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

117 lines
4.1 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 fastdeploy.utils import get_logger
logger = get_logger("prefix_cache_manager", "prefix_cache_manager.log")
class CacheMetrics:
"""
Cache Metrics used to record the cache hit time, token num, request num, etc.
"""
def __init__(self):
self.total_match_time = 0.0
self.avg_match_time = 0.0
self.min_match_time = 1e9
self.max_match_time = 0.0
# request level
self.req_count = 0
self.hit_req_count = 0
self.hit_req_ratio = 0.0
# token level
self.total_gpu_matched_token_num = 0
self.total_cpu_matched_token_num = 0
self.matched_token_num = 0
self.total_token_num = 0
self.hit_token_ratio = 0.0
self.cpu_hit_token_ratio = 0.0
self.gpu_hit_token_ratio = 0.0
def _update_history_hit_metrics(self):
"""
update hit ratio
"""
self.hit_req_ratio = self.hit_req_count / self.req_count
self.hit_token_ratio = self.matched_token_num / self.total_token_num
self.cpu_hit_token_ratio = self.total_cpu_matched_token_num / self.total_token_num
self.gpu_hit_token_ratio = self.total_gpu_matched_token_num / self.total_token_num
logger.info(
f"Metrics for all requests: req_count {self.req_count} hit_req_count {self.hit_req_count}"
+ f" hit_req_ratio {self.hit_req_ratio:.2f} hit_token_ratio {self.hit_token_ratio:.2f}"
+ f" gpu_hit_token_ratio {self.gpu_hit_token_ratio:.2f}"
+ f" cpu_hit_token_ratio {self.cpu_hit_token_ratio:.2f}"
+ f" total_gpu_matched_token_num {self.total_gpu_matched_token_num}"
+ f" total_cpu_matched_token_num {self.total_cpu_matched_token_num}"
+ f" total_matched_token_num {self.matched_token_num}"
+ f" total_token_num {self.total_token_num}"
)
def calculate_hit_metrics(
self,
req_id,
current_query_cpu_match_token_num,
current_query_gpu_match_token_num,
current_query_token_num,
):
"""
calculate hit metrics for current query
"""
cpu_cache_match_ratio = current_query_cpu_match_token_num / current_query_token_num
gpu_cache_match_ratio = current_query_gpu_match_token_num / current_query_token_num
total_match_ratio = cpu_cache_match_ratio + gpu_cache_match_ratio
self.total_cpu_matched_token_num += current_query_cpu_match_token_num
self.total_gpu_matched_token_num += current_query_gpu_match_token_num
self.matched_token_num += current_query_cpu_match_token_num + current_query_gpu_match_token_num
self.total_token_num += current_query_token_num
logger.info(
f"Metrics for req_id {req_id}: token_num {current_query_token_num}"
+ f" cpu_cache_match_ratio {cpu_cache_match_ratio}"
+ f" gpu_cache_match_ratio {gpu_cache_match_ratio}"
+ f" total_match_ratio {total_match_ratio}"
)
def reset_metrics(self):
"""
reset metrics
"""
self.total_match_time = 0.0
self.avg_match_time = 0.0
self.min_match_time = 1e9
self.max_match_time = 0.0
self.req_count = 0
self.hit_req_count = 0
self.hit_req_ratio = 0.0
self.total_gpu_matched_token_num = 0
self.total_cpu_matched_token_num = 0
self.matched_token_num = 0
self.total_token_num = 0
self.hit_token_ratio = 0.0
self.cpu_hit_token_ratio = 0.0
self.gpu_hit_token_ratio = 0.0