Repository: JuliaGPU/CUDA.jl

Issue #875: CuArrays don't seem to display correctly in VS code
Created: 2021-04-28T21:01:32Z
Describe the bug
Seems like CuArrays don't display correctly in the VS code REPL on windows, although the values seem to be ok. Don't have a linux install to test on. Also seems to work fine when running julia in windows terminal.
To reproduce
The Minimal Working Example (MWE) for this bug:
julia> xx = randn(3)
3-element Vector{Float64}:
 -0.2691051505932
 -0.38601362073983303
 -0.16102191893287385

julia> cu(xx)
3-element CuArray{Float32, 1}:
 0.0
 0.0
 0.0

julia> cu(xx) |> Array
3-element Vector{Float32}:
 -0.26910514
 -0.38601363
 -0.16102192

julia> cu(xx) .+ 10 |> Array
3-element Vector{Float32}:
 9.730895
 9.613986
 9.838978

# These seem to work, VS code uses some special MIME or something?
julia> display(cu(xx))
3-element CuArray{Float32, 1}:
 -0.26910514
 -0.38601363
 -0.16102192

julia> show(cu(xx))
Float32[-0.26910514, -0.38601363, -0.16102192]

# This is a bit puzzling...
julia> @show cu(xx)
cu(xx) = Float32[-0.26910514, -0.38601363, -0.16102192]
3-element CuArray{Float32, 1}:
 -0.26910514
 -0.38601363
 -0.16102192

# These are not 100% reproducible. Threading issue?
julia> cu(xx) |> identity
3-element CuArray{Float32, 1}:
 -0.26910514
 -0.38601363
 -0.16102192

julia> cu(xx) .+ 10
3-element CuArray{Float32, 1}:
 -0.26910514
 -0.38601363
 -0.16102192
Manifest.toml

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

[[AbstractFFTs]]
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[[ArgTools]]
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[[BFloat16s]]
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[[Dates]]
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[[DelimitedFiles]]
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[[Distributed]]
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[[Downloads]]
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[[ExprTools]]
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[[GPUArrays]]
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[[GPUCompiler]]
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[[InteractiveUtils]]
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[[JLLWrappers]]
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[[LLVM]]
deps = ["CEnum", "Libdl", "Printf", "Unicode"]
git-tree-sha1 = "b616937c31337576360cb9fb872ec7633af7b194"
uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
version = "3.6.0"

[[LazyArtifacts]]
deps = ["Artifacts", "Pkg"]
uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"

[[LibCURL]]
deps = ["LibCURL_jll", "MozillaCACerts_jll"]
uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"

[[LibCURL_jll]]
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[[LibGit2]]
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[[LibSSH2_jll]]
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[[Libdl]]
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[[LinearAlgebra]]
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[[Logging]]
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[[MacroTools]]
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[[MbedTLS_jll]]
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[[Memoize]]
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[[Mmap]]
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[[MozillaCACerts_jll]]
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[[NetworkOptions]]
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[[OpenSpecFun_jll]]
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[[OrderedCollections]]
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[[Pkg]]
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[[Preferences]]
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[[Printf]]
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[[REPL]]
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[[Random]]
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[[RandomNumbers]]
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[[Requires]]
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[[SHA]]
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[[Scratch]]
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[[Serialization]]
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[[SharedArrays]]
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[[UUIDs]]
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[[Zlib_jll]]
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[[nghttp2_jll]]
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[[p7zip_jll]]
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Expected behavior
Values should be represented correctly.
Version info
Details on Julia:
# please post the output of:
julia> versioninfo()
Julia Version 1.6.1
Commit 6aaedecc44 (2021-04-23 05:59 UTC)
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-11.0.1 (ORCJIT, haswell)
Environment:
  JULIA_DEPOT_PATH = E:/Programs/julia/.julia
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 12

Details on CUDA:
# please post the output of:
julia> CUDA.version()
v"11.3.0"

Additional context
Add any other context about the problem here.

Duplicate issue #1471: Some kind of issue with either allocation or display synchronization
Created: 2022-04-08T21:39:36Z
I'm not sure how replicable this issue is, but using CuArray() to convert a matrix sometimes gives back the uninitialized memory in the CuArray. The issues seems to be averted using convert() and fixed for a while using Cuda.@sync
julia> a = Float32.(rand(3,3))
3×3 Matrix{Float32}:
 0.0120305  0.971768   0.965004
 0.793499   0.0451779  0.89302
 0.124688   0.895949   0.0113215
julia> b = CuArray(a)
3×3 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 0.0  0.0  0.0
 0.0  0.0  0.0
 0.0  0.0  0.0
julia> ba = CuArray(a)
3×3 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 0.524379  0.260636  0.0440601
 0.116213  0.41118   0.59073
 0.497124  0.941929  0.0188319
julia> c = convert(CuArray, a)
3×3 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 0.0120305  0.971768   0.965004
 0.793499   0.0451779  0.89302
 0.124688   0.895949   0.0113215
julia> CUDA.@sync d = CuArray(a)
3×3 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 0.0120305  0.971768   0.965004
 0.793499   0.0451779  0.89302
 0.124688   0.895949   0.0113215
julia> e = CuArray(a)
3×3 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 0.0120305  0.971768   0.965004
 0.793499   0.0451779  0.89302
 0.124688   0.895949   0.0113215

Closing comment (2022-04-09T08:18:53Z): You're probably using VSCode. This is a bug in VSCode, so dup of #875. #837 fixed this for the regular REPL.