JuliaGPU/CUDA.jl

Inconsistency between CUDA.jl and Base for sum!

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#2,506 opened on Sep 25, 2024

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Description

Hi, thanks again for putting CUDA.jl together!

I found that the return type of the sum! function can be different between Array and CuArray: the Array return type is the same as the left argument while the CuArray return type retains the singleton dimension.

using CUDA
X = rand(Float32, (50, 50, 5));
Y = similar(X, (50,50));
res = sum!(Y, X);
size(res) # (50,50)

X_d = CuArray(X);
Y_d = CuArray(Y);
res_d = sum!(Y_d, X_d);
size(res_d) # (50,50,1)

I would expect to get the same type as Y_d in this case.

Version info

Julia Version 1.10.5
Commit 6f3fdf7b36 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × 11th Gen Intel(R) Core(TM) i9-11900K @ 3.50GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, rocketlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)

CUDA runtime 12.6, artifact installation
CUDA driver 12.4
NVIDIA driver 551.61.0

CUDA libraries:
- CUBLAS: 12.6.1
- CURAND: 10.3.7
- CUFFT: 11.2.6
- CUSOLVER: 11.6.4
- CUSPARSE: 12.5.3
- CUPTI: 2024.3.1 (API 24.0.0)
- NVML: 12.0.0+551.61

Julia packages:
- CUDA: 5.5.1
- CUDA_Driver_jll: 0.10.2+0
- CUDA_Runtime_jll: 0.15.2+0

Toolchain:
- Julia: 1.10.5
- LLVM: 15.0.7

1 device:
  0: NVIDIA RTX A5000 (sm_86, 21.757 GiB / 23.988 GiB available)

Contributor guide

Tech stack
None
Domain
backend
Issue type
bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
JuliaCUDA.jlGPGPU
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50
Research direction
Investigate the implementation of `sum!` for CuArray in CUDA.jl, likely in the file `src/accumulate.jl` or similar. Compare with the Base Julia implementation to identify where the singleton dimension is retained. The fix may involve modifying the reduction logic to drop singleton dimensions consistent with Base. Review the issue at https://github.com/JuliaGPU/CUDA.jl/issues/2506 for full details.