# An unexpected clash between NonlinearSolve and ForwardDiff

**URL:** https://discourse.julialang.org/t/an-unexpected-clash-between-nonlinearsolve-and-forwarddiff/137034
**Category:** General Usage
**Tags:** package, nonlinearsolve
**Created:** [May 8, 2026, 9:26am UTC](https://discourse.julialang.org/t/an-unexpected-clash-between-nonlinearsolve-and-forwarddiff/137034 "2026-05-08T09:26:00Z")
**Posts on this page:** 3
**Page:** 1

<div class="post-metadata">

### Author: ![ij05](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ij05/32/209328_2.png) [@ij05](https://discourse.julialang.org/u/ij05)
#### Post date: [May 8, 2026, 9:26am UTC](https://discourse.julialang.org/t/an-unexpected-clash-between-nonlinearsolve-and-forwarddiff/137034/1 "2026-05-08T09:26:00Z")

</div>

Hello,

I was successfully using the following block of code which uses ForwardDiff.gradient on a function, that has a Nonlinearsolve.NonlinearLeastSquaresProblem. It was giving out correct gradients (and jacobians) until recently, and now, it no longer works. I would really appreciate any help in figuring out why this is happening.

Here is a MWE

```julia-auto
using ForwardDiff, LinearAlgebra
import NonlinearSolve as NLS

l1 = 10.0;
l2 = 5.0;

function objective!(F,u,p)
   th1 = u[1];
   th2 = u[2];

    x = p[1]; y = p[2];

    F[1] = l1*cos(th1)+l2*cos(th1+th2)-x;
    F[2] = l1*sin(th1)+l2*sin(th1+th2)-y;

    return F
end

function cog_pos(pos)

    T = eltype(pos)

    u0 = T[0.0; 0.0]; # initial guess
    prob2 = NLS.NonlinearLeastSquaresProblem(objective!, u0, pos)
    sol = NLS.solve(prob2)    
    
    th1 = sol.u[1];
    th2 = sol.u[2];    

    cog_pos_x = 0.5*l1*cos(th1);
    

    return cog_pos_x
end

p = [5.0; 7.0];
ForwardDiff.gradient(cog_pos,p)

```

I get a very large MethodError: no method matching Float64, which I am not sharing here. The stacktrace is as follows:

```julia-auto
Stacktrace:
  [1] convert(::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBaseForwardDiffExt.var"#6#7"{NonlinearSolveBase.var"#nlls_generate_vjp_function##8#nlls_generate_vjp_function##9"{typeof(objective!), SciMLBase.NonlinearSolution{Float64, 1, Vector{Float64}, Vector{Float64}, SciMLBase.NonlinearLeastSquaresProblem{Vector{Float64}, true, Vector{Float64}, SciMLBase.NonlinearFunction{true, SciMLBase.AutoSpecialize, NonlinearSolveBase.AutoSpecializeCallable{FunctionWrappersWrappers.FunctionWrappersWrapper{Tuple{FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{Float64}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{Float64}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}}}}, FunctionWrappersWrappers.AllowNonIsBits, FunctionWrappersWrappers.SingleCacheStorage}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, Nothing, Nothing}, NonlinearSolveBase.NonlinearSolvePolyAlgorithm{Val{6}, Tuple{NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, Missing, NonlinearSolveBase.NewtonDescent{Nothing}, Nothing, Nothing, Nothing, Nothing, Val{false}}, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, NonlinearSolveFirstOrder.LevenbergMarquardtTrustRegion{Float64}, NonlinearSolveBase.DampedNewtonDescent{Nothing, Float64, NonlinearSolveFirstOrder.LevenbergMarquardtDampingFunction{Float64, Float64, Float64}}, Nothing, Nothing, Nothing, Nothing, Val{true}}, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, NonlinearSolveFirstOrder.GenericTrustRegionScheme{NonlinearSolveFirstOrder.RadiusUpdateSchemes. __Simple, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}}, NonlinearSolveBase.Dogleg{NonlinearSolveBase.NewtonDescent{Nothing}, NonlinearSolveBase.SteepestDescent{Nothing}}, Nothing, Nothing, Nothing, Nothing, Val{false}}, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{LineSearch.BackTracking{Nothing, Float64, Float64, Float64, Val{3}, Float64, Bool}, Missing, NonlinearSolveBase.NewtonDescent{Nothing}, Nothing, Nothing, Nothing, Nothing, Val{false}}, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, NonlinearSolveFirstOrder.GenericTrustRegionScheme{NonlinearSolveFirstOrder.RadiusUpdateSchemes.__ Bastin, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}, Rational{Int64}}, NonlinearSolveBase.Dogleg{NonlinearSolveBase.NewtonDescent{Nothing}, NonlinearSolveBase.SteepestDescent{Nothing}}, Nothing, Nothing, Nothing, Nothing, Val{false}}, NonlinearSolveFirstOrder.GeneralizedFirstOrderAlgorithm{Missing, NonlinearSolveFirstOrder.LevenbergMarquardtTrustRegion{Float64}, NonlinearSolveBase.GeodesicAcceleration{NonlinearSolveBase.DampedNewtonDescent{Nothing, Float64, NonlinearSolveFirstOrder.LevenbergMarquardtDampingFunction{Float64, Float64, Float64}}, Float64, Float64}, Nothing, Nothing, Nothing, Nothing, Val{true}}}}, Any, Nothing, SciMLBase.NLStats, NonlinearSolveBase.NonlinearSolveTrace{Val{false}, Val{false}, Nothing, NonlinearSolveBase.NonlinearSolveTracing{Val{:minimal}}, SciMLBase.NonlinearLeastSquaresProblem{Vector{Float64}, true, Vector{Float64}, SciMLBase.NonlinearFunction{true, SciMLBase.AutoSpecialize, NonlinearSolveBase.AutoSpecializeCallable{FunctionWrappersWrappers.FunctionWrappersWrapper{Tuple{FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{Float64}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}}}, FunctionWrappers.FunctionWrapper{Nothing, Tuple{Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}, Vector{Float64}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{NonlinearSolveBase.NonlinearSolveTag, Float64}, Float64, 1}}}}}, FunctionWrappersWrappers.AllowNonIsBits, FunctionWrappersWrappers.SingleCacheStorage}}, UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing}, @Kwargs{}, Nothing, Nothing}}}, ADTypes.AutoForwardDiff{nothing, Nothing}, Nothing}, Vector{Float64}}, Float64}, Float64, 2})
    @ Base .\number.jl:7
  [2] setindex!
    @ .\array.jl:985 [inlined]
  [3] macro expansion
    @ .\broadcast.jl:995 [inlined]
  [4] macro expansion
    @ .\simdloop.jl:77 [inlined]
  [5] copyto!
    @ .\broadcast.jl:994 [inlined]
  [6] copyto!
...
    @ ForwardDiff C:\Users\user\.julia\packages\ForwardDiff\z3jRk\src\jacobian.jl:19
 [40] jacobian(f::typeof(cog_pos), x::Vector{Float64})
    @ ForwardDiff C:\Users\user\.julia\packages\ForwardDiff\z3jRk\src\jacobian.jl:19
 [41] top-level scope
    @ c:\Users\user\Documents\final chapter julia report\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_X21sZmlsZQ==.jl:1
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

```

The output of `Pkg.status()` is

```julia-auto
  [26cc04aa] FiniteDifferences v0.12.33
  [f6369f11] ForwardDiff v1.3.3
  [e9467ef8] GLMakie v0.13.10
  [ee78f7c6] Makie v0.24.10
  [8913a72c] NonlinearSolve v4.19.0
  [1ed8b502] SciMLSensitivity v7.106.0
  [e88e6eb3] Zygote v0.7.10
  [37e2e46d] LinearAlgebra v1.12.0

```

---

<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 8, 2026, 10:40am UTC](https://discourse.julialang.org/t/an-unexpected-clash-between-nonlinearsolve-and-forwarddiff/137034/2 "2026-05-08T10:40:18Z")

</div>

Yes, I’m trying to figure out if it’s possible to work around, or if we need to do a bugfix in ForwardDiff

> <https://github.com/JuliaDiff/ForwardDiff.jl/issues/714>
>
> Hi, I found an issue when working with precompilation and nested derivatives.
> 
> …
> The tag count system is setup such that the inner derivative receives a tag with a higher \`tagcount\` than the outer derivative. When precompiling, it can happen that only the outer derivative is precompiled, for example because of runtime dispatch that depends on the value of some parameters. When running the code later, the atomic counter for tags starts from 0 again, which can cause the inner derivative to be given a tag with a lower count. This leads to a \`Cannot determine ordering of Dual tags\` error.
> 
> Here is a reproduction example: https://github.com/Technici4n/FDPrecompilationIssue.
> Precompilation should succeed, and running the test should fail with the following output:
> \`\`\`
> Testing Running tests...
> Tag count of Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,1) is 1
> Tag count of Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#1#2"{ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}}}(Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,0),Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,2)) is 0
> ERROR: LoadError: Cannot determine ordering of Dual tags ForwardDiff.Tag{FDPrecompilationIssue.var"#1#2"{ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}} and ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}
> \`\`\`
> 
> (I encountered this issue was encountered in the context of https://github.com/mfherbst/AiidaDFTK.jl but the setup is more complicated than in this minimal reproduction example. AiidaDFTK precompiles a stress computation (outer derivative) using the LDA functional (does not require a nested derivative). I encountered the issue when running a stress computation through AiidaDFTK using a PBE functional (which requires a nested gradient). You can think of \`α\` from the reproduction example as the parameter that selects the functional, LDA as \`α = 0\`, PBE as \`α = 1\`.)

> <https://github.com/JuliaDiff/ForwardDiff.jl/pull/724>
>
> This fixes #714:
> \`\`\`
> Testing Running tests...
> Tag count of Dual{ForwardD…iff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,1) is 1
> Tag count of Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#1#2"{ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}, Int64, 1}}}(Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,0),Dual{ForwardDiff.Tag{FDPrecompilationIssue.var"#3#4"{Int64}, Int64}}(0,2)) is 2
> Testing FDPrecompilationIssue tests passed
> \`\`\`
> 
> This adds an atomic operation for each \`Tag\` instantiation. This might be problematic when computing many fast derivatives in a tight loop?

It’s a rather tricky one, but I’ve had some solutions open for a few days and I just need to pick one by tomorrow.

---

<div class="post-metadata">

### Author: ![ij05](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ij05/32/209328_2.png) [@ij05](https://discourse.julialang.org/u/ij05)
#### Post date: [May 8, 2026, 12:36pm UTC](https://discourse.julialang.org/t/an-unexpected-clash-between-nonlinearsolve-and-forwarddiff/137034/3 "2026-05-08T12:36:52Z")

</div>

Hi Chris,

Thanks for your reply. I look forward to the solutions. I have changed

```julia-auto
prob2 = NLS.NonlinearLeastSquaresProblem(objective!, u0, pos)

```

to

```julia-auto
prob2 = NLS.NonlinearProblem(objective!, u0, pos)

```

and the method error vanished, returning the expected gradients.