aphrodite-engine/aphrodite/endpoints/openai/api_server.py

import asyncio
import importlib
import inspect
import json
import multiprocessing
import os
import re
import tempfile
from argparse import Namespace
from contextlib import asynccontextmanager
from http import HTTPStatus
from typing import AsyncGenerator, AsyncIterator, List, Set, Tuple

import uvloop
from fastapi import APIRouter, FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import (HTMLResponse, JSONResponse, Response,
                               StreamingResponse)
from loguru import logger
from starlette.routing import Mount

from aphrodite.common.config import ModelConfig
from aphrodite.common.outputs import RequestOutput
from aphrodite.common.sampling_params import _SAMPLING_EPS, SamplingParams
from aphrodite.common.utils import (FlexibleArgumentParser,
                                    get_open_zmq_ipc_path, random_uuid)
from aphrodite.endpoints.logger import RequestLogger
from aphrodite.endpoints.openai.args import make_arg_parser
# yapf: disable
from aphrodite.endpoints.openai.protocol import (ChatCompletionRequest,
                                                 ChatCompletionResponse,
                                                 CompletionRequest,
                                                 DetokenizeRequest,
                                                 DetokenizeResponse,
                                                 EmbeddingRequest,
                                                 ErrorResponse,
                                                 KAIGenerationInputSchema,
                                                 TokenizeRequest,
                                                 TokenizeResponse)
from aphrodite.endpoints.openai.rpc.client import AsyncEngineRPCClient
from aphrodite.endpoints.openai.rpc.server import run_rpc_server
# yapf: enable
from aphrodite.endpoints.openai.serving_chat import OpenAIServingChat
from aphrodite.endpoints.openai.serving_completions import (
    OpenAIServingCompletion)
from aphrodite.endpoints.openai.serving_embedding import OpenAIServingEmbedding
from aphrodite.endpoints.openai.serving_engine import (LoRAModulePath,
                                                       PromptAdapterPath)
from aphrodite.endpoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
from aphrodite.engine.args_tools import AsyncEngineArgs
from aphrodite.engine.async_aphrodite import AsyncAphrodite
from aphrodite.engine.protocol import AsyncEngineClient
from aphrodite.server import serve_http
from aphrodite.transformers_utils.tokenizer import get_tokenizer
from aphrodite.version import __version__ as APHRODITE_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
router = APIRouter()
kai_api = APIRouter()
extra_api = APIRouter()
kobold_lite_ui = ""
sampler_json = ""
gen_cache: dict = {}
prometheus_multiproc_dir: tempfile.TemporaryDirectory

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


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


@asynccontextmanager
async def lifespan(app: 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 AsyncAphrodite in process.
    # TODO: support embedding model via RPC.
    if (model_is_embedding(args.model, args.trust_remote_code)
            or args.disable_frontend_multiprocessing):
        async_engine_client = AsyncAphrodite.from_engine_args(engine_args)
        yield async_engine_client
        return

    # Otherwise, use the multiprocessing AsyncAphrodite.
    else:
        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 Aphrodite runs or "
                "you will find inaccurate metrics. Unset the variable "
                "and Aphrodite will properly handle cleanup.")
        # Select random path for IPC.
        rpc_path = get_open_zmq_ipc_path()
        logger.info(f"Multiprocessing frontend to use {rpc_path} for RPC Path."
                    )
        # Start RPCServer in separate process (holds the AsyncAphrodite).
        context = multiprocessing.get_context("spawn")
        # the current process might have CUDA context,
        # so we need to spawn a new process
        rpc_server_process = context.Process(
            target=run_rpc_server,
            args=(engine_args, rpc_path))
        rpc_server_process.start()
        logger.info(
            f"Started engine process with PID {rpc_server_process.pid}")

        # Build RPCClient, which conforms to AsyncEngineClient Protocol.
        async_engine_client = AsyncEngineRPCClient(rpc_path)

        try:
            while True:
                try:
                    await async_engine_client.setup()
                    break
                except TimeoutError as e:
                    if not rpc_server_process.is_alive():
                        raise RuntimeError(
                            "The server process died before "
                            "responding to the readiness probe") from e
            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()

            # 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
            multiprocess.mark_process_dead(rpc_server_process.pid)


def mount_metrics(app: FastAPI):
    # 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 (CollectorRegistry, make_asgi_app,
                                   multiprocess)
    prometheus_multiproc_dir_path = os.getenv("PROMETHEUS_MULTIPROC_DIR", None)
    if prometheus_multiproc_dir_path is not None:
        logger.info("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
                    prometheus_multiproc_dir_path)
        registry = CollectorRegistry()
        multiprocess.MultiProcessCollector(registry)
        # Add prometheus asgi middleware to route /metrics requests
        metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
    else:
        # 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("/v1/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("/v1/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": APHRODITE_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())
    

@router.post("/v1/lora/load")
async def load_lora(lora: LoRAModulePath):
    openai_serving_completion.add_lora(lora)
    if engine_args.enable_lora is False:
        logger.error("LoRA is not enabled in the engine. "
                     "Please start the server with the "
                     "--enable-lora flag!")
    return JSONResponse(content={"status": "success"})


@router.delete("/v1/lora/unload")
async def unload_lora(lora_name: str):
    openai_serving_completion.remove_lora(lora_name)
    return JSONResponse(content={"status": "success"})


@router.post("/v1/soft_prompt/load")
async def load_soft_prompt(soft_prompt: PromptAdapterPath):
    openai_serving_completion.add_prompt_adapter(soft_prompt)
    if engine_args.enable_prompt_adapter is False:
        logger.error("Prompt Adapter is not enabled in the engine. "
                     "Please start the server with the "
                     "--enable-prompt-adapter flag!")
    return JSONResponse(content={"status": "success"})

@router.delete("/v1/soft_prompt/unload")
async def unload_soft_prompt(soft_prompt_name: str):
    openai_serving_completion.remove_prompt_adapter(soft_prompt_name)
    return JSONResponse(content={"status": "success"})


# ============ KoboldAI API ============ #


def _set_badwords(tokenizer, hf_config):  # pylint: disable=redefined-outer-name
    # pylint: disable=global-variable-undefined
    global badwordsids
    if hf_config.bad_words_ids is not None:
        badwordsids = hf_config.bad_words_ids
        return

    badwordsids = [
        v for k, v in tokenizer.get_vocab().items()
        if any(c in str(k) for c in "[]")
    ]
    if tokenizer.pad_token_id in badwordsids:
        badwordsids.remove(tokenizer.pad_token_id)
    badwordsids.append(tokenizer.eos_token_id)


def prepare_engine_payload(
        kai_payload: KAIGenerationInputSchema
) -> Tuple[SamplingParams, List[int]]:
    """Create SamplingParams and truncated input tokens for AsyncEngine"""

    if not kai_payload.genkey:
        kai_payload.genkey = f"kai-{random_uuid()}"

    # if kai_payload.max_context_length > engine_args.max_model_len:
    #     raise ValueError(
    #         f"max_context_length ({kai_payload.max_context_length}) "
    #         "must be less than or equal to "
    #         f"max_model_len ({engine_args.max_model_len})")

    kai_payload.top_k = kai_payload.top_k if kai_payload.top_k != 0.0 else -1
    kai_payload.tfs = max(_SAMPLING_EPS, kai_payload.tfs)
    if kai_payload.temperature < _SAMPLING_EPS:
        kai_payload.n = 1
        kai_payload.top_p = 1.0
        kai_payload.top_k = -1

    sampling_params = SamplingParams(
        n=kai_payload.n,
        best_of=kai_payload.n,
        repetition_penalty=kai_payload.rep_pen,
        temperature=kai_payload.temperature,
        smoothing_factor=kai_payload.smoothing_factor,
        smoothing_curve=kai_payload.smoothing_curve,
        tfs=kai_payload.tfs,
        top_p=kai_payload.top_p,
        top_k=kai_payload.top_k,
        top_a=kai_payload.top_a,
        min_p=kai_payload.min_p,
        typical_p=kai_payload.typical,
        eta_cutoff=kai_payload.eta_cutoff,
        epsilon_cutoff=kai_payload.eps_cutoff,
        stop=kai_payload.stop_sequence,
        include_stop_str_in_output=kai_payload.include_stop_str_in_output,
        custom_token_bans=badwordsids
        if kai_payload.use_default_badwordsids else [],
        max_tokens=kai_payload.max_length,
        seed=kai_payload.sampler_seed,
    )

    max_input_tokens = max(
        1, kai_payload.max_context_length - kai_payload.max_length)
    input_tokens = tokenizer(kai_payload.prompt).input_ids[-max_input_tokens:]

    return sampling_params, input_tokens


@kai_api.post("/generate")
async def generate(kai_payload: KAIGenerationInputSchema) -> JSONResponse:
    sampling_params, input_tokens = prepare_engine_payload(kai_payload)
    result_generator = async_engine_client.generate(
        {
            "prompt": kai_payload.prompt,
            "prompt_token_ids": input_tokens,
        },
        sampling_params,
        kai_payload.genkey,
    )

    final_res: RequestOutput = None
    previous_output = ""
    async for res in result_generator:
        final_res = res
        new_chunk = res.outputs[0].text[len(previous_output):]
        previous_output += new_chunk
        gen_cache[kai_payload.genkey] = previous_output

    assert final_res is not None
    del gen_cache[kai_payload.genkey]

    return JSONResponse(
        {"results": [{
            "text": output.text
        } for output in final_res.outputs]})


@extra_api.post("/generate/stream")
async def generate_stream(
        kai_payload: KAIGenerationInputSchema) -> StreamingResponse:

    sampling_params, input_tokens = prepare_engine_payload(kai_payload)
    results_generator = async_engine_client.generate(
        {
            "prompt": kai_payload.prompt,
            "prompt_token_ids": input_tokens,
        },
        sampling_params,
        kai_payload.genkey,
    )

    async def stream_kobold() -> AsyncGenerator[bytes, None]:
        previous_output = ""
        async for res in results_generator:
            new_chunk = res.outputs[0].text[len(previous_output):]
            previous_output += new_chunk
            yield b"event: message\n"
            yield f"data: {json.dumps({'token': new_chunk})}\n\n".encode()

    return StreamingResponse(stream_kobold(),
                             headers={
                                 "Cache-Control": "no-cache",
                                 "Connection": "keep-alive",
                             },
                             media_type="text/event-stream")


@extra_api.post("/generate/check")
@extra_api.get("/generate/check")
async def check_generation(request: Request):
    text = ""
    try:
        request_dict = await request.json()
        if "genkey" in request_dict and request_dict["genkey"] in gen_cache:
            text = gen_cache[request_dict["genkey"]]
    except json.JSONDecodeError:
        pass

    return JSONResponse({"results": [{"text": text}]})


@extra_api.post("/abort")
async def abort_generation(request: Request):
    try:
        request_dict = await request.json()
        if "genkey" in request_dict:
            await async_engine_client.abort(request_dict["genkey"])
    except json.JSONDecodeError:
        pass

    return JSONResponse({})


@extra_api.post("/tokencount")
async def count_tokens(request: TokenizeRequest):
    """Tokenize string and return token count"""

    generator = await openai_serving_tokenization.create_tokenize(request)
    return JSONResponse({"value": generator.model_dump()["tokens"]})


@kai_api.get("/info/version")
async def get_version():
    """Impersonate KAI"""
    return JSONResponse({"result": "1.2.4"})


@kai_api.get("/model")
async def get_model():
    return JSONResponse({"result": f"aphrodite/{served_model_names[0]}"})


@kai_api.get("/config/soft_prompts_list")
async def get_available_softprompts():
    """Stub for compatibility"""
    return JSONResponse({"values": []})


@kai_api.get("/config/soft_prompt")
async def get_current_softprompt():
    """Stub for compatibility"""
    return JSONResponse({"value": ""})


@kai_api.put("/config/soft_prompt")
async def set_current_softprompt():
    """Stub for compatibility"""
    return JSONResponse({})


@kai_api.get("/config/max_length")
async def get_max_length() -> JSONResponse:
    max_length = args.max_length
    return JSONResponse({"value": max_length})


@kai_api.get("/config/max_context_length")
@extra_api.get("/true_max_context_length")
async def get_max_context_length() -> JSONResponse:
    max_context_length = engine_args.max_model_len
    return JSONResponse({"value": max_context_length})


@extra_api.get("/preloadstory")
async def get_preloaded_story() -> JSONResponse:
    """Stub for compatibility"""
    return JSONResponse({})


@extra_api.get("/version")
async def get_extra_version():
    """Impersonate KoboldCpp"""
    return JSONResponse({"result": "KoboldCpp", "version": "1.63"})


@router.get("/")
async def get_kobold_lite_ui():
    """Serves a cached copy of the Kobold Lite UI, loading it from disk
    on demand if needed."""
    global kobold_lite_ui
    if kobold_lite_ui == "":
        scriptpath = os.path.dirname(os.path.abspath(__file__))
        klitepath = os.path.join(scriptpath, "../kobold/klite.embd")
        klitepath = os.path.normpath(klitepath)  # Normalize the path
        if os.path.exists(klitepath):
            with open(klitepath, "r") as f:
                kobold_lite_ui = f.read()
        else:
            logger.error("Kobold Lite UI not found at " + klitepath)
    return HTMLResponse(content=kobold_lite_ui)


# ============ KoboldAI API ============ #


def build_app(args: Namespace) -> FastAPI:
    app = FastAPI(lifespan=lifespan)
    app.include_router(router)
    app.root_path = args.root_path
    if args.launch_kobold_api:
        logger.warning("Launching Kobold API server in addition to OpenAI. "
                       "Keep in mind that the Kobold API routes are NOT "
                       "protected via the API key.")
        app.include_router(kai_api, prefix="/api/v1")
        app.include_router(kai_api,
                           prefix="/api/latest",
                           include_in_schema=False)
        app.include_router(extra_api, prefix="/api/extra")

    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_completion.create_error_response(message=str(exc))
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

    if token := os.environ.get("APHRODITE_API_KEY") or args.api_keys:
        admin_key = os.environ.get("APHRODITE_ADMIN_KEY") or args.admin_key

        if admin_key is None:
            logger.warning("Admin key not provided. Admin operations will "
                           "be disabled.")

        @app.middleware("http")
        async def authentication(request: Request, call_next):
            excluded_paths = ["/api"]
            if any(
                    request.url.path.startswith(path)
                    for path in excluded_paths):
                return await call_next(request)
            if not request.url.path.startswith("/v1"):
                return await call_next(request)

            # Browsers may send OPTIONS requests to check CORS headers
            # before sending the actual request. We should allow these
            # requests to pass through without authentication.
            # See https://github.com/PygmalionAI/aphrodite-engine/issues/434
            if request.method == "OPTIONS":
                return await call_next(request)

            auth_header = request.headers.get("Authorization")
            api_key_header = request.headers.get("x-api-key")

            if request.url.path.startswith(("/v1/lora", "/v1/soft_prompt")):
                if admin_key is not None and api_key_header == admin_key:
                    return await call_next(request)
                return JSONResponse(content={"error": "Unauthorized"},
                                    status_code=401)

            if auth_header != "Bearer " + token and api_key_header != 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 init_app(
    async_engine_client: AsyncEngineClient,
    args: Namespace,
) -> FastAPI:
    app = build_app(args)

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

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

    if args.uvloop:
        uvloop.install()

    global tokenizer

    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

    tokenizer = get_tokenizer(
        tokenizer_name=engine_args.tokenizer,
        tokenizer_mode=engine_args.tokenizer_mode,
        trust_remote_code=engine_args.trust_remote_code,
        revision=engine_args.revision,
    )

    if args.launch_kobold_api:
        _set_badwords(tokenizer, model_config.hf_config)

    return app


async def run_server(args, **uvicorn_kwargs) -> None:

    async with build_async_engine_client(args) as async_engine_client:
        app = await init_app(async_engine_client, args)

        shutdown_task = await serve_http(
            app,
            engine=async_engine_client,
            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,
        )

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


if __name__ == "__main__":
    # NOTE:
    # This section should be in sync with aphrodite/endpoints/cli.py
    # for CLI entrypoints.
    parser = FlexibleArgumentParser(
        description="Aphrodite OpenAI-Compatible RESTful API Server")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
    asyncio.run(run_server(args))