apeforest commented on a change in pull request #14613: [MXNET-978] Higher
order gradient support for some unary operators
URL: https://github.com/apache/incubator-mxnet/pull/14613#discussion_r290913893
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File path: src/operator/tensor/elemwise_unary_op_basic.cc
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@@ -85,8 +85,23 @@ The storage type of ``relu`` output depends upon the input
storage type:
)code" ADD_FILELINE)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{"_backward_relu"});
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_relu,
-
unary_bwd<mshadow_op::relu_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU(_backward_relu,
unary_bwd<mshadow_op::relu_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+ [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+ std::vector<nnvm::NodeEntry> ret;
+ // ograds[0]: d^2L/dx^2
+ // inputs[0]: dL/dy
+ // inputs[1]: y
+ // f(x) -> relu(x)
+ // f'(x) = 1 if x > 0 else 0
+ // f''(x) = 0
+ auto gx = nnvm::NodeEntry{n}; // f'(x)
+ ret.emplace_back(MakeNode("elemwise_mul", n->attrs.name +
"_backward_grad_grad",
+ {ograds[0], gx}, nullptr, &n));
Review comment:
Updated
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