def _testSparseSparse(self, transpose_a, transpose_b, adjoint_a,
adjoint_b):
if not self._gpu_available:
return
sparsify = lambda m: m * (m > 0)
dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
dense_shape_b = [5, 15, 13
] if transpose_b or adjoint_b else [5, 13, 15]
for dtype in np.float32, np.complex64:
a_mats = sparsify(
(np.random.randn(*dense_shape_a) +
1.j * np.random.randn(*dense_shape_a))).astype(dtype)
b_mats = sparsify(
(np.random.randn(*dense_shape_b) +
1.j * np.random.randn(*dense_shape_b))).astype(dtype)
a_sm = sparse_csr_matrix_ops.CSRSparseMatrix(a_mats)
b_sm = sparse_csr_matrix_ops.CSRSparseMatrix(b_mats)
c_dense = math_ops.matmul(a_mats,
b_mats,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_sm = sparse_csr_matrix_ops.matmul(a_sm,
b_sm,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
self.assertIsInstance(c_sm, sparse_csr_matrix_ops.CSRSparseMatrix)
c_sm_dense = c_sm.to_dense()
c_dense, c_sm_dense = self.evaluate([c_dense, c_sm_dense])
self.assertAllClose(c_dense, c_sm_dense)
def _testDenseSparse(self, transpose_a, transpose_b, adjoint_a, adjoint_b):
if not self._gpu_available:
return
sparsify = lambda m: m * (m > 0)
dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
dense_shape_b = [5, 15, 13] if transpose_b or adjoint_b else [5, 13, 15]
dtypes_to_test = [np.float32, np.complex64]
for dtype in dtypes_to_test:
a_mats = (np.random.randn(*dense_shape_a) +
1.j * np.random.randn(*dense_shape_a)).astype(dtype)
b_mats = sparsify((np.random.randn(*dense_shape_b) +
1.j * np.random.randn(*dense_shape_b))).astype(dtype)
b_sm = sparse_csr_matrix_ops.CSRSparseMatrix(b_mats)
c_dense = test_util.matmul_without_tf32(
a_mats,
b_mats,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_sm_dense = sparse_csr_matrix_ops.matmul(
a_mats,
b_sm,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_dense, c_sm_dense = self.evaluate([c_dense, c_sm_dense])
self.assertAllClose(c_dense, c_sm_dense)
def _testDenseSparse(self, transpose_a, transpose_b, adjoint_a, adjoint_b):
if not self._gpu_available:
return
sparsify = lambda m: m * (m > 0)
dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
dense_shape_b = [5, 15, 13
] if transpose_b or adjoint_b else [5, 13, 15]
dtypes_to_test = [np.float32]
if not test.is_built_with_rocm():
# complex type is not supported on the ROCm platform
dtypes_to_test += [np.complex64]
for dtype in dtypes_to_test:
a_mats = (np.random.randn(*dense_shape_a) +
1.j * np.random.randn(*dense_shape_a)).astype(dtype)
b_mats = sparsify(
(np.random.randn(*dense_shape_b) +
1.j * np.random.randn(*dense_shape_b))).astype(dtype)
b_sm = sparse_csr_matrix_ops.CSRSparseMatrix(b_mats)
c_dense = math_ops.matmul(a_mats,
b_mats,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_sm_dense = sparse_csr_matrix_ops.matmul(a_mats,
b_sm,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
c_dense, c_sm_dense = self.evaluate([c_dense, c_sm_dense])
self.assertAllClose(c_dense, c_sm_dense)