Using Custom Structs in Kernels

Kernels often need more than plain arrays — you may want to pass your own struct bundling several arrays and parameters. The difficulty is that a ROCArray lives in host memory management, but inside a kernel it must appear as a device array (ROCDeviceArray). AMDGPU.jl handles this conversion automatically for arguments to @roc, using Adapt.jl to reach inside your struct and convert its array fields.

Declare how your struct should be adapted with Adapt.@adapt_structure, then pass an instance to a kernel like any other argument:

julia

using AMDGPU, Adapt

struct Model{A}
    weights::A
    scale::Float32
end
Adapt.@adapt_structure Model

function apply!(out, m)
    i = workitemIdx().x + (workgroupIdx().x - 1) * workgroupDim().x
    if i <= length(out)
        out[i] = m.weights[i] * m.scale
    end
    return
end

w = AMDGPU.ones(Float32, 16)
m = Model(w, 3f0)
out = AMDGPU.zeros(Float32, 16)

@roc groupsize=16 apply!(out, m)   # out .== 3.0

@adapt_structure generates the rule that rebuilds Model with each field adapted. When @roc launches the kernel it calls AMDGPU.rocconvert on m, which turns the weights::ROCArray field into a ROCDeviceArray while leaving scale untouched. Inside the kernel m.weights is therefore a device array you can index directly.

The type parameter A matters: it lets the struct hold a ROCArray on the host and a ROCDeviceArray on the device without you writing two types. Keep the remaining fields isbits (numbers, tuples, other adapted structs) so the whole struct can be passed to the GPU.

Using Custom Structs in Kernels ​

Using Custom Structs in Kernels