Training a HyperNetwork on MNIST and FashionMNIST

Package Imports

using Lux, ADTypes, ComponentArrays, LuxCUDA, MLDatasets, MLUtils, OneHotArrays, Optimisers,
      Printf, Random, Setfield, Statistics, Zygote

CUDA.allowscalar(false)

Loading Datasets

function load_dataset(::Type{dset}, n_train::Int, n_eval::Int, batchsize::Int) where {dset}
    imgs, labels = dset(:train)[1:n_train]
    x_train, y_train = reshape(imgs, 28, 28, 1, n_train), onehotbatch(labels, 0:9)

    imgs, labels = dset(:test)[1:n_eval]
    x_test, y_test = reshape(imgs, 28, 28, 1, n_eval), onehotbatch(labels, 0:9)

    return (DataLoader((x_train, y_train); batchsize=min(batchsize, n_train), shuffle=true),
        DataLoader((x_test, y_test); batchsize=min(batchsize, n_eval), shuffle=false))
end

function load_datasets(n_train=1024, n_eval=32, batchsize=256)
    return load_dataset.((MNIST, FashionMNIST), n_train, n_eval, batchsize)
end

load_datasets (generic function with 4 methods)

Implement a HyperNet Layer

function HyperNet(
        weight_generator::Lux.AbstractLuxLayer, core_network::Lux.AbstractLuxLayer)
    ca_axes = Lux.initialparameters(Random.default_rng(), core_network) |>
              ComponentArray |>
              getaxes
    return @compact(; ca_axes, weight_generator, core_network, dispatch=:HyperNet) do (x, y)
        # Generate the weights
        ps_new = ComponentArray(vec(weight_generator(x)), ca_axes)
        @return core_network(y, ps_new)
    end
end

HyperNet (generic function with 1 method)

Defining functions on the CompactLuxLayer requires some understanding of how the layer is structured, as such we don't recommend doing it unless you are familiar with the internals. In this case, we simply write it to ignore the initialization of the core_network parameters.

function Lux.initialparameters(rng::AbstractRNG, hn::CompactLuxLayer{:HyperNet})
    return (; weight_generator=Lux.initialparameters(rng, hn.layers.weight_generator),)
end

Create and Initialize the HyperNet

function create_model()
    # Doesn't need to be a MLP can have any Lux Layer
    core_network = Chain(FlattenLayer(), Dense(784, 256, relu), Dense(256, 10))
    weight_generator = Chain(Embedding(2 => 32), Dense(32, 64, relu),
        Dense(64, Lux.parameterlength(core_network)))

    model = HyperNet(weight_generator, core_network)
    return model
end

create_model (generic function with 1 method)

Define Utility Functions

const loss = CrossEntropyLoss(; logits=Val(true))

function accuracy(model, ps, st, dataloader, data_idx)
    total_correct, total = 0, 0
    st = Lux.testmode(st)
    for (x, y) in dataloader
        target_class = onecold(y)
        predicted_class = onecold(first(model((data_idx, x), ps, st)))
        total_correct += sum(target_class .== predicted_class)
        total += length(target_class)
    end
    return total_correct / total
end

accuracy (generic function with 1 method)

Training

function train()
    model = create_model()
    dataloaders = load_datasets()

    dev = gpu_device()
    rng = Xoshiro(0)
    ps, st = Lux.setup(rng, model) |> dev

    train_state = Training.TrainState(model, ps, st, Adam(3.0f-4))

    ### Lets train the model
    nepochs = 25
    for epoch in 1:nepochs, data_idx in 1:2
        train_dataloader, test_dataloader = dataloaders[data_idx] .|> dev

        stime = time()
        for (x, y) in train_dataloader
            (_, _, _, train_state) = Training.single_train_step!(
                AutoZygote(), loss, ((data_idx, x), y), train_state)
        end
        ttime = time() - stime

        train_acc = round(
            accuracy(model, train_state.parameters,
                train_state.states, train_dataloader, data_idx) * 100;
            digits=2)
        test_acc = round(
            accuracy(model, train_state.parameters,
                train_state.states, test_dataloader, data_idx) * 100;
            digits=2)

        data_name = data_idx == 1 ? "MNIST" : "FashionMNIST"

        @printf "[%3d/%3d] \t %12s \t Time %.5fs \t Training Accuracy: %.2f%% \t Test \
                 Accuracy: %.2f%%\n" epoch nepochs data_name ttime train_acc test_acc
    end

    println()

    test_acc_list = [0.0, 0.0]
    for data_idx in 1:2
        train_dataloader, test_dataloader = dataloaders[data_idx] .|> dev
        train_acc = round(
            accuracy(model, train_state.parameters,
                train_state.states, train_dataloader, data_idx) * 100;
            digits=2)
        test_acc = round(
            accuracy(model, train_state.parameters,
                train_state.states, test_dataloader, data_idx) * 100;
            digits=2)

        data_name = data_idx == 1 ? "MNIST" : "FashionMNIST"

        @printf "[FINAL] \t %12s \t Training Accuracy: %.2f%% \t Test Accuracy: \
                 %.2f%%\n" data_name train_acc test_acc
        test_acc_list[data_idx] = test_acc
    end
    return test_acc_list
end

test_acc_list = train()

[  1/ 25] 	        MNIST 	 Time 70.81018s 	 Training Accuracy: 24.32% 	 Test Accuracy: 25.00%
[  1/ 25] 	 FashionMNIST 	 Time 0.02807s 	 Training Accuracy: 30.66% 	 Test Accuracy: 25.00%
[  2/ 25] 	        MNIST 	 Time 0.02779s 	 Training Accuracy: 50.10% 	 Test Accuracy: 34.38%
[  2/ 25] 	 FashionMNIST 	 Time 0.02846s 	 Training Accuracy: 55.08% 	 Test Accuracy: 50.00%
[  3/ 25] 	        MNIST 	 Time 0.04007s 	 Training Accuracy: 58.69% 	 Test Accuracy: 59.38%
[  3/ 25] 	 FashionMNIST 	 Time 0.06524s 	 Training Accuracy: 60.25% 	 Test Accuracy: 56.25%
[  4/ 25] 	        MNIST 	 Time 0.02630s 	 Training Accuracy: 67.38% 	 Test Accuracy: 50.00%
[  4/ 25] 	 FashionMNIST 	 Time 0.02169s 	 Training Accuracy: 64.55% 	 Test Accuracy: 46.88%
[  5/ 25] 	        MNIST 	 Time 0.02157s 	 Training Accuracy: 72.85% 	 Test Accuracy: 56.25%
[  5/ 25] 	 FashionMNIST 	 Time 0.02178s 	 Training Accuracy: 72.75% 	 Test Accuracy: 65.62%
[  6/ 25] 	        MNIST 	 Time 0.02269s 	 Training Accuracy: 80.57% 	 Test Accuracy: 71.88%
[  6/ 25] 	 FashionMNIST 	 Time 0.02321s 	 Training Accuracy: 78.42% 	 Test Accuracy: 71.88%
[  7/ 25] 	        MNIST 	 Time 0.02294s 	 Training Accuracy: 81.45% 	 Test Accuracy: 71.88%
[  7/ 25] 	 FashionMNIST 	 Time 0.02224s 	 Training Accuracy: 76.07% 	 Test Accuracy: 62.50%
[  8/ 25] 	        MNIST 	 Time 0.03901s 	 Training Accuracy: 85.45% 	 Test Accuracy: 71.88%
[  8/ 25] 	 FashionMNIST 	 Time 0.02053s 	 Training Accuracy: 78.22% 	 Test Accuracy: 62.50%
[  9/ 25] 	        MNIST 	 Time 0.02069s 	 Training Accuracy: 88.57% 	 Test Accuracy: 68.75%
[  9/ 25] 	 FashionMNIST 	 Time 0.02065s 	 Training Accuracy: 82.03% 	 Test Accuracy: 71.88%
[ 10/ 25] 	        MNIST 	 Time 0.02026s 	 Training Accuracy: 91.11% 	 Test Accuracy: 71.88%
[ 10/ 25] 	 FashionMNIST 	 Time 0.02051s 	 Training Accuracy: 84.18% 	 Test Accuracy: 62.50%
[ 11/ 25] 	        MNIST 	 Time 0.02054s 	 Training Accuracy: 93.07% 	 Test Accuracy: 68.75%
[ 11/ 25] 	 FashionMNIST 	 Time 0.02045s 	 Training Accuracy: 84.96% 	 Test Accuracy: 65.62%
[ 12/ 25] 	        MNIST 	 Time 0.02095s 	 Training Accuracy: 94.63% 	 Test Accuracy: 71.88%
[ 12/ 25] 	 FashionMNIST 	 Time 0.01996s 	 Training Accuracy: 86.52% 	 Test Accuracy: 62.50%
[ 13/ 25] 	        MNIST 	 Time 0.02076s 	 Training Accuracy: 96.09% 	 Test Accuracy: 68.75%
[ 13/ 25] 	 FashionMNIST 	 Time 0.02051s 	 Training Accuracy: 88.18% 	 Test Accuracy: 65.62%
[ 14/ 25] 	        MNIST 	 Time 0.02184s 	 Training Accuracy: 96.00% 	 Test Accuracy: 71.88%
[ 14/ 25] 	 FashionMNIST 	 Time 0.02051s 	 Training Accuracy: 86.33% 	 Test Accuracy: 71.88%
[ 15/ 25] 	        MNIST 	 Time 0.02056s 	 Training Accuracy: 97.07% 	 Test Accuracy: 65.62%
[ 15/ 25] 	 FashionMNIST 	 Time 0.02065s 	 Training Accuracy: 85.35% 	 Test Accuracy: 59.38%
[ 16/ 25] 	        MNIST 	 Time 0.02053s 	 Training Accuracy: 98.24% 	 Test Accuracy: 65.62%
[ 16/ 25] 	 FashionMNIST 	 Time 0.03046s 	 Training Accuracy: 89.06% 	 Test Accuracy: 65.62%
[ 17/ 25] 	        MNIST 	 Time 0.02052s 	 Training Accuracy: 98.54% 	 Test Accuracy: 65.62%
[ 17/ 25] 	 FashionMNIST 	 Time 0.02163s 	 Training Accuracy: 90.04% 	 Test Accuracy: 68.75%
[ 18/ 25] 	        MNIST 	 Time 0.02009s 	 Training Accuracy: 99.12% 	 Test Accuracy: 65.62%
[ 18/ 25] 	 FashionMNIST 	 Time 0.02051s 	 Training Accuracy: 91.21% 	 Test Accuracy: 71.88%
[ 19/ 25] 	        MNIST 	 Time 0.02046s 	 Training Accuracy: 99.32% 	 Test Accuracy: 65.62%
[ 19/ 25] 	 FashionMNIST 	 Time 0.02136s 	 Training Accuracy: 88.48% 	 Test Accuracy: 68.75%
[ 20/ 25] 	        MNIST 	 Time 0.02050s 	 Training Accuracy: 99.51% 	 Test Accuracy: 68.75%
[ 20/ 25] 	 FashionMNIST 	 Time 0.02031s 	 Training Accuracy: 92.38% 	 Test Accuracy: 65.62%
[ 21/ 25] 	        MNIST 	 Time 0.02788s 	 Training Accuracy: 97.46% 	 Test Accuracy: 68.75%
[ 21/ 25] 	 FashionMNIST 	 Time 0.02166s 	 Training Accuracy: 89.36% 	 Test Accuracy: 65.62%
[ 22/ 25] 	        MNIST 	 Time 0.02052s 	 Training Accuracy: 96.88% 	 Test Accuracy: 65.62%
[ 22/ 25] 	 FashionMNIST 	 Time 0.02063s 	 Training Accuracy: 85.35% 	 Test Accuracy: 62.50%
[ 23/ 25] 	        MNIST 	 Time 0.02184s 	 Training Accuracy: 99.80% 	 Test Accuracy: 68.75%
[ 23/ 25] 	 FashionMNIST 	 Time 0.02431s 	 Training Accuracy: 88.57% 	 Test Accuracy: 75.00%
[ 24/ 25] 	        MNIST 	 Time 0.03535s 	 Training Accuracy: 99.51% 	 Test Accuracy: 68.75%
[ 24/ 25] 	 FashionMNIST 	 Time 0.01995s 	 Training Accuracy: 89.75% 	 Test Accuracy: 62.50%
[ 25/ 25] 	        MNIST 	 Time 0.03472s 	 Training Accuracy: 99.22% 	 Test Accuracy: 68.75%
[ 25/ 25] 	 FashionMNIST 	 Time 0.05311s 	 Training Accuracy: 88.67% 	 Test Accuracy: 68.75%

[FINAL] 	        MNIST 	 Training Accuracy: 99.41% 	 Test Accuracy: 71.88%
[FINAL] 	 FashionMNIST 	 Training Accuracy: 88.67% 	 Test Accuracy: 68.75%

Appendix

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 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 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 12.5, artifact installation
CUDA driver 12.5
NVIDIA driver 555.42.6

CUDA libraries: 
- CUBLAS: 12.5.3
- CURAND: 10.3.6
- CUFFT: 11.2.3
- CUSOLVER: 11.6.3
- CUSPARSE: 12.5.1
- CUPTI: 2024.2.1 (API 23.0.0)
- NVML: 12.0.0+555.42.6

Julia packages: 
- CUDA: 5.4.3
- CUDA_Driver_jll: 0.9.2+0
- CUDA_Runtime_jll: 0.14.1+0

Toolchain:
- Julia: 1.10.5
- LLVM: 15.0.7

Environment:
- JULIA_CUDA_HARD_MEMORY_LIMIT: 100%

1 device:
  0: NVIDIA A100-PCIE-40GB MIG 1g.5gb (sm_80, 2.232 GiB / 4.750 GiB available)

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