ci: standardise workflows using SciML's reusable workflows

.github/workflows/FormatCheck.yml

@@ -0,0 +1,13 @@
+name: "Format Check"
+
+on:
+  push:
+    branches:
+      - 'master'
+    tags: '*'
+  pull_request:
+
+jobs:
+  format-check:
+    name: "Format Check"
+    uses: "SciML/.github/.github/workflows/format-suggestions-on-pr.yml@v1"

.github/workflows/Invalidations.yml

@@ -1,9 +1,9 @@
-name: Invalidations
+name: "Invalidations"
 
 on:
   pull_request:
     paths-ignore:
-        - 'docs/**'
+      - 'docs/**'
 
 concurrency:
   # Skip intermediate builds: always.
@@ -12,31 +12,6 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  evaluate:
-    # Only run on PRs to the default branch.
-    # In the PR trigger above branches can be specified only explicitly whereas this check should work for master, main, or any other default branch
-    if: github.base_ref == github.event.repository.default_branch
-    runs-on: ubuntu-latest
-    steps:
-    - uses: julia-actions/setup-julia@v1
-      with:
-        version: '1'
-    - uses: actions/checkout@v4
-    - uses: julia-actions/julia-buildpkg@v1
-    - uses: julia-actions/julia-invalidations@v1
-      id: invs_pr
-
-    - uses: actions/checkout@v4
-      with:
-        ref: ${{ github.event.repository.default_branch }}
-    - uses: julia-actions/julia-buildpkg@v1
-    - uses: julia-actions/julia-invalidations@v1
-      id: invs_default
-
-    - name: Report invalidation counts
-      run: |
-        echo "Invalidations on default branch: ${{ steps.invs_default.outputs.total }} (${{ steps.invs_default.outputs.deps }} via deps)" >> $GITHUB_STEP_SUMMARY
-        echo "This branch: ${{ steps.invs_pr.outputs.total }} (${{ steps.invs_pr.outputs.deps }} via deps)" >> $GITHUB_STEP_SUMMARY
-    - name: Check if the PR does increase number of invalidations
-      if: steps.invs_pr.outputs.total > steps.invs_default.outputs.total
-      run: exit 1
+  evaluate-invalidations:
+    name: "Evaluate Invalidations"
+    uses: "SciML/.github/.github/workflows/invalidations.yml@v1"

.github/workflows/Tests.yml

@@ -0,0 +1,37 @@
+name: "Tests"
+
+on:
+  pull_request:
+    branches:
+      - master
+      - 'release-'
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'docs/**'
+
+concurrency:
+  # Skip intermediate builds: always, but for the master branch.
+  # Cancel intermediate builds: always, but for the master branch.
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/master' }}
+
+jobs:
+  tests:
+    name: "Tests"
+    strategy:
+      fail-fast: false
+      matrix:
+        group:
+          - InterfaceI
+          - InterfaceII
+          - Extensions
+          - Downstream
+          - RegressionI
+    uses: "SciML/.github/.github/workflows/tests.yml@v1"
+    with:
+      group: "${{ matrix.group }}"
+    secrets: "inherit"

test/hierarchical_initialization_eqs.jl

@@ -142,6 +142,6 @@ syslist = ModelingToolkit.get_systems(model)
 u0 = []
 prob = ODEProblem(structural_simplify(model), u0, (0.0, 10.0))
 sol = solve(prob, Rodas5P())
-@test length(sol[end]) == 2
+@test length(sol.u[end]) == 2
 @test length(equations(prob.f.initializeprob.f.sys)) == 0
 @test length(unknowns(prob.f.initializeprob.f.sys)) == 0

test/modelingtoolkitize.jl

@@ -59,13 +59,13 @@ sys = complete(modelingtoolkitize(prob)) # symbolicitize me captain!
 
 prob = OptimizationProblem(sys, x0, p, grad = true, hess = true)
 sol = solve(prob, NelderMead())
-@test sol.minimum < 1e-8
+@test sol.objective < 1e-8
 
 sol = solve(prob, BFGS())
-@test sol.minimum < 1e-8
+@test sol.objective < 1e-8
 
 sol = solve(prob, Newton())
-@test sol.minimum < 1e-8
+@test sol.objective < 1e-8
 
 ## SIR System Regression Test
 

test/optimizationsystem.jl

@@ -50,7 +50,7 @@ using ModelingToolkit: get_metadata
         cons_h = true)
     @test prob.f.sys === combinedsys
     sol = solve(prob, Ipopt.Optimizer(); print_level = 0)
-    @test sol.minimum < -1e5
+    @test sol.objective < -1e5
 end
 
 @testset "inequality constraint" begin
@@ -66,14 +66,14 @@ end
         grad = true, hess = true, cons_j = true, cons_h = true)
     @test prob.f.sys === sys
     sol = solve(prob, IPNewton())
-    @test sol.minimum < 1.0
+    @test sol.objective < 1.0
     sol = solve(prob, Ipopt.Optimizer(); print_level = 0)
-    @test sol.minimum < 1.0
+    @test sol.objective < 1.0
 
     prob = OptimizationProblem(sys, [x => 0.0, y => 0.0], [a => 1.0, b => 1.0],
         grad = false, hess = false, cons_j = false, cons_h = false)
     sol = solve(prob, AmplNLWriter.Optimizer(Ipopt_jll.amplexe))
-    @test_skip sol.minimum < 1.0
+    @test_skip sol.objective < 1.0
 end
 
 @testset "equality constraint" begin
@@ -88,18 +88,18 @@ end
     prob = OptimizationProblem(sys, [x => 0.0, y => 0.0, z => 0.0], [a => 1.0, b => 1.0],
         grad = true, hess = true, cons_j = true, cons_h = true)
     sol = solve(prob, IPNewton())
-    @test sol.minimum < 1.0
+    @test sol.objective < 1.0
     @test sol.u≈[0.808, -0.064] atol=1e-3
     @test sol[x]^2 + sol[y]^2 ≈ 1.0
     sol = solve(prob, Ipopt.Optimizer(); print_level = 0)
-    @test sol.minimum < 1.0
+    @test sol.objective < 1.0
     @test sol.u≈[0.808, -0.064] atol=1e-3
     @test sol[x]^2 + sol[y]^2 ≈ 1.0
 
     prob = OptimizationProblem(sys, [x => 0.0, y => 0.0, z => 0.0], [a => 1.0, b => 1.0],
         grad = false, hess = false, cons_j = false, cons_h = false)
     sol = solve(prob, AmplNLWriter.Optimizer(Ipopt_jll.amplexe))
-    @test_skip sol.minimum < 1.0
+    @test_skip sol.objective < 1.0
     @test_skip sol.u≈[0.808, -0.064] atol=1e-3
     @test_skip sol[x]^2 + sol[y]^2 ≈ 1.0
 end
@@ -108,7 +108,7 @@ end
     rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
     x0 = zeros(2)
     p = [1.0, 100.0]
-    f = OptimizationFunction(rosenbrock, Optimization.AutoModelingToolkit())
+    f = OptimizationFunction(rosenbrock, Optimization.AutoSymbolics())
     prob = OptimizationProblem(f, x0, p)
     sol = solve(prob, Newton())
     @test sol.u ≈ [1.0, 1.0]
@@ -215,15 +215,15 @@ end
         Ipopt.Optimizer();
         print_level = 0))
     #=
-     @test sol.minimum < -1e5
+     @test sol.objective < -1e5
 
      prob = OptimizationProblem(sys2, [x => 0.0, y => 0.0], [a => 1.0, b => 100.0],
          grad = true, hess = true, cons_j = true, cons_h = true)
      @test prob.f.sys === sys2
      sol = solve(prob, IPNewton())
-     @test sol.minimum < 1.0
+     @test sol.objective < 1.0
      sol = solve(prob, Ipopt.Optimizer(); print_level = 0)
-     @test sol.minimum < 1.0
+     @test sol.objective < 1.0
      =#
 end
 
@@ -298,11 +298,11 @@ end
     loss = (a - x)^2 + b * (y - x^2)^2
     @named sys = OptimizationSystem(loss, [x, y], [a, b], constraints = [x^2 + y^2 ≲ 0.0])
     sys = complete(sys)
-    @test_throws ArgumentError OptimizationProblem(sys,
+    @test_throws ErrorException OptimizationProblem(sys,
         [x => 0.0, y => 0.0],
         [a => 1.0, b => 100.0],
         lcons = [0.0])
-    @test_throws ArgumentError OptimizationProblem(sys,
+    @test_throws ErrorException OptimizationProblem(sys,
         [x => 0.0, y => 0.0],
         [a => 1.0, b => 100.0],
         ucons = [0.0])

test/sdesystem.jl

@@ -693,7 +693,7 @@ let # test to make sure that scalar noise always receive the same kicks
     @mtkbuild de = System(eqs, t)
     prob = SDEProblem(de, [x => 0, y => 0], (0.0, 10.0), [])
     sol = solve(prob, SOSRI())
-    @test sol[end][1] == sol[end][2]
+    @test sol.u[end][1] == sol.u[end][2]
 end
 
 let # test that diagonal noise is correctly handled

test/variable_parsing.jl

@@ -33,10 +33,6 @@ s1 = Num(Sym{Real}(:s))
 @test ModelingToolkit.isparameter(s)
 @test ModelingToolkit.isparameter(σ)
 
-@derivatives D' ~ t
-D1 = Differential(t)
-@test D1 == D
-
 @test @macroexpand(@parameters x, y, z(t)) == @macroexpand(@parameters x y z(t))
 @test @macroexpand(@variables x, y, z(t)) == @macroexpand(@variables x y z(t))