import argparse import os import torch from torch import nn def main(): p = argparse.ArgumentParser() p.add_argument("--batch", type=int, default=1024) p.add_argument("--in_dim", type=int, default=32) p.add_argument("--out_dim", type=int, default=64) p.add_argument("--compile", action="store_true") p.add_argument("--trace_dir", default="./traces/02_linear") args = p.parse_args() device = "cuda" x = torch.randn(args.batch, args.in_dim, device=device, dtype=torch.bfloat16) linear_layer = nn.Linear(args.in_dim, args.out_dim, bias=True).to( device, dtype=torch.bfloat16 ) linear_layer.eval() print(linear_layer.weight.shape) print(linear_layer.bias.shape) fwd = torch.compile(linear_layer) if args.compile else linear_layer def step(): with torch.profiler.record_function("linear_fwd"), torch.no_grad(): return fwd(x) # warmup for _ in range(3): step() torch.cuda.synchronize() os.makedirs(args.trace_dir, exist_ok=True) compile_tag = "compile" if args.compile else "eager" tag = f"{args.batch}_{args.in_dim}_{args.out_dim}_{compile_tag}" table_path = os.path.join(args.trace_dir, f"{tag}.txt") trace_path = os.path.join(args.trace_dir, f"{tag}.json") schedule = torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=1) with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA, ], schedule=schedule, record_shapes=False, # adds CPU overhead profile_memory=False, # adds CPU overhead with_stack=False, ) as prof: for _ in range(5): step() prof.step() torch.cuda.synchronize() print(f"saving traces ... {trace_path}") prof.export_chrome_trace(trace_path) with open(table_path, "w") as f: f.write(prof.key_averages().table(sort_by="cuda_time_total", row_limit=15)) if __name__ == "__main__": main()