[Nightly] Add op performance regression check

.github/scripts/op_calculate_best_perf.py

@@ -0,0 +1,153 @@
+"""
+To update the op perf baseline, use the better performance value
+# usage
+python op_calculate_best_perf.py --xpu /path/to/xpu/performance/result/dir/forward.csv --baseline /path/to/baseline/dir/new_baseline.csv -r
+
+"""
+
+import csv
+import argparse
+from pathlib import Path
+
+updated_rows = []
+added_cases = []
+updated_cases = []
+removed_cases = []
+
+def update_baseline(xpu_file, baseline_file, remove_missing=False):
+    with open(xpu_file) as f:
+        xpu_reader = csv.DictReader(f, delimiter=';')
+        xpu_rows = list(xpu_reader)
+        xpu_fieldnames = xpu_reader.fieldnames  # Keep original field order
+        fieldnames = [f for f in xpu_fieldnames if f != 'time(us)']
+        xpu_data = {make_key(row, fieldnames): (float(row['time(us)']), row) for row in xpu_rows}
+
+    with open(baseline_file) as f:
+        baseline_reader = csv.DictReader(f, delimiter=';')
+        baseline_rows = list(baseline_reader)
+        baseline_fieldnames = baseline_reader.fieldnames
+
+    # To add new parameter of new ops into baseline file
+    all_fieldnames = xpu_fieldnames + [f for f in baseline_fieldnames if f not in xpu_fieldnames]
+    fieldnames = [f for f in all_fieldnames if f != 'time(us)']
+
+    baseline_keys = {make_key(row, fieldnames) for row in baseline_rows}
+    xpu_keys = set(xpu_data.keys())
+
+    # Resolve existing cases
+    for row in baseline_rows:
+        key = make_key(row, fieldnames)
+        if key in xpu_data:
+            xpu_time, xpu_row = xpu_data[key]
+            baseline_time = float(row['time(us)'])
+
+            if xpu_time < baseline_time:
+                updated_row = {}
+                for field in all_fieldnames:
+                    updated_row[field] = xpu_row.get(field, row.get(field, ''))
+                updated_row['time(us)'] = str(xpu_time)
+                updated_cases.append((key, baseline_time, xpu_time, updated_row))
+                updated_rows.append(updated_row)
+            else:
+                ordered_row = {}
+                for field in all_fieldnames:
+                    ordered_row[field] = row.get(field, '')
+                updated_rows.append(ordered_row)
+        elif not remove_missing:
+            ordered_row = {}
+            for field in all_fieldnames:
+                ordered_row[field] = row.get(field, '')
+            updated_rows.append(ordered_row)
+
+    # Add new cases
+    for key in xpu_keys - baseline_keys:
+        xpu_time, xpu_row = xpu_data[key]
+        new_row = {}
+        for field in all_fieldnames:
+            new_row[field] = xpu_row.get(field, '')
+        new_row['time(us)'] = str(xpu_time)
+        updated_rows.append(new_row)
+        added_cases.append((key, xpu_time, new_row))
+
+    # Resolve removed cases
+    if remove_missing:
+        for key in baseline_keys - xpu_keys:
+            removed_case = next(row for row in baseline_rows if make_key(row, fieldnames) == key)
+            removed_cases.append((key, float(removed_case['time(us)']), removed_case))
+
+    if added_cases:
+        print(f"\nAdded {len(added_cases)} new case(s):")
+        for key, time, row in added_cases:
+            print(f"\n[New Case] {format_case(key)}")
+            print(f"Time: {time} us")
+            print("Parameters:")
+            for k, v in row.items():
+                if k != 'time(us)':
+                    print(f"  {k}: {v}")
+        print("-" * 60)
+
+    if updated_cases:
+        print(f"\nUpdated {len(updated_cases)} case(s):")
+        for key, old_time, new_time, row in updated_cases:
+            print(f"\n[Updated] {format_case(key)}")
+            print(f"Time: {old_time} us → {new_time} us")
+            print("Parameters:")
+            for k, v in row.items():
+                if k != 'time(us)':
+                    print(f"  {k}: {v}")
+        print("-" * 60)
+
+    if remove_missing and removed_cases:
+        print(f"\nRemoved {len(removed_cases)} case(s):")
+        for key, time, row in removed_cases:
+            print(f"\n[Removed] {format_case(key)}")
+            print(f"Time: {time} us")
+            print("Parameters:")
+            for k, v in row.items():
+                if k != 'time(us)':
+                    print(f"  {k}: {v}")
+        print("-" * 60)
+
+    if not (added_cases or updated_cases or (remove_missing and removed_cases)):
+        print("\nNo changes detected between files.")
+
+    backup_file = baseline_file.replace('.csv', '_backup.csv')
+    Path(baseline_file).rename(backup_file)
+
+    with open(baseline_file, 'w', newline='') as f:
+        writer = csv.DictWriter(f, fieldnames=all_fieldnames, delimiter=';')
+        writer.writeheader()
+        writer.writerows(updated_rows)
+
+    print("\n" + "-" * 80)
+    print(f"Update complete! Total cases in new baseline: {len(updated_rows)}")
+    print(f"Updated baseline saved to {baseline_file}")
+    print(f"Original backup created at {backup_file}")
+
+def make_key(row, fieldnames):
+    return tuple(str(row.get(field, '')) for field in fieldnames)
+
+def format_case(key):
+    return f"{key[0]} | {key[1]} | {key[2]} (shape: {key[3]})"
+
+def main():
+    parser = argparse.ArgumentParser(description='Compare and synchronize operation performance data')
+    parser.add_argument('-x', '--xpu', required=True, help='Path to xpu_op_summary.csv')
+    parser.add_argument('-b', '--baseline', required=True, help='Path to baseline_op_summary.csv')
+    parser.add_argument('-r', '--remove-missing', action='store_true',
+                      help='Remove cases not present in xpu file')
+
+    args = parser.parse_args()
+
+    if not Path(args.xpu).exists():
+        print(f"Error: XPU file not found at {args.xpu}")
+        return
+    if not Path(args.baseline).exists():
+        print(f"Error: Baseline file not found at {args.baseline}")
+        return
+
+    update_baseline(args.xpu, args.baseline, args.remove_missing)
+
+
+if __name__ == "__main__":
+    main()

.github/scripts/op_perf_comparison.py

@@ -0,0 +1,201 @@
+"""
+To compare the op perf diff
+# usage
+python op_perf_comparison.py --xpu_file /path/to/xpu/performance/result/dir/forward.csv --baseline_file /path/to/baselineence/dir/baseline.csv
+
+"""
+
+import pandas as pd
+import argparse
+import os
+from ast import literal_eval
+from tabulate import tabulate
+
+def preprocess_row(row):
+    processed = {}
+    for col, val in row.items():
+        if pd.isna(val):
+            processed[col] = "NULL"
+        else:
+            try:
+                processed[col] = literal_eval(str(val))
+            except (ValueError, SyntaxError):
+                processed[col] = val
+    return processed
+
+def display_row(record):
+    formatted = {}
+    for key, value in record.items():
+        if isinstance(value, (list, tuple, dict)):
+            formatted[key] = str(value)
+        elif value == "NULL":
+            formatted[key] = "NULL"
+        else:
+            formatted[key] = value
+    return formatted
+
+def write_to_github_summary(content):
+    github_step_summary = os.getenv('GITHUB_STEP_SUMMARY')
+    if github_step_summary:
+        with open(github_step_summary, 'a') as f:
+            f.write(content + "\n")
+
+def display_comparison(results, threshold):
+    if results.empty:
+        print(f"\n No outlier exceeding ({threshold:.0%})")
+        write_to_github_summary(f"## No outlier exceeding ({threshold:.0%})")
+        return
+
+    regression = results[results['change'] == '↓']
+    improvement = results[results['change'] == '↑']
+
+    if not regression.empty:
+        print("\n🔴 Regression:")
+        display_records = []
+        for _, row in regression.iterrows():
+            record = display_row(row)
+            display_records.append({
+                **{k: v for k, v in record.items() if k not in ['time_xpu_file', 'time_baseline_file', 'difference', 'change']},
+                'Current Time(us)': record['time_xpu_file'],
+                'Baseline Time(us)': record['time_baseline_file'],
+                'Difference': record['difference']
+            })
+
+        print(tabulate(
+            display_records,
+            headers="keys",
+            tablefmt='grid',
+            showindex=False,
+            floatfmt=".2f"
+        ))
+
+    if not improvement.empty:
+        print("\n🟢 Improvement:")
+        display_records = []
+        for _, row in improvement.iterrows():
+            record = display_row(row)
+            display_records.append({
+                **{k: v for k, v in record.items() if k not in ['time_xpu_file', 'time_baseline_file', 'difference', 'change']},
+                'Current Time(us)': record['time_xpu_file'],
+                'Baseline Time(us)': record['time_baseline_file'],
+                'Difference': record['difference']
+            })
+
+        print(tabulate(
+            display_records,
+            headers="keys",
+            tablefmt='grid',
+            showindex=False,
+            floatfmt=".2f"
+        ))
+    # Print Summary on Github Action Summary
+    summary_output = "## Performance Comparison Results\n"
+    if not regression.empty:
+        summary_output += "\n### 🔴 Regression\n"
+        display_records = []
+        for _, row in regression.iterrows():
+            record = display_row(row)
+            display_records.append({
+                **{k: v for k, v in record.items() if k not in ['time_xpu_file', 'time_baseline_file', 'difference', 'change']},
+                'Current Time(us)': record['time_xpu_file'],
+                'Baseline Time(us)': record['time_baseline_file'],
+                'Difference': record['difference']
+            })
+
+        summary_output += tabulate(
+            display_records,
+            headers="keys",
+            tablefmt='github',
+            showindex=False,
+            floatfmt=".2f"
+        ) + "\n"
+
+    if not improvement.empty:
+        summary_output += "\n### 🟢 Improvement\n"
+        display_records = []
+        for _, row in improvement.iterrows():
+            record = display_row(row)
+            display_records.append({
+                **{k: v for k, v in record.items() if k not in ['time_xpu_file', 'time_baseline_file', 'difference', 'change']},
+                'Current Time(us)': record['time_xpu_file'],
+                'Baseline Time(us)': record['time_baseline_file'],
+                'Difference': record['difference']
+            })
+
+        summary_output += tabulate(
+            display_records,
+            headers="keys",
+            tablefmt='github',
+            showindex=False,
+            floatfmt=".2f"
+        ) + "\n"
+
+    write_to_github_summary(summary_output)
+
+def compare_op_time_values(xpu_file, baseline_file, threshold=0.05, output_file=None):
+    df_xpu = pd.read_csv(xpu_file, sep=';')
+    df_baseline = pd.read_csv(baseline_file, sep=';')
+
+    records_xpu = [preprocess_row(row) for _, row in df_xpu.iterrows()]
+    records_baseline = [preprocess_row(row) for _, row in df_baseline.iterrows()]
+
+    dict_xpu = {
+        tuple((k, str(v)) for k, v in record.items() if k != 'time(us)'):
+        record['time(us)']
+        for record in records_xpu
+    }
+    dict_baseline = {
+        tuple((k, str(v)) for k, v in record.items() if k != 'time(us)'):
+        record['time(us)']
+        for record in records_baseline
+    }
+    common_keys = set(dict_xpu.keys()) & set(dict_baseline.keys())
+    results = []
+
+    for key in common_keys:
+        time_xpu = dict_xpu[key]
+        time_baseline = dict_baseline[key]
+
+        # Skip comparison if time_xpu is 0
+        if time_xpu == 0:
+            continue
+
+        diff = (time_baseline - time_xpu) / time_xpu
+        # Compare Time, Lower is better
+        if abs(diff) > threshold:
+            record = dict(key)
+            print(record)
+            record.update({
+                'time_xpu_file': time_xpu,
+                'time_baseline_file': time_baseline,
+                'difference': f"{diff:.2%}",
+                'change': "↑" if diff > 0 else "↓"
+            })
+            results.append(record)
+
+    result_df = pd.DataFrame(results) if results else pd.DataFrame()
+    display_comparison(result_df, threshold)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Compare time values between two CSV files')
+    parser.add_argument('-x', '--xpu_file', required=True, help='XPU OP performance result csv files dir')
+    parser.add_argument('-b', '--baseline_file', required=True, help="XPU OP baseline result csv files dir")
+    parser.add_argument('-t', '--threshold', type=float, default=0.10,
+                       help='Threshold for time difference (default: 0.10 for 10%)')
+    args = parser.parse_args()
+
+    print(f" Compared file: {args.xpu_file} 和 {args.baseline_file}")
+    print(f" Threshold: {args.threshold:.0%}")
+    write_to_github_summary("## Performance Comparison Set")
+    write_to_github_summary(f"- Threshold: {args.threshold:.0%}")
+
+    compare_op_time_values(
+        xpu_file=args.xpu_file,
+        baseline_file=args.baseline_file,
+        threshold=args.threshold,
+    )
+
+
+if __name__ == "__main__":
+    main()