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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for quantized instancenorm operator."""
instancenorm_configs_short = op_bench.cross_product_configs(
dims=(
(32, 8, 16),
(32, 8, 56, 56),
),
dtype=(torch.qint8,),
tags=["short"],
)
class QInstanceNormBenchmark(op_bench.TorchBenchmarkBase):
def init(self, dims, dtype):
X = (torch.rand(*dims) - 0.5) * 256
num_channels = dims[1]
scale = 1.0
zero_point = 0
self.inputs = {
"qX": torch.quantize_per_tensor(
X, scale=scale, zero_point=zero_point, dtype=dtype),
"weight": torch.rand(num_channels, dtype=torch.float),
"bias": torch.rand(num_channels, 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.instance_norm(
qX, weight=weight, bias=bias,
eps=eps, output_scale=Y_scale,
output_zero_point=Y_zero_point)
op_bench.generate_pt_test(instancenorm_configs_short, QInstanceNormBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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