wehub-resource-sync
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
# 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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import argparse
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import re
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from collections import defaultdict
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gpu_time_categories = {
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"within_1%": 0,
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"increase_1_to_5%": 0,
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"increase_above_5_to_10%": 0,
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"increase_above_10%": 0,
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"decrease_1_to_5%": 0,
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"decrease_above_5%": 0,
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}
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total_time_categories = {
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"within_1%": 0,
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"increase_1_to_5%": 0,
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"increase_above_5_to_10%": 0,
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"increase_above_10%": 0,
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"decrease_1_to_5%": 0,
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"decrease_above_5%": 0,
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}
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parser = argparse.ArgumentParser(
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description="Analyze time changes in log files"
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)
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parser.add_argument('file_name', type=str, help='The name of the log file')
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args = parser.parse_args()
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gpu_time_pattern = re.compile(r"GPU time change: ([\d.-]*)")
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total_time_pattern = re.compile(r"Total time change: ([\d.-]+)%")
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error_pattern = re.compile(r'Check speed result with case "(.*?)"')
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gpu_time_lines = 0
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error_cases = defaultdict(int)
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with open(args.file_name, 'r') as file:
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for line in file:
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if "GPU time change" in line:
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gpu_time_lines += 1
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gpu_time_match = gpu_time_pattern.search(line)
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if gpu_time_match:
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gpu_time_change_str = gpu_time_match.group(1)
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gpu_time_change = (
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float(gpu_time_change_str) if gpu_time_change_str else 0.0
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)
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if -1 < gpu_time_change < 1:
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gpu_time_categories["within_1%"] += 1
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elif 1 <= gpu_time_change < 5:
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gpu_time_categories["increase_1_to_5%"] += 1
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elif 5 <= gpu_time_change < 10:
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gpu_time_categories["increase_above_5_to_10%"] += 1
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elif gpu_time_change >= 10:
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gpu_time_categories["increase_above_10%"] += 1
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elif -5 < gpu_time_change <= -1:
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gpu_time_categories["decrease_1_to_5%"] += 1
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elif gpu_time_change <= -5:
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gpu_time_categories["decrease_above_5%"] += 1
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elif "Total time change" in line:
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total_time_match = total_time_pattern.search(line)
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if total_time_match:
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total_time_change = float(total_time_match.group(1))
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if -1 < total_time_change < 1:
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total_time_categories["within_1%"] += 1
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elif 1 <= total_time_change < 5:
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total_time_categories["increase_1_to_5%"] += 1
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elif 5 <= total_time_change < 10:
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total_time_categories["increase_above_5_to_10%"] += 1
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elif total_time_change >= 10:
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total_time_categories["increase_above_10%"] += 1
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elif -5 < total_time_change <= -1:
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total_time_categories["decrease_1_to_5%"] += 1
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elif total_time_change <= -5:
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total_time_categories["decrease_above_5%"] += 1
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elif error_pattern.search(line):
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error_match = error_pattern.search(line)
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if error_match:
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case_name = error_match.group(1)
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error_cases[case_name] += 1
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def print_categories(categories, title):
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total = sum(categories.values())
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print(f"\n{title} Categories:")
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for category, count in categories.items():
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percentage = (count / total * 100) if total > 0 else 0
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print(f"{category}: {count} ({percentage:.2f}%)")
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print_categories(gpu_time_categories, "GPU Time Change")
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print_categories(total_time_categories, "Total Time Change")
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total_errors = sum(error_cases.values())
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error_percentage = (
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(total_errors / gpu_time_lines * 100) if gpu_time_lines > 0 else 0
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)
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unique_errors = len(error_cases)
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print(f"\nError Cases Total: {total_errors}")
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print(f"Error Lines Percentage: {error_percentage:.2f}%")
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print(f"Unique Error OP: {unique_errors}\n")
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for case, count in error_cases.items():
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print(f"OP '{case}': {count} occurrences")
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