fix

Author: tmigot

test/runtests.jl

@@ -28,16 +28,13 @@ const list_problems_PureJuMP = setdiff(list_problems, list_problems_not_PureJuMP
 include("test-defined-problems.jl")
 @everywhere include("test-utils.jl")
 
-@test ndef == OptimizationProblems.PureJuMP.default_nvar
-@test ndef == OptimizationProblems.ADNLPProblems.default_nvar
-
-@everywhere function make_ad_nlp(prob::Symbol; simp_backend="")
+@everywhere function make_ad_nlp(prob::Symbol; kwargs...)
     mod = ADNLPProblems
     if !isdefined(mod, prob)
         error("Problem $(prob) is not defined in ADNLPProblems on pid $(myid()).")
     end
     ctor = getfield(mod, prob)
-    return isempty(simp_backend) ? ctor() : ctor(eval(Meta.parse(simp_backend)))
+    return ctor(matrix_free = true; kwargs...)
 end
 
 @everywhere function test_one_problem(prob::Symbol)
@@ -46,12 +43,7 @@ end
   nvar = OptimizationProblems.eval(Symbol(:get_, prob, :_nvar))()
   ncon = OptimizationProblems.eval(Symbol(:get_, prob, :_ncon))()
 
-  nlp_ad = if (nvar + ncon < 10000)
-    make_ad_nlp(prob)
-  else
-    # Avoid SparseADJacobian for too large problem as it requires a lot of memory for CIs
-    make_ad_nlp(prob, simp_backend = simp_backend)
-  end
+  nlp_ad = make_ad_nlp(prob)
 
   @test nlp_ad.meta.name == pb
 
@@ -103,29 +95,6 @@ end
 
 pmap(test_one_problem, list_problems_ADNLPProblems)
 
-names_pb_vars = meta[
-  meta.variable_nvar .== true,
-  [:nvar, :name, :best_known_upper_bound, :best_known_lower_bound],
-]
-adproblems = (
-  eval(Meta.parse("ADNLPProblems.$(pb[:name])(" * simp_backend * ")")) for
-  pb in eachrow(names_pb_vars)
-)
-adproblems11 = (
-  eval(Meta.parse("ADNLPProblems.$(pb[:name])(n=$(13 * ndef), " * simp_backend * ")")) for
-  pb in eachrow(names_pb_vars)
-)
-
-@testset "Test scalable problems" begin
-  @testset "problem: $pb" for (pb, nlp, nlp11) in
-                              zip(eachrow(names_pb_vars), adproblems, adproblems11)
-    @test pb[:nvar] == nlp.meta.nvar
-    n11 = OptimizationProblems.eval(Symbol(:get_, pb[:name], :_nvar))(n = 13 * ndef)
-    @test n11 == nlp11.meta.nvar
-
-    # test that the problem is actually scalable
-    @test n11 != pb[:nvar]
-  end
-end
+include("test-scalable.jl")
 
 rmprocs()

test/test-defined-problems.jl

@@ -8,17 +8,13 @@
     return (pid = myid(), missing = missing)
 end
 
-const list_problems_ADNLPProblems =
-    setdiff(list_problems, list_problems_not_ADNLPProblems)
 probes = @sync begin
     for pid in workers()
         @async remotecall_fetch(probe_missing, pid, ADNLPProblems, list_problems_ADNLPProblems)
     end
 end
 @info "ADNLPProblems missing per worker" probes
 
-const list_problems_PureJuMP =
-    setdiff(list_problems, list_problems_not_PureJuMP)
 probes = @sync begin
     for pid in workers()
         @async remotecall_fetch(probe_missing, pid, PureJuMP, list_problems_PureJuMP)

test/test-scalable.jl

@@ -0,0 +1,20 @@
+@everywhere function test_scalable(pb)
+  @testset "problem: $(pb[:name])" begin
+    nlp = make_ad_nlp(Symbol(pb[:name]))
+    @test pb[:nvar] == nlp.meta.nvar
+    nlp11 = make_ad_nlp(Symbol(pb[:name]); n = 13 * ndef)
+    n11 = OptimizationProblems.eval(Symbol(:get_, pb[:name], :_nvar))(n = 13 * ndef)
+    @test n11 == nlp11.meta.nvar
+
+    # test that the problem is actually scalable
+    @test n11 != pb[:nvar]
+  end
+end
+
+@testset "Test scalable problems" begin
+  names_pb_vars = meta[
+    meta.variable_nvar .== true,
+    [:nvar, :name, :best_known_upper_bound, :best_known_lower_bound],
+  ]
+  pmap(test_scalable, eachrow(names_pb_vars))
+end

test/test-utils.jl

@@ -1,10 +1,11 @@
 @everywhere const ndef = OptimizationProblems.default_nvar
+
+@test ndef == OptimizationProblems.PureJuMP.default_nvar
+@test ndef == OptimizationProblems.ADNLPProblems.default_nvar
+
 @everywhere const test_nvar = Int(round(ndef / 2))
 @everywhere meta = OptimizationProblems.meta
 
-# Avoid SparseADJacobian/Hessian for too large problem as it requires a lot of memory for CIs
-@everywhere const simp_backend = "jacobian_backend = ADNLPModels.ForwardDiffADJacobian, hessian_backend = ADNLPModels.ForwardDiffADHessian"
-
 @everywhere function meta_sanity_check(prob::Symbol, nlp::AbstractNLPModel)
   meta = OptimizationProblems.eval(Symbol(prob, :_meta))
   getnvar = OptimizationProblems.eval(Symbol(:get_, prob, :_nvar))(n = test_nvar)
@@ -71,12 +72,7 @@ end
   nvar = OptimizationProblems.eval(Symbol(:get_, prob, :_nvar))()
   ncon = OptimizationProblems.eval(Symbol(:get_, prob, :_ncon))()
 
-  nlp_ad = if (nvar + ncon < 10000)
-    eval(Meta.parse("ADNLPProblems.$(prob)()"))
-  else
-    # Avoid SparseADJacobian for too large problem as it requires a lot of memory for CIs
-    eval(Meta.parse("ADNLPProblems.$(prob)(" * simp_backend * ")"))
-  end
+  nlp_ad = make_ad_nlp(prob)
 
   return test_compatibility(prob, nlp_jump, nlp_ad, ndef)
 end