Hessian AD for logjoint and second-order optim interface broken

[bgroenks96]

Support for second-order forward-mode differentiation seems to be broken on the latest version (v0.21.10).

MWE:

using Turing
using Optim
using ForwardDiff
@model function gaussian()
    μ ~ Normal(0,1)
    σ ~ InverseGamma(2,3)
    x ~ Normal(μ, σ)
    return x
end
m = gaussian()
nlj(x) = -logjoint(m, (μ=x[1],σ=x[2]))
ForwardDiff.hessian(nlj, [0.0,3.0]) # fails

Error:

ERROR: TypeError: in setfield!, expected Float64, got a value of type ForwardDiff.Dual{Nothing, ForwardDiff.Dual{Nothing, Float64, 2}, 2}
Stacktrace:
  [1] setproperty!
    @ .\Base.jl:43 [inlined]
  [2] setindex!
    @ .\refvalue.jl:57 [inlined]
  [3] acclogp!!
    @ C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\threadsafe.jl:19 [inlined]
  [4] tilde_assume!!
    @ C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\context_implementations.jl:118 [inlined]
  [5] gaussian(__model__::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, __varinfo__::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.SimpleVarInfo{NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, 
ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}}, Float64, DynamicPPL.NoTransformation}, Vector{Base.RefValue{Float64}}}, __context__::DynamicPPL.DefaultContext)
    @ Main c:\Users\bgroenke\dmawi\data\sparc\personal_accounts\03_PhD\Brian\repos\CryoGrid\CryoGridML\experiments\ensemble\eks_inference.jl:40
  [6] macro expansion
    @ C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\model.jl:493 [inlined]
  [7] _evaluate!!(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.SimpleVarInfo{NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}}, Float64, DynamicPPL.NoTransformation}, Vector{Base.RefValue{Float64}}}, context::DynamicPPL.DefaultContext)
    @ DynamicPPL C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\model.jl:476
  [8] evaluate_threadsafe!!(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.SimpleVarInfo{NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}}, Float64, DynamicPPL.NoTransformation}, context::DynamicPPL.DefaultContext)
    @ DynamicPPL C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\model.jl:467
  [9] evaluate!!(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.SimpleVarInfo{NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}}, Float64, DynamicPPL.NoTransformation}, context::DynamicPPL.DefaultContext)
    @ DynamicPPL C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\model.jl:402
 [10] logjoint(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.SimpleVarInfo{NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}}, Float64, DynamicPPL.NoTransformation})
    @ DynamicPPL C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\model.jl:548
 [11] logjoint(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.DefaultContext}, θ::NamedTuple{(:μ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}})
    @ DynamicPPL C:\Users\bgroenke\.julia\packages\DynamicPPL\1qg3U\src\simple_varinfo.jl:570
 [12] nlj(x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}})
    @ Main c:\Users\bgroenke\dmawi\data\sparc\personal_accounts\03_PhD\Brian\repos\CryoGrid\CryoGridML\experiments\ensemble\eks_inference.jl:47
 [13] vector_mode_dual_eval!
    @ C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\apiutils.jl:37 [inlined]
 [14] vector_mode_gradient(f::typeof(nlj), x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 
2}}})
    @ ForwardDiff C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\gradient.jl:106
 [15] gradient
    @ C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\gradient.jl:19 [inlined]
 [16] #114
    @ C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\hessian.jl:16 [inlined]
 [17] vector_mode_dual_eval!
    @ C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\apiutils.jl:37 [inlined]
 [18] vector_mode_jacobian(f::ForwardDiff.var"#114#115"{typeof(nlj), ForwardDiff.HessianConfig{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), 
Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}}}, x::Vector{Float64}, cfg::ForwardDiff.JacobianConfig{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}})
    @ ForwardDiff C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\jacobian.jl:148
 [19] jacobian(f::Function, x::Vector{Float64}, cfg::ForwardDiff.JacobianConfig{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}}, ::Val{false})
    @ ForwardDiff C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\jacobian.jl:21
 [20] hessian(f::Function, x::Vector{Float64}, cfg::ForwardDiff.HessianConfig{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}}, ::Val{true})
    @ ForwardDiff C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\hessian.jl:17
 [21] hessian(f::Function, x::Vector{Float64}, cfg::ForwardDiff.HessianConfig{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}, 2}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(nlj), Float64}, Float64, 2}}}) (repeats 2 times)
    @ ForwardDiff C:\Users\bgroenke\.julia\packages\ForwardDiff\pDtsf\src\hessian.jl:15
 [22] top-level scope
    @ c:\Users\bgroenke\dmawi\data\sparc\personal_accounts\03_PhD\Brian\repos\CryoGrid\CryoGridML\experiments\ensemble\eks_inference.jl:52

Also with the Optim interface:

optimize(Turing.condition(m, x=1.0), MAP(), Newton()) # also fails

ERROR: type TwiceDifferentiableHV has no field h_calls
Stacktrace:
  [1] getproperty
    @ .\Base.jl:42 [inlined]
  [2] dotgetproperty(x::NLSolversBase.TwiceDifferentiableHV{Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}}, f::Symbol)
    @ Base .\Base.jl:45
  [3] hessian!!(obj::NLSolversBase.TwiceDifferentiableHV{Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}}, x::Vector{Float64})
    @ NLSolversBase C:\Users\bgroenke\.julia\packages\NLSolversBase\cfJrN\src\interface.jl:93
  [4] initial_state(method::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing}, d::NLSolversBase.TwiceDifferentiableHV{Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}}, initial_x::Vector{Float64})
    @ Optim C:\Users\bgroenke\.julia\packages\Optim\rpjtl\src\multivariate\solvers\second_order\newton.jl:46
  [5] optimize(d::NLSolversBase.TwiceDifferentiableHV{Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}}, initial_x::Vector{Float64}, method::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing})
    @ Optim C:\Users\bgroenke\.julia\packages\Optim\rpjtl\src\multivariate\optimize\optimize.jl:36
  [6] optimize(f::NLSolversBase.InplaceObjective{Nothing, Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, 
Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, Nothing, Nothing, Nothing}, initial_x::Vector{Float64}, method::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing}; inplace::Bool, autodiff::Symbol)
    @ Optim C:\Users\bgroenke\.julia\packages\Optim\rpjtl\src\multivariate\optimize\interface.jl:142
  [7] optimize(f::NLSolversBase.InplaceObjective{Nothing, Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, 
Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, Nothing, Nothing, Nothing}, initial_x::Vector{Float64}, method::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing})
    @ Optim C:\Users\bgroenke\.julia\packages\Optim\rpjtl\src\multivariate\optimize\interface.jl:141
  [8] _optimize(::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, 
Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, ::Vector{Float64}, ::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, ::Optim.Options{Float64, Nothing}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:242
  [9] _optimize(::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, 
Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, ::Vector{Float64}, ::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, ::Optim.Options{Float64, Nothing})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:233
 [10] _optimize(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, f::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, optimizer::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, args::Optim.Options{Float64, Nothing}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:199
 [11] _optimize(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, f::Turing.LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:μ, :σ), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:μ, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:μ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}, DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:σ, Setfield.IdentityLens}, Int64}, Vector{InverseGamma{Float64}}, Vector{AbstractPPL.VarName{:σ, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, DynamicPPL.SampleFromPrior, Turing.ModeEstimation.OptimizationContext{DynamicPPL.DefaultContext}}, optimizer::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, args::Optim.Options{Float64, Nothing})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:199
 [12] _map_optimize(::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, ::Vararg{Any}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:184
 [13] _map_optimize(::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, ::Vararg{Any})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:183
 [14] optimize(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::MAP, optimizer::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:169
 [15] optimize(model::DynamicPPL.Model{typeof(gaussian), (), (), (), Tuple{}, Tuple{}, DynamicPPL.ConditionContext{(:x,), NamedTuple{(:x,), Tuple{Float64}}, DynamicPPL.DefaultContext}}, ::MAP, optimizer::Newton{LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}}, options::Optim.Options{Float64, Nothing}) (repeats 2 times)
    @ Turing C:\Users\bgroenke\.julia\packages\Turing\Tpj0b\src\modes\OptimInterface.jl:169
 [16] top-level scope
    @ c:\Users\bgroenke\dmawi\data\sparc\personal_accounts\03_PhD\Brian\repos\CryoGrid\CryoGridML\experiments\ensemble\eks_inference.jl:53

I think there have been some changes to the JuliaOptim ecosystem packages recently. Perhaps this broke something?

[devmotion]

IIRC second order derivatives have never been supported (at least in the Optim interface).

[bgroenks96]

So what was going on in #1232 and #1369 then?

I do recall there being an error previously when trying to use Newton saying that second-order derivatives were not supported; however, this error seems to no longer be there. Instead we get the weird NLSolversBase error from above.

Is there a discussion somewhere on why second-order derivatives are not currently possible and what needs to be done to make it happen?

[bgroenks96]

@cpfiffer Could you clarify?

[torfjelde]

It seems like it's just an issue with ThreadSafeVarInfo, no?