larroy commented on a change in pull request #15120: [bug] fix higher grad log
URL: https://github.com/apache/incubator-mxnet/pull/15120#discussion_r290561369
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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:
can you try
`y_grad_grad = autograd.grad(y_grad, x, ..., create_graph = False...)[0]`
and in validation
`assert_almost_equal(expected_heads_grad, y_grad_grad.asnumpy())`
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