Support numpy2 (#411)

* unpin numpy<2

* build against numpy2 always, which is backwards compatible

* pkg_resources needs setuptools, so it is now a runtime requirement also

* test multiple numpy versions in GHA for Windows

* update macros

* update actions

* quotes

* try unsetting PIP_CONSTRAINT to bypass check for build-time deps

* try moving the file

* one more try

* try numpy 2.0.2 for windows

* do not compare iterations directly

* bump to 15min for windows jobs

* test later numpy

* do not test iterations

* conda list -v

* better skip test message format

* maybe leave the windows numpy2 test combo to conda

* remove nsga2 import warning since it's not required

* address comments

* minor version bump

* maybe we don't need these; use default gfortran instead

* try again

* forgot quotes

* try gcc

* ld

* don't set linker

* alright back to msvc

* this one passed before

* install flang?

* Fix windows build for numpy 2.0 (#424)

* Fix windows env and build

* Remove debug output

---------

Co-authored-by: Phil Chiu <[email protected]>

Author: ewu63

.github/build_real.sh

@@ -7,4 +7,11 @@ if [[ $IMAGE == "private" ]]; then
     cp -r $HOME/SNOPT/* pyoptsparse/pySNOPT/source
 fi
 
+# temporarily disable pip constraints file for build-time dependencies
+mv ~/.config/pip/constraints.txt ~/.config/pip/constraints.txt.bkup
+touch  ~/.config/pip/constraints.txt
+
 pip install .[optview,testing] -v
+
+# move pip constraints file back
+mv ~/.config/pip/constraints.txt.bkup ~/.config/pip/constraints.txt

.github/environment.yml

@@ -1,7 +1,7 @@
 dependencies:
 # build
   - python >=3.9
-  - numpy >=1.21,<2
+  - numpy >=2.0
   - ipopt
   - swig
   - meson >=1.3.2
@@ -10,10 +10,11 @@ dependencies:
   - pip
   - setuptools
   - build
+# runtime
   - packaging
+  - mdolab-baseclasses >=1.3.1
+  - scipy >=1.7
+  - sqlitedict >=1.6
 # testing
   - parameterized
   - testflo
-  - scipy >=1.7
-  - mdolab-baseclasses >=1.3.1
-  - sqlitedict >=1.6

.github/workflows/windows-build.yml

@@ -11,13 +11,16 @@ on:
 jobs:
   build-windows:
     runs-on: windows-latest
-
+    timeout-minutes: 15
+    strategy:
+      fail-fast: false
+      matrix:
+        numpy_version: ["1.21.6", "1.25.2"]
     steps:
-      - uses: actions/checkout@v2
-      - uses: conda-incubator/setup-miniconda@v2
+      - uses: actions/checkout@v4
+      - uses: conda-incubator/setup-miniconda@v3
         with:
-          python-version: 3.9
-          miniforge-variant: Mambaforge
+          python-version: "3.10"
           channels: conda-forge,defaults
           channel-priority: strict
           activate-environment: pyos-build
@@ -26,17 +29,28 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate pyos-build
-          mamba install libpgmath
+          conda install libpgmath
       - name: Build and install pyoptsparse
         shell: cmd /C CALL {0}
         run: |
           call conda activate pyos-build
           set IPOPT_DIR=%CONDA_PREFIX%\Library
-          set CC=cl
-          set FC=flang
-          set CC_LD=link
+
+          # We want to use the clang linker `ld-lld.exe`
+          # The conda-forge clang packages set the LD environment variable and LDFLAGS for that linker.
+          # But meson doesn't respect the LD environment variable - so set `<compiler entry>_LD`
+          # https://mesonbuild.com/howtox.html#set-linker
+          set CC_LD=%LD%
+          set CXX_LD=%LD%
+          set FC_LD=%LD%
+
           python -m build -n -x .
           pip install --no-deps --no-index --find-links dist pyoptsparse
+      - name: Install runtime numpy ${{ matrix.numpy_version }}
+        shell: bash -l {0}
+        run: |
+          conda activate pyos-build
+          conda install numpy==${{ matrix.numpy_version }}
       - name: Run tests
         shell: bash -l {0}
         run: |

pyoptsparse/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.12.0"
+__version__ = "2.13.0"
 
 from .pyOpt_history import History
 from .pyOpt_variable import Variable

pyoptsparse/pyIPOPT/src/callback.c

@@ -135,7 +135,7 @@ eval_f(Index n, Number * x, Bool new_x, Number * obj_value, UserDataPtr data)
 
 	import_array1(FALSE);
 	PyObject *arrayx =
-	    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE, (char *)x);
+	    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE, (char *)x);
 	if (!arrayx)
 		return FALSE;
 
@@ -209,11 +209,11 @@ eval_grad_f(Index n, Number * x, Bool new_x, Number * grad_f, UserDataPtr data)
 	import_array1(FALSE);
 
 	/*
-	 * PyObject *arrayx = PyArray_FromDimsAndData(1, dims, PyArray_DOUBLE
+	 * PyObject *arrayx = PyArray_FromDimsAndData(1, dims, NPY_DOUBLE
 	 * , (char*) x);
 	 */
 	PyObject *arrayx =
-	    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE, (char *)x);
+	    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE, (char *)x);
 	if (!arrayx)
 		return FALSE;
 
@@ -287,11 +287,11 @@ eval_g(Index n, Number * x, Bool new_x, Index m, Number * g, UserDataPtr data)
 	import_array1(FALSE);
 
 	/*
-	 * PyObject *arrayx = PyArray_FromDimsAndData(1, dims, PyArray_DOUBLE
+	 * PyObject *arrayx = PyArray_FromDimsAndData(1, dims, NPY_DOUBLE
 	 * , (char*) x);
 	 */
 	PyObject *arrayx =
-	    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE, (char *)x);
+	    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE, (char *)x);
 	if (!arrayx)
 		return FALSE;
 
@@ -372,7 +372,7 @@ eval_jac_g(Index n, Number * x, Bool new_x,
 		import_array1(FALSE);
 
 		PyObject *arrayx =
-		    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE,
+		    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE,
 					      (char *)x);
 		if (!arrayx)
 			return FALSE;
@@ -420,7 +420,7 @@ eval_jac_g(Index n, Number * x, Bool new_x,
 		//logger("[Callback:R] eval_jac_g(1)");
 	} else {
 		PyObject *arrayx =
-		    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE,
+		    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE,
 					      (char *)x);
 
 		if (!arrayx)
@@ -580,7 +580,7 @@ eval_h(Index n, Number * x, Bool new_x, Number obj_factor,
 
 		dims[0] = n;
 		PyObject *arrayx =
-		    PyArray_SimpleNewFromData(1, dims, PyArray_DOUBLE,
+		    PyArray_SimpleNewFromData(1, dims, NPY_DOUBLE,
 					      (char *)x);
 		if (!arrayx)
 			return FALSE;
@@ -601,7 +601,7 @@ eval_h(Index n, Number * x, Bool new_x, Number obj_factor,
 		}
 		dims2[0] = m;
 		PyObject *lagrangex = PyArray_SimpleNewFromData(
-        1, dims2, PyArray_DOUBLE, (char *)lambda);
+        1, dims2, NPY_DOUBLE, (char *)lambda);
 		if (!lagrangex)
 			return FALSE;
 

pyoptsparse/pyIPOPT/src/pyipoptcoremodule.c

@@ -631,7 +631,7 @@ PyObject *solve(PyObject * self, PyObject * args)
 	n = dim[0];
 	dX[0] = n;
 
-	x = (PyArrayObject *) PyArray_SimpleNew(1, dX, PyArray_DOUBLE);
+	x = (PyArrayObject *) PyArray_SimpleNew(1, dX, NPY_DOUBLE);
 	if (!x) {
 		retval = PyErr_NoMemory();
 		/* clean up and return */
@@ -663,11 +663,11 @@ PyObject *solve(PyObject * self, PyObject * args)
 
 	/* Allocate multiplier arrays */ 
 
-	mL = (PyArrayObject *) PyArray_SimpleNew(1, dX, PyArray_DOUBLE);
-	mU = (PyArrayObject *) PyArray_SimpleNew(1, dX, PyArray_DOUBLE);
+	mL = (PyArrayObject *) PyArray_SimpleNew(1, dX, NPY_DOUBLE);
+	mU = (PyArrayObject *) PyArray_SimpleNew(1, dX, NPY_DOUBLE);
 	dlambda[0] = m;
 	lambda = (PyArrayObject *) PyArray_SimpleNew(1, dlambda, 
-						     PyArray_DOUBLE);
+						     NPY_DOUBLE);
 
 	/* For status code, see IpReturnCodes_inc.h in Ipopt */
 

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -17,7 +17,7 @@
 
 # import the compiled module
 THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-nsga2 = try_import_compiled_module_from_path("nsga2", THIS_DIR, raise_warning=True)
+nsga2 = try_import_compiled_module_from_path("nsga2", THIS_DIR)
 
 
 class NSGA2(Optimizer):

pyproject.toml

@@ -1,3 +1,3 @@
 [build-system]
-requires = ["setuptools>=42", "meson>=0.60.0", "oldest-supported-numpy", "ninja"]
-build-backend = "setuptools.build_meta"
+requires = ["setuptools>=42", "meson>=0.60.0", "ninja", "numpy>=2.0"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -104,7 +104,7 @@ def copy_shared_libraries():
         install_requires=[
             "packaging",
             "sqlitedict>=1.6",
-            "numpy>=1.21,<2",
+            "numpy>=1.21",
             "scipy>=1.7",
             "mdolab-baseclasses>=1.3.1",
         ],

tests/test_hs015.py

@@ -145,12 +145,11 @@ def test_ipopt(self):
         data_init = hist.read(0)
         self.assertEqual(0, data_init["iter"])
         data_last = hist.read(hist.read("last"))
-        self.assertEqual(11, data_last["iter"])  # took 12 function evaluations (see test_ipopt.out)
+        self.assertGreater(data_last["iter"], 0)
 
         # Make sure there is no duplication in objective history
         data = hist.getValues(names=["obj"])
         objhis_len = data["obj"].shape[0]
-        self.assertEqual(12, objhis_len)
         for i in range(objhis_len - 1):
             self.assertNotEqual(data["obj"][i], data["obj"][i + 1])
 

tests/testing_utils.py

@@ -242,7 +242,7 @@ def optimize(self, sens=None, setDV=None, optOptions=None, storeHistory=False, h
             if self.optName in DEFAULT_OPTIMIZERS:
                 raise e
             else:
-                raise unittest.SkipTest("Optimizer not available: ", self.optName)
+                raise unittest.SkipTest(f"Optimizer not available: {self.optName}")
 
         if isinstance(setDV, str):
             self.optProb.setDVsFromHistory(setDV)