Merge branch 'main' into zhiwei/int4pack

.github/scripts/apply_torch_pr.py

@@ -12,8 +12,7 @@
         # Fallback to CPU for XPU FP64
         "https://github.com/pytorch/pytorch/pull/126516",
         # Modify the tolerance level in TIMM benchmark
-        # "https://github.com/pytorch/pytorch/pull/129735",
-        "https://github.com/mengfei25/pytorch/pull/21",
+        "https://github.com/pytorch/pytorch/pull/143739",
     ]
 )
 parser.add_argument('--extra-pr-list', '-e', nargs='+',default=[])

.github/scripts/env.sh

@@ -4,6 +4,8 @@ if [ "$1" != "nightly_wheel" ];then
     source /opt/intel/oneapi/compiler/latest/env/vars.sh
     source /opt/intel/oneapi/umf/latest/env/vars.sh
     source /opt/intel/oneapi/pti/latest/env/vars.sh
+    source /opt/intel/oneapi/ccl/latest/env/vars.sh
+    source /opt/intel/oneapi/mpi/latest/env/vars.sh
 else
     echo "Don't need to source DL-Essential for nightly wheel"
 fi

.github/scripts/inductor_summary.py

@@ -3,6 +3,8 @@
 import pandas as pd
 from scipy.stats import gmean
 from styleframe import StyleFrame, Styler, utils
+import numpy as np
+from openpyxl import Workbook
 
 parser = argparse.ArgumentParser(description="Generate report")
 parser.add_argument('-s', '--suite', default=["huggingface"], nargs='*', type=str, help='model suite name')
@@ -665,6 +667,73 @@ def update_summary(excel, scenario, suite):
             sf.set_row_height(j, 30)
         sf.to_excel(sheet_name=suite + '_'  + scenario + '_Summary', excel_writer=excel)
 
+def summary_conclusion(scenario, excel):
+    excel.book.save(excel)
+    df = pd.read_excel(excel, sheet_name = None, header = None)
+    #df = pd.DataFrame(excel)
+    if scenario == 'performance':
+        sheet_names = list(df.keys())
+        sheet_names = [s for s in sheet_names if 'Summary' in s and 'performance' in s]
+        sheet_names.sort()
+        print(f"Merge excel as below:\n{sheet_names}")
+        print("\n")
+        features = [[]] * 21
+        for sheet_name in sheet_names:
+            df_sheet = df[sheet_name]
+            df_sheet = df_sheet.values
+            features = np.hstack((features, df_sheet))
+
+        if len(sheet_names) == 1:
+            print("sheet not merge")
+        elif len(sheet_names) == 2:
+            print("2 sheets merge")
+            if 'huggingface' in sheet_names[0]:
+                features[:, 4:5] = features[:, 14:15]
+                features[:, 6:7] = features[:, 16:17]
+            else:
+                features[:, 4:5] = features[:, 14:15]
+        else:
+            print("3 sheets merge")
+            features[:, 4:5] = features[:, 24:25]
+            features[:, 6:7] = features[:, 16:17]
+
+        df_concat = StyleFrame(pd.DataFrame(features).iloc[:,:10])
+        for i in range(10):
+            df_concat.set_column_width(i, 22)
+        for j in range(1, 23):
+            df_concat.set_row_height(j, 30)
+        df_concat.to_excel(sheet_name='Perf_Summary', excel_writer=excel, index=False)
+    else:
+        sheet_names = list(df.keys())
+        sheet_names = [s for s in sheet_names if 'Summary' in s and 'accuracy' in s]
+        sheet_names.sort()
+        print(f"Merge excel as below:\n{sheet_names}")
+        print("\n")
+        features = [[]] * 11
+        for sheet_name in sheet_names:
+            df_sheet = df[sheet_name]
+            df_sheet = df_sheet.values
+            features = np.hstack((features, df_sheet))
+        if len(sheet_names) == 1:
+            print("sheet not merge")
+        elif len(sheet_names) == 2:
+            print("2 sheets merge")
+            if 'huggingface' in sheet_names[0]:
+                features[:, 3:4] = features[:, 12:13]
+                features[:, 5:6] = features[:, 14:15]
+            else:
+                features[:, 3:4] = features[:, 12:13]
+        else:
+            print("3 sheets merge")
+            features[:, 3:4] = features[:, 21:22]
+            features[:, 5:6] = features[:, 14:15]
+
+        df_concat = StyleFrame(pd.DataFrame(features).iloc[:,:9])
+        for i in range(10):
+            df_concat.set_column_width(i, 22)
+        for j in range(1, 13):
+            df_concat.set_row_height(j, 30)
+        df_concat.to_excel(sheet_name='Acc_Summary', excel_writer=excel, index=False)
 
 def generate_report(excel, scenario_list, precision_list, mode_list, suite_list):
     for sc in scenario_list:
@@ -693,8 +762,19 @@ def excel_postprocess(file, scenario, precison, mode, suite):
                     wdt.merge_cells(start_row=1, end_row=1, start_column=13, end_column=16)
             wb.save(file)
 
+        if len(scenario) == 2:
+            wb.move_sheet("Perf_Summary", -(len(wb.worksheets)-1))
+            wb.move_sheet("Acc_Summary", -(len(wb.worksheets)-1))
+        elif len(scenario) == 1 and sc == 'accuracy':
+            wb.move_sheet("Acc_Summary", -(len(wb.worksheets)-1))
+        else:
+            wb.move_sheet("Perf_Summary", -(len(wb.worksheets)-1))
+
 
 if __name__ == '__main__':
     excel = StyleFrame.ExcelWriter('inductor_log/Inductor_E2E_Test_Report.xlsx')
     generate_report(excel, args.scenario, args.precision, args.mode, args.suite)
+    for sc in args.scenario:
+        summary_conclusion(sc, excel)
     excel_postprocess(excel, args.scenario, args.precision, args.mode, args.suite)
+    excel.close()

.github/scripts/spec.py

@@ -0,0 +1,7 @@
+import torch
+
+DEVICE_NAME = 'xpu'
+
+MANUAL_SEED_FN = torch.xpu.manual_seed
+EMPTY_CACHE_FN = torch.xpu.empty_cache
+DEVICE_COUNT_FN = torch.xpu.device_count