MLJFlux 仅在 GPU 上崩溃，并出现 MethodError: no methodmatchingparent(...)

问题描述 投票：0回答：1

我尝试使用 MLJFlux 在 Julia 中训练一个简单的密集神经网络。代码在 CPU 上运行，但是当我想在 GPU 上运行它时，它崩溃了，并在 fit!(machine) 命令中出现 MethodError，说该方法

parent(::Type{SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{ Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true}})

未知。我在下面发布了 MWE 和完整的堆栈跟踪。有人可以指出我正确的方向这段代码有什么问题吗？

using Flux
using CUDA
using MLJ
using MLUtils
using cuDNN


x = Float32.(rand(1000, 9))
y = Float32.(rand(1000))

NeuralNetworkRegressor = MLJ.@load NeuralNetworkRegressor pkg=MLJFlux verbosity=0

builder = MLJFlux.@builder begin
        Chain(
            Dense(9, 32, relu ),
            Dense(32, 64, relu),
            Dense(64, 32, relu),
            Dense(32, 1),
        )
    end

model = NeuralNetworkRegressor(
         builder=builder,
         acceleration = CUDALibs(), #if I comment this line, the code works without error
         batch_size = 512,
         epochs=10)

mach = machine(model, x, y)
fit!(mach)

此代码在 CPU 上运行，但在 GPU 上，最后一行 fit!() 崩溃了

┌ Error: Problem fitting the machine machine(NeuralNetworkRegressor(builder = GenericBuilder(apply = #3), …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/fEiP2/src/machines.jl:682
[ Info: Running type checks... 
[ Info: Type checks okay. 
ERROR: LoadError: MethodError: no method matching parent(::Type{SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true}})

Closest candidates are:
  parent(::Union{LinearAlgebra.Adjoint{T, S}, LinearAlgebra.Transpose{T, S}} where {T, S})
   @ LinearAlgebra ~/software/julia-1.9.2/share/julia/stdlib/v1.9/LinearAlgebra/src/adjtrans.jl:341
  parent(::Union{LinearAlgebra.LowerTriangular{T, S} where S<:AbstractMatrix{T}, LinearAlgebra.UnitLowerTriangular{T, S} where S<:AbstractMatrix{T}, LinearAlgebra.UnitUpperTriangular{T, S} where S<:AbstractMatrix{T}, LinearAlgebra.UpperTriangular{T, S} where S<:AbstractMatrix{T}} where T)
   @ LinearAlgebra ~/software/julia-1.9.2/share/julia/stdlib/v1.9/LinearAlgebra/src/triangular.jl:164
  parent(::Union{LinearAlgebra.Hermitian{T, S}, LinearAlgebra.Symmetric{T, S}} where {T, S})
   @ LinearAlgebra ~/software/julia-1.9.2/share/julia/stdlib/v1.9/LinearAlgebra/src/symmetric.jl:275
  ...

Stacktrace:
  [1] backend(#unused#::Type{SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true}})
    @ GPUArraysCore ~/.julia/packages/GPUArraysCore/uOYfN/src/GPUArraysCore.jl:151
  [2] backend(x::SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true})
    @ GPUArraysCore ~/.julia/packages/GPUArraysCore/uOYfN/src/GPUArraysCore.jl:149
  [3] _copyto!
    @ ~/.julia/packages/GPUArrays/5XhED/src/host/broadcast.jl:65 [inlined]
  [4] materialize!
    @ ~/.julia/packages/GPUArrays/5XhED/src/host/broadcast.jl:41 [inlined]
  [5] materialize!
    @ ./broadcast.jl:881 [inlined]
  [6] ∇getindex!(dx::Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, dy::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, inds::Int64)
    @ ChainRules ~/.julia/packages/ChainRules/DSuXy/src/rulesets/Base/indexing.jl:147
  [7] ∇getindex(x::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, dy::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, inds::Int64)
    @ ChainRules ~/.julia/packages/ChainRules/DSuXy/src/rulesets/Base/indexing.jl:89
  [8] (::ChainRules.var"#1583#1585"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}})()
    @ ChainRules ~/.julia/packages/ChainRules/DSuXy/src/rulesets/Base/indexing.jl:69
  [9] unthunk
    @ ~/.julia/packages/ChainRulesCore/7MWx2/src/tangent_types/thunks.jl:204 [inlined]
 [10] unthunk(x::ChainRulesCore.InplaceableThunk{ChainRulesCore.Thunk{ChainRules.var"#1583#1585"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}}}, ChainRules.var"#1582#1584"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}}})
    @ ChainRulesCore ~/.julia/packages/ChainRulesCore/7MWx2/src/tangent_types/thunks.jl:237
 [11] wrap_chainrules_output
    @ ~/.julia/packages/Zygote/YYT6v/src/compiler/chainrules.jl:110 [inlined]
 [12] map
    @ ./tuple.jl:275 [inlined]
 [13] wrap_chainrules_output
    @ ~/.julia/packages/Zygote/YYT6v/src/compiler/chainrules.jl:111 [inlined]
 [14] (::Zygote.ZBack{ChainRules.var"#getindex_pullback#1581"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Int64}, Tuple{ChainRulesCore.NoTangent}}})(dy::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote ~/.julia/packages/Zygote/YYT6v/src/compiler/chainrules.jl:211
 [15] Pullback
    @ ~/.julia/packages/MLJFlux/V1aWg/src/core.jl:41 [inlined]
 [16] (::Zygote.Pullback{Tuple{MLJFlux.var"#23#24"{MLJFlux.Penalty{MLJFlux.Penalizer{Nothing}}, Chain{Tuple{Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Int64, typeof(Flux.Losses.mse), Int64, Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}}}, Any})(Δ::Float32)
    @ Zygote ~/.julia/packages/Zygote/YYT6v/src/compiler/interface2.jl:0
 [17] (::Zygote.var"#122#123"{Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}, Zygote.Pullback{Tuple{MLJFlux.var"#23#24"{MLJFlux.Penalty{MLJFlux.Penalizer{Nothing}}, Chain{Tuple{Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Int64, typeof(Flux.Losses.mse), Int64, Zygote.Params{Zygote.Buffer{Any, Vector{Any}}}}}, Any}, Zygote.Context{true}})(Δ::Float32)
    @ Zygote ~/.julia/packages/Zygote/YYT6v/src/compiler/interface.jl:419
 [18] gradient
    @ ~/.julia/packages/Zygote/YYT6v/src/compiler/interface.jl:97 [inlined]
 [19] train!(model::MLJFlux.NeuralNetworkRegressor{MLJFlux.GenericBuilder{var"#3#4"}, Adam, typeof(Flux.Losses.mse)}, penalty::MLJFlux.Penalty{MLJFlux.Penalizer{Nothing}}, chain::Chain{Tuple{Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}}, optimiser::Adam, X::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, y::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}})
    @ MLJFlux ~/.julia/packages/MLJFlux/V1aWg/src/core.jl:39
 [20] fit!(model::MLJFlux.NeuralNetworkRegressor{MLJFlux.GenericBuilder{var"#3#4"}, Adam, typeof(Flux.Losses.mse)}, penalty::MLJFlux.Penalty{MLJFlux.Penalizer{Nothing}}, chain::Chain{Tuple{Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(relu), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}, Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}}, optimiser::Adam, epochs::Int64, verbosity::Int64, X::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, y::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}})
    @ MLJFlux ~/.julia/packages/MLJFlux/V1aWg/src/core.jl:107
 [21] fit(model::MLJFlux.NeuralNetworkRegressor{MLJFlux.GenericBuilder{var"#3#4"}, Adam, typeof(Flux.Losses.mse)}, verbosity::Int64, X::Matrix{Float32}, y::Vector{Float32})
    @ MLJFlux ~/.julia/packages/MLJFlux/V1aWg/src/mlj_model_interface.jl:79
 [22] fit_only!(mach::Machine{MLJFlux.NeuralNetworkRegressor{MLJFlux.GenericBuilder{var"#3#4"}, Adam, typeof(Flux.Losses.mse)}, true}; rows::Nothing, verbosity::Int64, force::Bool, composite::Nothing)
    @ MLJBase ~/.julia/packages/MLJBase/fEiP2/src/machines.jl:680
 [23] fit_only!
    @ ~/.julia/packages/MLJBase/fEiP2/src/machines.jl:606 [inlined]
 [24] #fit!#63
    @ ~/.julia/packages/MLJBase/fEiP2/src/machines.jl:777 [inlined]
 [25] fit!(mach::Machine{MLJFlux.NeuralNetworkRegressor{MLJFlux.GenericBuilder{var"#3#4"}, Adam, typeof(Flux.Losses.mse)}, true})
    @ MLJBase ~/.julia/packages/MLJBase/fEiP2/src/machines.jl:774
 [26] top-level scope
    @ /path/to/file/file.jl:29
in expression starting at /path/to/file/file.jl:29
deep-learning julia
1个回答
0
投票

更新 Julia 软件包已解决该问题。该代码在 Julia 1.9.2、Flux 0.14.8、CUDA 5.1.1、MLJFlux 0.4.0 和 MLJ 0.20.2 下运行

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