Training Lux Models using Optimization.jl

Lux's native Training.TrainState is a great API for gradient-based learning of neural networks, however, it is geared towards using Optimisers.jl as the backend. However, often times we want to train the neural networks with other optimization methods like BFGS, LBFGS, etc. In this tutorial, we will show how to train Lux models with Optimization.jl that provides a simple unified interface to various optimization methods.

We will base our tutorial on the minibatching tutorial from the official Optimization.jl docs.

Neural ODE

This tutorial uses a Neural ODE, however, we won't discuss that part in this tutorial. Please refer to the Neural ODE tutorial for more information.

Imports packages

julia

using Lux, Optimization, OptimizationOptimisers, OptimizationOptimJL, OrdinaryDiffEqTsit5,
      SciMLSensitivity, Random, MLUtils, CairoMakie, ComponentArrays, Printf
using LuxCUDA

const gdev = gpu_device()
const cdev = cpu_device()

(::MLDataDevices.CPUDevice) (generic function with 2 methods)

Generate some training data

julia

function lotka_volterra(du, u, p, t)
    x, y = u
    α, β, δ, γ = p
    du[1] = α * x - β * x * y
    du[2] = -δ * y + γ * x * y
    return nothing
end

u0 = [1.0f0, 1.0f0]

datasize = 32
tspan = (0.0f0, 2.0f0)

const t = range(tspan[1], tspan[2]; length=datasize)
true_prob = ODEProblem(lotka_volterra, u0, (tspan[1], tspan[2]), [1.5, 1.0, 3.0, 1.0])
const ode_data = Array(solve(true_prob, Tsit5(); saveat=t))

begin
    fig = Figure()
    ax = CairoMakie.Axis(fig[1, 1])
    lines!(ax, t, ode_data[1, :]; label=L"u_1(t)", color=:blue, linestyle=:dot, linewidth=4)
    lines!(ax, t, ode_data[2, :]; label=L"u_2(t)", color=:red, linestyle=:dot, linewidth=4)
    axislegend(ax; position=:lt)
    fig
end

Define the DataLoader

We will define the DataLoader to batch over the data, additionally we will pipe it through the gdev device to move the data to the GPU on each iteration.

By default gdev will move all objects to the GPU. But we don't want to move the time vector to the GPU. So we will wrap it in a struct and mark it as a leaf using MLDataDevices.isleaf

julia

struct TimeWrapper{T}
    t::T
end

MLDataDevices.isleaf(::TimeWrapper) = true

Base.length(t::TimeWrapper) = length(t.t)

Base.getindex(t::TimeWrapper, i) = TimeWrapper(t.t[i])

dataloader = DataLoader((ode_data, TimeWrapper(t)); batchsize=8) |> gdev

MLDataDevices.DeviceIterator{MLDataDevices.CUDADevice{Nothing}, MLUtils.DataLoader{Tuple{Matrix{Float32}, Main.var"##225".TimeWrapper{StepRangeLen{Float32, Float64, Float64, Int64}}}, Random._GLOBAL_RNG, Val{nothing}}}(MLDataDevices.CUDADevice{Nothing}(nothing), DataLoader(::Tuple{Matrix{Float32}, Main.var"##225".TimeWrapper{StepRangeLen{Float32, Float64, Float64, Int64}}}, batchsize=8))

Training the model

Here we are using different optimization methods for demonstration purposes. This problem is trivial enough to not require this.

Optimization.jl requires an abstract array as the parameters, hence we will construct a ComponentArray to store the parameters.

Parameter Estimation vs State Estimation

Optimization.jl performs state estimation, which effectively means for a function f(u, p), it is trying to compute the optimal u for a given p. This terminology might be confusing to ML practitioners, since in the ML world, we usually do parameter estimation. This effectively means that the u in Optimization.jl corresponds to our model parameters that is being optimized.

julia

function train_model(dataloader)
    model = Chain(Dense(2, 32, tanh), Dense(32, 32, tanh), Dense(32, 2))
    ps, st = Lux.setup(Random.default_rng(), model)

    ps_ca = ComponentArray(ps) |> gdev
    st = st |> gdev

    function callback(state, l)
        state.iter % 25 == 1 && @printf "Iteration: %5d, Loss: %.6e\n" state.iter l
        return l < 1e-8 ## Terminate if loss is small
    end

    smodel = StatefulLuxLayer{true}(model, nothing, st)

    function loss_adjoint(θ, (u_batch, t_batch))
        t_batch = t_batch.t
        u0 = u_batch[:, 1]
        dudt(u, p, t) = smodel(u, p)
        prob = ODEProblem(dudt, u0, (t_batch[1], t_batch[end]), θ)
        sol = solve(prob, Tsit5(); sensealg=InterpolatingAdjoint(), saveat=t_batch)
        pred = stack(sol.u)
        return MSELoss()(pred, u_batch)
    end

    # Define the Optimization Function that takes in the optimization state (our parameters)
    # and optimization parameters (nothing in our case) and data from the dataloader and
    # returns the loss.
    opt_func = OptimizationFunction(loss_adjoint, Optimization.AutoZygote())
    opt_prob = OptimizationProblem(opt_func, ps_ca, dataloader)

    epochs = 25
    res_adam = solve(opt_prob, Optimisers.Adam(0.001); callback, epochs)

    # Let's finetune a bit with L-BFGS
    opt_prob = OptimizationProblem(opt_func, res_adam.u, (gdev(ode_data), TimeWrapper(t)))
    res_lbfgs = solve(opt_prob, LBFGS(); callback, maxiters=epochs)

    # Now that we have a good fit, let's train it on the entire dataset without
    # Minibatching. We need to do this since ODE solves can lead to accumulated errors if
    # the model was trained on individual parts (without a data-shooting approach).
    opt_prob = remake(opt_prob; u0=res_lbfgs.u)
    res = solve(opt_prob, Optimisers.Adam(0.005); maxiters=500, callback)

    return StatefulLuxLayer{true}(model, res.u, smodel.st)
end

trained_model = train_model(dataloader)

Iteration:     1, Loss: 1.781349e-01
Iteration:    26, Loss: 1.440252e-01
Iteration:    51, Loss: 2.220804e-01
Iteration:    76, Loss: 2.880028e-01
Iteration:     1, Loss: 3.225771e-01
Iteration:     1, Loss: 2.401903e-02
Iteration:    26, Loss: 2.741694e-02
Iteration:    51, Loss: 2.546618e-02
Iteration:    76, Loss: 2.340044e-02
Iteration:   101, Loss: 2.169080e-02
Iteration:   126, Loss: 2.006200e-02
Iteration:   151, Loss: 1.844056e-02
Iteration:   176, Loss: 1.681341e-02
Iteration:   201, Loss: 2.121204e-02
Iteration:   226, Loss: 1.646748e-02
Iteration:   251, Loss: 1.490256e-02
Iteration:   276, Loss: 1.558172e-02
Iteration:   301, Loss: 1.414660e-02
Iteration:   326, Loss: 1.312022e-02
Iteration:   351, Loss: 1.415232e-02
Iteration:   376, Loss: 1.298136e-02
Iteration:   401, Loss: 1.209131e-02
Iteration:   426, Loss: 1.170124e-02
Iteration:   451, Loss: 1.158293e-02
Iteration:   476, Loss: 2.675874e-02

Plotting the results

julia

dudt(u, p, t) = trained_model(u, p)
prob = ODEProblem(dudt, gdev(u0), (tspan[1], tspan[2]), trained_model.ps)
sol = solve(prob, Tsit5(); saveat=t)
pred = convert(AbstractArray, sol) |> cdev

begin
    fig = Figure()
    ax = CairoMakie.Axis(fig[1, 1])
    lines!(ax, t, ode_data[1, :]; label=L"u_1(t)", color=:blue, linestyle=:dot, linewidth=4)
    lines!(ax, t, ode_data[2, :]; label=L"u_2(t)", color=:red, linestyle=:dot, linewidth=4)
    lines!(ax, t, pred[1, :]; label=L"\hat{u}_1(t)", color=:blue, linewidth=4)
    lines!(ax, t, pred[2, :]; label=L"\hat{u}_2(t)", color=:red, linewidth=4)
    axislegend(ax; position=:lt)
    fig
end

Appendix

julia

using InteractiveUtils
InteractiveUtils.versioninfo()

if @isdefined(MLDataDevices)
    if @isdefined(CUDA) && MLDataDevices.functional(CUDADevice)
        println()
        CUDA.versioninfo()
    end

    if @isdefined(AMDGPU) && MLDataDevices.functional(AMDGPUDevice)
        println()
        AMDGPU.versioninfo()
    end
end

Julia Version 1.10.6
Commit 67dffc4a8ae (2024-10-28 12:23 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 48 × AMD EPYC 7402 24-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 48 default, 0 interactive, 24 GC (on 2 virtual cores)
Environment:
  JULIA_CPU_THREADS = 2
  JULIA_DEPOT_PATH = /root/.cache/julia-buildkite-plugin/depots/01872db4-8c79-43af-ab7d-12abac4f24f6
  LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64
  JULIA_PKG_SERVER = 
  JULIA_NUM_THREADS = 48
  JULIA_CUDA_HARD_MEMORY_LIMIT = 100%
  JULIA_PKG_PRECOMPILE_AUTO = 0
  JULIA_DEBUG = Literate

CUDA runtime 12.6, artifact installation
CUDA driver 12.6
NVIDIA driver 560.35.3

CUDA libraries: 
- CUBLAS: 12.6.3
- CURAND: 10.3.7
- CUFFT: 11.3.0
- CUSOLVER: 11.7.1
- CUSPARSE: 12.5.4
- CUPTI: 2024.3.2 (API 24.0.0)
- NVML: 12.0.0+560.35.3

Julia packages: 
- CUDA: 5.5.2
- CUDA_Driver_jll: 0.10.3+0
- CUDA_Runtime_jll: 0.15.4+0

Toolchain:
- Julia: 1.10.6
- LLVM: 15.0.7

Environment:
- JULIA_CUDA_HARD_MEMORY_LIMIT: 100%

1 device:
  0: NVIDIA A100-PCIE-40GB MIG 1g.5gb (sm_80, 4.453 GiB / 4.750 GiB available)

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Training Lux Models using Optimization.jl ​

Imports packages ​

Generate some training data ​

Define the DataLoader ​

Training the model ​

Plotting the results ​

Appendix ​

Training Lux Models using Optimization.jl

Imports packages

Generate some training data

Define the DataLoader

Training the model

Plotting the results

Appendix