Source code for torch.ao.nn.intrinsic.modules.fused
# mypy: allow-untyped-defs
import torch
from torch.nn import Conv1d, Conv2d, Conv3d, ReLU, Linear, BatchNorm1d, BatchNorm2d, BatchNorm3d
from torch.nn.utils.parametrize import type_before_parametrizations
__all__ = ['ConvReLU1d', 'ConvReLU2d', 'ConvReLU3d', 'LinearReLU', 'ConvBn1d', 'ConvBn2d',
'ConvBnReLU1d', 'ConvBnReLU2d', 'ConvBn3d', 'ConvBnReLU3d', 'BNReLU2d', 'BNReLU3d',
'LinearBn1d', 'LinearLeakyReLU', 'LinearTanh', 'ConvAdd2d', 'ConvAddReLU2d']
# Used for identifying intrinsic modules used in quantization
class _FusedModule(torch.nn.Sequential):
pass
[docs]class ConvReLU1d(_FusedModule):
r"""This is a sequential container which calls the Conv1d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
super().__init__(conv, relu)
[docs]class ConvReLU2d(_FusedModule):
r"""This is a sequential container which calls the Conv2d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
super().__init__(conv, relu)
[docs]class ConvReLU3d(_FusedModule):
r"""This is a sequential container which calls the Conv3d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(relu)}'
super().__init__(conv, relu)
[docs]class LinearReLU(_FusedModule):
r"""This is a sequential container which calls the Linear and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, linear, relu):
assert type_before_parametrizations(linear) == Linear and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(relu)}'
super().__init__(linear, relu)
[docs]class ConvBn1d(_FusedModule):
r"""This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(bn) == BatchNorm1d, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
super().__init__(conv, bn)
[docs]class ConvBn2d(_FusedModule):
r"""This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(bn) == BatchNorm2d, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
super().__init__(conv, bn)
[docs]class ConvBnReLU1d(_FusedModule):
r"""This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type_before_parametrizations(conv) == Conv1d and type_before_parametrizations(bn) == BatchNorm1d and \
type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}' # noqa: B950
super().__init__(conv, bn, relu)
[docs]class ConvBnReLU2d(_FusedModule):
r"""This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type_before_parametrizations(conv) == Conv2d and type_before_parametrizations(bn) == BatchNorm2d and \
type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}' # noqa: B950
super().__init__(conv, bn, relu)
[docs]class ConvBn3d(_FusedModule):
r"""This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(bn) == BatchNorm3d, \
f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}'
super().__init__(conv, bn)
[docs]class ConvBnReLU3d(_FusedModule):
r"""This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type_before_parametrizations(conv) == Conv3d and type_before_parametrizations(bn) == BatchNorm3d and \
type_before_parametrizations(relu) == ReLU, f'Incorrect types for input modules{type_before_parametrizations(conv)}{type_before_parametrizations(bn)}{type_before_parametrizations(relu)}' # noqa: B950
super().__init__(conv, bn, relu)
[docs]class BNReLU2d(_FusedModule):
r"""This is a sequential container which calls the BatchNorm 2d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, batch_norm, relu):
assert type_before_parametrizations(batch_norm) == BatchNorm2d and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}'
super().__init__(batch_norm, relu)
[docs]class BNReLU3d(_FusedModule):
r"""This is a sequential container which calls the BatchNorm 3d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, batch_norm, relu):
assert type_before_parametrizations(batch_norm) == BatchNorm3d and type_before_parametrizations(relu) == ReLU, \
f'Incorrect types for input modules{type_before_parametrizations(batch_norm)}{type_before_parametrizations(relu)}'
super().__init__(batch_norm, relu)
class LinearBn1d(_FusedModule):
r"""This is a sequential container which calls the Linear and BatchNorm1d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, linear, bn):
assert type_before_parametrizations(linear) == Linear and type_before_parametrizations(bn) == BatchNorm1d, \
f'Incorrect types for input modules{type_before_parametrizations(linear)}{type_before_parametrizations(bn)}'
super().__init__(linear, bn)
class LinearLeakyReLU(_FusedModule):
r"""This is a sequential container which calls the Linear and LeakyReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, linear, leaky_relu):
assert type(linear) == Linear and type(leaky_relu) == torch.nn.LeakyReLU, \
f'Incorrect types for input modules{type(linear)}{type(leaky_relu)}'
super().__init__(linear, leaky_relu)
class LinearTanh(_FusedModule):
r"""This is a sequential container which calls the Linear and Tanh modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, linear, tanh):
assert type(linear) == Linear and type(tanh) == torch.nn.Tanh, \
f'Incorrect types for input modules{type(linear)}{type(tanh)}'
super().__init__(linear, tanh)
class ConvAdd2d(_FusedModule):
r"""This is a sequential container which calls the Conv2d modules with extra Add.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, add):
super().__init__(conv)
self.add = add
def forward(self, x1, x2):
return self.add(self[0](x1), x2)
class ConvAddReLU2d(_FusedModule):
r"""This is a sequential container which calls the Conv2d, add, Relu.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, add, relu):
super().__init__(conv)
self.add = add
self.relu = relu
def forward(self, x1, x2):
return self.relu(self.add(self[0](x1), x2))