feat: make MLUtils into a weakdep & suppport MLDataDevices

lib/OptimizationOptimisers/Project.toml

@@ -1,17 +1,25 @@
 name = "OptimizationOptimisers"
 uuid = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 authors = ["Vaibhav Dixit <[email protected]> and contributors"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
+[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"
 Optimisers = "0.2, 0.3"
 Optimization = "4"
@@ -20,9 +28,14 @@ Reexport = "1.2"
 julia = "1"
 
 [extras]
+ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["ForwardDiff", "Test", "Zygote"]
+test = ["ComponentArrays", "ForwardDiff", "Lux", "MLDataDevices", "MLUtils", "Random", "Test", "Zygote"]

lib/OptimizationOptimisers/ext/OptimizationOptimisersMLDataDevicesExt.jl

@@ -0,0 +1,8 @@
+module OptimizationOptimisersMLDataDevicesExt
+
+using MLDataDevices
+using OptimizationOptimisers
+
+OptimizationOptimisers.isa_dataiterator(::DeviceIterator) = (@show "dkjht"; true)
+
+end

lib/OptimizationOptimisers/ext/OptimizationOptimisersMLUtilsExt.jl

@@ -0,0 +1,8 @@
+module OptimizationOptimisersMLUtilsExt
+
+using MLUtils
+using OptimizationOptimisers
+
+OptimizationOptimisers.isa_dataiterator(::MLUtils.DataLoader) = true
+
+end

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -2,7 +2,7 @@ module OptimizationOptimisers
 
 using Reexport, Printf, ProgressLogging
 @reexport using Optimisers, Optimization
-using Optimization.SciMLBase, MLUtils
+using Optimization.SciMLBase
 
 SciMLBase.supports_opt_cache_interface(opt::AbstractRule) = true
 SciMLBase.requiresgradient(opt::AbstractRule) = true
@@ -16,6 +16,8 @@ function SciMLBase.__init(
         kwargs...)
 end
 
+isa_dataiterator(data) = false
+
 function SciMLBase.__solve(cache::OptimizationCache{
         F,
         RC,
@@ -57,13 +59,14 @@ function SciMLBase.__solve(cache::OptimizationCache{
         throw(ArgumentError("The number of epochs must be specified as the epochs or maxiters kwarg."))
     end
 
-    if cache.p isa MLUtils.DataLoader
+    if isa_dataiterator(cache.p)
         data = cache.p
         dataiterate = true
     else
         data = [cache.p]
         dataiterate = false
     end
+
     opt = cache.opt
     θ = copy(cache.u0)
     G = copy(θ)
@@ -114,7 +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, 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 = 10000)
+
+    @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 = 10000)
+
+    @test res.objective < 1e-4
+end