21de2613ce
* Add model loading time record in csv for all-in-one benchmark * Small fix * Small fix to number after .
182 lines
8.1 KiB
Python
182 lines
8.1 KiB
Python
#
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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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# Python program to convert CSV to HTML Table
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import os
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import sys
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import argparse
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import pandas as pd
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def highlight_vals(val, max=3.0, color1='red', color2='green'):
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if isinstance(val, float):
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if val > max:
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return 'background-color: %s' % color2
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elif val <= -max:
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return 'background-color: %s' % color1
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else:
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return ''
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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, threshold=5.0):
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return not any(diff < (-threshold) for diff in diff1 + diff2 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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dict[key] = []
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dict[key].append(value)
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def best_in_dict(dict, key, value):
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if key in dict:
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best_value = nonzero_min(dict[key])
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if best_value < value or value <= 0.0:
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return best_value
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return value
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return value
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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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help="The directory which stores the .csv file", default="/mnt/disk1/nightly_perf_gpu/")
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parser.add_argument("-t", "--threshold", type=float, dest="threshold",
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help="the threshold of highlight values", default=3.0)
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parser.add_argument("-b", "--baseline_path", type=str, dest="baseline_path",
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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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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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if os.path.isfile(file_path) and file_name.endswith(".csv"):
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csv_files.append(file_path)
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csv_files.sort(reverse=True)
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highlight_threshold=args.threshold
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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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diffs_within_normal_range = True
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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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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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latency_1st_token='1st token avg latency (ms)'
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latency_2_avg='2+ avg latency (ms/token)'
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csv_dict = {}
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for csv_file in csv_files:
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current_csv = pd.read_csv(csv_file, index_col=0)
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for current_csv_ind,current_csv_row in current_csv.iterrows():
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current_csv_model=current_csv_row['model'].strip()
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current_csv_input_output_pairs=current_csv_row['input/output tokens'].strip()
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current_csv_model_input_1st=current_csv_model+'-'+current_csv_input_output_pairs+'-'+'1st'
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current_csv_model_input_2nd=current_csv_model+'-'+current_csv_input_output_pairs+'-'+'2nd'
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add_to_dict(csv_dict, current_csv_model_input_1st, current_csv_row[latency_1st_token])
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add_to_dict(csv_dict, current_csv_model_input_2nd, current_csv_row[latency_2_avg])
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for latest_csv_ind,latest_csv_row in latest_csv.iterrows():
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latest_csv_model=latest_csv_row['model'].strip()
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latest_csv_input_output_pairs=latest_csv_row['input/output tokens'].strip()
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latest_1st_token_latency=latest_csv_row[latency_1st_token]
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latest_2_avg_latency=latest_csv_row[latency_2_avg]
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key1=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+'1st'
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key2=latest_csv_model+'-'+latest_csv_input_output_pairs+'-'+'2nd'
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best_last1_value=best_in_dict(csv_dict, key1, latest_1st_token_latency)
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best_last2_value=best_in_dict(csv_dict, key2, latest_2_avg_latency)
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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_1st_token_latency)*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_2_avg_latency)*100/best_last2_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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previous_csv_model=previous_csv_row['model'].strip()
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previous_csv_input_output_pairs=previous_csv_row['input/output tokens'].strip()
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if latest_csv_model==previous_csv_model and latest_csv_input_output_pairs==previous_csv_input_output_pairs:
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previous_1st_token_latency=previous_csv_row[latency_1st_token]
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previous_2_avg_latency=previous_csv_row[latency_2_avg]
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if previous_1st_token_latency > 0.0 and previous_2_avg_latency > 0.0:
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last1[latest_csv_ind]=previous_1st_token_latency
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diff1[latest_csv_ind]=round((previous_1st_token_latency-latest_1st_token_latency)*100/previous_1st_token_latency,2)
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last2[latest_csv_ind]=previous_2_avg_latency
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diff2[latest_csv_ind]=round((previous_2_avg_latency-latest_2_avg_latency)*100/previous_2_avg_latency,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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latest_csv.insert(loc=3,column='last1',value=last1)
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latest_csv.insert(loc=4,column='diff1(%)',value=diff1)
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latest_csv.insert(loc=5,column='last2',value=last2)
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latest_csv.insert(loc=6,column='diff2(%)',value=diff2)
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latest_csv.insert(loc=7,column='best 1',value=best_last1)
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latest_csv.insert(loc=8,column='best diff1(%)',value=best_diff1)
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latest_csv.insert(loc=9,column='best 2',value=best_last2)
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latest_csv.insert(loc=10,column='best diff2(%)',value=best_diff2)
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diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, threshold=highlight_threshold)
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subset1=['diff1(%)','diff2(%)']
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subset2=['best diff1(%)','best diff2(%)']
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columns={'1st token avg latency (ms)': '{:.2f}', '2+ avg latency (ms/token)': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}',
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'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'encoder time (ms)': '{:.2f}', 'peak mem (GB)': '{:.2f}',
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'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}', 'model loading time (s)': '{:.2f}'}
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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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with open(daily_html, 'w') as f:
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f.write(html_output)
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else:
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latest_csv.to_html(daily_html)
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if args.baseline_path and not diffs_within_normal_range:
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print("The diffs are outside the normal range: %" + str(highlight_threshold))
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return 1
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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