Cache subexpressions when building MOI.ScalarNonlinearExpression

src/JuMP.jl

@@ -138,6 +138,8 @@ mutable struct GenericModel{T<:Real} <: AbstractModel
     # A dictionary to store timing information from the JuMP macros.
     enable_macro_timing::Bool
     macro_times::Dict{Tuple{LineNumberNode,String},Float64}
+    # We use `Any` as key because we haven't defined `GenericNonlinearExpr` yet
+    subexpressions::Dict{Any,MOI.ScalarNonlinearFunction}
 end
 
 value_type(::Type{GenericModel{T}}) where {T} = T
@@ -251,6 +253,7 @@ function direct_generic_model(
         Dict{Any,MOI.ConstraintIndex}(),
         false,
         Dict{Tuple{LineNumberNode,String},Float64}(),
+        Dict{Any,MOI.ScalarNonlinearFunction}(),
     )
 end
 

src/constraints.jl

@@ -760,6 +760,10 @@ function moi_function(constraint::AbstractConstraint)
     return moi_function(jump_function(constraint))
 end
 
+function moi_function(constraint::AbstractConstraint, model)
+    return moi_function(jump_function(constraint), model)
+end
+
 """
     moi_set(constraint::AbstractConstraint)
 
@@ -1016,6 +1020,17 @@ function _moi_add_constraint(
     return MOI.add_constraint(model, f, s)
 end
 
+function check_belongs_to_model(f::Vector, model)
+    for func in f
+        check_belongs_to_model(func, model)
+    end
+end
+
+function moi_function(f, model)
+    check_belongs_to_model(f, model)
+    return moi_function(f)
+end
+
 """
     add_constraint(
         model::GenericModel,
@@ -1032,10 +1047,9 @@ function add_constraint(
     name::String = "",
 )
     con = model_convert(model, con)
+    func, set = moi_function(con, model), moi_set(con)
     # The type of backend(model) is unknown so we directly redirect to another
     # function.
-    check_belongs_to_model(con, model)
-    func, set = moi_function(con), moi_set(con)
     cindex = _moi_add_constraint(
         backend(model),
         func,

src/nlp_expr.jl

@@ -552,18 +552,31 @@ end
 
 moi_function(x::Number) = x
 
-function moi_function(f::GenericNonlinearExpr{V}) where {V}
+function moi_function(
+    f::GenericNonlinearExpr{V},
+    model::JuMP.GenericModel,
+) where {V}
+    cache = model.subexpressions
+    if haskey(cache, f)
+        return cache[f]
+    end
     ret = MOI.ScalarNonlinearFunction(f.head, similar(f.args))
     stack = Tuple{MOI.ScalarNonlinearFunction,Int,GenericNonlinearExpr{V}}[]
     for i in length(f.args):-1:1
         if f.args[i] isa GenericNonlinearExpr{V}
             push!(stack, (ret, i, f.args[i]))
+        elseif f.args[i] isa AbstractJuMPScalar
+            ret.args[i] = moi_function(model, f.args[i])
         else
             ret.args[i] = moi_function(f.args[i])
         end
     end
     while !isempty(stack)
         parent, i, arg = pop!(stack)
+        if haskey(cache, arg)
+            parent.args[i] = cache[arg]
+            continue
+        end
         child = MOI.ScalarNonlinearFunction(arg.head, similar(arg.args))
         parent.args[i] = child
         for j in length(arg.args):-1:1
@@ -573,7 +586,9 @@ function moi_function(f::GenericNonlinearExpr{V}) where {V}
                 child.args[j] = moi_function(arg.args[j])
             end
         end
+        cache[arg] = child
     end
+    cache[f] = ret
     return ret
 end