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Adjoint not supported on Diagonal arrays

kiranshila opened this issue · comments

commented

Describe the bug

Trying to compute the adjoint (A') of a Diagonal backed by a CuArray reverts to scalar indexing.

To reproduce

The Minimal Working Example (MWE) for this bug:

using CUDA, LinearAlgebra

x = CuArray(rand(ComplexF32,5))
A = Diagonal(x)
A' # Errors

This will work in a REPL, but not running in VSCode (for some reason). The problem becomes more obvious when you try to use the result in computation, like a matmul with a dense array (this might also mean that diagonal multiplication has the issue, but I'm having difficulty determining that).

y  = CuArray(rand(ComplexF32,5,5))
y * A' # Errors

The adjoint in other contexts seems to work fine

x * x' # Works fine
y * y' # Works fine

And multiplication with non-adjointed diagonals work fine as well

y * A # Works fine
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Expected behavior

For Adjoints of diagonal arrays to match CPU behavior.

Version info

Details on Julia:

Julia Version 1.10.1
Commit 7790d6f0641 (2024-02-13 20:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 20 × Intel(R) Core(TM) i9-9900X CPU @ 3.50GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake-avx512)
Threads: 1 default, 0 interactive, 1 GC (on 20 virtual cores)
Environment:
  JULIA_DEPOT_PATH = /home/kiran/.local/share/julia:/home/kiran/.local/share/julia:

Details on CUDA:

CUDA runtime 12.3, artifact installation
CUDA driver 12.3
NVIDIA driver 545.29.6

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+545.29.6

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

Toolchain:
- Julia: 1.10.1
- LLVM: 15.0.7

1 device:
  0: NVIDIA GeForce RTX 2080 Ti (sm_75, 9.607 GiB / 11.000 GiB available)
commented

This is the well-known issue of multiple array wrappers 'breaking' method dispatch, so I think this can be closed in favor of JuliaGPU/Adapt.jl#21.

And FWIW:

This will work in a REPL, but not running in VSCode (for some reason).

It doesn't 'work' in the REPL, it's just that we allow scalar iteration with a warning in an interactive session. It looks like VSCode isn't recognized as such, resulting in scalar iteration generating a hard error.

Since it has proven tricky to solve this from the Julia side (see the linked issue, which point to a couple of Base PRs that have stranded) I'm considering switching to unified memory by default such that scalar fallbacks at least perform somewhat decently, but they still wouldn't be executing on the GPU.