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
|
import operator_benchmark as op_bench
import torch
"""Microbenchmarks for MatMul operator"""
# Configs for PT Matmul operator
mm_short_configs = op_bench.config_list(
attr_names=["M", "N", "K", "trans_a", "trans_b"],
attrs=[
[1, 1, 1, True, False],
[128, 128, 128, True, False],
[256, 256, 256, False, True],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=["short"],
)
mm_long_configs = op_bench.cross_product_configs(
M=[32],
N=[512, 128],
K=[64],
trans_a=[False, True],
trans_b=[True, False],
device=['cpu', 'cuda'],
tags=["long"]
)
class MatMulBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K, trans_a, trans_b, device):
self.inputs = {
"input_one": torch.rand(M, N, device=device)
if trans_a
else torch.rand(N, M, device=device).t(),
"input_two": torch.rand(N, K, device=device)
if trans_b
else torch.rand(K, N, device=device).t(),
}
self.set_module_name("matmul")
def forward(self, input_one, input_two):
return torch.matmul(input_one, input_two)
op_bench.generate_pt_test(mm_long_configs + mm_short_configs, MatMulBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
