FastDeploy/fastdeploy/cache_manager/cache_transfer_manager.py

"""
# 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 argparse
import asyncio
import concurrent.futures
import gc
import json
import os
import queue
import threading
import time
import traceback
from typing import List

import numpy as np
import paddle
import yaml

from fastdeploy import envs
from fastdeploy.cache_manager.cache_data import CacheStatus
from fastdeploy.cache_manager.cache_tasks import ReadStorageTask, WriteStorageTask
from fastdeploy.cache_manager.ops import (
    cuda_host_alloc,
    cuda_host_free,
    set_data_ipc,
    set_device,
    share_external_data_,
    swap_cache_all_layers,
    swap_cache_layout,
    unset_data_ipc,
)
from fastdeploy.cache_manager.transfer_factory import (
    AttentionStore,
    FileStore,
    MooncakeStore,
)
from fastdeploy.config import CacheConfig, SpeculativeConfig
from fastdeploy.engine.request import ControlRequest, ControlResponse
from fastdeploy.inter_communicator import EngineCacheQueue, IPCSignal, KVCacheStatus
from fastdeploy.inter_communicator.fmq import FMQ
from fastdeploy.platforms import current_platform
from fastdeploy.utils import console_logger, get_logger


def parse_args():
    """
    从命令行解析参数
    """
    parser = argparse.ArgumentParser("Cache transfer manager")
    parser.add_argument(
        "--splitwise_role",
        type=str,
        default="mixed",
        help="splitwise role, can be decode, prefill or mixed",
    )
    parser.add_argument("--rank", type=int, default=0, help="local tp rank")
    parser.add_argument("--device_id", type=int, default=0, help="device id")
    parser.add_argument("--max_model_len", type=int, default=32768, help="max model length")
    parser.add_argument("--num_layers", type=int, default=1, help="model num layers")
    parser.add_argument("--mp_num", type=int, default=1, help="number of model parallel")
    parser.add_argument(
        "--cache_dtype",
        type=str,
        default="bfloat16",
        choices=["uint8", "bfloat16", "block_wise_fp8"],
        help="cache dtype",
    )
    parser.add_argument(
        "--default_dtype",
        type=str,
        default="bfloat16",
        choices=["float16", "bfloat16", "uint8"],
        help="paddle default dtype, swap_cache_batch only support float16、bfloat16 and uint8 now",
    )
    parser.add_argument("--key_cache_shape", type=str, default="", help="key cache shape")
    parser.add_argument("--value_cache_shape", type=str, default="", help="value cache shape")
    parser.add_argument("--cache_queue_port", type=int, default=9923, help="cache queue port")
    parser.add_argument("--enable_splitwise", type=int, default=0, help="enable splitwise ")
    parser.add_argument("--pod_ip", type=str, default="0.0.0.0", help="pod ip")
    parser.add_argument(
        "--engine_worker_queue_port",
        type=int,
        default=9923,
        help="engine worker queue port",
    )
    parser.add_argument("--num_cpu_blocks", type=int, default=4, help="cpu cache block number")
    parser.add_argument(
        "--protocol",
        type=str,
        default="ipc",
        help="cache transfer protocol, only support ipc now",
    )
    parser.add_argument("--local_data_parallel_id", type=int, default=0)
    parser.add_argument("--rdma_port", type=str, default="", help="rmda port")
    parser.add_argument(
        "--speculative_config",
        type=json.loads,
        default="{}",
        help="speculative config",
    )
    parser.add_argument("--create_cache_tensor", action="store_true")
    parser.add_argument(
        "--kvcache_storage_backend",
        type=str,
        default=None,
        choices=["mooncake", "attention_store", "file"],
        help="The storage backend for kvcache storage. If not set, storage backend is disabled.",
    )
    parser.add_argument(
        "--write_policy",
        type=str,
        choices=["write_through"],
        default="write_through",
        help="KVCache write policy",
    )
    parser.add_argument("--model_path", type=str, help="The path of model")

    args = parser.parse_args()
    return args


def get_key_prefix_from_version(version_file_path):
    # the format of version string is RL-STEP{xx}-{timestamp}-{uuid4}
    with open(version_file_path, "r", encoding="utf-8") as f:
        data = yaml.safe_load(f)
        version = data["version"]
        parts = version.split("-", 2)
        key_prefix = "-".join(parts[:2])
        return key_prefix


class CacheTransferManager:
    """
    管理CPU和GPU之间缓存的交换传输
    """

    def __init__(self, args):
        """
        初始化CacheTransferManager
        """
        self.gpu_cache_kvs = {}
        self.cpu_cache_kvs = {}
        self.gpu_cache_k_tensors = []
        self.gpu_cache_v_tensors = []
        self.gpu_cache_scales_k_tensors = []
        self.gpu_cache_scales_v_tensors = []
        self.speculative_config = SpeculativeConfig(args.speculative_config)

        # parse kv cache shape
        self.key_cache_shape = [int(i) for i in args.key_cache_shape.split(",")]
        self.value_cache_shape = []
        if args.value_cache_shape:
            self.value_cache_shape = [int(i) for i in args.value_cache_shape.split(",")]

        # extract kv cache shape into fields
        self.num_gpu_blocks = self.key_cache_shape[0]
        self.head_num = self.key_cache_shape[1]
        self.block_size = self.key_cache_shape[2]
        self.head_dim = self.key_cache_shape[3]

        # compute cache bytes
        self.cache_dtype = args.cache_dtype
        self.cache_item_bytes = CacheConfig.get_cache_bytes(self.cache_dtype)
        self.scale_item_bytes = CacheConfig.get_cache_bytes(paddle.get_default_dtype())
        self.has_cache_scale = self.cache_dtype == "block_wise_fp8"
        if self.has_cache_scale:
            self.cache_scale_shape = [self.num_gpu_blocks, self.head_num, self.block_size]

        # kv cache storage
        self.storage_backend_type = args.kvcache_storage_backend
        self.key_prefix = ""

        # extract other arg values
        self.model_path = args.model_path
        self.model_id = os.path.basename(args.model_path.rstrip("/"))
        self.n_ranks = args.mp_num
        self.rank = args.rank
        self.device = args.device_id
        self.num_layers = args.num_layers
        self.create_cache_tensor = args.create_cache_tensor
        self.local_data_parallel_id = args.local_data_parallel_id
        self.num_extra_layers = self.speculative_config.num_extra_cache_layer
        self.num_extra_layer_gpu_blocks = int(self.num_gpu_blocks * self.speculative_config.num_gpu_block_expand_ratio)
        self.cache_queue_port = args.cache_queue_port
        paddle.set_default_dtype(args.default_dtype)

        self.swap_to_cpu_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.swap_to_gpu_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.read_storage_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.write_back_storage_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.timeout_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=2)
        self.control_task_thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        self.transfer_task_queue = queue.Queue()  # 用来接收传输任务
        self.tansfer_done_queue = queue.Queue()  # 用来告知任务执行完毕
        self.ctrl_output_queue = None

        address = (args.pod_ip, args.cache_queue_port)
        self.cache_task_queue = EngineCacheQueue(
            address=address,
            is_server=False,
            num_client=args.mp_num,
            client_id=self.rank,
            local_data_parallel_id=0,
        )

        cache_ready_signal_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
        self.cache_ready_signal = IPCSignal(
            name="cache_ready_signal",
            array=cache_ready_signal_data,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )
        swap_space_ready_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
        self.swap_space_ready_signal = IPCSignal(
            name="swap_space_ready_signal",
            array=swap_space_ready_data,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )

        self.num_cpu_blocks = args.num_cpu_blocks

        self._init_gpu_cache()
        if self.num_cpu_blocks > 0:
            self._init_cpu_cache()
        if self.storage_backend_type is not None:
            self._init_storage(args)
        self._init_control()

        cache_task_broadcast_data = np.zeros(shape=[1], dtype=np.int32)
        self.cache_task_broadcast_signal = IPCSignal(
            name="cache_task_broadcast_signal",
            array=cache_task_broadcast_data,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )

        # NOTE: `cache_task_is_paused_signal` indicates if do_data_transfer thread
        # of the FIRST rank (rank#0) has received a pause signal
        self.cache_task_is_paused_signal = IPCSignal(
            name="cache_task_is_paused",
            array=np.zeros([1], dtype=np.int32),
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )
        # NOTE: `cache_task_inflight_signal` indicates if do_data_transfer thread
        # of each rank has finished remaining tasks and finally paused
        self.cache_task_inflight_signal = IPCSignal(
            name="cache_task_inflight",
            array=np.zeros([self.n_ranks], dtype=np.int32),
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )

        max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
        array_size = min(max_chips_per_node, args.mp_num)
        worker_healthy_live_array = np.zeros(shape=[array_size], dtype=np.int32)
        self.worker_healthy_live_signal = IPCSignal(
            name="worker_healthy_live_signal",
            array=worker_healthy_live_array,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )

        # Initialize update/clear signals for RL
        self.kv_cache_status_signal = IPCSignal(
            name="kv_cache_status",
            array=np.zeros([1], dtype=np.int32),
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )
        threading.Thread(target=self.check_cache_status, args=[args], daemon=True).start()

        self.is_paused = False  # transfer manager state
        self.is_sleeping = False
        self.inflight = 0  # number of inflight transfer tasks
        self.inflight_tasks = {}

        cache_transfer_inited_signal_data = np.zeros(shape=[args.mp_num], dtype=np.int32)
        self.cache_transfer_inited_signal = IPCSignal(
            name="cache_transfer_inited_signal",
            array=cache_transfer_inited_signal_data,
            dtype=np.int32,
            suffix=args.engine_worker_queue_port,
            create=False,
        )
        self.cache_transfer_inited_signal.value[self.rank] = 1

    def _init_control(self):
        dp_rank = self.local_data_parallel_id
        tp_rank = self.rank
        tp_size = self.n_ranks
        cq_port = self.cache_queue_port
        name = f"ctrl_c2e_rank{tp_rank+tp_size*dp_rank}_{cq_port}"
        self.ctrl_output_queue = FMQ().queue(name, "producer")
        logger.info(f"Init control output queue: {name} (producer)")

    def _init_storage(self, args):
        try:
            # TODO: support cache scale for other backend
            if self.has_cache_scale and self.storage_backend_type is not None:
                if self.storage_backend_type not in ["mooncake"]:
                    raise ValueError(
                        f"Unsupported storage backend ({self.storage_backend_type}) "
                        "when cache quantization is block_wise_fp8"
                    )

            if self.storage_backend_type is None:
                self.storage_backend = None
            elif self.storage_backend_type == "mooncake":
                logger.info("Start initialize mooncake store...")
                self.storage_backend = MooncakeStore(tp_rank=self.rank)
                self._init_storage_buffer(args)
                logger.info("Initialized mooncake store successfully")
            elif self.storage_backend_type == "attention_store":
                logger.info("Start initialize attention store...")
                # TODO: support different model version in rl
                self.storage_backend = AttentionStore(
                    namespace=self.model_id,
                    shard_id=self.rank,
                    shard_num=self.n_ranks,
                    layer_num=self.num_layers + self.num_extra_layers,
                    block_token_size=self.block_size,
                    bytes_per_shard_layer_per_block=self.head_num
                    * self.block_size
                    * self.head_dim
                    * self.cache_item_bytes,
                    device_id=self.device,
                    dp_id=self.local_data_parallel_id,
                )
                logger.info("Initialized attention store successfully!")
            elif args.kvcache_storage_backend == "file":
                logger.info("Start initialize file store...")
                self.storage_backend = FileStore(
                    namespace=self.model_id,
                    tp_rank=self.rank,
                    tp_size=self.n_ranks,
                )
                self._init_storage_buffer(args)
                logger.info("Initialized file store successfully")
            else:
                raise NotImplementedError(f"Unsupported storage backend: {self.storage_backend_type}")
        except Exception as e:
            err_msg = f"Fail to initialize storage backend, {e}, traceback: {traceback.format_exc()}"
            logger.error(err_msg)
            console_logger.error(err_msg)  # print error message to console
            raise

        if args.write_policy not in ["write_through"]:
            raise ValueError(f"Invalid write policy: {args.write_policy}")
        self.write_policy = args.write_policy

        self._update_key_prefix()

        logger.info("Initialize cache storage successfully")

    def _update_key_prefix(self):
        # use key_prefix to distinguish cache for different version of weight in rl
        version_file_path = os.path.join(self.model_path, "version.yaml")
        if os.path.exists(version_file_path):
            self.key_prefix = get_key_prefix_from_version(version_file_path)
            logger.info(f"Update key_prefix of cache storage to {self.key_prefix}")
        else:
            logger.error(f"version.yaml not found at {version_file_path}")

    def _init_storage_buffer(self, args):
        """
        Initialize pinned memory buffer that can hold the cache for a longest request
        cache layout: layer_num * [block_num, head_num, block_size, head_dim]
        scale layout: layer_num * [block_num, head_num, block_size]
        cache buffer layout: [block_num, layer_num, head_num, block_size, head_dim]
        scale buffer layout: [block_num, layer_num, head_num, block_size]
        """
        layer_num = self.num_layers + self.num_extra_layers
        block_num = (args.max_model_len + self.block_size - 1) // self.block_size
        logger.info(
            f"Creating cache buffer for storage with shape: "
            f"[{block_num}, {layer_num}, {self.head_num}, {self.block_size}, {self.head_dim}]"
        )

        self.cache_buffer_stride_bytes = (
            layer_num * self.head_num * self.block_size * self.head_dim * self.cache_item_bytes
        )
        cache_buffer_total_bytes = block_num * self.cache_buffer_stride_bytes * 2  # key and value

        logger.info(f"Creating cache cpu buffer for all layers: {cache_buffer_total_bytes / 1024 ** 3:.2f}GB")
        read_buffer = cuda_host_alloc(cache_buffer_total_bytes)
        self.storage_key_read_buffer = read_buffer
        self.storage_value_read_buffer = read_buffer + cache_buffer_total_bytes // 2
        self.storage_backend.register_buffer(read_buffer, cache_buffer_total_bytes)

        write_buffer = cuda_host_alloc(cache_buffer_total_bytes)
        self.storage_key_write_buffer = write_buffer
        self.storage_value_write_buffer = write_buffer + cache_buffer_total_bytes // 2
        self.storage_backend.register_buffer(write_buffer, cache_buffer_total_bytes)

        if self.has_cache_scale:
            self.scale_buffer_stride_bytes = layer_num * self.head_num * self.block_size * self.scale_item_bytes
            scale_buffer_total_bytes = block_num * self.scale_buffer_stride_bytes * 2
            logger.info(
                f"Creating scale cpu buffer cache for all layers: {scale_buffer_total_bytes / 1024 ** 3:.2f}GB"
            )

            read_buffer = cuda_host_alloc(scale_buffer_total_bytes)
            self.storage_key_scale_read_buffer = read_buffer
            self.storage_value_scale_read_buffer = read_buffer + scale_buffer_total_bytes // 2
            self.storage_backend.register_buffer(read_buffer, scale_buffer_total_bytes)

            write_buffer = cuda_host_alloc(scale_buffer_total_bytes)
            self.storage_key_scale_write_buffer = write_buffer
            self.storage_value_scale_write_buffer = write_buffer + scale_buffer_total_bytes // 2
            self.storage_backend.register_buffer(write_buffer, scale_buffer_total_bytes)

    def _init_gpu_cache(self):

        if not self.create_cache_tensor:
            logger.info("Waiting for runners or messagers to create kv cache.")
            while self.cache_ready_signal.value[self.rank] != 1:
                time.sleep(0.1)
            logger.info("OK! Stop waiting.")

        if self.cache_dtype == "block_wise_fp8":
            cache_type = "uint8"
        else:
            cache_type = self.cache_dtype

        logger.info("Initializing kv cache for all layers.")
        set_device(self.device)
        for i in range(self.num_layers + self.num_extra_layers):
            # NOTE: num_extra_layer_gpu_blocks is usually equal to num_gpu_blocks
            num_gpu_blocks = self.num_gpu_blocks if i < self.num_layers else self.num_extra_layer_gpu_blocks
            key_name = f"key_caches_{i}_rank{self.rank}.device{self.device}"
            val_name = f"value_caches_{i}_rank{self.rank}.device{self.device}"
            key_cache_scales_name = f"key_cache_scales_{i}_rank{self.rank}.device{self.device}"
            value_cache_scales_name = f"value_cache_scales_{i}_rank{self.rank}.device{self.device}"
            key_cache_shape = [
                num_gpu_blocks,
                self.key_cache_shape[1],
                self.key_cache_shape[2],
                self.key_cache_shape[3],
            ]
            value_cache_shape = []
            if self.value_cache_shape:
                value_cache_shape = [
                    num_gpu_blocks,
                    self.value_cache_shape[1],
                    self.value_cache_shape[2],
                    self.value_cache_shape[3],
                ]
            if self.create_cache_tensor:
                logger.info(f"..creating kv cache for layer {i}: {key_cache_shape} {value_cache_shape}")
                key_cache = paddle.full(shape=key_cache_shape, fill_value=0, dtype=cache_type)
                set_data_ipc(key_cache, key_name)

                if self.cache_dtype == "block_wise_fp8":
                    key_cache_scales = paddle.full(
                        shape=[num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
                        fill_value=0,
                        dtype=paddle.get_default_dtype(),
                    )
                    set_data_ipc(key_cache_scales, key_cache_scales_name)
                if self.value_cache_shape:
                    val_cache = paddle.full(shape=value_cache_shape, fill_value=0, dtype=cache_type)
                    set_data_ipc(val_cache, val_name)

                    if self.cache_dtype == "block_wise_fp8":
                        value_cache_scales = paddle.full(
                            shape=[num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
                            fill_value=0,
                            dtype=paddle.get_default_dtype(),
                        )
                        set_data_ipc(value_cache_scales, value_cache_scales_name)
            else:
                logger.info(f"..attaching kv cache for layer {i}: {key_cache_shape} {value_cache_shape}")
                key_cache = paddle.empty(shape=[], dtype=cache_type)
                val_cache = paddle.empty(shape=[], dtype=cache_type)
                key_cache = share_external_data_(key_cache, key_name, key_cache_shape, True)
                if self.cache_dtype == "block_wise_fp8":
                    key_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                    key_cache_scales = share_external_data_(
                        key_cache_scales,
                        key_cache_scales_name,
                        [num_gpu_blocks, self.key_cache_shape[1], self.key_cache_shape[2]],
                        True,
                    )
                if self.value_cache_shape:
                    val_cache = share_external_data_(val_cache, val_name, value_cache_shape, True)
                    if self.cache_dtype == "block_wise_fp8":
                        value_cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
                        value_cache_scales = share_external_data_(
                            value_cache_scales,
                            value_cache_scales_name,
                            [num_gpu_blocks, self.value_cache_shape[1], self.value_cache_shape[2]],
                            True,
                        )

            self.gpu_cache_kvs[key_name] = key_cache
            self.gpu_cache_k_tensors.append(self.gpu_cache_kvs[key_name])
            if self.cache_dtype == "block_wise_fp8":
                self.gpu_cache_kvs[key_cache_scales_name] = key_cache_scales
                self.gpu_cache_scales_k_tensors.append(self.gpu_cache_kvs[key_cache_scales_name])
            if self.value_cache_shape:
                self.gpu_cache_kvs[val_name] = val_cache
                self.gpu_cache_v_tensors.append(self.gpu_cache_kvs[val_name])
                if self.cache_dtype == "block_wise_fp8":
                    self.gpu_cache_kvs[value_cache_scales_name] = value_cache_scales
                    self.gpu_cache_scales_v_tensors.append(self.gpu_cache_kvs[value_cache_scales_name])

        if self.create_cache_tensor:
            self.cache_ready_signal.value[self.rank] = 1
            while np.sum(self.cache_ready_signal.value) != self.n_ranks:
                time.sleep(0.1)

        logger.info("GPU KV cache is initialized")

    def _clear_gpu_cache(self):
        if self.create_cache_tensor:
            logger.debug("Waiting for gpu runner to unlink cuda ipc")
            while self.cache_ready_signal.value[self.rank] != 0:
                time.sleep(0.1)
            logger.debug("Stop waiting! gpu runner has unlinked cuda ipc")
            self.gpu_cache_kvs.clear()
            self.gpu_cache_k_tensors.clear()
            self.gpu_cache_v_tensors.clear()
            if hasattr(self, "gpu_cache_scales_k_tensors"):
                self.gpu_cache_scales_k_tensors.clear()
            if hasattr(self, "gpu_cache_scales_v_tensors"):
                self.gpu_cache_scales_v_tensors.clear()
            paddle.device.cuda.empty_cache()
        else:
            for name, tensor in self.gpu_cache_kvs.items():
                unset_data_ipc(tensor, name, True, False)
            logger.debug("Successfully unlinked gpu caches cuda ipc")
            self.cache_ready_signal.value[self.rank] = 0

        while np.sum(self.cache_ready_signal.value) != 0:
            time.sleep(0.1)
        logger.info("All ranks cleared gpu caches")

    def _init_cpu_cache(self):
        if self.num_cpu_blocks == 0:
            return
        paddle.set_device("cpu")
        key_cache_size = self.key_cache_shape[1] * self.key_cache_shape[2] * self.key_cache_shape[3]
        if self.value_cache_shape:
            value_cache_size = self.value_cache_shape[1] * self.value_cache_shape[2] * self.value_cache_shape[3]
        else:
            value_cache_size = 0
        cache_item_bytes = CacheConfig.get_cache_bytes(self.cache_dtype)
        key_need_to_allocate_bytes = self.num_cpu_blocks * cache_item_bytes * key_cache_size
        value_need_to_allocate_bytes = self.num_cpu_blocks * cache_item_bytes * value_cache_size
        logger.info("Initializing swap space (cpu cache) for all layers.")
        if self.cache_dtype == "block_wise_fp8":
            cache_scales = paddle.empty(shape=[], dtype=paddle.get_default_dtype())
            cache_scales_size = self.key_cache_shape[1] * self.key_cache_shape[2]
            scales_key_need_to_allocate_bytes = self.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
            scales_value_need_to_allocate_bytes = self.num_cpu_blocks * cache_scales.element_size() * cache_scales_size
        self.k_dst_ptrs = []
        self.v_dst_ptrs = []
        self.k_scales_ptrs = []
        self.v_scales_ptrs = []
        for i in range(self.num_layers + self.num_extra_layers):
            key_name = f"key_caches_{i}_rank{self.rank}"
            val_name = f"value_caches_{i}_rank{self.rank}"
            key_cache_scales_name = f"key_cache_scales_{i}_rank{self.rank}"
            value_cache_scales_name = f"value_cache_scales_{i}_rank{self.rank}"
            logger.info(
                f"..creating cpu cache for layer {i}: {(key_need_to_allocate_bytes + value_need_to_allocate_bytes) / 1024 ** 3:.2f}GB"
            )
            self.cpu_cache_kvs[key_name] = cuda_host_alloc(key_need_to_allocate_bytes)
            self.k_dst_ptrs.append(self.cpu_cache_kvs[key_name])
            if self.cache_dtype == "block_wise_fp8":
                self.cpu_cache_kvs[key_cache_scales_name] = cuda_host_alloc(scales_key_need_to_allocate_bytes)
                self.k_scales_ptrs.append(self.cpu_cache_kvs[key_cache_scales_name])
            if value_need_to_allocate_bytes > 0:
                self.cpu_cache_kvs[val_name] = cuda_host_alloc(value_need_to_allocate_bytes)
                self.v_dst_ptrs.append(self.cpu_cache_kvs[val_name])
                if self.cache_dtype == "block_wise_fp8":
                    self.cpu_cache_kvs[value_cache_scales_name] = cuda_host_alloc(scales_value_need_to_allocate_bytes)
                    self.v_scales_ptrs.append(self.cpu_cache_kvs[value_cache_scales_name])
        logger.info("Swap space (cpu cache) is ready!")
        self.swap_space_ready_signal.value[self.rank] = 1

        while np.sum(self.swap_space_ready_signal.value) != self.n_ranks:
            time.sleep(0.1)
        logger.info("All ranks init cpu caches")

    def _clear_cpu_cache(self):
        for ptrs in self.k_dst_ptrs + self.v_dst_ptrs:
            cuda_host_free(ptrs)
        self.cpu_cache_kvs.clear()
        self.k_dst_ptrs.clear()
        self.v_dst_ptrs.clear()
        if hasattr(self, "k_scales_ptrs"):
            self.k_scales_ptrs.clear()
        if hasattr(self, "v_scales_ptrs"):
            self.v_scales_ptrs.clear()
        gc.collect()
        self.swap_space_ready_signal.value[self.rank] = 0

        while np.sum(self.swap_space_ready_signal.value) != 0:
            time.sleep(0.1)
        logger.info("All ranks cleared cpu caches")

    def _run_read_storage(
        self,
        task_id: str,
        token_ids: List[int],
        start_read_block_idx: int,
        k_cache_keys: List[str],
        v_cache_keys: List[str],
        k_scale_keys: List[str],
        v_scale_keys: List[str],
        gpu_block_ids: List[int],
        cpu_block_ids: List[int],
        timeout: float,
    ):
        """
        Read storage data from the given blocks to the corresponding cache tensors on the current rank's GPU.
        """
        try:
            if self.storage_backend_type in ("mooncake", "file"):
                block_num = len(gpu_block_ids)
                keys = k_cache_keys + v_cache_keys
                k_cache_ptrs = [
                    self.storage_key_read_buffer + i * self.cache_buffer_stride_bytes for i in cpu_block_ids
                ]
                v_cache_ptrs = [
                    self.storage_value_read_buffer + i * self.cache_buffer_stride_bytes for i in cpu_block_ids
                ]
                target_locations = k_cache_ptrs + v_cache_ptrs
                target_sizes = [self.cache_buffer_stride_bytes] * block_num * 2  # key and value
                if k_scale_keys and v_scale_keys:
                    keys.extend(k_scale_keys + v_scale_keys)
                    k_scale_ptrs = [
                        self.storage_key_scale_read_buffer + i * self.scale_buffer_stride_bytes for i in cpu_block_ids
                    ]
                    v_scale_ptrs = [
                        self.storage_value_scale_read_buffer + i * self.scale_buffer_stride_bytes
                        for i in cpu_block_ids
                    ]
                    target_locations.extend(k_scale_ptrs + v_scale_ptrs)
                    target_sizes.extend([self.scale_buffer_stride_bytes] * block_num * 2)

                start_time = time.time()
                result = self.storage_backend.batch_get(
                    keys=keys, target_locations=target_locations, target_sizes=target_sizes
                )
                read_cost_time = time.time() - start_time

                if k_scale_keys and v_scale_keys:
                    k_result, v_result = result[:block_num], result[block_num : 2 * block_num]
                    k_scale_result, v_scale_result = result[2 * block_num : 3 * block_num], result[3 * block_num :]
                    success_block_num = 0
                    for k, v, k_scale, v_scale in zip(k_result, v_result, k_scale_result, v_scale_result):
                        if not (k > 0 and v > 0 and k_scale > 0 and v_scale > 0):
                            break
                        success_block_num += 1
                else:
                    k_result, v_result = result[:block_num], result[block_num : 2 * block_num]
                    success_block_num = 0
                    for k, v in zip(k_result, v_result):
                        if not (k > 0 and v > 0):
                            break
                        success_block_num += 1
                logger.debug(f"_run_read_storage, success_block_num: {success_block_num}")
                valid_gpu_block_ids = gpu_block_ids[:success_block_num]
                valid_cpu_block_ids = cpu_block_ids[:success_block_num]

                mode = 1  # cpu ==> gpu
                start_time = time.time()
                swap_cache_layout(
                    self.gpu_cache_k_tensors,
                    self.storage_key_read_buffer,
                    self.key_cache_shape,
                    valid_gpu_block_ids,
                    valid_cpu_block_ids,
                    self.device,
                    mode,
                )
                swap_cache_layout(
                    self.gpu_cache_v_tensors,
                    self.storage_value_read_buffer,
                    self.value_cache_shape,
                    valid_gpu_block_ids,
                    valid_cpu_block_ids,
                    self.device,
                    mode,
                )
                if k_scale_keys and v_scale_keys:
                    swap_cache_layout(
                        self.gpu_cache_scales_k_tensors,
                        self.storage_key_scale_read_buffer,
                        self.cache_scale_shape,
                        valid_gpu_block_ids,
                        valid_cpu_block_ids,
                        self.device,
                        mode,
                    )
                    swap_cache_layout(
                        self.gpu_cache_scales_v_tensors,
                        self.storage_value_scale_read_buffer,
                        self.cache_scale_shape,
                        valid_gpu_block_ids,
                        valid_cpu_block_ids,
                        self.device,
                        mode,
                    )
                swap_cost_time = time.time() - start_time
                logger.debug(
                    f"_run_read_storage, swap_cost_time: {swap_cost_time:.6f}s, read_cost_time: {read_cost_time:.6f}s"
                )

            elif self.storage_backend_type == "attention_store":
                key_cache = []
                val_cache = []
                for i in range(self.num_layers + self.num_extra_layers):
                    key_cache.append(self.gpu_cache_kvs[f"key_caches_{i}_rank{self.rank}.device{self.device}"])
                    val_cache.append(self.gpu_cache_kvs[f"value_caches_{i}_rank{self.rank}.device{self.device}"])

                start_time = time.time()
                read_block_num = self.storage_backend.read(
                    task_id, key_cache, val_cache, token_ids, gpu_block_ids, start_read_block_idx, timeout
                )
                read_cost_time = time.time() - start_time
                valid_gpu_block_ids = gpu_block_ids[:read_block_num]
                logger.debug(f"_run_read_storage, read_cost_time: {read_cost_time:.6f}s")

            return valid_gpu_block_ids

        except Exception as e:
            logger.error(
                f"An error occurred in _run_read_storage, " f"error: {e}, traceback:\n{traceback.format_exc()}"
            )
            raise

    def read_storage_task(self, task: ReadStorageTask):
        """Read cache from the storage backend to the GPU memory."""
        assert (
            self.storage_backend
        ), f"storage_backend not initialized, storage_backend_type: {self.storage_backend_type}"

        try:
            gpu_block_ids = task.gpu_block_ids.copy()
            cpu_block_ids = [i for i in range(len(gpu_block_ids))]
            k_cache_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_key" for key in task.keys]
            v_cache_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_value" for key in task.keys]
            if not self.has_cache_scale:
                k_scale_keys = None
                v_scale_keys = None
            else:
                k_scale_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_key_scale" for key in task.keys]
                v_scale_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_value_scale" for key in task.keys]

            match_block_num = 0
            if self.storage_backend_type in ("mooncake", "file"):
                match_block_num = self.storage_backend.query(
                    k_cache_keys, v_cache_keys, k_scale_keys, v_scale_keys, task.timeout
                )
            elif self.storage_backend_type == "attention_store":
                match_block_num = self.storage_backend.query(
                    task.task_id, task.token_ids, task.start_read_block_idx, task.timeout
                )
            logger.info(f"Matched {match_block_num} blocks in cache storage for read task {task.task_id}")

            k_cache_keys = k_cache_keys[:match_block_num]
            v_cache_keys = v_cache_keys[:match_block_num]
            k_scale_keys = k_scale_keys[:match_block_num] if k_scale_keys else None
            v_scale_keys = v_scale_keys[:match_block_num] if v_scale_keys else None
            gpu_block_ids = gpu_block_ids[:match_block_num]
            cpu_block_ids = cpu_block_ids[:match_block_num]
            valid_gpu_block_ids = []
            if match_block_num > 0:
                # TODO: support timeout with actual block count
                try:
                    valid_gpu_block_ids = self._run_read_storage(
                        task.task_id,
                        task.token_ids[: match_block_num * self.block_size] if task.token_ids else None,
                        task.start_read_block_idx,
                        k_cache_keys,
                        v_cache_keys,
                        k_scale_keys,
                        v_scale_keys,
                        gpu_block_ids,
                        cpu_block_ids,
                        task.timeout,
                    )
                    logger.info(
                        f"Successfully read {len(valid_gpu_block_ids)} blocks from cache storage for task {task.task_id}"
                    )
                except Exception as e:
                    logger.error(
                        f"Failed to read cache for task {task.task_id}, error: {e}, traceback: {traceback.format_exc()}"
                    )
                    valid_gpu_block_ids = []
                finally:
                    try:
                        if (self.rank == 0) and self.storage_backend_type == "attention_store":
                            self.storage_backend.flush_token_index(task.task_id, task.token_ids, 0, True)
                        logger.info(f"Report cache index in HBM to cache storage for task {task.task_id}")
                    except Exception as e:
                        logger.info(
                            f"Failed to report cache index in HBM to cache storage for task {task.task_id}, error: {e}"
                        )

            result = (CacheStatus.STORAGE2GPU, task.task_id, task.keys, valid_gpu_block_ids)
            self.cache_task_queue.swap_storage_to_gpu_barrier.wait()
            self.cache_task_queue.swap_storage_to_gpu_barrier.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"read_storage_task: put transfer done signal for {task.task_id}")

        except Exception as e:
            logger.error(
                f"An error occurred in read_storage_task: "
                f"task_id: {task.task_id}, error:{e}, {traceback.format_exc()}"
            )

    def _run_write_back_storage(
        self,
        task_id,
        token_ids,
        start_write_block_idx,
        k_cache_keys,
        v_cache_keys,
        k_scale_keys,
        v_scale_keys,
        gpu_block_ids,
        cpu_block_ids,
        timeout,
    ):
        try:
            if self.storage_backend_type in ("mooncake", "file"):
                mode = 0  # gpu ==> cpu
                start_time = time.time()
                swap_cache_layout(
                    self.gpu_cache_k_tensors,
                    self.storage_key_write_buffer,
                    self.key_cache_shape,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    mode,
                )
                swap_cache_layout(
                    self.gpu_cache_v_tensors,
                    self.storage_value_write_buffer,
                    self.key_cache_shape,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    mode,
                )
                if k_scale_keys and v_scale_keys:
                    swap_cache_layout(
                        self.gpu_cache_scales_k_tensors,
                        self.storage_key_scale_write_buffer,
                        self.cache_scale_shape,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        mode,
                    )
                    swap_cache_layout(
                        self.gpu_cache_scales_v_tensors,
                        self.storage_value_scale_write_buffer,
                        self.cache_scale_shape,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        mode,
                    )
                swap_cost_time = time.time() - start_time

                block_num = len(gpu_block_ids)
                keys = k_cache_keys + v_cache_keys
                k_cache_ptrs = [
                    self.storage_key_write_buffer + i * self.cache_buffer_stride_bytes for i in cpu_block_ids
                ]
                v_cache_ptrs = [
                    self.storage_value_write_buffer + i * self.cache_buffer_stride_bytes for i in cpu_block_ids
                ]
                target_locations = k_cache_ptrs + v_cache_ptrs
                target_sizes = [self.cache_buffer_stride_bytes] * block_num * 2  # key and value
                if k_scale_keys and v_scale_keys:
                    keys.extend(k_scale_keys + v_scale_keys)
                    k_scale_ptrs = [
                        self.storage_key_scale_write_buffer + i * self.scale_buffer_stride_bytes for i in cpu_block_ids
                    ]
                    v_scale_ptrs = [
                        self.storage_value_scale_write_buffer + i * self.scale_buffer_stride_bytes
                        for i in cpu_block_ids
                    ]
                    target_locations.extend(k_scale_ptrs + v_scale_ptrs)
                    target_sizes.extend([self.scale_buffer_stride_bytes] * block_num * 2)

                start_time = time.time()
                self.storage_backend.batch_set(keys=keys, target_locations=target_locations, target_sizes=target_sizes)
                write_cost_time = time.time() - start_time

                logger.debug(
                    f"_run_write_back_storage, swap_cost_time: {swap_cost_time:.6f}s, write_cost_time: {write_cost_time:.6f}s"
                )
                return block_num

            elif self.storage_backend_type == "attention_store":
                key_cache = []
                val_cache = []
                for i in range(self.num_layers + self.num_extra_layers):
                    key_cache.append(self.gpu_cache_kvs[f"key_caches_{i}_rank{self.rank}.device{self.device}"])
                    val_cache.append(self.gpu_cache_kvs[f"value_caches_{i}_rank{self.rank}.device{self.device}"])

                start_time = time.time()
                write_block_num = self.storage_backend.write(
                    task_id, key_cache, val_cache, token_ids, gpu_block_ids, start_write_block_idx, timeout
                )
                write_cost_time = time.time() - start_time
                logger.debug(f"_run_write_back_storage, write_cost_time: {write_cost_time:.6f}s")
                return write_block_num

        except Exception as e:
            logger.error(
                f"An error occurred in _run_write_back_storage, " f"error: {e}, traceback:\n{traceback.format_exc()}"
            )
            return 0

    def write_back_storage_task(self, task: WriteStorageTask):
        """
        Write cache to the storage backend from the GPU memory.
        """
        assert (
            self.storage_backend
        ), f"storage_backend not initialized, storage_backend_type: {self.storage_backend_type}"

        try:
            gpu_block_ids = task.gpu_block_ids.copy()
            cpu_block_ids = [i for i in range(len(gpu_block_ids))]
            k_cache_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_key" for key in task.keys]
            v_cache_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_value" for key in task.keys]
            if not self.has_cache_scale:
                k_scale_keys = None
                v_scale_keys = None
            else:
                k_scale_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_key_scale" for key in task.keys]
                v_scale_keys = [f"prefix{self.key_prefix}_{key}_{self.rank}_value_scale" for key in task.keys]

            match_block_num = 0
            if self.storage_backend_type in ("mooncake", "file"):
                match_block_num = self.storage_backend.query(
                    k_cache_keys, v_cache_keys, k_scale_keys, v_scale_keys, task.timeout
                )
            elif self.storage_backend_type == "attention_store":
                match_block_num = self.storage_backend.query(task.task_id, task.token_ids, 0, task.timeout)
            logger.info(f"Matched {match_block_num} blocks in cache storage for write task {task.task_id}")

            if match_block_num >= len(k_cache_keys):
                logger.info(f"No uncached keys found for task {task.task_id}")
                gpu_block_ids = []
            else:
                try:
                    k_cache_keys = k_cache_keys[match_block_num:]
                    v_cache_keys = v_cache_keys[match_block_num:]
                    k_scale_keys = k_scale_keys[match_block_num:] if k_scale_keys else None
                    v_scale_keys = v_scale_keys[match_block_num:] if v_scale_keys else None
                    gpu_block_ids = gpu_block_ids[match_block_num:]
                    cpu_block_ids = cpu_block_ids[match_block_num:]
                    # TODO: support timeout with actual block count
                    write_block_num = self._run_write_back_storage(
                        task.task_id,
                        task.token_ids,
                        match_block_num,
                        k_cache_keys,
                        v_cache_keys,
                        k_scale_keys,
                        v_scale_keys,
                        gpu_block_ids,
                        cpu_block_ids,
                        task.timeout,
                    )
                    logger.info(
                        f"Successfully wrote {write_block_num} blocks to cache storage for task {task.task_id}"
                    )
                except Exception as e:
                    logger.error(f"Error in write back storage task: {e}, traceback:{traceback.format_exc()}")
                    gpu_block_ids = []
                finally:
                    try:
                        if (self.rank == 0) and self.storage_backend_type == "attention_store":
                            self.storage_backend.flush_token_index(task.task_id, task.token_ids, 0, False)
                        logger.info(f"Report cache index out HBM to cache storage for task {task.task_id}")
                    except Exception as e:
                        logger.info(
                            f"Failed to report cache index out HBM to cache storage for task {task.task_id}, error: {e}"
                        )

            result = (CacheStatus.GPU2STORAGE, task.task_id, task.keys, gpu_block_ids)
            self.cache_task_queue.swap_to_storage_barrier.wait()
            if self.rank == 0:  # 只有当rank为0时执行同步操作
                self.cache_task_queue.swap_to_storage_barrier.reset()
                self.cache_task_queue.put_transfer_done_signal(result)  # 发送传输完成信号
                logger.debug(f"write_back_storage_task: put_transfer_done_signal {result}")
        except Exception as e:
            logger.error(
                f"An error occurred in write_back_storage_task, " f"error: {e}, traceback:\n{traceback.format_exc()}"
            )

    def _do_swap_to_cpu_task(
        self,
        swap_node_ids,
        gpu_block_id,
        cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        swap cache GPU->CPU
        """
        self.cache_task_queue.swap_to_cpu_barrier1.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_cpu_barrier1.reset()
        result = self._transfer_data(
            swap_node_ids,
            gpu_block_id,
            cpu_block_id,
            event_type,
            transfer_task_id,
        )
        self.cache_task_queue.swap_to_cpu_barrier2.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_cpu_barrier2.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"_do_swap_to_cpu_task: put_transfer_done_signal {result}")
            logger.info(f"_do_swap_to_cpu_task: put_transfer_done_signal for transfer_task_id {transfer_task_id}")

    def _do_swap_to_gpu_task(
        self,
        swap_node_ids,
        gpu_block_id,
        cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        swap cache CPU->GPU
        """
        self.cache_task_queue.swap_to_gpu_barrier1.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_gpu_barrier1.reset()
        result = self._transfer_data(
            swap_node_ids,
            gpu_block_id,
            cpu_block_id,
            event_type,
            transfer_task_id,
        )
        self.cache_task_queue.swap_to_gpu_barrier2.wait()
        if self.rank == 0:
            self.cache_task_queue.swap_to_gpu_barrier2.reset()
            self.cache_task_queue.put_transfer_done_signal(result)
            logger.debug(f"_do_swap_to_gpu_task: put_transfer_done_signal {result}")
            logger.info(f"_do_swap_to_gpu_task: put_transfer_done_signal for transfer_task_id {transfer_task_id}")

    def _handle_pause(self):
        if self.is_paused:
            logger.info("💡 Cache transfer manager is already paused, no need to pause again!")
        else:
            self.pause()
            logger.info("✅ Successfully paused transfer")
        return True

    def _handle_resume(self):
        if not self.is_paused:
            logger.info("💡 Cache transfer manager is not paused, no need to resume!")
        else:
            self.resume()
            if self.storage_backend_type is not None:
                self._update_key_prefix()
            logger.info("✅ Successfully resumed transfer")
        return True

    def _handle_update_weights(self):
        if self.storage_backend_type is not None:
            self._update_key_prefix()
            logger.info("✅ Successfully updated cache key prefix after weight update")
        else:
            logger.info("💡 Cache storage backend is disabled, skip updating cache key prefix")
        return True

    def _handle_sleep(self):
        if self.is_sleeping:
            logger.info("💡 Cache transfer manager is already sleeping, no need to sleep again!")
        else:
            if self.num_cpu_blocks > 0 and envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                self._clear_cpu_cache()
            self._clear_gpu_cache()
            self.is_sleeping = True
            logger.info("✅ Successfully fell asleep (offloaded caches)")
        return True

    def _handle_wakeup(self):
        if not self.is_sleeping:
            logger.info("💡 Cache transfer manager is not sleeping, no need to wakeup!")
        else:
            if self.num_cpu_blocks > 0 and envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                self._init_cpu_cache()
            self._init_gpu_cache()
            self.is_sleeping = False
            logger.info("✅ Successfully wakeup (reload caches)")
        return True

    def control_task(self, task: ControlRequest):
        method = task.get_method()
        tags = task.args.get("tags", {})
        logger.info(f"Received control task: {method}, tags: {tags}")

        handlers = {
            "pause": self._handle_pause,
            "resume": self._handle_resume,
            "update_weights": self._handle_update_weights,
            "sleep": self._handle_sleep,
            "wakeup": self._handle_wakeup,
        }

        handler = handlers.get(method)
        error_code = 200
        error_message = "Success"

        if handler:
            try:
                handler()
            except Exception as e:
                error_code = 500
                error_message = f"Failed to execute {method}: {str(e)}"
                logger.error(f"Error in control_task: {traceback.format_exc()}")
        else:
            error_code = 400
            error_message = f"Unknown control method: {method}"
            logger.warning(error_message)

        self.cache_task_queue.barrier.wait()
        resp = ControlResponse(task.request_id, error_code, error_message)
        asyncio.run(self.ctrl_output_queue.put(resp))
        logger.info(f"Put response into output queue {self.ctrl_output_queue.name}: {resp}")

    def check_work_status(self, time_interval_threashold=envs.FD_CACHE_PROC_EXIT_TIMEOUT):
        """
        Check the health of the model server by checking whether all workers are alive.

        """
        if self.worker_healthy_live_signal.value[0]:
            elapsed_time = time.time() - self.worker_healthy_live_signal.value[0]
            if elapsed_time > time_interval_threashold:
                return False, "Worker Service Not Healthy"

        return True, ""

    def submit_task(self, thread_pool: concurrent.futures.ThreadPoolExecutor, task_fn, *args):

        def inflight_task(fn, *args):
            try:
                return fn(*args)
            finally:
                self.inflight -= 1
                logger.debug(f"submit_task: {fn.__name__} finished, args: {args}, current inflight: {self.inflight}")

        self.inflight += 1
        thread_pool.submit(inflight_task, task_fn, *args)
        logger.debug(
            f"submit_task: {task_fn.__name__} submitted to thread pool, args: {args}, current inflight: {self.inflight}"
        )

    def do_data_transfer(self):
        """
        do data transfer task
        """

        consecutive_error_count = 0
        max_errors = (
            envs.FD_CACHE_PROC_ERROR_COUNT
        )  # After this many consecutive errors, check if the worker process exists.
        is_paused = False

        while True:
            try:
                if self.rank == 0:
                    self.cache_task_is_paused_signal.value[0] = 1 if self.is_paused else 0
                if self.n_ranks > 1:
                    self.cache_task_queue.barrier0.wait()
                    if self.rank == 0:
                        self.cache_task_queue.barrier0.reset()

                # Ensure all ranks synchronically do one of the following things:
                # (1) If rank#0 is paused, wait for inflight tasks to finish first, then only process control tasks;
                # (2) otherwise, all ranks are allowed to pull all tasks from cache task queue
                if self.cache_task_is_paused_signal.value[0] == 1:
                    # wait for inflight tasks to finish first
                    while self.inflight != 0:
                        time.sleep(0.1)
                    # mark the current rank as not having inflight tasks
                    self.cache_task_inflight_signal.value[self.rank] = 0
                    is_paused = True
                else:
                    self.cache_task_inflight_signal.value[self.rank] = 1
                    is_paused = False

                if self.rank == 0:
                    if not self.cache_task_queue.empty():
                        self.cache_task_broadcast_signal.value[0] = 1
                if self.n_ranks > 1:
                    self.cache_task_queue.barrier1.wait()
                    if self.rank == 0:
                        self.cache_task_queue.barrier1.reset()

                if self.cache_task_broadcast_signal.value[0] == 1:
                    data, read_finish = self.cache_task_queue.get_transfer_task()
                    logger.debug(f"do_data_transfer: {data}")
                    if read_finish:
                        self.cache_task_broadcast_signal.value[0] = 0
                    event_type, event_args = data[0], data[1:]

                    # control task is the only task allowed to execute when loop is paused
                    if is_paused and event_type.value != CacheStatus.CTRL.value:
                        continue

                    if event_type.value == CacheStatus.SWAP2CPU.value:
                        transfer_task_id, swap_node_ids, gpu_block_id, cpu_block_id = event_args
                        self.submit_task(
                            self.swap_to_cpu_thread_pool,
                            self._do_swap_to_cpu_task,
                            swap_node_ids,
                            gpu_block_id,
                            cpu_block_id,
                            event_type,
                            transfer_task_id,
                        )
                    elif event_type.value == CacheStatus.SWAP2GPU.value:
                        transfer_task_id, swap_node_ids, gpu_block_id, cpu_block_id = event_args
                        self.submit_task(
                            self.swap_to_gpu_thread_pool,
                            self._do_swap_to_gpu_task,
                            swap_node_ids,
                            gpu_block_id,
                            cpu_block_id,
                            event_type,
                            transfer_task_id,
                        )
                    elif event_type.value == CacheStatus.STORAGE2GPU.value:
                        read_storage_task = event_args[0]
                        self.submit_task(
                            self.read_storage_thread_pool,
                            self.read_storage_task,
                            read_storage_task,
                        )
                    elif event_type.value == CacheStatus.GPU2STORAGE.value:
                        write_storage_task = event_args[0]
                        self.submit_task(
                            self.write_back_storage_thread_pool,
                            self.write_back_storage_task,
                            write_storage_task,
                        )
                    elif event_type.value == CacheStatus.CTRL.value:
                        control_task = event_args[0]
                        self.control_task_thread_pool.submit(
                            self.control_task,
                            control_task,
                        )

                else:
                    if self.n_ranks > 1:
                        self.cache_task_queue.barrier2.wait()
                        if self.rank == 0:
                            self.cache_task_queue.barrier2.reset()
                    continue

                if self.n_ranks > 1:
                    self.cache_task_queue.barrier3.wait()
                    if self.rank == 0:
                        self.cache_task_queue.barrier3.reset()

                consecutive_error_count = 0

            except (BrokenPipeError, EOFError, ConnectionResetError) as e:
                # When a cache_transfer_manager process remains, it keeps printing error logs and may exhaust disk space.
                # Add a check to see if the worker process is alive; if it has ended, exit the loop to stop continuous logging.
                logger.error(f"[CacheTransferManager] Connection broken: {e}")
                consecutive_error_count += 1
                if consecutive_error_count > max_errors:
                    try:
                        status, msg = self.check_work_status()
                    except Exception:
                        status = True

                    if status is False:
                        logger.critical(
                            f"The Worker process has been inactive for over {envs.FD_CACHE_PROC_EXIT_TIMEOUT} seconds, and the Cache process will automatically terminate (the waiting timeout can be extended via FD_CACHE_PROC_EXIT_TIMEOUT)."
                        )
                        break
                time.sleep(1)
                continue

            except Exception as e:
                logger.info(f"do_data_transfer: error: {e}, {str(traceback.format_exc())}")

    def _transfer_data(
        self,
        swap_node_ids,
        task_gpu_block_id,
        task_cpu_block_id,
        event_type,
        transfer_task_id,
    ):
        """
        transfer data
        task_gpu_block_id format: [[block_id0, [fold_block_id0, fold_block_id1]],
            [block_id1, [fold_block_id0, fold_block_id1]], ...]
        """
        logger.debug(
            f"transfer data: transfer_task_id {transfer_task_id}: swap_node_ids {swap_node_ids}"
            + f"task_gpu_block_id {task_gpu_block_id} task_cpu_block_id {task_cpu_block_id} event_type {event_type}"
        )
        start_time = time.time()
        try:
            # transform block id
            assert len(task_gpu_block_id) == len(task_cpu_block_id)
            gpu_block_ids = task_gpu_block_id
            cpu_block_ids = task_cpu_block_id

            if event_type.value == CacheStatus.SWAP2CPU.value:
                swap_cache_all_layers(
                    self.gpu_cache_k_tensors,
                    self.k_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    0,
                )
                swap_cache_all_layers(
                    self.gpu_cache_v_tensors,
                    self.v_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    0,
                )
                if self.cache_dtype == "block_wise_fp8":
                    swap_cache_all_layers(
                        self.gpu_cache_scales_k_tensors,
                        self.k_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        0,
                    )
                    swap_cache_all_layers(
                        self.gpu_cache_scales_v_tensors,
                        self.v_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        0,
                    )

            elif event_type.value == CacheStatus.SWAP2GPU.value:
                swap_cache_all_layers(
                    self.gpu_cache_k_tensors,
                    self.k_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    1,
                )
                swap_cache_all_layers(
                    self.gpu_cache_v_tensors,
                    self.v_dst_ptrs,
                    self.num_cpu_blocks,
                    gpu_block_ids,
                    cpu_block_ids,
                    self.device,
                    1,
                )
                if self.cache_dtype == "block_wise_fp8":
                    swap_cache_all_layers(
                        self.gpu_cache_scales_k_tensors,
                        self.k_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        1,
                    )
                    swap_cache_all_layers(
                        self.gpu_cache_scales_v_tensors,
                        self.v_scales_ptrs,
                        self.num_cpu_blocks,
                        gpu_block_ids,
                        cpu_block_ids,
                        self.device,
                        1,
                    )
            else:
                logger.warning(
                    f"transfer data: Get unexpected event type {event_type}, only SWAP2CPU and SWAP2GPU supported"
                )
        except Exception as e:
            logger.error(f"transfer data: error: {e}")
            raise e
        end_time = time.time()
        elasped_time = end_time - start_time
        logger.info(
            f"transfer data: transfer_task_id {transfer_task_id} event_type {event_type}: "
            + f"transfer {len(gpu_block_ids)} blocks done  elapsed_time {elasped_time:.4f}"
        )
        return (
            event_type,
            transfer_task_id,
            swap_node_ids,
            task_gpu_block_id,
            task_cpu_block_id,
        )

    def check_cache_status(self, args):
        # TODO XPU support RL
        if unset_data_ipc is None:
            return
        logger.info(
            "check_cache_status: Launch a thread to clear/restore kv cache when model weights are cleared/updated."
        )
        while True:
            # handle cache clearing/restoring
            if self.kv_cache_status_signal.value[0] == KVCacheStatus.CLEARING:
                assert args.splitwise_role == "mixed", "Only mixed mode supports clearing cache."
                try:
                    # pause transfer
                    self.pause()

                    # clear caches
                    logger.info("check_cache_status: start clearing caches")
                    if self.num_cpu_blocks > 0 and envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                        self._clear_cpu_cache()
                    self._clear_gpu_cache()
                    self.kv_cache_status_signal.value[0] = KVCacheStatus.CLEARED
                    self._log_memory("check_cache_status: after clearing caches")
                except Exception as e:
                    logger.error(f"check_cache_status: failed to clear caches: {e}")

            elif self.kv_cache_status_signal.value[0] == KVCacheStatus.UPDATING:
                assert args.splitwise_role == "mixed", "Only mixed mode supports updating cache."
                try:
                    logger.info("check_cache_status: start restoring caches")
                    if self.num_cpu_blocks > 0 and envs.FD_ENABLE_SWAP_SPACE_CLEARING:
                        self._init_cpu_cache()
                    self._init_gpu_cache()

                    # update key prefix for kv cache backend
                    if self.storage_backend_type is not None:
                        self._update_key_prefix()

                    # resume transfer
                    self.resume()
                    self.kv_cache_status_signal.value[0] = KVCacheStatus.NORMAL
                    self._log_memory("check_cache_status: after restoring caches")

                except Exception as e:
                    logger.error(f"check_cache_status: failed to restore caches: {e}")

            time.sleep(0.1)

    def pause(self):
        if self.n_ranks > 1:
            self.cache_task_queue.pause_barrier.wait()
            if self.rank == 0:
                self.cache_task_queue.pause_barrier.reset()
        logger.info("pause: 🟠 wait for inflight transfer tasks to finish")
        self.is_paused = True
        while np.sum(self.cache_task_inflight_signal.value) != 0:
            time.sleep(0.1)
        logger.info("pause: 🔴 pause transfer manager and stop do transfer tasks")

    def resume(self):
        if self.n_ranks > 1:
            self.cache_task_queue.resume_barrier.wait()
            if self.rank == 0:
                self.cache_task_queue.resume_barrier.reset()
        self.is_paused = False
        while np.sum(self.cache_task_inflight_signal.value) != self.n_ranks:
            time.sleep(0.1)
        logger.info("resume: 🟢 resume transfer manager and start to do transfer tasks")

    def _log_memory(self, context: str):
        """Log current GPU memory usage."""
        max_alloc = paddle.device.cuda.max_memory_allocated() / (1024**3)
        max_reserved = paddle.device.cuda.max_memory_reserved() / (1024**3)
        curr_alloc = paddle.device.cuda.memory_allocated() / (1024**3)
        curr_reserved = paddle.device.cuda.memory_reserved() / (1024**3)

        logger.info(
            f"{context}: "
            f"max_allocated: {max_alloc:.2f}GB "
            f"max_reserved: {max_reserved:.2f}GB "
            f"current_allocated: {curr_alloc:.2f}GB "
            f"current_reserved: {curr_reserved:.2f}GB"
        )


def main():
    """
    启动cache manager
    """
    cache_manager = CacheTransferManager(args)
    cache_manager.do_data_transfer()


if __name__ == "__main__":

    args = parse_args()
    rank_id = args.rank + args.local_data_parallel_id * args.mp_num
    if args.mp_num > 1:
        logger = get_logger("cache_transfer", f"cache_transfer_{rank_id}.log")
    else:
        logger = get_logger("cache_transfer", "cache_transfer.log")

    logger.info(f"args: {vars(args)}")
    set_device(args.device_id)
    try:
        main()
    except Exception as e:
        logger.error(f"cache_transfer_manager failed with error: {e}, traceback: {traceback.format_exc()}")
        raise