Simple SDE only works with one trajectory in ensemble

Based on the DiffEq documentation, I've written this minimal example. If I set trajectories = 1, it compiles properly and everything is fine. But when trajectories = 32, say, I get errors relating to calling overdub from within the kernel. I'm very much a Julia noob, but this seems weird to me. The CUDA debugging settings aren't really helping shed any light on the situation. I've been working on this all day and can't seem to make forward progress. Hoping to get some help here!

Example code:

using CUDA
using Plots
using DifferentialEquations
using DiffEqGPU

n = 16

function μ_cox(du, u, p, t)
    du .= -1.0f0 * u
    return nothing
end

function σ_cox(du, u, p, t)
    du .= 0.1f0 * u .^ 2;
    return nothing
end

params = CUDA.zeros(Float32, 1)
initial = CUDA.ones(Float32, n)
prob = SDEProblem(μ_cox, σ_cox, initial, (0.0f0, 1.0f1), p = params)

ensembleprob = EnsembleProblem(prob, safetycopy = false)
sol = solve(ensembleprob, Tsit5(), EnsembleGPUArray(), trajectories = 32)

Example error:

InvalidIRError: compiling kernel gpu_gpu_kernel(Cassette.Context{nametype(CUDACtx), KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, 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}}}}}, Nothing, KernelAbstractions.var"##PassType#257", Nothing, Cassette.DisableHooks}, typeof(DiffEqGPU.gpu_gpu_kernel), typeof(μ_cox), CuDeviceMatrix{Float32, 1}, CuDeviceMatrix{Float32, 1}, CuDeviceMatrix{SciMLBase.NullParameters, 1}, Float32) resulted in invalid LLVM IR
Reason: unsupported call to the Julia runtime (call to jl_f_tuple)
Stacktrace:
 [1] overdub
   @ ~/.julia/packages/Cassette/r4kKQ/src/overdub.jl:635
 [2] throw_inexacterror(::Symbol, ::Type{UInt64}, ::Int64)
   @ ./boot.jl:602
 [3] overdub
   @ ./boot.jl:602
 [4] overdub
   @ ~/.julia/packages/Cassette/r4kKQ/src/overdub.jl:0
 [5] multiple call sites
   @ unknown:0

Start with the minimal Julia example in the issue and run the EnsembleGPUArray solve with trajectories set to 1 and then 32. Inspect the InvalidIRError and its overdub/jl_f_tuple stack trace while tracing the DiffEqGPU GPU kernel entry point. Done means the provided example runs successfully with multiple trajectories without the invalid LLVM IR error.