def test_seq_mult(shape_1: Tuple[int, ...], num_arrays: int,
data: st.DataObject):
shape_2 = data.draw(hnp.broadcastable_shapes(shape_1), label="shape_2")
shapes = [shape_1, shape_2]
pair = shapes
for i in range(num_arrays):
# ensure sequence of shapes is mutually-broadcastable
broadcasted = _broadcast_shapes(*pair)
shapes.append(
data.draw(hnp.broadcastable_shapes(broadcasted),
label="shape_{}".format(i + 3)))
pair = [broadcasted, shapes[-1]]
tensors = [
Tensor(
data.draw(
hnp.arrays(shape=shape,
dtype=np.float32,
elements=st.floats(-10, 10, width=32))))
for shape in shapes
]
note("tensors: {}".format(tensors))
tensors_copy = [x.copy() for x in tensors]
f = multiply_sequence(*tensors)
f1 = reduce(lambda x, y: x * y,
(var for n, var in enumerate(tensors_copy)))
assert_allclose(f.data, f1.data)
f.sum().backward()
f1.sum().backward()
assert_allclose(f.data, f1.data, rtol=1e-4, atol=1e-4)
for n, (expected, actual) in enumerate(zip(tensors_copy, tensors)):
assert_allclose(
expected.grad,
actual.grad,
rtol=1e-3,
atol=1e-3,
err_msg="tensor-{}".format(n),
)
f.null_gradients()
assert all(x.grad is None for x in tensors)
assert all(not x._ops for x in tensors)
def test_seq_mult():
a = Tensor(3.)
b = Tensor([1., 2., 3.])
c = Tensor([[1., 2., 3.], [2., 3., 4.]])
f = multiply_sequence(a, b, c, constant=False)
f.sum().backward()
a1 = Tensor(3.)
b1 = Tensor([1., 2., 3.])
c1 = Tensor([[1., 2., 3.], [2., 3., 4.]])
f1 = a1 * b1 * c1
f1.sum().backward()
assert_allclose(f.data, f1.data)
assert_allclose(f.grad, f1.grad)
assert_allclose(a.grad, a1.grad)
assert_allclose(b.grad, b1.grad)
assert_allclose(c.grad, c1.grad)
def test_input_validation(arrays):
with pytest.raises(ValueError):
add_sequence(*arrays)
with pytest.raises(ValueError):
multiply_sequence(*arrays)