core.py 82.1 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
import contextlib
4
import os
5
import queue
6
import signal
7
8
import threading
import time
9
from collections import defaultdict, deque
10
from collections.abc import Callable, Generator
11
from concurrent.futures import Future
Rui Qiao's avatar
Rui Qiao committed
12
from contextlib import ExitStack, contextmanager
13
from enum import IntEnum
14
from functools import partial
15
from inspect import isclass, signature
16
from logging import DEBUG
17
from typing import Any, TypeVar, cast
18

19
import msgspec
20
21
import zmq

22
import vllm.envs as envs
23
24
from vllm.config import ParallelConfig, VllmConfig
from vllm.distributed import stateless_destroy_torch_distributed_process_group
25
from vllm.envs import enable_envs_cache
26
from vllm.logger import init_logger
27
from vllm.logging_utils.dump_input import dump_engine_exception
28
from vllm.lora.request import LoRARequest
29
from vllm.multimodal import MULTIMODAL_REGISTRY
30
from vllm.tasks import POOLING_TASKS, SupportedTask
31
from vllm.tracing import instrument, maybe_init_worker_tracer
32
from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
33
34
35
36
from vllm.utils.gc_utils import (
    freeze_gc_heap,
    maybe_attach_gc_debug_callback,
)
37
from vllm.utils.hashing import get_hash_fn_by_name
38
from vllm.utils.network_utils import make_zmq_socket
39
from vllm.utils.system_utils import decorate_logs, set_process_title
40
41
42
43
44
45
46
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,
)
47
from vllm.v1.core.sched.interface import PauseState, SchedulerInterface
48
from vllm.v1.core.sched.output import SchedulerOutput
49
from vllm.v1.engine import (
50
51
    EEP_NOTIFICATION_CALL_ID,
    EEPNotificationType,
52
    EngineCoreOutput,
53
54
55
    EngineCoreOutputs,
    EngineCoreRequest,
    EngineCoreRequestType,
56
    FinishReason,
57
    PauseMode,
58
59
60
61
62
63
64
65
    ReconfigureDistributedRequest,
    ReconfigureRankType,
    UtilityOutput,
    UtilityResult,
)
from vllm.v1.engine.utils import (
    EngineHandshakeMetadata,
    EngineZmqAddresses,
66
    SignalCallback,
67
68
    get_device_indices,
)
69
from vllm.v1.executor import Executor
70
from vllm.v1.kv_cache_interface import KVCacheConfig
71
from vllm.v1.metrics.stats import SchedulerStats
72
from vllm.v1.outputs import ModelRunnerOutput
73
from vllm.v1.request import Request, RequestStatus
74
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
75
from vllm.v1.structured_output import StructuredOutputManager
76
from vllm.v1.utils import compute_iteration_details
77
78
79
80
from vllm.version import __version__ as VLLM_VERSION

logger = init_logger(__name__)

81
HANDSHAKE_TIMEOUT_MINS = 5
82

83
_R = TypeVar("_R")  # Return type for collective_rpc
84

85
86
87
88

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

89
90
91
92
93
    def __init__(
        self,
        vllm_config: VllmConfig,
        executor_class: type[Executor],
        log_stats: bool,
94
        executor_fail_callback: Callable | None = None,
95
        include_finished_set: bool = False,
96
    ):
97
98
        # plugins need to be loaded at the engine/scheduler level too
        from vllm.plugins import load_general_plugins
99

100
101
        load_general_plugins()

102
        self.vllm_config = vllm_config
103
        if not vllm_config.parallel_config.data_parallel_rank_local:
104
105
106
107
108
            logger.info(
                "Initializing a V1 LLM engine (v%s) with config: %s",
                VLLM_VERSION,
                vllm_config,
            )
109

110
111
        self.log_stats = log_stats

112
113
        # Setup Model.
        self.model_executor = executor_class(vllm_config)
114
        if executor_fail_callback is not None:
115
            self.model_executor.register_failure_callback(executor_fail_callback)
116

117
118
        self.available_gpu_memory_for_kv_cache = -1

119
120
121
        if envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH:
            self._eep_scale_up_before_kv_init()

122
        # Setup KV Caches and update CacheConfig after profiling.
123
124
125
126
127
128
129
130
131
132
133
        try:
            num_gpu_blocks, num_cpu_blocks, kv_cache_config = (
                self._initialize_kv_caches(vllm_config)
            )
        except Exception:
            logger.exception(
                "EngineCore failed during KV cache initialization; "
                "shutting down executor."
            )
            self.model_executor.shutdown()
            raise
134

135
136
        vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks
        vllm_config.cache_config.num_cpu_blocks = num_cpu_blocks
137
        self.collective_rpc("initialize_cache", args=(num_gpu_blocks, num_cpu_blocks))
138

139
140
        self.structured_output_manager = StructuredOutputManager(vllm_config)

141
        # Setup scheduler.
142
        Scheduler = vllm_config.scheduler_config.get_scheduler_cls()
143

144
        if len(kv_cache_config.kv_cache_groups) == 0:  # noqa: SIM102
145
146
            # Encoder models without KV cache don't support
            # chunked prefill. But do SSM models?
147
148
149
            if vllm_config.scheduler_config.enable_chunked_prefill:
                logger.warning("Disabling chunked prefill for model without KVCache")
                vllm_config.scheduler_config.enable_chunked_prefill = False
150

151
152
153
        scheduler_block_size = (
            vllm_config.cache_config.block_size
            * vllm_config.parallel_config.decode_context_parallel_size
154
            * vllm_config.parallel_config.prefill_context_parallel_size
155
156
        )

157
        self.scheduler: SchedulerInterface = Scheduler(
158
            vllm_config=vllm_config,
159
160
            kv_cache_config=kv_cache_config,
            structured_output_manager=self.structured_output_manager,
161
            include_finished_set=include_finished_set,
162
            log_stats=self.log_stats,
163
            block_size=scheduler_block_size,
164
        )
165
        self.use_spec_decode = vllm_config.speculative_config is not None
166
        if self.scheduler.connector is not None:  # type: ignore
167
            self.model_executor.init_kv_output_aggregator(self.scheduler.connector)  # type: ignore
168

169
        self.mm_registry = mm_registry = MULTIMODAL_REGISTRY
170
171
        self.mm_receiver_cache = mm_registry.engine_receiver_cache_from_config(
            vllm_config
172
        )
173

174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
        # If a KV connector is initialized for scheduler, we want to collect
        # handshake metadata from all workers so the connector in the scheduler
        # will have the full context
        kv_connector = self.scheduler.get_kv_connector()
        if kv_connector is not None:
            # Collect and store KV connector xfer metadata from workers
            # (after KV cache registration)
            xfer_handshake_metadata = (
                self.model_executor.get_kv_connector_handshake_metadata()
            )

            if xfer_handshake_metadata:
                # xfer_handshake_metadata is list of dicts from workers
                # Each dict already has structure {tp_rank: metadata}
                # Merge all worker dicts into a single dict
                content: dict[int, Any] = {}
                for worker_dict in xfer_handshake_metadata:
                    if worker_dict is not None:
                        content.update(worker_dict)
                kv_connector.set_xfer_handshake_metadata(content)

195
196
197
198
199
        # 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
200
        self.batch_queue: (
201
            deque[tuple[Future[ModelRunnerOutput], SchedulerOutput, Future[Any]]] | None
202
        ) = None
203
        if self.batch_queue_size > 1:
204
            logger.debug("Batch queue is enabled with size %d", self.batch_queue_size)
205
            self.batch_queue = deque(maxlen=self.batch_queue_size)
206

207
208
209
        self.is_ec_consumer = (
            vllm_config.ec_transfer_config is None
            or vllm_config.ec_transfer_config.is_ec_consumer
210
        )
211
        self.is_pooling_model = vllm_config.model_config.runner_type == "pooling"
212

213
        self.request_block_hasher: Callable[[Request], list[BlockHash]] | None = None
214
        if vllm_config.cache_config.enable_prefix_caching or kv_connector is not None:
215
            caching_hash_fn = get_hash_fn_by_name(
216
217
                vllm_config.cache_config.prefix_caching_hash_algo
            )
218
219
220
            init_none_hash(caching_hash_fn)

            self.request_block_hasher = get_request_block_hasher(
221
                scheduler_block_size, caching_hash_fn
222
            )
223

224
225
226
        self.step_fn = (
            self.step if self.batch_queue is None else self.step_with_batch_queue
        )
227
        self.async_scheduling = vllm_config.scheduler_config.async_scheduling
228

229
        self.aborts_queue = queue.Queue[list[str]]()
230

231
        self._idle_state_callbacks: list[Callable] = []
232

233
234
235
        # Mark the startup heap as static so that it's ignored by GC.
        # Reduces pause times of oldest generation collections.
        freeze_gc_heap()
236
237
        # If enable, attach GC debugger after static variable freeze.
        maybe_attach_gc_debug_callback()
238
239
240
        # Enable environment variable cache (e.g. assume no more
        # environment variable overrides after this point)
        enable_envs_cache()
241

242
    @instrument(span_name="Prepare model")
243
    def _initialize_kv_caches(
244
245
        self, vllm_config: VllmConfig
    ) -> tuple[int, int, KVCacheConfig]:
246
        start = time.time()
247

248
        # Get all kv cache needed by the model
249
        kv_cache_specs = self.model_executor.get_kv_cache_specs()
250

251
252
        has_kv_cache = any(kv_cache_spec for kv_cache_spec in kv_cache_specs)
        if has_kv_cache:
253
254
255
256
            if envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH:
                # NOTE(yongji): should already be set
                # during _eep_scale_up_before_kv_init
                assert self.available_gpu_memory_for_kv_cache > 0
257
258
259
                available_gpu_memory = [self.available_gpu_memory_for_kv_cache] * len(
                    kv_cache_specs
                )
260
261
262
            else:
                # Profiles the peak memory usage of the model to determine how
                # much memory can be allocated for kv cache.
263
264
                available_gpu_memory = self.model_executor.determine_available_memory()
                self.available_gpu_memory_for_kv_cache = available_gpu_memory[0]
265
266
267
        else:
            # Attention free models don't need memory for kv cache
            available_gpu_memory = [0] * len(kv_cache_specs)
268

269
        assert len(kv_cache_specs) == len(available_gpu_memory)
270

271
272
273
        # Track max_model_len before KV cache config to detect auto-fit changes
        max_model_len_before = vllm_config.model_config.max_model_len

274
275
276
        kv_cache_configs = get_kv_cache_configs(
            vllm_config, kv_cache_specs, available_gpu_memory
        )
277
278
279
280
281
282
283
284

        # If auto-fit reduced max_model_len, sync the new value to workers.
        # This is needed because workers were spawned before memory profiling
        # and have the original (larger) max_model_len cached.
        max_model_len_after = vllm_config.model_config.max_model_len
        if max_model_len_after != max_model_len_before:
            self.collective_rpc("update_max_model_len", args=(max_model_len_after,))

285
        scheduler_kv_cache_config = generate_scheduler_kv_cache_config(kv_cache_configs)
286
        num_gpu_blocks = scheduler_kv_cache_config.num_blocks
287
        num_cpu_blocks = 0
288
289

        # Initialize kv cache and warmup the execution
290
        self.model_executor.initialize_from_config(kv_cache_configs)
291

292
        elapsed = time.time() - start
293
        logger.info_once(
294
            "init engine (profile, create kv cache, warmup model) took %.2f seconds",
295
            elapsed,
296
            scope="local",
297
        )
298
        return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config
299

300
301
302
    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return self.model_executor.supported_tasks

303
304
    def add_request(self, request: Request, request_wave: int = 0):
        """Add request to the scheduler.
305

306
307
308
        `request_wave`: indicate which wave of requests this is expected to
        belong to in DP case
        """
309
310
311
        # Validate the request_id type.
        if not isinstance(request.request_id, str):
            raise TypeError(
312
313
                f"request_id must be a string, got {type(request.request_id)}"
            )
314

315
        if pooling_params := request.pooling_params:
316
            supported_pooling_tasks = [
317
                task for task in self.get_supported_tasks() if task in POOLING_TASKS
318
319
            ]

320
            if pooling_params.task not in supported_pooling_tasks:
321
322
323
324
                raise ValueError(
                    f"Unsupported task: {pooling_params.task!r} "
                    f"Supported tasks: {supported_pooling_tasks}"
                )
325

326
        if request.kv_transfer_params is not None and (
327
328
329
330
331
332
            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
333

334
        self.scheduler.add_request(request)
335

336
    def abort_requests(self, request_ids: list[str]):
337
338
339
340
341
        """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).
342
        self.scheduler.finish_requests(request_ids, RequestStatus.FINISHED_ABORTED)
343

344
345
    @contextmanager
    def log_error_detail(self, scheduler_output: SchedulerOutput):
346
        """Execute the model and log detailed info on failure."""
347
        try:
348
            yield
349
350
351
352
353
        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.

354
            # NOTE: This method is exception-free
355
356
357
            dump_engine_exception(
                self.vllm_config, scheduler_output, self.scheduler.make_stats()
            )
358
359
            raise err

360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
    @contextmanager
    def log_iteration_details(self, scheduler_output: SchedulerOutput):
        if not self.vllm_config.observability_config.enable_logging_iteration_details:
            yield
            return
        self._iteration_index = getattr(self, "_iteration_index", 0)
        iteration_details = compute_iteration_details(scheduler_output)
        before = time.monotonic()
        yield
        logger.info(
            "".join(
                [
                    "Iteration(",
                    str(self._iteration_index),
                    "): ",
                    str(iteration_details.num_ctx_requests),
                    " context requests, ",
                    str(iteration_details.num_ctx_tokens),
                    " context tokens, ",
                    str(iteration_details.num_generation_requests),
                    " generation requests, ",
                    str(iteration_details.num_generation_tokens),
                    " generation tokens, iteration elapsed time: ",
                    format((time.monotonic() - before) * 1000, ".2f"),
                    " ms",
                ]
            )
        )
        self._iteration_index += 1

390
    def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]:
391
392
393
394
395
        """Schedule, execute, and make output.

        Returns tuple of outputs and a flag indicating whether the model
        was executed.
        """
396

397
398
399
        # Check for any requests remaining in the scheduler - unfinished,
        # or finished and not yet removed from the batch.
        if not self.scheduler.has_requests():
400
            return {}, False
401
402
403
        scheduler_output = self.scheduler.schedule()
        future = self.model_executor.execute_model(scheduler_output, non_block=True)
        grammar_output = self.scheduler.get_grammar_bitmask(scheduler_output)
404
405
406
407
        with (
            self.log_error_detail(scheduler_output),
            self.log_iteration_details(scheduler_output),
        ):
408
409
410
411
            model_output = future.result()
            if model_output is None:
                model_output = self.model_executor.sample_tokens(grammar_output)

412
413
414
        # Before processing the model output, process any aborts that happened
        # during the model execution.
        self._process_aborts_queue()
415
416
417
        engine_core_outputs = self.scheduler.update_from_output(
            scheduler_output, model_output
        )
418

419
        return engine_core_outputs, scheduler_output.total_num_scheduled_tokens > 0
420

421
    def post_step(self, model_executed: bool) -> None:
422
423
424
425
        # When using async scheduling we can't get draft token ids in advance,
        # so we update draft token ids in the worker process and don't
        # need to update draft token ids here.
        if not self.async_scheduling and self.use_spec_decode and model_executed:
426
427
428
429
430
            # 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)

431
    def step_with_batch_queue(
432
        self,
433
    ) -> tuple[dict[int, EngineCoreOutputs] | None, bool]:
434
435
436
437
        """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:
438
439
440
441
        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.
442
443
444
445
446
        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.
        """
447

448
449
        batch_queue = self.batch_queue
        assert batch_queue is not None
450

451
452
453
        # 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.
454
        assert len(batch_queue) < self.batch_queue_size
455

456
        model_executed = False
457
        deferred_scheduler_output = None
458
        if self.scheduler.has_requests():
459
460
461
462
            scheduler_output = self.scheduler.schedule()
            exec_future = self.model_executor.execute_model(
                scheduler_output, non_block=True
            )
463
            if self.is_ec_consumer:
464
                model_executed = scheduler_output.total_num_scheduled_tokens > 0
465

466
            if self.is_pooling_model or not model_executed:
467
468
                # No sampling required (no requests scheduled).
                future = cast(Future[ModelRunnerOutput], exec_future)
469
            else:
470
471
472
                if not scheduler_output.pending_structured_output_tokens:
                    # We aren't waiting for any tokens, get any grammar output
                    # and sample immediately.
473
474
475
                    grammar_output = self.scheduler.get_grammar_bitmask(
                        scheduler_output
                    )
476
477
478
                    future = self.model_executor.sample_tokens(
                        grammar_output, non_block=True
                    )
479
                else:
480
481
482
483
484
                    # We need to defer sampling until we have processed the model output
                    # from the prior step.
                    deferred_scheduler_output = scheduler_output

            if not deferred_scheduler_output:
485
                # Add this step's future to the queue.
486
                batch_queue.appendleft((future, scheduler_output, exec_future))
487
488
489
490
491
492
493
494
                if (
                    model_executed
                    and len(batch_queue) < self.batch_queue_size
                    and not batch_queue[-1][0].done()
                ):
                    # Don't block on next worker response unless the queue is full
                    # or there are no more requests to schedule.
                    return None, True
495
496
497
498
499
500

        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
501
502

        # Block until the next result is available.
503
        future, scheduler_output, exec_model_fut = batch_queue.pop()
504
505
506
507
        with (
            self.log_error_detail(scheduler_output),
            self.log_iteration_details(scheduler_output),
        ):
508
            model_output = future.result()
509
510
511
512
513
            if model_output is None:
                # None from sample_tokens() implies that the original execute_model()
                # call failed - raise that exception.
                exec_model_fut.result()
                raise RuntimeError("unexpected error")
514

515
516
517
        # Before processing the model output, process any aborts that happened
        # during the model execution.
        self._process_aborts_queue()
518
519
520
        engine_core_outputs = self.scheduler.update_from_output(
            scheduler_output, model_output
        )
521
522
523
524
525

        # NOTE(nick): We can either handle the deferred tasks here or save
        # in a field and do it immediately once step_with_batch_queue is
        # re-called. The latter slightly favors TTFT over TPOT/throughput.
        if deferred_scheduler_output:
526
527
528
529
530
531
532
533
534
535
536
537
            # If we are doing speculative decoding with structured output,
            # we need to get the draft token ids from the prior step before
            # we can compute the grammar bitmask for the deferred request.
            if self.use_spec_decode:
                draft_token_ids = self.model_executor.take_draft_token_ids()
                assert draft_token_ids is not None
                # Update the draft token ids in the scheduler output to
                # filter out the invalid spec tokens, which will be padded
                # with -1 and skipped by the grammar bitmask computation.
                self.scheduler.update_draft_token_ids_in_output(
                    draft_token_ids, deferred_scheduler_output
                )
538
539
540
541
542
543
            # We now have the tokens needed to compute the bitmask for the
            # deferred request. Get the bitmask and call sample tokens.
            grammar_output = self.scheduler.get_grammar_bitmask(
                deferred_scheduler_output
            )
            future = self.model_executor.sample_tokens(grammar_output, non_block=True)
544
            batch_queue.appendleft((future, deferred_scheduler_output, exec_future))
545

546
        return engine_core_outputs, model_executed
547

548
549
550
551
552
    def _process_aborts_queue(self):
        if not self.aborts_queue.empty():
            request_ids = []
            while not self.aborts_queue.empty():
                ids = self.aborts_queue.get_nowait()
553
554
                # Should be a list here, but also handle string just in case.
                request_ids.extend((ids,) if isinstance(ids, str) else ids)
555
556
557
            # More efficient to abort all as a single batch.
            self.abort_requests(request_ids)

558
    def shutdown(self):
559
        self.structured_output_manager.clear_backend()
560
561
        if self.model_executor:
            self.model_executor.shutdown()
562
563
        if self.scheduler:
            self.scheduler.shutdown()
564

565
566
    def profile(self, is_start: bool = True, profile_prefix: str | None = None):
        self.model_executor.profile(is_start, profile_prefix)
567

568
569
    def reset_mm_cache(self):
        # NOTE: Since this is mainly for debugging, we don't attempt to
570
        # re-sync the internal caches (P0 sender, P1 receiver)
571
        if self.scheduler.has_unfinished_requests():
572
573
574
575
            logger.warning(
                "Resetting the multi-modal cache when requests are "
                "in progress may lead to desynced internal caches."
            )
576

577
        # The cache either exists in EngineCore or WorkerWrapperBase
578
579
        if self.mm_receiver_cache is not None:
            self.mm_receiver_cache.clear_cache()
580

581
582
        self.model_executor.reset_mm_cache()

583
584
585
586
587
588
    def reset_prefix_cache(
        self, reset_running_requests: bool = False, reset_connector: bool = False
    ) -> bool:
        return self.scheduler.reset_prefix_cache(
            reset_running_requests, reset_connector
        )
589

590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
    def reset_encoder_cache(self) -> None:
        """Reset the encoder cache to invalidate all cached encoder outputs.

        This should be called when model weights are updated to ensure
        stale vision embeddings computed with old weights are not reused.
        Clears both the scheduler's cache manager and the GPU model runner's cache.
        """
        # NOTE: Since this is mainly for debugging, we don't attempt to
        # re-sync the internal caches (P0 sender, P1 receiver)
        if self.scheduler.has_unfinished_requests():
            logger.warning(
                "Resetting the encoder cache when requests are "
                "in progress may lead to desynced internal caches."
            )

        # Reset the scheduler's encoder cache manager (logical state)
        self.scheduler.reset_encoder_cache()
        # Reset the GPU model runner's encoder cache (physical storage)
        self.model_executor.reset_encoder_cache()

610
611
612
613
614
    def _reset_caches(self, reset_running_requests=True) -> None:
        self.reset_prefix_cache(reset_running_requests=reset_running_requests)
        self.reset_mm_cache()
        self.reset_encoder_cache()

615
616
    def pause_scheduler(
        self, mode: PauseMode = "abort", clear_cache: bool = True
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
    ) -> Future | None:
        """Pause generation; behavior depends on mode.

        All pause modes queue new adds -- "abort" and "keep" skip step();
        "wait" allows step() so in-flight requests can drain.

        - ``abort``: Set PAUSED_NEW, abort all requests, wait for abort
          outputs to be sent (when running with output_queue), optionally
          clear caches, then complete the returned Future.
        - ``wait``: Set PAUSED_NEW (queue adds, keep stepping); when drained,
          optionally clear caches, then complete the returned Future.
        - ``keep``: Set PAUSED_ALL; return a Future that completes when the
          output queue is empty.
        """
        if mode not in ("keep", "abort", "wait"):
            raise ValueError(f"Invalid pause mode: {mode}")
        if mode == "wait":
            raise ValueError("'wait' mode can't be used in inproc-engine mode")

        if mode == "abort":
            self.scheduler.finish_requests(None, RequestStatus.FINISHED_ABORTED)

        pause_state = PauseState.PAUSED_ALL if mode == "keep" else PauseState.PAUSED_NEW
        self.scheduler.set_pause_state(pause_state)
        if clear_cache:
            self._reset_caches()

644
645
646
        return None

    def resume_scheduler(self) -> None:
647
648
        """Resume the scheduler and flush any requests queued while paused."""
        self.scheduler.set_pause_state(PauseState.UNPAUSED)
649
650

    def is_scheduler_paused(self) -> bool:
651
652
        """Return whether the scheduler is in any pause state."""
        return self.scheduler.pause_state != PauseState.UNPAUSED
653

654
    def sleep(self, level: int = 1, mode: PauseMode = "abort") -> None | Future:
655
656
657
658
659
660
661
662
        """Put the engine to sleep at the specified level.

        Args:
            level: Sleep level.
                - Level 0: Pause scheduling only. Requests are still accepted
                           but not processed. No GPU memory changes.
                - Level 1: Offload model weights to CPU, discard KV cache.
                - Level 2: Discard all GPU memory.
663
664
            mode: Pause mode - how to deal with any existing requests, see
                documentation of pause_scheduler method.
665
        """
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690

        # Pause scheduler before sleeping.
        clear_prefix_cache = level >= 1
        pause_future = self.pause_scheduler(mode=mode, clear_cache=clear_prefix_cache)
        if level < 1:
            return pause_future

        # Level 1+: Delegate to executor for GPU memory management
        model_executor = self.model_executor
        if pause_future is None:
            model_executor.sleep(level)
            return None

        future = Future[Any]()

        def pause_complete(f: Future):
            try:
                f.result()  # propagate any exception
                future.set_result(model_executor.sleep(level))
            except Exception as e:
                future.set_exception(e)

        logger.info("Waiting for in-flight requests to complete before sleeping...")
        pause_future.add_done_callback(pause_complete)
        return future
691

692
    def wake_up(self, tags: list[str] | None = None):
693
694
695
696
697
698
        """Wake up the engine from sleep.

        Args:
            tags: Tags to wake up. Use ["scheduling"] for level 0 wake up.
        """
        if tags is not None and "scheduling" in tags:
699
700
701
702
            # Remove "scheduling" from tags if there are other tags to process.
            tags = [t for t in tags if t != "scheduling"]

        if tags is None or tags:
703
            self.model_executor.wake_up(tags)
704

705
706
707
        # Resume scheduling (applies to all levels)
        self.resume_scheduler()

708
    def is_sleeping(self) -> bool:
709
        """Check if engine is sleeping at any level."""
710
        return self.is_scheduler_paused() or self.model_executor.is_sleeping
711

712
    def execute_dummy_batch(self):
713
        self.model_executor.execute_dummy_batch()
714

715
716
717
718
719
720
    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)

721
    def list_loras(self) -> set[int]:
722
723
724
725
        return self.model_executor.list_loras()

    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)
726

727
728
729
    def save_sharded_state(
        self,
        path: str,
730
731
        pattern: str | None = None,
        max_size: int | None = None,
732
    ) -> None:
733
734
735
736
737
738
        self.model_executor.save_sharded_state(
            path=path, pattern=pattern, max_size=max_size
        )

    def collective_rpc(
        self,
739
740
        method: str | Callable[..., _R],
        timeout: float | None = None,
741
        args: tuple = (),
742
        kwargs: dict[str, Any] | None = None,
743
744
    ) -> list[_R]:
        return self.model_executor.collective_rpc(method, timeout, args, kwargs)
745

746
    def preprocess_add_request(self, request: EngineCoreRequest) -> tuple[Request, int]:
747
        """Preprocess the request.
748

749
750
751
        This function could be directly used in input processing thread to allow
        request initialization running in parallel with Model forward
        """
752
753
        # Note on thread safety: no race condition.
        # `mm_receiver_cache` is reset at the end of LLMEngine init,
754
        # and will only be accessed in the input processing thread afterwards.
755
        if self.mm_receiver_cache is not None and request.mm_features:
756
757
758
            request.mm_features = self.mm_receiver_cache.get_and_update_features(
                request.mm_features
            )
759

760
        req = Request.from_engine_core_request(request, self.request_block_hasher)
761
762
763
764
765
766
767
768
769
        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

770
771
772
773
774
775
776
777
778
779
    def _eep_scale_up_before_kv_init(self):
        raise NotImplementedError

    def _eep_send_engine_core_notification(
        self,
        notification_type: EEPNotificationType,
        vllm_config: VllmConfig | None = None,
    ):
        raise NotImplementedError

780

781
782
783
784
785
786
class EngineShutdownState(IntEnum):
    RUNNING = 0
    REQUESTED = 1
    SHUTTING_DOWN = 2


787
788
789
class EngineCoreProc(EngineCore):
    """ZMQ-wrapper for running EngineCore in background process."""

790
    ENGINE_CORE_DEAD = b"ENGINE_CORE_DEAD"
791
    addresses: EngineZmqAddresses
792

793
    @instrument(span_name="EngineCoreProc init")
794
795
    def __init__(
        self,
796
        vllm_config: VllmConfig,
797
        local_client: bool,
798
        handshake_address: str,
799
        executor_class: type[Executor],
800
        log_stats: bool,
801
        client_handshake_address: str | None = None,
802
        *,
803
        engine_index: int = 0,
804
    ):
Rui Qiao's avatar
Rui Qiao committed
805
        self.input_queue = queue.Queue[tuple[EngineCoreRequestType, Any]]()
806
        self.output_queue = queue.Queue[tuple[int, EngineCoreOutputs] | bytes]()
Rui Qiao's avatar
Rui Qiao committed
807
        executor_fail_callback = lambda: self.input_queue.put_nowait(
808
809
            (EngineCoreRequestType.EXECUTOR_FAILED, b"")
        )
810

Rui Qiao's avatar
Rui Qiao committed
811
812
813
        self.engine_index = engine_index
        identity = self.engine_index.to_bytes(length=2, byteorder="little")
        self.engines_running = False
814
        self.shutdown_state = EngineShutdownState.RUNNING
815

816
817
818
819
820
821
822
        with self._perform_handshakes(
            handshake_address,
            identity,
            local_client,
            vllm_config,
            client_handshake_address,
        ) as addresses:
823
            self.client_count = len(addresses.outputs)
824
825

            # Set up data parallel environment.
826
            self.has_coordinator = addresses.coordinator_output is not None
827
            self.frontend_stats_publish_address = (
828
829
830
831
832
833
834
                addresses.frontend_stats_publish_address
            )
            logger.debug(
                "Has DP Coordinator: %s, stats publish address: %s",
                self.has_coordinator,
                self.frontend_stats_publish_address,
            )
835
            internal_dp_balancing = (
836
                self.has_coordinator
837
838
                and not vllm_config.parallel_config.data_parallel_external_lb
            )
839
840
841
            # Only publish request queue stats to coordinator for "internal"
            # and "hybrid" LB modes.
            self.publish_dp_lb_stats = internal_dp_balancing
842

843
844
845
846
847
848
849
            self.addresses = addresses
            self.process_input_queue_block = True
            if envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH:
                self._eep_send_engine_core_notification(
                    EEPNotificationType.NEW_CORE_ENGINES_INIT_READY,
                    vllm_config=vllm_config,
                )
850
851
            self._init_data_parallel(vllm_config)

852
            super().__init__(
853
854
855
856
857
                vllm_config,
                executor_class,
                log_stats,
                executor_fail_callback,
                internal_dp_balancing,
858
            )
859

860
861
862
863
864
865
            # 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()
866
867
868
869
870
871
872
873
874
875
            input_thread = threading.Thread(
                target=self.process_input_sockets,
                args=(
                    addresses.inputs,
                    addresses.coordinator_input,
                    identity,
                    ready_event,
                ),
                daemon=True,
            )
876
877
878
879
            input_thread.start()

            self.output_thread = threading.Thread(
                target=self.process_output_sockets,
880
881
882
883
884
885
886
                args=(
                    addresses.outputs,
                    addresses.coordinator_output,
                    self.engine_index,
                ),
                daemon=True,
            )
887
888
889
890
891
892
            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():
893
                    raise RuntimeError("Input socket thread died during startup")
894
895
896
                assert addresses.coordinator_input is not None
                logger.info("Waiting for READY message from DP Coordinator...")

Rui Qiao's avatar
Rui Qiao committed
897
    @contextmanager
898
899
900
901
902
903
    def _perform_handshakes(
        self,
        handshake_address: str,
        identity: bytes,
        local_client: bool,
        vllm_config: VllmConfig,
904
        client_handshake_address: str | None,
Rui Qiao's avatar
Rui Qiao committed
905
    ) -> Generator[EngineZmqAddresses, None, None]:
906
907
908
909
910
        """
        Perform startup handshakes.

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

911
        For DP>1 with internal load-balancing this is with the shared front-end
912
913
        process which may reside on a different node.

914
        For DP>1 with external or hybrid load-balancing, two handshakes are
915
        performed:
916
917
918
919
            - 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.
920
921
        with the exception of the rank 0 and colocated engines themselves which
        don't require the second handshake.
922
923
924
925
926
927

        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
928
        input_ctx = zmq.Context()
929
        is_local = local_client and client_handshake_address is None
930
        headless = not local_client
931
932
933
934
935
936
937
938
939
        handshake = self._perform_handshake(
            input_ctx,
            handshake_address,
            identity,
            is_local,
            headless,
            vllm_config,
            vllm_config.parallel_config,
        )
940
941
942
943
        if client_handshake_address is None:
            with handshake as addresses:
                yield addresses
        else:
944
            assert local_client
945
            local_handshake = self._perform_handshake(
946
947
                input_ctx, client_handshake_address, identity, True, False, vllm_config
            )
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
            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,
963
        headless: bool,
964
        vllm_config: VllmConfig,
965
        parallel_config_to_update: ParallelConfig | None = None,
966
    ) -> Generator[EngineZmqAddresses, None, None]:
967
968
969
970
971
972
973
974
        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
975
            # Register engine with front-end.
976
977
978
            addresses = self.startup_handshake(
                handshake_socket, local_client, headless, parallel_config_to_update
            )
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
            exc_during_init = False
            try:
                yield addresses
            except Exception:
                exc_during_init = True
                raise
            finally:
                if exc_during_init:
                    # Send FAILED status so the front-end detects init
                    # failure immediately via ZMQ instead of waiting for
                    # process sentinel (which may be delayed by cleanup).
                    with contextlib.suppress(Exception):
                        handshake_socket.send(
                            msgspec.msgpack.encode(
                                {
                                    "status": "FAILED",
                                    "local": local_client,
                                    "headless": headless,
                                }
                            )
                        )
                else:
                    # Send ready message.
                    num_gpu_blocks = vllm_config.cache_config.num_gpu_blocks
                    # 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

                    # Include config hash for DP configuration validation
                    ready_msg = {
                        "status": "READY",
                        "local": local_client,
                        "headless": headless,
                        "num_gpu_blocks": num_gpu_blocks,
                        "dp_stats_address": dp_stats_address,
                    }
                    if vllm_config.parallel_config.data_parallel_size > 1:
                        ready_msg["parallel_config_hash"] = (
                            vllm_config.parallel_config.compute_hash()
                        )

                    handshake_socket.send(msgspec.msgpack.encode(ready_msg))
Rui Qiao's avatar
Rui Qiao committed
1022

1023
    @staticmethod
1024
    def startup_handshake(
1025
1026
        handshake_socket: zmq.Socket,
        local_client: bool,
1027
        headless: bool,
1028
        parallel_config: ParallelConfig | None = None,
1029
    ) -> EngineZmqAddresses:
1030
        # Send registration message.
1031
        handshake_socket.send(
1032
1033
1034
1035
1036
1037
1038
1039
            msgspec.msgpack.encode(
                {
                    "status": "HELLO",
                    "local": local_client,
                    "headless": headless,
                }
            )
        )
1040
1041

        # Receive initialization message.
1042
        logger.debug("Waiting for init message from front-end.")
1043
        if not handshake_socket.poll(timeout=HANDSHAKE_TIMEOUT_MINS * 60_000):
1044
1045
1046
1047
1048
            raise RuntimeError(
                "Did not receive response from front-end "
                f"process within {HANDSHAKE_TIMEOUT_MINS} "
                f"minutes"
            )
1049
1050
        init_bytes = handshake_socket.recv()
        init_message: EngineHandshakeMetadata = msgspec.msgpack.decode(
1051
1052
            init_bytes, type=EngineHandshakeMetadata
        )
1053
1054
        logger.debug("Received init message: %s", init_message)

1055
1056
1057
        if parallel_config is not None:
            for key, value in init_message.parallel_config.items():
                setattr(parallel_config, key, value)
1058

1059
        return init_message.addresses
1060
1061

    @staticmethod
1062
    def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs):
1063
1064
        """Launch EngineCore busy loop in background process."""

1065
1066
1067
        # Ensure we can serialize transformer config after spawning
        maybe_register_config_serialize_by_value()

1068
        engine_core: EngineCoreProc | None = None
1069
        signal_callback: SignalCallback | None = None
1070
        try:
1071
1072
1073
1074
1075
            vllm_config: VllmConfig = kwargs["vllm_config"]
            parallel_config: ParallelConfig = vllm_config.parallel_config
            data_parallel = parallel_config.data_parallel_size > 1 or dp_rank > 0
            if data_parallel:
                parallel_config.data_parallel_rank_local = local_dp_rank
1076
1077
1078
1079
1080
                maybe_init_worker_tracer(
                    instrumenting_module_name="vllm.engine_core",
                    process_kind="engine_core",
                    process_name=f"EngineCore_DP{dp_rank}",
                )
1081
                set_process_title("EngineCore", f"DP{dp_rank}")
1082
            else:
1083
1084
1085
1086
1087
                maybe_init_worker_tracer(
                    instrumenting_module_name="vllm.engine_core",
                    process_kind="engine_core",
                    process_name="EngineCore",
                )
1088
1089
1090
                set_process_title("EngineCore")
            decorate_logs()

1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
            if data_parallel and 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}"
                )
                logger.debug(
                    "Setting kv_transfer_config.engine_id to %s",
                    vllm_config.kv_transfer_config.engine_id,
                )

1102
1103
            parallel_config.data_parallel_index = dp_rank
            if data_parallel and vllm_config.model_config.is_moe:
1104
1105
1106
1107
                # Set data parallel rank for this engine process.
                parallel_config.data_parallel_rank = dp_rank
                engine_core = DPEngineCoreProc(*args, **kwargs)
            else:
1108
1109
1110
1111
1112
1113
1114
                # Non-MoE DP ranks are completely independent, so treat like DP=1.
                # Note that parallel_config.data_parallel_index will still reflect
                # the original DP rank.
                parallel_config.data_parallel_size = 1
                parallel_config.data_parallel_size_local = 1
                parallel_config.data_parallel_rank = 0
                engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
1115

1116
            assert engine_core is not None
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132

            def wakeup_engine():
                # Wakes up idle engine via input_queue when shutdown is requested
                # Not safe in a signal handler - we may interrupt the main thread
                # while it is holding the non-reentrant input_queue.mutex
                engine_core.input_queue.put_nowait((EngineCoreRequestType.WAKEUP, None))

            signal_callback = SignalCallback(wakeup_engine)

            def signal_handler(signum, frame):
                engine_core.shutdown_state = EngineShutdownState.REQUESTED
                signal_callback.trigger()

            signal.signal(signal.SIGTERM, signal_handler)
            signal.signal(signal.SIGINT, signal_handler)

1133
1134
            engine_core.run_busy_loop()

1135
        except SystemExit:
1136
            logger.debug("EngineCore exiting.")
1137
            raise
1138
1139
1140
1141
1142
1143
1144
        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
1145
        finally:
1146
1147
1148
1149
            signal.signal(signal.SIGTERM, signal.SIG_DFL)
            signal.signal(signal.SIGINT, signal.SIG_DFL)
            if signal_callback is not None:
                signal_callback.stop()
1150
1151
1152
            if engine_core is not None:
                engine_core.shutdown()

1153
1154
1155
    def _init_data_parallel(self, vllm_config: VllmConfig):
        pass

1156
1157
1158
1159
1160
1161
1162
1163
    def has_work(self) -> bool:
        """Returns true if the engine should be stepped."""
        return (
            self.engines_running
            or self.scheduler.has_requests()
            or bool(self.batch_queue)
        )

1164
1165
1166
1167
    def is_running(self) -> bool:
        """Returns true if shutdown has not been requested."""
        return self.shutdown_state == EngineShutdownState.RUNNING

1168
1169
    def run_busy_loop(self):
        """Core busy loop of the EngineCore."""
1170
        while self._handle_shutdown():
1171
            # 1) Poll the input queue until there is work to do.
1172
1173
            self._process_input_queue()
            # 2) Step the engine core and return the outputs.
1174
            self._process_engine_step()
1175

1176
1177
        raise SystemExit

1178
1179
1180
1181
    def _process_input_queue(self):
        """Exits when an engine step needs to be performed."""

        waited = False
1182
        while not self.has_work() and self.is_running():
1183
1184
            # Notify callbacks waiting for engine to become idle.
            self._notify_idle_state_callbacks()
1185
1186
1187
1188
1189
1190
1191
            if self.input_queue.empty():
                # Drain aborts queue; all aborts are also processed via input_queue.
                with self.aborts_queue.mutex:
                    self.aborts_queue.queue.clear()
                if logger.isEnabledFor(DEBUG):
                    logger.debug("EngineCore waiting for work.")
                    waited = True
1192
1193
1194
1195
1196
1197
1198
1199
            block = self.process_input_queue_block
            try:
                req = self.input_queue.get(block=block)
                self._handle_client_request(*req)
            except queue.Empty:
                break
            if not block:
                break
1200
1201

        if waited:
1202
            logger.debug("EngineCore loop active.")
1203
1204
1205
1206
1207
1208

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

1209
    def _process_engine_step(self) -> bool:
1210
1211
1212
        """Called only when there are unfinished local requests."""

        # Step the engine core.
1213
        outputs, model_executed = self.step_fn()
1214
        # Put EngineCoreOutputs into the output queue.
1215
        for output in outputs.items() if outputs else ():
1216
            self.output_queue.put_nowait(output)
1217
1218
        # Post-step hook.
        self.post_step(model_executed)
1219

1220
1221
1222
1223
1224
1225
1226
        # If no model execution happened but there are waiting requests
        # (e.g., WAITING_FOR_REMOTE_KVS), yield the GIL briefly to allow
        # background threads (like NIXL handshake) to make progress.
        # Without this, the tight polling loop can starve background threads.
        if not model_executed and self.scheduler.has_unfinished_requests():
            time.sleep(0.001)

1227
1228
        return model_executed

1229
1230
1231
1232
    def _notify_idle_state_callbacks(self) -> None:
        while self._idle_state_callbacks:
            callback = self._idle_state_callbacks.pop()
            callback(self)
1233

1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
    def _handle_shutdown(self) -> bool:
        # Check if shutdown was requested and handle it
        if self.shutdown_state == EngineShutdownState.RUNNING:
            return True

        if self.shutdown_state == EngineShutdownState.REQUESTED:
            shutdown_timeout = self.vllm_config.shutdown_timeout

            logger.info("Shutdown initiated (timeout=%d)", shutdown_timeout)

            if shutdown_timeout == 0:
                num_requests = self.scheduler.get_num_unfinished_requests()
                if num_requests > 0:
                    logger.info("Aborting %d requests", num_requests)
                aborted_reqs = self.scheduler.finish_requests(
                    None, RequestStatus.FINISHED_ABORTED
                )
                self._send_abort_outputs(aborted_reqs)
            else:
                num_requests = self.scheduler.get_num_unfinished_requests()
                if num_requests > 0:
                    logger.info(
                        "Draining %d in-flight requests (timeout=%ds)",
                        num_requests,
                        shutdown_timeout,
                    )

            self.shutdown_state = EngineShutdownState.SHUTTING_DOWN

        # Exit when no work remaining
        if not self.has_work():
            logger.info("Shutdown complete")
            return False

        return True

1270
1271
1272
    def _handle_client_request(
        self, request_type: EngineCoreRequestType, request: Any
    ) -> None:
1273
        """Dispatch request from client."""
1274

1275
1276
1277
        if request_type == EngineCoreRequestType.WAKEUP:
            return
        elif request_type == EngineCoreRequestType.ADD:
1278
            req, request_wave = request
1279
1280
            if self._reject_add_in_shutdown(req):
                return
1281
            self.add_request(req, request_wave)
1282
        elif request_type == EngineCoreRequestType.ABORT:
1283
            self.abort_requests(request)
1284
        elif request_type == EngineCoreRequestType.UTILITY:
1285
            client_idx, call_id, method_name, args = request
1286
1287
            if self._reject_utility_in_shutdown(client_idx, call_id, method_name):
                return
1288
            output = UtilityOutput(call_id)
1289
1290
1291
1292
1293
1294
            # Lazily look-up utility method so that failure will be handled/returned.
            get_result = lambda: (method := getattr(self, method_name)) and method(
                *self._convert_msgspec_args(method, args)
            )
            enqueue_output = lambda out: self.output_queue.put_nowait(
                (client_idx, EngineCoreOutputs(utility_output=out))
1295
            )
1296
            self._invoke_utility_method(method_name, get_result, output, enqueue_output)
1297
1298
1299
        elif request_type == EngineCoreRequestType.EXECUTOR_FAILED:
            raise RuntimeError("Executor failed.")
        else:
1300
1301
1302
            logger.error(
                "Unrecognized input request type encountered: %s", request_type
            )
1303

1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
    def _reject_add_in_shutdown(self, request: Request) -> bool:
        if self.shutdown_state == EngineShutdownState.RUNNING:
            return False

        logger.info("Rejecting request %s (server shutting down)", request.request_id)
        self._send_abort_outputs_to_client([request.request_id], request.client_index)
        return True

    def _reject_utility_in_shutdown(
        self, client_idx: int, call_id: int, method_name: str
    ) -> bool:
        if self.shutdown_state == EngineShutdownState.RUNNING:
            return False

        logger.warning("Rejecting utility call %s (server shutting down)", method_name)
        output = UtilityOutput(call_id, failure_message="Server shutting down")
        self.output_queue.put_nowait(
            (client_idx, EngineCoreOutputs(utility_output=output))
        )
        return True

1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
    @staticmethod
    def _invoke_utility_method(
        name: str, get_result: Callable, output: UtilityOutput, enqueue_output: Callable
    ):
        try:
            result = get_result()
            if isinstance(result, Future):
                # Defer utility output handling until future completion.
                callback = lambda future: EngineCoreProc._invoke_utility_method(
                    name, future.result, output, enqueue_output
                )
                result.add_done_callback(callback)
                return
            output.result = UtilityResult(result)
        except Exception as e:
            logger.exception("Invocation of %s method failed", name)
            output.failure_message = f"Call to {name} method failed: {str(e)}"
        enqueue_output(output)

1344
1345
1346
    @staticmethod
    def _convert_msgspec_args(method, args):
        """If a provided arg type doesn't match corresponding target method
1347
        arg type, try converting to msgspec object."""
1348
1349
1350
1351
1352
        if not args:
            return args
        arg_types = signature(method).parameters.values()
        assert len(args) <= len(arg_types)
        return tuple(
1353
1354
            msgspec.convert(v, type=p.annotation)
            if isclass(p.annotation)
1355
            and issubclass(p.annotation, msgspec.Struct)
1356
1357
1358
1359
            and not isinstance(v, p.annotation)
            else v
            for v, p in zip(args, arg_types)
        )
1360

1361
1362
1363
1364
1365
1366
1367
1368
1369
    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():
1370
1371
1372
1373
            logger.fatal(
                "vLLM shutdown signal from EngineCore failed "
                "to send. Please report this issue."
            )
1374

1375
1376
1377
    def process_input_sockets(
        self,
        input_addresses: list[str],
1378
        coord_input_address: str | None,
1379
1380
1381
        identity: bytes,
        ready_event: threading.Event,
    ):
1382
1383
1384
        """Input socket IO thread."""

        # Msgpack serialization decoding.
1385
1386
        add_request_decoder = MsgpackDecoder(EngineCoreRequest)
        generic_decoder = MsgpackDecoder()
1387

1388
1389
1390
        with ExitStack() as stack, zmq.Context() as ctx:
            input_sockets = [
                stack.enter_context(
1391
1392
1393
1394
                    make_zmq_socket(
                        ctx, input_address, zmq.DEALER, identity=identity, bind=False
                    )
                )
1395
1396
1397
1398
1399
1400
                for input_address in input_addresses
            ]
            if coord_input_address is None:
                coord_socket = None
            else:
                coord_socket = stack.enter_context(
1401
1402
1403
1404
1405
1406
1407
1408
                    make_zmq_socket(
                        ctx,
                        coord_input_address,
                        zmq.XSUB,
                        identity=identity,
                        bind=False,
                    )
                )
1409
                # Send subscription message to coordinator.
1410
                coord_socket.send(b"\x01")
1411
1412
1413
1414
1415
1416
1417

            # 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.
1418
                input_socket.send(b"")
1419
                poller.register(input_socket, zmq.POLLIN)
1420

1421
            if coord_socket is not None:
1422
1423
                # Wait for ready message from coordinator.
                assert coord_socket.recv() == b"READY"
1424
                poller.register(coord_socket, zmq.POLLIN)
1425

1426
1427
            ready_event.set()
            del ready_event
1428
1429
1430
            while True:
                for input_socket, _ in poller.poll():
                    # (RequestType, RequestData)
1431
                    type_frame, *data_frames = input_socket.recv_multipart(copy=False)
1432
1433
1434
1435
1436
                    # NOTE(yongji): ignore READY message sent by DP coordinator
                    # that is used to notify newly started engines
                    if type_frame.buffer == b"READY":
                        assert input_socket == coord_socket
                        continue
1437
                    request_type = EngineCoreRequestType(bytes(type_frame.buffer))
1438
1439

                    # Deserialize the request data.
1440
                    request: Any
1441
                    if request_type == EngineCoreRequestType.ADD:
1442
1443
1444
1445
1446
1447
                        req: EngineCoreRequest = add_request_decoder.decode(data_frames)
                        try:
                            request = self.preprocess_add_request(req)
                        except Exception:
                            self._handle_request_preproc_error(req)
                            continue
1448
1449
                    else:
                        request = generic_decoder.decode(data_frames)
1450

1451
1452
1453
1454
1455
1456
1457
                        if request_type == EngineCoreRequestType.ABORT:
                            # Aborts are added to *both* queues, allows us to eagerly
                            # process aborts while also ensuring ordering in the input
                            # queue to avoid leaking requests. This is ok because
                            # aborting in the scheduler is idempotent.
                            self.aborts_queue.put_nowait(request)

1458
1459
1460
                    # Push to input queue for core busy loop.
                    self.input_queue.put_nowait((request_type, request))

1461
1462
1463
    def process_output_sockets(
        self,
        output_paths: list[str],
1464
        coord_output_path: str | None,
1465
1466
        engine_index: int,
    ):
1467
1468
1469
        """Output socket IO thread."""

        # Msgpack serialization encoding.
1470
        encoder = MsgpackEncoder()
1471
1472
1473
1474
1475
1476
        # 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]]()
1477

1478
1479
        # We must set linger to ensure the ENGINE_CORE_DEAD
        # message is sent prior to closing the socket.
1480
1481
1482
        with ExitStack() as stack, zmq.Context() as ctx:
            sockets = [
                stack.enter_context(
1483
1484
                    make_zmq_socket(ctx, output_path, zmq.PUSH, linger=4000)
                )
1485
1486
                for output_path in output_paths
            ]
1487
1488
1489
1490
1491
1492
1493
1494
1495
            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
            )
1496
1497
            max_reuse_bufs = len(sockets) + 1

1498
            while True:
1499
1500
1501
1502
                output = self.output_queue.get()
                if output == EngineCoreProc.ENGINE_CORE_DEAD:
                    for socket in sockets:
                        socket.send(output)
1503
                    break
1504
1505
                assert not isinstance(output, bytes)
                client_index, outputs = output
1506
                outputs.engine_index = engine_index
1507

1508
1509
1510
1511
1512
1513
1514
                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

1515
1516
1517
1518
1519
                # 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()
1520
                buffers = encoder.encode_into(outputs, buffer)
1521
1522
1523
                tracker = sockets[client_index].send_multipart(
                    buffers, copy=False, track=True
                )
1524
1525
1526
                if not tracker.done:
                    ref = outputs if len(buffers) > 1 else None
                    pending.appendleft((tracker, ref, buffer))
1527
1528
                elif len(reuse_buffers) < max_reuse_bufs:
                    # Limit the number of buffers to reuse.
1529
                    reuse_buffers.append(buffer)
1530

1531
1532
1533
1534
1535
1536
1537
    def _handle_request_preproc_error(self, request: EngineCoreRequest) -> None:
        """Log and return a request-scoped error response for exceptions raised
        from the add request preprocessing in the input socket processing thread.
        """
        logger.exception(
            "Unexpected error pre-processing request %s", request.request_id
        )
1538
        self._send_error_outputs_to_client([request.request_id], request.client_index)
1539

1540
1541
1542
1543
1544
    def pause_scheduler(
        self, mode: PauseMode = "abort", clear_cache: bool = True
    ) -> Future | None:
        """Pause generation; behavior depends on mode.

1545
1546
1547
1548
1549
1550
1551
1552
1553
        All pause modes queue new adds -- "abort" and "keep" skip step();
        "wait" allows step() so in-flight requests can drain.

        - ``abort``: Set PAUSED_NEW, abort all requests, wait for abort
          outputs to be sent (when running with output_queue), optionally
          clear caches, then complete the returned Future.
        - ``wait``: Set PAUSED_NEW (queue adds, keep stepping); when drained,
          optionally clear caches, then complete the returned Future.
        - ``keep``: Set PAUSED_ALL; return a Future that completes when the
1554
1555
1556
1557
1558
          output queue is empty.
        """
        if mode not in ("keep", "abort", "wait"):
            raise ValueError(f"Invalid pause mode: {mode}")

1559
        def engine_idle_callback(engine: "EngineCoreProc", future: Future[Any]) -> None:
1560
            if clear_cache:
1561
                engine._reset_caches()
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
            future.set_result(None)

        if mode == "abort":
            aborted_reqs = self.scheduler.finish_requests(
                None, RequestStatus.FINISHED_ABORTED
            )
            self._send_abort_outputs(aborted_reqs)

        pause_state = PauseState.PAUSED_ALL if mode == "keep" else PauseState.PAUSED_NEW
        self.scheduler.set_pause_state(pause_state)
1572
1573
1574
1575
1576
1577
1578
1579
        if not self.has_work():
            if clear_cache:
                self._reset_caches()
            return None

        future = Future[Any]()
        self._idle_state_callbacks.append(partial(engine_idle_callback, future=future))
        return future
1580

1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
    def _send_finish_outputs_to_client(
        self, req_ids: list[str], client_index: int, finish_reason: FinishReason
    ) -> None:
        outputs = [
            EngineCoreOutput(req_id, [], finish_reason=finish_reason)
            for req_id in req_ids
        ]
        eco = EngineCoreOutputs(finished_requests=req_ids, outputs=outputs)
        self.output_queue.put_nowait((client_index, eco))

    def _send_abort_outputs_to_client(
        self, req_ids: list[str], client_index: int
    ) -> None:
        self._send_finish_outputs_to_client(req_ids, client_index, FinishReason.ABORT)

    def _send_error_outputs_to_client(
        self, req_ids: list[str], client_index: int
    ) -> None:
        self._send_finish_outputs_to_client(req_ids, client_index, FinishReason.ERROR)

1601
    def _send_abort_outputs(self, aborted_reqs: list[tuple[str, int]]) -> None:
1602
        # TODO(nick) this will be moved inside the scheduler
1603
1604
1605
1606
1607
1608
        if aborted_reqs:
            # Map client_index to list of request_ids that belong to that client.
            by_client = defaultdict[int, set[str]](set)
            for req_id, client_index in aborted_reqs:
                by_client[client_index].add(req_id)
            for client_index, req_ids in by_client.items():
1609
                self._send_abort_outputs_to_client(list(req_ids), client_index)
1610

1611
1612
1613
1614
1615
1616
1617
1618

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

    def __init__(
        self,
        vllm_config: VllmConfig,
1619
        local_client: bool,
1620
        handshake_address: str,
1621
1622
        executor_class: type[Executor],
        log_stats: bool,
1623
        client_handshake_address: str | None = None,
1624
    ):
1625
1626
1627
1628
        assert vllm_config.model_config.is_moe, (
            "DPEngineCoreProc should only be used for MoE models"
        )

1629
1630
        # Counts forward-passes of the model so that we can synchronize
        # finished with DP peers every N steps.
1631
        self.step_counter = 0
1632
        self.current_wave = 0
Rui Qiao's avatar
Rui Qiao committed
1633
        self.last_counts = (0, 0)
1634

1635
1636
1637
1638
        from vllm.distributed.elastic_ep.elastic_state import ElasticEPScalingState

        self.eep_scaling_state: ElasticEPScalingState | None = None

1639
1640
        # Initialize the engine.
        dp_rank = vllm_config.parallel_config.data_parallel_rank
1641
1642
1643
1644
1645
1646
1647
        super().__init__(
            vllm_config,
            local_client,
            handshake_address,
            executor_class,
            log_stats,
            client_handshake_address,
1648
            engine_index=dp_rank,
1649
        )
1650
1651
1652

    def _init_data_parallel(self, vllm_config: VllmConfig):
        # Configure GPUs and stateless process group for data parallel.
1653
1654
1655
1656
        parallel_config = vllm_config.parallel_config
        dp_rank = parallel_config.data_parallel_rank
        dp_size = parallel_config.data_parallel_size
        local_dp_rank = parallel_config.data_parallel_rank_local
1657
1658

        assert dp_size > 1
1659
        assert local_dp_rank is not None
1660
1661
        assert 0 <= local_dp_rank <= dp_rank < dp_size

1662
        self.dp_rank = dp_rank
1663
1664
        dp_group, dp_store = parallel_config.stateless_init_dp_group(return_store=True)
        self.dp_group, self.dp_store = dp_group, dp_store
1665
1666
1667
1668
1669
1670

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

1671
    def add_request(self, request: Request, request_wave: int = 0):
1672
        super().add_request(request, request_wave)
1673
1674
1675
        if self.has_coordinator and request_wave != self.current_wave:
            if request_wave > self.current_wave:
                self.current_wave = request_wave
1676
1677
1678
1679
            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(
1680
1681
                    (-1, EngineCoreOutputs(start_wave=self.current_wave))
                )
1682

1683
1684
    def resume_scheduler(self):
        super().resume_scheduler()
1685
1686
1687
1688
1689
        if (
            self.has_coordinator
            and not self.engines_running
            and self.scheduler.has_unfinished_requests()
        ):
1690
1691
1692
1693
            # Wake up other DP engines.
            self.output_queue.put_nowait(
                (-1, EngineCoreOutputs(start_wave=self.current_wave))
            )
1694

1695
1696
1697
    def _handle_client_request(
        self, request_type: EngineCoreRequestType, request: Any
    ) -> None:
1698
        if request_type == EngineCoreRequestType.START_DP_WAVE:
1699
1700
            new_wave, exclude_eng_index = request
            if exclude_eng_index != self.engine_index and (
1701
1702
                new_wave >= self.current_wave
            ):
1703
1704
                self.current_wave = new_wave
                if not self.engines_running:
1705
                    logger.debug("EngineCore starting idle loop for wave %d.", new_wave)
1706
1707
1708
1709
                    self.engines_running = True
        else:
            super()._handle_client_request(request_type, request)

1710
    def _maybe_publish_request_counts(self):
1711
        if not self.publish_dp_lb_stats:
1712
1713
1714
1715
1716
1717
            return

        # Publish our request counts (if they've changed).
        counts = self.scheduler.get_request_counts()
        if counts != self.last_counts:
            self.last_counts = counts
1718
1719
1720
1721
            stats = SchedulerStats(
                *counts, step_counter=self.step_counter, current_wave=self.current_wave
            )
            self.output_queue.put_nowait((-1, EngineCoreOutputs(scheduler_stats=stats)))
1722

1723
1724
1725
1726
    def run_busy_loop(self):
        """Core busy loop of the EngineCore for data parallel case."""

        # Loop until process is sent a SIGINT or SIGTERM
1727
        while self._handle_shutdown():
1728
1729
1730
            # 1) Poll the input queue until there is work to do.
            self._process_input_queue()

1731
1732
1733
1734
1735
1736
            if self.eep_scaling_state is not None:
                _ = self.eep_scaling_state.progress()
                if self.eep_scaling_state.is_complete():
                    self.process_input_queue_block = True
                    self.eep_scaling_state = None

1737
            executed = self._process_engine_step()
1738
            self._maybe_publish_request_counts()
1739

1740
            local_unfinished_reqs = self.scheduler.has_unfinished_requests()
1741
1742
            if not executed:
                if not local_unfinished_reqs and not self.engines_running:
1743
1744
1745
                    # All engines are idle.
                    continue

1746
1747
                # We are in a running state and so must execute a dummy pass
                # if the model didn't execute any ready requests.
1748
1749
1750
                self.execute_dummy_batch()

            # 3) All-reduce operation to determine global unfinished reqs.
1751
            self.engines_running = self._has_global_unfinished_reqs(
1752
1753
                local_unfinished_reqs
            )
1754

1755
            if not self.engines_running:
1756
                if self.dp_rank == 0 or not self.has_coordinator:
1757
                    # Notify client that we are pausing the loop.
1758
1759
1760
                    logger.debug(
                        "Wave %d finished, pausing engine loop.", self.current_wave
                    )
1761
1762
1763
1764
                    # 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
1765
                    self.output_queue.put_nowait(
1766
1767
1768
1769
1770
                        (
                            client_index,
                            EngineCoreOutputs(wave_complete=self.current_wave),
                        )
                    )
1771
                # Increment wave count and reset step counter.
1772
                self.current_wave += 1
1773
                self.step_counter = 0
1774

1775
1776
        raise SystemExit

1777
    def _has_global_unfinished_reqs(self, local_unfinished: bool) -> bool:
1778
        # Optimization - only perform finish-sync all-reduce every 32 steps.
1779
1780
        self.step_counter += 1
        if self.step_counter % 32 != 0:
1781
1782
            return True

1783
        return ParallelConfig.has_unfinished_dp(self.dp_group, local_unfinished)
Rui Qiao's avatar
Rui Qiao committed
1784

1785
    def reinitialize_distributed(
1786
1787
        self, reconfig_request: ReconfigureDistributedRequest
    ) -> None:
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
        from copy import deepcopy

        from vllm.distributed.elastic_ep.elastic_state import ElasticEPScalingState

        new_parallel_config = deepcopy(self.vllm_config.parallel_config)
        old_dp_size = new_parallel_config.data_parallel_size
        new_parallel_config.data_parallel_size = reconfig_request.new_data_parallel_size
        if (
            reconfig_request.new_data_parallel_rank
            != ReconfigureRankType.KEEP_CURRENT_RANK
        ):
            new_parallel_config.data_parallel_rank = (
                reconfig_request.new_data_parallel_rank
            )
        new_parallel_config.data_parallel_master_ip = (
1803
            reconfig_request.new_data_parallel_master_ip
1804
        )
1805
        new_parallel_config.data_parallel_master_port = (
1806
            reconfig_request.new_data_parallel_master_port
1807
        )
1808
1809
        new_parallel_config._data_parallel_master_port_list = (
            reconfig_request.new_data_parallel_master_port_list
1810
        )
1811

1812
1813
        is_scale_down = reconfig_request.new_data_parallel_size < old_dp_size
        is_shutdown = (
1814
1815
            reconfig_request.new_data_parallel_rank
            == ReconfigureRankType.SHUTDOWN_CURRENT_RANK
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
        )

        self.eep_scaling_state = ElasticEPScalingState(
            model_executor=self.model_executor,
            engine_core=self,
            vllm_config=self.vllm_config,
            new_parallel_config=new_parallel_config,
            worker_type="removing" if is_shutdown else "existing",
            scale_type="scale_down" if is_scale_down else "scale_up",
            reconfig_request=reconfig_request,
        )
        self.process_input_queue_block = False
        logger.info(
            "[Elastic EP] Received reconfiguration request and starting scaling up/down"
        )

    def _eep_send_engine_core_notification(
        self,
        notification_type: EEPNotificationType,
        vllm_config: VllmConfig | None = None,
    ):
        """
        Send notifications to EngineCoreClient, which can then forward
        the notifications to other engine core processes. It is used for:
Jiayi Yan's avatar
Jiayi Yan committed
1840
        1) In scale up: new core engines to notify existing core engines
1841
1842
1843
           that they are ready;
        2) In scale down: removing core engines to notify EngineCoreClient
           so EngineCoreClient can release their ray placement groups;
Jiayi Yan's avatar
Jiayi Yan committed
1844
        3) Both scale up/down: to notify EngineCoreClient that existing
1845
1846
1847
1848
           core engines have already switched to the new parallel setup.
        """
        if vllm_config is None:
            dp_rank = self.vllm_config.parallel_config.data_parallel_rank
1849
        else:
1850
1851
1852
1853
1854
1855
            dp_rank = vllm_config.parallel_config.data_parallel_rank
        notification_data = (notification_type.value, dp_rank)
        outputs = EngineCoreOutputs(
            utility_output=UtilityOutput(
                call_id=EEP_NOTIFICATION_CALL_ID,
                result=UtilityResult(notification_data),
1856
            )
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
        )
        outputs.engine_index = self.engine_index

        if hasattr(self, "output_thread") and self.output_thread.is_alive():
            self.output_queue.put_nowait((0, outputs))
        else:
            encoder = MsgpackEncoder()
            with (
                zmq.Context() as ctx,
                make_zmq_socket(
                    ctx, self.addresses.outputs[0], zmq.PUSH, linger=4000
                ) as socket,
            ):
                socket.send_multipart(encoder.encode(outputs))

    def eep_handle_engine_core_notification(
        self, notification_type: str | EEPNotificationType
    ):
        """
        Handle notification received from EngineCoreClient
        (forwarded from new core engines).
        """
        assert self.eep_scaling_state is not None
        if isinstance(notification_type, str):
            notification_type = EEPNotificationType(notification_type)
        self.eep_scaling_state.handle_notification(notification_type)

    def _eep_scale_up_before_kv_init(self):
        from vllm.distributed.elastic_ep.elastic_state import ElasticEPScalingState

        self.eep_scaling_state = ElasticEPScalingState(
            model_executor=self.model_executor,
            engine_core=self,
            vllm_config=self.vllm_config,
            new_parallel_config=self.vllm_config.parallel_config,
            worker_type="new",
            scale_type="scale_up",
            reconfig_request=None,
        )
        self.model_executor.collective_rpc("init_device")
        self.model_executor.collective_rpc("load_model")
        self._eep_send_engine_core_notification(
            EEPNotificationType.NEW_CORE_ENGINES_WEIGHTS_INIT_READY
        )
        self.model_executor.collective_rpc(
            "elastic_ep_execute", args=("receive_weights",)
        )
        self.available_gpu_memory_for_kv_cache = (
            ParallelConfig.sync_kv_cache_memory_size(self.dp_group, -1)
        )
        self.model_executor.collective_rpc(
            "elastic_ep_execute", args=("prepare_new_worker",)
        )
        self.process_input_queue_block = False
1911

Rui Qiao's avatar
Rui Qiao committed
1912

1913
class EngineCoreActorMixin:
Rui Qiao's avatar
Rui Qiao committed
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
    """
    Ray actor for running EngineCore in a data parallel context
    """

    def __init__(
        self,
        vllm_config: VllmConfig,
        addresses: EngineZmqAddresses,
        dp_rank: int = 0,
        local_dp_rank: int = 0,
    ):
1925
1926
1927
1928
1929
1930
1931
        # Initialize tracer for distributed tracing if configured.
        maybe_init_worker_tracer(
            instrumenting_module_name="vllm.engine_core",
            process_kind="engine_core",
            process_name=f"DPEngineCoreActor_DP{dp_rank}",
        )

Rui Qiao's avatar
Rui Qiao committed
1932
        self.addresses = addresses
1933
        vllm_config.parallel_config.data_parallel_index = dp_rank
1934
        vllm_config.parallel_config.data_parallel_rank_local = local_dp_rank
Rui Qiao's avatar
Rui Qiao committed
1935

1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
        # 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
1946
1947
1948
1949
1950
1951
1952
        # 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.
1953
        self._set_visible_devices(vllm_config, local_dp_rank)
Rui Qiao's avatar
Rui Qiao committed
1954

1955
    def _set_visible_devices(self, vllm_config: VllmConfig, local_dp_rank: int):
1956
        from vllm.platforms import current_platform
1957

1958
1959
1960
1961
        if current_platform.is_xpu():
            pass
        else:
            device_control_env_var = current_platform.device_control_env_var
1962
1963
1964
            self._set_cuda_visible_devices(
                vllm_config, local_dp_rank, device_control_env_var
            )
1965

1966
1967
1968
    def _set_cuda_visible_devices(
        self, vllm_config: VllmConfig, local_dp_rank: int, device_control_env_var: str
    ):
1969
1970
1971
        world_size = vllm_config.parallel_config.world_size
        # Set CUDA_VISIBLE_DEVICES or equivalent.
        try:
1972
1973
1974
            value = get_device_indices(
                device_control_env_var, local_dp_rank, world_size
            )
1975
            os.environ[device_control_env_var] = value
1976
1977
1978
1979
1980
        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}) "
1981
1982
                f'base value: "{os.getenv(device_control_env_var)}"'
            ) from e
1983

Rui Qiao's avatar
Rui Qiao committed
1984
    @contextmanager
1985
1986
1987
1988
1989
1990
    def _perform_handshakes(
        self,
        handshake_address: str,
        identity: bytes,
        local_client: bool,
        vllm_config: VllmConfig,
1991
        client_handshake_address: str | None,
1992
    ):
Rui Qiao's avatar
Rui Qiao committed
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
        """
        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:
2015
            self.run_busy_loop()  # type: ignore[attr-defined]
Rui Qiao's avatar
Rui Qiao committed
2016
2017
2018
2019
2020
2021
2022
        except SystemExit:
            logger.debug("EngineCore exiting.")
            raise
        except Exception:
            logger.exception("EngineCore encountered a fatal error.")
            raise
        finally:
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
            self.shutdown()  # type: ignore[attr-defined]


class DPMoEEngineCoreActor(EngineCoreActorMixin, DPEngineCoreProc):
    """Used for MoE model data parallel cases."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_client: bool,
        addresses: EngineZmqAddresses,
        executor_class: type[Executor],
        log_stats: bool,
        dp_rank: int = 0,
        local_dp_rank: int = 0,
    ):
        vllm_config.parallel_config.data_parallel_rank = dp_rank

        EngineCoreActorMixin.__init__(
            self, vllm_config, addresses, dp_rank, local_dp_rank
        )
        DPEngineCoreProc.__init__(
            self, vllm_config, local_client, "", executor_class, log_stats
        )


class EngineCoreActor(EngineCoreActorMixin, EngineCoreProc):
    """Used for non-MoE and/or non-DP cases."""

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_client: bool,
        addresses: EngineZmqAddresses,
        executor_class: type[Executor],
        log_stats: bool,
        dp_rank: int = 0,
        local_dp_rank: int = 0,
    ):
        vllm_config.parallel_config.data_parallel_size = 1
        vllm_config.parallel_config.data_parallel_size_local = 1
        vllm_config.parallel_config.data_parallel_rank = 0

        EngineCoreActorMixin.__init__(
            self, vllm_config, addresses, dp_rank, local_dp_rank
        )
        EngineCoreProc.__init__(
            self,
            vllm_config,
            local_client,
            "",
            executor_class,
            log_stats,
            engine_index=dp_rank,
        )