more... the dimensionmismatch bug is not here

src/destructure.jl

@@ -55,21 +55,24 @@ Base.length(re::Restructure) = re.length
 
 # This flattens a model, and returns a web of offsets for later use:
 function _flatten(x)
-  isnumeric(x) && return vcat(vec(x)), 0, length(x)  # trivial case
+  isnumeric(x) && return vcat(_vec(x)), 0, length(x)  # trivial case
   arrays = AbstractVector[]
   len = Ref(0)
   off = fmap(x; exclude = isnumeric, walk = (f, z) -> map(f, _trainable(z))) do y
-    push!(arrays, vec(y))
+    push!(arrays, _vec(y))
     o = len[]
     len[] = o + length(y)
     o
   end
   reduce(vcat, arrays), off, len[]
 end
 
+_vec(x::Number) = LinRange(x,x,1)
+_vec(x::AbstractArray) = vec(x)
+
 function ChainRulesCore.rrule(::typeof(_flatten), x)
   flat, off, len = _flatten(x)
-  _flatten_back((dflat, _, _)) = (NoT, _rebuild(x, off, dflat, len; walk = _Tangent_biwalk, prune = NoT))
+  _flatten_back((dflat, _, _)) = (NoT, _rebuild(x, off, unthunk(dflat), len; walk = _Tangent_biwalk, prune = NoT))
   (flat, off, len), _flatten_back
 end
 
@@ -92,7 +95,7 @@ function _trainable_biwalk(f, x, aux)
 end
 
 function _trainmap(f, ch, tr, aux)
-  map(ch, tr, aux) do c, t, a  # isnothing(t) indicates non-trainable field, safe given isnumeric(c)??
+  map(ch, tr, aux) do c, t, a  # isnothing(t) indicates non-trainable field, safe given isnumeric(c)
     isnothing(t) ? c : f(t, a)
   end
 end
@@ -121,7 +124,7 @@ ChainRulesCore.@non_differentiable _zero(x)
 # This is the gradient of model reconstruction, accumulating duplicates:
 function _grad!(x, dx, off, flat::AbstractVector)
   x′, _ = functor(typeof(x), x)
-  dx′, _ = functor(typeof(x), dx)
+  dx′, _ = functor(typeof(x), base(dx))
   off′, _ = functor(typeof(x), off)
   foreach((xᵢ, dxᵢ, oᵢ) -> _grad!(xᵢ, dxᵢ, oᵢ, flat), x′, dx′, off′)
   flat
@@ -134,7 +137,6 @@ _grad!(x, dx::Zero, off, flat::AbstractVector) = dx
 _grad!(x, dx::Zero, off::Integer, flat::AbstractVector) = dx  # ambiguity
 
 function ChainRulesCore.rrule(::typeof(_grad!), x, dx, off, flat)
-  println("grad! fwd ", length(flat))
   _grad_back(dflat) = (NoT, NoT, _rebuild(x, off, unthunk(dflat); walk = _Tangent_biwalk, prune = NoT), NoT, NoT)
   _grad!(x, dx, off, flat), _grad_back
 end

src/interface.jl

@@ -1,6 +1,7 @@
 
 using ChainRulesCore: canonicalize, backing, Tangent, AbstractZero
 base(dx::Tangent) = backing(canonicalize(dx))
+base(dx::Tangent{<:Tangent}) = backing(dx).backing  # might be needed for gradient(gradient(destructure))
 base(dx) = dx
 const Zero = Union{Nothing, AbstractZero}  # Union{Zygote, Diffractor}
 

test/destructure.jl

@@ -2,7 +2,7 @@
 m1 = collect(1:3.0)
 m2 = (collect(1:3.0), collect(4:6.0))
 m3 = (x = m1, y = sin, z = collect(4:6.0))
-m4 = (x = m1, y = m1, z = collect(4:6.0))
+m4 = (x = m1, y = m1, z = collect(4:6.0))  # tied
 m5 = (a = (m3, true), b = (m1, false), c = (m4, true))
 m6 = (a = m1, b = [4.0 + im], c = m1)
 m7 = TwoThirds((sin, collect(1:3.0)), (cos, collect(4:6.0)), (tan, collect(7:9.0)))
@@ -75,10 +75,18 @@ end
     @test_broken gradient([1,2,3.0]) do v
       sum(abs2, gradient(m -> sum(abs2, destructure(m)[1]), (v, [4,5,6.0]))[1][1])
     end[1] ≈ [8,16,24]
+    # With Diffractor, non-leaf _grad!(x, dx, off, flat::AbstractVector) gets double-wrapped dx:
+    # off = (0, 3), dx = Tangent{Tangent{Tuple{Vector{Float64}, Vector{Float64}}, ...
+    # until you add explicit double-unwrap: base(dx::Tangent{<:Tangent}) = backing(dx).backing
 
-    @test_skip gradient([1,2,3.0]) do v
-      sum(gradient(m -> sum(destructure(m)[1]), (v, [4,5,6.0]))[1][1])
-    end
+    @test_broken gradient([1,2,3.0]) do v
+      sum(gradient(m -> sum(destructure(m)[1])^3, (v, [4,5,6.0]))[1][1])
+    end[1] == [378, 378, 378]
+
+    @test_broken gradient([1,2,3.0]) do v
+      sum(abs2, gradient(m -> sum(abs2, destructure(m)[1]), (x = v, y = sin, z = [4,5,6.0]))[1][1])
+    end[1] ≈ [8,16,24]
+    # Diffractor error in perform_optic_transform
   end
 end
 
@@ -109,15 +117,17 @@ end
   @test gradient(x -> only(sum(re8(x)[3]))^2, v8)[1] == [0,0,0,0,10]
 
   @testset "second derivative" begin
-    # ERROR: Need an adjoint for constructor ChainRulesCore.Tangent{Any, Tuple{Vector{Float64}, ChainRulesCore.ZeroTangent}}. Gradient is of type Tuple{Vector{Float64}, Vector{Float64}}
     @test_broken gradient(collect(1:6.0)) do y
       sum(abs2, gradient(x -> sum(abs2, re2(x)[1]), y)[1])
     end[1] ≈ [8,16,24,0,0,0]
-    # This fixes it!
+    # ERROR: Need an adjoint for constructor ChainRulesCore.Tangent{Any, Tuple{Vector{Float64}, ChainRulesCore.ZeroTangent}}. Gradient is of type Tuple{Vector{Float64}, Vector{Float64}}
+    # with Zygote, which can be fixed by:
     # Zygote.@adjoint Tangent{T,B}(x::Tuple) where {T,B<:Tuple} = Tangent{T,B}(x), dx -> (dx,)
-    @test_skip gradient(collect(1:6.0)) do y
+
+    @test_broken gradient(collect(1:6.0)) do y
       sum(abs2, gradient(x -> sum(abs2, re3(x).z), y)[1])
-    end[1]
+    end[1] ≈ [0,0,0,32,40,48]
+    # Not fixed by this:
     # Zygote.@adjoint Tangent{T,B}(x::NamedTuple) where {T,B<:NamedTuple} = Tangent{T,B}(x), dx -> (dx,)
   end
 end