#!/usr/bin/env python3 import sys import os import argparse import pandas as pd import csv from convert_miopen_driver_to_profiler import get_parser, init_const_args, process_miopen_driver_name, \ get_ck_grouped_conv_fwd_cmd, get_ck_grouped_conv_bwd_data_cmd, get_ck_grouped_conv_bwd_weight_cmd def parse_cli_args(): """Parse command line arguments""" parser = argparse.ArgumentParser(description="Run CK convolution profiler.") parser.add_argument("--fremont-csv-file", type=str, dest="fremont_csv_file", required=True, help="Path to the CSV file containing Fremont test cases.") parser.add_argument("--ktn-csv-file", type=str, dest="ktn_csv_file", required=True, help="Path to the CSV file containing KTN test cases.") parser.add_argument("--fwd-only", action="store_true", help="Run only forward convolution.") parser.add_argument("--bwd-data-only", action="store_true", help="Run only backward data convolution.") parser.add_argument("--bwd-weight-only", action="store_true", help="Run only backward weight convolution.") parser.add_argument("--no-verification", action="store_true", help="Disable verification in the CK profiler.") parser.add_argument("--full-set", action="store_true", help="Create a full set of tests. By default only a subset of the raw data is used.") parser.add_argument("--output-path", type=str, dest="output_path", default=".", help="Path to save the output files. Default is current directory.") args, unknown_args = parser.parse_known_args() if unknown_args: print(f"Unknown arguments: {unknown_args}", file=sys.stderr) sys.exit(1) return args def parse_profiler_command(args, fwd_only=False, bwd_data_only=False, bwd_weight_only=False): # MIOpen get number of channel per all groups, CK profiler get number of # channel per group args.in_channels = int(args.in_channels / args.group_count) args.out_channels = int(args.out_channels / args.group_count) cmd = None if fwd_only: args.forw = 1 cmd = get_ck_grouped_conv_fwd_cmd(args) if bwd_data_only: args.forw = 2 cmd = get_ck_grouped_conv_bwd_data_cmd(args) if bwd_weight_only: args.forw = 4 cmd = get_ck_grouped_conv_bwd_weight_cmd(args) return cmd def parse_fremont_profiler_commands(csv_file, no_verification=False, fwd_only=False, bwd_data_only=False, bwd_weight_only=False): if not os.path.isfile(csv_file): print(f"Error: The specified CSV file '{csv_file}' does not exist.", file=sys.stderr) sys.exit(1) df = pd.read_csv(csv_file) shapes = df['Shape'].tolist() parser = get_parser() commands = [] for i, line in enumerate(shapes): try: args, unknown = parser.parse_known_args(line.split()) init_const_args(args) process_miopen_driver_name(args, unknown) assert len(unknown) == 4 and unknown[0] == "--fil_layout" and unknown[2] == "--out_layout" and unknown[1] == unknown[3], \ f"Error: Unknown arguments do not match: {unknown}" assert unknown[1] == args.in_layout, \ f"Error: Input layout does not match unknown arguments: {unknown[1]} != {args.in_layout}" if no_verification: args.verify = 0 # Ensure we run always the timing. args.time = 1 command = parse_profiler_command(args, fwd_only=fwd_only, bwd_data_only=bwd_data_only, bwd_weight_only=bwd_weight_only) if command is not None: commands.append(command) except AttributeError as e: print(f"Error processing line {i}: {line}. Skipping the line.") continue return commands def process_miopen_driver(args, unknown): if "convint8" in unknown: args.data_type = 'int8' elif "convbfp16" in unknown: args.data_type = 'bfp16' elif "convfp16" in unknown: args.data_type = 'fp16' elif "conv" in unknown: args.data_type = 'fp32' else: print('Not supported driver (supported: conv, convfp16, convint8,' ' convbfp16).') exit(1) def parse_ktn_command(csv_file, no_verification=False, fwd_only=False, bwd_data_only=False, bwd_weight_only=False): if not os.path.isfile(csv_file): print(f"Error: The specified CSV file '{csv_file}' does not exist.", file=sys.stderr) sys.exit(1) df = pd.read_csv(csv_file) # Remove the KTN commands where column 'Group Size' has value 1 df = df[df['Group Size'] != 1] assert (df['Group Size'] == 1).sum() == 0, "Filtering failed!" assert (df['Group Size'] != 1).sum() > 0, "Filtering failed!" print("Unique Group Size values:", df['Group Size'].unique()) print("Data types:", df.dtypes) commands = [] parser = get_parser() for i, cmd in enumerate(df['Command']): cmd = cmd.split() args, _ = parser.parse_known_args(cmd) init_const_args(args) process_miopen_driver(args, cmd[0]) if no_verification: args.verify = 0 else: args.verify = 1 # Ensure we run always the timing. args.time = 1 command = parse_profiler_command(args, fwd_only=fwd_only, bwd_data_only=bwd_data_only, bwd_weight_only=bwd_weight_only) if command is not None: commands.append(command) return commands def main(): args = parse_cli_args() # Initialize random seed for reproducibility seed = 42 n_fremont_shapes = 1000 n_ktn_shapes = 1000 fremont_commands = parse_fremont_profiler_commands(args.fremont_csv_file, no_verification=args.no_verification, fwd_only=args.fwd_only, bwd_data_only=args.bwd_data_only, bwd_weight_only=args.bwd_weight_only) ktn_commands = parse_ktn_command(args.ktn_csv_file, no_verification=args.no_verification, fwd_only=args.fwd_only, bwd_data_only=args.bwd_data_only, bwd_weight_only=args.bwd_weight_only) # Create a DataFrame to hold the commands commands_fremont_df = pd.DataFrame({ 'Command': fremont_commands, }) commands_ktn_df = pd.DataFrame({ 'Command': ktn_commands, }) if not args.full_set: # The hardest cases are at the beginning of the Fremont CSV file. commands_fremont_df = commands_fremont_df.sample(n=min(n_fremont_shapes, len(commands_fremont_df))) commands_ktn_df = commands_ktn_df.sample(n=min(n_ktn_shapes, len(commands_ktn_df)), random_state=seed) # Combine the two DataFrames commands_df = pd.concat([commands_fremont_df, commands_ktn_df], ignore_index=True) # Randomly permute the commands commands_df = commands_df.sample(frac=1, random_state=seed).reset_index(drop=True) output_file = os.path.join(args.output_path, "ck_profiler_commands.csv") with open(output_file, "w") as f: csv_writer = csv.writer(f, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['profiler', 'op', 'datatype', 'layout', 'verify', 'init', 'log', 'time', 'Ndims', 'G', 'N', 'K', 'C', 'Y', 'X', 'Hi', 'Wi', 'Sy', 'Sx', 'Dy', 'Dx', 'LeftPy', 'LeftPx', 'RightPy', 'RightPx', 'SplitK']) for command in commands_df['Command']: csv_writer.writerow(command) print(f"Commands saved to {output_file}") if __name__ == "__main__": main()