disallow trivial Duplicated

ext/FluxEnzymeExt/FluxEnzymeExt.jl

@@ -5,6 +5,7 @@ using Flux: _make_zero!
 import Flux.Train: _enzyme_train!, _rule_to_state
 import Flux.Optimise
 import Optimisers
+import Functors
 import Enzyme
 using Enzyme: EnzymeRules, Active, Const, Duplicated, autodiff, ReverseWithPrimal
 using Enzyme: autodiff_thunk, ReverseSplitWithPrimal
@@ -17,11 +18,17 @@ EnzymeRules.inactive(::typeof(Flux.Losses._check_sizes), args...) = true
 function Flux._enzyme_gradient(f, args::Union{Const, Duplicated}...; zero::Bool=true)
   for x in args
     zero && x isa Duplicated && _make_zero!(x.dval)
+    _check_mutable(x)
   end
   Enzyme.autodiff(ReverseWithPrimal, f, Active, args...)
   map(_grad_or_nothing, args)
 end
 
+_check_mutable(x::Const) = nothing
+_check_mutable(x::Duplicated) = Functors.anymutable(x) || error(
+    """`Flux.gradient(f, Duplicatged(x), ...)` expects `x` to contain mutable parameter arrays."""
+)
+
 # This function strips the returned gradient to be Zygote-like:
 _grad_or_nothing(dup::Duplicated) = Flux.fmapstructure(_grad_or_nothing, dup.dval; prune=nothing)
 _grad_or_nothing(::Const) = nothing
@@ -30,6 +37,7 @@ _grad_or_nothing(x) = Optimisers.isnumeric(x) ? x : nothing
 function Flux._enzyme_withgradient(f, args::Union{Const, Duplicated}...; zero::Bool=true)
   for x in args
     zero && x isa Duplicated && _make_zero!(x.dval)
+    _check_mutable(x)
   end
 
   # _, val = Enzyme.autodiff(ReverseWithPrimal, f, Active, args...)