Dense instead of sparse matrix returned during differentiation

[vboussange]

Hi there,
I found an inconsistent behaviour when differentiating a function which takes in an AbstractCuSparseArray versus the same function differentiating an AbstractSparseArray

using CUDA
using Zygote
using SparseArrays

logpos(a) = a > 0 ? log(a) : zero(a)
l(A) = sum(logpos.(A))

A = sprandn(Float32, 10,10,0.4)

dA = gradient(l, A)[1] # returns a sparse array, as expected

Acu = sparse(CuArray(A))
dAcu = gradient(l, Acu)[1] # returns a dense CuArray

Maybe someone here has an idea of where could this come from?

[ToucheSir]

I suspect the issue is with missing rule(s) in ChainRules since Zygote has almost nothing in the way of machinery for diffing sparse arrays. What happens if you set CUDA.allowscalar(false) before running the MWE?