ayo/ipex-llm
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LLM: add peak diff (#9789)

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* add peak diff

* small fix

* revert yml file
This commit is contained in:
WeiguangHan 2024-01-03 18:18:19 +08:00 committed by GitHub
parent f4eb5da42d
commit 9a14465560
1 changed files with 49 additions and 3 deletions
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52
python/llm/test/benchmark/csv_to_html.py
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@ -33,6 +33,19 @@ def highlight_vals(val, max=3.0):
def is_diffs_within_normal_range(diff1, diff2, threshold=5.0):
return not any(diff < (-threshold) for diff in diff1 + diff2 if isinstance(diff, float))
def add_to_dict(dict, key, value):
if key not in dict:
dict[key] = []
dict[key].append(value)
def best_in_dict(dict, key, value):
if key in dict:
best_value=min(dict[key])
if best_value < value:
return best_value
return value
return value
def main():
parser = argparse.ArgumentParser(description="convert .csv file to .html file")
parser.add_argument("-f", "--folder_path", type=str, dest="folder_path",
@ -68,9 +81,25 @@ def main():
last2=['']*len(latest_csv.index)
diff2=['']*len(latest_csv.index)
best_last1=['']*len(latest_csv.index)
best_diff1=['']*len(latest_csv.index)
best_last2=['']*len(latest_csv.index)
best_diff2=['']*len(latest_csv.index)
latency_1st_token='1st token avg latency (ms)'
latency_2_avg='2+ avg latency (ms/token)'
csv_dict = {}
for csv_file in csv_files:
current_csv = pd.read_csv(csv_file, index_col=0)
for current_csv_ind,current_csv_row in current_csv.iterrows():
current_csv_model=current_csv_row['model'].strip()
current_csv_input_output_pairs=current_csv_row['input/output tokens'].strip()
current_csv_model_input_1st=current_csv_model+'-'+current_csv_input_output_pairs+'-'+'1st'
current_csv_model_input_2nd=current_csv_model+'-'+current_csv_input_output_pairs+'-'+'2nd'
add_to_dict(csv_dict, current_csv_model_input_1st, current_csv_row[latency_1st_token])
add_to_dict(csv_dict, current_csv_model_input_2nd, current_csv_row[latency_2_avg])
for latest_csv_ind,latest_csv_row in latest_csv.iterrows():
latest_csv_model=latest_csv_row['model'].strip()
@ -78,6 +107,17 @@ def main():
latest_1st_token_latency=latest_csv_row[latency_1st_token]
latest_2_avg_latency=latest_csv_row[latency_2_avg]
key1=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+'1st'
key2=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+'2nd'
best_last1_value=best_in_dict(csv_dict, key1, latest_1st_token_latency)
best_last2_value=best_in_dict(csv_dict, key2, latest_2_avg_latency)
best_last1[latest_csv_ind]=best_last1_value
best_diff1[latest_csv_ind]=round((best_last1_value-latest_1st_token_latency)*100/best_last1_value,2)
best_last2[latest_csv_ind]=best_last2_value
best_diff2[latest_csv_ind]=round((best_last2_value-latest_2_avg_latency)*100/best_last2_value,2)
in_previous_flag=False
for previous_csv_ind,previous_csv_row in previous_csv.iterrows():
@ -106,11 +146,17 @@ def main():
latest_csv.insert(loc=5,column='last2',value=last2)
latest_csv.insert(loc=6,column='diff2(%)',value=diff2)
diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, highlight_threshold)
latest_csv.insert(loc=7,column='best 1',value=best_last1)
latest_csv.insert(loc=8,column='best diff1(%)',value=best_diff1)
latest_csv.insert(loc=9,column='best 2',value=best_last2)
latest_csv.insert(loc=10,column='best diff2(%)',value=best_diff2)
subset=['diff1(%)','diff2(%)']
diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, threshold=highlight_threshold)
subset=['diff1(%)','diff2(%)','best diff1(%)','best 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}'}
'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}', 'peak mem (GB)': '{:.2f}',
'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}'}
with open(daily_html, 'w') as f:
f.write(latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold), subset)