def _testLargeBatchSparseMatrixMatMulGrad(
self,
datatype,
transpose_a,
transpose_b,
adjoint_a,
adjoint_b,
transpose_output,
conjugate_output,
batched_inputs,
):
if batched_inputs:
a_shape = (3, 5, 11)
b_shape = (3, 11, 13)
transpose = lambda x: np.transpose(x, (0, 2, 1))
else:
a_shape = (5, 11)
b_shape = (11, 13)
transpose = np.transpose
sparsify = lambda m: m * (m > 0)
a_mats_val = sparsify(
np.random.randn(*a_shape) +
1.j * np.random.randn(*a_shape)).astype(datatype)
if transpose_a or adjoint_a:
a_mats_val = transpose(a_mats_val)
if adjoint_a:
a_mats_val = np.conj(a_mats_val)
b_mats_val = (np.random.randn(*b_shape) +
1.j * np.random.randn(*b_shape)).astype(datatype)
if transpose_b or adjoint_b:
b_mats_val = transpose(b_mats_val)
if adjoint_b:
b_mats_val = np.conj(b_mats_val)
with self.test_session():
a_mats = ops.convert_to_tensor(a_mats_val, dtype=datatype)
b_mats = ops.convert_to_tensor(b_mats_val, dtype=datatype)
locs = array_ops.where(abs(a_mats_val) > 0)
a_sm = sparse_csr_matrix_ops.dense_to_csr_sparse_matrix(
a_mats, locs)
c_mats = sparse_csr_matrix_ops.sparse_matrix_mat_mul(
a_sm,
b_mats,
transpose_a=transpose_a,
transpose_b=transpose_b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b,
transpose_output=transpose_output,
conjugate_output=conjugate_output)
for [ten, val, nn] in [[a_mats, a_mats_val, "a"],
[b_mats, b_mats_val, "b"]]:
tf_logging.info("Testing gradients for %s" % nn)
theoretical, numerical = gradient_checker.compute_gradient(
ten,
ten.get_shape().as_list(),
c_mats,
c_mats.get_shape().as_list(),
x_init_value=val,
delta=1e-3)
self.assertAllClose(theoretical,
numerical,
atol=1e-3,
rtol=1e-3)
def dense_to_csr_sparse_matrix(dense):
dense_t = ops.convert_to_tensor(dense)
locs = array_ops.stop_gradient(array_ops.where(math_ops.abs(dense_t) > 0))
return sparse_csr_matrix_ops.dense_to_csr_sparse_matrix(dense_t, locs)
def dense_and_sparse_from_vals(vals, datatype):
locs = array_ops.where(math_ops.abs(vals) > 0)
dense_t = ops.convert_to_tensor(vals, dtype=datatype)
return (dense_t,
sparse_csr_matrix_ops.dense_to_csr_sparse_matrix(dense_t, locs))
