api_server.py 42.1 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

3
import asyncio
4
import atexit
5
import gc
6
7
import importlib
import inspect
8
import multiprocessing
9
import os
10
import re
11
import signal
12
import socket
13
import tempfile
14
import uuid
15
from argparse import Namespace
16
from collections.abc import AsyncIterator
17
from contextlib import asynccontextmanager
18
from functools import partial
19
from http import HTTPStatus
20
from typing import Annotated, Optional, Union
21

22
import uvloop
23
from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Request
Zhuohan Li's avatar
Zhuohan Li committed
24
25
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
26
from fastapi.responses import JSONResponse, Response, StreamingResponse
27
from starlette.concurrency import iterate_in_threadpool
28
from starlette.datastructures import State
29
from starlette.routing import Mount
30
from typing_extensions import assert_never
Zhuohan Li's avatar
Zhuohan Li committed
31

32
import vllm.envs as envs
33
from vllm.config import ModelConfig
Woosuk Kwon's avatar
Woosuk Kwon committed
34
from vllm.engine.arg_utils import AsyncEngineArgs
35
from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
36
37
38
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
from vllm.engine.protocol import EngineClient
39
40
41
from vllm.entrypoints.chat_utils import (load_chat_template,
                                         resolve_hf_chat_template,
                                         resolve_mistral_chat_template)
42
from vllm.entrypoints.launcher import serve_http
43
from vllm.entrypoints.logger import RequestLogger
44
45
from vllm.entrypoints.openai.cli_args import (make_arg_parser,
                                              validate_parsed_serve_args)
46
47
# yapf conflicts with isort for this block
# yapf: disable
48
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
49
                                              ChatCompletionResponse,
50
                                              CompletionRequest,
51
                                              CompletionResponse,
52
53
                                              DetokenizeRequest,
                                              DetokenizeResponse,
54
55
                                              EmbeddingChatRequest,
                                              EmbeddingCompletionRequest,
56
                                              EmbeddingRequest,
57
58
59
                                              EmbeddingResponse,
                                              EmbeddingResponseData,
                                              ErrorResponse,
60
                                              LoadLoRAAdapterRequest,
61
62
                                              PoolingChatRequest,
                                              PoolingCompletionRequest,
63
                                              PoolingRequest, PoolingResponse,
64
                                              RerankRequest, RerankResponse,
65
                                              ScoreRequest, ScoreResponse,
66
                                              TokenizeRequest,
67
                                              TokenizeResponse,
68
69
                                              TranscriptionRequest,
                                              TranscriptionResponse,
70
                                              UnloadLoRAAdapterRequest)
71
# yapf: enable
72
73
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
74
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
75
76
77
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
                                                    OpenAIServingModels)
78
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
79
from vllm.entrypoints.openai.serving_score import ServingScores
80
81
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
82
83
from vllm.entrypoints.openai.serving_transcription import (
    OpenAIServingTranscription)
84
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
85
from vllm.entrypoints.utils import load_aware_call, with_cancellation
86
from vllm.logger import init_logger
87
from vllm.reasoning import ReasoningParserManager
88
89
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
90
from vllm.transformers_utils.tokenizer import MistralTokenizer
yhu422's avatar
yhu422 committed
91
from vllm.usage.usage_lib import UsageContext
92
from vllm.utils import (Device, FlexibleArgumentParser, get_open_zmq_ipc_path,
93
                        is_valid_ipv6_address, set_ulimit)
94
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
95

96
TIMEOUT_KEEP_ALIVE = 5  # seconds
Zhuohan Li's avatar
Zhuohan Li committed
97

98
prometheus_multiproc_dir: tempfile.TemporaryDirectory
99

100
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
101
logger = init_logger('vllm.entrypoints.openai.api_server')
102

103
_running_tasks: set[asyncio.Task] = set()
104

105

106
@asynccontextmanager
107
async def lifespan(app: FastAPI):
108
109
    try:
        if app.state.log_stats:
110
            engine_client: EngineClient = app.state.engine_client
111
112
113

            async def _force_log():
                while True:
114
115
                    await asyncio.sleep(10.)
                    await engine_client.do_log_stats()
116
117
118
119
120
121

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
122
123
124
125
126

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
127
128
129
130
131
132
133
134
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
135
136


137
@asynccontextmanager
138
async def build_async_engine_client(
139
        args: Namespace) -> AsyncIterator[EngineClient]:
140

141
    # Context manager to handle engine_client lifecycle
142
143
144
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

145
146
147
148
149
150
151
152
153
    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,
154
) -> AsyncIterator[EngineClient]:
155
    """
156
    Create EngineClient, either:
157
158
159
160
161
162
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

163
164
165
166
167
168
169
170
171
172
173
174
175
176
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
        try:
            async_llm = AsyncLLM.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)
            yield async_llm
        finally:
            if async_llm:
                async_llm.shutdown()

    # V0 AsyncLLM.
    elif (MQLLMEngineClient.is_unsupported_config(vllm_config)
          or disable_frontend_multiprocessing):
190

191
192
        engine_client: Optional[EngineClient] = None
        try:
193
194
195
196
197
            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)
198
199
200
201
            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()
202

203
    # V0MQLLMEngine.
204
    else:
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
        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.")

220
        # Select random path for IPC.
221
        ipc_path = get_open_zmq_ipc_path()
222
223
        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)
224

225
        # Start RPCServer in separate process (holds the LLMEngine).
226
227
        # the current process might have CUDA context,
        # so we need to spawn a new process
228
229
        context = multiprocessing.get_context("spawn")

230
231
232
        # Ensure we can serialize transformer config before spawning
        maybe_register_config_serialize_by_value()

233
234
235
236
        # 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)
237
238
239
240
241
        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))
242
        engine_process.start()
243
        engine_pid = engine_process.pid
244
        assert engine_pid is not None, "Engine process failed to start."
245
        logger.info("Started engine process with PID %d", engine_pid)
246

247
248
249
250
251
252
253
254
        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)

255
        # Build RPCClient, which conforms to EngineClient Protocol.
256
        build_client = partial(MQLLMEngineClient, ipc_path, vllm_config,
257
258
259
                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
260
        try:
261
262
            while True:
                try:
263
                    await mq_engine_client.setup()
264
                    break
265
                except TimeoutError:
266
267
                    if (not engine_process.is_alive()
                            or not engine_alive.value):
268
                        raise RuntimeError(
269
270
                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None
271

272
            yield mq_engine_client  # type: ignore[misc]
273
274
        finally:
            # Ensure rpc server process was terminated
275
            engine_process.terminate()
276
277

            # Close all open connections to the backend
278
            mq_engine_client.close()
279

280
281
282
283
284
            # 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()
285

286
287
288
289
290
            # 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
291
            multiprocess.mark_process_dead(engine_process.pid)
292

293

294
295
async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
296
297
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
298
299
300
301
302
303
        raise HTTPException(
            status_code=HTTPStatus.UNSUPPORTED_MEDIA_TYPE,
            detail="Unsupported Media Type: Only 'application/json' is allowed"
        )


Ethan Xu's avatar
Ethan Xu committed
304
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
305

306

307
def mount_metrics(app: FastAPI):
308
309
310
311
312
313
    # 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)
314
    from prometheus_fastapi_instrumentator import Instrumentator
315
316
317

    prometheus_multiproc_dir_path = os.getenv("PROMETHEUS_MULTIPROC_DIR", None)
    if prometheus_multiproc_dir_path is not None:
318
319
        logger.debug("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
                     prometheus_multiproc_dir_path)
320
321
        registry = CollectorRegistry()
        multiprocess.MultiProcessCollector(registry)
322
323
324
325
326
327
328
329
330
331
        Instrumentator(
            excluded_handlers=[
                "/metrics",
                "/health",
                "/load",
                "/ping",
                "/version",
            ],
            registry=registry,
        ).add().instrument(app).expose(app)
332
333
334
335
336
337
338

        # 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())

339
    # Workaround for 307 Redirect for /metrics
340
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
341
    app.routes.append(metrics_route)
342
343


344
345
346
347
348
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


349
350
351
352
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


353
def chat(request: Request) -> Optional[OpenAIServingChat]:
354
355
356
    return request.app.state.openai_serving_chat


357
def completion(request: Request) -> Optional[OpenAIServingCompletion]:
358
359
360
    return request.app.state.openai_serving_completion


361
362
363
364
def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


365
366
def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
367
368


369
def score(request: Request) -> Optional[ServingScores]:
370
371
372
    return request.app.state.openai_serving_scores


373
374
def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores
375
376


377
378
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
379
380


381
382
383
384
def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


385
def engine_client(request: Request) -> EngineClient:
386
387
388
    return request.app.state.engine_client


Ethan Xu's avatar
Ethan Xu committed
389
@router.get("/health")
390
async def health(raw_request: Request) -> Response:
391
    """Health check."""
392
    await engine_client(raw_request).check_health()
393
394
395
    return Response(status_code=200)


396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
@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
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
    return JSONResponse(
        content={'server_load': request.app.state.server_load_metrics})


414
415
416
417
418
419
@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)


420
@router.post("/tokenize", dependencies=[Depends(validate_json_request)])
421
@with_cancellation
422
async def tokenize(request: TokenizeRequest, raw_request: Request):
423
424
    handler = tokenization(raw_request)

425
    generator = await handler.create_tokenize(request, raw_request)
426
427
428
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
429
    elif isinstance(generator, TokenizeResponse):
430
431
        return JSONResponse(content=generator.model_dump())

432
433
    assert_never(generator)

434

435
@router.post("/detokenize", dependencies=[Depends(validate_json_request)])
436
@with_cancellation
437
async def detokenize(request: DetokenizeRequest, raw_request: Request):
438
439
    handler = tokenization(raw_request)

440
    generator = await handler.create_detokenize(request, raw_request)
441
442
443
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
444
    elif isinstance(generator, DetokenizeResponse):
445
446
        return JSONResponse(content=generator.model_dump())

447
448
    assert_never(generator)

449

Ethan Xu's avatar
Ethan Xu committed
450
@router.get("/v1/models")
451
async def show_available_models(raw_request: Request):
452
    handler = models(raw_request)
453

454
455
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
456
457


Ethan Xu's avatar
Ethan Xu committed
458
@router.get("/version")
459
async def show_version():
460
    ver = {"version": VLLM_VERSION}
461
462
463
    return JSONResponse(content=ver)


464
465
@router.post("/v1/chat/completions",
             dependencies=[Depends(validate_json_request)])
466
@with_cancellation
467
@load_aware_call
468
469
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
470
471
472
473
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")
474

475
    generator = await handler.create_chat_completion(request, raw_request)
476

477
478
479
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
480

481
    elif isinstance(generator, ChatCompletionResponse):
482
        return JSONResponse(content=generator.model_dump())
483

484
485
    return StreamingResponse(content=generator, media_type="text/event-stream")

486

487
@router.post("/v1/completions", dependencies=[Depends(validate_json_request)])
488
@with_cancellation
489
@load_aware_call
490
async def create_completion(request: CompletionRequest, raw_request: Request):
491
492
493
494
495
496
    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)
497
498
499
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
500
    elif isinstance(generator, CompletionResponse):
501
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
502

503
504
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
505

506
@router.post("/v1/embeddings", dependencies=[Depends(validate_json_request)])
507
@with_cancellation
508
@load_aware_call
509
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
510
511
    handler = embedding(raw_request)
    if handler is None:
512
513
514
515
516
517
518
519
520
521
522
        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)
523
524

        generator: Union[ErrorResponse, EmbeddingResponse]
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
        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)
543

544
545
546
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
547
    elif isinstance(generator, EmbeddingResponse):
548
549
        return JSONResponse(content=generator.model_dump())

550
551
    assert_never(generator)

552

553
@router.post("/pooling", dependencies=[Depends(validate_json_request)])
554
@with_cancellation
555
@load_aware_call
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
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)


572
@router.post("/score", dependencies=[Depends(validate_json_request)])
573
@with_cancellation
574
@load_aware_call
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
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)


591
@router.post("/v1/score", dependencies=[Depends(validate_json_request)])
592
@with_cancellation
593
@load_aware_call
594
595
596
597
598
599
600
601
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)


602
603
@router.post("/v1/audio/transcriptions")
@with_cancellation
604
@load_aware_call
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
async def create_transcriptions(request: Annotated[TranscriptionRequest,
                                                   Form()],
                                raw_request: Request):
    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")


627
@router.post("/rerank", dependencies=[Depends(validate_json_request)])
628
@with_cancellation
629
@load_aware_call
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
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)


645
@router.post("/v1/rerank", dependencies=[Depends(validate_json_request)])
646
647
@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
648
    logger.warning_once(
649
        "To indicate that the rerank API is not part of the standard OpenAI"
650
        " API, we have located it at `/rerank`. Please update your client "
651
652
653
654
655
        "accordingly. (Note: Conforms to JinaAI rerank API)")

    return await do_rerank(request, raw_request)


656
@router.post("/v2/rerank", dependencies=[Depends(validate_json_request)])
657
658
659
660
661
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


662
TASK_HANDLERS: dict[str, dict[str, tuple]] = {
663
664
665
666
667
668
669
670
671
    "generate": {
        "messages": (ChatCompletionRequest, create_chat_completion),
        "default": (CompletionRequest, create_completion),
    },
    "embed": {
        "messages": (EmbeddingChatRequest, create_embedding),
        "default": (EmbeddingCompletionRequest, create_embedding),
    },
    "score": {
672
673
674
675
        "default": (RerankRequest, do_rerank)
    },
    "rerank": {
        "default": (RerankRequest, do_rerank)
676
677
678
679
680
681
682
683
684
685
686
    },
    "reward": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
    "classify": {
        "messages": (PoolingChatRequest, create_pooling),
        "default": (PoolingCompletionRequest, create_pooling),
    },
}

687
688
689
690
691
692
693
694
if envs.VLLM_SERVER_DEV_MODE:

    @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.
        """
695
696
697
698
699
700
        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)
701
702
        return Response(status_code=200)

703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
    @router.post("/sleep")
    async def sleep(raw_request: Request):
        # get POST params
        level = raw_request.query_params.get("level", "1")
        logger.info("sleep the engine with level %s", level)
        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):
        logger.info("wake up the engine")
        await engine_client(raw_request).wake_up()
        # 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)

721
722
723
724
725
726
    @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})

727

728
@router.post("/invocations", dependencies=[Depends(validate_json_request)])
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
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)


753
754
755
756
757
758
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")
759
    async def start_profile(raw_request: Request):
760
        logger.info("Starting profiler...")
761
        await engine_client(raw_request).start_profile()
762
763
764
765
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
766
    async def stop_profile(raw_request: Request):
767
        logger.info("Stopping profiler...")
768
        await engine_client(raw_request).stop_profile()
769
770
771
772
        logger.info("Profiler stopped.")
        return Response(status_code=200)


773
774
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
775
        "LoRA dynamic loading & unloading is enabled in the API server. "
776
777
        "This should ONLY be used for local development!")

778
779
    @router.post("/v1/load_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
780
    async def load_lora_adapter(request: LoadLoRAAdapterRequest,
781
                                raw_request: Request):
782
783
784
785
786
        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)
787
788
789

        return Response(status_code=200, content=response)

790
791
    @router.post("/v1/unload_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
792
    async def unload_lora_adapter(request: UnloadLoRAAdapterRequest,
793
                                  raw_request: Request):
794
795
796
797
798
        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)
799
800
801
802

        return Response(status_code=200, content=response)


803
def build_app(args: Namespace) -> FastAPI:
804
805
806
807
808
809
810
    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
811
812
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
813

814
815
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
816
817
818
819
820
821
822
823
    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
824
825
    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(_, exc):
826
827
828
        err = ErrorResponse(message=str(exc),
                            type="BadRequestError",
                            code=HTTPStatus.BAD_REQUEST)
Ethan Xu's avatar
Ethan Xu committed
829
830
831
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

832
833
    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
    if token := args.api_key or envs.VLLM_API_KEY:
834
835
836

        @app.middleware("http")
        async def authentication(request: Request, call_next):
837
838
            if request.method == "OPTIONS":
                return await call_next(request)
839
840
841
842
            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"):
843
844
845
846
847
848
                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)

849
850
851
852
853
854
855
856
857
858
859
860
    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
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875

    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))
            logger.info("response_body={%s}", response_body[0].decode())
            return response
876

877
878
879
880
    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):
881
            app.add_middleware(imported)  # type: ignore[arg-type]
882
883
884
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
885
886
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")
887

Ethan Xu's avatar
Ethan Xu committed
888
889
890
    return app


891
async def init_app_state(
892
    engine_client: EngineClient,
893
894
    model_config: ModelConfig,
    state: State,
895
    args: Namespace,
896
) -> None:
897
    if args.served_model_name is not None:
898
        served_model_names = args.served_model_name
899
    else:
900
        served_model_names = [args.model]
901

902
903
904
905
906
    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

907
908
909
910
911
    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

912
    state.engine_client = engine_client
913
    state.log_stats = not args.disable_log_stats
Ethan Xu's avatar
Ethan Xu committed
914

915
    resolved_chat_template = load_chat_template(args.chat_template)
916
    if resolved_chat_template is not None:
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
        # 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(
                tokenizer,
                chat_template=None,
                tools=None,
                trust_remote_code=model_config.trust_remote_code)

            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)
937

938
    state.openai_serving_models = OpenAIServingModels(
939
        engine_client=engine_client,
940
941
942
943
944
        model_config=model_config,
        base_model_paths=base_model_paths,
        lora_modules=args.lora_modules,
        prompt_adapters=args.prompt_adapters,
    )
945
    await state.openai_serving_models.init_static_loras()
946
    state.openai_serving_chat = OpenAIServingChat(
947
        engine_client,
948
        model_config,
949
        state.openai_serving_models,
950
951
        args.response_role,
        request_logger=request_logger,
952
953
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
954
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
955
        enable_auto_tools=args.enable_auto_tool_choice,
956
        tool_parser=args.tool_call_parser,
957
958
        enable_reasoning=args.enable_reasoning,
        reasoning_parser=args.reasoning_parser,
959
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
960
    ) if model_config.runner_type == "generate" else None
961
    state.openai_serving_completion = OpenAIServingCompletion(
962
        engine_client,
963
        model_config,
964
        state.openai_serving_models,
965
        request_logger=request_logger,
966
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
967
    ) if model_config.runner_type == "generate" else None
968
    state.openai_serving_pooling = OpenAIServingPooling(
969
        engine_client,
970
        model_config,
971
        state.openai_serving_models,
972
        request_logger=request_logger,
973
974
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
975
    ) if model_config.runner_type == "pooling" else None
976
977
978
    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
979
        state.openai_serving_models,
980
981
982
983
        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
984
    state.openai_serving_scores = ServingScores(
985
986
        engine_client,
        model_config,
987
        state.openai_serving_models,
988
989
990
        request_logger=request_logger) if model_config.task in (
            "score", "embed", "pooling") else None
    state.jinaai_serving_reranking = ServingScores(
991
992
993
994
995
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger
    ) if model_config.task == "score" else None
996
    state.openai_serving_tokenization = OpenAIServingTokenization(
997
        engine_client,
998
        model_config,
999
        state.openai_serving_models,
1000
        request_logger=request_logger,
1001
1002
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1003
    )
1004
1005
1006
1007
1008
1009
    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
1010
    state.task = model_config.task
1011

1012
1013
1014
    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

1015

1016
def create_server_socket(addr: tuple[str, int]) -> socket.socket:
1017
1018
1019
1020
1021
1022
    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)
1023
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
1024
1025
1026
1027
1028
    sock.bind(addr)

    return sock


1029
async def run_server(args, **uvicorn_kwargs) -> None:
1030
1031
1032
    logger.info("vLLM API server version %s", VLLM_VERSION)
    logger.info("args: %s", args)

1033
1034
1035
    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
        ToolParserManager.import_tool_parser(args.tool_parser_plugin)

1036
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
1037
    if args.enable_auto_tool_choice \
1038
        and args.tool_call_parser not in valid_tool_parses:
1039
        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
1040
                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
1041

1042
1043
1044
1045
1046
1047
1048
    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
    if args.enable_reasoning \
        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)} }})")

1049
1050
1051
    # 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
1052
1053
    sock_addr = (args.host or "", args.port)
    sock = create_server_socket(sock_addr)
1054

1055
1056
1057
1058
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

1059
1060
1061
1062
1063
1064
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

1065
    async with build_async_engine_client(args) as engine_client:
1066
1067
        app = build_app(args)

1068
        model_config = await engine_client.get_model_config()
1069
        await init_app_state(engine_client, model_config, app.state, args)
1070

1071
1072
1073
1074
1075
        def _listen_addr(a: str) -> str:
            if is_valid_ipv6_address(a):
                return '[' + a + ']'
            return a or "0.0.0.0"

1076
1077
1078
1079
        is_ssl = args.ssl_keyfile and args.ssl_certfile
        logger.info("Starting vLLM API server on http%s://%s:%d",
                    "s" if is_ssl else "", _listen_addr(sock_addr[0]),
                    sock_addr[1])
1080

1081
1082
        shutdown_task = await serve_http(
            app,
1083
            sock=sock,
1084
            enable_ssl_refresh=args.enable_ssl_refresh,
1085
1086
1087
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
1088
1089
1090
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
1091
1092
1093
1094
1095
            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,
1096
1097
1098
            **uvicorn_kwargs,
        )

1099
1100
    # NB: Await server shutdown only after the backend context is exited
    await shutdown_task
1101

1102
1103
    sock.close()

Ethan Xu's avatar
Ethan Xu committed
1104
1105
1106

if __name__ == "__main__":
    # NOTE(simon):
1107
1108
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
Ethan Xu's avatar
Ethan Xu committed
1109
1110
1111
1112
    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
1113
    validate_parsed_serve_args(args)
1114

1115
    uvloop.run(run_server(args))