Fix rand!/randn! dispatch for non-Number array eltypes#743
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Drop the `<:Number` bound on the generic device `rand!` so isbits eltypes with a custom sampler (e.g. ColorTypes colorants) generate on-device instead of falling into the ambiguous host-array/CPU-RNG fallbacks. Matches the unconstrained `rand!` in the Random stdlib. Fixes JuliaGPU/CUDA.jl#3179. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The host-array `randn!` fallback was unconstrained, so for a GPU RNG and a
non-float eltype it matched itself recursively (`similar(AT{T})` is again a
GPU array) and stack-overflowed. Constrain it to floats, like the sibling
`randn!` methods, so non-float eltypes raise a clean MethodError instead.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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rand!(::RNG, ::AnyGPUArray{T})was constrained toT<:Number, so isbitseltypes with a custom sampler (e.g. ColorTypes colorants) fell through to an
ambiguity between the host-array and CPU-RNG fallbacks. Dropping the bound lets
them generate element-wise on-device, matching the unconstrained
rand!in theRandom stdlib.
While auditing, found a sibling bug: the host-array
randn!fallback wasunconstrained, so a GPU RNG + non-float eltype matched itself recursively and
stack-overflowed. Constrained it to floats like the other
randn!methods, sonon-float eltypes now raise a clean
MethodError.Both paths covered by new tests in the random testsuite.
Fixes JuliaGPU/CUDA.jl#3179.