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import operator_benchmark as op_bench
import torch
import torch.nn.functional as F
"""Microbenchmarks for groupnorm operator."""
groupnorm_configs_short = op_bench.cross_product_configs(
dims=(
(32, 8, 16),
(32, 8, 56, 56),
),
num_groups=(2, 4),
tags=["short"],
)
class GroupNormBenchmark(op_bench.TorchBenchmarkBase):
def init(self, dims, num_groups):
num_channels = dims[1]
self.inputs = {
"input": (torch.rand(*dims) - 0.5) * 256,
"num_groups": num_groups,
"weight": torch.rand(num_channels, dtype=torch.float),
"bias": torch.rand(num_channels, dtype=torch.float),
"eps": 1e-5,
}
def forward(self, input, num_groups: int, weight, bias, eps: float):
return F.group_norm(input, num_groups, weight=weight, bias=bias, eps=eps)
op_bench.generate_pt_test(groupnorm_configs_short, GroupNormBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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