api_server.py 68.3 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 gc
6
import hashlib
7
8
import importlib
import inspect
9
import json
10
import multiprocessing
11
import multiprocessing.forkserver as forkserver
12
import os
13
import secrets
14
import signal
15
import socket
16
import tempfile
17
import uuid
18
from argparse import Namespace
19
from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Callable
20
from contextlib import asynccontextmanager
21
from http import HTTPStatus
22
from typing import Annotated, Any, Literal
23

24
import prometheus_client
25
import pydantic
26
import regex as re
27
import uvloop
28
from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Query, Request
Zhuohan Li's avatar
Zhuohan Li committed
29
30
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
31
from fastapi.responses import JSONResponse, Response, StreamingResponse
32
33
from prometheus_client import make_asgi_app
from prometheus_fastapi_instrumentator import Instrumentator
34
from starlette.concurrency import iterate_in_threadpool
35
from starlette.datastructures import URL, Headers, MutableHeaders, State
36
from starlette.routing import Mount
37
from starlette.types import ASGIApp, Message, Receive, Scope, Send
38
from typing_extensions import assert_never
Zhuohan Li's avatar
Zhuohan Li committed
39

40
import vllm.envs as envs
41
from vllm.config import VllmConfig
Woosuk Kwon's avatar
Woosuk Kwon committed
42
from vllm.engine.arg_utils import AsyncEngineArgs
43
from vllm.engine.protocol import EngineClient
44
from vllm.entrypoints.launcher import serve_http
45
from vllm.entrypoints.logger import RequestLogger
46
47
48
49
50
51
52
53
54
55
from vllm.entrypoints.openai.cli_args import make_arg_parser, validate_parsed_serve_args
from vllm.entrypoints.openai.protocol import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    ClassificationRequest,
    ClassificationResponse,
    CompletionRequest,
    CompletionResponse,
    DetokenizeRequest,
    DetokenizeResponse,
56
    EmbeddingBytesResponse,
57
58
59
60
61
62
    EmbeddingRequest,
    EmbeddingResponse,
    ErrorInfo,
    ErrorResponse,
    IOProcessorResponse,
    LoadLoRAAdapterRequest,
63
    PoolingBytesResponse,
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
    PoolingRequest,
    PoolingResponse,
    RerankRequest,
    RerankResponse,
    ResponsesRequest,
    ResponsesResponse,
    ScoreRequest,
    ScoreResponse,
    StreamingResponsesResponse,
    TokenizeRequest,
    TokenizeResponse,
    TranscriptionRequest,
    TranscriptionResponse,
    TranslationRequest,
    TranslationResponse,
    UnloadLoRAAdapterRequest,
)
81
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
82
from vllm.entrypoints.openai.serving_classification import ServingClassification
83
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
84
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
85
from vllm.entrypoints.openai.serving_engine import OpenAIServing
86
87
88
89
from vllm.entrypoints.openai.serving_models import (
    BaseModelPath,
    OpenAIServingModels,
)
90
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
91
from vllm.entrypoints.openai.serving_responses import OpenAIServingResponses
92
from vllm.entrypoints.openai.serving_score import ServingScores
93
from vllm.entrypoints.openai.serving_tokenization import OpenAIServingTokenization
94
from vllm.entrypoints.openai.serving_transcription import (
95
96
97
    OpenAIServingTranscription,
    OpenAIServingTranslation,
)
98
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
99
100
101
102
103
from vllm.entrypoints.tool_server import DemoToolServer, MCPToolServer, ToolServer
from vllm.entrypoints.utils import (
    cli_env_setup,
    load_aware_call,
    log_non_default_args,
104
105
    process_chat_template,
    process_lora_modules,
106
107
    with_cancellation,
)
108
from vllm.logger import init_logger
109
from vllm.reasoning import ReasoningParserManager
yhu422's avatar
yhu422 committed
110
from vllm.usage.usage_lib import UsageContext
111
from vllm.utils import Device, FlexibleArgumentParser, set_ulimit
112
from vllm.utils.network_utils import is_valid_ipv6_address
113
from vllm.utils.system_utils import decorate_logs
114
from vllm.v1.engine.exceptions import EngineDeadError
115
from vllm.v1.metrics.prometheus import get_prometheus_registry
116
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
117

118
prometheus_multiproc_dir: tempfile.TemporaryDirectory
119

120
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
121
logger = init_logger("vllm.entrypoints.openai.api_server")
122

123
_running_tasks: set[asyncio.Task] = set()
124

125

126
@asynccontextmanager
127
async def lifespan(app: FastAPI):
128
129
    try:
        if app.state.log_stats:
130
            engine_client: EngineClient = app.state.engine_client
131
132
133

            async def _force_log():
                while True:
134
                    await asyncio.sleep(envs.VLLM_LOG_STATS_INTERVAL)
135
                    await engine_client.do_log_stats()
136
137
138
139
140
141

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
142
143
144
145
146

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
147
148
149
150
151
152
153
154
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
155
156


157
@asynccontextmanager
158
async def build_async_engine_client(
159
    args: Namespace,
160
161
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
162
163
    disable_frontend_multiprocessing: bool | None = None,
    client_config: dict[str, Any] | None = None,
164
) -> AsyncIterator[EngineClient]:
165
166
167
168
    if os.getenv("VLLM_WORKER_MULTIPROC_METHOD") == "forkserver":
        # The executor is expected to be mp.
        # Pre-import heavy modules in the forkserver process
        logger.debug("Setup forkserver with pre-imports")
169
        multiprocessing.set_start_method("forkserver")
170
171
172
173
        multiprocessing.set_forkserver_preload(["vllm.v1.engine.async_llm"])
        forkserver.ensure_running()
        logger.debug("Forkserver setup complete!")

174
    # Context manager to handle engine_client lifecycle
175
176
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)
177
178
179
    if client_config:
        engine_args._api_process_count = client_config.get("client_count", 1)
        engine_args._api_process_rank = client_config.get("client_index", 0)
180

181
    if disable_frontend_multiprocessing is None:
182
        disable_frontend_multiprocessing = bool(args.disable_frontend_multiprocessing)
183

184
    async with build_async_engine_client_from_engine_args(
185
186
187
188
        engine_args,
        usage_context=usage_context,
        disable_frontend_multiprocessing=disable_frontend_multiprocessing,
        client_config=client_config,
189
    ) as engine:
190
191
192
193
194
195
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
196
197
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
198
    disable_frontend_multiprocessing: bool = False,
199
    client_config: dict[str, Any] | None = None,
200
) -> AsyncIterator[EngineClient]:
201
    """
202
    Create EngineClient, either:
203
204
205
206
207
208
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

209
210
211
212
    # Create the EngineConfig (determines if we can use V1).
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    # V1 AsyncLLM.
213
    assert envs.VLLM_USE_V1
214

215
216
217
    if disable_frontend_multiprocessing:
        logger.warning(
            "V1 is enabled, but got --disable-frontend-multiprocessing. "
218
219
            "To disable frontend multiprocessing, set VLLM_USE_V1=0."
        )
220

221
    from vllm.v1.engine.async_llm import AsyncLLM
222

223
    async_llm: AsyncLLM | None = None
224
225
226
227
228
229

    # Don't mutate the input client_config
    client_config = dict(client_config) if client_config else {}
    client_count = client_config.pop("client_count", 1)
    client_index = client_config.pop("client_index", 0)

230
231
232
233
234
    try:
        async_llm = AsyncLLM.from_vllm_config(
            vllm_config=vllm_config,
            usage_context=usage_context,
            enable_log_requests=engine_args.enable_log_requests,
235
            aggregate_engine_logging=engine_args.aggregate_engine_logging,
236
237
238
            disable_log_stats=engine_args.disable_log_stats,
            client_addresses=client_config,
            client_count=client_count,
239
240
            client_index=client_index,
        )
241
242
243
244
245
246
247
248

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

        yield async_llm
    finally:
        if async_llm:
            async_llm.shutdown()
249
250


251
252
async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
253
254
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
255
256
257
        raise RequestValidationError(
            errors=["Unsupported Media Type: Only 'application/json' is allowed"]
        )
258
259


Ethan Xu's avatar
Ethan Xu committed
260
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
261

262

263
264
265
266
class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


267
def mount_metrics(app: FastAPI):
268
269
270
    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()
271

272
273
274
275
    # `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
276
277
278
279
280
281
282
283
284
285
    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
286
    ).add().instrument(app).expose(app, response_class=PrometheusResponse)
287
288
289

    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
290

291
    # Workaround for 307 Redirect for /metrics
292
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
293
    app.routes.append(metrics_route)
294
295


296
297
298
299
300
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


301
302
303
304
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


305
def responses(request: Request) -> OpenAIServingResponses | None:
306
307
308
    return request.app.state.openai_serving_responses


309
def chat(request: Request) -> OpenAIServingChat | None:
310
311
312
    return request.app.state.openai_serving_chat


313
def completion(request: Request) -> OpenAIServingCompletion | None:
314
315
316
    return request.app.state.openai_serving_completion


317
def pooling(request: Request) -> OpenAIServingPooling | None:
318
319
320
    return request.app.state.openai_serving_pooling


321
def embedding(request: Request) -> OpenAIServingEmbedding | None:
322
    return request.app.state.openai_serving_embedding
323
324


325
def score(request: Request) -> ServingScores | None:
326
327
328
    return request.app.state.openai_serving_scores


329
def classify(request: Request) -> ServingClassification | None:
330
331
332
    return request.app.state.openai_serving_classification


333
def rerank(request: Request) -> ServingScores | None:
334
    return request.app.state.openai_serving_scores
335
336


337
338
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
339
340


341
342
343
344
def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


345
346
347
348
def translation(request: Request) -> OpenAIServingTranslation:
    return request.app.state.openai_serving_translation


349
def engine_client(request: Request) -> EngineClient:
350
351
352
    return request.app.state.engine_client


353
354
@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
355
    """Health check."""
356
357
358
359
360
    try:
        await engine_client(raw_request).check_health()
        return Response(status_code=200)
    except EngineDeadError:
        return Response(status_code=503)
361
362


363
364
365
366
367
368
369
@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
370
    # - /v1/audio/translations
371
372
    # - /v1/embeddings
    # - /pooling
373
    # - /classify
374
375
376
377
378
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
379
    return JSONResponse(content={"server_load": request.app.state.server_load_metrics})
380
381


382
383
384
@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
385
386
387
388
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


389
390
391
392
393
394
395
396
397
398
@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},
    },
)
399
@with_cancellation
400
async def tokenize(request: TokenizeRequest, raw_request: Request):
401
402
    handler = tokenization(raw_request)

403
404
405
    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
406
407
408
        raise HTTPException(
            status_code=HTTPStatus.NOT_IMPLEMENTED.value, detail=str(e)
        ) from e
409
    except Exception as e:
410
411
412
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
413

414
    if isinstance(generator, ErrorResponse):
415
416
417
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
418
    elif isinstance(generator, TokenizeResponse):
419
420
        return JSONResponse(content=generator.model_dump())

421
422
    assert_never(generator)

423

424
425
426
427
428
429
430
431
432
@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},
    },
)
433
@with_cancellation
434
async def detokenize(request: DetokenizeRequest, raw_request: Request):
435
436
    handler = tokenization(raw_request)

437
438
439
440
441
    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
442
443
444
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
445

446
    if isinstance(generator, ErrorResponse):
447
448
449
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
450
    elif isinstance(generator, DetokenizeResponse):
451
452
        return JSONResponse(content=generator.model_dump())

453
454
    assert_never(generator)

455

456
457
def maybe_register_tokenizer_info_endpoint(args):
    """Conditionally register the tokenizer info endpoint if enabled."""
458
    if getattr(args, "enable_tokenizer_info_endpoint", False):
459
460
461
462
463

        @router.get("/tokenizer_info")
        async def get_tokenizer_info(raw_request: Request):
            """Get comprehensive tokenizer information."""
            result = await tokenization(raw_request).get_tokenizer_info()
464
465
466
467
468
469
            return JSONResponse(
                content=result.model_dump(),
                status_code=result.error.code
                if isinstance(result, ErrorResponse)
                else 200,
            )
470
471


Ethan Xu's avatar
Ethan Xu committed
472
@router.get("/v1/models")
473
async def show_available_models(raw_request: Request):
474
    handler = models(raw_request)
475

476
477
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
478
479


Ethan Xu's avatar
Ethan Xu committed
480
@router.get("/version")
481
async def show_version():
482
    ver = {"version": VLLM_VERSION}
483
484
485
    return JSONResponse(content=ver)


486
async def _convert_stream_to_sse_events(
487
    generator: AsyncGenerator[StreamingResponsesResponse, None],
488
) -> AsyncGenerator[str, None]:
489
490
    """Convert the generator to a stream of events in SSE format"""
    async for event in generator:
491
        event_type = getattr(event, "type", "unknown")
492
        # https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format
493
494
495
        event_data = (
            f"event: {event_type}\ndata: {event.model_dump_json(indent=None)}\n\n"
        )
496
497
498
        yield event_data


499
500
501
502
503
504
505
506
507
508
@router.post(
    "/v1/responses",
    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},
    },
)
509
510
511
512
513
@with_cancellation
async def create_responses(request: ResponsesRequest, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
514
515
            message="The model does not support Responses API"
        )
516
517
518
    try:
        generator = await handler.create_responses(request, raw_request)
    except Exception as e:
519
520
521
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
522
523

    if isinstance(generator, ErrorResponse):
524
525
526
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
527
528
    elif isinstance(generator, ResponsesResponse):
        return JSONResponse(content=generator.model_dump())
529

530
531
532
    return StreamingResponse(
        content=_convert_stream_to_sse_events(generator), media_type="text/event-stream"
    )
533
534
535


@router.get("/v1/responses/{response_id}")
536
537
538
async def retrieve_responses(
    response_id: str,
    raw_request: Request,
539
540
    starting_after: int | None = None,
    stream: bool | None = False,
541
):
542
543
544
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
545
546
            message="The model does not support Responses API"
        )
547

548
    try:
549
550
551
552
553
        response = await handler.retrieve_responses(
            response_id,
            starting_after=starting_after,
            stream=stream,
        )
554
    except Exception as e:
555
556
557
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
558
559

    if isinstance(response, ErrorResponse):
560
561
562
        return JSONResponse(
            content=response.model_dump(), status_code=response.error.code
        )
563
564
    elif isinstance(response, ResponsesResponse):
        return JSONResponse(content=response.model_dump())
565
566
567
    return StreamingResponse(
        content=_convert_stream_to_sse_events(response), media_type="text/event-stream"
    )
568
569
570
571
572
573
574


@router.post("/v1/responses/{response_id}/cancel")
async def cancel_responses(response_id: str, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
575
576
            message="The model does not support Responses API"
        )
577

578
579
580
    try:
        response = await handler.cancel_responses(response_id)
    except Exception as e:
581
582
583
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
584
585

    if isinstance(response, ErrorResponse):
586
587
588
        return JSONResponse(
            content=response.model_dump(), status_code=response.error.code
        )
589
590
591
    return JSONResponse(content=response.model_dump())


592
593
594
595
596
597
598
599
600
601
@router.post(
    "/v1/chat/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},
    },
)
602
@with_cancellation
603
@load_aware_call
604
async def create_chat_completion(request: ChatCompletionRequest, raw_request: Request):
605
606
607
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
608
609
            message="The model does not support Chat Completions API"
        )
610
611
612
    try:
        generator = await handler.create_chat_completion(request, raw_request)
    except Exception as e:
613
614
615
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
616
    if isinstance(generator, ErrorResponse):
617
618
619
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
620

621
    elif isinstance(generator, ChatCompletionResponse):
622
        return JSONResponse(content=generator.model_dump())
623

624
625
    return StreamingResponse(content=generator, media_type="text/event-stream")

626

627
628
629
630
631
632
633
634
635
636
@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},
    },
)
637
@with_cancellation
638
@load_aware_call
639
async def create_completion(request: CompletionRequest, raw_request: Request):
640
641
642
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
643
644
            message="The model does not support Completions API"
        )
645

646
647
648
    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
649
650
651
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value, detail=str(e)
        ) from e
652
    except Exception as e:
653
654
655
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
656

657
    if isinstance(generator, ErrorResponse):
658
659
660
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
661
    elif isinstance(generator, CompletionResponse):
662
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
663

664
665
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
666

667
668
669
670
671
672
673
674
@router.post(
    "/v1/embeddings",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
675
@with_cancellation
676
@load_aware_call
677
678
679
680
async def create_embedding(
    request: EmbeddingRequest,
    raw_request: Request,
):
681
682
    handler = embedding(raw_request)
    if handler is None:
683
        return base(raw_request).create_error_response(
684
685
            message="The model does not support Embeddings API"
        )
686

687
688
689
    try:
        generator = await handler.create_embedding(request, raw_request)
    except Exception as e:
690
691
692
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
693

694
    if isinstance(generator, ErrorResponse):
695
696
697
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
698
    elif isinstance(generator, EmbeddingResponse):
699
        return JSONResponse(content=generator.model_dump())
700
701
702
703
704
705
    elif isinstance(generator, EmbeddingBytesResponse):
        return StreamingResponse(
            content=generator.body,
            headers={"metadata": generator.metadata},
            media_type=generator.media_type,
        )
706

707
708
    assert_never(generator)

709

710
711
712
713
714
715
716
717
@router.post(
    "/pooling",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
718
@with_cancellation
719
@load_aware_call
720
721
722
723
async def create_pooling(request: PoolingRequest, raw_request: Request):
    handler = pooling(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
724
725
            message="The model does not support Pooling API"
        )
726
727
728
    try:
        generator = await handler.create_pooling(request, raw_request)
    except Exception as e:
729
730
731
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
732
    if isinstance(generator, ErrorResponse):
733
734
735
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
736
    elif isinstance(generator, (PoolingResponse, IOProcessorResponse)):
737
        return JSONResponse(content=generator.model_dump())
738
739
740
741
742
743
    elif isinstance(generator, PoolingBytesResponse):
        return StreamingResponse(
            content=generator.body,
            headers={"metadata": generator.metadata},
            media_type=generator.media_type,
        )
744
745
746
747

    assert_never(generator)


748
749
750
@router.post("/classify", dependencies=[Depends(validate_json_request)])
@with_cancellation
@load_aware_call
751
async def create_classify(request: ClassificationRequest, raw_request: Request):
752
753
754
    handler = classify(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
755
756
            message="The model does not support Classification API"
        )
757

758
759
760
    try:
        generator = await handler.create_classify(request, raw_request)
    except Exception as e:
761
762
763
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
764
    if isinstance(generator, ErrorResponse):
765
766
767
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
768
769
770
771
772
773
774

    elif isinstance(generator, ClassificationResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


775
776
777
778
779
780
781
782
@router.post(
    "/score",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
783
@with_cancellation
784
@load_aware_call
785
786
787
788
async def create_score(request: ScoreRequest, raw_request: Request):
    handler = score(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
789
790
            message="The model does not support Score API"
        )
791

792
793
794
    try:
        generator = await handler.create_score(request, raw_request)
    except Exception as e:
795
796
797
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
798
    if isinstance(generator, ErrorResponse):
799
800
801
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
802
803
804
805
806
807
    elif isinstance(generator, ScoreResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


808
809
810
811
812
813
814
815
@router.post(
    "/v1/score",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
816
@with_cancellation
817
@load_aware_call
818
819
820
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 "
821
822
        "have moved it to `/score`. Please update your client accordingly."
    )
823
824
825
826

    return await create_score(request, raw_request)


827
828
829
830
831
832
833
834
835
@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},
    },
)
836
@with_cancellation
837
@load_aware_call
838
839
840
async def create_transcriptions(
    raw_request: Request, request: Annotated[TranscriptionRequest, Form()]
):
841
842
843
    handler = transcription(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
844
845
            message="The model does not support Transcriptions API"
        )
846
847

    audio_data = await request.file.read()
848
    try:
849
        generator = await handler.create_transcription(audio_data, request, raw_request)
850
    except Exception as e:
851
852
853
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
854
855

    if isinstance(generator, ErrorResponse):
856
857
858
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
859
860
861
862
863
864
865

    elif isinstance(generator, TranscriptionResponse):
        return JSONResponse(content=generator.model_dump())

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


866
867
868
869
870
871
872
873
874
@router.post(
    "/v1/audio/translations",
    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},
    },
)
875
876
@with_cancellation
@load_aware_call
877
878
879
async def create_translations(
    request: Annotated[TranslationRequest, Form()], raw_request: Request
):
880
881
882
    handler = translation(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
883
884
            message="The model does not support Translations API"
        )
885
886

    audio_data = await request.file.read()
887
    try:
888
        generator = await handler.create_translation(audio_data, request, raw_request)
889
    except Exception as e:
890
891
892
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
893
894

    if isinstance(generator, ErrorResponse):
895
896
897
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
898
899
900
901
902
903
904

    elif isinstance(generator, TranslationResponse):
        return JSONResponse(content=generator.model_dump())

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


905
906
907
908
909
910
911
912
@router.post(
    "/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
913
@with_cancellation
914
@load_aware_call
915
916
917
918
async def do_rerank(request: RerankRequest, raw_request: Request):
    handler = rerank(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
919
920
            message="The model does not support Rerank (Score) API"
        )
921
922
923
    try:
        generator = await handler.do_rerank(request, raw_request)
    except Exception as e:
924
925
926
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
927
    if isinstance(generator, ErrorResponse):
928
929
930
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
931
932
933
934
935
936
    elif isinstance(generator, RerankResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


937
938
939
940
941
942
943
944
@router.post(
    "/v1/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
945
946
@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
947
    logger.warning_once(
948
        "To indicate that the rerank API is not part of the standard OpenAI"
949
        " API, we have located it at `/rerank`. Please update your client "
950
951
        "accordingly. (Note: Conforms to JinaAI rerank API)"
    )
952
953
954
955

    return await do_rerank(request, raw_request)


956
957
958
959
960
961
962
963
@router.post(
    "/v2/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
964
965
966
967
968
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


969
if envs.VLLM_SERVER_DEV_MODE:
970
971
972
973
    logger.warning(
        "SECURITY WARNING: Development endpoints are enabled! "
        "This should NOT be used in production!"
    )
974

975
976
    PydanticVllmConfig = pydantic.TypeAdapter(VllmConfig)

977
    @router.get("/server_info")
978
979
    async def show_server_info(
        raw_request: Request,
980
        config_format: Annotated[Literal["text", "json"], Query()] = "text",
981
982
983
    ):
        vllm_config: VllmConfig = raw_request.app.state.vllm_config
        server_info = {
984
985
986
            "vllm_config": str(vllm_config)
            if config_format == "text"
            else PydanticVllmConfig.dump_python(vllm_config, mode="json", fallback=str)
987
988
            # fallback=str is needed to handle e.g. torch.dtype
        }
989
990
        return JSONResponse(content=server_info)

991
992
993
994
995
996
    @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.
        """
997
998
999
1000
1001
1002
        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)
1003
1004
        return Response(status_code=200)

1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
    @router.post("/reset_mm_cache")
    async def reset_mm_cache(raw_request: Request):
        """
        Reset the multi-modal cache. Note that we currently do not check if the
        multi-modal cache is successfully reset in the API server.
        """
        logger.info("Resetting multi-modal cache...")
        await engine_client(raw_request).reset_mm_cache()
        return Response(status_code=200)

1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
    @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):
1026
1027
1028
1029
1030
1031
        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)
1032
1033
1034
1035
        # 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)

1036
1037
1038
1039
1040
1041
    @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})

1042
1043
1044
1045
1046
    @router.post("/collective_rpc")
    async def collective_rpc(raw_request: Request):
        try:
            body = await raw_request.json()
        except json.JSONDecodeError as e:
1047
1048
1049
1050
            raise HTTPException(
                status_code=HTTPStatus.BAD_REQUEST.value,
                detail=f"JSON decode error: {e}",
            ) from e
1051
1052
        method = body.get("method")
        if method is None:
1053
1054
1055
1056
            raise HTTPException(
                status_code=HTTPStatus.BAD_REQUEST.value,
                detail="Missing 'method' in request body",
            )
1057
        # For security reason, only serialized string args/kwargs are passed.
1058
        # User-defined `method` is responsible for deserialization if needed.
1059
1060
        args: list[str] = body.get("args", [])
        kwargs: dict[str, str] = body.get("kwargs", {})
1061
        timeout: float | None = body.get("timeout")
1062
        results = await engine_client(raw_request).collective_rpc(
1063
1064
            method=method, timeout=timeout, args=tuple(args), kwargs=kwargs
        )
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
        if results is None:
            return Response(status_code=200)
        response: list[Any] = []
        for result in results:
            if result is None or isinstance(result, (dict, list)):
                response.append(result)
            else:
                response.append(str(result))
        return JSONResponse(content={"results": response})

1075

1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
@router.post(
    "/scale_elastic_ep",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.OK.value: {"model": dict},
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.REQUEST_TIMEOUT.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
1086
1087
1088
1089
async def scale_elastic_ep(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
1090
        raise HTTPException(status_code=400, detail="Invalid JSON format") from e  # noqa: B904
1091
1092
1093
1094
1095

    new_data_parallel_size = body.get("new_data_parallel_size")
    drain_timeout = body.get("drain_timeout", 120)  # Default 2 minutes

    if new_data_parallel_size is None:
1096
1097
1098
        raise HTTPException(
            status_code=400, detail="new_data_parallel_size is required"
        )
1099

1100
    if not isinstance(new_data_parallel_size, int) or new_data_parallel_size <= 0:
1101
        raise HTTPException(
1102
1103
            status_code=400, detail="new_data_parallel_size must be a positive integer"
        )
1104
1105

    if not isinstance(drain_timeout, int) or drain_timeout <= 0:
1106
1107
1108
        raise HTTPException(
            status_code=400, detail="drain_timeout must be a positive integer"
        )
1109
1110
1111
1112
1113
1114
1115

    # Set scaling flag to prevent new requests
    global _scaling_elastic_ep
    _scaling_elastic_ep = True
    client = engine_client(raw_request)
    try:
        await client.scale_elastic_ep(new_data_parallel_size, drain_timeout)
1116
1117
1118
1119
1120
        return JSONResponse(
            {
                "message": f"Scaled to {new_data_parallel_size} data parallel engines",
            }
        )
1121
    except TimeoutError as e:
1122
1123
1124
1125
1126
        raise HTTPException(
            status_code=408,
            detail="Scale failed due to request drain timeout "
            f"after {drain_timeout} seconds",
        ) from e
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
    except Exception as e:
        logger.error("Scale failed: %s", e)
        raise HTTPException(status_code=500, detail="Scale failed") from e
    finally:
        _scaling_elastic_ep = False


@router.post("/is_scaling_elastic_ep")
async def is_scaling_elastic_ep(raw_request: Request):
    return JSONResponse({"is_scaling_elastic_ep": _scaling_elastic_ep})


1139
1140
1141
# TODO: RequestType = TypeForm[BaseModel] when recognized by type checkers
# (requires typing_extensions >= 4.13)
RequestType = Any
1142
GetHandlerFn = Callable[[Request], OpenAIServing | None]
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
EndpointFn = Callable[[RequestType, Request], Awaitable[Any]]

# NOTE: Items defined earlier take higher priority
INVOCATION_TYPES: list[tuple[RequestType, tuple[GetHandlerFn, EndpointFn]]] = [
    (ChatCompletionRequest, (chat, create_chat_completion)),
    (CompletionRequest, (completion, create_completion)),
    (EmbeddingRequest, (embedding, create_embedding)),
    (ClassificationRequest, (classify, create_classify)),
    (ScoreRequest, (score, create_score)),
    (RerankRequest, (rerank, do_rerank)),
    (PoolingRequest, (pooling, create_pooling)),
]

# NOTE: Construct the TypeAdapters only once
INVOCATION_VALIDATORS = [
    (pydantic.TypeAdapter(request_type), (get_handler, endpoint))
    for request_type, (get_handler, endpoint) in INVOCATION_TYPES
]


1163
1164
1165
1166
1167
1168
1169
1170
1171
@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},
    },
)
1172
async def invocations(raw_request: Request):
1173
    """For SageMaker, routes requests based on the request type."""
1174
1175
    try:
        body = await raw_request.json()
1176
    except json.JSONDecodeError as e:
1177
1178
1179
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value, detail=f"JSON decode error: {e}"
        ) from e
1180

1181
1182
1183
1184
1185
    valid_endpoints = [
        (validator, endpoint)
        for validator, (get_handler, endpoint) in INVOCATION_VALIDATORS
        if get_handler(raw_request) is not None
    ]
1186

1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
    for request_validator, endpoint in valid_endpoints:
        try:
            request = request_validator.validate_python(body)
        except pydantic.ValidationError:
            continue

        return await endpoint(request, raw_request)

    type_names = [
        t.__name__ if isinstance(t := validator._type, type) else str(t)
        for validator, _ in valid_endpoints
    ]
1199
    msg = f"Cannot find suitable handler for request. Expected one of: {type_names}"
1200
    res = base(raw_request).create_error_response(message=msg)
1201
    return JSONResponse(content=res.model_dump(), status_code=res.error.code)
1202
1203


1204
1205
1206
if envs.VLLM_TORCH_PROFILER_DIR:
    logger.warning(
        "Torch Profiler is enabled in the API server. This should ONLY be "
1207
1208
        "used for local development!"
    )
1209
1210

    @router.post("/start_profile")
1211
    async def start_profile(raw_request: Request):
1212
        logger.info("Starting profiler...")
1213
        await engine_client(raw_request).start_profile()
1214
1215
1216
1217
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
1218
    async def stop_profile(raw_request: Request):
1219
        logger.info("Stopping profiler...")
1220
        await engine_client(raw_request).stop_profile()
1221
1222
1223
1224
        logger.info("Profiler stopped.")
        return Response(status_code=200)


1225
1226
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
1227
        "LoRA dynamic loading & unloading is enabled in the API server. "
1228
1229
        "This should ONLY be used for local development!"
    )
1230

1231
1232
    @router.post("/v1/load_lora_adapter", dependencies=[Depends(validate_json_request)])
    async def load_lora_adapter(request: LoadLoRAAdapterRequest, raw_request: Request):
1233
1234
1235
        handler = models(raw_request)
        response = await handler.load_lora_adapter(request)
        if isinstance(response, ErrorResponse):
1236
1237
1238
            return JSONResponse(
                content=response.model_dump(), status_code=response.error.code
            )
1239
1240
1241

        return Response(status_code=200, content=response)

1242
1243
1244
1245
1246
1247
    @router.post(
        "/v1/unload_lora_adapter", dependencies=[Depends(validate_json_request)]
    )
    async def unload_lora_adapter(
        request: UnloadLoRAAdapterRequest, raw_request: Request
    ):
1248
1249
1250
        handler = models(raw_request)
        response = await handler.unload_lora_adapter(request)
        if isinstance(response, ErrorResponse):
1251
1252
1253
            return JSONResponse(
                content=response.model_dump(), status_code=response.error.code
            )
1254
1255
1256
1257

        return Response(status_code=200, content=response)


1258
def load_log_config(log_config_file: str | None) -> dict | None:
1259
1260
1261
1262
1263
1264
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
1265
1266
1267
        logger.warning(
            "Failed to load log config from file %s: error %s", log_config_file, e
        )
1268
1269
1270
        return None


1271
1272
1273
class AuthenticationMiddleware:
    """
    Pure ASGI middleware that authenticates each request by checking
1274
    if the Authorization Bearer token exists and equals anyof "{api_key}".
1275
1276
1277
1278
1279
1280
1281
1282

    Notes
    -----
    There are two cases in which authentication is skipped:
        1. The HTTP method is OPTIONS.
        2. The request path doesn't start with /v1 (e.g. /health).
    """

1283
    def __init__(self, app: ASGIApp, tokens: list[str]) -> None:
1284
        self.app = app
1285
        self.api_tokens = [hashlib.sha256(t.encode("utf-8")).digest() for t in tokens]
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302

    def verify_token(self, headers: Headers) -> bool:
        authorization_header_value = headers.get("Authorization")
        if not authorization_header_value:
            return False

        scheme, _, param = authorization_header_value.partition(" ")
        if scheme.lower() != "bearer":
            return False

        param_hash = hashlib.sha256(param.encode("utf-8")).digest()

        token_match = False
        for token_hash in self.api_tokens:
            token_match |= secrets.compare_digest(param_hash, token_hash)

        return token_match
1303

1304
1305
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http", "websocket") or scope["method"] == "OPTIONS":
1306
1307
1308
1309
1310
1311
1312
            # scope["type"] can be "lifespan" or "startup" for example,
            # in which case we don't need to do anything
            return self.app(scope, receive, send)
        root_path = scope.get("root_path", "")
        url_path = URL(scope=scope).path.removeprefix(root_path)
        headers = Headers(scope=scope)
        # Type narrow to satisfy mypy.
1313
        if url_path.startswith("/v1") and not self.verify_token(headers):
1314
            response = JSONResponse(content={"error": "Unauthorized"}, status_code=401)
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
            return response(scope, receive, send)
        return self.app(scope, receive, send)


class XRequestIdMiddleware:
    """
    Middleware the set's the X-Request-Id header for each response
    to a random uuid4 (hex) value if the header isn't already
    present in the request, otherwise use the provided request id.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

1329
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
        if scope["type"] not in ("http", "websocket"):
            return self.app(scope, receive, send)

        # Extract the request headers.
        request_headers = Headers(scope=scope)

        async def send_with_request_id(message: Message) -> None:
            """
            Custom send function to mutate the response headers
            and append X-Request-Id to it.
            """
            if message["type"] == "http.response.start":
                response_headers = MutableHeaders(raw=message["headers"])
1343
                request_id = request_headers.get("X-Request-Id", uuid.uuid4().hex)
1344
1345
1346
1347
1348
1349
                response_headers.append("X-Request-Id", request_id)
            await send(message)

        return self.app(scope, receive, send_with_request_id)


1350
1351
1352
1353
1354
1355
1356
1357
# Global variable to track scaling state
_scaling_elastic_ep = False


class ScalingMiddleware:
    """
    Middleware that checks if the model is currently scaling and
    returns a 503 Service Unavailable response if it is.
1358

1359
1360
1361
1362
1363
1364
1365
    This middleware applies to all HTTP requests and prevents
    processing when the model is in a scaling state.
    """

    def __init__(self, app: ASGIApp) -> None:
        self.app = app

1366
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
1367
1368
1369
1370
1371
1372
1373
        if scope["type"] != "http":
            return self.app(scope, receive, send)

        # Check global scaling state
        global _scaling_elastic_ep
        if _scaling_elastic_ep:
            # Return 503 Service Unavailable response
1374
1375
1376
1377
1378
1379
            response = JSONResponse(
                content={
                    "error": "The model is currently scaling. Please try again later."
                },
                status_code=503,
            )
1380
1381
1382
1383
1384
            return response(scope, receive, send)

        return self.app(scope, receive, send)


1385
1386
1387
1388
def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
1389
1390
1391
            ChatCompletionStreamResponse,
            CompletionStreamResponse,
        )
1392
1393

        # Try using Completion types for type-safe parsing
1394
1395
        if chunk_data.get("object") == "chat.completion.chunk":
            chat_response = ChatCompletionStreamResponse.model_validate(chunk_data)
1396
1397
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
1398
1399
1400
        elif chunk_data.get("object") == "text_completion":
            completion_response = CompletionStreamResponse.model_validate(chunk_data)
            if completion_response.choices and completion_response.choices[0].text:
1401
1402
1403
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
1404
1405
1406
1407
1408
1409
        if "choices" in chunk_data and chunk_data["choices"]:
            choice = chunk_data["choices"][0]
            if "delta" in choice and choice["delta"].get("content"):
                return choice["delta"]["content"]
            elif choice.get("text"):
                return choice["text"]
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
    return ""


class SSEDecoder:
    """Robust Server-Sent Events decoder for streaming responses."""

    def __init__(self):
        self.buffer = ""
        self.content_buffer = []

    def decode_chunk(self, chunk: bytes) -> list[dict]:
        """Decode a chunk of SSE data and return parsed events."""
        import json

        try:
1425
            chunk_str = chunk.decode("utf-8")
1426
1427
1428
1429
1430
1431
1432
1433
        except UnicodeDecodeError:
            # Skip malformed chunks
            return []

        self.buffer += chunk_str
        events = []

        # Process complete lines
1434
1435
1436
        while "\n" in self.buffer:
            line, self.buffer = self.buffer.split("\n", 1)
            line = line.rstrip("\r")  # Handle CRLF
1437

1438
            if line.startswith("data: "):
1439
                data_str = line[6:].strip()
1440
1441
                if data_str == "[DONE]":
                    events.append({"type": "done"})
1442
1443
1444
                elif data_str:
                    try:
                        event_data = json.loads(data_str)
1445
                        events.append({"type": "data", "data": event_data})
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
                    except json.JSONDecodeError:
                        # Skip malformed JSON
                        continue

        return events

    def extract_content(self, event_data: dict) -> str:
        """Extract content from event data."""
        return _extract_content_from_chunk(event_data)

    def add_content(self, content: str) -> None:
        """Add content to the buffer."""
        if content:
            self.content_buffer.append(content)

    def get_complete_content(self) -> str:
        """Get the complete buffered content."""
1463
        return "".join(self.content_buffer)
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483


def _log_streaming_response(response, response_body: list) -> None:
    """Log streaming response with robust SSE parsing."""
    from starlette.concurrency import iterate_in_threadpool

    sse_decoder = SSEDecoder()
    chunk_count = 0

    def buffered_iterator():
        nonlocal chunk_count

        for chunk in response_body:
            chunk_count += 1
            yield chunk

            # Parse SSE events from chunk
            events = sse_decoder.decode_chunk(chunk)

            for event in events:
1484
1485
                if event["type"] == "data":
                    content = sse_decoder.extract_content(event["data"])
1486
                    sse_decoder.add_content(content)
1487
                elif event["type"] == "done":
1488
1489
1490
1491
1492
1493
1494
1495
                    # Log complete content when done
                    full_content = sse_decoder.get_complete_content()
                    if full_content:
                        # Truncate if too long
                        if len(full_content) > 2048:
                            full_content = full_content[:2048] + ""
                            "...[truncated]"
                        logger.info(
1496
                            "response_body={streaming_complete: "
1497
                            "content='%s', chunks=%d}",
1498
1499
1500
                            full_content,
                            chunk_count,
                        )
1501
1502
                    else:
                        logger.info(
1503
1504
1505
                            "response_body={streaming_complete: no_content, chunks=%d}",
                            chunk_count,
                        )
1506
1507
1508
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
1509
    logger.info("response_body={streaming_started: chunks=%d}", len(response_body))
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520


def _log_non_streaming_response(response_body: list) -> None:
    """Log non-streaming response."""
    try:
        decoded_body = response_body[0].decode()
        logger.info("response_body={%s}", decoded_body)
    except UnicodeDecodeError:
        logger.info("response_body={<binary_data>}")


1521
def build_app(args: Namespace) -> FastAPI:
1522
    if args.disable_fastapi_docs:
1523
1524
1525
        app = FastAPI(
            openapi_url=None, docs_url=None, redoc_url=None, lifespan=lifespan
        )
1526
1527
    else:
        app = FastAPI(lifespan=lifespan)
Ethan Xu's avatar
Ethan Xu committed
1528
1529
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
1530

1531
1532
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
1533
1534
1535
1536
1537
1538
1539
1540
    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

1541
1542
    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
1543
        err = ErrorResponse(
1544
1545
1546
1547
1548
1549
            error=ErrorInfo(
                message=exc.detail,
                type=HTTPStatus(exc.status_code).phrase,
                code=exc.status_code,
            )
        )
1550
1551
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

Ethan Xu's avatar
Ethan Xu committed
1552
    @app.exception_handler(RequestValidationError)
1553
    async def validation_exception_handler(_: Request, exc: RequestValidationError):
1554
1555
1556
1557
1558
1559
1560
1561
        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

1562
1563
1564
1565
1566
1567
1568
1569
        err = ErrorResponse(
            error=ErrorInfo(
                message=message,
                type=HTTPStatus.BAD_REQUEST.phrase,
                code=HTTPStatus.BAD_REQUEST,
            )
        )
        return JSONResponse(err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)
Ethan Xu's avatar
Ethan Xu committed
1570

1571
    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
1572
1573
    if tokens := [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]:
        app.add_middleware(AuthenticationMiddleware, tokens=tokens)
1574

1575
    if args.enable_request_id_headers:
1576
        app.add_middleware(XRequestIdMiddleware)
1577

1578
1579
1580
    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

1581
    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
1582
1583
1584
1585
1586
        logger.warning(
            "CAUTION: Enabling log response in the API Server. "
            "This can include sensitive information and should be "
            "avoided in production."
        )
1587
1588
1589
1590

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
1591
            response_body = [section async for section in response.body_iterator]
1592
            response.body_iterator = iterate_in_threadpool(iter(response_body))
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
            # Check if this is a streaming response by looking at content-type
            content_type = response.headers.get("content-type", "")
            is_streaming = content_type == "text/event-stream; charset=utf-8"

            # Log response body based on type
            if not response_body:
                logger.info("response_body={<empty>}")
            elif is_streaming:
                _log_streaming_response(response, response_body)
            else:
                _log_non_streaming_response(response_body)
1604
            return response
1605

1606
1607
1608
1609
    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):
1610
            app.add_middleware(imported)  # type: ignore[arg-type]
1611
1612
1613
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
1614
1615
1616
            raise ValueError(
                f"Invalid middleware {middleware}. Must be a function or a class."
            )
1617

Ethan Xu's avatar
Ethan Xu committed
1618
1619
1620
    return app


1621
async def init_app_state(
1622
    engine_client: EngineClient,
1623
    state: State,
1624
    args: Namespace,
1625
) -> None:
1626
1627
    vllm_config = engine_client.vllm_config

1628
    if args.served_model_name is not None:
1629
        served_model_names = args.served_model_name
1630
    else:
1631
        served_model_names = [args.model]
1632

1633
    if args.enable_log_requests:
1634
        request_logger = RequestLogger(max_log_len=args.max_log_len)
1635
1636
    else:
        request_logger = None
1637

1638
    base_model_paths = [
1639
        BaseModelPath(name=name, model_path=args.model) for name in served_model_names
1640
1641
    ]

1642
    state.engine_client = engine_client
1643
    state.log_stats = not args.disable_log_stats
1644
    state.vllm_config = vllm_config
Ethan Xu's avatar
Ethan Xu committed
1645

1646
    supported_tasks = await engine_client.get_supported_tasks()
1647
    logger.info("Supported tasks: %s", supported_tasks)
1648

1649
1650
1651
    resolved_chat_template = await process_chat_template(
        args.chat_template, engine_client, vllm_config.model_config
    )
1652

1653
    if args.tool_server == "demo":
1654
        tool_server: ToolServer | None = DemoToolServer()
1655
1656
        assert isinstance(tool_server, DemoToolServer)
        await tool_server.init_and_validate()
1657
1658
1659
    elif args.tool_server:
        tool_server = MCPToolServer()
        await tool_server.add_tool_server(args.tool_server)
1660
1661
1662
    else:
        tool_server = None

1663
    # Merge default_mm_loras into the static lora_modules
1664
1665
1666
1667
1668
    default_mm_loras = (
        vllm_config.lora_config.default_mm_loras
        if vllm_config.lora_config is not None
        else {}
    )
1669

1670
1671
1672
1673
1674
1675
    default_mm_loras = (
        vllm_config.lora_config.default_mm_loras
        if vllm_config.lora_config is not None
        else {}
    )
    lora_modules = process_lora_modules(args.lora_modules, default_mm_loras)
1676

1677
    state.openai_serving_models = OpenAIServingModels(
1678
        engine_client=engine_client,
1679
        base_model_paths=base_model_paths,
1680
        lora_modules=lora_modules,
1681
    )
1682
    await state.openai_serving_models.init_static_loras()
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
    state.openai_serving_responses = (
        OpenAIServingResponses(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            chat_template=resolved_chat_template,
            chat_template_content_format=args.chat_template_content_format,
            return_tokens_as_token_ids=args.return_tokens_as_token_ids,
            enable_auto_tools=args.enable_auto_tool_choice,
            tool_parser=args.tool_call_parser,
            tool_server=tool_server,
            reasoning_parser=args.structured_outputs_config.reasoning_parser,
            enable_prompt_tokens_details=args.enable_prompt_tokens_details,
            enable_force_include_usage=args.enable_force_include_usage,
            enable_log_outputs=args.enable_log_outputs,
            log_error_stack=args.log_error_stack,
        )
        if "generate" in supported_tasks
        else None
    )
    state.openai_serving_chat = (
        OpenAIServingChat(
            engine_client,
            state.openai_serving_models,
            args.response_role,
            request_logger=request_logger,
            chat_template=resolved_chat_template,
            chat_template_content_format=args.chat_template_content_format,
            trust_request_chat_template=args.trust_request_chat_template,
            return_tokens_as_token_ids=args.return_tokens_as_token_ids,
            enable_auto_tools=args.enable_auto_tool_choice,
            exclude_tools_when_tool_choice_none=args.exclude_tools_when_tool_choice_none,
            tool_parser=args.tool_call_parser,
            reasoning_parser=args.structured_outputs_config.reasoning_parser,
            enable_prompt_tokens_details=args.enable_prompt_tokens_details,
            enable_force_include_usage=args.enable_force_include_usage,
            enable_log_outputs=args.enable_log_outputs,
            log_error_stack=args.log_error_stack,
        )
        if "generate" in supported_tasks
        else None
    )
    state.openai_serving_completion = (
        OpenAIServingCompletion(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            return_tokens_as_token_ids=args.return_tokens_as_token_ids,
            enable_prompt_tokens_details=args.enable_prompt_tokens_details,
            enable_force_include_usage=args.enable_force_include_usage,
            log_error_stack=args.log_error_stack,
        )
        if "generate" in supported_tasks
        else None
    )
    state.openai_serving_pooling = (
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
        (
            OpenAIServingPooling(
                engine_client,
                state.openai_serving_models,
                supported_tasks=supported_tasks,
                request_logger=request_logger,
                chat_template=resolved_chat_template,
                chat_template_content_format=args.chat_template_content_format,
                trust_request_chat_template=args.trust_request_chat_template,
                log_error_stack=args.log_error_stack,
            )
1750
        )
1751
        if ("token_embed" in supported_tasks or "token_classify" in supported_tasks)
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
        else None
    )
    state.openai_serving_embedding = (
        OpenAIServingEmbedding(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            chat_template=resolved_chat_template,
            chat_template_content_format=args.chat_template_content_format,
            trust_request_chat_template=args.trust_request_chat_template,
            log_error_stack=args.log_error_stack,
        )
        if "embed" in supported_tasks
        else None
    )
    state.openai_serving_classification = (
        ServingClassification(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
        )
        if "classify" in supported_tasks
        else None
    )
    state.openai_serving_scores = (
        ServingScores(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
        )
        if ("embed" in supported_tasks or "score" in supported_tasks)
        else None
    )
1787
    state.openai_serving_tokenization = OpenAIServingTokenization(
1788
        engine_client,
1789
        state.openai_serving_models,
1790
        request_logger=request_logger,
1791
1792
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1793
        trust_request_chat_template=args.trust_request_chat_template,
1794
        log_error_stack=args.log_error_stack,
1795
    )
1796
1797
1798
1799
1800
1801
    state.openai_serving_transcription = (
        OpenAIServingTranscription(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
1802
            enable_force_include_usage=args.enable_force_include_usage,
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
        )
        if "transcription" in supported_tasks
        else None
    )
    state.openai_serving_translation = (
        OpenAIServingTranslation(
            engine_client,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
1813
            enable_force_include_usage=args.enable_force_include_usage,
1814
1815
1816
1817
        )
        if "transcription" in supported_tasks
        else None
    )
1818

1819
1820
1821
    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

1822

1823
def create_server_socket(addr: tuple[str, int]) -> socket.socket:
1824
1825
1826
1827
1828
1829
    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)
1830
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
1831
1832
1833
1834
1835
    sock.bind(addr)

    return sock


1836
1837
1838
1839
1840
1841
def create_server_unix_socket(path: str) -> socket.socket:
    sock = socket.socket(family=socket.AF_UNIX, type=socket.SOCK_STREAM)
    sock.bind(path)
    return sock


1842
def validate_api_server_args(args):
1843
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
1844
1845
1846
1847
1848
    if args.enable_auto_tool_choice and args.tool_call_parser not in valid_tool_parses:
        raise KeyError(
            f"invalid tool call parser: {args.tool_call_parser} "
            f"(chose from {{ {','.join(valid_tool_parses)} }})"
        )
1849

1850
    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
1851
1852
1853
    if (
        reasoning_parser := args.structured_outputs_config.reasoning_parser
    ) and reasoning_parser not in valid_reasoning_parses:
1854
        raise KeyError(
1855
            f"invalid reasoning parser: {reasoning_parser} "
1856
1857
            f"(chose from {{ {','.join(valid_reasoning_parses)} }})"
        )
1858

1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871

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)

1872
1873
1874
    # 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
1875
1876
1877
1878
1879
    if args.uds:
        sock = create_server_unix_socket(args.uds)
    else:
        sock_addr = (args.host or "", args.port)
        sock = create_server_socket(sock_addr)
1880

1881
1882
1883
1884
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

1885
1886
1887
1888
1889
1890
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

1891
1892
1893
1894
1895
    if args.uds:
        listen_address = f"unix:{args.uds}"
    else:
        addr, port = sock_addr
        is_ssl = args.ssl_keyfile and args.ssl_certfile
1896
        host_part = f"[{addr}]" if is_valid_ipv6_address(addr) else addr or "0.0.0.0"
1897
        listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"
1898
1899
1900
1901
1902
    return listen_address, sock


async def run_server(args, **uvicorn_kwargs) -> None:
    """Run a single-worker API server."""
1903
1904

    # Add process-specific prefix to stdout and stderr.
1905
    decorate_logs("APIServer")
1906

1907
1908
1909
1910
    listen_address, sock = setup_server(args)
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)


1911
1912
1913
async def run_server_worker(
    listen_address, sock, args, client_config=None, **uvicorn_kwargs
) -> None:
1914
1915
1916
1917
1918
    """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)

1919
1920
1921
    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
1922
        uvicorn_kwargs["log_config"] = log_config
1923

1924
    async with build_async_engine_client(
1925
1926
        args,
        client_config=client_config,
1927
    ) as engine_client:
1928
        maybe_register_tokenizer_info_endpoint(args)
1929
1930
        app = build_app(args)

1931
        await init_app_state(engine_client, app.state, args)
1932

1933
1934
        logger.info(
            "Starting vLLM API server %d on %s",
1935
            engine_client.vllm_config.parallel_config._api_process_rank,
1936
1937
            listen_address,
        )
1938
1939
        shutdown_task = await serve_http(
            app,
1940
            sock=sock,
1941
            enable_ssl_refresh=args.enable_ssl_refresh,
1942
1943
1944
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
1945
1946
1947
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
1948
            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
1949
1950
1951
1952
            ssl_keyfile=args.ssl_keyfile,
            ssl_certfile=args.ssl_certfile,
            ssl_ca_certs=args.ssl_ca_certs,
            ssl_cert_reqs=args.ssl_cert_reqs,
1953
1954
            h11_max_incomplete_event_size=args.h11_max_incomplete_event_size,
            h11_max_header_count=args.h11_max_header_count,
1955
1956
1957
            **uvicorn_kwargs,
        )

1958
    # NB: Await server shutdown only after the backend context is exited
1959
1960
1961
1962
    try:
        await shutdown_task
    finally:
        sock.close()
1963

Ethan Xu's avatar
Ethan Xu committed
1964
1965
1966

if __name__ == "__main__":
    # NOTE(simon):
1967
1968
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
1969
    cli_env_setup()
Ethan Xu's avatar
Ethan Xu committed
1970
    parser = FlexibleArgumentParser(
1971
1972
        description="vLLM OpenAI-Compatible RESTful API server."
    )
Ethan Xu's avatar
Ethan Xu committed
1973
1974
    parser = make_arg_parser(parser)
    args = parser.parse_args()
1975
    validate_parsed_serve_args(args)
1976

1977
    uvloop.run(run_server(args))