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LLM: modify the script to generate html results more accurately (#9445)

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* modify the script to generate html results more accurately

* resolve some comments

* revert some codes
This commit is contained in:
WeiguangHan 2023-11-16 19:50:23 +08:00 committed by GitHub
parent c0ef70df02
commit bc06bec90e
1 changed files with 34 additions and 13 deletions
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47
python/llm/test/benchmark/csv_to_html.py
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@ -34,26 +34,47 @@ def main():
csv_files.append(file_path) csv_files.append(file_path)
csv_files.sort(reverse=True) csv_files.sort(reverse=True)
data1 = pd.read_csv(csv_files[0], index_col=0) latest_csv = pd.read_csv(csv_files[0], index_col=0)
if len(csv_files)>1: if len(csv_files)>1:
data2 = pd.read_csv(csv_files[1], index_col=0) previous_csv = pd.read_csv(csv_files[1], index_col=0)
origin_column_1='1st token avg latency (ms)' last1=['']*len(latest_csv.index)
origin_column_2='2+ avg latency (ms/token)' diff1=['']*len(latest_csv.index)
last2=['']*len(latest_csv.index)
diff2=['']*len(latest_csv.index)
added_column_1='last1' latency_1st_token='1st token avg latency (ms)'
added_column_2='diff1(%)' latency_2_avg='2+ avg latency (ms/token)'
added_column_3='last2'
added_column_4='diff2(%)'
data1.insert(loc=3,column=added_column_1,value=data2[origin_column_1]) for latest_csv_ind,latest_csv_row in latest_csv.iterrows():
data1.insert(loc=4,column=added_column_2,value=round((data2[origin_column_1]-data1[origin_column_1])*100/data2[origin_column_1],2))
data1.insert(loc=5,column=added_column_3,value=data2[origin_column_2]) latest_csv_model=latest_csv_row['model'].strip()
data1.insert(loc=6,column=added_column_4,value=round((data2[origin_column_2]-data1[origin_column_2])*100/data2[origin_column_2],2)) latest_csv_input_output_pairs=latest_csv_row['input/output tokens'].strip()
latest_1st_token_latency=latest_csv_row[latency_1st_token]
latest_2_avg_latency=latest_csv_row[latency_2_avg]
for previous_csv_ind,previous_csv_row in previous_csv.iterrows():
previous_csv_model=previous_csv_row['model'].strip()
previous_csv_input_output_pairs=previous_csv_row['input/output tokens'].strip()
if latest_csv_model==previous_csv_model and latest_csv_input_output_pairs==previous_csv_input_output_pairs:
previous_1st_token_latency=previous_csv_row[latency_1st_token]
previous_2_avg_latency=previous_csv_row[latency_2_avg]
last1[latest_csv_ind]=previous_1st_token_latency
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)
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" daily_html=csv_files[0].split(".")[0]+".html"
data1.to_html(daily_html) latest_csv.to_html(daily_html)
if __name__ == "__main__": if __name__ == "__main__":
sys.exit(main()) sys.exit(main())