Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

amd gpu give different results when nested loop is used #517

Open
ww1g11 opened this issue Sep 5, 2024 · 18 comments
Open

amd gpu give different results when nested loop is used #517

ww1g11 opened this issue Sep 5, 2024 · 18 comments

Comments

@ww1g11
Copy link

ww1g11 commented Sep 5, 2024

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

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?

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)
@vchuravy
Copy link
Member

vchuravy commented Sep 5, 2024

Can you also print the output?

@ww1g11
Copy link
Author

ww1g11 commented Sep 5, 2024 •
edited
Loading

Can you also print the output?

The output is

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;;;]
julia> versioninfo()
Julia Version 1.10.0
Commit 3120989f39 (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 12 × Intel(R) Core(TM) i7-9850H CPU @ 2.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
  Threads: 1 on 12 virtual cores

[ Info: AMDGPU versioninfo
┌───────────┬──────────────────┬───────────┬───────────────────────────────────────────────────────────────────────────────────────────────┐
│ Available │ Name             │ Version   │ Path                                                                                          │
├───────────┼──────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────────────────┤
│     +     │ LLD              │ -         │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\ld.lld.exe                                            │
│     +     │ Device Libraries │ -         │ D:\\Softwares\\julia_pkg\\artifacts\\5ad5ecb46e3c334821f54c1feecc6c152b7b6a45\\amdgcn/bitcode │
│     +     │ HIP              │ 5.7.32000 │ C:\\WINDOWS\\SYSTEM32\\amdhip64.DLL                                                           │
│     +     │ rocBLAS          │ 3.1.0     │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocblas.dll                                           │
│     +     │ rocSOLVER        │ 3.23.0    │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocsolver.dll                                         │
│     +     │ rocALUTION       │ -         │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocalution.dll                                        │
│     +     │ rocSPARSE        │ -         │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocsparse.dll                                         │
│     +     │ rocRAND          │ 2.10.5    │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocrand.dll                                           │
│     +     │ rocFFT           │ 1.0.27    │ C:\\Program Files\\AMD\\ROCm\\5.7\\bin\\rocfft.dll                                            │
│     -     │ MIOpen           │ -         │ -                                                                                             │
└───────────┴──────────────────┴───────────┴───────────────────────────────────────────────────────────────────────────────────────────────┘

[ Info: AMDGPU devices
┌────┬────────────────────────┬──────────┬───────────┬───────────┐
│ Id │                   Name │ GCN arch │ Wavefront │    Memory │
├────┼────────────────────────┼──────────┼───────────┼───────────┤
│  1 │ AMD Radeon RX 7600M XT │  gfx1102 │        32 │ 7.984 GiB │
└────┴────────────────────────┴──────────┴───────────┴───────────┘

@vchuravy
Copy link
Member

vchuravy commented Sep 5, 2024

Yeah that's concerning... @pxl-th any ideas?

@luraess
Copy link

luraess commented Sep 5, 2024

AMDGPU on Windows right? Could one have sum differently to discern if p or q are not executed?

@ww1g11
Copy link
Author

ww1g11 commented Sep 5, 2024

An error occurs if the kernel function changes to

@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
        for q in (-Ny):Ny
            sum += 2.0
        end
    end
    @inbounds tensor[i, j, k] = sum
end

the error msg:

julia> include("amd.jl")
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;;;]
ERROR: LoadError: Not implemented
Stacktrace:
  [1] error(s::String)
    @ Base .\error.jl:35
  [2] runtime_module(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\interface.jl:176
  [3] build_runtime(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:102
  [4] (::GPUCompiler.var"#140#142"{GPUCompiler.CompilerJob{…}})()
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:143
  [5] lock(f::GPUCompiler.var"#140#142"{GPUCompiler.CompilerJob{…}}, l::ReentrantLock)
    @ Base .\lock.jl:229
  [6] macro expansion
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:121 [inlined]
  [7] load_runtime(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:103
  [8] macro expansion
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:304 [inlined]
  [9]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:103
 [10] emit_llvm
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:97 [inlined]
 [11]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:136
 [12] codegen
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:115 [inlined]
 [13]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:111
 [14] compile
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:103 [inlined]
 [15] #40
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:170 [inlined]
 [16] JuliaContext(f::AMDGPU.Compiler.var"#40#41"{GPUCompiler.CompilerJob{…}}; kwargs::@Kwargs{})
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:52
 [17] JuliaContext(f::Function)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:42
 [18] hipcompile(job::GPUCompiler.CompilerJob)
    @ AMDGPU.Compiler D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:169
 [19] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(AMDGPU.Compiler.hipcompile), linker::typeof(AMDGPU.Compiler.hiplink))
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\execution.jl:128
 [20] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\execution.jl:103
 [21] macro expansion
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:137 [inlined]
 [22] macro expansion
    @ .\lock.jl:267 [inlined]
 [23] hipfunction(f::GPUArrays.var"#6#7", tt::Type{Tuple{…}}; kwargs::@Kwargs{name::Nothing})
    @ AMDGPU.Compiler D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:131
 [24] hipfunction
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:130 [inlined]
 [25] macro expansion
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\highlevel.jl:172 [inlined]
 [26] #gpu_call#55
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\gpuarrays.jl:8 [inlined]
 [27] gpu_call
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\gpuarrays.jl:5 [inlined]
 [28] gpu_call(::GPUArrays.var"#6#7", ::ROCArray{…}, ::Float64; target::ROCArray{…}, elements::Nothing, threads::Nothing, blocks::Nothing, name::Nothing)
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\device\execution.jl:65
 [29] gpu_call
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\device\execution.jl:34 [inlined]
 [30] fill!(A::ROCArray{Float64, 3, AMDGPU.Runtime.Mem.HIPBuffer}, x::Float64)
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\host\construction.jl:14
 [31] zeros(::Type, ::Int64, ::Vararg{Int64})
    @ AMDGPU D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\array.jl:244

@luraess
Copy link

luraess commented Sep 5, 2024 •
edited
Loading

Testing on RX 7800 XT Ubuntu 22.04 I can reproduce the issue.

The following does not errors though for me:

@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
        for q in (-Ny):Ny
            sum += 2.0
        end
    end
    @inbounds tensor[i, j, k] = sum
end

and produces the same wrong results as the original version.

From testing, it seems that -Ny brakes the thing as following produces correct result:

@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
        for q in (-1):Ny
            sum += 2.0
        end
    end
    @inbounds tensor[i, j, k] = sum
end

EDIT: (-Ny):Ny seems to be the deal-breaker in both loop-variants.

@luraess
Copy link

luraess commented Sep 5, 2024

Also the "plain" AMDGPU version works fine:

using AMDGPU

function compute_amdgpu(tensor, kernel_fun, Nx, Ny, Nz)
    groupsize = (16, 4, 2)                     # nthreads
    gridsize  = cld.(size(tensor), groupsize)  # nblocks
    @roc groupsize=groupsize gridsize=gridsize kernel_fun(tensor, Nx, Ny, Nz)
    AMDGPU.synchronize()
    return nothing
end

function kernel_xx2!(tensor, Nx, Ny, Nz)
    i = (workgroupIdx().x - 1) * workgroupDim().x + workitemIdx().x
    j = (workgroupIdx().y - 1) * workgroupDim().y + workitemIdx().y
    k = (workgroupIdx().z - 1) * workgroupDim().z + workitemIdx().z
    sum = zero(eltype(tensor))
    for p in (-Nx):Nx, q in (-Ny):Ny
        sum += 2.0
    end
    if i ∈ axes(tensor, 1) && j ∈ axes(tensor, 2) && k ∈ axes(tensor, 3)
        @inbounds tensor[i, j, k] = sum
    end
    return nothing
end

nx, ny, nz = 10, 1, 1
Nx, Ny, Nz = 1, 1, 1

tensor2 = AMDGPU.zeros(Float64, nx, ny, nz)
compute_amdgpu(tensor2, kernel_xx2!, Nx, Ny, Nz)
println("amdgpu:", tensor2)

@pxl-th
Copy link
Collaborator

pxl-th commented Sep 5, 2024 •
edited
Loading

This seems to appear only when using NTuple index type.
Changing to linear or cartesian works fine:

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

@pxl-th
Copy link
Collaborator

pxl-th commented Sep 5, 2024

Or more generally, when passing size(x) to ndrage instead of length(x).

@pxl-th
Copy link
Collaborator

pxl-th commented Sep 5, 2024 •
edited
Loading

Here's optimized LLVM IR for:

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

@vchuravy in size(x) case, there's lot's of comparisons >= 0 and terminations in case of false. I suspect that's why it fails:

%29 = icmp sgt i64 %.fca.1.1.0.1.0.extract, 0
br i1 %29, label %pass6, label %fail5
  • ndrange=size(x):
; ModuleID = 'start'
source_filename = "start"
target datalayout = "e-p:64:64-p1:64:64-p2:32:32-p3:32:32-p4:64:64-p5:32:32-p6:32:32-i64:64-v16:16-v24:32-v32:32-v48:64-v96:128-v192:256-v256:256-v512:512-v1024:1024-v2048:2048-n32:64-S32-A5-G1-ni:10:11:12:13"
target triple = "amdgcn-amd-amdhsa"

; Function Attrs: nounwind readnone speculatable willreturn
declare i32 @llvm.amdgcn.workgroup.id.x() #0

; Function Attrs: nounwind readnone speculatable willreturn
declare i32 @llvm.amdgcn.workitem.id.x() #0

; Function Attrs: cold noreturn nounwind
declare void @llvm.amdgcn.endpgm() #1

; Function Attrs: nocallback nofree nosync nounwind readnone speculatable willreturn
declare i64 @llvm.smax.i64(i64, i64) #2

define amdgpu_kernel void @_Z14gpu_kernel_xx_16CompilerMetadataI11DynamicSize12DynamicCheckv16CartesianIndicesILl3E5TupleI5OneToI5Int64ES4_IS5_ES4_IS5_EEE7NDRangeILl3ES0_S0_S2_ILl3ES3_IS4_IS5_ES4_IS5_ES4_IS5_EEES2_ILl3ES3_IS4_IS5_ES4_IS5_ES4_IS5_EEEEE14ROCDeviceArrayI7Float64Ll3ELl1EES5_S5_S5_({ i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, { [3 x i64], i8 addrspace(1)*, i64 } %1, i64 signext %2, i64 signext %3, i64 signext %4) local_unnamed_addr #3 {
conversion:
  %.fca.0.0.0.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 0, 0, 0, 0
  %.fca.0.0.1.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 0, 0, 1, 0
  %.fca.0.0.2.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 0, 0, 2, 0
  %.fca.1.0.0.0.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 1, 0, 0, 0, 0
  %.fca.1.0.0.1.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 1, 0, 0, 1, 0
  %.fca.1.1.0.0.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 1, 1, 0, 0, 0
  %.fca.1.1.0.1.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 1, 1, 0, 1, 0
  %.fca.1.1.0.2.0.extract = extractvalue { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] } %0, 1, 1, 0, 2, 0
  %.fca.1.extract = extractvalue { [3 x i64], i8 addrspace(1)*, i64 } %1, 1
  %5 = call i32 @llvm.amdgcn.workitem.id.x(), !range !7
  %6 = call i64 @llvm.smax.i64(i64 %.fca.1.0.0.1.0.extract, i64 0)
  %7 = icmp sgt i64 %.fca.1.0.0.0.0.extract, 0
  br i1 %7, label %pass, label %fail

L674:                                             ; preds = %L674.preheader, %L706
  %value_phi17 = phi i64 [ %10, %L706 ], [ %65, %L674.preheader ]
  %value_phi18 = phi double [ %value_phi26, %L706 ], [ 0.000000e+00, %L674.preheader ]
  br i1 %.not127.not, label %L706, label %L693

L693:                                             ; preds = %L693, %L674
  %value_phi22 = phi double [ %8, %L693 ], [ %value_phi18, %L674 ]
  %value_phi23 = phi i64 [ %9, %L693 ], [ %67, %L674 ]
  %8 = fadd double %value_phi22, 2.000000e+00
  %.not128 = icmp eq i64 %value_phi23, %value_phi19
  %9 = add i64 %value_phi23, 1
  br i1 %.not128, label %L706, label %L693

L706:                                             ; preds = %L693, %L674
  %value_phi26 = phi double [ %value_phi18, %L674 ], [ %8, %L693 ]
  %.not129 = icmp eq i64 %value_phi17, %value_phi
  %10 = add i64 %value_phi17, 1
  br i1 %.not129, label %L732, label %L674

L732:                                             ; preds = %pass10, %L706
  %value_phi29 = phi double [ 0.000000e+00, %pass10 ], [ %value_phi26, %L706 ]
  %11 = add i64 %64, %32
  %reass.add137 = add i64 %11, %reass.mul135
  %reass.mul138 = mul i64 %reass.add137, %60
  %12 = add i64 %reass.mul138, %31
  %13 = add i64 %12, %reass.mul
  %14 = bitcast i8 addrspace(1)* %.fca.1.extract to double addrspace(1)*
  %15 = getelementptr inbounds double, double addrspace(1)* %14, i64 %13
  store double %value_phi29, double addrspace(1)* %15, align 8, !tbaa !8
  br label %L738

L738:                                             ; preds = %pass6, %L732
  ret void

fail:                                             ; preds = %conversion
  %state.i.fca.0.extract.i = extractvalue { i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, 0
  %16 = inttoptr i64 %state.i.fca.0.extract.i to i32*
  store i32 1, i32* %16, align 1
  call void @llvm.amdgcn.endpgm()
  unreachable

pass:                                             ; preds = %conversion
  %17 = call i32 @llvm.amdgcn.workgroup.id.x(), !range !11
  %18 = zext i32 %17 to i64
  %19 = udiv i64 %18, %.fca.1.0.0.0.0.extract
  %20 = mul i64 %19, %.fca.1.0.0.0.0.extract
  %21 = icmp sgt i64 %.fca.1.0.0.1.0.extract, 0
  br i1 %21, label %pass2, label %fail1

fail1:                                            ; preds = %pass
  %state.i.fca.0.extract.i28 = extractvalue { i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, 0
  %22 = inttoptr i64 %state.i.fca.0.extract.i28 to i32*
  store i32 1, i32* %22, align 1
  call void @llvm.amdgcn.endpgm()
  unreachable

pass2:                                            ; preds = %pass
  %23 = udiv i64 %19, %6
  %24 = mul i64 %23, %6
  %25 = sub i64 %19, %24
  %26 = call i64 @llvm.smax.i64(i64 %.fca.1.1.0.1.0.extract, i64 0)
  %27 = icmp sgt i64 %.fca.1.1.0.0.0.extract, 0
  br i1 %27, label %pass4, label %fail3

fail3:                                            ; preds = %pass2
  %state.i.fca.0.extract.i42 = extractvalue { i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, 0
  %28 = inttoptr i64 %state.i.fca.0.extract.i42 to i32*
  store i32 1, i32* %28, align 1
  call void @llvm.amdgcn.endpgm()
  unreachable

pass4:                                            ; preds = %pass2
  %29 = icmp sgt i64 %.fca.1.1.0.1.0.extract, 0
  br i1 %29, label %pass6, label %fail5

fail5:                                            ; preds = %pass4
  %state.i.fca.0.extract.i56 = extractvalue { i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, 0
  %30 = inttoptr i64 %state.i.fca.0.extract.i56 to i32*
  store i32 1, i32* %30, align 1
  call void @llvm.amdgcn.endpgm()
  unreachable

pass6:                                            ; preds = %pass4
  %31 = zext i32 %5 to i64
  %32 = udiv i64 %31, %.fca.1.1.0.0.0.extract
  %33 = udiv i64 %32, %26
  %34 = mul i64 %33, %26
  %35 = add i64 %20, %32
  %reass.add = sub i64 %18, %35
  %reass.mul = mul i64 %reass.add, %.fca.1.1.0.0.0.extract
  %36 = add nuw nsw i64 %31, 1
  %37 = add i64 %36, %reass.mul
  %38 = mul i64 %25, %.fca.1.1.0.1.0.extract
  %39 = add i64 %38, 1
  %40 = add i64 %39, %32
  %41 = sub i64 %40, %34
  %42 = mul i64 %23, %.fca.1.1.0.2.0.extract
  %43 = add i64 %42, 1
  %44 = add i64 %43, %33
  %45 = icmp sgt i64 %37, 0
  %46 = icmp sle i64 %37, %.fca.0.0.0.0.extract
  %47 = and i1 %45, %46
  %48 = icmp sgt i64 %41, 0
  %49 = icmp sle i64 %41, %.fca.0.0.1.0.extract
  %50 = and i1 %48, %49
  %51 = icmp sgt i64 %44, 0
  %52 = icmp sle i64 %44, %.fca.0.0.2.0.extract
  %53 = and i1 %51, %52
  %54 = and i1 %47, %50
  %55 = and i1 %53, %54
  br i1 %55, label %pass10, label %L738

pass10:                                           ; preds = %pass6
  %56 = udiv i64 %19, %.fca.1.0.0.1.0.extract
  %57 = mul i64 %56, %.fca.1.0.0.1.0.extract
  %58 = udiv i64 %32, %.fca.1.1.0.1.0.extract
  %59 = mul i64 %56, %.fca.1.1.0.2.0.extract
  %60 = call i64 @llvm.smax.i64(i64 %.fca.0.0.0.0.extract, i64 0)
  %61 = call i64 @llvm.smax.i64(i64 %.fca.0.0.1.0.extract, i64 0)
  %62 = add i64 %57, %58
  %reass.add134 = sub i64 %19, %62
  %reass.mul135 = mul i64 %reass.add134, %.fca.1.1.0.1.0.extract
  %63 = add i64 %58, %59
  %64 = mul i64 %63, %61
  %65 = sub i64 0, %2
  %.not = icmp sgt i64 %65, %2
  %66 = sext i1 %.not to i64
  %value_phi = xor i64 %66, %2
  %.not125.not = icmp slt i64 %value_phi, %65
  br i1 %.not125.not, label %L732, label %L674.preheader

L674.preheader:                                   ; preds = %pass10
  %67 = sub i64 0, %3
  %.not126 = icmp sgt i64 %67, %3
  %68 = sext i1 %.not126 to i64
  %value_phi19 = xor i64 %68, %3
  %.not127.not = icmp slt i64 %value_phi19, %67
  br label %L674
}

attributes #0 = { nounwind readnone speculatable willreturn }
attributes #1 = { cold noreturn nounwind }
attributes #2 = { nocallback nofree nosync nounwind readnone speculatable willreturn }
attributes #3 = { "amdgpu-unsafe-fp-atomics"="true" "target-cpu"="gfx1100" "target-features"="+wavefrontsize32,-wavefrontsize64" }

!llvm.module.flags = !{!0, !1, !2, !3}
!opencl.ocl.version = !{!4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4}
!llvm.ident = !{!5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5}
!julia.kernel = !{!6}

!0 = !{i32 2, !"Dwarf Version", i32 4}
!1 = !{i32 2, !"Debug Info Version", i32 3}
!2 = !{i32 1, !"wchar_size", i32 4}
!3 = !{i32 7, !"PIC Level", i32 1}
!4 = !{i32 2, i32 0}
!5 = !{!"clang version 15.0.0 (/cache/yggdrasil/downloads/clones/llvm-project.git-974efd367bc513231526d317489c66cb27727ef3caa41108e3819c131a8acf57 f3d695fc2985a8dfdd5f4219d351fdeac3038867)"}
!6 = !{void ({ i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 }, { [1 x [3 x [1 x i64]]], [2 x [1 x [3 x [1 x i64]]]] }, { [3 x i64], i8 addrspace(1)*, i64 }, i64, i64, i64)* @_Z14gpu_kernel_xx_16CompilerMetadataI11DynamicSize12DynamicCheckv16CartesianIndicesILl3E5TupleI5OneToI5Int64ES4_IS5_ES4_IS5_EEE7NDRangeILl3ES0_S0_S2_ILl3ES3_IS4_IS5_ES4_IS5_ES4_IS5_EEES2_ILl3ES3_IS4_IS5_ES4_IS5_ES4_IS5_EEEEE14ROCDeviceArrayI7Float64Ll3ELl1EES5_S5_S5_}
!7 = !{i32 0, i32 1023}
!8 = !{!9, !9, i64 0, i64 0}
!9 = !{!"custom_tbaa_addrspace(1)", !10, i64 0}
!10 = !{!"custom_tbaa"}
!11 = !{i32 0, i32 -2}
  • ndrange=length(x):
; ModuleID = 'start'
source_filename = "start"
target datalayout = "e-p:64:64-p1:64:64-p2:32:32-p3:32:32-p4:64:64-p5:32:32-p6:32:32-i64:64-v16:16-v24:32-v32:32-v48:64-v96:128-v192:256-v256:256-v512:512-v1024:1024-v2048:2048-n32:64-S32-A5-G1-ni:10:11:12:13"
target triple = "amdgcn-amd-amdhsa"

; Function Attrs: nounwind readnone speculatable willreturn
declare i32 @llvm.amdgcn.workgroup.id.x() #0

; Function Attrs: nounwind readnone speculatable willreturn
declare i32 @llvm.amdgcn.workitem.id.x() #0

define amdgpu_kernel void @_Z14gpu_kernel_xx_16CompilerMetadataI11DynamicSize12DynamicCheckv16CartesianIndicesILl1E5TupleI5OneToI5Int64EEE7NDRangeILl1ES0_S0_S2_ILl1ES3_IS4_IS5_EEES2_ILl1ES3_IS4_IS5_EEEEE14ROCDeviceArrayI7Float64Ll3ELl1EES5_S5_S5_({ i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 } %state, { [1 x [1 x [1 x i64]]], [2 x [1 x [1 x [1 x i64]]]] } %0, { [3 x i64], i8 addrspace(1)*, i64 } %1, i64 signext %2, i64 signext %3, i64 signext %4) local_unnamed_addr #1 {
conversion:
  %.fca.0.0.0.0.extract = extractvalue { [1 x [1 x [1 x i64]]], [2 x [1 x [1 x [1 x i64]]]] } %0, 0, 0, 0, 0
  %.fca.1.1.0.0.0.extract = extractvalue { [1 x [1 x [1 x i64]]], [2 x [1 x [1 x [1 x i64]]]] } %0, 1, 1, 0, 0, 0
  %.fca.1.extract = extractvalue { [3 x i64], i8 addrspace(1)*, i64 } %1, 1
  %5 = call i32 @llvm.amdgcn.workgroup.id.x(), !range !7
  %6 = call i32 @llvm.amdgcn.workitem.id.x(), !range !8
  %7 = add nuw nsw i32 %6, 1
  %8 = zext i32 %7 to i64
  %9 = zext i32 %5 to i64
  %10 = mul i64 %.fca.1.1.0.0.0.extract, %9
  %11 = add i64 %10, %8
  %12 = icmp slt i64 %11, 1
  %13 = icmp sgt i64 %11, %.fca.0.0.0.0.extract
  %14 = or i1 %12, %13
  br i1 %14, label %L299, label %L103

L103:                                             ; preds = %conversion
  %15 = sub i64 0, %2
  %.not = icmp sgt i64 %15, %2
  %16 = sext i1 %.not to i64
  %value_phi = xor i64 %16, %2
  %.not6.not = icmp slt i64 %value_phi, %15
  br i1 %.not6.not, label %L293, label %L235.preheader

L235.preheader:                                   ; preds = %L103
  %17 = sub i64 0, %3
  %.not7 = icmp sgt i64 %17, %3
  %18 = sext i1 %.not7 to i64
  %value_phi5 = xor i64 %18, %3
  %.not8.not = icmp slt i64 %value_phi5, %17
  br label %L235

L235:                                             ; preds = %L267, %L235.preheader
  %value_phi3 = phi i64 [ %21, %L267 ], [ %15, %L235.preheader ]
  %value_phi4 = phi double [ %value_phi12, %L267 ], [ 0.000000e+00, %L235.preheader ]
  br i1 %.not8.not, label %L267, label %L254

L254:                                             ; preds = %L254, %L235
  %value_phi8 = phi double [ %19, %L254 ], [ %value_phi4, %L235 ]
  %value_phi9 = phi i64 [ %20, %L254 ], [ %17, %L235 ]
  %19 = fadd double %value_phi8, 2.000000e+00
  %.not9 = icmp eq i64 %value_phi9, %value_phi5
  %20 = add i64 %value_phi9, 1
  br i1 %.not9, label %L267, label %L254

L267:                                             ; preds = %L254, %L235
  %value_phi12 = phi double [ %value_phi4, %L235 ], [ %19, %L254 ]
  %.not10 = icmp eq i64 %value_phi3, %value_phi
  %21 = add i64 %value_phi3, 1
  br i1 %.not10, label %L293, label %L235

L293:                                             ; preds = %L267, %L103
  %value_phi15 = phi double [ 0.000000e+00, %L103 ], [ %value_phi12, %L267 ]
  %22 = add nsw i64 %8, -1
  %23 = add i64 %22, %10
  %24 = bitcast i8 addrspace(1)* %.fca.1.extract to double addrspace(1)*
  %25 = getelementptr inbounds double, double addrspace(1)* %24, i64 %23
  store double %value_phi15, double addrspace(1)* %25, align 8, !tbaa !9
  br label %L299

L299:                                             ; preds = %L293, %conversion
  ret void
}

attributes #0 = { nounwind readnone speculatable willreturn }
attributes #1 = { "amdgpu-unsafe-fp-atomics"="true" "target-cpu"="gfx1100" "target-features"="+wavefrontsize32,-wavefrontsize64" }

!llvm.module.flags = !{!0, !1, !2, !3}
!opencl.ocl.version = !{!4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4, !4}
!llvm.ident = !{!5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5, !5}
!julia.kernel = !{!6}

!0 = !{i32 2, !"Dwarf Version", i32 4}
!1 = !{i32 2, !"Debug Info Version", i32 3}
!2 = !{i32 1, !"wchar_size", i32 4}
!3 = !{i32 7, !"PIC Level", i32 1}
!4 = !{i32 2, i32 0}
!5 = !{!"clang version 15.0.0 (/cache/yggdrasil/downloads/clones/llvm-project.git-974efd367bc513231526d317489c66cb27727ef3caa41108e3819c131a8acf57 f3d695fc2985a8dfdd5f4219d351fdeac3038867)"}
!6 = !{void ({ i64, i64, i64, i64, i64, i64, i32, i32, i64, i64, i64, i64 }, { [1 x [1 x [1 x i64]]], [2 x [1 x [1 x [1 x i64]]]] }, { [3 x i64], i8 addrspace(1)*, i64 }, i64, i64, i64)* @_Z14gpu_kernel_xx_16CompilerMetadataI11DynamicSize12DynamicCheckv16CartesianIndicesILl1E5TupleI5OneToI5Int64EEE7NDRangeILl1ES0_S0_S2_ILl1ES3_IS4_IS5_EEES2_ILl1ES3_IS4_IS5_EEEEE14ROCDeviceArrayI7Float64Ll3ELl1EES5_S5_S5_}
!7 = !{i32 0, i32 -2}
!8 = !{i32 0, i32 1023}
!9 = !{!10, !10, i64 0, i64 0}
!10 = !{!"custom_tbaa_addrspace(1)", !11, i64 0}
!11 = !{!"custom_tbaa"}

@ww1g11
Copy link
Author

ww1g11 commented Sep 9, 2024

In my computer, the linear or cartesian also give the wrong results:

cpu:[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;;;]

This seems to appear only when using NTuple index type. Changing to linear or cartesian works fine:

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

@ww1g11
Copy link
Author

ww1g11 commented Sep 9, 2024

This time this kernel works well (the results are still wrong).

An error occurs if the kernel function changes to

@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
        for q in (-Ny):Ny
            sum += 2.0
        end
    end
    @inbounds tensor[i, j, k] = sum
end

the error msg:

julia> include("amd.jl")
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;;;]
ERROR: LoadError: Not implemented
Stacktrace:
  [1] error(s::String)
    @ Base .\error.jl:35
  [2] runtime_module(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\interface.jl:176
  [3] build_runtime(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:102
  [4] (::GPUCompiler.var"#140#142"{GPUCompiler.CompilerJob{…}})()
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:143
  [5] lock(f::GPUCompiler.var"#140#142"{GPUCompiler.CompilerJob{…}}, l::ReentrantLock)
    @ Base .\lock.jl:229
  [6] macro expansion
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\rtlib.jl:121 [inlined]
  [7] load_runtime(job::GPUCompiler.CompilerJob)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:103
  [8] macro expansion
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:304 [inlined]
  [9]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:103
 [10] emit_llvm
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\utils.jl:97 [inlined]
 [11]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:136
 [12] codegen
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:115 [inlined]
 [13]
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:111
 [14] compile
    @ D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:103 [inlined]
 [15] #40
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:170 [inlined]
 [16] JuliaContext(f::AMDGPU.Compiler.var"#40#41"{GPUCompiler.CompilerJob{…}}; kwargs::@Kwargs{})
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:52
 [17] JuliaContext(f::Function)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\driver.jl:42
 [18] hipcompile(job::GPUCompiler.CompilerJob)
    @ AMDGPU.Compiler D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:169
 [19] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(AMDGPU.Compiler.hipcompile), linker::typeof(AMDGPU.Compiler.hiplink))
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\execution.jl:128
 [20] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler D:\Softwares\julia_pkg\packages\GPUCompiler\nWT2N\src\execution.jl:103
 [21] macro expansion
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:137 [inlined]
 [22] macro expansion
    @ .\lock.jl:267 [inlined]
 [23] hipfunction(f::GPUArrays.var"#6#7", tt::Type{Tuple{…}}; kwargs::@Kwargs{name::Nothing})
    @ AMDGPU.Compiler D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:131
 [24] hipfunction
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\compiler\codegen.jl:130 [inlined]
 [25] macro expansion
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\highlevel.jl:172 [inlined]
 [26] #gpu_call#55
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\gpuarrays.jl:8 [inlined]
 [27] gpu_call
    @ D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\gpuarrays.jl:5 [inlined]
 [28] gpu_call(::GPUArrays.var"#6#7", ::ROCArray{…}, ::Float64; target::ROCArray{…}, elements::Nothing, threads::Nothing, blocks::Nothing, name::Nothing)
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\device\execution.jl:65
 [29] gpu_call
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\device\execution.jl:34 [inlined]
 [30] fill!(A::ROCArray{Float64, 3, AMDGPU.Runtime.Mem.HIPBuffer}, x::Float64)
    @ GPUArrays D:\Softwares\julia_pkg\packages\GPUArrays\qt4ax\src\host\construction.jl:14
 [31] zeros(::Type, ::Int64, ::Vararg{Int64})
    @ AMDGPU D:\Softwares\julia_pkg\packages\AMDGPU\WqMSe\src\array.jl:244

@pxl-th
Copy link
Collaborator

pxl-th commented Sep 9, 2024 •
edited
Loading

In my computer, the linear or cartesian also give the wrong results:

Did you change how you launch the code?
You need to specity ndrange=length(x) instead of ndrange=size(x).
Because that's the main issue, passing a tuple to ndrange.

@ww1g11
Copy link
Author

ww1g11 commented Sep 12, 2024

In my computer, the linear or cartesian also give the wrong results:

Did you change how you launch the code? You need to specity ndrange=length(x) instead of ndrange=size(x). Because that's the main issue, passing a tuple to ndrange.

I just tried ndrange=length(x), and it give the correct results.

I am using ndrange=size(x) because I want to know its Cartesian Indices, it seems I can cast the linear index to CartesianIndices manually:

    idx = @index(Global)
    i, j = Tuple(CartesianIndices(tensor)[idx])

so using ndrange=length(x) solves the problem.

@pxl-th
Copy link
Collaborator

pxl-th commented Sep 12, 2024 •
edited
Loading

You can also access size of the array within the kernel and compute i, j indices from there.
And if you do only element-wise operations, you can just index with idx since it will iterate all elements of x anyway:

x[2, 2] == x[4]

@ww1g11
Copy link
Author

ww1g11 commented Sep 12, 2024

You can also access size of the array within the kernel and compute i, j indices from there. And if you do only element-wise operations, you can just index with idx since it will iterate all elements of x anyway:

x[2, 2] == x[4]

thanks~

@vchuravy
Copy link
Member

So this is smelling more and more like a compiler bug...

The below code is an attempt of mine to remove the KA syntax sugar. Next step is the inline expand and then we should have a KA free reproducer.

import KernelAbstractions as KA
function gpu_kernel_xx!(ndrange, iterspace, tensor, Nx::Int64, Ny::Int64; )
    I = @inbounds KA.expand(iterspace, AMDGPU.blockIdx().x, AMDGPU.threadIdx().x)
    if I in ndrange
        (i, j, k) = @inbounds KA.expand(iterspace, AMDGPU.blockIdx().x, AMDGPU.threadIdx().x).I
        sum = zero(eltype(tensor))
        for p = 1:Nx + 2
            for q = -Ny:Ny
                sum += 1.0
            end
        end
        @inbounds tensor[i, j, k] = sum
    end
    return nothing
end

obj = kernel_xx!(ROCBackend())
ndrange, wgsize, iterspace, dynamic = KernelAbstractions.launch_config(obj, (10, 1, 1), nothing)
ctx = KernelAbstractions.mkcontext(obj, ndrange, iterspace)

tensor = AMDGPU.zeros(Float64, nx, ny, nz)
@roc groupsize=512 gridsize=size(tensor) gpu_kernel_xx!(CartesianIndices((10, 1, 1)), iterspace, tensor, Nx, Ny)
println("ka_direct:", tensor)

@vchuravy
Copy link
Member

This is now KA free:

"""
    assume(cond::Bool)

Assume that the condition `cond` is true. This is a hint to the compiler, possibly enabling
it to optimize more aggressively.
"""
@inline assume(cond::Bool) = Base.llvmcall(("""
        declare void @llvm.assume(i1)

        define void @entry(i8) #0 {
            %cond = icmp eq i8 %0, 1
            call void @llvm.assume(i1 %cond)
            ret void
        }

        attributes #0 = { alwaysinline }""", "entry"),
    Nothing, Tuple{Bool}, cond)

@inline function assume_nonzero(CI::CartesianIndices)
    ntuple(Val(ndims(CI))) do I
        @inline
        indices = CI.indices[I]
        assume(indices.stop > 0)
    end
end

Base.@propagate_inbounds function expand(blocks, workitems, groupidx::Integer, idx::Integer)
    # this causes a exception branch and a div
    assume_nonzero(blocks)
    assume_nonzero(workitems)
    expand(blocks, workitems, blocks[groupidx], workitems[idx])
end

@inline function expand(blocks, workitems, groupidx::CartesianIndex{N}, idx::CartesianIndex{N}) where {N}
    nI = ntuple(Val(N)) do I
        Base.@_inline_meta
        stride = size(workitems, I)
        gidx = groupidx.I[I]
        (gidx - 1) * stride + idx.I[I]
    end
    CartesianIndex(nI)
end

function gpu_kernel_xx!(ndrange, blocks, workitems, tensor, Nx::Int64, Ny::Int64; )
    I = @inbounds expand(blocks, workitems, AMDGPU.blockIdx().x, AMDGPU.threadIdx().x)
    if I in ndrange
        (i, j, k) = @inbounds expand(blocks, workitems, AMDGPU.blockIdx().x, AMDGPU.threadIdx().x).I
        sum = zero(eltype(tensor))
        for p = 1:Nx + 2
            for q = -Ny:Ny
                sum += 1.0
            end
        end
        @inbounds tensor[i, j, k] = sum
    end
    return nothing
end

tensor = AMDGPU.zeros(Float64, nx, ny, nz)

ndrange = CartesianIndices((10,1,1))
blocks = CartesianIndices((1, 1, 1))
workitems = CartesianIndices((10, 1, 1))

@roc groupsize=512 gridsize=size(tensor) gpu_kernel_xx!(ndrange, blocks, workitems, tensor, Nx, Ny)
println("ka_direct:", tensor)

@vchuravy vchuravy mentioned this issue Sep 12, 2024
Open
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
4 participants
@vchuravy @ww1g11 @pxl-th @luraess