[Nonlinear.ReverseAD] improve test coverage (#2646)

Author: odow

src/Nonlinear/ReverseAD/graph_tools.jl

@@ -228,12 +228,7 @@ function _compute_hessian_sparsity(
     end
     for k in 2:length(nodes)
         nod = nodes[k]
-        if nod.type == Nonlinear.NODE_MOI_VARIABLE
-            error(
-                "Internal error: Invalid to compute sparsity if Nonlinear.NODE_MOI_VARIABLE " *
-                "nodes are present.",
-            )
-        end
+        @assert nod.type != Nonlinear.NODE_MOI_VARIABLE
         if nonlinear_wrt_output[k]
             continue # already seen this node one way or another
         elseif input_linearity[k] == CONSTANT
@@ -473,20 +468,3 @@ function _order_subexpressions(
     )
     return full_sort, individual_sorts
 end
-
-"""
-    _order_subexpressions(
-        main_expression::Vector{Nonlinear.Node},
-        subexpressions::Vector{Vector{Nonlinear.Node}},
-    )
-
-Return a `Vector{Int}` containing the topologically ordered list of
-subexpression-indices that need to be evaluated to compute `main_expression`.
-"""
-function _order_subexpressions(
-    main_expression::Vector{Nonlinear.Node},
-    subexpressions::Vector{Vector{Nonlinear.Node}},
-)
-    s = _list_subexpressions(main_expression)
-    return _topological_sort(s, subexpressions)
-end

test/Nonlinear/ReverseAD.jl

@@ -1233,6 +1233,77 @@ function test_unsafe_vector_view()
     return
 end
 
+function test_classify_linearity_ifelse()
+    x = MOI.VariableIndex(1)
+    y = MOI.VariableIndex(2)
+    model = MOI.Nonlinear.Model()
+    MOI.Nonlinear.set_objective(model, :(ifelse($y, $x, 1)))
+    evaluator = MOI.Nonlinear.Evaluator(
+        model,
+        MOI.Nonlinear.SparseReverseMode(),
+        [x, y],
+    )
+    MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+    @test MOI.eval_objective(evaluator, [1.2, 1.0]) == 1.2
+    @test MOI.eval_objective(evaluator, [1.2, 0.0]) == 1.0
+    @test isempty(MOI.hessian_lagrangian_structure(evaluator))
+    return
+end
+
+function test_classify_linearity_logic()
+    x = MOI.VariableIndex(1)
+    y = MOI.VariableIndex(2)
+    model = MOI.Nonlinear.Model()
+    MOI.Nonlinear.set_objective(model, :($x && $y))
+    evaluator = MOI.Nonlinear.Evaluator(
+        model,
+        MOI.Nonlinear.SparseReverseMode(),
+        [x, y],
+    )
+    MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+    @test MOI.eval_objective(evaluator, [1.0, 1.0]) == 1.0
+    @test MOI.eval_objective(evaluator, [0.0, 1.0]) == 0.0
+    @test MOI.eval_objective(evaluator, [1.0, 0.0]) == 0.0
+    @test MOI.eval_objective(evaluator, [0.0, 0.0]) == 0.0
+    @test isempty(MOI.hessian_lagrangian_structure(evaluator))
+    return
+end
+
+function test_hessian_sparsity_with_subexpressions()
+    x = MOI.VariableIndex(1)
+    y = MOI.VariableIndex(2)
+    model = MOI.Nonlinear.Model()
+    expr = MOI.Nonlinear.add_expression(model, :($x * $y))
+    expr2 = MOI.Nonlinear.add_expression(model, :($expr))
+    MOI.Nonlinear.set_objective(model, :(sin($expr2)))
+    evaluator = MOI.Nonlinear.Evaluator(
+        model,
+        MOI.Nonlinear.SparseReverseMode(),
+        [x, y],
+    )
+    MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+    MOI.hessian_lagrangian_structure(evaluator)
+    return
+end
+
+function test_toposort_subexpressions()
+    x = MOI.VariableIndex(1)
+    model = MOI.Nonlinear.Model()
+    a = MOI.Nonlinear.add_expression(model, :($x))
+    b = MOI.Nonlinear.add_expression(model, :($x))
+    c = MOI.Nonlinear.add_expression(model, :($a + $b))
+    d = MOI.Nonlinear.add_expression(model, :($c + $b))
+    MOI.Nonlinear.add_constraint(model, :($d), MOI.LessThan(1.0))
+    MOI.Nonlinear.add_constraint(model, :($c), MOI.LessThan(1.0))
+    evaluator =
+        MOI.Nonlinear.Evaluator(model, MOI.Nonlinear.SparseReverseMode(), [x])
+    MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+    g = [NaN, NaN]
+    MOI.eval_constraint(evaluator, g, [2.0])
+    @test g == [6.0, 4.0]
+    return
+end
+
 end  # module
 
 TestReverseAD.runtests()