torch.svd¶

torch.
svd
(input, some=True, compute_uv=True, *, out=None) > (Tensor, Tensor, Tensor)¶ Computes the singular value decomposition of either a matrix or batch of matrices
input
. The singular value decomposition is represented as a namedtuple (U, S, V), such thatinput
= U diag(S) Vᴴ. where Vᴴ is the transpose of V for real inputs, and the conjugate transpose of V for complex inputs. Ifinput
is a batch of matrices, then U, S, and V are also batched with the same batch dimensions asinput
.If
some
is True (default), the method returns the reduced singular value decomposition. In this case, if the last two dimensions ofinput
are m and n, then the returned U and V matrices will contain only min(n, m) orthonormal columns.If
compute_uv
is False, the returned U and V will be zerofilled matrices of shape (m, m) and (n, n) respectively, and the same device asinput
. The argumentsome
has no effect whencompute_uv
is False.Supports
input
of float, double, cfloat and cdouble data types. The dtypes of U and V are the same asinput
’s. S will always be realvalued, even ifinput
is complex.Warning
torch.svd()
is deprecated. Please usetorch.linalg.svd()
instead, which is similar to NumPy’s numpy.linalg.svd.Note
Differences with
torch.linalg.svd()
:some
is the opposite oftorch.linalg.svd()
’sfull_matrices
. Note that default value for both is True, so the default behavior is effectively the opposite.torch.svd()
returns V, whereastorch.linalg.svd()
returns Vh, that is, Vᴴ.If
compute_uv
is False,torch.svd()
returns zerofilled tensors for U and Vh, whereastorch.linalg.svd()
returns empty tensors.
Note
The singular values are returned in descending order. If
input
is a batch of matrices, then the singular values of each matrix in the batch are returned in descending order.Note
The S tensor can only be used to compute gradients if
compute_uv
is True.Note
When
some
is False, the gradients on U[…, :, min(m, n):] and V[…, :, min(m, n):] will be ignored in the backward pass, as those vectors can be arbitrary bases of the corresponding subspaces.Note
The implementation of
torch.linalg.svd()
on CPU uses LAPACK’s routine ?gesdd (a divideandconquer algorithm) instead of ?gesvd for speed. Analogously, on GPU, it uses cuSOLVER’s routines gesvdj and gesvdjBatched on CUDA 10.1.243 and later, and MAGMA’s routine gesdd on earlier versions of CUDA.Note
The returned U will not be contiguous. The matrix (or batch of matrices) will be represented as a columnmajor matrix (i.e. Fortrancontiguous).
Warning
The gradients with respect to U and V will only be finite when the input does not have zero nor repeated singular values.
Warning
If the distance between any two singular values is close to zero, the gradients with respect to U and V will be numerically unstable, as they depends on $\frac{1}{\min_{i \neq j} \sigma_i^2  \sigma_j^2}$ . The same happens when the matrix has small singular values, as these gradients also depend on S⁻¹.
Warning
For complexvalued
input
the singular value decomposition is not unique, as U and V may be multiplied by an arbitrary phase factor $e^{i \phi}$ on every column. The same happens wheninput
has repeated singular values, where one may multiply the columns of the spanning subspace in U and V by a rotation matrix and the resulting vectors will span the same subspace. Different platforms, like NumPy, or inputs on different device types, may produce different U and V tensors. Parameters
input (Tensor) – the input tensor of size (*, m, n) where * is zero or more batch dimensions consisting of (m, n) matrices.
some (bool, optional) – controls whether to compute the reduced or full decomposition, and consequently, the shape of returned U and V. Default: True.
compute_uv (bool, optional) – controls whether to compute U and V. Default: True.
 Keyword Arguments
out (tuple, optional) – the output tuple of tensors
Example:
>>> a = torch.randn(5, 3) >>> a tensor([[ 0.2364, 0.7752, 0.6372], [ 1.7201, 0.7394, 0.0504], [0.3371, 1.0584, 0.5296], [ 0.3550, 0.4022, 1.5569], [ 0.2445, 0.0158, 1.1414]]) >>> u, s, v = torch.svd(a) >>> u tensor([[ 0.4027, 0.0287, 0.5434], [0.1946, 0.8833, 0.3679], [ 0.4296, 0.2890, 0.5261], [ 0.6604, 0.2717, 0.2618], [ 0.4234, 0.2481, 0.4733]]) >>> s tensor([2.3289, 2.0315, 0.7806]) >>> v tensor([[0.0199, 0.8766, 0.4809], [0.5080, 0.4054, 0.7600], [ 0.8611, 0.2594, 0.4373]]) >>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t())) tensor(8.6531e07) >>> a_big = torch.randn(7, 5, 3) >>> u, s, v = torch.svd(a_big) >>> torch.dist(a_big, torch.matmul(torch.matmul(u, torch.diag_embed(s)), v.transpose(2, 1))) tensor(2.6503e06)