make len positional, fix a bug

src/destructure.jl

@@ -48,7 +48,7 @@ struct Restructure{T,S}
   offsets::S
   length::Int
 end
-(re::Restructure)(flat::AbstractVector) = _rebuild(re.model, re.offsets, flat; len = re.length)
+(re::Restructure)(flat::AbstractVector) = _rebuild(re.model, re.offsets, flat, re.length)
 (re::Restructure)(x, flat::AbstractVector) = re(flat)(x)
 Base.show(io::IO, re::Restructure{T}) where T = print(io, "Restructure(", T.name.name, ", ..., ", re.length, ")")
 Base.length(re::Restructure) = re.length
@@ -69,13 +69,13 @@ end
 
 function ChainRulesCore.rrule(::typeof(_flatten), x)
   flat, off, len = _flatten(x)
-  _flatten_back((dflat, _)) = (NoT, _rebuild(x, off, dflat; walk = _Tangent_biwalk, prune = NoT, len))
+  _flatten_back((dflat, _, _)) = (NoT, _rebuild(x, off, dflat, len; walk = _Tangent_biwalk, prune = NoT))
   (flat, off, len), _flatten_back
 end
 
 # This reconstructs either a model like x, or a gradient for it:
-function _rebuild(x, off, flat::AbstractVector; len, walk = _trainable_biwalk, kw...)
-  len == length(flat) || error("wrong length")
+function _rebuild(x, off, flat::AbstractVector, len = length(flat); walk = _trainable_biwalk, kw...)
+  len == length(flat) || throw(DimensionMismatch("Rebuild expected a vector of length $len, got $(length(flat))"))
   fmap(x, off; exclude = isnumeric, walk, kw...) do y, o
     _getat(y, o, flat)
   end
@@ -105,12 +105,14 @@ function _Tangent_biwalk(f, x, aux)  # use with prune = NoT
   Tangent{typeof(x), typeof(y)}(y)
 end
 
-function ChainRulesCore.rrule(::typeof(_rebuild), x, off, flat; len)
-  dflat = map!(zero, similar(flat, float(eltype(flat))), flat)
-  _rebuild_back(dx) = (NoT, NoT, NoT, _grad!(x, unthunk(dx), off, dflat))
-  _rebuild(x, off, flat; len), _rebuild_back
+function ChainRulesCore.rrule(::typeof(_rebuild), x, off, flat, len; kw...)
+  _rebuild_back(dx) = (NoT, NoT, NoT, _grad!(x, unthunk(dx), off, _zero(flat)), NoT)
+  _rebuild(x, off, flat, len; kw...), _rebuild_back
 end
 
+_zero(x) = map!(zero, similar(x, float(eltype(x))), x)  # mutable zero array for _grad!
+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)