Remove out-dated precompile.jl and simplify compile_workload

src/JuMP.jl

@@ -1367,13 +1367,7 @@ PrecompileTools.@compile_workload begin
     # at lowering time, not much of this would get precompiled without `@eval`
     @eval begin
         let
-            model = Model(
-                () -> MOI.Utilities.MockOptimizer(
-                    MOI.Utilities.UniversalFallback(
-                        MOI.Utilities.Model{Float64}(),
-                    ),
-                ),
-            )
+            model = Model()
             @variables(model, begin
                 x1 >= 0
                 0 <= x2 <= 1
@@ -1387,6 +1381,7 @@ PrecompileTools.@compile_workload begin
             @expressions(model, begin
                 a, -1 + x1 + x2
                 b, 1 + x1^2 + x2
+                nl_expr, sin(x)
             end)
             @constraints(model, begin
                 c1, a >= 0
@@ -1400,42 +1395,13 @@ PrecompileTools.@compile_workload begin
                 [x1, x2, x1] in SecondOrderCone()
                 [1.0*x1 x2; x2 x1] >= 0, PSDCone()
                 1.0 * x1 ⟂ x2
+                nl_expr <= 1
             end)
             @objective(model, Min, x1)
             @objective(model, Max, a)
             @objective(model, Min, b)
-            @NLconstraint(model, c9, 1 * sin(x1) + 2.0 * sin(a) + sin(b) <= 1)
-            @NLparameter(model, p == 2)
-            @NLexpression(model, expr, x1^p)
-            @NLobjective(model, Min, 1 + expr)
-            optimize!(model)
-            # This block could sit in MOI, but it's a public API at the JuMP
-            # level, so it can go here.
-            #
-            # We evaluate with a `view` because it's a common type for solvers
-            # like Ipopt to use.
-            d = NLPEvaluator(model)
-            MOI.features_available(d)
-            MOI.initialize(d, [:Grad, :Jac, :Hess])
-            g = zeros(num_nonlinear_constraints(model))
-            v_g = view(g, 1:length(g))
-            x = zeros(num_variables(model))
-            MOI.eval_objective(d, x)
-            MOI.eval_constraint(d, g, x)
-            MOI.eval_constraint(d, v_g, x)
-            MOI.eval_objective_gradient(d, g, x)
-            J = zeros(length(MOI.jacobian_structure(d)))
-            MOI.eval_constraint_jacobian(d, J, x)
-            v_J = view(J, 1:length(J))
-            MOI.eval_constraint_jacobian(d, v_J, x)
-            H = zeros(length(MOI.hessian_lagrangian_structure(d)))
-            v_H = view(H, 1:length(H))
-            MOI.eval_hessian_lagrangian(d, v_H, x, 1.0, v_g)
         end
     end
 end
 
-include("precompile.jl")
-_precompile_()
-
 end