"vllm/vscode:/vscode.git/clone" did not exist on "64e307c7931bb284aa41fde575046b3752beab19"
core.py 51.9 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
import gc
4
import os
5
import queue
6
import signal
7
8
import threading
import time
9
from collections import deque
Rui Qiao's avatar
Rui Qiao committed
10
from collections.abc import Generator
11
from concurrent.futures import Future
Rui Qiao's avatar
Rui Qiao committed
12
from contextlib import ExitStack, contextmanager
13
from inspect import isclass, signature
14
from logging import DEBUG
15
from typing import Any, Callable, Optional, TypeVar, Union
16

17
import msgspec
18
19
import zmq

20
21
from vllm.config import ParallelConfig, VllmConfig
from vllm.distributed import stateless_destroy_torch_distributed_process_group
22
from vllm.logger import init_logger
23
from vllm.logging_utils.dump_input import dump_engine_exception
24
from vllm.lora.request import LoRARequest
25
from vllm.multimodal import MULTIMODAL_REGISTRY
26
from vllm.multimodal.cache import engine_receiver_cache_from_config
27
from vllm.tasks import POOLING_TASKS, SupportedTask
28
29
30
31
32
33
34
35
from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
from vllm.utils import (
    decorate_logs,
    get_hash_fn_by_name,
    make_zmq_socket,
    resolve_obj_by_qualname,
    set_process_title,
)
36
from vllm.utils.gc_utils import maybe_attach_gc_debug_callback
37
38
39
40
41
42
43
from vllm.v1.core.kv_cache_utils import (
    BlockHash,
    generate_scheduler_kv_cache_config,
    get_kv_cache_configs,
    get_request_block_hasher,
    init_none_hash,
)
44
from vllm.v1.core.sched.interface import SchedulerInterface
45
46
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.core.sched.scheduler import Scheduler as V1Scheduler
47
48
49
50
51
52
53
54
55
56
57
58
59
60
from vllm.v1.engine import (
    EngineCoreOutputs,
    EngineCoreRequest,
    EngineCoreRequestType,
    ReconfigureDistributedRequest,
    ReconfigureRankType,
    UtilityOutput,
    UtilityResult,
)
from vllm.v1.engine.utils import (
    EngineHandshakeMetadata,
    EngineZmqAddresses,
    get_device_indices,
)
61
from vllm.v1.executor.abstract import Executor
62
from vllm.v1.kv_cache_interface import KVCacheConfig
63
from vllm.v1.metrics.stats import SchedulerStats
64
from vllm.v1.outputs import ModelRunnerOutput
65
from vllm.v1.request import Request, RequestStatus
66
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
67
from vllm.v1.structured_output import StructuredOutputManager
68
69
70
71
from vllm.version import __version__ as VLLM_VERSION

logger = init_logger(__name__)

72
POLLING_TIMEOUT_S = 2.5
73
HANDSHAKE_TIMEOUT_MINS = 5
74

75
_R = TypeVar("_R")  # Return type for collective_rpc
76

77
78
79
80

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

81
82
83
84
85
86
87
    def __init__(
        self,
        vllm_config: VllmConfig,
        executor_class: type[Executor],
        log_stats: bool,
        executor_fail_callback: Optional[Callable] = None,
    ):
88
89
        # plugins need to be loaded at the engine/scheduler level too
        from vllm.plugins import load_general_plugins
90

91
92
        load_general_plugins()

93
        self.vllm_config = vllm_config
94
95
96
97
98
        logger.info(
            "Initializing a V1 LLM engine (v%s) with config: %s",
            VLLM_VERSION,
            vllm_config,
        )
99

100
101
        self.log_stats = log_stats

102
103
        # Setup Model.
        self.model_executor = executor_class(vllm_config)
104
        if executor_fail_callback is not None:
105
            self.model_executor.register_failure_callback(executor_fail_callback)
106

107
108
        self.available_gpu_memory_for_kv_cache = -1

109
        # Setup KV Caches and update CacheConfig after profiling.
110
111
112
        num_gpu_blocks, num_cpu_blocks, kv_cache_config = self._initialize_kv_caches(
            vllm_config
        )
113

114
115
        vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks
        vllm_config.cache_config.num_cpu_blocks = num_cpu_blocks
116
        self.collective_rpc("initialize_cache", args=(num_gpu_blocks, num_cpu_blocks))
117

118
119
        self.structured_output_manager = StructuredOutputManager(vllm_config)

120
        # Setup scheduler.
121
        if isinstance(vllm_config.scheduler_config.scheduler_cls, str):
122
            Scheduler = resolve_obj_by_qualname(
123
124
                vllm_config.scheduler_config.scheduler_cls
            )
125
126
127
128
129
130
131
        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:
132
133
134
135
            logger.warning(
                "Using configured V1 scheduler class %s. "
                "This scheduler interface is not public and "
                "compatibility may not be maintained.",
136
137
                vllm_config.scheduler_config.scheduler_cls,
            )
138

139
140
141
142
143
144
        if len(kv_cache_config.kv_cache_groups) == 0:
            # Encoder models without KV cache don't support
            # chunked prefill. But do SSM models?
            logger.info("Disabling chunked prefill for model without KVCache")
            vllm_config.scheduler_config.chunked_prefill_enabled = False

145
        self.scheduler: SchedulerInterface = Scheduler(
146
            vllm_config=vllm_config,
147
148
            kv_cache_config=kv_cache_config,
            structured_output_manager=self.structured_output_manager,
149
            include_finished_set=vllm_config.parallel_config.data_parallel_size > 1,
150
            log_stats=self.log_stats,
151
        )
152
        self.use_spec_decode = vllm_config.speculative_config is not None
153
154
        if self.scheduler.connector is not None:  # type: ignore
            self.model_executor.init_kv_output_aggregator(
155
156
                self.scheduler.connector.get_finished_count()  # type: ignore
            )
157

158
        self.mm_registry = mm_registry = MULTIMODAL_REGISTRY
159
        self.mm_receiver_cache = engine_receiver_cache_from_config(
160
161
            vllm_config, mm_registry
        )
162

163
164
165
166
167
        # 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
168
169
170
        self.batch_queue: Optional[
            deque[tuple[Future[ModelRunnerOutput], SchedulerOutput]]
        ] = None
171
        if self.batch_queue_size > 1:
172
            logger.info("Batch queue is enabled with size %d", self.batch_queue_size)
173
            self.batch_queue = deque(maxlen=self.batch_queue_size)
174

175
176
177
178
179
        self.request_block_hasher: Optional[Callable[[Request], list[BlockHash]]] = None
        if (
            self.vllm_config.cache_config.enable_prefix_caching
            or self.scheduler.get_kv_connector() is not None
        ):
180
181
            block_size = vllm_config.cache_config.block_size
            caching_hash_fn = get_hash_fn_by_name(
182
183
                vllm_config.cache_config.prefix_caching_hash_algo
            )
184
185
186
            init_none_hash(caching_hash_fn)

            self.request_block_hasher = get_request_block_hasher(
187
188
                block_size, caching_hash_fn
            )
189

190
191
192
        self.step_fn = (
            self.step if self.batch_queue is None else self.step_with_batch_queue
        )
193

194
    def _initialize_kv_caches(
195
196
        self, vllm_config: VllmConfig
    ) -> tuple[int, int, KVCacheConfig]:
197
        start = time.time()
198

199
        # Get all kv cache needed by the model
200
        kv_cache_specs = self.model_executor.get_kv_cache_specs()
201

202
203
        has_kv_cache = any(kv_cache_spec for kv_cache_spec in kv_cache_specs)
        if has_kv_cache:
204
205
206
            if os.environ.get("VLLM_ELASTIC_EP_SCALE_UP_LAUNCH") == "1":
                dp_group = getattr(self, "dp_group", None)
                assert dp_group is not None
207
                self.available_gpu_memory_for_kv_cache = (
208
                    ParallelConfig.sync_kv_cache_memory_size(dp_group, -1)
209
210
211
212
                )
                available_gpu_memory = [self.available_gpu_memory_for_kv_cache] * len(
                    kv_cache_specs
                )
213
214
215
            else:
                # Profiles the peak memory usage of the model to determine how
                # much memory can be allocated for kv cache.
216
217
                available_gpu_memory = self.model_executor.determine_available_memory()
                self.available_gpu_memory_for_kv_cache = available_gpu_memory[0]
218
219
220
        else:
            # Attention free models don't need memory for kv cache
            available_gpu_memory = [0] * len(kv_cache_specs)
221

222
        assert len(kv_cache_specs) == len(available_gpu_memory)
223

224
225
226
227
        kv_cache_configs = get_kv_cache_configs(
            vllm_config, kv_cache_specs, available_gpu_memory
        )
        scheduler_kv_cache_config = generate_scheduler_kv_cache_config(kv_cache_configs)
228
        num_gpu_blocks = scheduler_kv_cache_config.num_blocks
229
        num_cpu_blocks = 0
230
231

        # Initialize kv cache and warmup the execution
232
        self.model_executor.initialize_from_config(kv_cache_configs)
233

234
        elapsed = time.time() - start
235
236
237
238
        logger.info(
            ("init engine (profile, create kv cache, warmup model) took %.2f seconds"),
            elapsed,
        )
239
        return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config
240

241
242
243
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return self.model_executor.supported_tasks

244
245
    def add_request(self, request: Request, request_wave: int = 0):
        """Add request to the scheduler.
246

247
248
249
        `request_wave`: indicate which wave of requests this is expected to
        belong to in DP case
        """
250
251
252
        # Validate the request_id type.
        if not isinstance(request.request_id, str):
            raise TypeError(
253
254
                f"request_id must be a string, got {type(request.request_id)}"
            )
255

256
        if pooling_params := request.pooling_params:
257
            supported_pooling_tasks = [
258
                task for task in self.get_supported_tasks() if task in POOLING_TASKS
259
260
            ]

261
            if pooling_params.task not in supported_pooling_tasks:
262
263
264
265
                raise ValueError(
                    f"Unsupported task: {pooling_params.task!r} "
                    f"Supported tasks: {supported_pooling_tasks}"
                )
266

267
        if request.kv_transfer_params is not None and (
268
269
270
271
272
273
            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
274

275
        self.scheduler.add_request(request)
276

277
    def abort_requests(self, request_ids: list[str]):
278
279
280
281
282
        """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).
283
        self.scheduler.finish_requests(request_ids, RequestStatus.FINISHED_ABORTED)
284

285
286
287
288
289
290
    def execute_model_with_error_logging(
        self,
        model_fn: Callable[[SchedulerOutput], ModelRunnerOutput],
        scheduler_output: SchedulerOutput,
    ) -> ModelRunnerOutput:
        """Execute the model and log detailed info on failure."""
291
        try:
292
            return model_fn(scheduler_output)
293
294
295
296
297
        except Exception as err:
            # We do not want to catch BaseException here since we're only
            # interested in dumping info when the exception is due to an
            # error from execute_model itself.

298
            # NOTE: This method is exception-free
299
300
301
            dump_engine_exception(
                self.vllm_config, scheduler_output, self.scheduler.make_stats()
            )
302
303
            raise err

304
    def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]:
305
306
307
308
309
        """Schedule, execute, and make output.

        Returns tuple of outputs and a flag indicating whether the model
        was executed.
        """
310

311
312
313
        # Check for any requests remaining in the scheduler - unfinished,
        # or finished and not yet removed from the batch.
        if not self.scheduler.has_requests():
314
            return {}, False
315
        scheduler_output = self.scheduler.schedule()
316
317
        model_output = self.execute_model_with_error_logging(
            self.model_executor.execute_model,  # type: ignore
318
319
            scheduler_output,
        )
320
        engine_core_outputs = self.scheduler.update_from_output(
321
322
            scheduler_output, model_output
        )  # type: ignore
323

324
        return (engine_core_outputs, scheduler_output.total_num_scheduled_tokens > 0)
325

326
327
328
329
330
331
332
    def post_step(self, model_executed: bool) -> None:
        if self.use_spec_decode and model_executed:
            # Take the draft token ids.
            draft_token_ids = self.model_executor.take_draft_token_ids()
            if draft_token_ids is not None:
                self.scheduler.update_draft_token_ids(draft_token_ids)

333
    def step_with_batch_queue(
334
335
        self,
    ) -> tuple[Optional[dict[int, EngineCoreOutputs]], bool]:
336
337
338
339
        """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:
340
341
342
343
        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.
344
345
346
347
348
        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.
        """
349
350
        batch_queue = self.batch_queue
        assert batch_queue is not None
351

352
353
354
        # 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.
355
        assert len(batch_queue) < self.batch_queue_size
356

357
358
359
        model_executed = False
        if self.scheduler.has_requests():
            scheduler_output = self.scheduler.schedule()
360
361
            future = self.model_executor.execute_model(scheduler_output, non_block=True)
            batch_queue.appendleft((future, scheduler_output))  # type: ignore[arg-type]
362
363

            model_executed = scheduler_output.total_num_scheduled_tokens > 0
364
365
366
367
368
            if (
                model_executed
                and len(batch_queue) < self.batch_queue_size
                and not batch_queue[-1][0].done()
            ):
369
370
371
372
373
374
375
376
377
378
379
380
381
                # Don't block on next worker response unless the queue is full
                # or there are no more requests to schedule.
                return None, True

        elif not batch_queue:
            # Queue is empty. We should not reach here since this method should
            # only be called when the scheduler contains requests or the queue
            # is non-empty.
            return None, False

        # Block until the next result is available.
        future, scheduler_output = batch_queue.pop()
        model_output = self.execute_model_with_error_logging(
382
383
            lambda _: future.result(), scheduler_output
        )
384

385
        engine_core_outputs = self.scheduler.update_from_output(
386
387
            scheduler_output, model_output
        )
388

389
        return engine_core_outputs, model_executed
390

391
    def shutdown(self):
392
        self.structured_output_manager.clear_backend()
393
394
        if self.model_executor:
            self.model_executor.shutdown()
395
396
        if self.scheduler:
            self.scheduler.shutdown()
397

398
    def profile(self, is_start: bool = True):
399
        self.model_executor.profile(is_start)
400

401
402
403
    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)
404
        if self.scheduler.has_unfinished_requests():
405
406
407
408
            logger.warning(
                "Resetting the multi-modal cache when requests are "
                "in progress may lead to desynced internal caches."
            )
409

410
411
        if self.mm_receiver_cache is not None:
            self.mm_receiver_cache.clear_cache()
412

413
414
415
    def reset_prefix_cache(self):
        self.scheduler.reset_prefix_cache()

416
417
418
    def sleep(self, level: int = 1):
        self.model_executor.sleep(level)

419
420
    def wake_up(self, tags: Optional[list[str]] = None):
        self.model_executor.wake_up(tags)
421

422
423
424
    def is_sleeping(self) -> bool:
        return self.model_executor.is_sleeping

425
    def execute_dummy_batch(self):
426
        self.model_executor.execute_dummy_batch()
427

428
429
430
431
432
433
    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)

434
    def list_loras(self) -> set[int]:
435
436
437
438
        return self.model_executor.list_loras()

    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)
439

440
441
442
443
444
445
    def save_sharded_state(
        self,
        path: str,
        pattern: Optional[str] = None,
        max_size: Optional[int] = None,
    ) -> None:
446
447
448
449
450
451
452
453
454
455
456
457
        self.model_executor.save_sharded_state(
            path=path, pattern=pattern, max_size=max_size
        )

    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)
458

459
460
461
462
463
    def save_tensorized_model(
        self,
        tensorizer_config,
    ) -> None:
        self.model_executor.save_tensorized_model(
464
465
            tensorizer_config=tensorizer_config,
        )
466

467
    def preprocess_add_request(self, request: EngineCoreRequest) -> tuple[Request, int]:
468
        """Preprocess the request.
469

470
471
472
        This function could be directly used in input processing thread to allow
        request initialization running in parallel with Model forward
        """
473
474
        # Note on thread safety: no race condition.
        # `mm_receiver_cache` is reset at the end of LLMEngine init,
475
        # and will only be accessed in the input processing thread afterwards.
476
        if self.mm_receiver_cache is not None and request.mm_features:
477
478
479
            request.mm_features = self.mm_receiver_cache.get_and_update_features(
                request.mm_features
            )
480

481
        req = Request.from_engine_core_request(request, self.request_block_hasher)
482
483
484
485
486
487
488
489
490
        if req.use_structured_output:
            # Note on thread safety: no race condition.
            # `grammar_init` is only invoked in input processing thread. For
            # `structured_output_manager`, each request is independent and
            # grammar compilation is async. Scheduler always checks grammar
            # compilation status before scheduling request.
            self.structured_output_manager.grammar_init(req)
        return req, request.current_wave

491
492
493
494

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

495
    ENGINE_CORE_DEAD = b"ENGINE_CORE_DEAD"
496

497
498
    def __init__(
        self,
499
        vllm_config: VllmConfig,
500
        local_client: bool,
501
        handshake_address: str,
502
        executor_class: type[Executor],
503
        log_stats: bool,
504
        client_handshake_address: Optional[str] = None,
505
        engine_index: int = 0,
506
    ):
Rui Qiao's avatar
Rui Qiao committed
507
        self.input_queue = queue.Queue[tuple[EngineCoreRequestType, Any]]()
508
        self.output_queue = queue.Queue[Union[tuple[int, EngineCoreOutputs], bytes]]()
Rui Qiao's avatar
Rui Qiao committed
509
        executor_fail_callback = lambda: self.input_queue.put_nowait(
510
511
            (EngineCoreRequestType.EXECUTOR_FAILED, b"")
        )
512

Rui Qiao's avatar
Rui Qiao committed
513
514
515
        self.engine_index = engine_index
        identity = self.engine_index.to_bytes(length=2, byteorder="little")
        self.engines_running = False
516

517
518
519
520
521
522
523
        with self._perform_handshakes(
            handshake_address,
            identity,
            local_client,
            vllm_config,
            client_handshake_address,
        ) as addresses:
524
            self.client_count = len(addresses.outputs)
525
526

            # Set up data parallel environment.
527
            self.has_coordinator = addresses.coordinator_output is not None
528
            self.frontend_stats_publish_address = (
529
530
531
532
533
534
535
                addresses.frontend_stats_publish_address
            )
            logger.debug(
                "Has DP Coordinator: %s, stats publish address: %s",
                self.has_coordinator,
                self.frontend_stats_publish_address,
            )
536
            # Only publish request queue stats to coordinator for "internal"
537
            # and "hybrid" LB modes .
538
539
            self.publish_dp_lb_stats = (
                self.has_coordinator
540
541
                and not vllm_config.parallel_config.data_parallel_external_lb
            )
542

543
544
            self._init_data_parallel(vllm_config)

545
546
547
            super().__init__(
                vllm_config, executor_class, log_stats, executor_fail_callback
            )
548

549
550
551
552
553
554
            # 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.
            ready_event = threading.Event()
555
556
557
558
559
560
561
562
563
564
            input_thread = threading.Thread(
                target=self.process_input_sockets,
                args=(
                    addresses.inputs,
                    addresses.coordinator_input,
                    identity,
                    ready_event,
                ),
                daemon=True,
            )
565
566
567
568
            input_thread.start()

            self.output_thread = threading.Thread(
                target=self.process_output_sockets,
569
570
571
572
573
574
575
                args=(
                    addresses.outputs,
                    addresses.coordinator_output,
                    self.engine_index,
                ),
                daemon=True,
            )
576
577
578
579
580
581
            self.output_thread.start()

            # Don't complete handshake until DP coordinator ready message is
            # received.
            while not ready_event.wait(timeout=10):
                if not input_thread.is_alive():
582
                    raise RuntimeError("Input socket thread died during startup")
583
584
585
                assert addresses.coordinator_input is not None
                logger.info("Waiting for READY message from DP Coordinator...")

586
587
588
589
590
        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        gc.collect()
        gc.freeze()

591
592
593
        # If enable, attach GC debugger after static variable freeze.
        maybe_attach_gc_debug_callback()

Rui Qiao's avatar
Rui Qiao committed
594
    @contextmanager
595
596
597
598
599
600
601
    def _perform_handshakes(
        self,
        handshake_address: str,
        identity: bytes,
        local_client: bool,
        vllm_config: VllmConfig,
        client_handshake_address: Optional[str],
Rui Qiao's avatar
Rui Qiao committed
602
    ) -> Generator[EngineZmqAddresses, None, None]:
603
604
605
606
607
        """
        Perform startup handshakes.

        For DP=1 or offline mode, this is with the colocated front-end process.

608
        For DP>1 with internal load-balancing this is with the shared front-end
609
610
        process which may reside on a different node.

611
        For DP>1 with external or hybrid load-balancing, two handshakes are
612
        performed:
613
614
615
616
            - With the rank 0 front-end process which retrieves the
              DP Coordinator ZMQ addresses and DP process group address.
            - With the colocated front-end process which retrieves the
              client input/output socket addresses.
617
618
        with the exception of the rank 0 and colocated engines themselves which
        don't require the second handshake.
619
620
621
622
623
624

        Here, "front-end" process can mean the process containing the engine
        core client (which is the API server process in the case the API
        server is not scaled out), OR the launcher process running the
        run_multi_api_server() function in serve.py.
        """
Rui Qiao's avatar
Rui Qiao committed
625
        input_ctx = zmq.Context()
626
        is_local = local_client and client_handshake_address is None
627
        headless = not local_client
628
629
630
631
632
633
634
635
636
        handshake = self._perform_handshake(
            input_ctx,
            handshake_address,
            identity,
            is_local,
            headless,
            vllm_config,
            vllm_config.parallel_config,
        )
637
638
639
640
        if client_handshake_address is None:
            with handshake as addresses:
                yield addresses
        else:
641
            assert local_client
642
            local_handshake = self._perform_handshake(
643
644
                input_ctx, client_handshake_address, identity, True, False, vllm_config
            )
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
            with handshake as addresses, local_handshake as client_addresses:
                addresses.inputs = client_addresses.inputs
                addresses.outputs = client_addresses.outputs
                yield addresses

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

    @contextmanager
    def _perform_handshake(
        self,
        ctx: zmq.Context,
        handshake_address: str,
        identity: bytes,
        local_client: bool,
660
        headless: bool,
661
662
663
        vllm_config: VllmConfig,
        parallel_config_to_update: Optional[ParallelConfig] = None,
    ) -> Generator[EngineZmqAddresses, None, None]:
664
665
666
667
668
669
670
671
        with make_zmq_socket(
            ctx,
            handshake_address,
            zmq.DEALER,
            identity=identity,
            linger=5000,
            bind=False,
        ) as handshake_socket:
Rui Qiao's avatar
Rui Qiao committed
672
            # Register engine with front-end.
673
674
675
            addresses = self.startup_handshake(
                handshake_socket, local_client, headless, parallel_config_to_update
            )
Rui Qiao's avatar
Rui Qiao committed
676
677
678
679
            yield addresses

            # Send ready message.
            num_gpu_blocks = vllm_config.cache_config.num_gpu_blocks
680
681
682
683
            # We pass back the coordinator stats update address here for the
            # external LB case for our colocated front-end to use (coordinator
            # only runs with rank 0).
            dp_stats_address = self.frontend_stats_publish_address
Rui Qiao's avatar
Rui Qiao committed
684
            handshake_socket.send(
685
686
687
688
689
690
691
692
693
694
                msgspec.msgpack.encode(
                    {
                        "status": "READY",
                        "local": local_client,
                        "headless": headless,
                        "num_gpu_blocks": num_gpu_blocks,
                        "dp_stats_address": dp_stats_address,
                    }
                )
            )
Rui Qiao's avatar
Rui Qiao committed
695

696
    @staticmethod
697
    def startup_handshake(
698
699
        handshake_socket: zmq.Socket,
        local_client: bool,
700
        headless: bool,
701
702
        parallel_config: Optional[ParallelConfig] = None,
    ) -> EngineZmqAddresses:
703
        # Send registration message.
704
        handshake_socket.send(
705
706
707
708
709
710
711
712
            msgspec.msgpack.encode(
                {
                    "status": "HELLO",
                    "local": local_client,
                    "headless": headless,
                }
            )
        )
713
714
715

        # Receive initialization message.
        logger.info("Waiting for init message from front-end.")
716
        if not handshake_socket.poll(timeout=HANDSHAKE_TIMEOUT_MINS * 60_000):
717
718
719
720
721
            raise RuntimeError(
                "Did not receive response from front-end "
                f"process within {HANDSHAKE_TIMEOUT_MINS} "
                f"minutes"
            )
722
723
        init_bytes = handshake_socket.recv()
        init_message: EngineHandshakeMetadata = msgspec.msgpack.decode(
724
725
            init_bytes, type=EngineHandshakeMetadata
        )
726
727
        logger.debug("Received init message: %s", init_message)

728
729
730
        if parallel_config is not None:
            for key, value in init_message.parallel_config.items():
                setattr(parallel_config, key, value)
731

732
        return init_message.addresses
733
734

    @staticmethod
735
    def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs):
736
737
        """Launch EngineCore busy loop in background process."""

738
739
740
741
742
        # 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

743
744
745
        # Ensure we can serialize transformer config after spawning
        maybe_register_config_serialize_by_value()

746
747
748
749
750
751
752
753
754
755
        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)

756
        engine_core: Optional[EngineCoreProc] = None
757
        try:
758
            parallel_config: ParallelConfig = kwargs["vllm_config"].parallel_config
759
            if parallel_config.data_parallel_size > 1 or dp_rank > 0:
760
                set_process_title("EngineCore", f"DP{dp_rank}")
761
                decorate_logs()
762
763
764
765
766
                # 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:
767
                set_process_title("EngineCore")
768
                decorate_logs()
769
770
                engine_core = EngineCoreProc(*args, **kwargs)

771
772
            engine_core.run_busy_loop()

773
        except SystemExit:
774
            logger.debug("EngineCore exiting.")
775
            raise
776
777
778
779
780
781
782
        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
783
784
785
786
        finally:
            if engine_core is not None:
                engine_core.shutdown()

787
788
789
    def _init_data_parallel(self, vllm_config: VllmConfig):
        pass

790
791
792
    def run_busy_loop(self):
        """Core busy loop of the EngineCore."""

793
794
        # Loop until process is sent a SIGINT or SIGTERM
        while True:
795
            # 1) Poll the input queue until there is work to do.
796
797
798
799
800
801
802
803
            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
804
805
806
807
808
        while (
            not self.engines_running
            and not self.scheduler.has_requests()
            and not self.batch_queue
        ):
809
810
811
812
813
814
815
            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:
816
            logger.debug("EngineCore loop active.")
817
818
819
820
821
822

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

823
    def _process_engine_step(self) -> bool:
824
825
826
        """Called only when there are unfinished local requests."""

        # Step the engine core.
827
        outputs, model_executed = self.step_fn()
828
        # Put EngineCoreOutputs into the output queue.
829
        for output in outputs.items() if outputs else ():
830
            self.output_queue.put_nowait(output)
831
832
        # Post-step hook.
        self.post_step(model_executed)
833

834
835
        return model_executed

836
837
838
    def _handle_client_request(
        self, request_type: EngineCoreRequestType, request: Any
    ) -> None:
839
        """Dispatch request from client."""
840

841
        if request_type == EngineCoreRequestType.ADD:
842
843
            req, request_wave = request
            self.add_request(req, request_wave)
844
        elif request_type == EngineCoreRequestType.ABORT:
845
            self.abort_requests(request)
846
        elif request_type == EngineCoreRequestType.UTILITY:
847
            client_idx, call_id, method_name, args = request
848
849
850
            output = UtilityOutput(call_id)
            try:
                method = getattr(self, method_name)
851
852
                result = method(*self._convert_msgspec_args(method, args))
                output.result = UtilityResult(result)
853
854
            except BaseException as e:
                logger.exception("Invocation of %s method failed", method_name)
855
856
857
                output.failure_message = (
                    f"Call to {method_name} method failed: {str(e)}"
                )
858
            self.output_queue.put_nowait(
859
860
                (client_idx, EngineCoreOutputs(utility_output=output))
            )
861
862
863
        elif request_type == EngineCoreRequestType.EXECUTOR_FAILED:
            raise RuntimeError("Executor failed.")
        else:
864
865
866
            logger.error(
                "Unrecognized input request type encountered: %s", request_type
            )
867
868
869
870

    @staticmethod
    def _convert_msgspec_args(method, args):
        """If a provided arg type doesn't match corresponding target method
871
        arg type, try converting to msgspec object."""
872
873
874
875
876
        if not args:
            return args
        arg_types = signature(method).parameters.values()
        assert len(args) <= len(arg_types)
        return tuple(
877
878
            msgspec.convert(v, type=p.annotation)
            if isclass(p.annotation)
879
            and issubclass(p.annotation, msgspec.Struct)
880
881
882
883
            and not isinstance(v, p.annotation)
            else v
            for v, p in zip(args, arg_types)
        )
884

885
886
887
888
889
890
891
892
893
    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():
894
895
896
897
            logger.fatal(
                "vLLM shutdown signal from EngineCore failed "
                "to send. Please report this issue."
            )
898

899
900
901
902
903
904
905
    def process_input_sockets(
        self,
        input_addresses: list[str],
        coord_input_address: Optional[str],
        identity: bytes,
        ready_event: threading.Event,
    ):
906
907
908
        """Input socket IO thread."""

        # Msgpack serialization decoding.
909
910
        add_request_decoder = MsgpackDecoder(EngineCoreRequest)
        generic_decoder = MsgpackDecoder()
911

912
913
914
        with ExitStack() as stack, zmq.Context() as ctx:
            input_sockets = [
                stack.enter_context(
915
916
917
918
                    make_zmq_socket(
                        ctx, input_address, zmq.DEALER, identity=identity, bind=False
                    )
                )
919
920
921
922
923
924
                for input_address in input_addresses
            ]
            if coord_input_address is None:
                coord_socket = None
            else:
                coord_socket = stack.enter_context(
925
926
927
928
929
930
931
932
                    make_zmq_socket(
                        ctx,
                        coord_input_address,
                        zmq.XSUB,
                        identity=identity,
                        bind=False,
                    )
                )
933
                # Send subscription message to coordinator.
934
                coord_socket.send(b"\x01")
935
936
937
938
939
940
941

            # Register sockets with poller.
            poller = zmq.Poller()
            for input_socket in input_sockets:
                # Send initial message to each input socket - this is required
                # before the front-end ROUTER socket can send input messages
                # back to us.
942
                input_socket.send(b"")
943
                poller.register(input_socket, zmq.POLLIN)
944

945
            if coord_socket is not None:
946
947
                # Wait for ready message from coordinator.
                assert coord_socket.recv() == b"READY"
948
                poller.register(coord_socket, zmq.POLLIN)
949

950
951
            ready_event.set()
            del ready_event
952
953
954
            while True:
                for input_socket, _ in poller.poll():
                    # (RequestType, RequestData)
955
956
                    type_frame, *data_frames = input_socket.recv_multipart(copy=False)
                    request_type = EngineCoreRequestType(bytes(type_frame.buffer))
957
958

                    # Deserialize the request data.
959
960
961
962
963
                    if request_type == EngineCoreRequestType.ADD:
                        request = add_request_decoder.decode(data_frames)
                        request = self.preprocess_add_request(request)
                    else:
                        request = generic_decoder.decode(data_frames)
964
965
966
967

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

968
969
970
971
972
973
    def process_output_sockets(
        self,
        output_paths: list[str],
        coord_output_path: Optional[str],
        engine_index: int,
    ):
974
975
976
        """Output socket IO thread."""

        # Msgpack serialization encoding.
977
        encoder = MsgpackEncoder()
978
979
980
981
982
983
        # 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]]()
984

985
986
        # We must set linger to ensure the ENGINE_CORE_DEAD
        # message is sent prior to closing the socket.
987
988
989
        with ExitStack() as stack, zmq.Context() as ctx:
            sockets = [
                stack.enter_context(
990
991
                    make_zmq_socket(ctx, output_path, zmq.PUSH, linger=4000)
                )
992
993
                for output_path in output_paths
            ]
994
995
996
997
998
999
1000
1001
1002
            coord_socket = (
                stack.enter_context(
                    make_zmq_socket(
                        ctx, coord_output_path, zmq.PUSH, bind=False, linger=4000
                    )
                )
                if coord_output_path is not None
                else None
            )
1003
1004
            max_reuse_bufs = len(sockets) + 1

1005
            while True:
1006
1007
1008
1009
                output = self.output_queue.get()
                if output == EngineCoreProc.ENGINE_CORE_DEAD:
                    for socket in sockets:
                        socket.send(output)
1010
                    break
1011
1012
                assert not isinstance(output, bytes)
                client_index, outputs = output
1013
                outputs.engine_index = engine_index
1014

1015
1016
1017
1018
1019
1020
1021
                if client_index == -1:
                    # Don't reuse buffer for coordinator message
                    # which will be very small.
                    assert coord_socket is not None
                    coord_socket.send_multipart(encoder.encode(outputs))
                    continue

1022
1023
1024
1025
1026
                # 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()
1027
                buffers = encoder.encode_into(outputs, buffer)
1028
1029
1030
                tracker = sockets[client_index].send_multipart(
                    buffers, copy=False, track=True
                )
1031
1032
1033
                if not tracker.done:
                    ref = outputs if len(buffers) > 1 else None
                    pending.appendleft((tracker, ref, buffer))
1034
1035
                elif len(reuse_buffers) < max_reuse_bufs:
                    # Limit the number of buffers to reuse.
1036
                    reuse_buffers.append(buffer)
1037
1038
1039
1040
1041
1042
1043
1044
1045


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

    def __init__(
        self,
        vllm_config: VllmConfig,
1046
        local_client: bool,
1047
        handshake_address: str,
1048
1049
        executor_class: type[Executor],
        log_stats: bool,
1050
        client_handshake_address: Optional[str] = None,
1051
    ):
1052
1053
        # Counts forward-passes of the model so that we can synchronize
        # finished with DP peers every N steps.
1054
        self.step_counter = 0
1055
        self.current_wave = 0
Rui Qiao's avatar
Rui Qiao committed
1056
        self.last_counts = (0, 0)
1057
1058
1059

        # Initialize the engine.
        dp_rank = vllm_config.parallel_config.data_parallel_rank
1060
1061
1062
1063
1064
1065
1066
1067
1068
        super().__init__(
            vllm_config,
            local_client,
            handshake_address,
            executor_class,
            log_stats,
            client_handshake_address,
            dp_rank,
        )
1069
1070
1071

    def _init_data_parallel(self, vllm_config: VllmConfig):
        # Configure GPUs and stateless process group for data parallel.
1072
        dp_rank = vllm_config.parallel_config.data_parallel_rank
1073
        dp_size = vllm_config.parallel_config.data_parallel_size
1074
1075
1076
1077
1078
        local_dp_rank = vllm_config.parallel_config.data_parallel_rank_local

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

1079
1080
1081
1082
1083
1084
        if vllm_config.kv_transfer_config is not None:
            # modify the engine_id and append the local_dp_rank to it to ensure
            # that the kv_transfer_config is unique for each DP rank.
            vllm_config.kv_transfer_config.engine_id = (
                f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}"
            )
1085
1086
1087
1088
            logger.debug(
                "Setting kv_transfer_config.engine_id to %s",
                vllm_config.kv_transfer_config.engine_id,
            )
1089

1090
        self.dp_rank = dp_rank
1091
1092
1093
1094
1095
1096
1097
        self.dp_group = vllm_config.parallel_config.stateless_init_dp_group()

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

1098
1099
1100
1101
    def add_request(self, request: Request, request_wave: int = 0):
        if self.has_coordinator and request_wave != self.current_wave:
            if request_wave > self.current_wave:
                self.current_wave = request_wave
1102
1103
1104
1105
            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(
1106
1107
                    (-1, EngineCoreOutputs(start_wave=self.current_wave))
                )
1108

1109
        super().add_request(request, request_wave)
1110

1111
1112
1113
    def _handle_client_request(
        self, request_type: EngineCoreRequestType, request: Any
    ) -> None:
1114
        if request_type == EngineCoreRequestType.START_DP_WAVE:
1115
1116
            new_wave, exclude_eng_index = request
            if exclude_eng_index != self.engine_index and (
1117
1118
                new_wave >= self.current_wave
            ):
1119
1120
                self.current_wave = new_wave
                if not self.engines_running:
1121
                    logger.debug("EngineCore starting idle loop for wave %d.", new_wave)
1122
1123
1124
1125
                    self.engines_running = True
        else:
            super()._handle_client_request(request_type, request)

1126
    def _maybe_publish_request_counts(self):
1127
        if not self.publish_dp_lb_stats:
1128
1129
1130
1131
1132
1133
            return

        # Publish our request counts (if they've changed).
        counts = self.scheduler.get_request_counts()
        if counts != self.last_counts:
            self.last_counts = counts
1134
1135
1136
1137
            stats = SchedulerStats(
                *counts, step_counter=self.step_counter, current_wave=self.current_wave
            )
            self.output_queue.put_nowait((-1, EngineCoreOutputs(scheduler_stats=stats)))
1138

1139
1140
1141
1142
1143
1144
1145
1146
    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()

1147
1148
            # 2) Step the engine core.
            executed = self._process_engine_step()
1149
1150
            self._maybe_publish_request_counts()

1151
            local_unfinished_reqs = self.scheduler.has_unfinished_requests()
1152
1153
            if not executed:
                if not local_unfinished_reqs and not self.engines_running:
1154
1155
1156
                    # All engines are idle.
                    continue

1157
1158
                # We are in a running state and so must execute a dummy pass
                # if the model didn't execute any ready requests.
1159
1160
1161
                self.execute_dummy_batch()

            # 3) All-reduce operation to determine global unfinished reqs.
1162
            self.engines_running = self._has_global_unfinished_reqs(
1163
1164
                local_unfinished_reqs
            )
1165

1166
            if not self.engines_running:
1167
                if self.dp_rank == 0 or not self.has_coordinator:
1168
                    # Notify client that we are pausing the loop.
1169
1170
1171
                    logger.debug(
                        "Wave %d finished, pausing engine loop.", self.current_wave
                    )
1172
1173
1174
1175
                    # In the coordinator case, dp rank 0 sends updates to the
                    # coordinator. Otherwise (offline spmd case), each rank
                    # sends the update to its colocated front-end process.
                    client_index = -1 if self.has_coordinator else 0
1176
                    self.output_queue.put_nowait(
1177
1178
1179
1180
1181
                        (
                            client_index,
                            EngineCoreOutputs(wave_complete=self.current_wave),
                        )
                    )
1182
                # Increment wave count and reset step counter.
1183
                self.current_wave += 1
1184
                self.step_counter = 0
1185
1186

    def _has_global_unfinished_reqs(self, local_unfinished: bool) -> bool:
1187
        # Optimization - only perform finish-sync all-reduce every 32 steps.
1188
1189
        self.step_counter += 1
        if self.step_counter % 32 != 0:
1190
1191
            return True

1192
        return ParallelConfig.has_unfinished_dp(self.dp_group, local_unfinished)
Rui Qiao's avatar
Rui Qiao committed
1193

1194
    def reinitialize_distributed(
1195
1196
        self, reconfig_request: ReconfigureDistributedRequest
    ) -> None:
1197
1198
1199
1200
1201
        stateless_destroy_torch_distributed_process_group(self.dp_group)
        self.shutdown()

        parallel_config = self.vllm_config.parallel_config
        old_dp_size = parallel_config.data_parallel_size
1202
        parallel_config.data_parallel_size = reconfig_request.new_data_parallel_size
1203
        if reconfig_request.new_data_parallel_rank != -1:
1204
            parallel_config.data_parallel_rank = reconfig_request.new_data_parallel_rank
1205
        # local rank specifies device visibility, it should not be changed
1206
1207
1208
1209
1210
        assert (
            reconfig_request.new_data_parallel_rank_local
            == ReconfigureRankType.KEEP_CURRENT_RANK
        )
        parallel_config.data_parallel_master_ip = (
1211
            reconfig_request.new_data_parallel_master_ip
1212
1213
        )
        parallel_config.data_parallel_master_port = (
1214
            reconfig_request.new_data_parallel_master_port
1215
        )
1216
1217
1218
        if reconfig_request.new_data_parallel_rank != -2:
            self.dp_rank = parallel_config.data_parallel_rank
            self.dp_group = parallel_config.stateless_init_dp_group()
1219
        reconfig_request.new_data_parallel_master_port = (
1220
            parallel_config.data_parallel_master_port
1221
        )
1222
1223
1224
1225
1226
1227
1228
1229

        self.model_executor.reinitialize_distributed(reconfig_request)
        if reconfig_request.new_data_parallel_size > old_dp_size:
            assert self.available_gpu_memory_for_kv_cache > 0
            # pass available_gpu_memory_for_kv_cache from existing
            # engine-cores to new engine-cores so they can directly
            # use it in _initialize_kv_caches() rather than profiling.
            ParallelConfig.sync_kv_cache_memory_size(
1230
1231
                self.dp_group, self.available_gpu_memory_for_kv_cache
            )
1232
1233
1234
            # NOTE(yongji): newly joined workers require dummy_run even
            # CUDA graph is not used
            self.model_executor.collective_rpc("compile_or_warm_up_model")
1235
1236
1237
1238
        if (
            reconfig_request.new_data_parallel_rank
            == ReconfigureRankType.SHUTDOWN_CURRENT_RANK
        ):
1239
1240
1241
            self.shutdown()
            logger.info("DPEngineCoreProc %s shutdown", self.dp_rank)
        else:
1242
1243
1244
            logger.info(
                "Distributed environment reinitialized for DP rank %s", self.dp_rank
            )
1245

Rui Qiao's avatar
Rui Qiao committed
1246
1247
1248
1249
1250
1251
1252
1253
1254

class DPEngineCoreActor(DPEngineCoreProc):
    """
    Ray actor for running EngineCore in a data parallel context
    """

    def __init__(
        self,
        vllm_config: VllmConfig,
1255
        local_client: bool,
Rui Qiao's avatar
Rui Qiao committed
1256
1257
1258
1259
1260
1261
1262
1263
        addresses: EngineZmqAddresses,
        executor_class: type[Executor],
        log_stats: bool,
        dp_rank: int = 0,
        local_dp_rank: int = 0,
    ):
        self.addresses = addresses
        vllm_config.parallel_config.data_parallel_rank = dp_rank
1264
        vllm_config.parallel_config.data_parallel_rank_local = local_dp_rank
Rui Qiao's avatar
Rui Qiao committed
1265

1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
        # Set CUDA_VISIBLE_DEVICES as early as possible in actor life cycle
        # NOTE: in MP we set CUDA_VISIBLE_DEVICES at process creation time,
        # and this cannot be done in the same way for Ray because:
        # 1) Ray manages life cycle of all ray workers (including
        # DPEngineCoreActor)
        # 2) Ray sets CUDA_VISIBLE_DEVICES based on num_gpus configuration
        # To bypass 2, we need to also set
        # RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES, but vLLM workers created
        # thereafter would have CUDA_VISIBLE_DEVICES set, which is sticky:
        # https://github.com/ray-project/ray/blob/e752fc319ddedd9779a0989b6d3613909bad75c9/python/ray/_private/worker.py#L456 # noqa: E501
1276
1277
1278
1279
1280
1281
1282
        # This is problematic because when the vLLM worker (a Ray actor)
        # executes a task, it indexes into the sticky CUDA_VISIBLE_DEVICES
        # rather than directly using the GPU ID, potentially resulting in
        # index out of bounds error. See:
        # https://github.com/ray-project/ray/pull/40461/files#diff-31e8159767361e4bc259b6d9883d9c0d5e5db780fcea4a52ead4ee3ee4a59a78R1860 # noqa: E501
        # and get_accelerator_ids_for_accelerator_resource() in worker.py
        # of ray.
1283
        self._set_visible_devices(vllm_config, local_dp_rank)
Rui Qiao's avatar
Rui Qiao committed
1284

1285
        super().__init__(vllm_config, local_client, "", executor_class, log_stats)
Rui Qiao's avatar
Rui Qiao committed
1286

1287
    def _set_visible_devices(self, vllm_config: VllmConfig, local_dp_rank: int):
1288
        from vllm.platforms import current_platform
1289

1290
1291
1292
1293
        if current_platform.is_xpu():
            pass
        else:
            device_control_env_var = current_platform.device_control_env_var
1294
1295
1296
            self._set_cuda_visible_devices(
                vllm_config, local_dp_rank, device_control_env_var
            )
1297

1298
1299
1300
    def _set_cuda_visible_devices(
        self, vllm_config: VllmConfig, local_dp_rank: int, device_control_env_var: str
    ):
1301
1302
1303
        world_size = vllm_config.parallel_config.world_size
        # Set CUDA_VISIBLE_DEVICES or equivalent.
        try:
1304
1305
1306
            value = get_device_indices(
                device_control_env_var, local_dp_rank, world_size
            )
1307
            os.environ[device_control_env_var] = value
1308
1309
1310
1311
1312
        except IndexError as e:
            raise Exception(
                f"Error setting {device_control_env_var}: "
                f"local range: [{local_dp_rank * world_size}, "
                f"{(local_dp_rank + 1) * world_size}) "
1313
1314
                f'base value: "{os.getenv(device_control_env_var)}"'
            ) from e
1315

Rui Qiao's avatar
Rui Qiao committed
1316
    @contextmanager
1317
1318
1319
1320
1321
1322
1323
1324
    def _perform_handshakes(
        self,
        handshake_address: str,
        identity: bytes,
        local_client: bool,
        vllm_config: VllmConfig,
        client_handshake_address: Optional[str],
    ):
Rui Qiao's avatar
Rui Qiao committed
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
        """
        For Ray, we don't need to actually perform handshake.
        All addresses information is known before the actor creation.
        Therefore, we simply yield these addresses.
        """
        yield self.addresses

    def wait_for_init(self):
        """
        Wait until the engine core is initialized.

        This is just an empty method. When ray.get() on this method
        (or any other method of the actor) returns, it is guaranteed
        that actor creation (i.e., __init__) is complete.
        """
        pass

    def run(self):
        """
        Run the engine core busy loop.
        """
        try:
            self.run_busy_loop()
        except SystemExit:
            logger.debug("EngineCore exiting.")
            raise
        except Exception:
            logger.exception("EngineCore encountered a fatal error.")
            raise
        finally:
            self.shutdown()