def run_gpu_solve(self, A_val, x_val):
b_val = numpy.dot(A_val, x_val)
b_val_trans = numpy.dot(A_val.T, x_val)
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
b_trans = theano.tensor.matrix("b", dtype="float32")
solver = gpu_solve(A, b)
solver_trans = gpu_solve(A, b_trans, trans='T')
fn = theano.function([A, b, b_trans], [solver, solver_trans], mode=mode_with_gpu)
res = fn(A_val, b_val, b_val_trans)
x_res = numpy.array(res[0])
x_res_trans = numpy.array(res[1])
utt.assert_allclose(x_res, x_val)
utt.assert_allclose(x_res_trans, x_val)
def run_gpu_solve(self, A_val, x_val):
b_val = numpy.dot(A_val, x_val)
b_val_trans = numpy.dot(A_val.T, x_val)
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
b_trans = theano.tensor.matrix("b", dtype="float32")
solver = gpu_solve(A, b)
solver_trans = gpu_solve(A, b_trans, trans='T')
fn = theano.function([A, b, b_trans], [solver, solver_trans],
mode=mode_with_gpu)
res = fn(A_val, b_val, b_val_trans)
x_res = numpy.array(res[0])
x_res_trans = numpy.array(res[1])
utt.assert_allclose(x_res, x_val)
utt.assert_allclose(x_res_trans, x_val)
def run_gpu_solve(self, A_val, x_val, A_struct=None):
b_val = np.dot(A_val, x_val)
b_val_trans = np.dot(A_val.T, x_val)
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
b_trans = theano.tensor.matrix("b", dtype="float32")
if A_struct is None:
solver = gpu_solve(A, b)
solver_trans = gpu_solve(A, b_trans, trans="T")
else:
solver = gpu_solve(A, b, A_struct)
solver_trans = gpu_solve(A, b_trans, A_struct, trans="T")
fn = theano.function([A, b, b_trans], [solver, solver_trans],
mode=mode_with_gpu)
res = fn(A_val, b_val, b_val_trans)
x_res = np.array(res[0])
x_res_trans = np.array(res[1])
utt.assert_allclose(x_val, x_res)
utt.assert_allclose(x_val, x_res_trans)
def test_linalgerr_solve(self):
numpy.random.seed(1)
A_val = numpy.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = numpy.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
# make A singular
A_val[:, 2] = 0
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
solver = gpu_solve(A, b, trans='T')
fn = theano.function([A, b], [solver], mode=mode_with_gpu)
self.assertRaises(LinAlgError, fn, A_val, x_val)
def test_linalgerr_solve(self):
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = np.random.uniform(-0.4, 0.4,
(A_val.shape[1], 4)).astype("float32")
# make A singular
A_val[:, 2] = 0
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
solver = gpu_solve(A, b, trans='T')
fn = theano.function([A, b], [solver], mode=mode_with_gpu)
self.assertRaises(LinAlgError, fn, A_val, x_val)
def test_linalgerrsym_solve(self):
np.random.seed(1)
A_val = np.random.uniform(-0.4, 0.4, (5, 5)).astype("float32")
x_val = np.random.uniform(-0.4, 0.4, (A_val.shape[1], 4)).astype("float32")
A_val = np.dot(A_val.T, A_val)
# make A singular
A_val[:, 2] = A_val[:, 1] + A_val[:, 3]
A = theano.tensor.matrix("A", dtype="float32")
b = theano.tensor.matrix("b", dtype="float32")
solver = gpu_solve(A, b, "symmetric")
fn = theano.function([A, b], [solver], mode=mode_with_gpu)
with pytest.raises(LinAlgError):
fn(A_val, x_val)
