        # Extract the request headers.
        request_headers = Headers(scope=scope)

        async def send_with_request_id(message: Message) -> None:
            """
            Custom send function to mutate the response headers
            and append X-Request-Id to it.
            """
            if message["type"] == "http.response.start":
                response_headers = MutableHeaders(raw=message["headers"])
                request_id = request_headers.get("X-Request-Id",
                                                 uuid.uuid4().hex)
                response_headers.append("X-Request-Id", request_id)
            await send(message)

        return self.app(scope, receive, send_with_request_id)


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# Global variable to track scaling state
_scaling_elastic_ep = False


class ScalingMiddleware:
    """
    Middleware that checks if the model is currently scaling and
    returns a 503 Service Unavailable response if it is.
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    This middleware applies to all HTTP requests and prevents
    processing when the model is in a scaling state.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] != "http":
            return self.app(scope, receive, send)

        # Check global scaling state
        global _scaling_elastic_ep
        if _scaling_elastic_ep:
            # Return 503 Service Unavailable response
            response = JSONResponse(content={
                "error":
                "The model is currently scaling. Please try again later."
            },
                                    status_code=503)
            return response(scope, receive, send)

        return self.app(scope, receive, send)


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def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
            ChatCompletionStreamResponse, CompletionStreamResponse)

        # Try using Completion types for type-safe parsing
        if chunk_data.get('object') == 'chat.completion.chunk':
            chat_response = ChatCompletionStreamResponse.model_validate(
                chunk_data)
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
        elif chunk_data.get('object') == 'text_completion':
            completion_response = CompletionStreamResponse.model_validate(
                chunk_data)
            if completion_response.choices and completion_response.choices[
                    0].text:
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
        if 'choices' in chunk_data and chunk_data['choices']:
            choice = chunk_data['choices'][0]
            if 'delta' in choice and choice['delta'].get('content'):
                return choice['delta']['content']
            elif choice.get('text'):
                return choice['text']
    return ""


class SSEDecoder:
    """Robust Server-Sent Events decoder for streaming responses."""

    def __init__(self):
        self.buffer = ""
        self.content_buffer = []

    def decode_chunk(self, chunk: bytes) -> list[dict]:
        """Decode a chunk of SSE data and return parsed events."""
        import json

        try:
            chunk_str = chunk.decode('utf-8')
        except UnicodeDecodeError:
            # Skip malformed chunks
            return []

        self.buffer += chunk_str
        events = []

        # Process complete lines
        while '\n' in self.buffer:
            line, self.buffer = self.buffer.split('\n', 1)
            line = line.rstrip('\r')  # Handle CRLF

            if line.startswith('data: '):
                data_str = line[6:].strip()
                if data_str == '[DONE]':
                    events.append({'type': 'done'})
                elif data_str:
                    try:
                        event_data = json.loads(data_str)
                        events.append({'type': 'data', 'data': event_data})
                    except json.JSONDecodeError:
                        # Skip malformed JSON
                        continue

        return events

    def extract_content(self, event_data: dict) -> str:
        """Extract content from event data."""
        return _extract_content_from_chunk(event_data)

    def add_content(self, content: str) -> None:
        """Add content to the buffer."""
        if content:
            self.content_buffer.append(content)

    def get_complete_content(self) -> str:
        """Get the complete buffered content."""
        return ''.join(self.content_buffer)


def _log_streaming_response(response, response_body: list) -> None:
    """Log streaming response with robust SSE parsing."""
    from starlette.concurrency import iterate_in_threadpool

    sse_decoder = SSEDecoder()
    chunk_count = 0

    def buffered_iterator():
        nonlocal chunk_count

        for chunk in response_body:
            chunk_count += 1
            yield chunk

            # Parse SSE events from chunk
            events = sse_decoder.decode_chunk(chunk)

            for event in events:
                if event['type'] == 'data':
                    content = sse_decoder.extract_content(event['data'])
                    sse_decoder.add_content(content)
                elif event['type'] == 'done':
                    # Log complete content when done
                    full_content = sse_decoder.get_complete_content()
                    if full_content:
                        # Truncate if too long
                        if len(full_content) > 2048:
                            full_content = full_content[:2048] + ""
                            "...[truncated]"
                        logger.info(
                            "response_body={streaming_complete: " \
                            "content='%s', chunks=%d}",
                            full_content, chunk_count)
                    else:
                        logger.info(
                            "response_body={streaming_complete: " \
                            "no_content, chunks=%d}",
                            chunk_count)
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
    logger.info("response_body={streaming_started: chunks=%d}",
                len(response_body))


def _log_non_streaming_response(response_body: list) -> None:
    """Log non-streaming response."""
    try:
        decoded_body = response_body[0].decode()
        logger.info("response_body={%s}", decoded_body)
    except UnicodeDecodeError:
        logger.info("response_body={<binary_data>}")


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def build_app(args: Namespace) -> FastAPI:
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    if args.disable_fastapi_docs:
        app = FastAPI(openapi_url=None,
                      docs_url=None,
                      redoc_url=None,
                      lifespan=lifespan)
    else:
        app = FastAPI(lifespan=lifespan)
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    app.include_router(router)
    app.root_path = args.root_path
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    mount_metrics(app)

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    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

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    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
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        err = ErrorResponse(
            error=ErrorInfo(message=exc.detail,
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                            type=HTTPStatus(exc.status_code).phrase,
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                            code=exc.status_code))
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        return JSONResponse(err.model_dump(), status_code=exc.status_code)

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    @app.exception_handler(RequestValidationError)
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    async def validation_exception_handler(_: Request,
                                           exc: RequestValidationError):
        exc_str = str(exc)
        errors_str = str(exc.errors())

        if exc.errors() and errors_str and errors_str != exc_str:
            message = f"{exc_str} {errors_str}"
        else:
            message = exc_str

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        err = ErrorResponse(error=ErrorInfo(message=message,
                                            type=HTTPStatus.BAD_REQUEST.phrase,
                                            code=HTTPStatus.BAD_REQUEST))
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        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

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    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
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    if tokens := [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]:
        app.add_middleware(AuthenticationMiddleware, tokens=tokens)
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    if args.enable_request_id_headers:
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        app.add_middleware(XRequestIdMiddleware)
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    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

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    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
        logger.warning("CAUTION: Enabling log response in the API Server. "
                       "This can include sensitive information and should be "
                       "avoided in production.")

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
            response_body = [
                section async for section in response.body_iterator
            ]
            response.body_iterator = iterate_in_threadpool(iter(response_body))
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            # Check if this is a streaming response by looking at content-type
            content_type = response.headers.get("content-type", "")
            is_streaming = content_type == "text/event-stream; charset=utf-8"

            # Log response body based on type
            if not response_body:
                logger.info("response_body={<empty>}")
            elif is_streaming:
                _log_streaming_response(response, response_body)
            else:
                _log_non_streaming_response(response_body)
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            return response
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    for middleware in args.middleware:
        module_path, object_name = middleware.rsplit(".", 1)
        imported = getattr(importlib.import_module(module_path), object_name)
        if inspect.isclass(imported):
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            app.add_middleware(imported)  # type: ignore[arg-type]
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        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
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            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")
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    return app


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async def init_app_state(
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    engine_client: EngineClient,
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    vllm_config: VllmConfig,
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    state: State,
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    args: Namespace,
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) -> None:
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    if args.served_model_name is not None:
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        served_model_names = args.served_model_name
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    else:
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        served_model_names = [args.model]
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    if args.enable_log_requests:
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        request_logger = RequestLogger(max_log_len=args.max_log_len)
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    else:
        request_logger = None
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    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

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    state.engine_client = engine_client
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    state.log_stats = not args.disable_log_stats
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    state.vllm_config = vllm_config
    model_config = vllm_config.model_config
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    if envs.VLLM_USE_V1:
        supported_tasks = await engine_client \
            .get_supported_tasks()  # type: ignore
    else:
        supported_tasks = model_config.supported_tasks

    logger.info("Supported_tasks: %s", supported_tasks)

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    resolved_chat_template = load_chat_template(args.chat_template)
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    if resolved_chat_template is not None:
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        # Get the tokenizer to check official template
        tokenizer = await engine_client.get_tokenizer()

        if isinstance(tokenizer, MistralTokenizer):
            # The warning is logged in resolve_mistral_chat_template.
            resolved_chat_template = resolve_mistral_chat_template(
                chat_template=resolved_chat_template)
        else:
            hf_chat_template = resolve_hf_chat_template(
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                tokenizer=tokenizer,
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                chat_template=None,
                tools=None,
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                model_config=vllm_config.model_config,
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            )
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            if hf_chat_template != resolved_chat_template:
                logger.warning(
                    "Using supplied chat template: %s\n"
                    "It is different from official chat template '%s'. "
                    "This discrepancy may lead to performance degradation.",
                    resolved_chat_template, args.model)
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    if args.tool_server == "demo":
        tool_server: Optional[ToolServer] = DemoToolServer()
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        assert isinstance(tool_server, DemoToolServer)
        await tool_server.init_and_validate()
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    elif args.tool_server:
        tool_server = MCPToolServer()
        await tool_server.add_tool_server(args.tool_server)
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    else:
        tool_server = None

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    # Merge default_mm_loras into the static lora_modules
    default_mm_loras = (vllm_config.lora_config.default_mm_loras
                        if vllm_config.lora_config is not None else {})

    lora_modules = args.lora_modules
    if default_mm_loras:
        default_mm_lora_paths = [
            LoRAModulePath(
                name=modality,
                path=lora_path,
            ) for modality, lora_path in default_mm_loras.items()
        ]
        if args.lora_modules is None:
            lora_modules = default_mm_lora_paths
        else:
            lora_modules += default_mm_lora_paths

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    state.openai_serving_models = OpenAIServingModels(
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        engine_client=engine_client,
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        model_config=model_config,
        base_model_paths=base_model_paths,
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        lora_modules=lora_modules,
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    )
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    await state.openai_serving_models.init_static_loras()
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    state.openai_serving_responses = OpenAIServingResponses(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
        enable_auto_tools=args.enable_auto_tool_choice,
        tool_parser=args.tool_call_parser,
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        tool_server=tool_server,
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        reasoning_parser=args.reasoning_parser,
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
        enable_force_include_usage=args.enable_force_include_usage,
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        enable_log_outputs=args.enable_log_outputs,
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        log_error_stack=args.log_error_stack,
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    ) if "generate" in supported_tasks else None
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    state.openai_serving_chat = OpenAIServingChat(
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        engine_client,
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        model_config,
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        state.openai_serving_models,
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        args.response_role,
        request_logger=request_logger,
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        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
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        enable_auto_tools=args.enable_auto_tool_choice,
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        exclude_tools_when_tool_choice_none=args.
        exclude_tools_when_tool_choice_none,
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        tool_parser=args.tool_call_parser,
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        reasoning_parser=args.reasoning_parser,
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        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
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        enable_force_include_usage=args.enable_force_include_usage,
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        enable_log_outputs=args.enable_log_outputs,
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        log_error_stack=args.log_error_stack,
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    ) if "generate" in supported_tasks else None
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    state.openai_serving_completion = OpenAIServingCompletion(
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        engine_client,
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        model_config,
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        state.openai_serving_models,
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        request_logger=request_logger,
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        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
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        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
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        enable_force_include_usage=args.enable_force_include_usage,
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        log_error_stack=args.log_error_stack,
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    ) if "generate" in supported_tasks else None
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    state.openai_serving_pooling = OpenAIServingPooling(
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        engine_client,
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        vllm_config,
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        state.openai_serving_models,
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        request_logger=request_logger,
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        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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        log_error_stack=args.log_error_stack,
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    ) if "encode" in supported_tasks else None
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    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
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        state.openai_serving_models,
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        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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        log_error_stack=args.log_error_stack,
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    ) if "embed" in supported_tasks else None
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    state.openai_serving_classification = ServingClassification(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
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        log_error_stack=args.log_error_stack,
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    ) if "classify" in supported_tasks else None
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    state.openai_serving_scores = ServingScores(
        engine_client,
        model_config,
        state.openai_serving_models,
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        request_logger=request_logger,
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        log_error_stack=args.log_error_stack,
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    ) if ("embed" in supported_tasks or "score" in supported_tasks) else None
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    state.openai_serving_tokenization = OpenAIServingTokenization(
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        model_config,
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        state.openai_serving_models,
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        request_logger=request_logger,
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        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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        log_error_stack=args.log_error_stack,
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    )
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    state.openai_serving_transcription = OpenAIServingTranscription(
        engine_client,
        model_config,
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        state.openai_serving_models,
        request_logger=request_logger,
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        log_error_stack=args.log_error_stack,
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    ) if "transcription" in supported_tasks else None
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    state.openai_serving_translation = OpenAIServingTranslation(
        engine_client,
        model_config,
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        state.openai_serving_models,
        request_logger=request_logger,
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        log_error_stack=args.log_error_stack,
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    ) if "transcription" in supported_tasks else None
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    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

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def create_server_socket(addr: tuple[str, int]) -> socket.socket:
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    family = socket.AF_INET
    if is_valid_ipv6_address(addr[0]):
        family = socket.AF_INET6

    sock = socket.socket(family=family, type=socket.SOCK_STREAM)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
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    sock.bind(addr)

    return sock


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def create_server_unix_socket(path: str) -> socket.socket:
    sock = socket.socket(family=socket.AF_UNIX, type=socket.SOCK_STREAM)
    sock.bind(path)
    return sock


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def validate_api_server_args(args):
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    valid_tool_parses = ToolParserManager.tool_parsers.keys()
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    if args.enable_auto_tool_choice \
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            and args.tool_call_parser not in valid_tool_parses:
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        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
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                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
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    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
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    if args.reasoning_parser \
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        and args.reasoning_parser not in valid_reasoning_parses:
        raise KeyError(
            f"invalid reasoning parser: {args.reasoning_parser} "
            f"(chose from {{ {','.join(valid_reasoning_parses)} }})")

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def setup_server(args):
    """Validate API server args, set up signal handler, create socket
    ready to serve."""

    logger.info("vLLM API server version %s", VLLM_VERSION)
    log_non_default_args(args)

    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

    validate_api_server_args(args)

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    # workaround to make sure that we bind the port before the engine is set up.
    # This avoids race conditions with ray.
    # see https://github.com/vllm-project/vllm/issues/8204
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    if args.uds:
        sock = create_server_unix_socket(args.uds)
    else:
        sock_addr = (args.host or "", args.port)
        sock = create_server_socket(sock_addr)
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    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

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    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

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    if args.uds:
        listen_address = f"unix:{args.uds}"
    else:
        addr, port = sock_addr
        is_ssl = args.ssl_keyfile and args.ssl_certfile
        host_part = f"[{addr}]" if is_valid_ipv6_address(
            addr) else addr or "0.0.0.0"
        listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"
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    return listen_address, sock


async def run_server(args, **uvicorn_kwargs) -> None:
    """Run a single-worker API server."""
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    # Add process-specific prefix to stdout and stderr.
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    decorate_logs("APIServer")
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    listen_address, sock = setup_server(args)
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)


async def run_server_worker(listen_address,
                            sock,
                            args,
                            client_config=None,
                            **uvicorn_kwargs) -> None:
    """Run a single API server worker."""

    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

    server_index = client_config.get("client_index", 0) if client_config else 0
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    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
        uvicorn_kwargs['log_config'] = log_config

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    async with build_async_engine_client(
            args,
            client_config=client_config,
    ) as engine_client:
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        maybe_register_tokenizer_info_endpoint(args)
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        app = build_app(args)

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        vllm_config = await engine_client.get_vllm_config()
        await init_app_state(engine_client, vllm_config, app.state, args)
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        logger.info("Starting vLLM API server %d on %s", server_index,
                    listen_address)
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        shutdown_task = await serve_http(
            app,
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            sock=sock,
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            enable_ssl_refresh=args.enable_ssl_refresh,
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            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
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            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
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            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
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            ssl_keyfile=args.ssl_keyfile,
            ssl_certfile=args.ssl_certfile,
            ssl_ca_certs=args.ssl_ca_certs,
            ssl_cert_reqs=args.ssl_cert_reqs,
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            h11_max_incomplete_event_size=args.h11_max_incomplete_event_size,
            h11_max_header_count=args.h11_max_header_count,
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            **uvicorn_kwargs,
        )

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    # NB: Await server shutdown only after the backend context is exited
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    try:
        await shutdown_task
    finally:
        sock.close()
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Ethan Xu committed
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if __name__ == "__main__":
    # NOTE(simon):
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    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
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    cli_env_setup()
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Ethan Xu committed
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    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
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    validate_parsed_serve_args(args)
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    uvloop.run(run_server(args))