# There was a problem with ForwardDiff-based Optimization when re-executing the code

**URL:** <https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682>\
**Category:** General Usage\
**Tags:** optimization\
**Created:** [May 1, 2024, 5:24am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682 "2024-05-01T05:24:19Z")\
**Posts on this page:** 14\
**Page:** 1

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 5:24am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/1 "2024-05-01T05:24:19Z")

</div>

I’m using Optimization.jl to optimize some parameters of ODEProblem, where AutoForwardDiff is used for adtype, when the code is executed for the first time, the program does not show any errors, but when I execute the program again without restarting julia, I get the following error:

```julia
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{OptimizationForwardDiffExt.var"#37#55"{…}, Float64}, Float64, 6})

Closest candidates are:
  (::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat
   @ Base rounding.jl:207
  (::Type{T})(::T) where T<:Number
   @ Core boot.jl:792
  Float64(::UInt8)
   @ Base float.jl:165
  ...

Stacktrace:
  [1] convert
    @ D:\software\Julia-1.10.0\pkg\packages\ForwardDiff\PcZ48\src\dual.jl:433 [inlined]
  [2] setindex!(A::Vector{ForwardDiff.Dual{…}}, x::ForwardDiff.Dual{ForwardDiff.Tag{…}, ForwardDiff.Dual{…}, 6}, i1::Int64)
    @ Base .\array.jl:1021
  [3] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\SymbolicUtils\c0xQb\src\code.jl:418 [inlined]
  [4] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\Symbolics\Eas9m\src\build_function.jl:546 [inlined]
  [5] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\SymbolicUtils\c0xQb\src\code.jl:375 [inlined]
  [6] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\RuntimeGeneratedFunctions\M9ZX8\src\RuntimeGeneratedFunctions.jl:163 [inlined]
  [7] macro expansion
    @ .\none:0 [inlined]
  [8] generated_callfunc
    @ .\none:0 [inlined]
  [9] (::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{…})(::Vector{…}, ::Vector{…}, ::Vector{…}, ::Float64)
    @ RuntimeGeneratedFunctions D:\software\Julia-1.10.0\pkg\packages\RuntimeGeneratedFunctions\M9ZX8\src\RuntimeGeneratedFunctions.jl:150
 [10] (::ModelingToolkit.var"#f#709"{…})(du::Vector{…}, u::Vector{…}, p::ModelingToolkit.MTKParameters{…}, t::Float64)
    @ ModelingToolkit D:\software\Julia-1.10.0\pkg\packages\ModelingToolkit\kByuD\src\systems\diffeqs\abstractodesystem.jl:344
 [11] (::SciMLBase.Void{ModelingToolkit.var"#f#709"{…}})(::Vector{ForwardDiff.Dual{…}}, ::Vararg{Any})
    @ SciMLBase D:\software\Julia-1.10.0\pkg\packages\SciMLBase\QEvkv\src\utils.jl:482
 [12] (::FunctionWrappers.CallWrapper{…})(f::SciMLBase.Void{…}, arg1::Vector{…}, arg2::Vector{…}, arg3::ModelingToolkit.MTKParameters{…}, arg4::Float64)
    @ FunctionWrappers D:\software\Julia-1.10.0\pkg\packages\FunctionWrappers\Q5cBx\src\FunctionWrappers.jl:65
 [13] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\FunctionWrappers\Q5cBx\src\FunctionWrappers.jl:137 [inlined]
 [14] do_ccall
    @ D:\software\Julia-1.10.0\pkg\packages\FunctionWrappers\Q5cBx\src\FunctionWrappers.jl:125 [inlined]
 [15] FunctionWrapper
    @ D:\software\Julia-1.10.0\pkg\packages\FunctionWrappers\Q5cBx\src\FunctionWrappers.jl:144 [inlined]
 [16] _call
    @ D:\software\Julia-1.10.0\pkg\packages\FunctionWrappersWrappers\9XR0m\src\FunctionWrappersWrappers.jl:12 [inlined]
 [17] FunctionWrappersWrapper
    @ D:\software\Julia-1.10.0\pkg\packages\FunctionWrappersWrappers\9XR0m\src\FunctionWrappersWrappers.jl:10 [inlined]
 [18] ODEFunction
    @ D:\software\Julia-1.10.0\pkg\packages\SciMLBase\QEvkv\src\scimlfunctions.jl:2296 [inlined]
 [19] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{…}, cache::OrdinaryDiffEq.Rosenbrock23Cache{…})
    @ OrdinaryDiffEq D:\software\Julia-1.10.0\pkg\packages\OrdinaryDiffEq\ZbQoo\src\perform_step\rosenbrock_perform_step.jl:10
 [20] __init(prob::ODEProblem{…}, alg::OrdinaryDiffEq.Rosenbrock23{…}, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{…}; saveat::Float64, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Float64, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Rational{…}, abstol::Float64, reltol::Float64, qmin::Rational{…}, qmax::Int64, qsteady_min::Int64, qsteady_max::Rational{…}, beta1::Nothing, beta2::Nothing, qoldinit::Rational{…}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(LinearAlgebra.opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), progress_id::Symbol, userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::@Kwargs{})
    @ OrdinaryDiffEq D:\software\Julia-1.10.0\pkg\packages\OrdinaryDiffEq\ZbQoo\src\solve.jl:518
 [21] __init (repeats 5 times)
    @ D:\software\Julia-1.10.0\pkg\packages\OrdinaryDiffEq\ZbQoo\src\solve.jl:11 [inlined]
 [22] #__solve#761
    @ D:\software\Julia-1.10.0\pkg\packages\OrdinaryDiffEq\ZbQoo\src\solve.jl:6 [inlined]
 [23] __solve
    @ D:\software\Julia-1.10.0\pkg\packages\OrdinaryDiffEq\ZbQoo\src\solve.jl:1 [inlined]
 [24] solve_call(_prob::ODEProblem{…}, args::OrdinaryDiffEq.Rosenbrock23{…}; merge_callbacks::Bool, kwargshandle::Nothing, kwargs::@Kwargs{…})
    @ DiffEqBase D:\software\Julia-1.10.0\pkg\packages\DiffEqBase\NaUtB\src\solve.jl:612
 [25] solve_up(prob::ODEProblem{…}, sensealg::SciMLSensitivity.ForwardSensitivity{…}, u0::Vector{…}, p::ModelingToolkit.MTKParameters{…}, args::OrdinaryDiffEq.Rosenbrock23{…}; kwargs::@Kwargs{…})
    @ DiffEqBase D:\software\Julia-1.10.0\pkg\packages\DiffEqBase\NaUtB\src\solve.jl:1080
 [26] solve_up
    @ D:\software\Julia-1.10.0\pkg\packages\DiffEqBase\NaUtB\src\solve.jl:1066 [inlined]
 [27] solve(prob::ODEProblem{…}, args::OrdinaryDiffEq.Rosenbrock23{…}; sensealg::SciMLSensitivity.ForwardSensitivity{…}, u0::Nothing, p::Nothing, wrap::Val{…}, kwargs::@Kwargs{…})
    @ DiffEqBase D:\software\Julia-1.10.0\pkg\packages\DiffEqBase\NaUtB\src\solve.jl:1003
 [28] (::Main.DeepFlex.ODESolver)(ode_prob::ODEProblem{…}, state_names::Vector{…})
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\utils\solver.jl:13
 [29] (::Main.DeepFlex.HydroElement{…})(input::@NamedTuple{…}, pas::ComponentVector{…}; solver::Main.DeepFlex.ODESolver)
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\element.jl:90
 [30] (::Vector{…})(input::@NamedTuple{…}, pas::ComponentVector{…}; solver::Main.DeepFlex.ODESolver)
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\element.jl:148
 [31] (::Main.DeepFlex.HydroNode)(input::@NamedTuple{…}, pas::ComponentVector{…}; solver::Main.DeepFlex.ODESolver)
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\node.jl:54
 [32] (::Main.DeepFlex.HydroNode)(input::@NamedTuple{…}, pas::ComponentVector{…})
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\node.jl:46
 [33] (::Main.DeepFlex.var"#predict_func#39"{…})(x::Vector{…}, p::SciMLBase.NullParameters)
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\utils\optimize.jl:74
 [34] (::Main.DeepFlex.var"#objective#40"{…})(x::Vector{…}, p::SciMLBase.NullParameters)
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\utils\optimize.jl:77
 [35] (::OptimizationForwardDiffExt.var"#37#55"{…})(::Vector{…})
    @ OptimizationForwardDiffExt D:\software\Julia-1.10.0\pkg\packages\OptimizationBase\rRpJs\ext\OptimizationForwardDiffExt.jl:98
 [36] #39
    @ D:\software\Julia-1.10.0\pkg\packages\OptimizationBase\rRpJs\ext\OptimizationForwardDiffExt.jl:102 [inlined]
 [37] vector_mode_dual_eval!
    @ D:\software\Julia-1.10.0\pkg\packages\ForwardDiff\PcZ48\src\apiutils.jl:24 [inlined]
 [38] vector_mode_gradient!(result::Vector{…}, f::OptimizationForwardDiffExt.var"#39#57"{…}, x::Vector{…}, cfg::ForwardDiff.GradientConfig{…})
    @ ForwardDiff D:\software\Julia-1.10.0\pkg\packages\ForwardDiff\PcZ48\src\gradient.jl:96
 [39] gradient!
    @ ForwardDiff D:\software\Julia-1.10.0\pkg\packages\ForwardDiff\PcZ48\src\gradient.jl:37 [inlined]
 [40] (::OptimizationForwardDiffExt.var"#38#56"{…})(::Vector{…}, ::Vector{…})
    @ OptimizationForwardDiffExt D:\software\Julia-1.10.0\pkg\packages\OptimizationBase\rRpJs\ext\OptimizationForwardDiffExt.jl:102
 [41] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\OptimizationOptimisers\AOkbT\src\OptimizationOptimisers.jl:68 [inlined]
 [42] macro expansion
    @ D:\software\Julia-1.10.0\pkg\packages\Optimization\5DEdF\src\utils.jl:32 [inlined]
 [43] __solve(cache::OptimizationCache{…})
    @ OptimizationOptimisers D:\software\Julia-1.10.0\pkg\packages\OptimizationOptimisers\AOkbT\src\OptimizationOptimisers.jl:66
 [44] solve!(cache::OptimizationCache{…})
    @ SciMLBase D:\software\Julia-1.10.0\pkg\packages\SciMLBase\QEvkv\src\solve.jl:188
 [45] solve(::OptimizationProblem{…}, ::Adam; kwargs::@Kwargs{…})
    @ SciMLBase D:\software\Julia-1.10.0\pkg\packages\SciMLBase\QEvkv\src\solve.jl:96
 [46] solve
    @ D:\software\Julia-1.10.0\pkg\packages\SciMLBase\QEvkv\src\solve.jl:93 [inlined]
 [47] param_grad_optim(component::Main.DeepFlex.HydroNode; tunable_pas::ComponentVector{…}, const_pas::ComponentVector{…}, input::@NamedTuple{…}, target::@NamedTuple{…}, kwargs::@Kwargs{})
    @ Main.DeepFlex f:\julia\DeepFlex.jl\src\utils\optimize.jl:82
 [48] top-level scope
    @ f:\julia\DeepFlex.jl\test\optimization\test_node_grad_optimization.jl:38
Some type information was truncated. Use `show(err)` to see complete types.

```

This problem comes from a project I am currently writing, I have the above problem when I use ModelingToolkit.jl to build ODEProblem and use AutoForwardDiff as the Adtype, and I don’t have this problem when I build ODEProblem directly through the Function or use AutoFiniteDiff, see the detailed execution file [DeepFlex.jl/test/optimization/test\_node\_grad\_optimization.jl at main · chooron/DeepFlex.jl · GitHub](https://github.com/chooron/DeepFlex.jl/blob/main/test/optimization/test_node_grad_optimization.jl)

---

<div class="post-metadata">

**Author:** ![gdalle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gdalle/32/27854_2.png) [@gdalle](https://discourse.julialang.org/u/gdalle)\
**Post date:** [May 1, 2024, 6:24am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/2 "2024-05-01T06:24:44Z")

</div>

This is due to ForwardDiff.jl needing to pass numbers of a custom (`Dual`) type to the functions you provide, see [https://juliadiff.org/ForwardDiff.jl/stable/user/limitations/](https://juliadiff.org/ForwardDiff.jl/stable/user/limitations/). It is likely that somewhere in your code you constrain numbers to be of type `Float64`, perhaps without knowing it (like an array initialization with `zeros(n)`, which amounts to `zeros(Float64, n)`).

We can help you debug if you provide a complete example

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 9:06am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/3 "2024-05-01T09:06:05Z")

</div>

Why didn’t I get the problem the first time I executed the code, but I got this error the second time, is it because of the cache? Is it possible to find out where the problem is by debugging, and how to debug, because this is a model building framework, and I will integrate it into a main file in the next few days

---

<div class="post-metadata">

**Author:** ![gdalle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gdalle/32/27854_2.png) [@gdalle](https://discourse.julialang.org/u/gdalle)\
**Post date:** [May 1, 2024, 9:26am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/4 "2024-05-01T09:26:21Z")

</div>

It’s really hard to help you debug without the actual code you’re running. Can you distill a minimum working example?

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 10:02am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/5 "2024-05-01T10:02:03Z")

</div>

I’ve uploaded the code to github, see [GitHub - chooron/DeepFlex.jl: julia implement of superflexpy](https://github.com/chooron/DeepFlex.jl), you can run this [code](https://github.com/chooron/DeepFlex.jl/blob/main/test/optimization/test_node_grad_optimization.jl) to debug the previously mentioned bugs, thank you very much for your help 😀！

---

<div class="post-metadata">

**Author:** ![gdalle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gdalle/32/27854_2.png) [@gdalle](https://discourse.julialang.org/u/gdalle)\
**Post date:** [May 1, 2024, 10:15am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/6 "2024-05-01T10:15:46Z")

</div>

That’s a very big example with lots of dependencies, can you try to isolate the specific aspect that makes it fail?

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 11:21am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/7 "2024-05-01T11:21:33Z")

</div>

Yes, it’s a simple framework for building a hydrological model, if necessary I’ll write a standalone code in the last few days the same way you did, similar code is visible in [https://github.com/chooron/DeepFlex.jl/blob/main/test/pkgs/test\_itp\_ode\_ad.jl](https://github.com/chooron/DeepFlex.jl/blob/main/test/pkgs/test_itp_ode_ad.jl)  
However, this code does not have the aforementioned problem, so I suspect that there is still a problem with one piece of code in the project

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [May 1, 2024, 11:43am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/8 "2024-05-01T11:43:31Z")

</div>

Is the loss function also defined as a ModelingToolkit model remake call? Can you show where that is done?

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 11:56am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/10 "2024-05-01T11:56:16Z")

</div>

In the [utils/optimize.jl](https://github.com/chooron/DeepFlex.jl/blob/main/src/utils/optimize.jl), I built a loss function (line70 -78) (this component object contains an ODEProblem, and the result will be calculated based on some functions after solving)  
I’ll try to build this OptimizationProblem with ModelingToolKit

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [May 1, 2024, 12:01pm UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/11 "2024-05-01T12:01:10Z")

</div>

Is it building or `remake`ing?

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 1, 2024, 12:10pm UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/12 "2024-05-01T12:10:06Z")

</div>

I’ll build an ODEProblem after getting the input data, see [DeepFlex.jl/src/element.jl at main · chooron/DeepFlex.jl · GitHub](https://github.com/chooron/DeepFlex.jl/blob/main/src/element.jl)

```julia
# line 163-179
function setup_input(
    ele::HydroElement;
    input::AbstractVector,
    time::AbstractVector
)
    #* build interpolation system
    itp_eqs = Equation[getproperty(ele.sys, key) ~ @itpfn(key, input[key], time) for key in keys(input)]
    compose_sys = compose(ODESystem(itp_eqs, t; name=Symbol(ele.name, :comp_sys)), ele.sys)
    sys = structural_simplify(compose_sys)
    build_u0 = Pair[]
    for func in filter(func -> func isa AbstractNNFlux, ele.funcs)
        func_nn_sys = getproperty(ele.sys, func.param_names)
        push!(build_u0, getproperty(getproperty(func_nn_sys, :input), :u) => zeros(eltype(eltype(input)), length(get_input_names(func))))
    end
    prob = ODEProblem(sys, [], (time[1], time[end]), [], warn_initialize_determined=true)
    prob
end

```

Then after getting the arguments, I’ll build the problem of remake

```julia
function setup_prob(
    ele::HydroElement,
    prob::ODEProblem;
    input::AbstractVector,
    params::ComponentVector,
    init_states::ComponentVector,
)
    ele_input_names = get_input_names(ele.funcs, ele.dfuncs)
    #* setup init states
    u0 = [getproperty(ele.sys, nm) => init_states[nm] for nm in keys(init_states) if nm in get_state_names(ele.dfuncs)]
    for func in filter(func -> func isa AbstractNNFlux, ele.funcs)
        sol_0 = get_sol_u0(ele, namedtuple(ele_input_names, [input[nm][1] for nm in ele_input_names]),
            params, namedtuple(keys(init_states), [init_states[nm] for nm in keys(init_states)]))
        func_nn_sys = getproperty(ele.sys, func.param_names)
        u0 = vcat(u0, [getproperty(getproperty(func_nn_sys, :input), :u)[idx] => sol_0[nm] for (idx, nm) in enumerate(get_input_names(func))])
    end
    #* setup parameters
    p = Pair[]
    for nm in ModelingToolkit.parameters(ele.sys)
        if contains(string(nm), "₊")
            tmp_nn = split(string(nm), "₊")[1]
            push!(p, getproperty(getproperty(ele.sys, Symbol(tmp_nn)), :p) => Vector(params[Symbol(tmp_nn)]))
        else
            push!(p, getproperty(ele.sys, Symbol(nm)) => params[Symbol(nm)])
        end
    end
    p = [getproperty(ele.sys, Symbol(nm)) => params[Symbol(nm)] for nm in ModelingToolkit.parameters(ele.sys)]
    new_prob = remake(prob, p=p, u0=u0)
    new_prob
end

```

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [May 4, 2024, 10:46am UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/13 "2024-05-04T10:46:07Z")

</div>

Is this with the latest MTK?

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 5, 2024, 3:10pm UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/14 "2024-05-05T15:10:33Z")

</div>

Hello, the mtk version is `ModelingToolkit v9.12.1`.

---

<div class="post-metadata">

**Author:** ![chooron](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chooron/32/208300_2.png) [@chooron](https://discourse.julialang.org/u/chooron)\
**Post date:** [May 5, 2024, 3:10pm UTC](https://discourse.julialang.org/t/there-was-a-problem-with-forwarddiff-based-optimization-when-re-executing-the-code/113682/15 "2024-05-05T15:10:49Z")

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@gdalle @ChrisRackauckas  
I may have discovered the cause of this problem. The previous problem description mentioned that the above problem will occur when the code is re-run. To be precise, this problem is because I re-imported a module I built (this module will load all mycode). This error will appear when I reload the module. On the contrary, it will not appear when the module is only loaded once.  
the module loading code is:

```julia
include("../../src/DeepFlex.jl")

```