Add AbstractChar array support to MLDataDevices by Copilot · Pull Request #1582 · LuxDL/Lux.jl

Copilot · 2025-12-05T21:00:53Z

Transferring character arrays to GPU devices failed with MethodError: no method matching array_adapt(::typeof(cu), ::Type{CuArray}, ::Type{Missing}, ::Vector{Char}).

Changes

Added array_adapt methods for AbstractArray{<:AbstractChar} in lib/MLDataDevices/src/internal.jl
- Three methods following the same pattern as Number types: Missing, Nothing, and parameterized T eltype cases
- Element type is preserved (no conversion), consistent with non-float numeric types
Added test coverage in cuda_tests.jl and misc_tests.jl

Example

using MLDataDevices

dev = gpu_device()
dev(['a', 'b', 'c'])  # Now returns CuVector{Char} instead of MethodError

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Original prompt

This section details on the original issue you should resolve

<issue_title>[MLDataDevices] failure at transfering non numerical array</issue_title>
<issue_description>This used to work (it is tested in Flux) and returned a CuVector{Char}:

julia> using CUDA, cuDNN, MLDataDevices

julia> dev = gpu_device()
(::CUDADevice{Nothing, Missing}) (generic function with 1 method)

julia> dev(['a', 'b', 'c'])
ERROR: MethodError: no method matching array_adapt(::typeof(cu), ::Type{CuArray}, ::Type{Missing}, ::Vector{Char})
The function `array_adapt` exists, but no method is defined for this combination of argument types.

Closest candidates are:
  array_adapt(::F, ::Type{aType}, ::Type{Missing}, ::AbstractArray{<:AbstractFloat}) where {F, aType}
   @ MLDataDevices ~/.julia/packages/MLDataDevices/f5bYy/src/internal.jl:289
  array_adapt(::F, ::Type{aType}, ::Type{Missing}, ::AbstractArray{<:Complex{<:AbstractFloat}}) where {F, aType}
   @ MLDataDevices ~/.julia/packages/MLDataDevices/f5bYy/src/internal.jl:294
  array_adapt(::F, ::Type{aType}, ::Type{Missing}, ::AbstractArray{<:Number}) where {F, aType}
   @ MLDataDevices ~/.julia/packages/MLDataDevices/f5bYy/src/internal.jl:299
  ...

Stacktrace:
  [1] cuda_array_adapt(::Type{Missing}, x::Vector{Char})
    @ MLDataDevicesCUDAExt ~/.julia/packages/MLDataDevices/f5bYy/ext/MLDataDevicesCUDAExt.jl:61
  [2] adapt_storage(::CUDADevice{Nothing, Missing}, x::Vector{Char})
    @ MLDataDevicesCUDAExt ~/.julia/packages/MLDataDevices/f5bYy/ext/MLDataDevicesCUDAExt.jl:65
  [3] adapt_structure(to::Function, x::Vector{Char})
    @ Adapt ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:57
  [4] adapt(to::Function, x::Vector{Char})
    @ Adapt ~/.julia/packages/Adapt/2UZ81/src/Adapt.jl:40
  [5] (::Base.Fix1{typeof(Adapt.adapt), CUDADevice{Nothing, Missing}})(arg::Vector{Char}; kws::@Kwargs{})
    @ Base ./operators.jl:1193
  [6] ExcludeWalk
    @ ~/.julia/packages/Functors/LbNAu/src/walks.jl:126 [inlined]
  [7] (::Functors.CachedWalk{Functors.ExcludeWalk{…}, Functors.NoKeyword, Functors.WalkCache{…}})(::Function, ::Vector{Char})
    @ Functors ~/.julia/packages/Functors/LbNAu/src/walks.jl:177
  [8] execute
    @ ~/.julia/packages/Functors/LbNAu/src/walks.jl:55 [inlined]
  [9] #fmap#32
    @ ~/.julia/packages/Functors/LbNAu/src/maps.jl:11 [inlined]
 [10] fmap
    @ ~/.julia/packages/Functors/LbNAu/src/maps.jl:3 [inlined]
 [11] (::CUDADevice{Nothing, Missing})(x::Vector{Char})
    @ MLDataDevices ~/.julia/packages/MLDataDevices/f5bYy/src/public.jl:503
 [12] top-level scope
    @ REPL[4]:1
Some type information was truncated. Use `show(err)` to see complete types.
```</issue_description>

## Comments on the Issue (you are @copilot in this section)

<comments>
<comment_new><author>@avik-pal</author><body>
We are missing dispatches for AbstractChar (it should mimic Number) in https://github.com/LuxDL/Lux.jl/blob/1f1fbb629739bf896ca43ca1dc729853a0c88fbf/lib/MLDataDevices/src/internal.jl#L289-L335</body></comment_new>
</comments>

Fixes [MLDataDevices] failure at transfering non numerical array #1580

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

- Add array_adapt methods for AbstractArray{<:AbstractChar} (3 methods for Missing, Nothing, and T eltype cases) - Add tests for character array transfer in cuda_tests.jl and misc_tests.jl - Fixes issue where transferring character arrays to GPU devices would fail with MethodError Co-authored-by: avik-pal <30564094+avik-pal@users.noreply.github.com>

github-actions · 2025-12-05T21:36:51Z

Benchmark Results (Julia v1.11)

Time benchmarks

	main	`deea48f`...	main / `deea48f`...
basics/MHA	4.19 ± 0.34 μs	4.27 ± 0.36 μs	0.981 ± 0.12
basics/MHA (first run)	4.43 ± 0.46 μs	4.53 ± 0.61 μs	0.978 ± 0.17
basics/MHA reactant	0.0688 ± 0.0099 ms	0.0669 ± 0.0051 ms	1.03 ± 0.17
basics/MHA reactant (comp + run)	0.157 ± 0.0073 s	0.149 ± 0.0036 s	1.06 ± 0.055
basics/conv	13 ± 17 μs	12.8 ± 17 μs	1.01 ± 1.8
basics/conv (first run)	10.8 ± 3.2 μs	10.7 ± 0.88 μs	1.02 ± 0.31
basics/conv reactant	0.0554 ± 0.0055 ms	0.0541 ± 0.0042 ms	1.02 ± 0.13
basics/conv reactant (comp + run)	0.104 ± 0.0034 s	0.106 ± 0.0035 s	0.976 ± 0.046
basics/dense	0.181 ± 0.01 μs	0.18 ± 0.001 μs	1.01 ± 0.056
basics/dense (first run)	0.2 ± 0.01 μs	0.2 ± 0.011 μs	1 ± 0.074
basics/dense reactant	0.0522 ± 0.0049 ms	0.0539 ± 0.0051 ms	0.969 ± 0.13
basics/dense reactant (comp + run)	0.0846 ± 0.0023 s	0.0863 ± 0.0025 s	0.98 ± 0.039
time_to_load	0.918 ± 0.018 s	0.947 ± 0.013 s	0.97 ± 0.023

Memory benchmarks

	main	`deea48f`...	main / `deea48f`...
basics/MHA	0.087 k allocs: 6.05 kB	0.087 k allocs: 6.05 kB	1
basics/MHA (first run)	0.087 k allocs: 6.05 kB	0.087 k allocs: 6.05 kB	1
basics/MHA reactant	19 allocs: 0.578 kB	19 allocs: 0.578 kB	1
basics/MHA reactant (comp + run)	18 k allocs: 1.37 MB	18 k allocs: 1.37 MB	1
basics/conv	0.038 k allocs: 5.12 kB	0.038 k allocs: 5.12 kB	1
basics/conv (first run)	0.038 k allocs: 5.12 kB	0.038 k allocs: 5.12 kB	1
basics/conv reactant	15 allocs: 0.438 kB	15 allocs: 0.438 kB	1
basics/conv reactant (comp + run)	6.15 k allocs: 0.813 MB	6.14 k allocs: 0.813 MB	1
basics/dense	2 allocs: 0.109 kB	2 allocs: 0.109 kB	1
basics/dense (first run)	2 allocs: 0.109 kB	2 allocs: 0.109 kB	1
basics/dense reactant	15 allocs: 0.422 kB	15 allocs: 0.422 kB	1
basics/dense reactant (comp + run)	5.9 k allocs: 0.796 MB	5.91 k allocs: 0.796 MB	1
time_to_load	0.159 k allocs: 11.2 kB	0.159 k allocs: 11.2 kB	1

Initial plan

28cb5b8

Copilot AI assigned Copilot and avik-pal Dec 5, 2025

Copilot started work on behalf of avik-pal December 5, 2025 21:01 View session

Copilot AI changed the title ~~[WIP] Fix failure in transferring non-numerical array with MLDataDevices~~ Add AbstractChar array support to MLDataDevices Dec 5, 2025

Copilot AI requested a review from avik-pal December 5, 2025 21:12

Copilot finished work on behalf of avik-pal December 5, 2025 21:12

avik-pal marked this pull request as ready for review December 5, 2025 22:40

avik-pal merged commit e203e27 into main Dec 5, 2025
70 of 121 checks passed

avik-pal deleted the copilot/fix-transfer-non-numerical-array branch December 5, 2025 22:40

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Add AbstractChar array support to MLDataDevices#1582

Add AbstractChar array support to MLDataDevices#1582
avik-pal merged 2 commits intomainfrom
copilot/fix-transfer-non-numerical-array

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Copilot AI commented Dec 5, 2025 •

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github-actions bot commented Dec 5, 2025 •

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