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")
