Perf analysis scripts.

script/plot_navi_perf.py

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+#!/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()