[ADD] EKFAC #127
[ADD] EKFAC #127
Conversation
Pull Request Test Coverage Report for Build 10975103378Details
💛 - Coveralls |
|
@f-dangel One thing that is not tested and that could be wrong is the per-example gradient computation when there is weight sharing. |
There was a problem hiding this comment.
Gave some refactoring comments.
Overall, while reading through the diff, I was wondering if there is a better way to separate the eigenvalue correction of EKFAC. Ideally, I was imagining we can keep KFAC as is and implement EKFAC separately, e.g. by inheriting EKFAC from KFAC.
Do you have a good idea how to do this? Otherwise I believe this PR will make the code a lot more complex, and long-term complicate extending KFAC, especially for developers that are less familiar with EKFAC.
| # Delete the cached activations | ||
| self._cached_activations.clear() |
There was a problem hiding this comment.
Are these cached activations concatenated over batches? Why don't they have to be cleared inside the data loop?
There was a problem hiding this comment.
No they will just be overwritten, this avoids redundant clearing of the cache before it is filled up again anyway. Do you think it is cleaner to clear the cache explicitly every iteration?
| "d_out1 d_out2, ... d_out1 d_in1, d_in1 d_in2 -> ... d_out2 d_in2", | ||
| ) | ||
| .square_() | ||
| .sum(dim=0) |
There was a problem hiding this comment.
Is this sum correct, or do you want to sum out the ... of the einsum result?
There was a problem hiding this comment.
Based on the above variable, I would change .sum(dim=0) into .sum(list(range(shared_axes)))
There was a problem hiding this comment.
Also check the else branch below for the same suggestions.
| per_example_gradient = einsum( | ||
| g, | ||
| self._cached_activations[module_name], | ||
| "shared d_out, shared d_in -> shared d_out d_in", |
There was a problem hiding this comment.
shared should be replaced by ...
There was a problem hiding this comment.
Then, add a line shared_axes = g.ndim - 2.
|
Will continue this PR in ~2 weeks. |
Implements EKFAC (and its inverse) support (resolves #116).
I think we should at some point refactor
KFACLinearOperatorandKFACInverseLinearOperatorto inherit fromKroneckerProductLinearOperatorandEigendecomposedKroneckerProductLinearOperator(or similar) classes sincetorch_matmatand other methods can be shared. Also, currentlyKFACInverseLinearOperatordoesn't support trace, det, etc. properties which can also be shared. I created #126 for this.