问基于DiffEqFlux的神经ODE反向传播过程中的标量运算
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Stack Overflow用户

提问于 2019-12-27 10:45:14

回答 1查看 219关注 0票数 2

我正在使用Julia中的DiffEqFlux包来实现一个神经ODE。我在让它在GPU上工作时遇到了问题。

最简单的例子如下：

using DifferentialEquations
using Flux, DiffEqFlux
using CuArrays
CuArrays.allowscalar(false)

x = Float32[2.; 0.]|>gpu
tspan = Float32.((0.0f0,25.0f0))
dudt = Chain(Dense(2,50,tanh),Dense(50,2))|>gpu

loss() = sum(neural_ode(dudt,x,tspan,Tsit5(),save_everystep=false,save_start=false))

@show(loss())
Flux.back!(loss())

和stacktrace：

loss() = -24.529072f0 (tracked)
ERROR: LoadError: scalar getindex is disallowed
Stacktrace:
 [1] error(::String) at ./error.jl:33
 [2] assertscalar(::String) at /home/vshanka2/.julia/packages/GPUArrays/1wgPO/src/indexing.jl:14
 [3] getindex at /home/vshanka2/.julia/packages/GPUArrays/1wgPO/src/indexing.jl:54 [inlined]
 [4] getindex at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.1/LinearAlgebra/src/adjtrans.jl:129 [inlined]
 [5] _unsafe_getindex_rs at ./reshapedarray.jl:245 [inlined]
 [6] _unsafe_getindex at ./reshapedarray.jl:242 [inlined]
 [7] getindex at ./reshapedarray.jl:231 [inlined]
 [8] macro expansion at ./multidimensional.jl:671 [inlined]
 [9] macro expansion at ./cartesian.jl:64 [inlined]
 [10] macro expansion at ./multidimensional.jl:666 [inlined]
 [11] _unsafe_getindex! at ./multidimensional.jl:662 [inlined]
 [12] _unsafe_getindex(::IndexLinear, ::Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}, ::UnitRange{Int64}) at ./multidimensional.jl:656
 [13] _getindex at ./multidimensional.jl:642 [inlined]
 [14] getindex(::Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}, ::UnitRange{Int64}) at ./abstractarray.jl:927
 [15] (::getfield(Tracker, Symbol("##429#432")){Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}})(::TrackedArray{…,CuArray{Float32,1,CuArray{Float32,2,Nothing}}}) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/lib/array.jl:196
 [16] iterate at ./generator.jl:47 [inlined]
 [17] collect(::Base.Generator{Tuple{TrackedArray{…,CuArray{Float32,1,CuArray{Float32,2,Nothing}}},TrackedArray{…,CuArray{Float32,1,Nothing}},TrackedArray{…,CuArray{Float32,1,CuArray{Float32,2,Nothing}}},TrackedArray{…,CuArray{Float32,1,Nothing}}},getfield(Tracker, Symbol("##429#432")){Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}}}) at ./array.jl:606
 [18] #428 at /home/vshanka2/.julia/packages/Tracker/cpxco/src/lib/array.jl:193 [inlined]
 [19] back_(::Tracker.Call{getfield(Tracker, Symbol("##428#431")){Tuple{TrackedArray{…,CuArray{Float32,1,CuArray{Float32,2,Nothing}}},TrackedArray{…,CuArray{Float32,1,Nothing}},TrackedArray{…,CuArray{Float32,1,CuArray{Float32,2,Nothing}}},TrackedArray{…,CuArray{Float32,1,Nothing}}}},Tuple{Tracker.Tracked{CuArray{Float32,1,CuArray{Float32,2,Nothing}}},Tracker.Tracked{CuArray{Float32,1,Nothing}},Tracker.Tracked{CuArray{Float32,1,CuArray{Float32,2,Nothing}}},Tracker.Tracked{CuArray{Float32,1,Nothing}}}}, ::Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:35
 [20] back(::Tracker.Tracked{CuArray{Float32,1,Nothing}}, ::Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:58
 [21] (::getfield(Tracker, Symbol("##13#14")){Bool})(::Tracker.Tracked{CuArray{Float32,1,Nothing}}, ::Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}}) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:38
 [22] foreach(::Function, ::Tuple{Tracker.Tracked{CuArray{Float32,1,Nothing}},Nothing,Nothing,Nothing}, ::Tuple{Base.ReshapedArray{Float32,1,LinearAlgebra.Adjoint{Float32,CuArray{Float32,1,Nothing}},Tuple{}},CuArray{Float32,1,Nothing},Nothing,Nothing}) at ./abstractarray.jl:1867
 [23] back_(::Tracker.Call{getfield(DiffEqFlux, Symbol("##25#28")){DiffEqSensitivity.SensitivityAlg{0,true,Val{:central}},Base.Iterators.Pairs{Symbol,Bool,Tuple{Symbol},NamedTuple{(:save_everystep,),Tuple{Bool}}},TrackedArray{…,CuArray{Float32,1,Nothing}},CuArray{Float32,1,Nothing},Tuple{Tsit5},ODESolution{Float32,2,Array{CuArray{Float32,1,Nothing},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,1,Nothing},1},1},ODEProblem{CuArray{Float32,1,Nothing},Tuple{Float32,Float32},false,CuArray{Float32,1,Nothing},ODEFunction{false,getfield(DiffEqFlux, Symbol("#dudt_#32")){Chain{Tuple{Dense{typeof(tanh),TrackedArray{…,CuArray{Float32,2,Nothing}},TrackedArray{…,CuArray{Float32,1,Nothing}}},Dense{typeof(identity),TrackedArray{…,CuArray{Float32,2,Nothing}},TrackedArray{…,CuArray{Float32,1,Nothing}}}}}},LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,getfield(DiffEqFlux, Symbol("#dudt_#32")){Chain{Tuple{Dense{typeof(tanh),TrackedArray{…,CuArray{Float32,2,Nothing}},TrackedArray{…,CuArray{Float32,1,Nothing}}},Dense{typeof(identity),TrackedArray{…,CuArray{Float32,2,Nothing}},TrackedArray{…,CuArray{Float32,1,Nothing}}}}}},LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Array{CuArray{Float32,1,Nothing},1},Array{Float32,1},Array{Array{CuArray{Float32,1,Nothing},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats}},Tuple{Tracker.Tracked{CuArray{Float32,1,Nothing}},Nothing,Nothing,Nothing}}, ::CuArray{Float32,1,Nothing}, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:38
 [24] back(::Tracker.Tracked{CuArray{Float32,1,Nothing}}, ::CuArray{Float32,1,Nothing}, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:58
 [25] #13 at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:38 [inlined]
 [26] foreach at ./abstractarray.jl:1867 [inlined]
 [27] back_(::Tracker.Call{getfield(Tracker, Symbol("##484#485")){TrackedArray{…,CuArray{Float32,1,Nothing}}},Tuple{Tracker.Tracked{CuArray{Float32,1,Nothing}}}}, ::Float32, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:38
 [28] back(::Tracker.Tracked{Float32}, ::Int64, ::Bool) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:58
 [29] #back!#15 at /home/vshanka2/.julia/packages/Tracker/cpxco/src/back.jl:77 [inlined]
 [30] #back! at ./none:0 [inlined]
 [31] #back!#32 at /home/vshanka2/.julia/packages/Tracker/cpxco/src/lib/real.jl:16 [inlined]
 [32] back!(::Tracker.TrackedReal{Float32}) at /home/vshanka2/.julia/packages/Tracker/cpxco/src/lib/real.jl:14
 [33] top-level scope at none:0
 [34] include at ./boot.jl:326 [inlined]
 [35] include_relative(::Module, ::String) at ./loading.jl:1038
 [36] include(::Module, ::String) at ./sysimg.jl:29
 [37] include(::String) at ./client.jl:403
 [38] top-level scope at none:0

正向传递可以很好地工作，但向后调用会导致标量getindex。如果我允许标量，当然没有问题，但它比cpu慢得多(即使是对于我正在处理的更大的问题)。

我不确定它是否与任何包依赖关系有关，但以下是我目前安装的。

  [fbb218c0] BSON v0.2.4
  [c5f51814] CUDAdrv v5.0.1
  [be33ccc6] CUDAnative v2.7.0
  [3a865a2d] CuArrays v1.6.0
  [aae7a2af] DiffEqFlux v0.7.0
  [9fdde737] DiffEqOperators v4.6.1
  [0c46a032] DifferentialEquations v6.9.0
  [587475ba] Flux v0.8.3
  [a98d9a8b] Interpolations v0.12.5
  [15e1cf62] NPZ v0.4.0
  [91a5bcdd] Plots v0.28.4