ReverseDiffAdjoint does not support non-Array types

[JTaets]

On the latest version of DiffEqSensitivity, using ComponentArrays with ReverseDiffSensitivity fails.

using Pkg, DiffEqSensitivity, OrdinaryDiffEq, Zygote, ComponentArrays, ReverseDiff
Pkg.status("DiffEqSensitivity")
# DiffEqSensitivity v6.58.0

## Proof that ReverseDiff works with ComponentArrays
ReverseDiff.gradient(x->sum(x),ComponentArray(a=1.,b=7.,c=8.))
# ComponentVector{Float64}(a = 1.0, b = 1.0, c = 1.0)


## Proof that ReverseDiff for ODEs doesn't work with ComponentArrays
u0 = [1.0;1.0]
p = ComponentArray(a=1.5,b=1.0,c=3.0,d=1.0)
function fiip(du,u,p,t)
  du[1] = dx = p.a*u[1] - p.b*u[1]*u[2]
  du[2] = dy = -p.c*u[2] + p.d*u[1]*u[2]
end
prob = ODEProblem(fiip,u0,(0.0,10.0),p)

# ForwardDiff works
loss(p) = sum(solve(prob,Tsit5(),u0=u0,p=p,saveat=0.1,sensealg=ForwardDiffSensitivity()))
dp = Zygote.gradient(loss,p)[1]
# ComponentVector{Float64}(a = 7.349039781610272, b = -159.31079871982794, c = 74.93924771425637, d = -339.3272371527868)

# ReverseDiff fails
loss2(p) = sum(solve(prob,Tsit5(),u0=u0,p=p,saveat=0.1,sensealg=ReverseDiffAdjoint()))
dp2 = Zygote.gradient(loss2,p)[1]
# ComponentVector{Float64}(a = 0.0, b = 0.0, c = 0.0, d = 0.0)

[ChrisRackauckas]

This is an upstream issue. The real problem is that ReverseDiff doesn't work with non-Array types, so this probably isn't fixable.

[JTaets]

But something like this works. Am I missing something?

ReverseDiff.gradient(x->sum(x),ComponentArray(a=1.,b=7.,c=8.))
# ComponentVector{Float64}(a = 1.0, b = 1.0, c = 1.0)

(If it is due to ReverseDiff, than you can close this issue if you want.)

[ChrisRackauckas]

do something like sum((x+x).a + (x+x).b)? I think it's that the output of no operations can result in an AbstractArray type IIRC. The pushforward of the operations will change it internally from a ComponentVector to a Vector, but only in the context of the AD pushforward.

(If it is due to ReverseDiff, than you can close this issue if you want.)

This is just a known feature of ReverseDiff, and not something that can be fixed due to its design IIRC.

[acertain]

@ChrisRackauckas are you sure? SciML/ComponentArrays.jl#37 , also your ReverseDiff.gradient(x->sum((x+x).a+(x+x).b),ComponentArray(a=1.,b=7.,c=8.)) example works:

julia> ReverseDiff.gradient(x->sum((x+x).a+(x+x).b),ComponentArray(a=1.,b=7.,c=8.))
ComponentVector{Float64}(a = 2.0, b = 2.0, c = 0.0)

[ChrisRackauckas]

This response was written in 2021. It means nothing for today.

[acertain]

Then maybe this should be reopened? afaict all of the reverse mode adjoints are either broken (Zygote) or don't work with ComponentArrays (ReverseDiff, Tracker).

[ChrisRackauckas]

But those are all upstream issues. Nothing in this library can fix the limitations of ReverseDiff.jl, Tracker.jl, or Zygote.jl. Those are issues that would need to be addressed on those respective libraries.

If anything is going to fix this issue, it's to make EnzymeAdjoint work out, which is SciML/OrdinaryDiffEq.jl#2282 and a few other threads.

[acertain]

What's the current problem with ReverseDiff?

[ChrisRackauckas]

ReverseDiff.jl only supports defining @grad overloads on arrays and automatically converts all AbstractArray objects to an Array on gradient application. That's why it has issues with alternative array types.