aihwkit.nn.modules.conv module¶

Convolution layers.

class aihwkit.nn.modules.conv.AnalogConv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', rpu_config=None, realistic_read_write=False, weight_scaling_omega=0.0)¶

Bases: aihwkit.nn.modules.conv._AnalogConvNd

1D convolution layer that uses an analog tile.

Applies a 1D convolution over an input signal composed of several input planes, using an analog tile for its forward, backward and update passes.

Note

The tensor parameters of this layer (.weight and .bias) are not guaranteed to contain the same values as the internal weights and biases stored in the analog tile. Please use set_weights and get_weights when attempting to read or modify the weight/bias. This read/write process can simulate the (noisy and inexact) analog writing and reading of the resistive elements.

Parameters

in_channels – number of channels in the input image.
out_channels – number of channels produced by the convolution.
kernel_size – size of the convolving kernel.
stride – stride of the convolution.
padding – zero-padding added to both sides of the input.
dilation – spacing between kernel elements.
groups – number of blocked connections from input channels to output channels.
bias – whether to use a bias row on the analog tile or not.
padding_mode – padding strategy. Only 'zeros' is supported.
rpu_config – resistive processing unit configuration.
realistic_read_write – whether to enable realistic read/write for setting initial weights and read out of weights.
weight_scaling_omega – the weight value where the max weight will be scaled to. If zero, no weight scaling will be performed.

dilation¶

fold_indices¶

get_tile_size(in_channels, groups, kernel_size)¶

Calculate the tile size.

Parameters

in_channels (int) –
groups (int) –
kernel_size (Tuple[int, ..]) –

Return type

int

groups¶

in_channels¶

in_features¶

input_size¶

kernel_size¶

out_channels¶

out_features¶

output_padding¶

padding¶

padding_mode¶

realistic_read_write¶

recalculate_indexes(x_input)¶

Calculate and set the indexes of the analog tile.

Parameters: x_input (torch.Tensor) –
Return type: None

stride¶

transposed¶

weight_scaling_omega¶

class aihwkit.nn.modules.conv.AnalogConv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', rpu_config=None, realistic_read_write=False, weight_scaling_omega=0.0)¶

Bases: aihwkit.nn.modules.conv._AnalogConvNd

2D convolution layer that uses an analog tile.

Applies a 2D convolution over an input signal composed of several input planes, using an analog tile for its forward, backward and update passes.

Note

The tensor parameters of this layer (.weight and .bias) are not guaranteed to contain the same values as the internal weights and biases stored in the analog tile. Please use set_weights and get_weights when attempting to read or modify the weight/bias. This read/write process can simulate the (noisy and inexact) analog writing and reading of the resistive elements.

Parameters

in_channels – number of channels in the input image.
out_channels – number of channels produced by the convolution.
kernel_size – size of the convolving kernel.
stride – stride of the convolution.
padding – zero-padding added to both sides of the input.
dilation – spacing between kernel elements.
groups – number of blocked connections from input channels to output channels.
bias – whether to use a bias row on the analog tile or not.
padding_mode – padding strategy. Only 'zeros' is supported.
rpu_config – resistive processing unit configuration.
realistic_read_write – whether to enable realistic read/write for setting initial weights and read out of weights.
weight_scaling_omega – the weight value where the max weight will be scaled to. If zero, no weight scaling will be performed.

dilation¶

fold_indices¶

get_tile_size(in_channels, groups, kernel_size)¶

Calculate the tile size.

Parameters

in_channels (int) –
groups (int) –
kernel_size (Tuple[int, ..]) –

Return type

int

groups¶

in_channels¶

in_features¶

input_size¶

kernel_size¶

out_channels¶

out_features¶

output_padding¶

padding¶

padding_mode¶

realistic_read_write¶

recalculate_indexes(x_input)¶

Calculate and set the indexes of the analog tile.

Parameters: x_input (torch.Tensor) –
Return type: None

stride¶

transposed¶

weight_scaling_omega¶

class aihwkit.nn.modules.conv.AnalogConv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', rpu_config=None, realistic_read_write=False, weight_scaling_omega=0.0)¶

Bases: aihwkit.nn.modules.conv._AnalogConvNd

3D convolution layer that uses an analog tile.

Applies a 3D convolution over an input signal composed of several input planes, using an analog tile for its forward, backward and update passes.

Note

The tensor parameters of this layer (.weight and .bias) are not guaranteed to contain the same values as the internal weights and biases stored in the analog tile. Please use set_weights and get_weights when attempting to read or modify the weight/bias. This read/write process can simulate the (noisy and inexact) analog writing and reading of the resistive elements.

Parameters

in_channels – number of channels in the input image.
out_channels – number of channels produced by the convolution.
kernel_size – size of the convolving kernel.
stride – stride of the convolution.
padding – zero-padding added to both sides of the input.
dilation – spacing between kernel elements.
groups – number of blocked connections from input channels to output channels.
bias – whether to use a bias row on the analog tile or not.
padding_mode – padding strategy. Only 'zeros' is supported.
rpu_config – resistive processing unit configuration.
realistic_read_write – whether to enable realistic read/write for setting initial weights and read out of weights.
weight_scaling_omega – the weight value where the max weight will be scaled to. If zero, no weight scaling will be performed.

dilation¶

fold_indices¶

get_tile_size(in_channels, groups, kernel_size)¶

Calculate the tile size.

Parameters

in_channels (int) –
groups (int) –
kernel_size (Tuple[int, ..]) –

Return type

int

groups¶

in_channels¶

in_features¶

input_size¶

kernel_size¶

out_channels¶

out_features¶

output_padding¶

padding¶

padding_mode¶

realistic_read_write¶

recalculate_indexes(x_input)¶

Calculate and set the indexes of the analog tile.

Parameters: x_input (torch.Tensor) –
Return type: None

stride¶

transposed¶

weight_scaling_omega¶