# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import os import queue import signal import sys import threading import time from collections import deque from collections.abc import Generator from concurrent.futures import Future from contextlib import ExitStack, contextmanager from inspect import isclass, signature from logging import DEBUG from typing import Any, Callable, Optional, TypeVar, Union import msgspec import zmq from vllm.config import ParallelConfig, VllmConfig from vllm.distributed import stateless_destroy_torch_distributed_process_group from vllm.executor.multiproc_worker_utils import _add_prefix from vllm.logger import init_logger from vllm.logging_utils.dump_input import dump_engine_exception from vllm.lora.request import LoRARequest from vllm.transformers_utils.config import ( maybe_register_config_serialize_by_value) from vllm.utils import make_zmq_socket, resolve_obj_by_qualname from vllm.v1.core.kv_cache_utils import (get_kv_cache_config, unify_kv_cache_configs) from vllm.v1.core.sched.interface import SchedulerInterface from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.core.sched.scheduler import Scheduler as V1Scheduler from vllm.v1.engine import (EngineCoreOutputs, EngineCoreRequest, EngineCoreRequestType, UtilityOutput) from vllm.v1.engine.mm_input_cache import MirroredProcessingCache from vllm.v1.executor.abstract import Executor from vllm.v1.kv_cache_interface import KVCacheConfig from vllm.v1.metrics.stats import SchedulerStats from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.request import Request, RequestStatus from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder from vllm.v1.structured_output import StructuredOutputManager from vllm.v1.utils import EngineHandshakeMetadata, EngineZmqAddresses from vllm.version import __version__ as VLLM_VERSION logger = init_logger(__name__) POLLING_TIMEOUT_S = 2.5 HANDSHAKE_TIMEOUT_MINS = 5 _R = TypeVar('_R') # Return type for collective_rpc class EngineCore: """Inner loop of vLLM's Engine.""" def __init__(self, vllm_config: VllmConfig, executor_class: type[Executor], log_stats: bool, executor_fail_callback: Optional[Callable] = None): assert vllm_config.model_config.runner_type != "pooling" # plugins need to be loaded at the engine/scheduler level too from vllm.plugins import load_general_plugins load_general_plugins() self.vllm_config = vllm_config logger.info("Initializing a V1 LLM engine (v%s) with config: %s", VLLM_VERSION, vllm_config) self.log_stats = log_stats # Setup Model. self.model_executor = executor_class(vllm_config) if executor_fail_callback is not None: self.model_executor.register_failure_callback( executor_fail_callback) # Setup KV Caches and update CacheConfig after profiling. num_gpu_blocks, num_cpu_blocks, kv_cache_config = \ self._initialize_kv_caches(vllm_config) vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks vllm_config.cache_config.num_cpu_blocks = num_cpu_blocks self.structured_output_manager = StructuredOutputManager(vllm_config) # Setup scheduler. if isinstance(vllm_config.scheduler_config.scheduler_cls, str): Scheduler = resolve_obj_by_qualname( vllm_config.scheduler_config.scheduler_cls) else: Scheduler = vllm_config.scheduler_config.scheduler_cls # This warning can be removed once the V1 Scheduler interface is # finalized and we can maintain support for scheduler classes that # implement it if Scheduler is not V1Scheduler: logger.warning( "Using configured V1 scheduler class %s. " "This scheduler interface is not public and " "compatibility may not be maintained.", vllm_config.scheduler_config.scheduler_cls) self.scheduler: SchedulerInterface = Scheduler( vllm_config=vllm_config, kv_cache_config=kv_cache_config, structured_output_manager=self.structured_output_manager, include_finished_set=vllm_config.parallel_config.data_parallel_size > 1, log_stats=self.log_stats, ) # Setup MM Input Mapper. self.mm_input_cache_server = MirroredProcessingCache( vllm_config.model_config) # 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 self.batch_queue: Optional[queue.Queue[tuple[Future[ModelRunnerOutput], SchedulerOutput]]] = None if self.batch_queue_size > 1: logger.info("Batch queue is enabled with size %d", self.batch_queue_size) self.batch_queue = queue.Queue(self.batch_queue_size) def _initialize_kv_caches( self, vllm_config: VllmConfig) -> tuple[int, int, KVCacheConfig]: start = time.time() # Get all kv cache needed by the model kv_cache_specs = self.model_executor.get_kv_cache_specs() # Profiles the peak memory usage of the model to determine how much # memory can be allocated for kv cache. available_gpu_memory = self.model_executor.determine_available_memory() assert len(kv_cache_specs) == len(available_gpu_memory) # Get the kv cache tensor size kv_cache_configs = [ get_kv_cache_config(vllm_config, kv_cache_spec_one_worker, available_gpu_memory_one_worker) for kv_cache_spec_one_worker, available_gpu_memory_one_worker in zip(kv_cache_specs, available_gpu_memory) ] # Since we use a shared centralized controller, we need the # `kv_cache_config` to be consistent across all workers to make sure # all the memory operators can be applied to all workers. unify_kv_cache_configs(kv_cache_configs) # All workers have the same kv_cache_config except layer names, so use # an arbitrary one to initialize the scheduler. assert all([ cfg.num_blocks == kv_cache_configs[0].num_blocks for cfg in kv_cache_configs ]) num_gpu_blocks = kv_cache_configs[0].num_blocks num_cpu_blocks = 0 scheduler_kv_cache_config = kv_cache_configs[0] # Initialize kv cache and warmup the execution self.model_executor.initialize_from_config(kv_cache_configs) elapsed = time.time() - start logger.info(("init engine (profile, create kv cache, " "warmup model) took %.2f seconds"), elapsed) return num_gpu_blocks, num_cpu_blocks, scheduler_kv_cache_config def add_request(self, request: EngineCoreRequest): """Add request to the scheduler.""" if request.mm_hashes is not None: # Here, if hash exists for a multimodal input, then it will be # fetched from the cache, else it will be added to the cache. # Note that the cache here is mirrored with the client cache, so # anything that has a hash must have a HIT cache entry here # as well. assert request.mm_inputs is not None request.mm_inputs = self.mm_input_cache_server.get_and_update_p1( request.mm_inputs, request.mm_hashes) req = Request.from_engine_core_request(request) if req.use_structured_output: # Start grammar compilation asynchronously self.structured_output_manager.grammar_init(req) if req.kv_transfer_params is not None and ( not self.scheduler.get_kv_connector()): logger.warning("Got kv_transfer_params, but no KVConnector found. " "Disabling KVTransfer for this request.") self.scheduler.add_request(req) def abort_requests(self, request_ids: list[str]): """Abort requests from the scheduler.""" # TODO: The scheduler doesn't really need to know the # specific finish reason, TBD whether we propagate that # (i.e. client-aborted vs stop criteria met). self.scheduler.finish_requests(request_ids, RequestStatus.FINISHED_ABORTED) def execute_model(self, scheduler_output: SchedulerOutput): try: return self.model_executor.execute_model(scheduler_output) except BaseException as err: # NOTE: This method is exception-free dump_engine_exception(self.vllm_config, scheduler_output, self.scheduler.make_stats()) # Re-raise exception raise err def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]: """Schedule, execute, and make output. Returns tuple of outputs and a flag indicating whether the model was executed. """ # Check for any requests remaining in the scheduler - unfinished, # or finished and not yet removed from the batch. if not self.scheduler.has_requests(): return {}, False scheduler_output = self.scheduler.schedule() model_output = self.execute_model(scheduler_output) engine_core_outputs = self.scheduler.update_from_output( scheduler_output, model_output) # type: ignore return (engine_core_outputs, scheduler_output.total_num_scheduled_tokens > 0) def step_with_batch_queue( self) -> tuple[Optional[dict[int, EngineCoreOutputs]], bool]: """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: 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. 2. If there is no new scheduled batch, meaning that the batch queue is full or no other requests can be scheduled, we block until the first batch in the job queue is finished. 3. Update the scheduler from the output. """ assert self.batch_queue is not None engine_core_outputs = None scheduler_output = None # Try to schedule a new batch if the batch queue is not full, but # the scheduler may return an empty batch if all requests are scheduled. # Note that this is not blocking. if not self.batch_queue.full(): scheduler_output = self.scheduler.schedule() if scheduler_output.total_num_scheduled_tokens > 0: future = self.model_executor.execute_model(scheduler_output) self.batch_queue.put_nowait( (future, scheduler_output)) # type: ignore scheduled_batch = (scheduler_output is not None and scheduler_output.total_num_scheduled_tokens > 0) # If no more requests can be scheduled and the job queue is not empty, # block until the first batch in the job queue is finished. # TODO(comaniac): Ideally we should peek the first batch in the # job queue to check if it's finished before scheduling a new batch, # but peeking the first element in a queue is not thread-safe, # so we need more work. if not scheduled_batch and not self.batch_queue.empty(): future, scheduler_output = self.batch_queue.get_nowait() # Blocking until the first result is available. model_output = future.result() self.batch_queue.task_done() engine_core_outputs = (self.scheduler.update_from_output( scheduler_output, model_output)) return engine_core_outputs, scheduled_batch def shutdown(self): self.structured_output_manager.clear_backend() if self.model_executor: self.model_executor.shutdown() if self.scheduler: self.scheduler.shutdown() def profile(self, is_start: bool = True): self.model_executor.profile(is_start) def reset_mm_cache(self): # NOTE: Since this is mainly for debugging, we don't attempt to # re-sync the internal caches (P0 processor, P0 mirror, P1 mirror) if self.scheduler.has_unfinished_requests(): logger.warning("Resetting the multi-modal cache when requests are " "in progress may lead to desynced internal caches.") self.mm_input_cache_server.reset() def reset_prefix_cache(self): self.scheduler.reset_prefix_cache() def sleep(self, level: int = 1): self.model_executor.sleep(level) def wake_up(self, tags: Optional[list[str]] = None): self.model_executor.wake_up(tags) def is_sleeping(self) -> bool: return self.model_executor.is_sleeping def execute_dummy_batch(self): self.model_executor.collective_rpc("execute_dummy_batch") 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) def list_loras(self) -> set[int]: return self.model_executor.list_loras() def pin_lora(self, lora_id: int) -> bool: return self.model_executor.pin_lora(lora_id) def save_sharded_state( self, path: str, pattern: Optional[str] = None, max_size: Optional[int] = None, ) -> None: self.model_executor.save_sharded_state(path=path, pattern=pattern, max_size=max_size) def collective_rpc(self, method: Union[str, Callable[..., _R]], timeout: Optional[float] = None, args: tuple = (), kwargs: Optional[dict[str, Any]] = None) -> list[_R]: return self.model_executor.collective_rpc(method, timeout, args, kwargs) def save_tensorized_model( self, tensorizer_config, ) -> None: self.model_executor.save_tensorized_model( tensorizer_config=tensorizer_config, ) class EngineCoreProc(EngineCore): """ZMQ-wrapper for running EngineCore in background process.""" ENGINE_CORE_DEAD = b'ENGINE_CORE_DEAD' def __init__( self, vllm_config: VllmConfig, on_head_node: bool, handshake_address: str, executor_class: type[Executor], log_stats: bool, engine_index: int = 0, ): self.input_queue = queue.Queue[tuple[EngineCoreRequestType, Any]]() self.output_queue = queue.Queue[Union[tuple[int, EngineCoreOutputs], bytes]]() executor_fail_callback = lambda: self.input_queue.put_nowait( (EngineCoreRequestType.EXECUTOR_FAILED, b'')) self.engine_index = engine_index identity = self.engine_index.to_bytes(length=2, byteorder="little") self.engines_running = False with self._perform_handshake(handshake_address, identity, on_head_node, vllm_config) as addresses: self.client_count = len(addresses.outputs) # Set up data parallel environment. self.has_coordinator = addresses.coordinator_output is not None self._init_data_parallel(vllm_config) super().__init__(vllm_config, executor_class, log_stats, executor_fail_callback) self.step_fn = (self.step if self.batch_queue is None else self.step_with_batch_queue) # 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. threading.Thread(target=self.process_input_sockets, args=(addresses.inputs, addresses.coordinator_input, identity), daemon=True).start() self.output_thread = threading.Thread( target=self.process_output_sockets, args=(addresses.outputs, addresses.coordinator_output, self.engine_index), daemon=True) self.output_thread.start() @contextmanager def _perform_handshake( self, handshake_address: str, identity: bytes, on_head_node: bool, vllm_config: VllmConfig ) -> Generator[EngineZmqAddresses, None, None]: input_ctx = zmq.Context() with make_zmq_socket(input_ctx, handshake_address, zmq.DEALER, identity=identity, linger=5000, bind=False) as handshake_socket: # Register engine with front-end. addresses = self.startup_handshake(handshake_socket, on_head_node, vllm_config.parallel_config) # Update config which may have changed from the handshake vllm_config.__post_init__() yield addresses # Send ready message. num_gpu_blocks = vllm_config.cache_config.num_gpu_blocks handshake_socket.send( msgspec.msgpack.encode({ "status": "READY", "local": on_head_node, "num_gpu_blocks": num_gpu_blocks, })) @staticmethod def startup_handshake( handshake_socket: zmq.Socket, on_head_node: bool, parallel_config: ParallelConfig) -> EngineZmqAddresses: # Send registration message. handshake_socket.send( msgspec.msgpack.encode({ "status": "HELLO", "local": on_head_node, })) # Receive initialization message. logger.info("Waiting for init message from front-end.") if not handshake_socket.poll(timeout=HANDSHAKE_TIMEOUT_MINS * 60_000): raise RuntimeError("Did not receive response from front-end " f"process within {HANDSHAKE_TIMEOUT_MINS} " f"minutes") init_bytes = handshake_socket.recv() init_message: EngineHandshakeMetadata = msgspec.msgpack.decode( init_bytes, type=EngineHandshakeMetadata) logger.debug("Received init message: %s", init_message) received_parallel_config = init_message.parallel_config for key, value in received_parallel_config.items(): setattr(parallel_config, key, value) return init_message.addresses @staticmethod def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs): """Launch EngineCore busy loop in background process.""" # Signal handler used for graceful termination. # SystemExit exception is only raised once to allow this and worker # processes to terminate without error shutdown_requested = False # Ensure we can serialize transformer config after spawning maybe_register_config_serialize_by_value() def signal_handler(signum, frame): nonlocal shutdown_requested if not shutdown_requested: shutdown_requested = True raise SystemExit() # Either SIGTERM or SIGINT will terminate the engine_core signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) engine_core: Optional[EngineCoreProc] = None try: parallel_config: ParallelConfig = kwargs[ "vllm_config"].parallel_config if parallel_config.data_parallel_size > 1 or dp_rank > 0: # Set data parallel rank for this engine process. parallel_config.data_parallel_rank = dp_rank parallel_config.data_parallel_rank_local = local_dp_rank engine_core = DPEngineCoreProc(*args, **kwargs) else: engine_core = EngineCoreProc(*args, **kwargs) engine_core.run_busy_loop() except SystemExit: logger.debug("EngineCore exiting.") raise 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 finally: if engine_core is not None: engine_core.shutdown() def _init_data_parallel(self, vllm_config: VllmConfig): pass def run_busy_loop(self): """Core busy loop of the EngineCore.""" # Loop until process is sent a SIGINT or SIGTERM while True: # 1) Poll the input queue until there is work to do. self._process_input_queue() # 2) Step the engine core and return the outputs. self._process_engine_step() def _process_input_queue(self): """Exits when an engine step needs to be performed.""" waited = False while not self.engines_running and not self.scheduler.has_requests(): if logger.isEnabledFor(DEBUG) and self.input_queue.empty(): logger.debug("EngineCore waiting for work.") waited = True req = self.input_queue.get() self._handle_client_request(*req) if waited: logger.debug("EngineCore loop active.") # Handle any more client requests. while not self.input_queue.empty(): req = self.input_queue.get_nowait() self._handle_client_request(*req) def _process_engine_step(self) -> bool: """Called only when there are unfinished local requests.""" # Step the engine core. outputs, model_executed = self.step_fn() # Put EngineCoreOutputs into the output queue. for output in (outputs.items() if outputs else ()): self.output_queue.put_nowait(output) return model_executed def _handle_client_request(self, request_type: EngineCoreRequestType, request: Any) -> None: """Dispatch request from client.""" if request_type == EngineCoreRequestType.ADD: self.add_request(request) elif request_type == EngineCoreRequestType.ABORT: self.abort_requests(request) elif request_type == EngineCoreRequestType.UTILITY: client_idx, call_id, method_name, args = request output = UtilityOutput(call_id) try: method = getattr(self, method_name) output.result = method( *self._convert_msgspec_args(method, args)) except BaseException as e: logger.exception("Invocation of %s method failed", method_name) output.failure_message = (f"Call to {method_name} method" f" failed: {str(e)}") self.output_queue.put_nowait( (client_idx, EngineCoreOutputs(utility_output=output))) elif request_type == EngineCoreRequestType.EXECUTOR_FAILED: raise RuntimeError("Executor failed.") else: logger.error("Unrecognized input request type encountered: %s", request_type) @staticmethod def _convert_msgspec_args(method, args): """If a provided arg type doesn't match corresponding target method arg type, try converting to msgspec object.""" if not args: return args arg_types = signature(method).parameters.values() assert len(args) <= len(arg_types) return tuple( msgspec.convert(v, type=p.annotation) if isclass(p.annotation) and issubclass(p.annotation, msgspec.Struct) and not isinstance(v, p.annotation) else v for v, p in zip(args, arg_types)) def _send_engine_dead(self): """Send EngineDead status to the EngineCoreClient.""" # Put ENGINE_CORE_DEAD in the queue. self.output_queue.put_nowait(EngineCoreProc.ENGINE_CORE_DEAD) # Wait until msg sent by the daemon before shutdown. self.output_thread.join(timeout=5.0) if self.output_thread.is_alive(): logger.fatal("vLLM shutdown signal from EngineCore failed " "to send. Please report this issue.") def process_input_sockets(self, input_addresses: list[str], coord_input_address: Optional[str], identity: bytes): """Input socket IO thread.""" # Msgpack serialization decoding. add_request_decoder = MsgpackDecoder(EngineCoreRequest) generic_decoder = MsgpackDecoder() with ExitStack() as stack, zmq.Context() as ctx: input_sockets = [ stack.enter_context( make_zmq_socket(ctx, input_address, zmq.DEALER, identity=identity, bind=False)) for input_address in input_addresses ] if coord_input_address is None: coord_socket = None else: coord_socket = stack.enter_context( make_zmq_socket(ctx, coord_input_address, zmq.XSUB, identity=identity, bind=False)) # Send subscription message to coordinator. coord_socket.send(b'\x01') # 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. input_socket.send(b'') poller.register(input_socket, zmq.POLLIN) if coord_socket is not None: poller.register(coord_socket, zmq.POLLIN) while True: for input_socket, _ in poller.poll(): # (RequestType, RequestData) type_frame, *data_frames = input_socket.recv_multipart( copy=False) request_type = EngineCoreRequestType( bytes(type_frame.buffer)) # Deserialize the request data. decoder = add_request_decoder if ( request_type == EngineCoreRequestType.ADD) else generic_decoder request = decoder.decode(data_frames) # Push to input queue for core busy loop. self.input_queue.put_nowait((request_type, request)) def process_output_sockets(self, output_paths: list[str], coord_output_path: Optional[str], engine_index: int): """Output socket IO thread.""" # Msgpack serialization encoding. encoder = MsgpackEncoder() # 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]]() # We must set linger to ensure the ENGINE_CORE_DEAD # message is sent prior to closing the socket. with ExitStack() as stack, zmq.Context() as ctx: sockets = [ stack.enter_context( make_zmq_socket(ctx, output_path, zmq.PUSH, linger=4000)) for output_path in output_paths ] 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 max_reuse_bufs = len(sockets) + 1 while True: output = self.output_queue.get() if output == EngineCoreProc.ENGINE_CORE_DEAD: for socket in sockets: socket.send(output) break assert not isinstance(output, bytes) client_index, outputs = output outputs.engine_index = engine_index 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 # 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() buffers = encoder.encode_into(outputs, buffer) tracker = sockets[client_index].send_multipart(buffers, copy=False, track=True) if not tracker.done: ref = outputs if len(buffers) > 1 else None pending.appendleft((tracker, ref, buffer)) elif len(reuse_buffers) < max_reuse_bufs: # Limit the number of buffers to reuse. reuse_buffers.append(buffer) class DPEngineCoreProc(EngineCoreProc): """ZMQ-wrapper for running EngineCore in background process in a data parallel context.""" def __init__( self, vllm_config: VllmConfig, on_head_node: bool, handshake_address: str, executor_class: type[Executor], log_stats: bool, ): self._decorate_logs() # Counts forward-passes of the model so that we can synchronize # finished with DP peers every N steps. self.counter = 0 self.current_wave = 0 self.last_counts = (0, 0) # Initialize the engine. dp_rank = vllm_config.parallel_config.data_parallel_rank super().__init__(vllm_config, on_head_node, handshake_address, executor_class, log_stats, dp_rank) def _decorate_logs(self): # Add process-specific prefix to stdout and stderr before # we initialize the engine. from multiprocessing import current_process process_name = current_process().name pid = os.getpid() _add_prefix(sys.stdout, process_name, pid) _add_prefix(sys.stderr, process_name, pid) def _init_data_parallel(self, vllm_config: VllmConfig): # Configure GPUs and stateless process group for data parallel. dp_rank = vllm_config.parallel_config.data_parallel_rank dp_size = vllm_config.parallel_config.data_parallel_size local_dp_rank = vllm_config.parallel_config.data_parallel_rank_local assert dp_size > 1 assert 0 <= local_dp_rank <= dp_rank < dp_size if vllm_config.kv_transfer_config is not None: # modify the engine_id and append the local_dp_rank to it to ensure # that the kv_transfer_config is unique for each DP rank. vllm_config.kv_transfer_config.engine_id = ( f"{vllm_config.kv_transfer_config.engine_id}_dp{local_dp_rank}" ) logger.debug("Setting kv_transfer_config.engine_id to %s", vllm_config.kv_transfer_config.engine_id) from vllm.platforms import current_platform device_control_env_var = current_platform.device_control_env_var world_size = vllm_config.parallel_config.world_size os.environ[device_control_env_var] = ",".join( str(current_platform.device_id_to_physical_device_id(i)) for i in range(local_dp_rank * world_size, (local_dp_rank + 1) * world_size)) os.environ["MACA_VISIBLE_DEVICES"] = os.environ[device_control_env_var] self.dp_rank = dp_rank self.dp_group = vllm_config.parallel_config.stateless_init_dp_group() def shutdown(self): super().shutdown() if dp_group := getattr(self, "dp_group", None): stateless_destroy_torch_distributed_process_group(dp_group) def add_request(self, request: EngineCoreRequest): if self.has_coordinator and request.current_wave != self.current_wave: if request.current_wave > self.current_wave: self.current_wave = request.current_wave elif not self.engines_running: # Request received for an already-completed wave, notify # front-end that we need to start the next one. self.output_queue.put_nowait( (-1, EngineCoreOutputs(start_wave=self.current_wave))) super().add_request(request) def _handle_client_request(self, request_type: EngineCoreRequestType, request: Any) -> None: if request_type == EngineCoreRequestType.START_DP_WAVE: new_wave, exclude_eng_index = request if exclude_eng_index != self.engine_index and ( new_wave >= self.current_wave): self.current_wave = new_wave if not self.engines_running: logger.debug("EngineCore starting idle loop for wave %d.", new_wave) self.engines_running = True else: super()._handle_client_request(request_type, request) def _maybe_publish_request_counts(self): if not self.has_coordinator: return # Publish our request counts (if they've changed). counts = self.scheduler.get_request_counts() if counts != self.last_counts: self.last_counts = counts stats = SchedulerStats(*counts) self.output_queue.put_nowait( (-1, EngineCoreOutputs(scheduler_stats=stats))) def run_busy_loop(self): """Core busy loop of the EngineCore for data parallel case.""" # Loop until process is sent a SIGINT or SIGTERM while True: # 1) Poll the input queue until there is work to do. self._process_input_queue() # 2) Step the engine core. executed = self._process_engine_step() self._maybe_publish_request_counts() local_unfinished_reqs = self.scheduler.has_unfinished_requests() if not executed: if not local_unfinished_reqs and not self.engines_running: # All engines are idle. continue # We are in a running state and so must execute a dummy pass # if the model didn't execute any ready requests. self.execute_dummy_batch() # 3) All-reduce operation to determine global unfinished reqs. self.engines_running = self._has_global_unfinished_reqs( local_unfinished_reqs) if not self.engines_running: if self.dp_rank == 0: # Notify client that we are pausing the loop. logger.debug("Wave %d finished, pausing engine loop.", self.current_wave) self.output_queue.put_nowait( (-1, EngineCoreOutputs(wave_complete=self.current_wave))) self.current_wave += 1 def _has_global_unfinished_reqs(self, local_unfinished: bool) -> bool: # Optimization - only perform finish-sync all-reduce every 24 steps. self.counter += 1 if self.counter != 24: return True self.counter = 0 return ParallelConfig.has_unfinished_dp(self.dp_group, local_unfinished) class DPEngineCoreActor(DPEngineCoreProc): """ Ray actor for running EngineCore in a data parallel context """ def __init__( self, vllm_config: VllmConfig, on_head_node: bool, addresses: EngineZmqAddresses, executor_class: type[Executor], log_stats: bool, dp_rank: int = 0, local_dp_rank: int = 0, ): self.addresses = addresses vllm_config.parallel_config.data_parallel_rank = dp_rank vllm_config.parallel_config.data_parallel_rank_local = \ local_dp_rank # Ray sets CUDA_VISIBLE_DEVICES to empty string, # we clean this up to be able to properly initialize # data parallel groups. # del os.environ['CUDA_VISIBLE_DEVICES'] super().__init__(vllm_config, on_head_node, "", executor_class, log_stats) def _decorate_logs(self): pass @contextmanager def _perform_handshake(self, handshake_address: str, identity: bytes, on_head_node: bool, vllm_config: VllmConfig): """ For Ray, we don't need to actually perform handshake. All addresses information is known before the actor creation. Therefore, we simply yield these addresses. """ yield self.addresses def wait_for_init(self): """ Wait until the engine core is initialized. This is just an empty method. When ray.get() on this method (or any other method of the actor) returns, it is guaranteed that actor creation (i.e., __init__) is complete. """ pass def run(self): """ Run the engine core busy loop. """ try: self.run_busy_loop() except SystemExit: logger.debug("EngineCore exiting.") raise except Exception: logger.exception("EngineCore encountered a fatal error.") raise finally: self.shutdown()