Problem with Mutating array not supported in constructing a Wasserstein GAN in Flux

I am trying to construct a Wasserstein GAN as in Gulrajani, et al, Improved Training of Wasserstein GANs. Part of the code is below. The discriminator loss function contains a function of the derivatives of the discriminator with respect to the inputs. This creates problems in differentiating the discriminator loss which I cannot solve.

using CUDA, Flux, Distributions, CSV, DataFrames, ForwardDiff, StatsBase, GLM, Random, Statistics, Base
using Flux.Losses: logitbinarycrossentropy
using Parameters: @with_kw
using Flux.Optimise: update!
using Base.Iterators: partition
using Flux: params
using ReverseDiff
using Distances

global const ε=Float32(1e-6)

function Discriminator()
return Chain(
Dense(14, 21, elu),
Dense(21, 1,sigmoid))

function Grad_discriminator_x(dscr,Z)
f = z → dscr(z)[1]
g = z → ReverseDiff.gradient(f,z)
x = mapslices(g, Z; dims=1)
return x

function discriminator_loss(dscr, real_input, fake_input)
real_loss = mean(dscr(real_input))
fake_loss = mean(dscr(fake_input))
mix=ε * real_input +(1-ε)fake_input
x = mapslices(Z->Grad_discriminator_x(Discriminator(),Z), mix; dims=1)
norms = colwise(Euclidean(), x, zeros(14))
penalty = λ
return fake_loss-real_loss+penalty

function Grad_discriminator_loss(dscr, real_input, fake_input)
gradient(()->discriminator_loss(dscr, real_input, fake_input),Flux.params(dscr))

real_input=randn(Float64,14, 5);
fake_input=randn(Float64,14, 5);

Grad_discriminator_x(Discriminator(),randn(Float64,14, 5))

discriminator_loss(Discriminator(), real_input, fake_input)

Grad_discriminator_loss(Discriminator(), real_input, fake_input)

Calculating the gradient of the discriminator with respect to the inputs, or the discriminator loss is not problem. However, when I try to evaluate Grad_discriminator_loss I get the following error. Any help would be greatly appreciated.

Grad_discriminator_loss(Discriminator(), real_input, fake_input)
ERROR: Mutating arrays is not supported
[1] error(s::String)
@ Base ./error.jl:33
[2] (::Zygote.var"#403#404")(#unused#::Nothing)
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/lib/array.jl:58
[3] (::Zygote.var"#2259#back#405"{Zygote.var"#403#404"})(Δ::Nothing)
@ Zygote ~/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
[4] Pullback
@ ~/.julia/packages/Distances/gnt89/src/generic.jl:83 [inlined]
[5] (::typeof(∂(colwise!)))(Δ::Vector{Float64})
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface2.jl:0
[6] Pullback
@ ~/.julia/packages/Distances/gnt89/src/generic.jl:163 [inlined]
[7] (::typeof(∂(colwise)))(Δ::Vector{Float64})
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface2.jl:0
[8] Pullback
@ ./REPL[11]:8 [inlined]
[9] (::typeof(∂(discriminator_loss)))(Δ::Float64)
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface2.jl:0
[10] Pullback
@ ./REPL[12]:2 [inlined]
[11] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface2.jl:0
[12] (::Zygote.var"#69#70"{Zygote.Params, typeof(∂(λ)), Zygote.Context})(Δ::Float64)
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface.jl:255
[13] gradient(f::Function, args::Zygote.Params)
@ Zygote ~/.julia/packages/Zygote/i1R8y/src/compiler/interface.jl:59
[14] Grad_discriminator_loss(dscr::Chain{Tuple{Dense{typeof(elu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(σ), Matrix{Float32}, Vector{Float32}}}}, real_input::Matrix{Float64}, fake_input::Matrix{Float64})
@ Main ./REPL[12]:2
[15] top-level scope
@ REPL[18]:1

Did you have already a look at this or this?

Btw. Quoting the code would help the reader of your post :slight_smile:

Thank you for the links. Although I have googled extensively, I haven’t seen them.

1 Like

This error usually means some array-level function isn’t supported, and internally it is making a new array & writing into it. Seeing “packages/Distances” in the stack trace is a clue, and if you look here you can check what methods are supported:

julia> jacobian(x -> colwise(Euclidean(), x, zeros(14,1)), rand(14,1))  # makes a 1-element vector
([0.19770084950458103 0.03806289602200381 … 0.31432031223232176 0.03768309247814568],)

julia> jacobian(x -> colwise(Euclidean(), x, zeros(14)), rand(14))
ERROR: Mutating arrays is not supported
  [4] Pullback
    @ ~/.julia/packages/Distances/gnt89/src/generic.jl:63 [inlined]
  [5] (::typeof(∂(colwise!)))(Δ::Vector{Float64})
    @ Zygote ~/.julia/dev/Zygote/src/compiler/interface2.jl:0
  [6] Pullback
    @ ~/.julia/packages/Distances/gnt89/src/generic.jl:129 [inlined]
  [7] (::typeof(∂(colwise)))(Δ::Vector{Float64})
    @ Zygote ~/.julia/dev/Zygote/src/compiler/interface2.jl:0

julia> jacobian(x -> mapslices(identity, x, dims=1), [1 2; 3 4])
ERROR: Mutating arrays is not supported
  [2] (::Zygote.var"#440#441")(#unused#::Nothing)
    @ Zygote ~/.julia/dev/Zygote/src/lib/array.jl:76
  [7] (::typeof(∂(concatenate_setindex!)))(Δ::Nothing)
    @ Zygote ~/.julia/dev/Zygote/src/compiler/interface2.jl:0

julia> using SliceMap

julia> jacobian(x -> mapcols(identity, x), [1 2; 3 4])
([1 0 0 0; 0 1 0 0; 0 0 1 0; 0 0 0 1],)

The next problem you will hit is mapslices. You can probably re-arrange things to use solid arrays not slices, but if you must, then SliceMap.jl is one way around that. (How this will work with 2nd derivatives you’ll have to see, MapCols which uses ForwardDiff may behave better.)

Many thanks for your help. This is really helpful to understand what is going wrong.