tltorch.CPLinear
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class
tltorch.CPLinear(in_features, out_features, tensorized_shape, rank, bias=True)[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 (CP) 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
See also
Attributes: full_weightReturns the reconstruced matrix weight of the linear layer
Methods
forward(input)Inference using the tensorized and factorized weight matrix get_decomposition()Returns the decomposition parametrizing the layer init_from_decomposition(cp_tensor[, bias])Initializes the factorization from the given decomposition init_from_random([decompose_full_weight])Initializes the factorization randomly init_from_tensor(tensor[, bias, …])Initializes the layer by decomposing a full tensor -
forward(input)[source] Inference using the tensorized and factorized weight matrix
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init_from_random(decompose_full_weight=True)[source] Initializes the factorization randomly
Parameters: - decompose_full_weightbool, default is True
if True, a full weight is created and decomposed to initialize the factors otherwise, the factors of the decomposition are directly initialized
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init_from_decomposition(cp_tensor, bias=None)[source] Initializes the factorization from the given decomposition
Parameters: - tucker_tensor(weights, factors)
values to initialize the decomposition parametrizing the layer to
- biastorch.Tensor or None, default is None
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init_from_tensor(tensor, bias=None, decomposition_kwargs={'init': 'random'})[source] Initializes the layer by decomposing a full tensor
Parameters: - tensortorch.Tensor
must be either a matrix or a tensor must verify
np.prod(tensor.shape) == np.prod(self.tensorized_shape)- biastorch.Tensor or None, default is None
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get_decomposition()[source] Returns the decomposition parametrizing the layer
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property
full_weight Returns the reconstruced matrix weight of the linear layer