File size: 2,460 Bytes
8e23137 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import argparse
import os
import torch
def parse_arguments():
p = argparse.ArgumentParser()
p.add_argument("--size", type=int, default=64)
p.add_argument("--dtype", choices=["bf16", "fp32"], default="bf16")
p.add_argument("--compile", action="store_true")
p.add_argument("--warmup", action="store_true")
p.add_argument("--trace_dir", default="./traces/01_matmul_add")
return p.parse_args()
def main():
args = parse_arguments()
device = "cuda"
dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32
x = torch.randn(args.size, args.size, device=device, dtype=dtype)
w = torch.randn(args.size, args.size, device=device, dtype=dtype)
b = torch.randn(args.size, args.size, device=device, dtype=dtype)
def fn(x, w, b):
return torch.add(torch.matmul(x, w), b)
fn = torch.compile(fn) if args.compile else fn
def step():
with torch.profiler.record_function("matmul_add"):
return fn(x, w, b)
if args.warmup:
for _ in range(3):
step()
# you're flushing the queue so the upcoming profiled steps aren't credited for prior work
torch.cuda.synchronize()
os.makedirs(args.trace_dir, exist_ok=True)
compile_tag = "compile" if args.compile else "eager"
warmup_tag = "warm" if args.warmup else "cold"
tag = f"{args.size}_{args.dtype}_{warmup_tag}_{compile_tag}"
table_path = os.path.join(args.trace_dir, f"{tag}.txt")
trace_path = os.path.join(args.trace_dir, f"{tag}.json")
# wait skips noisy init, warmup runs through the profiler without
# recording (so caches/autotune settle), active is what shows up in
# the table/trace.
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()
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