Add minibatching tests

Author: Vaibhavdixit02

lib/OptimizationOptimisers/Project.toml

@@ -10,14 +10,14 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
-[extensions]
-OptimizationOptimisersMLDataDevicesExt = "MLDataDevices"
-OptimizationOptimisersMLUtilsExt = "MLUtils"
-
 [weakdeps]
 MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 
+[extensions]
+OptimizationOptimisersMLDataDevicesExt = "MLDataDevices"
+OptimizationOptimisersMLUtilsExt = "MLUtils"
+
 [compat]
 MLDataDevices = "1.1"
 MLUtils = "0.4.4"

lib/OptimizationOptimisers/ext/OptimizationOptimisersMLDataDevicesExt.jl

@@ -3,6 +3,6 @@ module OptimizationOptimisersMLDataDevicesExt
 using MLDataDevices
 using OptimizationOptimisers
 
-OptimizationOptimisers.isa_dataiterator(::DeviceIterator) = true
+OptimizationOptimisers.isa_dataiterator(::DeviceIterator) = (@show "dkjht"; true)
 
 end

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -117,7 +117,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
                         opt = min_opt
                         x = min_err
                         θ = min_θ
-                        cache.f.grad(G, θ, d...)
+                        cache.f.grad(G, θ, d)
                         opt_state = Optimization.OptimizationState(iter = i,
                             u = θ,
                             objective = x[1],

lib/OptimizationOptimisers/test/runtests.jl

@@ -68,3 +68,43 @@ using Zygote
 
     @test_throws ArgumentError sol=solve(prob, Optimisers.Adam())
 end
+
+@testset "Minibatching" begin
+    using Optimization, OptimizationOptimisers, Lux, Zygote, MLUtils, Statistics, Plots, Random, ComponentArrays
+
+    x = rand(10000)
+    y = sin.(x)
+    data = MLUtils.DataLoader((x, y), batchsize = 100)
+
+    # Define the neural network
+    model = Chain(Dense(1, 32, tanh), Dense(32, 1))
+    ps, st = Lux.setup(Random.default_rng(), model)
+    ps_ca = ComponentArray(ps)
+    smodel = StatefulLuxLayer{true}(model, nothing, st)
+
+    function callback(state, l)
+        state.iter % 25 == 1 && @show "Iteration: %5d, Loss: %.6e\n" state.iter l
+        return l < 1e-4
+    end
+
+    function loss(ps, data)
+        ypred = [smodel([data[1][i]], ps)[1] for i in eachindex(data[1])]
+        return sum(abs2, ypred .- data[2])
+    end
+
+    optf = OptimizationFunction(loss, AutoZygote())
+    prob = OptimizationProblem(optf, ps_ca, data)
+
+    res = Optimization.solve(prob, Optimisers.Adam(), callback = callback, epochs = 100)
+
+    @test res.objective < 1e-4
+
+    using MLDataDevices
+    data = CPUDevice()(data)
+    optf = OptimizationFunction(loss, AutoZygote())
+    prob = OptimizationProblem(optf, ps_ca, data)
+
+    res = Optimization.solve(prob, Optimisers.Adam(), callback = callback, epochs = 100)
+
+    @test res.objective < 1e-4
+end