adding atomic support with atomix by leios · Pull Request #299 · JuliaGPU/KernelAbstractions.jl

leios · 2022-05-25T09:15:12Z

After some discussions on #282, we decided to use Atomix for atomic support in KA.

A few quick questions:

Because Base (and CUDA) both have an @atomic macro, we need to specify that we are using the Atomix.@atomic macro in code that needs atomic operations. Should we overdub any @atomic macros in KA to specifically use Atomix?
Should we add in the tests from Atomic attempts #282?
What about atomic primitives like atomic_add!(...), and atomic_sub!(...) from Atomic attempts #282? These come from either CUDA or Core.Intrinsics. Maybe it's a good idea to use Atomix on top of Atomic attempts #282? I don't know how many people will use the primitives over the macro, to be honest.

Note, this should not be merged until JuliaRegistries/General#61002 is automerged.

tkf

KernelAbstractions.jl doesn't have to depend on UnsafeAtomicsLLVM.jl (and LLVM.jl)

Co-authored-by: Takafumi Arakaki <takafumi.a@gmail.com>

vchuravy

Looks great!

Probably needs docs as well as AMDGPU support.

Co-authored-by: Valentin Churavy <vchuravy@users.noreply.github.com>

pxl-th · 2022-05-31T09:19:51Z

Hi!

As for 3. What about atomic primitives like atomic_add!(...), I'd like to say that I have several kernels that use atomic_add! specifically because it returns the old value after adding. I'm not sure if this is achievable with macros.

Also I'm curious if it will support things like:

@atomic max(x[i], v)

Co-authored-by: Takafumi Arakaki <takafumi.a@gmail.com>

leios · 2022-05-31T19:02:18Z

I don't mind reworking this PR and #282 so we get both the macro and better ordering support from Atomix and also the atomic_... functions from either Core.Intrinsics or CUDA. I have a branch locally that basically does this and it works fine for my purposes.

I figure most people will want to use the macro, but some people will prefer the atomic_... functions, so why not just do both?

vchuravy · 2022-05-31T19:05:39Z

Let's merge this for now and then you can open a second PR?

leios · 2022-05-31T19:06:33Z

This one is not ready to be merged

vchuravy · 2022-05-31T19:16:26Z

Oops. I got excited that it passed tests :)

leios · 2022-05-31T19:28:08Z

It was missing docs and tests, at least... I will add them when I get the chance. To be fair, atomix should have all the necessary tests, I just wanted to double check here. Documentation does not need to be long, but having a section for atomics with an example would go a long way.

leios · 2022-05-31T19:29:39Z

I was just waiting to add docs until we settled the atomic "primitive" discussion.

claforte · 2022-05-31T20:24:18Z

Thanks a lot @leios ! @pxl-th and a few others in my team are very much looking forward to this PR being merged for our Instant NeRF (3D reconstruction) Julia implementation. If you'd like a sneak preview, let me know, I can invite you to our private Discord and Github. :-)

pxl-th · 2022-06-04T08:09:15Z

I've tried this PR and it looks like on CPU it only supports integer types.
While on GPU I get unsupported dynamic function invocation (call to modify!) for any type.
I'm on Julia 1.8.0-rc1, but the same errors are present on 1.7.2.

Error

ERROR: LoadError: InvalidIRError: compiling kernel #gpu_splat!(KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.StaticSize{(512,)}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}) resulted in invalid LLVM IR
Reason: unsupported dynamic function invocation (call to modify!)
Stacktrace:
 [1] modify!
   @ ~/.julia/packages/Atomix/F9VIX/src/core.jl:33
 [2] macro expansion
   @ ~/code/a.jl:28
 [3] gpu_splat!
   @ ~/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80
 [4] gpu_splat!
   @ ./none:0
Hint: catch this exception as `err` and call `code_typed(err; interactive = true)` to introspect the erronous code
Stacktrace:
  [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(gpu_splat!), Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.StaticSize{(512,)}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}}}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/validation.jl:139
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:409 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/LDL7n/src/TimerOutput.jl:252 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:407 [inlined]
  [5] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/utils.jl:64
  [6] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:354
  [7] #224
    @ ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:347 [inlined]
  [8] JuliaContext(f::CUDA.var"#224#225"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(gpu_splat!), Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.StaticSize{(512,)}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:74
  [9] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:346
 [10] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/cache.jl:90
 [11] cufunction(f::typeof(gpu_splat!), tt::Type{Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.StaticSize{(512,)}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:299
 [12] cufunction(f::typeof(gpu_splat!), tt::Type{Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.StaticSize{(512,)}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}})
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:293
 [13] macro expansion
    @ ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:102 [inlined]
 [14] (::KernelAbstractions.Kernel{CUDADevice, KernelAbstractions.NDIteration.StaticSize{(512,)}, KernelAbstractions.NDIteration.DynamicSize, typeof(gpu_splat!)})(::CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, ::Vararg{Any}; ndrange::Int64, dependencies::CUDAKernels.CudaEvent, workgroupsize::Nothing, progress::Function)
    @ CUDAKernels ~/.julia/packages/CUDAKernels/4VLF4/src/CUDAKernels.jl:272
 [15] main()
    @ Main ~/code/a.jl:40
 [16] top-level scope
    @ ~/code/a.jl:42
in expression starting at /home/pxl-th/code/a.jl:42

MWE:

Code

using CUDA
using CUDAKernels
using KernelAbstractions
using KernelAbstractions: @atomic

CUDA.allowscalar(false)

n_threads(::CPU) = Threads.nthreads()
n_threads(::CUDADevice) = 512

Base.rand(::CPU, T, shape) = rand(T, shape)
Base.rand(::CUDADevice, T, shape) = CUDA.rand(T, shape)

to_device(::CPU, x) = copy(x)
to_device(::CUDADevice, x) = CuArray(x)

@kernel function splat!(grid, @Const(indices), @Const(mlp_out))
    i = @index(Global)
    idx = indices[i]
    @atomic max(grid[idx], mlp_out[i])
end

function main()
    #device = CPU()
    device = CUDADevice()

    n = 16
    indices = to_device(device, UInt32.(collect(1:n)))
    mlp_out = rand(device, Int64, n) # errors on CPU with Float32
    grid = rand(device, Int64, n) # errors on CPU with Float32

    wait(splat!(device, n_threads(device), n)(grid, indices, mlp_out))
end
main()

leios · 2022-06-04T09:32:22Z

I was unable to replicate this error by running the provided code with 1.7.1 and 1.8.0-beta3 (just pulled from git). What OS are you using? Also, could you show the outputs of ] st?

pxl-th · 2022-06-04T11:17:11Z

I'm on Ubuntu 22.04
CPU: AMD Ryzen 7 5800HS
GPU: NVIDIA GeForce RTX 3060

]st:

(@v1.8) pkg> st
Status `~/.julia/environments/v1.8/Project.toml`
  [a9b6321e] Atomix v0.1.0
  [052768ef] CUDA v3.10.1
  [72cfdca4] CUDAKernels v0.4.1
  [5789e2e9] FileIO v1.14.0
  [a09fc81d] ImageCore v0.9.3
  [82e4d734] ImageIO v0.6.5
  [02fcd773] ImageTransformations v0.9.4
  [b835a17e] JpegTurbo v0.1.1
  [63c18a36] KernelAbstractions v0.8.1 `https://github.com/JuliaGPU/KernelAbstractions.jl.git#master`

pxl-th · 2022-06-04T11:20:45Z

I've just updated MWE code, before I included code that does not error :)
You can also change grid & mlp_out eltypes to Float32 and to see that it does not work with them.

leios · 2022-06-04T12:25:24Z

Right, I see the comments now, sorry!

try ]add CUDAKernels#master?

pxl-th · 2022-06-04T15:16:21Z

Yes, that works, thanks!

Although there is another issue, which is not critical for me, but might be worth mentioning:

MWE:

Code

using CUDA
using CUDAKernels
using KernelAbstractions
using KernelAbstractions: @atomic

CUDA.allowscalar(false)

const NERF_STEPS = UInt32(1024)
const MIN_CONE_STEPSIZE = √3f0 / NERF_STEPS

n_threads(::CPU) = Threads.nthreads()
n_threads(::CUDADevice) = 512

Base.rand(::CPU, T, shape) = rand(T, shape)
Base.rand(::CUDADevice, T, shape) = CUDA.rand(T, shape)

Base.zeros(::CPU, T, shape) = zeros(T, shape)
Base.zeros(::CUDADevice, T, shape) = CUDA.zeros(T, shape)

to_device(::CPU, x) = copy(x)
to_device(::CUDADevice, x) = CuArray(x)

@inline density_activation(x) = exp(x)

@kernel function splat!(grid, @Const(indices), @Const(mlp_out))
    i = @index(Global)
    idx = indices[i]
    old, new = @atomic max(grid[idx], mlp_out[i])
    @atomic grid[idx] = old
end

function main()
    # device = CPU()
    device = CUDADevice()

    n = 16
    indices = to_device(device, UInt32.(collect(1:n)))
    mlp_out = rand(device, Int64, n)
    grid = zeros(device, Int64, n)

    wait(splat!(device, n_threads(device), n)(grid, indices, mlp_out))
end
main()

Error

ERROR: LoadError: LLVM error: Cannot select: 0x77adc60: ch = AtomicStore<(store seq_cst (s64) into %ir.41, addrspace 1)> 0x4926e08:1, 0x6f629c8, 0x4926e08, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/base.jl:40 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:245 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:201 @[ /home/pxl-th/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/internal.jl:11 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:14 @[ /home/pxl-th/code/a.jl:29 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
  0x6f629c8: i64 = add 0x6f626f0, Constant:i64<-8>, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
    0x6f626f0: i64 = add 0x6f62ea8, 0x7195f28, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
      0x6f62ea8: i64,ch = CopyFromReg 0x65cc278, Register:i64 %0, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
        0x4926cd0: i64 = Register %0
      0x7195f28: i64 = shl nuw nsw 0x77ae5b8, Constant:i32<3>, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
        0x77ae5b8: i64 = AssertZext 0x71ce208, ValueType:ch:i32, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
          0x71ce208: i64,ch = CopyFromReg 0x65cc278, Register:i64 %9, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
            0x66aca88: i64 = Register %9
        0x66ac0c8: i32 = Constant<3>
    0x4926720: i64 = Constant<-8>
  0x4926e08: i64,ch = AtomicLoadMax<(load store seq_cst (s64) on %ir.39, addrspace 1)> 0x7195c50:1, 0x6f629c8, 0x7195c50, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/base.jl:40 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:270 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:270 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:374 @[ /home/pxl-th/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/internal.jl:18 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:33 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ] ]
    0x6f629c8: i64 = add 0x6f626f0, Constant:i64<-8>, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
      0x6f626f0: i64 = add 0x6f62ea8, 0x7195f28, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
        0x6f62ea8: i64,ch = CopyFromReg 0x65cc278, Register:i64 %0, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ]
          0x4926cd0: i64 = Register %0
        0x7195f28: i64 = shl nuw nsw 0x77ae5b8, Constant:i32<3>, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
          0x77ae5b8: i64 = AssertZext 0x71ce208, ValueType:ch:i32, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
            0x71ce208: i64,ch = CopyFromReg 0x65cc278, Register:i64 %9, int.jl:88 @[ abstractarray.jl:1189 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:80 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/references.jl:99 @[ /home/pxl-th/.julia/packages/Atomix/F9VIX/src/core.jl:30 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
              0x66aca88: i64 = Register %9
          0x66ac0c8: i32 = Constant<3>
      0x4926720: i64 = Constant<-8>
    0x7195c50: i64,ch = llvm.nvvm.ldg.global.i<(load (s64) from %ir.34, addrspace 1)> 0x65cc278, TargetConstant:i64<5104>, 0x6f62b68, Constant:i32<8>, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/base.jl:40 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:120 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:120 @[ /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:219 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:40 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ] ] ] ]
      0x49271b0: i64 = TargetConstant<5104>
      0x6f62b68: i64 = add 0x4926b98, Constant:i64<-8>, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:39 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
        0x4926b98: i64 = add 0x6f62278, 0x4926ed8, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:39 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
          0x6f62278: i64,ch = CopyFromReg 0x65cc278, Register:i64 %4, /home/pxl-th/.julia/packages/LLVM/YSJ2s/src/interop/pointer.jl:110 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:39 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
            0x7803a58: i64 = Register %4
          0x4926ed8: i64 = shl nuw nsw 0x71ce2d8, Constant:i32<3>, int.jl:88 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:39 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
            0x71ce2d8: i64,ch = CopyFromReg 0x65cc278, Register:i64 %8, int.jl:88 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:39 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/pointer.jl:48 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:184 @[ /home/pxl-th/.julia/packages/CUDA/GGwVa/src/device/array.jl:232 @[ /home/pxl-th/code/a.jl:28 @[ /home/pxl-th/.julia/packages/KernelAbstractions/I3MhZ/src/macros.jl:80 @[ none:0 ] ] ] ] ] ] ]
              0x77ae620: i64 = Register %8
            0x66ac0c8: i32 = Constant<3>
        0x4926720: i64 = Constant<-8>
      0x7196880: i32 = Constant<8>
In function: _Z21julia_gpu_splat__430516CompilerMetadataI10StaticSizeI5_16__E12DynamicCheckvv7NDRangeILi1ES0_I4_1__ES0_I6_512__EvvEE13CuDeviceArrayI5Int64Li1ELi1EES3_I6UInt32Li1ELi1EES3_IS4_Li1ELi1EE
Stacktrace:
  [1] handle_error(reason::Cstring)
    @ LLVM ~/.julia/packages/LLVM/YSJ2s/src/core/context.jl:105
  [2] LLVMTargetMachineEmitToMemoryBuffer
    @ ~/.julia/packages/LLVM/YSJ2s/lib/13/libLLVM_h.jl:947 [inlined]
  [3] emit(tm::LLVM.TargetMachine, mod::LLVM.Module, filetype::LLVM.API.LLVMCodeGenFileType)
    @ LLVM ~/.julia/packages/LLVM/YSJ2s/src/targetmachine.jl:45
  [4] mcgen(job::GPUCompiler.CompilerJob, mod::LLVM.Module, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/mcgen.jl:74
  [5] macro expansion
    @ ~/.julia/packages/TimerOutputs/LDL7n/src/TimerOutput.jl:252 [inlined]
  [6] macro expansion
    @ ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:421 [inlined]
  [7] macro expansion
    @ ~/.julia/packages/TimerOutputs/LDL7n/src/TimerOutput.jl:252 [inlined]
  [8] macro expansion
    @ ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:418 [inlined]
  [9] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/utils.jl:64
 [10] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:354
 [11] #224
    @ ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:347 [inlined]
 [12] JuliaContext(f::CUDA.var"#224#225"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(gpu_splat!), Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.StaticSize{(16,)}, KernelAbstractions.NDIteration.DynamicCheck, Nothing, Nothing, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.StaticSize{(1,)}, KernelAbstractions.NDIteration.StaticSize{(512,)}, Nothing, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/driver.jl:74
 [13] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:346
 [14] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/XyxTy/src/cache.jl:90
 [15] cufunction(f::typeof(gpu_splat!), tt::Type{Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.StaticSize{(16,)}, KernelAbstractions.NDIteration.DynamicCheck, Nothing, Nothing, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.StaticSize{(1,)}, KernelAbstractions.NDIteration.StaticSize{(512,)}, Nothing, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:299
 [16] cufunction(f::typeof(gpu_splat!), tt::Type{Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.StaticSize{(16,)}, KernelAbstractions.NDIteration.DynamicCheck, Nothing, Nothing, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.StaticSize{(1,)}, KernelAbstractions.NDIteration.StaticSize{(512,)}, Nothing, Nothing}}, CuDeviceVector{Int64, 1}, CuDeviceVector{UInt32, 1}, CuDeviceVector{Int64, 1}}})
    @ CUDA ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:292
 [17] macro expansion
    @ ~/.julia/packages/CUDA/GGwVa/src/compiler/execution.jl:102 [inlined]
 [18] (::KernelAbstractions.Kernel{CUDADevice, KernelAbstractions.NDIteration.StaticSize{(512,)}, KernelAbstractions.NDIteration.StaticSize{(16,)}, typeof(gpu_splat!)})(::CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, ::Vararg{Any}; ndrange::Nothing, dependencies::CUDAKernels.CudaEvent, workgroupsize::Nothing, progress::Function)
    @ CUDAKernels ~/.julia/packages/CUDAKernels/JJJ1U/src/CUDAKernels.jl:273
 [19] Kernel
    @ ~/.julia/packages/CUDAKernels/JJJ1U/src/CUDAKernels.jl:268 [inlined]
 [20] main()
    @ Main ~/code/a.jl:42
 [21] top-level scope
    @ ~/code/a.jl:45
in expression starting at /home/pxl-th/code/a.jl:45

leios · 2022-06-04T16:47:31Z

Ah, I can replicate this error, but I am not sure if it is an Atomix or KernelAbstractions issue. It seems like the CPU version works fine, so maybe it's with UnsafeAtomicsLLVM?

Would you be willing to open up a new issue either here or on Atomix (https://github.com/JuliaConcurrent/Atomix.jl) and ping @tkf?

tkf · 2022-06-05T04:45:35Z

It's an LLVM issue but workaroundable at the level of (e.g.) CUDA.jl. See: JuliaConcurrent/Atomix.jl#33

leios · 2022-06-14T06:27:31Z

@pxl-th, if you are still having trouble with Atomix, I created a separate PR with the atomic support from Core.Intrinsics and CUDA directly in #306. I also added the pkg commands to load in the subdirectory of CUDAKernels in a comment so you can just use it for now if you need.

I've been struggling to get things to work as well, so I also added testing infrastructure for Atomix in #308. Hopefully we can iron out all the details there and get all this sorted. If you have run into any issues, please document them there!

atting atomic attempts with atomix

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tkf reviewed May 25, 2022

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Co-authored-by: Takafumi Arakaki <takafumi.a@gmail.com>

vchuravy reviewed May 25, 2022

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Co-authored-by: Valentin Churavy <vchuravy@users.noreply.github.com>

tkf reviewed May 25, 2022

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leios changed the title ~~atting atomic support with atomix~~ adding atomic support with atomix May 31, 2022

vchuravy marked this pull request as ready for review May 31, 2022 18:25

Update examples/histogram.jl

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Co-authored-by: Takafumi Arakaki <takafumi.a@gmail.com>

vchuravy merged commit 6374613 into JuliaGPU:master May 31, 2022

This was referenced Jun 8, 2022

Atomic attempts #282

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Support atomics #7

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leios mentioned this pull request Jun 15, 2022

adding atomix tests #308

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