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Auto-promotion does not work while using NeuralODE likelihood in Turing.jl #893

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canbozdogan opened this issue Dec 29, 2023 · 0 comments
Open

Auto-promotion does not work while using NeuralODE likelihood in Turing.jl #893

canbozdogan opened this issue Dec 29, 2023 · 0 comments
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bug Something isn't working

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@canbozdogan
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Describe the bug 🐞

When using a NeuralODE model within Turing.jl, specifically with parameters and data in Float64, the auto-promotion of parameters to Dual type for AD seems not to work correctly. This results in a type conversion error (MethodError related to converting ForwardDiff.Dual to Float64) during the sampling process.

Expected behavior
The expectation is that the parameters of the NeuralODE would automatically promote to the appropriate Dual type for AD, as they do in the usual ODEProblem. This should happen without manual intervention, ensuring compatibility with Turing.jl's sampling process.

Minimal Reproducible Example 👇

You can download trained NeuralODE parameters from here:
nn_parameters.zip

using DifferentialEquations, LinearAlgebra, JLD2, Turing
using Lux, Statistics, Plots, ForwardDiff 
using Random, DiffEqFlux, ComponentArrays, Optimization, OptimizationOptimisers


C_0 = 0.0014
rel_start = 0
rel_end = 10
N = 10
tspan = (rel_start,rel_end)
datasize = 50
tsteps = range(tspan[1], tspan[2]; length = datasize)
y_0 = fill(C_0, N)


function diffusion!(dOx, Ox, p, t)
    C_eq, h, N, D_z = p

    h0 = h / N
    N = Int(N)

    dOx[1] = (D_z * (Ox[2] - Ox[1]) - D_z * (Ox[1] - C_eq)) / h0^2
    dOx[2:N-1] = (D_z * (Ox[3:N] - Ox[2:N-1]) - D_z * (Ox[2:N-1] - Ox[1:N-2])) / h0^2
    dOx[N] = - D_z * (Ox[N] - Ox[N-1]) / h0^2
end

p_ode = [40.6205, 0.0015, 10, 1.9340e-08]

J_prototype = Tridiagonal(fill(1.0, N-1), fill(1.0, N), fill(1.0, N-1))
fun =  ODEFunction(diffusion!, jac_prototype=J_prototype)
prob = ODEProblem(fun,  y_0, tspan, p_ode)
ode_data = Array(solve(prob,Rodas5();saveat=tsteps))

true_ode_data = mean(ode_data, dims=1)


struct DiffusionLayer <: Lux.AbstractExplicitLayer
    C_eq::Float64
    h::Float64
    D_z::Float64

end


function DiffusionLayer(C_eq, h, D_z)
    return DiffusionLayer(C_eq, h, D_z)
end

# No trainable parameters in this layer
Lux.initialparameters(::AbstractRNG, ::DiffusionLayer) = NamedTuple()

# States are the ODE parameters C_eq, h, D_z, and N
Lux.initialstates(::AbstractRNG, layer::DiffusionLayer) = (C_eq=layer.C_eq, h=layer.h, D_z=layer.D_z)

function (layer::DiffusionLayer)(x, ps, st)
    N = length(x)
    h0 = st.h / N
    D = st.D_z / h0^2


    main_diag = [i == 1 ? -3 * D : i == N ? -D : -2 * D for i in 1:N]

    off_diag = fill(D, N - 1)

    A = Tridiagonal(off_diag, main_diag, off_diag)

    modified_x = vcat(x[1] - st.C_eq, x[2:end])

    dOx = A * modified_x


    return dOx, st
end

nn = Lux.Chain(
    DiffusionLayer(40.6205, 0.0015, 1.9340e-08),
    Lux.Dense(N, 50, tanh),
    Lux.Dense(50, N)
)


rng = Xoshiro(31)
p_nn , st = Lux.setup(rng, nn)


neural_ode = NeuralODE(nn, tspan, Tsit5(), saveat=tsteps)
neural_ode(y_0, p_nn, st)


function predict_neuralode(y_0,p_nn,st)
    
    solution = first(neural_ode(y_0, p_nn, st)) 

    return reduce(hcat,solution.u), solution.retcode
end

nn_param = load_object("nn_parameters.jld2")

@model function inverse_model(data)

    global st
    # Prior distribution
    D_z ~ Normal(1.9340e-08 , 1e-7) 

    ct = (layer_1 = (C_eq = 40.6205, h = 0.0015, D_z = D_z), layer_2 = NamedTuple(), 
    layer_3 = NamedTuple())


    predicted, retcode = predict_neuralode(y_0, nn_param.u, ct)
    avg_concentration = mean(predicted, dims=1)[:]


    if retcode !== ReturnCode.Success 
        Turing.@addlogprob! -Inf
        return nothing
    end


    data[:] ~ MvNormal(avg_concentration,1)

    return nothing
end

model = inverse_model(true_ode_data)
chain = sample(model, NUTS(), MCMCSerial(), 100, 1; progress=true)

Error & Stacktrace ⚠️

julia> chain = sample(model, NUTS(), MCMCSerial(), 100, 1; progress=true)
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, 
Float64}, Float64, 1})

Closest candidates are:
  (::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat
   @ Base rounding.jl:207
  (::Type{T})(::T) where T<:Number
   @ Core boot.jl:792
  (::Type{T})(::AbstractChar) where T<:Union{AbstractChar, Number}
   @ Base char.jl:50
  ...

Stacktrace:
  [1] convert(#unused#::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1})
    @ Base .\number.jl:7
  [2] setindex!(A::Vector{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, 
Float64, 1}, i1::Int64)
    @ Base .\array.jl:969
  [3] _unsafe_copyto!(dest::Vector{Float64}, doffs::Int64, src::Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, soffs::Int64, n::Int64)
    @ Base .\array.jl:250
  [4] unsafe_copyto!
    @ .\array.jl:304 [inlined]
  [5] _copyto_impl!
    @ .\array.jl:327 [inlined]
  [6] copyto!
    @ .\array.jl:314 [inlined]
  [7] copyto!
    @ .\array.jl:339 [inlined]
  [8] copyto_axcheck!
    @ .\abstractarray.jl:1182 [inlined]
  [9] Vector{Float64}(x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}})
    @ Base .\array.jl:621
 [10] convert
    @ .\array.jl:613 [inlined]
 [11] setproperty!
    @ .\Base.jl:38 [inlined]
 [12] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, false, Vector{Float64}, Nothing, Float64, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, Float64, 
Float64, Float64, Float64, Vector{Vector{Float64}}, ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), 
Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Tsit5ConstantCache}, DiffEqBase.Stats, Nothing}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, OrdinaryDiffEq.Tsit5ConstantCache, OrdinaryDiffEq.DEOptions{Float64, Float64, Float64, Float64, PIController{Rational{Int64}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(opnorm), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float64, DataStructures.FasterForward}, Nothing, Nothing, Int64, 
Tuple{}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{}}, Vector{Float64}, Float64, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Tsit5ConstantCache)
    @ OrdinaryDiffEq C:\Users\bozdoc\.julia\packages\OrdinaryDiffEq\FFFcA\src\perform_step\low_order_rk_perform_step.jl:726
 [13] __init(prob::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, alg::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{Val{true}}; saveat::StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float64, dtmin::Nothing, dtmax::Float64, force_dtmin::Bool, adaptive::Bool, gamma::Rational{Int64}, abstol::Nothing, reltol::Nothing, qmin::Rational{Int64}, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, beta1::Nothing, beta2::Nothing, qoldinit::Rational{Int64}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::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, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})  
    @ OrdinaryDiffEq C:\Users\bozdoc\.julia\packages\OrdinaryDiffEq\FFFcA\src\solve.jl:502
 [14] __init (repeats 5 times)
    @ C:\Users\bozdoc\.julia\packages\OrdinaryDiffEq\FFFcA\src\solve.jl:10 [inlined]
 [15] __solve(::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), 
Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, 
NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}}})
    @ OrdinaryDiffEq C:\Users\bozdoc\.julia\packages\OrdinaryDiffEq\FFFcA\src\solve.jl:5
 [16] __solve
    @ C:\Users\bozdoc\.julia\packages\OrdinaryDiffEq\FFFcA\src\solve.jl:1 [inlined]
 [17] #solve_call#34
    @ C:\Users\bozdoc\.julia\packages\DiffEqBase\s433k\src\solve.jl:559 [inlined]
 [18] solve_up(prob::ODEProblem{Vector{Float64}, Tuple{Int64, Int64}, false, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, 
Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ZygoteVJP}, u0::Vector{Float64}, p::ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Symbol}, NamedTuple{(:saveat,), 
Tuple{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}}})  
    @ DiffEqBase C:\Users\bozdoc\.julia\packages\DiffEqBase\s433k\src\solve.jl:1020
 [19] solve_up
    @ C:\Users\bozdoc\.julia\packages\DiffEqBase\s433k\src\solve.jl:993 [inlined]
 [20] solve(prob::ODEProblem{Vector{Float64}, Tuple{Int64, Int64}, false, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, ODEFunction{false, SciMLBase.FullSpecialize, DiffEqFlux.var"#dudt#24"{Lux.Experimental.StatefulLuxLayer{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Nothing, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}}, UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; sensealg::InterpolatingAdjoint{0, true, Val{:central}, ZygoteVJP}, u0::Nothing, p::Nothing, wrap::Val{true}, kwargs::Base.Pairs{Symbol, 
StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}}})
    @ DiffEqBase C:\Users\bozdoc\.julia\packages\DiffEqBase\s433k\src\solve.jl:930
 [21] solve
    @ C:\Users\bozdoc\.julia\packages\DiffEqBase\s433k\src\solve.jl:920 [inlined]
 [22] (::NeuralODE{Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{DiffusionLayer, Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}}, Nothing}, Tuple{Int64, Int64}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}}}})(x::Vector{Float64}, p::ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, st::NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}})
    @ DiffEqFlux C:\Users\bozdoc\.julia\packages\DiffEqFlux\7OfDv\src\neural_de.jl:53
 [23] predict_neuralode(y_0::Vector{Float64}, p_nn::ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:176, Axis(weight = ViewAxis(1:160, ShapedAxis((16, 10), NamedTuple())), bias = ViewAxis(161:176, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(177:346, Axis(weight = ViewAxis(1:160, ShapedAxis((10, 16), NamedTuple())), bias = ViewAxis(161:170, ShapedAxis((10, 1), NamedTuple())))))}}}, st::NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(:C_eq, :h, :D_z), Tuple{Float64, Float64, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}})
    @ Main c:\Users\bozdoc\Desktop\CanRepo\Phase-1\minimalworking.jl:92
 [24] inverse_model(__model__::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, __varinfo__::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}}, 
__context__::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}, data::Matrix{Float64})
    @ Main c:\Users\bozdoc\Desktop\CanRepo\Phase-1\minimalworking.jl:109
 [25] _evaluate!!(model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG})
    @ DynamicPPL C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\model.jl:963
 [26] evaluate_threadsafe!!(model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG})
    @ DynamicPPL C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\model.jl:952
 [27] evaluate!!
    @ C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\model.jl:887 [inlined]
 [28] logdensity(f::LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}, θ::Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}})
    @ DynamicPPL C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\logdensityfunction.jl:94      
 [29] Fix1
    @ .\operators.jl:1108 [inlined]
 [30] vector_mode_dual_eval!
    @ C:\Users\bozdoc\.julia\packages\ForwardDiff\PcZ48\src\apiutils.jl:24 [inlined]
 [31] vector_mode_gradient!
    @ C:\Users\bozdoc\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:96 [inlined]
 [32] gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), 
Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}, ::Val{true})
    @ ForwardDiff C:\Users\bozdoc\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:37
 [33] gradient!
    @ C:\Users\bozdoc\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:35 [inlined]
 [34] logdensity_and_gradient
    @ C:\Users\bozdoc\.julia\packages\LogDensityProblemsAD\OQ0BL\ext\LogDensityProblemsADForwardDiffExt.jl:118 [inlined]
 [35] ∂logπ∂θ
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\hmc.jl:160 [inlined]
 [36] ∂H∂θ
    @ C:\Users\bozdoc\.julia\packages\AdvancedHMC\LJv94\src\hamiltonian.jl:38 [inlined]
 [37] phasepoint(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}, ForwardDiff.Chunk{1}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}}}, Turing.Inference.var"#∂logπ∂θ#36"{LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}, ForwardDiff.Chunk{1}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}}}}, θ::Vector{Float64}, r::Vector{Float64})
    @ AdvancedHMC C:\Users\bozdoc\.julia\packages\AdvancedHMC\LJv94\src\hamiltonian.jl:80
 [38] phasepoint(rng::TaskLocalRNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}, ForwardDiff.Chunk{1}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, 
Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1}}}}}, Turing.Inference.var"#∂logπ∂θ#36"{LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, TaskLocalRNG}}, ForwardDiff.Chunk{1}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 1, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 
1}}}}}})
    @ AdvancedHMC C:\Users\bozdoc\.julia\packages\AdvancedHMC\LJv94\src\hamiltonian.jl:159
 [39] initialstep(rng::TaskLocalRNG, model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:D_z,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}, Int64}, Vector{Normal{Float64}}, Vector{AbstractPPL.VarName{:D_z, Setfield.IdentityLens}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\hmc.jl:164
 [40] step(rng::TaskLocalRNG, model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ DynamicPPL C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\sampler.jl:111
 [41] step
    @ C:\Users\bozdoc\.julia\packages\DynamicPPL\oX6N7\src\sampler.jl:84 [inlined]
 [42] macro expansion
    @ C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:125 [inlined]
 [43] macro expansion
    @ C:\Users\bozdoc\.julia\packages\ProgressLogging\6KXlp\src\ProgressLogging.jl:328 [inlined]    
 [44] macro expansion
    @ C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\logging.jl:9 [inlined]
 [45] mcmcsample(rng::TaskLocalRNG, model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Union{Nothing, Int64}, Tuple{Symbol, Symbol}, NamedTuple{(:nadapts, :init_params), Tuple{Int64, Nothing}}})
    @ AbstractMCMC C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:116
 [46] mcmcsample
    @ C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:95 [inlined]
 [47] #sample#34
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\hmc.jl:121 [inlined]
 [48] sample
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\hmc.jl:91 [inlined]
 [49] (::AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, TaskLocalRNG, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, Int64})(i::Int64, seed::UInt64, init_params::Nothing)
    @ AbstractMCMC C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:511
 [50] sample_chain
    @ C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:508 [inlined]
 [51] #4
    @ .\generator.jl:36 [inlined]
 [52] iterate
    @ .\generator.jl:47 [inlined]
 [53] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{UnitRange{Int64}, Vector{UInt64}}}, Base.var"#4#5"{AbstractMCMC.var"#sample_chain#78"{String, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, 
NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}}, TaskLocalRNG, DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, Int64, 
Int64}}})
    @ Base .\array.jl:782
 [54] map
    @ .\abstractarray.jl:3385 [inlined]
 [55] mcmcsample(rng::TaskLocalRNG, model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ::MCMCSerial, N::Int64, nchains::Int64; progressname::String, init_params::Nothing, kwargs::Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol}, NamedTuple{(:chain_type, :progress), Tuple{UnionAll, Bool}}})
    @ AbstractMCMC C:\Users\bozdoc\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:523
 [56] sample(rng::TaskLocalRNG, model::DynamicPPL.Model{typeof(inverse_model), (:data,), (), (), Tuple{Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, ensemble::MCMCSerial, N::Int64, n_chains::Int64; chain_type::Type, progress::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\Inference.jl:265
 [57] sample
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\Inference.jl:254 [inlined]
 [58] #sample#6
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\Inference.jl:250 [inlined]
 [59] sample
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\Inference.jl:241 [inlined]
 [60] #sample#5
    @ C:\Users\bozdoc\.julia\packages\Turing\UCuzt\src\mcmc\Inference.jl:237 [inlined]

Environment (please complete the following information):

  • Output of using Pkg; Pkg.status()
julia> Pkg.status()
Status `C:\Users\bozdoc\Desktop\CanRepo\MyJuliaProject\Project.toml`
  [70b36510] AutomaticDocstrings v1.0.5
  [336ed68f] CSV v0.10.11
  [052768ef] CUDA v5.1.1
  [c3611d14] ColorVectorSpace v0.10.0
⌃ [b0b7db55] ComponentArrays v0.15.6
  [a93c6f00] DataFrames v1.6.1
  [31a5f54b] Debugger v0.7.8
⌃ [aae7a2af] DiffEqFlux v3.2.0
  [071ae1c0] DiffEqGPU v3.3.0
⌃ [0c46a032] DifferentialEquations v7.10.0
  [31c24e10] Distributions v0.25.104
  [ffbed154] DocStringExtensions v0.9.3
⌅ [28b8d3ca] GR v0.72.10
  [c27321d9] Glob v1.3.1
  [5903a43b] Infiltrator v1.6.4
  [2fda8390] LsqFit v0.15.0
  [b2108857] Lux v0.5.13
  [d0bbae9a] LuxCUDA v0.3.1
  [94925ecb] MethodOfLines v0.10.4
⌃ [961ee093] ModelingToolkit v8.70.0
  [3bd65402] Optimisers v0.3.1
⌃ [7f7a1694] Optimization v3.19.3
  [36348300] OptimizationOptimJL v0.1.14
  [42dfb2eb] OptimizationOptimisers v0.1.6
  [a03496cd] PlotlyBase v0.8.19
  [f0f68f2c] PlotlyJS v0.18.11
  [91a5bcdd] Plots v1.39.0
  [aea7be01] PrecompileTools v1.2.0
  [92933f4c] ProgressMeter v1.9.0
  [efcf1570] Setfield v1.1.1
  [6303bc30] Signals v1.2.0
⌅ [2913bbd2] StatsBase v0.33.21
  [f3b207a7] StatsPlots v0.15.6
⌃ [c3572dad] Sundials v4.20.1
⌃ [fce5fe82] Turing v0.29.3
  [ea0860ee] TuringCallbacks v0.4.0
⌃ [b8865327] UnicodePlots v3.6.0
  [e88e6eb3] Zygote v0.6.68
  [fa267f1f] TOML v1.0.3
  • Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
julia> Pkg.status(; mode = PKGMODE_MANIFEST)
Status `C:\Users\bozdoc\Desktop\CanRepo\MyJuliaProject\Manifest.toml`
⌃ [47edcb42] ADTypes v0.2.5
  [c3fe647b] AbstractAlgebra v0.34.7
  [621f4979] AbstractFFTs v1.5.0
⌅ [80f14c24] AbstractMCMC v4.4.2
⌅ [7a57a42e] AbstractPPL v0.6.2
  [1520ce14] AbstractTrees v0.4.4
⌅ [79e6a3ab] Adapt v3.7.2
⌅ [0bf59076] AdvancedHMC v0.4.4
⌅ [5b7e9947] AdvancedMH v0.7.5
⌅ [576499cb] AdvancedPS v0.4.3
  [b5ca4192] AdvancedVI v0.2.4
  [dce04be8] ArgCheck v2.3.0
  [ec485272] ArnoldiMethod v0.2.0
  [7d9fca2a] Arpack v0.5.4
  [4fba245c] ArrayInterface v7.7.0
  [4c555306] ArrayLayouts v1.4.5
  [bf4720bc] AssetRegistry v0.1.0
  [a9b6321e] Atomix v0.1.0
  [70b36510] AutomaticDocstrings v1.0.5
⌃ [13072b0f] AxisAlgorithms v1.0.1
  [39de3d68] AxisArrays v0.4.7
  [ab4f0b2a] BFloat16s v0.4.2
⌅ [aae01518] BandedMatrices v0.17.38
  [198e06fe] BangBang v0.3.39
  [9718e550] Baselet v0.1.1
  [e2ed5e7c] Bijections v0.1.6
  [76274a88] Bijectors v0.13.8
  [d1d4a3ce] BitFlags v0.1.8
  [62783981] BitTwiddlingConvenienceFunctions v0.1.5
  [ad839575] Blink v0.12.8
⌅ [764a87c0] BoundaryValueDiffEq v4.0.1
  [e1450e63] BufferedStreams v1.2.1
⌅ [fa961155] CEnum v0.4.2
  [2a0fbf3d] CPUSummary v0.2.4
  [00ebfdb7] CSTParser v3.3.6
  [336ed68f] CSV v0.10.11
  [052768ef] CUDA v5.1.1
  [1af6417a] CUDA_Runtime_Discovery v0.2.2
  [49dc2e85] Calculus v0.5.1
  [7057c7e9] Cassette v0.3.12
  [082447d4] ChainRules v1.58.1
  [d360d2e6] ChainRulesCore v1.19.0
  [9e997f8a] ChangesOfVariables v0.1.8
  [fb6a15b2] CloseOpenIntervals v0.1.12
  [aaaa29a8] Clustering v0.15.6
  [da1fd8a2] CodeTracking v1.3.5
  [944b1d66] CodecZlib v0.7.3
  [35d6a980] ColorSchemes v3.24.0
  [3da002f7] ColorTypes v0.11.4
  [c3611d14] ColorVectorSpace v0.10.0
  [5ae59095] Colors v0.12.10
  [861a8166] Combinatorics v1.0.2
  [a80b9123] CommonMark v0.8.12
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.0
  [34da2185] Compat v4.10.1
⌃ [b0b7db55] ComponentArrays v0.15.6
  [b152e2b5] CompositeTypes v0.1.3
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [f0e56b4a] ConcurrentUtilities v2.3.0
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.5.4
  [d38c429a] Contour v0.6.2
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [9a962f9c] DataAPI v1.15.0
  [a93c6f00] DataFrames v1.6.1
  [864edb3b] DataStructures v0.18.15
  [e2d170a0] DataValueInterfaces v1.0.0
  [31a5f54b] Debugger v0.7.8
  [244e2a9f] DefineSingletons v0.1.2
⌃ [bcd4f6db] DelayDiffEq v5.44.0
  [8bb1440f] DelimitedFiles v1.9.1
  [b429d917] DensityInterface v0.4.0
⌃ [2b5f629d] DiffEqBase v6.130.0
⌃ [459566f4] DiffEqCallbacks v2.35.0
⌃ [aae7a2af] DiffEqFlux v3.2.0
  [071ae1c0] DiffEqGPU v3.3.0
  [77a26b50] DiffEqNoiseProcess v5.20.0
  [163ba53b] DiffResults v1.1.0
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⌅ [d5909c97] GroupsCore v0.4.2
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⌅ [8913a72c] NonlinearSolve v1.10.1
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⌃ [7f7a1694] Optimization v3.19.3
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⌃ [1dea7af3] OrdinaryDiffEq v6.58.2
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⌅ [0bca4576] SciMLBase v1.98.1
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⌅ [727e6d20] SimpleNonlinearSolve v0.1.23
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⌃ [a2af1166] SortingAlgorithms v1.2.0
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⌅ [2913bbd2] StatsBase v0.33.21
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⌅ [9672c7b4] SteadyStateDiffEq v1.16.1
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⌃ [c3572dad] Sundials v4.20.1
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⌅ [3bb67fe8] TranscodingStreams v0.9.13
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⌃ [fce5fe82] Turing v0.29.3
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⌃ [b8865327] UnicodePlots v3.6.0
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⌅ [efce3f68] WoodburyMatrices v0.5.6
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  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.0.5+0
  [deac9b47] LibCURL_jll v7.84.0+0
  [29816b5a] LibSSH2_jll v1.10.2+0
  [c8ffd9c3] MbedTLS_jll v2.28.2+0
  [14a3606d] MozillaCACerts_jll v2022.10.11
  [4536629a] OpenBLAS_jll v0.3.21+4
  [05823500] OpenLibm_jll v0.8.1+0
  [efcefdf7] PCRE2_jll v10.42.0+0
  [bea87d4a] SuiteSparse_jll v5.10.1+6
  [83775a58] Zlib_jll v1.2.13+0
  [8e850b90] libblastrampoline_jll v5.8.0+0
  [8e850ede] nghttp2_jll v1.48.0+0
  [3f19e933] p7zip_jll v17.4.0+0
Info Packages marked with ⌃ and ⌅ have new versions available, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
  • Output of versioninfo()
Julia Version 1.9.3
Commit bed2cd540a (2023-08-24 14:43 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 8 × Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
  Threads: 8 on 8 virtual cores
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 8

Additional context

In a discussion with @ChrisRackauckas, it was suggested that the issue might be related to auto-promotion not working as expected in the NeuralODE context with Turing.jl. The workaround eltype(D_z).(nn_param.u) was suggested and worked, implying that the auto-promotion should have handled this case.
@canbozdogan canbozdogan added the bug Something isn't working label Dec 29, 2023
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