# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Python program to convert CSV to HTML Table import os import sys import argparse import pandas as pd def highlight_vals(val, max=3.0, color1='red', color2='green'): if isinstance(val, float): if val > max: return 'background-color: %s' % color2 elif val <= -max: return 'background-color: %s' % color1 else: return '' 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(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 = nonzero_min(dict[key]) if best_value < value or value <= 0.0: 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", help="The directory which stores the .csv file", default="/mnt/disk1/nightly_perf_gpu/") parser.add_argument("-t", "--threshold", type=float, dest="threshold", help="the threshold of highlight values", default=3.0) parser.add_argument("-b", "--baseline_path", type=str, dest="baseline_path", help="the baseline path which stores the baseline.csv file") args = parser.parse_args() csv_files = [] for file_name in os.listdir(args.folder_path): file_path = os.path.join(args.folder_path, file_name) if os.path.isfile(file_path) and file_name.endswith(".csv"): csv_files.append(file_path) csv_files.sort(reverse=True) highlight_threshold=args.threshold latest_csv = pd.read_csv(csv_files[0], index_col=0) daily_html=csv_files[0].split(".")[0]+".html" diffs_within_normal_range = True 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) last1=['']*len(latest_csv.index) diff1=['']*len(latest_csv.index) 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() try: current_csv_batch_size=str(current_csv_row['batch_size']) current_csv_model_input_1st=current_csv_model+'-'+current_csv_input_output_pairs+'-'+current_csv_batch_size+'-'+'1st' current_csv_model_input_2nd=current_csv_model+'-'+current_csv_input_output_pairs+'-'+current_csv_batch_size+'-'+'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]) except KeyError: #Old csv/html files didn't include 'batch_size' pass for latest_csv_ind,latest_csv_row in latest_csv.iterrows(): latest_csv_model=latest_csv_row['model'].strip() latest_csv_input_output_pairs=latest_csv_row['input/output tokens'].strip() latest_1st_token_latency=latest_csv_row[latency_1st_token] latest_2_avg_latency=latest_csv_row[latency_2_avg] latest_csv_batch_size=str(latest_csv_row['batch_size']) key1=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+latest_csv_batch_size+'-'+'1st' key2=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+latest_csv_batch_size+'-'+'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(): previous_csv_model=previous_csv_row['model'].strip() previous_csv_input_output_pairs=previous_csv_row['input/output tokens'].strip() previous_csv_batch_size=str(previous_csv_row['batch_size']) if latest_csv_model==previous_csv_model and latest_csv_input_output_pairs==previous_csv_input_output_pairs and latest_csv_batch_size==previous_csv_batch_size: previous_1st_token_latency=previous_csv_row[latency_1st_token] previous_2_avg_latency=previous_csv_row[latency_2_avg] if previous_1st_token_latency > 0.0 and previous_2_avg_latency > 0.0: last1[latest_csv_ind]=previous_1st_token_latency diff1[latest_csv_ind]=round((previous_1st_token_latency-latest_1st_token_latency)*100/previous_1st_token_latency,2) last2[latest_csv_ind]=previous_2_avg_latency diff2[latest_csv_ind]=round((previous_2_avg_latency-latest_2_avg_latency)*100/previous_2_avg_latency,2) in_previous_flag=True if not in_previous_flag: last1[latest_csv_ind]=pd.NA diff1[latest_csv_ind]=pd.NA last2[latest_csv_ind]=pd.NA diff2[latest_csv_ind]=pd.NA latest_csv.insert(loc=3,column='last1',value=last1) latest_csv.insert(loc=4,column='diff1(%)',value=diff1) latest_csv.insert(loc=5,column='last2',value=last2) latest_csv.insert(loc=6,column='diff2(%)',value=diff2) 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) diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, threshold=highlight_threshold) subset1=['diff1(%)','diff2(%)'] subset2=['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}', 'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}', 'model loading time (s)': '{:.2f}'} styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, color1='red', color2='green'), subset=subset1) styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=3.0, color1='yellow'), subset=subset2) html_output = styled_df.set_table_attributes("border=1").render() with open(daily_html, 'w') as f: f.write(html_output) else: 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)) return 1 return 0 if __name__ == "__main__": sys.exit(main())