larroy commented on a change in pull request #15120: [bug] fix higher grad log
URL: https://github.com/apache/incubator-mxnet/pull/15120#discussion_r290873336
##########
File path: tests/python/unittest/test_higher_order_grad.py
##########
@@ -27,52 +27,79 @@ def test_log():
def log(x):
return nd.log(x)
+ def grad_op(x):
+ return 1/x
+
def grad_grad_op(x):
return -1/(x**2)
arrays = random_arrays((2, 2), (2, 3), (4, 5, 2), (3, 1, 4, 5))
for array in arrays:
- check_second_order_unary(array, log, grad_grad_op)
+ check_second_order_unary(array, log, grad_op, grad_grad_op)
@with_seed()
def test_log2():
def log2(x):
return nd.log2(x)
+ def grad_op(x):
+ return 1/(x * math.log(2))
+
def grad_grad_op(x):
return -1/((x**2) * math.log(2))
arrays = random_arrays((2, 2), (2, 3), (4, 5, 2), (3, 1, 4, 5))
for array in arrays:
- check_second_order_unary(array, log2, grad_grad_op)
+ check_second_order_unary(array, log2, grad_op, grad_grad_op)
@with_seed()
def test_log10():
def log10(x):
return nd.log10(x)
+ def grad_op(x):
+ return 1/(x * math.log(10))
+
def grad_grad_op(x):
return -1/((x**2) * math.log(10))
arrays = random_arrays((2, 2), (2, 3), (4, 5, 2), (3, 1, 4, 5))
for array in arrays:
- check_second_order_unary(array, log10, grad_grad_op)
+ check_second_order_unary(array, log10, grad_op, grad_grad_op)
-def check_second_order_unary(x, op, grad_grad_op):
+def check_second_order_unary(x, op, grad_op, grad_grad_op):
x = nd.array(x)
- expect_grad_grad = grad_grad_op(x)
+ grad_x = grad_op(x)
+ grad_grad_x = grad_grad_op(x)
x.attach_grad()
+
+ # Manual head_grads.
+ head_grads = nd.random.normal(shape=x.shape)
+ head_grad_grads = nd.random.normal(shape=x.shape)
+ head_grads.attach_grad()
+
+ # Perform compute.
with autograd.record():
y = op(x)
- y_grad = autograd.grad(y, x, create_graph=True, retain_graph=True)[0]
- y_grad.backward()
- assert_almost_equal(expect_grad_grad.asnumpy(), x.grad.asnumpy())
+ y_grad = autograd.grad(y, x, head_grads=head_grads,
+ create_graph=True, retain_graph=True)[0]
+
+ y_grad.backward(head_grad_grads)
+
+ # Compute expected values.
+ expected_grad_grad = grad_grad_x.asnumpy() * head_grad_grads.asnumpy() * \
+ head_grads.asnumpy()
+ expected_heads_grad = grad_x.asnumpy()
+
+ # Validate the gradients.
+ assert_almost_equal(expected_grad_grad, x.grad.asnumpy())
+ assert_almost_equal(expected_heads_grad, head_grads.grad.asnumpy())
Review comment:
y_grad.backward(head_grad_grads) indicate that head_grad_grads are the head
gradients passed from "upstream". Calling (output variable).backward It will
update all the independent input variables (from which those output are
dependent), which have attached gradient. In this case head_grad_grads is not
an input to the graph, so your problem that the grad doesn't get updated is
expected:
https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/ndarray/ndarray.py#L2188
https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/autograd.py#L270
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