async_llm.py 21.7 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
import asyncio
3
from collections.abc import AsyncGenerator, Mapping
4
from copy import copy
5
from typing import Any, Optional, Union
6

7
8
import numpy as np

9
import vllm.envs as envs
10
11
12
from vllm.config import ModelConfig, VllmConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.protocol import EngineClient
13
from vllm.envs import VLLM_V1_OUTPUT_PROC_CHUNK_SIZE
14
from vllm.inputs import PromptType
15
from vllm.inputs.preprocess import InputPreprocessor
16
17
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
18
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
19
from vllm.outputs import RequestOutput
20
21
from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
22
from vllm.sampling_params import SamplingParams
23
24
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
25
26
27
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
from vllm.usage.usage_lib import UsageContext
28
from vllm.utils import Device, cdiv
29
from vllm.v1.engine import EngineCoreRequest
Rui Qiao's avatar
Rui Qiao committed
30
31
from vllm.v1.engine.core_client import (AsyncMPClient, DPAsyncMPClient,
                                        RayDPClient)
32
from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
33
34
from vllm.v1.engine.output_processor import (OutputProcessor,
                                             RequestOutputCollector)
35
from vllm.v1.engine.parallel_sampling import ParentRequest
36
from vllm.v1.engine.processor import Processor
37
from vllm.v1.executor.abstract import Executor
38
39
from vllm.v1.metrics.loggers import (StatLoggerBase, StatLoggerFactory,
                                     setup_default_loggers)
40
from vllm.v1.metrics.prometheus import shutdown_prometheus
41
from vllm.v1.metrics.stats import IterationStats, SchedulerStats
42
43
44
45
46
47
48
49
50

logger = init_logger(__name__)


class AsyncLLM(EngineClient):

    def __init__(
        self,
        vllm_config: VllmConfig,
51
        executor_class: type[Executor],
52
53
        log_stats: bool,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
54
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
55
56
57
        use_cached_outputs: bool = False,
        log_requests: bool = True,
        start_engine_loop: bool = True,
58
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
59
60
        client_addresses: Optional[dict[str, str]] = None,
        client_index: int = 0,
61
    ) -> None:
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
        """
        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
        """
82
83
84
85
86
87
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github.")
88

89
90
91
        # Ensure we can serialize custom transformer configs
        maybe_register_config_serialize_by_value()

92
        self.model_config = vllm_config.model_config
93
        self.vllm_config = vllm_config
94
95
        self.log_requests = log_requests
        self.log_stats = log_stats
96
97

        # Set up stat loggers; independent set for each DP rank.
98
99
100
101
102
103
        self.stat_loggers: list[list[StatLoggerBase]] = setup_default_loggers(
            vllm_config=vllm_config,
            log_stats=self.log_stats,
            engine_num=vllm_config.parallel_config.data_parallel_size,
            custom_stat_loggers=stat_loggers,
        )
104
105
106
107
108

        # Tokenizer (+ ensure liveness if running in another process).
        self.tokenizer = init_tokenizer_from_configs(
            model_config=vllm_config.model_config,
            scheduler_config=vllm_config.scheduler_config,
109
            lora_config=vllm_config.lora_config)
110
111

        # Processor (converts Inputs --> EngineCoreRequests).
112
        self.processor = Processor(
113
            vllm_config=vllm_config,
114
            tokenizer=self.tokenizer,
115
            mm_registry=mm_registry,
116
        )
117

118
119
120
        # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
        self.output_processor = OutputProcessor(self.tokenizer,
                                                log_stats=self.log_stats)
121
122

        # EngineCore (starts the engine in background process).
Rui Qiao's avatar
Rui Qiao committed
123
124
125
126
127
128
129
        core_client_class: type[AsyncMPClient]
        if vllm_config.parallel_config.data_parallel_size == 1:
            core_client_class = AsyncMPClient
        elif vllm_config.parallel_config.data_parallel_backend == "ray":
            core_client_class = RayDPClient
        else:
            core_client_class = DPAsyncMPClient
130
131

        self.engine_core = core_client_class(
132
133
            vllm_config=vllm_config,
            executor_class=executor_class,
134
            log_stats=self.log_stats,
135
136
            client_addresses=client_addresses,
            client_index=client_index,
137
        )
138
139
140
        if self.stat_loggers:
            for stat_logger in self.stat_loggers[0]:
                stat_logger.log_engine_initialized()
141
        self.output_handler: Optional[asyncio.Task] = None
142
143
144
145
146
147
        try:
            # Start output handler eagerly if we are in the asyncio eventloop.
            asyncio.get_running_loop()
            self._run_output_handler()
        except RuntimeError:
            pass
148

149
150
151
152
153
154
    @classmethod
    def from_vllm_config(
        cls,
        vllm_config: VllmConfig,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
155
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
156
157
        disable_log_requests: bool = False,
        disable_log_stats: bool = False,
158
159
        client_addresses: Optional[dict[str, str]] = None,
        client_index: int = 0,
160
161
162
163
164
165
166
167
168
169
170
171
172
    ) -> "AsyncLLM":
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github.")

        # Create the LLMEngine.
        return cls(
            vllm_config=vllm_config,
            executor_class=Executor.get_class(vllm_config),
            start_engine_loop=start_engine_loop,
173
            stat_loggers=stat_loggers,
174
175
176
            log_requests=not disable_log_requests,
            log_stats=not disable_log_stats,
            usage_context=usage_context,
177
178
            client_addresses=client_addresses,
            client_index=client_index,
179
180
        )

181
182
183
184
185
186
    @classmethod
    def from_engine_args(
        cls,
        engine_args: AsyncEngineArgs,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
187
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
188
    ) -> "AsyncLLM":
189
190
191
        """Create an AsyncLLM from the EngineArgs."""

        # Create the engine configs.
192
        vllm_config = engine_args.create_engine_config(usage_context)
193
        executor_class = Executor.get_class(vllm_config)
194
195
196
197
198
199
200
201
202

        # Create the AsyncLLM.
        return cls(
            vllm_config=vllm_config,
            executor_class=executor_class,
            log_requests=not engine_args.disable_log_requests,
            log_stats=not engine_args.disable_log_stats,
            start_engine_loop=start_engine_loop,
            usage_context=usage_context,
203
            stat_loggers=stat_loggers,
204
205
        )

206
207
208
    def __del__(self):
        self.shutdown()

209
210
211
    def shutdown(self):
        """Shutdown, cleaning up the background proc and IPC."""

212
213
        shutdown_prometheus()

214
215
        if engine_core := getattr(self, "engine_core", None):
            engine_core.shutdown()
216
217
218
219
220
221
222
223
224
225
226

        if handler := getattr(self, "output_handler", None):
            handler.cancel()

    async def add_request(
        self,
        request_id: str,
        prompt: PromptType,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: Optional[float] = None,
        lora_request: Optional[LoRARequest] = None,
227
        tokenization_kwargs: Optional[dict[str, Any]] = None,
228
229
230
        trace_headers: Optional[Mapping[str, str]] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        priority: int = 0,
231
    ) -> RequestOutputCollector:
232
233
        """Add new request to the AsyncLLM."""

234
235
236
        if self.errored:
            raise EngineDeadError()

237
238
239
240
241
        assert isinstance(params, SamplingParams), \
            "Pooling is not supported in V1"

        # Create a new output collector for the request.
        queue = RequestOutputCollector(output_kind=params.output_kind)
242

243
        # Convert Input --> Request.
244
245
        prompt_str, request = self.processor.process_inputs(
            request_id, prompt, params, arrival_time, lora_request,
246
247
            tokenization_kwargs, trace_headers, prompt_adapter_request,
            priority)
248

249
        if params.n == 1:
250
            await self._add_request(request, prompt_str, None, 0, queue)
251
252
253
254
            return queue

        # Fan out child requests (for n>1).
        parent_request = ParentRequest(request_id, params)
255
        for idx in range(params.n):
256
            request_id, params = parent_request.get_child_info(idx)
257
            child_request = request if idx == params.n - 1 else copy(request)
258
259
            child_request.request_id = request_id
            child_request.sampling_params = params
260
261
            await self._add_request(child_request, prompt_str, parent_request,
                                    idx, queue)
262
        return queue
263

264
    async def _add_request(self, request: EngineCoreRequest,
265
                           prompt: Optional[str],
266
                           parent_req: Optional[ParentRequest], index: int,
267
                           queue: RequestOutputCollector):
268

269
        # Add the request to OutputProcessor (this process).
270
271
        self.output_processor.add_request(request, prompt, parent_req, index,
                                          queue)
272

273
274
        # Add the EngineCoreRequest to EngineCore (separate process).
        await self.engine_core.add_request_async(request)
275

276
277
        if self.log_requests:
            logger.info("Added request %s.", request.request_id)
278
279
280
281
282
283

    # 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.
284
    async def generate(
285
286
287
288
289
290
291
292
293
294
295
296
        self,
        prompt: PromptType,
        sampling_params: SamplingParams,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        priority: int = 0,
    ) -> AsyncGenerator[RequestOutput, None]:
        """
        Main function called by the API server to kick off a request
            * 1) Making an AsyncStream corresponding to the Request.
297
            * 2) Processing the Input.
298
299
300
            * 3) Adding the Request to the Detokenizer.
            * 4) Adding the Request to the EngineCore (separate process).

301
302
        A separate output_handler loop runs in a background AsyncIO task,
        pulling outputs from EngineCore and putting them into the
303
304
305
306
307
308
        per-request AsyncStream.

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

309
310
311
312
        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.
313
            self._run_output_handler()
314
315

            q = await self.add_request(
316
317
318
319
320
321
322
                request_id,
                prompt,
                sampling_params,
                lora_request=lora_request,
                trace_headers=trace_headers,
                prompt_adapter_request=prompt_adapter_request,
                priority=priority,
323
            )
324

325
326
            # The output_handler task pushes items into the queue.
            # This task pulls from the queue and yields to caller.
327
328
            finished = False
            while not finished:
329
330
                # Note: drain queue without await if possible (avoids
                # task switching under load which helps performance).
331
                out = q.get_nowait() or await q.get()
332

333
                # Note: both OutputProcessor and EngineCore handle their
334
                # own request cleanup based on finished.
335
                finished = out.finished
336
337
                yield out

338
339
        # If the request is disconnected by the client, generate()
        # is cancelled. So, we abort the request if we end up here.
340
341
        except asyncio.CancelledError:
            await self.abort(request_id)
342
343
            if self.log_requests:
                logger.info("Request %s aborted.", request_id)
344
            raise
345

346
347
348
349
350
        # 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
351

352
353
354
355
356
        # Request validation error.
        except ValueError:
            if self.log_requests:
                logger.info("Request %s failed (bad request).", request_id)
            raise
357

358
        # Unexpected error in the generate() task (possibly recoverable).
359
        except Exception as e:
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
            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
        stat_loggers = self.stat_loggers if log_stats else None

        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)

                    iteration_stats = IterationStats() if (
                        log_stats and num_outputs) else None

                    # 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.
                    if num_outputs <= VLLM_V1_OUTPUT_PROC_CHUNK_SIZE:
                        slices = (outputs.outputs, )
                    else:
                        slices = np.array_split(
                            outputs.outputs,
                            cdiv(num_outputs, VLLM_V1_OUTPUT_PROC_CHUNK_SIZE))

                    for i, outputs_slice in enumerate(slices):
                        # 2) Process EngineCoreOutputs.
                        processed_outputs = output_processor.process_outputs(
                            outputs_slice, outputs.timestamp, iteration_stats)
                        # 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(
                            processed_outputs.reqs_to_abort)

                    # 4) Logging.
                    # TODO(rob): make into a coroutine and launch it in
                    # background thread once Prometheus overhead is non-trivial.
                    if stat_loggers:
                        AsyncLLM._record_stats(
                            stat_loggers[outputs.engine_index],
                            scheduler_stats=outputs.scheduler_stats,
                            iteration_stats=iteration_stats,
                        )
            except Exception as e:
                logger.exception("AsyncLLM output_handler failed.")
                output_processor.propagate_error(e)

        self.output_handler = asyncio.create_task(output_handler())
427
428

    async def abort(self, request_id: str) -> None:
429
        """Abort RequestId in OutputProcessor and EngineCore."""
430

431
        request_ids = self.output_processor.abort_requests((request_id, ))
432
433
        await self.engine_core.abort_requests_async(request_ids)

434
435
        if self.log_requests:
            logger.info("Aborted request %s.", request_id)
436

437
    @staticmethod
438
    def _record_stats(
439
        stat_loggers: list[StatLoggerBase],
440
        scheduler_stats: Optional[SchedulerStats],
441
        iteration_stats: Optional[IterationStats],
442
    ):
443
444
445
        """static so that it can be used from the output_handler task
        without a circular ref to AsyncLLM."""
        for stat_logger in stat_loggers:
446
447
            stat_logger.record(scheduler_stats=scheduler_stats,
                               iteration_stats=iteration_stats)
448

449
450
451
452
453
454
455
456
457
458
459
    def encode(
        self,
        prompt: PromptType,
        pooling_params: PoolingParams,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
    ):
        raise ValueError("Not Supported on V1 yet.")

460
461
462
    async def get_vllm_config(self) -> VllmConfig:
        return self.vllm_config

463
464
465
466
467
468
    async def get_model_config(self) -> ModelConfig:
        return self.model_config

    async def get_decoding_config(self):
        raise ValueError("Not Supported on V1 yet.")

469
470
471
    async def get_input_preprocessor(self) -> InputPreprocessor:
        return self.processor.input_preprocessor

472
473
474
475
    async def get_tokenizer(
        self,
        lora_request: Optional[LoRARequest] = None,
    ) -> AnyTokenizer:
476
        return self.tokenizer.get_lora_tokenizer(lora_request)
477
478
479
480
481
482
483
484
485

    async def is_tracing_enabled(self) -> bool:
        return False

    async def do_log_stats(
        self,
        scheduler_outputs=None,
        model_output=None,
    ) -> None:
486
487
488
        for loggers in self.stat_loggers:
            for stat_logger in loggers:
                stat_logger.log()
489
490
491
492
493

    async def check_health(self) -> None:
        logger.debug("Called check_health.")

    async def start_profile(self) -> None:
494
        await self.engine_core.profile_async(True)
495
496

    async def stop_profile(self) -> None:
497
        await self.engine_core.profile_async(False)
498

499
500
501
502
503
    async def reset_mm_cache(self) -> None:
        self.processor.mm_registry.reset_processor_cache()
        self.processor.mm_input_cache_client.reset()
        await self.engine_core.reset_mm_cache_async()

504
505
506
507
    async def reset_prefix_cache(self,
                                 device: Optional[Device] = None) -> None:
        if device == Device.CPU:
            raise ValueError("Not supported on CPU.")
508
509
        await self.engine_core.reset_prefix_cache_async()

510
511
512
    async def sleep(self, level: int = 1) -> None:
        await self.engine_core.sleep_async(level)

513
514
    async def wake_up(self, tags: Optional[list[str]] = None) -> None:
        await self.engine_core.wake_up_async(tags)
515

516
517
518
    async def is_sleeping(self) -> bool:
        return await self.engine_core.is_sleeping_async()

519
    async def add_lora(self, lora_request: LoRARequest) -> bool:
520
        """Load a new LoRA adapter into the engine for future requests."""
521
522
523
524
525
526
        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)

527
    async def list_loras(self) -> set[int]:
528
529
530
531
532
533
        """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)
534

535
536
537
538
539
540
541
542
543
544
545
    async def collective_rpc(self,
                             method: str,
                             timeout: Optional[float] = None,
                             args: tuple = (),
                             kwargs: Optional[dict] = None):
        """
        Perform a collective RPC call to the given path.
        """
        return await self.engine_core.collective_rpc_async(
            method, timeout, args, kwargs)

546
547
    @property
    def is_running(self) -> bool:
548
549
        # Is None before the loop is started.
        return self.output_handler is None or not self.output_handler.done()
550
551
552

    @property
    def is_stopped(self) -> bool:
553
        return self.errored
554
555
556

    @property
    def errored(self) -> bool:
557
        return self.engine_core.resources.engine_dead or not self.is_running
558
559
560

    @property
    def dead_error(self) -> BaseException:
561
        return EngineDeadError()