Broadcasting over random static array errors on Julia 1.11

[jgreener64]

With CUDA v5.5.2 and Julia 1.10.3 this works:

using CUDA, StaticArrays, Random
f(x, rng) = SVector(rand(rng), rand(rng))
f.(CuArray(ones(5)), Random.GLOBAL_RNG)

5-element CuArray{SVector{2, Float64}, 1, CUDA.DeviceMemory}:
 [0.36764910175195165, 0.4794875174237414]
 [0.010348667621972396, 0.6326148712929516]
 [0.5735762055272076, 0.42568438586826485]
 [0.4907457866677476, 0.19396317615182368]
 [0.02563223451906871, 0.553653790980341]

However with Julia 1.11.0 it errors:

ERROR: InvalidIRError: compiling MethodInstance for (::GPUArrays.var"#34#36")(::CUDA.CuKernelContext, ::CuDeviceVector{…}, ::Base.Broadcast.Broadcasted{…}, ::Int64) resulted in invalid LLVM IR
Reason: unsupported call to an unknown function (call to julia.get_pgcstack)
Stacktrace:
 [1] _broadcast_getindex_evalf
   @ ./broadcast.jl:673
 [2] _broadcast_getindex
   @ ./broadcast.jl:646
 [3] getindex
   @ ./broadcast.jl:605
 [4] #34
   @ ~/.julia/packages/GPUArrays/qt4ax/src/host/broadcast.jl:59
Hint: catch this exception as `err` and call `code_typed(err; interactive = true)` to introspect the erronous code with Cthulhu.jl
Stacktrace:
  [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/validation.jl:147
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:382 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/NRdsv/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:381 [inlined]
  [5] 
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/utils.jl:108
  [6] emit_llvm
    @ ~/.julia/packages/GPUCompiler/2CW9L/src/utils.jl:106 [inlined]
  [7] 
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:100
  [8] codegen
    @ ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:82 [inlined]
  [9] compile(target::Symbol, job::GPUCompiler.CompilerJob; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:79
 [10] compile
    @ ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:74 [inlined]
 [11] #1145
    @ ~/.julia/dev/CUDA/src/compiler/compilation.jl:250 [inlined]
 [12] JuliaContext(f::CUDA.var"#1145#1148"{GPUCompiler.CompilerJob{…}}; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:34
 [13] JuliaContext(f::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/driver.jl:25
 [14] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/dev/CUDA/src/compiler/compilation.jl:249
 [15] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/execution.jl:237
 [16] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/2CW9L/src/execution.jl:151
 [17] macro expansion
    @ ~/.julia/dev/CUDA/src/compiler/execution.jl:380 [inlined]
 [18] macro expansion
    @ ./lock.jl:273 [inlined]
 [19] cufunction(f::GPUArrays.var"#34#36", tt::Type{Tuple{…}}; kwargs::@Kwargs{})
    @ CUDA ~/.julia/dev/CUDA/src/compiler/execution.jl:375
 [20] cufunction
    @ ~/.julia/dev/CUDA/src/compiler/execution.jl:372 [inlined]
 [21] macro expansion
    @ ~/.julia/dev/CUDA/src/compiler/execution.jl:112 [inlined]
 [22] #launch_heuristic#1200
    @ ~/.julia/dev/CUDA/src/gpuarrays.jl:17 [inlined]
 [23] launch_heuristic
    @ ~/.julia/dev/CUDA/src/gpuarrays.jl:15 [inlined]
 [24] _copyto!
    @ ~/.julia/packages/GPUArrays/qt4ax/src/host/broadcast.jl:78 [inlined]
 [25] copyto!
    @ ~/.julia/packages/GPUArrays/qt4ax/src/host/broadcast.jl:44 [inlined]
 [26] copy
    @ ~/.julia/packages/GPUArrays/qt4ax/src/host/broadcast.jl:29 [inlined]
 [27] materialize(bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{…}, Nothing, typeof(f), Tuple{…}})
    @ Base.Broadcast ./broadcast.jl:867
 [28] top-level scope
    @ REPL[3]:1
Some type information was truncated. Use `show(err)` to see complete types.

I know there are some complexities with broadcasting random numbers (#1480) but I wonder if this is an intended regression.

My CUDA.versioninfo() is:

CUDA runtime 12.6, artifact installation
CUDA driver 12.6
NVIDIA driver 535.183.1

CUDA libraries: 
- CUBLAS: 12.6.3
- 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+535.183.1

Julia packages: 
- CUDA: 5.5.2
- CUDA_Driver_jll: 0.10.3+0
- CUDA_Runtime_jll: 0.15.3+0

Toolchain:
- Julia: 1.11.0
- LLVM: 16.0.6

2 devices:
  0: NVIDIA RTX A6000 (sm_86, 46.941 GiB / 47.988 GiB available)
  1: NVIDIA RTX A6000 (sm_86, 45.888 GiB / 47.988 GiB available)

[maleadt]

Random.GLOBAL_RNG

This is wrong. Use Random.default_rng().

[jgreener64]

In this case I get errors with f.(CuArray(ones(5)), Random.default_rng()) and f.(CuArray(ones(5)), (Random.default_rng(),)) on both Julia 1.10 and 1.11.