Instruction selection error from loads and stores on CUDA #33

tkf · 2022-06-05T04:44:30Z

(This is not really an Atomix issue but it's not clear where to track this.)

Currently, LLVM does not seem to select atomic store and load on CUDA (see Examples below). Until it is fixed in LLVM, as a short-term solution, it may be nice to use @device_override in CUDA.jl to emit appropriate device-specific instructions through LLVM.Interop.atomic_pointer* APIs.

Ref:
JuliaGPU/CUDA.jl#1353
JuliaGPU/CUDA.jl#1393

Workaround

If you really need to establish some atomic ordering semantics, sometimes it can be done by using some stronger operations such as:

x = Atomix.@atomic xs[i] += 0  # load (for numeric elements)
Atomix.@atomicswap xs[i] = x   # store

Examples

julia> function load(xs)
           Atomix.@atomic xs[1]
           nothing
       end;

julia> @cuda load(CUDA.zeros(1))
ERROR: LLVM error: Cannot select: 0x6b13098: f32,ch = AtomicLoad<(load seq_cst 4 from %ir.2, addrspace
1)> 0x704a6d8, 0x6b13b90, /home/tkf/.julia/packages/LLVM/YSJ2s/src/interop/base.jl:45 @[ /home/tkf/.jul
ia/packages/LLVM/YSJ2s/src/interop/atomics.jl:166 @[ /home/tkf/.julia/packages/LLVM/YSJ2s/src/interop/a
tomics.jl:166 @[ /home/tkf/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/internal.jl:7 @[ /home/tkf/.juli
a/packages/Atomix/F9VIX/src/core.jl:5 @[ REPL[11]:2 ] ] ] ] ]
  0x6b13b90: i64,ch = CopyFromReg 0x704a6d8, Register:i64 %0, abstractarray.jl:656 @[ /home/tkf/.julia/
packages/Atomix/F9VIX/src/references.jl:95 @[ REPL[11]:2 ] ]
    0x6b12fc8: i64 = Register %0

julia> function store!(xs)
           Atomix.@atomic xs[1] = zero(eltype(xs))
           nothing
       end;

julia> @cuda store!(CUDA.zeros(1))
ERROR: LLVM error: Cannot select: 0x854a080: ch = AtomicStore<(store seq_cst 4 into %ir.2, addrspace 1)
> 0x863d288, 0x85273f0, ConstantFP:f32<0.000000e+00>, /home/tkf/.julia/packages/LLVM/YSJ2s/src/interop/
base.jl:45 @[ /home/tkf/.julia/packages/LLVM/YSJ2s/src/interop/atomics.jl:245 @[ /home/tkf/.julia/packa
ges/LLVM/YSJ2s/src/interop/atomics.jl:201 @[ /home/tkf/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/inte
rnal.jl:11 @[ /home/tkf/.julia/packages/Atomix/F9VIX/src/core.jl:14 @[ REPL[13]:2 ] ] ] ] ]
  0x85273f0: i64,ch = CopyFromReg 0x863d288, Register:i64 %0, abstractarray.jl:656 @[ /home/tkf/.julia/
packages/Atomix/F9VIX/src/references.jl:95 @[ REPL[13]:2 ] ]
    0x854a220: i64 = Register %0
  0x8527ad8: f32 = ConstantFP<0.000000e+00>

(jl_4BkM8Y) pkg> st
      Status `/tmp/jl_4BkM8Y/Project.toml`
  [a9b6321e] Atomix v0.1.0
  [052768ef] CUDA v3.10.1
  [d80eeb9a] UnsafeAtomicsLLVM v0.1.0

julia> versioninfo()
Julia Version 1.7.2
Commit bf53498635 (2022-02-06 15:21 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Xeon(R) CPU E5-2603 v4 @ 1.70GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-12.0.1 (ORCJIT, broadwell)

The text was updated successfully, but these errors were encountered:

jgreener64 · 2023-01-10T15:10:17Z

I am trying to create a minimal example for EnzymeAD/Enzyme.jl#511 but am struggling to get Atomix to work on the GPU.

The error I get for the above examples is different to that shown. For example:

using Atomix, CUDA
function load(xs)
    Atomix.@atomic xs[1]
    nothing
end
@cuda load(CUDA.zeros(1))

ERROR: GPU compilation of kernel #load(CuDeviceVector{Float32, 1}) failed
KernelError: kernel returns a value of type `Union{}`

Make sure your kernel function ends in `return`, `return nothing` or `nothing`.
If the returned value is of type `Union{}`, your Julia code probably throws an exception.
Inspect the code with `@device_code_warntype` for more details.

Stacktrace:
  [1] check_method(job::GPUCompiler.CompilerJob)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/validation.jl:41
  [2] macro expansion
    @ ~/.julia/packages/TimerOutputs/LHjFw/src/TimerOutput.jl:253 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/GPUCompiler/hi5Wg/src/driver.jl:152 [inlined]
  [4] emit_julia(job::GPUCompiler.CompilerJob)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/utils.jl:68
  [5] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:352
  [6] #224
    @ ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:347 [inlined]
  [7] JuliaContext(f::CUDA.var"#224#225"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(load), Tuple{CuDeviceVector{Float32, 1}}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/driver.jl:76
  [8] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:346
  [9] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/cache.jl:90
 [10] cufunction(f::typeof(load), tt::Type{Tuple{CuDeviceVector{Float32, 1}}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:299
 [11] cufunction(f::typeof(load), tt::Type{Tuple{CuDeviceVector{Float32, 1}}})
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:292
 [12] top-level scope
    @ ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:102
 [13] top-level scope
    @ ~/.julia/packages/CUDA/Ey3w2/src/initialization.jl:52

I also get a similar error with the suggestion at the top:

function load2(xs)
    x = Atomix.@atomic xs[1] += 0
    nothing
end
@cuda load2(CUDA.zeros(1))

If I try the following I get a different error:

function load3(xs)
    if threadIdx().x == 1
        Atomix.@atomic xs[1]
    end
    nothing
end
@cuda load3(CUDA.zeros(1))

ERROR: InvalidIRError: compiling kernel #load3(CuDeviceVector{Float32, 1}) resulted in invalid LLVM IR
Reason: unsupported dynamic function invocation (call to load)
Stacktrace:
 [1] get
   @ ~/.julia/packages/Atomix/F9VIX/src/core.jl:5
 [2] load3
   @ ./REPL[11]: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, GPUCompiler.FunctionSpec{typeof(load3), Tuple{CuDeviceVector{Float32, 1}}}}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/validation.jl:141
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/hi5Wg/src/driver.jl:418 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/LHjFw/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/hi5Wg/src/driver.jl:416 [inlined]
  [5] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/utils.jl:68
  [6] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:354
  [7] #224
    @ ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:347 [inlined]
  [8] JuliaContext(f::CUDA.var"#224#225"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(load3), Tuple{CuDeviceVector{Float32, 1}}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/hi5Wg/src/driver.jl:76
  [9] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/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/hi5Wg/src/cache.jl:90
 [11] cufunction(f::typeof(load3), tt::Type{Tuple{CuDeviceVector{Float32, 1}}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:299
 [12] cufunction(f::typeof(load3), tt::Type{Tuple{CuDeviceVector{Float32, 1}}})
    @ CUDA ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:292
 [13] top-level scope
    @ ~/.julia/packages/CUDA/Ey3w2/src/compiler/execution.jl:102
 [14] top-level scope
    @ ~/.julia/packages/CUDA/Ey3w2/src/initialization.jl:52

I am on Julia 1.8.2, Atomix 0.1.0 and CUDA 3.12.1. System info:

Julia Version 1.8.2
Commit 36034abf260 (2022-09-29 15:21 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 36 × Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, cascadelake)
  Threads: 16 on 36 virtual cores
Environment:
  LD_LIBRARY_PATH = /usr/local/gromacs/lib

CUDA toolkit 11.7, artifact installation
NVIDIA driver 470.161.3, for CUDA 11.4
CUDA driver 11.7

Libraries: 
- CUBLAS: 11.10.1
- CURAND: 10.2.10
- CUFFT: 10.7.2
- CUSOLVER: 11.3.5
- CUSPARSE: 11.7.3
- CUPTI: 17.0.0
- NVML: 11.0.0+470.161.3
- CUDNN: 8.30.2 (for CUDA 11.5.0)
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)

Toolchain:
- Julia: 1.8.2
- LLVM: 13.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0, 7.1, 7.2
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86

2 devices:
  0: NVIDIA RTX A6000 (sm_86, 47.249 GiB / 47.544 GiB available)
  1: NVIDIA RTX A6000 (sm_86, 46.256 GiB / 47.541 GiB available)

vchuravy · 2023-01-11T15:24:09Z

So to get the actual LLVM error

julia> using CUDA, Atomix, UnsafeAtomicsLLVM

julia> function load3(xs)
           if threadIdx().x == 1
               Atomix.@atomic xs[1]
           end
           nothing
       end
load3 (generic function with 1 method)

julia> @cuda load3(CUDA.zeros(1))

Then you get:

ERROR: LLVM error: Cannot select: 0x51076a0: f32,ch = AtomicLoad<(load seq_cst (s32) from %ir.3, addrspace 1)> 0x47b3938, 0x450a590, /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/base.jl:40 @[ /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/atomics.jl:166 @[ /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/atomics.jl:166 @[ /home/vchuravy/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/internal.jl:7 @[ /home/vchuravy/.julia/packages/Atomix/F9VIX/src/core.jl:5 @[ REPL[2]:3 ] ] ] ] ]
  0x450a590: i64,ch = CopyFromReg 0x47b3938, Register:i64 %0, REPL[2]:2
    0x450a5f8: i64 = Register %0
In function: _Z16julia_load3_185713CuDeviceArrayI7Float32Li1ELi1EE

One way around this would be JuliaGPU/CUDA.jl#1644

jgreener64 · 2023-01-11T16:26:48Z

One way around this would be JuliaGPU/CUDA.jl#1644

Is that expected to work currently? I get the same error when using that branch.

vchuravy · 2023-01-11T16:30:25Z

No not yet fully, and I haven't done the work to integrate it with Atomix.

vchuravy · 2023-01-11T18:14:30Z

This works though:

julia> function load3(xs)
           if threadIdx().x == 1
               Atomix.@atomic :monotonic xs[1]
           end
           nothing
       end
load3 (generic function with 1 method)

julia> @cuda load3(CUDA.zeros(1))
CUDA.HostKernel{typeof(load3), Tuple{CuDeviceVector{Float32, 1}}}(load3, CuFunction(Ptr{Nothing} @0x0000000006513120, CuModule(Ptr{Nothing} @0x00000000065535c0, CuContext(0x00000000024c8b20, instance 1658ac4f1e894673))), CUDA.KernelState(Ptr{Nothing} @0x00007f9baf400000))

jgreener64 · 2023-01-12T16:37:10Z

Works for me, I'll see how this plays with Enzyme gradients.

vchuravy · 2023-01-13T15:48:58Z

But the atomic decrement is still wrong

julia> function load3(xs)
                  if threadIdx().x == 1
                      Atomix.@atomic :monotonic xs[1] -= 1.0
                  end
                  nothing
              end
load3 (generic function with 1 method)

julia> @cuda load3(CUDA.zeros(1))
ERROR: LLVM error: Cannot select: 0xfc25d28: f32,ch = <<Unknown DAG Node>><(load store monotonic (s32) on %ir.3, addrspace 1)> 0x885c938, 0xfc24cb0, ConstantFP:f32<1.000000e+00>, /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/base.jl:40 @[ /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/atomics.jl:270 @[ /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/atomics.jl:270 @[ /home/vchuravy/.julia/packages/LLVM/9gCXO/src/interop/atomics.jl:374 @[ /home/vchuravy/.julia/packages/UnsafeAtomicsLLVM/i4GMj/src/internal.jl:18 @[ /home/vchuravy/.julia/packages/Atomix/F9VIX/src/core.jl:33 @[ REPL[33]:3 ] ] ] ] ] ]
  0xfc24cb0: i64,ch = CopyFromReg 0x885c938, Register:i64 %0, REPL[33]:2
    0xfc24d18: i64 = Register %0
  0xfc25cc0: f32 = ConstantFP<1.000000e+00>
In function: _Z16julia_load3_842013CuDeviceArrayI7Float32Li1ELi1EE

vchuravy · 2023-01-13T15:50:19Z

But ...

 function load3(xs)
                  if threadIdx().x == 1
                      Atomix.@atomic :monotonic xs[1] += -1.0
                  end
                  nothing
              end

tkf mentioned this issue Jun 5, 2022

adding atomic support with atomix JuliaGPU/KernelAbstractions.jl#299

Merged

tkf mentioned this issue Jun 14, 2022

Atomic attempts 3 JuliaGPU/KernelAbstractions.jl#306

Closed

tkf mentioned this issue Oct 14, 2022

CUDA.@atomic error in GPU kernel EnzymeAD/Enzyme.jl#511

Open

jgreener64 mentioned this issue Jan 27, 2023

Add scoped atomic_thread_fence JuliaGPU/CUDA.jl#1644

Draft

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Instruction selection error from loads and stores on CUDA #33

Instruction selection error from loads and stores on CUDA #33

tkf commented Jun 5, 2022 •

edited

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jgreener64 commented Jan 10, 2023

vchuravy commented Jan 11, 2023

jgreener64 commented Jan 11, 2023

vchuravy commented Jan 11, 2023

vchuravy commented Jan 11, 2023

jgreener64 commented Jan 12, 2023

vchuravy commented Jan 13, 2023

vchuravy commented Jan 13, 2023

Instruction selection error from loads and stores on CUDA #33

Instruction selection error from loads and stores on CUDA #33

Comments

tkf commented Jun 5, 2022 • edited Loading

Workaround

Examples

jgreener64 commented Jan 10, 2023

vchuravy commented Jan 11, 2023

jgreener64 commented Jan 11, 2023

vchuravy commented Jan 11, 2023

vchuravy commented Jan 11, 2023

jgreener64 commented Jan 12, 2023

vchuravy commented Jan 13, 2023

vchuravy commented Jan 13, 2023

tkf commented Jun 5, 2022 •

edited

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