From afa895877cc46d98a5bd6fdccfb8af5f5ac2d4bf Mon Sep 17 00:00:00 2001 From: WeiguangHan Date: Mon, 11 Dec 2023 19:14:57 +0800 Subject: [PATCH] LLM: fix the issue that may generate blank html (#9650) * LLM: fix the issue that may generate blank html * reslove some comments --- python/llm/test/benchmark/csv_to_html.py | 41 ++++++++++++++++-------- 1 file changed, 28 insertions(+), 13 deletions(-) diff --git a/python/llm/test/benchmark/csv_to_html.py b/python/llm/test/benchmark/csv_to_html.py index 3240215a..ace12ffc 100644 --- a/python/llm/test/benchmark/csv_to_html.py +++ b/python/llm/test/benchmark/csv_to_html.py @@ -22,17 +22,20 @@ import argparse import pandas as pd def highlight_vals(val, max=3.0): - if val > max: - return 'background-color: %s' % 'green' - elif val < -max: - return 'background-color: %s' % 'red' + if isinstance(val, float): + if val > max: + return 'background-color: %s' % 'green' + elif val <= -max: + return 'background-color: %s' % 'red' else: return '' def main(): parser = argparse.ArgumentParser(description="convert .csv file to .html file") parser.add_argument("-f", "--folder_path", type=str, dest="folder_path", - help="The directory which stores the .csv file", default="/mnt/disk1/nightly_perf/") + help="The directory which stores the .csv file", default="/mnt/disk1/nightly_perf_gpu/") + parser.add_argument("-t", "--threshold", type=float, dest="threshold", + help="the threshold of highlight values", default=3.0) args = parser.parse_args() csv_files = [] @@ -42,7 +45,10 @@ def main(): csv_files.append(file_path) csv_files.sort(reverse=True) + highlight_threshold=args.threshold + latest_csv = pd.read_csv(csv_files[0], index_col=0) + daily_html=csv_files[0].split(".")[0]+".html" if len(csv_files)>1: previous_csv = pd.read_csv(csv_files[1], index_col=0) @@ -62,6 +68,8 @@ def main(): latest_1st_token_latency=latest_csv_row[latency_1st_token] latest_2_avg_latency=latest_csv_row[latency_2_avg] + in_previous_flag=False + for previous_csv_ind,previous_csv_row in previous_csv.iterrows(): previous_csv_model=previous_csv_row['model'].strip() @@ -75,21 +83,28 @@ def main(): diff1[latest_csv_ind]=round((previous_1st_token_latency-latest_1st_token_latency)*100/previous_1st_token_latency,2) last2[latest_csv_ind]=previous_2_avg_latency diff2[latest_csv_ind]=round((previous_2_avg_latency-latest_2_avg_latency)*100/previous_2_avg_latency,2) + in_previous_flag=True + + if not in_previous_flag: + last1[latest_csv_ind]=pd.NA + diff1[latest_csv_ind]=pd.NA + last2[latest_csv_ind]=pd.NA + diff2[latest_csv_ind]=pd.NA latest_csv.insert(loc=3,column='last1',value=last1) latest_csv.insert(loc=4,column='diff1(%)',value=diff1) latest_csv.insert(loc=5,column='last2',value=last2) latest_csv.insert(loc=6,column='diff2(%)',value=diff2) - daily_html=csv_files[0].split(".")[0]+".html" + subset=['diff1(%)','diff2(%)'] + columns={'1st token avg latency (ms)': '{:.2f}', '2+ avg latency (ms/token)': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}', + 'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}', 'peak mem (GB)': '{:.2f}'} - subset=['diff1(%)','diff2(%)'] - columns={'1st token avg latency (ms)': '{:.2f}', '2+ avg latency (ms/token)': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}', - 'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}'} - - with open(daily_html, 'w') as f: - f.write(latest_csv.style.format(columns).applymap(highlight_vals, subset) - .set_table_attributes("border=1").render()) + with open(daily_html, 'w') as f: + f.write(latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold), subset) + .set_table_attributes("border=1").render()) + else: + latest_csv.to_html(daily_html) if __name__ == "__main__": sys.exit(main()) \ No newline at end of file