ayo/ipex-llm
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LLM: fix the issue that may generate blank html (#9650)

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* LLM: fix the issue that may generate blank html

* reslove some comments
This commit is contained in:
WeiguangHan 2023-12-11 19:14:57 +08:00 committed by GitHub
parent a04a027b4c
commit afa895877c
1 changed files with 28 additions and 13 deletions
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41
python/llm/test/benchmark/csv_to_html.py
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@ -22,17 +22,20 @@ import argparse
import pandas as pd import pandas as pd
def highlight_vals(val, max=3.0): def highlight_vals(val, max=3.0):
if val > max: if isinstance(val, float):
return 'background-color: %s' % 'green' if val > max:
elif val < -max: return 'background-color: %s' % 'green'
return 'background-color: %s' % 'red' elif val <= -max:
return 'background-color: %s' % 'red'
else: else:
return '' return ''
def main(): def main():
parser = argparse.ArgumentParser(description="convert .csv file to .html file") parser = argparse.ArgumentParser(description="convert .csv file to .html file")
parser.add_argument("-f", "--folder_path", type=str, dest="folder_path", 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() args = parser.parse_args()
csv_files = [] csv_files = []
@ -42,7 +45,10 @@ def main():
csv_files.append(file_path) csv_files.append(file_path)
csv_files.sort(reverse=True) csv_files.sort(reverse=True)
highlight_threshold=args.threshold
latest_csv = pd.read_csv(csv_files[0], index_col=0) latest_csv = pd.read_csv(csv_files[0], index_col=0)
daily_html=csv_files[0].split(".")[0]+".html"
if len(csv_files)>1: if len(csv_files)>1:
previous_csv = pd.read_csv(csv_files[1], index_col=0) 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_1st_token_latency=latest_csv_row[latency_1st_token]
latest_2_avg_latency=latest_csv_row[latency_2_avg] 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(): for previous_csv_ind,previous_csv_row in previous_csv.iterrows():
previous_csv_model=previous_csv_row['model'].strip() 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) 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 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) 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=3,column='last1',value=last1)
latest_csv.insert(loc=4,column='diff1(%)',value=diff1) latest_csv.insert(loc=4,column='diff1(%)',value=diff1)
latest_csv.insert(loc=5,column='last2',value=last2) latest_csv.insert(loc=5,column='last2',value=last2)
latest_csv.insert(loc=6,column='diff2(%)',value=diff2) 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(%)'] with open(daily_html, 'w') as f:
columns={'1st token avg latency (ms)': '{:.2f}', '2+ avg latency (ms/token)': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}', f.write(latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold), subset)
'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}'} .set_table_attributes("border=1").render())
else:
with open(daily_html, 'w') as f: latest_csv.to_html(daily_html)
f.write(latest_csv.style.format(columns).applymap(highlight_vals, subset)
.set_table_attributes("border=1").render())
if __name__ == "__main__": if __name__ == "__main__":
sys.exit(main()) sys.exit(main())