async_llm.py 33.2 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
import asyncio
4
5
import os
import socket
6
import time
7
from collections.abc import AsyncGenerator, Iterable, Mapping
8
from copy import copy
9
from typing import Any, cast
10

11
import numpy as np
12
import torch
13
from typing_extensions import deprecated
14

15
import vllm.envs as envs
16
from vllm.config import VllmConfig
17
from vllm.engine.arg_utils import AsyncEngineArgs
18
from vllm.engine.protocol import EngineClient
19
from vllm.entrypoints.utils import _validate_truncation_size
20
from vllm.inputs import PromptType
21
22
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
23
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
24
from vllm.outputs import PoolingRequestOutput, RequestOutput
25
from vllm.plugins.io_processors import get_io_processor
26
from vllm.pooling_params import PoolingParams
27
from vllm.sampling_params import SamplingParams
28
from vllm.tasks import SupportedTask
29
from vllm.tracing import init_tracer
30
from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
31
from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
32
from vllm.usage.usage_lib import UsageContext
33
34
from vllm.utils.async_utils import cancel_task_threadsafe
from vllm.utils.collection_utils import as_list
35
from vllm.utils.math_utils import cdiv
36
from vllm.v1.engine import EngineCoreRequest
37
from vllm.v1.engine.core_client import EngineCoreClient
38
from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
39
from vllm.v1.engine.input_processor import InputProcessor
40
from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
41
from vllm.v1.engine.parallel_sampling import ParentRequest
42
from vllm.v1.executor import Executor
43
44
45
46
47
from vllm.v1.metrics.loggers import (
    StatLoggerFactory,
    StatLoggerManager,
    load_stat_logger_plugin_factories,
)
48
from vllm.v1.metrics.prometheus import shutdown_prometheus
49
from vllm.v1.metrics.stats import IterationStats
50
51
52
53
54
55
56
57

logger = init_logger(__name__)


class AsyncLLM(EngineClient):
    def __init__(
        self,
        vllm_config: VllmConfig,
58
        executor_class: type[Executor],
59
60
        log_stats: bool,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
61
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
62
63
64
        use_cached_outputs: bool = False,
        log_requests: bool = True,
        start_engine_loop: bool = True,
65
        stat_loggers: list[StatLoggerFactory] | None = None,
66
        aggregate_engine_logging: bool = False,
67
        client_addresses: dict[str, str] | None = None,
68
        client_count: int = 1,
69
        client_index: int = 0,
70
    ) -> None:
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
        """
        Create an AsyncLLM.

        Args:
            vllm_config: global configuration.
            executor_class: an Executor impl, e.g. MultiprocExecutor.
            log_stats: Whether to log stats.
            usage_context: Usage context of the LLM.
            mm_registry: Multi-modal registry.
            use_cached_outputs: Whether to use cached outputs.
            log_requests: Whether to log requests.
            start_engine_loop: Whether to start the engine loop.
            stat_loggers: customized stat loggers for the engine.
                If not provided, default stat loggers will be used.
                PLEASE BE AWARE THAT STAT LOGGER IS NOT STABLE
                IN V1, AND ITS BASE CLASS INTERFACE MIGHT CHANGE.

        Returns:
            None
        """
91
92
93
        # Ensure we can serialize custom transformer configs
        maybe_register_config_serialize_by_value()

94
        self.model_config = vllm_config.model_config
95
        self.vllm_config = vllm_config
96
        self.observability_config = vllm_config.observability_config
97
        self.log_requests = log_requests
98

99
100
101
102
103
104
        custom_stat_loggers = list(stat_loggers or [])
        custom_stat_loggers.extend(load_stat_logger_plugin_factories())

        has_custom_loggers = bool(custom_stat_loggers)
        self.log_stats = log_stats or has_custom_loggers
        if not log_stats and has_custom_loggers:
105
            logger.info(
106
107
108
                "AsyncLLM created with log_stats=False, "
                "but custom stat loggers were found; "
                "enabling logging without default stat loggers."
109
            )
110

111
112
113
114
115
        if self.model_config.skip_tokenizer_init:
            tokenizer = None
        else:
            tokenizer = init_tokenizer_from_configs(self.model_config)

116
        self.input_processor = InputProcessor(self.vllm_config, tokenizer)
117
118
119
120
        self.io_processor = get_io_processor(
            self.vllm_config,
            self.model_config.io_processor_plugin,
        )
121

122
        # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
123
        stream_interval = self.vllm_config.scheduler_config.stream_interval
124
        self.output_processor = OutputProcessor(
125
            self.tokenizer, log_stats=self.log_stats, stream_interval=stream_interval
126
        )
127
128
129
        endpoint = self.observability_config.otlp_traces_endpoint
        if endpoint is not None:
            tracer = init_tracer("vllm.llm_engine", endpoint)
130
            self.output_processor.tracer = tracer
131
132

        # EngineCore (starts the engine in background process).
133
        self.engine_core = EngineCoreClient.make_async_mp_client(
134
135
            vllm_config=vllm_config,
            executor_class=executor_class,
136
            log_stats=self.log_stats,
137
            client_addresses=client_addresses,
138
            client_count=client_count,
139
            client_index=client_index,
140
        )
141
142

        # Loggers.
143
        self.logger_manager: StatLoggerManager | None = None
144
145
146
        if self.log_stats:
            self.logger_manager = StatLoggerManager(
                vllm_config=vllm_config,
147
                engine_idxs=self.engine_core.engine_ranks_managed,
148
                custom_stat_loggers=custom_stat_loggers,
149
                enable_default_loggers=log_stats,
150
                client_count=client_count,
151
                aggregate_engine_logging=aggregate_engine_logging,
152
153
154
            )
            self.logger_manager.log_engine_initialized()

155
156
157
158
        # Pause / resume state for async RL workflows.
        self._pause_cond = asyncio.Condition()
        self._paused = False

159
        self.output_handler: asyncio.Task | None = None
160
161
162
163
164
165
        try:
            # Start output handler eagerly if we are in the asyncio eventloop.
            asyncio.get_running_loop()
            self._run_output_handler()
        except RuntimeError:
            pass
166

167
168
169
170
        if (
            envs.VLLM_TORCH_PROFILER_DIR
            and not envs.VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM
        ):
171
172
            logger.info(
                "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s",  # noqa: E501
173
174
                envs.VLLM_TORCH_PROFILER_DIR,
            )
175
176
177
178
179
180
181
182
183
            if envs.VLLM_PROFILER_MAX_ITERS > 0 or envs.VLLM_PROFILER_DELAY_ITERS > 0:
                logger.warning_once(
                    "Torch profiler received max_iters or delay_iters setting. These "
                    "are not compatible with the AsyncLLM profiler and will be ignored "
                    "for the AsyncLLM process. Engine process profiling will still "
                    "respect these settings. Consider setting "
                    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM=1 to disable "
                    "AsyncLLM profiling."
                )
184
185
186
187
188
189
190
            worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                ],
                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
191
192
193
                    envs.VLLM_TORCH_PROFILER_DIR, worker_name=worker_name, use_gzip=True
                ),
            )
194
195
196
        else:
            self.profiler = None

197
198
199
200
201
202
203
204
    @property
    @deprecated(
        "`AsyncLLM.processor` has been renamed to `AsyncLLM.input_processor`. "
        "The old name will be removed in v0.13."
    )
    def processor(self):
        return self.input_processor

205
206
    @classmethod
    def from_vllm_config(
207
208
209
210
        cls,
        vllm_config: VllmConfig,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
211
        stat_loggers: list[StatLoggerFactory] | None = None,
212
        enable_log_requests: bool = False,
213
        aggregate_engine_logging: bool = False,
214
        disable_log_stats: bool = False,
215
        client_addresses: dict[str, str] | None = None,
216
217
        client_count: int = 1,
        client_index: int = 0,
218
219
220
221
222
223
    ) -> "AsyncLLM":
        # Create the LLMEngine.
        return cls(
            vllm_config=vllm_config,
            executor_class=Executor.get_class(vllm_config),
            start_engine_loop=start_engine_loop,
224
            stat_loggers=stat_loggers,
225
            log_requests=enable_log_requests,
226
            log_stats=not disable_log_stats,
227
            aggregate_engine_logging=aggregate_engine_logging,
228
            usage_context=usage_context,
229
            client_addresses=client_addresses,
230
            client_count=client_count,
231
            client_index=client_index,
232
233
        )

234
235
236
237
238
239
    @classmethod
    def from_engine_args(
        cls,
        engine_args: AsyncEngineArgs,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
240
        stat_loggers: list[StatLoggerFactory] | None = None,
241
    ) -> "AsyncLLM":
242
243
244
        """Create an AsyncLLM from the EngineArgs."""

        # Create the engine configs.
245
        vllm_config = engine_args.create_engine_config(usage_context)
246
        executor_class = Executor.get_class(vllm_config)
247
248
249
250
251

        # Create the AsyncLLM.
        return cls(
            vllm_config=vllm_config,
            executor_class=executor_class,
252
            log_requests=engine_args.enable_log_requests,
253
254
255
            log_stats=not engine_args.disable_log_stats,
            start_engine_loop=start_engine_loop,
            usage_context=usage_context,
256
            stat_loggers=stat_loggers,
257
258
        )

259
260
261
    def __del__(self):
        self.shutdown()

262
263
264
    def shutdown(self):
        """Shutdown, cleaning up the background proc and IPC."""

265
266
        shutdown_prometheus()

267
268
        if engine_core := getattr(self, "engine_core", None):
            engine_core.shutdown()
269

270
271
272
        handler = getattr(self, "output_handler", None)
        if handler is not None:
            cancel_task_threadsafe(handler)
273

274
275
276
    async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return await self.engine_core.get_supported_tasks_async()

277
278
279
    async def add_request(
        self,
        request_id: str,
280
281
282
283
284
285
        prompt: EngineCoreRequest | PromptType,
        params: SamplingParams | PoolingParams,
        arrival_time: float | None = None,
        lora_request: LoRARequest | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        trace_headers: Mapping[str, str] | None = None,
286
        priority: int = 0,
287
288
        data_parallel_rank: int | None = None,
        prompt_text: str | None = None,
289
    ) -> RequestOutputCollector:
290
291
        """Add new request to the AsyncLLM."""

292
293
294
        if self.errored:
            raise EngineDeadError()

295
        is_pooling = isinstance(params, PoolingParams)
296
297
298

        # Create a new output collector for the request.
        queue = RequestOutputCollector(output_kind=params.output_kind)
299

300
        # Convert Input --> Request.
301
302
303
304
        if isinstance(prompt, EngineCoreRequest):
            request = prompt
        else:
            assert prompt_text is None
305
            request = self.input_processor.process_inputs(
306
307
308
309
310
311
312
313
314
315
                request_id,
                prompt,
                params,
                arrival_time,
                lora_request,
                tokenization_kwargs,
                trace_headers,
                priority,
                data_parallel_rank,
            )
316
317
318
319
            if isinstance(prompt, str):
                prompt_text = prompt
            elif isinstance(prompt, Mapping):
                prompt_text = cast(str | None, prompt.get("prompt"))
320

321
322
323
        # Use cloned params that may have been updated in process_inputs()
        params = request.params

324
        if is_pooling or params.n == 1:
325
            await self._add_request(request, prompt_text, None, 0, queue)
326
327
            return queue

328
329
        parent_params = params
        assert isinstance(parent_params, SamplingParams)
330

331
        # Fan out child requests (for n>1).
332
333
334
        parent_request = ParentRequest(request_id, parent_params)
        for idx in range(parent_params.n):
            request_id, child_params = parent_request.get_child_info(idx)
335
            child_request = request if idx == parent_params.n - 1 else copy(request)
336
            child_request.request_id = request_id
337
            child_request.sampling_params = child_params
338
339
340
            await self._add_request(
                child_request, prompt_text, parent_request, idx, queue
            )
341
        return queue
342

343
344
345
    async def _add_request(
        self,
        request: EngineCoreRequest,
346
347
        prompt: str | None,
        parent_req: ParentRequest | None,
348
349
350
        index: int,
        queue: RequestOutputCollector,
    ):
351
        # Add the request to OutputProcessor (this process).
352
        self.output_processor.add_request(request, prompt, parent_req, index, queue)
353

354
355
        # Add the EngineCoreRequest to EngineCore (separate process).
        await self.engine_core.add_request_async(request)
356

357
358
        if self.log_requests:
            logger.info("Added request %s.", request.request_id)
359
360
361
362
363
364

    # TODO: we should support multiple prompts in one call, as you
    # can do with LLM.generate. So that for multi-prompt completion
    # requests we don't need to send multiple messages to core proc,
    # and so we don't need multiple streams which then get
    # re-multiplexed in the API server anyhow.
365
    async def generate(
366
        self,
367
        prompt: EngineCoreRequest | PromptType,
368
369
        sampling_params: SamplingParams,
        request_id: str,
370
        *,
371
372
373
374
        prompt_text: str | None = None,
        lora_request: LoRARequest | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        trace_headers: Mapping[str, str] | None = None,
375
        priority: int = 0,
376
        data_parallel_rank: int | None = None,
377
378
379
380
    ) -> AsyncGenerator[RequestOutput, None]:
        """
        Main function called by the API server to kick off a request
            * 1) Making an AsyncStream corresponding to the Request.
381
            * 2) Processing the Input.
382
383
384
            * 3) Adding the Request to the Detokenizer.
            * 4) Adding the Request to the EngineCore (separate process).

385
386
        A separate output_handler loop runs in a background AsyncIO task,
        pulling outputs from EngineCore and putting them into the
387
388
389
390
391
392
        per-request AsyncStream.

        The caller of generate() iterates the returned AsyncGenerator,
        returning the RequestOutput back to the caller.
        """

393
394
395
396
        if (
            self.vllm_config.cache_config.kv_sharing_fast_prefill
            and sampling_params.prompt_logprobs
        ):
397
398
399
            raise ValueError(
                "--kv-sharing-fast-prefill produces incorrect logprobs for "
                "prompt tokens, please disable it when the requests need "
400
401
                "prompt logprobs"
            )
402

403
404
405
406
        try:
            # We start the output_handler on the first call to generate() so
            # we can call __init__ before the event loop, which enables us
            # to handle startup failure gracefully in the OpenAI server.
407
            self._run_output_handler()
408

409
410
411
412
            # Wait until generation is resumed if the engine is paused.
            async with self._pause_cond:
                await self._pause_cond.wait_for(lambda: not self._paused)

413
414
415
416
417
418
419
420
421
422
            if tokenization_kwargs is None:
                tokenization_kwargs = {}
                truncate_prompt_tokens = sampling_params.truncate_prompt_tokens

                _validate_truncation_size(
                    self.model_config.max_model_len,
                    truncate_prompt_tokens,
                    tokenization_kwargs,
                )

423
424
425
426
427
428
429
430
431
432
433
            q = await self.add_request(
                request_id,
                prompt,
                sampling_params,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
                trace_headers=trace_headers,
                priority=priority,
                data_parallel_rank=data_parallel_rank,
                prompt_text=prompt_text,
            )
434

435
436
            # The output_handler task pushes items into the queue.
            # This task pulls from the queue and yields to caller.
437
438
            finished = False
            while not finished:
439
440
                # Note: drain queue without await if possible (avoids
                # task switching under load which helps performance).
441
                out = q.get_nowait() or await q.get()
442

443
                # Note: both OutputProcessor and EngineCore handle their
444
                # own request cleanup based on finished.
445
                finished = out.finished
446
                assert isinstance(out, RequestOutput)
447
448
                yield out

449
        # If the request is disconnected by the client, generate()
450
451
452
        # is cancelled or the generator is garbage collected. So,
        # we abort the request if we end up here.
        except (asyncio.CancelledError, GeneratorExit):
453
            await self.abort(request_id)
454
455
            if self.log_requests:
                logger.info("Request %s aborted.", request_id)
456
            raise
457

458
459
460
461
462
        # Engine is dead. Do not abort since we shut down.
        except EngineDeadError:
            if self.log_requests:
                logger.info("Request %s failed (engine dead).", request_id)
            raise
463

464
465
466
467
468
        # Request validation error.
        except ValueError:
            if self.log_requests:
                logger.info("Request %s failed (bad request).", request_id)
            raise
469

470
        # Unexpected error in the generate() task (possibly recoverable).
471
        except Exception as e:
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s failed.", request_id)
            raise EngineGenerateError() from e

    def _run_output_handler(self):
        """Background loop: pulls from EngineCore and pushes to AsyncStreams."""

        if self.output_handler is not None:
            return

        # Ensure that the task doesn't have a circular ref back to the AsyncLLM
        # object, or else it won't be garbage collected and cleaned up properly.
        engine_core = self.engine_core
        output_processor = self.output_processor
        log_stats = self.log_stats
488
        logger_manager = self.logger_manager
489
        input_processor = self.input_processor
490
491
492
493
494
495
496
497

        async def output_handler():
            try:
                while True:
                    # 1) Pull EngineCoreOutputs from the EngineCore.
                    outputs = await engine_core.get_output_async()
                    num_outputs = len(outputs.outputs)

498
499
500
                    iteration_stats = (
                        IterationStats() if (log_stats and num_outputs) else None
                    )
501
502
503
504

                    # Split outputs into chunks of at most
                    # VLLM_V1_OUTPUT_PROC_CHUNK_SIZE, so that we don't block the
                    # event loop for too long.
505
                    if num_outputs <= envs.VLLM_V1_OUTPUT_PROC_CHUNK_SIZE:
506
                        slices = (outputs.outputs,)
507
508
509
                    else:
                        slices = np.array_split(
                            outputs.outputs,
510
                            cdiv(num_outputs, envs.VLLM_V1_OUTPUT_PROC_CHUNK_SIZE),
511
                        )
512
513
514
515

                    for i, outputs_slice in enumerate(slices):
                        # 2) Process EngineCoreOutputs.
                        processed_outputs = output_processor.process_outputs(
516
517
                            outputs_slice, outputs.timestamp, iteration_stats
                        )
518
519
520
521
522
523
524
525
526
                        # NOTE: RequestOutputs are pushed to their queues.
                        assert not processed_outputs.request_outputs

                        # Allow other asyncio tasks to run between chunks
                        if i + 1 < len(slices):
                            await asyncio.sleep(0)

                        # 3) Abort any reqs that finished due to stop strings.
                        await engine_core.abort_requests_async(
527
528
                            processed_outputs.reqs_to_abort
                        )
529

530
531
                    output_processor.update_scheduler_stats(outputs.scheduler_stats)

532
533
534
                    # 4) Logging.
                    # TODO(rob): make into a coroutine and launch it in
                    # background thread once Prometheus overhead is non-trivial.
535
536
537
                    if logger_manager:
                        logger_manager.record(
                            engine_idx=outputs.engine_index,
538
539
                            scheduler_stats=outputs.scheduler_stats,
                            iteration_stats=iteration_stats,
540
                            mm_cache_stats=input_processor.stat_mm_cache(),
541
542
543
544
545
546
                        )
            except Exception as e:
                logger.exception("AsyncLLM output_handler failed.")
                output_processor.propagate_error(e)

        self.output_handler = asyncio.create_task(output_handler())
547

548
    async def abort(self, request_id: str | Iterable[str]) -> None:
549
        """Abort RequestId in OutputProcessor and EngineCore."""
550

551
552
553
        request_ids = (
            (request_id,) if isinstance(request_id, str) else as_list(request_id)
        )
554
555
        all_request_ids = self.output_processor.abort_requests(request_ids)
        await self.engine_core.abort_requests_async(all_request_ids)
556

557
        if self.log_requests:
558
            logger.info("Aborted request(s) %s.", ",".join(request_ids))
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
    async def pause_generation(
        self,
        *,
        wait_for_inflight_requests: bool = False,
        clear_cache: bool = True,
    ) -> None:
        """
        Pause generation to allow model weight updates.

        New generation/encoding requests are blocked until resume.

        Args:
            wait_for_inflight_requests: When ``True`` waits for in-flight
                requests to finish before pausing. When ``False`` (default),
                immediately aborts any in-flight requests.
            clear_cache: Whether to clear KV cache and prefix cache after
                draining. Set to ``False`` to preserve cache for faster resume.
                Default is ``True`` (clear caches).
        """

        async with self._pause_cond:
            if self._paused:
                return
            self._paused = True

        if not wait_for_inflight_requests:
            request_ids = list(self.output_processor.request_states.keys())
            if request_ids:
                await self.abort(request_ids)

        # Wait for running requests to drain before clearing cache.
        if self.output_processor.has_unfinished_requests():
            await self.output_processor.wait_for_requests_to_drain()

        # Clear cache
        if clear_cache:
            await self.reset_prefix_cache()
            await self.reset_mm_cache()

    async def resume_generation(self) -> None:
        """Resume generation after :meth:`pause_generation`."""

        async with self._pause_cond:
            self._paused = False
            self._pause_cond.notify_all()  # Wake up all waiting requests

    async def is_paused(self) -> bool:
        """Return whether the engine is currently paused."""

        async with self._pause_cond:
            return self._paused

612
    async def encode(
613
614
615
616
        self,
        prompt: PromptType,
        pooling_params: PoolingParams,
        request_id: str,
617
618
        lora_request: LoRARequest | None = None,
        trace_headers: Mapping[str, str] | None = None,
619
        priority: int = 0,
620
621
        truncate_prompt_tokens: int | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
    ) -> AsyncGenerator[PoolingRequestOutput, None]:
        """
        Main function called by the API server to kick off a request
            * 1) Making an AsyncStream corresponding to the Request.
            * 2) Processing the Input.
            * 3) Adding the Request to the EngineCore (separate process).

        A separate output_handler loop runs in a background AsyncIO task,
        pulling outputs from EngineCore and putting them into the
        per-request AsyncStream.

        The caller of generate() iterates the returned AsyncGenerator,
        returning the RequestOutput back to the caller.
        """

        try:
            # We start the output_handler on the first call to generate() so
            # we can call __init__ before the event loop, which enables us
            # to handle startup failure gracefully in the OpenAI server.
            self._run_output_handler()

643
644
645
646
            # Respect pause state before accepting new requests.
            async with self._pause_cond:
                await self._pause_cond.wait_for(lambda: not self._paused)

647
            if tokenization_kwargs is None:
648
                tokenization_kwargs = {}
649
650
651
652
653
654
            _validate_truncation_size(
                self.model_config.max_model_len,
                truncate_prompt_tokens,
                tokenization_kwargs,
            )

655
656
657
658
659
            q = await self.add_request(
                request_id,
                prompt,
                pooling_params,
                lora_request=lora_request,
660
                tokenization_kwargs=tokenization_kwargs,
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
                trace_headers=trace_headers,
                priority=priority,
            )

            # The output_handler task pushes items into the queue.
            # This task pulls from the queue and yields to caller.
            finished = False
            while not finished:
                # Note: drain queue without await if possible (avoids
                # task switching under load which helps performance).
                out = q.get_nowait() or await q.get()
                assert isinstance(out, PoolingRequestOutput)
                # Note: both OutputProcessor and EngineCore handle their
                # own request cleanup based on finished.
                finished = out.finished
                yield out

        # If the request is disconnected by the client, generate()
        # is cancelled. So, we abort the request if we end up here.
        except asyncio.CancelledError:
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s aborted.", request_id)
            raise

        # Engine is dead. Do not abort since we shut down.
        except EngineDeadError:
            if self.log_requests:
                logger.info("Request %s failed (engine dead).", request_id)
            raise

        # Request validation error.
        except ValueError:
            if self.log_requests:
                logger.info("Request %s failed (bad request).", request_id)
            raise

        # Unexpected error in the generate() task (possibly recoverable).
        except Exception as e:
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s failed.", request_id)
            raise EngineGenerateError() from e
704

705
    @property
706
    def tokenizer(self) -> AnyTokenizer | None:
707
        return self.input_processor.tokenizer
708

709
    @tokenizer.setter
710
    def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
711
        self.input_processor.tokenizer = tokenizer
712

713
    async def get_tokenizer(self) -> AnyTokenizer:
714
        if self.tokenizer is None:
715
716
717
            raise ValueError(
                "Unable to get tokenizer because skip_tokenizer_init is True"
            )
718

719
        return self.tokenizer
720
721

    async def is_tracing_enabled(self) -> bool:
722
        return self.observability_config.otlp_traces_endpoint is not None  # type: ignore
723

724
    async def do_log_stats(self) -> None:
725
726
        if self.logger_manager:
            self.logger_manager.log()
727
728
729

    async def check_health(self) -> None:
        logger.debug("Called check_health.")
730
731
        if self.errored:
            raise self.dead_error
732
733

    async def start_profile(self) -> None:
734
735
736
737
        coros = [self.engine_core.profile_async(True)]
        if self.profiler is not None:
            coros.append(asyncio.to_thread(self.profiler.start))
        await asyncio.gather(*coros)
738
739

    async def stop_profile(self) -> None:
740
741
742
743
        coros = [self.engine_core.profile_async(False)]
        if self.profiler is not None:
            coros.append(asyncio.to_thread(self.profiler.stop))
        await asyncio.gather(*coros)
744

745
    async def reset_mm_cache(self) -> None:
746
        self.input_processor.clear_mm_cache()
747
748
        await self.engine_core.reset_mm_cache_async()

749
    async def reset_prefix_cache(self) -> None:
750
751
        await self.engine_core.reset_prefix_cache_async()

752
    async def sleep(self, level: int = 1) -> None:
753
        await self.reset_prefix_cache()
754
755
        await self.engine_core.sleep_async(level)

756
757
758
        if self.logger_manager is not None:
            self.logger_manager.record_sleep_state(1, level)

759
    async def wake_up(self, tags: list[str] | None = None) -> None:
760
        await self.engine_core.wake_up_async(tags)
761

762
763
764
        if self.logger_manager is not None:
            self.logger_manager.record_sleep_state(0, 0)

765
766
767
    async def is_sleeping(self) -> bool:
        return await self.engine_core.is_sleeping_async()

768
    async def add_lora(self, lora_request: LoRARequest) -> bool:
769
        """Load a new LoRA adapter into the engine for future requests."""
770
771
772
773
774
775
        return await self.engine_core.add_lora_async(lora_request)

    async def remove_lora(self, lora_id: int) -> bool:
        """Remove an already loaded LoRA adapter."""
        return await self.engine_core.remove_lora_async(lora_id)

776
    async def list_loras(self) -> set[int]:
777
778
779
780
781
782
        """List all registered adapters."""
        return await self.engine_core.list_loras_async()

    async def pin_lora(self, lora_id: int) -> bool:
        """Prevent an adapter from being evicted."""
        return await self.engine_core.pin_lora_async(lora_id)
783

784
785
786
    async def collective_rpc(
        self,
        method: str,
787
        timeout: float | None = None,
788
        args: tuple = (),
789
        kwargs: dict | None = None,
790
    ):
791
792
793
794
        """
        Perform a collective RPC call to the given path.
        """
        return await self.engine_core.collective_rpc_async(
795
796
            method, timeout, args, kwargs
        )
797

798
799
800
801
802
803
804
805
    async def wait_for_requests_to_drain(self, drain_timeout: int = 300):
        """Wait for all requests to be drained."""
        start_time = time.time()
        while time.time() - start_time < drain_timeout:
            if not self.engine_core.dp_engines_running():
                logger.info("Engines are idle, requests have been drained")
                return

806
            logger.info("Engines are still running, waiting for requests to drain...")
807
808
            await asyncio.sleep(1)  # Wait 1 second before checking again

809
810
811
812
        raise TimeoutError(
            f"Timeout reached after {drain_timeout} seconds "
            "waiting for requests to drain."
        )
813

814
815
816
    async def scale_elastic_ep(
        self, new_data_parallel_size: int, drain_timeout: int = 300
    ):
817
818
819
820
821
822
823
824
        """
        Scale up or down the data parallel size by adding or removing
        engine cores.
        Args:
            new_data_parallel_size: The new number of data parallel workers
            drain_timeout:
                Maximum time to wait for requests to drain (seconds)
        """
825
        old_data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
826
        if old_data_parallel_size == new_data_parallel_size:
827
828
829
830
            logger.info(
                "Data parallel size is already %s, skipping scale",
                new_data_parallel_size,
            )
831
832
            return
        logger.info(
833
834
835
            "Waiting for requests to drain before scaling up to %s engines...",
            new_data_parallel_size,
        )
836
837
        await self.wait_for_requests_to_drain(drain_timeout)
        logger.info(
838
839
840
            "Requests have been drained, proceeding with scale to %s engines",
            new_data_parallel_size,
        )
841
        await self.engine_core.scale_elastic_ep(new_data_parallel_size)
842
        self.vllm_config.parallel_config.data_parallel_size = new_data_parallel_size
843
844

        # recreate stat loggers
845
846
847
848
849
850
        if new_data_parallel_size > old_data_parallel_size and self.log_stats:
            # TODO(rob): fix this after talking with Ray team.
            # This resets all the prometheus metrics since we
            # unregister during initialization. Need to understand
            # the intended behavior here better.
            self.logger_manager = StatLoggerManager(
851
                vllm_config=self.vllm_config,
852
                engine_idxs=list(range(new_data_parallel_size)),
853
854
855
                custom_stat_loggers=None,
            )

856
857
    @property
    def is_running(self) -> bool:
858
859
        # Is None before the loop is started.
        return self.output_handler is None or not self.output_handler.done()
860
861
862

    @property
    def is_stopped(self) -> bool:
863
        return self.errored
864
865
866

    @property
    def errored(self) -> bool:
867
        return self.engine_core.resources.engine_dead or not self.is_running
868
869
870

    @property
    def dead_error(self) -> BaseException:
871
        return EngineDeadError()