api_server.py 30.7 KB
Newer Older
1
import asyncio
2
import atexit
3
4
import importlib
import inspect
5
import multiprocessing
6
import os
7
import re
8
import signal
9
import socket
10
import sys
11
import tempfile
12
import uuid
13
from argparse import Namespace
14
from contextlib import asynccontextmanager
15
from functools import partial
16
from http import HTTPStatus
17
from typing import AsyncIterator, Dict, Optional, Set, Tuple, Union
18

19
import uvloop
20
from fastapi import APIRouter, FastAPI, HTTPException, Request
Zhuohan Li's avatar
Zhuohan Li committed
21
22
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
23
from fastapi.responses import JSONResponse, Response, StreamingResponse
24
from starlette.datastructures import State
25
from starlette.routing import Mount
26
from typing_extensions import assert_never
Zhuohan Li's avatar
Zhuohan Li committed
27

28
import vllm.envs as envs
29
from vllm.config import ModelConfig
Woosuk Kwon's avatar
Woosuk Kwon committed
30
from vllm.engine.arg_utils import AsyncEngineArgs
31
from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
32
33
34
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
from vllm.engine.protocol import EngineClient
35
from vllm.entrypoints.chat_utils import load_chat_template
36
from vllm.entrypoints.launcher import serve_http
37
from vllm.entrypoints.logger import RequestLogger
38
39
from vllm.entrypoints.openai.cli_args import (make_arg_parser,
                                              validate_parsed_serve_args)
40
41
# yapf conflicts with isort for this block
# yapf: disable
42
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
43
                                              ChatCompletionResponse,
44
                                              CompletionRequest,
45
                                              CompletionResponse,
46
47
                                              DetokenizeRequest,
                                              DetokenizeResponse,
48
49
                                              EmbeddingChatRequest,
                                              EmbeddingCompletionRequest,
50
                                              EmbeddingRequest,
51
52
53
                                              EmbeddingResponse,
                                              EmbeddingResponseData,
                                              ErrorResponse,
54
                                              LoadLoraAdapterRequest,
55
56
                                              PoolingChatRequest,
                                              PoolingCompletionRequest,
57
                                              PoolingRequest, PoolingResponse,
58
                                              ScoreRequest, ScoreResponse,
59
                                              TokenizeRequest,
60
61
62
                                              TokenizeResponse,
                                              UnloadLoraAdapterRequest)
# yapf: enable
63
64
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
65
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
66
67
68
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
                                                    OpenAIServingModels)
69
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
70
from vllm.entrypoints.openai.serving_score import OpenAIServingScores
71
72
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
73
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
74
from vllm.entrypoints.utils import with_cancellation
75
from vllm.logger import init_logger
yhu422's avatar
yhu422 committed
76
from vllm.usage.usage_lib import UsageContext
77
from vllm.utils import (FlexibleArgumentParser, get_open_zmq_ipc_path,
78
                        is_valid_ipv6_address, set_ulimit)
79
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
80

81
TIMEOUT_KEEP_ALIVE = 5  # seconds
Zhuohan Li's avatar
Zhuohan Li committed
82

83
prometheus_multiproc_dir: tempfile.TemporaryDirectory
84

85
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
86
logger = init_logger('vllm.entrypoints.openai.api_server')
87

88
_running_tasks: Set[asyncio.Task] = set()
89

90

91
@asynccontextmanager
92
async def lifespan(app: FastAPI):
93
94
    try:
        if app.state.log_stats:
95
            engine_client: EngineClient = app.state.engine_client
96
97
98

            async def _force_log():
                while True:
99
100
                    await asyncio.sleep(10.)
                    await engine_client.do_log_stats()
101
102
103
104
105
106
107
108
109
110
111
112
113
114

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
115
116


117
@asynccontextmanager
118
async def build_async_engine_client(
119
        args: Namespace) -> AsyncIterator[EngineClient]:
120

121
    # Context manager to handle engine_client lifecycle
122
123
124
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

125
126
127
128
129
130
131
132
133
    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,
134
) -> AsyncIterator[EngineClient]:
135
    """
136
    Create EngineClient, either:
137
138
139
140
141
142
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

143
    # AsyncLLMEngine.
144
    if (MQLLMEngineClient.is_unsupported_config(engine_args)
145
            or envs.VLLM_USE_V1 or disable_frontend_multiprocessing):
146

147
148
149
150
151
152
153
154
155
        engine_client: Optional[EngineClient] = None
        try:
            engine_client = AsyncLLMEngine.from_engine_args(
                engine_args=engine_args,
                usage_context=UsageContext.OPENAI_API_SERVER)
            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()
156

157
    # MQLLMEngine.
158
    else:
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
        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.")

174
        # Select random path for IPC.
175
        ipc_path = get_open_zmq_ipc_path()
176
177
        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)
178

179
        # Start RPCServer in separate process (holds the LLMEngine).
180
181
        # the current process might have CUDA context,
        # so we need to spawn a new process
182
183
        context = multiprocessing.get_context("spawn")

184
185
186
187
        # 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)
188
189
190
        engine_process = context.Process(target=run_mp_engine,
                                         args=(engine_args,
                                               UsageContext.OPENAI_API_SERVER,
191
                                               ipc_path, engine_alive))
192
        engine_process.start()
193
        engine_pid = engine_process.pid
194
        assert engine_pid is not None, "Engine process failed to start."
195
        logger.info("Started engine process with PID %d", engine_pid)
196

197
198
199
200
201
202
203
204
        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)

205
206
        # Build RPCClient, which conforms to EngineClient Protocol.
        engine_config = engine_args.create_engine_config()
207
208
209
210
        build_client = partial(MQLLMEngineClient, ipc_path, engine_config,
                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
211
        try:
212
213
            while True:
                try:
214
                    await mq_engine_client.setup()
215
                    break
216
                except TimeoutError:
217
218
                    if (not engine_process.is_alive()
                            or not engine_alive.value):
219
                        raise RuntimeError(
220
221
                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None
222

223
            yield mq_engine_client  # type: ignore[misc]
224
225
        finally:
            # Ensure rpc server process was terminated
226
            engine_process.terminate()
227
228

            # Close all open connections to the backend
229
            mq_engine_client.close()
230

231
232
233
234
235
            # 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()
236

237
238
239
240
241
            # 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
242
            multiprocess.mark_process_dead(engine_process.pid)
243

244

Ethan Xu's avatar
Ethan Xu committed
245
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
246

247

248
def mount_metrics(app: FastAPI):
249
250
251
252
253
254
255
256
257
    # 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:
258
259
        logger.debug("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
                     prometheus_multiproc_dir_path)
260
261
262
263
264
265
266
267
268
        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())

269
    # Workaround for 307 Redirect for /metrics
270
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
271
    app.routes.append(metrics_route)
272
273


274
275
276
277
278
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


279
280
281
282
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


283
def chat(request: Request) -> Optional[OpenAIServingChat]:
284
285
286
    return request.app.state.openai_serving_chat


287
def completion(request: Request) -> Optional[OpenAIServingCompletion]:
288
289
290
    return request.app.state.openai_serving_completion


291
292
293
294
def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


295
296
def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
297
298


299
300
301
302
def score(request: Request) -> Optional[OpenAIServingScores]:
    return request.app.state.openai_serving_scores


303
304
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
305
306


307
def engine_client(request: Request) -> EngineClient:
308
309
310
    return request.app.state.engine_client


Ethan Xu's avatar
Ethan Xu committed
311
@router.get("/health")
312
async def health(raw_request: Request) -> Response:
313
    """Health check."""
314
    await engine_client(raw_request).check_health()
315
316
317
    return Response(status_code=200)


318
319
320
321
322
323
@router.api_route("/ping", methods=["GET", "POST"])
async def ping(raw_request: Request) -> Response:
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


Ethan Xu's avatar
Ethan Xu committed
324
@router.post("/tokenize")
325
@with_cancellation
326
async def tokenize(request: TokenizeRequest, raw_request: Request):
327
328
    handler = tokenization(raw_request)

329
    generator = await handler.create_tokenize(request, raw_request)
330
331
332
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
333
    elif isinstance(generator, TokenizeResponse):
334
335
        return JSONResponse(content=generator.model_dump())

336
337
    assert_never(generator)

338

Ethan Xu's avatar
Ethan Xu committed
339
@router.post("/detokenize")
340
@with_cancellation
341
async def detokenize(request: DetokenizeRequest, raw_request: Request):
342
343
    handler = tokenization(raw_request)

344
    generator = await handler.create_detokenize(request, raw_request)
345
346
347
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
348
    elif isinstance(generator, DetokenizeResponse):
349
350
        return JSONResponse(content=generator.model_dump())

351
352
    assert_never(generator)

353

Ethan Xu's avatar
Ethan Xu committed
354
@router.get("/v1/models")
355
async def show_available_models(raw_request: Request):
356
    handler = models(raw_request)
357

358
359
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
360
361


Ethan Xu's avatar
Ethan Xu committed
362
@router.get("/version")
363
async def show_version():
364
    ver = {"version": VLLM_VERSION}
365
366
367
    return JSONResponse(content=ver)


Ethan Xu's avatar
Ethan Xu committed
368
@router.post("/v1/chat/completions")
369
@with_cancellation
370
371
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
372
373
374
375
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")
376

377
    generator = await handler.create_chat_completion(request, raw_request)
378

379
380
381
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
382

383
    elif isinstance(generator, ChatCompletionResponse):
384
        return JSONResponse(content=generator.model_dump())
385

386
387
    return StreamingResponse(content=generator, media_type="text/event-stream")

388

Ethan Xu's avatar
Ethan Xu committed
389
@router.post("/v1/completions")
390
@with_cancellation
391
async def create_completion(request: CompletionRequest, raw_request: Request):
392
393
394
395
396
397
    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)
398
399
400
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
401
    elif isinstance(generator, CompletionResponse):
402
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
403

404
405
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
406

Ethan Xu's avatar
Ethan Xu committed
407
@router.post("/v1/embeddings")
408
@with_cancellation
409
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
410
411
    handler = embedding(raw_request)
    if handler is None:
412
413
414
415
416
417
418
419
420
421
422
        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)
423
424

        generator: Union[ErrorResponse, EmbeddingResponse]
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
        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)
443

444
445
446
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
447
    elif isinstance(generator, EmbeddingResponse):
448
449
        return JSONResponse(content=generator.model_dump())

450
451
    assert_never(generator)

452

453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
@router.post("/pooling")
@with_cancellation
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)


471
@router.post("/score")
472
@with_cancellation
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
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)


489
@router.post("/v1/score")
490
@with_cancellation
491
492
493
494
495
496
497
498
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)


499
TASK_HANDLERS: Dict[str, Dict[str, tuple]] = {
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
    "generate": {
        "messages": (ChatCompletionRequest, create_chat_completion),
        "default": (CompletionRequest, create_completion),
    },
    "embed": {
        "messages": (EmbeddingChatRequest, create_embedding),
        "default": (EmbeddingCompletionRequest, create_embedding),
    },
    "score": {
        "default": (ScoreRequest, create_score),
    },
    "reward": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
    "classify": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
}


@router.post("/invocations")
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)


547
548
549
550
551
552
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")
553
    async def start_profile(raw_request: Request):
554
        logger.info("Starting profiler...")
555
        await engine_client(raw_request).start_profile()
556
557
558
559
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
560
    async def stop_profile(raw_request: Request):
561
        logger.info("Stopping profiler...")
562
        await engine_client(raw_request).stop_profile()
563
564
565
566
        logger.info("Profiler stopped.")
        return Response(status_code=200)


567
568
569
570
571
572
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
        "Lora dynamic loading & unloading is enabled in the API server. "
        "This should ONLY be used for local development!")

    @router.post("/v1/load_lora_adapter")
573
574
    async def load_lora_adapter(request: LoadLoraAdapterRequest,
                                raw_request: Request):
575
576
577
578
579
        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)
580
581
582
583

        return Response(status_code=200, content=response)

    @router.post("/v1/unload_lora_adapter")
584
585
    async def unload_lora_adapter(request: UnloadLoraAdapterRequest,
                                  raw_request: Request):
586
587
588
589
590
        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)
591
592
593
594

        return Response(status_code=200, content=response)


595
def build_app(args: Namespace) -> FastAPI:
596
597
598
599
600
601
602
    if args.disable_fastapi_docs:
        app = FastAPI(openapi_url=None,
                      docs_url=None,
                      redoc_url=None,
                      lifespan=lifespan)
    else:
        app = FastAPI(lifespan=lifespan)
Ethan Xu's avatar
Ethan Xu committed
603
604
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
605

606
607
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
608
609
610
611
612
613
614
615
    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

Ethan Xu's avatar
Ethan Xu committed
616
617
    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(_, exc):
618
619
620
        err = ErrorResponse(message=str(exc),
                            type="BadRequestError",
                            code=HTTPStatus.BAD_REQUEST)
Ethan Xu's avatar
Ethan Xu committed
621
622
623
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

624
    if token := envs.VLLM_API_KEY or args.api_key:
625
626
627

        @app.middleware("http")
        async def authentication(request: Request, call_next):
628
629
            if request.method == "OPTIONS":
                return await call_next(request)
630
631
632
633
            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"):
634
635
636
637
638
639
                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)

640
641
642
643
644
645
646
647
648
649
650
651
    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
652

653
654
655
656
    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):
657
            app.add_middleware(imported)  # type: ignore[arg-type]
658
659
660
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
661
662
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")
663

Ethan Xu's avatar
Ethan Xu committed
664
665
666
    return app


667
async def init_app_state(
668
    engine_client: EngineClient,
669
670
    model_config: ModelConfig,
    state: State,
671
    args: Namespace,
672
) -> None:
673
    if args.served_model_name is not None:
674
        served_model_names = args.served_model_name
675
    else:
676
        served_model_names = [args.model]
677

678
679
680
681
682
    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

683
684
685
686
687
    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

688
    state.engine_client = engine_client
689
    state.log_stats = not args.disable_log_stats
Ethan Xu's avatar
Ethan Xu committed
690

691
692
693
    resolved_chat_template = load_chat_template(args.chat_template)
    logger.info("Using supplied chat template:\n%s", resolved_chat_template)

694
    state.openai_serving_models = OpenAIServingModels(
695
        engine_client=engine_client,
696
697
698
699
700
        model_config=model_config,
        base_model_paths=base_model_paths,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
    )
701
    await state.openai_serving_models.init_static_loras()
702
    state.openai_serving_chat = OpenAIServingChat(
703
        engine_client,
704
        model_config,
705
        state.openai_serving_models,
706
707
        args.response_role,
        request_logger=request_logger,
708
709
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
710
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
711
        enable_auto_tools=args.enable_auto_tool_choice,
712
        tool_parser=args.tool_call_parser,
713
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
714
    ) if model_config.runner_type == "generate" else None
715
    state.openai_serving_completion = OpenAIServingCompletion(
716
        engine_client,
717
        model_config,
718
        state.openai_serving_models,
719
        request_logger=request_logger,
720
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
721
    ) if model_config.runner_type == "generate" else None
722
    state.openai_serving_pooling = OpenAIServingPooling(
723
        engine_client,
724
        model_config,
725
        state.openai_serving_models,
726
        request_logger=request_logger,
727
728
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
729
    ) if model_config.runner_type == "pooling" else None
730
731
732
    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
733
        state.openai_serving_models,
734
735
736
737
        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
738
739
740
    state.openai_serving_scores = OpenAIServingScores(
        engine_client,
        model_config,
741
        state.openai_serving_models,
742
        request_logger=request_logger
743
    ) if model_config.task == "score" else None
744
    state.openai_serving_tokenization = OpenAIServingTokenization(
745
        engine_client,
746
        model_config,
747
        state.openai_serving_models,
748
        request_logger=request_logger,
749
750
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
751
    )
752
    state.task = model_config.task
753
754


755
756
757
758
759
760
761
762
763
764
765
766
def create_server_socket(addr: Tuple[str, int]) -> socket.socket:
    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)
    sock.bind(addr)

    return sock


767
async def run_server(args, **uvicorn_kwargs) -> None:
768
769
770
    logger.info("vLLM API server version %s", VLLM_VERSION)
    logger.info("args: %s", args)

771
772
773
    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

774
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
775
    if args.enable_auto_tool_choice \
776
        and args.tool_call_parser not in valid_tool_parses:
777
        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
778
                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
779

780
781
782
    # 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
783
784
    sock_addr = (args.host or "", args.port)
    sock = create_server_socket(sock_addr)
785

786
787
788
789
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

790
791
792
793
794
795
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

796
    async with build_async_engine_client(args) as engine_client:
797
798
        app = build_app(args)

799
        model_config = await engine_client.get_model_config()
800
        await init_app_state(engine_client, model_config, app.state, args)
801
802
803
804
805
806
807
808
809
810
811

        shutdown_task = await serve_http(
            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,
812
813
            # Workaround to work on macOS
            fd=sock.fileno() if sys.platform.startswith("darwin") else None,
814
815
816
            **uvicorn_kwargs,
        )

817
818
    # NB: Await server shutdown only after the backend context is exited
    await shutdown_task
819

820
821
    sock.close()

Ethan Xu's avatar
Ethan Xu committed
822
823
824
825
826
827
828
829

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()
830
    validate_parsed_serve_args(args)
831

832
    uvloop.run(run_server(args))