Hi everybody,
I’m trying to use PolyesterForwardDiff in my code but stumbling upon an error for which the MWE is inspired by the doc:
julia> using Optimization, OptimizationOptimJL, ForwardDiff, PolyesterForwardDiff
julia> rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
rosenbrock (generic function with 1 method)
julia> x0 = zeros(2)
2-element Vector{Float64}:
0.0
0.0
julia> _p = [1.0, 100.0]
2-element Vector{Float64}:
1.0
100.0
julia> optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
(::OptimizationFunction{true, AutoForwardDiff{nothing, Nothing}, typeof(rosenbrock), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}) (generic function with 1 method)
julia> prob = OptimizationProblem(optf, x0, _p)
OptimizationProblem. In-place: true
u0: 2-element Vector{Float64}:
0.0
0.0
julia> sol = solve(prob, BFGS())
retcode: Success
u: 2-element Vector{Float64}:
0.9999999999373603
0.9999999998686199
julia> optf = OptimizationFunction(rosenbrock, Optimization.AutoPolyesterForwardDiff())
(::OptimizationFunction{true, AutoPolyesterForwardDiff{nothing}, typeof(rosenbrock), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}) (generic function with 1 method)
julia> prob = OptimizationProblem(optf, x0, _p)
OptimizationProblem. In-place: true
u0: 2-element Vector{Float64}:
0.0
0.0
julia> sol = solve(prob, BFGS())
ERROR: ArgumentError: The passed automatic differentiation backend choice is not available. Please load the corresponding AD package PolyesterForwardDiff.
Stacktrace:
[1] instantiate_function(f::Function, x::Optimization.ReInitCache{…}, adtype::AutoPolyesterForwardDiff{…}, p::Int64, num_cons::Int64)
@ Optimization ~/.julia/packages/Optimization/xbuNJ/src/function.jl:114
[2] instantiate_function(f::Function, x::Optimization.ReInitCache{…}, adtype::AutoPolyesterForwardDiff{…}, p::Int64)
@ Optimization ~/.julia/packages/Optimization/xbuNJ/src/function.jl:106
[3] OptimizationCache(prob::OptimizationProblem{…}, opt::BFGS{…}, data::Base.Iterators.Cycle{…}; callback::Function, maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, progress::Bool, kwargs::@Kwargs{})
@ Optimization ~/.julia/packages/Optimization/xbuNJ/src/cache.jl:27
[4] __init(prob::OptimizationProblem{…}, opt::BFGS{…}, data::Base.Iterators.Cycle{…}; callback::Function, maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, progress::Bool, kwargs::@Kwargs{})
@ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/yMF3E/src/OptimizationOptimJL.jl:101
[5] __init(prob::OptimizationProblem{…}, opt::BFGS{…}, data::Base.Iterators.Cycle{…})
@ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/yMF3E/src/OptimizationOptimJL.jl:68
[6] init(::OptimizationProblem{…}, ::BFGS{…}; kwargs::@Kwargs{})
@ SciMLBase ~/.julia/packages/SciMLBase/Dwomw/src/solve.jl:166
[7] init(::OptimizationProblem{…}, ::BFGS{…})
@ SciMLBase ~/.julia/packages/SciMLBase/Dwomw/src/solve.jl:164
[8] solve(::OptimizationProblem{…}, ::BFGS{…}; kwargs::@Kwargs{})
@ SciMLBase ~/.julia/packages/SciMLBase/Dwomw/src/solve.jl:96
[9] solve(::OptimizationProblem{…}, ::BFGS{…})
@ SciMLBase ~/.julia/packages/SciMLBase/Dwomw/src/solve.jl:93
[10] top-level scope
@ REPL[17]:1
Some type information was truncated. Use `show(err)` to see complete types.
I looked at the code for OptimizationCache but couldn’t figure it out. Has somebody encountered this?
My package versions:
(Test) pkg> st
Project Test v0.1.0
Status `~/workspace/julia/1.10/Test/Project.toml`
[052768ef] CUDA v5.2.0
[5ae59095] Colors v0.12.10
[a93c6f00] DataFrames v1.6.1
[aae7a2af] DiffEqFlux v3.3.1
[071ae1c0] DiffEqGPU v3.4.1
[0c46a032] DifferentialEquations v7.12.0
[31c24e10] Distributions v0.25.107
[587475ba] Flux v0.14.12
[f6369f11] ForwardDiff v0.10.36
[e9467ef8] GLMakie v0.9.9
[cd3eb016] HTTP v1.10.2
[7f7a1694] Optimization v3.23.0
[36348300] OptimizationOptimJL v0.2.2
[42dfb2eb] OptimizationOptimisers v0.2.1
[1dea7af3] OrdinaryDiffEq v6.73.1
[f0f68f2c] PlotlyJS v0.18.13
[91a5bcdd] Plots v1.40.1
[98d1487c] PolyesterForwardDiff v0.1.1
[92933f4c] ProgressMeter v1.9.0
[ce6b1742] RDatasets v0.7.7
[1ed8b502] SciMLSensitivity v7.56.0
[789caeaf] StochasticDiffEq v6.65.1
[e88e6eb3] Zygote v0.6.69
[02a925ec] cuDNN v1.3.0
[ea8e919c] SHA v0.7.0
[10745b16] Statistics v1.10.0
(Test) pkg>
Note that I’m able to successfully call PolyesterForwardDiff.threaded_gradient!(f, dx, x, ForwardDiff.Chunk(8)); for MWE style values of f, dx and x
