Widen in _grad! (#66)

src/destructure.jl

@@ -127,15 +127,22 @@ function _grad!(x, dx, off, flat::AbstractVector)
   x′, _ = functor(typeof(x), x)
   dx′, _ = functor(typeof(x), base(dx))
   off′, _ = functor(typeof(x), off)
-  foreach((xᵢ, dxᵢ, oᵢ) -> _grad!(xᵢ, dxᵢ, oᵢ, flat), x′, dx′, off′)
+  for (xᵢ, dxᵢ, oᵢ) in zip(x′, dx′, off′)
+    flat = _grad!(xᵢ, dxᵢ, oᵢ, flat)
+  end
   flat
 end
-function _grad!(x, dx, off::Integer, flat::AbstractVector)
-  @views flat[off .+ (1:length(x))] .+= vec(dx)  # must visit all tied nodes
+function _grad!(x, dx, off::Integer, flat::AbstractVector{T}) where T
+  dx_un = unthunk(dx)
+  T2 = promote_type(T, eltype(dx_un))
+  if T != T2  # then we must widen the type
+    flat = copyto!(similar(flat, T2), flat)
+  end
+  @views flat[off .+ (1:length(x))] .+= vec(dx_un)  # must visit all tied nodes
   flat
 end
-_grad!(x, dx::Zero, off, flat::AbstractVector) = dx
-_grad!(x, dx::Zero, off::Integer, flat::AbstractVector) = dx  # ambiguity
+_grad!(x, dx::Zero, off, flat::AbstractVector) = flat
+_grad!(x, dx::Zero, off::Integer, flat::AbstractVector) = flat  # ambiguity
 
 # These are only needed for 2nd derivatives:
 function ChainRulesCore.rrule(::typeof(_grad!), x, dx, off, flat)

test/destructure.jl

@@ -180,3 +180,43 @@ 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 "DiffEqFlux issue 699" begin
+  # The gradient of `re` is a vector into which we accumulate contributions, and the issue
+  # is that one contribution may have a wider type than `v`, especially for `Dual` numbers.
+  v, re = destructure((x=Float32[1,2], y=Float32[3,4,5]))
+  _, bk = Zygote.pullback(re, ones(Float32, 5))
+  # Testing with `Complex` isn't ideal, but this was an error on 0.2.1.
+  # If some upgrade inserts ProjectTo, this will fail, and can be changed:
+  @test bk((x=[1.0,im], y=nothing)) == ([1,im,0,0,0],)
+
+  @test bk((x=nothing, y=[10,20,30]))[1] isa Vector{Float32}  # despite some ZeroTangent
+  @test bk((x=nothing, y=nothing)) == ([0,0,0,0,0],) 
+  @test bk((x=nothing, y=@thunk [1,2,3] .* 10.0)) == ([0,0,10,20,30],)
+  @test bk((x=[1.2, 3.4], y=Float32[5,6,7])) == ([1.2, 3.4, 5, 6, 7],)
+end
+
+#=
+
+# Adapted from https://github.com/SciML/DiffEqFlux.jl/pull/699#issuecomment-1092846657
+using ForwardDiff, Zygote, Flux, Optimisers, Test
+
+y = Float32[0.8564646, 0.21083355]
+p = randn(Float32, 27);
+t = 1.5f0
+λ = [ForwardDiff.Dual(0.87135935, 1, 0, 0, 0, 0, 0), ForwardDiff.Dual(1.5225363, 0, 1, 0, 0, 0, 0)]
+
+model = Chain(x -> x .^ 3,
+    Dense(2 => 5, tanh),
+    Dense(5 => 2))
+
+p,re = Optimisers.destructure(model)
+f(u, p, t) = re(p)(u)
+_dy, back = Zygote.pullback(y, p) do u, p
+    vec(f(u, p, t))
+end
+tmp1, tmp2 = back(λ);
+tmp1
+@test tmp2 isa Vector{<:ForwardDiff.Dual}
+
+=#