Reuse intermediate results over multiple backwards grad_inputs#3526
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I also pushed a fairly hefty piece of documentation on the top of |
colesbury
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Signed-off-by: Edward Z. Yang <[email protected]>
Signed-off-by: Edward Z. Yang <[email protected]>
…atives.yaml Signed-off-by: Edward Z. Yang <[email protected]>
Signed-off-by: Edward Z. Yang <[email protected]>
Signed-off-by: Edward Z. Yang <[email protected]>
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Pushed some PR comment fixes, and squashed some lint commits. I'll merge this when CI passes. |
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The first two commits are just a little bit of refactoring.
The next two support defining gradient for multiple inputs simultaneously in
derivatives.yaml, and then an example of how to use it viaatan2. The basic model is, instead of sayingoutput1: returns a tensorandoutput2: returns a tensor, you just sayoutput1, output2: returns a tuple of tensorsI am not entirely sure I have done the
output_maskhandling idiomatically. This definitely seems like an opportunity for some compiler-y techniques. For example, one way to implement this assuming you have a working compiler is to define the computation once assuming every output is needed, and then for every output_mask permutation, DCE the unneeded outputs. (This is, of course, assuming that there isn't a totally different algorithm that is applicable when you can remove required grads; in the case of atan2, this is definitely not the case.) In any case I don't plan to do this on this PR.Looking at derivatives.yaml, here are some more opportunities for reusing intermediate computations with addbmm and dist (which already have gradients.) I'll do these once I confirm the pattern looks good.