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Mark Color Modification

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jenniew 2024-04-12 13:26:36 +08:00
parent b151a9b672
commit bb34c6e325
1 changed files with 97 additions and 85 deletions
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146
python/llm/test/benchmark/perplexity/ppl_csv_to_html.py
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@ -28,7 +28,7 @@ def highlight_vals(val, max=3.0, color1='red', color2='green', color3='yellow',
return 'background-color: %s' % color1 return 'background-color: %s' % color1
elif val <= -max: elif val <= -max:
return 'background-color: %s' % color2 return 'background-color: %s' % color2
elif val != 0.0 and not pd.isna(val) and is_last: elif val != 0.0 and is_last:
return 'background-color: %s' % color3 return 'background-color: %s' % color3
else: else:
return '' return ''
@ -38,7 +38,7 @@ def nonzero_min(lst):
return min(non_zero_lst) if non_zero_lst else None return min(non_zero_lst) if non_zero_lst else None
def is_diffs_within_normal_range(diff_ppl_result, threshold=5.0): def is_diffs_within_normal_range(diff_ppl_result, threshold=5.0):
return not any(diff < (-threshold) for diff in diff_ppl_result + diff_ppl_result if isinstance(diff, float))#diff_en + diff_zh return not any(diff < (-threshold) for diff in diff_ppl_result if isinstance(diff, float))
def add_to_dict(dict, key, value): def add_to_dict(dict, key, value):
if key not in dict: if key not in dict:
@ -58,17 +58,11 @@ def create_fp16_dict(fp16_path):
fp16_dict = {} fp16_dict = {}
for _, row in fp16_df.iterrows(): for _, row in fp16_df.iterrows():
model = row['Model'] model = row['Model']
# print("I want to test the ppl_result row", row['ppl_result'])
# Formalize the data to have 2 decimal places
# fp16_dict[model] = {
# 'en': "{:.2f}".format(row['en']),
# 'zh': "{:.2f}".format(row['zh'])
# }
fp16_dict[model] = { fp16_dict[model] = {
'ppl_result': "{:.2f}".format(row['ppl_result']) 'ppl_result': "{:.2f}".format(row['ppl_result'])
# 'zh': "{:.2f}".format(row['zh'])
} }
# print(fp16_dict[model])
return fp16_dict return fp16_dict
def calculate_percentage_difference(current, fp16): def calculate_percentage_difference(current, fp16):
@ -112,9 +106,8 @@ def main():
# Add display of FP16 values for each model and add percentage difference column # Add display of FP16 values for each model and add percentage difference column
for task in ['ppl_result']:#['en', 'zh']: task = 'ppl_result'
latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A')) latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A'))
# print("This is the stuff I want to check",latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A') == 'N/A'))
latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1) latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1)
print(csv_files) print(csv_files)
@ -127,13 +120,8 @@ def main():
last_ppl_result=['']*len(latest_csv.index) last_ppl_result=['']*len(latest_csv.index)
diff_ppl_result=['']*len(latest_csv.index) diff_ppl_result=['']*len(latest_csv.index)
# last_en=['']*len(latest_csv.index)
# diff_en=['']*len(latest_csv.index)
# last_zh=['']*len(latest_csv.index)
# diff_zh=['']*len(latest_csv.index)
ppl_result = 'ppl_result'#en='en' ppl_result = 'ppl_result'
#zh='zh'
csv_dict = {} csv_dict = {}
for csv_file in csv_files: for csv_file in csv_files:
@ -143,20 +131,15 @@ def main():
current_csv_precision=current_csv_row['Precision'].strip() current_csv_precision=current_csv_row['Precision'].strip()
current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result' current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result'
# current_csv_model_en=current_csv_model+'-'+current_csv_precision+'-'+'en'
# current_csv_model_zh=current_csv_model+'-'+current_csv_precision+'-'+'zh'
add_to_dict(csv_dict, current_csv_model_ppl_result, current_csv_row[ppl_result]) add_to_dict(csv_dict, current_csv_model_ppl_result, current_csv_row[ppl_result])
# add_to_dict(csv_dict, current_csv_model_en, current_csv_row[en])
# add_to_dict(csv_dict, current_csv_model_zh, current_csv_row[zh])
for latest_csv_ind,latest_csv_row in latest_csv.iterrows(): for latest_csv_ind,latest_csv_row in latest_csv.iterrows():
latest_csv_model=latest_csv_row['Model'].strip() latest_csv_model=latest_csv_row['Model'].strip()
latest_csv_precision=latest_csv_row['Precision'].strip() latest_csv_precision=latest_csv_row['Precision'].strip()
latest_ppl_result=latest_csv_row[ppl_result]#latest_en=latest_csv_row[en] latest_ppl_result=latest_csv_row[ppl_result]
print("This is the latest_ppl_result",latest_ppl_result) print("This is the latest_ppl_result",latest_ppl_result)
# latest_zh=latest_csv_row[zh]
in_previous_flag=False in_previous_flag=False
@ -167,67 +150,40 @@ def main():
if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision: if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision:
previous_ppl_result=previous_csv_row[ppl_result] #previous_en=previous_csv_row[en] previous_ppl_result=previous_csv_row[ppl_result]
print("This is the previous_ppl_result", previous_ppl_result) print("This is the previous_ppl_result", previous_ppl_result)
# previous_zh=previous_csv_row[zh]
if previous_ppl_result > 0.0:# or previous_zh > 0.0: if previous_ppl_result > 0.0:
last_ppl_result[latest_csv_ind]=previous_ppl_result last_ppl_result[latest_csv_ind]=previous_ppl_result
diff_ppl_result[latest_csv_ind]=round((latest_ppl_result-previous_ppl_result)*100/previous_ppl_result,2) diff_ppl_result[latest_csv_ind]=round((latest_ppl_result-previous_ppl_result)*100/previous_ppl_result,2)
# last_zh[latest_csv_ind]=previous_zh
# diff_zh[latest_csv_ind]=round((latest_zh-previous_zh)*100/previous_zh,2)
in_previous_flag=True in_previous_flag=True
# last_en[latest_csv_ind]=previous_en
# diff_en[latest_csv_ind]=round((latest_en-previous_en)*100/previous_en,2)
# last_zh[latest_csv_ind]=previous_zh
# diff_zh[latest_csv_ind]=round((latest_zh-previous_zh)*100/previous_zh,2)
# in_previous_flag=True
if not in_previous_flag: if not in_previous_flag:
last_ppl_result[latest_csv_ind]=pd.NA last_ppl_result[latest_csv_ind]=pd.NA
diff_ppl_result[latest_csv_ind]=pd.NA diff_ppl_result[latest_csv_ind]=pd.NA
# last_zh[latest_csv_ind]=pd.NA
# diff_zh[latest_csv_ind]=pd.NA
latest_csv.insert(loc=6,column='last_ppl_result',value=last_ppl_result) latest_csv.insert(loc=6,column='last_ppl_result',value=last_ppl_result)
latest_csv.insert(loc=7,column='last_diff_ppl_result(%)',value=diff_ppl_result) latest_csv.insert(loc=7,column='ppl_result_diff_last(%)',value=diff_ppl_result)
# latest_csv.insert(loc=11,column='last_zh',value=last_zh)
# latest_csv.insert(loc=12,column='diff_zh(%)',value=diff_zh)
diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)#en, diff_zh, threshold=highlight_threshold) diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)
subset1=['last_diff_ppl_result(%)']#['diff_en(%)','diff_zh(%)'] subset1='ppl_result_diff_last(%)'
columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'last_diff_ppl_result(%)': '{:.2f}'} # columns will be different: columns_sec={'ppl_result': '{:.2f}'}
#{'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}', columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'ppl_result_diff_last(%)': '{:.2f}'}
#'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'} # This is the same
latest_csv.drop('Index', axis=1, inplace=True) latest_csv.drop('Index', axis=1, inplace=True)
# subset is different
styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1) styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=True), subset=['ppl_result_diff_last(%)'])
for task in ['ppl_result']:#['en', 'zh']: styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)'])
styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
# add css style to restrict width and wrap text
styled_df.set_table_styles([{
'selector': 'th, td',
'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
}], overwrite=False)
html_output = styled_df.set_table_attributes("border=1").to_html()
with open(daily_html, 'w') as f:
f.write(html_output)
else: else:
columns={'ppl_result': '{:.2f}'}
latest_csv.drop('Index', axis=1, inplace=True) latest_csv.drop('Index', axis=1, inplace=True)
columns_sec={'ppl_result': '{:.2f}'} styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)'])
#{'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}',
#'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
styled_df = latest_csv.style.format(columns_sec).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True))#, subset=subset1)
for task in ['ppl_result']:#['en', 'zh']:
styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
# add css style to restrict width and wrap text # add css style to restrict width and wrap text
styled_df.set_table_styles([{ styled_df.set_table_styles([{
@ -239,7 +195,6 @@ def main():
with open(daily_html, 'w') as f: with open(daily_html, 'w') as f:
f.write(html_output) f.write(html_output)
# latest_csv.to_html(daily_html)
if args.baseline_path and not diffs_within_normal_range: if args.baseline_path and not diffs_within_normal_range:
print("The diffs are outside the normal range: %" + str(highlight_threshold)) print("The diffs are outside the normal range: %" + str(highlight_threshold))
@ -248,3 +203,60 @@ def main():
if __name__ == "__main__": if __name__ == "__main__":
sys.exit(main()) sys.exit(main())
# styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1)
# # These are the same:
# task = 'ppl_result'
# styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
# # add css style to restrict width and wrap text
# styled_df.set_table_styles([{
# 'selector': 'th, td',
# 'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
# }], overwrite=False)
# html_output = styled_df.set_table_attributes("border=1").to_html()
# with open(daily_html, 'w') as f:
# f.write(html_output)
# else:
# latest_csv.drop('Index', axis=1, inplace=True)
# columns_sec={'ppl_result': '{:.2f}'}
# styled_df = latest_csv.style.format(columns_sec).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True))#, subset=subset1)
# task = 'ppl_result'
# styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
# # add css style to restrict width and wrap text
# styled_df.set_table_styles([{
# 'selector': 'th, td',
# 'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
# }], overwrite=False)
# html_output = styled_df.set_table_attributes("border=1").to_html()
# with open(daily_html, 'w') as f:
# f.write(html_output)
# if args.baseline_path and not diffs_within_normal_range:
# print("The diffs are outside the normal range: %" + str(highlight_threshold))
# return 1
# return 0
# if __name__ == "__main__":
# sys.exit(main())