fix(benchmark): store 'compare' and 'one' perf results in csv files and visualize them

benchmark/bench_flash_mla.py

@@ -1,15 +1,16 @@
 # MLA Triton kernel is from: https://github.com/monellz/vllm/commit/feebaa7c063be6bfb590a876741aeef1c5f58cf8#diff-7b2e1c9032522f7266051b9887246a65753871dfb3625a258fee40109fe6e87a
+import argparse
 import math
 import random
 
+import flashinfer
 import torch
 import triton
 import triton.language as tl
-import argparse
 
 # pip install flashinfer-python
-from flash_mla import get_mla_metadata, flash_mla_with_kvcache
-import flashinfer
+from flash_mla import flash_mla_with_kvcache, get_mla_metadata
+
 
 def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False):
     query = query.float()
@@ -443,6 +444,7 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal
     bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8)
     print(f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10 ** 9 / perf_a:.0f} TFLOPS, {bytes / 10 ** 6 / perf_a:.0f} GB/s")
     print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s")
+    return bytes / 10 ** 6 / perf_a, bytes / 10 ** 6 / perf_b
 
 
 def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype):
@@ -501,14 +503,18 @@ def get_args():
 
 if __name__ == "__main__":
     args = get_args()
-    with open("all_perf.csv", "w") as fout:
+    benchmark_type = "all" if args.all else f"{args.baseline}_vs_{args.target}" if args.compare else args.target
+    with open(f"{benchmark_type}_perf.csv", "w") as fout:
         fout.write("name,batch,seqlen,head,bw\n")
         for shape in shape_configs:
             if args.all:
                 for target in available_targets:
                     perf = compare_a(target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
                     fout.write(f'{target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n')
             elif args.compare:
-                compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
+                perfa, prefb = compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
+                fout.write(f'{args.baseline},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perfa:.0f}\n')
+                fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n')
             elif args.one:
-                compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
+                perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], shape["causal"], shape["dtype"])
+                fout.write(f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n')

benchmark/visualize.py

@@ -1,7 +1,17 @@
+import argparse
+
 import matplotlib.pyplot as plt
 import pandas as pd
 
-file_path = 'all_perf.csv'
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Visualize benchmark results')
+    parser.add_argument('--file', type=str, default='all_perf.csv',
+                        help='Path to the CSV file with benchmark results (default: all_perf.csv)')
+    return parser.parse_args()
+
+args = parse_args()
+file_path = args.file
 
 df = pd.read_csv(file_path)
 
@@ -16,4 +26,4 @@
 plt.ylabel('bw (GB/s)')
 plt.legend()
 
-plt.savefig('bandwidth_vs_seqlen.png')
+plt.savefig(f'{file_path.split(".")[0].split("/")[-1]}_bandwidth_vs_seqlen.png')