Merge pull request #253 from ValentinKaisermayer/patch-opfunc

changes all OptimizationFunction constructors to outer one

Author: ChrisRackauckas

Project.toml

@@ -20,13 +20,17 @@ TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
 
 [compat]
 ArrayInterfaceCore = "0.1.1"
+ArrayInterface = "6"
 ConsoleProgressMonitor = "0.1"
 DiffResults = "1.0"
 DocStringExtensions = "0.8"
 LoggingExtras = "0.4"
 ProgressLogging = "0.1"
 Reexport = "0.2, 1.0"
 Requires = "1.0"
-SciMLBase = "1.32"
+SciMLBase = "1.34"
 TerminalLoggers = "0.1"
 julia = "1.6"
+
+[extras]
+ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

src/function/finitediff.jl

@@ -32,5 +32,7 @@ function instantiate_function(f, x, adtype::AutoFiniteDiff, p, num_cons = 0)
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoFiniteDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,adtype,grad,hess,hv,nothing,nothing,nothing)
+    return OptimizationFunction{false}(f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=nothing, cons_j=nothing, cons_h=nothing,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/forwarddiff.jl

@@ -11,7 +11,7 @@ function default_chunk_size(len)
     end
 end
 
-function instantiate_function(f::OptimizationFunction{true}, x, ::AutoForwardDiff{_chunksize}, p, num_cons = 0) where _chunksize
+function instantiate_function(f::OptimizationFunction{true}, x, adtype::AutoForwardDiff{_chunksize}, p, num_cons = 0) where _chunksize
 
     chunksize = _chunksize === nothing ? default_chunk_size(length(x)) : _chunksize
 
@@ -67,5 +67,7 @@ function instantiate_function(f::OptimizationFunction{true}, x, ::AutoForwardDif
         cons_h = f.cons_h
     end
 
-    return OptimizationFunction{true,AutoForwardDiff,typeof(f.f),typeof(grad),typeof(hess),typeof(hv),typeof(cons),typeof(cons_j),typeof(cons_h)}(f.f,AutoForwardDiff(),grad,hess,hv,cons,cons_j,cons_h)
+    return OptimizationFunction{true}(f.f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=cons, cons_j=cons_j, cons_h=cons_h,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/function.jl

@@ -6,9 +6,7 @@ function instantiate_function(f, x, ::AbstractADType, p, num_cons = 0)
     cons_j = f.cons_j === nothing ? nothing : (res,x)->f.cons_j(res,x,p)
     cons_h = f.cons_h === nothing ? nothing : (res,x)->f.cons_h(res,x,p)
 
-    OptimizationFunction{true,SciMLBase.NoAD,typeof(f.f),typeof(grad),
-                         typeof(hess),typeof(hv),typeof(cons),
-                         typeof(cons_j),typeof(cons_h)}(f.f,
-                         SciMLBase.NoAD(),grad,hess,hv,cons,
-                         cons_j,cons_h)
+    return OptimizationFunction{true}(f.f, SciMLBase.NoAD(); grad=grad, hess=hess, hv=hv, 
+        cons=cons, cons_j=cons_j, cons_h=cons_h,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/mtk.jl

@@ -5,7 +5,7 @@ end
 
 AutoModelingToolkit() = AutoModelingToolkit(false, false)
 
-function instantiate_function(f, x, ad::AutoModelingToolkit, p, num_cons=0)
+function instantiate_function(f, x, adtype::AutoModelingToolkit, p, num_cons=0)
     p = isnothing(p) ? SciMLBase.NullParameters() : p
     sys = ModelingToolkit.modelingtoolkitize(OptimizationProblem(f, x, p))
 
@@ -17,7 +17,7 @@ function instantiate_function(f, x, ad::AutoModelingToolkit, p, num_cons=0)
     end
 
     if f.hess === nothing
-        hess_oop, hess_iip = ModelingToolkit.generate_hessian(sys, expression=Val{false}, sparse = ad.obj_sparse)
+        hess_oop, hess_iip = ModelingToolkit.generate_hessian(sys, expression=Val{false}, sparse = adtype.obj_sparse)
         hess(H, u) = (hess_iip(H, u, p); H)
     else
         hess = f.hess
@@ -41,7 +41,7 @@ function instantiate_function(f, x, ad::AutoModelingToolkit, p, num_cons=0)
     end
 
     if f.cons !== nothing && f.cons_j === nothing
-        jac_oop, jac_iip = ModelingToolkit.generate_jacobian(cons_sys, expression=Val{false}, sparse=ad.cons_sparse)
+        jac_oop, jac_iip = ModelingToolkit.generate_jacobian(cons_sys, expression=Val{false}, sparse=adtype.cons_sparse)
         cons_j = function (J, θ)
             jac_iip(J, θ, p)
         end
@@ -50,13 +50,15 @@ function instantiate_function(f, x, ad::AutoModelingToolkit, p, num_cons=0)
     end
 
     if f.cons !== nothing && f.cons_h === nothing
-        cons_hess_oop, cons_hess_iip = ModelingToolkit.generate_hessian(cons_sys, expression=Val{false}, sparse=ad.cons_sparse)
+        cons_hess_oop, cons_hess_iip = ModelingToolkit.generate_hessian(cons_sys, expression=Val{false}, sparse=adtype.cons_sparse)
         cons_h = function (res, θ)
             cons_hess_iip(res, θ, p)
         end
     else
         cons_h = f.cons_h
     end
 
-    return OptimizationFunction{true,AutoModelingToolkit,typeof(f.f),typeof(grad),typeof(hess),typeof(hv),typeof(cons),typeof(cons_j),typeof(cons_h)}(f.f, AutoModelingToolkit(), grad, hess, hv, cons, cons_j, cons_h)
+    return OptimizationFunction{true}(f.f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=cons, cons_j=cons_j, cons_h=cons_h,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/reversediff.jl

@@ -1,6 +1,6 @@
 struct AutoReverseDiff <: AbstractADType end
 
-function instantiate_function(f, x, ::AutoReverseDiff, p=SciMLBase.NullParameters(), num_cons = 0)
+function instantiate_function(f, x, adtype::AutoReverseDiff, p=SciMLBase.NullParameters(), num_cons = 0)
     num_cons != 0 && error("AutoReverseDiff does not currently support constraints")
 
     _f = (θ, args...) -> first(f.f(θ,p, args...))
@@ -39,5 +39,7 @@ function instantiate_function(f, x, ::AutoReverseDiff, p=SciMLBase.NullParameter
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoReverseDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoReverseDiff(),grad,hess,hv,nothing,nothing,nothing)
+    return OptimizationFunction{false}(f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=nothing, cons_j=nothing, cons_h=nothing,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/tracker.jl

@@ -1,6 +1,6 @@
 struct AutoTracker <: AbstractADType end
 
-function instantiate_function(f, x, ::AutoTracker, p, num_cons = 0)
+function instantiate_function(f, x, adtype::AutoTracker, p, num_cons = 0)
     num_cons != 0 && error("AutoTracker does not currently support constraints")
     _f = (θ, args...) -> first(f.f(θ, p, args...))
 
@@ -23,5 +23,7 @@ function instantiate_function(f, x, ::AutoTracker, p, num_cons = 0)
     end
 
 
-    return OptimizationFunction{false,AutoTracker,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoTracker(),grad,hess,hv,nothing,nothing,nothing)
+    return OptimizationFunction{false}(f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=nothing, cons_j=nothing, cons_h=nothing,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end

src/function/zygote.jl

@@ -1,6 +1,6 @@
 struct AutoZygote <: AbstractADType end
 
-function instantiate_function(f, x, ::AutoZygote, p, num_cons = 0)
+function instantiate_function(f, x, adtype::AutoZygote, p, num_cons = 0)
     num_cons != 0 && error("AutoZygote does not currently support constraints")
 
     _f = (θ, args...) -> f(θ,p,args...)[1]
@@ -37,5 +37,7 @@ function instantiate_function(f, x, ::AutoZygote, p, num_cons = 0)
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoZygote,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoZygote(),grad,hess,hv,nothing,nothing,nothing)
+    return OptimizationFunction{false}(f, adtype; grad=grad, hess=hess, hv=hv, 
+        cons=nothing, cons_j=nothing, cons_h=nothing,
+        hess_prototype=nothing, cons_jac_prototype=nothing, cons_hess_prototype=nothing)
 end