Fix code

.JuliaFormatter.toml

@@ -1,2 +1,9 @@
 style = "sciml"
-whitespace_in_kwargs = false
+whitespace_in_kwargs = false
+always_use_return = true
+margin = 92
+indent = 2
+format_docstrings = true
+join_lines_based_on_source = true
+separate_kwargs_with_semicolon = true
+always_for_in = true

Project.toml

@@ -1,15 +1,14 @@
 name = "DeepEquilibriumNetworks"
 uuid = "6748aba7-0e9b-415e-a410-ae3cc0ecb334"
 authors = ["Avik Pal <[email protected]>"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
-DiffEqSensitivity = "41bf760c-e81c-5289-8e54-58b1f1f8abe2"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
@@ -18,6 +17,7 @@ MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
+SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -31,13 +31,13 @@ ChainRulesCore = "1"
 ComponentArrays = "0.11, 0.12"
 DiffEqBase = "6"
 DiffEqCallbacks = "2.20.1"
-DiffEqSensitivity = "6.64"
 Functors = "0.2, 0.3"
 LinearSolve = "1"
 Lux = "0.4"
 MLUtils = "0.2"
 OrdinaryDiffEq = "6"
 SciMLBase = "1.19"
+SciMLSensitivity = "7"
 Setfield = "1"
 Static = "0.6, 0.7"
 SteadyStateDiffEq = "1.6"

docs/make.jl

@@ -1,27 +1,28 @@
 using Documenter, DocumenterCitations, DeepEquilibriumNetworks
 
-bib = CitationBibliography(joinpath(@__DIR__, "ref.bib"), sorting=:nyt)
+bib = CitationBibliography(joinpath(@__DIR__, "ref.bib"); sorting=:nyt)
 
-makedocs(bib,
+makedocs(bib;
          sitename="Fast Deep Equilibrium Networks",
          authors="Avik Pal et al.",
          clean=true,
          doctest=false,
          modules=[DeepEquilibriumNetworks],
          format=Documenter.HTML(#  analytics = "",
                                 #  assets = ["assets/favicon.ico"],
+                                ;
                                 canonical="https://deepequilibriumnetworks.sciml.ai/stable/"),
          pages=[
-             "Home" => "index.md",
-             "Manual" => [
-                 "Dynamical Systems" => "manual/solvers.md",
-                 "Non Linear Solvers" => "manual/nlsolve.md",
-                 "General Purpose Layers" => "manual/layers.md",
-                 "DEQ Layers" => "manual/deqs.md",
-                 "Miscellaneous" => "manual/misc.md",
-             ],
-             "References" => "references.md",
+           "Home" => "index.md",
+           "Manual" => [
+             "Dynamical Systems" => "manual/solvers.md",
+             "Non Linear Solvers" => "manual/nlsolve.md",
+             "General Purpose Layers" => "manual/layers.md",
+             "DEQ Layers" => "manual/deqs.md",
+             "Miscellaneous" => "manual/misc.md",
+           ],
+           "References" => "references.md",
          ])
 
-deploydocs(repo="github.com/SciML/DeepEquilibriumNetworks.jl.git";
+deploydocs(; repo="github.com/SciML/DeepEquilibriumNetworks.jl.git",
            push_preview=true)

src/DeepEquilibriumNetworks.jl

@@ -5,7 +5,7 @@ using ChainRulesCore,
       CUDA,
       DiffEqBase,
       DiffEqCallbacks,
-      DiffEqSensitivity,
+      SciMLSensitivity,
       Functors,
       LinearAlgebra,
       LinearSolve,
@@ -20,7 +20,7 @@ using ChainRulesCore,
       UnPack,
       Zygote
 
-import DiffEqSensitivity: AbstractAdjointSensitivityAlgorithm
+import SciMLSensitivity: AbstractAdjointSensitivityAlgorithm
 import Lux: AbstractExplicitContainerLayer, initialparameters, initialstates,
             parameterlength, statelength
 import Random: AbstractRNG

src/adjoint.jl

@@ -1,83 +1,84 @@
 neg(x::Any) = hasmethod(-, (typeof(x),)) ? -x : x
 neg(nt::NamedTuple) = fmap(neg, nt)
 
-@noinline function DiffEqSensitivity.SteadyStateAdjointProblem(sol::EquilibriumSolution,
-                                                               sensealg::DeepEquilibriumAdjoint,
-                                                               g::Nothing, dg;
-                                                               save_idxs=nothing)
-    @unpack f, p, u0 = sol.prob
+@noinline function SciMLSensitivity.SteadyStateAdjointProblem(sol::EquilibriumSolution,
+                                                              sensealg::DeepEquilibriumAdjoint,
+                                                              g::Nothing, dg;
+                                                              save_idxs=nothing, kwargs...)
+  @unpack f, p, u0 = sol.prob
 
-    diffcache, y = DiffEqSensitivity.adjointdiffcache(g, sensealg, false, sol, dg, f;
-                                                      quad=false, needs_jac=false)
+  diffcache, y = SciMLSensitivity.adjointdiffcache(g, sensealg, false, sol, dg, f;
+                                                   quad=false, needs_jac=false)
 
-    _save_idxs = save_idxs === nothing ? Colon() : save_idxs
-    if dg !== nothing
-        if typeof(_save_idxs) <: Number
-            diffcache.dg_val[_save_idxs] = dg[_save_idxs]
-        elseif typeof(dg) <: Number
-            @. diffcache.dg_val[_save_idxs] = dg
-        else
-            @. diffcache.dg_val[_save_idxs] = dg[_save_idxs]
-        end
-    end
-
-    if check_adjoint_mode(sensealg, Val(:vanilla))
-        # Solve the Linear Problem
-        _val, back = Zygote.pullback(x -> f(x, p, nothing), y)
-        s_val = size(_val)
-        op = ZygotePullbackMultiplyOperator{eltype(y), typeof(back), typeof(s_val)}(back,
-                                                                                    s_val)
-        linear_problem = LinearProblem(op, vec(diffcache.dg_val))
-        λ = solve(linear_problem, sensealg.linsolve).u
-    elseif check_adjoint_mode(sensealg, Val(:jfb))
-        # Jacobian Free Backpropagation
-        λ = diffcache.dg_val
+  _save_idxs = save_idxs === nothing ? Colon() : save_idxs
+  if dg !== nothing
+    if typeof(_save_idxs) <: Number
+      diffcache.dg_val[_save_idxs] = dg[_save_idxs]
+    elseif typeof(dg) <: Number
+      @. diffcache.dg_val[_save_idxs] = dg
     else
-        error("Unknown adjoint mode")
+      @. diffcache.dg_val[_save_idxs] = dg[_save_idxs]
     end
+  end
 
-    # Compute the VJP
-    _, back = Zygote.pullback(p -> vec(f(y, p, nothing)), p)
-    dp = back(vec(λ))[1]
+  if check_adjoint_mode(sensealg, Val(:vanilla))
+    # Solve the Linear Problem
+    _val, back = Zygote.pullback(x -> f(x, p, nothing), y)
+    s_val = size(_val)
+    op = ZygotePullbackMultiplyOperator{eltype(y), typeof(back), typeof(s_val)}(back,
+                                                                                s_val)
+    linear_problem = LinearProblem(op, vec(diffcache.dg_val))
+    λ = solve(linear_problem, sensealg.linsolve).u
+  elseif check_adjoint_mode(sensealg, Val(:jfb))
+    # Jacobian Free Backpropagation
+    λ = diffcache.dg_val
+  else
+    error("Unknown adjoint mode")
+  end
 
-    return neg(dp)
+  # Compute the VJP
+  _, back = Zygote.pullback(p -> vec(f(y, p, nothing)), p)
+  dp = back(vec(λ))[1]
+
+  return neg(dp)
 end
 
 function DiffEqBase._concrete_solve_adjoint(prob::SteadyStateProblem, alg,
                                             sensealg::DeepEquilibriumAdjoint, u0, p,
-                                            args...; save_idxs=nothing, kwargs...)
-    _prob = remake(prob; u0=u0, p=p)
-    sol = solve(_prob, alg, args...; kwargs...)
-    _save_idxs = save_idxs === nothing ? Colon() : save_idxs
+                                            ::SciMLBase.ADOriginator, args...;
+                                            save_idxs=nothing, kwargs...)
+  _prob = remake(prob; u0=u0, p=p)
+  sol = solve(_prob, alg, args...; kwargs...)
+  _save_idxs = save_idxs === nothing ? Colon() : save_idxs
 
-    out = save_idxs === nothing ? sol :
-          DiffEqBase.sensitivity_solution(sol, sol[_save_idxs])
+  out = save_idxs === nothing ? sol :
+        DiffEqBase.sensitivity_solution(sol, sol[_save_idxs])
 
-    function steadystatebackpass(Δ)
-        # Δ = dg/dx or diffcache.dg_val
-        # del g/del p = 0
-        dp = adjoint_sensitivities(sol, alg; sensealg=sensealg, g=nothing, dg=Δ,
-                                   save_idxs=save_idxs)
-        return (NoTangent(),
-                NoTangent(),
-                NoTangent(),
-                NoTangent(),
-                dp,
-                NoTangent(),
-                ntuple(_ -> NoTangent(), length(args))...)
-    end
+  function steadystatebackpass(Δ)
+    # Δ = dg/dx or diffcache.dg_val
+    # del g/del p = 0
+    dp = adjoint_sensitivities(sol, alg; sensealg=sensealg, g=nothing, dg=Δ,
+                               save_idxs=save_idxs)
+    return (NoTangent(),
+            NoTangent(),
+            NoTangent(),
+            NoTangent(),
+            dp,
+            NoTangent(),
+            ntuple(_ -> NoTangent(), length(args))...)
+  end
 
-    return out, steadystatebackpass
+  return out, steadystatebackpass
 end
 
-function DiffEqSensitivity._adjoint_sensitivities(sol, sensealg::DeepEquilibriumAdjoint,
-                                                  alg, g, dg=nothing; abstol=1e-6,
-                                                  reltol=1e-3, kwargs...)
-    return DiffEqSensitivity.SteadyStateAdjointProblem(sol, sensealg, g, dg; kwargs...)
+function SciMLSensitivity._adjoint_sensitivities(sol, sensealg::DeepEquilibriumAdjoint,
+                                                 alg, g, dg=nothing; abstol=1e-6,
+                                                 reltol=1e-3, kwargs...)
+  return SciMLSensitivity.SteadyStateAdjointProblem(sol, sensealg, g, dg; kwargs...)
 end
 
-function DiffEqSensitivity._adjoint_sensitivities(sol, sensealg::DeepEquilibriumAdjoint,
-                                                  alg; g=nothing, dg=nothing, abstol=1e-6,
-                                                  reltol=1e-3, kwargs...)
-    return DiffEqSensitivity.SteadyStateAdjointProblem(sol, sensealg, g, dg; kwargs...)
+function SciMLSensitivity._adjoint_sensitivities(sol, sensealg::DeepEquilibriumAdjoint,
+                                                 alg; g=nothing, dg=nothing, abstol=1e-6,
+                                                 reltol=1e-3, kwargs...)
+  return SciMLSensitivity.SteadyStateAdjointProblem(sol, sensealg, g, dg; kwargs...)
 end

src/layers/chain.jl

@@ -3,47 +3,47 @@
 
 Sequence of layers divided into 3 chunks --
 
-* `pre_deq` -- layers that are executed before DEQ is applied
-* `deq` -- The Deep Equilibrium Layer
-* `post_deq` -- layers that are executed after DEQ is applied
+  - `pre_deq` -- layers that are executed before DEQ is applied
+  - `deq` -- The Deep Equilibrium Layer
+  - `post_deq` -- layers that are executed after DEQ is applied
 
 Constraint: Must have one DEQ layer in `layers`
 """
 struct DEQChain{P1, D, P2} <: AbstractExplicitContainerLayer{(:pre_deq, :deq, :post_deq)}
-    pre_deq::P1
-    deq::D
-    post_deq::P2
+  pre_deq::P1
+  deq::D
+  post_deq::P2
 end
 
 function DEQChain(layers...)
-    pre_deq, post_deq, deq, encounter_deq = [], [], nothing, false
-    for l in layers
-        if l isa AbstractDeepEquilibriumNetwork || l isa AbstractSkipDeepEquilibriumNetwork
-            @assert !encounter_deq "Can have only 1 DEQ Layer in the Chain!!!"
-            deq = l
-            encounter_deq = true
-            continue
-        end
-        push!(encounter_deq ? post_deq : pre_deq, l)
+  pre_deq, post_deq, deq, encounter_deq = [], [], nothing, false
+  for l in layers
+    if l isa AbstractDeepEquilibriumNetwork || l isa AbstractSkipDeepEquilibriumNetwork
+      @assert !encounter_deq "Can have only 1 DEQ Layer in the Chain!!!"
+      deq = l
+      encounter_deq = true
+      continue
     end
-    @assert encounter_deq "No DEQ Layer in the Chain!!! Maybe you wanted to use Chain"
-    pre_deq = length(pre_deq) == 0 ? NoOpLayer() : Chain(pre_deq...)
-    post_deq = length(post_deq) == 0 ? NoOpLayer() : Chain(post_deq...)
-    return DEQChain(pre_deq, deq, post_deq)
+    push!(encounter_deq ? post_deq : pre_deq, l)
+  end
+  @assert encounter_deq "No DEQ Layer in the Chain!!! Maybe you wanted to use Chain"
+  pre_deq = length(pre_deq) == 0 ? NoOpLayer() : Chain(pre_deq...)
+  post_deq = length(post_deq) == 0 ? NoOpLayer() : Chain(post_deq...)
+  return DEQChain(pre_deq, deq, post_deq)
 end
 
 function get_deq_return_type(deq::DEQChain{P1,
                                            <:Union{MultiScaleDeepEquilibriumNetwork,
                                                    MultiScaleSkipDeepEquilibriumNetwork}},
                              ::T) where {P1, T}
-    return NTuple{length(deq.deq.scales), T}
+  return NTuple{length(deq.deq.scales), T}
 end
 get_deq_return_type(::DEQChain, ::T) where {T} = T
 
 function (deq::DEQChain)(x, ps::Union{ComponentArray, NamedTuple}, st::NamedTuple)
-    T = get_deq_return_type(deq, x)
-    x1, st1 = deq.pre_deq(x, ps.pre_deq, st.pre_deq)
-    (x2::T, deq_soln), st2 = deq.deq(x1, ps.deq, st.deq)
-    x3, st3 = deq.post_deq(x2, ps.post_deq, st.post_deq)
-    return (x3, deq_soln), (pre_deq=st1, deq=st2, post_deq=st3)
+  T = get_deq_return_type(deq, x)
+  x1, st1 = deq.pre_deq(x, ps.pre_deq, st.pre_deq)
+  (x2::T, deq_soln), st2 = deq.deq(x1, ps.deq, st.deq)
+  x3, st3 = deq.post_deq(x2, ps.post_deq, st.post_deq)
+  return (x3, deq_soln), (pre_deq=st1, deq=st2, post_deq=st3)
 end

src/layers/core.jl

@@ -1,15 +1,15 @@
 abstract type AbstractDeepEquilibriumNetwork <: AbstractExplicitContainerLayer{(:model,)} end
 
 function initialstates(rng::AbstractRNG, deq::AbstractDeepEquilibriumNetwork)
-    return (model=initialstates(rng, deq.model), fixed_depth=Val(0))
+  return (model=initialstates(rng, deq.model), fixed_depth=Val(0))
 end
 
 abstract type AbstractSkipDeepEquilibriumNetwork <:
               AbstractExplicitContainerLayer{(:model, :shortcut)} end
 
 function initialstates(rng::AbstractRNG, deq::AbstractSkipDeepEquilibriumNetwork)
-    return (model=initialstates(rng, deq.model), shortcut=initialstates(rng, deq.shortcut),
-            fixed_depth=Val(0))
+  return (model=initialstates(rng, deq.model), shortcut=initialstates(rng, deq.shortcut),
+          fixed_depth=Val(0))
 end
 
 @inline check_unrolled_mode(::Val{0})::Bool = false
@@ -27,27 +27,28 @@ ChainRulesCore.@non_differentiable get_unrolled_depth(::Any)
 Stores the solution of a DeepEquilibriumNetwork and its variants.
 
 ## Fields
+
     * `z_star`: Steady-State or the value reached due to maxiters
     * `u₀`: Initial Condition
     * `residual`: Difference of the ``z^*`` and ``f(z^*, x)``
     * `jacobian_loss`: Jacobian Stabilization Loss (see individual networks to see how it can be computed)
     * `nfe`: Number of Function Evaluations
 """
 struct DeepEquilibriumSolution{T, R <: AbstractFloat}
-    z_star::T
-    u₀::T
-    residual::T
-    jacobian_loss::R
-    nfe::Int
+  z_star::T
+  u₀::T
+  residual::T
+  jacobian_loss::R
+  nfe::Int
 end
 
 function Base.show(io::IO, l::DeepEquilibriumSolution)
-    print(io, "DeepEquilibriumSolution(")
-    print(io, "z_star: ", l.z_star)
-    print(io, ", initial_condition: ", l.u₀)
-    print(io, ", residual: ", l.residual)
-    print(io, ", jacobian_loss: ", l.jacobian_loss)
-    print(io, ", NFE: ", l.nfe)
-    print(io, ")")
-    return nothing
+  print(io, "DeepEquilibriumSolution(")
+  print(io, "z_star: ", l.z_star)
+  print(io, ", initial_condition: ", l.u₀)
+  print(io, ", residual: ", l.residual)
+  print(io, ", jacobian_loss: ", l.jacobian_loss)
+  print(io, ", NFE: ", l.nfe)
+  print(io, ")")
+  return nothing
 end