let Flux own the function update! to avoid piracy

ext/FluxEnzymeExt/FluxEnzymeExt.jl

@@ -2,7 +2,7 @@ module FluxEnzymeExt
 
 using Flux
 using Flux: _make_zero!
-import Flux.Train: _enzyme_train!, _rule_to_state
+import Flux.Train: _enzyme_train!, _rule_to_state, _grad_or_nothing
 import Flux.Optimise
 import Optimisers
 import Enzyme
@@ -21,11 +21,6 @@ function Flux._enzyme_gradient(f, args::Union{Const, Duplicated}...; zero::Bool=
   map(_grad_or_nothing, args)
 end
 
-# 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
-_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)
@@ -52,31 +47,10 @@ function _enzyme_train!(loss, model::Duplicated, data, opt; cb = nothing)
     if !isfinite(l)
       throw(DomainError(lazy"Loss is $l on data item $i, stopping training"))
     end
-    opt, model2 = Optimisers.update!(opt, model.val, model.dval)
-    model = Duplicated(model2, model.dval)
+    Flux.update!(opt, model)
 
     @logprogress Base.haslength(data) ? i/length(data) : nothing
   end
 end
 
-
-### Optimisers.update!, piracy, for now!
-
-"""
-    Flux.update!(opt_state, model::Duplicated)
-
-Method of `update!` for use with Enzyme, and in particular with `gradient(loss, Duplicated(model))`.
-Since `Duplicated(model)` stores the gradient, `update!` can read it & update the model itself,
-by calling `Flux.update!(opt_state, model.val, model.dval)`.
-
-!!! warning "Experimental"
-    Enzyme support like this is new and somewhat experimental.
-    This method is piracy, and must either move to Optimisers.jl
-    or else Flux should own this function, and fall back to Optimisers.
-"""
-function Flux.update!(opt_state, model::Duplicated)
-  Flux.update!(opt_state, model.val, _grad_or_nothing(model))
-  model
-end
-
 end # FluxEnzymeExt

src/gradient.jl

@@ -62,6 +62,7 @@ With the keyword `zero=false`, the new gradient will instead be added to what is
 
 !!! warning "Experimental"
     Enzyme support like this is new and somewhat experimental.
+    This method was added in Flux 0.15.
 
 # Example
 ```
@@ -169,6 +170,10 @@ Only available when Enzyme is loaded!
 
 Does not at present allow `f` to return a tuple of `(loss, aux)` the way `Zygote.withgradient` does.
 
+!!! warning "Experimental"
+    Enzyme support like this is new and somewhat experimental.
+    This method was added in Flux 0.15.
+
 # Example
 
 ```

src/optimise/train.jl

@@ -1,9 +1,9 @@
 using ProgressLogging: @progress, @withprogress, @logprogress
 import Zygote: Params, gradient, withgradient
 
-# Add methods to Optimisers.jl's function, so that there is just one Flux.update!
-# for both explicit and implicit parameters.
-import Optimisers.update!
+# Flux 0.13 and 0.14 used Optimisers.jl's function, but Flux 0.15 owns it:
+# import Optimisers.update!
+function update! end
 
 """
     update!(opt, p, g)

src/train.jl

@@ -3,14 +3,15 @@ module Train
 using LinearAlgebra
 using Optimisers: Optimisers
 using Functors: fmap, fmapstructure
-using ..Flux: Flux # used only in docstring
 import ..Flux.Optimise: train!, update!, Optimise  # during 0.13, we add methods to the old functions
 
-export setup, train!
+using ..Flux: Flux # used only in docstring
+
+export setup, train!, update!
 
 using ProgressLogging: @progress, @withprogress, @logprogress
 using Zygote: Zygote, Params
-using EnzymeCore: Duplicated
+using EnzymeCore: EnzymeCore, Duplicated, Const
 
 """
     opt_state = setup(rule, model)
@@ -169,4 +170,100 @@ function train!(loss, model::Duplicated, data, rule::Optimisers.AbstractRule; cb
   train!(loss, model, data, _rule_to_state(model, rule); cb)
 end
 
+""""
+    update!(opt_state, model, grad)
+
+Uses the optimiser and the gradient to change the trainable parameters in the model.
+The optimisers state comes from `setup(rule, model)`, and the gradient from `grad = gradient(loss, model, args...)[1]`.
+
+This is a version of `Optimisers.update!`, which differs in that it returns `nothing`.
+It also differs in having a method which accepts a model and gradient packaged together as `Duplicated(model, grad)`,
+which is convenient for use with Enzyme.
+
+# Example
+
+```jldoctest
+julia> model = Chain(Embedding([1;;2;;3.0;;]), Dense([4;-5.0;;], true, relu))
+Chain(
+  Embedding(3 => 1),                    # 3 parameters
+  Dense(1 => 2, relu),                  # 4 parameters
+)                   # Total: 3 arrays, 7 parameters, 216 bytes.
+
+julia> opt_state = Flux.setup(Momentum(1/9), model)
+(layers = ((weight = Leaf(Momentum(0.111111, 0.9), [0.0 0.0 0.0]),), (weight = Leaf(Momentum(0.111111, 0.9), [0.0; 0.0;;]), bias = Leaf(Momentum(0.111111, 0.9), [0.0, 0.0]), σ = ())),)
+
+julia> val, grads = Flux.withgradient(m -> first(m(2)), model)
+(val = 8.0, grad = ((layers = ((weight = [0.0 4.0 0.0],), (weight = [2.0; 0.0;;], bias = [1.0, 0.0], σ = nothing)),),))
+
+julia> Flux.update!(opt_state, model, grads[1]);
+
+julia> round.(model(2); digits=3)  # has changed! Compare val = 8.0
+2-element Vector{Float64}:
+ 5.765
+ 0.0
+
+julia> opt_state  # has also changed
+(layers = ((weight = Leaf(Momentum(0.111111, 0.9), [0.0 0.444444 0.0]),), (weight = Leaf(Momentum(0.111111, 0.9), [0.222222; 0.0;;]), bias = Leaf(Momentum(0.111111, 0.9), [0.111111, 0.0]), σ = ())),)
+```
+
+"""
+update!(opt_state, model, grad) = Optimisers.update!(opt_state, model, grad)
+
+"""
+    update!(opt_state, model::Duplicated)
+
+Method of `update!` for use with Enzyme, and in particular with `gradient(loss, Duplicated(model))`.
+Since `Duplicated(model)` stores the gradient, `update!` can read it & update the model itself.
+Approximately equivalent to calling `Flux.update!(opt_state, model.val, model.dval)`,
+but more careful about shared parameters.
+
+!!! warning "Experimental"
+    Enzyme support like this is new and somewhat experimental.
+    This method was added in Flux 0.15.
+
+# Example
+
+```julia
+julia> using Flux, Enzyme
+
+julia> dup_model = Chain(Embedding([1;;2;;3.0;;]), Dense([4;-5.0;;], true, relu)) |> Duplicated
+Duplicated(
+  Chain(
+    Embedding(3 => 1),                  # 3 parameters
+    Dense(1 => 2, relu),                # 4 parameters
+  ),
+  # norm(∇) ≈ 0.0
+)                   # Total: 3 arrays, 7 parameters, 216 bytes.
+
+julia> dup_model(2)
+2-element Vector{Float64}:
+ 8.0
+ 0.0
+
+julia> opt_state = Flux.setup(Momentum(1/9), dup_model)
+(layers = ((weight = Leaf(Momentum(0.111111, 0.9), [0.0 0.0 0.0]),), (weight = Leaf(Momentum(0.111111, 0.9), [0.0; 0.0;;]), bias = Leaf(Momentum(0.111111, 0.9), [0.0, 0.0]), σ = ())),)
+
+julia> Flux.gradient(m -> first(m(2)), dup_model);  # updates gradient within Duplicated
+
+julia> Flux.update!(opt_state, dup_model);
+
+julia> round.(dup_model(2); digits=3)  # has changed! Compare val = 8.0
+2-element Vector{Float64}:
+ 5.765
+ 0.0
+
+julia> opt_state  # has also changed
+(layers = ((weight = Leaf(Momentum(0.111111, 0.9), [0.0 0.444444 0.0]),), (weight = Leaf(Momentum(0.111111, 0.9), [0.222222; 0.0;;]), bias = Leaf(Momentum(0.111111, 0.9), [0.111111, 0.0]), σ = ())),)
+```
+"""
+function update!(opt_state, model::Duplicated)
+  update!(opt_state, model.val, _grad_or_nothing(model))
+  model
+end
+
+# 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
+_grad_or_nothing(x) = Optimisers.isnumeric(x) ? x : nothing
+
 end # module Train