ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

edit csv_to_html to combine en & zh

Browse source
This commit is contained in:
jenniew 2024-04-11 17:35:36 +08:00
parent 591bae092c
commit b151a9b672
2 changed files with 92 additions and 48 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

16
python/llm/test/benchmark/perplexity/fp16.csv
View file

@ -1,8 +1,8 @@
Index,Model,Precision,en,zh
0,Llama-2-7b-chat-hf,fp16,4.7019,
1,chatglm2-6b,fp16,,22.321
2,chatglm3-6b,fp16,,30.1281
3,Baichuan2-7B-Chat,fp16,,10.7676
4,mpt-7b-chat,fp16,5.7882,
5,falcon-7b-instruct-with-patch,fp16,5.2532,
6,mistral-7b-v0.1,fp16,3.6597,
Index,Model,Precision,ppl_result
0,Llama-2-7b-chat-hf,fp16,4.7019
1,chatglm2-6b,fp16,22.321
2,chatglm3-6b,fp16,30.1281
3,Baichuan2-7B-Chat,fp16,10.7676
4,mpt-7b-chat,fp16,5.7882
5,falcon-7b-instruct-with-patch,fp16,5.2532
6,Mistral-7B-v0.1,fp16,3.6597

1 Index Model Precision en ppl_result zh
2 0 Llama-2-7b-chat-hf fp16 4.7019 4.7019
3 1 chatglm2-6b fp16 22.321 22.321
4 2 chatglm3-6b fp16 30.1281 30.1281
5 3 Baichuan2-7B-Chat fp16 10.7676 10.7676
6 4 mpt-7b-chat fp16 5.7882 5.7882
7 5 falcon-7b-instruct-with-patch fp16 5.2532 5.2532
8 6 mistral-7b-v0.1 Mistral-7B-v0.1 fp16 3.6597 3.6597

124
python/llm/test/benchmark/perplexity/ppl_csv_to_html.py
View file

@ -37,8 +37,8 @@ def nonzero_min(lst):
non_zero_lst = [num for num in lst if num > 0.0]
return min(non_zero_lst) if non_zero_lst else None
def is_diffs_within_normal_range(diff_en, diff_zh, threshold=5.0):
return not any(diff < (-threshold) for diff in diff_en + diff_zh if isinstance(diff, float))
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
def add_to_dict(dict, key, value):
if key not in dict:
@ -58,11 +58,17 @@ def create_fp16_dict(fp16_path):
fp16_dict = {}
for _, row in fp16_df.iterrows():
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] = {
'en': "{:.2f}".format(row['en']),
'zh': "{:.2f}".format(row['zh'])
'ppl_result': "{:.2f}".format(row['ppl_result'])
# 'zh': "{:.2f}".format(row['zh'])
}
# print(fp16_dict[model])
return fp16_dict
def calculate_percentage_difference(current, fp16):
@ -104,24 +110,30 @@ def main():
diffs_within_normal_range = True
# Add display of FP16 values for each model and add percentage difference column
for task in ['en', 'zh']:
# Add display of FP16 values for each model and add percentage difference column
for task in ['ppl_result']:#['en', 'zh']:
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)
print(csv_files)
if len(csv_files)>1:
if args.baseline_path:
previous_csv = pd.read_csv(args.baseline_path, index_col=0)
else:
previous_csv = pd.read_csv(csv_files[1], index_col=0)
last_en=['']*len(latest_csv.index)
diff_en=['']*len(latest_csv.index)
last_zh=['']*len(latest_csv.index)
diff_zh=['']*len(latest_csv.index)
last_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)
en='en'
zh='zh'
ppl_result = 'ppl_result'#en='en'
#zh='zh'
csv_dict = {}
for csv_file in csv_files:
@ -129,17 +141,22 @@ def main():
for current_csv_ind,current_csv_row in current_csv.iterrows():
current_csv_model=current_csv_row['Model'].strip()
current_csv_precision=current_csv_row['Precision'].strip()
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_en, current_csv_row[en])
add_to_dict(csv_dict, current_csv_model_zh, current_csv_row[zh])
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_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():
latest_csv_model=latest_csv_row['Model'].strip()
latest_csv_precision=latest_csv_row['Precision'].strip()
latest_en=latest_csv_row[en]
latest_zh=latest_csv_row[zh]
latest_ppl_result=latest_csv_row[ppl_result]#latest_en=latest_csv_row[en]
print("This is the latest_ppl_result",latest_ppl_result)
# latest_zh=latest_csv_row[zh]
in_previous_flag=False
@ -150,40 +167,48 @@ def main():
if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision:
previous_en=previous_csv_row[en]
previous_zh=previous_csv_row[zh]
if previous_en > 0.0 or previous_zh > 0.0:
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)
previous_ppl_result=previous_csv_row[ppl_result] #previous_en=previous_csv_row[en]
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:
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)
# 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
# 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:
last_en[latest_csv_ind]=pd.NA
diff_en[latest_csv_ind]=pd.NA
last_zh[latest_csv_ind]=pd.NA
diff_zh[latest_csv_ind]=pd.NA
last_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=9,column='last_en',value=last_en)
latest_csv.insert(loc=10,column='diff_en(%)',value=diff_en)
latest_csv.insert(loc=11,column='last_zh',value=last_zh)
latest_csv.insert(loc=12,column='diff_zh(%)',value=diff_zh)
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=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_en, diff_zh, threshold=highlight_threshold)
diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)#en, diff_zh, threshold=highlight_threshold)
subset1=['diff_en(%)','diff_zh(%)']
subset1=['last_diff_ppl_result(%)']#['diff_en(%)','diff_zh(%)']
columns={'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}',
'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'last_diff_ppl_result(%)': '{:.2f}'}
#{'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}',
#'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
latest_csv.drop('Index', axis=1, inplace=True)
styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1)
for task in ['en', 'zh']:
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
styled_df.set_table_styles([{
'selector': 'th, td',
@ -195,7 +220,26 @@ def main():
with open(daily_html, 'w') as f:
f.write(html_output)
else:
latest_csv.to_html(daily_html)
latest_csv.drop('Index', axis=1, inplace=True)
columns_sec={'ppl_result': '{:.2f}'}
#{'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
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)
# latest_csv.to_html(daily_html)
if args.baseline_path and not diffs_within_normal_range:
print("The diffs are outside the normal range: %" + str(highlight_threshold))