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SciML / NonlinearSolve.jl

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
https://docs.sciml.ai/NonlinearSolve/stable/
MIT License
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MethodError: anyeltypedual(::Type{Union{}}) when passing ODEProblem as a parameter #440

Open EllissoideRotondo opened 1 month ago

EllissoideRotondo commented 1 month ago

I am trying to implement a shooting method using a NonlinearProblem. An ODEProblem is passed as a parameter to the NonlinearProblem (to avoid recreating a new ODEProblem at each iteration), and during evaluation of the nonlinear function the properties of the ODEProblem (u0, tspan, p, etc.) are modified. However, when passing the ODEProblem as a parameter, I get the error MethodError: anyeltypedual(::Type{Union{}}) is ambiguous. Everything works correctly if the ODEProblem is created inside the nonlinear function, but being able to pass it as a parameter would be more convenient.

Minimal Reproducible Example


using OrdinaryDiffEq, NonlinearSolve, StaticArrays

# ODE problem
f(u, p, t) = SA[u[2], 1.5u[1]^2]
ode = ODEProblem(f, SA[0.0, 0.0], (0, 1))

## Example 1: ODE is not passed as a parameter, no errors
fun_no_ode(u, _) = begin
    ode = ODEProblem(f, SA[4.0; u], (0, 1))
    sol = solve(ode, Tsit5(); reltol = 1e-8, abstol = 1e-9)
    return sol[end][1] - 1
end
prob1 = NonlinearProblem(fun_no_ode, -2.0)
sol = solve(prob1, TrustRegion(), show_trace = Val(true))

## Example 2: ODE is passed as a parameter, error is thrown
fun_with_ode(u, ode) = begin
    ode = remake(ode, u0 = SA[4.0; u])
    sol = solve(ode, Tsit5(); reltol = 1e-8, abstol = 1e-9)
    return sol[end][1] - 1
end
prob2 = NonlinearProblem(objective, -2.0, ode)
sol = solve(prob2, TrustRegion())

Error & Stacktrace

ERROR: MethodError: anyeltypedual(::Type{Union{}}) is ambiguous.

Candidates:
  anyeltypedual(::Type{T}) where T<:(Tuple{Vararg{T, N}} where {N, T})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:180
  anyeltypedual(::Type{T}) where T<:ForwardDiff.Dual
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:161
  anyeltypedual(x::Type{T}) where T<:DiffResults.DiffResult
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:129
  anyeltypedual(::Type{T}) where T<:Union{Set, AbstractArray}
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:165
  anyeltypedual(x::Type{T}) where T<:ForwardDiff.AbstractConfig
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:120

Possible fix, define
  anyeltypedual(::Type{Union{}})

Stacktrace:
  [1] (::Base.MappingRF{typeof(DiffEqBase.anyeltypedual), Base.BottomRF{typeof(DiffEqBase.promote_dual)}})(acc::Type, x::Type)
    @ Base .\reduce.jl:100
  [2] _foldl_impl(op::Base.MappingRF{typeof(DiffEqBase.anyeltypedual), Base.BottomRF{…}}, init::Type, itr::Core.SimpleVector)
    @ Base .\reduce.jl:62
  [3] foldl_impl
    @ .\reduce.jl:48 [inlined]
  [4] mapfoldl_impl
    @ .\reduce.jl:44 [inlined]
  [5] mapfoldl
    @ .\reduce.jl:175 [inlined]
  [6] mapreduce
    @ .\reduce.jl:307 [inlined]
  [7] __anyeltypedual(::Type{@Kwargs{}})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:153
  [8] anyeltypedual(::Type{@Kwargs{}}, ::Type{Val{1}})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:159
  [9] (::DiffEqBase.var"#80#81"{Int64})(x::Type)
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:102
 [10] (::Base.MappingRF{DiffEqBase.var"#80#81"{Int64}, Base.BottomRF{typeof(DiffEqBase.promote_dual)}})(acc::Type, x::Type)
    @ Base .\reduce.jl:100
 [11] _foldl_impl(op::Base.MappingRF{DiffEqBase.var"#80#81"{…}, Base.BottomRF{…}}, init::Type, itr::Core.SimpleVector)
    @ Base .\reduce.jl:62
 [12] foldl_impl
    @ .\reduce.jl:48 [inlined]
 [13] mapfoldl_impl
    @ .\reduce.jl:44 [inlined]
 [14] mapfoldl
    @ .\reduce.jl:175 [inlined]
 [15] mapreduce
    @ .\reduce.jl:307 [inlined]
 [16] diffeqmapreduce(f::DiffEqBase.var"#80#81"{Int64}, op::typeof(DiffEqBase.promote_dual), x::Core.SimpleVector)
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:55
 [17] #s86#79
    @ C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:102 [inlined]
 [18] var"#s86#79"(counter::Any, ::Any, x::Any, ::Any)
    @ DiffEqBase .\none:0
 [19] (::Core.GeneratedFunctionStub)(::UInt64, ::LineNumberNode, ::Any, ::Vararg{Any})
    @ Core .\boot.jl:602
 [20] anyeltypedual
    @ C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:95 [inlined]
 [21] promote_u0
    @ C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\forwarddiff.jl:269 [inlined]
 [22] get_concrete_problem(prob::NonlinearProblem{…}, isadapt::Bool; kwargs::@Kwargs{…})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1141
 [23] get_concrete_problem
    @ C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1139 [inlined]
 [24] solve_up(prob::NonlinearProblem{…}, sensealg::Nothing, u0::Float64, p::ODEProblem{…}, args::GeneralizedFirstOrderAlgorithm{…}; kwargs::@Kwargs{})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1070
 [25] solve_up
    @ C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1066 [inlined]
 [26] solve(prob::NonlinearProblem{…}, args::GeneralizedFirstOrderAlgorithm{…}; sensealg::Nothing, u0::Nothing, p::Nothing, wrap::Val{…}, kwargs::@Kwargs{})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1060
 [27] solve(prob::NonlinearProblem{…}, args::GeneralizedFirstOrderAlgorithm{…})
    @ DiffEqBase C:\Users\alexp\.julia\packages\DiffEqBase\PBhFc\src\solve.jl:1050
 [28] top-level scope
    @ c:\Users\alexp\test\mwe.jl:23

Environment:

Status `C:\Users\alexp\test\Project.toml`
  [8913a72c] NonlinearSolve v3.12.3
  [1dea7af3] OrdinaryDiffEq v6.80.1
  [90137ffa] StaticArrays v1.9.4
Status `C:\Users\alexp\test\Manifest.toml`
  [47edcb42] ADTypes v1.2.1
  [7d9f7c33] Accessors v0.1.36
  [79e6a3ab] Adapt v4.0.4
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.10.0
  [4c555306] ArrayLayouts v1.9.3
  [62783981] BitTwiddlingConvenienceFunctions v0.1.5
  [2a0fbf3d] CPUSummary v0.2.5
  [d360d2e6] ChainRulesCore v1.23.0
  [fb6a15b2] CloseOpenIntervals v0.1.12
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.0
  [34da2185] Compat v4.15.0
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [187b0558] ConstructionBase v1.5.5
  [adafc99b] CpuId v0.3.1
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.18.20
  [e2d170a0] DataValueInterfaces v1.0.0
  [2b5f629d] DiffEqBase v6.151.2
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
⌅ [a0c0ee7d] DifferentiationInterface v0.4.2
  [ffbed154] DocStringExtensions v0.9.3
  [4e289a0a] EnumX v1.0.4
  [f151be2c] EnzymeCore v0.7.3
  [d4d017d3] ExponentialUtilities v1.26.1
  [e2ba6199] ExprTools v0.1.10
  [7034ab61] FastBroadcast v0.3.0
  [9aa1b823] FastClosures v0.3.2
  [29a986be] FastLapackInterface v2.0.4
  [1a297f60] FillArrays v1.11.0
  [6a86dc24] FiniteDiff v2.23.1
  [f6369f11] ForwardDiff v0.10.36
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v0.1.3
  [46192b85] GPUArraysCore v0.1.6
  [c145ed77] GenericSchur v0.5.4
  [86223c79] Graphs v1.11.0
  [3e5b6fbb] HostCPUFeatures v0.1.16
  [615f187c] IfElse v0.1.1
  [d25df0c9] Inflate v0.1.5
  [3587e190] InverseFunctions v0.1.14
  [92d709cd] IrrationalConstants v0.2.2
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.5.0
  [ef3ab10e] KLU v0.6.0
  [ba0b0d4f] Krylov v0.9.6
  [10f19ff3] LayoutPointers v0.1.15
  [5078a376] LazyArrays v2.0.2
  [d3d80556] LineSearches v7.2.0
  [7ed4a6bd] LinearSolve v2.30.1
  [2ab3a3ac] LogExpFunctions v0.3.27
  [bdcacae8] LoopVectorization v0.12.170
  [1914dd2f] MacroTools v0.5.13
  [d125e4d3] ManualMemory v0.1.8
  [bb5d69b7] MaybeInplace v0.1.3
  [46d2c3a1] MuladdMacro v0.2.4
  [d41bc354] NLSolversBase v7.8.3
  [77ba4419] NaNMath v1.0.2
  [8913a72c] NonlinearSolve v3.12.3
  [6fe1bfb0] OffsetArrays v1.14.0
  [bac558e1] OrderedCollections v1.6.3
  [1dea7af3] OrdinaryDiffEq v6.80.1
  [65ce6f38] PackageExtensionCompat v1.0.2
  [d96e819e] Parameters v0.12.3
  [f517fe37] Polyester v0.7.14
  [1d0040c9] PolyesterWeave v0.2.1
  [d236fae5] PreallocationTools v0.4.21
  [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [3cdcf5f2] RecipesBase v1.3.4
  [731186ca] RecursiveArrayTools v3.20.0
  [f2c3362d] RecursiveFactorization v0.2.23
  [189a3867] Reexport v1.2.2
  [ae029012] Requires v1.3.0
  [7e49a35a] RuntimeGeneratedFunctions v0.5.13
  [94e857df] SIMDTypes v0.1.0
  [476501e8] SLEEFPirates v0.6.42
  [0bca4576] SciMLBase v2.39.0
  [c0aeaf25] SciMLOperators v0.3.8
  [53ae85a6] SciMLStructures v1.2.0
  [efcf1570] Setfield v1.1.1
  [727e6d20] SimpleNonlinearSolve v1.9.0
  [699a6c99] SimpleTraits v0.9.4
  [ce78b400] SimpleUnPack v1.1.0
  [47a9eef4] SparseDiffTools v2.19.0
  [0a514795] SparseMatrixColorings v0.3.2
  [e56a9233] Sparspak v0.3.9
  [276daf66] SpecialFunctions v2.4.0
  [aedffcd0] Static v0.8.10
  [0d7ed370] StaticArrayInterface v1.5.0
  [90137ffa] StaticArrays v1.9.4
  [1e83bf80] StaticArraysCore v1.4.2
  [7792a7ef] StrideArraysCore v0.5.6
  [2efcf032] SymbolicIndexingInterface v0.3.22
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.11.1
  [8290d209] ThreadingUtilities v0.5.2
  [a759f4b9] TimerOutputs v0.5.24
  [d5829a12] TriangularSolve v0.2.0
  [410a4b4d] Tricks v0.1.8
  [781d530d] TruncatedStacktraces v1.4.0
  [3a884ed6] UnPack v1.0.2
  [3d5dd08c] VectorizationBase v0.21.68
  [19fa3120] VertexSafeGraphs v0.2.0
  [1d5cc7b8] IntelOpenMP_jll v2024.1.0+0
  [856f044c] MKL_jll v2024.1.0+0
  [efe28fd5] OpenSpecFun_jll v0.5.5+0
  [1317d2d5] oneTBB_jll v2021.12.0+0
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [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
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
  [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
Julia Version 1.10.3
Commit 0b4590a550 (2024-04-30 10:59 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 32 × AMD Ryzen 9 5950X 16-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 16 default, 0 interactive, 8 GC (on 32 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 16
avik-pal commented 1 month ago

Don't provide the ODE as a parameter :sweat:. Use a functor if you don't want to Box the ODE.

ChrisRackauckas commented 1 month ago

No we can just fix this. anyeltypedual on this just needs to lower to u0 and p

EllissoideRotondo commented 3 weeks ago

I don't know if this is useful to know, but I just realized I have some code I wrote 6 months ago where this behavior is used and the code runs without any errors, with the only difference being it uses the NLsolveJL solver instead of TrustRegion. I tried switching to NLsolveJL solver for the MWE above, but it still throws the same error with the current version.

With this environment and NLsolveJL solver, the MWE above runs successfully:

Status C:\Users\alexp\Documents\GitHub\test_bug\Project.toml [0c46a032] DifferentialEquations v7.13.0 ⌅ [8913a72c] NonlinearSolve v3.4.0 ⌅ [1dea7af3] OrdinaryDiffEq v6.66.0 [e9a6253c] SciMLNLSolve v0.1.9 [90137ffa] StaticArrays v1.9.5

Status C:\Users\alexp\Documents\GitHub\test_bug\Manifest.toml ⌅ [47edcb42] ADTypes v0.2.7 ⌅ [79e6a3ab] Adapt v3.7.2 [66dad0bd] AliasTables v1.1.3 [ec485272] ArnoldiMethod v0.4.0 ⌅ [4fba245c] ArrayInterface v7.7.1 [4c555306] ArrayLayouts v1.9.4 [aae01518] BandedMatrices v1.7.2 [62783981] BitTwiddlingConvenienceFunctions v0.1.5 ⌅ [764a87c0] BoundaryValueDiffEq v5.6.3 [fa961155] CEnum v0.5.0 [2a0fbf3d] CPUSummary v0.2.5 [49dc2e85] Calculus v0.5.1 [fb6a15b2] CloseOpenIntervals v0.1.12 [38540f10] CommonSolve v0.2.4 [bbf7d656] CommonSubexpressions v0.3.0 [34da2185] Compat v4.15.0 [2569d6c7] ConcreteStructs v0.2.3 [187b0558] ConstructionBase v1.5.5 [adafc99b] CpuId v0.3.1 [9a962f9c] DataAPI v1.16.0 [864edb3b] DataStructures v0.18.20 [e2d170a0] DataValueInterfaces v1.0.0 ⌃ [bcd4f6db] DelayDiffEq v5.45.1 ⌃ [2b5f629d] DiffEqBase v6.145.6 ⌅ [459566f4] DiffEqCallbacks v2.36.1 [77a26b50] DiffEqNoiseProcess v5.21.0 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [0c46a032] DifferentialEquations v7.13.0 [b4f34e82] Distances v0.10.11 [31c24e10] Distributions v0.25.109 [ffbed154] DocStringExtensions v0.9.3 [fa6b7ba4] DualNumbers v0.6.8 [4e289a0a] EnumX v1.0.4 ⌅ [f151be2c] EnzymeCore v0.6.6 ⌅ [d4d017d3] ExponentialUtilities v1.25.0 [e2ba6199] ExprTools v0.1.10 [9d29842c] FastAlmostBandedMatrices v0.1.2 ⌅ [7034ab61] FastBroadcast v0.2.8 [9aa1b823] FastClosures v0.3.2 [29a986be] FastLapackInterface v2.0.4 [1a297f60] FillArrays v1.11.0 ⌅ [6a86dc24] FiniteDiff v2.22.0 [f6369f11] ForwardDiff v0.10.36 [069b7b12] FunctionWrappers v1.1.3 [77dc65aa] FunctionWrappersWrappers v0.1.3 [d9f16b24] Functors v0.4.11 ⌃ [46192b85] GPUArraysCore v0.1.5 [c145ed77] GenericSchur v0.5.4 [86223c79] Graphs v1.11.1 [3e5b6fbb] HostCPUFeatures v0.1.16 [34004b35] HypergeometricFunctions v0.3.23 [615f187c] IfElse v0.1.1 [d25df0c9] Inflate v0.1.5 [92d709cd] IrrationalConstants v0.2.2 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.5.0 ⌅ [ccbc3e58] JumpProcesses 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ChrisRackauckas commented 3 weeks ago

It's trivial to fix but I just haven't gotten around to it. But if you help me a little bit this could be done in a few hours. Try the following in your REPL:

function DiffEqBase.anyeltypedual(prob::ODEProblem,
    ::Type{Val{counter}} = Val{0}) where {counter} where {N, T <: NTuple{N, <:Number}}
    DiffEqBase.anyeltypedual(prob.u0, prob.p, prob.tspan)
end

and then run it.

EllissoideRotondo commented 3 weeks ago

I'm not a Julia expert at all, but I'm happy to help. I had to slightly modify your code snippet to avoid errors. If I pass the inputs as a Tuple, i.e. DiffEqBase.anyeltypedual((prob.u0, prob.p, prob.tspan)), then the code runs fine. Thank you!

ChrisRackauckas commented 3 weeks ago

https://github.com/SciML/DiffEqBase.jl/pull/1058 handles this and throws a much better error message.