"vscode:/vscode.git/clone" did not exist on "67e02fa8a405e1e1df0eb7428ad45eed20b0934b"
api_server.py 69.3 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 atexit
6
import gc
7
8
import importlib
import inspect
9
import json
10
import multiprocessing
11
import os
12
import signal
13
import socket
14
import tempfile
15
import uuid
16
from argparse import Namespace
17
from collections.abc import AsyncIterator, Awaitable
18
from contextlib import asynccontextmanager
19
from functools import partial
20
from http import HTTPStatus
21
from typing import Annotated, Any, Callable, Optional
22

23
import prometheus_client
24
import pydantic
25
import regex as re
26
import uvloop
27
from fastapi import APIRouter, Depends, FastAPI, Form, HTTPException, Request
Zhuohan Li's avatar
Zhuohan Li committed
28
29
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
30
from fastapi.responses import JSONResponse, Response, StreamingResponse
31
32
from prometheus_client import make_asgi_app
from prometheus_fastapi_instrumentator import Instrumentator
33
from starlette.concurrency import iterate_in_threadpool
34
from starlette.datastructures import URL, Headers, MutableHeaders, State
35
from starlette.routing import Mount
36
from starlette.types import ASGIApp, Message, Receive, Scope, Send
37
from typing_extensions import assert_never
Zhuohan Li's avatar
Zhuohan Li committed
38

39
import vllm.envs as envs
40
from vllm.config import VllmConfig
Woosuk Kwon's avatar
Woosuk Kwon committed
41
from vllm.engine.arg_utils import AsyncEngineArgs
42
from vllm.engine.async_llm_engine import AsyncLLMEngine  # type: ignore
43
44
45
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.engine.multiprocessing.engine import run_mp_engine
from vllm.engine.protocol import EngineClient
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
from vllm.entrypoints.openai.cli_args import (log_non_default_args,
                                              make_arg_parser,
53
                                              validate_parsed_serve_args)
54
55
# yapf conflicts with isort for this block
# yapf: disable
56
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
57
                                              ChatCompletionResponse,
58
59
                                              ClassificationRequest,
                                              ClassificationResponse,
60
                                              CompletionRequest,
61
                                              CompletionResponse,
62
63
                                              DetokenizeRequest,
                                              DetokenizeResponse,
64
                                              EmbeddingRequest,
65
                                              EmbeddingResponse, ErrorResponse,
66
                                              LoadLoRAAdapterRequest,
67
                                              PoolingRequest, PoolingResponse,
68
                                              RerankRequest, RerankResponse,
69
70
71
                                              ResponsesRequest,
                                              ResponsesResponse, ScoreRequest,
                                              ScoreResponse, TokenizeRequest,
72
                                              TokenizeResponse,
73
74
                                              TranscriptionRequest,
                                              TranscriptionResponse,
75
76
                                              TranslationRequest,
                                              TranslationResponse,
77
                                              UnloadLoRAAdapterRequest)
78
# yapf: enable
79
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
80
81
from vllm.entrypoints.openai.serving_classification import (
    ServingClassification)
82
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
83
from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
84
85
from vllm.entrypoints.openai.serving_engine import OpenAIServing
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
86
                                                    LoRAModulePath,
87
                                                    OpenAIServingModels)
88
from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
89
from vllm.entrypoints.openai.serving_responses import OpenAIServingResponses
90
from vllm.entrypoints.openai.serving_score import ServingScores
91
92
from vllm.entrypoints.openai.serving_tokenization import (
    OpenAIServingTokenization)
93
from vllm.entrypoints.openai.serving_transcription import (
94
    OpenAIServingTranscription, OpenAIServingTranslation)
95
from vllm.entrypoints.openai.tool_parsers import ToolParserManager
96
97
from vllm.entrypoints.utils import (cli_env_setup, load_aware_call,
                                    with_cancellation)
98
from vllm.logger import init_logger
99
from vllm.reasoning import ReasoningParserManager
100
101
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
102
from vllm.transformers_utils.tokenizer import MistralTokenizer
yhu422's avatar
yhu422 committed
103
from vllm.usage.usage_lib import UsageContext
104
from vllm.utils import (Device, FlexibleArgumentParser, get_open_zmq_ipc_path,
105
                        is_valid_ipv6_address, set_ulimit)
106
from vllm.v1.metrics.prometheus import get_prometheus_registry
107
from vllm.version import __version__ as VLLM_VERSION
Zhuohan Li's avatar
Zhuohan Li committed
108

109
prometheus_multiproc_dir: tempfile.TemporaryDirectory
110

111
# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
112
logger = init_logger('vllm.entrypoints.openai.api_server')
113

114
_running_tasks: set[asyncio.Task] = set()
115

116

117
@asynccontextmanager
118
async def lifespan(app: FastAPI):
119
120
    try:
        if app.state.log_stats:
121
            engine_client: EngineClient = app.state.engine_client
122
123
124

            async def _force_log():
                while True:
125
126
                    await asyncio.sleep(10.)
                    await engine_client.do_log_stats()
127
128
129
130
131
132

            task = asyncio.create_task(_force_log())
            _running_tasks.add(task)
            task.add_done_callback(_running_tasks.remove)
        else:
            task = None
133
134
135
136
137

        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()
138
139
140
141
142
143
144
145
        try:
            yield
        finally:
            if task is not None:
                task.cancel()
    finally:
        # Ensure app state including engine ref is gc'd
        del app.state
146
147


148
@asynccontextmanager
149
async def build_async_engine_client(
150
151
152
    args: Namespace,
    client_config: Optional[dict[str, Any]] = None,
) -> AsyncIterator[EngineClient]:
153

154
    # Context manager to handle engine_client lifecycle
155
156
157
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)

158
    async with build_async_engine_client_from_engine_args(
159
160
            engine_args, args.disable_frontend_multiprocessing,
            client_config) as engine:
161
162
163
164
165
166
167
        yield engine


@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
    disable_frontend_multiprocessing: bool = False,
168
    client_config: Optional[dict[str, Any]] = None,
169
) -> AsyncIterator[EngineClient]:
170
    """
171
    Create EngineClient, either:
172
173
174
175
176
177
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

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

178
179
180
181
182
183
184
185
186
187
188
189
190
    # Create the EngineConfig (determines if we can use V1).
    usage_context = UsageContext.OPENAI_API_SERVER
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    # V1 AsyncLLM.
    if envs.VLLM_USE_V1:
        if disable_frontend_multiprocessing:
            logger.warning(
                "V1 is enabled, but got --disable-frontend-multiprocessing. "
                "To disable frontend multiprocessing, set VLLM_USE_V1=0.")

        from vllm.v1.engine.async_llm import AsyncLLM
        async_llm: Optional[AsyncLLM] = None
191
192
        client_index = client_config.pop(
            "client_index") if client_config else 0
193
194
195
196
197
        try:
            async_llm = AsyncLLM.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
198
199
200
                disable_log_stats=engine_args.disable_log_stats,
                client_addresses=client_config,
                client_index=client_index)
201
202
203
204

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

205
206
207
208
209
210
211
212
            yield async_llm
        finally:
            if async_llm:
                async_llm.shutdown()

    # V0 AsyncLLM.
    elif (MQLLMEngineClient.is_unsupported_config(vllm_config)
          or disable_frontend_multiprocessing):
213

214
215
        engine_client: Optional[EngineClient] = None
        try:
216
217
218
219
220
            engine_client = AsyncLLMEngine.from_vllm_config(
                vllm_config=vllm_config,
                usage_context=usage_context,
                disable_log_requests=engine_args.disable_log_requests,
                disable_log_stats=engine_args.disable_log_stats)
221
222
223
224
            yield engine_client
        finally:
            if engine_client and hasattr(engine_client, "shutdown"):
                engine_client.shutdown()
225

226
    # V0MQLLMEngine.
227
    else:
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
        if "PROMETHEUS_MULTIPROC_DIR" not in os.environ:
            # Make TemporaryDirectory for prometheus multiprocessing
            # Note: global TemporaryDirectory will be automatically
            #   cleaned up upon exit.
            global prometheus_multiproc_dir
            prometheus_multiproc_dir = tempfile.TemporaryDirectory()
            os.environ[
                "PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
        else:
            logger.warning(
                "Found PROMETHEUS_MULTIPROC_DIR was set by user. "
                "This directory must be wiped between vLLM runs or "
                "you will find inaccurate metrics. Unset the variable "
                "and vLLM will properly handle cleanup.")

243
        # Select random path for IPC.
244
        ipc_path = get_open_zmq_ipc_path()
245
246
        logger.debug("Multiprocessing frontend to use %s for IPC Path.",
                     ipc_path)
247

248
        # Start RPCServer in separate process (holds the LLMEngine).
249
250
        # the current process might have CUDA context,
        # so we need to spawn a new process
251
252
        context = multiprocessing.get_context("spawn")

253
254
255
        # Ensure we can serialize transformer config before spawning
        maybe_register_config_serialize_by_value()

256
257
258
259
        # The Process can raise an exception during startup, which may
        # not actually result in an exitcode being reported. As a result
        # we use a shared variable to communicate the information.
        engine_alive = multiprocessing.Value('b', True, lock=False)
260
261
262
263
264
        engine_process = context.Process(
            target=run_mp_engine,
            args=(vllm_config, UsageContext.OPENAI_API_SERVER, ipc_path,
                  engine_args.disable_log_stats,
                  engine_args.disable_log_requests, engine_alive))
265
        engine_process.start()
266
        engine_pid = engine_process.pid
267
        assert engine_pid is not None, "Engine process failed to start."
268
        logger.info("Started engine process with PID %d", engine_pid)
269

270
271
272
273
274
275
276
277
        def _cleanup_ipc_path():
            socket_path = ipc_path.replace("ipc://", "")
            if os.path.exists(socket_path):
                os.remove(socket_path)

        # Ensure we clean up the local IPC socket file on exit.
        atexit.register(_cleanup_ipc_path)

278
        # Build RPCClient, which conforms to EngineClient Protocol.
279
        build_client = partial(MQLLMEngineClient, ipc_path, vllm_config,
280
281
282
                               engine_pid)
        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
            None, build_client)
283
        try:
284
285
            while True:
                try:
286
                    await mq_engine_client.setup()
287
                    break
288
                except TimeoutError:
289
290
                    if (not engine_process.is_alive()
                            or not engine_alive.value):
291
                        raise RuntimeError(
292
293
                            "Engine process failed to start. See stack "
                            "trace for the root cause.") from None
294

295
            yield mq_engine_client  # type: ignore[misc]
296
297
        finally:
            # Ensure rpc server process was terminated
298
            engine_process.terminate()
299
300

            # Close all open connections to the backend
301
            mq_engine_client.close()
302

303
304
305
306
307
            # Wait for engine process to join
            engine_process.join(4)
            if engine_process.exitcode is None:
                # Kill if taking longer than 5 seconds to stop
                engine_process.kill()
308

309
310
311
312
313
            # Lazy import for prometheus multiprocessing.
            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
            # before prometheus_client is imported.
            # See https://prometheus.github.io/client_python/multiprocess/
            from prometheus_client import multiprocess
314
            multiprocess.mark_process_dead(engine_process.pid)
315

316

317
318
async def validate_json_request(raw_request: Request):
    content_type = raw_request.headers.get("content-type", "").lower()
319
320
    media_type = content_type.split(";", maxsplit=1)[0]
    if media_type != "application/json":
321
322
323
        raise RequestValidationError(errors=[
            "Unsupported Media Type: Only 'application/json' is allowed"
        ])
324
325


Ethan Xu's avatar
Ethan Xu committed
326
router = APIRouter()
Zhuohan Li's avatar
Zhuohan Li committed
327

328

329
330
331
332
class PrometheusResponse(Response):
    media_type = prometheus_client.CONTENT_TYPE_LATEST


333
def mount_metrics(app: FastAPI):
334
335
336
    """Mount prometheus metrics to a FastAPI app."""

    registry = get_prometheus_registry()
337

338
339
340
341
    # `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
342
343
344
345
346
347
348
349
350
351
    Instrumentator(
        excluded_handlers=[
            "/metrics",
            "/health",
            "/load",
            "/ping",
            "/version",
            "/server_info",
        ],
        registry=registry,
352
    ).add().instrument(app).expose(app, response_class=PrometheusResponse)
353
354
355

    # Add prometheus asgi middleware to route /metrics requests
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
356

357
    # Workaround for 307 Redirect for /metrics
358
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
359
    app.routes.append(metrics_route)
360
361


362
363
364
365
366
def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)


367
368
369
370
def models(request: Request) -> OpenAIServingModels:
    return request.app.state.openai_serving_models


371
372
373
374
def responses(request: Request) -> Optional[OpenAIServingResponses]:
    return request.app.state.openai_serving_responses


375
def chat(request: Request) -> Optional[OpenAIServingChat]:
376
377
378
    return request.app.state.openai_serving_chat


379
def completion(request: Request) -> Optional[OpenAIServingCompletion]:
380
381
382
    return request.app.state.openai_serving_completion


383
384
385
386
def pooling(request: Request) -> Optional[OpenAIServingPooling]:
    return request.app.state.openai_serving_pooling


387
388
def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
    return request.app.state.openai_serving_embedding
389
390


391
def score(request: Request) -> Optional[ServingScores]:
392
393
394
    return request.app.state.openai_serving_scores


395
396
397
398
def classify(request: Request) -> Optional[ServingClassification]:
    return request.app.state.openai_serving_classification


399
400
def rerank(request: Request) -> Optional[ServingScores]:
    return request.app.state.openai_serving_scores
401
402


403
404
def tokenization(request: Request) -> OpenAIServingTokenization:
    return request.app.state.openai_serving_tokenization
405
406


407
408
409
410
def transcription(request: Request) -> OpenAIServingTranscription:
    return request.app.state.openai_serving_transcription


411
412
413
414
def translation(request: Request) -> OpenAIServingTranslation:
    return request.app.state.openai_serving_translation


415
def engine_client(request: Request) -> EngineClient:
416
417
418
    return request.app.state.engine_client


419
420
@router.get("/health", response_class=Response)
async def health(raw_request: Request) -> Response:
421
    """Health check."""
422
    await engine_client(raw_request).check_health()
423
    return Response(status_code=200)
424
425


426
427
428
429
430
431
432
@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
433
    # - /v1/audio/translations
434
435
    # - /v1/embeddings
    # - /pooling
436
    # - /classify
437
438
439
440
441
442
443
444
445
    # - /score
    # - /v1/score
    # - /rerank
    # - /v1/rerank
    # - /v2/rerank
    return JSONResponse(
        content={'server_load': request.app.state.server_load_metrics})


446
447
448
@router.get("/ping", response_class=Response)
@router.post("/ping", response_class=Response)
async def ping(raw_request: Request) -> Response:
449
450
451
452
    """Ping check. Endpoint required for SageMaker"""
    return await health(raw_request)


453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
@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
                 },
             })
469
@with_cancellation
470
async def tokenize(request: TokenizeRequest, raw_request: Request):
471
472
    handler = tokenization(raw_request)

473
474
475
476
477
478
479
480
481
    try:
        generator = await handler.create_tokenize(request, raw_request)
    except NotImplementedError as e:
        raise HTTPException(status_code=HTTPStatus.NOT_IMPLEMENTED.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

482
483
484
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
485
    elif isinstance(generator, TokenizeResponse):
486
487
        return JSONResponse(content=generator.model_dump())

488
489
    assert_never(generator)

490

491
492
493
494
495
496
497
498
499
500
501
502
503
@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
                 },
             })
504
@with_cancellation
505
async def detokenize(request: DetokenizeRequest, raw_request: Request):
506
507
    handler = tokenization(raw_request)

508
509
510
511
512
513
514
515
    try:
        generator = await handler.create_detokenize(request, raw_request)
    except OverflowError as e:
        raise RequestValidationError(errors=[str(e)]) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

516
517
518
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
519
    elif isinstance(generator, DetokenizeResponse):
520
521
        return JSONResponse(content=generator.model_dump())

522
523
    assert_never(generator)

524

525
526
527
528
529
530
531
532
533
534
535
536
537
def maybe_register_tokenizer_info_endpoint(args):
    """Conditionally register the tokenizer info endpoint if enabled."""
    if getattr(args, 'enable_tokenizer_info_endpoint', False):

        @router.get("/tokenizer_info")
        async def get_tokenizer_info(raw_request: Request):
            """Get comprehensive tokenizer information."""
            result = await tokenization(raw_request).get_tokenizer_info()
            return JSONResponse(content=result.model_dump(),
                                status_code=result.code if isinstance(
                                    result, ErrorResponse) else 200)


Ethan Xu's avatar
Ethan Xu committed
538
@router.get("/v1/models")
539
async def show_available_models(raw_request: Request):
540
    handler = models(raw_request)
541

542
543
    models_ = await handler.show_available_models()
    return JSONResponse(content=models_.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
544
545


Ethan Xu's avatar
Ethan Xu committed
546
@router.get("/version")
547
async def show_version():
548
    ver = {"version": VLLM_VERSION}
549
550
551
    return JSONResponse(content=ver)


552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
@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
                 },
             })
@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(
            message="The model does not support Responses API")

    generator = await handler.create_responses(request, raw_request)

    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, ResponsesResponse):
        return JSONResponse(content=generator.model_dump())
    return StreamingResponse(content=generator, media_type="text/event-stream")


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

    response = await handler.retrieve_responses(response_id)

    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
                            status_code=response.code)
    return JSONResponse(content=response.model_dump())


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

    response = await handler.cancel_responses(response_id)

    if isinstance(response, ErrorResponse):
        return JSONResponse(content=response.model_dump(),
                            status_code=response.code)
    return JSONResponse(content=response.model_dump())


617
@router.post("/v1/chat/completions",
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
             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
                 }
             })
635
@with_cancellation
636
@load_aware_call
637
638
async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
639
640
641
642
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API")
643

644
    generator = await handler.create_chat_completion(request, raw_request)
645

646
647
648
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
649

650
    elif isinstance(generator, ChatCompletionResponse):
651
        return JSONResponse(content=generator.model_dump())
652

653
654
    return StreamingResponse(content=generator, media_type="text/event-stream")

655

656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
@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
                 },
             })
674
@with_cancellation
675
@load_aware_call
676
async def create_completion(request: CompletionRequest, raw_request: Request):
677
678
679
680
681
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Completions API")

682
683
684
685
686
687
688
689
690
    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=str(e)) from e
    except Exception as e:
        raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
                            detail=str(e)) from e

691
692
693
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
694
    elif isinstance(generator, CompletionResponse):
695
        return JSONResponse(content=generator.model_dump())
Zhuohan Li's avatar
Zhuohan Li committed
696

697
698
    return StreamingResponse(content=generator, media_type="text/event-stream")

Zhuohan Li's avatar
Zhuohan Li committed
699

700
701
702
703
704
705
706
707
708
709
@router.post("/v1/embeddings",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
710
@with_cancellation
711
@load_aware_call
712
async def create_embedding(request: EmbeddingRequest, raw_request: Request):
713
714
    handler = embedding(raw_request)
    if handler is None:
715
716
717
718
        return base(raw_request).create_error_response(
            message="The model does not support Embeddings API")

    generator = await handler.create_embedding(request, raw_request)
719

720
721
722
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
723
    elif isinstance(generator, EmbeddingResponse):
724
725
        return JSONResponse(content=generator.model_dump())

726
727
    assert_never(generator)

728

729
730
731
732
733
734
735
736
737
738
@router.post("/pooling",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
739
@with_cancellation
740
@load_aware_call
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
async def create_pooling(request: PoolingRequest, raw_request: Request):
    handler = pooling(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Pooling API")

    generator = await handler.create_pooling(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, PoolingResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
@router.post("/classify", dependencies=[Depends(validate_json_request)])
@with_cancellation
@load_aware_call
async def create_classify(request: ClassificationRequest,
                          raw_request: Request):
    handler = classify(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Classification API")

    generator = await handler.create_classify(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)

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

    assert_never(generator)


778
779
780
781
782
783
784
785
786
787
@router.post("/score",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
788
@with_cancellation
789
@load_aware_call
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
async def create_score(request: ScoreRequest, raw_request: Request):
    handler = score(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Score API")

    generator = await handler.create_score(request, raw_request)
    if isinstance(generator, ErrorResponse):
        return JSONResponse(content=generator.model_dump(),
                            status_code=generator.code)
    elif isinstance(generator, ScoreResponse):
        return JSONResponse(content=generator.model_dump())

    assert_never(generator)


806
807
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
821
822
823
824
825
async def create_score_v1(request: ScoreRequest, raw_request: Request):
    logger.warning(
        "To indicate that Score API is not part of standard OpenAI API, we "
        "have moved it to `/score`. Please update your client accordingly.")

    return await create_score(request, raw_request)


826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
@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
                 },
             })
843
@with_cancellation
844
@load_aware_call
845
846
847
async def create_transcriptions(raw_request: Request,
                                request: Annotated[TranscriptionRequest,
                                                   Form()]):
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
    handler = transcription(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Transcriptions API")

    audio_data = await request.file.read()
    generator = await handler.create_transcription(audio_data, request,
                                                   raw_request)

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

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

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


867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
@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
                 },
             })
@with_cancellation
@load_aware_call
async def create_translations(request: Annotated[TranslationRequest,
                                                 Form()],
                              raw_request: Request):
    handler = translation(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Translations API")

    audio_data = await request.file.read()
    generator = await handler.create_translation(audio_data, request,
                                                 raw_request)

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

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

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


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

    assert_never(generator)


935
936
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
952
953
954
        "accordingly. (Note: Conforms to JinaAI rerank API)")

    return await do_rerank(request, raw_request)


955
956
957
958
959
960
961
962
963
964
@router.post("/v2/rerank",
             dependencies=[Depends(validate_json_request)],
             responses={
                 HTTPStatus.BAD_REQUEST.value: {
                     "model": ErrorResponse
                 },
                 HTTPStatus.INTERNAL_SERVER_ERROR.value: {
                     "model": ErrorResponse
                 },
             })
965
966
967
968
969
@with_cancellation
async def do_rerank_v2(request: RerankRequest, raw_request: Request):
    return await do_rerank(request, raw_request)


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

974
975
976
977
978
    @router.get("/server_info")
    async def show_server_info(raw_request: Request):
        server_info = {"vllm_config": str(raw_request.app.state.vllm_config)}
        return JSONResponse(content=server_info)

979
980
981
982
983
984
    @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.
        """
985
986
987
988
989
990
        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)
991
992
        return Response(status_code=200)

993
994
995
996
997
998
999
1000
1001
1002
1003
    @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):
1004
1005
1006
1007
1008
1009
        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)
1010
1011
1012
1013
        # 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)

1014
1015
1016
1017
1018
1019
    @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})

1020

1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
@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
                 },
             })
async def scale_elastic_ep(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
        raise HTTPException(status_code=400,
                            detail="Invalid JSON format") from e  # noqa: B904

    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:
        raise HTTPException(status_code=400,
                            detail="new_data_parallel_size is required")

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

    if not isinstance(drain_timeout, int) or drain_timeout <= 0:
        raise HTTPException(status_code=400,
                            detail="drain_timeout must be a positive integer")

    # 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)
        return JSONResponse({
            "message":
            f"Scaled to {new_data_parallel_size} "
            "data parallel engines",
        })
    except TimeoutError as e:
        raise HTTPException(status_code=408,
                            detail="Scale failed due to request drain timeout "
                            f"after {drain_timeout} seconds") from e
    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})


1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
# 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
]


1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
@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
                 },
             })
1125
async def invocations(raw_request: Request):
1126
    """For SageMaker, routes requests based on the request type."""
1127
1128
    try:
        body = await raw_request.json()
1129
    except json.JSONDecodeError as e:
1130
1131
1132
        raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
                            detail=f"JSON decode error: {e}") from e

1133
1134
1135
1136
    valid_endpoints = [(validator, endpoint)
                       for validator, (get_handler,
                                       endpoint) in INVOCATION_VALIDATORS
                       if get_handler(raw_request) is not None]
1137

1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
    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
    ]
    msg = ("Cannot find suitable handler for request. "
           f"Expected one of: {type_names}")
    res = base(raw_request).create_error_response(message=msg)
    return JSONResponse(content=res.model_dump(), status_code=res.code)
1154
1155


1156
1157
1158
1159
1160
1161
if envs.VLLM_TORCH_PROFILER_DIR:
    logger.warning(
        "Torch Profiler is enabled in the API server. This should ONLY be "
        "used for local development!")

    @router.post("/start_profile")
1162
    async def start_profile(raw_request: Request):
1163
        logger.info("Starting profiler...")
1164
        await engine_client(raw_request).start_profile()
1165
1166
1167
1168
        logger.info("Profiler started.")
        return Response(status_code=200)

    @router.post("/stop_profile")
1169
    async def stop_profile(raw_request: Request):
1170
        logger.info("Stopping profiler...")
1171
        await engine_client(raw_request).stop_profile()
1172
1173
1174
1175
        logger.info("Profiler stopped.")
        return Response(status_code=200)


1176
1177
if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
    logger.warning(
1178
        "LoRA dynamic loading & unloading is enabled in the API server. "
1179
1180
        "This should ONLY be used for local development!")

1181
1182
    @router.post("/v1/load_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
1183
    async def load_lora_adapter(request: LoadLoRAAdapterRequest,
1184
                                raw_request: Request):
1185
1186
1187
1188
1189
        handler = models(raw_request)
        response = await handler.load_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
                                status_code=response.code)
1190
1191
1192

        return Response(status_code=200, content=response)

1193
1194
    @router.post("/v1/unload_lora_adapter",
                 dependencies=[Depends(validate_json_request)])
1195
    async def unload_lora_adapter(request: UnloadLoRAAdapterRequest,
1196
                                  raw_request: Request):
1197
1198
1199
1200
1201
        handler = models(raw_request)
        response = await handler.unload_lora_adapter(request)
        if isinstance(response, ErrorResponse):
            return JSONResponse(content=response.model_dump(),
                                status_code=response.code)
1202
1203
1204
1205

        return Response(status_code=200, content=response)


1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
def load_log_config(log_config_file: Optional[str]) -> Optional[dict]:
    if not log_config_file:
        return None
    try:
        with open(log_config_file) as f:
            return json.load(f)
    except Exception as e:
        logger.warning("Failed to load log config from file %s: error %s",
                       log_config_file, e)
        return None


1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
class AuthenticationMiddleware:
    """
    Pure ASGI middleware that authenticates each request by checking
    if the Authorization header exists and equals "Bearer {api_key}".

    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).
    """

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

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        if scope["type"] not in ("http",
                                 "websocket") or scope["method"] == "OPTIONS":
            # 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.
        if url_path.startswith("/v1") and headers.get(
                "Authorization") != f"Bearer {self.api_token}":
            response = JSONResponse(content={"error": "Unauthorized"},
                                    status_code=401)
            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

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        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"])
                request_id = request_headers.get("X-Request-Id",
                                                 uuid.uuid4().hex)
                response_headers.append("X-Request-Id", request_id)
            await send(message)

        return self.app(scope, receive, send_with_request_id)


1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
# 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.
    
    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

    def __call__(self, scope: Scope, receive: Receive,
                 send: Send) -> Awaitable[None]:
        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
            response = JSONResponse(content={
                "error":
                "The model is currently scaling. Please try again later."
            },
                                    status_code=503)
            return response(scope, receive, send)

        return self.app(scope, receive, send)


1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
            ChatCompletionStreamResponse, CompletionStreamResponse)

        # Try using Completion types for type-safe parsing
        if chunk_data.get('object') == 'chat.completion.chunk':
            chat_response = ChatCompletionStreamResponse.model_validate(
                chunk_data)
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
        elif chunk_data.get('object') == 'text_completion':
            completion_response = CompletionStreamResponse.model_validate(
                chunk_data)
            if completion_response.choices and completion_response.choices[
                    0].text:
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
        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']
    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:
            chunk_str = chunk.decode('utf-8')
        except UnicodeDecodeError:
            # Skip malformed chunks
            return []

        self.buffer += chunk_str
        events = []

        # Process complete lines
        while '\n' in self.buffer:
            line, self.buffer = self.buffer.split('\n', 1)
            line = line.rstrip('\r')  # Handle CRLF

            if line.startswith('data: '):
                data_str = line[6:].strip()
                if data_str == '[DONE]':
                    events.append({'type': 'done'})
                elif data_str:
                    try:
                        event_data = json.loads(data_str)
                        events.append({'type': 'data', 'data': event_data})
                    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."""
        return ''.join(self.content_buffer)


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:
                if event['type'] == 'data':
                    content = sse_decoder.extract_content(event['data'])
                    sse_decoder.add_content(content)
                elif event['type'] == 'done':
                    # 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(
                            "response_body={streaming_complete: " \
                            "content='%s', chunks=%d}",
                            full_content, chunk_count)
                    else:
                        logger.info(
                            "response_body={streaming_complete: " \
                            "no_content, chunks=%d}",
                            chunk_count)
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
    logger.info("response_body={streaming_started: chunks=%d}",
                len(response_body))


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>}")


1457
def build_app(args: Namespace) -> FastAPI:
1458
1459
1460
1461
1462
1463
1464
    if args.disable_fastapi_docs:
        app = FastAPI(openapi_url=None,
                      docs_url=None,
                      redoc_url=None,
                      lifespan=lifespan)
    else:
        app = FastAPI(lifespan=lifespan)
Ethan Xu's avatar
Ethan Xu committed
1465
1466
    app.include_router(router)
    app.root_path = args.root_path
Zhuohan Li's avatar
Zhuohan Li committed
1467

1468
1469
    mount_metrics(app)

Zhuohan Li's avatar
Zhuohan Li committed
1470
1471
1472
1473
1474
1475
1476
1477
    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

1478
1479
1480
1481
1482
1483
1484
    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
        err = ErrorResponse(message=exc.detail,
                            type=HTTPStatus(exc.status_code).phrase,
                            code=exc.status_code)
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

Ethan Xu's avatar
Ethan Xu committed
1485
    @app.exception_handler(RequestValidationError)
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
    async def validation_exception_handler(_: Request,
                                           exc: RequestValidationError):
        exc_str = str(exc)
        errors_str = str(exc.errors())

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

        err = ErrorResponse(message=message,
                            type=HTTPStatus.BAD_REQUEST.phrase,
1498
                            code=HTTPStatus.BAD_REQUEST)
Ethan Xu's avatar
Ethan Xu committed
1499
1500
1501
        return JSONResponse(err.model_dump(),
                            status_code=HTTPStatus.BAD_REQUEST)

1502
1503
    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
    if token := args.api_key or envs.VLLM_API_KEY:
1504
        app.add_middleware(AuthenticationMiddleware, api_token=token)
1505

1506
    if args.enable_request_id_headers:
1507
        app.add_middleware(XRequestIdMiddleware)
1508

1509
1510
1511
    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
        logger.warning("CAUTION: Enabling log response in the API Server. "
                       "This can include sensitive information and should be "
                       "avoided in production.")

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
            response_body = [
                section async for section in response.body_iterator
            ]
            response.body_iterator = iterate_in_threadpool(iter(response_body))
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
            # 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)
1535
            return response
1536

1537
1538
1539
1540
    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):
1541
            app.add_middleware(imported)  # type: ignore[arg-type]
1542
1543
1544
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
1545
1546
            raise ValueError(f"Invalid middleware {middleware}. "
                             f"Must be a function or a class.")
1547

Ethan Xu's avatar
Ethan Xu committed
1548
1549
1550
    return app


1551
async def init_app_state(
1552
    engine_client: EngineClient,
1553
    vllm_config: VllmConfig,
1554
    state: State,
1555
    args: Namespace,
1556
) -> None:
1557
    if args.served_model_name is not None:
1558
        served_model_names = args.served_model_name
1559
    else:
1560
        served_model_names = [args.model]
1561

1562
1563
1564
1565
1566
    if args.disable_log_requests:
        request_logger = None
    else:
        request_logger = RequestLogger(max_log_len=args.max_log_len)

1567
1568
1569
1570
1571
    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model)
        for name in served_model_names
    ]

1572
    state.engine_client = engine_client
1573
    state.log_stats = not args.disable_log_stats
1574
1575
    state.vllm_config = vllm_config
    model_config = vllm_config.model_config
Ethan Xu's avatar
Ethan Xu committed
1576

1577
    resolved_chat_template = load_chat_template(args.chat_template)
1578
    if resolved_chat_template is not None:
1579
1580
1581
1582
1583
1584
1585
1586
1587
        # Get the tokenizer to check official template
        tokenizer = await engine_client.get_tokenizer()

        if isinstance(tokenizer, MistralTokenizer):
            # The warning is logged in resolve_mistral_chat_template.
            resolved_chat_template = resolve_mistral_chat_template(
                chat_template=resolved_chat_template)
        else:
            hf_chat_template = resolve_hf_chat_template(
1588
                tokenizer=tokenizer,
1589
1590
                chat_template=None,
                tools=None,
1591
                model_config=vllm_config.model_config,
1592
            )
1593
1594
1595
1596
1597
1598
1599

            if hf_chat_template != resolved_chat_template:
                logger.warning(
                    "Using supplied chat template: %s\n"
                    "It is different from official chat template '%s'. "
                    "This discrepancy may lead to performance degradation.",
                    resolved_chat_template, args.model)
1600

1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
    # Merge default_mm_loras into the static lora_modules
    default_mm_loras = (vllm_config.lora_config.default_mm_loras
                        if vllm_config.lora_config is not None else {})

    lora_modules = args.lora_modules
    if default_mm_loras:
        default_mm_lora_paths = [
            LoRAModulePath(
                name=modality,
                path=lora_path,
            ) for modality, lora_path in default_mm_loras.items()
        ]
        if args.lora_modules is None:
            lora_modules = default_mm_lora_paths
        else:
            lora_modules += default_mm_lora_paths

1618
    state.openai_serving_models = OpenAIServingModels(
1619
        engine_client=engine_client,
1620
1621
        model_config=model_config,
        base_model_paths=base_model_paths,
1622
        lora_modules=lora_modules,
1623
    )
1624
    await state.openai_serving_models.init_static_loras()
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
    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,
        reasoning_parser=args.reasoning_parser,
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
        enable_force_include_usage=args.enable_force_include_usage,
1638
    ) if "generate" in model_config.supported_tasks else None
1639
    state.openai_serving_chat = OpenAIServingChat(
1640
        engine_client,
1641
        model_config,
1642
        state.openai_serving_models,
1643
1644
        args.response_role,
        request_logger=request_logger,
1645
1646
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1647
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
1648
        enable_auto_tools=args.enable_auto_tool_choice,
1649
        tool_parser=args.tool_call_parser,
1650
        reasoning_parser=args.reasoning_parser,
1651
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
1652
        enable_force_include_usage=args.enable_force_include_usage,
1653
    ) if "generate" in model_config.supported_tasks else None
1654
    state.openai_serving_completion = OpenAIServingCompletion(
1655
        engine_client,
1656
        model_config,
1657
        state.openai_serving_models,
1658
        request_logger=request_logger,
1659
        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
1660
        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
1661
        enable_force_include_usage=args.enable_force_include_usage,
1662
    ) if "generate" in model_config.supported_tasks else None
1663
    state.openai_serving_pooling = OpenAIServingPooling(
1664
        engine_client,
1665
        model_config,
1666
        state.openai_serving_models,
1667
        request_logger=request_logger,
1668
1669
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1670
    ) if "encode" in model_config.supported_tasks else None
1671
1672
1673
    state.openai_serving_embedding = OpenAIServingEmbedding(
        engine_client,
        model_config,
1674
        state.openai_serving_models,
1675
1676
1677
        request_logger=request_logger,
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1678
    ) if "embed" in model_config.supported_tasks else None
1679
1680
1681
1682
1683
    state.openai_serving_classification = ServingClassification(
        engine_client,
        model_config,
        state.openai_serving_models,
        request_logger=request_logger,
1684
    ) if "classify" in model_config.supported_tasks else None
1685

1686
1687
1688
    enable_serving_reranking = ("classify" in model_config.supported_tasks
                                and getattr(model_config.hf_config,
                                            "num_labels", 0) == 1)
1689
1690
1691
1692
    state.openai_serving_scores = ServingScores(
        engine_client,
        model_config,
        state.openai_serving_models,
1693
1694
1695
        request_logger=request_logger,
    ) if ("embed" in model_config.supported_tasks
          or enable_serving_reranking) else None
1696

1697
    state.openai_serving_tokenization = OpenAIServingTokenization(
1698
        engine_client,
1699
        model_config,
1700
        state.openai_serving_models,
1701
        request_logger=request_logger,
1702
1703
        chat_template=resolved_chat_template,
        chat_template_content_format=args.chat_template_content_format,
1704
    )
1705
1706
1707
    state.openai_serving_transcription = OpenAIServingTranscription(
        engine_client,
        model_config,
1708
1709
        state.openai_serving_models,
        request_logger=request_logger,
1710
    ) if "transcription" in model_config.supported_tasks else None
1711
1712
1713
    state.openai_serving_translation = OpenAIServingTranslation(
        engine_client,
        model_config,
1714
1715
        state.openai_serving_models,
        request_logger=request_logger,
1716
    ) if "transcription" in model_config.supported_tasks else None
1717
    state.task = model_config.task
1718

1719
1720
1721
    state.enable_server_load_tracking = args.enable_server_load_tracking
    state.server_load_metrics = 0

1722

1723
def create_server_socket(addr: tuple[str, int]) -> socket.socket:
1724
1725
1726
1727
1728
1729
    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)
1730
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
1731
1732
1733
1734
1735
    sock.bind(addr)

    return sock


1736
def validate_api_server_args(args):
1737
    valid_tool_parses = ToolParserManager.tool_parsers.keys()
1738
    if args.enable_auto_tool_choice \
1739
            and args.tool_call_parser not in valid_tool_parses:
1740
        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
1741
                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
1742

1743
    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
1744
    if args.reasoning_parser \
1745
1746
1747
1748
1749
        and args.reasoning_parser not in valid_reasoning_parses:
        raise KeyError(
            f"invalid reasoning parser: {args.reasoning_parser} "
            f"(chose from {{ {','.join(valid_reasoning_parses)} }})")

1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762

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)

1763
1764
1765
    # 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
1766
1767
    sock_addr = (args.host or "", args.port)
    sock = create_server_socket(sock_addr)
1768

1769
1770
1771
1772
    # workaround to avoid footguns where uvicorn drops requests with too
    # many concurrent requests active
    set_ulimit()

1773
1774
1775
1776
1777
1778
    def signal_handler(*_) -> None:
        # Interrupt server on sigterm while initializing
        raise KeyboardInterrupt("terminated")

    signal.signal(signal.SIGTERM, signal_handler)

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
1804
1805
    addr, port = sock_addr
    is_ssl = args.ssl_keyfile and args.ssl_certfile
    host_part = f"[{addr}]" if is_valid_ipv6_address(
        addr) else addr or "0.0.0.0"
    listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"

    return listen_address, sock


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


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

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

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

1806
1807
1808
1809
1810
    # Load logging config for uvicorn if specified
    log_config = load_log_config(args.log_config_file)
    if log_config is not None:
        uvicorn_kwargs['log_config'] = log_config

1811
    async with build_async_engine_client(args, client_config) as engine_client:
1812
        maybe_register_tokenizer_info_endpoint(args)
1813
1814
        app = build_app(args)

1815
1816
        vllm_config = await engine_client.get_vllm_config()
        await init_app_state(engine_client, vllm_config, app.state, args)
1817

1818
1819
        logger.info("Starting vLLM API server %d on %s", server_index,
                    listen_address)
1820
1821
        shutdown_task = await serve_http(
            app,
1822
            sock=sock,
1823
            enable_ssl_refresh=args.enable_ssl_refresh,
1824
1825
1826
            host=args.host,
            port=args.port,
            log_level=args.uvicorn_log_level,
1827
1828
1829
            # NOTE: When the 'disable_uvicorn_access_log' value is True,
            # no access log will be output.
            access_log=not args.disable_uvicorn_access_log,
1830
            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
1831
1832
1833
1834
            ssl_keyfile=args.ssl_keyfile,
            ssl_certfile=args.ssl_certfile,
            ssl_ca_certs=args.ssl_ca_certs,
            ssl_cert_reqs=args.ssl_cert_reqs,
1835
1836
1837
            **uvicorn_kwargs,
        )

1838
    # NB: Await server shutdown only after the backend context is exited
1839
1840
1841
1842
    try:
        await shutdown_task
    finally:
        sock.close()
1843

Ethan Xu's avatar
Ethan Xu committed
1844
1845
1846

if __name__ == "__main__":
    # NOTE(simon):
1847
1848
    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
    # entrypoints.
1849
    cli_env_setup()
Ethan Xu's avatar
Ethan Xu committed
1850
1851
1852
1853
    parser = FlexibleArgumentParser(
        description="vLLM OpenAI-Compatible RESTful API server.")
    parser = make_arg_parser(parser)
    args = parser.parse_args()
1854
    validate_parsed_serve_args(args)
1855

1856
    uvloop.run(run_server(args))