Merge pull request #819 from SciML/bumvers

docs updates

.github/workflows/Documentation.yml

@@ -16,15 +16,15 @@ jobs:
         with:
           version: '1'
       - name: Install dependencies
-        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(vcat(PackageSpec(path = pwd()), [PackageSpec(path = joinpath("lib", dir)) for dir in readdir("lib") if dir !== "OptimizationQuadDIRECT"])); Pkg.instantiate()'
+        run: julia --project=docs/ -e 'using Pkg; Pkg.develop(vcat(PackageSpec(path = pwd()), [PackageSpec(path = joinpath("lib", dir)) for dir in readdir("lib") if (dir !== "OptimizationQuadDIRECT" && dir !== "OptimizationMultistartOptimization")])); Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # For authentication with SSH deploy key
         run: julia --project=docs/ --code-coverage=user docs/make.jl
       - uses: julia-actions/julia-processcoverage@v1
         with:
-          directories: src,lib/OptimizationBBO/src,lib/OptimizationCMAEvolutionStrategy/src,lib/OptimizationEvolutionary/src,lib/OptimizationFlux/src,lib/OptimizationGCMAES/src,lib/OptimizationMOI/src,lib/OptimizationMetaheuristics/src,lib/OptimizationMultistartOptimization/src,lib/OptimizationNLopt/src,lib/OptimizationNOMAD/src,lib/OptimizationOptimJL/src,lib/OptimizationOptimisers/src,lib/OptimizationPolyalgorithms/src,lib/OptimizationQuadDIRECT/src,lib/OptimizationSpeedMapping/src
+          directories: src,lib/OptimizationBBO/src,lib/OptimizationCMAEvolutionStrategy/src,lib/OptimizationEvolutionary/src,lib/OptimizationGCMAES/src,lib/OptimizationMOI/src,lib/OptimizationMetaheuristics/src,lib/OptimizationMultistartOptimization/src,lib/OptimizationNLopt/src,lib/OptimizationNOMAD/src,lib/OptimizationOptimJL/src,lib/OptimizationOptimisers/src,lib/OptimizationPolyalgorithms/src,lib/OptimizationQuadDIRECT/src,lib/OptimizationSpeedMapping/src
       - uses: codecov/codecov-action@v4
         with:
           file: lcov.info

docs/Project.toml

@@ -1,16 +1,18 @@
 [deps]
 AmplNLWriter = "7c4d4715-977e-5154-bfe0-e096adeac482"
+ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Ipopt_jll = "9cc047cb-c261-5740-88fc-0cf96f7bdcc7"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
 Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
@@ -20,12 +22,10 @@ OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationBase = "bca83a33-5cc9-4baa-983d-23429ab6bcbb"
 OptimizationCMAEvolutionStrategy = "bd407f91-200f-4536-9381-e4ba712f53f8"
 OptimizationEvolutionary = "cb963754-43f6-435e-8d4b-99009ff27753"
-OptimizationFlux = "253f991c-a7b2-45f8-8852-8b9a9df78a86"
 OptimizationGCMAES = "6f0a0517-dbc2-4a7a-8a20-99ae7f27e911"
 OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OptimizationManopt = "e57b7fff-7ee7-4550-b4f0-90e9476e9fb6"
 OptimizationMetaheuristics = "3aafef2f-86ae-4776-b337-85a36adf0b55"
-OptimizationMultistartOptimization = "e4316d97-8bbb-4fd3-a7d8-3851d2a72823"
 OptimizationNLPModels = "064b21be-54cf-11ef-1646-cdfee32b588f"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationNOMAD = "2cab0595-8222-4775-b714-9828e6a9e01b"
@@ -35,6 +35,8 @@ OptimizationPRIMA = "72f8369c-a2ea-4298-9126-56167ce9cbc2"
 OptimizationPolyalgorithms = "500b13db-7e66-49ce-bda4-eed966be6282"
 OptimizationSpeedMapping = "3d669222-0d7d-4eb9-8a9f-d8528b0d9b91"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
@@ -45,40 +47,42 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 AmplNLWriter = "1"
+ComponentArrays = "0.15"
 Documenter = "1"
 FiniteDiff = ">= 2.8.1"
-Flux = "0.13, 0.14"
 ForwardDiff = ">= 0.10.19"
 HiGHS = "1"
 Ipopt = "1"
 IterTools = "1"
 Juniper = "0.9"
+Lux = "1"
 Manifolds = "0.9"
 Manopt = "0.4"
+MLUtils = "0.4.4"
 ModelingToolkit = "9"
 NLPModels = "0.21"
 NLPModelsTest = "0.10"
 NLopt = "0.6, 1"
-Optimization = "3"
-OptimizationBBO = "0.1, 0.2, 0.3"
-OptimizationBase = "0.0.5, 0.0.6, 0.0.7, 1"
-OptimizationCMAEvolutionStrategy = "0.1, 0.2"
-OptimizationEvolutionary = "0.1, 0.2, 0.3"
-OptimizationFlux = "0.2.1"
-OptimizationGCMAES = "0.1, 0.2"
-OptimizationMOI = "0.1, 0.2, 0.3, 0.4"
-OptimizationManopt = "0.0.2, 0.0.3"
-OptimizationMetaheuristics = "0.1, 0.2"
-OptimizationMultistartOptimization = "0.1, 0.2"
-OptimizationNLPModels = "0.0.1"
-OptimizationNLopt = "0.1, 0.2"
-OptimizationNOMAD = "0.1, 0.2"
-OptimizationOptimJL = "0.1, 0.2, 0.3"
-OptimizationOptimisers = "0.1, 0.2"
-OptimizationPRIMA = "0.1.0, 0.2"
-OptimizationPolyalgorithms = "0.1, 0.2"
-OptimizationSpeedMapping = "0.1, 0.2"
+Optimization = "4"
+OptimizationBBO = "0.4"
+OptimizationBase = "2"
+OptimizationCMAEvolutionStrategy = "0.3"
+OptimizationEvolutionary = "0.4"
+OptimizationGCMAES = "0.3"
+OptimizationMOI = "0.5"
+OptimizationManopt = "0.0.4"
+OptimizationMetaheuristics = "0.3"
+OptimizationNLPModels = "0.0.2"
+OptimizationNLopt = "0.3"
+OptimizationNOMAD = "0.3"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
+OptimizationPRIMA = "0.3"
+OptimizationPolyalgorithms = "0.3"
+OptimizationSpeedMapping = "0.3"
 OrdinaryDiffEq = "6"
+Plots = "1"
+Random = "1"
 ReverseDiff = ">= 1.9.0"
 SciMLBase = "2.30.0"
 SciMLSensitivity = "7"

docs/src/optimization_packages/multistartoptimization.md

@@ -31,7 +31,7 @@ constraint equations. However, lower and upper constraints set by `lb` and `ub`
 
 The Rosenbrock function can be optimized using `MultistartOptimization.TikTak()` with 100 initial points and the local method `NLopt.LD_LBFGS()` as follows:
 
-```@example MultiStart
+```julia
 using Optimization, OptimizationMultistartOptimization, OptimizationNLopt
 rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
@@ -43,7 +43,7 @@ sol = solve(prob, MultistartOptimization.TikTak(100), NLopt.LD_LBFGS())
 
 You can use any `Optimization` optimizers you like. The global method of the `MultistartOptimization` is a positional argument and followed by the local method. For example, we can perform a multistartoptimization with LBFGS as the optimizer using either the `NLopt.jl` or `Optim.jl` implementation as follows. Moreover, this interface allows you to access and adjust all the optimizer settings as you normally would:
 
-```@example MultiStart
+```julia
 using OptimizationOptimJL
 f = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
 prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])

docs/src/optimization_packages/optimisers.md

@@ -12,27 +12,7 @@ Pkg.add("OptimizationOptimisers");
 In addition to the optimisation algorithms provided by the Optimisers.jl package this subpackage
 also provides the Sophia optimisation algorithm.
 
-## Local Unconstrained Optimizers
-
-  - `Sophia`: Based on the recent paper https://arxiv.org/abs/2305.14342. It incorporates second order information
-    in the form of the diagonal of the Hessian matrix hence avoiding the need to compute the complete hessian. It has been shown to converge faster than other first order methods such as Adam and SGD.
-
-      + `solve(problem, Sophia(; η, βs, ϵ, λ, k, ρ))`
-
-      + `η` is the learning rate
-      + `βs` are the decay of momentums
-      + `ϵ` is the epsilon value
-      + `λ` is the weight decay parameter
-      + `k` is the number of iterations to re-compute the diagonal of the Hessian matrix
-      + `ρ` is the momentum
-      + Defaults:
-
-          * `η = 0.001`
-          * `βs = (0.9, 0.999)`
-          * `ϵ = 1e-8`
-          * `λ = 0.1`
-          * `k = 10`
-          * `ρ = 0.04`
+## List of optimizers
 
   - [`Optimisers.Descent`](https://fluxml.ai/Optimisers.jl/dev/api/#Optimisers.Descent): **Classic gradient descent optimizer with learning rate**
 
@@ -42,6 +22,7 @@ also provides the Sophia optimisation algorithm.
       + Defaults:
 
           * `η = 0.1`
+
   - [`Optimisers.Momentum`](https://fluxml.ai/Optimisers.jl/dev/api/#Optimisers.Momentum): **Classic gradient descent optimizer with learning rate and momentum**
 
       + `solve(problem, Momentum(η, ρ))`

docs/src/optimization_packages/optimization.md

@@ -4,15 +4,35 @@ There are some solvers that are available in the Optimization.jl package directl
 
 ## Methods
 
-`LBFGS`: The popular quasi-Newton method that leverages limited memory BFGS approximation of the inverse of the Hessian. Through a wrapper over the [L-BFGS-B](https://users.iems.northwestern.edu/%7Enocedal/lbfgsb.html) fortran routine accessed from the [LBFGSB.jl](https://github.com/Gnimuc/LBFGSB.jl/) package. It directly supports box-constraints.
+  - `LBFGS`: The popular quasi-Newton method that leverages limited memory BFGS approximation of the inverse of the Hessian. Through a wrapper over the [L-BFGS-B](https://users.iems.northwestern.edu/%7Enocedal/lbfgsb.html) fortran routine accessed from the [LBFGSB.jl](https://github.com/Gnimuc/LBFGSB.jl/) package. It directly supports box-constraints.
 
 This can also handle arbitrary non-linear constraints through a Augmented Lagrangian method with bounds constraints described in 17.4 of Numerical Optimization by Nocedal and Wright. Thus serving as a general-purpose nonlinear optimization solver available directly in Optimization.jl.
 
+  - `Sophia`: Based on the recent paper https://arxiv.org/abs/2305.14342. It incorporates second order information in the form of the diagonal of the Hessian matrix hence avoiding the need to compute the complete hessian. It has been shown to converge faster than other first order methods such as Adam and SGD.
+
+        + `solve(problem, Sophia(; η, βs, ϵ, λ, k, ρ))`
+
+        + `η` is the learning rate
+        + `βs` are the decay of momentums
+        + `ϵ` is the epsilon value
+        + `λ` is the weight decay parameter
+        + `k` is the number of iterations to re-compute the diagonal of the Hessian matrix
+        + `ρ` is the momentum
+        + Defaults:
+
+            * `η = 0.001`
+            * `βs = (0.9, 0.999)`
+            * `ϵ = 1e-8`
+            * `λ = 0.1`
+            * `k = 10`
+            * `ρ = 0.04`
+
 ## Examples
 
 ### Unconstrained rosenbrock problem
 
 ```@example L-BFGS
+
 using Optimization, Zygote
 
 rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
@@ -27,6 +47,7 @@ sol = solve(prob, Optimization.LBFGS())
 ### With nonlinear and bounds constraints
 
 ```@example L-BFGS
+
 function con2_c(res, x, p)
     res .= [x[1]^2 + x[2]^2, (x[2] * sin(x[1]) + x[1]) - 5]
 end
@@ -37,3 +58,35 @@ prob = OptimizationProblem(optf, x0, p, lcons = [1.0, -Inf],
     ub = [1.0, 1.0])
 res = solve(prob, Optimization.LBFGS(), maxiters = 100)
 ```
+
+### Train NN with Sophia
+
+```@example Sophia
+
+using Optimization, 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-1 ## Terminate if loss is small
+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, Optimization.Sophia(), callback = callback)
+```

docs/src/tutorials/minibatch.md

@@ -1,14 +1,16 @@
 # Data Iterators and Minibatching
 
-It is possible to solve an optimization problem with batches using a `Flux.Data.DataLoader`, which is passed to `Optimization.solve` with `ncycles`. All data for the batches need to be passed as a tuple of vectors.
+It is possible to solve an optimization problem with batches using a `MLUtils.DataLoader`, which is passed to `Optimization.solve` with `ncycles`. All data for the batches need to be passed as a tuple of vectors.
 
 !!! note
 
     This example uses the OptimizationOptimisers.jl package. See the
     [Optimisers.jl page](@ref optimisers) for details on the installation and usage.
 
-```@example
-using Flux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, SciMLSensitivity
+```@example minibatch
+
+using Lux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, SciMLSensitivity, MLUtils,
+      Random, ComponentArrays
 
 function newtons_cooling(du, u, p, t)
     temp = u[1]
@@ -21,14 +23,16 @@ function true_sol(du, u, p, t)
     newtons_cooling(du, u, true_p, t)
 end
 
-ann = Chain(Dense(1, 8, tanh), Dense(8, 1, tanh))
-pp, re = Flux.destructure(ann)
+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 dudt_(u, p, t)
-    re(p)(u) .* u
+    smodel(u, p) .* u
 end
 
-callback = function (state, l, pred; doplot = false) #callback function to observe training
+function callback(state, l, pred; doplot = false) #callback function to observe training
     display(l)
     # plot current prediction against data
     if doplot
@@ -47,30 +51,30 @@ t = range(tspan[1], tspan[2], length = datasize)
 true_prob = ODEProblem(true_sol, u0, tspan)
 ode_data = Array(solve(true_prob, Tsit5(), saveat = t))
 
-prob = ODEProblem{false}(dudt_, u0, tspan, pp)
+prob = ODEProblem{false}(dudt_, u0, tspan, ps_ca)
 
 function predict_adjoint(fullp, time_batch)
     Array(solve(prob, Tsit5(), p = fullp, saveat = time_batch))
 end
 
-function loss_adjoint(fullp, batch, time_batch)
+function loss_adjoint(fullp, data)
+    batch, time_batch = data
     pred = predict_adjoint(fullp, time_batch)
     sum(abs2, batch .- pred), pred
 end
 
 k = 10
 # Pass the data for the batches as separate vectors wrapped in a tuple
-train_loader = Flux.Data.DataLoader((ode_data, t), batchsize = k)
+train_loader = MLUtils.DataLoader((ode_data, t), batchsize = k)
 
 numEpochs = 300
-l1 = loss_adjoint(pp, train_loader.data[1], train_loader.data[2])[1]
+l1 = loss_adjoint(ps_ca, train_loader.data)[1]
 
 optfun = OptimizationFunction(
-    (θ, p, batch, time_batch) -> loss_adjoint(θ, batch,
-        time_batch),
+    loss_adjoint,
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfun, pp)
+optprob = OptimizationProblem(optfun, ps_ca, train_loader)
 using IterTools: ncycle
-res1 = Optimization.solve(optprob, Optimisers.ADAM(0.05), ncycle(train_loader, numEpochs),
-    callback = callback)
+res1 = Optimization.solve(
+    optprob, Optimisers.ADAM(0.05); callback = callback, epochs = 1000)
 ```

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -52,7 +52,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
         cache.solver_args.epochs
     end
 
-    maxiters = Optimization._check_and_convert_maxiters(cache.solver_args.maxiters)
+    maxiters = Optimization._check_and_convert_maxiters(maxiters)
     if maxiters === nothing
         throw(ArgumentError("The number of epochs must be specified as the epochs or maxiters kwarg."))
     end

lib/OptimizationPolyalgorithms/Project.toml

@@ -11,8 +11,8 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
 [compat]
 Optimization = "4"
-OptimizationOptimJL = "0.1, 0.2, 0.3"
-OptimizationOptimisers = "0.1, 0.2"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
 Reexport = "1.2"
 julia = "1.6"
 

test/minibatch.jl

@@ -58,11 +58,10 @@ optfun = OptimizationFunction(loss_adjoint,
     Optimization.AutoZygote())
 optprob = OptimizationProblem(optfun, pp, train_loader)
 
-# res1 = Optimization.solve(optprob,
-#     Optimization.Sophia(; η = 0.5,
-#         λ = 0.0), callback = callback,
-#     maxiters = 1000)
-# @test 10res1.objective < l1
+res1 = Optimization.solve(optprob,
+    Optimization.Sophia(), callback = callback,
+    maxiters = 1000)
+@test 10res1.objective < l1
 
 optfun = OptimizationFunction(loss_adjoint,
     Optimization.AutoForwardDiff())