Files
FastDeploy/fastdeploy/cache_manager/cache_data.py
2025-07-19 23:19:27 +08:00

161 lines
4.8 KiB
Python

"""
# 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.
"""
from enum import Enum
from fastdeploy.utils import get_logger
logger = get_logger("prefix_cache_manager", "prefix_cache_manager.log")
class CacheStatus(Enum):
"""
cache status enum class
"""
GPU = 0
SWAP2CPU = 1
SWAP2GPU = 2
CPU = 3
class BlockNode:
"""
BlockNode: store the information of a block node
"""
def __init__(
self,
node_id,
input_ids,
input_hash_value,
depth,
block_id,
token_num,
hash_value,
last_used_time,
parent=None,
shared_count=1,
reverved_dec_block_ids=[],
cache_status=CacheStatus.GPU,
is_persistent=False,
persistent_shared_count=0,
):
"""
Args:
node_id: Unique identifier of the node
depth: Depth of the node
block_id: Assigned block ID (CPU block ID if on CPU, GPU block ID if on GPU)
token_num: Number of tokens in the current block
hash_value: Hash value of the current block
last_used_time: Timestamp of last usage
parent: Parent node
shared_count: Reference count of requests currently using this node
reserved_dec_block_ids: Pre-allocated block IDs reserved for decoding, formatted as [block_id, block_id,...]
cache_status: Current cache state (USING, SWAP2CPU, SWAP2GPU, FREE)
is_persistent: Whether the node is persistently stored
persistent_shared_count: Reference count of persistent cache requests
"""
self.node_id = node_id
self.depth = depth
self.parent = parent
self.hash_value = hash_value
self.token_num = token_num
self.input_ids = input_ids
self.input_hash_value = input_hash_value
self.children = {}
self.shared_count = shared_count
self.last_used_time = last_used_time
self.block_id = block_id
self.reverved_dec_block_ids = reverved_dec_block_ids
self.cache_status = cache_status
self.is_persistent = is_persistent
self.persistent_shared_count = persistent_shared_count
self.req_id_set = set()
def __lt__(self, other):
"""
override the less than operator
"""
if self.last_used_time < other.last_used_time:
return True
elif self.last_used_time > other.last_used_time:
return False
else:
return self.depth > other.depth
def __str__(self):
"""
return node info
"""
if self.parent is not None:
parent_node_id = self.parent.node_id
else:
parent_node_id = None
return (
f"node_id {self.node_id}: depth {self.depth} hash_value {self.hash_value}"
+ f" shared_count {self.shared_count} is_gpu_leaf_node {self.is_gpu_leaf_node}"
+ f" is_cpu_leaf_node {self.is_cpu_leaf_node} block_id {self.block_id} "
+ f"has_in_gpu {self.has_in_gpu} "
+ f"cache_status {self.cache_status} parent {parent_node_id} with children number "
+ f"{len(self.children)} req_id_set {self.req_id_set}"
)
@property
def has_in_gpu(self):
"""
check if the node has been allocated in GPU
"""
return self.cache_status == CacheStatus.GPU
def increment_shared_count(self):
"""
increment shared count
"""
self.shared_count += 1
def decrement_shared_count(self):
"""
decrement shared count
"""
self.shared_count -= 1
@property
def is_cpu_leaf_node(self):
"""
check if the node is a leaf node in CPU
"""
if (self.cache_status == CacheStatus.CPU) and (len(self.children) == 0):
return True
return False
@property
def is_gpu_leaf_node(self):
"""
check if the node is a leaf node in GPU
"""
if self.has_in_gpu is False:
return False
else:
if len(self.children) == 0:
return True
for child in self.children.values():
if child.has_in_gpu is True:
return False
return True