[Nonlinear] Change Vector{T} to AbstractVector{T} in _extract_reverse_pass and add test (#1934)

Author: abelsiqueira

src/Nonlinear/ReverseAD/reverse_mode.jl

@@ -312,7 +312,7 @@ end
 
 """
     _extract_reverse_pass(
-        g::Vector{T},
+        g::AbstractVector{T},
         d::NLPEvaluator,
         f::Union{_FunctionStorage,_SubexpressionStorage},
     ) where {T}
@@ -321,7 +321,7 @@ Fill the gradient vector `g` with the values from the reverse pass. Assumes you
 have already called `_reverse_eval_all(d, x)`.
 """
 function _extract_reverse_pass(
-    g::Vector{T},
+    g::AbstractVector{T},
     d::NLPEvaluator,
     f::Union{_FunctionStorage,_SubexpressionStorage},
 ) where {T}

test/Nonlinear/ReverseAD.jl

@@ -850,6 +850,35 @@ function test_gradient_nested_subexpressions()
     return
 end
 
+function test_gradient_view()
+    x = MOI.VariableIndex(1)
+    y = MOI.VariableIndex(2)
+    model = Nonlinear.Model()
+    Nonlinear.set_objective(model, :(($x - 1)^2 + 4 * ($y - $x^2)^2))
+    evaluator = Nonlinear.Evaluator(
+        model,
+        Nonlinear.SparseReverseMode(),
+        MOI.VariableIndex[x, y],
+    )
+    MOI.initialize(evaluator, [:Grad])
+    X_input = [0.0; 1.0; 2.0; 3.0]
+    for idx in [1:2, 3:4, [1, 3], 4:-2:2]
+        x_input = view(X_input, idx)
+        x_vec = Vector(x_input)
+        ∇f = fill(Inf, 2)
+        ∇fv = fill(Inf, 2)
+        MOI.eval_objective_gradient(evaluator, ∇f, x_input)
+        MOI.eval_objective_gradient(evaluator, ∇fv, x_vec)
+        @test ∇f == ∇fv
+        ∇f = fill(Inf, 2)
+        ∇fv = view(fill(Inf, 4), idx)
+        MOI.eval_objective_gradient(evaluator, ∇f, x_input)
+        MOI.eval_objective_gradient(evaluator, ∇fv, x_input)
+        @test ∇f == ∇fv
+    end
+    return
+end
+
 function _dense_hessian(hessian_sparsity, V, n)
     I = [i for (i, _) in hessian_sparsity]
     J = [j for (_, j) in hessian_sparsity]