Clean up and update to OptimizationBase@v4

.github/workflows/CI.yml

@@ -66,9 +66,54 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
       - if: ${{ matrix.group == 'OptimizationQuadDIRECT' }}
         run: julia --project -e 'using Pkg; Pkg.Registry.add(RegistrySpec(url  = "https://github.com/HolyLab/HolyLabRegistry.git")); Pkg.add("QuadDIRECT")'
-      - uses: julia-actions/julia-runtest@v1
+      - name: ${{ matrix.group }}
         env:
-          GROUP: ${{ matrix.group }}
+            GROUP: ${{ matrix.group }}
+        shell: julia --color=yes --check-bounds=yes --depwarn=yes {0}
+        run: |
+          using Pkg
+          const GROUP = get(ENV, "GROUP", "Core")
+
+          function dev_subpkg(subpkg)
+            subpkg_path = "lib/$subpkg"
+            Pkg.develop(PackageSpec(path = subpkg_path))
+          end
+
+          if GROUP == "Core"
+            Pkg.activate(".")
+          else
+            subpkg_path = "lib/${{ matrix.group }}"
+            Pkg.activate(subpkg_path)
+          end
+
+          if VERSION < v"1.11"
+            @info "Preparing env"
+            if GROUP == "Core"
+              @info "Testing Core"
+              dev_subpkg("OptimizationBase")
+              dev_subpkg("OptimizationLBFGSB")
+              dev_subpkg("OptimizationMOI")
+              dev_subpkg("OptimizationOptimJL")
+              dev_subpkg("OptimizationOptimisers")
+            elseif GROUP == "OptimizationBase"
+              dev_subpkg("OptimizationLBFGSB")
+              dev_subpkg("OptimizationManopt")
+            elseif GROUP == "OptimizationAuglag"
+              dev_subpkg("OptimizationOptimisers")
+            elseif GROUP == "OptimizationMultistartOptimization"
+              dev_subpkg("OptimizationNLopt")
+            elseif GROUP == "GPU" || GROUP == "OptimizationPolyalgorithms"
+              # special case
+              Pkg.develop([PackageSpec(path = "lib/OptimizationOptimJL"), PackageSpec(path = "lib/OptimizationOptimisers")])
+            elseif GROUP == "OptimizationNLPModels"
+              dev_subpkg("OptimizationMOI")
+              dev_subpkg("OptimizationOptimJL")
+              dev_subpkg("OptimizationLBFGSB")
+            end
+          end
+
+          @info "Starting tests"
+          Pkg.test()
       - uses: julia-actions/julia-processcoverage@v1
         with:
           directories: src,lib/OptimizationBase/src,lib/OptimizationBBO/src,lib/OptimizationCMAEvolutionStrategy/src,lib/OptimizationEvolutionary/src,lib/OptimizationGCMAES/src,lib/OptimizationIpopt/src,lib/OptimizationMadNLP/src,lib/OptimizationManopt/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

docs/Project.toml

@@ -19,8 +19,8 @@ NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationBase = "bca83a33-5cc9-4baa-983d-23429ab6bcbb"
+OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationCMAEvolutionStrategy = "bd407f91-200f-4536-9381-e4ba712f53f8"
 OptimizationEvolutionary = "cb963754-43f6-435e-8d4b-99009ff27753"
 OptimizationGCMAES = "6f0a0517-dbc2-4a7a-8a20-99ae7f27e911"
@@ -47,6 +47,26 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
+[sources]
+Optimization = {path = ".."}
+OptimizationBase = {path = "../lib/OptimizationBase"}
+OptimizationBBO = {path = "../lib/OptimizationBBO"}
+OptimizationCMAEvolutionStrategy = {path = "../lib/OptimizationCMAEvolutionStrategy"}
+OptimizationEvolutionary = {path = "../lib/OptimizationEvolutionary"}
+OptimizationGCMAES = {path = "../lib/OptimizationGCMAES"}
+OptimizationIpopt = {path = "../lib/OptimizationIpopt"}
+OptimizationMOI = {path = "../lib/OptimizationMOI"}
+OptimizationManopt = {path = "../lib/OptimizationManopt"}
+OptimizationMetaheuristics = {path = "../lib/OptimizationMetaheuristics"}
+OptimizationNLPModels = {path = "../lib/OptimizationNLPModels"}
+OptimizationNLopt = {path = "../lib/OptimizationNLopt"}
+OptimizationNOMAD = {path = "../lib/OptimizationNOMAD"}
+OptimizationOptimJL = {path = "../lib/OptimizationOptimJL"}
+OptimizationOptimisers = {path = "../lib/OptimizationOptimisers"}
+OptimizationPRIMA = {path = "../lib/OptimizationPRIMA"}
+OptimizationPolyalgorithms = {path = "../lib/OptimizationPolyalgorithms"}
+OptimizationSpeedMapping = {path = "../lib/OptimizationSpeedMapping"}
+
 [compat]
 ADTypes = "1"
 AmplNLWriter = "1"
@@ -66,9 +86,9 @@ ModelingToolkit = "10.23"
 NLPModels = "0.21"
 NLPModelsTest = "0.10"
 NLopt = "0.6, 1"
-Optimization = "4, 5"
+Optimization = "5"
+OptimizationBase = "4"
 OptimizationBBO = "0.4"
-OptimizationBase = "2, 3"
 OptimizationCMAEvolutionStrategy = "0.3"
 OptimizationEvolutionary = "0.4"
 OptimizationGCMAES = "0.3"
@@ -88,7 +108,7 @@ OrdinaryDiffEq = "6"
 Plots = "1"
 Random = "1"
 ReverseDiff = ">= 1.9.0"
-SciMLBase = "2.30.0"
+SciMLBase = "2.122.1"
 SciMLSensitivity = "7"
 SymbolicAnalysis = "0.3"
 Symbolics = "6"

lib/OptimizationAuglag/Project.toml

@@ -14,14 +14,17 @@ MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
+[sources]
+OptimizationOptimisers = {path = "../OptimizationOptimisers"}
+
 [compat]
 ForwardDiff = "1.0.1"
-OptimizationBase = "3"
+OptimizationBase = "4"
 MLUtils = "0.4.8"
 LinearAlgebra = "1.10"
 OptimizationOptimisers = "0.3.8"
 Test = "1.10.0"
-SciMLBase = "2.58"
+SciMLBase = "2.122.1"
 julia = "1.10"
 
 [targets]

lib/OptimizationAuglag/src/OptimizationAuglag.jl

@@ -16,12 +16,7 @@ using LinearAlgebra: norm
     ϵ = 1e-8
 end
 
-@static if isdefined(SciMLBase, :supports_opt_cache_interface)
-    SciMLBase.supports_opt_cache_interface(::AugLag) = true
-end
-@static if isdefined(OptimizationBase, :supports_opt_cache_interface)
-    OptimizationBase.supports_opt_cache_interface(::AugLag) = true
-end
+SciMLBase.has_init(::AugLag) = true
 SciMLBase.allowsbounds(::AugLag) = true
 SciMLBase.requiresgradient(::AugLag) = true
 SciMLBase.allowsconstraints(::AugLag) = true
@@ -59,32 +54,7 @@ function __map_optimizer_args(cache::OptimizationBase.OptimizationCache, opt::Au
     return mapped_args
 end
 
-function SciMLBase.__solve(cache::OptimizationCache{
-        F,
-        RC,
-        LB,
-        UB,
-        LC,
-        UC,
-        S,
-        O,
-        D,
-        P,
-        C
-}) where {
-        F,
-        RC,
-        LB,
-        UB,
-        LC,
-        UC,
-        S,
-        O <:
-        AugLag,
-        D,
-        P,
-        C
-}
+function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: AugLag}
     maxiters = OptimizationBase._check_and_convert_maxiters(cache.solver_args.maxiters)
 
     local x
@@ -121,7 +91,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end
-            return x[1] + sum(@. λ * cons_tmp[eq_inds] + ρ / 2 * (cons_tmp[eq_inds] .^ 2)) + 1 / (2 * ρ) * sum((max.(Ref(0.0), μ .+ (ρ .* cons_tmp[ineq_inds]))) .^ 2)
+            return x[1] + sum(@. λ * cons_tmp[eq_inds] + ρ / 2 * (cons_tmp[eq_inds] .^ 2)) +
+                   1 / (2 * ρ) * sum((max.(Ref(0.0), μ .+ (ρ .* cons_tmp[ineq_inds]))) .^ 2)
         end
 
         prev_eqcons = zero(λ)

lib/OptimizationBBO/Project.toml

@@ -14,8 +14,8 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [compat]
 julia = "1.10"
 BlackBoxOptim = "0.6"
-OptimizationBase = "3"
-SciMLBase = "2.58"
+OptimizationBase = "4"
+SciMLBase = "2.122.1"
 Reexport = "1.2"
 
 [targets]

lib/OptimizationBBO/src/OptimizationBBO.jl

@@ -1,21 +1,16 @@
 module OptimizationBBO
 
 using Reexport
-import OptimizationBase
-import OptimizationBase: SciMLBase
-import BlackBoxOptim
-import SciMLBase: MultiObjectiveOptimizationFunction
+using OptimizationBase
+using SciMLBase
+using BlackBoxOptim: BlackBoxOptim
 
 abstract type BBO end
 
 SciMLBase.requiresbounds(::BBO) = true
 SciMLBase.allowsbounds(::BBO) = true
-@static if isdefined(SciMLBase, :supports_opt_cache_interface)
-    SciMLBase.supports_opt_cache_interface(opt::BBO) = true
-end
-@static if isdefined(OptimizationBase, :supports_opt_cache_interface)
-    OptimizationBase.supports_opt_cache_interface(opt::BBO) = true
-end
+
+SciMLBase.has_init(opt::BBO) = true
 
 for j in string.(BlackBoxOptim.SingleObjectiveMethodNames)
     eval(Meta.parse("Base.@kwdef struct BBO_" * j * " <: BBO method=:" * j * " end"))
@@ -36,19 +31,19 @@ function decompose_trace(opt::BlackBoxOptim.OptRunController, progress)
         if iszero(max_time)
             # we stop at either convergence or max_steps
             n_steps = BlackBoxOptim.num_steps(opt)
-            Base.@logmsg(Base.LogLevel(-1), msg, progress=n_steps/maxiters,
+            Base.@logmsg(Base.LogLevel(-1), msg, progress=n_steps / maxiters,
                 _id=:OptimizationBBO)
         else
             # we stop at either convergence or max_time
             elapsed = BlackBoxOptim.elapsed_time(opt)
-            Base.@logmsg(Base.LogLevel(-1), msg, progress=elapsed/max_time,
+            Base.@logmsg(Base.LogLevel(-1), msg, progress=elapsed / max_time,
                 _id=:OptimizationBBO)
         end
     end
     return BlackBoxOptim.best_candidate(opt)
 end
 
-function __map_optimizer_args(prob::OptimizationBase.OptimizationCache, opt::BBO;
+function __map_optimizer_args(prob::OptimizationCache, opt::BBO;
         callback = nothing,
         maxiters::Union{Number, Nothing} = nothing,
         maxtime::Union{Number, Nothing} = nothing,
@@ -96,32 +91,7 @@ function map_objective(obj::BlackBoxOptim.IndexedTupleFitness)
     obj.orig
 end
 
-function SciMLBase.__solve(cache::OptimizationBase.OptimizationCache{
-        F,
-        RC,
-        LB,
-        UB,
-        LC,
-        UC,
-        S,
-        O,
-        D,
-        P,
-        C
-}) where {
-        F,
-        RC,
-        LB,
-        UB,
-        LC,
-        UC,
-        S,
-        O <:
-        BBO,
-        D,
-        P,
-        C
-}
+function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: BBO}
     function _cb(trace)
         if cache.callback === OptimizationBase.DEFAULT_CALLBACK
             cb_call = false

lib/OptimizationBase/Project.toml

@@ -55,7 +55,7 @@ ModelingToolkit = "10.23"
 PDMats = "0.11"
 Reexport = "1.2"
 ReverseDiff = "1.14"
-SciMLBase = "2.122"
+SciMLBase = "2.122.1"
 SparseConnectivityTracer = "0.6, 1"
 SparseMatrixColorings = "0.4"
 SymbolicAnalysis = "0.3"

lib/OptimizationBase/ext/OptimizationEnzymeExt.jl

@@ -199,12 +199,17 @@ function OptimizationBase.instantiate_function(f::OptimizationFunction{true}, x,
 
     if hv == true && f.hv === nothing
         function hv!(H, θ, v, p = p)
-            x = Duplicated(θ, v)
-            dx = Enzyme.make_zero(x)
-            H .= Enzyme.autodiff(
-                fmode, hv_f2_alloc, Const(rmode), Duplicated(x,dx),
-                Const(f.f), Const(p)
-            )[1].dval
+            dθ = zero(θ)
+            Enzyme.make_zero!(H)
+            Enzyme.autodiff(
+                fmode,
+                inner_grad,
+                Const(rmode),
+                Duplicated(θ, v),
+                Duplicated(dθ, H),
+                Const(f.f),
+                Const(p)
+            )
         end
     elseif hv == true
         hv! = (H, θ, v, p = p) -> f.hv(H, θ, v, p)
@@ -553,13 +558,20 @@ function OptimizationBase.instantiate_function(f::OptimizationFunction{false}, x
     end
 
     if hv == true && f.hv === nothing
+        H = zero(x)
         function hv!(θ, v, p = p)
-            x = Duplicated(θ, v)
-            dx = Enzyme.make_zero(x)
-            return Enzyme.autodiff(
-                fmode, hv_f2_alloc, DuplicatedNoNeed, Const(rmode), Duplicated(x, dx), 
-                Const(_f), Const(f.f), Const(p)
-            )[1].dval
+            dθ = zero(θ)
+            Enzyme.make_zero!(H)
+            Enzyme.autodiff(
+                fmode,
+                inner_grad,
+                Const(rmode),
+                Duplicated(θ, v),
+                Duplicated(dθ, H),
+                Const(f.f),
+                Const(p)
+            )
+            return H
         end
     elseif hv == true
         hv! = (θ, v, p = p) -> f.hv(θ, v, p)