larroy commented on a change in pull request #15253: [MXNET-978] Add higher
order gradient support `tan`, `tanh`
URL: https://github.com/apache/incubator-mxnet/pull/15253#discussion_r302241795
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File path: src/operator/tensor/elemwise_unary_op_trig.cc
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@@ -139,7 +139,31 @@ The storage type of ``tan`` output depends upon the input
storage type:
)code" ADD_FILELINE)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseOut{ "_backward_tan" });
-MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tan,
unary_bwd<mshadow_op::tan_grad>);
+MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_tan,
unary_bwd<mshadow_op::tan_grad>)
+.set_attr<nnvm::FGradient>("FGradient",
+ [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
+ // NodeEntry{n} : y_grad * f'(x)
+ // n->inputs[0] : y_grad
+ // n->inputs[1] : f(x) = tan(x)
+ // ograds[0] : head_grads
+ // f'(x) = sec^2(x)
+ // f''(x) = 2 * f'(x) * f(x)
+ const std::unordered_map<std::string, std::string> args = {{"scalar",
"2.0"}};
+ auto two_y = MakeNode("_mul_scalar", n->attrs.name + "_mul_two",
{n->inputs[1]}, &args, &n);
+ auto grad_grad_mid = MakeNode("elemwise_mul", n->attrs.name +
"_grad_mul",
Review comment:
Can we clarify / add a comment on why is correct to multiply by y_grad (the
first head gradient?) again? This would help readers as is not obvious, as well
as the very non-obvious implicit multiplication by f'(x) it compounds.
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