core.py 32.5 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
import os
3
import queue
4
import signal
5
import sys
6
7
import threading
import time
8
from collections import deque
9
from concurrent.futures import Future
10
from inspect import isclass, signature
11
from logging import DEBUG
12
from typing import Any, Callable, Optional, TypeVar, Union
13

14
import msgspec
15
16
import zmq

17
18
19
from vllm.config import ParallelConfig, VllmConfig
from vllm.distributed import stateless_destroy_torch_distributed_process_group
from vllm.executor.multiproc_worker_utils import _add_prefix
20
from vllm.logger import init_logger
21
from vllm.logging_utils.dump_input import dump_engine_exception
22
from vllm.lora.request import LoRARequest
23
24
from vllm.transformers_utils.config import (
    maybe_register_config_serialize_by_value)
25
from vllm.utils import make_zmq_socket, resolve_obj_by_qualname, zmq_socket_ctx
26
27
from vllm.v1.core.kv_cache_utils import (get_kv_cache_config,
                                         unify_kv_cache_configs)
28
from vllm.v1.core.sched.interface import SchedulerInterface
29
30
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.core.sched.scheduler import Scheduler as V1Scheduler
31
from vllm.v1.engine import (EngineCoreOutputs, EngineCoreRequest,
32
                            EngineCoreRequestType, UtilityOutput)
33
from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
34
from vllm.v1.executor.abstract import Executor
35
from vllm.v1.kv_cache_interface import KVCacheConfig
36
from vllm.v1.outputs import ModelRunnerOutput
37
from vllm.v1.request import Request, RequestStatus
38
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
39
from vllm.v1.structured_output import StructuredOutputManager
40
41
42
43
from vllm.version import __version__ as VLLM_VERSION

logger = init_logger(__name__)

44
POLLING_TIMEOUT_S = 2.5
45
HANDSHAKE_TIMEOUT_MINS = 5
46

47
48
_R = TypeVar('_R')  # Return type for collective_rpc

49
50
51
52

class EngineCore:
    """Inner loop of vLLM's Engine."""

53
54
55
56
57
    def __init__(self,
                 vllm_config: VllmConfig,
                 executor_class: type[Executor],
                 log_stats: bool,
                 executor_fail_callback: Optional[Callable] = None):
58
        assert vllm_config.model_config.runner_type != "pooling"
59

60
61
62
63
        # plugins need to be loaded at the engine/scheduler level too
        from vllm.plugins import load_general_plugins
        load_general_plugins()

64
        self.vllm_config = vllm_config
65
        logger.info("Initializing a V1 LLM engine (v%s) with config: %s",
66
67
                    VLLM_VERSION, vllm_config)

68
69
        self.log_stats = log_stats

70
71
        # Setup Model.
        self.model_executor = executor_class(vllm_config)
72
73
74
        if executor_fail_callback is not None:
            self.model_executor.register_failure_callback(
                executor_fail_callback)
75
76

        # Setup KV Caches and update CacheConfig after profiling.
77
78
79
        num_gpu_blocks, num_cpu_blocks, kv_cache_config = \
            self._initialize_kv_caches(vllm_config)

80
81
82
        vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks
        vllm_config.cache_config.num_cpu_blocks = num_cpu_blocks

83
84
        self.structured_output_manager = StructuredOutputManager(vllm_config)

85
        # Setup scheduler.
86
        if isinstance(vllm_config.scheduler_config.scheduler_cls, str):
87
88
89
90
91
92
93
94
95
            Scheduler = resolve_obj_by_qualname(
                vllm_config.scheduler_config.scheduler_cls)
        else:
            Scheduler = vllm_config.scheduler_config.scheduler_cls

        # This warning can be removed once the V1 Scheduler interface is
        # finalized and we can maintain support for scheduler classes that
        # implement it
        if Scheduler is not V1Scheduler:
96
97
98
99
100
            logger.warning(
                "Using configured V1 scheduler class %s. "
                "This scheduler interface is not public and "
                "compatibility may not be maintained.",
                vllm_config.scheduler_config.scheduler_cls)
101

102
        self.scheduler: SchedulerInterface = Scheduler(
103
            vllm_config=vllm_config,
104
105
            kv_cache_config=kv_cache_config,
            structured_output_manager=self.structured_output_manager,
106
107
            include_finished_set=vllm_config.parallel_config.data_parallel_size
            > 1,
108
            log_stats=self.log_stats,
109
        )
110

111
        # Setup MM Input Mapper.
112
        self.mm_input_cache_server = MirroredProcessingCache(
113
            vllm_config.model_config)
114

115
116
117
118
119
        # Setup batch queue for pipeline parallelism.
        # Batch queue for scheduled batches. This enables us to asynchronously
        # schedule and execute batches, and is required by pipeline parallelism
        # to eliminate pipeline bubbles.
        self.batch_queue_size = self.model_executor.max_concurrent_batches
120
        self.batch_queue: Optional[queue.Queue[tuple[Future[ModelRunnerOutput],
121
122
123
124
125
126
                                                     SchedulerOutput]]] = None
        if self.batch_queue_size > 1:
            logger.info("Batch queue is enabled with size %d",
                        self.batch_queue_size)
            self.batch_queue = queue.Queue(self.batch_queue_size)

127
128
    def _initialize_kv_caches(
            self, vllm_config: VllmConfig) -> tuple[int, int, KVCacheConfig]:
129
        start = time.time()
130

131
        # Get all kv cache needed by the model
132
        kv_cache_specs = self.model_executor.get_kv_cache_specs()
133
134
135

        # Profiles the peak memory usage of the model to determine how much
        # memory can be allocated for kv cache.
136
        available_gpu_memory = self.model_executor.determine_available_memory()
137

138
        assert len(kv_cache_specs) == len(available_gpu_memory)
139
        # Get the kv cache tensor size
140
141
142
143
144
145
146
147
148
149
150
151
152
        kv_cache_configs = [
            get_kv_cache_config(vllm_config, kv_cache_spec_one_worker,
                                available_gpu_memory_one_worker)
            for kv_cache_spec_one_worker, available_gpu_memory_one_worker in
            zip(kv_cache_specs, available_gpu_memory)
        ]

        # Since we use a shared centralized controller, we need the
        # `kv_cache_config` to be consistent across all workers to make sure
        # all the memory operators can be applied to all workers.
        unify_kv_cache_configs(kv_cache_configs)

        # All workers have the same kv_cache_config except layer names, so use
153
        # an arbitrary one to initialize the scheduler.
154
155
156
157
158
        assert all([
            cfg.num_blocks == kv_cache_configs[0].num_blocks
            for cfg in kv_cache_configs
        ])
        num_gpu_blocks = kv_cache_configs[0].num_blocks
159
        num_cpu_blocks = 0
160
        scheduler_kv_cache_config = kv_cache_configs[0]
161
162

        # Initialize kv cache and warmup the execution
163
        self.model_executor.initialize_from_config(kv_cache_configs)
164

165
166
167
        elapsed = time.time() - start
        logger.info(("init engine (profile, create kv cache, "
                     "warmup model) took %.2f seconds"), elapsed)
168
        return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config
169
170
171

    def add_request(self, request: EngineCoreRequest):
        """Add request to the scheduler."""
172
173

        if request.mm_hashes is not None:
174
175
176
177
178
            # Here, if hash exists for a multimodal input, then it will be
            # fetched from the cache, else it will be added to the cache.
            # Note that the cache here is mirrored with the client cache, so
            # anything that has a hash must have a HIT cache entry here
            # as well.
179
            assert request.mm_inputs is not None
180
            request.mm_inputs = self.mm_input_cache_server.get_and_update_p1(
181
                request.mm_inputs, request.mm_hashes)
182

183
        req = Request.from_engine_core_request(request)
184
185
        if req.use_structured_output:
            # Start grammar compilation asynchronously
186
            self.structured_output_manager.grammar_init(req)
187

188
189
190
191
        if req.kv_transfer_params is not None and (
                not self.scheduler.get_kv_connector()):
            logger.warning("Got kv_transfer_params, but no KVConnector found. "
                           "Disabling KVTransfer for this request.")
Robert Shaw's avatar
Robert Shaw committed
192

193
194
        self.scheduler.add_request(req)

195
    def abort_requests(self, request_ids: list[str]):
196
197
198
199
200
201
202
203
        """Abort requests from the scheduler."""

        # TODO: The scheduler doesn't really need to know the
        # specific finish reason, TBD whether we propagate that
        # (i.e. client-aborted vs stop criteria met).
        self.scheduler.finish_requests(request_ids,
                                       RequestStatus.FINISHED_ABORTED)

204
205
206
207
208
209
210
211
212
213
    def execute_model(self, scheduler_output: SchedulerOutput):
        try:
            return self.model_executor.execute_model(scheduler_output)
        except BaseException as err:
            # NOTE: This method is exception-free
            dump_engine_exception(self.vllm_config, scheduler_output,
                                  self.scheduler.make_stats())
            # Re-raise exception
            raise err

214
    def step(self) -> EngineCoreOutputs:
215
216
        """Schedule, execute, and make output."""

217
218
219
        # Check for any requests remaining in the scheduler - unfinished,
        # or finished and not yet removed from the batch.
        if not self.scheduler.has_requests():
220
            return EngineCoreOutputs(
221
222
223
                outputs=[],
                scheduler_stats=self.scheduler.make_stats(),
            )
224
        scheduler_output = self.scheduler.schedule()
225
        model_output = self.execute_model(scheduler_output)
226
        engine_core_outputs = self.scheduler.update_from_output(
227
            scheduler_output, model_output)  # type: ignore
228

229
230
231
232
233
234
235
        return engine_core_outputs

    def step_with_batch_queue(self) -> Optional[EngineCoreOutputs]:
        """Schedule and execute batches with the batch queue.
        Note that if nothing to output in this step, None is returned.

        The execution flow is as follows:
236
237
238
239
        1. Try to schedule a new batch if the batch queue is not full.
        If a new batch is scheduled, directly return an empty engine core
        output. In other words, fulfilling the batch queue has a higher priority
        than getting model outputs.
240
241
242
243
244
245
246
247
248
        2. If there is no new scheduled batch, meaning that the batch queue
        is full or no other requests can be scheduled, we block until the first
        batch in the job queue is finished.
        3. Update the scheduler from the output.
        """
        assert self.batch_queue is not None

        engine_core_outputs = None
        scheduler_output = None
249
250
251
252
        # Try to schedule a new batch if the batch queue is not full, but
        # the scheduler may return an empty batch if all requests are scheduled.
        # Note that this is not blocking.
        if not self.batch_queue.full():
253
254
255
256
257
258
            scheduler_output = self.scheduler.schedule()
            if scheduler_output.total_num_scheduled_tokens > 0:
                future = self.model_executor.execute_model(scheduler_output)
                self.batch_queue.put_nowait(
                    (future, scheduler_output))  # type: ignore

259
260
261
262
        scheduled_batch = (scheduler_output is not None
                           and scheduler_output.total_num_scheduled_tokens > 0)

        # If no more requests can be scheduled and the job queue is not empty,
263
        # block until the first batch in the job queue is finished.
264
265
266
267
        # TODO(comaniac): Ideally we should peek the first batch in the
        # job queue to check if it's finished before scheduling a new batch,
        # but peeking the first element in a queue is not thread-safe,
        # so we need more work.
268
269
270
271
272
273
274
        if not scheduled_batch and not self.batch_queue.empty():
            future, scheduler_output = self.batch_queue.get_nowait()
            # Blocking until the first result is available.
            model_output = future.result()
            self.batch_queue.task_done()
            engine_core_outputs = self.scheduler.update_from_output(
                scheduler_output, model_output)
275

276
277
        return engine_core_outputs

278
    def shutdown(self):
279
        self.structured_output_manager.clear_backend()
280
281
        if self.model_executor:
            self.model_executor.shutdown()
282
283
        if self.scheduler:
            self.scheduler.shutdown()
284

285
    def profile(self, is_start: bool = True):
286
        self.model_executor.profile(is_start)
287

288
289
290
    def reset_mm_cache(self):
        # NOTE: Since this is mainly for debugging, we don't attempt to
        # re-sync the internal caches (P0 processor, P0 mirror, P1 mirror)
291
        if self.scheduler.has_unfinished_requests():
292
293
294
295
296
            logger.warning("Resetting the multi-modal cache when requests are "
                           "in progress may lead to desynced internal caches.")

        self.mm_input_cache_server.reset()

297
298
299
    def reset_prefix_cache(self):
        self.scheduler.reset_prefix_cache()

300
301
302
    def sleep(self, level: int = 1):
        self.model_executor.sleep(level)

303
304
    def wake_up(self, tags: Optional[list[str]] = None):
        self.model_executor.wake_up(tags)
305

306
307
308
    def is_sleeping(self) -> bool:
        return self.model_executor.is_sleeping

309
310
311
    def execute_dummy_batch(self):
        self.model_executor.collective_rpc("execute_dummy_batch")

312
313
314
315
316
317
    def add_lora(self, lora_request: LoRARequest) -> bool:
        return self.model_executor.add_lora(lora_request)

    def remove_lora(self, lora_id: int) -> bool:
        return self.model_executor.remove_lora(lora_id)

318
    def list_loras(self) -> set[int]:
319
320
321
322
        return self.model_executor.list_loras()

    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)
323

324
325
326
327
328
329
330
331
332
333
    def save_sharded_state(
        self,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
        self.model_executor.save_sharded_state(path=path,
                                               pattern=pattern,
                                               max_size=max_size)

334
335
336
337
338
339
340
341
    def collective_rpc(self,
                       method: Union[str, Callable[..., _R]],
                       timeout: Optional[float] = None,
                       args: tuple = (),
                       kwargs: Optional[dict[str, Any]] = None) -> list[_R]:
        return self.model_executor.collective_rpc(method, timeout, args,
                                                  kwargs)

342
343
344
345
346
347
348
    def save_tensorized_model(
        self,
        tensorizer_config,
    ) -> None:
        self.model_executor.save_tensorized_model(
            tensorizer_config=tensorizer_config, )

349
350
351
352

class EngineCoreProc(EngineCore):
    """ZMQ-wrapper for running EngineCore in background process."""

353
354
    ENGINE_CORE_DEAD = b'ENGINE_CORE_DEAD'

355
356
    def __init__(
        self,
357
        vllm_config: VllmConfig,
358
359
        on_head_node: bool,
        input_address: str,
360
        executor_class: type[Executor],
361
        log_stats: bool,
362
        engine_index: int = 0,
363
    ):
364
365
366
367
368
        input_queue = queue.Queue[tuple[EngineCoreRequestType, Any]]()

        executor_fail_callback = lambda: input_queue.put_nowait(
            (EngineCoreRequestType.EXECUTOR_FAILED, b''))

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
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
        # Create input socket.
        input_ctx = zmq.Context()
        identity = engine_index.to_bytes(length=2, byteorder="little")
        input_socket = make_zmq_socket(input_ctx,
                                       input_address,
                                       zmq.DEALER,
                                       identity=identity,
                                       bind=False)
        try:
            # Register engine with front-end.
            output_address = self.startup_handshake(
                input_socket, on_head_node, vllm_config.parallel_config)

            # Update config which may have changed from the handshake.
            vllm_config.__post_init__()

            # Set up data parallel environment.
            self._init_data_parallel(vllm_config)

            # Initialize engine core and model.
            super().__init__(vllm_config, executor_class, log_stats,
                             executor_fail_callback)

            self.step_fn = (self.step if self.batch_queue is None else
                            self.step_with_batch_queue)
            self.engines_running = False

            # Send ready message.
            num_gpu_blocks = vllm_config.cache_config.num_gpu_blocks
            input_socket.send(
                msgspec.msgpack.encode({
                    "status": "READY",
                    "local": on_head_node,
                    "num_gpu_blocks": num_gpu_blocks,
                }))

            # Background Threads and Queues for IO. These enable us to
            # overlap ZMQ socket IO with GPU since they release the GIL,
            # and to overlap some serialization/deserialization with the
            # model forward pass.
            # Threads handle Socket <-> Queues and core_busy_loop uses Queue.
            self.input_queue = input_queue
            self.output_queue = queue.Queue[Union[EngineCoreOutputs, bytes]]()
            threading.Thread(target=self.process_input_socket,
                             args=(input_socket, ),
                             daemon=True).start()
            input_socket = None
            self.output_thread = threading.Thread(
                target=self.process_output_socket,
                args=(output_address, engine_index),
                daemon=True)
            self.output_thread.start()
        finally:
            if input_socket is not None:
                input_socket.close(linger=0)

    @staticmethod
    def startup_handshake(input_socket: zmq.Socket, on_head_node: bool,
                          parallel_config: ParallelConfig) -> str:

        # Send registration message.
        input_socket.send(
            msgspec.msgpack.encode({
                "status": "HELLO",
                "local": on_head_node,
            }))

        # Receive initialization message.
        logger.info("Waiting for init message from front-end.")
        if not input_socket.poll(timeout=HANDSHAKE_TIMEOUT_MINS * 60 * 1000):
            raise RuntimeError("Did not receive response from front-end "
                               f"process within {HANDSHAKE_TIMEOUT_MINS} "
                               f"minutes")
        init_bytes = input_socket.recv()
        init_message = msgspec.msgpack.decode(init_bytes)
        logger.debug("Received init message: %s", init_message)

        output_socket_address = init_message["output_socket_address"]
        #TBD(nick) maybe replace IP with configured head node address

        received_parallel_config = init_message["parallel_config"]
        for key, value in received_parallel_config.items():
            setattr(parallel_config, key, value)

        return output_socket_address
454
455

    @staticmethod
456
457
458
459
    def run_engine_core(*args,
                        dp_rank: int = 0,
                        local_dp_rank: int = 0,
                        **kwargs):
460
461
        """Launch EngineCore busy loop in background process."""

462
463
464
465
466
        # Signal handler used for graceful termination.
        # SystemExit exception is only raised once to allow this and worker
        # processes to terminate without error
        shutdown_requested = False

467
468
469
        # Ensure we can serialize transformer config after spawning
        maybe_register_config_serialize_by_value()

470
471
472
473
474
475
476
477
478
479
        def signal_handler(signum, frame):
            nonlocal shutdown_requested
            if not shutdown_requested:
                shutdown_requested = True
                raise SystemExit()

        # Either SIGTERM or SIGINT will terminate the engine_core
        signal.signal(signal.SIGTERM, signal_handler)
        signal.signal(signal.SIGINT, signal_handler)

480
        engine_core: Optional[EngineCoreProc] = None
481
        try:
482
483
            parallel_config: ParallelConfig = kwargs[
                "vllm_config"].parallel_config
484
            if parallel_config.data_parallel_size > 1 or dp_rank > 0:
485
486
487
488
489
490
491
                # Set data parallel rank for this engine process.
                parallel_config.data_parallel_rank = dp_rank
                parallel_config.data_parallel_rank_local = local_dp_rank
                engine_core = DPEngineCoreProc(*args, **kwargs)
            else:
                engine_core = EngineCoreProc(*args, **kwargs)

492
493
            engine_core.run_busy_loop()

494
        except SystemExit:
495
            logger.debug("EngineCore exiting.")
496
            raise
497
498
499
500
501
502
503
        except Exception as e:
            if engine_core is None:
                logger.exception("EngineCore failed to start.")
            else:
                logger.exception("EngineCore encountered a fatal error.")
                engine_core._send_engine_dead()
            raise e
504
505
506
507
        finally:
            if engine_core is not None:
                engine_core.shutdown()

508
509
510
    def _init_data_parallel(self, vllm_config: VllmConfig):
        pass

511
512
513
    def run_busy_loop(self):
        """Core busy loop of the EngineCore."""

514
515
        # Loop until process is sent a SIGINT or SIGTERM
        while True:
516
            # 1) Poll the input queue until there is work to do.
517
518
519
520
521
522
523
524
            self._process_input_queue()
            # 2) Step the engine core and return the outputs.
            self._process_engine_step()

    def _process_input_queue(self):
        """Exits when an engine step needs to be performed."""

        waited = False
525
        while not self.engines_running and not (self.scheduler.has_requests()):
526
527
528
529
530
531
532
            if logger.isEnabledFor(DEBUG) and self.input_queue.empty():
                logger.debug("EngineCore waiting for work.")
                waited = True
            req = self.input_queue.get()
            self._handle_client_request(*req)

        if waited:
533
            logger.debug("EngineCore loop active.")
534
535
536
537
538
539
540
541
542
543
544
545
546
547

        # Handle any more client requests.
        while not self.input_queue.empty():
            req = self.input_queue.get_nowait()
            self._handle_client_request(*req)

    def _process_engine_step(self):
        """Called only when there are unfinished local requests."""

        # Step the engine core.
        outputs = self.step_fn()
        # Put EngineCoreOutputs into the output queue.
        if outputs is not None:
            self.output_queue.put_nowait(outputs)
548

549
550
551
    def _handle_client_request(self, request_type: EngineCoreRequestType,
                               request: Any) -> None:
        """Dispatch request from client."""
552

553
        if request_type == EngineCoreRequestType.ADD:
554
            self.add_request(request)
555
        elif request_type == EngineCoreRequestType.ABORT:
556
            self.abort_requests(request)
557
558
559
560
561
562
563
564
565
566
567
568
569
        elif request_type == EngineCoreRequestType.UTILITY:
            call_id, method_name, args = request
            output = UtilityOutput(call_id)
            try:
                method = getattr(self, method_name)
                output.result = method(
                    *self._convert_msgspec_args(method, args))
            except BaseException as e:
                logger.exception("Invocation of %s method failed", method_name)
                output.failure_message = (f"Call to {method_name} method"
                                          f" failed: {str(e)}")
            self.output_queue.put_nowait(
                EngineCoreOutputs(utility_output=output))
570
571
572
573
574
        elif request_type == EngineCoreRequestType.EXECUTOR_FAILED:
            raise RuntimeError("Executor failed.")
        else:
            logger.error("Unrecognized input request type encountered: %s",
                         request_type)
575
576
577
578
579
580
581
582
583
584
585
586
587
588

    @staticmethod
    def _convert_msgspec_args(method, args):
        """If a provided arg type doesn't match corresponding target method
         arg type, try converting to msgspec object."""
        if not args:
            return args
        arg_types = signature(method).parameters.values()
        assert len(args) <= len(arg_types)
        return tuple(
            msgspec.convert(v, type=p.annotation) if isclass(p.annotation)
            and issubclass(p.annotation, msgspec.Struct)
            and not isinstance(v, p.annotation) else v
            for v, p in zip(args, arg_types))
589

590
591
592
593
594
595
596
597
598
599
600
601
    def _send_engine_dead(self):
        """Send EngineDead status to the EngineCoreClient."""

        # Put ENGINE_CORE_DEAD in the queue.
        self.output_queue.put_nowait(EngineCoreProc.ENGINE_CORE_DEAD)

        # Wait until msg sent by the daemon before shutdown.
        self.output_thread.join(timeout=5.0)
        if self.output_thread.is_alive():
            logger.fatal("vLLM shutdown signal from EngineCore failed "
                         "to send. Please report this issue.")

602
    def process_input_socket(self, input_socket: zmq.Socket):
603
604
605
        """Input socket IO thread."""

        # Msgpack serialization decoding.
606
607
        add_request_decoder = MsgpackDecoder(EngineCoreRequest)
        generic_decoder = MsgpackDecoder()
608

609
610
611
612
        while True:
            # (RequestType, RequestData)
            type_frame, *data_frames = input_socket.recv_multipart(copy=False)
            request_type = EngineCoreRequestType(bytes(type_frame.buffer))
613

614
615
616
617
            # Deserialize the request data.
            decoder = add_request_decoder if (
                request_type == EngineCoreRequestType.ADD) else generic_decoder
            request = decoder.decode(data_frames)
618

619
620
            # Push to input queue for core busy loop.
            self.input_queue.put_nowait((request_type, request))
621

622
    def process_output_socket(self, output_path: str, engine_index: int):
623
624
625
        """Output socket IO thread."""

        # Msgpack serialization encoding.
626
        encoder = MsgpackEncoder()
627
628
629
630
631
632
        # Send buffers to reuse.
        reuse_buffers: list[bytearray] = []
        # Keep references to outputs and buffers until zmq is finished
        # with them (outputs may contain tensors/np arrays whose
        # backing buffers were extracted for zero-copy send).
        pending = deque[tuple[zmq.MessageTracker, Any, bytearray]]()
633

634
635
636
637
        # We must set linger to ensure the ENGINE_CORE_DEAD
        # message is sent prior to closing the socket.
        with zmq_socket_ctx(output_path, zmq.constants.PUSH,
                            linger=4000) as socket:
638
            while True:
639
                outputs = self.output_queue.get()
640
641
642
643
                if outputs == EngineCoreProc.ENGINE_CORE_DEAD:
                    socket.send(outputs, copy=False)
                    break
                assert not isinstance(outputs, bytes)
644
                outputs.engine_index = engine_index
645
646
647
648
649
650

                # Reclaim buffers that zmq is finished with.
                while pending and pending[-1][0].done:
                    reuse_buffers.append(pending.pop()[2])

                buffer = reuse_buffers.pop() if reuse_buffers else bytearray()
651
                buffers = encoder.encode_into(outputs, buffer)
652
653
654
655
656
657
658
659
660
                tracker = socket.send_multipart(buffers,
                                                copy=False,
                                                track=True)
                if not tracker.done:
                    ref = outputs if len(buffers) > 1 else None
                    pending.appendleft((tracker, ref, buffer))
                elif len(reuse_buffers) < 2:
                    # Keep at most 2 buffers to reuse.
                    reuse_buffers.append(buffer)
661
662
663
664
665
666
667
668
669


class DPEngineCoreProc(EngineCoreProc):
    """ZMQ-wrapper for running EngineCore in background process
    in a data parallel context."""

    def __init__(
        self,
        vllm_config: VllmConfig,
670
671
        on_head_node: bool,
        input_address: str,
672
673
674
675
676
677
678
679
680
681
682
        executor_class: type[Executor],
        log_stats: bool,
    ):
        # Add process-specific prefix to stdout and stderr before
        # we initialize the engine.
        from multiprocessing import current_process
        process_name = current_process().name
        pid = os.getpid()
        _add_prefix(sys.stdout, process_name, pid)
        _add_prefix(sys.stderr, process_name, pid)

683
684
685
686
687
688
689
690
691
692
693
694
        # Counts forward-passes of the model so that we can synchronize
        # finished with DP peers every N steps.
        self.counter = 0

        # Initialize the engine.
        dp_rank = vllm_config.parallel_config.data_parallel_rank
        super().__init__(vllm_config, on_head_node, input_address,
                         executor_class, log_stats, dp_rank)

    def _init_data_parallel(self, vllm_config: VllmConfig):

        # Configure GPUs and stateless process group for data parallel.
695
        dp_rank = vllm_config.parallel_config.data_parallel_rank
696
        dp_size = vllm_config.parallel_config.data_parallel_size
697
698
699
700
701
702
        local_dp_rank = vllm_config.parallel_config.data_parallel_rank_local

        assert dp_size > 1
        assert 0 <= local_dp_rank <= dp_rank < dp_size

        from vllm.platforms import current_platform
703
        device_control_env_var = current_platform.device_control_env_var
704
        world_size = vllm_config.parallel_config.world_size
705
706
        os.environ[device_control_env_var] = ",".join(
            str(current_platform.device_id_to_physical_device_id(i))
707
708
            for i in range(local_dp_rank * world_size, (local_dp_rank + 1) *
                           world_size))
709

710
        self.dp_rank = dp_rank
711
        self.dp_group = vllm_config.parallel_config.stateless_init_dp_group()
712
        self.current_wave = 0
713
714
715
716
717
718

    def shutdown(self):
        super().shutdown()
        if dp_group := getattr(self, "dp_group", None):
            stateless_destroy_torch_distributed_process_group(dp_group)

719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
    def add_request(self, request: EngineCoreRequest):
        if request.current_wave != self.current_wave:
            if request.current_wave > self.current_wave:
                self.current_wave = request.current_wave
            elif not self.engines_running:
                # Request received for an already-completed wave, notify
                # front-end that we need to start the next one.
                self.output_queue.put_nowait(
                    EngineCoreOutputs(start_wave=self.current_wave))

        super().add_request(request)

    def _handle_client_request(self, request_type: EngineCoreRequestType,
                               request: Any) -> None:
        if request_type == EngineCoreRequestType.START_DP_WAVE:
            new_wave: int = request
            if new_wave >= self.current_wave:
                self.current_wave = new_wave
                if not self.engines_running:
                    logger.debug("EngineCore starting idle loop for wave %d.",
                                 new_wave)
                    self.engines_running = True
        else:
            super()._handle_client_request(request_type, request)

744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
    def run_busy_loop(self):
        """Core busy loop of the EngineCore for data parallel case."""

        # Loop until process is sent a SIGINT or SIGTERM
        while True:
            # 1) Poll the input queue until there is work to do.
            self._process_input_queue()

            local_unfinished_reqs = self.scheduler.has_unfinished_requests()

            if local_unfinished_reqs:
                # 2) Step the engine core.
                self._process_engine_step()

                # Check if we have now finished all requests.
                local_unfinished_reqs = (
                    self.scheduler.has_unfinished_requests())
            else:
                if self.scheduler.has_finished_requests():
                    # There are no unfinished requests, but there are some
                    # finished requests remaining to be removed from the
                    # batch state. This engine step won't perform a forward
                    # pass but will flush the finished requests to ensure
                    # up-to-date state is returned in the engine outputs.
                    self._process_engine_step()

770
                if not self.engines_running:
771
772
773
774
775
776
777
778
                    # All engines are idle.
                    continue

                # There must be unfinished requests in DP peers, run a
                # dummy forward pass.
                self.execute_dummy_batch()

            # 3) All-reduce operation to determine global unfinished reqs.
779
            self.engines_running = self._has_global_unfinished_reqs(
780
781
                local_unfinished_reqs)

782
            if not self.engines_running:
783
                if self.dp_rank == 0:
784
785
786
787
788
789
                    # Notify client that we are pausing the loop.
                    logger.debug("Wave %d finished, pausing engine loop.",
                                 self.current_wave)
                    self.output_queue.put_nowait(
                        EngineCoreOutputs(wave_complete=self.current_wave))
                self.current_wave += 1
790
791
792

    def _has_global_unfinished_reqs(self, local_unfinished: bool) -> bool:

793
        # Optimization - only perform finish-sync all-reduce every 24 steps.
794
        self.counter += 1
795
        if self.counter != 24:
796
797
798
799
800
            return True
        self.counter = 0

        return ParallelConfig.has_unfinished_dp(self.dp_group,
                                                local_unfinished)