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android / platform / external / pytorch / c78a4a52d2 / . / torch / linalg / __init__.py
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import sys
import torch
from torch._C import _add_docstr, _linalg # type: ignore
Tensor = torch.Tensor
# Note: This not only adds doc strings for functions in the linalg namespace, but
# also connects the torch.linalg Python namespace to the torch._C._linalg builtins.
cholesky = _add_docstr(_linalg.linalg_cholesky, r"""
linalg.cholesky(input, *, out=None) -> Tensor
Computes the Cholesky decomposition of a Hermitian (or symmetric for real-valued matrices)
positive-definite matrix or the Cholesky decompositions for a batch of such matrices.
Each decomposition has the form:
.. math::
\text{input} = LL^H
where :math:`L` is a lower-triangular matrix and :math:`L^H` is the conjugate transpose of :math:`L`,
which is just a transpose for the case of real-valued input matrices.
In code it translates to ``input = L @ L.t()`` if :attr:`input` is real-valued and
``input = L @ L.conj().t()`` if :attr:`input` is complex-valued.
The batch of :math:`L` matrices is returned.
Supports real-valued and complex-valued inputs.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: LAPACK's `potrf` is used for CPU inputs, and MAGMA's `potrf` is used for CUDA inputs.
.. note:: If :attr:`input` is not a Hermitian positive-definite matrix, or if it's a batch of matrices
and one or more of them is not a Hermitian positive-definite matrix, then a RuntimeError will be thrown.
If :attr:`input` is a batch of matrices, then the error message will include the batch index
of the first matrix that is not Hermitian positive-definite.
Args:
input (Tensor): the input tensor of size :math:`(*, n, n)` consisting of Hermitian positive-definite
:math:`n \times n` matrices, where :math:`*` is zero or more batch dimensions.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
Examples::
>>> a = torch.randn(2, 2, dtype=torch.complex128)
>>> a = torch.mm(a, a.t().conj()) # creates a Hermitian positive-definite matrix
>>> l = torch.linalg.cholesky(a)
>>> a
tensor([[2.5266+0.0000j, 1.9586-2.0626j],
[1.9586+2.0626j, 9.4160+0.0000j]], dtype=torch.complex128)
>>> l
tensor([[1.5895+0.0000j, 0.0000+0.0000j],
[1.2322+1.2976j, 2.4928+0.0000j]], dtype=torch.complex128)
>>> torch.mm(l, l.t().conj())
tensor([[2.5266+0.0000j, 1.9586-2.0626j],
[1.9586+2.0626j, 9.4160+0.0000j]], dtype=torch.complex128)
>>> a = torch.randn(3, 2, 2, dtype=torch.float64)
>>> a = torch.matmul(a, a.transpose(-2, -1)) # creates a symmetric positive-definite matrix
>>> l = torch.linalg.cholesky(a)
>>> a
tensor([[[ 1.1629, 2.0237],
[ 2.0237, 6.6593]],
[[ 0.4187, 0.1830],
[ 0.1830, 0.1018]],
[[ 1.9348, -2.5744],
[-2.5744, 4.6386]]], dtype=torch.float64)
>>> l
tensor([[[ 1.0784, 0.0000],
[ 1.8766, 1.7713]],
[[ 0.6471, 0.0000],
[ 0.2829, 0.1477]],
[[ 1.3910, 0.0000],
[-1.8509, 1.1014]]], dtype=torch.float64)
>>> torch.allclose(torch.matmul(l, l.transpose(-2, -1)), a)
True
""")
inv = _add_docstr(_linalg.linalg_inv, r"""
linalg.inv(input, *, out=None) -> Tensor
Computes the multiplicative inverse matrix of a square matrix :attr:`input`, or of each square matrix in a
batched :attr:`input`. The result satisfies the relation:
``matmul(inv(input),input)`` = ``matmul(input,inv(input))`` = ``eye(input.shape[0]).expand_as(input)``.
Supports input of float, double, cfloat and cdouble data types.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The inverse matrix is computed using LAPACK's `getrf` and `getri` routines for CPU inputs. For CUDA
inputs, cuSOLVER's `getrf` and `getrs` routines as well as cuBLAS' `getrf` and `getri` routines are
used if CUDA version >= 10.1.243, otherwise MAGMA's `getrf` and `getri` routines are used instead.
.. note:: If :attr:`input` is a non-invertible matrix or non-square matrix, or batch with at least one such matrix,
then a RuntimeError will be thrown.
Args:
input (Tensor): the square `(n, n)` matrix or the batch of such matrices of size
`(*, n, n)` where `*` is one or more batch dimensions.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default is ``None``.
Examples::
>>> x = torch.rand(4, 4)
>>> y = torch.linalg.inv(x)
>>> z = torch.mm(x, y)
>>> z
tensor([[ 1.0000, -0.0000, -0.0000, 0.0000],
[ 0.0000, 1.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 1.0000, 0.0000],
[ 0.0000, -0.0000, -0.0000, 1.0000]])
>>> torch.max(torch.abs(z - torch.eye(4))) # Max non-zero
tensor(1.1921e-07)
>>> # Batched inverse example
>>> x = torch.randn(2, 3, 4, 4)
>>> y = torch.linalg.inv(x)
>>> z = torch.matmul(x, y)
>>> torch.max(torch.abs(z - torch.eye(4).expand_as(x))) # Max non-zero
tensor(1.9073e-06)
>>> x = torch.rand(4, 4, dtype=torch.cdouble)
>>> y = torch.linalg.inv(x)
>>> z = torch.mm(x, y)
>>> z
tensor([[ 1.0000e+00+0.0000e+00j, -1.3878e-16+3.4694e-16j,
5.5511e-17-1.1102e-16j, 0.0000e+00-1.6653e-16j],
[ 5.5511e-16-1.6653e-16j, 1.0000e+00+6.9389e-17j,
2.2204e-16-1.1102e-16j, -2.2204e-16+1.1102e-16j],
[ 3.8858e-16-1.2490e-16j, 2.7756e-17+3.4694e-17j,
1.0000e+00+0.0000e+00j, -4.4409e-16+5.5511e-17j],
[ 4.4409e-16+5.5511e-16j, -3.8858e-16+1.8041e-16j,
2.2204e-16+0.0000e+00j, 1.0000e+00-3.4694e-16j]],
dtype=torch.complex128)
>>> torch.max(torch.abs(z - torch.eye(4, dtype=torch.cdouble))) # Max non-zero
tensor(7.5107e-16, dtype=torch.float64)
""")
det = _add_docstr(_linalg.linalg_det, r"""
linalg.det(input) -> Tensor
Computes the determinant of a square matrix :attr:`input`, or of each square matrix
in a batched :attr:`input`.
This function supports float, double, cfloat and cdouble dtypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The determinant is computed using LU factorization. LAPACK's `getrf` is used for CPU inputs,
and MAGMA's `getrf` is used for CUDA inputs.
.. note:: Backward through `det` internally uses :func:`torch.linalg.svd` when :attr:`input` is not
invertible. In this case, double backward through `det` will be unstable when :attr:`input`
doesn't have distinct singular values. See :func:`torch.linalg.svd` for more details.
Args:
input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size
`(*, n, n)` where `*` is one or more batch dimensions.
Example::
>>> a = torch.randn(3, 3)
>>> a
tensor([[ 0.9478, 0.9158, -1.1295],
[ 0.9701, 0.7346, -1.8044],
[-0.2337, 0.0557, 0.6929]])
>>> torch.linalg.det(a)
tensor(0.0934)
>>> a = torch.randn(3, 2, 2)
>>> a
tensor([[[ 0.9254, -0.6213],
[-0.5787, 1.6843]],
[[ 0.3242, -0.9665],
[ 0.4539, -0.0887]],
[[ 1.1336, -0.4025],
[-0.7089, 0.9032]]])
>>> torch.linalg.det(a)
tensor([1.1990, 0.4099, 0.7386])
""")
slogdet = _add_docstr(_linalg.linalg_slogdet, r"""
linalg.slogdet(input, *, out=None) -> (Tensor, Tensor)
Calculates the sign and natural logarithm of the absolute value of a square matrix's determinant,
or of the absolute values of the determinants of a batch of square matrices :attr:`input`.
The determinant can be computed with ``sign * exp(logabsdet)``.
Supports input of float, double, cfloat and cdouble datatypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The determinant is computed using LU factorization. LAPACK's `getrf` is used for CPU inputs,
and MAGMA's `getrf` is used for CUDA inputs.
.. note:: For matrices that have zero determinant, this returns ``(0, -inf)``.
If :attr:`input` is batched then the entries in the result tensors corresponding to matrices with
the zero determinant have sign 0 and the natural logarithm of the absolute value of the determinant -inf.
Args:
input (Tensor): the input matrix of size :math:`(n, n)` or the batch of matrices of size :math:`(*, n, n)`
where :math:`*` is one or more batch dimensions.
Keyword args:
out (tuple, optional): tuple of two tensors to write the output to.
Returns:
A namedtuple (sign, logabsdet) containing the sign of the determinant and the natural logarithm
of the absolute value of determinant, respectively.
Example::
>>> A = torch.randn(3, 3)
>>> A
tensor([[ 0.0032, -0.2239, -1.1219],
[-0.6690, 0.1161, 0.4053],
[-1.6218, -0.9273, -0.0082]])
>>> torch.linalg.det(A)
tensor(-0.7576)
>>> torch.linalg.logdet(A)
tensor(nan)
>>> torch.linalg.slogdet(A)
torch.return_types.linalg_slogdet(sign=tensor(-1.), logabsdet=tensor(-0.2776))
""")
eigh = _add_docstr(_linalg.linalg_eigh, r"""
linalg.eigh(input, UPLO='L', *, out=None) -> (Tensor, Tensor)
Computes the eigenvalues and eigenvectors of a complex Hermitian (or real symmetric)
matrix :attr:`input`, or of each such matrix in a batched :attr:`input`.
For a single matrix :attr:`input`, the tensor of eigenvalues `w` and the tensor of eigenvectors
`V` decompose the :attr:`input` such that `input = V diag(w) Vᴴ`, where `Vᴴ` is the transpose of `V`
for real-valued :attr:`input`, or the conjugate transpose of `V` for complex-valued :attr:`input`.
Since the matrix or matrices in :attr:`input` are assumed to be Hermitian, the imaginary part of their diagonals
is always treated as zero. When :attr:`UPLO` is "L", its default value, only the lower triangular part of each
matrix is used in the computation. When :attr:`UPLO` is "U" only the upper triangular part of each matrix is used.
Supports input of float, double, cfloat and cdouble dtypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The eigenvalues/eigenvectors are computed using LAPACK's `syevd` and `heevd` routines for CPU inputs,
and MAGMA's `syevd` and `heevd` routines for CUDA inputs.
.. note:: The eigenvalues of real symmetric or complex Hermitian matrices are always real.
.. note:: The eigenvectors of matrices are not unique, so any eigenvector multiplied by a constant remains
a valid eigenvector. This function may compute different eigenvector representations on
different device types. Usually the difference is only in the sign of the eigenvector.
.. note:: See :func:`torch.linalg.eigvalsh` for a related function that computes only eigenvalues.
However, that function is not differentiable.
Args:
input (Tensor): the Hermitian `n \times n` matrix or the batch of such matrices of size
`(*, n, n)` where `*` is one or more batch dimensions.
UPLO ('L', 'U', optional): controls whether to use the upper-triangular or the lower-triangular part
of :attr:`input` in the computations. Default is ``'L'``.
Keyword args:
out (tuple, optional): tuple of two tensors to write the output to. Default is ``None``.
Returns:
(Tensor, Tensor): A namedtuple (eigenvalues, eigenvectors) containing
- **eigenvalues** (*Tensor*): Shape `(*, m)`.
The eigenvalues in ascending order.
- **eigenvectors** (*Tensor*): Shape `(*, m, m)`.
The orthonormal eigenvectors of the :attr:`input`.
Examples::
>>> a = torch.randn(2, 2, dtype=torch.complex128)
>>> a = a + a.t().conj() # creates a Hermitian matrix
>>> a
tensor([[2.9228+0.0000j, 0.2029-0.0862j],
[0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
>>> w, v = torch.linalg.eigh(a)
>>> w
tensor([0.3277, 2.9415], dtype=torch.float64)
>>> v
tensor([[-0.0846+-0.0000j, -0.9964+0.0000j],
[ 0.9170+0.3898j, -0.0779-0.0331j]], dtype=torch.complex128)
>>> torch.allclose(torch.matmul(v, torch.matmul(w.to(v.dtype).diag_embed(), v.t().conj())), a)
True
>>> a = torch.randn(3, 2, 2, dtype=torch.float64)
>>> a = a + a.transpose(-2, -1) # creates a symmetric matrix
>>> w, v = torch.linalg.eigh(a)
>>> torch.allclose(torch.matmul(v, torch.matmul(w.diag_embed(), v.transpose(-2, -1))), a)
True
""")
eigvalsh = _add_docstr(_linalg.linalg_eigvalsh, r"""
linalg.eigvalsh(input, UPLO='L', *, out=None) -> Tensor
Computes the eigenvalues of a complex Hermitian (or real symmetric) matrix :attr:`input`,
or of each such matrix in a batched :attr:`input`. The eigenvalues are returned in ascending order.
Since the matrix or matrices in :attr:`input` are assumed to be Hermitian, the imaginary part of their diagonals
is always treated as zero. When :attr:`UPLO` is "L", its default value, only the lower triangular part of
each matrix is used in the computation. When :attr:`UPLO` is "U" only the upper triangular part of each matrix is used.
Supports input of float, double, cfloat and cdouble dtypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The eigenvalues are computed using LAPACK's `syevd` and `heevd` routines for CPU inputs,
and MAGMA's `syevd` and `heevd` routines for CUDA inputs.
.. note:: The eigenvalues of real symmetric or complex Hermitian matrices are always real.
.. note:: This function doesn't support backpropagation, please use :func:`torch.linalg.eigh` instead,
which also computes the eigenvectors.
.. note:: See :func:`torch.linalg.eigh` for a related function that computes both eigenvalues and eigenvectors.
Args:
input (Tensor): the Hermitian `n \times n` matrix or the batch
of such matrices of size `(*, n, n)` where `*` is one or more batch dimensions.
UPLO ('L', 'U', optional): controls whether to use the upper-triangular or the lower-triangular part
of :attr:`input` in the computations. Default is ``'L'``.
Keyword args:
out (Tensor, optional): tensor to write the output to. Default is ``None``.
Examples::
>>> a = torch.randn(2, 2, dtype=torch.complex128)
>>> a = a + a.t().conj() # creates a Hermitian matrix
>>> a
tensor([[2.9228+0.0000j, 0.2029-0.0862j],
[0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
>>> w = torch.linalg.eigvalsh(a)
>>> w
tensor([0.3277, 2.9415], dtype=torch.float64)
>>> a = torch.randn(3, 2, 2, dtype=torch.float64)
>>> a = a + a.transpose(-2, -1) # creates a symmetric matrix
>>> a
tensor([[[ 2.8050, -0.3850],
[-0.3850, 3.2376]],
[[-1.0307, -2.7457],
[-2.7457, -1.7517]],
[[ 1.7166, 2.2207],
[ 2.2207, -2.0898]]], dtype=torch.float64)
>>> w = torch.linalg.eigvalsh(a)
>>> w
tensor([[ 2.5797, 3.4629],
[-4.1605, 1.3780],
[-3.1113, 2.7381]], dtype=torch.float64)
""")
matrix_rank = _add_docstr(_linalg.linalg_matrix_rank, r"""
matrix_rank(input, tol=None, hermitian=False, *, out=None) -> Tensor
Computes the numerical rank of a matrix :attr:`input`, or of each matrix in a batched :attr:`input`.
The matrix rank is computed as the number of singular values (or absolute eigenvalues when :attr:`hermitian` is ``True``)
that are greater than the specified :attr:`tol` threshold.
If :attr:`tol` is not specified, :attr:`tol` is set to ``S.max(dim=-1)*max(input.shape[-2:])*eps``,
where ``S`` is the singular values (or absolute eigenvalues when :attr:`hermitian` is ``True``), and
``eps`` is the epsilon value for the datatype of :attr:`input`. The epsilon value can be obtained using
the ``eps`` attribute of :class:`torch.finfo`.
Supports input of float, double, cfloat and cdouble dtypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The matrix rank is computed using singular value decomposition (see :func:`torch.linalg.svd`) by default.
If :attr:`hermitian` is ``True``, then :attr:`input` is assumed to be Hermitian (symmetric if real-valued),
and the computation is done by obtaining the eigenvalues (see :func:`torch.linalg.eigvalsh`).
Args:
input (Tensor): the input matrix of size `(m, n)` or the batch of matrices of size `(*, m, n)`
where `*` is one or more batch dimensions.
tol (float, optional): the tolerance value. Default is ``None``
hermitian(bool, optional): indicates whether :attr:`input` is Hermitian. Default is ``False``.
Keyword args:
out (Tensor, optional): tensor to write the output to. Default is ``None``.
Examples::
>>> a = torch.eye(10)
>>> torch.linalg.matrix_rank(a)
tensor(10)
>>> b = torch.eye(10)
>>> b[0, 0] = 0
>>> torch.linalg.matrix_rank(b)
tensor(9)
>>> a = torch.randn(4, 3, 2)
>>> torch.linalg.matrix_rank(a)
tensor([2, 2, 2, 2])
>>> a = torch.randn(2, 4, 2, 3)
>>> torch.linalg.matrix_rank(a)
tensor([[2, 2, 2, 2],
[2, 2, 2, 2]])
>>> a = torch.randn(2, 4, 3, 3, dtype=torch.complex64)
>>> torch.linalg.matrix_rank(a)
tensor([[3, 3, 3, 3],
[3, 3, 3, 3]])
>>> torch.linalg.matrix_rank(a, hermitian=True)
tensor([[3, 3, 3, 3],
[3, 3, 3, 3]])
>>> torch.linalg.matrix_rank(a, tol=1.0)
tensor([[3, 2, 2, 2],
[1, 2, 1, 2]])
>>> torch.linalg.matrix_rank(a, tol=1.0, hermitian=True)
tensor([[2, 2, 2, 1],
[1, 2, 2, 2]])
""")
norm = _add_docstr(_linalg.linalg_norm, r"""
linalg.norm(input, ord=None, dim=None, keepdim=False, *, out=None, dtype=None) -> Tensor
Returns the matrix norm or vector norm of a given tensor.
This function can calculate one of eight different types of matrix norms, or one
of an infinite number of vector norms, depending on both the number of reduction
dimensions and the value of the `ord` parameter.
Args:
input (Tensor): The input tensor. If dim is None, x must be 1-D or 2-D, unless :attr:`ord`
is None. If both :attr:`dim` and :attr:`ord` are None, the 2-norm of the input flattened to 1-D
will be returned. Its data type must be either a floating point or complex type. For complex
inputs, the norm is calculated on of the absolute values of each element. If the input is
complex and neither :attr:`dtype` nor :attr:`out` is specified, the result's data type will
be the corresponding floating point type (e.g. float if :attr:`input` is complexfloat).
ord (int, float, inf, -inf, 'fro', 'nuc', optional): The order of norm.
inf refers to :attr:`float('inf')`, numpy's :attr:`inf` object, or any equivalent object.
The following norms can be calculated:
===== ============================ ==========================
ord norm for matrices norm for vectors
===== ============================ ==========================
None Frobenius norm 2-norm
'fro' Frobenius norm -- not supported --
'nuc' nuclear norm -- not supported --
inf max(sum(abs(x), dim=1)) max(abs(x))
-inf min(sum(abs(x), dim=1)) min(abs(x))
0 -- not supported -- sum(x != 0)
1 max(sum(abs(x), dim=0)) as below
-1 min(sum(abs(x), dim=0)) as below
2 2-norm (largest sing. value) as below
-2 smallest singular value as below
other -- not supported -- sum(abs(x)**ord)**(1./ord)
===== ============================ ==========================
Default: ``None``
dim (int, 2-tuple of ints, 2-list of ints, optional): If :attr:`dim` is an int,
vector norm will be calculated over the specified dimension. If :attr:`dim`
is a 2-tuple of ints, matrix norm will be calculated over the specified
dimensions. If :attr:`dim` is None, matrix norm will be calculated
when the input tensor has two dimensions, and vector norm will be
calculated when the input tensor has one dimension. Default: ``None``
keepdim (bool, optional): If set to True, the reduced dimensions are retained
in the result as dimensions with size one. Default: ``False``
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
dtype (:class:`torch.dtype`, optional): If specified, the input tensor is cast to
:attr:`dtype` before performing the operation, and the returned tensor's type
will be :attr:`dtype`. If this argument is used in conjunction with the
:attr:`out` argument, the output tensor's type must match this argument or a
RuntimeError will be raised. Default: ``None``
Examples::
>>> import torch
>>> from torch import linalg as LA
>>> a = torch.arange(9, dtype=torch.float) - 4
>>> a
tensor([-4., -3., -2., -1., 0., 1., 2., 3., 4.])
>>> b = a.reshape((3, 3))
>>> b
tensor([[-4., -3., -2.],
[-1., 0., 1.],
[ 2., 3., 4.]])
>>> LA.norm(a)
tensor(7.7460)
>>> LA.norm(b)
tensor(7.7460)
>>> LA.norm(b, 'fro')
tensor(7.7460)
>>> LA.norm(a, float('inf'))
tensor(4.)
>>> LA.norm(b, float('inf'))
tensor(9.)
>>> LA.norm(a, -float('inf'))
tensor(0.)
>>> LA.norm(b, -float('inf'))
tensor(2.)
>>> LA.norm(a, 1)
tensor(20.)
>>> LA.norm(b, 1)
tensor(7.)
>>> LA.norm(a, -1)
tensor(0.)
>>> LA.norm(b, -1)
tensor(6.)
>>> LA.norm(a, 2)
tensor(7.7460)
>>> LA.norm(b, 2)
tensor(7.3485)
>>> LA.norm(a, -2)
tensor(0.)
>>> LA.norm(b.double(), -2)
tensor(1.8570e-16, dtype=torch.float64)
>>> LA.norm(a, 3)
tensor(5.8480)
>>> LA.norm(a, -3)
tensor(0.)
Using the :attr:`dim` argument to compute vector norms::
>>> c = torch.tensor([[1., 2., 3.],
... [-1, 1, 4]])
>>> LA.norm(c, dim=0)
tensor([1.4142, 2.2361, 5.0000])
>>> LA.norm(c, dim=1)
tensor([3.7417, 4.2426])
>>> LA.norm(c, ord=1, dim=1)
tensor([6., 6.])
Using the :attr:`dim` argument to compute matrix norms::
>>> m = torch.arange(8, dtype=torch.float).reshape(2, 2, 2)
>>> LA.norm(m, dim=(1,2))
tensor([ 3.7417, 11.2250])
>>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
(tensor(3.7417), tensor(11.2250))
""")
svd = _add_docstr(_linalg.linalg_svd, r"""
linalg.svd(input, full_matrices=True, compute_uv=True, *, out=None) -> (Tensor, Tensor, Tensor)
Computes the singular value decomposition of either a matrix or batch of
matrices :attr:`input`." The singular value decomposition is represented as a
namedtuple ``(U, S, Vh)``, such that :math:`input = U \mathbin{@} diag(S) \times
Vh`. If :attr:`input` is a batch of tensors, then ``U``, ``S``, and ``Vh`` are
also batched with the same batch dimensions as :attr:`input`.
If :attr:`full_matrices` is ``False`` (default), the method returns the reduced singular
value decomposition i.e., if the last two dimensions of :attr:`input` are
``m`` and ``n``, then the returned `U` and `V` matrices will contain only
:math:`min(n, m)` orthonormal columns.
If :attr:`compute_uv` is ``False``, the returned `U` and `Vh` will be empy
tensors with no elements and the same device as :attr:`input`. The
:attr:`full_matrices` argument has no effect when :attr:`compute_uv` is False.
The dtypes of ``U`` and ``V`` are the same as :attr:`input`'s. ``S`` will
always be real-valued, even if :attr:`input` is complex.
.. note:: Unlike NumPy's ``linalg.svd``, this always returns a namedtuple of
three tensors, even when :attr:`compute_uv=False`. This behavior may
change in a future PyTorch release.
.. note:: The singular values are returned in descending order. If :attr:`input` is a batch of matrices,
then the singular values of each matrix in the batch is returned in descending order.
.. note:: The implementation of SVD on CPU uses the LAPACK routine `?gesdd` (a divide-and-conquer
algorithm) instead of `?gesvd` for speed. Analogously, the SVD on GPU uses the cuSOLVER routines
`gesvdj` and `gesvdjBatched` on CUDA 10.1.243 and later, and uses the MAGMA routine `gesdd`
on earlier versions of CUDA.
.. note:: The returned matrix `U` will be transposed, i.e. with strides
:code:`U.contiguous().transpose(-2, -1).stride()`.
.. note:: Gradients computed using `U` and `Vh` may be unstable if
:attr:`input` is not full rank or has non-unique singular values.
.. note:: When :attr:`full_matrices` = ``True``, the gradients on :code:`U[..., :, min(m, n):]`
and :code:`V[..., :, min(m, n):]` will be ignored in backward as those vectors
can be arbitrary bases of the subspaces.
.. note:: The `S` tensor can only be used to compute gradients if :attr:`compute_uv` is True.
.. note:: Since `U` and `V` of an SVD is not unique, each vector can be multiplied by
an arbitrary phase factor :math:`e^{i \phi}` while the SVD result is still correct.
Different platforms, like Numpy, or inputs on different device types, may produce different
`U` and `V` tensors.
Args:
input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more
batch dimensions consisting of :math:`m \times n` matrices.
full_matrices (bool, optional): controls whether to compute the full or reduced decomposition, and
consequently the shape of returned ``U`` and ``V``. Defaults to True.
compute_uv (bool, optional): whether to compute `U` and `V` or not. Defaults to True.
out (tuple, optional): a tuple of three tensors to use for the outputs. If compute_uv=False,
the 1st and 3rd arguments must be tensors, but they are ignored. E.g. you can
pass `(torch.Tensor(), out_S, torch.Tensor())`
Example::
>>> import torch
>>> a = torch.randn(5, 3)
>>> a
tensor([[-0.3357, -0.2987, -1.1096],
[ 1.4894, 1.0016, -0.4572],
[-1.9401, 0.7437, 2.0968],
[ 0.1515, 1.3812, 1.5491],
[-1.8489, -0.5907, -2.5673]])
>>>
>>> # reconstruction in the full_matrices=False case
>>> u, s, vh = torch.linalg.svd(a, full_matrices=False)
>>> u.shape, s.shape, vh.shape
(torch.Size([5, 3]), torch.Size([3]), torch.Size([3, 3]))
>>> torch.dist(a, u @ torch.diag(s) @ vh)
tensor(1.0486e-06)
>>>
>>> # reconstruction in the full_matrices=True case
>>> u, s, vh = torch.linalg.svd(a)
>>> u.shape, s.shape, vh.shape
(torch.Size([5, 5]), torch.Size([3]), torch.Size([3, 3]))
>>> torch.dist(a, u[:, :3] @ torch.diag(s) @ vh)
>>> torch.dist(a, u[:, :3] @ torch.diag(s) @ vh)
tensor(1.0486e-06)
>>>
>>> # extra dimensions
>>> a_big = torch.randn(7, 5, 3)
>>> u, s, vh = torch.linalg.svd(a_big, full_matrices=False)
>>> torch.dist(a_big, u @ torch.diag_embed(s) @ vh)
tensor(3.0957e-06)
""")
cond = _add_docstr(_linalg.linalg_cond, r"""
linalg.cond(input, p=None, *, out=None) -> Tensor
Computes the condition number of a matrix :attr:`input`, or of each matrix in
a batched :attr:`input`, using the matrix norm defined by :attr:`p`.
For norms `{'fro', 'nuc', inf, -inf, 1, -1}` this is defined as the matrix norm of :attr:`input`
times the matrix norm of the inverse of :attr:`input` computed using :func:`torch.linalg.norm`. While
for norms `{None, 2, -2}` this is defined as the ratio between the largest and smallest singular
values computed using :func:`torch.linalg.svd`.
This function supports float, double, cfloat and cdouble dtypes.
.. note:: When given inputs on a CUDA device, this function may synchronize that device with the CPU depending
on which norm :attr:`p` is used.
.. note:: For norms `{None, 2, -2}`, :attr:`input` may be a non-square matrix or batch of non-square matrices.
For other norms, however, :attr:`input` must be a square matrix or a batch of square matrices,
and if this requirement is not satisfied a RuntimeError will be thrown.
.. note:: For norms `{'fro', 'nuc', inf, -inf, 1, -1}` if :attr:`input` is a non-invertible matrix then
a tensor containing infinity will be returned. If :attr:`input` is a batch of matrices and one
or more of them is not invertible then a RuntimeError will be thrown.
Args:
input (Tensor): the input matrix of size `(m, n)` or the batch of matrices of size `(*, m, n)`
where `*` is one or more batch dimensions.
p (int, float, inf, -inf, 'fro', 'nuc', optional): the type of the matrix norm to use in the computations.
inf refers to :attr:`float('inf')`, numpy's :attr:`inf` object, or any equivalent object.
The following norms can be used:
===== ============================
p norm for matrices
===== ============================
None ratio of the largest singular value to the smallest singular value
'fro' Frobenius norm
'nuc' nuclear norm
inf max(sum(abs(x), dim=1))
-inf min(sum(abs(x), dim=1))
1 max(sum(abs(x), dim=0))
-1 min(sum(abs(x), dim=0))
2 ratio of the largest singular value to the smallest singular value
-2 ratio of the smallest singular value to the largest singular value
===== ============================
Default: ``None``
Keyword args:
out (Tensor, optional): tensor to write the output to. Default is ``None``.
Returns:
The condition number of :attr:`input`. The output dtype is always real valued
even for complex inputs (e.g. float if :attr:`input` is cfloat).
Examples::
>>> a = torch.randn(3, 4, 4, dtype=torch.complex64)
>>> torch.linalg.cond(a)
>>> a = torch.tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])
>>> torch.linalg.cond(a)
tensor([1.4142])
>>> torch.linalg.cond(a, 'fro')
tensor(3.1623)
>>> torch.linalg.cond(a, 'nuc')
tensor(9.2426)
>>> torch.linalg.cond(a, float('inf'))
tensor(2.)
>>> torch.linalg.cond(a, float('-inf'))
tensor(1.)
>>> torch.linalg.cond(a, 1)
tensor(2.)
>>> torch.linalg.cond(a, -1)
tensor(1.)
>>> torch.linalg.cond(a, 2)
tensor([1.4142])
>>> torch.linalg.cond(a, -2)
tensor([0.7071])
>>> a = torch.randn(2, 3, 3)
>>> a
tensor([[[-0.9204, 1.1140, 1.2055],
[ 0.3988, -0.2395, -0.7441],
[-0.5160, 0.3115, 0.2619]],
[[-2.2128, 0.9241, 2.1492],
[-1.1277, 2.7604, -0.8760],
[ 1.2159, 0.5960, 0.0498]]])
>>> torch.linalg.cond(a)
tensor([[9.5917],
[3.2538]])
>>> a = torch.randn(2, 3, 3, dtype=torch.complex64)
>>> a
tensor([[[-0.4671-0.2137j, -0.1334-0.9508j, 0.6252+0.1759j],
[-0.3486-0.2991j, -0.1317+0.1252j, 0.3025-0.1604j],
[-0.5634+0.8582j, 0.1118-0.4677j, -0.1121+0.7574j]],
[[ 0.3964+0.2533j, 0.9385-0.6417j, -0.0283-0.8673j],
[ 0.2635+0.2323j, -0.8929-1.1269j, 0.3332+0.0733j],
[ 0.1151+0.1644j, -1.1163+0.3471j, -0.5870+0.1629j]]])
>>> torch.linalg.cond(a)
tensor([[4.6245],
[4.5671]])
>>> torch.linalg.cond(a, 1)
tensor([9.2589, 9.3486])
""")
pinv = _add_docstr(_linalg.linalg_pinv, r"""
linalg.pinv(input, rcond=1e-15, hermitian=False, *, out=None) -> Tensor
Computes the pseudo-inverse (also known as the Moore-Penrose inverse) of a matrix :attr:`input`,
or of each matrix in a batched :attr:`input`.
The singular values (or the absolute values of the eigenvalues when :attr:`hermitian` is ``True``)
that are below the specified :attr:`rcond` threshold are treated as zero and discarded in the computation.
Supports input of float, double, cfloat and cdouble datatypes.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
.. note:: The pseudo-inverse is computed using singular value decomposition (see :func:`torch.linalg.svd`) by default.
If :attr:`hermitian` is ``True``, then :attr:`input` is assumed to be Hermitian (symmetric if real-valued),
and the computation of the pseudo-inverse is done by obtaining the eigenvalues and eigenvectors
(see :func:`torch.linalg.eigh`).
.. note:: If singular value decomposition or eigenvalue decomposition algorithms do not converge
then a RuntimeError will be thrown.
Args:
input (Tensor): the input matrix of size `(m, n)` or the batch of matrices of size `(*, m, n)`
where `*` is one or more batch dimensions.
rcond (float, Tensor, optional): the tolerance value to determine the cutoff for small singular values.
Must be broadcastable to the singular values of :attr:`input` as returned
by :func:`torch.svd`. Default is ``1e-15``.
hermitian(bool, optional): indicates whether :attr:`input` is Hermitian. Default is ``False``.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default is ``None``.
Examples::
>>> input = torch.randn(3, 5)
>>> input
tensor([[ 0.5495, 0.0979, -1.4092, -0.1128, 0.4132],
[-1.1143, -0.3662, 0.3042, 1.6374, -0.9294],
[-0.3269, -0.5745, -0.0382, -0.5922, -0.6759]])
>>> torch.linalg.pinv(input)
tensor([[ 0.0600, -0.1933, -0.2090],
[-0.0903, -0.0817, -0.4752],
[-0.7124, -0.1631, -0.2272],
[ 0.1356, 0.3933, -0.5023],
[-0.0308, -0.1725, -0.5216]])
Batched linalg.pinv example
>>> a = torch.randn(2, 6, 3)
>>> b = torch.linalg.pinv(a)
>>> torch.matmul(b, a)
tensor([[[ 1.0000e+00, 1.6391e-07, -1.1548e-07],
[ 8.3121e-08, 1.0000e+00, -2.7567e-07],
[ 3.5390e-08, 1.4901e-08, 1.0000e+00]],
[[ 1.0000e+00, -8.9407e-08, 2.9802e-08],
[-2.2352e-07, 1.0000e+00, 1.1921e-07],
[ 0.0000e+00, 8.9407e-08, 1.0000e+00]]])
Hermitian input example
>>> a = torch.randn(3, 3, dtype=torch.complex64)
>>> a = a + a.t().conj() # creates a Hermitian matrix
>>> b = torch.linalg.pinv(a, hermitian=True)
>>> torch.matmul(b, a)
tensor([[ 1.0000e+00+0.0000e+00j, -1.1921e-07-2.3842e-07j,
5.9605e-08-2.3842e-07j],
[ 5.9605e-08+2.3842e-07j, 1.0000e+00+2.3842e-07j,
-4.7684e-07+1.1921e-07j],
[-1.1921e-07+0.0000e+00j, -2.3842e-07-2.9802e-07j,
1.0000e+00-1.7897e-07j]])
Non-default rcond example
>>> rcond = 0.5
>>> a = torch.randn(3, 3)
>>> torch.linalg.pinv(a)
tensor([[ 0.2971, -0.4280, -2.0111],
[-0.0090, 0.6426, -0.1116],
[-0.7832, -0.2465, 1.0994]])
>>> torch.linalg.pinv(a, rcond)
tensor([[-0.2672, -0.2351, -0.0539],
[-0.0211, 0.6467, -0.0698],
[-0.4400, -0.3638, -0.0910]])
Matrix-wise rcond example
>>> a = torch.randn(5, 6, 2, 3, 3)
>>> rcond = torch.rand(2) # different rcond values for each matrix in a[:, :, 0] and a[:, :, 1]
>>> torch.linalg.pinv(a, rcond)
>>> rcond = torch.randn(5, 6, 2) # different rcond value for each matrix in 'a'
>>> torch.linalg.pinv(a, rcond)
""")
solve = _add_docstr(_linalg.linalg_solve, r"""
linalg.solve(input, other, *, out=None) -> Tensor
Computes the solution ``x`` to the matrix equation ``matmul(input, x) = other``
with a square matrix, or batches of such matrices, :attr:`input` and one or more right-hand side vectors :attr:`other`.
If :attr:`input` is batched and :attr:`other` is not, then :attr:`other` is broadcast
to have the same batch dimensions as :attr:`input`.
The resulting tensor has the same shape as the (possibly broadcast) :attr:`other`.
Supports input of ``float``, ``double``, ``cfloat`` and ``cdouble`` dtypes.
.. note:: If :attr:`input` is a non-square or non-invertible matrix, or a batch containing non-square matrices
or one or more non-invertible matrices, then a RuntimeError will be thrown.
.. note:: When given inputs on a CUDA device, this function synchronizes that device with the CPU.
Args:
input (Tensor): the square :math:`n \times n` matrix or the batch
of such matrices of size :math:`(*, n, n)` where ``*`` is one or more batch dimensions.
other (Tensor): right-hand side tensor of shape :math:`(*, n)` or :math:`(*, n, k)`,
where :math:`k` is the number of right-hand side vectors.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
Examples::
>>> A = torch.eye(3)
>>> b = torch.randn(3)
>>> x = torch.linalg.solve(A, b)
>>> torch.allclose(A @ x, b)
True
Batched input::
>>> A = torch.randn(2, 3, 3)
>>> b = torch.randn(3, 1)
>>> x = torch.linalg.solve(A, b)
>>> torch.allclose(A @ x, b)
True
>>> b = torch.rand(3) # b is broadcast internally to (*A.shape[:-2], 3)
>>> x = torch.linalg.solve(A, b)
>>> x.shape
torch.Size([2, 3])
>>> Ax = A @ x.unsqueeze(-1)
>>> torch.allclose(Ax, b.unsqueeze(-1).expand_as(Ax))
True
""")
tensorinv = _add_docstr(_linalg.linalg_tensorinv, r"""
linalg.tensorinv(input, ind=2, *, out=None) -> Tensor
Computes a tensor ``input_inv`` such that ``tensordot(input_inv, input, ind) == I_n`` (inverse tensor equation),
where ``I_n`` is the n-dimensional identity tensor and ``n`` is equal to ``input.ndim``.
The resulting tensor ``input_inv`` has shape equal to ``input.shape[ind:] + input.shape[:ind]``.
Supports input of ``float``, ``double``, ``cfloat`` and ``cdouble`` data types.
.. note:: If :attr:`input` is not invertible or does not satisfy the requirement
``prod(input.shape[ind:]) == prod(input.shape[:ind])``,
then a RuntimeError will be thrown.
.. note:: When :attr:`input` is a 2-dimensional tensor and ``ind=1``, this function computes the
(multiplicative) inverse of :attr:`input`, equivalent to calling :func:`torch.inverse`.
Args:
input (Tensor): A tensor to invert. Its shape must satisfy ``prod(input.shape[:ind]) == prod(input.shape[ind:])``.
ind (int): A positive integer that describes the inverse tensor equation. See :func:`torch.tensordot` for details. Default: 2.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
Examples::
>>> a = torch.eye(4 * 6).reshape((4, 6, 8, 3))
>>> ainv = torch.linalg.tensorinv(a, ind=2)
>>> ainv.shape
torch.Size([8, 3, 4, 6])
>>> b = torch.randn(4, 6)
>>> torch.allclose(torch.tensordot(ainv, b), torch.linalg.tensorsolve(a, b))
True
>>> a = torch.randn(4, 4)
>>> a_tensorinv = torch.linalg.tensorinv(a, ind=1)
>>> a_inv = torch.inverse(a)
>>> torch.allclose(a_tensorinv, a_inv)
True
""")
tensorsolve = _add_docstr(_linalg.linalg_tensorsolve, r"""
linalg.tensorsolve(input, other, dims=None, *, out=None) -> Tensor
Computes a tensor ``x`` such that ``tensordot(input, x, dims=x.ndim) = other``.
The resulting tensor ``x`` has the same shape as ``input[other.ndim:]``.
Supports real-valued and complex-valued inputs.
.. note:: If :attr:`input` does not satisfy the requirement
``prod(input.shape[other.ndim:]) == prod(input.shape[:other.ndim])``
after (optionally) moving the dimensions using :attr:`dims`, then a RuntimeError will be thrown.
Args:
input (Tensor): "left-hand-side" tensor, it must satisfy the requirement
``prod(input.shape[other.ndim:]) == prod(input.shape[:other.ndim])``.
other (Tensor): "right-hand-side" tensor of shape ``input.shape[other.ndim]``.
dims (Tuple[int]): dimensions of :attr:`input` to be moved before the computation.
Equivalent to calling ``input = movedim(input, dims, range(len(dims) - input.ndim, 0))``.
If None (default), no dimensions are moved.
Keyword args:
out (Tensor, optional): The output tensor. Ignored if ``None``. Default: ``None``
Examples::
>>> a = torch.eye(2 * 3 * 4).reshape((2 * 3, 4, 2, 3, 4))
>>> b = torch.randn(2 * 3, 4)
>>> x = torch.linalg.tensorsolve(a, b)
>>> x.shape
torch.Size([2, 3, 4])
>>> torch.allclose(torch.tensordot(a, x, dims=x.ndim), b)
True
>>> a = torch.randn(6, 4, 4, 3, 2)
>>> b = torch.randn(4, 3, 2)
>>> x = torch.linalg.tensorsolve(a, b, dims=(0, 2))
>>> x.shape
torch.Size([6, 4])
>>> a = a.permute(1, 3, 4, 0, 2)
>>> a.shape[b.ndim:]
torch.Size([6, 4])
>>> torch.allclose(torch.tensordot(a, x, dims=x.ndim), b, atol=1e-6)
True
""")
qr = _add_docstr(_linalg.linalg_qr, r"""
qr(input, mode='reduced', *, out=None) -> (Tensor, Tensor)
Computes the QR decomposition of a matrix or a batch of matrices :attr:`input`,
and returns a namedtuple (Q, R) of tensors such that :math:`\text{input} = Q R`
with :math:`Q` being an orthogonal matrix or batch of orthogonal matrices and
:math:`R` being an upper triangular matrix or batch of upper triangular matrices.
Depending on the value of :attr:`mode` this function returns the reduced or
complete QR factorization. See below for a list of valid modes.
.. note:: **Differences with** ``numpy.linalg.qr``:
* ``mode='raw'`` is not implemented
* unlike ``numpy.linalg.qr``, this function always returns a
tuple of two tensors. When ``mode='r'``, the `Q` tensor is an
empty tensor. This behavior may change in a future PyTorch release.
.. note::
Backpropagation is not supported for ``mode='r'``. Use ``mode='reduced'`` instead.
Backpropagation is also not supported if the first
:math:`\min(input.size(-1), input.size(-2))` columns of any matrix
in :attr:`input` are not linearly independent. While no error will
be thrown when this occurs the values of the "gradient" produced may
be anything. This behavior may change in the future.
.. note:: This function uses LAPACK for CPU inputs and MAGMA for CUDA inputs,
and may produce different (valid) decompositions on different device types
or different platforms.
Args:
input (Tensor): the input tensor of size :math:`(*, m, n)` where `*` is zero or more
batch dimensions consisting of matrices of dimension :math:`m \times n`.
mode (str, optional): if `k = min(m, n)` then:
* ``'reduced'`` : returns `(Q, R)` with dimensions (m, k), (k, n) (default)
* ``'complete'``: returns `(Q, R)` with dimensions (m, m), (m, n)
* ``'r'``: computes only `R`; returns `(Q, R)` where `Q` is empty and `R` has dimensions (k, n)
Keyword args:
out (tuple, optional): tuple of `Q` and `R` tensors.
The dimensions of `Q` and `R` are detailed in the description of :attr:`mode` above.
Example::
>>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]])
>>> q, r = torch.linalg.qr(a)
>>> q
tensor([[-0.8571, 0.3943, 0.3314],
[-0.4286, -0.9029, -0.0343],
[ 0.2857, -0.1714, 0.9429]])
>>> r
tensor([[ -14.0000, -21.0000, 14.0000],
[ 0.0000, -175.0000, 70.0000],
[ 0.0000, 0.0000, -35.0000]])
>>> torch.mm(q, r).round()
tensor([[ 12., -51., 4.],
[ 6., 167., -68.],
[ -4., 24., -41.]])
>>> torch.mm(q.t(), q).round()
tensor([[ 1., 0., 0.],
[ 0., 1., -0.],
[ 0., -0., 1.]])
>>> q2, r2 = torch.linalg.qr(a, mode='r')
>>> q2
tensor([])
>>> torch.equal(r, r2)
True
>>> a = torch.randn(3, 4, 5)
>>> q, r = torch.linalg.qr(a, mode='complete')
>>> torch.allclose(torch.matmul(q, r), a)
True
>>> torch.allclose(torch.matmul(q.transpose(-2, -1), q), torch.eye(5))
True
""")