Reversediff with in optimization problem

[mmbosschaert]

Describe the bug 🐞

Changing the differentiation method in the optimization example to AutoReverseDiff() or AutoZygote() yields errors.

Expected behavior

To be able to use reverse differentiation.

Minimal Reproducible Example 👇

using ModelingToolkit
using ModelingToolkit: t_nounits as t, D_nounits as D

@parameters α β γ δ
@variables x(t) y(t)
eqs = [D(x) ~ (α - β * y) * x
  D(y) ~ (δ * x - γ) * y]
@mtkbuild odesys = ODESystem(eqs, t)

using OrdinaryDiffEq

odeprob = ODEProblem(
  odesys, [x => 1.0, y => 1.0], (0.0, 10.0), [α => 1.5, β => 1.0, γ => 3.0, δ => 1.0])
timesteps = 0.0:0.1:10.0
sol = solve(odeprob, Tsit5(); saveat=timesteps)
data = Array(sol)
# add some random noise
data = data + 0.01 * randn(size(data))

using SymbolicIndexingInterface: parameter_values, state_values
using SciMLStructures: Tunable, replace, replace!

function loss(x, p)
  odeprob = p[1] # ODEProblem stored as parameters to avoid using global variables
  ps = parameter_values(odeprob) # obtain the parameter object from the problem
  ps = replace(Tunable(), ps, x) # create a copy with the values passed to the loss function
  # remake the problem, passing in our new parameter object
  newprob = remake(odeprob; p=ps)
  timesteps = p[2]
  sol = solve(newprob, AutoTsit5(Rosenbrock23()); saveat=timesteps)
  truth = p[3]
  data = Array(sol)
  return sum((truth .- data) .^ 2) / length(truth)
end

using Optimization
using OptimizationOptimJL

# manually create an OptimizationFunction to ensure usage of `ForwardDiff`, which will
# require changing the types of parameters from `Float64` to `ForwardDiff.Dual`
optfn = OptimizationFunction(loss, Optimization.AutoForwardDiff())
# parameter object is a tuple, to store differently typed objects together
optprob = OptimizationProblem(
  optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4))
sol = solve(optprob, BFGS())

using Zygote
using ReverseDiff
using SciMLSensitivity

optfn = OptimizationFunction(loss, Optimization.AutoReverseDiff())
optprob = OptimizationProblem(
  optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4))
sol = solve(optprob, BFGS())

optfn = OptimizationFunction(loss, Optimization.AutoZygote())
optprob = OptimizationProblem(
  optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4))
sol = solve(optprob, BFGS())

Error & Stacktrace ⚠️

With AutoReverseDiff

julia> optfn = OptimizationFunction(loss, Optimization.AutoReverseDiff())
(::OptimizationFunction{true, AutoReverseDiff{false}, typeof(loss), Nothing, Nothing, Nothing, Nothing, 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> optprob = OptimizationProblem(
         optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4))
OptimizationProblem. In-place: true
u0: 4-element Vector{Float64}:
 0.35815540168643545
 0.1319150897371667
 0.9690776505305917
 0.2801614947750005


julia> sol = solve(optprob, BFGS())
ERROR: StackOverflowError:
Stacktrace:
     [1] anyeltypedual(::Type{T}, ::Type{Val{counter}}) where {T<:Union{Set, AbstractArray}, counter}
       @ DiffEqBase ~/.julia/packages/DiffEqBase/sCsah/src/forwarddiff.jl:242
--- the last 1 lines are repeated 1 more time ---
     [3] (::Base.MappingRF{typeof(DiffEqBase.anyeltypedual), Base.BottomRF{typeof(DiffEqBase.promote_dual)}})(acc::Type, x::Type)
       @ Base ./reduce.jl:100
     [4] _foldl_impl(op::Base.MappingRF{typeof(DiffEqBase.anyeltypedual), Base.BottomRF{typeof(DiffEqBase.promote_dual)}}, init::Type, itr::Core.SimpleVector)
       @ Base ./reduce.jl:62
     [5] foldl_impl
       @ ./reduce.jl:48 [inlined]
     [6] mapfoldl_impl
       @ ./reduce.jl:44 [inlined]
     [7] mapfoldl
       @ ./reduce.jl:175 [inlined]
     [8] mapreduce
       @ ./reduce.jl:307 [inlined]
     [9] __anyeltypedual(::Type{ReverseDiff.TrackedReal{Float64, Float64, ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}}})
       @ DiffEqBase ~/.julia/packages/DiffEqBase/sCsah/src/forwarddiff.jl:227
    [10] anyeltypedual(::Type{ReverseDiff.TrackedReal{Float64, Float64, ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}}}, ::Type{Val{0}})
       @ DiffEqBase ~/.julia/packages/DiffEqBase/sCsah/src/forwarddiff.jl:233
--- the last 1 lines are repeated 1 more time ---
--- the last 11 lines are repeated 6151 more times ---
 [67673] anyeltypedual(::Type{T}, ::Type{Val{counter}}) where {T<:Union{Set, AbstractArray}, counter}
       @ DiffEqBase ~/.julia/packages/DiffEqBase/sCsah/src/forwarddiff.jl:242

and with AutoZygote

julia> optfn = OptimizationFunction(loss, Optimization.AutoZygote())
(::OptimizationFunction{true, AutoZygote, typeof(loss), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciML
Base.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}) (generic function with 1 method)

julia> optprob = OptimizationProblem(
         optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4))
OptimizationProblem. In-place: true
u0: 4-element Vector{Float64}:
 0.31077860782384037
 0.9341453115627105
 0.34756647375349614
 0.798118214086817

julia> sol = solve(optprob, BFGS())
ERROR: Need an adjoint for constructor MTKParameters{Vector{Float64}, StaticArraysCore.SizedVector{0, Any, Vector{Any}}, Tuple{}, Tuple{}}. Gradient is of type MTKParameters{Vector{Floa
t64}, StaticArraysCore.SizedVector{0, Any, Vector{Any}}, Tuple{}, Tuple{}}
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:35
  [2] (::Zygote.Jnew{MTKParameters{…}, Nothing, false})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/lib/lib.jl:330
  [3] (::Zygote.var"#2210#back#313"{Zygote.Jnew{MTKParameters{…}, Nothing, false}})(Δ::MTKParameters{Vector{Float64}, StaticArraysCore.SizedVector{0, Any, Vector{…}}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/ZygoteRules/M4xmc/src/adjoint.jl:72
  [4] MTKParameters
    @ ~/.julia/packages/ModelingToolkit/2KZCu/src/systems/parameter_buffer.jl:7 [inlined]
  [5] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
  [6] setfields_object
    @ ~/.julia/packages/ConstructionBase/c2lWA/src/ConstructionBase.jl:195 [inlined]
  [7] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
  [8] setproperties_object
    @ ~/.julia/packages/ConstructionBase/c2lWA/src/ConstructionBase.jl:208 [inlined]
  [9] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [10] setproperties
    @ ~/.julia/packages/ConstructionBase/c2lWA/src/ConstructionBase.jl:136 [inlined]
 [11] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [12] set
    @ ~/.julia/packages/Setfield/PdKfV/src/lens.jl:122 [inlined]
 [13] replace
    @ ~/.julia/packages/Setfield/PdKfV/src/sugar.jl:197 [inlined]
 [14] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::MTKParameters{Vector{…}, StaticArraysCore.SizedVector{…}, Tuple{}, Tuple{}})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [15] loss
    @ ./REPL[16]:4 [inlined]
 [16] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [17] #291
    @ ~/.julia/packages/Zygote/nsBv0/src/lib/lib.jl:206 [inlined]
 [18] #2169#back
    @ ~/.julia/packages/ZygoteRules/M4xmc/src/adjoint.jl:72 [inlined]
 [19] OptimizationFunction
    @ ~/.julia/packages/SciMLBase/OXryf/src/scimlfunctions.jl:3812 [inlined]
 [20] #291
    @ ~/.julia/packages/Zygote/nsBv0/src/lib/lib.jl:206 [inlined]
 [21] (::Zygote.var"#2169#back#293"{Zygote.var"#291#292"{Tuple{Tuple{…}, Tuple{}}, Zygote.Pullback{Tuple{…}, Tuple{…}}}})(Δ::Float64)
    @ Zygote ~/.julia/packages/ZygoteRules/M4xmc/src/adjoint.jl:72
 [22] #37
    @ ~/.julia/packages/OptimizationBase/KIIy3/ext/OptimizationZygoteExt.jl:94 [inlined]
 [23] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [24] #291
    @ ~/.julia/packages/Zygote/nsBv0/src/lib/lib.jl:206 [inlined]
 [25] #2169#back
    @ ~/.julia/packages/ZygoteRules/M4xmc/src/adjoint.jl:72 [inlined]
 [26] #39
    @ ~/.julia/packages/OptimizationBase/KIIy3/ext/OptimizationZygoteExt.jl:97 [inlined]
 [27] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface2.jl:0
 [28] (::Zygote.var"#75#76"{Zygote.Pullback{Tuple{…}, Tuple{…}}})(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface.jl:91
 [29] gradient(f::Function, args::Vector{Float64})
    @ Zygote ~/.julia/packages/Zygote/nsBv0/src/compiler/interface.jl:148
 [30] (::OptimizationZygoteExt.var"#38#56"{OptimizationZygoteExt.var"#37#55"{OptimizationFunction{…}, OptimizationBase.ReInitCache{…}}})(::Vector{Float64}, ::Vector{Float64})
    @ OptimizationZygoteExt ~/.julia/packages/OptimizationBase/KIIy3/ext/OptimizationZygoteExt.jl:97
 [31] (::OptimizationOptimJL.var"#19#23"{OptimizationCache{…}, OptimizationOptimJL.var"#18#22"{…}})(G::Vector{Float64}, θ::Vector{Float64})
    @ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/hDX5k/src/OptimizationOptimJL.jl:297
 [32] value_gradient!!(obj::OnceDifferentiable{Float64, Vector{Float64}, Vector{Float64}}, x::Vector{Float64})
    @ NLSolversBase ~/.julia/packages/NLSolversBase/kavn7/src/interface.jl:82
 [33] value_gradient!!(bw::Optim.BarrierWrapper{OnceDifferentiable{…}, Optim.BoxBarrier{…}, Float64, Float64, Vector{…}}, x::Vector{Float64})
    @ Optim ~/.julia/packages/Optim/ZhuZN/src/multivariate/solvers/constrained/fminbox.jl:81
 [34] initial_state(method::BFGS{…}, options::Optim.Options{…}, d::Optim.BarrierWrapper{…}, initial_x::Vector{…})
    @ Optim ~/.julia/packages/Optim/ZhuZN/src/multivariate/solvers/first_order/bfgs.jl:94
 [35] optimize(df::OnceDifferentiable{…}, l::Vector{…}, u::Vector{…}, initial_x::Vector{…}, F::Fminbox{…}, options::Optim.Options{…})
    @ Optim ~/.julia/packages/Optim/ZhuZN/src/multivariate/solvers/constrained/fminbox.jl:322
 [36] __solve(cache::OptimizationCache{…})
    @ OptimizationOptimJL ~/.julia/packages/OptimizationOptimJL/hDX5k/src/OptimizationOptimJL.jl:321
 [37] solve!(cache::OptimizationCache{…})
    @ SciMLBase ~/.julia/packages/SciMLBase/OXryf/src/solve.jl:188
 [38] solve(::OptimizationProblem{…}, ::BFGS{…}; kwargs::@Kwargs{})
    @ SciMLBase ~/.julia/packages/SciMLBase/OXryf/src/solve.jl:96
 [39] solve(::OptimizationProblem{…}, ::BFGS{…})
    @ SciMLBase ~/.julia/packages/SciMLBase/OXryf/src/solve.jl:93
 [40] top-level scope
    @ REPL[56]:1
Some type information was truncated. Use `show(err)` to see complete types.

julia> 

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

(@v1.10) pkg> st
Status `~/.julia/environments/v1.10/Project.toml`
  [cbdf2221] AlgebraOfGraphics v0.8.1
  [6e4b80f9] BenchmarkTools v1.5.0
  [ad839575] Blink v0.12.9
  [336ed68f] CSV v0.10.14
  [052768ef] CUDA v5.4.3
  [13f3f980] CairoMakie v0.12.7
  [324d7699] CategoricalArrays v0.10.8
  [35d6a980] ColorSchemes v3.26.0
  [5ae59095] Colors v0.12.11
  [a93c6f00] DataFrames v1.6.1
  [0c46a032] DifferentialEquations v7.13.0
  [31c24e10] Distributions v0.25.110
  [e30172f5] Documenter v1.5.0
  [bbc10e6e] DynamicHMC v3.4.7
  [366bfd00] DynamicPPL v0.28.4
  [48062228] FilePathsBase v0.9.21
  [1a297f60] FillArrays v1.12.0
  [6a86dc24] FiniteDiff v2.24.0
  [f6369f11] ForwardDiff v0.10.36
  [38e38edf] GLM v1.9.0
  [e9467ef8] GLMakie v0.10.7
  [c27321d9] Glob v1.3.1
  [f67ccb44] HDF5 v0.17.2
  [cd3eb016] HTTP v1.10.8
  [7073ff75] IJulia v1.25.0
  [5903a43b] Infiltrator v1.8.3
  [4138dd39] JLD v0.13.5
  [033835bb] JLD2 v0.4.51
  [0f8b85d8] JSON3 v1.14.0
  [23fbe1c1] Latexify v0.16.5
  [2ab3a3ac] LogExpFunctions v0.3.28
  [961ee093] ModelingToolkit v9.33.1
  [2774e3e8] NLsolve v4.5.1
  [429524aa] Optim v1.9.4
  [7f7a1694] Optimization v3.27.0
  [36348300] OptimizationOptimJL v0.3.2
  [1dea7af3] OrdinaryDiffEq v6.87.0
  [d96e819e] Parameters v0.12.3
  [14b8a8f1] PkgTemplates v0.7.51
  [91a5bcdd] Plots v1.40.5
  [f27b6e38] Polynomials v4.0.11
  [d236fae5] PreallocationTools v0.4.24
  [8162dcfd] PrettyPrint v0.2.0
  [438e738f] PyCall v1.96.4
  [df47a6cb] RData v1.0.0
  [37e2e3b7] ReverseDiff v1.15.3
  [295af30f] Revise v3.5.18
  [0bca4576] SciMLBase v2.50.1
  [1ed8b502] SciMLSensitivity v7.65.0
  [53ae85a6] SciMLStructures v1.4.2
  [efcf1570] Setfield v1.1.1
  [2913bbd2] StatsBase v0.34.3
  [4c63d2b9] StatsFuns v1.3.1
  [f3b207a7] StatsPlots v0.15.7
  [2efcf032] SymbolicIndexingInterface v0.3.28
  [0c5d862f] Symbolics v6.2.0
  [40c74d1a] TableView v0.8.0
  [9e3dc215] TimeSeries v0.24.2
  [fce5fe82] Turing v0.33.3
  [0004c1f4] TuringGLM v2.11.0
  [3a884ed6] UnPack v1.0.2
  [276b4fcb] WGLMakie v0.10.7
  [e88e6eb3] Zygote v0.6.70
  [9abbd945] Profile

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

Status `~/.julia/environments/v1.10/Manifest.toml`
  [47edcb42] ADTypes v1.7.1
  [a4c015fc] ANSIColoredPrinters v0.0.1
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.2.0
  [7a57a42e] AbstractPPL v0.8.4
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.37
  [79e6a3ab] Adapt v4.0.4
  [35492f91] AdaptivePredicates v1.1.1
  [0bf59076] AdvancedHMC v0.6.1
  [5b7e9947] AdvancedMH v0.8.2
  [576499cb] AdvancedPS v0.6.0
⌃ [b5ca4192] AdvancedVI v0.2.5
  [cbdf2221] AlgebraOfGraphics v0.8.1
  [66dad0bd] AliasTables v1.1.3
  [27a7e980] Animations v0.4.1
  [dce04be8] ArgCheck v2.3.0
⌅ [ec485272] ArnoldiMethod v0.2.0
⌅ [7d9fca2a] Arpack v0.5.3
  [4fba245c] ArrayInterface v7.15.0
  [4c555306] ArrayLayouts v1.10.3
  [69666777] Arrow v2.7.2
  [31f734f8] ArrowTypes v2.3.0
  [bf4720bc] AssetRegistry v0.1.0
  [a9b6321e] Atomix v0.1.0
  [67c07d97] Automa v1.0.4
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
  [ab4f0b2a] BFloat16s v0.5.0
  [093aae92] BSplineKit v0.17.6
  [aae01518] BandedMatrices v1.7.3
  [198e06fe] BangBang v0.4.3
  [9718e550] Baselet v0.1.1
  [6e4b80f9] BenchmarkTools v1.5.0
  [e2ed5e7c] Bijections v0.1.7
⌃ [76274a88] Bijectors v0.13.12
  [d1d4a3ce] BitFlags v0.1.9
  [c3b6d118] BitIntegers v0.3.1
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [ad839575] Blink v0.12.9
⌅ [8e7c35d0] BlockArrays v0.16.43
  [a74b3585] Blosc v0.7.3
  [824d6782] Bonito v3.1.2
  [764a87c0] BoundaryValueDiffEq v5.9.1
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
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  [336ed68f] CSV v0.10.14
  [052768ef] CUDA v5.4.3
  [1af6417a] CUDA_Runtime_Discovery v0.3.4
  [159f3aea] Cairo v1.1.0
  [13f3f980] CairoMakie v0.12.7
  [7057c7e9] Cassette v0.3.13
  [324d7699] CategoricalArrays v0.10.8
  [082447d4] ChainRules v1.69.0
  [d360d2e6] ChainRulesCore v1.24.0
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  [a2cac450] ColorBrewer v0.4.0
  [35d6a980] ColorSchemes v3.26.0
  [3da002f7] ColorTypes v0.11.5
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  [5ae59095] Colors v0.12.11
  [861a8166] Combinatorics v1.0.2
  [a80b9123] CommonMark v0.8.12
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.0
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.16.0
  [b152e2b5] CompositeTypes v0.1.4
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  [f0e56b4a] ConcurrentUtilities v2.4.2
  [8f4d0f93] Conda v1.10.2
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.5.7
  [d38c429a] Contour v0.6.3
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [717857b8] DSP v0.7.9
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.6.1
  [864edb3b] DataStructures v0.18.20
  [e2d170a0] DataValueInterfaces v1.0.0
  [244e2a9f] DefineSingletons v0.1.2
  [927a84f5] DelaunayTriangulation v1.1.3
  [bcd4f6db] DelayDiffEq v5.48.0
  [8bb1440f] DelimitedFiles v1.9.1
  [b429d917] DensityInterface v0.4.0
  [85a47980] Dictionaries v0.4.2
  [2b5f629d] DiffEqBase v6.154.0
  [459566f4] DiffEqCallbacks v3.8.0
  [77a26b50] DiffEqNoiseProcess v5.23.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [0c46a032] DifferentialEquations v7.13.0
  [a0c0ee7d] DifferentiationInterface v0.5.14
  [b4f34e82] Distances v0.10.11
  [31c24e10] Distributions v0.25.110
  [ced4e74d] DistributionsAD v0.6.55
  [ffbed154] DocStringExtensions v0.9.3
  [e30172f5] Documenter v1.5.0
  [5b8099bc] DomainSets v0.7.14
  [bbc10e6e] DynamicHMC v3.4.7
  [366bfd00] DynamicPPL v0.28.4
⌅ [7c1d4256] DynamicPolynomials v0.5.7
⌅ [06fc5a27] DynamicQuantities v0.13.2
  [b7d42ee7] Einsum v0.4.1
  [da5c29d0] EllipsisNotation v1.8.0
  [cad2338a] EllipticalSliceSampling v2.0.0
  [4e289a0a] EnumX v1.0.4
  [7da242da] Enzyme v0.12.31
  [f151be2c] EnzymeCore v0.7.8
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  [c0090381] Qt6Base_jll v6.7.1+1
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  [ce943373] Qt6ShaderTools_jll v6.7.1+1
  [e99dba38] Qt6Wayland_jll v6.7.1+1
⌅ [f50d1b31] Rmath_jll v0.4.3+0
⌅ [fb77eaff] Sundials_jll v5.2.2+0
  [a44049a8] Vulkan_Loader_jll v1.3.243+0
  [a2964d1f] Wayland_jll v1.21.0+1
  [2381bf8a] Wayland_protocols_jll v1.31.0+0
  [02c8fc9c] XML2_jll v2.13.1+0
  [aed1982a] XSLT_jll v1.1.41+0
  [ffd25f8a] XZ_jll v5.4.6+0
  [f67eecfb] Xorg_libICE_jll v1.1.1+0
  [c834827a] Xorg_libSM_jll v1.2.4+0
  [4f6342f7] Xorg_libX11_jll v1.8.6+0
  [0c0b7dd1] Xorg_libXau_jll v1.0.11+0
  [935fb764] Xorg_libXcursor_jll v1.2.0+4
  [a3789734] Xorg_libXdmcp_jll v1.1.4+0
  [1082639a] Xorg_libXext_jll v1.3.6+0
  [d091e8ba] Xorg_libXfixes_jll v5.0.3+4
  [a51aa0fd] Xorg_libXi_jll v1.7.10+4
  [d1454406] Xorg_libXinerama_jll v1.1.4+4
  [ec84b674] Xorg_libXrandr_jll v1.5.2+4
  [ea2f1a96] Xorg_libXrender_jll v0.9.11+0
  [14d82f49] Xorg_libpthread_stubs_jll v0.1.1+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.0+0
  [cc61e674] Xorg_libxkbfile_jll v1.1.2+0
  [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.4+0
  [12413925] Xorg_xcb_util_image_jll v0.4.0+1
  [2def613f] Xorg_xcb_util_jll v0.4.0+1
  [975044d2] Xorg_xcb_util_keysyms_jll v0.4.0+1
  [0d47668e] Xorg_xcb_util_renderutil_jll v0.3.9+1
  [c22f9ab0] Xorg_xcb_util_wm_jll v0.4.1+1
  [35661453] Xorg_xkbcomp_jll v1.4.6+0
  [33bec58e] Xorg_xkeyboard_config_jll v2.39.0+0
  [c5fb5394] Xorg_xtrans_jll v1.5.0+0
  [8f1865be] ZeroMQ_jll v4.3.5+0
  [3161d3a3] Zstd_jll v1.5.6+0
  [35ca27e7] eudev_jll v3.2.9+0
⌅ [214eeab7] fzf_jll v0.43.0+0
  [1a1c6b14] gperf_jll v3.1.1+0
  [9a68df92] isoband_jll v0.2.3+0
  [477f73a3] libaec_jll v1.1.2+0
  [a4ae2306] libaom_jll v3.9.0+0
  [0ac62f75] libass_jll v0.15.1+0
  [1183f4f0] libdecor_jll v0.2.2+0
  [2db6ffa8] libevdev_jll v1.11.0+0
  [f638f0a6] libfdk_aac_jll v2.0.2+0
  [36db933b] libinput_jll v1.18.0+0
  [b53b4c65] libpng_jll v1.6.43+1
  [075b6546] libsixel_jll v1.10.3+0
  [a9144af2] libsodium_jll v1.0.20+0
  [f27f6e37] libvorbis_jll v1.3.7+2
  [009596ad] mtdev_jll v1.1.6+0
  [1317d2d5] oneTBB_jll v2021.12.0+0
  [1270edf5] x264_jll v2021.5.5+0
  [dfaa095f] x265_jll v3.5.0+0
  [d8fb68d0] xkbcommon_jll v1.4.1+1
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
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  [ade2ca70] Dates
  [8ba89e20] Distributed
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  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
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  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
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  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
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  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
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  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
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  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.8.0+1
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why us
e `status --outdated -m`

• Output of versioninfo()

Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 20 × 13th Gen Intel(R) Core(TM) i7-13700H
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, goldmont)
Threads: 1 default, 0 interactive, 1 GC (on 20 virtual cores)

[ChrisRackauckas]

@DhairyaLGandhi are the examples here covered by the tests you've added?

[DhairyaLGandhi]

It seems like avoiding returning MTKParameters and instead returning the Tangent type might be better. I am not sure this is hitting the correct rules. I'll take a look

[DhairyaLGandhi]

I have a branch SciMLSensitivity#dg/nnrev2 which should take care of the Zygote issue. ReverseDiff does still feel like it needs a little more digging into.

julia> optfn = OptimizationFunction(loss, Optimization.AutoZygote());

julia> optprob = OptimizationProblem(
  optfn, rand(4), (odeprob, timesteps, data), lb=0.1zeros(4), ub=3ones(4));

julia> sol = solve(optprob, BFGS())
retcode: Success
u: 4-element Vector{Float64}:
 0.8990195546222982
 0.5906955967141667
 0.02737487567837998
 1.090967054832e-12