tltorch.factorized_layers.FactorizedLinear

class tltorch.factorized_layers.FactorizedLinear(in_tensorized_features, out_tensorized_features, bias=True, factorization='cp', rank='same', n_layers=1)[source]

Tensorized Fully-Connected Layers

The weight matrice is tensorized to a tensor of size tensorized_shape. That tensor is expressed as a low-rank tensor. During inference, the full tensor is reconstructed, and unfolded back into a matrix, used for the forward pass in a regular linear layer.

Parameters
in_featuresint
out_featuresint
tensorized_shapeint tuple
rankint tuple or str
biasbool, default is True

Methods

forward(x[, indices])

Defines the computation performed at every call.

from_linear(linear, in_tensorized_features, …)

Class method to create an instance from an existing linear layer

from_linear_list(linear_list, …[, bias, …])

Class method to create an instance from an existing linear layer

get_linear

reset_parameters

forward(x, indices=0)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

classmethod from_linear(linear, in_tensorized_features, out_tensorized_features, rank, bias=True, factorization='CP', decomposition_kwargs={})[source]

Class method to create an instance from an existing linear layer

Parameters
lineartorch.nn.Linear

layer to tensorize

tensorized_shapetuple

shape to tensorized the factorized_weight matrix to. Must verify np.prod(tensorized_shape) == np.prod(linear.factorized_weight.shape)

rank{rank of the decomposition, ‘same’, float}

if float, percentage of parameters of the original factorized_weights to use if ‘same’ use the same number of parameters

biasbool, default is True
classmethod from_linear_list(linear_list, in_tensorized_features, out_tensorized_features, rank, bias=True, factorization='CP', decomposition_kwargs={'init': 'random'})[source]

Class method to create an instance from an existing linear layer

Parameters
lineartorch.nn.Linear

layer to tensorize

tensorized_shapetuple

shape to tensorized the weight matrix to. Must verify np.prod(tensorized_shape) == np.prod(linear.weight.shape)

rank{rank of the decomposition, ‘same’, float}

if float, percentage of parameters of the original weights to use if ‘same’ use the same number of parameters

biasbool, default is True