Attempt to fix #62

src/destructure.jl

@@ -91,10 +91,13 @@ _getat(y::AbstractArray, o::Int, flat::AbstractVector) =
 
 function _trainable_biwalk(f, x, aux)
   ch, re = functor(typeof(x), x)
-  au, _ = functor(typeof(x), aux)
+  au = _aux_children(aux) 
   _trainmap(f, ch, _trainable(x), au) |> re
 end
 
+_aux_children(off) = functor(off)[1]
+_aux_children(off::AbstractArray) = off  # leaflike according to Functors, but we need to see each offset
+
 function _trainmap(f, ch, tr, aux)
   map(ch, tr, aux) do c, t, a  # isnothing(t) indicates non-trainable field, safe given isnumeric(c)
     isnothing(t) ? c : f(t, a)
@@ -103,7 +106,7 @@ end
 
 function _Tangent_biwalk(f, x, aux)  # use with prune = NoT
   ch, re = functor(typeof(x), x)
-  au, _ = functor(typeof(x), aux)
+  au = _aux_children(aux)
   y = _trainmap(f, ch, _trainable(x), au)
   y isa Tuple{} && return NoT
   p = ProjectTo(x)
@@ -126,7 +129,7 @@ ChainRulesCore.@non_differentiable _zero(x)
 function _grad!(x, dx, off, flat::AbstractVector)
   x′, _ = functor(typeof(x), x)
   dx′, _ = functor(typeof(x), base(dx))
-  off′, _ = functor(typeof(x), off)
+  off′ = _aux_children(off)
   foreach((xᵢ, dxᵢ, oᵢ) -> _grad!(xᵢ, dxᵢ, oᵢ, flat), x′, dx′, off′)
   flat
 end

test/destructure.jl

@@ -79,7 +79,7 @@ end
   g8 = gradient(m -> sum(abs2, destructure(m)[1]), m8)[1]
   @test g8[1].x == [2,4,6]
   @test g8[2].b.x == [8]
-  @test g8[3] == [[10.0]]
+  @test g8[3] == [[10.0]]  # fails
 
   g9 = gradient(m -> sum(sqrt, destructure(m)[1]), m9)[1]
   @test g9.c === nothing
@@ -180,3 +180,44 @@ end
     4(sum(m.x) + sum(m.y)) + 13*sum(m.z)  # again two gradients are ===, so it eliminates one
   end == ([17,17,4,4],)  # Flux gave ([4.0, 4.0, 13.0, 13.0],)
 end
+
+@testset "issue 62" begin
+  # Flux.Chain used to have children which aren't its own fields, which Skip immitates.
+
+  sk = Skip([1.0, 2.0], (x=3, y=[4.0, 5.0]))
+  @test fmap(identity, sk) == sk
+
+  gk = gradient(x -> sum(x[2].y), sk)[1]
+  @test fmap(Zygote.accum, sk, gk) isa Skip  # this relies on functor(typeof(x), dx)
+
+  st = fmapstructure(identity, sk)
+  @test st isa Tuple{Vector, NamedTuple}
+  @test_throws Exception fmap(+, sk, st)  # this fails because of functor(typeof(x), dx)
+
+  v, re = destructure(sk)
+  @test v == [1,2,4,5]
+  @test re(10v) isa Skip
+  @test re(10v)[1] == [10, 20]
+
+  @test gradient(zero(v)) do w
+    re(w)[2].y[1]
+  end == ([0,0,1,0],)
+
+  gradient(sk) do x
+    w, _ = destructure(x)
+    w[1]
+  end
+#=
+
+ERROR: ArgumentError: Tangent for the primal Skip{Tuple{Vector{Float64}, NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}}} should be backed by a NamedTuple type, not by Tuple{Vector{Float64}, ChainRulesCore.Tangent{NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}, NamedTuple{(:x, :y), Tuple{ChainRulesCore.NoTangent, Vector{Float64}}}}}.
+Stacktrace:
+  [1] _backing_error(P::Type, G::Type, E::Type)
+    @ ChainRulesCore ~/.julia/packages/ChainRulesCore/RbX5a/src/tangent_types/tangent.jl:62
+  [2] ChainRulesCore.Tangent{Skip{Tuple{Vector{Float64}, NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}}}, Tuple{Vector{Float64}, ChainRulesCore.Tangent{NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}, NamedTuple{(:x, :y), Tuple{ChainRulesCore.NoTangent, Vector{Float64}}}}}}(backing::Tuple{Vector{Float64}, ChainRulesCore.Tangent{NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}, NamedTuple{(:x, :y), Tuple{ChainRulesCore.NoTangent, Vector{Float64}}}}})
+    @ ChainRulesCore ~/.julia/packages/ChainRulesCore/RbX5a/src/tangent_types/tangent.jl:36
+  [3] _Tangent_biwalk(f::Function, x::Skip{Tuple{Vector{Float64}, NamedTuple{(:x, :y), Tuple{Int64, Vector{Float64}}}}}, aux::Tuple{Int64, NamedTuple{(:x, :y), Tuple{Tuple{}, Int64}}})
+    @ Optimisers ~/.julia/dev/Optimisers/src/destructure.jl:116
+
+=#
+
+end

test/runtests.jl

@@ -13,6 +13,13 @@ struct TwoThirds a; b; c; end
 Functors.@functor TwoThirds (a, c)
 Optimisers.trainable(x::TwoThirds) = (a = x.a,)
 
+struct Skip{T}  # like Flux 0.12's Chain
+  layers::T
+  Skip(ls...) = new{typeof(ls)}(ls)
+end
+Base.getindex(x::Skip, i::Integer) = x.layers[i]
+Functors.functor(::Type{<:Skip}, x) = x.layers, ls -> Skip(ls...)
+
 @testset verbose=true "Optimisers.jl" begin
   @testset verbose=true "Features" begin
 
@@ -165,6 +172,16 @@ Optimisers.trainable(x::TwoThirds) = (a = x.a,)
       @test_throws ArgumentError Optimisers.setup(ADAMW(), m2)
     end
 
+    @testset "issue 62" begin
+      m62 = (s = Skip([1.0, 2.0], Foo([3.0], false)), t = [4.0, 5.0])
+      s62 = Optimisers.setup(Descent(), m62)
+      g62 = gradient(m -> m.s[2].x[1] + 3 * m.t[2], m62)
+      s, m = Optimisers.update(s62, m62, g62...)
+      @test m.s isa Skip
+      @test m.s[2].x ≈ [2.9]
+      @test m.t ≈ [4, 4.7]
+    end
+
   end
   @testset verbose=true "Destructure" begin
     include("destructure.jl")