Performance Tips

Performance Tips

All the usual Julia performance tips apply. As always profiling your code is generally a useful way of finding bottlenecks. Below follow some Flux specific tips/reminders.

Don't use more precision than you need.

Flux works great with all kinds of number types. But often you do not need to be working with say Float64 (let alone BigFloat). Switching to Float32 can give you a significant speed up, not because the operations are faster, but because the memory usage is halved. Which means allocations occur much faster. And you use less memory.

Make sure your custom activation functions preserve the type of their inputs

Not only should your activation functions be type-stable, they should also preserve the type of their inputs.

A very artificial example using an activatioon function like

    my_tanh(x) = Float64(tanh(x))

will result in performance on Float32 input orders of magnitude slower than the normal tanh would, because it results in having to use slow mixed type multiplication in the dense layers.

Which means if you change your data say from Float64 to Float32 (which should give a speedup: see above), you will see a large slow-down

This can occur sneakily, because you can cause type-promotion by interacting with a numeric literals. E.g. the following will have run into the same problem as above:

    leaky_tanh(x) = 0.01x + tanh(x)

While one could change your activation function (e.g. to use 0.01f0x) to avoid this when ever your inputs change, the idiomatic (and safe way) is to use oftype.

    leaky_tanh(x) = oftype(x/1, 0.01) + tanh(x)

Evaluate batches as Matrices of features, rather than sequences of Vector features

While it can sometimes be tempting to process your observations (feature vectors) one at a time e.g.

function loss_total(xs::AbstractVector{<:Vector}, ys::AbstractVector{<:Vector})
    sum(zip(xs, ys)) do (x, y_target)
        y_pred = model(x) #  evaluate the model
        return loss(y_pred, y_target)

It is much faster to concatenate them into a matrix, as this will hit BLAS matrix-matrix multiplication, which is much faster than the equivalent sequence of matrix-vector multiplications. Even though this means allocating new memory to store them contiguously.

x_batch = reduce(hcat, xs)
y_batch = reduce(hcat, ys)
function loss_total(x_batch::Matrix, y_batch::Matrix)
    y_preds = model(x_batch)
    sum(loss.(y_preds, y_batch))

When doing this kind of concatenation use reduce(hcat, xs) rather than hcat(xs...). This will avoid the splatting penality, and will hit the optimised reduce method.