def cholesky(a):
'''Cholesky decomposition.
Decompose a given two-dimensional square matrix into ``L * L.T``,
where ``L`` is a lower-triangular matrix and ``.T`` is a conjugate
transpose operator. Note that in the current implementation ``a`` must be
a real matrix, and only float32 and float64 are supported.
Args:
a (cupy.ndarray): The input matrix with dimension ``(N, N)``
.. seealso:: :func:`numpy.linalg.cholesky`
'''
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_assert_cupy_array(a)
_assert_rank2(a)
_assert_nd_squareness(a)
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
x = a.astype(dtype, copy=True)
n = len(a)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
buffersize = cusolver.spotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.spotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'd'
buffersize = cusolver.dpotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dpotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status > 0:
raise linalg.LinAlgError(
'The leading minor of order {} '
'is not positive definite'.format(status))
elif status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
_tril(x, k=0)
return x
def _lu_factor(a, overwrite_a=False, check_finite=True):
a = cupy.asarray(a)
_util._assert_rank2(a)
dtype = a.dtype
if dtype.char == 'f':
getrf = cusolver.sgetrf
getrf_bufferSize = cusolver.sgetrf_bufferSize
elif dtype.char == 'd':
getrf = cusolver.dgetrf
getrf_bufferSize = cusolver.dgetrf_bufferSize
elif dtype.char == 'F':
getrf = cusolver.cgetrf
getrf_bufferSize = cusolver.cgetrf_bufferSize
elif dtype.char == 'D':
getrf = cusolver.zgetrf
getrf_bufferSize = cusolver.zgetrf_bufferSize
else:
msg = 'Only float32, float64, complex64 and complex128 are supported.'
raise NotImplementedError(msg)
a = a.astype(dtype, order='F', copy=(not overwrite_a))
if check_finite:
if a.dtype.kind == 'f' and not cupy.isfinite(a).all():
raise ValueError('array must not contain infs or NaNs')
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
m, n = a.shape
ipiv = cupy.empty((min(m, n), ), dtype=numpy.intc)
buffersize = getrf_bufferSize(cusolver_handle, m, n, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
# LU factorization
getrf(cusolver_handle, m, n, a.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
if dev_info[0] < 0:
raise ValueError('illegal value in %d-th argument of '
'internal getrf (lu_factor)' % -dev_info[0])
elif dev_info[0] > 0:
warn('Diagonal number %d is exactly zero. Singular matrix.' %
dev_info[0],
RuntimeWarning,
stacklevel=2)
# cuSolver uses 1-origin while SciPy uses 0-origin
ipiv -= 1
return (a, ipiv)
def cholesky(a):
'''Cholesky decomposition.
Decompose a given two-dimensional square matrix into ``L * L.T``,
where ``L`` is a lower-triangular matrix and ``.T`` is a conjugate
transpose operator. Note that in the current implementation ``a`` must be
a real matrix, and only float32 and float64 are supported.
Args:
a (cupy.ndarray): The input matrix with dimension ``(N, N)``
.. seealso:: :func:`numpy.linalg.cholesky`
'''
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_assert_cupy_array(a)
_assert_rank2(a)
_assert_nd_squareness(a)
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
x = a.astype(dtype, order='C', copy=True)
n = len(a)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
buffersize = cusolver.spotrf_bufferSize(handle,
cublas.CUBLAS_FILL_MODE_UPPER,
n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.spotrf(handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr,
n, workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'd'
buffersize = cusolver.dpotrf_bufferSize(handle,
cublas.CUBLAS_FILL_MODE_UPPER,
n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dpotrf(handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr,
n, workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status > 0:
raise linalg.LinAlgError('The leading minor of order {} '
'is not positive definite'.format(status))
elif status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
_tril(x, k=0)
return x
def solve(a, b):
"""Solves a linear matrix equation.
It computes the exact solution of ``x`` in ``ax = b``,
where ``a`` is a square and full rank matrix.
Args:
a (cupy.ndarray): The matrix with dimension ``(..., M, M)``.
b (cupy.ndarray): The matrix with dimension ``(...,M)`` or
``(..., M, K)``.
Returns:
cupy.ndarray:
The matrix with dimension ``(..., M)`` or ``(..., M, K)``.
.. seealso:: :func:`numpy.linalg.solve`
"""
# NOTE: Since cusolver in CUDA 8.0 does not support gesv,
# we manually solve a linear system with QR decomposition.
# For details, please see the following:
# https://docs.nvidia.com/cuda/cusolver/index.html#qr_examples
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
util._assert_cupy_array(a, b)
util._assert_nd_squareness(a)
if not ((a.ndim == b.ndim or a.ndim == b.ndim + 1)
and a.shape[:-1] == b.shape[:a.ndim - 1]):
raise ValueError(
'a must have (..., M, M) shape and b must have (..., M) '
'or (..., M, K)')
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ())
cublas_handle = device.get_cublas_handle()
cusolver_handle = device.get_cusolver_handle()
a = a.astype(dtype)
b = b.astype(dtype)
if a.ndim == 2:
return _solve(a, b, cublas_handle, cusolver_handle)
x = cupy.empty_like(b)
shape = a.shape[:-2]
for i in six.moves.range(numpy.prod(shape)):
index = numpy.unravel_index(i, shape)
x[index] = _solve(a[index], b[index], cublas_handle, cusolver_handle)
return x
def _potrf_batched(a):
"""Batched Cholesky decomposition.
Decompose a given array of two-dimensional square matrices into
``L * L.T``, where ``L`` is a lower-triangular matrix and ``.T``
is a conjugate transpose operator.
Args:
a (cupy.ndarray): The input array of matrices
with dimension ``(..., N, N)``
Returns:
cupy.ndarray: The lower-triangular matrix.
"""
if not check_availability('potrfBatched'):
raise RuntimeError('potrfBatched is not available')
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.promote_types(a.dtype.char, 'f').char
if dtype == 'f':
potrfBatched = cusolver.spotrfBatched
elif dtype == 'd':
potrfBatched = cusolver.dpotrfBatched
elif dtype == 'F':
potrfBatched = cusolver.cpotrfBatched
else: # dtype == 'D':
potrfBatched = cusolver.zpotrfBatched
x = a.astype(dtype, order='C', copy=True)
xp = cupy.core._mat_ptrs(x)
n = x.shape[-1]
ldx = x.strides[-2] // x.dtype.itemsize
handle = device.get_cusolver_handle()
batch_size = internal.prod(x.shape[:-2])
dev_info = cupy.empty(batch_size, dtype=numpy.int32)
potrfBatched(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, xp.data.ptr, ldx,
dev_info.data.ptr, batch_size)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
potrfBatched, dev_info)
return cupy.tril(x)
def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, dtype=numpy.int32)
dev_info = cupy.empty((), dtype=numpy.int32)
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
else:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr, ipiv.data.ptr,
dev_info.data.ptr)
# dev_info < 0 means illegal value (in dimensions, strides, and etc.) that
# should never happen even if the matrix contains nan or inf.
# TODO(kataoka): assert dev_info >= 0 if synchronization is allowed for
# debugging purposes.
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
singular = dev_info > 0
return (
cupy.where(singular, dtype.type(0), sign),
cupy.where(singular, dtype.type('-inf'), logdet),
)
def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, dtype=numpy.int32)
dev_info = cupy.empty(1, dtype=numpy.int32)
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
else:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
try:
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
getrf, dev_info)
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
except linalg.LinAlgError:
sign = cupy.array(0.0, dtype=dtype)
logdet = cupy.array(float('-inf'), dtype)
return sign, logdet
def _slogdet_one(a):
util._assert_rank2(a)
util._assert_nd_squareness(a)
dtype = a.dtype
handle = device.get_cusolver_handle()
m = len(a)
ipiv = cupy.empty(m, 'i')
info = cupy.empty((), 'i')
# Need to make a copy because getrf works inplace
a_copy = a.copy(order='F')
if dtype == 'f':
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
else:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
buffersize = getrf_bufferSize(handle, m, m, a_copy.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, m, m, a_copy.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, info.data.ptr)
if info[()] == 0:
diag = cupy.diag(a_copy)
# ipiv is 1-origin
non_zero = (cupy.count_nonzero(ipiv != cupy.arange(1, m + 1)) +
cupy.count_nonzero(diag < 0))
# Note: sign == -1 ** (non_zero % 2)
sign = (non_zero % 2) * -2 + 1
logdet = cupy.log(abs(diag)).sum()
else:
sign = cupy.array(0.0, dtype=dtype)
logdet = cupy.array(float('-inf'), dtype)
return sign, logdet
def _solve(a, b):
a = cupy.asfortranarray(a)
b = cupy.asfortranarray(b)
dtype = a.dtype
m, k = (b.size, 1) if b.ndim == 1 else b.shape
cusolver_handle = device.get_cusolver_handle()
cublas_handle = device.get_cublas_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
geqrf = cusolver.sgeqrf
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
ormqr = cusolver.sormqr
trsm = cublas.strsm
else: # dtype == 'd'
geqrf = cusolver.dgeqrf
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
ormqr = cusolver.dormqr
trsm = cublas.dtrsm
# 1. QR decomposition (A = Q * R)
buffersize = geqrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(m, dtype=dtype)
geqrf(cusolver_handle, m, m, a.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 2. ormqr (Q^T * B)
ormqr(cusolver_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_OP_T, m, k,
m, a.data.ptr, m, tau.data.ptr, b.data.ptr, m, workspace.data.ptr,
buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 3. trsm (X = R^{-1} * (Q^T * B))
trsm(cublas_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_FILL_MODE_UPPER,
cublas.CUBLAS_OP_N, cublas.CUBLAS_DIAG_NON_UNIT, m, k, 1, a.data.ptr,
m, b.data.ptr, m)
return b
def invh(a):
"""Compute the inverse of a Hermitian matrix.
This function computes a inverse of a real symmetric or complex hermitian
positive-definite matrix using Cholesky factorization. If matrix ``a`` is
not positive definite, Cholesky factorization fails and it raises an error.
Args:
a (cupy.ndarray): Real symmetric or complex hermitian maxtix.
Returns:
cupy.ndarray: The inverse of matrix ``a``.
"""
# to prevent `a` from being overwritten
a = a.copy()
util._assert_cupy_array(a)
util._assert_rank2(a)
util._assert_nd_squareness(a)
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.promote_types(a.dtype.char, 'f').char
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
potrf = cusolver.spotrf
potrf_bufferSize = cusolver.spotrf_bufferSize
potrs = cusolver.spotrs
elif dtype == 'd':
potrf = cusolver.dpotrf
potrf_bufferSize = cusolver.dpotrf_bufferSize
potrs = cusolver.dpotrs
elif dtype == 'F':
potrf = cusolver.cpotrf
potrf_bufferSize = cusolver.cpotrf_bufferSize
potrs = cusolver.cpotrs
elif dtype == 'D':
potrf = cusolver.zpotrf
potrf_bufferSize = cusolver.zpotrf_bufferSize
potrs = cusolver.zpotrs
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
m = a.shape[0]
uplo = cublas.CUBLAS_FILL_MODE_LOWER
worksize = potrf_bufferSize(cusolver_handle, uplo, m, a.data.ptr, m)
workspace = cupy.empty(worksize, dtype=dtype)
# Cholesky factorization
potrf(cusolver_handle, uplo, m, a.data.ptr, m, workspace.data.ptr,
worksize, dev_info.data.ptr)
info = dev_info[0]
if info != 0:
if info < 0:
msg = '\tThe {}-th parameter is wrong'.format(-info)
else:
msg = ('\tThe leading minor of order {} is not positive definite'
.format(info))
raise RuntimeError('matrix inversion failed at potrf.\n' + msg)
b = cupy.eye(m, dtype=dtype)
# Solve: A * X = B
potrs(cusolver_handle, uplo, m, m, a.data.ptr, m, b.data.ptr, m,
dev_info.data.ptr)
info = dev_info[0]
if info > 0:
assert False, ('Unexpected output returned by potrs (actual: {})'
.format(info))
elif info < 0:
raise RuntimeError('matrix inversion failed at potrs.\n'
'\tThe {}-th parameter is wrong'.format(-info))
return b
def lu_factor(a, overwrite_a=False, check_finite=True):
"""LU decomposition.
Decompose a given two-dimensional square matrix into ``P * L * U``,
where ``P`` is a permutation matrix, ``L`` lower-triangular with
unit diagonal elements, and ``U`` upper-triangular matrix.
Note that in the current implementation ``a`` must be
a real matrix, and only :class:`numpy.float32` and :class:`numpy.float64`
are supported.
Args:
a (cupy.ndarray): The input matrix with dimension ``(M, N)``
overwrite_a (bool): Allow overwriting data in ``a`` (may enhance
performance)
check_finite (bool): Whether to check that the input matrices contain
only finite numbers. Disabling may give a performance gain, but may
result in problems (crashes, non-termination) if the inputs do
contain infinities or NaNs.
Returns:
tuple:
``(lu, piv)`` where ``lu`` is a :class:`cupy.ndarray`
storing ``U`` in its upper triangle, and ``L`` without
unit diagonal elements in its lower triangle, and ``piv`` is
a :class:`cupy.ndarray` storing pivot indices representing
permutation matrix ``P``. For ``0 <= i < min(M,N)``, row
``i`` of the matrix was interchanged with row ``piv[i]``
.. seealso:: :func:`scipy.linalg.lu_factor`
.. note::
Current implementation returns result different from SciPy when the
matrix singular. SciPy returns an array containing ``0.`` while the
current implementation returns an array containing ``nan``.
>>> import numpy as np
>>> import scipy.linalg
>>> scipy.linalg.lu_factor(np.array([[0, 1], [0, 0]], \
dtype=np.float32))
(array([[0., 1.],
[0., 0.]], dtype=float32), array([0, 1], dtype=int32))
>>> import cupy as cp
>>> import cupyx.scipy.linalg
>>> cupyx.scipy.linalg.lu_factor(cp.array([[0, 1], [0, 0]], \
dtype=cp.float32))
(array([[ 0., 1.],
[nan, nan]], dtype=float32), array([0, 1], dtype=int32))
"""
a = cupy.asarray(a)
util._assert_rank2(a)
dtype = a.dtype
if dtype.char == 'f':
getrf = cusolver.sgetrf
getrf_bufferSize = cusolver.sgetrf_bufferSize
elif dtype.char == 'd':
getrf = cusolver.dgetrf
getrf_bufferSize = cusolver.dgetrf_bufferSize
else:
raise NotImplementedError('Only float32 and float64 are supported.')
a = a.astype(dtype, order='F', copy=(not overwrite_a))
if check_finite:
if a.dtype.kind == 'f' and not cupy.isfinite(a).all():
raise ValueError('array must not contain infs or NaNs')
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
m, n = a.shape
ipiv = cupy.empty((min(m, n), ), dtype=numpy.intc)
buffersize = getrf_bufferSize(cusolver_handle, m, n, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
# LU factorization
getrf(cusolver_handle, m, n, a.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
if dev_info[0] < 0:
raise ValueError('illegal value in %d-th argument of '
'internal getrf (lu_factor)' % -dev_info[0])
elif dev_info[0] > 0:
warn('Diagonal number %d is exactly zero. Singular matrix.' %
dev_info[0],
RuntimeWarning,
stacklevel=2)
# cuSolver uses 1-origin while SciPy uses 0-origin
ipiv -= 1
return (a, ipiv)
def qr(a, mode='reduced'):
'''QR decomposition.
Decompose a given two-dimensional matrix into ``Q * R``, where ``Q``
is an orthonormal and ``R`` is an upper-triangular matrix.
Args:
a (cupy.ndarray): The input matrix.
mode (str): The mode of decomposition. Currently 'reduced',
'complete', 'r', and 'raw' modes are supported. The default mode
is 'reduced', and decompose a matrix ``A = (M, N)`` into ``Q``,
``R`` with dimensions ``(M, K)``, ``(K, N)``, where
``K = min(M, N)``.
.. seealso:: :func:`numpy.linalg.qr`
'''
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_assert_cupy_array(a)
_assert_rank2(a)
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full', 'e', 'economic'):
msg = 'The deprecated mode \'{}\' is not supported'.format(mode)
raise ValueError(msg)
else:
raise ValueError('Unrecognized mode \'{}\''.format(mode))
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
m, n = a.shape
x = a.transpose().astype(dtype, copy=True)
mn = min(m, n)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
# compute working space of geqrf and ormqr, and solve R
if dtype == 'f':
buffersize = cusolver.sgeqrf_bufferSize(handle, m, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
tau = cupy.empty(mn, dtype=numpy.float32)
cusolver.sgeqrf(
handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'd'
buffersize = cusolver.dgeqrf_bufferSize(handle, n, m, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
tau = cupy.empty(mn, dtype=numpy.float64)
cusolver.dgeqrf(
handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
if mode == 'r':
r = x[:, :mn].transpose()
return _triu(r)
if mode == 'raw':
if a.dtype.char == 'f':
# The original numpy.linalg.qr returns float64 in raw mode,
# whereas the cusolver returns float32. We agree that the
# following code would be inappropriate, however, in this time
# we explicitly convert them to float64 for compatibility.
return x.astype(numpy.float64), tau.astype(numpy.float64)
return x, tau
if mode == 'complete' and m > n:
mc = m
q = cupy.empty((m, m), dtype)
else:
mc = mn
q = cupy.empty((n, m), dtype)
q[:n] = x
# solve Q
if dtype == 'f':
buffersize = cusolver.sorgqr_bufferSize(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.sorgqr(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
else:
buffersize = cusolver.dorgqr_bufferSize(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dorgqr(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
q = q[:mc].transpose()
r = x[:, :mc].transpose()
return q, _triu(r)
def qr(a, mode='reduced'):
'''QR decomposition.
Decompose a given two-dimensional matrix into ``Q * R``, where ``Q``
is an orthonormal and ``R`` is an upper-triangular matrix.
Args:
a (cupy.ndarray): The input matrix.
mode (str): The mode of decomposition. Currently 'reduced',
'complete', 'r', and 'raw' modes are supported. The default mode
is 'reduced', and decompose a matrix ``A = (M, N)`` into ``Q``,
``R`` with dimensions ``(M, K)``, ``(K, N)``, where
``K = min(M, N)``.
.. seealso:: :func:`numpy.linalg.qr`
'''
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_assert_cupy_array(a)
_assert_rank2(a)
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full', 'e', 'economic'):
msg = 'The deprecated mode \'{}\' is not supported'.format(mode)
raise ValueError(msg)
else:
raise ValueError('Unrecognized mode \'{}\''.format(mode))
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
m, n = a.shape
x = a.transpose().astype(dtype, copy=True)
mn = min(m, n)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
# compute working space of geqrf and ormqr, and solve R
if dtype == 'f':
buffersize = cusolver.sgeqrf_bufferSize(handle, m, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
tau = cupy.empty(mn, dtype=numpy.float32)
cusolver.sgeqrf(
handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'd'
buffersize = cusolver.dgeqrf_bufferSize(handle, n, m, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
tau = cupy.empty(mn, dtype=numpy.float64)
cusolver.dgeqrf(
handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
if mode == 'r':
r = x[:, :mn].transpose()
return _triu(r)
if mode == 'raw':
if a.dtype.char == 'f':
# The original numpy.linalg.qr returns float64 in raw mode,
# whereas the cusolver returns float32. We agree that the
# following code would be inappropriate, however, in this time
# we explicitly convert them to float64 for compatibility.
return x.astype(numpy.float64), tau.astype(numpy.float64)
return x, tau
if mode == 'complete' and m > n:
mc = m
q = cupy.empty((m, m), dtype)
else:
mc = mn
q = cupy.empty((n, m), dtype)
q[:n] = x
# solve Q
if dtype == 'f':
buffersize = cusolver.sorgqr_bufferSize(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.sorgqr(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
else:
buffersize = cusolver.dorgqr_bufferSize(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dorgqr(
handle, m, mc, mn, q.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
q = q[:mc].transpose()
r = x[:, :mc].transpose()
return q, _triu(r)
def gesv(a, b):
"""Solve a linear matrix equation using cusolverDn<t>getr[fs]().
Computes the solution to a system of linear equation ``ax = b``.
Args:
a (cupy.ndarray): The matrix with dimension ``(M, M)``.
b (cupy.ndarray): The matrix with dimension ``(M)`` or ``(M, K)``.
Returns:
cupy.ndarray:
The matrix with dimension ``(M)`` or ``(M, K)``.
Note: ``a`` and ``b`` will be overwritten.
"""
if a.ndim != 2:
raise ValueError('a.ndim must be 2 (actual: {})'.format(a.ndim))
if b.ndim not in (1, 2):
raise ValueError('b.ndim must be 1 or 2 (actual: {})'.format(b.ndim))
if a.shape[0] != a.shape[1]:
raise ValueError('a must be a square matrix.')
if a.shape[0] != b.shape[0]:
raise ValueError('shape mismatch (a: {}, b: {}).'.format(
a.shape, b.shape))
if a.dtype != b.dtype:
raise TypeError('dtype mismatch (a: {}, b: {})'.format(
a.dtype, b.dtype))
dtype = a.dtype
if dtype == 'f':
t = 's'
elif dtype == 'd':
t = 'd'
elif dtype == 'F':
t = 'c'
elif dtype == 'D':
t = 'z'
else:
raise TypeError('unsupported dtype (actual:{})'.format(a.dtype))
helper = getattr(_cusolver, t + 'getrf_bufferSize')
getrf = getattr(_cusolver, t + 'getrf')
getrs = getattr(_cusolver, t + 'getrs')
n = b.shape[0]
nrhs = b.shape[1] if b.ndim == 2 else 1
if a._f_contiguous:
trans = _cublas.CUBLAS_OP_N
elif a._c_contiguous:
trans = _cublas.CUBLAS_OP_T
else:
raise ValueError('a must be F-contiguous or C-contiguous.')
if not b._f_contiguous:
raise ValueError('b must be F-contiguous.')
handle = _device.get_cusolver_handle()
dipiv = _cupy.empty(n, dtype=_numpy.int32)
dinfo = _cupy.empty(1, dtype=_numpy.int32)
lwork = helper(handle, n, n, a.data.ptr, n)
dwork = _cupy.empty(lwork, dtype=a.dtype)
# LU factrization (A = L * U)
getrf(handle, n, n, a.data.ptr, n, dwork.data.ptr, dipiv.data.ptr,
dinfo.data.ptr)
_cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
getrf, dinfo)
# Solves Ax = b
getrs(handle, trans, n, nrhs, a.data.ptr, n, dipiv.data.ptr, b.data.ptr, n,
dinfo.data.ptr)
_cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
getrs, dinfo)
def inv(a):
"""Computes the inverse of a matrix.
This function computes matrix ``a_inv`` from n-dimensional regular matrix
``a`` such that ``dot(a, a_inv) == eye(n)``.
Args:
a (cupy.ndarray): The regular matrix
Returns:
cupy.ndarray: The inverse of a matrix.
.. seealso:: :func:`numpy.linalg.inv`
"""
if a.ndim >= 3:
return _batched_inv(a)
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# to prevent `a` to be overwritten
a = a.copy()
util._assert_cupy_array(a)
util._assert_rank2(a)
util._assert_nd_squareness(a)
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
ipiv = cupy.empty((a.shape[0], 1), dtype=numpy.intc)
if dtype == 'f':
getrf = cusolver.sgetrf
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrs = cusolver.sgetrs
elif dtype == 'd':
getrf = cusolver.dgetrf
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrs = cusolver.dgetrs
elif dtype == 'F':
getrf = cusolver.cgetrf
getrf_bufferSize = cusolver.cgetrf_bufferSize
getrs = cusolver.cgetrs
elif dtype == 'D':
getrf = cusolver.zgetrf
getrf_bufferSize = cusolver.zgetrf_bufferSize
getrs = cusolver.zgetrs
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
m = a.shape[0]
buffersize = getrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
# LU factorization
getrf(cusolver_handle, m, m, a.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
b = cupy.eye(m, dtype=dtype)
# solve for the inverse
getrs(cusolver_handle, 0, m, m, a.data.ptr, m, ipiv.data.ptr, b.data.ptr,
m, dev_info.data.ptr)
return b
def gesv(a, b):
"""Solve a linear matrix equation using cusolverDn<t1><t2>gesv().
Computes the solution to a system of linear equation ``ax = b``.
Args:
a (cupy.ndarray): The matrix with dimension ``(M, M)``.
b (cupy.ndarray): The matrix with dimension ``(M)`` or ``(M, K)``.
Returns:
cupy.ndarray:
The matrix with dimension ``(M)`` or ``(M, K)``.
"""
if not check_availability('gesv'):
raise RuntimeError('gesv is not available.')
if a.ndim != 2:
raise ValueError('a.ndim must be 2 (actual:{})'.format(a.ndim))
if b.ndim not in (1, 2):
raise ValueError('b.ndim must be 1 or 2 (actual:{})'.format(b.ndim))
if a.shape[0] != a.shape[1]:
raise ValueError('a must be a square matrix.')
if a.shape[0] != b.shape[0]:
raise ValueError('shape mismatch (a:{}, b:{}).'.
format(a.shape, b.shape))
if a.dtype != b.dtype:
raise ValueError('dtype mismatch (a:{}, b:{}).'.
format(a.dtype, b.dtype))
if b.ndim == 2:
n, nrhs = b.shape
else:
n, nrhs = b.shape[0], 1
compute_type = _linalg.get_compute_type(a.dtype)
if a.dtype.char in 'fd':
if a.dtype.char == 'f':
t1 = t2 = 's'
else:
t1 = t2 = 'd'
if compute_type == _linalg.COMPUTE_TYPE_FP16:
t2 = 'h'
elif compute_type == _linalg.COMPUTE_TYPE_TF32:
t2 = 'x'
elif compute_type == _linalg.COMPUTE_TYPE_FP32:
t2 = 's'
elif a.dtype.char in 'FD':
if a.dtype.char == 'F':
t1 = t2 = 'c'
else:
t1 = t2 = 'z'
if compute_type == _linalg.COMPUTE_TYPE_FP16:
t2 = 'k'
elif compute_type == _linalg.COMPUTE_TYPE_TF32:
t2 = 'y'
elif compute_type == _linalg.COMPUTE_TYPE_FP32:
t2 = 'c'
else:
raise ValueError('unsupported dtype (actual:{})'.format(a.dtype))
solver_name = t1 + t2 + 'gesv'
solver = getattr(_cusolver, solver_name)
helper = getattr(_cusolver, solver_name + '_bufferSize')
a = a.copy(order='F')
b = b.copy(order='F')
x = _cupy.empty_like(b)
dipiv = _cupy.empty(n, dtype=_numpy.int32)
dinfo = _cupy.empty(1, dtype=_numpy.int32)
handle = _device.get_cusolver_handle()
lwork = helper(handle, n, nrhs, a.data.ptr, n, dipiv.data.ptr,
b.data.ptr, n, x.data.ptr, n, 0)
dwork = _cupy.empty(lwork, dtype=_numpy.int8)
niters = solver(handle, n, nrhs, a.data.ptr, n, dipiv.data.ptr,
b.data.ptr, n, x.data.ptr, n, dwork.data.ptr, lwork,
dinfo.data.ptr)
if niters < 0:
raise RuntimeError('gesv has failed ({}).'.format(niters))
return x
def solve(a, b):
'''Solves a linear matrix equation.
It computes the exact solution of ``x`` in ``ax = b``,
where ``a`` is a square and full rank matrix.
Args:
a (cupy.ndarray): The matrix with dimension ``(M, M)``
b (cupy.ndarray): The vector with ``M`` elements, or
the matrix with dimension ``(M, K)``
Returns:
cupy.ndarray:
The vector with ``M`` elements, or the matrix with dimension
``(M, K)``.
.. seealso:: :func:`numpy.linalg.solve`
'''
# NOTE: Since cusolver in CUDA 8.0 does not support gesv,
# we manually solve a linear system with QR decomposition.
# For details, please see the following:
# https://docs.nvidia.com/cuda/cusolver/index.html#qr_examples
if not cuda.cusolver_enabled:
raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a, b)
util._assert_rank2(a)
util._assert_nd_squareness(a)
if 2 < b.ndim:
raise linalg.LinAlgError('{}-dimensional array given. Array must be '
'one or two-dimensional'.format(b.ndim))
if len(a) != len(b):
raise linalg.LinAlgError('The number of rows of array a must be '
'the same as that of array b')
# Cast to float32 or float64
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
m, k = (b.size, 1) if b.ndim == 1 else b.shape
a = a.transpose().astype(dtype, order='C', copy=True)
b = b.transpose().astype(dtype, order='C', copy=True)
cusolver_handle = device.get_cusolver_handle()
cublas_handle = device.get_cublas_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
geqrf = cusolver.sgeqrf
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
ormqr = cusolver.sormqr
trsm = cublas.strsm
else: # dtype == 'd'
geqrf = cusolver.dgeqrf
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
ormqr = cusolver.dormqr
trsm = cublas.dtrsm
# 1. QR decomposition (A = Q * R)
buffersize = geqrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(m, dtype=dtype)
geqrf(cusolver_handle, m, m, a.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 2. ormqr (Q^T * B)
ormqr(cusolver_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_OP_T, m, k,
m, a.data.ptr, m, tau.data.ptr, b.data.ptr, m, workspace.data.ptr,
buffersize, dev_info.data.ptr)
_check_status(dev_info)
# 3. trsm (X = R^{-1} * (Q^T * B))
trsm(cublas_handle, cublas.CUBLAS_SIDE_LEFT, cublas.CUBLAS_FILL_MODE_UPPER,
cublas.CUBLAS_OP_N, cublas.CUBLAS_DIAG_NON_UNIT, m, k, 1, a.data.ptr,
m, b.data.ptr, m)
return b.transpose()
def cholesky(a):
"""Cholesky decomposition.
Decompose a given two-dimensional square matrix into ``L * L.T``,
where ``L`` is a lower-triangular matrix and ``.T`` is a conjugate
transpose operator.
Args:
a (cupy.ndarray): The input matrix with dimension ``(N, N)``
Returns:
cupy.ndarray: The lower-triangular matrix.
.. seealso:: :func:`numpy.linalg.cholesky`
"""
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a)
util._assert_rank2(a)
util._assert_nd_squareness(a)
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
x = a.astype(dtype, order='C', copy=True)
n = len(a)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
buffersize = cusolver.spotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float32)
cusolver.spotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
elif dtype == 'd':
buffersize = cusolver.dpotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.float64)
cusolver.dpotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
elif dtype == 'F':
buffersize = cusolver.cpotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.complex64)
cusolver.cpotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
else: # dtype == 'D':
buffersize = cusolver.zpotrf_bufferSize(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=numpy.complex128)
cusolver.zpotrf(
handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status > 0:
raise linalg.LinAlgError(
'The leading minor of order {} '
'is not positive definite'.format(status))
elif status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
util._tril(x, k=0)
return x
def qr(a, mode='reduced'):
"""QR decomposition.
Decompose a given two-dimensional matrix into ``Q * R``, where ``Q``
is an orthonormal and ``R`` is an upper-triangular matrix.
Args:
a (cupy.ndarray): The input matrix.
mode (str): The mode of decomposition. Currently 'reduced',
'complete', 'r', and 'raw' modes are supported. The default mode
is 'reduced', in which matrix ``A = (M, N)`` is decomposed into
``Q``, ``R`` with dimensions ``(M, K)``, ``(K, N)``, where
``K = min(M, N)``.
Returns:
cupy.ndarray, or tuple of ndarray:
Although the type of returned object depends on the mode,
it returns a tuple of ``(Q, R)`` by default.
For details, please see the document of :func:`numpy.linalg.qr`.
.. seealso:: :func:`numpy.linalg.qr`
"""
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a)
util._assert_rank2(a)
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full', 'e', 'economic'):
msg = 'The deprecated mode \'{}\' is not supported'.format(mode)
raise ValueError(msg)
else:
raise ValueError('Unrecognized mode \'{}\''.format(mode))
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
m, n = a.shape
x = a.transpose().astype(dtype, order='C', copy=True)
mn = min(m, n)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
# compute working space of geqrf and orgqr, and solve R
if dtype == 'f':
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
geqrf = cusolver.sgeqrf
elif dtype == 'd':
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
geqrf = cusolver.dgeqrf
elif dtype == 'F':
geqrf_bufferSize = cusolver.cgeqrf_bufferSize
geqrf = cusolver.cgeqrf
elif dtype == 'D':
geqrf_bufferSize = cusolver.zgeqrf_bufferSize
geqrf = cusolver.zgeqrf
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
buffersize = geqrf_bufferSize(handle, m, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(mn, dtype=dtype)
geqrf(handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
status = int(dev_info[0])
if status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
if mode == 'r':
r = x[:, :mn].transpose()
return util._triu(r)
if mode == 'raw':
if a.dtype.char == 'f':
# The original numpy.linalg.qr returns float64 in raw mode,
# whereas the cusolver returns float32. We agree that the
# following code would be inappropriate, however, in this time
# we explicitly convert them to float64 for compatibility.
return x.astype(numpy.float64), tau.astype(numpy.float64)
elif a.dtype.char == 'F':
# The same applies to complex64
return x.astype(numpy.complex128), tau.astype(numpy.complex128)
return x, tau
if mode == 'complete' and m > n:
mc = m
q = cupy.empty((m, m), dtype)
else:
mc = mn
q = cupy.empty((n, m), dtype)
q[:n] = x
# solve Q
if dtype == 'f':
orgqr_bufferSize = cusolver.sorgqr_bufferSize
orgqr = cusolver.sorgqr
elif dtype == 'd':
orgqr_bufferSize = cusolver.dorgqr_bufferSize
orgqr = cusolver.dorgqr
elif dtype == 'F':
orgqr_bufferSize = cusolver.cungqr_bufferSize
orgqr = cusolver.cungqr
elif dtype == 'D':
orgqr_bufferSize = cusolver.zungqr_bufferSize
orgqr = cusolver.zungqr
buffersize = orgqr_bufferSize(handle, m, mc, mn, q.data.ptr, m,
tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=dtype)
orgqr(handle, m, mc, mn, q.data.ptr, m, tau.data.ptr,
workspace.data.ptr, buffersize, dev_info.data.ptr)
q = q[:mc].transpose()
r = x[:, :mc].transpose()
return q, util._triu(r)
def qr(a, mode='reduced'):
"""QR decomposition.
Decompose a given two-dimensional matrix into ``Q * R``, where ``Q``
is an orthonormal and ``R`` is an upper-triangular matrix.
Args:
a (cupy.ndarray): The input matrix.
mode (str): The mode of decomposition. Currently 'reduced',
'complete', 'r', and 'raw' modes are supported. The default mode
is 'reduced', in which matrix ``A = (M, N)`` is decomposed into
``Q``, ``R`` with dimensions ``(M, K)``, ``(K, N)``, where
``K = min(M, N)``.
Returns:
cupy.ndarray, or tuple of ndarray:
Although the type of returned object depends on the mode,
it returns a tuple of ``(Q, R)`` by default.
For details, please see the document of :func:`numpy.linalg.qr`.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso:: :func:`numpy.linalg.qr`
"""
# TODO(Saito): Current implementation only accepts two-dimensional arrays
_util._assert_cupy_array(a)
_util._assert_rank2(a)
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full', 'e', 'economic'):
msg = 'The deprecated mode \'{}\' is not supported'.format(mode)
raise ValueError(msg)
else:
raise ValueError('Unrecognized mode \'{}\''.format(mode))
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.promote_types(a.dtype.char, 'f').char
m, n = a.shape
mn = min(m, n)
if mn == 0:
if mode == 'reduced':
return cupy.empty((m, 0), dtype), cupy.empty((0, n), dtype)
elif mode == 'complete':
return cupy.identity(m, dtype), cupy.empty((m, n), dtype)
elif mode == 'r':
return cupy.empty((0, n), dtype)
else: # mode == 'raw'
# compatibility with numpy.linalg.qr
dtype = numpy.promote_types(dtype, 'd')
return cupy.empty((n, m), dtype), cupy.empty((0, ), dtype)
x = a.transpose().astype(dtype, order='C', copy=True)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
geqrf = cusolver.sgeqrf
elif dtype == 'd':
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
geqrf = cusolver.dgeqrf
elif dtype == 'F':
geqrf_bufferSize = cusolver.cgeqrf_bufferSize
geqrf = cusolver.cgeqrf
elif dtype == 'D':
geqrf_bufferSize = cusolver.zgeqrf_bufferSize
geqrf = cusolver.zgeqrf
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
# compute working space of geqrf and solve R
buffersize = geqrf_bufferSize(handle, m, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(mn, dtype=dtype)
geqrf(handle, m, n, x.data.ptr, m, tau.data.ptr, workspace.data.ptr,
buffersize, dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
geqrf, dev_info)
if mode == 'r':
r = x[:, :mn].transpose()
return _util._triu(r)
if mode == 'raw':
if a.dtype.char == 'f':
# The original numpy.linalg.qr returns float64 in raw mode,
# whereas the cusolver returns float32. We agree that the
# following code would be inappropriate, however, in this time
# we explicitly convert them to float64 for compatibility.
return x.astype(numpy.float64), tau.astype(numpy.float64)
elif a.dtype.char == 'F':
# The same applies to complex64
return x.astype(numpy.complex128), tau.astype(numpy.complex128)
return x, tau
if mode == 'complete' and m > n:
mc = m
q = cupy.empty((m, m), dtype)
else:
mc = mn
q = cupy.empty((n, m), dtype)
q[:n] = x
# compute working space of orgqr and solve Q
if dtype == 'f':
orgqr_bufferSize = cusolver.sorgqr_bufferSize
orgqr = cusolver.sorgqr
elif dtype == 'd':
orgqr_bufferSize = cusolver.dorgqr_bufferSize
orgqr = cusolver.dorgqr
elif dtype == 'F':
orgqr_bufferSize = cusolver.cungqr_bufferSize
orgqr = cusolver.cungqr
elif dtype == 'D':
orgqr_bufferSize = cusolver.zungqr_bufferSize
orgqr = cusolver.zungqr
buffersize = orgqr_bufferSize(handle, m, mc, mn, q.data.ptr, m,
tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=dtype)
orgqr(handle, m, mc, mn, q.data.ptr, m, tau.data.ptr, workspace.data.ptr,
buffersize, dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
orgqr, dev_info)
q = q[:mc].transpose()
r = x[:, :mc].transpose()
return q, _util._triu(r)
def _lu_factor(a_t, dtype):
"""Compute pivoted LU decomposition.
Decompose a given batch of square matrices. Inputs and outputs are
transposed.
Args:
a_t (cupy.ndarray): The input matrix with dimension ``(..., N, N)``.
The dimension condition is not checked.
dtype (numpy.dtype): float32, float64, complex64, or complex128.
Returns:
lu_t (cupy.ndarray): ``L`` without its unit diagonal and ``U`` with
dimension ``(..., N, N)``.
piv (cupy.ndarray): 1-origin pivot indices with dimension
``(..., N)``.
dev_info (cupy.ndarray): ``getrf`` info with dimension ``(...)``.
.. seealso:: :func:`scipy.linalg.lu_factor`
"""
orig_shape = a_t.shape
n = orig_shape[-2]
# copy is necessary to present `a` to be overwritten.
a_t = a_t.astype(dtype, order='C').reshape(-1, n, n)
batch_size = a_t.shape[0]
ipiv = cupy.empty((batch_size, n), dtype=numpy.int32)
dev_info = cupy.empty((batch_size, ), dtype=numpy.int32)
# Heuristic condition from some performance test.
# TODO(kataoka): autotune
use_batched = batch_size * 65536 >= n * n
if use_batched:
handle = device.get_cublas_handle()
lda = n
step = n * lda * a_t.itemsize
start = a_t.data.ptr
stop = start + step * batch_size
a_array = cupy.arange(start, stop, step, dtype=cupy.uintp)
if dtype == numpy.float32:
getrfBatched = cupy.cuda.cublas.sgetrfBatched
elif dtype == numpy.float64:
getrfBatched = cupy.cuda.cublas.dgetrfBatched
elif dtype == numpy.complex64:
getrfBatched = cupy.cuda.cublas.cgetrfBatched
elif dtype == numpy.complex128:
getrfBatched = cupy.cuda.cublas.zgetrfBatched
else:
assert False
getrfBatched(handle, n, a_array.data.ptr, lda, ipiv.data.ptr,
dev_info.data.ptr, batch_size)
else:
handle = device.get_cusolver_handle()
if dtype == numpy.float32:
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrf = cusolver.sgetrf
elif dtype == numpy.float64:
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrf = cusolver.dgetrf
elif dtype == numpy.complex64:
getrf_bufferSize = cusolver.cgetrf_bufferSize
getrf = cusolver.cgetrf
elif dtype == numpy.complex128:
getrf_bufferSize = cusolver.zgetrf_bufferSize
getrf = cusolver.zgetrf
else:
assert False
for i in range(batch_size):
a_ptr = a_t[i].data.ptr
buffersize = getrf_bufferSize(handle, n, n, a_ptr, n)
workspace = cupy.empty(buffersize, dtype=dtype)
getrf(handle, n, n, a_ptr, n, workspace.data.ptr, ipiv[i].data.ptr,
dev_info[i].data.ptr)
return (
a_t.reshape(orig_shape),
ipiv.reshape(orig_shape[:-1]),
dev_info.reshape(orig_shape[:-2]),
)
def _batched_invh(a):
"""Compute the inverse of an array of Hermitian matrices.
This function computes an inverse of a real symmetric or complex hermitian
positive-definite matrix using Cholesky factorization. If matrix ``a[i]``
is not positive definite, Cholesky factorization fails and it raises
an error.
Args:
a (cupy.ndarray): Array of real symmetric or complex hermitian
matrices with dimension (..., N, N).
Returns:
cupy.ndarray: The array of inverses of matrices ``a[i]``.
"""
if not check_availability('potrsBatched'):
raise RuntimeError('potrsBatched is not available')
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.promote_types(a.dtype.char, 'f').char
if dtype == 'f':
potrfBatched = cusolver.spotrfBatched
potrsBatched = cusolver.spotrsBatched
elif dtype == 'd':
potrfBatched = cusolver.dpotrfBatched
potrsBatched = cusolver.dpotrsBatched
elif dtype == 'F':
potrfBatched = cusolver.cpotrfBatched
potrsBatched = cusolver.cpotrsBatched
elif dtype == 'D':
potrfBatched = cusolver.zpotrfBatched
potrsBatched = cusolver.zpotrsBatched
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
a = a.astype(dtype, order='C', copy=True)
ap = cupy.core.core._mat_ptrs(a)
n = a.shape[-1]
lda = a.strides[-2] // a.dtype.itemsize
handle = device.get_cusolver_handle()
uplo = cublas.CUBLAS_FILL_MODE_LOWER
batch_size = int(numpy.prod(a.shape[:-2]))
dev_info = cupy.empty(batch_size, dtype=numpy.int32)
# Cholesky factorization
potrfBatched(handle, uplo, n, ap.data.ptr, lda, dev_info.data.ptr,
batch_size)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
potrfBatched, dev_info)
identity_matrix = cupy.eye(n, dtype=dtype)
b = cupy.empty(a.shape, dtype)
b[...] = identity_matrix
nrhs = b.shape[-1]
ldb = b.strides[-2] // a.dtype.itemsize
bp = cupy.core.core._mat_ptrs(b)
dev_info = cupy.empty(1, dtype=numpy.int32)
# NOTE: potrsBatched does not currently support nrhs > 1 (CUDA v10.2)
# Solve: A[i] * X[i] = B[i]
potrsBatched(handle, uplo, n, nrhs, ap.data.ptr, lda, bp.data.ptr, ldb,
dev_info.data.ptr, batch_size)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
potrfBatched, dev_info)
return b
def qr(a, mode='reduced'):
"""QR decomposition.
Decompose a given two-dimensional matrix into ``Q * R``, where ``Q``
is an orthonormal and ``R`` is an upper-triangular matrix.
Args:
a (cupy.ndarray): The input matrix.
mode (str): The mode of decomposition. Currently 'reduced',
'complete', 'r', and 'raw' modes are supported. The default mode
is 'reduced', in which matrix ``A = (..., M, N)`` is decomposed
into ``Q``, ``R`` with dimensions ``(..., M, K)``, ``(..., K, N)``,
where ``K = min(M, N)``.
Returns:
cupy.ndarray, or tuple of ndarray:
Although the type of returned object depends on the mode,
it returns a tuple of ``(Q, R)`` by default.
For details, please see the document of :func:`numpy.linalg.qr`.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso:: :func:`numpy.linalg.qr`
"""
_util._assert_cupy_array(a)
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full', 'e', 'economic'):
msg = 'The deprecated mode \'{}\' is not supported'.format(mode)
else:
msg = 'Unrecognized mode \'{}\''.format(mode)
raise ValueError(msg)
if a.ndim > 2:
return _qr_batched(a, mode)
# support float32, float64, complex64, and complex128
dtype, out_dtype = _util.linalg_common_type(a)
m, n = a.shape
k = min(m, n)
if k == 0:
if mode == 'reduced':
return cupy.empty((m, 0), out_dtype), cupy.empty((0, n), out_dtype)
elif mode == 'complete':
return cupy.identity(m, out_dtype), cupy.empty((m, n), out_dtype)
elif mode == 'r':
return cupy.empty((0, n), out_dtype)
else: # mode == 'raw'
return cupy.empty((n, m), out_dtype), cupy.empty((0,), out_dtype)
x = a.transpose().astype(dtype, order='C', copy=True)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
geqrf_bufferSize = cusolver.sgeqrf_bufferSize
geqrf = cusolver.sgeqrf
elif dtype == 'd':
geqrf_bufferSize = cusolver.dgeqrf_bufferSize
geqrf = cusolver.dgeqrf
elif dtype == 'F':
geqrf_bufferSize = cusolver.cgeqrf_bufferSize
geqrf = cusolver.cgeqrf
elif dtype == 'D':
geqrf_bufferSize = cusolver.zgeqrf_bufferSize
geqrf = cusolver.zgeqrf
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
# compute working space of geqrf and solve R
buffersize = geqrf_bufferSize(handle, m, n, x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=dtype)
tau = cupy.empty(k, dtype=dtype)
geqrf(handle, m, n, x.data.ptr, m,
tau.data.ptr, workspace.data.ptr, buffersize, dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
geqrf, dev_info)
if mode == 'r':
r = x[:, :k].transpose()
return _util._triu(r).astype(out_dtype, copy=False)
if mode == 'raw':
return (
x.astype(out_dtype, copy=False),
tau.astype(out_dtype, copy=False))
if mode == 'complete' and m > n:
mc = m
q = cupy.empty((m, m), dtype)
else:
mc = k
q = cupy.empty((n, m), dtype)
q[:n] = x
# compute working space of orgqr and solve Q
if dtype == 'f':
orgqr_bufferSize = cusolver.sorgqr_bufferSize
orgqr = cusolver.sorgqr
elif dtype == 'd':
orgqr_bufferSize = cusolver.dorgqr_bufferSize
orgqr = cusolver.dorgqr
elif dtype == 'F':
orgqr_bufferSize = cusolver.cungqr_bufferSize
orgqr = cusolver.cungqr
elif dtype == 'D':
orgqr_bufferSize = cusolver.zungqr_bufferSize
orgqr = cusolver.zungqr
buffersize = orgqr_bufferSize(
handle, m, mc, k, q.data.ptr, m, tau.data.ptr)
workspace = cupy.empty(buffersize, dtype=dtype)
orgqr(
handle, m, mc, k, q.data.ptr, m, tau.data.ptr, workspace.data.ptr,
buffersize, dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
orgqr, dev_info)
q = q[:mc].transpose()
r = x[:, :mc].transpose()
return (
q.astype(out_dtype, copy=False),
_util._triu(r).astype(out_dtype, copy=False))
def gels(a, b):
"""Compute least square solution using cusolverDn<t1><t2>gels().
Computes the least square solution to a system of ``ax = b``.
Args:
a (cupy.ndarray): The matrix with dimension ``(M, N)``.
b (cupy.ndarray): The matrix with dimension ``(M)`` or ``(M, K)``.
Returns:
cupy.ndarray:
The matrix with dimension ``(N)`` or ``(N, K)``.
"""
if not check_availability('gels'):
raise RuntimeError('gels is not available.')
if a.ndim != 2:
raise ValueError('a.ndim must be 2 (actual:{})'.format(a.ndim))
if b.ndim == 1:
nrhs = 1
elif b.ndim == 2:
nrhs = b.shape[1]
else:
raise ValueError('b.ndim must be 1 or 2 (actual: {})'.format(b.ndim))
if a.shape[0] != b.shape[0]:
raise ValueError('shape mismatch (a:{}, b:{}).'.
format(a.shape, b.shape))
if a.dtype != b.dtype:
raise ValueError('dtype mismatch (a:{}, b:{}).'.
format(a.dtype, b.dtype))
m, n = a.shape
if m < n:
raise ValueError('m must be equal to or greater than n.')
max_mn = max(m, n)
b_ndim = b.ndim
compute_type = _linalg.get_compute_type(a.dtype)
if a.dtype.char in 'fd':
if a.dtype.char == 'f':
t1 = t2 = 's'
else:
t1 = t2 = 'd'
if compute_type == _linalg.COMPUTE_TYPE_FP16:
t2 = 'h'
elif compute_type == _linalg.COMPUTE_TYPE_TF32:
t2 = 'x'
elif compute_type == _linalg.COMPUTE_TYPE_FP32:
t2 = 's'
elif a.dtype.char in 'FD':
if a.dtype.char == 'F':
t1 = t2 = 'c'
else:
t1 = t2 = 'z'
if compute_type == _linalg.COMPUTE_TYPE_FP16:
t2 = 'k'
elif compute_type == _linalg.COMPUTE_TYPE_TF32:
t2 = 'y'
elif compute_type == _linalg.COMPUTE_TYPE_FP32:
t2 = 'c'
else:
raise ValueError('unsupported dtype (actual:{})'.format(a.dtype))
solver_name = t1 + t2 + 'gels'
solver = getattr(_cusolver, solver_name)
helper = getattr(_cusolver, solver_name + '_bufferSize')
a = a.copy(order='F')
org_nrhs = nrhs
if m > n and nrhs == 1:
# Note: this is workaround as there is bug in cusolverDn<T1><T2>gels()
# of CUDA 11.0/11.1 and it returns CUSOLVER_STATUS_IRS_NOT_SUPPORTED
# when m > n and nrhs == 1.
nrhs = 2
bb = b.reshape(m, 1)
b = _cupy.empty((max_mn, nrhs), dtype=a.dtype, order='F')
b[:m, :] = bb
else:
b = b.copy(order='F')
x = _cupy.empty((max_mn, nrhs), dtype=a.dtype, order='F')
dinfo = _cupy.empty(1, dtype=_numpy.int32)
handle = _device.get_cusolver_handle()
lwork = helper(handle, m, n, nrhs, a.data.ptr, m, b.data.ptr, m,
x.data.ptr, max_mn, 0)
dwork = _cupy.empty(lwork, dtype=_numpy.int8)
niters = solver(handle, m, n, nrhs, a.data.ptr, m, b.data.ptr, m,
x.data.ptr, max_mn, dwork.data.ptr, lwork, dinfo.data.ptr)
if niters < 0:
if niters <= -50:
_warnings.warn('gels reached maximum allowed iterations.')
else:
raise RuntimeError('gels has failed ({}).'.format(niters))
x = x[:n]
if org_nrhs != nrhs:
x = x[:, :org_nrhs]
if b_ndim == 1:
x = x.reshape(n)
return x
def cholesky(a):
"""Cholesky decomposition.
Decompose a given two-dimensional square matrix into ``L * L.T``,
where ``L`` is a lower-triangular matrix and ``.T`` is a conjugate
transpose operator.
Args:
a (cupy.ndarray): The input matrix with dimension ``(N, N)``
Returns:
cupy.ndarray: The lower-triangular matrix.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso:: :func:`numpy.linalg.cholesky`
"""
_util._assert_cupy_array(a)
_util._assert_nd_squareness(a)
if a.ndim > 2:
return _potrf_batched(a)
if a.dtype.char == 'f' or a.dtype.char == 'd':
dtype = a.dtype.char
else:
dtype = numpy.promote_types(a.dtype.char, 'f').char
x = a.astype(dtype, order='C', copy=True)
n = len(a)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if dtype == 'f':
potrf = cusolver.spotrf
potrf_bufferSize = cusolver.spotrf_bufferSize
elif dtype == 'd':
potrf = cusolver.dpotrf
potrf_bufferSize = cusolver.dpotrf_bufferSize
elif dtype == 'F':
potrf = cusolver.cpotrf
potrf_bufferSize = cusolver.cpotrf_bufferSize
else: # dtype == 'D':
potrf = cusolver.zpotrf
potrf_bufferSize = cusolver.zpotrf_bufferSize
buffersize = potrf_bufferSize(handle, cublas.CUBLAS_FILL_MODE_UPPER, n,
x.data.ptr, n)
workspace = cupy.empty(buffersize, dtype=dtype)
potrf(handle, cublas.CUBLAS_FILL_MODE_UPPER, n, x.data.ptr, n,
workspace.data.ptr, buffersize, dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
potrf, dev_info)
_util._tril(x, k=0)
return x
def lu_solve(lu_and_piv, b, trans=0, overwrite_b=False, check_finite=True):
"""Solve an equation system, ``a * x = b``, given the LU factorization of ``a``
Args:
lu_and_piv (tuple): LU factorization of matrix ``a`` (``(M, M)``)
together with pivot indices.
b (cupy.ndarray): The matrix with dimension ``(M,)`` or
``(M, N)``.
trans ({0, 1, 2}): Type of system to solve:
======== =========
trans system
======== =========
0 a x = b
1 a^T x = b
2 a^H x = b
======== =========
overwrite_b (bool): Allow overwriting data in b (may enhance
performance)
check_finite (bool): Whether to check that the input matrices contain
only finite numbers. Disabling may give a performance gain, but may
result in problems (crashes, non-termination) if the inputs do
contain infinities or NaNs.
Returns:
cupy.ndarray:
The matrix with dimension ``(M,)`` or ``(M, N)``.
.. seealso:: :func:`scipy.linalg.lu_solve`
"""
(lu, ipiv) = lu_and_piv
util._assert_cupy_array(lu)
util._assert_rank2(lu)
util._assert_nd_squareness(lu)
m = lu.shape[0]
if m != b.shape[0]:
raise ValueError('incompatible dimensions.')
dtype = lu.dtype
if dtype.char == 'f':
getrs = cusolver.sgetrs
elif dtype.char == 'd':
getrs = cusolver.dgetrs
else:
raise NotImplementedError('Only float32 and float64 are supported.')
if trans == 0:
trans = cublas.CUBLAS_OP_N
elif trans == 1:
trans = cublas.CUBLAS_OP_T
elif trans == 2:
trans = cublas.CUBLAS_OP_C
else:
raise ValueError('unknown trans')
lu = lu.astype(dtype, order='F', copy=False)
ipiv = ipiv.astype(ipiv.dtype, order='F', copy=True)
# cuSolver uses 1-origin while SciPy uses 0-origin
ipiv += 1
b = b.astype(dtype, order='F', copy=(not overwrite_b))
if check_finite:
if lu.dtype.kind == 'f' and not cupy.isfinite(lu).all():
raise ValueError(
'array must not contain infs or NaNs.\n'
'Note that when a singular matrix is given, unlike '
'scipy.linalg.lu_factor, cupyx.scipy.linalg.lu_factor '
'returns an array containing NaN.')
if b.dtype.kind == 'f' and not cupy.isfinite(b).all():
raise ValueError('array must not contain infs or NaNs')
n = 1 if b.ndim == 1 else b.shape[1]
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
# solve for the inverse
getrs(cusolver_handle, trans, m, n, lu.data.ptr, m, ipiv.data.ptr,
b.data.ptr, m, dev_info.data.ptr)
if dev_info[0] < 0:
raise ValueError('illegal value in %d-th argument of '
'internal getrs (lu_solve)' % -dev_info[0])
return b
def svd(a, full_matrices=True, compute_uv=True):
"""Singular Value Decomposition.
Factorizes the matrix ``a`` as ``u * np.diag(s) * v``, where ``u`` and
``v`` are unitary and ``s`` is an one-dimensional array of ``a``'s
singular values.
Args:
a (cupy.ndarray): The input matrix with dimension ``(..., M, N)``.
full_matrices (bool): If True, it returns u and v with dimensions
``(..., M, M)`` and ``(..., N, N)``. Otherwise, the dimensions
of u and v are ``(..., M, K)`` and ``(..., K, N)``, respectively,
where ``K = min(M, N)``.
compute_uv (bool): If ``False``, it only returns singular values.
Returns:
tuple of :class:`cupy.ndarray`:
A tuple of ``(u, s, v)`` such that ``a = u * np.diag(s) * v``.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. note::
On CUDA, when ``a.ndim > 2`` and the matrix dimensions <= 32, a fast
code path based on Jacobian method (``gesvdj``) is taken. Otherwise,
a QR method (``gesvd``) is used.
On ROCm, there is no such a fast code path that switches the underlying
algorithm.
.. seealso:: :func:`numpy.linalg.svd`
"""
_util._assert_cupy_array(a)
# Cast to float32 or float64
a_dtype = numpy.promote_types(a.dtype.char, 'f').char
if a_dtype == 'f':
s_dtype = 'f'
elif a_dtype == 'd':
s_dtype = 'd'
elif a_dtype == 'F':
s_dtype = 'f'
else: # a_dtype == 'D':
a_dtype = 'D'
s_dtype = 'd'
if a.ndim > 2:
return _svd_batched(a, a_dtype, full_matrices, compute_uv)
# Remark 1: gesvd only supports m >= n (WHAT?)
# Remark 2: gesvd returns matrix U and V^H
n, m = a.shape
if m == 0 or n == 0:
s = cupy.empty((0, ), s_dtype)
if compute_uv:
if full_matrices:
u = cupy.eye(n, dtype=a_dtype)
vt = cupy.eye(m, dtype=a_dtype)
else:
u = cupy.empty((n, 0), dtype=a_dtype)
vt = cupy.empty((0, m), dtype=a_dtype)
return u, s, vt
else:
return s
# `a` must be copied because xgesvd destroys the matrix
if m >= n:
x = a.astype(a_dtype, order='C', copy=True)
trans_flag = False
else:
m, n = a.shape
x = a.transpose().astype(a_dtype, order='C', copy=True)
trans_flag = True
k = n # = min(m, n) where m >= n is ensured above
if compute_uv:
if full_matrices:
u = cupy.empty((m, m), dtype=a_dtype)
vt = x[:, :n]
job_u = ord('A')
job_vt = ord('O')
else:
u = x
vt = cupy.empty((k, n), dtype=a_dtype)
job_u = ord('O')
job_vt = ord('S')
u_ptr, vt_ptr = u.data.ptr, vt.data.ptr
else:
u_ptr, vt_ptr = 0, 0 # Use nullptr
job_u = ord('N')
job_vt = ord('N')
s = cupy.empty(k, dtype=s_dtype)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if a_dtype == 'f':
gesvd = cusolver.sgesvd
gesvd_bufferSize = cusolver.sgesvd_bufferSize
elif a_dtype == 'd':
gesvd = cusolver.dgesvd
gesvd_bufferSize = cusolver.dgesvd_bufferSize
elif a_dtype == 'F':
gesvd = cusolver.cgesvd
gesvd_bufferSize = cusolver.cgesvd_bufferSize
else: # a_dtype == 'D':
gesvd = cusolver.zgesvd
gesvd_bufferSize = cusolver.zgesvd_bufferSize
buffersize = gesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
if not runtime.is_hip:
# rwork can be NULL if the information from supperdiagonal isn't needed
# https://docs.nvidia.com/cuda/cusolver/index.html#cuSolverDN-lt-t-gt-gesvd # noqa
rwork_ptr = 0
else:
rwork = cupy.empty(min(m, n) - 1, dtype=s_dtype)
rwork_ptr = rwork.data.ptr
gesvd(handle, job_u, job_vt, m, n, x.data.ptr, m, s.data.ptr, u_ptr, m,
vt_ptr, n, workspace.data.ptr, buffersize, rwork_ptr,
dev_info.data.ptr)
cupy.linalg._util._check_cusolver_dev_info_if_synchronization_allowed(
gesvd, dev_info)
# Note that the returned array may need to be transposed
# depending on the structure of an input
if compute_uv:
if trans_flag:
return u.transpose(), s, vt.transpose()
else:
return vt, s, u
else:
return s
def svd(a, full_matrices=True, compute_uv=True):
"""Singular Value Decomposition.
Factorizes the matrix ``a`` as ``u * np.diag(s) * v``, where ``u`` and
``v`` are unitary and ``s`` is an one-dimensional array of ``a``'s
singular values.
Args:
a (cupy.ndarray): The input matrix with dimension ``(M, N)``.
full_matrices (bool): If True, it returns u and v with dimensions
``(M, M)`` and ``(N, N)``. Otherwise, the dimensions of u and v
are respectively ``(M, K)`` and ``(K, N)``, where
``K = min(M, N)``.
compute_uv (bool): If ``False``, it only returns singular values.
Returns:
tuple of :class:`cupy.ndarray`:
A tuple of ``(u, s, v)`` such that ``a = u * np.diag(s) * v``.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso:: :func:`numpy.linalg.svd`
"""
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a)
util._assert_rank2(a)
# Cast to float32 or float64
a_dtype = numpy.promote_types(a.dtype.char, 'f').char
if a_dtype == 'f':
s_dtype = 'f'
elif a_dtype == 'd':
s_dtype = 'd'
elif a_dtype == 'F':
s_dtype = 'f'
else: # a_dtype == 'D':
a_dtype = 'D'
s_dtype = 'd'
# Remark 1: gesvd only supports m >= n (WHAT?)
# Remark 2: gesvd only supports jobu = 'A' and jobvt = 'A'
# Remark 3: gesvd returns matrix U and V^H
# Remark 4: Remark 2 is removed since cuda 8.0 (new!)
n, m = a.shape
# `a` must be copied because xgesvd destroys the matrix
if m >= n:
x = a.astype(a_dtype, order='C', copy=True)
trans_flag = False
else:
m, n = a.shape
x = a.transpose().astype(a_dtype, order='C', copy=True)
trans_flag = True
mn = min(m, n)
if compute_uv:
if full_matrices:
u = cupy.empty((m, m), dtype=a_dtype)
vt = cupy.empty((n, n), dtype=a_dtype)
else:
u = cupy.empty((mn, m), dtype=a_dtype)
vt = cupy.empty((mn, n), dtype=a_dtype)
u_ptr, vt_ptr = u.data.ptr, vt.data.ptr
else:
u_ptr, vt_ptr = 0, 0 # Use nullptr
s = cupy.empty(mn, dtype=s_dtype)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if compute_uv:
job = ord('A') if full_matrices else ord('S')
else:
job = ord('N')
if a_dtype == 'f':
gesvd = cusolver.sgesvd
gesvd_bufferSize = cusolver.sgesvd_bufferSize
elif a_dtype == 'd':
gesvd = cusolver.dgesvd
gesvd_bufferSize = cusolver.dgesvd_bufferSize
elif a_dtype == 'F':
gesvd = cusolver.cgesvd
gesvd_bufferSize = cusolver.cgesvd_bufferSize
else: # a_dtype == 'D':
gesvd = cusolver.zgesvd
gesvd_bufferSize = cusolver.zgesvd_bufferSize
buffersize = gesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
gesvd(handle, job, job, m, n, x.data.ptr, m, s.data.ptr, u_ptr, m, vt_ptr,
n, workspace.data.ptr, buffersize, 0, dev_info.data.ptr)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
gesvd, dev_info)
# Note that the returned array may need to be transporsed
# depending on the structure of an input
if compute_uv:
if trans_flag:
return u.transpose(), s, vt.transpose()
else:
return vt, s, u
else:
return s
def svd(a, full_matrices=True, compute_uv=True):
"""Singular Value Decomposition.
Factorizes the matrix ``a`` as ``u * np.diag(s) * v``, where ``u`` and
``v`` are unitary and ``s`` is an one-dimensional array of ``a``'s
singular values.
Args:
a (cupy.ndarray): The input matrix with dimension ``(M, N)``.
full_matrices (bool): If True, it returns u and v with dimensions
``(M, M)`` and ``(N, N)``. Otherwise, the dimensions of u and v
are respectively ``(M, K)`` and ``(K, N)``, where
``K = min(M, N)``.
compute_uv (bool): If ``False``, it only returns singular values.
Returns:
tuple of :class:`cupy.ndarray`:
A tuple of ``(u, s, v)`` such that ``a = u * np.diag(s) * v``.
.. seealso:: :func:`numpy.linalg.svd`
"""
# TODO(Saito): Current implementation only accepts two-dimensional arrays
util._assert_cupy_array(a)
util._assert_rank2(a)
# Cast to float32 or float64
a_dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
if a_dtype == 'f':
s_dtype = 'f'
elif a_dtype == 'd':
s_dtype = 'd'
elif a_dtype == 'F':
s_dtype = 'f'
else: # a_dtype == 'D':
a_dtype = 'D'
s_dtype = 'd'
# Remark 1: gesvd only supports m >= n (WHAT?)
# Remark 2: gesvd only supports jobu = 'A' and jobvt = 'A'
# Remark 3: gesvd returns matrix U and V^H
# Remark 4: Remark 2 is removed since cuda 8.0 (new!)
n, m = a.shape
# `a` must be copied because xgesvd destroys the matrix
if m >= n:
x = a.astype(a_dtype, order='C', copy=True)
trans_flag = False
else:
m, n = a.shape
x = a.transpose().astype(a_dtype, order='C', copy=True)
trans_flag = True
mn = min(m, n)
if compute_uv:
if full_matrices:
u = cupy.empty((m, m), dtype=a_dtype)
vt = cupy.empty((n, n), dtype=a_dtype)
else:
u = cupy.empty((mn, m), dtype=a_dtype)
vt = cupy.empty((mn, n), dtype=a_dtype)
u_ptr, vt_ptr = u.data.ptr, vt.data.ptr
else:
u_ptr, vt_ptr = 0, 0 # Use nullptr
s = cupy.empty(mn, dtype=s_dtype)
handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
if compute_uv:
job = ord('A') if full_matrices else ord('S')
else:
job = ord('N')
if a_dtype == 'f':
buffersize = cusolver.sgesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
cusolver.sgesvd(
handle, job, job, m, n, x.data.ptr, m,
s.data.ptr, u_ptr, m, vt_ptr, n,
workspace.data.ptr, buffersize, 0, dev_info.data.ptr)
elif a_dtype == 'd':
buffersize = cusolver.dgesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
cusolver.dgesvd(
handle, job, job, m, n, x.data.ptr, m,
s.data.ptr, u_ptr, m, vt_ptr, n,
workspace.data.ptr, buffersize, 0, dev_info.data.ptr)
elif a_dtype == 'F':
buffersize = cusolver.cgesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
cusolver.cgesvd(
handle, job, job, m, n, x.data.ptr, m,
s.data.ptr, u_ptr, m, vt_ptr, n,
workspace.data.ptr, buffersize, 0, dev_info.data.ptr)
else: # a_dtype == 'D':
buffersize = cusolver.zgesvd_bufferSize(handle, m, n)
workspace = cupy.empty(buffersize, dtype=a_dtype)
cusolver.zgesvd(
handle, job, job, m, n, x.data.ptr, m,
s.data.ptr, u_ptr, m, vt_ptr, n,
workspace.data.ptr, buffersize, 0, dev_info.data.ptr)
status = int(dev_info[0])
if status > 0:
raise linalg.LinAlgError(
'SVD computation does not converge')
elif status < 0:
raise linalg.LinAlgError(
'Parameter error (maybe caused by a bug in cupy.linalg?)')
# Note that the returned array may need to be transporsed
# depending on the structure of an input
if compute_uv:
if trans_flag:
return u.transpose(), s, vt.transpose()
else:
return vt, s, u
else:
return s
def gesvda(a, compute_uv=True):
"""Singular value decomposition using cusolverDn<t>gesvdaStridedBatched().
Factorizes the matrix ``a`` into two unitary matrices ``u`` and ``v`` and
a singular values vector ``s`` such that ``a == u @ diag(s) @ v*``.
Args:
a (cupy.ndarray): The input matrix with dimension ``(.., M, N)``.
compute_uv (bool): If ``False``, it only returns singular values.
Returns:
tuple of :class:`cupy.ndarray`:
A tuple of ``(u, s, v)``.
"""
if not check_availability('gesvda'):
raise RuntimeError('gesvda is not available.')
assert a.ndim >= 2
a_ndim = a.ndim
a_shape = a.shape
m, n = a_shape[-2:]
assert m >= n
if a.dtype == 'f':
helper = cusolver.sgesvdaStridedBatched_bufferSize
solver = cusolver.sgesvdaStridedBatched
s_dtype = 'f'
elif a.dtype == 'd':
helper = cusolver.dgesvdaStridedBatched_bufferSize
solver = cusolver.dgesvdaStridedBatched
s_dtype = 'd'
elif a.dtype == 'F':
helper = cusolver.cgesvdaStridedBatched_bufferSize
solver = cusolver.cgesvdaStridedBatched
s_dtype = 'f'
elif a.dtype == 'D':
helper = cusolver.zgesvdaStridedBatched_bufferSize
solver = cusolver.zgesvdaStridedBatched
s_dtype = 'd'
else:
raise TypeError
handle = device.get_cusolver_handle()
if compute_uv:
jobz = cusolver.CUSOLVER_EIG_MODE_VECTOR
else:
jobz = cusolver.CUSOLVER_EIG_MODE_NOVECTOR
rank = min(m, n)
if a_ndim == 2:
batch_size = 1
else:
batch_size = numpy.array(a_shape[:-2]).prod().item()
a = a.reshape((batch_size, m, n))
a = cupy.ascontiguousarray(a.transpose(0, 2, 1))
lda = m
stride_a = lda * n
s = cupy.empty((batch_size, rank), dtype=s_dtype)
stride_s = rank
ldu = m
ldv = n
u = cupy.empty((batch_size, rank, ldu), dtype=a.dtype, order='C')
v = cupy.empty((batch_size, rank, ldv), dtype=a.dtype, order='C')
stride_u = rank * ldu
stride_v = rank * ldv
lwork = helper(handle, jobz, rank, m, n, a.data.ptr, lda, stride_a,
s.data.ptr, stride_s, u.data.ptr, ldu, stride_u, v.data.ptr,
ldv, stride_v, batch_size)
work = cupy.empty((lwork, ), dtype=a.dtype)
info = cupy.empty((batch_size, ), dtype=numpy.int32)
r_norm = numpy.empty((batch_size, ), dtype=numpy.float64)
solver(handle, jobz, rank, m, n, a.data.ptr, lda, stride_a, s.data.ptr,
stride_s, u.data.ptr, ldu, stride_u, v.data.ptr, ldv, stride_v,
work.data.ptr, lwork, info.data.ptr, r_norm.ctypes.data, batch_size)
s = s.reshape(a_shape[:-2] + (s.shape[-1], ))
if not compute_uv:
return s
u = u.transpose(0, 2, 1)
v = v.transpose(0, 2, 1)
u = u.reshape(a_shape[:-2] + (u.shape[-2:]))
v = v.reshape(a_shape[:-2] + (v.shape[-2:]))
return u, s, v
def inv(a):
"""Computes the inverse of a matrix.
This function computes matrix ``a_inv`` from n-dimensional regular matrix
``a`` such that ``dot(a, a_inv) == eye(n)``.
Args:
a (cupy.ndarray): The regular matrix
Returns:
cupy.ndarray: The inverse of a matrix.
.. warning::
This function calls one or more cuSOLVER routine(s) which may yield
invalid results if input conditions are not met.
To detect these invalid results, you can set the `linalg`
configuration to a value that is not `ignore` in
:func:`cupyx.errstate` or :func:`cupyx.seterr`.
.. seealso:: :func:`numpy.linalg.inv`
"""
if a.ndim >= 3:
return _batched_inv(a)
# to prevent `a` to be overwritten
a = a.copy()
util._assert_cupy_array(a)
util._assert_rank2(a)
util._assert_nd_squareness(a)
# support float32, float64, complex64, and complex128
if a.dtype.char in 'fdFD':
dtype = a.dtype.char
else:
dtype = numpy.find_common_type((a.dtype.char, 'f'), ()).char
cusolver_handle = device.get_cusolver_handle()
dev_info = cupy.empty(1, dtype=numpy.int32)
ipiv = cupy.empty((a.shape[0], 1), dtype=numpy.intc)
if dtype == 'f':
getrf = cusolver.sgetrf
getrf_bufferSize = cusolver.sgetrf_bufferSize
getrs = cusolver.sgetrs
elif dtype == 'd':
getrf = cusolver.dgetrf
getrf_bufferSize = cusolver.dgetrf_bufferSize
getrs = cusolver.dgetrs
elif dtype == 'F':
getrf = cusolver.cgetrf
getrf_bufferSize = cusolver.cgetrf_bufferSize
getrs = cusolver.cgetrs
elif dtype == 'D':
getrf = cusolver.zgetrf
getrf_bufferSize = cusolver.zgetrf_bufferSize
getrs = cusolver.zgetrs
else:
msg = ('dtype must be float32, float64, complex64 or complex128'
' (actual: {})'.format(a.dtype))
raise ValueError(msg)
m = a.shape[0]
buffersize = getrf_bufferSize(cusolver_handle, m, m, a.data.ptr, m)
workspace = cupy.empty(buffersize, dtype=dtype)
# LU factorization
getrf(cusolver_handle, m, m, a.data.ptr, m, workspace.data.ptr,
ipiv.data.ptr, dev_info.data.ptr)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
getrf, dev_info)
b = cupy.eye(m, dtype=dtype)
# solve for the inverse
getrs(cusolver_handle, 0, m, m, a.data.ptr, m, ipiv.data.ptr, b.data.ptr,
m, dev_info.data.ptr)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
getrs, dev_info)
return b
def gesvdj(a, full_matrices=True, compute_uv=True, overwrite_a=False):
"""Singular value decomposition using cusolverDn<t>gesvdj().
Factorizes the matrix ``a`` into two unitary matrices ``u`` and ``v`` and
a singular values vector ``s`` such that ``a == u @ diag(s) @ v*``.
Args:
a (cupy.ndarray): The input matrix with dimension ``(M, N)``.
full_matrices (bool): If True, it returns u and v with dimensions
``(M, M)`` and ``(N, N)``. Otherwise, the dimensions of u and v
are respectively ``(M, K)`` and ``(K, N)``, where
``K = min(M, N)``.
compute_uv (bool): If ``False``, it only returns singular values.
overwrite_a (bool): If ``True``, matrix ``a`` might be overwritten.
Returns:
tuple of :class:`cupy.ndarray`:
A tuple of ``(u, s, v)``.
"""
if not check_availability('gesvdj'):
raise RuntimeError('gesvdj is not available.')
if a.ndim == 3:
return _gesvdj_batched(a, full_matrices, compute_uv, overwrite_a)
assert a.ndim == 2
if a.dtype == 'f':
helper = cusolver.sgesvdj_bufferSize
solver = cusolver.sgesvdj
s_dtype = 'f'
elif a.dtype == 'd':
helper = cusolver.dgesvdj_bufferSize
solver = cusolver.dgesvdj
s_dtype = 'd'
elif a.dtype == 'F':
helper = cusolver.cgesvdj_bufferSize
solver = cusolver.cgesvdj
s_dtype = 'f'
elif a.dtype == 'D':
helper = cusolver.zgesvdj_bufferSize
solver = cusolver.zgesvdj
s_dtype = 'd'
else:
raise TypeError
handle = device.get_cusolver_handle()
m, n = a.shape
a = cupy.array(a, order='F', copy=not overwrite_a)
lda = m
mn = min(m, n)
s = cupy.empty(mn, dtype=s_dtype)
ldu = m
ldv = n
if compute_uv:
jobz = cusolver.CUSOLVER_EIG_MODE_VECTOR
else:
jobz = cusolver.CUSOLVER_EIG_MODE_NOVECTOR
full_matrices = False
if full_matrices:
econ = 0
u = cupy.empty((ldu, m), dtype=a.dtype, order='F')
v = cupy.empty((ldv, n), dtype=a.dtype, order='F')
else:
econ = 1
u = cupy.empty((ldu, mn), dtype=a.dtype, order='F')
v = cupy.empty((ldv, mn), dtype=a.dtype, order='F')
params = cusolver.createGesvdjInfo()
lwork = helper(handle, jobz, econ, m, n, a.data.ptr, lda, s.data.ptr,
u.data.ptr, ldu, v.data.ptr, ldv, params)
work = cupy.empty(lwork, dtype=a.dtype)
info = cupy.empty(1, dtype=numpy.int32)
solver(handle, jobz, econ, m, n, a.data.ptr, lda, s.data.ptr, u.data.ptr,
ldu, v.data.ptr, ldv, work.data.ptr, lwork, info.data.ptr, params)
cupy.linalg.util._check_cusolver_dev_info_if_synchronization_allowed(
gesvdj, info)
cusolver.destroyGesvdjInfo(params)
if compute_uv:
return u, s, v
else:
return s
