""" # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ import threading import time import traceback from multiprocessing.managers import ( AcquirerProxy, BaseManager, ListProxy, Value, ValueProxy, ) from typing import Any, List, Tuple from fastdeploy.utils import get_logger logger = get_logger("cache_queue_manager", "cache_queue_manager.log") class EngineCacheQueue: """ Multiprocessing manager for cache queue between Engine and Worker. Manages shared resources using multiprocessing managers for inter-process communication. """ def __init__( self, address: Tuple[str, int] = ("127.0.0.1", 56666), authkey: bytes = b"cache_queue_service", is_server: bool = False, num_client: int = 1, # tensor parallel size client_id: int = -1, # tensor parallel id local_data_parallel_size: int = 1, # data parallel size local_data_parallel_id: int = 0, # local data parallel id ) -> None: """ Initialize the cache communication queue. Args: address: Network address (IP, port) for the queue server authkey: Authentication key for secure connection is_server: Whether this instance acts as a server num_client: Total number of expected clients client_id: Unique identifier for client instances local_data_parallel_size: data parallel size local_data_parallel_id: local data parallel id """ self.address: Tuple[str, int] = address self.authkey: bytes = authkey self.num_client: int = num_client self.client_id: int = client_id self.local_data_parallel_size = local_data_parallel_size self.local_data_parallel_id = local_data_parallel_id class QueueManager(BaseManager): """ Custom QueueManager for proxy object registration """ pass if is_server: # Server-side initialization for shared resources self.transfer_task_queue_init: List[List[Any]] = [list() for _ in range(self.local_data_parallel_size)] self.tansfer_done_queue_init: List[List[Any]] = [list() for _ in range(self.local_data_parallel_size)] self.cache_sync_value_init: List[Value] = [Value("i", 0) for _ in range(self.local_data_parallel_size)] self.transfer_task_lock_init: List[threading.Lock] = [ threading.Lock() for _ in range(self.local_data_parallel_size) ] self.transfer_task_done_lock_init: List[threading.Lock] = [ threading.Lock() for _ in range(self.local_data_parallel_size) ] # Initialize barriers self.barrier1_init = [threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)] self.barrier2_init = [threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)] self.barrier3_init = [threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size)] self.swap_to_cpu_barrier1_init = [ threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size) ] self.swap_to_cpu_barrier2_init = [ threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size) ] self.swap_to_gpu_barrier1_init = [ threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size) ] self.swap_to_gpu_barrier2_init = [ threading.Barrier(self.num_client) for _ in range(self.local_data_parallel_size) ] # Register shared objects with proxy types QueueManager.register( "get_transfer_task_queue", callable=lambda idx: self.transfer_task_queue_init[idx], proxytype=ListProxy, ) QueueManager.register( "get_tansfer_done_queue", callable=lambda idx: self.tansfer_done_queue_init[idx], proxytype=ListProxy, ) QueueManager.register( "get_cache_sync_value", callable=lambda idx: self.cache_sync_value_init[idx], proxytype=ValueProxy, ) QueueManager.register( "get_transfer_task_lock", callable=lambda idx: self.transfer_task_lock_init[idx], proxytype=AcquirerProxy, ) QueueManager.register( "get_transfer_task_done_lock", callable=lambda idx: self.transfer_task_done_lock_init[idx], proxytype=AcquirerProxy, ) QueueManager.register("get_barrier1", callable=lambda idx: self.barrier1_init[idx]) QueueManager.register("get_barrier2", callable=lambda idx: self.barrier2_init[idx]) QueueManager.register("get_barrier3", callable=lambda idx: self.barrier3_init[idx]) QueueManager.register( "get_swap_to_cpu_barrier1", callable=lambda idx: self.swap_to_cpu_barrier1_init[idx], ) QueueManager.register( "get_swap_to_cpu_barrier2", callable=lambda idx: self.swap_to_cpu_barrier2_init[idx], ) QueueManager.register( "get_swap_to_gpu_barrier1", callable=lambda idx: self.swap_to_gpu_barrier1_init[idx], ) QueueManager.register( "get_swap_to_gpu_barrier2", callable=lambda idx: self.swap_to_gpu_barrier2_init[idx], ) self.manager: BaseManager = QueueManager(address=self.address, authkey=self.authkey) self.manager.start() logger.info(f"EngineCacheQueue server started at {self.address}") else: # Client-side connection setup assert ( 0 <= self.client_id < self.num_client ), f"client_id must be between 0 and {self.num_client-1}, got {self.client_id}" QueueManager.register("get_transfer_task_queue") QueueManager.register("get_tansfer_done_queue") QueueManager.register("get_cache_sync_value") QueueManager.register("get_transfer_task_lock") QueueManager.register("get_transfer_task_done_lock") QueueManager.register("get_barrier1") QueueManager.register("get_barrier2") QueueManager.register("get_barrier3") QueueManager.register("get_swap_to_cpu_barrier1") QueueManager.register("get_swap_to_cpu_barrier2") QueueManager.register("get_swap_to_gpu_barrier1") QueueManager.register("get_swap_to_gpu_barrier2") self.manager = QueueManager(address=self.address, authkey=self.authkey) self._connect_with_retry() # Get proxy objects for shared resources self.transfer_task_queue = self.manager.get_transfer_task_queue(self.local_data_parallel_id) self.tansfer_done_queue = self.manager.get_tansfer_done_queue(self.local_data_parallel_id) self.task_sync_value = self.manager.get_cache_sync_value(self.local_data_parallel_id) self.task_lock = self.manager.get_transfer_task_lock(self.local_data_parallel_id) self.task_done_lock = self.manager.get_transfer_task_done_lock(self.local_data_parallel_id) # Get barrier proxies self.barrier1 = self.manager.get_barrier1(self.local_data_parallel_id) self.barrier2 = self.manager.get_barrier2(self.local_data_parallel_id) self.barrier3 = self.manager.get_barrier3(self.local_data_parallel_id) self.swap_to_cpu_barrier1 = self.manager.get_swap_to_cpu_barrier1(self.local_data_parallel_id) self.swap_to_cpu_barrier2 = self.manager.get_swap_to_cpu_barrier2(self.local_data_parallel_id) self.swap_to_gpu_barrier1 = self.manager.get_swap_to_gpu_barrier1(self.local_data_parallel_id) self.swap_to_gpu_barrier2 = self.manager.get_swap_to_gpu_barrier2(self.local_data_parallel_id) self.total_num: int = (1 << self.num_client) - 1 if not is_server: # Setup position and total_num for sync operations self.position: int = 1 << self.client_id logger.info(f"Connected EngineCacheQueue client_id: {self.client_id}") def _connect_with_retry(self, max_retries: int = 5, interval: int = 3) -> None: """ Connect to the server with retry mechanism. Args: max_retries: Maximum connection attempts interval: Retry interval in seconds Raises: ConnectionError: If all connection attempts fail """ for _ in range(max_retries): try: self.manager.connect() return except ConnectionRefusedError: time.sleep(interval) raise ConnectionError(f"EngineCacheQueue cannot connect to {self.address}") def put_transfer_task(self, item): """ put swap task """ self.task_lock.acquire() if 0 < self.task_sync_value.get() < self.total_num: self.task_lock.release() while 0 < self.task_sync_value.get() < self.total_num: time.sleep(0.001) self.task_lock.acquire() self.task_sync_value.set(0) self.transfer_task_queue.append(item) logger.info(f"put_transfer_task: put swap task {item[-1]} to queue successful") self.task_lock.release() def get_transfer_task(self): """ get swap task """ data = None read_finish = False self.task_lock.acquire() if self.task_sync_value.get() & self.position == 0 and len(self.transfer_task_queue) > 0: data = self.transfer_task_queue[0] logger.debug(f"get_transfer_task: Get {data} by {self.client_id} from queue successful") set_value = self.task_sync_value.get() | self.position logger.info(f"get_transfer_task: rank: {self.client_id} set_value: {set_value}") if set_value >= self.total_num: self.transfer_task_queue.pop(0) set_value = 0 read_finish = True self.task_sync_value.set(set_value) self.task_lock.release() return data, read_finish def put_transfer_done_signal(self, item): """ put swap result """ self.task_done_lock.acquire() self.tansfer_done_queue.append(item) self.task_done_lock.release() logger.info(f"put_transfer_done_signal: put swap task {item[-1]} finished signal to queue successful") def get_transfer_done_signal(self): """ get swap result """ data = None self.task_done_lock.acquire() if len(self.tansfer_done_queue) > 0: data = self.tansfer_done_queue.pop(0) logger.info(f"get_transfer_done_signal: Get swap task {data[-1]} finished signal from queue successful") self.task_done_lock.release() return data def empty(self): """ check if queue is empty """ try: return len(self.transfer_task_queue) == 0 except Exception as e: logger.error(f"empty function meets error: {e}, {str(traceback.format_exc())}") raise e