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Aliases in torch.ao#

Created On: Dec 01, 2025 | Last Updated On: Dec 01, 2025

The following are aliases to their counterparts in torch.ao in nested namespaces.

torch.ao.nn.intrinsic.qat.modules#

The following are aliases to their counterparts in torch.ao.nn.intrinsic.qat in the torch.ao.nn.intrinsic.qat.module namespace.

torch.ao.nn.intrinsic.qat.modules.conv_fused (Aliases)#

`conv_fused.ConvReLU1d`	A ConvReLU1d module is a fused module of Conv1d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.
`conv_fused.ConvReLU2d`	A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.
`conv_fused.ConvReLU3d`	A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.
`conv_fused.ConvBnReLU1d`	A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.
`conv_fused.ConvBnReLU2d`	A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.
`conv_fused.ConvBnReLU3d`	A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.

torch.ao.nn.intrinsic.qat.modules.linear_fused (Aliases)#

linear_fused.LinearBn1d

A LinearBn1d module is a module fused from Linear and BatchNorm1d, attached with FakeQuantize modules for weight, used in quantization aware training.

torch.ao.nn.intrinsic.qat.modules.linear_relu (Aliases)#

linear_relu.LinearReLU

A LinearReLU module fused from Linear and ReLU modules, attached with FakeQuantize modules for weight, used in quantization aware training.

torch.ao.nn.intrinsic.quantized.modules#

The following are aliases to their counterparts in torch.ao.nn.intrinsic.quantized in the torch.ao.nn.intrinsic.quantized.modules namespace.

torch.ao.nn.intrinsic.quantized.modules.conv_relu (Aliases)#

`conv_relu.ConvReLU1d`	A ConvReLU1d module is a fused module of Conv1d and ReLU
`conv_relu.ConvReLU2d`	A ConvReLU2d module is a fused module of Conv2d and ReLU
`conv_relu.ConvReLU3d`	A ConvReLU3d module is a fused module of Conv3d and ReLU

torch.ao.nn.intrinsic.quantized.modules.bn_relu (Aliases)#

`bn_relu.BNReLU2d`	A BNReLU2d module is a fused module of BatchNorm2d and ReLU
`bn_relu.BNReLU3d`	A BNReLU3d module is a fused module of BatchNorm3d and ReLU

torch.ao.nn.intrinsic.quantized.modules.conv_add (Aliases)#

`conv_add.ConvAdd2d`	A ConvAdd2d module is a fused module of Conv2d and Add
`conv_add.ConvAddReLU2d`	A ConvAddReLU2d module is a fused module of Conv2d, Add and Relu

torch.ao.nn.intrinsic.quantized.modules.linear_relu (Aliases)#

`linear_relu.LinearLeakyReLU`	For onednn backend only A LinearLeakyReLU module fused from Linear and LeakyReLU modules We adopt the same interface as `torch.ao.nn.quantized.Linear`.
`linear_relu.LinearReLU`	A LinearReLU module fused from Linear and ReLU modules
`linear_relu.LinearTanh`	A LinearTanh module fused from Linear and Tanh modules

torch.ao.nn.intrinsic.quantized.dynamic.modules#

The following are aliases to their counterparts in the torch.ao.nn.intrinsic.quantized.dynamic namespace.

torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu (Aliases)#

linear_relu.LinearReLU

A LinearReLU module fused from Linear and ReLU modules that can be used for dynamic quantization.

torch.ao.nn.intrinsic.modules#

The following are aliases to their counterparts in the torch.ao.nn.intrinsic namespace.

torch.ao.nn.intrinsic.modules.fused (Aliases)#

`fused.ConvAdd2d`	This is a sequential container which calls the Conv2d modules with extra Add.
`fused.ConvAddReLU2d`	This is a sequential container which calls the Conv2d, add, Relu.
`fused.LinearBn1d`	This is a sequential container which calls the Linear and BatchNorm1d modules.
`fused.LinearLeakyReLU`	This is a sequential container which calls the Linear and LeakyReLU modules.
`fused.LinearTanh`	This is a sequential container which calls the Linear and Tanh modules.

torch.ao.nn.intrinsic.modules.torch.ao.nn.qat.modules#

The following are aliases to their counterparts in the torch.ao.nn.qat namespace.

torch.ao.nn.intrinsic.modules.conv (Aliases)#

`conv.Conv1d`	A Conv1d module attached with FakeQuantize modules for weight, used for quantization aware training.
`conv.Conv2d`	A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training.
`conv.Conv3d`	A Conv3d module attached with FakeQuantize modules for weight, used for quantization aware training.

torch.ao.nn.intrinsic.modules.embedding_ops (Aliases)#

`embedding_ops.Embedding`	An embedding bag module attached with FakeQuantize modules for weight, used for quantization aware training.
`embedding_ops.EmbeddingBag`	An embedding bag module attached with FakeQuantize modules for weight, used for quantization aware training.

torch.ao.nn.intrinsic.modules.linear (Aliases)#

linear.Linear

A linear module attached with FakeQuantize modules for weight, used for quantization aware training.

torch.ao.nn.quantizable.modules#

The following are aliases to their counterparts in the torch.ao.nn.quantizable namespace.

torch.ao.nn.quantizable.modules.activation (Aliases)#

activation.MultiheadAttention

torch.ao.nn.quantizable.modules.rnn (Aliases)#

`rnn.LSTM`	A quantizable long short-term memory (LSTM).
`rnn.LSTMCell`	A quantizable long short-term memory (LSTM) cell.

torch.ao.nn.quantized.dynamic.modules#

The following are aliases to their counterparts in the torch.ao.nn.quantized.dynamic namespace.

torch.ao.nn.quantized.dynamic.modules.conv (Aliases)#

`conv.Conv1d`	A dynamically quantized conv module with floating point tensors as inputs and outputs.
`conv.Conv2d`	A dynamically quantized conv module with floating point tensors as inputs and outputs.
`conv.Conv3d`	A dynamically quantized conv module with floating point tensors as inputs and outputs.
`conv.ConvTranspose1d`	A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
`conv.ConvTranspose2d`	A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
`conv.ConvTranspose3d`	A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.

torch.ao.nn.quantized.dynamic.modules.linear (Aliases)#