Sparse Jacobian/Hessian not GPU-compatible

[tmigot]

See the following tests:

• ADNLPModels.jl/test/nls/nlpmodelstest.jl

  Line 42 in 53a494c

  multiple_precision_nls_array(T -> nls_from_T(T; jacobian_backend = ADNLPModels.ForwardDiffADJacobian, hessian_backend = ADNLPModels.ForwardDiffADHessian, jacobian_residual_backend = ADNLPModels.ForwardDiffADJacobian, hessian_residual_backend = ADNLPModels.ForwardDiffADHessian), CuArray, exclude = [jprod, jprod_residual, hprod_residual], linear_api = true)

• ADNLPModels.jl/test/nlp/nlpmodelstest.jl

  Line 25 in 53a494c

  multiple_precision_nlp_array(T -> nlp_from_T(T; jacobian_backend = ADNLPModels.ForwardDiffADJacobian, hessian_backend = ADNLPModels.ForwardDiffADHessian), CuArray, exclude = [jth_hprod, hprod, jprod], linear_api = true)

• ADNLPModels.jl/test/gpu.jl

  Line 28 in 53a494c

  multiple_precision_nls_array(T -> nls_from_T(T; jacobian_backend = ADNLPModels.ForwardDiffADJacobian, hessian_backend = ADNLPModels.ForwardDiffADHessian, jacobian_residual_backend = ADNLPModels.ForwardDiffADJacobian, hessian_residual_backend = ADNLPModels.ForwardDiffADHessian), CuArray, exclude = [jprod, jprod_residual, hprod_residual], linear_api = true)

• ADNLPModels.jl/test/gpu.jl

  Line 18 in 53a494c

  multiple_precision_nlp_array(T -> nlp_from_T(T; jacobian_backend = ADNLPModels.ForwardDiffADJacobian, hessian_backend = ADNLPModels.ForwardDiffADHessian), CuArray, exclude = [jth_hprod, hprod, jprod], linear_api = true)

  
  A MWE:

using CUDA, ADNLPModels, NLPModels, Symbolics

hs6_autodiff(::Type{T}; kwargs...) where {T <: Number} = hs6_autodiff(Vector{T}; kwargs...)
function hs6_autodiff(::Type{S} = Vector{Float64}; kwargs...) where {S}
  x0 = S([-12 // 10; 1])
  f(x) = (1 - x[1])^2
  c(x) = [10 * (x[2] - x[1]^2)]
  lcon = fill!(S(undef, 1), 0)
  ucon = fill!(S(undef, 1), 0)

  return ADNLPModel(f, x0, c, lcon, ucon, name = "hs6_autodiff"; kwargs...)
end
S = CuArray{Float64}
function c!(cx, x)
    cx .= [10 * (x[2] - x[1]^2)]
    return cx
end
x0 = S([-12 // 10; 1])
output = similar(x0, 1)
# nlp = hs6_autodiff(CuArray{Float64})
# ADNLPModels.SparseADJacobian(2, x -> (1 - x[1])^2, 1, c!, x0 = x0)
# J = ADNLPModels.compute_jacobian_sparsity(c!, output, x0)
J = Symbolics.jacobian_sparsity(c!, cx, x0)