api_server.py

from serving_engine import LoRA
from serving_completion import OpenAIServingCompletion
from serving_chat import OpenAIServingChat
from vllm.logger import init_logger
from protocol import CompletionRequest, ChatCompletionRequest, ErrorResponse
from vllm.engine.metrics import add_global_metrics_labels
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.engine.arg_utils import AsyncEngineArgs
from fastapi.responses import JSONResponse, StreamingResponse, Response
from fastapi.middleware.cors import CORSMiddleware
from fastapi.exceptions import RequestValidationError
from fastapi import Request
from http import HTTPStatus
import uvicorn
import fastapi
from aioprometheus.asgi.starlette import metrics
from aioprometheus import MetricsMiddleware
import inspect
import importlib
from contextlib import asynccontextmanager
import json
import asyncio
import argparse
import sys
import os

SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.dirname(SCRIPT_DIR))


TIMEOUT_KEEP_ALIVE = 5  # seconds

openai_serving_chat: OpenAIServingChat = None
openai_serving_completion: OpenAIServingCompletion = None
logger = init_logger(__name__)


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

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

    if not engine_args.disable_log_stats:
        asyncio.create_task(_force_log())

    yield


app = fastapi.FastAPI(lifespan=lifespan)


class LoRAParserAction(argparse.Action):

    def __call__(self, parser, namespace, values, option_string=None):
        lora_list = []
        for item in values:
            name, path = item.split('=')
            lora_list.append(LoRA(name, path))
        setattr(namespace, self.dest, lora_list)


def parse_args():
    parser = argparse.ArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser.add_argument("--host", type=str, default=None, help="host name")
    parser.add_argument("--port", type=int, default=8000, help="port number")
    parser.add_argument("--allow-credentials",
                        action="store_true",
                        help="allow credentials")
    parser.add_argument("--allowed-origins",
                        type=json.loads,
                        default=["*"],
                        help="allowed origins")
    parser.add_argument("--allowed-methods",
                        type=json.loads,
                        default=["*"],
                        help="allowed methods")
    parser.add_argument("--allowed-headers",
                        type=json.loads,
                        default=["*"],
                        help="allowed headers")
    parser.add_argument(
        "--api-key",
        type=str,
        default=None,
        help="If provided, the server will require this key to be presented in the header."
    )
    parser.add_argument("--served-model-name",
                        type=str,
                        default=None,
                        help="The model name used in the API. If not "
                        "specified, the model name will be the same as "
                        "the huggingface name.")
    parser.add_argument(
        "--lora-modules",
        type=str,
        default=None,
        nargs='+',
        action=LoRAParserAction,
        help="LoRA module configurations in the format name=path. Multiple modules can be specified."
    )
    parser.add_argument("--chat-template",
                        type=str,
                        default=None,
                        help="The file path to the chat template, "
                        "or the template in single-line form "
                        "for the specified model")
    parser.add_argument("--response-role",
                        type=str,
                        default="assistant",
                        help="The role name to return if "
                        "`request.add_generation_prompt=true`.")
    parser.add_argument("--ssl-keyfile",
                        type=str,
                        default=None,
                        help="The file path to the SSL key file")
    parser.add_argument("--ssl-certfile",
                        type=str,
                        default=None,
                        help="The file path to the SSL cert file")
    parser.add_argument(
        "--root-path",
        type=str,
        default=None,
        help="FastAPI root_path when app is behind a path based routing proxy")
    parser.add_argument(
        "--middleware",
        type=str,
        action="append",
        default=[],
        help="Additional ASGI middleware to apply to the app. "
        "We accept multiple --middleware arguments. "
        "The value should be an import path. "
        "If a function is provided, vLLM will add it to the server using @app.middleware('http'). "
        "If a class is provided, vLLM will add it to the server using app.add_middleware(). "
    )

    parser = AsyncEngineArgs.add_cli_args(parser)
    return parser.parse_args()


app.add_middleware(MetricsMiddleware)  # Trace HTTP server metrics
app.add_route("/metrics", metrics)  # Exposes HTTP metrics


@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)


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


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


@app.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:
        return JSONResponse(content=generator.model_dump())


@app.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())


if __name__ == "__main__":
    args = parse_args()

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

    if token := os.environ.get("VLLM_API_KEY") or args.api_key:

        @app.middleware("http")
        async def authentication(request: Request, call_next):
            if not request.url.path.startswith("/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}. Must be a function or a class."
            )

    logger.info(f"args: {args}")

    if args.served_model_name is not None:
        served_model = args.served_model_name
    else:
        served_model = args.model

    engine_args = AsyncEngineArgs.from_cli_args(args)
    engine = AsyncLLMEngine.from_engine_args(engine_args)
    openai_serving_chat = OpenAIServingChat(engine, served_model,
                                            args.response_role,
                                            args.lora_modules,
                                            args.chat_template)
    openai_serving_completion = OpenAIServingCompletion(
        engine, served_model, args.lora_modules)

    # Register labels for metrics
    add_global_metrics_labels(model_name=engine_args.model)

    app.root_path = args.root_path
    uvicorn.run(app,
                host=args.host,
                port=args.port,
                log_level="info",
                timeout_keep_alive=TIMEOUT_KEEP_ALIVE,
                ssl_keyfile=args.ssl_keyfile,
                ssl_certfile=args.ssl_certfile)