diff --git a/python/llm/dev/benchmark/perplexity/make_csv.py b/python/llm/dev/benchmark/perplexity/make_csv.py index 0876bec7..426e8d53 100644 --- a/python/llm/dev/benchmark/perplexity/make_csv.py +++ b/python/llm/dev/benchmark/perplexity/make_csv.py @@ -35,9 +35,8 @@ def make_csv(result_dict, output_path=None): current_date = datetime.datetime.now().strftime("%Y-%m-%d") file_name = f'results_{current_date}.csv' full_path = os.path.join(output_path, file_name) if output_path else file_name - print('Writing to', 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: 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 precision, prec_results in model_results.items(): row = [index, model, precision] - for language in headers[3:]: + for language in ["en","zh"]: task_results = prec_results.get(language.lower(), None) if task_results is None: - row.append("") + continue else: result = task_results["results"] row.append("%.4f" % result) @@ -89,6 +88,7 @@ def main(*args): output_path = args[2] merged_results = merge_results(input_path) + make_csv(merged_results, output_path) diff --git a/python/llm/dev/benchmark/perplexity/make_table.py b/python/llm/dev/benchmark/perplexity/make_table.py index 18906b5a..7a6c84fa 100644 --- a/python/llm/dev/benchmark/perplexity/make_table.py +++ b/python/llm/dev/benchmark/perplexity/make_table.py @@ -35,8 +35,8 @@ def make_table(result_dict): """Generate table of results.""" md_writer = MarkdownTableWriter() latex_writer = LatexTableWriter() - md_writer.headers = ["Model", "Precision", "en", "zh"] - latex_writer.headers = ["Model", "Precision", "en", "zh"] + md_writer.headers = ["Model", "Precision", "ppl_result"] + latex_writer.headers = ["Model", "Precision", "ppl_result"] languages = ["en", "zh"] values = [] @@ -46,7 +46,7 @@ def make_table(result_dict): for language in languages: task_results = prec_results.get(language, None) if task_results is None: - value.append("") + continue else: result = task_results["results"] value.append("%.4f" % result) diff --git a/python/llm/test/benchmark/perplexity/fp16.csv b/python/llm/test/benchmark/perplexity/fp16.csv index 2b812f84..00915746 100644 --- a/python/llm/test/benchmark/perplexity/fp16.csv +++ b/python/llm/test/benchmark/perplexity/fp16.csv @@ -1,8 +1,8 @@ -Index,Model,Precision,en,zh -0,Llama-2-7b-chat-hf,fp16,4.7019, -1,chatglm2-6b,fp16,,22.321 -2,chatglm3-6b,fp16,,30.1281 -3,Baichuan2-7B-Chat,fp16,,10.7676 -4,mpt-7b-chat,fp16,5.7882, -5,falcon-7b-instruct-with-patch,fp16,5.2532, -6,mistral-7b-v0.1,fp16,3.6597, \ No newline at end of file +Index,Model,Precision,ppl_result +0,Llama-2-7b-chat-hf,fp16,4.7019 +1,chatglm2-6b,fp16,22.321 +2,chatglm3-6b,fp16,30.1281 +3,Baichuan2-7B-Chat,fp16,10.7676 +4,mpt-7b-chat,fp16,5.7882 +5,falcon-7b-instruct-with-patch,fp16,5.2532 +6,Mistral-7B-v0.1,fp16,3.6597 diff --git a/python/llm/test/benchmark/perplexity/ppl_csv_to_html.py b/python/llm/test/benchmark/perplexity/ppl_csv_to_html.py index d1175d19..cef18a85 100644 --- a/python/llm/test/benchmark/perplexity/ppl_csv_to_html.py +++ b/python/llm/test/benchmark/perplexity/ppl_csv_to_html.py @@ -28,7 +28,7 @@ def highlight_vals(val, max=3.0, color1='red', color2='green', color3='yellow', return 'background-color: %s' % color1 elif val <= -max: 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 else: return '' @@ -37,8 +37,8 @@ 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(diff_en, diff_zh, threshold=5.0): - return not any(diff < (-threshold) for diff in diff_en + diff_zh if isinstance(diff, float)) +def is_diffs_within_normal_range(diff_ppl_result, threshold=5.0): + return not any(diff < (-threshold) for diff in diff_ppl_result if isinstance(diff, float)) def add_to_dict(dict, key, value): if key not in dict: @@ -60,9 +60,9 @@ def create_fp16_dict(fp16_path): model = row['Model'] # Formalize the data to have 2 decimal places fp16_dict[model] = { - 'en': "{:.2f}".format(row['en']), - 'zh': "{:.2f}".format(row['zh']) + 'ppl_result': "{:.2f}".format(row['ppl_result']) } + return fp16_dict def calculate_percentage_difference(current, fp16): @@ -105,9 +105,8 @@ def main(): diffs_within_normal_range = True # Add display of FP16 values for each model and add percentage difference column - for task in ['en', 'zh']: - latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A')) - latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1) + latest_csv['ppl_result_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get('ppl_result', '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) if len(csv_files)>1: if args.baseline_path: @@ -115,13 +114,10 @@ def main(): else: previous_csv = pd.read_csv(csv_files[1], index_col=0) - last_en=['']*len(latest_csv.index) - diff_en=['']*len(latest_csv.index) - last_zh=['']*len(latest_csv.index) - diff_zh=['']*len(latest_csv.index) + last_ppl_result=['']*len(latest_csv.index) + diff_ppl_result=['']*len(latest_csv.index) - en='en' - zh='zh' + ppl_result = 'ppl_result' csv_dict = {} for csv_file in csv_files: @@ -129,17 +125,14 @@ def main(): for current_csv_ind,current_csv_row in current_csv.iterrows(): current_csv_model=current_csv_row['Model'].strip() current_csv_precision=current_csv_row['Precision'].strip() - current_csv_model_en=current_csv_model+'-'+current_csv_precision+'-'+'en' - current_csv_model_zh=current_csv_model+'-'+current_csv_precision+'-'+'zh' - 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]) - + current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result' + add_to_dict(csv_dict, current_csv_model_ppl_result, current_csv_row[ppl_result]) + for latest_csv_ind,latest_csv_row in latest_csv.iterrows(): latest_csv_model=latest_csv_row['Model'].strip() latest_csv_precision=latest_csv_row['Precision'].strip() - latest_en=latest_csv_row[en] - latest_zh=latest_csv_row[zh] + latest_ppl_result=latest_csv_row[ppl_result] in_previous_flag=False @@ -150,57 +143,50 @@ def main(): if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision: - previous_en=previous_csv_row[en] - previous_zh=previous_csv_row[zh] - if previous_en > 0.0 or previous_zh > 0.0: - last_en[latest_csv_ind]=previous_en - diff_en[latest_csv_ind]=round((latest_en-previous_en)*100/previous_en,2) - last_zh[latest_csv_ind]=previous_zh - diff_zh[latest_csv_ind]=round((latest_zh-previous_zh)*100/previous_zh,2) + previous_ppl_result=previous_csv_row[ppl_result] + + if previous_ppl_result > 0.0: + last_ppl_result[latest_csv_ind]=previous_ppl_result + diff_ppl_result[latest_csv_ind]=round((latest_ppl_result-previous_ppl_result)*100/previous_ppl_result,2) in_previous_flag=True if not in_previous_flag: - last_en[latest_csv_ind]=pd.NA - diff_en[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) + last_ppl_result[latest_csv_ind]=pd.NA + diff_ppl_result[latest_csv_ind]=pd.NA - 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(%)'] + + diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold) - columns={'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}', - '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) - styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1) - for task in ['en', 'zh']: - styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)']) - - # add css style to restrict width and wrap text - styled_df.set_table_styles([{ - 'selector': 'th, td', - 'props': [('max-width', '88px'), ('word-wrap', 'break-word')] - }], overwrite=False) - - html_output = styled_df.set_table_attributes("border=1").to_html() - - with open(daily_html, 'w') as f: - f.write(html_output) + styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=True), subset=['ppl_result_diff_last(%)']) + styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)']) + else: - latest_csv.to_html(daily_html) - + 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 + styled_df.set_table_styles([{ + 'selector': 'th, td', + 'props': [('max-width', '88px'), ('word-wrap', 'break-word')] + }], overwrite=False) + + html_output = styled_df.set_table_attributes("border=1").to_html() + + with open(daily_html, 'w') as f: + f.write(html_output) + if args.baseline_path and not diffs_within_normal_range: print("The diffs are outside the normal range: %" + str(highlight_threshold)) - return 1 + return 1 return 0 - + if __name__ == "__main__": sys.exit(main())