Is it possible to train sparse neural networks in GPU?

I would like to train a sparse neural network using Flux. I can train it in CPU, but I would like to do it using GPU, profiting from CuSparseMatrixCSC. However, when I try to run the code below I get ERROR: LoadError: This object is not a GPU array.

Is it possible to train sparse networks in GPU?
If so, what is causing the error?



using CUDA, Flux, SparseArrays, MLDatasets

x_train, y_train = MLDatasets.MNIST.traindata(Float32)
x_train = Flux.flatten(x_train) |> gpu
y_train = Flux.onehotbatch(y_train, 0:9) |> gpu
data = Flux.Data.DataLoader((x_train, y_train), batchsize=256, shuffle=true)

model = Dense(sprand(10, 784, 1.0), zeros(Float32, 10)) |> gpu

opt = ADAM(3e-4)
loss(x, y) = Flux.Losses.logitcrossentropy(model(x), y)
parameters = Flux.params(model)

for (x, y) ∈ data
    gradients = gradient(() -> loss(x, y), parameters)
    Flux.Optimise.update!(opt, parameters, gradients)


  [1] error(s::String)
    @ Base ./error.jl:33
  [2] backend(#unused#::Type)
    @ GPUArrays ~/.julia/packages/GPUArrays/Zecv7/src/device/execution.jl:15
  [3] backend(x::Base.ReshapedArray{Float32, 1, CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}})
    @ GPUArrays ~/.julia/packages/GPUArrays/Zecv7/src/device/execution.jl:16
  [4] _copyto!
    @ ~/.julia/packages/GPUArrays/Zecv7/src/host/broadcast.jl:73 [inlined]
  [5] materialize!
    @ ~/.julia/packages/GPUArrays/Zecv7/src/host/broadcast.jl:51 [inlined]
  [6] materialize!
    @ ./broadcast.jl:868 [inlined]
  [7] materialize!
    @ ./broadcast.jl:864 [inlined]
  [8] restructure(x::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, y::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ ArrayInterfaceCore ~/.julia/packages/ArrayInterfaceCore/wwYvJ/src/ArrayInterfaceCore.jl:346
  [9] update!(opt::ADAM, x::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, x̄::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Flux.Optimise ~/.julia/packages/Flux/js6mP/src/optimise/train.jl:16
 [10] update!(opt::ADAM, xs::Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}, gs::Zygote.Grads)
    @ Flux.Optimise ~/.julia/packages/Flux/js6mP/src/optimise/train.jl:24
 [11] top-level scope
    @ .../test_sparse.jl:16
in expression starting at .../test_sparse.jl:14
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It looks like this can be isolated to just a missing method for reshaping a CuSparseMatrixCSC.

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Does that mean it is supposed to work, but is currently broken?

Yes, make an MWE about reshaping sparse CUDA matrices on CUDA.jl and it can get fixed up.

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