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# torch.nn.utils.parametrizations.orthogonal¶

torch.nn.utils.parametrizations.orthogonal(module, name='weight', orthogonal_map=None, *, use_trivialization=True)[source]

Applies an orthogonal or unitary parametrization to a matrix or a batch of matrices.

Letting $\mathbb{K}$ be $\mathbb{R}$ or $\mathbb{C}$, the parametrized matrix $Q \in \mathbb{K}^{m \times n}$ is orthogonal as

\begin{align*} Q^{\text{H}}Q &= \mathrm{I}_n \mathrlap{\qquad \text{if }m \geq n}\\ QQ^{\text{H}} &= \mathrm{I}_m \mathrlap{\qquad \text{if }m < n} \end{align*}

where $Q^{\text{H}}$ is the conjugate transpose when $Q$ is complex and the transpose when $Q$ is real-valued, and $\mathrm{I}_n$ is the n-dimensional identity matrix. In plain words, $Q$ will have orthonormal columns whenever $m \geq n$ and orthonormal rows otherwise.

If the tensor has more than two dimensions, we consider it as a batch of matrices of shape (…, m, n).

The matrix $Q$ may be parametrized via three different orthogonal_map in terms of the original tensor:

• "matrix_exp"/"cayley": the matrix_exp() $Q = \exp(A)$ and the Cayley map $Q = (\mathrm{I}_n + A/2)(\mathrm{I}_n - A/2)^{-1}$ are applied to a skew-symmetric $A$ to give an orthogonal matrix.

• "householder": computes a product of Householder reflectors (householder_product()).

"matrix_exp"/"cayley" often make the parametrized weight converge faster than "householder", but they are slower to compute for very thin or very wide matrices.

If use_trivialization=True (default), the parametrization implements the “Dynamic Trivialization Framework”, where an extra matrix $B \in \mathbb{K}^{n \times n}$ is stored under module.parametrizations.weight.base. This helps the convergence of the parametrized layer at the expense of some extra memory use. See Trivializations for Gradient-Based Optimization on Manifolds .

Initial value of $Q$: If the original tensor is not parametrized and use_trivialization=True (default), the initial value of $Q$ is that of the original tensor if it is orthogonal (or unitary in the complex case) and it is orthogonalized via the QR decomposition otherwise (see torch.linalg.qr()). Same happens when it is not parametrized and orthogonal_map="householder" even when use_trivialization=False. Otherwise, the initial value is the result of the composition of all the registered parametrizations applied to the original tensor.

Note

This function is implemented using the parametrization functionality in register_parametrization().

Parameters:
• module (nn.Module) – module on which to register the parametrization.

• name (str, optional) – name of the tensor to make orthogonal. Default: "weight".

• orthogonal_map (str, optional) – One of the following: "matrix_exp", "cayley", "householder". Default: "matrix_exp" if the matrix is square or complex, "householder" otherwise.

• use_trivialization (bool, optional) – whether to use the dynamic trivialization framework. Default: True.

Returns:

The original module with an orthogonal parametrization registered to the specified weight

Return type:

Module

Example:

>>> orth_linear = orthogonal(nn.Linear(20, 40))
>>> orth_linear
ParametrizedLinear(
in_features=20, out_features=40, bias=True
(parametrizations): ModuleDict(
(weight): ParametrizationList(
(0): _Orthogonal()
)
)
)
>>> Q = orth_linear.weight
>>> torch.dist(Q.T @ Q, torch.eye(20))
tensor(4.9332e-07) ## Docs

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