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c0ed38e644
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
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for quantized 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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dtype=(torch.qint8,),
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tags=["short"],
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)
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class QLayerNormBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, dims, dtype):
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X = (torch.rand(*dims) - 0.5) * 256
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scale = 1.0
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zero_point = 0
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self.qX = torch.quantize_per_tensor(
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X, scale=scale, zero_point=zero_point, dtype=dtype
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)
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self.inputs = {
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"qX": self.qX,
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"weight": torch.rand(*self.qX.size()[1:], dtype=torch.float),
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"bias": torch.rand(*self.qX.size()[1:], dtype=torch.float),
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"eps": 1e-5,
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"Y_scale": 0.1,
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"Y_zero_point": 0,
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}
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def forward(self, qX, weight, bias, eps: float, Y_scale: float, Y_zero_point: int):
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return torch.ops.quantized.layer_norm(
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qX,
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qX.size()[1:],
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weight=weight,
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bias=bias,
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eps=eps,
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output_scale=Y_scale,
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output_zero_point=Y_zero_point,
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)
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op_bench.generate_pt_test(layernorm_configs_short, QLayerNormBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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