PrecompileTools.jl with CUDA.jl causes kernels to fail to run on 1.11

[gbaraldi]

julia> using CUDA

julia> using Reactant

julia> function square!(x)
           CUDA.@cuda blocks = 1 threads = length(x) square_kernel!(x)
           return nothing
       end
square! (generic function with 1 method)

julia> function square_kernel!(x)
                           i = CUDA.threadIdx().x
                           x[i] *= x[i]
                           return nothing
                       end
square_kernel! (generic function with 1 method)

julia> y2 = CuArray([2.0])
1-element CuArray{Float64, 1, CUDA.DeviceMemory}:
 2.0

julia> square!(y2)
ERROR: InvalidIRError: compiling MethodInstance for square_kernel!(::CuDeviceVector{Float64, 1}) resulted in invalid LLVM IR
Reason: unsupported dynamic function invocation (call to throw_boundserror() @ CUDA ~/.julia/packages/CUDA/1kIOw/src/device/quirks.jl:51)
Stacktrace:
 [1] #throw_boundserror
   @ ~/.julia/packages/CUDA/1kIOw/src/device/quirks.jl:53
 [2] checkbounds
   @ ./abstractarray.jl:699
 [3] #arrayref
   @ ~/.julia/packages/CUDA/1kIOw/src/device/array.jl:81
 [4] getindex
   @ ~/.julia/packages/CUDA/1kIOw/src/device/array.jl:164
 [5] square_kernel!
   @ ./REPL[4]:3
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/Nxf8r/src/validation.jl:167
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:382 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/6KVfH/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:381 [inlined]
  [5] emit_llvm(job::GPUCompiler.CompilerJob; toplevel::Bool, libraries::Bool, optimize::Bool, cleanup::Bool, validate::Bool, only_entry::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/utils.jl:108
  [6] 
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:100
  [7] codegen(output::Symbol, job::GPUCompiler.CompilerJob)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:82
  [8] compile(target::Symbol, job::GPUCompiler.CompilerJob; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:79
  [9] compile
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:74 [inlined]
 [10] #1147
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/compilation.jl:250 [inlined]
 [11] JuliaContext(f::CUDA.var"#1147#1150"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}}; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:34
 [12] JuliaContext(f::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:25
 [13] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/1kIOw/src/compiler/compilation.jl:249
 [14] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/execution.jl:237
 [15] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/execution.jl:151
 [16] macro expansion
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:380 [inlined]
 [17] macro expansion
    @ ./lock.jl:273 [inlined]
 [18] cufunction(f::typeof(square_kernel!), tt::Type{Tuple{CuDeviceVector{Float64, 1}}}; kwargs::@Kwargs{})
    @ CUDA ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:375
 [19] cufunction
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:372 [inlined]
 [20] macro expansion
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:112 [inlined]
 [21] square!(x::CuArray{Float64, 1, CUDA.DeviceMemory})
    @ Main ./REPL[3]:2
 [22] top-level scope
    @ REPL[6]:1
Some type information was truncated. Use `show(err)` to see complete types.

Reactant.jl has a precompileTools.jl workload that might cause issues https://github.com/EnzymeAD/Reactant.jl/blob/23a57dfc8cd56a4f12b79a6fafc72446a53ec058/ext/ReactantCUDAExt.jl#L871, but I suspect what's happening is that this is relying on an abstract type not having too many types defined on it, and adding a subtype in reactant causes things to blow up

[gbaraldi]

#2633 this is related ;)