async_llm_engine.py 45.1 KB
Newer Older
1
2
import asyncio
import time
Antoni Baum's avatar
Antoni Baum committed
3
from functools import partial
4
from typing import (AsyncGenerator, Callable, Dict, Iterable, List, Mapping,
5
                    Optional, Set, Tuple, Type, Union)
6

7
from transformers import PreTrainedTokenizer
8
from typing_extensions import assert_never
9

10
import vllm.envs as envs
11
12
from vllm.config import (DecodingConfig, EngineConfig, LoRAConfig, ModelConfig,
                         ParallelConfig, SchedulerConfig)
13
from vllm.core.scheduler import SchedulerOutputs
Woosuk Kwon's avatar
Woosuk Kwon committed
14
from vllm.engine.arg_utils import AsyncEngineArgs
15
from vllm.engine.async_timeout import asyncio_timeout
16
17
from vllm.engine.llm_engine import (DecoderPromptComponents, LLMEngine,
                                    PromptComponents)
18
from vllm.engine.metrics import StatLoggerBase
19
from vllm.executor.executor_base import ExecutorAsyncBase
20
from vllm.executor.ray_utils import initialize_ray_cluster, ray
21
22
23
from vllm.inputs import (EncoderDecoderLLMInputs, LLMInputs, PromptInputs,
                         SingletonPromptInputs)
from vllm.inputs.parse import is_explicit_encoder_decoder_prompt
Woosuk Kwon's avatar
Woosuk Kwon committed
24
from vllm.logger import init_logger
25
from vllm.lora.request import LoRARequest
26
27
from vllm.outputs import EmbeddingRequestOutput, RequestOutput
from vllm.pooling_params import PoolingParams
28
from vllm.prompt_adapter.request import PromptAdapterRequest
Woosuk Kwon's avatar
Woosuk Kwon committed
29
from vllm.sampling_params import SamplingParams
30
from vllm.sequence import ExecuteModelRequest, SamplerOutput
yhu422's avatar
yhu422 committed
31
from vllm.usage.usage_lib import UsageContext
32
33

logger = init_logger(__name__)
34
ENGINE_ITERATION_TIMEOUT_S = envs.VLLM_ENGINE_ITERATION_TIMEOUT_S
35

Antoni Baum's avatar
Antoni Baum committed
36

37
38
39
40
class AsyncEngineDeadError(RuntimeError):
    pass


41
42
43
44
45
46
47
def _log_task_completion(task: asyncio.Task,
                         error_callback: Callable[[Exception], None]) -> None:
    """This function is only intended for the `engine.run_engine_loop()` task.

    In particular, that task runs a `while True` loop that can only exit if
    there is an exception.
    """
48
49

    exception = None
50
    try:
51
52
53
54
55
56
57
58
        return_value = task.result()
        raise AssertionError(
            f"The engine background task should never finish without an "
            f"exception. {return_value}")
    except asyncio.exceptions.CancelledError:
        # We assume that if the task is cancelled, we are gracefully shutting
        # down. This should only happen on program exit.
        logger.info("Engine is gracefully shutting down.")
59
60
61
62
63
    except Exception as e:
        exception = e
        logger.error("Engine background task failed", exc_info=e)
        error_callback(exception)
        raise AsyncEngineDeadError(
64
            "Task finished unexpectedly. This should never happen! "
65
            "Please open an issue on Github. See stack trace above for the "
66
            "actual cause.") from e
67
68


69
70
71
STOP_ITERATION = Exception()  # Sentinel


Antoni Baum's avatar
Antoni Baum committed
72
class AsyncStream:
73
    """A stream of RequestOutputs or EmbeddingRequestOutputs for a request
74
    that can be iterated over asynchronously via an async generator."""
Antoni Baum's avatar
Antoni Baum committed
75

76
    def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
Antoni Baum's avatar
Antoni Baum committed
77
        self.request_id = request_id
78
        self._cancel = cancel
79
        self._queue: asyncio.Queue = asyncio.Queue()
Antoni Baum's avatar
Antoni Baum committed
80
81
        self._finished = False

82
83
    def put(self, item: Union[RequestOutput, EmbeddingRequestOutput,
                              Exception]) -> None:
Antoni Baum's avatar
Antoni Baum committed
84
85
86
87
        if self._finished:
            return
        self._queue.put_nowait(item)

88
89
90
91
    def finish(
        self,
        exception: Optional[Union[BaseException, Type[BaseException]]] = None,
    ) -> None:
92
93
94
        if not self._finished:
            self._finished = True
            self._queue.put_nowait(
95
                exception if exception is not None else STOP_ITERATION)
Antoni Baum's avatar
Antoni Baum committed
96
97
98
99
100

    @property
    def finished(self) -> bool:
        return self._finished

101
102
103
104
105
106
107
108
109
110
111
112
113
114
    async def generator(
        self
    ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
        try:
            while not self._finished:
                result = await self._queue.get()
                if isinstance(result, Exception):
                    if result == STOP_ITERATION:
                        return
                    raise result
                yield result
        except GeneratorExit:
            self._cancel(self.request_id)
            raise asyncio.CancelledError from None
Antoni Baum's avatar
Antoni Baum committed
115
116


117
118
119
120
121
class RequestTracker:
    """Synchronous abstraction for tracking requests."""

    def __init__(self) -> None:
        self._request_streams: Dict[str, AsyncStream] = {}
122
        self._aborted_requests: asyncio.Queue[str] = asyncio.Queue()
123
124
        self._new_requests: asyncio.Queue[Tuple[AsyncStream,
                                                dict]] = asyncio.Queue()
125
        self.new_requests_event = asyncio.Event()
126
127
128
129

    def __contains__(self, item):
        return item in self._request_streams

130
131
    def __len__(self) -> int:
        return len(self._request_streams)
132
133
134
135
136
137
138

    def propagate_exception(self,
                            exc: Exception,
                            request_id: Optional[str] = None) -> None:
        """Propagate an exception to request streams
        (all if request_id is None)."""
        if request_id is not None:
139
            self.abort_request(request_id, exception=exc)
140
        else:
141
            # NB: tuple() used here because self.abort_request pops the stream
142
            # out of self._request_streams, so we can't iterate on it directly
143
144
            for rid in tuple(self._request_streams.keys()):
                self.abort_request(rid, exception=exc)
145
146

    def process_request_output(self,
147
148
                               request_output: Union[RequestOutput,
                                                     EmbeddingRequestOutput],
149
150
151
152
                               *,
                               verbose: bool = False) -> None:
        """Process a request output from the engine."""
        request_id = request_output.request_id
153
        finished = request_output.finished
154

155
156
157
158
        if finished:
            stream = self._request_streams.pop(request_id, None)
        else:
            stream = self._request_streams.get(request_id)
159
160
        # Guard against a KeyError which can occur if the request was aborted
        # while the output was generated
161
        if stream is not None:
162
            stream.put(request_output)
163
164
165
166
167
            if finished:
                stream.finish()

        if verbose and finished:
            logger.info("Finished request %s.", request_id)
168

169
170
    def process_exception(self,
                          request_id: str,
171
                          exception: BaseException,
172
173
174
175
                          *,
                          verbose: bool = False) -> None:
        """Propagate an exception from the engine."""
        if verbose:
176
            logger.info("Finished request %s.", request_id)
177
        self.abort_request(request_id, exception=exception)
178

179
180
181
182
    def add_request(self,
                    request_id: str,
                    *,
                    verbose: bool = False,
183
184
185
186
187
188
                    **engine_add_request_kwargs) -> AsyncStream:
        """Add a request to be sent to the engine on the next background
        loop iteration."""
        if request_id in self._request_streams:
            raise KeyError(f"Request {request_id} already exists.")

189
190
        abort_request = partial(self.abort_request, verbose=verbose)
        stream = AsyncStream(request_id, abort_request)
191
192
193
194
        self._new_requests.put_nowait((stream, {
            "request_id": request_id,
            **engine_add_request_kwargs
        }))
195
196
197

        self.new_requests_event.set()

198
199
200
        if verbose:
            logger.info("Added request %s.", request_id)

201
202
        return stream

203
204
205
    def abort_request(self,
                      request_id: str,
                      *,
206
207
                      exception: Optional[Union[BaseException,
                                                Type[BaseException]]] = None,
208
                      verbose: bool = False) -> None:
209
210
        """Abort a request during next background loop iteration."""
        if verbose:
211
            logger.info("Aborted request %s.", request_id)
212

213
        self._aborted_requests.put_nowait(request_id)
214

215
216
        stream = self._request_streams.pop(request_id, None)
        if stream is not None:
217
            stream.finish(exception=exception)
218

219
    def get_new_and_aborted_requests(self) -> Tuple[List[Dict], Set[str]]:
220
221
        """Get the new requests and finished requests to be
        sent to the engine."""
222
        new_requests: List[Dict] = []
223
224
        finished_requests: Set[str] = set()

225
226
        while not self._aborted_requests.empty():
            request_id = self._aborted_requests.get_nowait()
227
228
229
230
            finished_requests.add(request_id)

        while not self._new_requests.empty():
            stream, new_request = self._new_requests.get_nowait()
231
232
            request_id = stream.request_id
            if request_id in finished_requests:
233
                # The request has already been aborted.
234
235
236
237
238
                stream.finish(asyncio.CancelledError)
                finished_requests.discard(request_id)
            else:
                self._request_streams[request_id] = stream
                new_requests.append(new_request)
239
240

        return new_requests, finished_requests
Antoni Baum's avatar
Antoni Baum committed
241

242
    async def wait_for_new_requests(self):
243
244
245
246
247
248
        if not self.has_new_requests():
            await self.new_requests_event.wait()
        self.new_requests_event.clear()

    def has_new_requests(self):
        return not self._new_requests.empty()
249

Antoni Baum's avatar
Antoni Baum committed
250
251
252
253

class _AsyncLLMEngine(LLMEngine):
    """Extension of LLMEngine to add async methods."""

254
    async def step_async(
255
256
        self, virtual_engine: int
    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
Antoni Baum's avatar
Antoni Baum committed
257
258
259
260
261
262
263
264
265
        """Performs one decoding iteration and returns newly generated results.
        The workers are ran asynchronously if possible.

        This function performs one decoding iteration of the engine. It first
        schedules the sequences to be executed in the next iteration and the
        token blocks to be swapped in/out/copy. Then, it executes the model
        and updates the scheduler with the model outputs. Finally, it decodes
        the sequences and returns the newly generated results.
        """
266
267
        seq_group_metadata_list, scheduler_outputs = self.scheduler[
            virtual_engine].schedule()
Antoni Baum's avatar
Antoni Baum committed
268

269
270
        if not scheduler_outputs.is_empty():
            # Execute the model.
271
272
            finished_requests_ids = self.scheduler[
                virtual_engine].get_and_reset_finished_requests_ids()
273
274
275
276
277
            execute_model_req = ExecuteModelRequest(
                seq_group_metadata_list=seq_group_metadata_list,
                blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
                blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
                blocks_to_copy=scheduler_outputs.blocks_to_copy,
278
                virtual_engine=virtual_engine,
279
280
                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                running_queue_size=scheduler_outputs.running_queue_size,
Mor Zusman's avatar
Mor Zusman committed
281
                finished_requests_ids=finished_requests_ids)
282
            output = await self.model_executor.execute_model_async(
283
                execute_model_req)
284
285
        else:
            output = []
Antoni Baum's avatar
Antoni Baum committed
286

287
        request_outputs = self._process_model_outputs(
288
            output, scheduler_outputs.scheduled_seq_groups,
289
            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
Antoni Baum's avatar
Antoni Baum committed
290

291
        # Log stats.
292
        self.do_log_stats(scheduler_outputs, output)
293

294
295
296
        # Tracing
        self.do_tracing(scheduler_outputs)

297
298
        return request_outputs

299
300
301
302
    async def stop_remote_worker_execution_loop_async(self) -> None:
        """Stop the remote worker execution loop."""
        await self.model_executor.stop_remote_worker_execution_loop_async()

303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
    async def _tokenize_prompt_async(
        self,
        prompt: str,
        request_id: str,
        lora_request: Optional[LoRARequest],
    ) -> List[int]:
        """Async version of :meth:`_tokenize_prompt`."""
        tokenizer = self.get_tokenizer_group("prompts must be None if "
                                             "skip_tokenizer_init is True")

        return await tokenizer.encode_async(request_id=request_id,
                                            prompt=prompt,
                                            lora_request=lora_request)

    async def _extract_prompt_components_async(
318
        self,
319
        inputs: SingletonPromptInputs,
320
        request_id: str,
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
        lora_request: Optional[LoRARequest] = None,
    ) -> PromptComponents:
        """Async version of :meth:`_extract_prompt_components`."""
        if isinstance(inputs, str):
            prompt = inputs
            prompt_token_ids = await self._tokenize_prompt_async(
                prompt,
                request_id=request_id,
                lora_request=lora_request,
            )
            multi_modal_data = None
        elif isinstance(inputs, dict):
            if "prompt_token_ids" in inputs:
                prompt = None
                prompt_token_ids = inputs["prompt_token_ids"]
            else:
                # NOTE: This extra assignment is required to pass mypy
                prompt = parsed_prompt = inputs["prompt"]
                prompt_token_ids = await self._tokenize_prompt_async(
                    parsed_prompt,
                    request_id=request_id,
                    lora_request=lora_request,
                )

            multi_modal_data = inputs.get("multi_modal_data")
        else:
            assert_never(inputs)

        return prompt, prompt_token_ids, multi_modal_data

    async def _process_encoder_decoder_prompt_async(
        self,
353
        inputs: PromptInputs,
354
355
356
357
358
359
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
        request_id: str,
    ) -> EncoderDecoderLLMInputs:
        """Async version of :meth:`_process_encoder_decoder_prompt`."""
        encoder_comps: PromptComponents
        decoder_comps: DecoderPromptComponents

        if is_explicit_encoder_decoder_prompt(inputs):
            encoder_task = self._extract_prompt_components_async(
                inputs["encoder_prompt"],
                request_id=request_id,
            )

            if (decoder_input := inputs["decoder_prompt"]) is None:
                encoder_comps = await encoder_task
                decoder_comps = None, None, None
            else:
                decoder_task = self._extract_prompt_components_async(
                    decoder_input,
                    request_id=request_id,
                )

                encoder_comps, decoder_comps = await asyncio.gather(
                    encoder_task, decoder_task)
        else:
            encoder_comps = await self._extract_prompt_components_async(
                inputs,
                request_id=request_id,
            )

            decoder_comps = None, None, None

        return self._build_enc_dec_llm_inputs(encoder_comps, decoder_comps)

    async def _process_decoder_only_prompt_async(
        self,
        inputs: SingletonPromptInputs,
        request_id: str,
391
        lora_request: Optional[LoRARequest] = None,
392
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
393
    ) -> LLMInputs:
394
395
396
397
398
399
        """Async version of :meth:`_process_decoder_only_prompt`."""
        prompt_comps = await self._extract_prompt_components_async(
            inputs,
            request_id=request_id,
            lora_request=lora_request,
        )
400

401
402
403
404
        return self._build_decoder_only_llm_inputs(
            prompt_comps,
            prompt_adapter_request=prompt_adapter_request,
        )
405

406
407
408
409
410
411
412
413
414
415
416
417
418
    async def process_model_inputs_async(
        self,
        inputs: PromptInputs,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
    ) -> Union[LLMInputs, EncoderDecoderLLMInputs]:
        """Async version of :meth:`process_model_inputs`."""
        if self.is_encoder_decoder_model():
            # Encoder-decoder model requires special mapping of
            # input prompts to encoder & decoder
            model_inputs = await self._process_encoder_decoder_prompt_async(
                inputs,
419
                request_id=request_id,
420
            )
421
        else:
422
423
424
            if is_explicit_encoder_decoder_prompt(inputs):
                raise ValueError("Cannot pass encoder-decoder prompt "
                                 "to decoder-only models")
425

426
427
428
429
430
431
432
            # Decoder-only operation
            model_inputs = await self._process_decoder_only_prompt_async(
                inputs,
                request_id=request_id,
                lora_request=lora_request,
                prompt_adapter_request=prompt_adapter_request,
            )
433

434
        return self.input_processor(model_inputs)
435
436

    async def add_request_async(
437
438
439
440
441
442
443
444
        self,
        request_id: str,
        inputs: PromptInputs,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: Optional[float] = None,
        lora_request: Optional[LoRARequest] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
445
    ) -> None:
446
        """Async version of :meth:`add_request`."""
447
448
449
450
451
        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")
        if arrival_time is None:
            arrival_time = time.time()
452
453

        processed_inputs = await self.process_model_inputs_async(
454
            inputs,
455
456
            request_id=request_id,
            lora_request=lora_request,
457
458
            prompt_adapter_request=prompt_adapter_request,
        )
459
460

        self._add_processed_request(
461
            request_id=request_id,
462
463
464
465
            processed_inputs=processed_inputs,
            params=params,
            arrival_time=arrival_time,
            lora_request=lora_request,
466
            prompt_adapter_request=prompt_adapter_request,
467
            trace_headers=trace_headers,
468
        )
469

470
    async def check_health_async(self) -> None:
471
472
        if self.tokenizer:
            self.tokenizer.check_health()
473
        self.model_executor.check_health()
474

475

476
class AsyncLLMEngine:
477
    """An asynchronous wrapper for :class:`LLMEngine`.
478

479
480
481
482
483
    This class is used to wrap the :class:`LLMEngine` class to make it
    asynchronous. It uses asyncio to create a background loop that keeps
    processing incoming requests. The :class:`LLMEngine` is kicked by the
    generate method when there are requests in the waiting queue. The generate
    method yields the outputs from the :class:`LLMEngine` to the caller.
484
485
486
487
488

    Args:
        worker_use_ray: Whether to use Ray for model workers. Required for
            distributed execution. Should be the same as
            `parallel_config.worker_use_ray`.
Zhuohan Li's avatar
Zhuohan Li committed
489
        engine_use_ray: Whether to make LLMEngine a Ray actor. If so, the
490
491
            async frontend will be executed in a separate process as the
            model workers.
492
        log_requests: Whether to log the requests.
493
494
        start_engine_loop: If True, the background task to run the engine
            will be automatically started in the generate call.
495
496
        *args: Arguments for :class:`LLMEngine`.
        **kwargs: Arguments for :class:`LLMEngine`.
497
    """
498

Antoni Baum's avatar
Antoni Baum committed
499
500
    _engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine

501
502
503
504
505
    def __init__(self,
                 worker_use_ray: bool,
                 engine_use_ray: bool,
                 *args,
                 log_requests: bool = True,
506
                 start_engine_loop: bool = True,
507
                 **kwargs) -> None:
508
        self.worker_use_ray = worker_use_ray
Zhuohan Li's avatar
Zhuohan Li committed
509
        self.engine_use_ray = engine_use_ray
510
        self.log_requests = log_requests
Antoni Baum's avatar
Antoni Baum committed
511
512
        self.engine = self._init_engine(*args, **kwargs)

513
        self.background_loop: Optional[asyncio.Future] = None
514
515
516
        # We need to keep a reference to unshielded
        # task as well to prevent it from being garbage
        # collected
517
        self._background_loop_unshielded: Optional[asyncio.Task] = None
518
        self.start_engine_loop = start_engine_loop
519
        self._errored_with: Optional[BaseException] = None
Antoni Baum's avatar
Antoni Baum committed
520

521
522
523
        # Lazy initialized fields
        self._request_tracker: RequestTracker

524
    @classmethod
525
526
    def _get_executor_cls(
            cls, engine_config: EngineConfig) -> Type[ExecutorAsyncBase]:
527
528
        distributed_executor_backend = (
            engine_config.parallel_config.distributed_executor_backend)
529
530
531
532
533
534
535
536
537
        if isinstance(distributed_executor_backend, type):
            if not issubclass(distributed_executor_backend, ExecutorAsyncBase):
                raise TypeError(
                    "distributed_executor_backend must be a subclass of "
                    f"ExecutorAsyncBase. Got {distributed_executor_backend}.")
            if distributed_executor_backend.uses_ray:  # type: ignore
                initialize_ray_cluster(engine_config.parallel_config)
            executor_class = distributed_executor_backend
        elif engine_config.device_config.device_type == "neuron":
538
539
            from vllm.executor.neuron_executor import NeuronExecutorAsync
            executor_class = NeuronExecutorAsync
540
        elif engine_config.device_config.device_type == "tpu":
541
542
543
544
545
546
547
548
            if distributed_executor_backend == "ray":
                initialize_ray_cluster(engine_config.parallel_config)
                from vllm.executor.ray_tpu_executor import RayTPUExecutorAsync
                executor_class = RayTPUExecutorAsync
            else:
                assert distributed_executor_backend is None
                from vllm.executor.tpu_executor import TPUExecutorAsync
                executor_class = TPUExecutorAsync
549
550
551
        elif engine_config.device_config.device_type == "cpu":
            from vllm.executor.cpu_executor import CPUExecutorAsync
            executor_class = CPUExecutorAsync
552
553
554
555
556
557
        elif engine_config.device_config.device_type == "openvino":
            assert distributed_executor_backend is None, (
                "Distributed execution is not supported with "
                "the OpenVINO backend.")
            from vllm.executor.openvino_executor import OpenVINOExecutorAsync
            executor_class = OpenVINOExecutorAsync
558
559
560
561
562
563
564
565
566
567
568
        elif engine_config.device_config.device_type == "xpu":
            if distributed_executor_backend is None:
                from vllm.executor.xpu_executor import XPUExecutorAsync
                executor_class = XPUExecutorAsync
            elif distributed_executor_backend == "ray":
                initialize_ray_cluster(engine_config.parallel_config)
                from vllm.executor.ray_xpu_executor import RayXPUExecutorAsync
                executor_class = RayXPUExecutorAsync
            else:
                raise RuntimeError(
                    "Not supported distributed execution model on XPU device.")
569
        elif distributed_executor_backend == "ray":
570
            initialize_ray_cluster(engine_config.parallel_config)
571
572
            from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync
            executor_class = RayGPUExecutorAsync
573
574
575
576
        elif distributed_executor_backend == "mp":
            from vllm.executor.multiproc_gpu_executor import (
                MultiprocessingGPUExecutorAsync)
            executor_class = MultiprocessingGPUExecutorAsync
577
578
579
        else:
            from vllm.executor.gpu_executor import GPUExecutorAsync
            executor_class = GPUExecutorAsync
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
        return executor_class

    @classmethod
    def from_engine_args(
        cls,
        engine_args: AsyncEngineArgs,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
    ) -> "AsyncLLMEngine":
        """Creates an async LLM engine from the engine arguments."""
        # Create the engine configs.
        engine_config = engine_args.create_engine_config()

        if engine_args.engine_use_ray:
            from vllm.executor import ray_utils
            ray_utils.assert_ray_available()

        executor_class = cls._get_executor_cls(engine_config)

600
        # Create the async LLM engine.
yhu422's avatar
yhu422 committed
601
        engine = cls(
602
            executor_class.uses_ray,
yhu422's avatar
yhu422 committed
603
            engine_args.engine_use_ray,
604
605
            **engine_config.to_dict(),
            executor_class=executor_class,
yhu422's avatar
yhu422 committed
606
607
608
609
            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,
610
            stat_loggers=stat_loggers,
yhu422's avatar
yhu422 committed
611
        )
612
613
        return engine

614
615
    @property
    def is_running(self) -> bool:
616
        return (self.background_loop is not None
617
                and self._background_loop_unshielded is not None
618
619
620
621
                and not self._background_loop_unshielded.done())

    @property
    def is_stopped(self) -> bool:
622
623
        return self.errored or (self.background_loop is not None and
                                self._background_loop_unshielded is not None
624
625
626
627
628
629
630
631
632
633
634
635
                                and self._background_loop_unshielded.done())

    @property
    def errored(self) -> bool:
        return self._errored_with is not None

    def set_errored(self, exc: Exception) -> None:
        self._errored_with = exc

    def _error_callback(self, exc: Exception) -> None:
        self.set_errored(exc)
        self._request_tracker.propagate_exception(exc)
636

637
638
639
640
    async def get_tokenizer(
        self,
        lora_request: Optional[LoRARequest] = None,
    ) -> "PreTrainedTokenizer":
641
        if self.engine_use_ray:
642
643
644
645
646
            return await self.engine.get_tokenizer.remote(  # type: ignore
                lora_request)

        return await (self.engine.get_tokenizer_group().
                      get_lora_tokenizer_async(lora_request))
647

648
    def start_background_loop(self) -> None:
Antoni Baum's avatar
Antoni Baum committed
649
        """Start the background loop."""
650
651
652
        if self.errored:
            raise AsyncEngineDeadError(
                "Background loop has errored already.") from self._errored_with
653
        if self.is_running:
Antoni Baum's avatar
Antoni Baum committed
654
            raise RuntimeError("Background loop is already running.")
655
656
        # Initialize the RequestTracker here so it uses the right event loop.
        self._request_tracker = RequestTracker()
657
658
659
660

        self._background_loop_unshielded = asyncio.get_event_loop(
        ).create_task(self.run_engine_loop())
        self._background_loop_unshielded.add_done_callback(
661
            partial(_log_task_completion, error_callback=self._error_callback))
662
        self.background_loop = asyncio.shield(self._background_loop_unshielded)
Antoni Baum's avatar
Antoni Baum committed
663

664
665
666
667
668
669
670
671
672
673
674
675
676
677
    def shutdown_background_loop(self) -> None:
        """
        Shut down the background loop.

        This method needs to be called during cleanup to remove
        references to `self` and properly GC the resources held
        by the async LLM engine (e.g., the executors as well as
        their resources).
        """
        if self._background_loop_unshielded is not None:
            self._background_loop_unshielded.cancel()
            self._background_loop_unshielded = None
        self.background_loop = None

Antoni Baum's avatar
Antoni Baum committed
678
679
    def _init_engine(self, *args,
                     **kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]:
Zhuohan Li's avatar
Zhuohan Li committed
680
        if not self.engine_use_ray:
Antoni Baum's avatar
Antoni Baum committed
681
            engine_class = self._engine_class
682
        elif self.worker_use_ray:
Antoni Baum's avatar
Antoni Baum committed
683
            engine_class = ray.remote(num_cpus=0)(self._engine_class).remote
684
        else:
Woosuk Kwon's avatar
Woosuk Kwon committed
685
686
            # FIXME(woosuk): This is a bit hacky. Be careful when changing the
            # order of the arguments.
687
688
            cache_config = kwargs["cache_config"]
            parallel_config = kwargs["parallel_config"]
689
690
            if (parallel_config.tensor_parallel_size == 1
                    and parallel_config.pipeline_parallel_size == 1):
Woosuk Kwon's avatar
Woosuk Kwon committed
691
692
693
694
695
                num_gpus = cache_config.gpu_memory_utilization
            else:
                num_gpus = 1
            engine_class = ray.remote(num_gpus=num_gpus)(
                self._engine_class).remote
Antoni Baum's avatar
Antoni Baum committed
696
697
        return engine_class(*args, **kwargs)

698
    async def engine_step(self, virtual_engine: int) -> bool:
699
700
701
        """Kick the engine to process the waiting requests.

        Returns True if there are in-progress requests."""
702

703
704
        new_requests, aborted_requests = (
            self._request_tracker.get_new_and_aborted_requests())
705
706
707
708

        for new_request in new_requests:
            # Add the request into the vLLM engine's waiting queue.
            # TODO: Maybe add add_request_batch to reduce Ray overhead
709
710
            try:
                if self.engine_use_ray:
711
712
                    await self.engine.add_request.remote(  # type: ignore
                        **new_request)
713
714
715
716
717
718
719
720
721
                else:
                    await self.engine.add_request_async(**new_request)
            except ValueError as e:
                # TODO: use a vLLM specific error for failed validation
                self._request_tracker.process_exception(
                    new_request["request_id"],
                    e,
                    verbose=self.log_requests,
                )
722

723
724
        if aborted_requests:
            await self._engine_abort(aborted_requests)
725

Zhuohan Li's avatar
Zhuohan Li committed
726
        if self.engine_use_ray:
727
            request_outputs = await self.engine.step.remote()  # type: ignore
728
        else:
729
            request_outputs = await self.engine.step_async(virtual_engine)
730

Antoni Baum's avatar
Antoni Baum committed
731
        # Put the outputs into the corresponding streams.
732
        finished = True
733
        for request_output in request_outputs:
734
            self._request_tracker.process_request_output(
735
                request_output, verbose=self.log_requests)
736
            finished = finished and request_output.finished
Antoni Baum's avatar
Antoni Baum committed
737

738
        return not finished
739

Antoni Baum's avatar
Antoni Baum committed
740
741
    async def _engine_abort(self, request_ids: Iterable[str]):
        if self.engine_use_ray:
742
            await self.engine.abort_request.remote(request_ids)  # type: ignore
Antoni Baum's avatar
Antoni Baum committed
743
744
745
746
        else:
            self.engine.abort_request(request_ids)

    async def run_engine_loop(self):
747
748
749
750
751
752
        if self.engine_use_ray:
            pipeline_parallel_size = 1  # type: ignore
        else:
            pipeline_parallel_size = \
                self.engine.parallel_config.pipeline_parallel_size
        has_requests_in_progress = [False] * pipeline_parallel_size
Antoni Baum's avatar
Antoni Baum committed
753
        while True:
754
            if not any(has_requests_in_progress):
755
                logger.debug("Waiting for new requests...")
756
757
758
759
760
761
762
763
764
765
766
767
                # Stop the execute model loop in parallel workers until there
                # are more requests to process. This avoids waiting
                # indefinitely in torch.distributed ops which may otherwise
                # timeout, and unblocks the RPC thread in the workers so that
                # they can process any other queued control plane messages,
                # such as add/remove lora adapters.
                if self.engine_use_ray:
                    await (self.engine.stop_remote_worker_execution_loop.
                           remote()  # type: ignore
                           )
                else:
                    await self.engine.stop_remote_worker_execution_loop_async()
768
                await self._request_tracker.wait_for_new_requests()
769
                logger.debug("Got new requests!")
770
771
772
773
774
                requests_in_progress = [
                    asyncio.create_task(self.engine_step(ve))
                    for ve in range(pipeline_parallel_size)
                ]
                has_requests_in_progress = [True] * pipeline_parallel_size
775
776
777
778

            # Abort if iteration takes too long due to unrecoverable errors
            # (eg. NCCL timeouts).
            try:
779
                async with asyncio_timeout(ENGINE_ITERATION_TIMEOUT_S):
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
                    done, _ = await asyncio.wait(
                        requests_in_progress,
                        return_when=asyncio.FIRST_COMPLETED)
                    for _ in range(pipeline_parallel_size):
                        await asyncio.sleep(0)
                for task in done:
                    result = task.result()
                    virtual_engine = requests_in_progress.index(task)
                    if self.engine_use_ray:
                        has_unfinished_requests = (
                            await (self.engine.
                                   has_unfinished_requests_for_virtual_engine.
                                   remote(  # type: ignore
                                       virtual_engine)))
                    else:
                        has_unfinished_requests = (
                            self.engine.
                            has_unfinished_requests_for_virtual_engine(
                                virtual_engine))
                    if result or has_unfinished_requests:
                        requests_in_progress[virtual_engine] = (
                            asyncio.create_task(
                                self.engine_step(virtual_engine)))
                        has_requests_in_progress[virtual_engine] = True
                    else:
                        has_requests_in_progress[virtual_engine] = False
806
807
808
809
810
            except asyncio.TimeoutError as exc:
                logger.error(
                    "Engine iteration timed out. This should never happen!")
                self.set_errored(exc)
                raise
Antoni Baum's avatar
Antoni Baum committed
811
812
            await asyncio.sleep(0)

813
814
    # This method does not need to be async, but kept that way
    # for backwards compatibility.
Antoni Baum's avatar
Antoni Baum committed
815
816
817
    async def add_request(
        self,
        request_id: str,
818
        inputs: PromptInputs,
819
        params: Union[SamplingParams, PoolingParams],
Antoni Baum's avatar
Antoni Baum committed
820
        arrival_time: Optional[float] = None,
821
        lora_request: Optional[LoRARequest] = None,
822
        trace_headers: Optional[Mapping[str, str]] = None,
823
        prompt_adapter_request: Optional[PromptAdapterRequest] = None
824
    ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]:
825
        if not self.is_running:
826
827
828
829
830
831
832
833
            if self.start_engine_loop:
                self.start_background_loop()
            else:
                raise AsyncEngineDeadError(
                    "Background loop is not running. If it was running, "
                    "inspect the output to find the stacktrace of the "
                    "error that caused the background loop to stop "
                    "(AsyncEngineDeadError).")
Antoni Baum's avatar
Antoni Baum committed
834

835
        stream = self._request_tracker.add_request(
836
            request_id,
837
            verbose=self.log_requests,
838
            inputs=inputs,
839
            params=params,
840
            arrival_time=arrival_time or time.time(),
841
            lora_request=lora_request,
842
            trace_headers=trace_headers,
843
            prompt_adapter_request=prompt_adapter_request)
Antoni Baum's avatar
Antoni Baum committed
844

845
        return stream.generator()
846

847
    async def generate(
848
        self,
849
        inputs: PromptInputs,
850
851
        sampling_params: SamplingParams,
        request_id: str,
852
        lora_request: Optional[LoRARequest] = None,
853
        trace_headers: Optional[Mapping[str, str]] = None,
854
        prompt_adapter_request: Optional[PromptAdapterRequest] = None
855
    ) -> AsyncGenerator[RequestOutput, None]:
856
857
858
        """Generate outputs for a request.

        Generate outputs for a request. This method is a coroutine. It adds the
859
860
        request into the waiting queue of the LLMEngine and streams the outputs
        from the LLMEngine to the caller.
861
862

        Args:
863
864
865
            inputs: The inputs to the LLM. See
                :class:`~vllm.inputs.PromptInputs`
                for more details about the format of each input.
866
867
            sampling_params: The sampling parameters of the request.
            request_id: The unique id of the request.
868
            lora_request: LoRA request to use for generation, if any.
869
            trace_headers: OpenTelemetry trace headers.
870
871
            prompt_adapter_request: Prompt Adapter request to use 
                                            for generation, if any.
872
873

        Yields:
874
875
            The output `RequestOutput` objects from the LLMEngine
            for the request.
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
908
909
910
911
912
913
914
915
916
917
918

        Details:
            - If the engine is not running, start the background loop,
              which iteratively invokes
              :meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`
              to process the waiting requests.
            - Add the request to the engine's `RequestTracker`.
              On the next background loop, this request will be sent to
              the underlying engine.
              Also, a corresponding `AsyncStream` will be created.
            - Wait for the request outputs from `AsyncStream` and yield them.

        Example:
            >>> # Please refer to entrypoints/api_server.py for
            >>> # the complete example.
            >>>
            >>> # initialize the engine and the example input
            >>> engine = AsyncLLMEngine.from_engine_args(engine_args)
            >>> example_input = {
            >>>     "prompt": "What is LLM?",
            >>>     "stream": False, # assume the non-streaming case
            >>>     "temperature": 0.0,
            >>>     "request_id": 0,
            >>> }
            >>>
            >>> # start the generation
            >>> results_generator = engine.generate(
            >>>    example_input["prompt"],
            >>>    SamplingParams(temperature=example_input["temperature"]),
            >>>    example_input["request_id"])
            >>>
            >>> # get the results
            >>> final_output = None
            >>> async for request_output in results_generator:
            >>>     if await request.is_disconnected():
            >>>         # Abort the request if the client disconnects.
            >>>         await engine.abort(request_id)
            >>>         # Return or raise an error
            >>>         ...
            >>>     final_output = request_output
            >>>
            >>> # Process and return the final output
            >>> ...
919
        """
920
        async for output in await self.add_request(
921
                request_id,
922
                inputs,
923
                sampling_params,
924
                lora_request=lora_request,
925
                trace_headers=trace_headers,
926
                prompt_adapter_request=prompt_adapter_request,
927
        ):
928
            yield LLMEngine.validate_output(output, RequestOutput)
929
930
931

    async def encode(
        self,
932
        inputs: PromptInputs,
933
934
935
        pooling_params: PoolingParams,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
936
        trace_headers: Optional[Mapping[str, str]] = None,
937
    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
938
939
940
941
942
943
944
        """Generate outputs for a request from an embedding model.

        Generate outputs for a request. This method is a coroutine. It adds the
        request into the waiting queue of the LLMEngine and streams the outputs
        from the LLMEngine to the caller.

        Args:
945
946
947
            inputs: The inputs to the LLM. See
                :class:`~vllm.inputs.PromptInputs`
                for more details about the format of each input.
948
949
950
            pooling_params: The pooling parameters of the request.
            request_id: The unique id of the request.
            lora_request: LoRA request to use for generation, if any.
951
            trace_headers: OpenTelemetry trace headers.
952
953

        Yields:
954
            The output `EmbeddingRequestOutput` objects from the LLMEngine
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
            for the request.

        Details:
            - If the engine is not running, start the background loop,
              which iteratively invokes
              :meth:`~vllm.engine.async_llm_engine.AsyncLLMEngine.engine_step`
              to process the waiting requests.
            - Add the request to the engine's `RequestTracker`.
              On the next background loop, this request will be sent to
              the underlying engine.
              Also, a corresponding `AsyncStream` will be created.
            - Wait for the request outputs from `AsyncStream` and yield them.

        Example:
            >>> # Please refer to entrypoints/api_server.py for
            >>> # the complete example.
            >>>
            >>> # initialize the engine and the example input
            >>> engine = AsyncLLMEngine.from_engine_args(engine_args)
            >>> example_input = {
            >>>     "input": "What is LLM?",
            >>>     "request_id": 0,
            >>> }
            >>>
            >>> # start the generation
            >>> results_generator = engine.encode(
            >>>    example_input["input"],
            >>>    PoolingParams(),
            >>>    example_input["request_id"])
            >>>
            >>> # get the results
            >>> final_output = None
            >>> async for request_output in results_generator:
            >>>     if await request.is_disconnected():
            >>>         # Abort the request if the client disconnects.
            >>>         await engine.abort(request_id)
            >>>         # Return or raise an error
            >>>         ...
            >>>     final_output = request_output
            >>>
            >>> # Process and return the final output
            >>> ...
        """
998
        async for output in await self.add_request(
999
                request_id,
1000
                inputs,
1001
                pooling_params,
1002
                lora_request=lora_request,
1003
                trace_headers=trace_headers,
1004
        ):
1005
            yield LLMEngine.validate_output(output, EmbeddingRequestOutput)
1006

Antoni Baum's avatar
Antoni Baum committed
1007
1008
    async def abort(self, request_id: str) -> None:
        """Abort a request.
1009

Antoni Baum's avatar
Antoni Baum committed
1010
1011
        Abort a submitted request. If the request is finished or not found,
        this method will be a no-op.
1012

Antoni Baum's avatar
Antoni Baum committed
1013
1014
1015
        Args:
            request_id: The unique id of the request.
        """
1016
1017
1018
1019
1020
1021
1022
        if not self.is_running:
            raise AsyncEngineDeadError(
                "Background loop is not running. If it was running, "
                "inspect the output to find the stacktrace of the "
                "error that caused the background loop to stop "
                "(AsyncEngineDeadError).")

Antoni Baum's avatar
Antoni Baum committed
1023
        return self._abort(request_id)
1024

Antoni Baum's avatar
Antoni Baum committed
1025
    def _abort(self, request_id: str) -> None:
1026
1027
1028
1029
1030
1031
1032
1033
        """Abort a request.

        Abort a submitted request. If the request is finished or not found,
        this method will be a no-op.

        Args:
            request_id: The unique id of the request.
        """
1034
        self._request_tracker.abort_request(request_id,
1035
                                            exception=asyncio.CancelledError,
1036
                                            verbose=self.log_requests)
1037

1038
1039
1040
    async def get_model_config(self) -> ModelConfig:
        """Get the model configuration of the vLLM engine."""
        if self.engine_use_ray:
1041
            return await self.engine.get_model_config.remote()  # type: ignore
1042
1043
1044
        else:
            return self.engine.get_model_config()

1045
1046
1047
1048
1049
1050
1051
1052
    async def get_parallel_config(self) -> ParallelConfig:
        """Get the parallel configuration of the vLLM engine."""
        if self.engine_use_ray:
            return await self.engine.get_parallel_config.remote(  # type: ignore
            )
        else:
            return self.engine.get_parallel_config()

1053
1054
1055
1056
1057
1058
1059
1060
    async def get_decoding_config(self) -> DecodingConfig:
        """Get the decoding configuration of the vLLM engine."""
        if self.engine_use_ray:
            return await self.engine.get_decoding_config.remote(  # type: ignore
            )
        else:
            return self.engine.get_decoding_config()

1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
    async def get_scheduler_config(self) -> SchedulerConfig:
        """Get the scheduling configuration of the vLLM engine."""
        if self.engine_use_ray:
            return await self.engine.get_scheduler_config.remote(  # type: ignore
            )
        else:
            return self.engine.get_scheduler_config()

    async def get_lora_config(self) -> LoRAConfig:
        """Get the lora configuration of the vLLM engine."""
        if self.engine_use_ray:
            return await self.engine.get_lora_config.remote(  # type: ignore
            )
        else:
            return self.engine.get_lora_config()

1077
1078
1079
1080
    async def do_log_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs] = None,
            model_output: Optional[List[SamplerOutput]] = None) -> None:
1081
        if self.engine_use_ray:
1082
1083
            await self.engine.do_log_stats.remote(  # type: ignore
                scheduler_outputs, model_output)
1084
1085
        else:
            self.engine.do_log_stats()
1086

1087
    async def check_health(self) -> None:
1088
1089
1090
1091
1092
1093
1094
1095
        """Raises an error if engine is unhealthy."""
        t = time.perf_counter()
        logger.debug("Starting health check...")
        if self.is_stopped:
            raise AsyncEngineDeadError("Background loop is stopped.")

        if self.engine_use_ray:
            try:
1096
                await self.engine.check_health.remote()  # type: ignore
1097
1098
1099
1100
            except ray.exceptions.RayActorError as e:
                raise RuntimeError("Engine is dead.") from e
        else:
            await self.engine.check_health_async()
1101
        logger.debug("Health check took %fs", time.perf_counter() - t)
1102
1103
1104
1105
1106
1107
1108

    async def is_tracing_enabled(self) -> bool:
        if self.engine_use_ray:
            return await self.engine.is_tracing_enabled.remote(  # type: ignore
            )
        else:
            return self.engine.is_tracing_enabled()
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124

    def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
        if self.engine_use_ray:
            ray.get(
                self.engine.add_logger.remote(  # type: ignore
                    logger_name=logger_name, logger=logger))
        else:
            self.engine.add_logger(logger_name=logger_name, logger=logger)

    def remove_logger(self, logger_name: str) -> None:
        if self.engine_use_ray:
            ray.get(
                self.engine.remove_logger.remote(  # type: ignore
                    logger_name=logger_name))
        else:
            self.engine.remove_logger(logger_name=logger_name)