torchlogix.layers.LogicDense

class torchlogix.layers.LogicDense(in_dim, out_dim, device='cpu', grad_factor=1.0, lut_rank=2, parametrization='raw', parametrization_kwargs=None, connections='fixed', connections_kwargs=None)

Fully-connected logic gate layer with differentiable learning.

This module provides the core implementation of Differentiable Deep Logic Gate Networks. Each neuron learns a Boolean logic function (LUT) that operates on a subset of input features.

Parameters:

• in_dim (int) – Number of input features.

• out_dim (int) – Number of neurons (output features).

• device (str) – Device to run the layer on (‘cpu’ or ‘cuda’).

• grad_factor (float) – Gradient scaling factor.

• lut_rank (int) – Rank of the LUTs used in the layer.

• parametrization (str) – Type of parametrization to use (‘raw’, ‘warp’, ‘light’).

• parametrization_kwargs (dict) – Additional keyword arguments for parametrization.

• connections (str) – Type of connections to use (‘fixed’, ‘learnable’, etc.).

• connections_kwargs (dict) – Additional keyword arguments for connections.

__init__(in_dim, out_dim, device='cpu', grad_factor=1.0, lut_rank=2, parametrization='raw', parametrization_kwargs=None, connections='fixed', connections_kwargs=None)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(in_dim, out_dim[, device, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x)	Applies the LogicDense transformation to the input.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training

__init__(in_dim, out_dim, device='cpu', grad_factor=1.0, lut_rank=2, parametrization='raw', parametrization_kwargs=None, connections='fixed', connections_kwargs=None)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Applies the LogicDense transformation to the input.

For each neuron, the layer: 1. Selects lut_rank input features according to the connection

pattern in self.indices.

2. Samples (or selects) LUT weights based on self.weight and the sampler strategy.

3. Evaluates the resulting binary operation.

Parameters:

x – Input tensor of shape (..., in_dim). The last dimension must match self.in_dim.

Returns:

A tensor of shape (..., out_dim) containing the neuron outputs.

extra_repr()

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

get_luts_and_ids(**kwargs)

Computes the most probable LUT and its ID for each neuron.

Method is dependent on the chosen parametrization.

Returns:

• luts: Boolean tensor of shape (out_dim, 2 ** lut_rank), where each row is the most probable LUT truth table for a neuron (entry is True for output 1, False for 0).

• ids: Integer tensor of shape (out_dim,) where each entry is the integer ID of the corresponding LUT, obtained by interpreting its truth table as a binary number (or None if not applicable for high lut_rank).

Return type:

Tuple[torch.Tensor, torch.Tensor]

get_luts(**kwargs)

Computes the most probable LUT for each neuron.

Method is dependent on the chosen parametrization.

Returns:

Boolean tensor of shape (out_dim, 2 ** lut_rank),

where each row is the most probable LUT truth table for a neuron (entry is True for output 1, False for 0).

Return type:

torch.Tensor

get_regularization_loss(regularizer)

Computes regularization loss for the layer.

Returns:

Scalar tensor representing the regularization loss.

Return type:

torch.Tensor

rescale_weights(method)

Rescales the weights of the layer according to the specified method.

Parameters:

method (str) – Rescaling method. Options are ‘clip’, ‘abs_sum’, ‘L2’.