mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-06-30 19:57:40 +00:00
89 lines
3.2 KiB
Python
Executable File
89 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
|
|
import os
|
|
import argparse
|
|
import subprocess
|
|
import sys
|
|
import matplotlib.pyplot as plt
|
|
# Non-interactive backend for matplotlib
|
|
plt.switch_backend('Agg')
|
|
import numpy as np
|
|
|
|
def parse_cli_args():
|
|
"""Parse command line arguments"""
|
|
parser = argparse.ArgumentParser(description="Run CK and CK Tile convolution profilers.")
|
|
parser.add_argument("--input-file-int8", type=str, dest="input_file_int8", required=False, help="Path to the file containing test results for int8.")
|
|
parser.add_argument("--input-file-fp16", type=str, dest="input_file_fp16", required=False, help="Path to the file containing test results for fp16.")
|
|
|
|
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_times(input_file):
|
|
with open(input_file, 'r') as f:
|
|
lines = f.readlines()
|
|
ang_time_lines = lines[3::4] # Every 4th line starting from line 3
|
|
avg_times = [float(line.strip().split("avg_time: ")[-1]) for line in ang_time_lines]
|
|
commnds = lines[0::4] # Every 4th line starting from line 0
|
|
|
|
# Create a dictionary of commands to their average times
|
|
cmd_time_dict = {}
|
|
for cmd, time in zip(commnds, avg_times):
|
|
cmd_time_dict[cmd.strip()] = time
|
|
|
|
return cmd_time_dict
|
|
|
|
def plot_perf(times_int8, times_fp16, output_file):
|
|
#n_samples = min(len(times_int8), len(times_fp16))
|
|
|
|
# From two dictionaries, extract the values where the key is present in both dictionaries
|
|
speedup_percentage = []
|
|
for cmd in times_int8:
|
|
print(cmd)
|
|
print(f"Times int8: {times_int8[cmd]}")
|
|
# TODO: WE need account for the different data types in the commands
|
|
if cmd in times_fp16:
|
|
time_int8 = times_int8[cmd]
|
|
time_fp16 = times_fp16[cmd]
|
|
print(f"int8 time: {time_int8}, fp16 time: {time_fp16}")
|
|
if time_fp16 > 0:
|
|
speedup = (time_fp16 - time_int8) / time_fp16 * 100
|
|
speedup_percentage.append(speedup)
|
|
|
|
n_samples = len(speedup_percentage)
|
|
x = np.arange(n_samples)
|
|
plt.figure(figsize=(10, 6))
|
|
plt.plot(x, speedup_percentage, marker='o')
|
|
plt.title('Speedup of int8 over fp16')
|
|
plt.xlabel('Sample Index')
|
|
plt.ylabel('Speedup (%)')
|
|
plt.grid(True)
|
|
plt.savefig(output_file)
|
|
plt.close()
|
|
|
|
def main():
|
|
args = parse_cli_args()
|
|
|
|
times_int8 = parse_times(args.input_file_int8)
|
|
times_fp16 = parse_times(args.input_file_fp16)
|
|
|
|
#avg_times_int8 = np.mean(np.array(times_int8.items()))
|
|
#avg_times_fp16 = np.mean(np.array(times_fp16.items()))
|
|
|
|
print(f"Got {len(times_int8)} int8 samples and {len(times_fp16)} fp16 samples.")
|
|
|
|
# print(f"Average time for int8: {avg_times_int8} ms")
|
|
# print(f"Average time for fp16: {avg_times_fp16} ms")
|
|
# print(f"Speedup (int8 over fp16): {avg_times_fp16 / avg_times_int8:.2f}x")
|
|
|
|
output_plot_file = "navi_perf_int8_vs_fp16.png"
|
|
output_path = os.path.join(os.getcwd(), output_plot_file)
|
|
plot_perf(times_int8, times_fp16, output_path)
|
|
print(f"Performance plot saved to {output_path}")
|
|
|
|
if __name__ == "__main__":
|
|
main() |