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https://github.com/zebrajr/pytorch.git
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dd3a77bc96
Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/105928 Approved by: https://github.com/albanD
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import torch
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import operator_benchmark as op_bench
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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(
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f_input, scale=scale, zero_point=zero_point, dtype=dtype
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)
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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 = {"q_input": self.q_input, "scale_factor": scale, "mode": mode}
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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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)
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op_bench.generate_pt_test(
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qinterpolate_short_configs + qinterpolate_long_configs, QInterpolateBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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