def test_cholesky_grad():
rng = np.random.RandomState(utt.fetch_seed())
r = rng.randn(5, 5).astype(config.floatX)
# The dots are inside the graph since Cholesky needs separable matrices
# Check the default.
utt.verify_grad(lambda r: gpu_cholesky(r.dot(r.T)), [r], 3, rng)
# Explicit lower-triangular.
utt.verify_grad(lambda r: GpuCholesky(lower=True)(r.dot(r.T)), [r], 3, rng)
# Explicit upper-triangular.
utt.verify_grad(lambda r: GpuCholesky(lower=False)(r.dot(r.T)), [r], 3, rng)
def test_cholesky_grad_indef():
x = theano.tensor.matrix()
matrix = np.array([[1, 0.2], [0.2, -2]]).astype(config.floatX)
cholesky = GpuCholesky(lower=True)
chol_f = theano.function([x], theano.tensor.grad(cholesky(x).sum(), [x]))
with pytest.raises(LinAlgError):
chol_f(matrix)
def get_gpu_cholesky_func(self, lower=True, inplace=False):
# Helper function to compile function from GPU Cholesky op.
A = theano.tensor.matrix("A", dtype="float64")
cholesky_op = GpuCholesky(lower=lower, inplace=inplace)
chol_A = cholesky_op(A)
return theano.function([A],
chol_A,
accept_inplace=inplace,
mode=mode_with_gpu)
def invalid_input_func():
A = theano.tensor.tensor3("A", dtype="float64")
GpuCholesky(lower=True, inplace=False)(A)
