ai-content-maker/.venv/Lib/site-packages/torch/_linalg_utils.py

165 lines
5.5 KiB
Python

"""Various linear algebra utility methods for internal use.
"""
from typing import Optional, Tuple
import torch
from torch import Tensor
def is_sparse(A):
"""Check if tensor A is a sparse tensor"""
if isinstance(A, torch.Tensor):
return A.layout == torch.sparse_coo
error_str = "expected Tensor"
if not torch.jit.is_scripting():
error_str += f" but got {type(A)}"
raise TypeError(error_str)
def get_floating_dtype(A):
"""Return the floating point dtype of tensor A.
Integer types map to float32.
"""
dtype = A.dtype
if dtype in (torch.float16, torch.float32, torch.float64):
return dtype
return torch.float32
def matmul(A: Optional[Tensor], B: Tensor) -> Tensor:
"""Multiply two matrices.
If A is None, return B. A can be sparse or dense. B is always
dense.
"""
if A is None:
return B
if is_sparse(A):
return torch.sparse.mm(A, B)
return torch.matmul(A, B)
def conjugate(A):
"""Return conjugate of tensor A.
.. note:: If A's dtype is not complex, A is returned.
"""
if A.is_complex():
return A.conj()
return A
def transpose(A):
"""Return transpose of a matrix or batches of matrices."""
ndim = len(A.shape)
return A.transpose(ndim - 1, ndim - 2)
def transjugate(A):
"""Return transpose conjugate of a matrix or batches of matrices."""
return conjugate(transpose(A))
def bform(X: Tensor, A: Optional[Tensor], Y: Tensor) -> Tensor:
"""Return bilinear form of matrices: :math:`X^T A Y`."""
return matmul(transpose(X), matmul(A, Y))
def qform(A: Optional[Tensor], S: Tensor):
"""Return quadratic form :math:`S^T A S`."""
return bform(S, A, S)
def basis(A):
"""Return orthogonal basis of A columns."""
return torch.linalg.qr(A).Q
def symeig(A: Tensor, largest: Optional[bool] = False) -> Tuple[Tensor, Tensor]:
"""Return eigenpairs of A with specified ordering."""
if largest is None:
largest = False
E, Z = torch.linalg.eigh(A, UPLO="U")
# assuming that E is ordered
if largest:
E = torch.flip(E, dims=(-1,))
Z = torch.flip(Z, dims=(-1,))
return E, Z
# These functions were deprecated and removed
# This nice error message can be removed in version 1.13+
def matrix_rank(input, tol=None, symmetric=False, *, out=None) -> Tensor:
raise RuntimeError(
"This function was deprecated since version 1.9 and is now removed.\n"
"Please use the `torch.linalg.matrix_rank` function instead. "
"The parameter 'symmetric' was renamed in `torch.linalg.matrix_rank()` to 'hermitian'."
)
def solve(input: Tensor, A: Tensor, *, out=None) -> Tuple[Tensor, Tensor]:
raise RuntimeError(
"This function was deprecated since version 1.9 and is now removed. "
"`torch.solve` is deprecated in favor of `torch.linalg.solve`. "
"`torch.linalg.solve` has its arguments reversed and does not return the LU factorization.\n\n"
"To get the LU factorization see `torch.lu`, which can be used with `torch.lu_solve` or `torch.lu_unpack`.\n"
"X = torch.solve(B, A).solution "
"should be replaced with:\n"
"X = torch.linalg.solve(A, B)"
)
def lstsq(input: Tensor, A: Tensor, *, out=None) -> Tuple[Tensor, Tensor]:
raise RuntimeError(
"This function was deprecated since version 1.9 and is now removed. "
"`torch.lstsq` is deprecated in favor of `torch.linalg.lstsq`.\n"
"`torch.linalg.lstsq` has reversed arguments and does not return the QR decomposition in "
"the returned tuple (although it returns other information about the problem).\n\n"
"To get the QR decomposition consider using `torch.linalg.qr`.\n\n"
"The returned solution in `torch.lstsq` stored the residuals of the solution in the "
"last m - n columns of the returned value whenever m > n. In torch.linalg.lstsq, "
"the residuals are in the field 'residuals' of the returned named tuple.\n\n"
"The unpacking of the solution, as in\n"
"X, _ = torch.lstsq(B, A).solution[:A.size(1)]\n"
"should be replaced with:\n"
"X = torch.linalg.lstsq(A, B).solution"
)
def _symeig(
input, eigenvectors=False, upper=True, *, out=None
) -> Tuple[Tensor, Tensor]:
raise RuntimeError(
"This function was deprecated since version 1.9 and is now removed. "
"The default behavior has changed from using the upper triangular portion of the matrix by default "
"to using the lower triangular portion.\n\n"
"L, _ = torch.symeig(A, upper=upper) "
"should be replaced with:\n"
"L = torch.linalg.eigvalsh(A, UPLO='U' if upper else 'L')\n\n"
"and\n\n"
"L, V = torch.symeig(A, eigenvectors=True) "
"should be replaced with:\n"
"L, V = torch.linalg.eigh(A, UPLO='U' if upper else 'L')"
)
def eig(
self: Tensor, eigenvectors: bool = False, *, e=None, v=None
) -> Tuple[Tensor, Tensor]:
raise RuntimeError(
"This function was deprecated since version 1.9 and is now removed. "
"`torch.linalg.eig` returns complex tensors of dtype `cfloat` or `cdouble` rather than real tensors "
"mimicking complex tensors.\n\n"
"L, _ = torch.eig(A) "
"should be replaced with:\n"
"L_complex = torch.linalg.eigvals(A)\n\n"
"and\n\n"
"L, V = torch.eig(A, eigenvectors=True) "
"should be replaced with:\n"
"L_complex, V_complex = torch.linalg.eig(A)"
)