File size: 2,290 Bytes
18633c7 | 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 | import argparse
import os
import torch
from kernels import get_kernel
def main():
p = argparse.ArgumentParser()
p.add_argument("--batch", type=int, default=64)
p.add_argument("--seq", type=int, default=128)
p.add_argument("--dim", type=int, default=768)
p.add_argument("--hidden", type=int, default=3072)
p.add_argument("--compile", action="store_true")
p.add_argument("--trace_dir", default="./traces/03_kernels_mlp")
args = p.parse_args()
device = "cuda"
x = torch.randn(args.batch, args.seq, args.dim, device=device, dtype=torch.bfloat16)
class Config:
hidden_size = args.dim
intermediate_size = args.hidden
hidden_act = "gelu_pytorch_tanh"
kernels_layers = get_kernel("kernels-community/liger-kernels", version=1).layers
kernels_geglu_mlp = kernels_layers.LigerGEGLUMLP
kernels_geglu_mlp = kernels_geglu_mlp(Config()).to(device=device, dtype=torch.bfloat16).eval()
fwd = torch.compile(kernels_geglu_mlp) if args.compile else kernels_geglu_mlp
def step():
with torch.profiler.record_function("kernels_mlp_fwd"), torch.no_grad():
return fwd(x)
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.seq}_{args.dim}_{args.hidden}_{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() |