api_server.py 44.1 KB
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# SPDX-License-Identifier: Apache-2.0

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import asyncio
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import atexit
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import gc
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import importlib
import inspect
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import multiprocessing
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import os
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import re
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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 AsyncIterator
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from contextlib import asynccontextmanager
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from functools import partial
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from http import HTTPStatus
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from typing import Annotated, Optional, Union
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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 starlette.concurrency import iterate_in_threadpool
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from starlette.datastructures import State
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from starlette.routing import Mount
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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.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
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 (log_non_default_args,
                                              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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                                              EmbeddingChatRequest,
                                              EmbeddingCompletionRequest,
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                                              EmbeddingRequest,
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                                              EmbeddingResponse,
                                              EmbeddingResponseData,
                                              ErrorResponse,
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                                              LoadLoRAAdapterRequest,
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                                              PoolingChatRequest,
                                              PoolingCompletionRequest,
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                                              PoolingRequest, PoolingResponse,
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                                              RerankRequest, RerankResponse,
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                                              ScoreRequest, ScoreResponse,
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                                              TokenizeRequest,
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                                              TokenizeResponse,
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                                              TranscriptionRequest,
                                              TranscriptionResponse,
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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,
                                                    OpenAIServingModels)
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from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
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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 (
    OpenAIServingTranscription)
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from vllm.entrypoints.openai.tool_parsers import ToolParserManager
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from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
                                    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.config import (
    maybe_register_config_serialize_by_value)
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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, get_open_zmq_ipc_path,
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                        is_valid_ipv6_address, set_ulimit)
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from vllm.version import __version__ as VLLM_VERSION
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TIMEOUT_KEEP_ALIVE = 5  # seconds
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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(10.)
                    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) -> AsyncIterator[EngineClient]:
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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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    async with build_async_engine_client_from_engine_args(
            engine_args, args.disable_frontend_multiprocessing) as engine:
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
    disable_frontend_multiprocessing: bool = False,
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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).
    usage_context = UsageContext.OPENAI_API_SERVER
    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
        try:
            async_llm = AsyncLLM.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
                disable_log_stats=engine_args.disable_log_stats)
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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.
    elif (MQLLMEngineClient.is_unsupported_config(vllm_config)
          or disable_frontend_multiprocessing):
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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,
                disable_log_requests=engine_args.disable_log_requests,
                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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    # V0MQLLMEngine.
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    else:
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        if "PROMETHEUS_MULTIPROC_DIR" not in os.environ:
            # Make TemporaryDirectory for prometheus multiprocessing
            # Note: global TemporaryDirectory will be automatically
            #   cleaned up upon exit.
            global prometheus_multiproc_dir
            prometheus_multiproc_dir = tempfile.TemporaryDirectory()
            os.environ[
                "PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
        else:
            logger.warning(
                "Found PROMETHEUS_MULTIPROC_DIR was set by user. "
                "This directory must be wiped between vLLM runs or "
                "you will find inaccurate metrics. Unset the variable "
                "and vLLM will properly handle cleanup.")

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        # Select random path for IPC.
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        ipc_path = get_open_zmq_ipc_path()
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        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)
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        # Start RPCServer in separate process (holds the LLMEngine).
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        # the current process might have CUDA context,
        # so we need to spawn a new process
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        context = multiprocessing.get_context("spawn")

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        # Ensure we can serialize transformer config before spawning
        maybe_register_config_serialize_by_value()

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        # The Process can raise an exception during startup, which may
        # not actually result in an exitcode being reported. As a result
        # we use a shared variable to communicate the information.
        engine_alive = multiprocessing.Value('b', True, lock=False)
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        engine_process = context.Process(
            target=run_mp_engine,
            args=(vllm_config, UsageContext.OPENAI_API_SERVER, ipc_path,
                  engine_args.disable_log_stats,
                  engine_args.disable_log_requests, engine_alive))
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        engine_process.start()
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        engine_pid = engine_process.pid
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        assert engine_pid is not None, "Engine process failed to start."
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        logger.info("Started engine process with PID %d", engine_pid)
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        def _cleanup_ipc_path():
            socket_path = ipc_path.replace("ipc://", "")
            if os.path.exists(socket_path):
                os.remove(socket_path)

        # Ensure we clean up the local IPC socket file on exit.
        atexit.register(_cleanup_ipc_path)

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        # Build RPCClient, which conforms to EngineClient Protocol.
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        build_client = partial(MQLLMEngineClient, ipc_path, vllm_config,
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                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
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        try:
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            while True:
                try:
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                    await mq_engine_client.setup()
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                    break
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                except TimeoutError:
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                    if (not engine_process.is_alive()
                            or not engine_alive.value):
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                        raise RuntimeError(
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                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None
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            yield mq_engine_client  # type: ignore[misc]
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        finally:
            # Ensure rpc server process was terminated
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            engine_process.terminate()
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            # Close all open connections to the backend
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            mq_engine_client.close()
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            # Wait for engine process to join
            engine_process.join(4)
            if engine_process.exitcode is None:
                # Kill if taking longer than 5 seconds to stop
                engine_process.kill()
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            # Lazy import for prometheus multiprocessing.
            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
            # before prometheus_client is imported.
            # See https://prometheus.github.io/client_python/multiprocess/
            from prometheus_client import multiprocess
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            multiprocess.mark_process_dead(engine_process.pid)
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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 HTTPException(
            status_code=HTTPStatus.UNSUPPORTED_MEDIA_TYPE,
            detail="Unsupported Media Type: Only 'application/json' is allowed"
        )


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router = APIRouter()
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def mount_metrics(app: FastAPI):
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    # Lazy import for prometheus multiprocessing.
    # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
    # before prometheus_client is imported.
    # See https://prometheus.github.io/client_python/multiprocess/
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    from prometheus_client import (REGISTRY, CollectorRegistry, make_asgi_app,
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                                   multiprocess)
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    from prometheus_fastapi_instrumentator import Instrumentator
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    registry = REGISTRY

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    prometheus_multiproc_dir_path = os.getenv("PROMETHEUS_MULTIPROC_DIR", None)
    if prometheus_multiproc_dir_path is not None:
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        logger.debug("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
                     prometheus_multiproc_dir_path)
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        registry = CollectorRegistry()
        multiprocess.MultiProcessCollector(registry)
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    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
    ).add().instrument(app).expose(app)

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


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@router.get("/health")
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async def health(raw_request: Request) -> JSONResponse:
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    """Health check."""
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    await engine_client(raw_request).check_health()
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    return JSONResponse(content={}, 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
    # - /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.api_route("/ping", methods=["GET", "POST"])
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async def ping(raw_request: Request) -> JSONResponse:
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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)])
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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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    generator = await handler.create_tokenize(request, raw_request)
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.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)])
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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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    generator = await handler.create_detokenize(request, raw_request)
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.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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@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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@router.post("/v1/chat/completions",
             dependencies=[Depends(validate_json_request)])
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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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    generator = await handler.create_chat_completion(request, raw_request)
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.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)])
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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")

    generator = await handler.create_completion(request, raw_request)
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.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)])
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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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        fallback_handler = pooling(raw_request)
        if fallback_handler is None:
            return base(raw_request).create_error_response(
                message="The model does not support Embeddings API")

        logger.warning(
            "Embeddings API will become exclusive to embedding models "
            "in a future release. To return the hidden states directly, "
            "use the Pooling API (`/pooling`) instead.")

        res = await fallback_handler.create_pooling(request, raw_request)
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        generator: Union[ErrorResponse, EmbeddingResponse]
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        if isinstance(res, PoolingResponse):
            generator = EmbeddingResponse(
                id=res.id,
                object=res.object,
                created=res.created,
                model=res.model,
                data=[
                    EmbeddingResponseData(
                        index=d.index,
                        embedding=d.data,  # type: ignore
                    ) for d in res.data
                ],
                usage=res.usage,
            )
        else:
            generator = res
    else:
        generator = await handler.create_embedding(request, raw_request)
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    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.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)])
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@with_cancellation
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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")

    generator = await handler.create_pooling(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, PoolingResponse):
        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")

    generator = await handler.create_classify(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

    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)])
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@with_cancellation
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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")

    generator = await handler.create_score(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    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)])
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@with_cancellation
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@load_aware_call
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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")
@with_cancellation
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@load_aware_call
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async def create_transcriptions(request: Annotated[TranscriptionRequest,
                                                   Form()],
                                raw_request: Request):
    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()
    generator = await handler.create_transcription(audio_data, request,
                                                   raw_request)

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

    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("/rerank", dependencies=[Depends(validate_json_request)])
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@load_aware_call
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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")
    generator = await handler.do_rerank(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    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)])
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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)])
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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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TASK_HANDLERS: dict[str, dict[str, tuple]] = {
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    "generate": {
        "messages": (ChatCompletionRequest, create_chat_completion),
        "default": (CompletionRequest, create_completion),
    },
    "embed": {
        "messages": (EmbeddingChatRequest, create_embedding),
        "default": (EmbeddingCompletionRequest, create_embedding),
    },
    "score": {
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        "default": (RerankRequest, do_rerank)
    },
    "rerank": {
        "default": (RerankRequest, do_rerank)
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    },
    "reward": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
    "classify": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
}

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if envs.VLLM_SERVER_DEV_MODE:

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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("/invocations", dependencies=[Depends(validate_json_request)])
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async def invocations(raw_request: Request):
    """
    For SageMaker, routes requests to other handlers based on model `task`.
    """
    body = await raw_request.json()
    task = raw_request.app.state.task

    if task not in TASK_HANDLERS:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported task: '{task}' for '/invocations'. "
            f"Expected one of {set(TASK_HANDLERS.keys())}")

    handler_config = TASK_HANDLERS[task]
    if "messages" in body:
        request_model, handler = handler_config["messages"]
    else:
        request_model, handler = handler_config["default"]

    # this is required since we lose the FastAPI automatic casting
    request = request_model.model_validate(body)
    return await handler(request, raw_request)


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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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if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    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(),
                                status_code=response.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(),
                                status_code=response.code)
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        return Response(status_code=200, content=response)


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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(RequestValidationError)
    async def validation_exception_handler(_, exc):
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        err = ErrorResponse(message=str(exc),
                            type="BadRequestError",
                            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
    if token := args.api_key or envs.VLLM_API_KEY:
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        @app.middleware("http")
        async def authentication(request: Request, call_next):
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            if request.method == "OPTIONS":
                return await call_next(request)
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            url_path = request.url.path
            if app.root_path and url_path.startswith(app.root_path):
                url_path = url_path[len(app.root_path):]
            if not url_path.startswith("/v1"):
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                return await call_next(request)
            if request.headers.get("Authorization") != "Bearer " + token:
                return JSONResponse(content={"error": "Unauthorized"},
                                    status_code=401)
            return await call_next(request)

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    if args.enable_request_id_headers:
        logger.warning(
            "CAUTION: Enabling X-Request-Id headers in the API Server. "
            "This can harm performance at high QPS.")

        @app.middleware("http")
        async def add_request_id(request: Request, call_next):
            request_id = request.headers.get(
                "X-Request-Id") or uuid.uuid4().hex
            response = await call_next(request)
            response.headers["X-Request-Id"] = request_id
            return response
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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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            logger.info("response_body={%s}",
                        response_body[0].decode() if response_body else None)
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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.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

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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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    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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    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,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
    )
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    await state.openai_serving_models.init_static_loras()
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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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        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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    ) if model_config.runner_type == "generate" 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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    ) if model_config.runner_type == "generate" else None
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    state.openai_serving_pooling = OpenAIServingPooling(
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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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        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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    ) if model_config.runner_type == "pooling" 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,
    ) if model_config.task == "embed" else None
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    state.openai_serving_scores = ServingScores(
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        engine_client,
        model_config,
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        state.openai_serving_models,
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        request_logger=request_logger) if model_config.task in (
            "score", "embed", "pooling") else None
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    state.openai_serving_classification = ServingClassification(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if model_config.task == "classify" else None
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    state.jinaai_serving_reranking = ServingScores(
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        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger
    ) if model_config.task == "score" else None
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    state.openai_serving_tokenization = OpenAIServingTokenization(
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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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        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
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    )
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    state.openai_serving_transcription = OpenAIServingTranscription(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if model_config.runner_type == "transcription" else None
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    state.task = model_config.task
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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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async def run_server(args, **uvicorn_kwargs) -> None:
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    logger.info("vLLM API server version %s", VLLM_VERSION)
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    log_non_default_args(args)
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    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

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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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    # 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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    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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    async with build_async_engine_client(args) as engine_client:
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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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        def _listen_addr(a: str) -> str:
            if is_valid_ipv6_address(a):
                return '[' + a + ']'
            return a or "0.0.0.0"

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        is_ssl = args.ssl_keyfile and args.ssl_certfile
        logger.info("Starting vLLM API server on http%s://%s:%d",
                    "s" if is_ssl else "", _listen_addr(sock_addr[0]),
                    sock_addr[1])
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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=TIMEOUT_KEEP_ALIVE,
            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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            **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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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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    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))