Eager parameter updating

src/lib/grad.jl

@@ -27,6 +27,46 @@ function Zygote._pullback(ctx::Zygote.AContext, ::typeof(checkpointed), f, xs...
     return y, pullback_checkpointed
 end
 
+
+
+"""
+
+    eager_update!(state, model, update!)
+
+Eagerly updates the model parameters, discarding the updated gradients to save memory.
+`model` stores the parameters to be updated, `state` is the optimization state (eg. from Optimisers.jl) matching your model component, and
+`update!` is the function that updates the parameters (eg. from `Optimisers.jl`), usually called as `update!(state, model, grads)`.
+
+If `f` is a function that takes a single layer, called as `h = f(model.layers[i], h, other_args...)` then we can eagerly update with:
+
+```
+h = f(Zygote.eager_update!(state.layers[i], model.layers[i], Optimisers.update!), h, other_args...)
+```
+
+or combine this with gradient checkpointing (for additional memory saving at the cost of increased execution time) with:
+
+```
+h = Zygote.checkpointed(f, eager_update!(state.layers[i], model.layers[i], Optimisers.update!), h, other_args...)
+```
+
+If `model.layers[i]` itself is callable, we can use the above by first wrapping it:
+
+```
+f(model, xs...) = model(xs...)
+h = f(Zygote.eager_update!(state.layers[i], model.layers[i], Optimisers.update!), h, other_args...)
+```
+
+!!! warning
+    If different layers share trainable parameters, then `eager_update!` will likely give wrong results.
+"""
+function eager_update!(state, model, update!)
+    function update_hook(dmodel)
+        update!(state, model, dmodel)
+        return nothing
+    end
+    return Zygote.hook(update_hook, model)
+end
+
 """
     hessian(f, x)