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