vllm/vllm/entrypoints/openai/api_server.py

import asyncio
import importlib
import inspect
import re
import signal
from contextlib import asynccontextmanager
from http import HTTPStatus
from multiprocessing import Process
from typing import AsyncIterator, Set

import fastapi
import uvicorn
from fastapi import APIRouter, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, Response, StreamingResponse
from prometheus_client import make_asgi_app
from starlette.routing import Mount

import vllm.envs as envs
from vllm.config import ModelConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.engine.protocol import AsyncEngineClient
from vllm.entrypoints.logger import RequestLogger
from vllm.entrypoints.openai.cli_args import make_arg_parser
# yapf conflicts with isort for this block
# yapf: disable
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              ChatCompletionResponse,
                                              CompletionRequest,
                                              DetokenizeRequest,
                                              DetokenizeResponse,
                                              EmbeddingRequest, ErrorResponse,
                                              TokenizeRequest,
                                              TokenizeResponse)
from vllm.entrypoints.openai.rpc.client import AsyncEngineRPCClient
from vllm.entrypoints.openai.rpc.server import run_rpc_server
# yapf: enable
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
from vllm.logger import init_logger
from vllm.usage.usage_lib import UsageContext
from vllm.utils import FlexibleArgumentParser, get_open_port
from vllm.version import __version__ as VLLM_VERSION

TIMEOUT_KEEP_ALIVE = 5  # seconds

async_engine_client: AsyncEngineClient
engine_args: AsyncEngineArgs
openai_serving_chat: OpenAIServingChat
openai_serving_completion: OpenAIServingCompletion
openai_serving_embedding: OpenAIServingEmbedding
openai_serving_tokenization: OpenAIServingTokenization

logger = init_logger('vllm.entrypoints.openai.api_server')

_running_tasks: Set[asyncio.Task] = set()


def model_is_embedding(model_name: str) -> bool:
    return ModelConfig(model=model_name,
                       tokenizer=model_name,
                       tokenizer_mode="auto",
                       trust_remote_code=False,
                       seed=0,
                       dtype="float16").embedding_mode


@asynccontextmanager
async def lifespan(app: fastapi.FastAPI):

    async def _force_log():
        while True:
            await asyncio.sleep(10)
            await async_engine_client.do_log_stats()

    if not engine_args.disable_log_stats:
        task = asyncio.create_task(_force_log())
        _running_tasks.add(task)
        task.add_done_callback(_running_tasks.remove)

    yield


@asynccontextmanager
async def build_async_engine_client(args) -> AsyncIterator[AsyncEngineClient]:
    # Context manager to handle async_engine_client lifecycle
    # Ensures everything is shutdown and cleaned up on error/exit
    global engine_args
    engine_args = AsyncEngineArgs.from_cli_args(args)

    # Backend itself still global for the silly lil' health handler
    global async_engine_client

    # If manually triggered or embedding model, use AsyncLLMEngine in process.
    # TODO: support embedding model via RPC.
    if (model_is_embedding(args.model)
            or args.disable_frontend_multiprocessing):
        async_engine_client = AsyncLLMEngine.from_engine_args(
            engine_args, usage_context=UsageContext.OPENAI_API_SERVER)
        yield async_engine_client
        return

    # Otherwise, use the multiprocessing AsyncLLMEngine.
    else:
        # Start RPCServer in separate process (holds the AsyncLLMEngine).
        port = get_open_port(envs.VLLM_RPC_PORT)
        rpc_server_process = Process(target=run_rpc_server,
                                     args=(engine_args,
                                           UsageContext.OPENAI_API_SERVER,
                                           port))
        rpc_server_process.start()

        # Build RPCClient, which conforms to AsyncEngineClient Protocol.
        async_engine_client = AsyncEngineRPCClient(port)
        await async_engine_client.setup()

        try:
            yield async_engine_client
        finally:
            # Ensure rpc server process was terminated
            rpc_server_process.terminate()

            # Close all open connections to the backend
            async_engine_client.close()

            # Wait for server process to join
            rpc_server_process.join()


router = APIRouter()


def mount_metrics(app: fastapi.FastAPI):
    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app())
    # Workaround for 307 Redirect for /metrics
    metrics_route.path_regex = re.compile('^/metrics(?P<path>.*)$')
    app.routes.append(metrics_route)


@router.get("/health")
async def health() -> Response:
    """Health check."""
    await async_engine_client.check_health()
    return Response(status_code=200)


@router.post("/tokenize")
async def tokenize(request: TokenizeRequest):
    generator = await openai_serving_tokenization.create_tokenize(request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    else:
        assert isinstance(generator, TokenizeResponse)
        return JSONResponse(content=generator.model_dump())


@router.post("/detokenize")
async def detokenize(request: DetokenizeRequest):
    generator = await openai_serving_tokenization.create_detokenize(request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    else:
        assert isinstance(generator, DetokenizeResponse)
        return JSONResponse(content=generator.model_dump())


@router.get("/v1/models")
async def show_available_models():
    models = await openai_serving_completion.show_available_models()
    return JSONResponse(content=models.model_dump())


@router.get("/version")
async def show_version():
    ver = {"version": VLLM_VERSION}
    return JSONResponse(content=ver)


@router.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
    generator = await openai_serving_chat.create_chat_completion(
        request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    if request.stream:
        return StreamingResponse(content=generator,
                                 media_type="text/event-stream")
    else:
        assert isinstance(generator, ChatCompletionResponse)
        return JSONResponse(content=generator.model_dump())


@router.post("/v1/completions")
async def create_completion(request: CompletionRequest, raw_request: Request):
    generator = await openai_serving_completion.create_completion(
        request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    if request.stream:
        return StreamingResponse(content=generator,
                                 media_type="text/event-stream")
    else:
        return JSONResponse(content=generator.model_dump())


@router.post("/v1/embeddings")
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
    generator = await openai_serving_embedding.create_embedding(
        request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    else:
        return JSONResponse(content=generator.model_dump())


def build_app(args):
    app = fastapi.FastAPI(lifespan=lifespan)
    app.include_router(router)
    app.root_path = args.root_path

    mount_metrics(app)

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

    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(_, exc):
        err = openai_serving_chat.create_error_response(message=str(exc))
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

    if token := envs.VLLM_API_KEY or args.api_key:

        @app.middleware("http")
        async def authentication(request: Request, call_next):
            root_path = "" if args.root_path is None else args.root_path
            if request.method == "OPTIONS":
                return await call_next(request)
            if not request.url.path.startswith(f"{root_path}/v1"):
                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)

    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):
            app.add_middleware(imported)
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")

    return app


async def build_server(
    async_engine_client: AsyncEngineClient,
    args,
    **uvicorn_kwargs,
) -> uvicorn.Server:
    app = build_app(args)

    if args.served_model_name is not None:
        served_model_names = args.served_model_name
    else:
        served_model_names = [args.model]

    model_config = await async_engine_client.get_model_config()

    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

    global openai_serving_chat
    global openai_serving_completion
    global openai_serving_embedding
    global openai_serving_tokenization

    openai_serving_chat = OpenAIServingChat(
        async_engine_client,
        model_config,
        served_model_names,
        args.response_role,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
        request_logger=request_logger,
        chat_template=args.chat_template,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
    )
    openai_serving_completion = OpenAIServingCompletion(
        async_engine_client,
        model_config,
        served_model_names,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
        request_logger=request_logger,
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
    )
    openai_serving_embedding = OpenAIServingEmbedding(
        async_engine_client,
        model_config,
        served_model_names,
        request_logger=request_logger,
    )
    openai_serving_tokenization = OpenAIServingTokenization(
        async_engine_client,
        model_config,
        served_model_names,
        lora_modules=args.lora_modules,
        request_logger=request_logger,
        chat_template=args.chat_template,
    )
    app.root_path = args.root_path

    logger.info("Available routes are:")
    for route in app.routes:
        if not hasattr(route, 'methods'):
            continue
        methods = ', '.join(route.methods)
        logger.info("Route: %s, Methods: %s", route.path, methods)

    config = uvicorn.Config(
        app,
        host=args.host,
        port=args.port,
        log_level=args.uvicorn_log_level,
        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,
        **uvicorn_kwargs,
    )

    return uvicorn.Server(config)


async def run_server(args, **uvicorn_kwargs) -> None:
    logger.info("vLLM API server version %s", VLLM_VERSION)
    logger.info("args: %s", args)

    shutdown_task = None
    async with build_async_engine_client(args) as async_engine_client:

        server = await build_server(
            async_engine_client,
            args,
            **uvicorn_kwargs,
        )

        loop = asyncio.get_running_loop()

        server_task = loop.create_task(server.serve())

        def signal_handler() -> None:
            # prevents the uvicorn signal handler to exit early
            server_task.cancel()

        loop.add_signal_handler(signal.SIGINT, signal_handler)
        loop.add_signal_handler(signal.SIGTERM, signal_handler)

        try:
            await server_task
        except asyncio.CancelledError:
            logger.info("Gracefully stopping http server")
            shutdown_task = server.shutdown()

    if shutdown_task:
        # NB: Await server shutdown only after the backend context is exited
        await shutdown_task


if __name__ == "__main__":
    # NOTE(simon):
    # This section should be in sync with vllm/scripts.py for CLI entrypoints.
    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
    asyncio.run(run_server(args))