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import torch
import operator_benchmark as op_bench
# 2D pooling will have input matrix of rank 3 or 4
qpool2d_long_configs = op_bench.config_list(
attrs=(
# C H W k s p
( 1, 3, 3, (3, 3), (1, 1), (0, 0)), # dummy # noqa: E201,E241
( 3, 64, 64, (3, 3), (2, 2), (1, 1)), # dummy # noqa: E201,E241
# VGG16 pools with original input shape: (-1, 3, 224, 224)
( 64, 224, 224, (2, 2), (2, 2), (0, 0)), # MaxPool2d-4 # noqa: E201
(256, 56, 56, (2, 2), (2, 2), (0, 0)), # MaxPool2d-16 # noqa: E241
),
attr_names=('C', 'H', 'W', # Input layout
'k', 's', 'p'), # Pooling parameters
cross_product_configs={
'N': (1, 4),
'contig': (False, True),
'dtype': (torch.quint8,),
},
tags=('long',)
)
qpool2d_short_configs = op_bench.config_list(
attrs=((1, 3, 3, (3, 3), (1, 1), (0, 0)),), # dummy
attr_names=('C', 'H', 'W', # Input layout
'k', 's', 'p'), # Pooling parameters
cross_product_configs={
'N': (2,),
'contig': (True,),
'dtype': (torch.qint32, torch.qint8, torch.quint8),
},
tags=('short',)
)
qadaptive_avgpool2d_long_configs = op_bench.cross_product_configs(
input_size=(
# VGG16 pools with original input shape: (-1, 3, 224, 224)
(112, 112), # MaxPool2d-9
),
output_size=(
(448, 448),
# VGG16 pools with original input shape: (-1, 3, 224, 224)
(224, 224), # MaxPool2d-4
(112, 112), # MaxPool2d-9
( 56, 56), # MaxPool2d-16 # noqa: E201,E241
( 14, 14), # MaxPool2d-30 # noqa: E201,E241
),
N=(1, 4),
C=(1, 3, 64, 128),
contig=(False, True),
dtype=(torch.quint8,),
tags=('long',)
)
qadaptive_avgpool2d_short_configs = op_bench.config_list(
attrs=((4, 3, (224, 224), (112, 112), True),),
attr_names=('N', 'C', 'input_size', 'output_size', 'contig'),
cross_product_configs={
'dtype': (torch.qint32, torch.qint8, torch.quint8),
},
tags=('short',)
)
class _QPool2dBenchmarkBase(op_bench.TorchBenchmarkBase):
def setup(self, N, C, H, W, dtype, contig):
# Input
if N == 0:
f_input = (torch.rand(C, H, W) - 0.5) * 256
else:
f_input = (torch.rand(N, C, H, W) - 0.5) * 256
scale = 1.0
zero_point = 0
# Quantize the tensor
self.q_input = torch.quantize_per_tensor(f_input, scale=scale,
zero_point=zero_point,
dtype=dtype)
if not contig:
# Permute into NHWC and back to make it non-contiguous
if N == 0:
self.q_input = self.q_input.permute(1, 2, 0).contiguous()
self.q_input = self.q_input.permute(2, 0, 1)
else:
self.q_input = self.q_input.permute(0, 2, 3, 1).contiguous()
self.q_input = self.q_input.permute(0, 3, 1, 2)
self.inputs = {
"q_input": self.q_input
}
def forward(self, q_input):
return self.pool_op(q_input)
class QMaxPool2dBenchmark(_QPool2dBenchmarkBase):
def init(self, N, C, H, W, k, s, p, contig, dtype):
self.pool_op = torch.nn.MaxPool2d(kernel_size=k, stride=s, padding=p,
dilation=(1, 1), ceil_mode=False,
return_indices=False)
super(QMaxPool2dBenchmark, self).setup(N, C, H, W, dtype, contig)
class QAvgPool2dBenchmark(_QPool2dBenchmarkBase):
def init(self, N, C, H, W, k, s, p, contig, dtype):
self.pool_op = torch.nn.AvgPool2d(kernel_size=k, stride=s, padding=p,
ceil_mode=False)
super(QAvgPool2dBenchmark, self).setup(N, C, H, W, dtype, contig)
class QAdaptiveAvgPool2dBenchmark(_QPool2dBenchmarkBase):
def init(self, N, C, input_size, output_size, contig, dtype):
self.pool_op = torch.nn.AdaptiveAvgPool2d(output_size=output_size)
super(QAdaptiveAvgPool2dBenchmark, self).setup(N, C, *input_size,
dtype=dtype,
contig=contig)
op_bench.generate_pt_test(qadaptive_avgpool2d_short_configs + qadaptive_avgpool2d_long_configs,
QAdaptiveAvgPool2dBenchmark)
op_bench.generate_pt_test(qpool2d_short_configs + qpool2d_long_configs,
QAvgPool2dBenchmark)
op_bench.generate_pt_test(qpool2d_short_configs + qpool2d_long_configs,
QMaxPool2dBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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