Update grid for CUDA#233
Open
aguevara22 wants to merge 1 commit into
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Hi Ziming!
I noticed using "update_grid_from_samples", which really improves the training on CPU, leads to NaN in the network and gradient when used on CUDA. The problem seems to be coming from the fit of the B spline coefficients in spline.py:
coef = torch.linalg.lstsq(mat.to(device), y_eval.unsqueeze(dim=2).to(device),
driver='gelsy' if device == 'cpu' else 'gels').solution[:, :, 0]
Here "mat" is the B spline function which are not a full rank matrix depending on the samples. It seems that the driver 'gels' cannot handle degenerate matrices. So I just sent that operation to the CPU, which allows to use 'gelsy' and handle degenerate matrices. Perhaps there is a better solution but I'm committing it just in case, since it worked for me on both CUDA and MPS.