DiffEqGPU.jl `EnsembleGPUKernel` fails with MTK generated problems

Describe the bug 🐞

When using codegen from MTK (ODEProblem created from a System) together with EnsembleProblem, I'm getting an inlining error, ERROR: CuArray only supports element types that are allocated inline.

Expected behavior

solve should work

Minimal Reproducible Example 👇

using DiffEqGPU
using OrdinaryDiffEqTsit5, ModelingToolkit, StaticArrays
using ModelingToolkit: t_nounits as t, D_nounits as D

@parameters σ ρ β
@variables x(t) y(t) z(t)

eqs = [D(D(x)) ~ σ * (y - x),
    D(y) ~ x * (ρ - z) - y,
    D(z) ~ x * y - β * z]

@mtkcompile sys = System(eqs, t) split=false

u0 = SA[D(x) => 2f0,
    x => 1f0,
    y => 0f0,
    z => 0f0]

p = SA[σ => 28f0,
    ρ => 10f0,
    β => 8f0 / 3f0]

tspan = (0f0, 100f0)
prob = ODEProblem{false, SciMLBase.FullSpecialize}(sys, [u0; p], tspan)

sol = solve(prob, Tsit5())

using SymbolicIndexingInterface
sym_setter = setsym_oop(sys, [σ, ρ, β])

using DiffEqGPU, CUDA
function prob_func2(prob, i, repeat)
    u0, p = sym_setter(prob, @SVector(rand(Float32, 3)))
    remake(prob, u0 = u0, p = p)
end
monteprob = EnsembleProblem(prob, prob_func = prob_func2, safetycopy = false)
sol = solve(monteprob, GPUTsit5(), EnsembleGPUKernel(CUDA.CUDABackend()),
    trajectories = 3)

Error & Stacktrace ⚠️

ERROR: CuArray only supports element types that are allocated inline.
SciMLBase.ImmutableODEProblem{SVector{4, Float32}, Tuple{Float32, Float32}, false, MTKParameters{SVector{3, Float32}, SVector{7, Float32}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, ODEFunction{false, SciMLBase.FullSpecialize, ODEFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x25c216d0, 0x66c0e4c1, 0xe13aea51, 0x4f9eab09, 0x3dd13079), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xdd29b6d5, 0x817a3551, 0x11322694, 0x076bbe1d, 0xa5e7e309), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, MTKParameters{SVector{11, Float32}, SizedVector{0, Float64, Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x7430b4d3, 0x97d8911b, 0x08ae4259, 0x2cbb6d11, 0xf5c706ae), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x57907240, 0xc9de2ec6, 0x10670211, 0xa2f8519c, 0x13c87d58), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#941#943"{DataType, DataType, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x8571ce9f, 0x112f2557, 0xf85a66c8, 0x85c94ce8, 0xa51a9d10), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x906afefd, 0x660eb8ef, 0x11e09109, 0x89630605, 0x6e701a15), Nothing}}}}, ModelingToolkit.var"#initprobpmap_split#930"{ModelingToolkit.var"#_getter#926"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x287e3ed8, 0x3b51fd67, 0x9d4b783d, 0xd06fdbcd, 0x589f4ec5), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x87c5e6f0, 0x751d5941, 0xc1c10010, 0xeb9e81ff, 0x082cfb93), Nothing}}}}, ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x4b0ea51c, 0xf4fee0f3, 0x486207e1, 0xde5253fa, 0x0a385a76), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x62c603c2, 0x912fa75c, 0xec4f37c0, 0x06090191, 0x4ad44c9b), Nothing}}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter#926"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xa813a202, 0xb2f17c4b, 0x43e81296, 0xa4b494ca, 0xd6311f19), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xdeb4e13d, 0x88d526ab, 0xb93c24b0, 0x2cc6ef78, 0x31934efb), Nothing}}}}, Returns{SizedVector{0, Float64, Vector{Float64}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}, ComposedFunction{ModelingToolkit.var"#941#943"{DataType, DataType, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca527220, 0x0cf0a445, 0xe5c2aab4, 0x65ead3ea, 0x2941ca6b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x6bad6112, 0xbe794d38, 0x19367e3e, 0x6329ca6d, 0x4da43351), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, MTKParameters{SVector{11, Float32}, SizedVector{0, Float64, Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x7430b4d3, 0x97d8911b, 0x08ae4259, 0x2cbb6d11, 0xf5c706ae), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x57907240, 0xc9de2ec6, 0x10670211, 0xa2f8519c, 0x13c87d58), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#941#943"{DataType, DataType, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x8571ce9f, 0x112f2557, 0xf85a66c8, 0x85c94ce8, 0xa51a9d10), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x906afefd, 0x660eb8ef, 0x11e09109, 0x89630605, 0x6e701a15), Nothing}}}}, ModelingToolkit.var"#initprobpmap_split#930"{ModelingToolkit.var"#_getter#926"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x287e3ed8, 0x3b51fd67, 0x9d4b783d, 0xd06fdbcd, 0x589f4ec5), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x87c5e6f0, 0x751d5941, 0xc1c10010, 0xeb9e81ff, 0x082cfb93), Nothing}}}}, ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x4b0ea51c, 0xf4fee0f3, 0x486207e1, 0xde5253fa, 0x0a385a76), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x62c603c2, 0x912fa75c, 0xec4f37c0, 0x06090191, 0x4ad44c9b), Nothing}}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter#926"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#945#946"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xa813a202, 0xb2f17c4b, 0x43e81296, 0xa4b494ca, 0xd6311f19), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xdeb4e13d, 0x88d526ab, 0xb93c24b0, 0x2cc6ef78, 0x31934efb), Nothing}}}}, Returns{SizedVector{0, Float64, Vector{Float64}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}, ComposedFunction{ModelingToolkit.var"#941#943"{DataType, DataType, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca527220, 0x0cf0a445, 0xe5c2aab4, 0x65ead3ea, 0x2941ca6b), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x6bad6112, 0xbe794d38, 0x19367e3e, 0x6329ca6d, 0x4da43351), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem} is a struct that's not allocated inline

Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:35
  [2] check_eltype
    @ ~/.julia/packages/CUDA/G7Cnt/src/array.jl:67 [inlined]
  [3] CuArray{SciMLBase.ImmutableODEProblem{SVector{…}, Tuple{…}, false, MTKParameters{…}, ODEFunction{…}, @Kwargs{}, SciMLBase.StandardODEProblem}, 1, CUDA.DeviceMemory}(::UndefInitializer, dims::Tuple{Int64})
    @ CUDA ~/.julia/packages/CUDA/G7Cnt/src/array.jl:82
  [4] CuArray
    @ ~/.julia/packages/CUDA/G7Cnt/src/array.jl:437 [inlined]
  [5] CuArray
    @ ~/.julia/packages/CUDA/G7Cnt/src/array.jl:442 [inlined]
  [6] CuArray
    @ ~/.julia/packages/CUDA/G7Cnt/src/array.jl:451 [inlined]
  [7] convert
    @ ~/.julia/packages/GPUArrays/w335n/src/host/construction.jl:4 [inlined]
  [8] adapt_storage
    @ ~/.julia/packages/CUDA/G7Cnt/src/array.jl:732 [inlined]
  [9] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [10] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [11] adapt_storage
    @ ~/.julia/packages/CUDA/G7Cnt/src/CUDAKernels.jl:36 [inlined]
 [12] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [13] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [14] batch_solve_up_kernel(ensembleprob::EnsembleProblem{…}, probs::Vector{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/GCPCp/src/solve.jl:276
 [15] batch_solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/GCPCp/src/solve.jl:173
 [16] macro expansion
    @ ./timing.jl:421 [inlined]
 [17] __solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}; trajectories::Int64, batch_size::Int64, unstable_check::Function, adaptive::Bool, kwargs::@Kwargs{})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/GCPCp/src/solve.jl:55
 [18] __solve
    @ ~/.julia/packages/DiffEqGPU/GCPCp/src/solve.jl:1 [inlined]
 [19] #solve#747
    @ ~/.julia/packages/SciMLBase/b4Q81/src/ensemble/basic_ensemble_solve.jl:359 [inlined]
 [20] top-level scope
    @ REPL[32]:1
Some type information was truncated. Use `show(err)` to see complete types.

Environment (please complete the following information):

Output of using Pkg; Pkg.status()

Status `/tmp/jl_Tb76zZ/Project.toml`
  [052768ef] CUDA v5.9.1
  [071ae1c0] DiffEqGPU v3.8.0
  [961ee093] ModelingToolkit v10.26.0
  [b1df2697] OrdinaryDiffEqTsit5 v1.5.0
  [90137ffa] StaticArrays v1.9.15
  [2efcf032] SymbolicIndexingInterface v0.3.46

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

Status `/tmp/jl_Tb76zZ/Manifest.toml`
  [47edcb42] ADTypes v1.18.0
  [621f4979] AbstractFFTs v1.5.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.21.0
  [4c555306] ArrayLayouts v1.12.0
  [a9b6321e] Atomix v1.1.2
  [ab4f0b2a] BFloat16s v0.6.0
  [e2ed5e7c] Bijections v0.2.2
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [8e7c35d0] BlockArrays v1.7.2
  [70df07ce] BracketingNonlinearSolve v1.5.0
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.7
  [052768ef] CUDA v5.9.1
  [1af6417a] CUDA_Runtime_Discovery v1.0.0
  [d360d2e6] ChainRulesCore v1.26.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [3da002f7] ColorTypes v0.12.1
  [5ae59095] Colors v0.13.1
⌅ [861a8166] Combinatorics v1.0.2
  [a80b9123] CommonMark v0.9.1
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [b152e2b5] CompositeTypes v0.1.4
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [187b0558] ConstructionBase v1.6.0
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.16.0
  [a93c6f00] DataFrames v1.8.1
  [864edb3b] DataStructures v0.19.1
  [e2d170a0] DataValueInterfaces v1.0.0
  [2b5f629d] DiffEqBase v6.190.2
  [459566f4] DiffEqCallbacks v4.10.1
  [071ae1c0] DiffEqGPU v3.8.0
  [77a26b50] DiffEqNoiseProcess v5.24.1
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.7.9
  [8d63f2c5] DispatchDoctor v0.4.26
  [31c24e10] Distributions v0.25.122
  [ffbed154] DocStringExtensions v0.9.5
  [5b8099bc] DomainSets v0.7.16
  [7c1d4256] DynamicPolynomials v0.6.4
  [06fc5a27] DynamicQuantities v1.10.0
  [4e289a0a] EnumX v1.0.5
  [f151be2c] EnzymeCore v0.8.14
  [e2ba6199] ExprTools v0.1.10
  [55351af7] ExproniconLite v0.10.14
  [7034ab61] FastBroadcast v0.3.5
  [9aa1b823] FastClosures v0.3.2
  [a4df4552] FastPower v1.1.3
  [1a297f60] FillArrays v1.14.0
  [64ca27bc] FindFirstFunctions v1.4.2
  [6a86dc24] FiniteDiff v2.29.0
  [53c48c17] FixedPointNumbers v0.8.5
  [1fa38f19] Format v1.3.7
⌃ [f6369f11] ForwardDiff v0.10.39
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v0.1.3
  [0c68f7d7] GPUArrays v11.2.6
  [46192b85] GPUArraysCore v0.2.0
  [61eb1bfa] GPUCompiler v1.7.2
  [096a3bc2] GPUToolbox v1.0.0
  [c27321d9] Glob v1.3.1
  [86223c79] Graphs v1.13.1
  [076d061b] HashArrayMappedTries v0.2.0
  [34004b35] HypergeometricFunctions v0.3.28
  [615f187c] IfElse v0.1.1
  [3263718b] ImplicitDiscreteSolve v1.2.0
  [d25df0c9] Inflate v0.1.5
  [842dd82b] InlineStrings v1.4.5
  [18e54dd8] IntegerMathUtils v0.1.3
  [8197267c] IntervalSets v0.7.11
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.6
  [82899510] IteratorInterfaceExtensions v1.0.0
  [692b3bcd] JLLWrappers v1.7.1
  [ae98c720] Jieko v0.2.1
  [98e50ef6] JuliaFormatter v2.1.6
⌅ [70703baa] JuliaSyntax v0.4.10
  [ccbc3e58] JumpProcesses v9.19.1
  [63c18a36] KernelAbstractions v0.9.38
  [ba0b0d4f] Krylov v0.10.2
  [929cbde3] LLVM v9.4.4
  [8b046642] LLVMLoopInfo v1.0.0
  [b964fa9f] LaTeXStrings v1.4.0
  [23fbe1c1] Latexify v0.16.10
  [10f19ff3] LayoutPointers v0.1.17
  [5078a376] LazyArrays v2.8.0
  [87fe0de2] LineSearch v0.1.4
  [d3d80556] LineSearches v7.4.0
  [7ed4a6bd] LinearSolve v3.44.0
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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 use `status --outdated -m`

Output of versioninfo()

Julia Version 1.11.7
Commit f2b3dbda30a (2025-09-08 12:10 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 32 × Intel(R) Core(TM) i9-14900K
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 1 default, 0 interactive, 1 GC (on 32 virtual cores)

I also tried on 1.12 and I got a similar error.

Additional context

This is based on https://github.com/SciML/DiffEqGPU.jl/pull/349. I also tried both split=true and split=false

Oct 20 '25 15:10 SebastianM-C

This fails with Metal too, some manner of stack overflow

ERROR: StackOverflowError:
Stacktrace:
  [1] fieldtypes(T::Type)
    @ Base ./reflection.jl:1069
  [2] contains_eltype(T::Type, X::Type)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:22
  [3] contains_eltype(T::Type, X::Type) (repeats 28828 times)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:23
  [4] contains_eltype(T::Type, X::Type)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:19
  [5] contains_eltype(T::Type, X::Type)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:23
  [6] contains_eltype(T::Type, X::Type)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:19
  [7] contains_eltype(T::Type, X::Type) (repeats 8 times)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:23
  [8] check_eltype(T::Type)
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:32
  [9] MtlVector{SciMLBase.ImmutableODEProblem{…}, Metal.PrivateStorage}(::UndefInitializer, dims::Tuple{Int64})
    @ Metal ~/.julia/packages/Metal/av3If/src/array.jl:54
 [10] MtlArray
    @ ~/.julia/packages/Metal/av3If/src/array.jl:204 [inlined]
 [11] MtlArray
    @ ~/.julia/packages/Metal/av3If/src/array.jl:291 [inlined]
 [12] MtlArray
    @ ~/.julia/packages/Metal/av3If/src/array.jl:307 [inlined]
 [13] convert
    @ ~/.julia/packages/GPUArrays/w335n/src/host/construction.jl:4 [inlined]
 [14] adapt_storage
    @ ~/.julia/packages/Metal/av3If/src/array.jl:456 [inlined]
 [15] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [16] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [17] adapt_storage
    @ ~/.julia/packages/Metal/av3If/src/MetalKernels.jl:39 [inlined]
 [18] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [19] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [20] batch_solve_up_kernel(ensembleprob::EnsembleProblem{…}, probs::Vector{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:280
 [21] batch_solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:174
 [22] macro expansion
    @ ./timing.jl:421 [inlined]
 [23] __solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}; trajectories::Int64, batch_size::Int64, unstable_check::Function, adaptive::Bool, kwargs::@Kwargs{})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:55
 [24] __solve
    @ ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:1 [inlined]
 [25] #solve#747
    @ ~/.julia/packages/SciMLBase/YE7xF/src/ensemble/basic_ensemble_solve.jl:359 [inlined]
Some type information was truncated. Use `show(err)` to see complete types.

We need this for parameter sweep so something has to be done here

Nov 10 '25 21:11 asinghvi17

It could be happening because MTKParameters is mutable?

Nov 11 '25 12:11 asinghvi17

They have to be turned into static, yes. @AayushSabharwal I thought we had that working 😅 seems like a regression.

Nov 11 '25 12:11 ChrisRackauckas

Judging from the type, the problem is likely

MTKParameters{SVector{11, Float32}, SizedVector{0, Float64, Vector{Float64}}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}

This, the parameter object of the initialization problem. I'm guessing CUDA doesn't like the SizedVector

Nov 11 '25 12:11 AayushSabharwal

Yes it doesn't, but just a few months ago it was converting into a n SVector properly

Nov 11 '25 12:11 ChrisRackauckas

Yeah we don't have a GPU test in MTK so it must have broken and wasn't noticed. I opened https://github.com/SciML/ModelingToolkit.jl/pull/4018.

Nov 13 '25 06:11 AayushSabharwal

I tried again now with [email protected] and I still get

julia> sol = solve(monteprob, GPUTsit5(), EnsembleGPUKernel(CUDA.CUDABackend()),
           trajectories = 3)
ERROR: CuArray only supports element types that are allocated inline.
SciMLBase.ImmutableODEProblem{SVector{4, Float32}, Tuple{Float32, Float32}, false, SVector{10, Float32}, ODEFunction{false, SciMLBase.FullSpecialize, ODEFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x2f80f09b, 0x34945c65, 0x4d08c907, 0x23a61ef1, 0x1f6460cc), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x0b48871c, 0x0f14cfd7, 0xc55fc7ef, 0x5255a84a, 0x66f090f0), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, SVector{11, Float32}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x62a641e7, 0x3b965720, 0x541cff97, 0xcdad5379, 0xb05cc369), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca84f691, 0xb391fa9e, 0x0bc2fc08, 0x3f6ccf65, 0xbb71880a), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem, Nothing, Nothing}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#get_u0_constructor##0#get_u0_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xf10928e1, 0xf9db2d21, 0xbba7556d, 0xa7107d47, 0x8470f1b0), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x459d02b8, 0x4da04e2f, 0x06a1011d, 0xfa801570, 0x11b13110), Nothing}}}}, ModelingToolkit.var"#initprobpmap_nosplit#978"{ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x59f00c30, 0xd56dd53b, 0x0b3dd998, 0x933a2c19, 0x25d4848e), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x5f54bbe0, 0x6dc6ac0f, 0x8f0bac4a, 0xab8d475c, 0x723be12f), Nothing}}}, ModelingToolkit.var"#get_p_constructor##0#get_p_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter2#975"{ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x9e48e8d5, 0x8cc9f4ec, 0xcd18042a, 0xd02ab88a, 0x97e4083c), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x02d86f18, 0x169882d2, 0x7dbd7b7f, 0x323885af, 0x6106180b), Nothing}}}, ModelingToolkit.var"#get_p_constructor##0#get_p_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}}}, ComposedFunction{ModelingToolkit.var"#get_u0_constructor##0#get_u0_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x4a7156c7, 0x380809ab, 0x22da56eb, 0xb9f71763, 0x154ec082), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca84f691, 0xb391fa9e, 0x0bc2fc08, 0x3f6ccf65, 0xbb71880a), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{Int64}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{Int64}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, SVector{11, Float32}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x62a641e7, 0x3b965720, 0x541cff97, 0xcdad5379, 0xb05cc369), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca84f691, 0xb391fa9e, 0x0bc2fc08, 0x3f6ccf65, 0xbb71880a), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem, Nothing, Nothing}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#get_u0_constructor##0#get_u0_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xf10928e1, 0xf9db2d21, 0xbba7556d, 0xa7107d47, 0x8470f1b0), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x459d02b8, 0x4da04e2f, 0x06a1011d, 0xfa801570, 0x11b13110), Nothing}}}}, ModelingToolkit.var"#initprobpmap_nosplit#978"{ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x59f00c30, 0xd56dd53b, 0x0b3dd998, 0x933a2c19, 0x25d4848e), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x5f54bbe0, 0x6dc6ac0f, 0x8f0bac4a, 0xab8d475c, 0x723be12f), Nothing}}}, ModelingToolkit.var"#get_p_constructor##0#get_p_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter2#975"{ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x9e48e8d5, 0x8cc9f4ec, 0xcd18042a, 0xd02ab88a, 0x97e4083c), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x02d86f18, 0x169882d2, 0x7dbd7b7f, 0x323885af, 0x6106180b), Nothing}}}, ModelingToolkit.var"#get_p_constructor##0#get_p_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}}}, ComposedFunction{ModelingToolkit.var"#get_u0_constructor##0#get_u0_constructor##1"{Type{SVector{7, Pair{Num, Float32}}}, Type{Float32}, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, false), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x4a7156c7, 0x380809ab, 0x22da56eb, 0xb9f71763, 0x154ec082), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :__mtk_arg_2, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xca84f691, 0xb391fa9e, 0x0bc2fc08, 0x3f6ccf65, 0xbb71880a), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{Int64}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{Int64}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, Base.Pairs{Symbol, Union{}, Nothing, @NamedTuple{}}, SciMLBase.StandardODEProblem} is a struct that's not allocated inline

The versions are:

(jl_mbDRSq) pkg> st
Status `/tmp/jl_mbDRSq/Project.toml`
  [052768ef] CUDA v5.9.4
  [071ae1c0] DiffEqGPU v3.9.0
  [961ee093] ModelingToolkit v10.29.0
  [b1df2697] OrdinaryDiffEqTsit5 v1.5.0
  [90137ffa] StaticArrays v1.9.15
  [2efcf032] SymbolicIndexingInterface v0.3.46

Am I missing something or is this not yet fully fixed?

Nov 18 '25 13:11 SebastianM-C

I don't see any MTKParameters in that type.

Nov 18 '25 13:11 AayushSabharwal

That's because the initial MWE was with split=false, if I set that to true, I get

julia> sol = solve(monteprob, GPUTsit5(), EnsembleGPUKernel(CUDA.CUDABackend()),
           trajectories = 3)
ERROR: CuArray only supports element types that are allocated inline.
SciMLBase.ImmutableODEProblem{SVector{4, Float32}, Tuple{Float32, Float32}, false, MTKParameters{SVector{3, Float32}, SVector{7, Float32}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, ODEFunction{false, SciMLBase.FullSpecialize, ODEFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x8aa797bf, 0x717cba03, 0xe6af37e5, 0xe8fe0b2e, 0x4ceaf46e), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xac69339f, 0x26225ec4, 0xe2725799, 0x72e94af1, 0xceeedb96), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, MTKParameters{SVector{11, Float32}, SVector{0, Float64}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xb64123ea, 0xd15b9fb5, 0xaa613676, 0x7ef16810, 0xb47f57a0), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xfc6c9e13, 0xec2cac1e, 0x97404e24, 0x1ca42954, 0x73ee6769), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem, Nothing, Nothing}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#967#969"{DataType, DataType, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x344cdcea, 0xacd061d5, 0x468e0c48, 0xbb5df19e, 0xe5e5d976), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x23bf3cb3, 0xc9d39c9c, 0xc06349b3, 0xde9aaccb, 0x0bc32afe), Nothing}}}}, ModelingToolkit.var"#initprobpmap_split#956"{ModelingToolkit.var"#_getter#952"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x370bf647, 0xa6f7d2f2, 0xa1ee7dd6, 0x504d28d7, 0xfa08ffbc), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x46a8607c, 0x0dc0459a, 0x21c8e67f, 0xa0270fc5, 0x4fc4c1f1), Nothing}}}}, ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xb3a2ea49, 0x21e13f70, 0x53e436e8, 0xd42523a0, 0x2bea1064), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x70b32fab, 0x4aab9ffc, 0xe1a20d50, 0x712c237d, 0xbe02a349), Nothing}}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter#952"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x626746da, 0xeb6557bb, 0x00fd2c93, 0x78bbe2ae, 0x77619a45), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xd08cf89e, 0x8702e893, 0x62877de1, 0xe00236d1, 0x7bd658ca), Nothing}}}}, Returns{SVector{0, Float64}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}, ComposedFunction{ModelingToolkit.var"#967#969"{DataType, DataType, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x0af3cec4, 0x2d24521c, 0x0d58314f, 0x114b214b, 0x8296ca56), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x06c30c47, 0x45f5fc43, 0x99088faa, 0x944e0112, 0x65173ce2), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing, SciMLBase.OverrideInitData{NonlinearProblem{Nothing, false, MTKParameters{SVector{11, Float32}, SVector{0, Float64}, Tuple{}, Tuple{}, Tuple{}, Tuple{}}, NonlinearFunction{false, SciMLBase.FullSpecialize, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xb64123ea, 0xd15b9fb5, 0xaa613676, 0x7ef16810, 0xb47f57a0), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xfc6c9e13, 0xec2cac1e, 0x97404e24, 0x1ca42954, 0x73ee6769), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, ModelingToolkit.ObservedFunctionCache{System}, Nothing, System, Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardNonlinearProblem, Nothing, Nothing}, typeof(ModelingToolkit.update_initializeprob!), ComposedFunction{ComposedFunction{ModelingToolkit.var"#967#969"{DataType, DataType, Bool}, typeof(ModelingToolkit.safe_float)}, SymbolicIndexingInterface.TimeIndependentObservedFunction{ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x344cdcea, 0xacd061d5, 0x468e0c48, 0xbb5df19e, 0xe5e5d976), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x23bf3cb3, 0xc9d39c9c, 0xc06349b3, 0xde9aaccb, 0x0bc32afe), Nothing}}}}, ModelingToolkit.var"#initprobpmap_split#956"{ModelingToolkit.var"#_getter#952"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x370bf647, 0xa6f7d2f2, 0xa1ee7dd6, 0x504d28d7, 0xfa08ffbc), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x46a8607c, 0x0dc0459a, 0x21c8e67f, 0xa0270fc5, 0x4fc4c1f1), Nothing}}}}, ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{false, ModelingToolkit.GeneratedFunctionWrapper{(2, 2, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xb3a2ea49, 0x21e13f70, 0x53e436e8, 0xd42523a0, 0x2bea1064), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x70b32fab, 0x4aab9ffc, 0xe1a20d50, 0x712c237d, 0xbe02a349), Nothing}}}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}}, ModelingToolkit.InitializationMetadata{ModelingToolkit.ReconstructInitializeprob{ModelingToolkit.var"#_getter#952"{Tuple{ComposedFunction{ModelingToolkit.PConstructorApplicator{ModelingToolkit.var"#971#972"{DataType, DataType}}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x626746da, 0xeb6557bb, 0x00fd2c93, 0x78bbe2ae, 0x77619a45), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0xd08cf89e, 0x8702e893, 0x62877de1, 0xe00236d1, 0x7bd658ca), Nothing}}}}, Returns{SVector{0, Float64}}, Returns{Tuple{}}, Returns{Tuple{}}, Returns{Tuple{}}}}, ComposedFunction{ModelingToolkit.var"#967#969"{DataType, DataType, Bool}, ModelingToolkit.ObservedWrapper{true, ModelingToolkit.GeneratedFunctionWrapper{(2, 3, true), RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x0af3cec4, 0x2d24521c, 0x0d58314f, 0x114b214b, 0x8296ca56), Nothing}, RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:ˍ₋out, :__mtk_arg_1, :___mtkparameters___, :t), ModelingToolkit.var"#_RGF_ModTag", ModelingToolkit.var"#_RGF_ModTag", (0x06c30c47, 0x45f5fc43, 0x99088faa, 0x944e0112, 0x65173ce2), Nothing}}}}}, ModelingToolkit.GetUpdatedU0{SymbolicIndexingInterface.MultipleGetters{ContinuousTimeseries, Vector{SymbolicUtils.BasicSymbolic{Real}}}, SymbolicIndexingInterface.MultipleParametersGetter{SymbolicIndexingInterface.IndexerNotTimeseries, Vector{SymbolicIndexingInterface.GetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}}, Nothing}}, ModelingToolkit.SetInitialUnknowns{SymbolicIndexingInterface.MultipleSetters{Vector{SymbolicIndexingInterface.ParameterHookWrapper{SymbolicIndexingInterface.SetParameterIndex{ModelingToolkit.ParameterIndex{SciMLStructures.Initials, Int64}}, SymbolicUtils.BasicSymbolic{Real}}}}}}, Val{true}}, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem} is a struct that's not allocated inline

Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:35
  [2] check_eltype
    @ ~/.julia/packages/CUDA/wPaDI/src/array.jl:67 [inlined]
  [3] CuArray{SciMLBase.ImmutableODEProblem{SVector{…}, Tuple{…}, false, MTKParameters{…}, ODEFunction{…}, @Kwargs{}, SciMLBase.StandardODEProblem}, 1, CUDA.DeviceMemory}(::UndefInitializer, dims::Tuple{Int64})
    @ CUDA ~/.julia/packages/CUDA/wPaDI/src/array.jl:82
  [4] CuArray
    @ ~/.julia/packages/CUDA/wPaDI/src/array.jl:437 [inlined]
  [5] CuArray
    @ ~/.julia/packages/CUDA/wPaDI/src/array.jl:442 [inlined]
  [6] CuArray
    @ ~/.julia/packages/CUDA/wPaDI/src/array.jl:451 [inlined]
  [7] convert
    @ ~/.julia/packages/GPUArrays/w335n/src/host/construction.jl:4 [inlined]
  [8] adapt_storage
    @ ~/.julia/packages/CUDA/wPaDI/src/array.jl:732 [inlined]
  [9] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [10] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [11] adapt_storage
    @ ~/.julia/packages/CUDA/wPaDI/src/CUDAKernels.jl:36 [inlined]
 [12] adapt_structure
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57 [inlined]
 [13] adapt
    @ ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40 [inlined]
 [14] batch_solve_up_kernel(ensembleprob::EnsembleProblem{…}, probs::Vector{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:280
 [15] batch_solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}, I::UnitRange{…}, adaptive::Bool; kwargs::@Kwargs{…})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:174
 [16] macro expansion
    @ ./timing.jl:421 [inlined]
 [17] __solve(ensembleprob::EnsembleProblem{…}, alg::GPUTsit5, ensemblealg::EnsembleGPUKernel{…}; trajectories::Int64, batch_size::Int64, unstable_check::Function, adaptive::Bool, kwargs::@Kwargs{})
    @ DiffEqGPU ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:55
 [18] __solve
    @ ~/.julia/packages/DiffEqGPU/JsBHq/src/solve.jl:1 [inlined]
 [19] #solve#747
    @ ~/.julia/packages/SciMLBase/TZ9Rx/src/ensemble/basic_ensemble_solve.jl:359 [inlined]
 [20] top-level scope
    @ REPL[33]:1
Some type information was truncated. Use `show(err)` to see complete types.

Nov 18 '25 13:11 SebastianM-C

I think this might need build_initializeprob = false

Nov 18 '25 13:11 AayushSabharwal

That seems too work! 🎉

Nov 18 '25 13:11 SebastianM-C

Awesome! It looks like we need Adapt methods for SII and a bunch of MTK stuff

Nov 18 '25 13:11 AayushSabharwal

Did that get looked at?

Dec 08 '25 19:12 ChrisRackauckas