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
|
import operator_benchmark as op_bench
import torch
"""Microbenchmarks for quantized layernorm operator."""
layernorm_configs_short = op_bench.cross_product_configs(
dims=(
(1, 8, 16),
(8, 8, 16),
(32, 8, 16),
(64, 128, 56, 56),
),
dtype=(torch.qint8,),
tags=["short"],
)
class QLayerNormBenchmark(op_bench.TorchBenchmarkBase):
def init(self, dims, dtype):
X = (torch.rand(*dims) - 0.5) * 256
scale = 1.0
zero_point = 0
self.qX = torch.quantize_per_tensor(
X, scale=scale, zero_point=zero_point, dtype=dtype)
self.inputs = {
"qX": self.qX,
"weight": torch.rand(*self.qX.size()[1:], dtype=torch.float),
"bias": torch.rand(*self.qX.size()[1:], dtype=torch.float),
"eps": 1e-5,
"Y_scale": 0.1,
"Y_zero_point": 0
}
def forward(self, qX, weight, bias, eps: float, Y_scale: float, Y_zero_point: int):
return torch.ops.quantized.layer_norm(
qX, qX.size()[1:], weight=weight, bias=bias,
eps=eps, output_scale=Y_scale,
output_zero_point=Y_zero_point)
op_bench.generate_pt_test(layernorm_configs_short, QLayerNormBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
