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