BFloat16 support broken on Julia 1.11

[jonas-schulze]

Describe the bug

Creating a BF16 array fails on Julia 1.11.

To reproduce

The Minimal Working Example (MWE) for this bug:

using BFloat16s # 0.5.0
using CUDA # 5.2.0

CUDA.zeros(BFloat16, 5)

which fails with

ERROR: MethodError: no method matching get_inference_world(::GPUCompiler.GPUInterpreter)
The function `get_inference_world` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  get_inference_world(::REPL.REPLCompletions.REPLInterpreter)
   @ REPL ~/.julia/juliaup/julia-1.11.0-alpha2+0.x64.linux.gnu/share/julia/stdlib/v1.11/REPL/src/REPLCompletions.jl:550
  get_inference_world(::Core.Compiler.NativeInterpreter)
   @ Core compiler/types.jl:402

Full error message

ERROR: MethodError: no method matching get_inference_world(::GPUCompiler.GPUInterpreter)
The function `get_inference_world` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  get_inference_world(::REPL.REPLCompletions.REPLInterpreter)
   @ REPL ~/.julia/juliaup/julia-1.11.0-alpha2+0.x64.linux.gnu/share/julia/stdlib/v1.11/REPL/src/REPLCompletions.jl:550
  get_inference_world(::Core.Compiler.NativeInterpreter)
   @ Core compiler/types.jl:402

Stacktrace:
  [1] Core.Compiler.InferenceState(result::Core.Compiler.InferenceResult, cache_mode::UInt8, interp::GPUCompiler.GPUInterpreter)
    @ Core.Compiler ./compiler/inferencestate.jl:493
  [2] Core.Compiler.InferenceState(result::Core.Compiler.InferenceResult, cache_mode::Symbol, interp::GPUCompiler.GPUInterpreter)
    @ Core.Compiler ./compiler/inferencestate.jl:499
  [3] typeinf(interp::GPUCompiler.GPUInterpreter, result::Core.Compiler.InferenceResult, cache_mode::Symbol)
    @ Core.Compiler ./compiler/typeinfer.jl:9
  [4] typeinf_type(interp::GPUCompiler.GPUInterpreter, mi::Core.MethodInstance)
    @ Core.Compiler ./compiler/typeinfer.jl:1072
  [5] typeinf_type(interp::GPUCompiler.GPUInterpreter, method::Method, atype::Any, sparams::Core.SimpleVector)
    @ Core.Compiler ./compiler/typeinfer.jl:1059
  [6] typeinf_type(mi::Core.MethodInstance; interp::GPUCompiler.GPUInterpreter)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/validation.jl:17
  [7] typeinf_type
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/validation.jl:16 [inlined]
  [8] check_method(job::GPUCompiler.CompilerJob)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/validation.jl:33
  [9] macro expansion
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:122 [inlined]
 [10] macro expansion
    @ ~/.julia/packages/TimerOutputs/RsWnF/src/TimerOutput.jl:253 [inlined]
 [11] codegen(output::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:121
 [12] codegen
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:110 [inlined]
 [13] compile(target::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:106
 [14] compile
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:98 [inlined]
 [15] #1072
    @ ~/.julia/packages/CUDA/htRwP/src/compiler/compilation.jl:247 [inlined]
 [16] JuliaContext(f::CUDA.var"#1072#1075"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:47
 [17] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/htRwP/src/compiler/compilation.jl:246
 [18] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/execution.jl:125
 [19] cached_compilation(cache::Dict{Any, CuFunction}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/execution.jl:103
 [20] macro expansion
    @ ~/.julia/packages/CUDA/htRwP/src/compiler/execution.jl:367 [inlined]
 [21] macro expansion
    @ ./lock.jl:273 [inlined]
 [22] cufunction(f::GPUArrays.var"#6#7", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceVector{BFloat16, 1}, BFloat16}}; kwargs::@Kwargs{})
    @ CUDA ~/.julia/packages/CUDA/htRwP/src/compiler/execution.jl:362
 [23] cufunction
    @ ~/.julia/packages/CUDA/htRwP/src/compiler/execution.jl:359 [inlined]
 [24] macro expansion
    @ ~/.julia/packages/CUDA/htRwP/src/compiler/execution.jl:112 [inlined]
 [25] #launch_heuristic#1122
    @ ~/.julia/packages/CUDA/htRwP/src/gpuarrays.jl:17 [inlined]
 [26] launch_heuristic
    @ ~/.julia/packages/CUDA/htRwP/src/gpuarrays.jl:15 [inlined]
 [27] gpu_call(::GPUArrays.var"#6#7", ::CuArray{…}, ::BFloat16; target::CuArray{…}, elements::Nothing, threads::Nothing, blocks::Nothing, name::Nothing)
    @ GPUArrays ~/.julia/packages/GPUArrays/Hd5Sk/src/device/execution.jl:61
 [28] gpu_call
    @ ~/.julia/packages/GPUArrays/Hd5Sk/src/device/execution.jl:34 [inlined]
 [29] fill!(A::CuArray{BFloat16, 1, CUDA.Mem.DeviceBuffer}, x::BFloat16)
    @ GPUArrays ~/.julia/packages/GPUArrays/Hd5Sk/src/host/construction.jl:14
 [30] zeros(T::Type, dims::Int64)
    @ CUDA ~/.julia/packages/CUDA/htRwP/src/array.jl:813
 [31] top-level scope
    @ REPL[7]:1
 [32] top-level scope
    @ ~/.julia/packages/CUDA/htRwP/src/initialization.jl:206
Some type information was truncated. Use `show(err)` to see complete types.

Manifest.toml

# This file is machine-generated - editing it directly is not advised

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Expected behavior

Adding the same versions of BFloat16s and CUDA to a fresh Julia 1.10 environment, I don't get any errors. I would expect the same for Julia 1.11.

Version info

Details on Julia:

Julia Version 1.11.0-alpha2
Commit 9dfd28ab751 (2024-03-18 20:35 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 16 × 12th Gen Intel(R) Core(TM) i5-12600K
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)
Environment:
  LD_LIBRARY_PATH = /scratch/nvidia/hpc_sdk/Linux_x86_64/22.7/math_libs/lib64:/home/jschulze/.local/lib:
  JULIA_PROJECT = @.

Details on CUDA:

CUDA runtime 12.3, artifact installation
CUDA driver 12.3
NVIDIA driver 525.147.5, originally for CUDA 12.0

CUDA libraries: 
- CUBLAS: 12.3.4
- CURAND: 10.3.4
- CUFFT: 11.0.12
- CUSOLVER: 11.5.4
- CUSPARSE: 12.2.0
- CUPTI: 21.0.0
- NVML: 12.0.0+525.147.5

Julia packages: 
- CUDA: 5.2.0
- CUDA_Driver_jll: 0.7.0+1
- CUDA_Runtime_jll: 0.11.1+0

Toolchain:
- Julia: 1.11.0-alpha2
- LLVM: 16.0.6

1 device:
  0: NVIDIA RTX A4000 (sm_86, 15.718 GiB / 15.992 GiB available)

Additional context

Originally, I was trying to verify that I can use BF16 on hardware on the GPU. While digging into the documentation/history, I found that BFloat16s.jl was (originally) only using emulated/software BF16. Using Julia 1.10, some dummy code was running ok, but I am not used to reading the outputs of @code_native and friends, so I kept reading. I could only find a wrapped BF16 GEMM in CUDA.jl but not where it was wired to be used for CuArray{BFloat16}. Starting with Julia 1.11 and BFloat16s 0.5, more of the work is put on LLVM to handle BF16, which I then thought was a necessity to support it on the GPU. This led me to try Julia 1.11-alpha2 and this bug. I hope this is the right place to report; I have never used an alpha version of Julia before.

... if you read this far; how do I verify that my code is actually using BF16 in hardware on the GPU? Thanks in advance for any pointers. 🙂

Related: #2241, #2291

[maleadt]

This is unrelated to BFloat16; 1.11 is just not supported yet.
You can try using #2241, but there's known issues. In any case, the specific issue here is that you need the latest GPUCompiler, which is incompatible with the latest released version of CUDA.jl

[maleadt]

how do I verify that my code is actually using BF16 in hardware on the GPU? Thanks in advance for any pointers. 🙂

You could look at the generated code, by doing @device_code_ptx and looking for bf16.