pointer(CuArray) is not defined

[una-dinosauria]

The demos from Metalhead.jl are broken if one tries to run them on the GPU (xref FluxML/Metalhead.jl#42), eg

julia> using CuArrays, Metalhead, Flux
julia> vgg = VGG19() |> gpu
julia> x = cu(rand(Float32, 224, 224, 3, 1));
julia> vgg(x)

The trace points to a an exception in NNlib:

ERROR: conversion to pointer not defined for CuArray{Float32,2}
Stacktrace:
 [1] error(::String) at ./error.jl:33
 [2] unsafe_convert(::Type{Ptr{Float32}}, ::CuArray{Float32,2}) at ./pointer.jl:67
 [3] pointer(::CuArray{Float32,2}) at ./abstractarray.jl:934
 [4] macro expansion at /home/julieta/.julia/packages/NNlib/mxWRT/src/impl/conv_im2col.jl:54 [inlined]
 [5] macro expansion at ./gcutils.jl:87 [inlined]
 [6] macro expansion at /home/julieta/.julia/packages/NNlib/mxWRT/src/impl/conv_im2col.jl:53 [inlined]
 [7] #conv_im2col!#231(::CuArray{Float32,2}, ::Float32, ::Float32, ::typeof(NNlib.conv_im2col!), ::CuArray{Float32,5}, ::CuArray{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),3,64,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/julieta/.julia/packages/TimerOutputs/7zSea/src/TimerOutput.jl:190
 [8] conv!(::CuArray{Float32,5}, ::CuArray{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),3,64,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/julieta/.julia/packages/TimerOutputs/7zSea/src/TimerOutput.jl:198
 [9] #conv!#56(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(conv!), ::CuArray{Float32,4}, ::CuArray{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),3,64,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/julieta/.julia/packages/NNlib/mxWRT/src/conv.jl:68
 [10] conv!(::CuArray{Float32,4}, ::CuArray{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),3,64,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/julieta/.julia/packages/NNlib/mxWRT/src/conv.jl:68
 [11] macro expansion at /home/julieta/.julia/packages/NNlib/mxWRT/src/conv.jl:114 [inlined]
 [12] #conv#97(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(conv), ::CuArray{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),3,64,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/julieta/.julia/packages/TimerOutputs/7zSea/src/TimerOutput.jl:190
 [13] conv(::CuArray{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),3,64,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/julieta/.julia/packages/TimerOutputs/7zSea/src/TimerOutput.jl:198
 [14] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::CuArray{Float32,4}) at /home/julieta/.julia/packages/Flux/dkJUV/src/layers/conv.jl:55
 [15] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},getfield(Metalhead, Symbol("##44#45")),Dense{typeof(relu),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},Dropout{Float32},Dense{typeof(relu),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},Dropout{Float32},Dense{typeof(identity),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},typeof(softmax)}, ::CuArray{Float32,4}) at /home/julieta/.julia/packages/Flux/dkJUV/src/layers/basic.jl:31
 [16] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},getfield(Metalhead, Symbol("##44#45")),Dense{typeof(relu),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},Dropout{Float32},Dense{typeof(relu),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},Dropout{Float32},Dense{typeof(identity),LinearAlgebra.Adjoint{Float32,Array{Float32,2}},Array{Float32,1}},typeof(softmax)}})(::CuArray{Float32,4}) at /home/julieta/.julia/packages/Flux/dkJUV/src/layers/basic.jl:33
 [17] (::VGG19)(::CuArray{Float32,4}) at /home/julieta/.julia/packages/Metalhead/1dQOk/src/vgg19.jl:46
 [18] top-level scope at REPL[40]:1

Which in this example comes from the NNlib kernel trying to obtain a pointer from a CuArray

NNlib.jl/src/impl/conv_im2col.jl

Line 54 in 342928e

col_ptr = pointer(col)

I believe to obtain the pointer of a CuArray one now does

julia> pointer(x.buf)
CUDAdrv.CuPtr{Nothing}(0x00007f09ffc40000)

But I have no idea how to rewrite the code to make this compatible between CPU and GPU arrays.

[ljluestc]

To make the code compatible between CPU and GPU arrays, you can modify the line in the Metalhead.jl package where the pointer() function is used. Instead of directly calling pointer(col), you can create a helper function that handles both CPU and GPU arrays.

[ToucheSir]

This is an ancient issue (MWE still uses CuArrays) and we explicitly test Metalhead models on GPU now, so closing as long past stale.