[GitHub] [incubator-mxnet] larroy commented on a change in pull request #15611: [MXNET-978] n-th order gradient test support

[GitBox <gi...@apache.org>]

larroy commented on a change in pull request #15611: [MXNET-978] n-th order gradient test support
URL: https://github.com/apache/incubator-mxnet/pull/15611#discussion_r306023572
 
 

 ##########
 File path: tests/python/unittest/test_higher_order_grad.py
 ##########
 @@ -148,30 +165,48 @@ def grad_grad_op(x):
         shape = rand_shape_nd(dim)
         array = random_arrays(shape)
         check_second_order_unary(array, sigmoid, grad_grad_op)
+        # TODO(kshitij12345): Remove
+        check_nth_order_unary(array, sigmoid, [grad_op, grad_grad_op], [1, 2])
+        check_nth_order_unary(array, sigmoid, grad_grad_op, 2)
 
 
 def check_second_order_unary(x, op, grad_grad_op):
+    check_nth_order_unary(x, op, grad_grad_op, 2)
+
+
+def check_nth_order_unary(x, op, grad_ops, orders):
+    if isinstance(orders, int):
+        orders = [orders]
+        grad_ops = [grad_ops]
+
     x = nd.array(x)
-    grad_grad_x = grad_grad_op(x)
     x.attach_grad()
 
-    # Manual head_grads.
-    y_grad = nd.random.normal(shape=x.shape)
-    head_grad_grads = nd.random.normal(shape=x.shape)
+    order = max(orders)
+    expected_grads = [grad_op(x) for grad_op in grad_ops]
+    computed_grads = []
+    head_grads = []
 
     # Perform compute.
     with autograd.record():
         y = op(x)
-        x_grad = autograd.grad(heads=y, variables=x, head_grads=y_grad,
-                               create_graph=True, retain_graph=True)[0]
-    x_grad.backward(head_grad_grads)
-
-    # Compute expected values.
-    expected_grad_grad = grad_grad_x.asnumpy() * head_grad_grads.asnumpy() * \
-        y_grad.asnumpy()
-
-    # Validate the gradients.
-    assert_almost_equal(expected_grad_grad, x.grad.asnumpy())
+        for current_order in range(1, order+1):
+            head_grad = nd.random.normal(shape=x.shape)
+            y = autograd.grad(heads=y, variables=x, head_grads=head_grad,
+                              create_graph=True, retain_graph=True)[0]
+            if current_order in orders:
 
 Review comment:
   If current_order is not in orders we might have problem zipping?  Is there a case where you wou want 1st and 3rd order but not second?

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