ChainRules.jl
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JuliaDiff
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
Many repos have a roadmap to v1. This plan goes a little further. The idea is: ## ChainRules/ChainRulesCore v1: - It pragmatically works. - It is completely usable, and is...
appears to solve the diffractor-zygote case in https://github.com/FluxML/Zygote.jl/issues/1301 @mcabbott
The CI is failing on julia 1.8.1 for x86 with either a Segmentation fault or an LLVM memory error. LLVM error example: https://github.com/JuliaDiff/ChainRules.jl/actions/runs/3087367821/jobs/4992664313 ``` LLVM ERROR: out of memory Allocation...
This: ``` using ChainRulesCore myplus(x,y) = x + y function ChainRulesCore.rrule(::typeof(myplus), x, y) println("myplus rrule forward") x+y, dz -> begin println("myplus rrule reverse, dz isa ", typeof(dz)) NoTangent(), @thunk(unthunk(dz)), @thunk...
Closes #507, closes #314 Some commit made https://github.com/dfdx/Yota.jl/issues/78 work, but it needs these lines: https://github.com/JuliaDiff/ChainRules.jl/pull/671/commits/4f144fdaef5f09e193f8a0535855277531cd6b69#diff-dbac688fe6656bd395f7931766da39b9a03d2e640f1dcc9864cddf506bbf65e1L20-L21
Found here: https://github.com/FluxML/Zygote.jl/issues/1297 ```julia julia> using ChainRules, ChainRulesCore, ChainRulesTestUtils julia> const CFG = ChainRulesTestUtils.TestConfig(); julia> foldl(/, (), init=0) 0 julia> rrule(CFG, foldl, /, (), init=0) ERROR: BoundsError: attempt to access...
```julia using Zygote using LinearAlgebra r = rand(8,8); Σ = r' * r foo(X) = tr(svd(X).U) _orthogonal(X) = svd(X).U * svd(X).V' Zygote.gradient(foo, Σ) # Works Zygote.gradient(foo, 1.0 * Matrix(I(5))) #...
Hi, I'd like to suggest including a rule for GPU reductions. ```julia using Zygote function my_loss(v) # This works: # l = sum(v) # This does not work: l =...
As discussed on Slack there is an issue with dictionaries appearing in gradients. The following is as minimum an example as I could make. This requires Molly master, Zygote master,...
