api_server.py 51.4 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import asyncio
5
import atexit
6
import gc
7
8
import importlib
import inspect
9
import json
10
import multiprocessing
11
import os
12
import signal
13
import socket
14
import tempfile
15
import uuid
16
from argparse import Namespace
17
from collections.abc import AsyncIterator
18
from contextlib import asynccontextmanager
19
from functools import partial
20
from http import HTTPStatus
21
from typing import Annotated, Any, Optional
22

23
import prometheus_client
24
import regex as re
25
import uvloop
26
from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Request
Zhuohan Li's avatar
Zhuohan Li committed
27
28
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
29
from fastapi.responses import JSONResponse, Response, StreamingResponse
30
31
from prometheus_client import make_asgi_app
from prometheus_fastapi_instrumentator import Instrumentator
32
from starlette.concurrency import iterate_in_threadpool
33
from starlette.datastructures import State
34
from starlette.routing import Mount
35
from typing_extensions import assert_never
Zhuohan Li's avatar
Zhuohan Li committed
36

37
import vllm.envs as envs
38
from vllm.config import VllmConfig
Woosuk Kwon's avatar
Woosuk Kwon committed
39
from vllm.engine.arg_utils import AsyncEngineArgs
40
from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
41
42
43
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
from vllm.engine.protocol import EngineClient
44
45
46
from vllm.entrypoints.chat_utils import (load_chat_template,
                                         resolve_hf_chat_template,
                                         resolve_mistral_chat_template)
47
from vllm.entrypoints.launcher import serve_http
48
from vllm.entrypoints.logger import RequestLogger
49
50
from vllm.entrypoints.openai.cli_args import (log_non_default_args,
                                              make_arg_parser,
51
                                              validate_parsed_serve_args)
52
53
# yapf conflicts with isort for this block
# yapf: disable
54
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
55
                                              ChatCompletionResponse,
56
57
                                              ClassificationRequest,
                                              ClassificationResponse,
58
                                              CompletionRequest,
59
                                              CompletionResponse,
60
61
                                              DetokenizeRequest,
                                              DetokenizeResponse,
62
63
                                              EmbeddingChatRequest,
                                              EmbeddingCompletionRequest,
64
                                              EmbeddingRequest,
65
                                              EmbeddingResponse, ErrorResponse,
66
                                              LoadLoRAAdapterRequest,
67
68
                                              PoolingChatRequest,
                                              PoolingCompletionRequest,
69
                                              PoolingRequest, PoolingResponse,
70
                                              RerankRequest, RerankResponse,
71
                                              ScoreRequest, ScoreResponse,
72
                                              TokenizeRequest,
73
                                              TokenizeResponse,
74
75
                                              TranscriptionRequest,
                                              TranscriptionResponse,
76
                                              UnloadLoRAAdapterRequest)
77
# yapf: enable
78
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
79
80
from vllm.entrypoints.openai.serving_classification import (
    ServingClassification)
81
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
82
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
83
84
85
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
                                                    OpenAIServingModels)
86
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
87
from vllm.entrypoints.openai.serving_score import ServingScores
88
89
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
90
91
from vllm.entrypoints.openai.serving_transcription import (
    OpenAIServingTranscription)
92
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
93
94
from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
                                    with_cancellation)
95
from vllm.logger import init_logger
96
from vllm.reasoning import ReasoningParserManager
97
98
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
99
from vllm.transformers_utils.tokenizer import MistralTokenizer
yhu422's avatar
yhu422 committed
100
from vllm.usage.usage_lib import UsageContext
101
from vllm.utils import (Device, FlexibleArgumentParser, get_open_zmq_ipc_path,
102
                        is_valid_ipv6_address, set_ulimit)
103
from vllm.v1.metrics.prometheus import get_prometheus_registry
104
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
105

106
TIMEOUT_KEEP_ALIVE = 5  # seconds
Zhuohan Li's avatar
Zhuohan Li committed
107

108
prometheus_multiproc_dir: tempfile.TemporaryDirectory
109

110
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
111
logger = init_logger('vllm.entrypoints.openai.api_server')
112

113
_running_tasks: set[asyncio.Task] = set()
114

115

116
@asynccontextmanager
117
async def lifespan(app: FastAPI):
118
119
    try:
        if app.state.log_stats:
120
            engine_client: EngineClient = app.state.engine_client
121
122
123

            async def _force_log():
                while True:
124
125
                    await asyncio.sleep(10.)
                    await engine_client.do_log_stats()
126
127
128
129
130
131

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
132
133
134
135
136

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
137
138
139
140
141
142
143
144
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
145
146


147
@asynccontextmanager
148
async def build_async_engine_client(
149
150
151
    args: Namespace,
    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:
152

153
    # Context manager to handle engine_client lifecycle
154
155
156
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

157
    async with build_async_engine_client_from_engine_args(
158
159
            engine_args, args.disable_frontend_multiprocessing,
            client_config) as engine:
160
161
162
163
164
165
166
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
    disable_frontend_multiprocessing: bool = False,
167
    client_config: Optional[dict[str, Any]] = None,
168
) -> AsyncIterator[EngineClient]:
169
    """
170
    Create EngineClient, either:
171
172
173
174
175
176
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

177
178
179
180
181
182
183
184
185
186
187
188
189
    # 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
190
191
        client_index = client_config.pop(
            "client_index") if client_config else 0
192
193
194
195
196
        try:
            async_llm = AsyncLLM.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
197
198
199
                disable_log_stats=engine_args.disable_log_stats,
                client_addresses=client_config,
                client_index=client_index)
200
201
202
203

            # Don't keep the dummy data in memory
            await async_llm.reset_mm_cache()

204
205
206
207
208
209
210
211
            yield async_llm
        finally:
            if async_llm:
                async_llm.shutdown()

    # V0 AsyncLLM.
    elif (MQLLMEngineClient.is_unsupported_config(vllm_config)
          or disable_frontend_multiprocessing):
212

213
214
        engine_client: Optional[EngineClient] = None
        try:
215
216
217
218
219
            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)
220
221
222
223
            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()
224

225
    # V0MQLLMEngine.
226
    else:
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
        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.")

242
        # Select random path for IPC.
243
        ipc_path = get_open_zmq_ipc_path()
244
245
        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)
246

247
        # Start RPCServer in separate process (holds the LLMEngine).
248
249
        # the current process might have CUDA context,
        # so we need to spawn a new process
250
251
        context = multiprocessing.get_context("spawn")

252
253
254
        # Ensure we can serialize transformer config before spawning
        maybe_register_config_serialize_by_value()

255
256
257
258
        # 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)
259
260
261
262
263
        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))
264
        engine_process.start()
265
        engine_pid = engine_process.pid
266
        assert engine_pid is not None, "Engine process failed to start."
267
        logger.info("Started engine process with PID %d", engine_pid)
268

269
270
271
272
273
274
275
276
        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)

277
        # Build RPCClient, which conforms to EngineClient Protocol.
278
        build_client = partial(MQLLMEngineClient, ipc_path, vllm_config,
279
280
281
                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
282
        try:
283
284
            while True:
                try:
285
                    await mq_engine_client.setup()
286
                    break
287
                except TimeoutError:
288
289
                    if (not engine_process.is_alive()
                            or not engine_alive.value):
290
                        raise RuntimeError(
291
292
                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None
293

294
            yield mq_engine_client  # type: ignore[misc]
295
296
        finally:
            # Ensure rpc server process was terminated
297
            engine_process.terminate()
298
299

            # Close all open connections to the backend
300
            mq_engine_client.close()
301

302
303
304
305
306
            # 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()
307

308
309
310
311
312
            # 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
313
            multiprocess.mark_process_dead(engine_process.pid)
314

315

316
317
async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
318
319
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
320
321
322
        raise RequestValidationError(errors=[
            "Unsupported Media Type: Only 'application/json' is allowed"
        ])
323
324


Ethan Xu's avatar
Ethan Xu committed
325
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
326

327

328
329
330
331
class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


332
def mount_metrics(app: FastAPI):
333
334
335
    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()
336

337
338
339
340
    # `response_class=PrometheusResponse` is needed to return an HTTP response
    # with header "Content-Type: text/plain; version=0.0.4; charset=utf-8"
    # instead of the default "application/json" which is incorrect.
    # See https://github.com/trallnag/prometheus-fastapi-instrumentator/issues/163#issue-1296092364
341
342
343
344
345
346
347
348
349
350
    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
351
    ).add().instrument(app).expose(app, response_class=PrometheusResponse)
352
353
354

    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
355

356
    # Workaround for 307 Redirect for /metrics
357
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
358
    app.routes.append(metrics_route)
359
360


361
362
363
364
365
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


366
367
368
369
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


370
def chat(request: Request) -> Optional[OpenAIServingChat]:
371
372
373
    return request.app.state.openai_serving_chat


374
def completion(request: Request) -> Optional[OpenAIServingCompletion]:
375
376
377
    return request.app.state.openai_serving_completion


378
379
380
381
def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


382
383
def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
384
385


386
def score(request: Request) -> Optional[ServingScores]:
387
388
389
    return request.app.state.openai_serving_scores


390
391
392
393
def classify(request: Request) -> Optional[ServingClassification]:
    return request.app.state.openai_serving_classification


394
395
def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores
396
397


398
399
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
400
401


402
403
404
405
def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


406
def engine_client(request: Request) -> EngineClient:
407
408
409
    return request.app.state.engine_client


410
411
@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
412
    """Health check."""
413
    await engine_client(raw_request).check_health()
414
    return Response(status_code=200)
415
416


417
418
419
420
421
422
423
424
425
@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
426
    # - /classify
427
428
429
430
431
432
433
434
435
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
    return JSONResponse(
        content={'server_load': request.app.state.server_load_metrics})


436
437
438
@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
439
440
441
442
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
@router.post("/tokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_IMPLEMENTED.value: {
                     "model": ErrorResponse
                 },
             })
459
@with_cancellation
460
async def tokenize(request: TokenizeRequest, raw_request: Request):
461
462
    handler = tokenization(raw_request)

463
464
465
466
467
468
469
470
471
    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
        raise HTTPException(status_code=HTTPStatus.NOT_IMPLEMENTED.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

472
473
474
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
475
    elif isinstance(generator, TokenizeResponse):
476
477
        return JSONResponse(content=generator.model_dump())

478
479
    assert_never(generator)

480

481
482
483
484
485
486
487
488
489
490
491
492
493
@router.post("/detokenize",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
494
@with_cancellation
495
async def detokenize(request: DetokenizeRequest, raw_request: Request):
496
497
    handler = tokenization(raw_request)

498
499
500
501
502
503
504
505
    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

506
507
508
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
509
    elif isinstance(generator, DetokenizeResponse):
510
511
        return JSONResponse(content=generator.model_dump())

512
513
    assert_never(generator)

514

Ethan Xu's avatar
Ethan Xu committed
515
@router.get("/v1/models")
516
async def show_available_models(raw_request: Request):
517
    handler = models(raw_request)
518

519
520
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
521
522


Ethan Xu's avatar
Ethan Xu committed
523
@router.get("/version")
524
async def show_version():
525
    ver = {"version": VLLM_VERSION}
526
527
528
    return JSONResponse(content=ver)


529
@router.post("/v1/chat/completions",
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 }
             })
547
@with_cancellation
548
@load_aware_call
549
550
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
551
552
553
554
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")
555

556
    generator = await handler.create_chat_completion(request, raw_request)
557

558
559
560
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
561

562
    elif isinstance(generator, ChatCompletionResponse):
563
        return JSONResponse(content=generator.model_dump())
564

565
566
    return StreamingResponse(content=generator, media_type="text/event-stream")

567

568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
@router.post("/v1/completions",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.NOT_FOUND.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
586
@with_cancellation
587
@load_aware_call
588
async def create_completion(request: CompletionRequest, raw_request: Request):
589
590
591
592
593
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Completions API")

594
595
596
597
598
599
600
601
602
    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

603
604
605
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
606
    elif isinstance(generator, CompletionResponse):
607
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
608

609
610
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
611

612
613
614
615
616
617
618
619
620
621
@router.post("/v1/embeddings",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
622
@with_cancellation
623
@load_aware_call
624
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
625
626
    handler = embedding(raw_request)
    if handler is None:
627
628
629
630
        return base(raw_request).create_error_response(
            message="The model does not support Embeddings API")

    generator = await handler.create_embedding(request, raw_request)
631

632
633
634
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
635
    elif isinstance(generator, EmbeddingResponse):
636
637
        return JSONResponse(content=generator.model_dump())

638
639
    assert_never(generator)

640

641
642
643
644
645
646
647
648
649
650
@router.post("/pooling",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
651
@with_cancellation
652
@load_aware_call
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
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)


669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
@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)


690
691
692
693
694
695
696
697
698
699
@router.post("/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
700
@with_cancellation
701
@load_aware_call
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
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)


718
719
720
721
722
723
724
725
726
727
@router.post("/v1/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
728
@with_cancellation
729
@load_aware_call
730
731
732
733
734
735
736
737
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)


738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
@router.post("/v1/audio/transcriptions",
             responses={
                 HTTPStatus.OK.value: {
                     "content": {
                         "text/event-stream": {}
                     }
                 },
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNPROCESSABLE_ENTITY.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
755
@with_cancellation
756
@load_aware_call
757
758
759
async def create_transcriptions(raw_request: Request,
                                request: Annotated[TranscriptionRequest,
                                                   Form()]):
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
    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")


779
780
781
782
783
784
785
786
787
788
@router.post("/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
789
@with_cancellation
790
@load_aware_call
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
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)


806
807
808
809
810
811
812
813
814
815
@router.post("/v1/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
816
817
@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
818
    logger.warning_once(
819
        "To indicate that the rerank API is not part of the standard OpenAI"
820
        " API, we have located it at `/rerank`. Please update your client "
821
822
823
824
825
        "accordingly. (Note: Conforms to JinaAI rerank API)")

    return await do_rerank(request, raw_request)


826
827
828
829
830
831
832
833
834
835
@router.post("/v2/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
836
837
838
839
840
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


841
TASK_HANDLERS: dict[str, dict[str, tuple]] = {
842
843
844
845
846
847
848
849
850
    "generate": {
        "messages": (ChatCompletionRequest, create_chat_completion),
        "default": (CompletionRequest, create_completion),
    },
    "embed": {
        "messages": (EmbeddingChatRequest, create_embedding),
        "default": (EmbeddingCompletionRequest, create_embedding),
    },
    "score": {
851
852
853
854
        "default": (RerankRequest, do_rerank)
    },
    "rerank": {
        "default": (RerankRequest, do_rerank)
855
856
857
858
859
860
861
862
863
864
865
    },
    "reward": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
    "classify": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
}

866
867
if envs.VLLM_SERVER_DEV_MODE:

868
869
870
871
872
    @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)

873
874
875
876
877
878
    @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.
        """
879
880
881
882
883
884
        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)
885
886
        return Response(status_code=200)

887
888
889
890
891
892
893
894
895
896
897
    @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):
898
899
900
901
902
903
        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)
904
905
906
907
        # 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)

908
909
910
911
912
913
    @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})

914

915
916
917
918
919
920
921
922
923
924
925
926
927
@router.post("/invocations",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.UNSUPPORTED_MEDIA_TYPE.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
928
929
930
931
async def invocations(raw_request: Request):
    """
    For SageMaker, routes requests to other handlers based on model `task`.
    """
932
933
    try:
        body = await raw_request.json()
934
    except json.JSONDecodeError as e:
935
936
937
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=f"JSON decode error: {e}") from e

938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
    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)


957
958
959
960
961
962
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")
963
    async def start_profile(raw_request: Request):
964
        logger.info("Starting profiler...")
965
        await engine_client(raw_request).start_profile()
966
967
968
969
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
970
    async def stop_profile(raw_request: Request):
971
        logger.info("Stopping profiler...")
972
        await engine_client(raw_request).stop_profile()
973
974
975
976
        logger.info("Profiler stopped.")
        return Response(status_code=200)


977
978
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
979
        "LoRA dynamic loading & unloading is enabled in the API server. "
980
981
        "This should ONLY be used for local development!")

982
983
    @router.post("/v1/load_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
984
    async def load_lora_adapter(request: LoadLoRAAdapterRequest,
985
                                raw_request: Request):
986
987
988
989
990
        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)
991
992
993

        return Response(status_code=200, content=response)

994
995
    @router.post("/v1/unload_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
996
    async def unload_lora_adapter(request: UnloadLoRAAdapterRequest,
997
                                  raw_request: Request):
998
999
1000
1001
1002
        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)
1003
1004
1005
1006

        return Response(status_code=200, content=response)


1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
def load_log_config(log_config_file: Optional[str]) -> Optional[dict]:
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
        logger.warning("Failed to load log config from file %s: error %s",
                       log_config_file, e)
        return None


1019
def build_app(args: Namespace) -> FastAPI:
1020
1021
1022
1023
1024
1025
1026
    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
1027
1028
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
1029

1030
1031
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
1032
1033
1034
1035
1036
1037
1038
1039
    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

1040
1041
1042
1043
1044
1045
1046
    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
        err = ErrorResponse(message=exc.detail,
                            type=HTTPStatus(exc.status_code).phrase,
                            code=exc.status_code)
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

Ethan Xu's avatar
Ethan Xu committed
1047
    @app.exception_handler(RequestValidationError)
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
    async def validation_exception_handler(_: Request,
                                           exc: RequestValidationError):
        exc_str = str(exc)
        errors_str = str(exc.errors())

        if exc.errors() and errors_str and errors_str != exc_str:
            message = f"{exc_str} {errors_str}"
        else:
            message = exc_str

        err = ErrorResponse(message=message,
                            type=HTTPStatus.BAD_REQUEST.phrase,
1060
                            code=HTTPStatus.BAD_REQUEST)
Ethan Xu's avatar
Ethan Xu committed
1061
1062
1063
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

1064
1065
    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
    if token := args.api_key or envs.VLLM_API_KEY:
1066
1067
1068

        @app.middleware("http")
        async def authentication(request: Request, call_next):
1069
1070
            if request.method == "OPTIONS":
                return await call_next(request)
1071
1072
1073
1074
            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"):
1075
1076
1077
1078
1079
1080
                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)

1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
    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
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105

    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))
1106
1107
            logger.info("response_body={%s}",
                        response_body[0].decode() if response_body else None)
1108
            return response
1109

1110
1111
1112
1113
    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):
1114
            app.add_middleware(imported)  # type: ignore[arg-type]
1115
1116
1117
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
1118
1119
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")
1120

Ethan Xu's avatar
Ethan Xu committed
1121
1122
1123
    return app


1124
async def init_app_state(
1125
    engine_client: EngineClient,
1126
    vllm_config: VllmConfig,
1127
    state: State,
1128
    args: Namespace,
1129
) -> None:
1130
    if args.served_model_name is not None:
1131
        served_model_names = args.served_model_name
1132
    else:
1133
        served_model_names = [args.model]
1134

1135
1136
1137
1138
1139
    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

1140
1141
1142
1143
1144
    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

1145
    state.engine_client = engine_client
1146
    state.log_stats = not args.disable_log_stats
1147
1148
    state.vllm_config = vllm_config
    model_config = vllm_config.model_config
Ethan Xu's avatar
Ethan Xu committed
1149

1150
    resolved_chat_template = load_chat_template(args.chat_template)
1151
    if resolved_chat_template is not None:
1152
1153
1154
1155
1156
1157
1158
1159
1160
        # 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(
1161
                tokenizer=tokenizer,
1162
1163
                chat_template=None,
                tools=None,
1164
                model_config=vllm_config.model_config,
1165
            )
1166
1167
1168
1169
1170
1171
1172

            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)
1173

1174
    state.openai_serving_models = OpenAIServingModels(
1175
        engine_client=engine_client,
1176
1177
1178
1179
1180
        model_config=model_config,
        base_model_paths=base_model_paths,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
    )
1181
    await state.openai_serving_models.init_static_loras()
1182
    state.openai_serving_chat = OpenAIServingChat(
1183
        engine_client,
1184
        model_config,
1185
        state.openai_serving_models,
1186
1187
        args.response_role,
        request_logger=request_logger,
1188
1189
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1190
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
1191
        enable_auto_tools=args.enable_auto_tool_choice,
1192
        tool_parser=args.tool_call_parser,
1193
        reasoning_parser=args.reasoning_parser,
1194
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
1195
    ) if model_config.runner_type == "generate" else None
1196
    state.openai_serving_completion = OpenAIServingCompletion(
1197
        engine_client,
1198
        model_config,
1199
        state.openai_serving_models,
1200
        request_logger=request_logger,
1201
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
1202
    ) if model_config.runner_type == "generate" else None
1203
    state.openai_serving_pooling = OpenAIServingPooling(
1204
        engine_client,
1205
        model_config,
1206
        state.openai_serving_models,
1207
        request_logger=request_logger,
1208
1209
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1210
    ) if model_config.runner_type == "pooling" else None
1211
1212
1213
    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
1214
        state.openai_serving_models,
1215
1216
1217
1218
        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
1219
    state.openai_serving_scores = ServingScores(
1220
1221
        engine_client,
        model_config,
1222
        state.openai_serving_models,
1223
1224
        request_logger=request_logger) if model_config.task in (
            "score", "embed", "pooling") else None
1225
1226
1227
1228
1229
1230
    state.openai_serving_classification = ServingClassification(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
    ) if model_config.task == "classify" else None
1231
    state.jinaai_serving_reranking = ServingScores(
1232
1233
1234
1235
1236
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger
    ) if model_config.task == "score" else None
1237
    state.openai_serving_tokenization = OpenAIServingTokenization(
1238
        engine_client,
1239
        model_config,
1240
        state.openai_serving_models,
1241
        request_logger=request_logger,
1242
1243
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1244
    )
1245
1246
1247
1248
1249
1250
    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
1251
    state.task = model_config.task
1252

1253
1254
1255
    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

1256

1257
def create_server_socket(addr: tuple[str, int]) -> socket.socket:
1258
1259
1260
1261
1262
1263
    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)
1264
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
1265
1266
1267
1268
1269
    sock.bind(addr)

    return sock


1270
def validate_api_server_args(args):
1271
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
1272
    if args.enable_auto_tool_choice \
1273
            and args.tool_call_parser not in valid_tool_parses:
1274
        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
1275
                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
1276

1277
    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
1278
    if args.reasoning_parser \
1279
1280
1281
1282
1283
        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)} }})")

1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296

def setup_server(args):
    """Validate API server args, set up signal handler, create socket
    ready to serve."""

    logger.info("vLLM API server version %s", VLLM_VERSION)
    log_non_default_args(args)

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

    validate_api_server_args(args)

1297
1298
1299
    # 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
1300
1301
    sock_addr = (args.host or "", args.port)
    sock = create_server_socket(sock_addr)
1302

1303
1304
1305
1306
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

1307
1308
1309
1310
1311
1312
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
    addr, port = sock_addr
    is_ssl = args.ssl_keyfile and args.ssl_certfile
    host_part = f"[{addr}]" if is_valid_ipv6_address(
        addr) else addr or "0.0.0.0"
    listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"

    return listen_address, sock


async def run_server(args, **uvicorn_kwargs) -> None:
    """Run a single-worker API server."""
    listen_address, sock = setup_server(args)
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)


async def run_server_worker(listen_address,
                            sock,
                            args,
                            client_config=None,
                            **uvicorn_kwargs) -> None:
    """Run a single API server worker."""

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

    server_index = client_config.get("client_index", 0) if client_config else 0

1340
1341
1342
1343
1344
    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
        uvicorn_kwargs['log_config'] = log_config

1345
    async with build_async_engine_client(args, client_config) as engine_client:
1346
1347
        app = build_app(args)

1348
1349
        vllm_config = await engine_client.get_vllm_config()
        await init_app_state(engine_client, vllm_config, app.state, args)
1350

1351
1352
        logger.info("Starting vLLM API server %d on %s", server_index,
                    listen_address)
1353
1354
        shutdown_task = await serve_http(
            app,
1355
            sock=sock,
1356
            enable_ssl_refresh=args.enable_ssl_refresh,
1357
1358
1359
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
1360
1361
1362
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
1363
1364
1365
1366
1367
            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,
1368
1369
1370
            **uvicorn_kwargs,
        )

1371
    # NB: Await server shutdown only after the backend context is exited
1372
1373
1374
1375
    try:
        await shutdown_task
    finally:
        sock.close()
1376

Ethan Xu's avatar
Ethan Xu committed
1377
1378
1379

if __name__ == "__main__":
    # NOTE(simon):
1380
1381
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
1382
    cli_env_setup()
Ethan Xu's avatar
Ethan Xu committed
1383
1384
1385
1386
    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
1387
    validate_parsed_serve_args(args)
1388

1389
    uvloop.run(run_server(args))