apeforest edited a comment on issue #15120: [bug] fix higher grad log
URL: https://github.com/apache/incubatormxnet/pull/15120#issuecomment499170409
@kshitij12345 I have some question about the equation `expected_head_grad = (grad_op(x)
* head_grad_grads).asnumpy()` in your test.
My understanding from the chain rule:
```
Given y =f(x)
dL/dx = dL/dy * dy/dx > this is the first backward pass. Let dL/dy be y_grad, we
get dL/dx (noted as x_grad)
Now we rewrite the above the equation:
input0: y_grad
input1: x
output: x_grad = y_grad * f'(x)
Another backward pass for this would be:
dL/d y_grad = dL/d x_grad * f'(x)
dL/dx = dL/d x_grad * y_grad * f''(x)
```
What is the meaning of dL/d y_grad? Are we treating y_grad as another input variable here?
Many thanks for your clarification.

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