hprod/jprod 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

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

nlp = hs6_autodiff(CuArray{Float64})
CUDA.allowscalar()
jth_hprod(nlp, nlp.meta.x0, nlp.meta.x0, 1) # same for hprod(nlp, nlp.meta.x0, nlp.meta.x0)
#=
ERROR: GPU compilation of MethodInstance for (::GPUArrays.var"#map_kernel#38"{…})(::CUDA.CuKernelContext, ::CuDeviceVector{…}, ::Base.Broadcast.Broadcasted{…}, ::Int64) failed
KernelError: passing and using non-bitstype argument

Argument 4 to your kernel function is of type Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Tuple{Base.OneTo{Int64}}, ForwardDiff.var"#85#86"{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}}, Tuple{Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}}}}, which is not isbits:
  .args is of type Tuple{Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}}} which is not isbits.
    .1 is of type Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}} which is not isbits.
      .x is of type Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}} which is not isbits.
=#