def test_nonconstant_s0_raises(s0, dropout: float, out_constant: bool):
T, N, C, D = 5, 1, 3, 2
X = Tensor(np.random.rand(T, N, C))
Wz, Wr, Wh = Tensor(np.random.rand(3, D, D))
Uz, Ur, Uh = Tensor(np.random.rand(3, C, D))
bz, br, bh = Tensor(np.random.rand(3, D))
with does_not_raise() if (
out_constant or s0 is None or isinstance(s0, np.ndarray) or s0.constant
) else pytest.raises(ValueError):
gru(
X,
Uz,
Wz,
bz,
Ur,
Wr,
br,
Uh,
Wh,
bh,
s0=s0,
dropout=dropout,
constant=out_constant,
)
def test_backpropping_non_numeric_gradient_raises(constant: bool,
arr: np.ndarray,
op_before: bool, op_after):
x = Tensor(arr, constant=constant)
if op_before:
x += 1
x = old_op(x)
if op_after:
x = x * 2
# if constant tensor, backprop should not be triggered - no exception raised
with (pytest.raises(InvalidGradient)
if not constant else does_not_raise()):
x.backward()
def test_backpropping_non_numeric_gradient_raises(constant: bool,
arr: np.ndarray,
op_before: bool,
op_after: bool):
x = Tensor(arr, constant=constant)
if op_before:
x += 1
x = old_op(x)
if op_after:
x = x * 2
# if constant tensor, backprop should not be triggered - no exception raised
with (pytest.raises(InvalidGradient)
if not constant else does_not_raise()) as exec_info:
x.backward()
if exec_info is not None:
err_msg = str(exec_info.value)
assert "NoneType" in err_msg
def test_invalid_gradient_raises(constant: bool):
x = Tensor(3, constant=constant) * 2
with (pytest.raises(InvalidGradient)
if not constant else does_not_raise()):
x.backward("bad")