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import argparse
import random
import torch
def bench(nt_a, nt_b, niter):
# Warmup
nt_a.bmm(nt_b)
torch.cuda.synchronize()
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
for iter in range(niter):
nt_a.bmm(nt_b)
end_event.record()
torch.cuda.synchronize()
runtime = (start_event.elapsed_time(end_event)) / niter
return runtime
def sweep_n(niter, dtype):
for ntensor in [4, 8, 16, 32, 64, 128, 256]:
tensors = [torch.randn(256, random.randint(100, 200)) for t in range(ntensor)]
nt_a = torch.nested.nested_tensor(
tensors,
dtype=dtype,
device="cuda",
)
nt_b = torch.nested.nested_tensor(
[t.t() for t in tensors],
dtype=dtype,
device="cuda",
)
runtime = bench(nt_a, nt_b, niter)
nt_a_size = torch.ops.aten._nested_tensor_size(nt_a)
lengths = nt_a_size[:, 1]
print(
",".join(
map(
str,
[
ntensor,
dtype,
lengths.min().item(),
lengths.float().mean().item(),
lengths.max().item(),
runtime,
],
)
)
)
if __name__ == "__main__":
random.seed(123)
parser = argparse.ArgumentParser(description="Nested Tensor BMM Benchmark")
parser.add_argument("--niter", default="10", type=int)
args = parser.parse_args()
niter = args.niter
print("ntensor,dtype,min_length,mean_length,max_length,runtime")
sweep_n(niter, torch.float32)
sweep_n(niter, torch.float16)
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