Conflicting dwarf version errors

[smillerc]

When I try to run some of the examples in the documentation (https://juliagpu.github.io/AcceleratedKernels.jl/stable/api/foreachindex/), I get dwarf version errors. For instance, when I run:

using CUDA
import AcceleratedKernels as AK
const x = CuArray(reshape(1:3000, 3, 1000))
const y = similar(x)
AK.foraxes(x, 2) do i
    for j in axes(x, 1)
        @inbounds y[j, i] = 2 * x[j, i] + 1
    end
end

I get this (and a bunch more LLVM errors)

warning: linking module flags 'Dwarf Version': IDs have conflicting values ('i32 4' from globals with 'i32 2' from start)
ERROR: InvalidIRError: compiling MethodInstance for AcceleratedKernels.gpu__forindices_global!(::KernelAbstractions.CompilerMetadata{…}, ::var"#1#2", ::Base.OneTo{…}) resulted in invalid LLVM IR

and more like

Reason: unsupported dynamic function invocation (call to convert)
...
Reason: unsupported call to an unknown function (call to julia.get_pgcstack)
...
Reason: unsupported call to a lazy-initialized function (call to jl_gc_run_pending_finalizers)
...
Reason: unsupported call to an external C function (call to jl_gc_have_pending_finalizers)
...

Here's my system info

julia> versioninfo()
Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 32 × Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, cascadelake)
Threads: 32 default, 0 interactive, 16 GC (on 32 virtual cores)

julia> CUDA.versioninfo()
CUDA runtime 12.6, artifact installation
CUDA driver 12.6
NVIDIA driver 550.120.0

CUDA libraries: 
- CUBLAS: 12.6.4
- CURAND: 10.3.7
- CUFFT: 11.3.0
- CUSOLVER: 11.7.1
- CUSPARSE: 12.5.4
- CUPTI: 2024.3.2 (API 24.0.0)
- NVML: 12.0.0+550.120

Julia packages: 
- CUDA: 5.5.2
- CUDA_Driver_jll: 0.10.4+0
- CUDA_Runtime_jll: 0.15.5+0

Toolchain:
- Julia: 1.11.2
- LLVM: 16.0.6

2 devices:
  0: NVIDIA RTX A4000 (sm_86, 15.525 GiB / 15.992 GiB available)
  1: NVIDIA RTX A4000 (sm_86, 13.911 GiB / 15.992 GiB available)

[anicusan]

I retried the code and got an error indeed, though seemingly different to yours. However, when put inside a function, it seems to work:

using CUDA
import AcceleratedKernels as AK

function addfunc()
    x = CuArray(reshape(1:3000, 3, 1000))
    y = similar(x)
    AK.foraxes(x, 2) do i
        for j in axes(x, 1)
            @inbounds y[j, i] = 2 * x[j, i] + 1
        end
    end
    y
end

addfunc()

Would that work on your end?

If not, it may be a CUDA configuration error - would you be able to run some higher-level, pure-CUDA.jl code, like:

using CUDA
x = CuArray(1:1000)
y = CuArray(1:1000)
z = x + y