replace main Zygote.adjoints with ChainRules rrules

[ChrisRackauckas]

Part of SciML/SciMLBase.jl#69

And needs to be done with SciML/SciMLSensitivity.jl#428

But currently getting:

using DiffEqSensitivity, OrdinaryDiffEq, Zygote
function fiip(du,u,p,t)
  du[1] = dx = p[1]*u[1] - p[2]*u[1]*u[2]
  du[2] = dy = -p[3]*u[2] + p[4]*u[1]*u[2]
end
function foop(u,p,t)
  dx = p[1]*u[1] - p[2]*u[1]*u[2]
  dy = -p[3]*u[2] + p[4]*u[1]*u[2]
  [dx,dy]
end

p = [1.5,1.0,3.0,1.0]; u0 = [1.0;1.0]
prob = ODEProblem(fiip,u0,(0.0,10.0),p)
du01,dp1 = Zygote.gradient((u0,p)->sum(solve(prob,Tsit5(),u0=u0,p=p,abstol=1e-14,
                            reltol=1e-14,saveat=0.1,sensealg=QuadratureAdjoint())),u0,p)

ArgumentError: tuple must be non-empty
first(#unused#::Tuple{}) at tuple.jl:134
_unapply(t::Nothing, xs::Tuple{}) at lib.jl:163
_unapply(t::Tuple{Nothing}, xs::Tuple{}) at lib.jl:167
_unapply(t::Tuple{Tuple{Nothing}}, xs::Tuple{}) at lib.jl:167
_unapply(t::Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, xs::Tuple{Nothing, Nothing, Nothing, Vector{Float64}, Vector{Float64}, Nothing}) at lib.jl:168
unapply(t::Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, xs::Tuple{Nothing, Nothing, Nothing, Vector{Float64}, Vector{Float64}, Nothing}) at lib.jl:177
#193 at lib.jl:195 [inlined]
(::Zygote.var"#1713#back#195"{Zygote.var"#193#194"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, Zygote.var"#kw_zpullback#40"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#179"{Base.Iterators.Pairs{Symbol, Float64, Tuple{Symbol, Symbol}, NamedTuple{(:abstol, :reltol), Tuple{Float64, Float64}}}, Tsit5, QuadratureAdjoint{0, true, Val{:central}, Bool}, Vector{Float64}, Vector{Float64}, Tuple{}, Colon, NamedTuple{(:abstol, :reltol), Tuple{Float64, Float64}}}}}})(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) at adjoint.jl:59
Pullback at solve.jl:70 [inlined]
(::typeof(∂(#solve#59)))(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) at interface2.jl:0
(::Zygote.var"#193#194"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#59))})(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) at lib.jl:194
(::Zygote.var"#1713#back#195"{Zygote.var"#193#194"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#59))}})(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) at adjoint.jl:59
Pullback at solve.jl:68 [inlined]
(::typeof(∂(solve##kw)))(Δ::FillArrays.Fill{Float64, 2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) at interface2.jl:0
Pullback at test.jl:14 [inlined]
(::typeof(∂(#7)))(Δ::Float64) at interface2.jl:0
(::Zygote.var"#46#47"{typeof(∂(#7))})(Δ::Float64) at interface.jl:41
gradient(::Function, ::Vector{Float64}, ::Vararg{Vector{Float64}, N} where N) at interface.jl:59
top-level scope at test.jl:14
eval at boot.jl:360 [inlined]

[oxinabox]

where is adjoint_sensitivity_backpass defined?

[ChrisRackauckas]

All of the adjoints are in DiffEqSensitivity since it has a ton of dependencies.

https://github.com/SciML/DiffEqSensitivity.jl/blob/b0cc6834a92047e7e85dc1155267b794b43694a5/src/concrete_solve.jl#L133