JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
ChebConv broken on gpu
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- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
As can be seen in the tests marked as broken in
https://github.com/CarloLucibello/GraphNeuralNetworks.jl/blob/master/test/layers/conv.jl
there are many issues with ChebConv.
- gradient with respect to weights is inaccurate
- CUDA support is lacking (mainly due to the lack of an eigen solver with gpu support)
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the broken ChebConv tests in test/layers/conv.jl and running that test file to reproduce the reported failures. Trace the gradient-with-respect-to-weights checks and the CUDA-related failures, then consider the work complete when the broken tests pass with accurate gradients and CUDA support is addressed.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100