Modify html table style and add fp16.csv in harness (#10169)
* Specify the version of pandas in harness evaluation workflow * Specify the version of pandas in harness evaluation workflow * Modify html table style and add fp16.csv in harness * Modify comments
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yb-peng
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6c09aed90d
commit
e31210ba00
3 changed files with 78 additions and 194 deletions
6
python/llm/dev/benchmark/harness/fp16.csv
Normal file
6
python/llm/dev/benchmark/harness/fp16.csv
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@ -0,0 +1,6 @@
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Index,Model,Precision,Arc,TruthfulQA,Winogrande
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0,falcon-7b-instruct-with-patch,fp16,46.16,44.08,67.96
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1,Llama2-7b-guanaco-dolphin-500,fp16,56.74,46.96,74.27
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2,Baichuan2-7B-Chat-LLaMAfied,fp16,52.47,48.04,69.14
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3,Mistral-7B-v0.1,fp16,59.98,42.15,78.37
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4,mpt-7b-chat,fp16,46.50,40.16,68.43
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@ -34,8 +34,8 @@ def nonzero_min(lst):
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non_zero_lst = [num for num in lst if num > 0.0]
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return min(non_zero_lst) if non_zero_lst else None
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def is_diffs_within_normal_range(diff1, diff2, diff3, threshold=5.0):
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return not any(diff < (-threshold) for diff in diff1 + diff2 + diff3 if isinstance(diff, float))
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def is_diffs_within_normal_range(diff_Arc, diff_TruthfulQA, diff_Winogrande, threshold=5.0):
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return not any(diff < (-threshold) for diff in diff_Arc + diff_TruthfulQA + diff_Winogrande if isinstance(diff, float))
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def add_to_dict(dict, key, value):
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if key not in dict:
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@ -50,6 +50,26 @@ def best_in_dict(dict, key, value):
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return value
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return value
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def create_fp16_dict(fp16_path):
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fp16_df = pd.read_csv(fp16_path)
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fp16_dict = {}
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for _, row in fp16_df.iterrows():
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model = row['Model']
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# Formalize the data to have 2 decimal places
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fp16_dict[model] = {
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'Arc': "{:.2f}".format(row['Arc']),
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'TruthfulQA': "{:.2f}".format(row['TruthfulQA']),
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'Winogrande': "{:.2f}".format(row['Winogrande'])
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}
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return fp16_dict
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def calculate_percentage_difference(current, fp16):
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if fp16 != 'N/A' and current != 'N/A' and float(fp16) != 0:
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return (float(current) - float(fp16)) / float(fp16) * 100
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else:
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return 'N/A'
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def main():
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parser = argparse.ArgumentParser(description="convert .csv file to .html file")
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parser.add_argument("-f", "--folder_path", type=str, dest="folder_path",
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@ -60,6 +80,8 @@ def main():
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help="the baseline path which stores the baseline.csv file")
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args = parser.parse_args()
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fp16_dict = create_fp16_dict('fp16.csv')
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csv_files = []
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for file_name in os.listdir(args.folder_path):
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file_path = os.path.join(args.folder_path, file_name)
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@ -72,27 +94,29 @@ def main():
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latest_csv = pd.read_csv(csv_files[0], index_col=0)
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daily_html=csv_files[0].split(".")[0]+".html"
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# Reset index
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latest_csv.reset_index(inplace=True)
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diffs_within_normal_range = True
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# Add display of FP16 values for each model and add percentage difference column
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for task in ['Arc', 'TruthfulQA', 'Winogrande']:
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latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A'))
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latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1)
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if len(csv_files)>1:
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if args.baseline_path:
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previous_csv = pd.read_csv(args.baseline_path, index_col=0)
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else:
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previous_csv = pd.read_csv(csv_files[1], index_col=0)
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last1=['']*len(latest_csv.index)
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diff1=['']*len(latest_csv.index)
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last2=['']*len(latest_csv.index)
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diff2=['']*len(latest_csv.index)
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last3=['']*len(latest_csv.index)
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diff3=['']*len(latest_csv.index)
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last_Arc=['']*len(latest_csv.index)
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diff_Arc=['']*len(latest_csv.index)
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last_TruthfulQA=['']*len(latest_csv.index)
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diff_TruthfulQA=['']*len(latest_csv.index)
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last_Winogrande=['']*len(latest_csv.index)
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diff_Winogrande=['']*len(latest_csv.index)
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best_last1=['']*len(latest_csv.index)
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best_diff1=['']*len(latest_csv.index)
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best_last2=['']*len(latest_csv.index)
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best_diff2=['']*len(latest_csv.index)
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best_last3=['']*len(latest_csv.index)
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best_diff3=['']*len(latest_csv.index)
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Arc='Arc'
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TruthfulQA='TruthfulQA'
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@ -119,21 +143,6 @@ def main():
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latest_truthfulqa=latest_csv_row[TruthfulQA]
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latest_winogrande=latest_csv_row[Winogrande]
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key1=latest_csv_model+'-'+latest_csv_precision+'-'+'Arc'
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key2=latest_csv_model+'-'+latest_csv_precision+'-'+'TruthfulQA'
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key3=latest_csv_model+'-'+latest_csv_precision+'-'+'Winogrande'
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best_last1_value=best_in_dict(csv_dict, key1, latest_arc)
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best_last2_value=best_in_dict(csv_dict, key2, latest_truthfulqa)
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best_last3_value=best_in_dict(csv_dict, key3, latest_winogrande)
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best_last1[latest_csv_ind]=best_last1_value
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best_diff1[latest_csv_ind]=round((best_last1_value-latest_arc)*100/best_last1_value,2)
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best_last2[latest_csv_ind]=best_last2_value
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best_diff2[latest_csv_ind]=round((best_last2_value-latest_truthfulqa)*100/best_last2_value,2)
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best_last3[latest_csv_ind]=best_last3_value
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best_diff3[latest_csv_ind]=round((best_last3_value-latest_winogrande)*100/best_last3_value,2)
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in_previous_flag=False
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for previous_csv_ind,previous_csv_row in previous_csv.iterrows():
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@ -147,48 +156,50 @@ def main():
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previous_truthfulqa=previous_csv_row[TruthfulQA]
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previous_winogrande=previous_csv_row[Winogrande]
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if previous_arc > 0.0 and previous_truthfulqa > 0.0 and previous_winogrande > 0.0:
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last1[latest_csv_ind]=previous_arc
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diff1[latest_csv_ind]=round((previous_arc-latest_arc)*100/previous_arc,2)
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last2[latest_csv_ind]=previous_truthfulqa
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diff2[latest_csv_ind]=round((previous_truthfulqa-latest_truthfulqa)*100/previous_truthfulqa,2)
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last3[latest_csv_ind]=previous_winogrande
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diff3[latest_csv_ind]=round((previous_winogrande-latest_winogrande)*100/previous_winogrande,2)
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last_Arc[latest_csv_ind]=previous_arc
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diff_Arc[latest_csv_ind]=round((previous_arc-latest_arc)*100/previous_arc,2)
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last_TruthfulQA[latest_csv_ind]=previous_truthfulqa
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diff_TruthfulQA[latest_csv_ind]=round((previous_truthfulqa-latest_truthfulqa)*100/previous_truthfulqa,2)
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last_Winogrande[latest_csv_ind]=previous_winogrande
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diff_Winogrande[latest_csv_ind]=round((previous_winogrande-latest_winogrande)*100/previous_winogrande,2)
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in_previous_flag=True
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if not in_previous_flag:
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last1[latest_csv_ind]=pd.NA
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diff1[latest_csv_ind]=pd.NA
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last2[latest_csv_ind]=pd.NA
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diff2[latest_csv_ind]=pd.NA
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last3[latest_csv_ind]=pd.NA
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diff3[latest_csv_ind]=pd.NA
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last_Arc[latest_csv_ind]=pd.NA
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diff_Arc[latest_csv_ind]=pd.NA
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last_TruthfulQA[latest_csv_ind]=pd.NA
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diff_TruthfulQA[latest_csv_ind]=pd.NA
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last_Winogrande[latest_csv_ind]=pd.NA
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diff_Winogrande[latest_csv_ind]=pd.NA
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latest_csv.insert(loc=5,column='last1',value=last1)
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latest_csv.insert(loc=6,column='diff1(%)',value=diff1)
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latest_csv.insert(loc=7,column='last2',value=last2)
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latest_csv.insert(loc=8,column='diff2(%)',value=diff2)
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latest_csv.insert(loc=9,column='last3',value=last3)
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latest_csv.insert(loc=10,column='diff3(%)',value=diff3)
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latest_csv.insert(loc=5,column='last_Arc',value=last_Arc)
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latest_csv.insert(loc=6,column='diff_Arc(%)',value=diff_Arc)
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latest_csv.insert(loc=7,column='last_TruthfulQA',value=last_TruthfulQA)
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latest_csv.insert(loc=8,column='diff_TruthfulQA(%)',value=diff_TruthfulQA)
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latest_csv.insert(loc=9,column='last_Winogrande',value=last_Winogrande)
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latest_csv.insert(loc=10,column='diff_Winogrande(%)',value=diff_Winogrande)
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latest_csv.insert(loc=11,column='best 1',value=best_last1)
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latest_csv.insert(loc=12,column='best diff1(%)',value=best_diff1)
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latest_csv.insert(loc=13,column='best 2',value=best_last2)
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latest_csv.insert(loc=14,column='best diff2(%)',value=best_diff2)
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latest_csv.insert(loc=15,column='best 3',value=best_last3)
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latest_csv.insert(loc=16,column='best diff3(%)',value=best_diff3)
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diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, diff3, threshold=highlight_threshold)
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diffs_within_normal_range = is_diffs_within_normal_range(diff_Arc, diff_TruthfulQA, diff_Winogrande, threshold=highlight_threshold)
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subset1=['diff1(%)','diff2(%)','diff3(%)' ]
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subset2=['best diff1(%)','best diff2(%)','best diff3(%)']
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subset1=['diff_Arc(%)','diff_TruthfulQA(%)','diff_Winogrande(%)' ]
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columns={'Arc': '{:.2f}', 'TruthfulQA': '{:.2f}', 'Winogrande': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}',
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'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'last3': '{:.2f}', 'diff3(%)': '{:.2f}',
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'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}', 'best 3': '{:.2f}', 'best diff3(%)': '{:.2f}'}
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columns={'Arc': '{:.2f}', 'TruthfulQA': '{:.2f}', 'Winogrande': '{:.2f}', 'last_Arc': '{:.2f}', 'diff_Arc(%)': '{:.2f}',
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'last_TruthfulQA': '{:.2f}', 'diff_TruthfulQA(%)': '{:.2f}', 'last_Winogrande': '{:.2f}', 'diff_Winogrande(%)': '{:.2f}'}
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latest_csv.drop('Index', axis=1, inplace=True)
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, color1='red', color2='green'), subset=subset1)
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=3.0, color1='yellow'), subset=subset2)
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html_output = styled_df.set_table_attributes("border=1").render()
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for task in ['Arc', 'TruthfulQA', 'Winogrande']:
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, color1='red', color2='green'), subset=[f'{task}_diff_FP16(%)'])
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# add css style to restrict width and wrap text
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styled_df.set_table_styles([{
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'selector': 'th, td',
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'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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}], overwrite=False)
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html_output = styled_df.set_table_attributes("border=1").to_html()
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with open(daily_html, 'w') as f:
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f.write(html_output)
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@ -1,133 +0,0 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Usage:
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python make_table_results.py <input_dir>
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"""
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import logging
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from pytablewriter import MarkdownTableWriter, LatexTableWriter
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import os
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import json
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import sys
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import csv
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import datetime
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from harness_to_leaderboard import task_to_metric
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def make_table(result_dict):
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"""Generate table of results."""
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md_writer = MarkdownTableWriter()
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latex_writer = LatexTableWriter()
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md_writer.headers = ["Model", "Precision", "Arc", "Hellaswag", "MMLU", "TruthfulQA","Winogrande", "GSM8K"]
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latex_writer.headers = ["Model", "Precision", "Arc", "Hellaswag", "MMLU", "TruthfulQA","Winogrande", "GSM8K"]
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tasks = ["arc", "hellaswag", "mmlu", "truthfulqa", "winogrande", "gsm8k"]
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values = []
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for model, model_results in result_dict.items():
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for precision, prec_results in model_results.items():
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value = [model, precision]
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for task in tasks:
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task_results = prec_results.get(task, None)
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if task_results is None:
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value.append("")
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else:
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m = task_to_metric[task]
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results = task_results["results"]
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if len(results) > 1:
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result = results[task]
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else:
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result = list(results.values())[0]
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value.append("%.2f" % (result[m] * 100))
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values.append(value)
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model = ""
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precision = ""
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md_writer.value_matrix = values
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latex_writer.value_matrix = values
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# todo: make latex table look good
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# print(latex_writer.dumps())
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return md_writer.dumps()
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def make_csv(result_dict, output_path=None):
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current_date = datetime.datetime.now().strftime("%Y-%m-%d")
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file_name = f'results_{current_date}.csv'
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full_path = os.path.join(output_path, file_name) if output_path else file_name
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print('Writing to', full_path)
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file_name = full_path
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headers = ["Index", "Model", "Precision", "Arc", "TruthfulQA", "Winogrande"]
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with open(file_name, mode='w', newline='') as csv_file:
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writer = csv.writer(csv_file)
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writer.writerow(headers)
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index = 0
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for model, model_results in result_dict.items():
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for precision, prec_results in model_results.items():
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row = [index, model, precision]
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for task in headers[3:]:
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task_results = prec_results.get(task.lower(), None)
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if task_results is None:
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row.append("")
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else:
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m = task_to_metric[task.lower()]
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results = task_results["results"]
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result = list(results.values())[0] if len(results) == 1 else results[task.lower()]
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row.append("%.2f" % (result[m] * 100))
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writer.writerow(row)
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index += 1
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def merge_results(args):
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# loop dirs and subdirs in results dir
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# for each dir, load json files
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path = args[1]
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print('Read from', path)
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merged_results = dict()
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for dirpath, dirnames, filenames in os.walk(sys.argv[1]):
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# skip dirs without files
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if not filenames:
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continue
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for filename in sorted([f for f in filenames if f.endswith("result.json")]):
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path = os.path.join(dirpath, filename)
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model, device, precision, task = dirpath.split('/')[-4:]
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with open(path, "r") as f:
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result_dict = json.load(f)
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if model not in merged_results:
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merged_results[model] = dict()
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if precision not in merged_results[model]:
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merged_results[model][precision] = dict()
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merged_results[model][precision][task] = result_dict
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# args[2] is the output path
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make_csv(merged_results, args[2])
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return merged_results
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def main(*args):
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merged_results = merge_results(args)
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print(make_table(merged_results))
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if __name__ == "__main__":
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# when running from the harness, the first argument is the script name
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# you must name the second argument and the third argument to be the input_dir and output_dir
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main(*sys.argv)
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