As functions are compiled as/when used in Julia for the given argument types (for C++ people: kind of like everything being a template argument by default), we can use custom structs and functions defined outside AcceleratedKernels.jl, which will be inlined and optimised as if they were hardcoded within the library. Normal Julia functions and code can be used, without special annotations like __device__, KOKKOS_LAMBDA or wrapping them in classes with overloaded operator().

As an example, let's compute the coordinate-wise minima of some points:

import AcceleratedKernels as AK
using Metal

struct Point
    x::Float32
    y::Float32
end

function compute_minima(points)
    AK.mapreduce(
        point -> (point.x, point.y),                    # Extract fields into tuple
        (a, b) -> (min(a[1], b[1]), min(a[2], b[2])),   # Keep each coordinate's minimum
        points,
        init=(typemax(Float32), typemax(Float32)),
    )
end

# Example output for Random.seed!(0):
#   minima = compute_minima(points) = (1.7966056f-5, 1.7797855f-6)
points = MtlArray([Point(rand(), rand()) for _ in 1:100_000])
@show minima = compute_minima(points)

Note that we did not have to explicitly type the function arguments in compute_minima - the types would be figured out when calling the function and compiled for the right backend automatically, e.g. CPU, oneAPI, ROCm, CUDA, Metal. Also, we used the standard Julia function min; it was not special-cased anywhere, it's just KernelAbstractions.jl inlining and compiling normal code, even from within the Julia.Base standard library.