Question about how to use on gpu

[yolhan83]

Hello, I wonder why this is not working on gpu :

julia> using CUDA,FiniteDiff
julia> f(x) = sum(abs2,x)
f (generic function with 1 method)

julia> x = rand(100,100) |> CuArray;

julia> f(x)
3298.8592709432687

julia> FiniteDiff.finite_difference_gradient(f,x);
ERROR: MethodError: no method matching compute_epsilon(::Val{:central}, ::CuArray{Float64, 2, CUDA.DeviceMemory}, ::Float64, ::Float64, ::Bool)
The function `compute_epsilon` exists, but no method is defined for this combination of argument types.

julia> FiniteDiff.finite_difference_gradient(f,collect(x)); # works

[ChrisRackauckas]

I don't currently have a GPU to debug this: can you share the full stack trace so I can see if I can eyeball it?

[yolhan83]

Yes, here it is

MethodError: no method matching compute_epsilon(::Val{:central}, ::CuArray{Float64, 2, CUDA.DeviceMemory}, ::Float64, ::Float64, ::Bool)
The function `compute_epsilon` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  compute_epsilon(::Val{:central}, ::T, ::Real, ::Real, ::Any) where T<:Number
   @ FiniteDiff C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\epsilons.jl:13
  compute_epsilon(::Val{:hcentral}, ::T, ::Real, ::Real, ::Any) where T<:Number
   @ FiniteDiff C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\epsilons.jl:17
  compute_epsilon(::Val{:central}, ::T, ::Real, ::Real) where T<:Number
   @ FiniteDiff C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\epsilons.jl:13
  ...


Stacktrace:
 [1] #finite_difference_gradient!#9
   @ C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\gradients.jl:280 [inlined]
 [2] finite_difference_gradient!
   @ C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\gradients.jl:228 [inlined]
 [3] finite_difference_gradient(f::typeof(f), x::CuArray{Float64, 2, CUDA.DeviceMemory}, fdtype::Val{:central}, returntype::Type, inplace::Val{true}, fx::Nothing, c1::Nothing, c2::Nothing; relstep::Float64, absstep::Float64, dir::Bool)
   @ FiniteDiff C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\gradients.jl:163
 [4] finite_difference_gradient(f::Function, x::CuArray{Float64, 2, CUDA.DeviceMemory}, fdtype::Val{:central}, returntype::Type, inplace::Val{true}, fx::Nothing, c1::Nothing, c2::Nothing)
   @ FiniteDiff C:\Users\yolha\.julia\packages\FiniteDiff\vund7\src\gradients.jl:129
 [5] top-level scope
   @ In[12]:3

meaning, it enters here in the code,

function finite_difference_gradient!(
    df,
    f,
    x,
    cache::GradientCache{T1,T2,T3,T4,fdtype,returntype,inplace};
    relstep=default_relstep(fdtype, eltype(x)),
    absstep=relstep,
    dir=true) where {T1,T2,T3,T4,fdtype,returntype,inplace}

    # NOTE: in this case epsilon is a vector, we need two arrays for epsilon and x1
    # c1 denotes x1, c2 is epsilon
    fx, c1, c2, c3 = cache.fx, cache.c1, cache.c2, cache.c3
    if fdtype != Val(:complex) && ArrayInterface.fast_scalar_indexing(c2)
        @. c2 = compute_epsilon(fdtype, x, relstep, absstep, dir)
        copyto!(c1, x)
    end
    copyto!(c3, x)
    if fdtype == Val(:forward)
        ......
    elseif fdtype == Val(:central)
        @inbounds for i ∈ eachindex(x)
            if ArrayInterface.fast_scalar_indexing(c2)
                epsilon = ArrayInterface.allowed_getindex(c2, i) * dir
            else
                epsilon = compute_epsilon(fdtype, x, relstep, absstep, dir) * dir # !!!!!!!!!!!!!!!!!!!!!!!!!!!
            end

and breaks because x should be a number and its the array that enters.
Also, it's the same for :forward