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https://github.com/zebrajr/pytorch.git
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5b011fc6eb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73130 Approved by: https://github.com/malfet
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import operator_benchmark as op_bench
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import torch
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'''Microbenchmarks for the quantized interpolate op.
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Note: We are not benchmarking `upsample` as it is being depricated, and calls
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the `interpolate` anyway.
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'''
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qinterpolate_long_configs = op_bench.config_list(
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attr_names=['M', 'N', 'K'],
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attrs=[
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[512, 512, 512],
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],
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cross_product_configs={
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'dtype': [torch.quint8, torch.qint8, torch.qint32],
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'mode': ['nearest', 'bilinear'],
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'scale': [0.5, 1.0, 2.0],
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'contig': [True], # TODO: Add `False` after #29435
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},
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tags=['long']
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)
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qinterpolate_short_configs = op_bench.config_list(
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attr_names=['M', 'N', 'K', 'dtype', 'mode', 'scale', 'contig'],
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attrs=[
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[32, 32, 32, torch.quint8, 'nearest', 0.5, True], # Downsample
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[32, 32, 32, torch.quint8, 'bilinear', 0.5, True], # Downsample
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[32, 32, 32, torch.quint8, 'nearest', 2.0, True], # Upsample
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[32, 32, 32, torch.quint8, 'bilinear', 2.0, True], # Upsample
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[3, 720, 1280, torch.quint8, 'bilinear', 0.83333, True], # Downsample
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],
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tags=['short'],
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)
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class QInterpolateBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, dtype, mode, scale, contig):
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f_input = (torch.rand(1, M, N, K) - 0.5) * 256
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scale = 0.1
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zero_point = 42
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self.q_input = torch.quantize_per_tensor(f_input, scale=scale,
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zero_point=zero_point,
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dtype=dtype)
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if not contig:
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permute_dims = list(range(self.q_input.ndim))[::-1]
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self.q_input = self.q_input.permute(permute_dims)
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self.inputs = {
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"q_input": self.q_input,
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"scale_factor": scale,
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"mode": mode
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}
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self.set_module_name('q_interpolate')
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def forward(self, q_input, scale_factor: float, mode: str):
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return torch.nn.functional.interpolate(
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q_input, scale_factor=scale_factor, mode=mode)
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op_bench.generate_pt_test(qinterpolate_short_configs + qinterpolate_long_configs,
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QInterpolateBenchmark)
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if __name__ == '__main__':
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op_bench.benchmark_runner.main()
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