Port MadNLP to MOI 0.10 and JuMP 0.22

.ci/ci.jl

@@ -1,9 +1,13 @@
-rm("Manifest.toml";force=true)
+
+rm(joinpath(@__DIR__, "Project.toml");force=true)
+rm(joinpath(@__DIR__, "Manifest.toml");force=true)
 
 using Pkg
 
+Pkg.activate(@__DIR__)
+
 if ARGS[1] == "full"
-    pkgs = ["MadNLPHSL","MadNLPPardiso","MadNLPMumps","MadNLPGPU","MadNLPGraph","MadNLPKrylov"]
+    pkgs = ["MadNLPHSL","MadNLPPardiso","MadNLPMumps","MadNLPGraph","MadNLPKrylov"]
 elseif ARGS[1] == "basic"
     pkgs = ["MadNLPMumps","MadNLPGraph","MadNLPKrylov"]
 else

Project.toml

@@ -17,20 +17,18 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SuiteSparse = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 
 [compat]
-AmplNLReader = "~0.11"
 MINLPTests = "~0.5"
 MadNLPTests = "~0.3"
-MathOptInterface = "~0.9"
+MathOptInterface = "0.10.5 - 0.10.8"
 NLPModels = "~0.17.2, 0.18"
 SolverCore = "~0.1,~0.2"
 julia = "1.3"
 
 [extras]
-AmplNLReader = "77dd3d4c-cb1d-5e09-9340-85030ff7ba66"
 MINLPTests = "ee0a3090-8ee9-5cdb-b8cb-8eeba3165522"
 MadNLPTests = "b52a2a03-04ab-4a5f-9698-6a2deff93217"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "MadNLPTests", "AmplNLReader", "MINLPTests", "Random"]
+test = ["Test", "MadNLPTests", "MINLPTests", "Random"]

lib/MadNLPGraph/Project.toml

@@ -12,7 +12,7 @@ Metis = "2679e427-3c69-5b7f-982b-ece356f1e94b"
 
 [compat]
 MadNLP = "~0.3"
-JuMP = "~0.21"
+JuMP = "~0.22"
 Plasmo = "~0.4"
 LightGraphs = "~1.3"
 Metis = "1"

lib/MadNLPGraph/src/MadNLPGraph.jl

@@ -16,7 +16,7 @@ import MadNLP:
     get_lcon, get_ucon, jac_structure!, hess_structure!, obj, grad!, cons!, jac_coord!, hess_coord!
 import Metis: partition
 import LightGraphs: Graph, Edge, add_edge!, edges, src, dst, neighbors, nv
-import Plasmo: OptiGraph, OptiNode, OptiEdge, all_nodes, all_edges, all_variables, num_all_nodes, getlinkconstraints, getnode, num_variables, num_constraints
+import Plasmo: OptiGraph, OptiNode, OptiEdge, all_nodes, all_edges, all_variables, num_all_nodes, link_constraints, getnode, num_variables, num_constraints
 import JuMP: _create_nlp_block_data, set_optimizer, GenericAffExpr
 
 include("plasmo_interface.jl")

lib/MadNLPGraph/src/plasmo_interface.jl

@@ -46,7 +46,7 @@ end
 
 function jacobian_structure(linkedge::OptiEdge,I,J,ninds,x_index_map,g_index_map)
     offset=1
-    for linkcon in getlinkconstraints(linkedge)
+    for linkcon in link_constraints(linkedge)
         offset += jacobian_structure(linkcon,I,J,ninds,x_index_map,g_index_map,offset)
     end
 end
@@ -107,7 +107,7 @@ function eval_function(aff::GenericAffExpr,x,ninds,x_index_map)
 end
 function eval_constraint(linkedge::OptiEdge,c,x,ninds,x_index_map)
     cnt = 1
-    for linkcon in getlinkconstraints(linkedge)
+    for linkcon in link_constraints(linkedge)
         c[cnt] = eval_function(get_func(linkcon),x,ninds,x_index_map)
         cnt += 1
     end
@@ -134,7 +134,7 @@ end
 
 function eval_constraint_jacobian(linkedge::OptiEdge,jac,x)
     offset=0
-    for linkcon in getlinkconstraints(linkedge)
+    for linkcon in link_constraints(linkedge)
         offset+=eval_constraint_jacobian(linkcon,jac,offset)
     end
 end
@@ -400,5 +400,9 @@ function optimize!(graph::OptiGraph; option_dict = Dict{Symbol,Any}(), kwargs...
             view(result.multipliers_L,nlp.ninds[k]),
             view(result.multipliers_U,nlp.ninds[k]),
             ips.cnt.k, ips.nlp.counters,ips.cnt.total_time)
+            # TODO: quick hack to specify to JuMP that the
+            # model is not dirty (so we do not run in `OptimizeNotCalled`
+            # exception).
+            nlp.modelnodes[k].model.is_model_dirty = false
     end
 end

lib/MadNLPTests/Project.toml

@@ -12,7 +12,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-JuMP = "~0.21"
+JuMP = "~0.22"
 MadNLP = "~0.3"
 NLPModels = "~0.17.2, 0.18"
 julia = "1.3"

test/MOI_interface_test.jl

@@ -1,130 +1,88 @@
-using MathOptInterface
-const MOI = MathOptInterface
-const MOIT = MOI.Test
-const MOIU = MOI.Utilities
-const MOIB = MOI.Bridges
+module TestMOIWrapper
 
-const config = MOIT.TestConfig(atol=1e-4, rtol=1e-4,
-                               optimal_status=MOI.LOCALLY_SOLVED)
-const config_no_duals = MOIT.TestConfig(atol=1e-4, rtol=1e-4, duals=false,
-                                        optimal_status=MOI.LOCALLY_SOLVED)
+using MadNLP
+using Test
 
-@testset "MOI utils" begin
-    optimizer = MadNLP.Optimizer()
-    @testset "SolverName" begin
-        @test MOI.get(optimizer, MOI.SolverName()) == "MadNLP"
-    end
-    @testset "supports_default_copy_to" begin
-        @test MOIU.supports_default_copy_to(optimizer, false)
-        @test !MOIU.supports_default_copy_to(optimizer, true)
-    end
-    @testset "MOI.Silent" begin
-        @test MOI.supports(optimizer, MOI.Silent())
-        @test MOI.get(optimizer, MOI.Silent()) == false
-        MOI.set(optimizer, MOI.Silent(), true)
-        @test MOI.get(optimizer, MOI.Silent()) == true
-    end
-    @testset "MOI.TimeLimitSec" begin
-        @test MOI.supports(optimizer, MOI.TimeLimitSec())
-        my_time_limit = 10.
-        MOI.set(optimizer, MOI.TimeLimitSec(), my_time_limit)
-        @test MOI.get(optimizer, MOI.TimeLimitSec()) == my_time_limit
-    end
-    @testset "MOI.MaxIter" begin
-        MOI.set(optimizer,MOI.RawParameter("max_iter"),1)
-        @test MOI.get(optimizer,MOI.RawParameter("max_iter")) == 1
+const MOI = MadNLP.MathOptInterface
+
+function runtests()
+    for name in names(@__MODULE__; all = true)
+        if startswith("$(name)", "test_")
+            @testset "$(name)" begin
+                getfield(@__MODULE__, name)()
+            end
+        end
     end
+    return
 end
 
-@testset "Testing getters" begin
-    MOIT.copytest(MOI.instantiate(()->MadNLP.Optimizer(print_level=MadNLP.ERROR),
-                                  with_bridge_type=Float64), MOIU.Model{Float64}())
-end
+function test_MOI_Test()
+    model = MOI.Bridges.full_bridge_optimizer(
+        MOI.Utilities.CachingOptimizer(
+            MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),
+            MadNLP.Optimizer(),
+        ),
+        Float64,
+    )
+    MOI.set(model, MOI.Silent(), true)
+    MOI.Test.runtests(
+        model,
+        MOI.Test.Config(
+            atol = 1e-4,
+            rtol = 1e-4,
+            optimal_status = MOI.LOCALLY_SOLVED,
+            exclude = Any[
+                MOI.delete,
+                MOI.ConstraintDual,
+                MOI.ConstraintBasisStatus,
+                MOI.DualObjectiveValue,
+                MOI.ObjectiveBound,
+            ]
+        );
+        exclude = String[
+            "test_modification",
+            # - Need to implement TimeLimitSec
+            "test_attribute_TimeLimitSec",
+            # - Wrong return type
+            "test_model_UpperBoundAlreadySet",
+            # - Final objective value is not equal to 0.0
+            "test_objective_FEASIBILITY_SENSE_clears_objective",
 
-@testset "Bounds set twice" begin
-    optimizer = MadNLP.Optimizer(print_level=MadNLP.ERROR)
-    MOIT.set_lower_bound_twice(optimizer, Float64)
-    MOIT.set_upper_bound_twice(optimizer, Float64)
-end
+            # TODO: Need to investigate why these tests are breaking
+            #   get(model, MOI.ConstraintPrimal(), c) returns the
+            #   opposite value: if 1.0 is expected, -1.0 is returned
+            "test_constraint_ScalarAffineFunction_EqualTo",
+            "test_quadratic_nonconvex_constraint_basic",
+            "test_linear_integration",
 
-@testset "MOI Linear tests" begin
-    optimizer = MadNLP.Optimizer(print_level=MadNLP.ERROR)
-    exclude = ["linear1", # modify constraints not allowed
-               "linear5", # modify constraints not allowed
-               "linear6", # constraint set for l/q not allowed
-               "linear7",  # VectorAffineFunction not supported.
-               "linear8a", # Behavior in infeasible case doesn't match test.
-               "linear8b", # Behavior in unbounded case doesn't match test.
-               "linear8c", # Behavior in unbounded case doesn't match test.
-               "linear10", # Interval not supported yet
-               "linear10b", # Interval not supported yet
-               "linear11", # Variable cannot be deleted
-               "linear12", # Behavior in infeasible case doesn't match test.
-               "linear14", # Variable cannot be deleted
-               "linear15", # VectorAffineFunction not supported.
-               ]
-    MOIT.contlineartest(optimizer, config_no_duals,exclude)
-end
+            # TODO: there might be an issue with VectorAffineFunction/VectorOfVariables
+            "test_conic_NormOneCone_VectorOfVariables",
+            "test_conic_NormOneCone_VectorAffineFunction",
+            "test_conic_NormInfinityCone_VectorOfVariables",
+            "test_conic_NormInfinityCone_VectorAffineFunction",
+            "test_conic_linear_VectorAffineFunction",
+            "test_conic_linear_VectorOfVariables",
 
-@testset "MOI NLP tests" begin
-    optimizer = MadNLP.Optimizer(print_level=MadNLP.ERROR)
-    exclude = [
-        "feasibility_sense_with_objective_and_no_hessian", # we need Hessians
-        "feasibility_sense_with_no_objective_and_no_hessian", # we need Hessians
-        "hs071_no_hessian", # we need Hessians
-        "hs071_hessian_vector_product_test", # Hessian-vector product is needed
-    ]
-    MOIT.nlptest(optimizer,config,exclude)
-end
+            # Tests excluded on purpose
+            # - Excluded as MadNLP returns LOCALLY_INFEASIBLE instead of INFEASIBLE
+            "INFEASIBLE",
+            "test_solve_DualStatus_INFEASIBILITY_CERTIFICATE_",
+            # - Excluded because Hessian information is needed
+            "test_nonlinear_hs071_hessian_vector_product",
+            # - Excluded because Hessian information is needed
+            "test_nonlinear_hs071_no_hessian",
+            # - Excluded because Hessian information is needed
+            "test_nonlinear_invalid",
 
-@testset "Unit" begin
-    bridged = MOIB.full_bridge_optimizer(MadNLP.Optimizer(print_level=MadNLP.ERROR),Float64)
-    exclude = ["delete_variable", # Deleting not supported.
-               "delete_variables", # Deleting not supported.
-               "getvariable", # Variable names not supported.
-               "solve_zero_one_with_bounds_1", # Variable names not supported.
-               "solve_zero_one_with_bounds_2", # Variable names not supported.
-               "solve_zero_one_with_bounds_3", # Variable names not supported.
-               "getconstraint", # Constraint names not suported.
-               "variablenames", # Variable names not supported.
-               "solve_with_upperbound", # loadfromstring!
-               "solve_with_lowerbound", # loadfromstring!
-               "solve_integer_edge_cases", # loadfromstring!
-               "solve_affine_lessthan", # loadfromstring!
-               "solve_affine_greaterthan", # loadfromstring!
-               "solve_affine_equalto", # loadfromstring!
-               "solve_affine_interval", # loadfromstring!
-               "get_objective_function", # Function getters not supported.
-               "solve_constant_obj",  # loadfromstring!
-               "solve_blank_obj", # loadfromstring!
-               "solve_singlevariable_obj", # loadfromstring!
-               "solve_objbound_edge_cases", # ObjectiveBound not supported.
-               "solve_affine_deletion_edge_cases", # Deleting not supported.
-               "solve_unbounded_model", # `NORM_LIMIT`
-               "number_threads", # NumberOfThreads not supported
-               "delete_nonnegative_variables", # get ConstraintFunction n/a.
-               "update_dimension_nonnegative_variables", # get ConstraintFunction n/a.
-               "delete_soc_variables", # VectorOfVar. in SOC not supported
-               "solve_result_index", # DualObjectiveValue not supported
-               "time_limit_sec", #time limit given as Flaot64?
-               "solve_farkas_interval_lower",
-               "solve_farkas_lessthan",
-               "solve_farkas_interval_upper",
-               "solve_farkas_greaterthan",
-               "solve_farkas_variable_lessthan_max",
-               "solve_farkas_variable_lessthan",
-               "solve_farkas_equalto_lower",
-               "solve_farkas_equalto_upper",
-               "solve_qp_edge_cases"
-               ]
-    MOIT.unittest(bridged, config, exclude)
+            #  - Excluded because this test is optional
+            "test_model_ScalarFunctionConstantNotZero",
+            #  - Excluded because MadNLP returns INVALID_MODEL instead of LOCALLY_SOLVED
+            "test_linear_VectorAffineFunction_empty_row",
+        ]
+    )
+    return
 end
 
-@testset "MOI QP/QCQP tests" begin
-    optimizer = MadNLP.Optimizer(print_level=MadNLP.ERROR)
-    qp_optimizer = MOIU.CachingOptimizer(MOIU.Model{Float64}(), optimizer)
-    MOIT.qptest(qp_optimizer, config)
-    exclude = ["qcp1", # VectorAffineFunction not supported.
-              ]
-    MOIT.qcptest(qp_optimizer, config_no_duals, exclude)
 end
+
+TestMOIWrapper.runtests()

test/runtests.jl

@@ -1,6 +1,6 @@
 using Test, MadNLP, MadNLPTests, MINLPTests
 import MathOptInterface
-import AmplNLReader: AmplModel
+# import AmplNLReader: AmplModel
 import SparseArrays: sparse
 
 @testset "MadNLP test" begin