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import operator_benchmark as op_bench
import torch
import torch.nn.functional as F
"""Microbenchmarks for layernorm operator."""
layernorm_configs_short = op_bench.cross_product_configs(
dims=(
(1, 8, 16),
(8, 8, 16),
(32, 8, 16),
(64, 128, 56, 56),
),
tags=["short"],
)
class LayerNormBenchmark(op_bench.TorchBenchmarkBase):
def init(self, dims):
input = (torch.rand(*dims) - 0.5) * 256
self.inputs = {
"input": input,
"weight": torch.rand(*input.size()[1:], dtype=torch.float),
"bias": torch.rand(*input.size()[1:], dtype=torch.float),
"eps": 1e-5,
}
def forward(self, input, weight, bias, eps: float):
return F.layer_norm(input, input.size()[1:], weight=weight, bias=bias, eps=eps)
op_bench.generate_pt_test(layernorm_configs_short, LayerNormBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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