216 lines
8 KiB
Python
216 lines
8 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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from pathlib import Path
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def highlight_vals(val, max=3.0, color1='red', color2='green', color3='yellow', is_last=False):
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if isinstance(val, float):
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if val > max:
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return 'background-color: %s' % color1
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elif val <= -max:
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return 'background-color: %s' % color2
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elif val != 0.0 and is_last:
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return 'background-color: %s' % color3
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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(diff_ppl_result, threshold=5.0):
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return not any(diff < (-threshold) for diff in diff_ppl_result 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 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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fp16_dict[model] = {
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'ppl_result': "{:.2f}".format(row['ppl_result'])
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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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help="The directory which stores the .csv file", default="/home/arda/yibo/BigDL/python/llm/dev/benchmark/harness")
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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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# fp16.csv is downloaded previously under the parent folder of the folder_path
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parent_dir = Path(args.folder_path).parent
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fp16_path = os.path.join(parent_dir, 'fp16.csv')
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fp16_dict = create_fp16_dict(fp16_path)
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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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# 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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task = 'ppl_result'
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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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print(csv_files)
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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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last_ppl_result=['']*len(latest_csv.index)
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diff_ppl_result=['']*len(latest_csv.index)
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ppl_result = 'ppl_result'
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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_precision=current_csv_row['Precision'].strip()
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current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result'
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add_to_dict(csv_dict, current_csv_model_ppl_result, current_csv_row[ppl_result])
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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_precision=latest_csv_row['Precision'].strip()
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latest_ppl_result=latest_csv_row[ppl_result]
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print("This is the latest_ppl_result",latest_ppl_result)
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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_precision=previous_csv_row['Precision'].strip()
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if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision:
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previous_ppl_result=previous_csv_row[ppl_result]
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print("This is the previous_ppl_result", previous_ppl_result)
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if previous_ppl_result > 0.0:
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last_ppl_result[latest_csv_ind]=previous_ppl_result
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diff_ppl_result[latest_csv_ind]=round((latest_ppl_result-previous_ppl_result)*100/previous_ppl_result,2)
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in_previous_flag=True
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if not in_previous_flag:
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last_ppl_result[latest_csv_ind]=pd.NA
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diff_ppl_result[latest_csv_ind]=pd.NA
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latest_csv.insert(loc=6,column='last_ppl_result',value=last_ppl_result)
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latest_csv.insert(loc=7,column='ppl_result_diff_last(%)',value=diff_ppl_result)
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diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)
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subset1='ppl_result_diff_last(%)'
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# columns will be different: columns_sec={'ppl_result': '{:.2f}'}
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columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'ppl_result_diff_last(%)': '{:.2f}'}
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# This is the same
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latest_csv.drop('Index', axis=1, inplace=True)
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# subset is different
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=True), subset=['ppl_result_diff_last(%)'])
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)'])
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else:
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columns={'ppl_result': '{:.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=highlight_threshold, is_last=False), subset=['ppl_result_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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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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