amd gpu give different results when nested loop is used

Hi, I noticed that the following script produces different results depending on the backend. On my machine, the output is:

```julia
cpu: [18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0;;;]
cuda: [18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0;;;]
amd: [6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0;;;]
```

Is there a mistake in the kernel function?

```julia
using CUDA
using AMDGPU
using KernelAbstractions

function compute_tensors(tensor, kernel_fun, Nx, Ny, Nz)
kernel! = kernel_fun(get_backend(tensor), 512)
kernel!(tensor, Nx, Ny, Nz; ndrange=size(tensor))
KernelAbstractions.synchronize(get_backend(tensor))
return nothing
end

@kernel function kernel_xx!(tensor, Nx::Int64, Ny::Int64, Nz::Int64)
i, j, k = @index(Global, NTuple)
sum = zero(eltype(tensor))
for p in (-Nx):Nx, q in (-Ny):Ny
sum += 2.0
end
@inbounds tensor[i, j, k] = sum
end

nx, ny, nz = 10, 1, 1
Nx, Ny, Nz = 1, 1, 1
tensor = zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("cpu:", tensor)

tensor = CUDA.zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("cuda:", tensor)

tensor = AMDGPU.zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("amd:", tensor)
```

Start by running the supplied Julia reproducer with the CPU, CUDA, and AMDGPU backends, then inspect how KernelAbstractions handles the nested loop in kernel_xx!. There are no files or tests named in the issue; done means determining why AMDGPU produces 6.0 instead of 18.0 and adding a regression test or correction for consistent results.

Written by the indexing model from the issue text.