Accessing retained values in computation tree for LRP?

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I’d like to implement layerwise relevance propagation (a saliency method that is different from backprop) automatically by taking advantage of the autograd compuatation tree, but I can’t figure out if there is a way to access retained values in pytorch.

Is there a way of walking the Function tree after a forward computation and reading out retained values?

For example, I’d like to know what the inputs (and/or outputs) of a given function node are for the various built-in functions such as AddmmBackward or ThresholdBackward1, but it appears that the various retained values are retained in C code and don’t seem to be visible to python.

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Hi,

Walking the three is possible using the output’s .grad_fn property to get the first functions, and using the .next_functions property to get higher in the three.
For Functions implemented in python, you can use this python object to access tensors that were saved with save_for_backward by using the saved_tensors or saved_variables properties.
For Functions implemented in C, it depends on the implementation and most of them do not expose these values.