Bump compats and update tutorials for Optimization v4

Project.toml

@@ -34,7 +34,7 @@ Boltz = "1"
 ChainRulesCore = "1"
 ComponentArrays = "0.15.17"
 ConcreteStructs = "0.2"
-DataInterpolations = "5, 6"
+DataInterpolations = "6.4"
 DelayDiffEq = "5.47.3"
 DiffEqCallbacks = "3.6.2"
 Distances = "0.10.11"
@@ -54,9 +54,9 @@ LuxLib = "1.2"
 NNlib = "0.9.22"
 OneHotArrays = "0.2.5"
 Optimisers = "0.3"
-Optimization = "3.25.0"
-OptimizationOptimJL = "0.3.0"
-OptimizationOptimisers = "0.2.1"
+Optimization = "4"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
 OrdinaryDiffEq = "6.76.0"
 Printf = "1.10"
 Random = "1.10"

docs/Project.toml

@@ -57,10 +57,10 @@ MLUtils = "0.4"
 NNlib = "0.9"
 OneHotArrays = "0.2"
 Optimisers = "0.3"
-Optimization = "3.9"
-OptimizationOptimJL = "0.2, 0.3"
-OptimizationOptimisers = "0.2"
-OptimizationPolyalgorithms = "0.2"
+Optimization = "4"
+OptimizationOptimJL = "0.4"
+OptimizationOptimisers = "0.3"
+OptimizationPolyalgorithms = "0.3"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 Printf = "1"

docs/src/examples/augmented_neural_ode.md

@@ -69,13 +69,13 @@ function plot_contour(model, ps, st, npoints = 300)
     return contour(x, y, sol; fill = true, linewidth = 0.0)
 end
 
-loss_node(model, x, y, ps, st) = mean((first(model(x, ps, st)) .- y) .^ 2)
+loss_node(model, data, ps, st) = mean((first(model(data[1], ps, st)) .- data[2]) .^ 2)
 
 dataloader = concentric_sphere(
     2, (0.0f0, 2.0f0), (3.0f0, 4.0f0), 2000, 2000; batch_size = 256)
 
 iter = 0
-cb = function (ps, l)
+cb = function (state, l)
     global iter
     iter += 1
     if iter % 10 == 0
@@ -87,15 +87,15 @@ end
 model, ps, st = construct_model(1, 2, 64, 0)
 opt = OptimizationOptimisers.Adam(0.005)
 
-loss_node(model, dataloader.data[1], dataloader.data[2], ps, st)
+loss_node(model, (dataloader.data[1], dataloader.data[2]), ps, st)
 
 println("Training Neural ODE")
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 1000)
 
 plt_node = plot_contour(model, res.u, st)
 
@@ -106,10 +106,10 @@ println()
 println("Training Augmented Neural ODE")
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 1000)
 
 plot_contour(model, res.u, st)
 ```
@@ -229,7 +229,7 @@ We use the L2 distance between the model prediction `model(x)` and the actual pr
 optimization objective.
 
 ```@example augneuralode
-loss_node(model, x, y, ps, st) = mean((first(model(x, ps, st)) .- y) .^ 2)
+loss_node(model, data, ps, st) = mean((first(model(data[1], ps, st)) .- data[2]) .^ 2)
 ```
 
 #### Dataset
@@ -248,7 +248,7 @@ Additionally, we define a callback function which displays the total loss at spe
 
 ```@example augneuralode
 iter = 0
-cb = function (ps, l)
+cb = function (state, l)
     global iter
     iter += 1
     if iter % 10 == 0
@@ -276,10 +276,10 @@ for `20` epochs.
 model, ps, st = construct_model(1, 2, 64, 0)
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 1000)
 
 plot_contour(model, res.u, st)
 ```
@@ -297,10 +297,10 @@ a function which can be expressed by the neural ode. For more details and proofs
 model, ps, st = construct_model(1, 2, 64, 1)
 
 optfunc = OptimizationFunction(
-    (x, p, data, target) -> loss_node(model, data, target, x, st),
+    (x, data) -> loss_node(model, data, x, st),
     Optimization.AutoZygote())
-optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev)
-res = solve(optprob, opt, IterTools.ncycle(dataloader, 5); callback = cb)
+optprob = OptimizationProblem(optfunc, ComponentArray(ps |> cdev) |> gdev, dataloader)
+res = solve(optprob, opt; callback = cb, epochs = 1000)
 
 plot_contour(model, res.u, st)
 ```

docs/src/examples/hamiltonian_nn.md

@@ -14,7 +14,7 @@ Before getting to the explanation, here's some code to start with. We will follo
 
 ```@example hamiltonian_cp
 using Lux, DiffEqFlux, OrdinaryDiffEq, Statistics, Plots, Zygote, ForwardDiff, Random,
-      ComponentArrays, Optimization, OptimizationOptimisers, IterTools
+      ComponentArrays, Optimization, OptimizationOptimisers, MLUtils
 
 t = range(0.0f0, 1.0f0; length = 1024)
 π_32 = Float32(π)
@@ -23,37 +23,33 @@ p_t = reshape(cos.(2π_32 * t), 1, :)
 dqdt = 2π_32 .* p_t
 dpdt = -2π_32 .* q_t
 
-data = vcat(q_t, p_t)
-target = vcat(dqdt, dpdt)
+data = cat(q_t, p_t; dims = 1)
+target = cat(dqdt, dpdt; dims = 1)
 B = 256
-NEPOCHS = 100
-dataloader = ncycle(
-    ((selectdim(data, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))),
-         selectdim(target, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))))
-    for i in 1:(size(data, 2) ÷ B)),
-    NEPOCHS)
+NEPOCHS = 1000
+dataloader = DataLoader((data, target); batchsize = B)
 
 hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
 
 opt = OptimizationOptimisers.Adam(0.01f0)
 
-function loss_function(ps, data, target)
+function loss_function(ps, databatch)
+    data, target = databatch
     pred, st_ = hnn(data, ps, st)
     return mean(abs2, pred .- target), pred
 end
 
-function callback(ps, loss, pred)
+function callback(state, loss)
     println("[Hamiltonian NN] Loss: ", loss)
     return false
 end
 
-opt_func = OptimizationFunction((ps, _, data, target) -> loss_function(ps, data, target),
-    Optimization.AutoForwardDiff())
-opt_prob = OptimizationProblem(opt_func, ps_c)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoForwardDiff())
+opt_prob = OptimizationProblem(opt_func, ps_c, dataloader)
 
-res = Optimization.solve(opt_prob, opt, dataloader; callback)
+res = Optimization.solve(opt_prob, opt; callback, epochs = NEPOCHS)
 
 ps_trained = res.u
 
@@ -75,7 +71,7 @@ The HNN predicts the gradients ``(\dot q, \dot p)`` given ``(q, p)``. Hence, we
 
 ```@example hamiltonian
 using Lux, DiffEqFlux, OrdinaryDiffEq, Statistics, Plots, Zygote, ForwardDiff, Random,
-      ComponentArrays, Optimization, OptimizationOptimisers, IterTools
+      ComponentArrays, Optimization, OptimizationOptimisers, MLUtils
 
 t = range(0.0f0, 1.0f0; length = 1024)
 π_32 = Float32(π)
@@ -87,12 +83,8 @@ dpdt = -2π_32 .* q_t
 data = cat(q_t, p_t; dims = 1)
 target = cat(dqdt, dpdt; dims = 1)
 B = 256
-NEPOCHS = 100
-dataloader = ncycle(
-    ((selectdim(data, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))),
-         selectdim(target, 2, ((i - 1) * B + 1):(min(i * B, size(data, 2)))))
-    for i in 1:(size(data, 2) ÷ B)),
-    NEPOCHS)
+NEPOCHS = 1000
+dataloader = DataLoader((data, target); batchsize = B)
 ```
 
 ### Training the HamiltonianNN
@@ -103,24 +95,25 @@ We parameterize the  with a small MultiLayered Perceptron. HNNs are trained by o
 hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
+hnn_stateful = StatefulLuxLayer{true}(hnn, ps_c, st)
 
 opt = OptimizationOptimisers.Adam(0.01f0)
 
-function loss_function(ps, data, target)
-    pred, st_ = hnn(data, ps, st)
+function loss_function(ps, databatch)
+    (data, target) = databatch
+    pred = hnn_stateful(data, ps)
     return mean(abs2, pred .- target), pred
 end
 
-function callback(ps, loss, pred)
+function callback(state, loss)
     println("[Hamiltonian NN] Loss: ", loss)
     return false
 end
 
-opt_func = OptimizationFunction(
-    (ps, _, data, target) -> loss_function(ps, data, target), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps_c)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps_c, dataloader)
 
-res = solve(opt_prob, opt, dataloader; callback)
+res = solve(opt_prob, opt; callback, epochs = NEPOCHS)
 
 ps_trained = res.u
 ```

docs/src/examples/mnist_conv_neural_ode.md

@@ -89,30 +89,30 @@ end
 # burn in accuracy
 accuracy(m, ((img, lab),), ps, st)
 
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, _ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
 # burn in loss
-loss_function(ps, img, lab)
+loss_function(ps, (img, lab))
 
 opt = OptimizationOptimisers.Adam(0.005)
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps);
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader);
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST Conv GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST Conv GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; maxiters = 5, callback)
+res = Optimization.solve(opt_prob, opt; epochs = 5, callback)
 acc = accuracy(m, dataloader, res.u, st)
 acc # hide
 ```

docs/src/examples/mnist_neural_ode.md

@@ -81,29 +81,29 @@ end
 
 accuracy(m, ((x_train1, y_train1),), ps, st) # burn in accuracy
 
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, st_ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
-loss_function(ps, x_train1, y_train1) # burn in loss
+loss_function(ps, (x_train1, y_train1)) # burn in loss
 
 opt = OptimizationOptimisers.Adam(0.05)
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader)
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; callback, maxiters = 5)
+res = Optimization.solve(opt_prob, opt; callback, epochs = 5)
 accuracy(m, dataloader, res.u, st)
 ```
 
@@ -285,12 +285,13 @@ final output of our model. `logitcrossentropy` takes in the prediction from our
 model `model(x)` and compares it to actual output `y`:
 
 ```@example mnist
-function loss_function(ps, x, y)
+function loss_function(ps, data)
+    (x, y) = data
     pred, st_ = m(x, ps, st)
-    return logitcrossentropy(pred, y), pred
+    return logitcrossentropy(pred, y)
 end
 
-loss_function(ps, x_train1, y_train1) # burn in loss
+loss_function(ps, (x_train1, y_train1)) # burn in loss
 ```
 
 #### Optimizer
@@ -309,14 +310,13 @@ This callback function is used to print both the training and testing accuracy a
 ```@example mnist
 iter = 0
 
-opt_func = OptimizationFunction(
-    (ps, _, x, y) -> loss_function(ps, x, y), Optimization.AutoZygote())
-opt_prob = OptimizationProblem(opt_func, ps)
+opt_func = OptimizationFunction(loss_function, Optimization.AutoZygote())
+opt_prob = OptimizationProblem(opt_func, ps, dataloader)
 
-function callback(ps, l, pred)
+function callback(state, l)
     global iter += 1
     iter % 10 == 0 &&
-        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, ps.u, st))"
+        @info "[MNIST GPU] Accuracy: $(accuracy(m, dataloader, state.u, st))"
     return false
 end
 ```
@@ -329,6 +329,6 @@ for Neural ODE is given by `nn_ode.p`:
 
 ```@example mnist
 # Train the NN-ODE and monitor the loss and weights.
-res = Optimization.solve(opt_prob, opt, dataloader; callback, maxiters = 5)
+res = Optimization.solve(opt_prob, opt; callback, epochs = 5)
 accuracy(m, dataloader, res.u, st)
 ```