api_server.py 68.7 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
20
from contextlib import asynccontextmanager
21
from http import HTTPStatus
22
from typing import Annotated, Any, Callable, Literal, Optional
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
45
46
47
48
from vllm.entrypoints.chat_utils import (
    load_chat_template,
    resolve_hf_chat_template,
    resolve_mistral_chat_template,
)
49
from vllm.entrypoints.launcher import serve_http
50
from vllm.entrypoints.logger import RequestLogger
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
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,
    EmbeddingRequest,
    EmbeddingResponse,
    ErrorInfo,
    ErrorResponse,
    IOProcessorResponse,
    LoadLoRAAdapterRequest,
    PoolingRequest,
    PoolingResponse,
    RerankRequest,
    RerankResponse,
    ResponsesRequest,
    ResponsesResponse,
    ScoreRequest,
    ScoreResponse,
    StreamingResponsesResponse,
    TokenizeRequest,
    TokenizeResponse,
    TranscriptionRequest,
    TranscriptionResponse,
    TranslationRequest,
    TranslationResponse,
    UnloadLoRAAdapterRequest,
)
84
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
85
from vllm.entrypoints.openai.serving_classification import ServingClassification
86
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
87
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
88
from vllm.entrypoints.openai.serving_engine import OpenAIServing
89
90
91
92
93
from vllm.entrypoints.openai.serving_models import (
    BaseModelPath,
    LoRAModulePath,
    OpenAIServingModels,
)
94
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
95
from vllm.entrypoints.openai.serving_responses import OpenAIServingResponses
96
from vllm.entrypoints.openai.serving_score import ServingScores
97
from vllm.entrypoints.openai.serving_tokenization import OpenAIServingTokenization
98
from vllm.entrypoints.openai.serving_transcription import (
99
100
101
    OpenAIServingTranscription,
    OpenAIServingTranslation,
)
102
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
103
104
105
106
107
108
109
from vllm.entrypoints.tool_server import DemoToolServer, MCPToolServer, ToolServer
from vllm.entrypoints.utils import (
    cli_env_setup,
    load_aware_call,
    log_non_default_args,
    with_cancellation,
)
110
from vllm.logger import init_logger
111
from vllm.reasoning import ReasoningParserManager
112
from vllm.transformers_utils.tokenizer import MistralTokenizer
yhu422's avatar
yhu422 committed
113
from vllm.usage.usage_lib import UsageContext
114
115
116
117
118
119
120
from vllm.utils import (
    Device,
    FlexibleArgumentParser,
    decorate_logs,
    is_valid_ipv6_address,
    set_ulimit,
)
121
from vllm.v1.engine.exceptions import EngineDeadError
122
from vllm.v1.metrics.prometheus import get_prometheus_registry
123
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
124

125
prometheus_multiproc_dir: tempfile.TemporaryDirectory
126

127
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
128
logger = init_logger("vllm.entrypoints.openai.api_server")
129

130
_running_tasks: set[asyncio.Task] = set()
131

132

133
@asynccontextmanager
134
async def lifespan(app: FastAPI):
135
136
    try:
        if app.state.log_stats:
137
            engine_client: EngineClient = app.state.engine_client
138
139
140

            async def _force_log():
                while True:
141
                    await asyncio.sleep(envs.VLLM_LOG_STATS_INTERVAL)
142
                    await engine_client.do_log_stats()
143
144
145
146
147
148

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
149
150
151
152
153

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
154
155
156
157
158
159
160
161
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
162
163


164
@asynccontextmanager
165
async def build_async_engine_client(
166
    args: Namespace,
167
168
169
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: Optional[bool] = None,
170
171
    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:
172
173
174
175
    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")
176
        multiprocessing.set_start_method("forkserver")
177
178
179
180
        multiprocessing.set_forkserver_preload(["vllm.v1.engine.async_llm"])
        forkserver.ensure_running()
        logger.debug("Forkserver setup complete!")

181
    # Context manager to handle engine_client lifecycle
182
183
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)
184
185
186
    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)
187

188
    if disable_frontend_multiprocessing is None:
189
        disable_frontend_multiprocessing = bool(args.disable_frontend_multiprocessing)
190

191
    async with build_async_engine_client_from_engine_args(
192
193
194
195
        engine_args,
        usage_context=usage_context,
        disable_frontend_multiprocessing=disable_frontend_multiprocessing,
        client_config=client_config,
196
    ) as engine:
197
198
199
200
201
202
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
203
204
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
205
    disable_frontend_multiprocessing: bool = False,
206
    client_config: Optional[dict[str, Any]] = None,
207
) -> AsyncIterator[EngineClient]:
208
    """
209
    Create EngineClient, either:
210
211
212
213
214
215
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

216
217
218
219
    # Create the EngineConfig (determines if we can use V1).
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    # V1 AsyncLLM.
220
    assert envs.VLLM_USE_V1
221

222
223
224
    if disable_frontend_multiprocessing:
        logger.warning(
            "V1 is enabled, but got --disable-frontend-multiprocessing. "
225
226
            "To disable frontend multiprocessing, set VLLM_USE_V1=0."
        )
227

228
    from vllm.v1.engine.async_llm import AsyncLLM
229

230
    async_llm: Optional[AsyncLLM] = None
231
232
233
234
235
236

    # 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)

237
238
239
240
241
242
243
244
    try:
        async_llm = AsyncLLM.from_vllm_config(
            vllm_config=vllm_config,
            usage_context=usage_context,
            enable_log_requests=engine_args.enable_log_requests,
            disable_log_stats=engine_args.disable_log_stats,
            client_addresses=client_config,
            client_count=client_count,
245
246
            client_index=client_index,
        )
247
248
249
250
251
252
253
254

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

        yield async_llm
    finally:
        if async_llm:
            async_llm.shutdown()
255
256


257
258
async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
259
260
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
261
262
263
        raise RequestValidationError(
            errors=["Unsupported Media Type: Only 'application/json' is allowed"]
        )
264
265


Ethan Xu's avatar
Ethan Xu committed
266
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
267

268

269
270
271
272
class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


273
def mount_metrics(app: FastAPI):
274
275
276
    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()
277

278
279
280
281
    # `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
282
283
284
285
286
287
288
289
290
291
    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
292
    ).add().instrument(app).expose(app, response_class=PrometheusResponse)
293
294
295

    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
296

297
    # Workaround for 307 Redirect for /metrics
298
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
299
    app.routes.append(metrics_route)
300
301


302
303
304
305
306
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


307
308
309
310
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


311
312
313
314
def responses(request: Request) -> Optional[OpenAIServingResponses]:
    return request.app.state.openai_serving_responses


315
def chat(request: Request) -> Optional[OpenAIServingChat]:
316
317
318
    return request.app.state.openai_serving_chat


319
def completion(request: Request) -> Optional[OpenAIServingCompletion]:
320
321
322
    return request.app.state.openai_serving_completion


323
324
325
326
def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


327
328
def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
329
330


331
def score(request: Request) -> Optional[ServingScores]:
332
333
334
    return request.app.state.openai_serving_scores


335
336
337
338
def classify(request: Request) -> Optional[ServingClassification]:
    return request.app.state.openai_serving_classification


339
340
def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores
341
342


343
344
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
345
346


347
348
349
350
def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


351
352
353
354
def translation(request: Request) -> OpenAIServingTranslation:
    return request.app.state.openai_serving_translation


355
def engine_client(request: Request) -> EngineClient:
356
357
358
    return request.app.state.engine_client


359
360
@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
361
    """Health check."""
362
363
364
365
366
    try:
        await engine_client(raw_request).check_health()
        return Response(status_code=200)
    except EngineDeadError:
        return Response(status_code=503)
367
368


369
370
371
372
373
374
375
@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
376
    # - /v1/audio/translations
377
378
    # - /v1/embeddings
    # - /pooling
379
    # - /classify
380
381
382
383
384
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
385
    return JSONResponse(content={"server_load": request.app.state.server_load_metrics})
386
387


388
389
390
@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
391
392
393
394
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


395
396
397
398
399
400
401
402
403
404
@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},
    },
)
405
@with_cancellation
406
async def tokenize(request: TokenizeRequest, raw_request: Request):
407
408
    handler = tokenization(raw_request)

409
410
411
    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
412
413
414
        raise HTTPException(
            status_code=HTTPStatus.NOT_IMPLEMENTED.value, detail=str(e)
        ) from e
415
    except Exception as e:
416
417
418
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
419

420
    if isinstance(generator, ErrorResponse):
421
422
423
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
424
    elif isinstance(generator, TokenizeResponse):
425
426
        return JSONResponse(content=generator.model_dump())

427
428
    assert_never(generator)

429

430
431
432
433
434
435
436
437
438
@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},
    },
)
439
@with_cancellation
440
async def detokenize(request: DetokenizeRequest, raw_request: Request):
441
442
    handler = tokenization(raw_request)

443
444
445
446
447
    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
448
449
450
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
451

452
    if isinstance(generator, ErrorResponse):
453
454
455
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
456
    elif isinstance(generator, DetokenizeResponse):
457
458
        return JSONResponse(content=generator.model_dump())

459
460
    assert_never(generator)

461

462
463
def maybe_register_tokenizer_info_endpoint(args):
    """Conditionally register the tokenizer info endpoint if enabled."""
464
    if getattr(args, "enable_tokenizer_info_endpoint", False):
465
466
467
468
469

        @router.get("/tokenizer_info")
        async def get_tokenizer_info(raw_request: Request):
            """Get comprehensive tokenizer information."""
            result = await tokenization(raw_request).get_tokenizer_info()
470
471
472
473
474
475
            return JSONResponse(
                content=result.model_dump(),
                status_code=result.error.code
                if isinstance(result, ErrorResponse)
                else 200,
            )
476
477


Ethan Xu's avatar
Ethan Xu committed
478
@router.get("/v1/models")
479
async def show_available_models(raw_request: Request):
480
    handler = models(raw_request)
481

482
483
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
484
485


Ethan Xu's avatar
Ethan Xu committed
486
@router.get("/version")
487
async def show_version():
488
    ver = {"version": VLLM_VERSION}
489
490
491
    return JSONResponse(content=ver)


492
async def _convert_stream_to_sse_events(
493
    generator: AsyncGenerator[StreamingResponsesResponse, None],
494
) -> AsyncGenerator[str, None]:
495
496
    """Convert the generator to a stream of events in SSE format"""
    async for event in generator:
497
        event_type = getattr(event, "type", "unknown")
498
        # https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format
499
500
501
        event_data = (
            f"event: {event_type}\ndata: {event.model_dump_json(indent=None)}\n\n"
        )
502
503
504
        yield event_data


505
506
507
508
509
510
511
512
513
514
@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},
    },
)
515
516
517
518
519
@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(
520
521
            message="The model does not support Responses API"
        )
522
523
524
    try:
        generator = await handler.create_responses(request, raw_request)
    except Exception as e:
525
526
527
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
528
529

    if isinstance(generator, ErrorResponse):
530
531
532
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
533
534
    elif isinstance(generator, ResponsesResponse):
        return JSONResponse(content=generator.model_dump())
535

536
537
538
    return StreamingResponse(
        content=_convert_stream_to_sse_events(generator), media_type="text/event-stream"
    )
539
540
541


@router.get("/v1/responses/{response_id}")
542
543
544
545
546
547
async def retrieve_responses(
    response_id: str,
    raw_request: Request,
    starting_after: Optional[int] = None,
    stream: Optional[bool] = False,
):
548
549
550
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
551
552
            message="The model does not support Responses API"
        )
553

554
    try:
555
556
557
558
559
        response = await handler.retrieve_responses(
            response_id,
            starting_after=starting_after,
            stream=stream,
        )
560
    except Exception as e:
561
562
563
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
564
565

    if isinstance(response, ErrorResponse):
566
567
568
        return JSONResponse(
            content=response.model_dump(), status_code=response.error.code
        )
569
570
    elif isinstance(response, ResponsesResponse):
        return JSONResponse(content=response.model_dump())
571
572
573
    return StreamingResponse(
        content=_convert_stream_to_sse_events(response), media_type="text/event-stream"
    )
574
575
576
577
578
579
580


@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(
581
582
            message="The model does not support Responses API"
        )
583

584
585
586
    try:
        response = await handler.cancel_responses(response_id)
    except Exception as e:
587
588
589
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
590
591

    if isinstance(response, ErrorResponse):
592
593
594
        return JSONResponse(
            content=response.model_dump(), status_code=response.error.code
        )
595
596
597
    return JSONResponse(content=response.model_dump())


598
599
600
601
602
603
604
605
606
607
@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},
    },
)
608
@with_cancellation
609
@load_aware_call
610
async def create_chat_completion(request: ChatCompletionRequest, raw_request: Request):
611
612
613
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
614
615
            message="The model does not support Chat Completions API"
        )
616
617
618
    try:
        generator = await handler.create_chat_completion(request, raw_request)
    except Exception as e:
619
620
621
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
622
    if isinstance(generator, ErrorResponse):
623
624
625
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
626

627
    elif isinstance(generator, ChatCompletionResponse):
628
        return JSONResponse(content=generator.model_dump())
629

630
631
    return StreamingResponse(content=generator, media_type="text/event-stream")

632

633
634
635
636
637
638
639
640
641
642
@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},
    },
)
643
@with_cancellation
644
@load_aware_call
645
async def create_completion(request: CompletionRequest, raw_request: Request):
646
647
648
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
649
650
            message="The model does not support Completions API"
        )
651

652
653
654
    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
655
656
657
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value, detail=str(e)
        ) from e
658
    except Exception as e:
659
660
661
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
662

663
    if isinstance(generator, ErrorResponse):
664
665
666
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
667
    elif isinstance(generator, CompletionResponse):
668
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
669

670
671
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
672

673
674
675
676
677
678
679
680
@router.post(
    "/v1/embeddings",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
681
@with_cancellation
682
@load_aware_call
683
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
684
685
    handler = embedding(raw_request)
    if handler is None:
686
        return base(raw_request).create_error_response(
687
688
            message="The model does not support Embeddings API"
        )
689

690
691
692
    try:
        generator = await handler.create_embedding(request, raw_request)
    except Exception as e:
693
694
695
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
696

697
    if isinstance(generator, ErrorResponse):
698
699
700
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
701
    elif isinstance(generator, EmbeddingResponse):
702
703
        return JSONResponse(content=generator.model_dump())

704
705
    assert_never(generator)

706

707
708
709
710
711
712
713
714
@router.post(
    "/pooling",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
715
@with_cancellation
716
@load_aware_call
717
718
719
720
async def create_pooling(request: PoolingRequest, raw_request: Request):
    handler = pooling(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
721
722
            message="The model does not support Pooling API"
        )
723
724
725
    try:
        generator = await handler.create_pooling(request, raw_request)
    except Exception as e:
726
727
728
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
729
    if isinstance(generator, ErrorResponse):
730
731
732
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
733
    elif isinstance(generator, (PoolingResponse, IOProcessorResponse)):
734
735
736
737
738
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


739
740
741
@router.post("/classify", dependencies=[Depends(validate_json_request)])
@with_cancellation
@load_aware_call
742
async def create_classify(request: ClassificationRequest, raw_request: Request):
743
744
745
    handler = classify(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
746
747
            message="The model does not support Classification API"
        )
748

749
750
751
    try:
        generator = await handler.create_classify(request, raw_request)
    except Exception as e:
752
753
754
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
755
    if isinstance(generator, ErrorResponse):
756
757
758
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
759
760
761
762
763
764
765

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

    assert_never(generator)


766
767
768
769
770
771
772
773
@router.post(
    "/score",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
774
@with_cancellation
775
@load_aware_call
776
777
778
779
async def create_score(request: ScoreRequest, raw_request: Request):
    handler = score(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
780
781
            message="The model does not support Score API"
        )
782

783
784
785
    try:
        generator = await handler.create_score(request, raw_request)
    except Exception as e:
786
787
788
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
789
    if isinstance(generator, ErrorResponse):
790
791
792
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
793
794
795
796
797
798
    elif isinstance(generator, ScoreResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


799
800
801
802
803
804
805
806
@router.post(
    "/v1/score",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
807
@with_cancellation
808
@load_aware_call
809
810
811
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 "
812
813
        "have moved it to `/score`. Please update your client accordingly."
    )
814
815
816
817

    return await create_score(request, raw_request)


818
819
820
821
822
823
824
825
826
@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},
    },
)
827
@with_cancellation
828
@load_aware_call
829
830
831
async def create_transcriptions(
    raw_request: Request, request: Annotated[TranscriptionRequest, Form()]
):
832
833
834
    handler = transcription(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
835
836
            message="The model does not support Transcriptions API"
        )
837
838

    audio_data = await request.file.read()
839
    try:
840
        generator = await handler.create_transcription(audio_data, request, raw_request)
841
    except Exception as e:
842
843
844
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
845
846

    if isinstance(generator, ErrorResponse):
847
848
849
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
850
851
852
853
854
855
856

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

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


857
858
859
860
861
862
863
864
865
@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},
    },
)
866
867
@with_cancellation
@load_aware_call
868
869
870
async def create_translations(
    request: Annotated[TranslationRequest, Form()], raw_request: Request
):
871
872
873
    handler = translation(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
874
875
            message="The model does not support Translations API"
        )
876
877

    audio_data = await request.file.read()
878
    try:
879
        generator = await handler.create_translation(audio_data, request, raw_request)
880
    except Exception as e:
881
882
883
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
884
885

    if isinstance(generator, ErrorResponse):
886
887
888
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
889
890
891
892
893
894
895

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

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


896
897
898
899
900
901
902
903
@router.post(
    "/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
904
@with_cancellation
905
@load_aware_call
906
907
908
909
async def do_rerank(request: RerankRequest, raw_request: Request):
    handler = rerank(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
910
911
            message="The model does not support Rerank (Score) API"
        )
912
913
914
    try:
        generator = await handler.do_rerank(request, raw_request)
    except Exception as e:
915
916
917
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
918
    if isinstance(generator, ErrorResponse):
919
920
921
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
922
923
924
925
926
927
    elif isinstance(generator, RerankResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


928
929
930
931
932
933
934
935
@router.post(
    "/v1/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
936
937
@with_cancellation
async def do_rerank_v1(request: RerankRequest, raw_request: Request):
938
    logger.warning_once(
939
        "To indicate that the rerank API is not part of the standard OpenAI"
940
        " API, we have located it at `/rerank`. Please update your client "
941
942
        "accordingly. (Note: Conforms to JinaAI rerank API)"
    )
943
944
945
946

    return await do_rerank(request, raw_request)


947
948
949
950
951
952
953
954
@router.post(
    "/v2/rerank",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
955
956
957
958
959
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


960
if envs.VLLM_SERVER_DEV_MODE:
961
962
963
964
    logger.warning(
        "SECURITY WARNING: Development endpoints are enabled! "
        "This should NOT be used in production!"
    )
965

966
967
    PydanticVllmConfig = pydantic.TypeAdapter(VllmConfig)

968
    @router.get("/server_info")
969
970
    async def show_server_info(
        raw_request: Request,
971
        config_format: Annotated[Literal["text", "json"], Query()] = "text",
972
973
974
    ):
        vllm_config: VllmConfig = raw_request.app.state.vllm_config
        server_info = {
975
976
977
            "vllm_config": str(vllm_config)
            if config_format == "text"
            else PydanticVllmConfig.dump_python(vllm_config, mode="json", fallback=str)
978
979
            # fallback=str is needed to handle e.g. torch.dtype
        }
980
981
        return JSONResponse(content=server_info)

982
983
984
985
986
987
    @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.
        """
988
989
990
991
992
993
        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)
994
995
        return Response(status_code=200)

996
997
998
999
1000
1001
1002
1003
1004
1005
1006
    @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):
1007
1008
1009
1010
1011
1012
        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)
1013
1014
1015
1016
        # 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)

1017
1018
1019
1020
1021
1022
    @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})

1023
1024
1025
1026
1027
    @router.post("/collective_rpc")
    async def collective_rpc(raw_request: Request):
        try:
            body = await raw_request.json()
        except json.JSONDecodeError as e:
1028
1029
1030
1031
            raise HTTPException(
                status_code=HTTPStatus.BAD_REQUEST.value,
                detail=f"JSON decode error: {e}",
            ) from e
1032
1033
        method = body.get("method")
        if method is None:
1034
1035
1036
1037
            raise HTTPException(
                status_code=HTTPStatus.BAD_REQUEST.value,
                detail="Missing 'method' in request body",
            )
1038
        # For security reason, only serialized string args/kwargs are passed.
1039
        # User-defined `method` is responsible for deserialization if needed.
1040
1041
1042
1043
        args: list[str] = body.get("args", [])
        kwargs: dict[str, str] = body.get("kwargs", {})
        timeout: Optional[float] = body.get("timeout")
        results = await engine_client(raw_request).collective_rpc(
1044
1045
            method=method, timeout=timeout, args=tuple(args), kwargs=kwargs
        )
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
        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})

1056

1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
@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},
    },
)
1067
1068
1069
1070
async def scale_elastic_ep(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
1071
        raise HTTPException(status_code=400, detail="Invalid JSON format") from e  # noqa: B904
1072
1073
1074
1075
1076

    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:
1077
1078
1079
        raise HTTPException(
            status_code=400, detail="new_data_parallel_size is required"
        )
1080

1081
    if not isinstance(new_data_parallel_size, int) or new_data_parallel_size <= 0:
1082
        raise HTTPException(
1083
1084
            status_code=400, detail="new_data_parallel_size must be a positive integer"
        )
1085
1086

    if not isinstance(drain_timeout, int) or drain_timeout <= 0:
1087
1088
1089
        raise HTTPException(
            status_code=400, detail="drain_timeout must be a positive integer"
        )
1090
1091
1092
1093
1094
1095
1096

    # 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)
1097
1098
1099
1100
1101
        return JSONResponse(
            {
                "message": f"Scaled to {new_data_parallel_size} data parallel engines",
            }
        )
1102
    except TimeoutError as e:
1103
1104
1105
1106
1107
        raise HTTPException(
            status_code=408,
            detail="Scale failed due to request drain timeout "
            f"after {drain_timeout} seconds",
        ) from e
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
    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})


1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
# TODO: RequestType = TypeForm[BaseModel] when recognized by type checkers
# (requires typing_extensions >= 4.13)
RequestType = Any
GetHandlerFn = Callable[[Request], Optional[OpenAIServing]]
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
]


1144
1145
1146
1147
1148
1149
1150
1151
1152
@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},
    },
)
1153
async def invocations(raw_request: Request):
1154
    """For SageMaker, routes requests based on the request type."""
1155
1156
    try:
        body = await raw_request.json()
1157
    except json.JSONDecodeError as e:
1158
1159
1160
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value, detail=f"JSON decode error: {e}"
        ) from e
1161

1162
1163
1164
1165
1166
    valid_endpoints = [
        (validator, endpoint)
        for validator, (get_handler, endpoint) in INVOCATION_VALIDATORS
        if get_handler(raw_request) is not None
    ]
1167

1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
    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
    ]
1180
    msg = f"Cannot find suitable handler for request. Expected one of: {type_names}"
1181
    res = base(raw_request).create_error_response(message=msg)
1182
    return JSONResponse(content=res.model_dump(), status_code=res.error.code)
1183
1184


1185
1186
1187
if envs.VLLM_TORCH_PROFILER_DIR:
    logger.warning(
        "Torch Profiler is enabled in the API server. This should ONLY be "
1188
1189
        "used for local development!"
    )
1190
1191

    @router.post("/start_profile")
1192
    async def start_profile(raw_request: Request):
1193
        logger.info("Starting profiler...")
1194
        await engine_client(raw_request).start_profile()
1195
1196
1197
1198
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
1199
    async def stop_profile(raw_request: Request):
1200
        logger.info("Stopping profiler...")
1201
        await engine_client(raw_request).stop_profile()
1202
1203
1204
1205
        logger.info("Profiler stopped.")
        return Response(status_code=200)


1206
1207
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
1208
        "LoRA dynamic loading & unloading is enabled in the API server. "
1209
1210
        "This should ONLY be used for local development!"
    )
1211

1212
1213
    @router.post("/v1/load_lora_adapter", dependencies=[Depends(validate_json_request)])
    async def load_lora_adapter(request: LoadLoRAAdapterRequest, raw_request: Request):
1214
1215
1216
        handler = models(raw_request)
        response = await handler.load_lora_adapter(request)
        if isinstance(response, ErrorResponse):
1217
1218
1219
            return JSONResponse(
                content=response.model_dump(), status_code=response.error.code
            )
1220
1221
1222

        return Response(status_code=200, content=response)

1223
1224
1225
1226
1227
1228
    @router.post(
        "/v1/unload_lora_adapter", dependencies=[Depends(validate_json_request)]
    )
    async def unload_lora_adapter(
        request: UnloadLoRAAdapterRequest, raw_request: Request
    ):
1229
1230
1231
        handler = models(raw_request)
        response = await handler.unload_lora_adapter(request)
        if isinstance(response, ErrorResponse):
1232
1233
1234
            return JSONResponse(
                content=response.model_dump(), status_code=response.error.code
            )
1235
1236
1237
1238

        return Response(status_code=200, content=response)


1239
1240
1241
1242
1243
1244
1245
def load_log_config(log_config_file: Optional[str]) -> Optional[dict]:
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
1246
1247
1248
        logger.warning(
            "Failed to load log config from file %s: error %s", log_config_file, e
        )
1249
1250
1251
        return None


1252
1253
1254
class AuthenticationMiddleware:
    """
    Pure ASGI middleware that authenticates each request by checking
1255
    if the Authorization Bearer token exists and equals anyof "{api_key}".
1256
1257
1258
1259
1260
1261
1262
1263

    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).
    """

1264
    def __init__(self, app: ASGIApp, tokens: list[str]) -> None:
1265
        self.app = app
1266
        self.api_tokens = [hashlib.sha256(t.encode("utf-8")).digest() for t in tokens]
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283

    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
1284

1285
1286
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http", "websocket") or scope["method"] == "OPTIONS":
1287
1288
1289
1290
1291
1292
1293
            # 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.
1294
        if url_path.startswith("/v1") and not self.verify_token(headers):
1295
            response = JSONResponse(content={"error": "Unauthorized"}, status_code=401)
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
            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

1310
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
        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"])
1324
                request_id = request_headers.get("X-Request-Id", uuid.uuid4().hex)
1325
1326
1327
1328
1329
1330
                response_headers.append("X-Request-Id", request_id)
            await send(message)

        return self.app(scope, receive, send_with_request_id)


1331
1332
1333
1334
1335
1336
1337
1338
# 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.
1339

1340
1341
1342
1343
1344
1345
1346
    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

1347
    def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]:
1348
1349
1350
1351
1352
1353
1354
        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
1355
1356
1357
1358
1359
1360
            response = JSONResponse(
                content={
                    "error": "The model is currently scaling. Please try again later."
                },
                status_code=503,
            )
1361
1362
1363
1364
1365
            return response(scope, receive, send)

        return self.app(scope, receive, send)


1366
1367
1368
1369
def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
1370
1371
1372
            ChatCompletionStreamResponse,
            CompletionStreamResponse,
        )
1373
1374

        # Try using Completion types for type-safe parsing
1375
1376
        if chunk_data.get("object") == "chat.completion.chunk":
            chat_response = ChatCompletionStreamResponse.model_validate(chunk_data)
1377
1378
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
1379
1380
1381
        elif chunk_data.get("object") == "text_completion":
            completion_response = CompletionStreamResponse.model_validate(chunk_data)
            if completion_response.choices and completion_response.choices[0].text:
1382
1383
1384
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
1385
1386
1387
1388
1389
1390
        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"]
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
    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:
1406
            chunk_str = chunk.decode("utf-8")
1407
1408
1409
1410
1411
1412
1413
1414
        except UnicodeDecodeError:
            # Skip malformed chunks
            return []

        self.buffer += chunk_str
        events = []

        # Process complete lines
1415
1416
1417
        while "\n" in self.buffer:
            line, self.buffer = self.buffer.split("\n", 1)
            line = line.rstrip("\r")  # Handle CRLF
1418

1419
            if line.startswith("data: "):
1420
                data_str = line[6:].strip()
1421
1422
                if data_str == "[DONE]":
                    events.append({"type": "done"})
1423
1424
1425
                elif data_str:
                    try:
                        event_data = json.loads(data_str)
1426
                        events.append({"type": "data", "data": event_data})
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
                    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."""
1444
        return "".join(self.content_buffer)
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464


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:
1465
1466
                if event["type"] == "data":
                    content = sse_decoder.extract_content(event["data"])
1467
                    sse_decoder.add_content(content)
1468
                elif event["type"] == "done":
1469
1470
1471
1472
1473
1474
1475
1476
                    # 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(
1477
                            "response_body={streaming_complete: "
1478
                            "content='%s', chunks=%d}",
1479
1480
1481
                            full_content,
                            chunk_count,
                        )
1482
1483
                    else:
                        logger.info(
1484
1485
1486
                            "response_body={streaming_complete: no_content, chunks=%d}",
                            chunk_count,
                        )
1487
1488
1489
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
1490
    logger.info("response_body={streaming_started: chunks=%d}", len(response_body))
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501


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>}")


1502
def build_app(args: Namespace) -> FastAPI:
1503
    if args.disable_fastapi_docs:
1504
1505
1506
        app = FastAPI(
            openapi_url=None, docs_url=None, redoc_url=None, lifespan=lifespan
        )
1507
1508
    else:
        app = FastAPI(lifespan=lifespan)
Ethan Xu's avatar
Ethan Xu committed
1509
1510
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
1511

1512
1513
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
1514
1515
1516
1517
1518
1519
1520
1521
    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

1522
1523
    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
1524
        err = ErrorResponse(
1525
1526
1527
1528
1529
1530
            error=ErrorInfo(
                message=exc.detail,
                type=HTTPStatus(exc.status_code).phrase,
                code=exc.status_code,
            )
        )
1531
1532
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

Ethan Xu's avatar
Ethan Xu committed
1533
    @app.exception_handler(RequestValidationError)
1534
    async def validation_exception_handler(_: Request, exc: RequestValidationError):
1535
1536
1537
1538
1539
1540
1541
1542
        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

1543
1544
1545
1546
1547
1548
1549
1550
        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
1551

1552
    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
1553
1554
    if tokens := [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]:
        app.add_middleware(AuthenticationMiddleware, tokens=tokens)
1555

1556
    if args.enable_request_id_headers:
1557
        app.add_middleware(XRequestIdMiddleware)
1558

1559
1560
1561
    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

1562
    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
1563
1564
1565
1566
1567
        logger.warning(
            "CAUTION: Enabling log response in the API Server. "
            "This can include sensitive information and should be "
            "avoided in production."
        )
1568
1569
1570
1571

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
1572
            response_body = [section async for section in response.body_iterator]
1573
            response.body_iterator = iterate_in_threadpool(iter(response_body))
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
            # 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)
1585
            return response
1586

1587
1588
1589
1590
    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):
1591
            app.add_middleware(imported)  # type: ignore[arg-type]
1592
1593
1594
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
1595
1596
1597
            raise ValueError(
                f"Invalid middleware {middleware}. Must be a function or a class."
            )
1598

Ethan Xu's avatar
Ethan Xu committed
1599
1600
1601
    return app


1602
async def init_app_state(
1603
    engine_client: EngineClient,
1604
    vllm_config: VllmConfig,
1605
    state: State,
1606
    args: Namespace,
1607
) -> None:
1608
    if args.served_model_name is not None:
1609
        served_model_names = args.served_model_name
1610
    else:
1611
        served_model_names = [args.model]
1612

1613
    if args.enable_log_requests:
1614
        request_logger = RequestLogger(max_log_len=args.max_log_len)
1615
1616
    else:
        request_logger = None
1617

1618
    base_model_paths = [
1619
        BaseModelPath(name=name, model_path=args.model) for name in served_model_names
1620
1621
    ]

1622
    state.engine_client = engine_client
1623
    state.log_stats = not args.disable_log_stats
1624
1625
    state.vllm_config = vllm_config
    model_config = vllm_config.model_config
Ethan Xu's avatar
Ethan Xu committed
1626

1627
    supported_tasks = await engine_client.get_supported_tasks()
1628
1629
1630

    logger.info("Supported_tasks: %s", supported_tasks)

1631
    resolved_chat_template = load_chat_template(args.chat_template)
1632
    if resolved_chat_template is not None:
1633
1634
1635
1636
1637
1638
        # 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(
1639
1640
                chat_template=resolved_chat_template
            )
1641
1642
        else:
            hf_chat_template = resolve_hf_chat_template(
1643
                tokenizer=tokenizer,
1644
1645
                chat_template=None,
                tools=None,
1646
                model_config=vllm_config.model_config,
1647
            )
1648
1649
1650
1651
1652
1653

            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.",
1654
1655
1656
                    resolved_chat_template,
                    args.model,
                )
1657

1658
1659
    if args.tool_server == "demo":
        tool_server: Optional[ToolServer] = DemoToolServer()
1660
1661
        assert isinstance(tool_server, DemoToolServer)
        await tool_server.init_and_validate()
1662
1663
1664
    elif args.tool_server:
        tool_server = MCPToolServer()
        await tool_server.add_tool_server(args.tool_server)
1665
1666
1667
    else:
        tool_server = None

1668
    # Merge default_mm_loras into the static lora_modules
1669
1670
1671
1672
1673
    default_mm_loras = (
        vllm_config.lora_config.default_mm_loras
        if vllm_config.lora_config is not None
        else {}
    )
1674
1675
1676
1677
1678
1679
1680

    lora_modules = args.lora_modules
    if default_mm_loras:
        default_mm_lora_paths = [
            LoRAModulePath(
                name=modality,
                path=lora_path,
1681
1682
            )
            for modality, lora_path in default_mm_loras.items()
1683
1684
1685
1686
1687
1688
        ]
        if args.lora_modules is None:
            lora_modules = default_mm_lora_paths
        else:
            lora_modules += default_mm_lora_paths

1689
    state.openai_serving_models = OpenAIServingModels(
1690
        engine_client=engine_client,
1691
1692
        model_config=model_config,
        base_model_paths=base_model_paths,
1693
        lora_modules=lora_modules,
1694
    )
1695
    await state.openai_serving_models.init_static_loras()
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
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
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
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
    state.openai_serving_responses = (
        OpenAIServingResponses(
            engine_client,
            model_config,
            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,
            model_config,
            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,
            model_config,
            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 = (
        OpenAIServingPooling(
            engine_client,
            vllm_config,
            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 "encode" in supported_tasks
        else None
    )
    state.openai_serving_embedding = (
        OpenAIServingEmbedding(
            engine_client,
            model_config,
            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,
            model_config,
            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,
            model_config,
            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
    )
1804
    state.openai_serving_tokenization = OpenAIServingTokenization(
1805
        engine_client,
1806
        model_config,
1807
        state.openai_serving_models,
1808
        request_logger=request_logger,
1809
1810
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1811
        trust_request_chat_template=args.trust_request_chat_template,
1812
        log_error_stack=args.log_error_stack,
1813
    )
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
    state.openai_serving_transcription = (
        OpenAIServingTranscription(
            engine_client,
            model_config,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
        )
        if "transcription" in supported_tasks
        else None
    )
    state.openai_serving_translation = (
        OpenAIServingTranslation(
            engine_client,
            model_config,
            state.openai_serving_models,
            request_logger=request_logger,
            log_error_stack=args.log_error_stack,
        )
        if "transcription" in supported_tasks
        else None
    )
1836

1837
1838
1839
    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

1840

1841
def create_server_socket(addr: tuple[str, int]) -> socket.socket:
1842
1843
1844
1845
1846
1847
    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)
1848
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
1849
1850
1851
1852
1853
    sock.bind(addr)

    return sock


1854
1855
1856
1857
1858
1859
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


1860
def validate_api_server_args(args):
1861
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
1862
1863
1864
1865
1866
    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)} }})"
        )
1867

1868
    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
1869
1870
1871
    if (
        reasoning_parser := args.structured_outputs_config.reasoning_parser
    ) and reasoning_parser not in valid_reasoning_parses:
1872
        raise KeyError(
1873
            f"invalid reasoning parser: {reasoning_parser} "
1874
1875
            f"(chose from {{ {','.join(valid_reasoning_parses)} }})"
        )
1876

1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889

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)

1890
1891
1892
    # 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
1893
1894
1895
1896
1897
    if args.uds:
        sock = create_server_unix_socket(args.uds)
    else:
        sock_addr = (args.host or "", args.port)
        sock = create_server_socket(sock_addr)
1898

1899
1900
1901
1902
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

1903
1904
1905
1906
1907
1908
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

1909
1910
1911
1912
1913
    if args.uds:
        listen_address = f"unix:{args.uds}"
    else:
        addr, port = sock_addr
        is_ssl = args.ssl_keyfile and args.ssl_certfile
1914
        host_part = f"[{addr}]" if is_valid_ipv6_address(addr) else addr or "0.0.0.0"
1915
        listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"
1916
1917
1918
1919
1920
    return listen_address, sock


async def run_server(args, **uvicorn_kwargs) -> None:
    """Run a single-worker API server."""
1921
1922

    # Add process-specific prefix to stdout and stderr.
1923
    decorate_logs("APIServer")
1924

1925
1926
1927
1928
    listen_address, sock = setup_server(args)
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)


1929
1930
1931
async def run_server_worker(
    listen_address, sock, args, client_config=None, **uvicorn_kwargs
) -> None:
1932
1933
1934
1935
1936
    """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)

1937
1938
1939
    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
1940
        uvicorn_kwargs["log_config"] = log_config
1941

1942
    async with build_async_engine_client(
1943
1944
        args,
        client_config=client_config,
1945
    ) as engine_client:
1946
        maybe_register_tokenizer_info_endpoint(args)
1947
1948
        app = build_app(args)

1949
1950
        vllm_config = await engine_client.get_vllm_config()
        await init_app_state(engine_client, vllm_config, app.state, args)
1951

1952
1953
1954
1955
1956
        logger.info(
            "Starting vLLM API server %d on %s",
            vllm_config.parallel_config._api_process_rank,
            listen_address,
        )
1957
1958
        shutdown_task = await serve_http(
            app,
1959
            sock=sock,
1960
            enable_ssl_refresh=args.enable_ssl_refresh,
1961
1962
1963
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
1964
1965
1966
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
1967
            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
1968
1969
1970
1971
            ssl_keyfile=args.ssl_keyfile,
            ssl_certfile=args.ssl_certfile,
            ssl_ca_certs=args.ssl_ca_certs,
            ssl_cert_reqs=args.ssl_cert_reqs,
1972
1973
            h11_max_incomplete_event_size=args.h11_max_incomplete_event_size,
            h11_max_header_count=args.h11_max_header_count,
1974
1975
1976
            **uvicorn_kwargs,
        )

1977
    # NB: Await server shutdown only after the backend context is exited
1978
1979
1980
1981
    try:
        await shutdown_task
    finally:
        sock.close()
1982

Ethan Xu's avatar
Ethan Xu committed
1983
1984
1985

if __name__ == "__main__":
    # NOTE(simon):
1986
1987
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
1988
    cli_env_setup()
Ethan Xu's avatar
Ethan Xu committed
1989
    parser = FlexibleArgumentParser(
1990
1991
        description="vLLM OpenAI-Compatible RESTful API server."
    )
Ethan Xu's avatar
Ethan Xu committed
1992
1993
    parser = make_arg_parser(parser)
    args = parser.parse_args()
1994
    validate_parsed_serve_args(args)
1995

1996
    uvloop.run(run_server(args))