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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
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for channel_shuffle operator."""
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# Configs for PT channel_shuffle operator
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channel_shuffle_long_configs = op_bench.cross_product_configs(
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batch_size=[4, 8],
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channels_per_group=[32, 64],
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height=[32, 64],
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width=[32, 64],
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groups=[4, 8],
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channel_last=[True, False],
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tags=["long"],
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)
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channel_shuffle_short_configs = op_bench.config_list(
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attr_names=["batch_size", "channels_per_group", "height", "width", "groups"],
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attrs=[
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[2, 16, 16, 16, 2],
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[2, 32, 32, 32, 2],
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[4, 32, 32, 32, 4],
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[4, 64, 64, 64, 4],
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[8, 64, 64, 64, 8],
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[16, 64, 64, 64, 16],
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],
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cross_product_configs={
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"channel_last": [True, False],
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},
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tags=["short"],
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)
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class ChannelSHuffleBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, batch_size, channels_per_group, height, width, groups, channel_last):
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channels = channels_per_group * groups
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data_shape = (batch_size, channels, height, width)
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input_data = torch.rand(data_shape)
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if channel_last:
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input_data = input_data.contiguous(memory_format=torch.channels_last)
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self.inputs = {"input_data": input_data, "groups": groups}
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self.set_module_name("channel_shuffle")
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def forward(self, input_data, groups: int):
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return torch.channel_shuffle(input_data, groups)
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op_bench.generate_pt_test(
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channel_shuffle_short_configs + channel_shuffle_long_configs,
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ChannelSHuffleBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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