def csrgeam(a, b, alpha=1, beta=1):
"""Matrix-matrix addition.
.. math::
C = \\alpha A + \\beta B
Args:
a (cupyx.scipy.sparse.csr_matrix): Sparse matrix A.
b (cupyx.scipy.sparse.csr_matrix): Sparse matrix B.
alpha (float): Coefficient for A.
beta (float): Coefficient for B.
Returns:
cupyx.scipy.sparse.csr_matrix: Result matrix.
"""
if not check_availability('csrgeam'):
raise RuntimeError('csrgeam is not available.')
if not isinstance(a, cupyx.scipy.sparse.csr_matrix):
raise TypeError('unsupported type (actual: {})'.format(type(a)))
if not isinstance(b, cupyx.scipy.sparse.csr_matrix):
raise TypeError('unsupported type (actual: {})'.format(type(b)))
assert a.has_canonical_format
assert b.has_canonical_format
if a.shape != b.shape:
raise ValueError('inconsistent shapes')
handle = device.get_cusparse_handle()
m, n = a.shape
a, b = _cast_common_type(a, b)
nnz = numpy.empty((), 'i')
cusparse.setPointerMode(handle, cusparse.CUSPARSE_POINTER_MODE_HOST)
c_descr = MatDescriptor.create()
c_indptr = cupy.empty(m + 1, 'i')
cusparse.xcsrgeamNnz(handle, m, n, a._descr.descriptor, a.nnz,
a.indptr.data.ptr, a.indices.data.ptr,
b._descr.descriptor, b.nnz, b.indptr.data.ptr,
b.indices.data.ptr, c_descr.descriptor,
c_indptr.data.ptr, nnz.ctypes.data)
c_indices = cupy.empty(int(nnz), 'i')
c_data = cupy.empty(int(nnz), a.dtype)
alpha = numpy.array(alpha, a.dtype).ctypes
beta = numpy.array(beta, a.dtype).ctypes
_call_cusparse('csrgeam', a.dtype, handle, m, n, alpha.data,
a._descr.descriptor, a.nnz, a.data.data.ptr,
a.indptr.data.ptr, a.indices.data.ptr, beta.data,
b._descr.descriptor, b.nnz, b.data.data.ptr,
b.indptr.data.ptr, b.indices.data.ptr, c_descr.descriptor,
c_data.data.ptr, c_indptr.data.ptr, c_indices.data.ptr)
c = cupyx.scipy.sparse.csr_matrix((c_data, c_indices, c_indptr),
shape=a.shape)
c._has_canonical_format = True
return c
def csrgeam(a, b, alpha=1, beta=1):
"""Matrix-matrix addition.
.. math::
C = \\alpha A + \\beta B
Args:
a (cupy.sparse.csr_matrix): Sparse matrix A.
b (cupy.sparse.csr_matrix): Sparse matrix B.
alpha (float): Coefficient for A.
beta (float): Coefficient for B.
Returns:
cupy.sparse.csr_matrix: Result matrix.
"""
if a.shape != b.shape:
raise ValueError('inconsistent shapes')
handle = device.get_cusparse_handle()
m, n = a.shape
a, b = _cast_common_type(a, b)
nnz = numpy.empty((), 'i')
cusparse.setPointerMode(
handle, cusparse.CUSPARSE_POINTER_MODE_HOST)
c_descr = MatDescriptor.create()
c_indptr = cupy.empty(m + 1, 'i')
cusparse.xcsrgeamNnz(
handle, m, n,
a._descr.descriptor, a.nnz, a.indptr.data.ptr, a.indices.data.ptr,
b._descr.descriptor, b.nnz, b.indptr.data.ptr, b.indices.data.ptr,
c_descr.descriptor, c_indptr.data.ptr, nnz.ctypes.data)
c_indices = cupy.empty(int(nnz), 'i')
c_data = cupy.empty(int(nnz), a.dtype)
alpha = numpy.array(alpha, a.dtype).ctypes
beta = numpy.array(beta, a.dtype).ctypes
_call_cusparse(
'csrgeam', a.dtype,
handle, m, n, alpha.data,
a._descr.descriptor, a.nnz, a.data.data.ptr,
a.indptr.data.ptr, a.indices.data.ptr, beta.data,
b._descr.descriptor, b.nnz, b.data.data.ptr,
b.indptr.data.ptr, b.indices.data.ptr,
c_descr.descriptor, c_data.data.ptr, c_indptr.data.ptr,
c_indices.data.ptr)
return cupy.sparse.csr_matrix((c_data, c_indices, c_indptr), shape=a.shape)
