SDEs with non-diagonal noise cause EnsembleGPU/CPUArray to throw an error

Comienza con src/ensemblegpuarray/kernels.jl y src/ensemblegpuarray/problem_generation.jl, siguiendo cómo EnsembleCPUArray y EnsembleGPUArray asignan y pasan el array de ruido para SRA1. Reproduce el ejemplo de Julia con un noise_rate_prototype no diagonal utilizando funciones de ruido in-place y out-of-place. La tarea estará terminada cuando los algoritmos SDE compatibles finalicen sin errores de dimensión y apliquen correctamente la matriz de ruido completa.

Describe the bug 🐞

• If EnsembleCPUArray or EnsembleGPUArray is provided with an SDE with non-diagonal noise and an in place noise function, then it crashes because the du array provided to the noise function does not match the dimensions of the noise_rate_prototype.
• This error happens regardless of the choice of SDE algorithm.
• If an out of place function is used for the noise process instead of an in place function, no error is thrown, but the noise process is not correctly applied. Specifically, the matrix returned for du has its first column interpreted as a diagonal noise vector.
• If you don't specify any SDE algorithm, it will return the correct solution, but this because of a different bug which causes the solve function to ignore the EnsembleCPU/GPUArray() option when a SDE algorithm is not specified.

Expected behavior
The expected behavior is for the solver to finish without throwing an error and return an accurate solution.

Minimal Reproducible Example 👇

using DifferentialEquations, DiffEqGPU, SparseArrays

function lorenz(du, u, p, t)
    du[1] = p[1] * (u[2] - u[1])
    du[2] = u[1] * (p[2] - u[3]) - u[2]
    du[3] = u[1] * u[2] - p[3] * u[3]
    du[4] = 0
end

function multiplicative_noise(du, u, p, t)
    du[1, 1] = 0.1
    du[2, 2] = 0.4
    du[4, 1] = 1.0
end

NRate = spzeros(4, 2)
NRate[1, 1] = 1
NRate[4, 1] = 1
NRate[2, 2] = 1

u0 = ComplexF32[1.0; 0.0; 0.0; 0.0]
tspan = (0.0f0, 10.0f0)
p = (10.0f0, 28.0f0, 8 / 3.0f0)
prob = SDEProblem(lorenz, multiplicative_noise, u0, tspan, p, noise_rate_prototype=NRate)

prob_func = (prob, i, repeat) -> remake(prob, p=p)
monteprob = EnsembleProblem(prob, prob_func=prob_func)

sol = solve(monteprob, SRA1(), EnsembleCPUArray(), trajectories=10_000, saveat=1.0f0)

Error & Stacktrace ⚠️

ERROR: LoadError: BoundsError: attempt to access 4-element view(::Matrix{ComplexF32}, :, 1) with eltype ComplexF32 at index [2, 2]
Stacktrace:
  [1] throw_boundserror(A::SubArray{ComplexF32, 1, Matrix{ComplexF32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, I::Tuple{Int64, Int64})
    @ Base .\abstractarray.jl:737
  [2] checkbounds
    @ .\abstractarray.jl:702 [inlined]
  [3] _setindex!
    @ .\abstractarray.jl:1418 [inlined]
  [4] setindex!
    @ .\abstractarray.jl:1396 [inlined]
  [5] multiplicative_noise
    @ Z:\Users\hshunt\LabNotebooks\DickeModel\ArraySolveTesting.jl:13 [inlined]
  [6] macro expansion
    @ C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\ensemblegpuarray\kernels.jl:45 [inlined]
  [7] cpu_gpu_kernel
    @ C:\Users\henhen724\.julia\packages\KernelAbstractions\MAxUm\src\macros.jl:287 [inlined]
  [8] cpu_gpu_kernel(__ctx__::KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, CartesianIndex{1}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}}, f::typeof(multiplicative_noise), du::Matrix{ComplexF32}, u::Matrix{ComplexF32}, p::Matrix{Tuple{Float32, Float32, Float32}}, t::Float32)
    @ DiffEqGPU .\none:0
  [9] __thread_run(tid::Int64, len::Int64, rem::Int64, obj::KernelAbstractions.Kernel{KernelAbstractions.CPU, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, typeof(DiffEqGPU.cpu_gpu_kernel)}, ndrange::Tuple{Int64}, iterspace::KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}, args::Tuple{typeof(multiplicative_noise), Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{Tuple{Float32, Float32, Float32}}, Float32}, dynamic::KernelAbstractions.NDIteration.DynamicCheck)
    @ KernelAbstractions C:\Users\henhen724\.julia\packages\KernelAbstractions\MAxUm\src\cpu.jl:117
 [10] __run(obj::KernelAbstractions.Kernel{KernelAbstractions.CPU, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, typeof(DiffEqGPU.cpu_gpu_kernel)}, ndrange::Tuple{Int64}, iterspace::KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}, args::Tuple{typeof(multiplicative_noise), Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{Tuple{Float32, Float32, Float32}}, Float32}, dynamic::KernelAbstractions.NDIteration.DynamicCheck, static_threads::Bool)
    @ KernelAbstractions C:\Users\henhen724\.julia\packages\KernelAbstractions\MAxUm\src\cpu.jl:84
 [11] (::KernelAbstractions.Kernel{KernelAbstractions.CPU, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, typeof(DiffEqGPU.cpu_gpu_kernel)})(::Function, ::Vararg{Any}; ndrange::Int64, workgroupsize::Int64)
    @ KernelAbstractions C:\Users\henhen724\.julia\packages\KernelAbstractions\MAxUm\src\cpu.jl:46
 [12] Kernel
    @ C:\Users\henhen724\.julia\packages\KernelAbstractions\MAxUm\src\cpu.jl:39 [inlined]
 [13] #21
    @ C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\ensemblegpuarray\problem_generation.jl:85 [inlined]       
 [14] sde_determine_initdt(u0::Matrix{ComplexF32}, t::Float32, tdir::Float32, dtmax::Float32, abstol::Float32, reltol::Float32, internalnorm::typeof(DiffEqGPU.diffeqgpunorm), prob::SDEProblem{Matrix{ComplexF32}, Tuple{Float32, Float32}, true, Matrix{Tuple{Float32, Float32, Float32}}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, @Kwargs{}, Nothing}, order::Rational{Int64}, integrator::StochasticDiffEq.SDEIntegrator{SRA1, true, Matrix{ComplexF32}, ComplexF32, Float32, Float32, Matrix{Tuple{Float32, Float32, Float32}}, Float32, Float32, ComplexF32, NoiseProcess{ComplexF32, 3, Float32, Matrix{ComplexF32}, Matrix{ComplexF32}, Vector{Matrix{ComplexF32}}, typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_BRIDGE), Nothing, true, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, 
ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, Matrix{ComplexF32}, RODESolution{ComplexF32, 3, Vector{Matrix{ComplexF32}}, Nothing, Nothing, Vector{Float32}, NoiseProcess{ComplexF32, 3, Float32, Matrix{ComplexF32}, Matrix{ComplexF32}, Vector{Matrix{ComplexF32}}, typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_BRIDGE), Nothing, true, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, SDEProblem{Matrix{ComplexF32}, Tuple{Float32, Float32}, true, Matrix{Tuple{Float32, Float32, Float32}}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, @Kwargs{}, Nothing}, SRA1, StochasticDiffEq.LinearInterpolationData{Vector{Matrix{ComplexF32}}, Vector{Float32}}, SciMLBase.DEStats, Nothing}, StochasticDiffEq.SRA1Cache{Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{ComplexF32}}, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, Nothing, StochasticDiffEq.SDEOptions{Float32, Float32, PIController{Float32}, typeof(DiffEqGPU.diffeqgpunorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), DiffEqGPU.var"#114#120", DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Float32, Float32, ComplexF32, Tuple{}, Float32, Tuple{}}, Nothing, ComplexF32, Nothing, Nothing})
    @ StochasticDiffEq C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\initdt.jl:34
 [15] auto_dt_reset!
    @ C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\integrators\integrator_interface.jl:355 [inlined]  
 [16] handle_dt!(integrator::StochasticDiffEq.SDEIntegrator{SRA1, true, Matrix{ComplexF32}, ComplexF32, Float32, Float32, Matrix{Tuple{Float32, Float32, Float32}}, Float32, Float32, ComplexF32, NoiseProcess{ComplexF32, 3, Float32, Matrix{ComplexF32}, Matrix{ComplexF32}, Vector{Matrix{ComplexF32}}, typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_BRIDGE), Nothing, true, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, Matrix{ComplexF32}, RODESolution{ComplexF32, 3, Vector{Matrix{ComplexF32}}, Nothing, Nothing, Vector{Float32}, NoiseProcess{ComplexF32, 3, Float32, Matrix{ComplexF32}, Matrix{ComplexF32}, Vector{Matrix{ComplexF32}}, typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.INPLACE_WHITE_NOISE_BRIDGE), Nothing, true, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, ResettableStacks.ResettableStack{Tuple{Float32, Matrix{ComplexF32}, Matrix{ComplexF32}}, true}, RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, SDEProblem{Matrix{ComplexF32}, Tuple{Float32, Float32}, true, Matrix{Tuple{Float32, Float32, Float32}}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, @Kwargs{}, Nothing}, SRA1, StochasticDiffEq.LinearInterpolationData{Vector{Matrix{ComplexF32}}, Vector{Float32}}, SciMLBase.DEStats, Nothing}, StochasticDiffEq.SRA1Cache{Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{ComplexF32}, Matrix{ComplexF32}}, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, 
Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, Nothing, StochasticDiffEq.SDEOptions{Float32, Float32, PIController{Float32}, typeof(DiffEqGPU.diffeqgpunorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), DiffEqGPU.var"#114#120", DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Float32, Float32, ComplexF32, Tuple{}, Float32, Tuple{}}, Nothing, ComplexF32, Nothing, Nothing})
    @ StochasticDiffEq C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\solve.jl:643
 [17] __init(_prob::SDEProblem{Matrix{ComplexF32}, Tuple{Float32, Float32}, true, Matrix{Tuple{Float32, Float32, Float32}}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, @Kwargs{}, Nothing}, alg::SRA1, timeseries_init::Vector{Any}, ts_init::Vector{Any}, ks_init::Type, recompile::Type{Val{true}}; saveat::Float32, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, 
save_noise::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float32, adaptive::Bool, gamma::Rational{Int64}, abstol::Nothing, reltol::Nothing, qmin::Rational{Int64}, qmax::Rational{Int64}, qsteady_min::Int64, qsteady_max::Int64, beta2::Nothing, beta1::Nothing, qoldinit::Rational{Int64}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, delta::Rational{Int64}, maxiters::Int64, dtmax::Float32, dtmin::Float32, internalnorm::typeof(DiffEqGPU.diffeqgpunorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::DiffEqGPU.var"#114#120", verbose::Bool, force_dtmin::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, initialize_integrator::Bool, seed::UInt64, alias_u0::Bool, alias_jumps::Bool, kwargs::@Kwargs{})
    @ StochasticDiffEq C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\solve.jl:596
 [18] __init (repeats 2 times)
    @ C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\solve.jl:18 [inlined]
 [19] #__solve#107
    @ C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\solve.jl:6 [inlined]
 [20] __solve (repeats 4 times)
    @ C:\Users\henhen724\.julia\packages\StochasticDiffEq\PgPd0\src\solve.jl:1 [inlined]
 [21] solve_call(_prob::SDEProblem{Matrix{ComplexF32}, Tuple{Float32, Float32}, true, Matrix{Tuple{Float32, Float32, Float32}}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, DiffEqGPU.var"#20#25"{typeof(lorenz), typeof(DiffEqGPU.gpu_kernel)}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, DiffEqGPU.var"#21#26"{typeof(multiplicative_noise), typeof(DiffEqGPU.gpu_kernel)}, @Kwargs{}, Nothing}, args::SRA1; merge_callbacks::Bool, kwargshandle::Nothing, kwargs::@Kwargs{adaptive::Bool, unstable_check::DiffEqGPU.var"#114#120", saveat::Float32, callback::Nothing, internalnorm::typeof(DiffEqGPU.diffeqgpunorm)})
    @ DiffEqBase C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:612
 [22] solve_call
    @ C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:569 [inlined]
 [23] #solve_up#53
    @ C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1080 [inlined]
 [24] solve_up
    @ C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1066 [inlined]
 [25] #solve#51
    @ C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1003 [inlined]
 [26] batch_solve_up(ensembleprob::EnsembleProblem{SDEProblem{Vector{ComplexF32}, Tuple{Float32, Float32}, true, Tuple{Float32, Float32, Float32}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, typeof(lorenz), typeof(multiplicative_noise), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, typeof(multiplicative_noise), @Kwargs{}, SparseMatrixCSC{Float64, Int64}}, var"#3#4", typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, probs::Vector{SDEProblem{Vector{ComplexF32}, Tuple{Float32, Float32}, true, Tuple{Float32, Float32, Float32}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, typeof(lorenz), typeof(multiplicative_noise), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, 
typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, typeof(multiplicative_noise), @Kwargs{}, SparseMatrixCSC{Float64, Int64}}}, alg::SRA1, ensemblealg::EnsembleCPUArray, I::UnitRange{Int64}, u0::Matrix{ComplexF32}, p::Matrix{Tuple{Float32, Float32, Float32}}; kwargs::@Kwargs{adaptive::Bool, unstable_check::DiffEqGPU.var"#114#120", saveat::Float32})    
    @ DiffEqGPU C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\solve.jl:315
 [27] batch_solve(ensembleprob::EnsembleProblem{SDEProblem{Vector{ComplexF32}, Tuple{Float32, Float32}, true, Tuple{Float32, Float32, Float32}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, typeof(lorenz), typeof(multiplicative_noise), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, typeof(multiplicative_noise), @Kwargs{}, SparseMatrixCSC{Float64, Int64}}, var"#3#4", typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), 
Nothing}, alg::SRA1, ensemblealg::EnsembleCPUArray, I::UnitRange{Int64}, adaptive::Bool; kwargs::@Kwargs{unstable_check::DiffEqGPU.var"#114#120", saveat::Float32})
    @ DiffEqGPU C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\solve.jl:242
 [28] macro expansion
    @ .\timing.jl:395 [inlined]
 [29] __solve(ensembleprob::EnsembleProblem{SDEProblem{Vector{ComplexF32}, Tuple{Float32, Float32}, true, Tuple{Float32, Float32, Float32}, Nothing, SDEFunction{true, SciMLBase.FullSpecialize, typeof(lorenz), typeof(multiplicative_noise), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, typeof(multiplicative_noise), @Kwargs{}, SparseMatrixCSC{Float64, Int64}}, var"#3#4", typeof(SciMLBase.DEFAULT_OUTPUT_FUNC), typeof(SciMLBase.DEFAULT_REDUCTION), Nothing}, alg::SRA1, ensemblealg::EnsembleCPUArray; trajectories::Int64, batch_size::Int64, unstable_check::Function, adaptive::Bool, kwargs::@Kwargs{saveat::Float32})
    @ DiffEqGPU C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\solve.jl:55
 [30] __solve
    @ C:\Users\henhen724\.julia\packages\DiffEqGPU\I999k\src\solve.jl:1 [inlined]
 [31] #solve#55
    @ C:\Users\henhen724\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1096 [inlined]
 [32] top-level scope
    @ Z:\Users\hshunt\LabNotebooks\DickeModel\ArraySolveTesting.jl:32
in expression starting at Z:\Users\hshunt\LabNotebooks\DickeModel\ArraySolveTesting.jl:32

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

julia> using Pkg; Pkg.status()
Status `\\levlabserver2.stanford.edu\commondrive\Users\hshunt\LabNotebooks\BugEnv\Project.toml`
  [071ae1c0] DiffEqGPU v3.4.1
  [0c46a032] DifferentialEquations v7.13.0
  [2f01184e] SparseArrays v1.10.0

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

julia> using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `\\levlabserver2.stanford.edu\commondrive\Users\hshunt\LabNotebooks\BugEnv\Manifest.toml`
  [47edcb42] ADTypes v1.6.1
⌃ [7d9f7c33] Accessors v0.1.36
  [79e6a3ab] Adapt v4.0.4
  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.12.0
  [4c555306] ArrayLayouts v1.10.2
  [a9b6321e] Atomix v0.1.0
  [aae01518] BandedMatrices v1.7.2
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [764a87c0] BoundaryValueDiffEq v5.9.0
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
  [49dc2e85] Calculus v0.5.1
  [d360d2e6] ChainRulesCore v1.24.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.0
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [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
  [bcd4f6db] DelayDiffEq v5.47.3
  [2b5f629d] DiffEqBase v6.151.5
  [459566f4] DiffEqCallbacks v3.6.2
  [071ae1c0] DiffEqGPU v3.4.1
  [77a26b50] DiffEqNoiseProcess v5.22.0
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [0c46a032] DifferentialEquations v7.13.0
  [a0c0ee7d] DifferentiationInterface v0.5.9
  [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 v0.7.7
  [d4d017d3] ExponentialUtilities v1.26.1
  [e2ba6199] ExprTools v0.1.10
  [9d29842c] FastAlmostBandedMatrices v0.1.3
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  [429524aa] Optim v1.9.4
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  [ae029012] Requires v1.3.0
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⌃ [0bca4576] SciMLBase v2.43.1
  [c0aeaf25] SciMLOperators v0.3.8
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⌃ [727e6d20] SimpleNonlinearSolve v1.10.1
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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> versioninfo()
Julia Version 1.10.2
Commit bd47eca2c8 (2024-03-01 10:14 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 8 × Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 1 default, 0 interactive, 1 GC (on 8 virtual cores)

Additional context
This error is caused by the assumption in the file src > ensemblegpuarray > kernels.jl that the time series for du can be written as a matrix (with one index for ODE coordinate and the next index for time). When a problem has non-diagonal noise you need two coordinate indexes and a time index, so the du time series needs to be a three index tensor or include some flatten and resize adaptor when evaluating the noise function.