update test to pass with numpy-2.3

Author: Vahid Tavanashad

.github/workflows/conda-package.yml

@@ -22,7 +22,7 @@ env:
   test-env-name: 'test'
   rerun-tests-on-failure: 'true'
   rerun-tests-max-attempts: 2
-  rerun-tests-timeout: 35
+  rerun-tests-timeout: 40
 
 jobs:
   build:

dpnp/tests/test_linalg.py

@@ -2104,11 +2104,9 @@ def test_empty(self, shape, ord, axis, keepdims):
                 assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
                 assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
             else:
-                # TODO: when similar changes in numpy are available, instead
-                # of assert_equal with zero, we should compare with numpy
-                # ord in [None, 1, 2]
-                assert_equal(dpnp.linalg.norm(ia, **kwarg), 0.0)
-                assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
+                result = dpnp.linalg.norm(ia, **kwarg)
+                expected = numpy.linalg.norm(a, **kwarg)
+                assert_dtype_allclose(result, expected)
         else:
             result = dpnp.linalg.norm(ia, **kwarg)
             expected = numpy.linalg.norm(a, **kwarg)

dpnp/tests/test_product.py

@@ -12,11 +12,9 @@
     assert_dtype_allclose,
     generate_random_numpy_array,
     get_all_dtypes,
-    get_complex_dtypes,
-    is_win_platform,
     numpy_version,
 )
-from .third_party.cupy import testing
+from .third_party.cupy.testing import with_requires
 
 # A list of selected dtypes including both integer and float dtypes
 # to test differennt backends: OneMath (for float) and dpctl (for integer)
@@ -141,7 +139,7 @@ def test_strided(self, dtype, stride):
         expected = numpy.cross(a[::stride], b[::stride])
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.0")
+    @with_requires("numpy>=2.0")
     @pytest.mark.parametrize("axis", [0, 1, -1])
     def test_linalg(self, axis):
         a = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
@@ -171,7 +169,7 @@ def test_error(self):
         with pytest.raises(TypeError):
             dpnp.cross(a, a)
 
-    @testing.with_requires("numpy>=2.0")
+    @with_requires("numpy>=2.0")
     def test_linalg_error(self):
         a = dpnp.arange(4)
         b = dpnp.arange(4)
@@ -706,7 +704,7 @@ def test_axes_ND_ND(self, axes):
         expected = numpy.matmul(a, b, axes=axes)
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.2")
+    @with_requires("numpy>=2.2")
     @pytest.mark.parametrize("func", ["matmul", "matvec"])
     @pytest.mark.parametrize(
         "axes",
@@ -725,7 +723,7 @@ def test_axes_ND_1D(self, func, axes):
         expected = getattr(numpy, func)(a, b, axes=axes)
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.2")
+    @with_requires("numpy>=2.2")
     @pytest.mark.parametrize("func", ["matmul", "vecmat"])
     @pytest.mark.parametrize(
         "axes",
@@ -845,6 +843,8 @@ def test_dtype_matrix(self, dt_in1, dt_in2, dt_out, shape1, shape2):
             assert_raises(TypeError, dpnp.matmul, ia, ib, out=iout)
             assert_raises(TypeError, numpy.matmul, a, b, out=out)
 
+    # TODO: include numpy-2.3 when numpy-issue-29164 is resolved
+    @with_requires("numpy<2.3")
     @pytest.mark.parametrize("dtype", _selected_dtypes)
     @pytest.mark.parametrize("order1", ["C", "F", "A"])
     @pytest.mark.parametrize("order2", ["C", "F", "A"])
@@ -882,6 +882,8 @@ def test_order(self, dtype, order1, order2, order, shape1, shape2):
         assert result.flags.f_contiguous == expected.flags.f_contiguous
         assert_dtype_allclose(result, expected)
 
+    # TODO: include numpy-2.3 when numpy-issue-29164 is resolved
+    @with_requires("numpy<2.3")
     @pytest.mark.parametrize("dtype", _selected_dtypes)
     @pytest.mark.parametrize(
         "stride",
@@ -971,7 +973,7 @@ def test_strided3(self, dtype, stride, transpose):
         assert result is iout
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.2")
+    @with_requires("numpy>=2.2")
     @pytest.mark.parametrize("dtype", _selected_dtypes)
     @pytest.mark.parametrize("func", ["matmul", "matvec"])
     @pytest.mark.parametrize("incx", [-2, 2])
@@ -1007,7 +1009,7 @@ def test_strided_mat_vec(self, dtype, func, incx, incy, transpose):
         assert result is iout
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.2")
+    @with_requires("numpy>=2.2")
     @pytest.mark.parametrize("dtype", _selected_dtypes)
     @pytest.mark.parametrize("func", ["matmul", "vecmat"])
     @pytest.mark.parametrize("incx", [-2, 2])
@@ -1198,7 +1200,7 @@ def test_bool(self):
         expected = numpy.matmul(a, b, out=out)
         assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.0")
+    @with_requires("numpy>=2.0")
     def test_linalg_matmul(self):
         a = numpy.ones((3, 4))
         b = numpy.ones((4, 5))
@@ -1434,7 +1436,7 @@ def test_invalid_axes(self, xp):
         assert_raises(AxisError, xp.matmul, a, b, axes=axes)
 
 
-@testing.with_requires("numpy>=2.2")
+@with_requires("numpy>=2.2")
 class TestMatvec:
     def setup_method(self):
         numpy.random.seed(42)
@@ -1735,7 +1737,7 @@ def test_strided(self, stride):
             expected = numpy.tensordot(a, a, axes=axes)
             assert_dtype_allclose(result, expected)
 
-    @testing.with_requires("numpy>=2.0")
+    @with_requires("numpy>=2.0")
     @pytest.mark.parametrize(
         "axes",
         [([0, 1]), ([0, 1], [1, 2]), ([-2, -3], [3, 2])],
@@ -1880,7 +1882,7 @@ def test_error(self):
             dpnp.vdot(b, a)
 
 
-@testing.with_requires("numpy>=2.0")
+@with_requires("numpy>=2.0")
 class TestVecdot:
     def setup_method(self):
         numpy.random.seed(42)
@@ -2160,7 +2162,7 @@ def test_error(self, xp):
         assert_raises(ValueError, xp.vecdot, a, a, axes=axes)
 
 
-@testing.with_requires("numpy>=2.2")
+@with_requires("numpy>=2.2")
 class TestVecmat:
     def setup_method(self):
         numpy.random.seed(42)

dpnp/tests/testing/array.py

@@ -29,6 +29,8 @@
 
 from dpnp.dpnp_utils import convert_item
 
+from ..helper import numpy_version
+
 assert_allclose_orig = numpy.testing.assert_allclose
 assert_almost_equal_orig = numpy.testing.assert_almost_equal
 assert_array_almost_equal_orig = numpy.testing.assert_array_almost_equal
@@ -49,7 +51,7 @@ def _assert(assert_func, result, expected, *args, **kwargs):
     ]
     # For numpy < 2.0, some tests will fail for dtype mismatch
     dev = dpctl.select_default_device()
-    if numpy.__version__ >= "2.0.0" and dev.has_aspect_fp64:
+    if numpy_version() >= "2.0.0" and dev.has_aspect_fp64:
         strict = kwargs.setdefault("strict", True)
         if flag:
             if strict:

dpnp/tests/third_party/cupy/manipulation_tests/test_add_remove.py

@@ -340,7 +340,8 @@ def test_unique_inverse(self, xp, dtype, attr):
         a = testing.shaped_random((100, 100), xp, dtype)
         return getattr(xp.unique_inverse(a), attr)
 
-    @testing.with_requires("numpy>=2.0")
+    # TODO: include numpy-2.3 when dpnp-issue-2476 is addressed
+    @testing.with_requires("numpy>=2.0", "numpy<2.3")
     @testing.for_all_dtypes(no_float16=True, no_bool=True, no_complex=True)
     @testing.numpy_cupy_array_equal()
     def test_unique_values(self, xp, dtype):

dpnp/tests/third_party/cupy/math_tests/test_matmul.py

@@ -83,6 +83,8 @@ def test_cupy_matmul(self, xp, dtype1, dtype2):
         return xp.matmul(x1, x2)
 
 
+# TODO: include numpy-2.3 when numpy-issue-29164 is resolved
+@testing.with_requires("numpy<2.3")
 @testing.parameterize(
     *testing.product(
         {