JuliaDiff / JuliaDiff/ForwardDiff.jl
dynamic NaN-safe mode switching
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- Dominant language
- Julia
- Stars
- 1k
- Forks
- 160
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Description
Follow-up issue to #179. From my comment there:
In a future PR, we could add
AbstractConfigconstructors likeGradientConfig(x, Chunk{N}, NaNSafe{true}). The generated dual numbers would then contain partials of typePartials{N,T,true}, which would dispatch to theNaN-safe versions of methods. Downstream modules could then support passing that option through their APIs (or simply setting it to a reasonable default for themselves internally).
Contributor guide
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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 with issue #179 and the referenced comment, then trace AbstractConfig, GradientConfig, Chunk, NaNSafe, and Partials in the Julia source. Check how generated dual numbers and downstream module APIs currently handle configuration; done means a reviewed design and implementation for dynamic NaN-safe mode switching.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100