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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
39 lines
963 B
Python
39 lines
963 B
Python
import operator_benchmark as op_bench
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import torch
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import torch.nn.functional as F
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"""Microbenchmarks for layernorm operator."""
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layernorm_configs_short = op_bench.cross_product_configs(
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dims=(
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(1, 8, 16),
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(8, 8, 16),
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(32, 8, 16),
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(64, 128, 56, 56),
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),
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tags=["short"],
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)
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class LayerNormBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, dims):
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input = (torch.rand(*dims) - 0.5) * 256
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self.inputs = {
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"input": input,
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"weight": torch.rand(*input.size()[1:], dtype=torch.float),
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"bias": torch.rand(*input.size()[1:], dtype=torch.float),
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"eps": 1e-5,
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}
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def forward(self, input, weight, bias, eps: float):
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return F.layer_norm(input, input.size()[1:], weight=weight, bias=bias, eps=eps)
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op_bench.generate_pt_test(layernorm_configs_short, LayerNormBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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