JuliaDiff / JuliaDiff/DifferentiationInterface.jl
AD-agnostic stop gradient
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 313
- Forks
- 35
- PR merge metrics
- No merged PRs in 30d
Description
Can we have in DI an AD-agnostic stop-gradient operator? That is the equivalent of to pytorch's .detach() or chainrules' ignore_derivatives.
Maybe something similar to what is done with DifferentiateWith can be done here.
Contributor guide
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 existing DifferentiateWith implementation and the surrounding DifferentiationInterface.jl APIs to understand how backend-independent behavior is expressed. Define the intended stop-gradient semantics from the issue's PyTorch detach and ChainRules ignore_derivatives references, then determine how the behavior should be validated across the supported AD backends.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100