[MXNET-978] Higher Order Gradient Support clip, dropout. (#15746)

* support clip, dropout for higher order grad

* add relevant tests

* retrigger CI

src/operator/nn/dropout.cc

@@ -171,14 +171,16 @@ Example::
 .add_arguments(DropoutParam::__FIELDS__());
 
 NNVM_REGISTER_OP(_backward_Dropout)
+.set_num_inputs(2)
 .set_num_outputs(1)
 .set_attr<bool>("TIsLayerOpBackward", true)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr_parser(ParamParser<DropoutParam>)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
   return std::vector<std::pair<int, int> >{{0, 0}};
 })
-.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", DropoutGradCompute<cpu>);
+.set_attr<FStatefulCompute>("FStatefulCompute<cpu>", DropoutGradCompute<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 }  // namespace op
 }  // namespace mxnet

src/operator/tensor/matrix_op.cc

@@ -779,7 +779,8 @@ NNVM_REGISTER_OP(_backward_clip)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<ClipParam>)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", ClipGrad_<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", ClipGrad_<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
 
 NNVM_REGISTER_OP(repeat)
 .add_alias("_np_repeat")

tests/python/unittest/test_higher_order_grad.py

@@ -215,6 +215,36 @@ def grad_grad_op(x):
         check_second_order_unary(array, abs, grad_grad_op)
 
 
+@with_seed()
+def test_clip():
+    def clip(x):
+        a_min, a_max = sorted([random.random(), random.random()])
+
+        return nd.clip(x, a_min, a_max)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, clip, grad_grad_op)
+
+
+@with_seed()
+def test_dropout():
+    def dropout(x):
+        return nd.Dropout(x)
+
+    def grad_grad_op(x):
+        return nd.zeros_like(x)
+
+    for dim in range(1, 5):
+        shape = rand_shape_nd(dim)
+        array = random_arrays(shape)
+        check_second_order_unary(array, dropout, grad_grad_op)
+
+
 def test_sigmoid():
     def sigmoid(x):
         return nd.sigmoid(x)