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Merge pull request #10697 from MargarettMao/ceval

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combine english and chinese, remove nan
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hxsz1997 2024-04-12 14:37:47 +08:00 committed by GitHub
commit 0d518aab8d
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4 changed files with 61 additions and 75 deletions
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8
python/llm/dev/benchmark/perplexity/make_csv.py
View file

@ -35,9 +35,8 @@ def make_csv(result_dict, output_path=None):
current_date = datetime.datetime.now().strftime("%Y-%m-%d") current_date = datetime.datetime.now().strftime("%Y-%m-%d")
file_name = f'results_{current_date}.csv' file_name = f'results_{current_date}.csv'
full_path = os.path.join(output_path, file_name) if output_path else file_name full_path = os.path.join(output_path, file_name) if output_path else file_name
print('Writing to', full_path)
file_name = full_path file_name = full_path
headers = ["Index", "Model", "Precision", "en", "zh"] headers = ["Index", "Model", "Precision", "ppl_result"]
with open(file_name, mode='w', newline='') as csv_file: with open(file_name, mode='w', newline='') as csv_file:
writer = csv.writer(csv_file) writer = csv.writer(csv_file)
@ -46,10 +45,10 @@ def make_csv(result_dict, output_path=None):
for model, model_results in result_dict.items(): for model, model_results in result_dict.items():
for precision, prec_results in model_results.items(): for precision, prec_results in model_results.items():
row = [index, model, precision] row = [index, model, precision]
for language in headers[3:]: for language in ["en","zh"]:
task_results = prec_results.get(language.lower(), None) task_results = prec_results.get(language.lower(), None)
if task_results is None: if task_results is None:
row.append("") continue
else: else:
result = task_results["results"] result = task_results["results"]
row.append("%.4f" % result) row.append("%.4f" % result)
@ -89,6 +88,7 @@ def main(*args):
output_path = args[2] output_path = args[2]
merged_results = merge_results(input_path) merged_results = merge_results(input_path)
make_csv(merged_results, output_path) make_csv(merged_results, output_path)

6
python/llm/dev/benchmark/perplexity/make_table.py
View file

@ -35,8 +35,8 @@ def make_table(result_dict):
"""Generate table of results.""" """Generate table of results."""
md_writer = MarkdownTableWriter() md_writer = MarkdownTableWriter()
latex_writer = LatexTableWriter() latex_writer = LatexTableWriter()
md_writer.headers = ["Model", "Precision", "en", "zh"] md_writer.headers = ["Model", "Precision", "ppl_result"]
latex_writer.headers = ["Model", "Precision", "en", "zh"] latex_writer.headers = ["Model", "Precision", "ppl_result"]
languages = ["en", "zh"] languages = ["en", "zh"]
values = [] values = []
@ -46,7 +46,7 @@ def make_table(result_dict):
for language in languages: for language in languages:
task_results = prec_results.get(language, None) task_results = prec_results.get(language, None)
if task_results is None: if task_results is None:
value.append("") continue
else: else:
result = task_results["results"] result = task_results["results"]
value.append("%.4f" % result) value.append("%.4f" % result)

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

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

76
python/llm/test/benchmark/perplexity/ppl_csv_to_html.py
View file

@ -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 ''
@ -37,8 +37,8 @@ def nonzero_min(lst):
non_zero_lst = [num for num in lst if num > 0.0] non_zero_lst = [num for num in lst if num > 0.0]
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_en, diff_zh, threshold=5.0): def is_diffs_within_normal_range(diff_ppl_result, threshold=5.0):
return not any(diff < (-threshold) for diff in diff_en + diff_zh if isinstance(diff, float)) 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:
@ -60,9 +60,9 @@ def create_fp16_dict(fp16_path):
model = row['Model'] model = row['Model']
# Formalize the data to have 2 decimal places # Formalize the data to have 2 decimal places
fp16_dict[model] = { fp16_dict[model] = {
'en': "{:.2f}".format(row['en']), 'ppl_result': "{:.2f}".format(row['ppl_result'])
'zh': "{:.2f}".format(row['zh'])
} }
return fp16_dict return fp16_dict
def calculate_percentage_difference(current, fp16): def calculate_percentage_difference(current, fp16):
@ -105,9 +105,8 @@ def main():
diffs_within_normal_range = True diffs_within_normal_range = True
# 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 ['en', 'zh']: latest_csv['ppl_result_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get('ppl_result', 'N/A'))
latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A')) latest_csv['ppl_result_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row['ppl_result'], row['ppl_result_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)
if len(csv_files)>1: if len(csv_files)>1:
if args.baseline_path: if args.baseline_path:
@ -115,13 +114,10 @@ def main():
else: else:
previous_csv = pd.read_csv(csv_files[1], index_col=0) previous_csv = pd.read_csv(csv_files[1], index_col=0)
last_en=['']*len(latest_csv.index) last_ppl_result=['']*len(latest_csv.index)
diff_en=['']*len(latest_csv.index) diff_ppl_result=['']*len(latest_csv.index)
last_zh=['']*len(latest_csv.index)
diff_zh=['']*len(latest_csv.index)
en='en' ppl_result = 'ppl_result'
zh='zh'
csv_dict = {} csv_dict = {}
for csv_file in csv_files: for csv_file in csv_files:
@ -129,17 +125,14 @@ def main():
for current_csv_ind,current_csv_row in current_csv.iterrows(): for current_csv_ind,current_csv_row in current_csv.iterrows():
current_csv_model=current_csv_row['Model'].strip() current_csv_model=current_csv_row['Model'].strip()
current_csv_precision=current_csv_row['Precision'].strip() current_csv_precision=current_csv_row['Precision'].strip()
current_csv_model_en=current_csv_model+'-'+current_csv_precision+'-'+'en' current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result'
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(): 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_en=latest_csv_row[en] latest_ppl_result=latest_csv_row[ppl_result]
latest_zh=latest_csv_row[zh]
in_previous_flag=False in_previous_flag=False
@ -150,39 +143,34 @@ 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_en=previous_csv_row[en] previous_ppl_result=previous_csv_row[ppl_result]
previous_zh=previous_csv_row[zh]
if previous_en > 0.0 or previous_zh > 0.0: if previous_ppl_result > 0.0:
last_en[latest_csv_ind]=previous_en last_ppl_result[latest_csv_ind]=previous_ppl_result
diff_en[latest_csv_ind]=round((latest_en-previous_en)*100/previous_en,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
if not in_previous_flag: if not in_previous_flag:
last_en[latest_csv_ind]=pd.NA last_ppl_result[latest_csv_ind]=pd.NA
diff_en[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)
diffs_within_normal_range = is_diffs_within_normal_range(diff_en, diff_zh, threshold=highlight_threshold) latest_csv.insert(loc=6,column='last_ppl_result',value=last_ppl_result)
latest_csv.insert(loc=7,column='ppl_result_diff_last(%)',value=diff_ppl_result)
subset1=['diff_en(%)','diff_zh(%)']
columns={'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}', diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)
'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'ppl_result_diff_last(%)': '{:.2f}'}
latest_csv.drop('Index', axis=1, inplace=True) 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) 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 ['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(%)'])
else:
columns={'ppl_result': '{:.2f}'}
latest_csv.drop('Index', axis=1, inplace=True)
styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_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([{
@ -194,8 +182,6 @@ def main():
with open(daily_html, 'w') as f: with open(daily_html, 'w') as f:
f.write(html_output) f.write(html_output)
else:
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))