Merge pull request #186 from ChrisRackauckas-Claude/runic-formatting

Switch from JuliaFormatter to Runic.jl for code formatting

Author: ChrisRackauckas

.JuliaFormatter.toml

@@ -1,13 +1,19 @@
-name: "Format Check"
+name: format-check
 
 on:
   push:
     branches:
+      - 'master'
       - 'main'
+      - 'release-'
     tags: '*'
   pull_request:
 
 jobs:
-  format-check:
-    name: "Format Check"
-    uses: "SciML/.github/.github/workflows/format-suggestions-on-pr.yml@v1"
+  runic:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: fredrikekre/runic-action@v1
+        with:
+          version: '1'

.github/workflows/FormatCheck.yml

@@ -1,21 +1,25 @@
 using Documenter, DocumenterCitations, DeepEquilibriumNetworks
 
-cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml"; force=true)
-cp("./docs/Project.toml", "./docs/src/assets/Project.toml"; force=true)
+cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml"; force = true)
+cp("./docs/Project.toml", "./docs/src/assets/Project.toml"; force = true)
 
-bib = CitationBibliography(joinpath(@__DIR__, "ref.bib"); style=:authoryear)
+bib = CitationBibliography(joinpath(@__DIR__, "ref.bib"); style = :authoryear)
 
 include("pages.jl")
 
-makedocs(; sitename="Deep Equilibrium Networks",
-    authors="Avik Pal et al.",
-    modules=[DeepEquilibriumNetworks],
-    clean=true,
-    doctest=false,  # Tested in CI
-    linkcheck=true,
-    format=Documenter.HTML(; assets=["assets/favicon.ico"],
-        canonical="https://docs.sciml.ai/DeepEquilibriumNetworks/stable/"),
-    plugins=[bib],
-    pages)
+makedocs(;
+    sitename = "Deep Equilibrium Networks",
+    authors = "Avik Pal et al.",
+    modules = [DeepEquilibriumNetworks],
+    clean = true,
+    doctest = false,  # Tested in CI
+    linkcheck = true,
+    format = Documenter.HTML(;
+        assets = ["assets/favicon.ico"],
+        canonical = "https://docs.sciml.ai/DeepEquilibriumNetworks/stable/"
+    ),
+    plugins = [bib],
+    pages
+)
 
-deploydocs(; repo="github.com/SciML/DeepEquilibriumNetworks.jl.git", push_preview=true)
+deploydocs(; repo = "github.com/SciML/DeepEquilibriumNetworks.jl.git", push_preview = true)

docs/make.jl

@@ -1,3 +1,5 @@
-pages = ["Home" => "index.md",
+pages = [
+    "Home" => "index.md",
     "Tutorials" => ["tutorials/basic_mnist_deq.md", "tutorials/reduced_dim_deq.md"],
-    "API References" => "api.md", "References" => "references.md"]
+    "API References" => "api.md", "References" => "references.md",
+]

docs/pages.jl

@@ -9,13 +9,13 @@ using NonlinearSolveBase: AbsNormTerminationMode
 using FastClosures: @closure
 using Random: Random, AbstractRNG, randn!
 using SciMLBase: SciMLBase, AbstractNonlinearAlgorithm, AbstractODEAlgorithm,
-                 NonlinearSolution, ODESolution, ODEFunction, ODEProblem,
-                 SteadyStateProblem, _unwrap_val
+    NonlinearSolution, ODESolution, ODEFunction, ODEProblem,
+    SteadyStateProblem, _unwrap_val
 using SciMLSensitivity: SteadyStateAdjoint, GaussAdjoint, ZygoteVJP
 using Static: StaticSymbol, StaticInt, known, static
 
 using Lux: Lux, LuxOps, BranchLayer, Chain, NoOpLayer, Parallel, RepeatedLayer,
-           StatefulLuxLayer, WrappedFunction
+    StatefulLuxLayer, WrappedFunction
 using LuxCore: LuxCore, AbstractLuxLayer, AbstractLuxContainerLayer, AbstractLuxWrapperLayer
 using NNlib: ⊠
 using SteadyStateDiffEq: DynamicSS, SSRootfind
@@ -30,7 +30,7 @@ include("precompilation.jl")
 
 # Exports
 export DEQs, DeepEquilibriumSolution, DeepEquilibriumNetwork, SkipDeepEquilibriumNetwork,
-       MultiScaleDeepEquilibriumNetwork, MultiScaleSkipDeepEquilibriumNetwork,
-       MultiScaleNeuralODE
+    MultiScaleDeepEquilibriumNetwork, MultiScaleSkipDeepEquilibriumNetwork,
+    MultiScaleNeuralODE
 
 end

src/DeepEquilibriumNetworks.jl

@@ -22,13 +22,17 @@ struct DeepEquilibriumSolution  # This is intentionally left untyped to allow up
     original
 end
 
-function CRC.rrule(::Type{<:DeepEquilibriumSolution}, z_star,
-        u0, residual, jacobian_loss, nfe, original)
+function CRC.rrule(
+        ::Type{<:DeepEquilibriumSolution}, z_star,
+        u0, residual, jacobian_loss, nfe, original
+    )
     sol = DeepEquilibriumSolution(z_star, u0, residual, jacobian_loss, nfe, original)
     ∇DeepEquilibriumSolution(::CRC.NoTangent) = ntuple(_ -> CRC.NoTangent(), 7)
     function ∇DeepEquilibriumSolution(∂sol)
-        return (CRC.NoTangent(), ∂sol.z_star, ∂sol.u0, ∂sol.residual,
-            ∂sol.jacobian_loss, ∂sol.nfe, CRC.NoTangent())
+        return (
+            CRC.NoTangent(), ∂sol.z_star, ∂sol.u0, ∂sol.residual,
+            ∂sol.jacobian_loss, ∂sol.nfe, CRC.NoTangent(),
+        )
     end
     return sol, ∇DeepEquilibriumSolution
 end
@@ -39,15 +43,32 @@ end
 
 function Base.show(io::IO, sol::DeepEquilibriumSolution)
     println(io, "DeepEquilibriumSolution")
-    println(io, " * Initial Guess: ", sprint(print, sol.u0; context=(
-        :compact => true, :limit => true)))
-    println(io, " * Steady State: ", sprint(print, sol.z_star; context=(
-        :compact => true, :limit => true)))
-    println(io, " * Residual: ", sprint(print, sol.residual; context=(
-        :compact => true, :limit => true)))
-    println(io, " * Jacobian Loss: ",
-        sprint(print, sol.jacobian_loss; context=(:compact => true, :limit => true)))
-    print(io, " * NFE: ", sol.nfe)
+    println(
+        io, " * Initial Guess: ", sprint(
+            print, sol.u0; context = (
+                :compact => true, :limit => true,
+            )
+        )
+    )
+    println(
+        io, " * Steady State: ", sprint(
+            print, sol.z_star; context = (
+                :compact => true, :limit => true,
+            )
+        )
+    )
+    println(
+        io, " * Residual: ", sprint(
+            print, sol.residual; context = (
+                :compact => true, :limit => true,
+            )
+        )
+    )
+    println(
+        io, " * Jacobian Loss: ",
+        sprint(print, sol.jacobian_loss; context = (:compact => true, :limit => true))
+    )
+    return print(io, " * NFE: ", sol.nfe)
 end
 
 # Core Model
@@ -65,31 +86,37 @@ const DEQ = DeepEquilibriumNetwork
 function LuxCore.initialstates(rng::AbstractRNG, deq::DEQ)
     rng = LuxCore.replicate(rng)
     randn(rng, 1)
-    return (; model=LuxCore.initialstates(rng, deq.model), fixed_depth=Val(0),
-        init=LuxCore.initialstates(rng, deq.init), solution=DeepEquilibriumSolution(), rng)
+    return (;
+        model = LuxCore.initialstates(rng, deq.model), fixed_depth = Val(0),
+        init = LuxCore.initialstates(rng, deq.init), solution = DeepEquilibriumSolution(), rng,
+    )
 end
 
 (deq::DEQ)(x, ps, st::NamedTuple) = deq(x, ps, st, check_unrolled_mode(st))
 
 ## Pretraining
 function (deq::DEQ)(x, ps, st::NamedTuple, ::Val{true})
     z, st = get_initial_condition(deq, x, ps, st)
-    repeated_model = RepeatedLayer(deq.model; repeats=st.fixed_depth)
+    repeated_model = RepeatedLayer(deq.model; repeats = st.fixed_depth)
 
     z_star, st_ = repeated_model((z, x), ps.model, st.model)
     model = StatefulLuxLayer{true}(deq.model, ps.model, st_)
     resid = CRC.ignore_derivatives(z_star .- model((z_star, x)))
 
     rng = LuxCore.replicate(st.rng)
     jac_loss = estimate_jacobian_trace(
-        LuxOps.getproperty(deq, Val(:jacobian_regularization)), model, z_star, x, rng)
+        LuxOps.getproperty(deq, Val(:jacobian_regularization)), model, z_star, x, rng
+    )
 
     solution = DeepEquilibriumSolution(
-        z_star, z, resid, zero(eltype(x)), _unwrap_val(st.fixed_depth), jac_loss)
-    res = split_and_reshape(z_star, LuxOps.getproperty(deq.model, Val(:split_idxs)),
-        LuxOps.getproperty(deq.model, Val(:scales)))
-
-    return res, (; st..., model=model.st, solution, rng)
+        z_star, z, resid, zero(eltype(x)), _unwrap_val(st.fixed_depth), jac_loss
+    )
+    res = split_and_reshape(
+        z_star, LuxOps.getproperty(deq.model, Val(:split_idxs)),
+        LuxOps.getproperty(deq.model, Val(:scales))
+    )
+
+    return res, (; st..., model = model.st, solution, rng)
 end
 
 function (deq::DEQ)(x, ps, st::NamedTuple, ::Val{false})
@@ -104,23 +131,29 @@ function (deq::DEQ)(x, ps, st::NamedTuple, ::Val{false})
         return y .- u
     end
 
-    prob = construct_prob(deq.kind, ODEFunction{false}(dudt), z, (; ps=ps.model, x))
+    prob = construct_prob(deq.kind, ODEFunction{false}(dudt), z, (; ps = ps.model, x))
     alg = normalize_alg(deq)
     termination_condition = AbsNormTerminationMode(Base.Fix1(maximum, abs))
-    sol = solve(prob, alg; sensealg=default_sensealg(prob), abstol=1e-3,
-        reltol=1e-3, termination_condition, maxiters=32, deq.kwargs...)
+    sol = solve(
+        prob, alg; sensealg = default_sensealg(prob), abstol = 1.0e-3,
+        reltol = 1.0e-3, termination_condition, maxiters = 32, deq.kwargs...
+    )
     z_star = get_steady_state(sol)
 
     rng = LuxCore.replicate(st.rng)
     jac_loss = estimate_jacobian_trace(
-        LuxOps.getproperty(deq, Val(:jacobian_regularization)), model, z_star, x, rng)
+        LuxOps.getproperty(deq, Val(:jacobian_regularization)), model, z_star, x, rng
+    )
 
     solution = DeepEquilibriumSolution(
-        z_star, z, LuxOps.getproperty(sol, Val(:resid)), jac_loss, get_nfe(sol), sol)
-    res = split_and_reshape(z_star, LuxOps.getproperty(deq.model, Val(:split_idxs)),
-        LuxOps.getproperty(deq.model, Val(:scales)))
-
-    return res, (; st..., model=model.st, solution, rng)
+        z_star, z, LuxOps.getproperty(sol, Val(:resid)), jac_loss, get_nfe(sol), sol
+    )
+    res = split_and_reshape(
+        z_star, LuxOps.getproperty(deq.model, Val(:split_idxs)),
+        LuxOps.getproperty(deq.model, Val(:scales))
+    )
+
+    return res, (; st..., model = model.st, solution, rng)
 end
 
 ## Constructors
@@ -168,17 +201,20 @@ See also: [`SkipDeepEquilibriumNetwork`](@ref), [`MultiScaleDeepEquilibriumNetwo
 [`MultiScaleSkipDeepEquilibriumNetwork`](@ref).
 """
 function DeepEquilibriumNetwork(
-        model, solver; init=missing, jacobian_regularization=nothing,
-        problem_type::Type=SteadyStateProblem{false}, kwargs...)
+        model, solver; init = missing, jacobian_regularization = nothing,
+        problem_type::Type = SteadyStateProblem{false}, kwargs...
+    )
     if init === missing # Regular DEQ
         init = WrappedFunction(Base.Fix1(zeros_init, LuxOps.getproperty(model, Val(:scales))))
     elseif init === nothing # SkipRegDEQ
         init = NoOpLayer()
     elseif !(init isa AbstractLuxLayer)
         error("init::$(typeof(init)) is not a valid input for DeepEquilibriumNetwork.")
     end
-    return DeepEquilibriumNetwork(init, model, solver, jacobian_regularization,
-        kwargs, problem_type_to_symbol(problem_type))
+    return DeepEquilibriumNetwork(
+        init, model, solver, jacobian_regularization,
+        kwargs, problem_type_to_symbol(problem_type)
+    )
 end
 
 """
@@ -192,7 +228,7 @@ function SkipDeepEquilibriumNetwork(model, init, solver; kwargs...)
 end
 
 function SkipDeepEquilibriumNetwork(model, solver; kwargs...)
-    return DeepEquilibriumNetwork(model, solver; init=nothing, kwargs...)
+    return DeepEquilibriumNetwork(model, solver; init = nothing, kwargs...)
 end
 
 ## MultiScale DEQ
@@ -242,8 +278,10 @@ julia> size.(first(model(x, ps, st)))
 ((4, 12), (3, 12), (2, 12), (1, 12))
 ```
 """
-function MultiScaleDeepEquilibriumNetwork(main_layers::Tuple, mapping_layers::Matrix,
-        post_fuse_layer::Union{Nothing, Tuple}, solver, scales; kwargs...)
+function MultiScaleDeepEquilibriumNetwork(
+        main_layers::Tuple, mapping_layers::Matrix,
+        post_fuse_layer::Union{Nothing, Tuple}, solver, scales; kwargs...
+    )
     l1 = Parallel(nothing, main_layers...)
     l2 = BranchLayer(Parallel.(+, map(x -> tuple(x...), eachrow(mapping_layers))...)...)
 
@@ -269,17 +307,23 @@ creates a [`MultiScaleDeepEquilibriumNetwork`](@ref) with `init` kwarg set to pa
 
 If `init` is not passed, it creates a MultiScale Regularized Deep Equilibrium Network.
 """
-function MultiScaleSkipDeepEquilibriumNetwork(main_layers::Tuple, mapping_layers::Matrix,
-        post_fuse_layer::Union{Nothing, Tuple}, init::Tuple, solver, scales; kwargs...)
+function MultiScaleSkipDeepEquilibriumNetwork(
+        main_layers::Tuple, mapping_layers::Matrix,
+        post_fuse_layer::Union{Nothing, Tuple}, init::Tuple, solver, scales; kwargs...
+    )
     init = Chain(Parallel(nothing, init...), flatten_vcat)
     return MultiScaleDeepEquilibriumNetwork(
-        main_layers, mapping_layers, post_fuse_layer, solver, scales; init, kwargs...)
+        main_layers, mapping_layers, post_fuse_layer, solver, scales; init, kwargs...
+    )
 end
 
-function MultiScaleSkipDeepEquilibriumNetwork(main_layers::Tuple, mapping_layers::Matrix,
-        post_fuse_layer::Union{Nothing, Tuple}, args...; kwargs...)
+function MultiScaleSkipDeepEquilibriumNetwork(
+        main_layers::Tuple, mapping_layers::Matrix,
+        post_fuse_layer::Union{Nothing, Tuple}, args...; kwargs...
+    )
     return MultiScaleDeepEquilibriumNetwork(
-        main_layers, mapping_layers, post_fuse_layer, args...; init=nothing, kwargs...)
+        main_layers, mapping_layers, post_fuse_layer, args...; init = nothing, kwargs...
+    )
 end
 
 """
@@ -289,19 +333,19 @@ Same arguments as [`MultiScaleDeepEquilibriumNetwork`](@ref) but sets `problem_t
 `ODEProblem{false}`.
 """
 function MultiScaleNeuralODE(args...; kwargs...)
-    return MultiScaleDeepEquilibriumNetwork(args...; kwargs..., problem_type=ODEProblem{false})
+    return MultiScaleDeepEquilibriumNetwork(args...; kwargs..., problem_type = ODEProblem{false})
 end
 
 ## Generate Initial Condition
 function get_initial_condition(deq::DEQ{NoOpLayer}, x, ps, st)
     zₓ = zeros_init(LuxOps.getproperty(deq.model, Val(:scales)), x)
     z, st_ = deq.model((zₓ, x), ps.model, st.model)
-    return z, (; st..., model=st_)
+    return z, (; st..., model = st_)
 end
 
 function get_initial_condition(deq::DEQ, x, ps, st)
     z, st_ = deq.init(x, ps.init, st.init)
-    return z, (; st..., init=st_)
+    return z, (; st..., init = st_)
 end
 
 # Other Layers

src/layers.jl

@@ -11,9 +11,9 @@ using PrecompileTools: @compile_workload, @setup_workload
         # Create a small model for precompilation
         # Using SSRootfind which is already imported from SteadyStateDiffEq
         model = DEQ(
-            Parallel(+, Lux.Dense(2, 2; use_bias=false), Lux.Dense(2, 2; use_bias=false)),
+            Parallel(+, Lux.Dense(2, 2; use_bias = false), Lux.Dense(2, 2; use_bias = false)),
             SSRootfind();
-            verbose=false
+            verbose = false
         )
 
         # Initialize parameters and state (very common operation)