def test_setitem_multiple_input():
"""
Ensures proper backprop through computational graph
in which variable that is set on serves as multiple
inputs to a single operation.
Ensures that null-gradient and clear-graph works properly.
"""
from mygrad import add_sequence
x = Tensor([1.0])
y = x + 0
assert_array_equal(y.data, np.array([1.0]))
o = add_sequence(y, y, y)
y[0] = 4
assert_array_equal(y.data, np.array([4.0]))
f = o * y # 3 * 4
f.backward()
assert_array_equal(o.data, np.array([3.0]))
assert_array_equal(f.data, np.array([12.0]))
assert_array_equal(x.grad, np.array([12.0]))
assert_array_equal(o.grad, np.array([4.0]))
assert_array_equal(y.grad, np.array([3.0]))
f.null_gradients()
assert x.grad is None and not x._ops and not x._accum_ops
assert y.grad is None and not y._ops and not y._accum_ops
assert o.grad is None and not o._ops and not o._accum_ops
assert f.grad is None and not f._ops and not f._accum_ops
def test_seq_add(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
# ensure sequence of shapes is mutually-broadcastable
for i in range(num_arrays):
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 = add_sequence(*tensors)
f1 = sum(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-4,
atol=1e-4,
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_add():
a = Tensor(3)
b = Tensor([1, 2, 3])
c = Tensor([[1, 2, 3], [2, 3, 4]])
f = add_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)
