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_interface.py
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_interface.py
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import warnings
import cupy
from cupyx.scipy import sparse
from cupyx.scipy.sparse import _util
class LinearOperator(object):
"""LinearOperator(shape, matvec, rmatvec=None, matmat=None, dtype=None, \
rmatmat=None)
Common interface for performing matrix vector products
To construct a concrete LinearOperator, either pass appropriate callables
to the constructor of this class, or subclass it.
Args:
shape (tuple): Matrix dimensions ``(M, N)``.
matvec (callable f(v)): Returns returns ``A * v``.
rmatvec (callable f(v)): Returns ``A^H * v``, where ``A^H`` is the
conjugate transpose of ``A``.
matmat (callable f(V)): Returns ``A * V``, where ``V`` is a dense
matrix with dimensions ``(N, K)``.
dtype (dtype): Data type of the matrix.
rmatmat (callable f(V)): Returns ``A^H * V``, where ``V`` is a dense
matrix with dimensions ``(M, K)``.
.. seealso:: :class:`scipy.sparse.linalg.LinearOperator`
"""
ndim = 2
def __new__(cls, *args, **kwargs):
if cls is LinearOperator:
# Operate as _CustomLinearOperator factory.
return super(LinearOperator, cls).__new__(_CustomLinearOperator)
else:
obj = super(LinearOperator, cls).__new__(cls)
if (type(obj)._matvec == LinearOperator._matvec
and type(obj)._matmat == LinearOperator._matmat):
warnings.warn('LinearOperator subclass should implement'
' at least one of _matvec and _matmat.',
category=RuntimeWarning, stacklevel=2)
return obj
def __init__(self, dtype, shape):
"""Initialize this :class:`LinearOperator`
"""
if dtype is not None:
dtype = cupy.dtype(dtype)
shape = tuple(shape)
if not _util.isshape(shape):
raise ValueError('invalid shape %r (must be 2-d)' % (shape,))
self.dtype = dtype
self.shape = shape
def _init_dtype(self):
"""Called from subclasses at the end of the `__init__` routine.
"""
if self.dtype is None:
v = cupy.zeros(self.shape[-1])
self.dtype = self.matvec(v).dtype
def _matmat(self, X):
"""Default matrix-matrix multiplication handler.
"""
return cupy.hstack([self.matvec(col.reshape(-1, 1)) for col in X.T])
def _matvec(self, x):
"""Default matrix-vector multiplication handler.
"""
return self.matmat(x.reshape(-1, 1))
def matvec(self, x):
"""Matrix-vector multiplication.
"""
M, N = self.shape
if x.shape != (N,) and x.shape != (N, 1):
raise ValueError('dimension mismatch')
y = self._matvec(x)
if x.ndim == 1:
y = y.reshape(M)
elif x.ndim == 2:
y = y.reshape(M, 1)
else:
raise ValueError('invalid shape returned by user-defined matvec()')
return y
def rmatvec(self, x):
"""Adjoint matrix-vector multiplication.
"""
M, N = self.shape
if x.shape != (M,) and x.shape != (M, 1):
raise ValueError('dimension mismatch')
y = self._rmatvec(x)
if x.ndim == 1:
y = y.reshape(N)
elif x.ndim == 2:
y = y.reshape(N, 1)
else:
raise ValueError(
'invalid shape returned by user-defined rmatvec()')
return y
def _rmatvec(self, x):
"""Default implementation of _rmatvec; defers to adjoint.
"""
if type(self)._adjoint == LinearOperator._adjoint:
# _adjoint not overridden, prevent infinite recursion
raise NotImplementedError
else:
return self.H.matvec(x)
def matmat(self, X):
"""Matrix-matrix multiplication.
"""
if X.ndim != 2:
raise ValueError('expected 2-d ndarray or matrix, not %d-d'
% X.ndim)
if X.shape[0] != self.shape[1]:
raise ValueError('dimension mismatch: %r, %r'
% (self.shape, X.shape))
Y = self._matmat(X)
return Y
def rmatmat(self, X):
"""Adjoint matrix-matrix multiplication.
"""
if X.ndim != 2:
raise ValueError('expected 2-d ndarray or matrix, not %d-d'
% X.ndim)
if X.shape[0] != self.shape[0]:
raise ValueError('dimension mismatch: %r, %r'
% (self.shape, X.shape))
Y = self._rmatmat(X)
return Y
def _rmatmat(self, X):
"""Default implementation of _rmatmat defers to rmatvec or adjoint."""
if type(self)._adjoint == LinearOperator._adjoint:
return cupy.hstack([self.rmatvec(col.reshape(-1, 1))
for col in X.T])
else:
return self.H.matmat(X)
def __call__(self, x):
return self*x
def __mul__(self, x):
return self.dot(x)
def dot(self, x):
"""Matrix-matrix or matrix-vector multiplication.
"""
if isinstance(x, LinearOperator):
return _ProductLinearOperator(self, x)
elif cupy.isscalar(x):
return _ScaledLinearOperator(self, x)
else:
if x.ndim == 1 or x.ndim == 2 and x.shape[1] == 1:
return self.matvec(x)
elif x.ndim == 2:
return self.matmat(x)
else:
raise ValueError('expected 1-d or 2-d array, got %r'
% x)
def __matmul__(self, other):
if cupy.isscalar(other):
raise ValueError('Scalar operands are not allowed, '
'use \'*\' instead')
return self.__mul__(other)
def __rmatmul__(self, other):
if cupy.isscalar(other):
raise ValueError('Scalar operands are not allowed, '
'use \'*\' instead')
return self.__rmul__(other)
def __rmul__(self, x):
if cupy.isscalar(x):
return _ScaledLinearOperator(self, x)
else:
return NotImplemented
def __pow__(self, p):
if cupy.isscalar(p):
return _PowerLinearOperator(self, p)
else:
return NotImplemented
def __add__(self, x):
if isinstance(x, LinearOperator):
return _SumLinearOperator(self, x)
else:
return NotImplemented
def __neg__(self):
return _ScaledLinearOperator(self, -1)
def __sub__(self, x):
return self.__add__(-x)
def __repr__(self):
M, N = self.shape
if self.dtype is None:
dt = 'unspecified dtype'
else:
dt = 'dtype=' + str(self.dtype)
return '<%dx%d %s with %s>' % (M, N, self.__class__.__name__, dt)
def adjoint(self):
"""Hermitian adjoint.
"""
return self._adjoint()
H = property(adjoint)
def transpose(self):
"""Transpose this linear operator.
"""
return self._transpose()
T = property(transpose)
def _adjoint(self):
"""Default implementation of _adjoint; defers to rmatvec."""
return _AdjointLinearOperator(self)
def _transpose(self):
""" Default implementation of _transpose; defers to rmatvec + conj"""
return _TransposedLinearOperator(self)
class _CustomLinearOperator(LinearOperator):
"""Linear operator defined in terms of user-specified operations."""
def __init__(self, shape, matvec, rmatvec=None, matmat=None,
dtype=None, rmatmat=None):
super(_CustomLinearOperator, self).__init__(dtype, shape)
self.args = ()
self.__matvec_impl = matvec
self.__rmatvec_impl = rmatvec
self.__rmatmat_impl = rmatmat
self.__matmat_impl = matmat
self._init_dtype()
def _matmat(self, X):
if self.__matmat_impl is not None:
return self.__matmat_impl(X)
else:
return super(_CustomLinearOperator, self)._matmat(X)
def _matvec(self, x):
return self.__matvec_impl(x)
def _rmatvec(self, x):
func = self.__rmatvec_impl
if func is None:
raise NotImplementedError('rmatvec is not defined')
return self.__rmatvec_impl(x)
def _rmatmat(self, X):
if self.__rmatmat_impl is not None:
return self.__rmatmat_impl(X)
else:
return super(_CustomLinearOperator, self)._rmatmat(X)
def _adjoint(self):
return _CustomLinearOperator(shape=(self.shape[1], self.shape[0]),
matvec=self.__rmatvec_impl,
rmatvec=self.__matvec_impl,
matmat=self.__rmatmat_impl,
rmatmat=self.__matmat_impl,
dtype=self.dtype)
class _AdjointLinearOperator(LinearOperator):
"""Adjoint of arbitrary Linear Operator"""
def __init__(self, A):
shape = (A.shape[1], A.shape[0])
super(_AdjointLinearOperator, self).__init__(
dtype=A.dtype, shape=shape)
self.A = A
self.args = (A,)
def _matvec(self, x):
return self.A._rmatvec(x)
def _rmatvec(self, x):
return self.A._matvec(x)
def _matmat(self, x):
return self.A._rmatmat(x)
def _rmatmat(self, x):
return self.A._matmat(x)
class _TransposedLinearOperator(LinearOperator):
"""Transposition of arbitrary Linear Operator"""
def __init__(self, A):
shape = (A.shape[1], A.shape[0])
super(_TransposedLinearOperator, self).__init__(
dtype=A.dtype, shape=shape)
self.A = A
self.args = (A,)
def _matvec(self, x):
# NB. cupy.conj works also on sparse matrices
return cupy.conj(self.A._rmatvec(cupy.conj(x)))
def _rmatvec(self, x):
return cupy.conj(self.A._matvec(cupy.conj(x)))
def _matmat(self, x):
# NB. cupy.conj works also on sparse matrices
return cupy.conj(self.A._rmatmat(cupy.conj(x)))
def _rmatmat(self, x):
return cupy.conj(self.A._matmat(cupy.conj(x)))
def _get_dtype(operators, dtypes=None):
if dtypes is None:
dtypes = []
for obj in operators:
if obj is not None and hasattr(obj, 'dtype'):
dtypes.append(obj.dtype)
return cupy.find_common_type(dtypes, [])
class _SumLinearOperator(LinearOperator):
def __init__(self, A, B):
if not isinstance(A, LinearOperator) or \
not isinstance(B, LinearOperator):
raise ValueError('both operands have to be a LinearOperator')
if A.shape != B.shape:
raise ValueError('cannot add %r and %r: shape mismatch'
% (A, B))
self.args = (A, B)
super(_SumLinearOperator, self).__init__(_get_dtype([A, B]), A.shape)
def _matvec(self, x):
return self.args[0].matvec(x) + self.args[1].matvec(x)
def _rmatvec(self, x):
return self.args[0].rmatvec(x) + self.args[1].rmatvec(x)
def _rmatmat(self, x):
return self.args[0].rmatmat(x) + self.args[1].rmatmat(x)
def _matmat(self, x):
return self.args[0].matmat(x) + self.args[1].matmat(x)
def _adjoint(self):
A, B = self.args
return A.H + B.H
class _ProductLinearOperator(LinearOperator):
def __init__(self, A, B):
if not isinstance(A, LinearOperator) or \
not isinstance(B, LinearOperator):
raise ValueError('both operands have to be a LinearOperator')
if A.shape[1] != B.shape[0]:
raise ValueError('cannot multiply %r and %r: shape mismatch'
% (A, B))
super(_ProductLinearOperator, self).__init__(_get_dtype([A, B]),
(A.shape[0], B.shape[1]))
self.args = (A, B)
def _matvec(self, x):
return self.args[0].matvec(self.args[1].matvec(x))
def _rmatvec(self, x):
return self.args[1].rmatvec(self.args[0].rmatvec(x))
def _rmatmat(self, x):
return self.args[1].rmatmat(self.args[0].rmatmat(x))
def _matmat(self, x):
return self.args[0].matmat(self.args[1].matmat(x))
def _adjoint(self):
A, B = self.args
return B.H * A.H
class _ScaledLinearOperator(LinearOperator):
def __init__(self, A, alpha):
if not isinstance(A, LinearOperator):
raise ValueError('LinearOperator expected as A')
if not cupy.isscalar(alpha):
raise ValueError('scalar expected as alpha')
dtype = _get_dtype([A], [type(alpha)])
super(_ScaledLinearOperator, self).__init__(dtype, A.shape)
self.args = (A, alpha)
def _matvec(self, x):
return self.args[1] * self.args[0].matvec(x)
def _rmatvec(self, x):
return cupy.conj(self.args[1]) * self.args[0].rmatvec(x)
def _rmatmat(self, x):
return cupy.conj(self.args[1]) * self.args[0].rmatmat(x)
def _matmat(self, x):
return self.args[1] * self.args[0].matmat(x)
def _adjoint(self):
A, alpha = self.args
return A.H * cupy.conj(alpha)
class _PowerLinearOperator(LinearOperator):
def __init__(self, A, p):
if not isinstance(A, LinearOperator):
raise ValueError('LinearOperator expected as A')
if A.shape[0] != A.shape[1]:
raise ValueError('square LinearOperator expected, got %r' % A)
if not _util.isintlike(p) or p < 0:
raise ValueError('non-negative integer expected as p')
super(_PowerLinearOperator, self).__init__(_get_dtype([A]), A.shape)
self.args = (A, p)
def _power(self, fun, x):
res = cupy.array(x, copy=True)
for i in range(self.args[1]):
res = fun(res)
return res
def _matvec(self, x):
return self._power(self.args[0].matvec, x)
def _rmatvec(self, x):
return self._power(self.args[0].rmatvec, x)
def _rmatmat(self, x):
return self._power(self.args[0].rmatmat, x)
def _matmat(self, x):
return self._power(self.args[0].matmat, x)
def _adjoint(self):
A, p = self.args
return A.H ** p
class MatrixLinearOperator(LinearOperator):
def __init__(self, A):
super(MatrixLinearOperator, self).__init__(A.dtype, A.shape)
self.A = A
self.__adj = None
self.args = (A,)
def _matmat(self, X):
return self.A.dot(X)
def _adjoint(self):
if self.__adj is None:
self.__adj = _AdjointMatrixOperator(self)
return self.__adj
class _AdjointMatrixOperator(MatrixLinearOperator):
def __init__(self, adjoint):
self.A = adjoint.A.T.conj()
self.__adjoint = adjoint
self.args = (adjoint,)
self.shape = adjoint.shape[1], adjoint.shape[0]
@property
def dtype(self):
return self.__adjoint.dtype
def _adjoint(self):
return self.__adjoint
class IdentityOperator(LinearOperator):
def __init__(self, shape, dtype=None):
super(IdentityOperator, self).__init__(dtype, shape)
def _matvec(self, x):
return x
def _rmatvec(self, x):
return x
def _rmatmat(self, x):
return x
def _matmat(self, x):
return x
def _adjoint(self):
return self
def aslinearoperator(A):
"""Return `A` as a LinearOperator.
Args:
A (array-like):
The input array to be converted to a `LinearOperator` object.
It may be any of the following types:
* :class:`cupy.ndarray`
* sparse matrix (e.g. ``csr_matrix``, ``coo_matrix``, etc.)
* :class:`cupyx.scipy.sparse.linalg.LinearOperator`
* object with ``.shape`` and ``.matvec`` attributes
Returns:
cupyx.scipy.sparse.linalg.LinearOperator: `LinearOperator` object
.. seealso:: :func:`scipy.sparse.aslinearoperator``
"""
if isinstance(A, LinearOperator):
return A
elif isinstance(A, cupy.ndarray):
if A.ndim > 2:
raise ValueError('array must have ndim <= 2')
A = cupy.atleast_2d(A)
return MatrixLinearOperator(A)
elif sparse.isspmatrix(A):
return MatrixLinearOperator(A)
else:
if hasattr(A, 'shape') and hasattr(A, 'matvec'):
rmatvec = None
rmatmat = None
dtype = None
if hasattr(A, 'rmatvec'):
rmatvec = A.rmatvec
if hasattr(A, 'rmatmat'):
rmatmat = A.rmatmat
if hasattr(A, 'dtype'):
dtype = A.dtype
return LinearOperator(A.shape, A.matvec, rmatvec=rmatvec,
rmatmat=rmatmat, dtype=dtype)
else:
raise TypeError('type not understood')