reapply formatter

Author: ArnoStrouwen

README.md

@@ -33,8 +33,10 @@ rng = Random.default_rng()
 Random.seed!(rng, seed)
 
 model = Chain(Dense(2 => 2),
-    DeepEquilibriumNetwork(Parallel(+, Dense(2 => 2; use_bias=false),
-            Dense(2 => 2; use_bias=false)), NewtonRaphson()))
+    DeepEquilibriumNetwork(
+        Parallel(+, Dense(2 => 2; use_bias=false),
+            Dense(2 => 2; use_bias=false)),
+        NewtonRaphson()))
 
 gdev = gpu_device()
 cdev = cpu_device()

docs/pages.jl

@@ -2,8 +2,8 @@ pages = [
     "Home" => "index.md",
     "Tutorials" => [
         "tutorials/basic_mnist_deq.md",
-        "tutorials/reduced_dim_deq.md",
+        "tutorials/reduced_dim_deq.md"
     ],
     "API References" => "api.md",
-    "References" => "references.md",
+    "References" => "references.md"
 ]

docs/src/index.md

@@ -25,8 +25,10 @@ rng = Random.default_rng()
 Random.seed!(rng, seed)
 
 model = Chain(Dense(2 => 2),
-    DeepEquilibriumNetwork(Parallel(+, Dense(2 => 2; use_bias=false),
-            Dense(2 => 2; use_bias=false)), NewtonRaphson()))
+    DeepEquilibriumNetwork(
+        Parallel(+, Dense(2 => 2; use_bias=false),
+            Dense(2 => 2; use_bias=false)),
+        NewtonRaphson()))
 
 gdev = gpu_device()
 cdev = cpu_device()

docs/src/tutorials/basic_mnist_deq.md

@@ -4,7 +4,7 @@ We will train a simple Deep Equilibrium Model on MNIST. First we load a few pack
 
 ```@example basic_mnist_deq
 using DeepEquilibriumNetworks, SciMLSensitivity, Lux, NonlinearSolve, OrdinaryDiffEq,
-    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
+      Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
 using MLDatasets: MNIST
 using MLDataUtils: LabelEnc, convertlabel, stratifiedobs, batchview
 
@@ -65,7 +65,8 @@ function construct_model(solver; model_type::Symbol=:deq)
         Conv((4, 4), 64 => 64; stride=2, pad=1))
 
     # The input layer of the DEQ
-    deq_model = Chain(Parallel(+,
+    deq_model = Chain(
+        Parallel(+,
             Conv((3, 3), 64 => 64, tanh; stride=1, pad=SamePad()),
             Conv((3, 3), 64 => 64, tanh; stride=1, pad=SamePad())),
         Conv((3, 3), 64 => 64, tanh; stride=1, pad=SamePad()))

docs/src/tutorials/reduced_dim_deq.md

@@ -6,7 +6,7 @@ same MNIST example as before, but this time we will use a reduced state size.
 
 ```@example reduced_dim_mnist
 using DeepEquilibriumNetworks, SciMLSensitivity, Lux, NonlinearSolve, OrdinaryDiffEq,
-    Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
+      Statistics, Random, Optimisers, LuxCUDA, Zygote, LinearSolve, LoggingExtras
 using MLDatasets: MNIST
 using MLDataUtils: LabelEnc, convertlabel, stratifiedobs, batchview
 

src/DeepEquilibriumNetworks.jl

@@ -4,14 +4,14 @@ import PrecompileTools: @recompile_invalidations
 
 @recompile_invalidations begin
     using ADTypes, DiffEqBase, FastClosures, LinearAlgebra, Lux, Random, SciMLBase,
-        Statistics, SteadyStateDiffEq
+          Statistics, SteadyStateDiffEq
 
     import ChainRulesCore as CRC
     import ConcreteStructs: @concrete
     import ConstructionBase: constructorof
     import Lux: AbstractExplicitLayer, AbstractExplicitContainerLayer
     import SciMLBase: AbstractNonlinearAlgorithm,
-        AbstractODEAlgorithm, _unwrap_val, NonlinearSolution
+                      AbstractODEAlgorithm, _unwrap_val, NonlinearSolution
     import TruncatedStacktraces: @truncate_stacktrace
 end
 
@@ -23,7 +23,7 @@ include("utils.jl")
 
 # Exports
 export DEQs, DeepEquilibriumSolution, DeepEquilibriumNetwork, SkipDeepEquilibriumNetwork,
-    MultiScaleDeepEquilibriumNetwork, MultiScaleSkipDeepEquilibriumNetwork,
-    MultiScaleNeuralODE
+       MultiScaleDeepEquilibriumNetwork, MultiScaleSkipDeepEquilibriumNetwork,
+       MultiScaleNeuralODE
 
 end

src/layers.jl

@@ -152,8 +152,10 @@ Deep Equilibrium Network as proposed in [baideep2019](@cite) and [pal2022mixing]
 ```julia
 julia> using DeepEquilibriumNetworks, Lux, Random, OrdinaryDiffEq
 
-julia> model = DeepEquilibriumNetwork(Parallel(+, Dense(2, 2; use_bias=false),
-               Dense(2, 2; use_bias=false)), VCABM3(); verbose=false)
+julia> model = DeepEquilibriumNetwork(
+           Parallel(+, Dense(2, 2; use_bias=false),
+               Dense(2, 2; use_bias=false)),
+           VCABM3(); verbose=false)
 DeepEquilibriumNetwork(
     model = Parallel(
         +
@@ -233,15 +235,17 @@ For keyword arguments, see [`DeepEquilibriumNetwork`](@ref).
 ```julia
 julia> using DeepEquilibriumNetworks, Lux, Random, NonlinearSolve
 
-julia> main_layers = (Parallel(+, Dense(4 => 4, tanh; use_bias=false),
-               Dense(4 => 4, tanh; use_bias=false)), Dense(3 => 3, tanh), Dense(2 => 2, tanh),
+julia> main_layers = (
+           Parallel(+, Dense(4 => 4, tanh; use_bias=false),
+               Dense(4 => 4, tanh; use_bias=false)),
+           Dense(3 => 3, tanh), Dense(2 => 2, tanh),
            Dense(1 => 1, tanh))
 (Parallel(), Dense(3 => 3, tanh_fast), Dense(2 => 2, tanh_fast), Dense(1 => 1, tanh_fast))
 
 julia> mapping_layers = [NoOpLayer() Dense(4 => 3, tanh) Dense(4 => 2, tanh) Dense(4 => 1, tanh);
-           Dense(3 => 4, tanh) NoOpLayer() Dense(3 => 2, tanh) Dense(3 => 1, tanh);
-           Dense(2 => 4, tanh) Dense(2 => 3, tanh) NoOpLayer() Dense(2 => 1, tanh);
-           Dense(1 => 4, tanh) Dense(1 => 3, tanh) Dense(1 => 2, tanh) NoOpLayer()]
+                         Dense(3 => 4, tanh) NoOpLayer() Dense(3 => 2, tanh) Dense(3 => 1, tanh);
+                         Dense(2 => 4, tanh) Dense(2 => 3, tanh) NoOpLayer() Dense(2 => 1, tanh);
+                         Dense(1 => 4, tanh) Dense(1 => 3, tanh) Dense(1 => 2, tanh) NoOpLayer()]
 4×4 Matrix{LuxCore.AbstractExplicitLayer}:
  NoOpLayer()               …  Dense(4 => 1, tanh_fast)
  Dense(3 => 4, tanh_fast)     Dense(3 => 1, tanh_fast)

test/layers.jl

@@ -1,5 +1,5 @@
 using ADTypes, DeepEquilibriumNetworks, DiffEqBase, NonlinearSolve, OrdinaryDiffEq,
-    SciMLSensitivity, SciMLBase, Test
+      SciMLSensitivity, SciMLBase, Test
 
 include("test_utils.jl")
 
@@ -16,7 +16,7 @@ end
 
     base_models = [
         Parallel(+, __get_dense_layer(2 => 2), __get_dense_layer(2 => 2)),
-        Parallel(+, __get_conv_layer((1, 1), 1 => 1), __get_conv_layer((1, 1), 1 => 1)),
+        Parallel(+, __get_conv_layer((1, 1), 1 => 1), __get_conv_layer((1, 1), 1 => 1))
     ]
     init_models = [__get_dense_layer(2 => 2), __get_conv_layer((1, 1), 1 => 1)]
     x_sizes = [(2, 14), (3, 3, 1, 3)]
@@ -31,7 +31,8 @@ end
     @testset "Solver: $(__nameof(solver))" for solver in solvers,
         mtype in model_type, jacobian_regularization in jacobian_regularizations
 
-        @testset "x_size: $(x_size)" for (base_model, init_model, x_size) in zip(base_models,
+        @testset "x_size: $(x_size)" for (base_model, init_model, x_size) in zip(
+            base_models,
             init_models, x_sizes)
             model = if mtype === :deq
                 DeepEquilibriumNetwork(base_model, solver; jacobian_regularization)
@@ -86,20 +87,20 @@ end
 
     main_layers = [
         (Parallel(+, __get_dense_layer(4 => 4), __get_dense_layer(4 => 4)),
-            __get_dense_layer(3 => 3), __get_dense_layer(2 => 2),
-            __get_dense_layer(1 => 1)),
+        __get_dense_layer(3 => 3), __get_dense_layer(2 => 2),
+        __get_dense_layer(1 => 1))
     ]
 
     mapping_layers = [
         [NoOpLayer() __get_dense_layer(4 => 3) __get_dense_layer(4 => 2) __get_dense_layer(4 => 1);
-            __get_dense_layer(3 => 4) NoOpLayer() __get_dense_layer(3 => 2) __get_dense_layer(3 => 1);
-            __get_dense_layer(2 => 4) __get_dense_layer(2 => 3) NoOpLayer() __get_dense_layer(2 => 1);
-            __get_dense_layer(1 => 4) __get_dense_layer(1 => 3) __get_dense_layer(1 => 2) NoOpLayer()],
+         __get_dense_layer(3 => 4) NoOpLayer() __get_dense_layer(3 => 2) __get_dense_layer(3 => 1);
+         __get_dense_layer(2 => 4) __get_dense_layer(2 => 3) NoOpLayer() __get_dense_layer(2 => 1);
+         __get_dense_layer(1 => 4) __get_dense_layer(1 => 3) __get_dense_layer(1 => 2) NoOpLayer()]
     ]
 
     init_layers = [
         (__get_dense_layer(4 => 4), __get_dense_layer(4 => 3), __get_dense_layer(4 => 2),
-            __get_dense_layer(4 => 1)),
+        __get_dense_layer(4 => 1))
     ]
 
     x_sizes = [(4, 3)]
@@ -113,16 +114,19 @@ end
 
     for mtype in model_type, jacobian_regularization in jacobian_regularizations
         @testset "Solver: $(__nameof(solver))" for solver in solvers
-            @testset "x_size: $(x_size)" for (main_layer, mapping_layer, init_layer, x_size, scale) in zip(main_layers,
+            @testset "x_size: $(x_size)" for (main_layer, mapping_layer, init_layer, x_size, scale) in zip(
+                main_layers,
                 mapping_layers, init_layers, x_sizes, scales)
                 model = if mtype === :deq
                     MultiScaleDeepEquilibriumNetwork(main_layer, mapping_layer, nothing,
                         solver, scale; jacobian_regularization)
                 elseif mtype === :skipdeq
-                    MultiScaleSkipDeepEquilibriumNetwork(main_layer, mapping_layer, nothing,
+                    MultiScaleSkipDeepEquilibriumNetwork(
+                        main_layer, mapping_layer, nothing,
                         init_layer, solver, scale; jacobian_regularization)
                 elseif mtype === :skipregdeq
-                    MultiScaleSkipDeepEquilibriumNetwork(main_layer, mapping_layer, nothing,
+                    MultiScaleSkipDeepEquilibriumNetwork(
+                        main_layer, mapping_layer, nothing,
                         solver, scale; jacobian_regularization)
                 elseif mtype === :node
                     solver isa SciMLBase.AbstractODEAlgorithm || continue