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