Provide CUDA support for the binarize() operation on sparse matrices #601
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
|
The segmentation fault in GNNGGraphs CUDA CI should be investigated |
Co-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>
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Outdated suggestions cannot be applied.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.
Made binarize() work on CUDA with sparse adjmat of type CUSPACE.CuSparseMatrixCSC.
Also, added a specialization of binarize() that takes an eltype as an additional arg and directly creates a binarized adjmat of that, avoiding potentially costly subsequent conversions.
However, note that for now this implementation won't be called during the fwd pass, as the CUDA propagate specialization falls back on the default gather/scatter approach, which doesn't use the adjecency matrix.
All tests on CPU and GPU correctly passed, except for 1 broken in "ChebConv GPU", but it is also broken on master.