fix Parametric Heteroscedastic Model

Author: yebai

examples/3-parametric-heteroscedastic/Project.toml

@@ -2,13 +2,14 @@
 AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"
 AbstractGPsMakie = "7834405d-1089-4985-bd30-732a30b92057"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 KernelFunctions = "ec8451be-7e33-11e9-00cf-bbf324bd1392"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
+Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 ParameterHandling = "2412ca09-6db7-441c-8e3a-88d5709968c5"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 AbstractGPs = "0.5"
@@ -18,4 +19,3 @@ KernelFunctions = "0.10"
 Literate = "2"
 Optim = "1"
 ParameterHandling = "0.4, 0.5"
-Zygote = "0.6, 0.7"

examples/3-parametric-heteroscedastic/script.jl

@@ -11,7 +11,7 @@
 using AbstractGPs
 using AbstractGPsMakie
 using CairoMakie
-using DifferentiationInterface
+import DifferentiationInterface as DI
 using KernelFunctions
 using Mooncake
 using Optim
@@ -51,11 +51,9 @@ end;
 # We do not derive and implement the gradient function manually here but instead use reverse-mode automatic differentiation with DifferentiationInterface + Mooncake.
 # When computing gradients, the objective function is evaluated as well.
 # We can exploit this and [avoid re-evaluating the objective function](https://julianlsolvers.github.io/Optim.jl/stable/#user/tipsandtricks/#avoid-repeating-computations) in such cases.
-backend = AutoMooncake()
 function objective_and_gradient(F, G, flat_θ)
     if G !== nothing
-        val, grad = value_and_gradient(objective, backend, flat_θ)
-        copyto!(G, only(grad))
+        val, grad = DI.value_and_gradient!(objective, G, AutoMooncake(), flat_θ)
         if F !== nothing
             return val
         end