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Use Base.IdDict instead of objectid to store subexpressions #4204

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Use Base.IdDict instead of objectid to store subexpressions #4204

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4 changes: 2 additions & 2 deletions src/JuMP.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,7 @@ mutable struct GenericModel{T<:Real} <: AbstractModel
enable_macro_timing::Bool
macro_times::Dict{Tuple{LineNumberNode,String},Float64}
# A cache to track common subexpressions based on their `objectid`.
subexpressions::Dict{UInt64,MOI.ScalarNonlinearFunction}
subexpressions::Base.IdDict{Any,MOI.ScalarNonlinearFunction}
end

value_type(::Type{GenericModel{T}}) where {T} = T
Expand Down Expand Up @@ -278,7 +278,7 @@ function direct_generic_model(
Dict{Any,MOI.ConstraintIndex}(),
false,
Dict{Tuple{LineNumberNode,String},Float64}(),
Dict{UInt64,MOI.ScalarNonlinearFunction}(),
Base.IdDict{Any,MOI.ScalarNonlinearFunction}(),
)
end

Expand Down
20 changes: 9 additions & 11 deletions src/jump_moi_function.jl
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Expand Up @@ -167,10 +167,9 @@ end
moi_function_type(::Type{<:GenericNonlinearExpr}) = MOI.ScalarNonlinearFunction

function moi_function(model::GenericModel, f::GenericNonlinearExpr{V}) where {V}
# key = objectid(f)
# if haskey(model.subexpressions, key)
# return model.subexpressions[key]
# end
if (subexpression = get(model.subexpressions, f, nothing)) !== nothing
return subexpression
end
ret = MOI.ScalarNonlinearFunction(f.head, similar(f.args))
stack = Tuple{MOI.ScalarNonlinearFunction,Int,GenericNonlinearExpr{V}}[]
for i in length(f.args):-1:1
Expand All @@ -182,11 +181,10 @@ function moi_function(model::GenericModel, f::GenericNonlinearExpr{V}) where {V}
end
while !isempty(stack)
parent, i, arg = pop!(stack)
# arg_key = objectid(arg)
# if haskey(model.subexpressions, arg_key)
# parent.args[i] = model.subexpressions[arg_key]
# continue
# end
if (subexpression = get(model.subexpressions, arg, nothing)) !== nothing
parent.args[i] = subexpression
continue
end
child = MOI.ScalarNonlinearFunction(arg.head, similar(arg.args))
parent.args[i] = child
for j in length(arg.args):-1:1
Expand All @@ -196,9 +194,9 @@ function moi_function(model::GenericModel, f::GenericNonlinearExpr{V}) where {V}
child.args[j] = moi_function(model, arg.args[j])
end
end
# model.subexpressions[arg_key] = child
model.subexpressions[arg] = child
end
# model.subexpressions[key] = ret
model.subexpressions[f] = ret
return ret
end

Expand Down
75 changes: 43 additions & 32 deletions test/test_nlp_expr.jl
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Expand Up @@ -1390,38 +1390,38 @@ function test_custom_array()
return
end

# function test_scalar_nonlinear_moi_function()
# model = Model()
# @variable(model, x)
# y1 = atan(x, 2)
# y1_moi = MOI.ScalarNonlinearFunction(:atan, Any[index(x), 2])
# y2 = y1 + y1
# y2_moi = MOI.ScalarNonlinearFunction(:+, Any[y1_moi, y1_moi])
# y3 = exp(y2)
# y3_moi = MOI.ScalarNonlinearFunction(:exp, Any[y2_moi])
# # Test y1
# @test isapprox(moi_function(y1), y1_moi)
# @test length(model.subexpressions) == 1
# @test isapprox(moi_function(model, y1), y1_moi)
# @test length(model.subexpressions) == 1
# @test isapprox(model.subexpressions[objectid(y1)], y1_moi)
# # Test y2
# @test isapprox(moi_function(y2), y2_moi)
# @test length(model.subexpressions) == 2
# @test isapprox(moi_function(model, y2), y2_moi)
# @test length(model.subexpressions) == 2
# @test isapprox(model.subexpressions[objectid(y1)], y1_moi)
# @test isapprox(model.subexpressions[objectid(y2)], y2_moi)
# # Test y3
# @test isapprox(moi_function(y3), y3_moi)
# @test length(model.subexpressions) == 3
# @test isapprox(moi_function(model, y3), y3_moi)
# @test length(model.subexpressions) == 3
# @test isapprox(model.subexpressions[objectid(y1)], y1_moi)
# @test isapprox(model.subexpressions[objectid(y2)], y2_moi)
# @test isapprox(model.subexpressions[objectid(y3)], y3_moi)
# return
# end
function test_scalar_nonlinear_moi_function()
model = Model()
@variable(model, x)
y1 = atan(x, 2)
y1_moi = MOI.ScalarNonlinearFunction(:atan, Any[index(x), 2])
y2 = y1 + y1
y2_moi = MOI.ScalarNonlinearFunction(:+, Any[y1_moi, y1_moi])
y3 = exp(y2)
y3_moi = MOI.ScalarNonlinearFunction(:exp, Any[y2_moi])
# Test y1
@test isapprox(moi_function(y1), y1_moi)
@test length(model.subexpressions) == 1
@test isapprox(moi_function(model, y1), y1_moi)
@test length(model.subexpressions) == 1
@test isapprox(model.subexpressions[y1], y1_moi)
# Test y2
@test isapprox(moi_function(y2), y2_moi)
@test length(model.subexpressions) == 2
@test isapprox(moi_function(model, y2), y2_moi)
@test length(model.subexpressions) == 2
@test isapprox(model.subexpressions[y1], y1_moi)
@test isapprox(model.subexpressions[y2], y2_moi)
# Test y3
@test isapprox(moi_function(y3), y3_moi)
@test length(model.subexpressions) == 3
@test isapprox(moi_function(model, y3), y3_moi)
@test length(model.subexpressions) == 3
@test isapprox(model.subexpressions[y1], y1_moi)
@test isapprox(model.subexpressions[y2], y2_moi)
@test isapprox(model.subexpressions[y3], y3_moi)
return
end

function test_addition_with_zero_Base_sum()
model = Model()
Expand Down Expand Up @@ -1482,4 +1482,15 @@ function test_extension_expression_bedmas_parentheses(
return
end

function test_issue_4203()
n = 25_000
model = Model()
@variable(model, x)
@constraint(model, cons[i in 1:n], sin(x) * i <= 0)
@testset "$i" for i in 1:n
@test isequal_canonical(constraint_object(cons[i]).func, sin(x) * i - 0)
end
return
end

end # module
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