Files
pytorch/benchmarks/operator_benchmark/pt/qinstancenorm_test.py
Xuehai Pan 7763c83af6 [5/N][Easy] fix typo for usort config in pyproject.toml (kown -> known): sort torch (#127126)
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
2024-05-27 04:22:18 +00:00

51 lines
1.3 KiB
Python

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()