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Source code for torch.ao.nn.intrinsic.modules.fused

# mypy: allow-untyped-defs
import torch
from torch.nn import (
    BatchNorm1d,
    BatchNorm2d,
    BatchNorm3d,
    Conv1d,
    Conv2d,
    Conv3d,
    Linear,
    ReLU,
)
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): # type: ignore[override] 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): # type: ignore[override] return self.relu(self.add(self[0](x1), x2))

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