Incorrect initialization of derivative storage

[andreasnoack]

In

NLSolversBase.jl/src/objective_types/twicedifferentiable.jl

Line 18 in 12ceb15

function TwiceDifferentiable(f, g, fg, h, x::TX, F::T = real(zero(eltype(x))), G::TG = similar(x), H::TH = alloc_H(x); inplace = true) where {T, TG, TH, TX}

, similar(x) is used for initialization of the gradient array but that need not be the right element type. In a nested derivative computation such as

julia> myf(x,y) = x^2+y^2
myf (generic function with 1 method)

julia> ForwardDiff.gradient(s -> optimize(t -> myf(t[1], s[1]), [0.0], Newton(), autodiff=:forward).minimum, [0.4])
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1})
Closest candidates are:
  Float64(::Real, ::RoundingMode) where T<:AbstractFloat at rounding.jl:194
  Float64(::T<:Number) where T<:Number at boot.jl:741
  Float64(::Int8) at float.jl:60
  ...
Stacktrace:
 [1] convert(::Type{Float64}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at ./number.jl:7
 [2] setproperty!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::Symbol, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at ./sysimg.jl:19
 [3] value!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at /Users/andreasnoack/.julia/packages/DiffResults/1LURj/src/DiffResults.jl:159
 [4] extract_gradient!(::Type{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64}}, ::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:70
 [5] vector_mode_gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:103
 [6] gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}, ::Val{true}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:35
 [7] gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:33
 [8] (::getfield(NLSolversBase, Symbol("##40#46")){getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}})(::Array{Float64,1}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/NLSolversBase/src/objective_types/twicedifferentiable.jl:122
 [9] value_gradient!!(::TwiceDifferentiable{Float64,Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/NLSolversBase/src/interface.jl:82
 [10] initial_state(::Newton{LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}}}, ::Optim.Options{Float64,Nothing}, ::TwiceDifferentiable{Float64,Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/Optim/src/multivariate/solvers/second_order/newton.jl:45
 [11] #optimize#87 at /Users/andreasnoack/.julia/dev/Optim/src/multivariate/optimize/optimize.jl:33 [inlined]
 [12] (::getfield(Optim, Symbol("#kw##optimize")))(::NamedTuple{(:autodiff,),Tuple{Symbol}}, ::typeof(optimize), ::Function, ::Array{Float64,1}, ::Newton{LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}}}, ::Optim.Options{Float64,Nothing}) at ./none:0 (repeats 2 times)
 [13] vector_mode_gradient(::getfield(Main, Symbol("##37#39")), ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}) at ./none:1
 [14] gradient(::Function, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Val{true}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:17
 [15] gradient(::Function, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:15 (repeats 2 times)
 [16] top-level scope at none:0

the gradient will be a vector of duals which therefore causes an error.

[mfherbst]

Still failing for me on NLSolversBase v7.6.0, Optim v0.20.0 and ForwardDiff v0.10.9.

[pkofod]

Still failing for me on NLSolversBase v7.6.0, Optim v0.20.0 and ForwardDiff v0.10.9.

Sorry, I was not clear here, the "fix" was simply that you can supply the types yourself, but it still defaults to the old behavior. Although, as you showed, it didn't really for complex gradients :)

[mfherbst]

Ah ... I see. Thanks for the clarification.