Stop rbf_kernel_grad and rbf_kernel_gradgrad creating the full covariance matrix unnecessarily #2388
Conversation
This file contains 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
Balandat
approved these changes
Aug 5, 2023
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
The master implementation creates a PyTorch tensor of zeros to store the full covariance matrix, even if only the diagonal elements are needed.
If diag=True then the (potentially very large) tensor K is created but never used. This can result in a GPU running out of memory in an avoidable situation.
I have moved the creation of the tensor to store the full covariance matrix into the if diag=False branch of the code.