StatsBase.transform fails on CuArray

[sdewaele]

The following code fails on a CuArray:

using CUDA
using StatsBase

A = 5randn(2,4).+2
t = fit(ZScoreTransform,A;dims=2)
Ac = cu(A)
Ag = StatsBase.transform(t,Ac)

Error message

┌ Warning: CUDA.jl only supports CUDNN 7.6 to 8.0
└ @ CUDA C:\Users\userid\.julia\packages\CUDA\dZvbp\src\initialization.jl:122
ERROR: GPU compilation of kernel broadcast_kernel(CUDA.CuKernelContext, LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}}, Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}, Int64) failed
KernelError: passing and using non-bitstype argument

Argument 4 to your kernel function is of type Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}}, which is not isbits:
  .args is of type Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}} which is not isbits.
    .2 is of type Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}} which is not isbits.
      .x is of type LinearAlgebra.Adjoint{Float64,Array{Float64,1}} which is not isbits.
        .parent is of type Array{Float64,1} which is not isbits.
    .3 is of type Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}} which is not isbits.
      .x is of type LinearAlgebra.Adjoint{Float64,Array{Float64,1}} which is not isbits.
        .parent is of type Array{Float64,1} which is not isbits.

Passing non-isbits types is only allowed if they they are unused by the kernel.

Stacktrace:
 [1] check_invocation(::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}, ::LLVM.Function) at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\validation.jl:75
 [2] macro expansion at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\driver.jl:240 [inlined]
 [3] macro expansion at C:\Users\userid\.julia\packages\TimerOutputs\dVnaw\src\TimerOutput.jl:206 [inlined]
 [4] codegen(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\driver.jl:239
 [5] compile(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\driver.jl:39
 [6] compile at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\driver.jl:35 [inlined]
 [7] _cufunction(::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}},Int64}}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\compiler\execution.jl:310
 [8] _cufunction at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\compiler\execution.jl:304 [inlined]
 [9] check_cache(::typeof(CUDA._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}},Int64}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\cache.jl:24
 [10] broadcast_kernel at C:\Users\userid\.julia\packages\GPUArrays\eVYIC\src\host\broadcast.jl:60 [inlined]
 [11] cached_compilation(::typeof(CUDA._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}},Int64}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\cache.jl:0
 [12] cached_compilation at C:\Users\userid\.julia\packages\GPUCompiler\GKp4B\src\cache.jl:44 [inlined]
 [13] cufunction(::GPUArrays.var"#broadcast_kernel#18", ::Type{Tuple{CUDA.CuKernelContext,LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64},Base.OneTo{Int64}},StatsBase.var"#186#187",Tuple{Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float32,CuDeviceArray{Float32,2,CUDA.AS.Global}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}},Base.Broadcast.Extruded{LinearAlgebra.Adjoint{Float64,Array{Float64,1}},Tuple{Bool,Bool},Tuple{Int64,Int64}}}},Int64}}; name::Nothing, kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\compiler\execution.jl:298
 [14] cufunction at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\compiler\execution.jl:293 [inlined]
 [15] #launch_heuristic#838 at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\gpuarrays.jl:19 [inlined]
 [16] launch_heuristic at C:\Users\userid\.julia\packages\CUDA\dZvbp\src\gpuarrays.jl:17 [inlined]
 [17] copyto! at C:\Users\userid\.julia\packages\GPUArrays\eVYIC\src\host\broadcast.jl:66 [inlined]
 [18] copyto! at .\broadcast.jl:864 [inlined]
 [19] materialize!(::LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}}, ::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{2},Nothing,StatsBase.var"#186#187",Tuple{LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}},LinearAlgebra.Adjoint{Float64,Array{Float64,1}},LinearAlgebra.Adjoint{Float64,Array{Float64,1}}}}) at .\broadcast.jl:823
 [20] broadcast!(::StatsBase.var"#186#187", ::LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}}, ::LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}}, ::LinearAlgebra.Adjoint{Float64,Array{Float64,1}}, ::LinearAlgebra.Adjoint{Float64,Array{Float64,1}}) at .\broadcast.jl:797
 [21] transform!(::LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}}, ::ZScoreTransform{Float64}, ::LinearAlgebra.Adjoint{Float32,CuArray{Float32,2}}) at C:\Users\userid\.julia\packages\StatsBase\ZxhK8\src\transformations.jl:166
 [22] transform!(::CuArray{Float32,2}, ::ZScoreTransform{Float64}, ::CuArray{Float32,2}) at C:\Users\userid\.julia\packages\StatsBase\ZxhK8\src\transformations.jl:171
 [23] transform(::ZScoreTransform{Float64}, ::CuArray{Float32,2}) at C:\Users\userid\.julia\packages\StatsBase\ZxhK8\src\transformations.jl:21
 [24] top-level scope at C:\Users\userid\.julia\dev\MigrateAI\scripts\cu-norm-issue.jl:9
 [25] include(::String) at .\client.jl:439
 [26] top-level scope at REPL[1]:1

This condensed reproduction of the transform! code may facilitate debugging:

x = Ag
y = similar(x)
y = y' # ' because t.dims == 2
x = x' # ' because t.dims == 2
t_ = ZScoreTransform(t.len, 1, t.mean, t.scale)
m = t_.mean
s = t_.scale
broadcast!((x,m,s)->(x-m)/s, y, x, m', s')

The error occurs on this line in StatsBase.transform!.

I know that it is not too hard to write code from scratch for this type of normalisation. For example, see Flux.normalise does something similar, although I do want to use precomputed mean and scale, that is why I tried StatsBase. However:

1. Doing controlled data normalization is common in machine learning so it would be nice to have something working out of the box.
2. This problem may point at something more fundamental in CUDA.jl. Or perhaps, StatsBase?

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Version information:

CUDA 1.3.3
StatsBase v0.33.1

julia> versioninfo()
Julia Version 1.4.0
Commit b8e9a9ecc6 (2020-03-21 16:36 UTC)
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: Intel(R) Core(TM) i7-8850H CPU @ 2.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
Environment:
  JULIA_CUDA_USE_BINARYBUILDER = false
  JULIA_NUM_THREADS = 5

Cuda version information:

CUDA toolkit 10.1.243, local installation
CUDA driver 10.1.0
NVIDIA driver 426.0.0

Libraries:
- CUBLAS: 10.2.1
- CURAND: 10.1.1
- CUFFT: 10.1.1
- CUSOLVER: 10.2.0
- CUSPARSE: 10.3.0
- CUPTI: 12.0.0
- NVML: 10.0.0+426.0
- CUDNN: 7.0.5 (for CUDA 9.1.0)
- CUTENSOR: missing

Toolchain:
- Julia: 1.4.0
- LLVM: 8.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3
- Device support: sm_30, sm_32, sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75

Environment:
- JULIA_CUDA_USE_BINARYBUILDER: false

1 device:
  0: Quadro P2000 (sm_61, 3.867 GiB / 4.000 GiB available)

NOTE: I have also reproduced the issue in Julia 1.5.0 on Linux, on a system where the CUDA.jl warning below does not occur and CUDA binaries were downloaded with binarybuilder.

[sdewaele]

I figured it out myself - the issue is caused by the fact that the transform mean and scale are on the CPU. When I transfer them to the GPU with cu, it works.

[lorrp1]

@sdewaele could you please post how did you used cu() to transfer transform mean and scale?

[sdewaele]

I actually created a type similar to ZScoreTransform that supports CuArrays. The issue is that the StatsBase.ZScoreTransform does not support CuArrays because the type definition is limited to Vector{T}. A better solution would be to update the StatsBase to directly support it of course.

[sdewaele]

Using this zscore-transform.jl, the following seems to do the job:

using CUDA
using StatsBase

include("zscore-transform.jl")

# Generic version
Ac = cu(5randn(3,4).+2)
tc = fit(ZScoreTransformGeneric,Ac;dims=2)
Agc = StatsBase.transform(tc,Ac)

# Compare to original ZScoreTransform result
using Test
A = collect(Ac)
t = fit(ZScoreTransform,A;dims=2)
Ag = StatsBase.transform(t,A)

@test collect(Agc)≈Ag

I may submit a PR to StatsBase for this. The changes with respect to the current ZScoreTransform version are not that many. There is a quite some code duplication in the gist just to make ZScoreTransformGeneric work.

Note that there is also Flux.normalize. It works for CuArrays. However, Flux.normalize cannot be used if you want to apply the normalization parameters from a training dataset on a test dataset, which is typical for machine learning.

[sdewaele]

When completed, this PR will resolve this issue: JuliaStats/StatsBase.jl#622

[sdewaele]

@lorrp1 The PR was merged into the master of StatsBase.

[lorrp1]

@lorrp1 The PR was merged into the master of StatsBase.

Thanks I’m going to check it

@sdewaele