JuliaGPU/CUDA.jl

Add `@non_differentiable` rules for basic random functions

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#2,153 opened on Nov 6, 2023

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 (2 comments) (0 reactions) (0 assignees)Julia (274 forks)batch import
enhancementgood first issue

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Description

I need to have these lines in my codes to make AD works.

@non_differentiable CUDA.zeros(::Any...)
@non_differentiable CUDA.rand(::Any...)
@non_differentiable CUDA.randn(::Any...)

Can we add them to the extension? Adapting from https://github.com/JuliaDiff/ChainRules.jl/blob/main/src/rulesets/Random/random.jl

Contributor guide

Research direction
Adapt the @non differentiable rules from ChainRules.jl's Random/random.jl for CUDA's random functions like zeros, rand, randn.
Tech stack
julia
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
1
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Under 1 hour
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Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Julia
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
90