MNIST Classification with SimpleChains

SimpleChains.jl is an excellent framework for training small neural networks. In this tutorial we will demonstrate how to use the same API as Lux.jl to train a model using SimpleChains.jl. We will use the tutorial from SimpleChains.jl as a reference.

Package Imports

using Lux, ADTypes, MLUtils, Optimisers, Zygote, OneHotArrays, Random, Statistics, Printf
using MLDatasets: MNIST
using SimpleChains: SimpleChains

Loading MNIST

function loadmnist(batchsize, train_split)
    # Load MNIST
    N = 2000
    dataset = MNIST(; split=:train)
    imgs = dataset.features[:, :, 1:N]
    labels_raw = dataset.targets[1:N]

    # Process images into (H, W, C, BS) batches
    x_data = Float32.(reshape(imgs, size(imgs, 1), size(imgs, 2), 1, size(imgs, 3)))
    y_data = onehotbatch(labels_raw, 0:9)
    (x_train, y_train), (x_test, y_test) = splitobs((x_data, y_data); at=train_split)

    return (
        # Use DataLoader to automatically minibatch and shuffle the data
        DataLoader(collect.((x_train, y_train)); batchsize, shuffle=true),
        # Don't shuffle the test data
        DataLoader(collect.((x_test, y_test)); batchsize, shuffle=false))
end

loadmnist (generic function with 1 method)

Define the Model

lux_model = Chain(Conv((5, 5), 1 => 6, relu), MaxPool((2, 2)),
    Conv((5, 5), 6 => 16, relu), MaxPool((2, 2)), FlattenLayer(3),
    Chain(Dense(256 => 128, relu), Dense(128 => 84, relu), Dense(84 => 10)))

Chain(
    layer_1 = Conv((5, 5), 1 => 6, relu),  # 156 parameters
    layer_2 = MaxPool((2, 2)),
    layer_3 = Conv((5, 5), 6 => 16, relu),  # 2_416 parameters
    layer_4 = MaxPool((2, 2)),
    layer_5 = Lux.FlattenLayer{Static.StaticInt{3}}(static(3)),
    layer_6 = Chain(
        layer_1 = Dense(256 => 128, relu),  # 32_896 parameters
        layer_2 = Dense(128 => 84, relu),  # 10_836 parameters
        layer_3 = Dense(84 => 10),      # 850 parameters
    ),
)         # Total: 47_154 parameters,
          #        plus 0 states.

We now need to convert the lux_model to SimpleChains.jl. We need to do this by defining the ToSimpleChainsAdaptor and providing the input dimensions.

adaptor = ToSimpleChainsAdaptor((28, 28, 1))
simple_chains_model = adaptor(lux_model)

SimpleChainsLayer(
    Chain(
        layer_1 = Conv((5, 5), 1 => 6, relu),  # 156 parameters
        layer_2 = MaxPool((2, 2)),
        layer_3 = Conv((5, 5), 6 => 16, relu),  # 2_416 parameters
        layer_4 = MaxPool((2, 2)),
        layer_5 = Lux.FlattenLayer{Static.StaticInt{3}}(static(3)),
        layer_6 = Chain(
            layer_1 = Dense(256 => 128, relu),  # 32_896 parameters
            layer_2 = Dense(128 => 84, relu),  # 10_836 parameters
            layer_3 = Dense(84 => 10),  # 850 parameters
        ),
    ),
)         # Total: 47_154 parameters,
          #        plus 0 states.

Helper Functions

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

function accuracy(model, ps, st, dataloader)
    total_correct, total = 0, 0
    st = Lux.testmode(st)
    for (x, y) in dataloader
        target_class = onecold(y)
        predicted_class = onecold(Array(first(model(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)

Define the Training Loop

function train(model; rng=Xoshiro(0), kwargs...)
    train_dataloader, test_dataloader = loadmnist(128, 0.9)
    ps, st = Lux.setup(rng, model)

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

    ### Warmup the model
    x_proto = randn(rng, Float32, 28, 28, 1, 1)
    y_proto = onehotbatch([1], 0:9)
    Training.compute_gradients(AutoZygote(), loss, (x_proto, y_proto), train_state)

    ### Lets train the model
    nepochs = 10
    tr_acc, te_acc = 0.0, 0.0
    for epoch in 1:nepochs
        stime = time()
        for (x, y) in train_dataloader
            gs, _, _, train_state = Training.single_train_step!(
                AutoZygote(), loss, (x, y), train_state)
        end
        ttime = time() - stime

        tr_acc = accuracy(
            model, train_state.parameters, train_state.states, train_dataloader) * 100
        te_acc = accuracy(
            model, train_state.parameters, train_state.states, test_dataloader) * 100

        @printf "[%2d/%2d] \t Time %.2fs \t Training Accuracy: %.2f%% \t Test Accuracy: \
                 %.2f%%\n" epoch nepochs ttime tr_acc te_acc
    end

    return tr_acc, te_acc
end

train (generic function with 1 method)

Finally Training the Model

First we will train the Lux model

tr_acc, te_acc = train(lux_model)

[ 1/10] 	 Time 94.07s 	 Training Accuracy: 22.72% 	 Test Accuracy: 18.50%
[ 2/10] 	 Time 72.76s 	 Training Accuracy: 45.28% 	 Test Accuracy: 44.00%
[ 3/10] 	 Time 70.07s 	 Training Accuracy: 59.00% 	 Test Accuracy: 60.00%
[ 4/10] 	 Time 85.03s 	 Training Accuracy: 68.61% 	 Test Accuracy: 64.00%
[ 5/10] 	 Time 83.05s 	 Training Accuracy: 74.06% 	 Test Accuracy: 71.50%
[ 6/10] 	 Time 78.81s 	 Training Accuracy: 77.67% 	 Test Accuracy: 76.00%
[ 7/10] 	 Time 69.71s 	 Training Accuracy: 80.22% 	 Test Accuracy: 77.50%
[ 8/10] 	 Time 69.87s 	 Training Accuracy: 82.78% 	 Test Accuracy: 81.50%
[ 9/10] 	 Time 70.71s 	 Training Accuracy: 83.83% 	 Test Accuracy: 81.00%
[10/10] 	 Time 68.55s 	 Training Accuracy: 85.56% 	 Test Accuracy: 85.00%

Now we will train the SimpleChains model

train(simple_chains_model)

[ 1/10] 	 Time 18.93s 	 Training Accuracy: 30.00% 	 Test Accuracy: 26.50%
[ 2/10] 	 Time 17.84s 	 Training Accuracy: 46.33% 	 Test Accuracy: 42.00%
[ 3/10] 	 Time 17.79s 	 Training Accuracy: 61.56% 	 Test Accuracy: 54.00%
[ 4/10] 	 Time 17.82s 	 Training Accuracy: 66.67% 	 Test Accuracy: 63.00%
[ 5/10] 	 Time 17.82s 	 Training Accuracy: 75.00% 	 Test Accuracy: 70.50%
[ 6/10] 	 Time 17.84s 	 Training Accuracy: 79.94% 	 Test Accuracy: 73.50%
[ 7/10] 	 Time 17.88s 	 Training Accuracy: 81.83% 	 Test Accuracy: 81.50%
[ 8/10] 	 Time 17.87s 	 Training Accuracy: 84.00% 	 Test Accuracy: 82.00%
[ 9/10] 	 Time 17.83s 	 Training Accuracy: 84.94% 	 Test Accuracy: 82.50%
[10/10] 	 Time 17.84s 	 Training Accuracy: 87.72% 	 Test Accuracy: 85.00%

On my local machine we see a 3-4x speedup when using SimpleChains.jl. The conditions of the server this documentation is being built on is not ideal for CPU benchmarking hence, the speedup may not be as significant and even there might be regressions.

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 1.10.6
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
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  LIBM: libopenlibm
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Threads: 48 default, 0 interactive, 24 GC (on 2 virtual cores)
Environment:
  JULIA_CPU_THREADS = 2
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  LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64
  JULIA_PKG_SERVER = 
  JULIA_NUM_THREADS = 48
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  JULIA_PKG_PRECOMPILE_AUTO = 0
  JULIA_DEBUG = Literate

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