ReverseDiff documentation shows issue that has been fixed? Nested differentiation of a closure? about reversediff.jl HOT 1 OPEN

import ForwardDiff, ReverseDiff, AbstractDifferentiation as AD

n = 1
x0 = Array(rand(n))
M0 = rand(n,n)

function proto(x,M)
    M*x |> sum
end

fw = AD.ForwardDiffBackend()
rv = AD.ReverseDiffBackend()

#Grads with regards to x
grad_x_FW(x,M) = AD.gradient(fw, x -> proto(x,M),x) |> first |> first
grad_x_RV(x,M) = AD.gradient(rv, x -> proto(x,M),x) |> first |> first


AD.gradient(fw, m -> grad_x_FW(x0,m),M0) #Forward-over-forward, correct
AD.gradient(rv, m -> grad_x_FW(x0,m),M0) #Reverse-over-Forward, ERROR
AD.gradient(fw, m -> grad_x_RV(x0,m),M0) #Forward-over-reverse, ERROR 
AD.gradient(rv, m -> grad_x_RV(x0,m),M0) #Reverse-over-reverse, wrong