Profiling Lux Training Loops

Only for Reactant

This tutorial is applicable iff you are using Reactant.jl (AutoEnzyme with ReactantDevice) for training.

To profile the training loop, wrap the training loop with Reactant.with_profiler and pass the path to the directory where the traces should be saved. Note that this will have some overhead and hence should be used only for debugging purposes.

A simple example is shown below:

using Reactant, Lux, Random, MLUtils, Optimisers

dev = reactant_device()

x_data = rand(Float32, 32, 1024)
y_data = x_data .^ 2 .- 1

dl = DataLoader((x_data, y_data); batchsize=32, shuffle=true) |> dev;

model = Chain(Dense(32 => 64, relu), Dense(64 => 32))
ps, st = Lux.setup(Random.default_rng(), model) |> dev;

Reactant.with_profiler("/tmp/traces/lux_training/") do
    train_state = Training.TrainState(model, ps, st, Adam(0.001))
    for epoch in 1:10
        for (x, y) in dl
            _, loss, _, train_state = Training.single_train_step!(
                AutoEnzyme(), MSELoss(), (x, y), train_state; return_gradients=Val(false)
            )
        end
    end
end

Once the run is completed, you can use xprof to analyze the traces. An example of the output is shown below: