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Refactor GenericNonlinearExpr and NonlinearOperator constructors #3489

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merged 7 commits into from
Sep 8, 2023
Merged

Refactor GenericNonlinearExpr and NonlinearOperator constructors #3489

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4f73dad
Add support for Array arguments in NonlinearOperator
odow Sep 7, 2023
3d27b58
Add test for inference
odow Sep 7, 2023
0189e80
Fix
odow Sep 7, 2023
fb54aa8
Move functions which could be omitted for now
odow Sep 7, 2023
8c4368e
Update test/test_nlp_expr.jl
odow Sep 7, 2023
fc522a7
Remove support
odow Sep 7, 2023
cdecbb3
Add back inferred AbstractVariableRef constructor
odow Sep 7, 2023
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58 changes: 14 additions & 44 deletions src/nlp_expr.jl
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Expand Up @@ -81,15 +81,11 @@ struct GenericNonlinearExpr{V<:AbstractVariableRef} <: AbstractJuMPScalar
head::Symbol
args::Vector{Any}

function GenericNonlinearExpr(head::Symbol, args::Vector{Any})
index = findfirst(Base.Fix2(isa, AbstractJuMPScalar), args)
if index === nothing
error(
"Unable to create a nonlinear expression because it did not " *
"contain any JuMP scalars. head = $head, args = $args.",
)
end
return new{variable_ref_type(args[index])}(head, args)
Comment on lines -84 to -92
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I understand the motivation for this. Unfortunately, it will break InfiniteOpt, but I can look into refactoring the workflow. If supporting array arguments necessitates this change, then I support it.

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We can probably just keep this method then. It's not necessary. Let me make some changes.

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Try this

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I confirmed that this works with InfiniteOpt: infiniteopt/InfiniteOpt.jl#321

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Thanks, you beat me to it :)

function GenericNonlinearExpr{V}(
head::Symbol,
args::Vararg{Any},
) where {V<:AbstractVariableRef}
return new{V}(head, Any[a for a in args])
end

function GenericNonlinearExpr{V}(
Expand All @@ -110,15 +106,6 @@ const NonlinearExpr = GenericNonlinearExpr{VariableRef}

variable_ref_type(::GenericNonlinearExpr{V}) where {V} = V

# We include this method so that we can refactor the internal representation of
# GenericNonlinearExpr without having to rewrite the method overloads.
function GenericNonlinearExpr{V}(
head::Symbol,
args...,
) where {V<:AbstractVariableRef}
return GenericNonlinearExpr{V}(head, Any[args...])
end

const _PREFIX_OPERATORS =
(:+, :-, :*, :/, :^, :||, :&&, :>, :<, :(<=), :(>=), :(==))

Expand Down Expand Up @@ -527,6 +514,8 @@ function moi_function(f::GenericNonlinearExpr{V}) where {V}
return ret
end

jump_function(::GenericModel{T}, x::Number) where {T} = convert(T, x)

function jump_function(model::GenericModel, f::MOI.ScalarNonlinearFunction)
V = variable_ref_type(typeof(model))
ret = GenericNonlinearExpr{V}(f.head, Any[])
Expand All @@ -542,8 +531,6 @@ function jump_function(model::GenericModel, f::MOI.ScalarNonlinearFunction)
for child in reverse(arg.args)
push!(stack, (new_ret, child))
end
elseif arg isa Number
push!(parent.args, arg)
else
push!(parent.args, jump_function(model, arg))
end
Expand Down Expand Up @@ -833,33 +820,16 @@ function Base.show(io::IO, f::NonlinearOperator)
return print(io, "NonlinearOperator($(f.func), :$(f.head))")
end

# Fast overload for unary calls

(f::NonlinearOperator)(x) = f.func(x)

(f::NonlinearOperator)(x::AbstractJuMPScalar) = NonlinearExpr(f.head, Any[x])

# Fast overload for binary calls

(f::NonlinearOperator)(x, y) = f.func(x, y)

function (f::NonlinearOperator)(x::AbstractJuMPScalar, y)
return GenericNonlinearExpr(f.head, Any[x, y])
end

function (f::NonlinearOperator)(x, y::AbstractJuMPScalar)
return GenericNonlinearExpr(f.head, Any[x, y])
end
variable_ref_type(::NonlinearOperator, ::Any) = nothing
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function (f::NonlinearOperator)(x::AbstractJuMPScalar, y::AbstractJuMPScalar)
return GenericNonlinearExpr(f.head, Any[x, y])
function variable_ref_type(::NonlinearOperator, x::AbstractJuMPScalar)
return variable_ref_type(x)
end

# Fallback for more arguments
function (f::NonlinearOperator)(x, y, z...)
args = (x, y, z...)
if any(Base.Fix2(isa, AbstractJuMPScalar), args)
return GenericNonlinearExpr(f.head, Any[a for a in args])
function (f::NonlinearOperator)(args::Vararg{Any,N}) where {N}
types = variable_ref_type.(Ref(f), args)
if (i = findfirst(!isnothing, types)) !== nothing
return GenericNonlinearExpr{types[i]}(f.head, args...)
end
return f.func(args...)
end
Expand Down
11 changes: 5 additions & 6 deletions src/operators.jl
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Expand Up @@ -195,28 +195,27 @@ function Base.:/(lhs::GenericAffExpr, rhs::_Constant)
return map_coefficients(c -> c / rhs, lhs)
end

function Base.:^(lhs::AbstractVariableRef, rhs::Integer)
T = value_type(typeof(lhs))
function Base.:^(lhs::V, rhs::Integer) where {V<:AbstractVariableRef}
if rhs == 0
return one(T)
return one(value_type(V))
elseif rhs == 1
return lhs
elseif rhs == 2
return lhs * lhs
else
return GenericNonlinearExpr(:^, Any[lhs, rhs])
return GenericNonlinearExpr{V}(:^, Any[lhs, rhs])
end
end

function Base.:^(lhs::GenericAffExpr{T}, rhs::Integer) where {T}
function Base.:^(lhs::GenericAffExpr{T,V}, rhs::Integer) where {T,V}
if rhs == 0
return one(T)
elseif rhs == 1
return lhs
elseif rhs == 2
return lhs * lhs
else
return GenericNonlinearExpr(:^, Any[lhs, rhs])
return GenericNonlinearExpr{V}(:^, Any[lhs, rhs])
end
end

Expand Down
12 changes: 6 additions & 6 deletions test/test_nlp.jl
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Expand Up @@ -1605,7 +1605,7 @@ function test_parse_expression_nonlinearexpr_call()
model = Model()
@variable(model, x)
@variable(model, y)
f = GenericNonlinearExpr(:ifelse, Any[x, 0, y])
f = NonlinearExpr(:ifelse, Any[x, 0, y])
@NLexpression(model, ref, f)
nlp = nonlinear_model(model)
expr = :(ifelse($x, 0, $y))
Expand All @@ -1617,7 +1617,7 @@ function test_parse_expression_nonlinearexpr_or()
model = Model()
@variable(model, x)
@variable(model, y)
f = GenericNonlinearExpr(:||, Any[x, y])
f = NonlinearExpr(:||, Any[x, y])
@NLexpression(model, ref, f)
nlp = nonlinear_model(model)
expr = :($x || $y)
Expand All @@ -1629,7 +1629,7 @@ function test_parse_expression_nonlinearexpr_and()
model = Model()
@variable(model, x)
@variable(model, y)
f = GenericNonlinearExpr(:&&, Any[x, y])
f = NonlinearExpr(:&&, Any[x, y])
@NLexpression(model, ref, f)
nlp = nonlinear_model(model)
expr = :($x && $y)
Expand All @@ -1641,7 +1641,7 @@ function test_parse_expression_nonlinearexpr_unsupported()
model = Model()
@variable(model, x)
@variable(model, y)
f = GenericNonlinearExpr(:foo, Any[x, y])
f = NonlinearExpr(:foo, Any[x, y])
@test_throws(
MOI.UnsupportedNonlinearOperator,
@NLexpression(model, ref, f),
Expand All @@ -1653,8 +1653,8 @@ function test_parse_expression_nonlinearexpr_nested_comparison()
model = Model()
@variable(model, x)
@variable(model, y)
f = GenericNonlinearExpr(:||, Any[x, y])
g = GenericNonlinearExpr(:&&, Any[f, x])
f = NonlinearExpr(:||, Any[x, y])
g = NonlinearExpr(:&&, Any[f, x])
@NLexpression(model, ref, g)
nlp = nonlinear_model(model)
expr = :(($x || $y) && $x)
Expand Down
56 changes: 26 additions & 30 deletions test/test_nlp_expr.jl
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Expand Up @@ -447,46 +447,46 @@ function test_extension_nl_macro(
@variable(model, x)
@test isequal_canonical(
@expression(model, ifelse(x, 1, 2)),
GenericNonlinearExpr(:ifelse, Any[x, 1, 2]),
GenericNonlinearExpr{VariableRefType}(:ifelse, Any[x, 1, 2]),
)
@test isequal_canonical(
@expression(model, x || 1),
GenericNonlinearExpr(:||, Any[x, 1]),
GenericNonlinearExpr{VariableRefType}(:||, Any[x, 1]),
)
@test isequal_canonical(
@expression(model, x && 1),
GenericNonlinearExpr(:&&, Any[x, 1]),
GenericNonlinearExpr{VariableRefType}(:&&, Any[x, 1]),
)
@test isequal_canonical(
@expression(model, x < 0),
GenericNonlinearExpr(:<, Any[x, 0]),
GenericNonlinearExpr{VariableRefType}(:<, Any[x, 0]),
)
@test isequal_canonical(
@expression(model, x > 0),
GenericNonlinearExpr(:>, Any[x, 0]),
GenericNonlinearExpr{VariableRefType}(:>, Any[x, 0]),
)
@test isequal_canonical(
@expression(model, x <= 0),
GenericNonlinearExpr(:<=, Any[x, 0]),
GenericNonlinearExpr{VariableRefType}(:<=, Any[x, 0]),
)
@test isequal_canonical(
@expression(model, x >= 0),
GenericNonlinearExpr(:>=, Any[x, 0]),
GenericNonlinearExpr{VariableRefType}(:>=, Any[x, 0]),
)
@test isequal_canonical(
@expression(model, x == 0),
GenericNonlinearExpr(:(==), Any[x, 0]),
GenericNonlinearExpr{VariableRefType}(:(==), Any[x, 0]),
)
@test isequal_canonical(
@expression(model, 0 < x <= 1),
GenericNonlinearExpr(
GenericNonlinearExpr{VariableRefType}(
:&&,
Any[@expression(model, 0 < x), @expression(model, x <= 1)],
),
)
@test isequal_canonical(
@expression(model, ifelse(x > 0, x^2, sin(x))),
GenericNonlinearExpr(
GenericNonlinearExpr{VariableRefType}(
:ifelse,
Any[@expression(model, x > 0), x^2, sin(x)],
),
Expand All @@ -501,7 +501,7 @@ function test_register_univariate()
@test f isa NonlinearOperator
@test sprint(show, f) == "NonlinearOperator($(f.func), :f)"
@test isequal_canonical(@expression(model, f(x)), f(x))
@test isequal_canonical(f(x), GenericNonlinearExpr(:f, Any[x]))
@test isequal_canonical(f(x), NonlinearExpr(:f, Any[x]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:f, 1) in attrs
return
Expand All @@ -522,7 +522,7 @@ function test_register_univariate_gradient()
@variable(model, x)
@operator(model, f, 1, x -> x^2, x -> 2 * x)
@test isequal_canonical(@expression(model, f(x)), f(x))
@test isequal_canonical(f(x), GenericNonlinearExpr(:f, Any[x]))
@test isequal_canonical(f(x), NonlinearExpr(:f, Any[x]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:f, 1) in attrs
return
Expand All @@ -533,7 +533,7 @@ function test_register_univariate_gradient_hessian()
@variable(model, x)
@operator(model, f, 1, x -> x^2, x -> 2 * x, x -> 2.0)
@test isequal_canonical(@expression(model, f(x)), f(x))
@test isequal_canonical(f(x), GenericNonlinearExpr(:f, Any[x]))
@test isequal_canonical(f(x), NonlinearExpr(:f, Any[x]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:f, 1) in attrs
return
Expand All @@ -545,7 +545,7 @@ function test_register_multivariate()
f = (x...) -> sum(x .^ 2)
@operator(model, foo, 2, f)
@test isequal_canonical(@expression(model, foo(x...)), foo(x...))
@test isequal_canonical(foo(x...), GenericNonlinearExpr(:foo, Any[x...]))
@test isequal_canonical(foo(x...), NonlinearExpr(:foo, Any[x...]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:foo, 2) in attrs
return
Expand All @@ -558,7 +558,7 @@ function test_register_multivariate_gradient()
∇f = (g, x...) -> (g .= 2 .* x)
@operator(model, foo, 2, f, ∇f)
@test isequal_canonical(@expression(model, foo(x...)), foo(x...))
@test isequal_canonical(foo(x...), GenericNonlinearExpr(:foo, Any[x...]))
@test isequal_canonical(foo(x...), NonlinearExpr(:foo, Any[x...]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:foo, 2) in attrs
return
Expand All @@ -576,7 +576,7 @@ function test_register_multivariate_gradient_hessian()
end
@operator(model, foo, 2, f, ∇f, ∇²f)
@test isequal_canonical(@expression(model, foo(x...)), foo(x...))
@test isequal_canonical(foo(x...), GenericNonlinearExpr(:foo, Any[x...]))
@test isequal_canonical(foo(x...), NonlinearExpr(:foo, Any[x...]))
attrs = MOI.get(model, MOI.ListOfModelAttributesSet())
@test MOI.UserDefinedFunction(:foo, 2) in attrs
return
Expand All @@ -587,7 +587,7 @@ function test_register_multivariate_many_args()
@variable(model, x[1:10])
f = (x...) -> sum(x .^ 2)
@operator(model, foo, 10, f)
@test isequal_canonical(foo(x...), GenericNonlinearExpr(:foo, Any[x...]))
@test isequal_canonical(foo(x...), NonlinearExpr(:foo, Any[x...]))
@test foo((1:10)...) == 385
return
end
Expand All @@ -607,18 +607,6 @@ function test_register_errors()
return
end

function test_expression_no_variable()
head, args = :sin, Any[1]
@test_throws(
ErrorException(
"Unable to create a nonlinear expression because it did not " *
"contain any JuMP scalars. head = $head, args = $args.",
),
GenericNonlinearExpr(head, args),
)
return
end

function test_value_expression()
model = Model()
@variable(model, x)
Expand Down Expand Up @@ -676,7 +664,7 @@ end
function test_nonlinear_expr_owner_model()
model = Model()
@variable(model, x)
f = GenericNonlinearExpr(:sin, Any[x])
f = NonlinearExpr(:sin, Any[x])
# This shouldn't happen in regular code, but let's test against it to check
# we get something similar to AffExpr and QuadExpr.
empty!(f.args)
Expand Down Expand Up @@ -900,4 +888,12 @@ function test_ma_zero_in_operate!!()
return
end

function test_nonlinear_operator_inferred()
model = Model()
@variable(model, x)
@inferred op_less_than_or_equal_to(x, 1)
@test @inferred(op_less_than_or_equal_to(1, 2)) == true
return
end

end # module
2 changes: 1 addition & 1 deletion test/test_operator.jl
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Expand Up @@ -621,7 +621,7 @@ function test_complex_pow()
@test y^0 == (1.0 + 0im)
@test y^1 == y
@test y^2 == y * y
@test isequal_canonical(y^3, GenericNonlinearExpr(:^, Any[y, 3]))
@test isequal_canonical(y^3, NonlinearExpr(:^, Any[y, 3]))
return
end

Expand Down