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 68.75160s 	 Training Accuracy: 23.24% 	 Test Accuracy: 25.00%
[  1/ 25] 	 FashionMNIST 	 Time 0.02731s 	 Training Accuracy: 30.76% 	 Test Accuracy: 18.75%
[  2/ 25] 	        MNIST 	 Time 0.02331s 	 Training Accuracy: 48.34% 	 Test Accuracy: 34.38%
[  2/ 25] 	 FashionMNIST 	 Time 0.02395s 	 Training Accuracy: 52.54% 	 Test Accuracy: 43.75%
[  3/ 25] 	        MNIST 	 Time 0.02325s 	 Training Accuracy: 59.08% 	 Test Accuracy: 56.25%
[  3/ 25] 	 FashionMNIST 	 Time 0.02429s 	 Training Accuracy: 54.69% 	 Test Accuracy: 46.88%
[  4/ 25] 	        MNIST 	 Time 0.02403s 	 Training Accuracy: 67.48% 	 Test Accuracy: 53.12%
[  4/ 25] 	 FashionMNIST 	 Time 0.02461s 	 Training Accuracy: 64.36% 	 Test Accuracy: 43.75%
[  5/ 25] 	        MNIST 	 Time 0.02330s 	 Training Accuracy: 74.12% 	 Test Accuracy: 65.62%
[  5/ 25] 	 FashionMNIST 	 Time 0.02051s 	 Training Accuracy: 70.12% 	 Test Accuracy: 62.50%
[  6/ 25] 	        MNIST 	 Time 0.02121s 	 Training Accuracy: 78.22% 	 Test Accuracy: 56.25%
[  6/ 25] 	 FashionMNIST 	 Time 0.02064s 	 Training Accuracy: 74.22% 	 Test Accuracy: 71.88%
[  7/ 25] 	        MNIST 	 Time 0.02084s 	 Training Accuracy: 82.13% 	 Test Accuracy: 65.62%
[  7/ 25] 	 FashionMNIST 	 Time 0.02181s 	 Training Accuracy: 78.42% 	 Test Accuracy: 62.50%
[  8/ 25] 	        MNIST 	 Time 0.02076s 	 Training Accuracy: 86.62% 	 Test Accuracy: 65.62%
[  8/ 25] 	 FashionMNIST 	 Time 0.02141s 	 Training Accuracy: 81.05% 	 Test Accuracy: 62.50%
[  9/ 25] 	        MNIST 	 Time 0.02179s 	 Training Accuracy: 86.04% 	 Test Accuracy: 65.62%
[  9/ 25] 	 FashionMNIST 	 Time 0.03888s 	 Training Accuracy: 81.54% 	 Test Accuracy: 62.50%
[ 10/ 25] 	        MNIST 	 Time 0.02146s 	 Training Accuracy: 90.04% 	 Test Accuracy: 68.75%
[ 10/ 25] 	 FashionMNIST 	 Time 0.02209s 	 Training Accuracy: 81.45% 	 Test Accuracy: 56.25%
[ 11/ 25] 	        MNIST 	 Time 0.02179s 	 Training Accuracy: 93.07% 	 Test Accuracy: 68.75%
[ 11/ 25] 	 FashionMNIST 	 Time 0.02217s 	 Training Accuracy: 82.62% 	 Test Accuracy: 59.38%
[ 12/ 25] 	        MNIST 	 Time 0.02153s 	 Training Accuracy: 92.97% 	 Test Accuracy: 65.62%
[ 12/ 25] 	 FashionMNIST 	 Time 0.02145s 	 Training Accuracy: 82.62% 	 Test Accuracy: 71.88%
[ 13/ 25] 	        MNIST 	 Time 0.02114s 	 Training Accuracy: 94.73% 	 Test Accuracy: 62.50%
[ 13/ 25] 	 FashionMNIST 	 Time 0.02240s 	 Training Accuracy: 83.01% 	 Test Accuracy: 62.50%
[ 14/ 25] 	        MNIST 	 Time 0.02893s 	 Training Accuracy: 96.19% 	 Test Accuracy: 65.62%
[ 14/ 25] 	 FashionMNIST 	 Time 0.02083s 	 Training Accuracy: 87.11% 	 Test Accuracy: 68.75%
[ 15/ 25] 	        MNIST 	 Time 0.02143s 	 Training Accuracy: 94.92% 	 Test Accuracy: 68.75%
[ 15/ 25] 	 FashionMNIST 	 Time 0.02212s 	 Training Accuracy: 83.20% 	 Test Accuracy: 62.50%
[ 16/ 25] 	        MNIST 	 Time 0.02130s 	 Training Accuracy: 97.27% 	 Test Accuracy: 62.50%
[ 16/ 25] 	 FashionMNIST 	 Time 0.02078s 	 Training Accuracy: 84.77% 	 Test Accuracy: 65.62%
[ 17/ 25] 	        MNIST 	 Time 0.02120s 	 Training Accuracy: 98.14% 	 Test Accuracy: 62.50%
[ 17/ 25] 	 FashionMNIST 	 Time 0.02212s 	 Training Accuracy: 87.89% 	 Test Accuracy: 65.62%
[ 18/ 25] 	        MNIST 	 Time 0.02139s 	 Training Accuracy: 98.24% 	 Test Accuracy: 62.50%
[ 18/ 25] 	 FashionMNIST 	 Time 0.02761s 	 Training Accuracy: 87.70% 	 Test Accuracy: 65.62%
[ 19/ 25] 	        MNIST 	 Time 0.02073s 	 Training Accuracy: 98.83% 	 Test Accuracy: 62.50%
[ 19/ 25] 	 FashionMNIST 	 Time 0.02146s 	 Training Accuracy: 87.99% 	 Test Accuracy: 62.50%
[ 20/ 25] 	        MNIST 	 Time 0.02160s 	 Training Accuracy: 99.22% 	 Test Accuracy: 62.50%
[ 20/ 25] 	 FashionMNIST 	 Time 0.02161s 	 Training Accuracy: 89.26% 	 Test Accuracy: 71.88%
[ 21/ 25] 	        MNIST 	 Time 0.02276s 	 Training Accuracy: 99.12% 	 Test Accuracy: 62.50%
[ 21/ 25] 	 FashionMNIST 	 Time 0.02150s 	 Training Accuracy: 88.09% 	 Test Accuracy: 59.38%
[ 22/ 25] 	        MNIST 	 Time 0.02120s 	 Training Accuracy: 97.75% 	 Test Accuracy: 62.50%
[ 22/ 25] 	 FashionMNIST 	 Time 0.02150s 	 Training Accuracy: 90.43% 	 Test Accuracy: 68.75%
[ 23/ 25] 	        MNIST 	 Time 0.02125s 	 Training Accuracy: 99.32% 	 Test Accuracy: 62.50%
[ 23/ 25] 	 FashionMNIST 	 Time 0.02158s 	 Training Accuracy: 88.87% 	 Test Accuracy: 65.62%
[ 24/ 25] 	        MNIST 	 Time 0.02130s 	 Training Accuracy: 99.51% 	 Test Accuracy: 62.50%
[ 24/ 25] 	 FashionMNIST 	 Time 0.02071s 	 Training Accuracy: 90.04% 	 Test Accuracy: 62.50%
[ 25/ 25] 	        MNIST 	 Time 0.02275s 	 Training Accuracy: 99.90% 	 Test Accuracy: 62.50%
[ 25/ 25] 	 FashionMNIST 	 Time 0.02149s 	 Training Accuracy: 91.11% 	 Test Accuracy: 65.62%

[FINAL] 	        MNIST 	 Training Accuracy: 99.80% 	 Test Accuracy: 62.50%
[FINAL] 	 FashionMNIST 	 Training Accuracy: 91.11% 	 Test Accuracy: 65.62%

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, 2.170 GiB / 4.750 GiB available)

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