Backport gh-2477 (#2491)

This PR backports of #2477 from development branch to
`maintenance/0.18.x`.

Author: antonwolfy

.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:

CHANGELOG.md

@@ -4,6 +4,17 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.18.1] - 06/DD/2025
+
+### Added
+
+### Changed
+
+### Fixed
+
+* Fixed a bug for calculating the norm (`dpnp.linalg.norm`) of empty arrays when `keepdims=True` is passed  [#2477](https://github.com/IntelPython/dpnp/pull/2477)
+
+
 ## [0.18.0] - 06/04/2025
 
 This release achieves 100% compliance with Python Array API specification (revision [2024.12](https://data-apis.org/array-api/2024.12/)).

dpnp/linalg/dpnp_utils_linalg.py

@@ -1187,7 +1187,10 @@ def _norm_int_axis(x, ord, axis, keepdims):
     if ord == dpnp.inf:
         if x.shape[axis] == 0:
             x = dpnp.moveaxis(x, axis, -1)
-            return dpnp.zeros_like(x, shape=x.shape[:-1])
+            res_shape = x.shape[:-1]
+            if keepdims:
+                res_shape += (1,)
+            return dpnp.zeros_like(x, shape=res_shape)
         return dpnp.abs(x).max(axis=axis, keepdims=keepdims)
     if ord == -dpnp.inf:
         return dpnp.abs(x).min(axis=axis, keepdims=keepdims)
@@ -1226,7 +1229,10 @@ def _norm_tuple_axis(x, ord, row_axis, col_axis, keepdims):
     flag = x.shape[row_axis] == 0 or x.shape[col_axis] == 0
     if flag and ord in [1, 2, dpnp.inf]:
         x = dpnp.moveaxis(x, axis, (-2, -1))
-        return dpnp.zeros_like(x, shape=x.shape[:-2])
+        res_shape = x.shape[:-2]
+        if keepdims:
+            res_shape += (1, 1)
+        return dpnp.zeros_like(x, shape=res_shape)
     if row_axis == col_axis:
         raise ValueError("Duplicate axes given.")
     if ord == 2:

dpnp/tests/test_linalg.py

@@ -23,7 +23,6 @@
     get_integer_float_dtypes,
     has_support_aspect64,
     is_cpu_device,
-    is_cuda_device,
     numpy_version,
     requires_intel_mkl_version,
 )
@@ -2104,11 +2103,14 @@ 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)
+                if numpy_version() >= "2.3.0":
+                    result = dpnp.linalg.norm(ia, **kwarg)
+                    expected = numpy.linalg.norm(a, **kwarg)
+                    assert_dtype_allclose(result, expected)
+                else:
+                    assert_equal(
+                        dpnp.linalg.norm(ia, **kwarg), 0.0, strict=False
+                    )
         else:
             result = dpnp.linalg.norm(ia, **kwarg)
             expected = numpy.linalg.norm(a, **kwarg)
@@ -2296,49 +2298,40 @@ def test_matrix_norm(self, ord, keepdims):
         expected = numpy.linalg.matrix_norm(a, ord=ord, keepdims=keepdims)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "xp",
-        [
-            dpnp,
-            pytest.param(
-                numpy,
-                marks=pytest.mark.skipif(
-                    numpy_version() < "2.3.0",
-                    reason="numpy raises an error",
-                ),
-            ),
-        ],
-    )
+    @testing.with_requires("numpy>=2.3")
     @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
     @pytest.mark.parametrize(
         "shape, axis", [[(2, 0), None], [(2, 0), (0, 1)], [(0, 2), (0, 1)]]
     )
     @pytest.mark.parametrize("ord", [None, "fro", "nuc", 1, 2, dpnp.inf])
-    def test_matrix_norm_empty(self, xp, dtype, shape, axis, ord):
-        x = xp.zeros(shape, dtype=dtype)
-        sc = dtype(0.0) if dtype == dpnp.float32 else 0.0
-        assert_equal(xp.linalg.norm(x, axis=axis, ord=ord), sc)
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_matrix_norm_empty(self, dtype, shape, axis, ord, keepdims):
+        a = numpy.zeros(shape, dtype=dtype)
+        ia = dpnp.array(a)
+        result = dpnp.linalg.norm(ia, axis=axis, ord=ord, keepdims=keepdims)
+        expected = numpy.linalg.norm(a, axis=axis, ord=ord, keepdims=keepdims)
+        assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "xp",
-        [
-            dpnp,
-            pytest.param(
-                numpy,
-                marks=pytest.mark.skipif(
-                    numpy_version() < "2.3.0",
-                    reason="numpy raises an error",
-                ),
-            ),
-        ],
-    )
+    @testing.with_requires("numpy>=2.3")
     @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
     @pytest.mark.parametrize("axis", [None, 0])
     @pytest.mark.parametrize("ord", [None, 1, 2, dpnp.inf])
-    def test_vector_norm_empty(self, xp, dtype, axis, ord):
-        x = xp.zeros(0, dtype=dtype)
-        sc = dtype(0.0) if dtype == dpnp.float32 else 0.0
-        assert_equal(xp.linalg.vector_norm(x, axis=axis, ord=ord), sc)
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_vector_norm_empty(self, dtype, axis, ord, keepdims):
+        a = numpy.zeros(0, dtype=dtype)
+        ia = dpnp.array(a)
+        result = dpnp.linalg.vector_norm(
+            ia, axis=axis, ord=ord, keepdims=keepdims
+        )
+        expected = numpy.linalg.vector_norm(
+            a, axis=axis, ord=ord, keepdims=keepdims
+        )
+        assert_dtype_allclose(result, expected)
+        if keepdims:
+            # norm and vector_norm have different paths in dpnp when keepdims=True,
+            # to cover both of them test with norm as well
+            result = dpnp.linalg.norm(ia, axis=axis, ord=ord, keepdims=keepdims)
+            assert_dtype_allclose(result, expected)
 
     @testing.with_requires("numpy>=2.0")
     @pytest.mark.parametrize(

dpnp/tests/test_product.py

@@ -12,8 +12,6 @@
     assert_dtype_allclose,
     generate_random_numpy_array,
     get_all_dtypes,
-    get_complex_dtypes,
-    is_win_platform,
     numpy_version,
 )
 from .third_party.cupy import testing
@@ -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
+    @testing.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
+    @testing.with_requires("numpy<2.3")
     @pytest.mark.parametrize("dtype", _selected_dtypes)
     @pytest.mark.parametrize(
         "stride",

dpnp/tests/testing/array.py

@@ -49,7 +49,10 @@ 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.lib.NumpyVersion(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

@@ -345,7 +345,8 @@ def test_unique_inverse(self, xp, dtype, attr):
     @testing.numpy_cupy_array_equal()
     def test_unique_values(self, xp, dtype):
         a = testing.shaped_random((100, 100), xp, dtype)
-        return xp.unique_values(a)
+        out = xp.unique_values(a)  # may not be sorted from NumPy 2.3.
+        return xp.sort(out)
 
 
 @testing.parameterize(*testing.product({"trim": ["fb", "f", "b"]}))

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

@@ -99,6 +99,8 @@ def test_cupy_matmul(self, xp, dtype1, dtype2):
 )
 class TestMatmulOut(unittest.TestCase):
 
+    # TODO: include numpy-2.3 when numpy-issue-29164 is resolved
+    @testing.with_requires("numpy<2.3")
     # no_int8=True is added to avoid overflow
     @testing.for_all_dtypes(name="dtype1", no_int8=True)
     @testing.for_all_dtypes(name="dtype2", no_int8=True)

pyproject.toml

@@ -135,7 +135,7 @@ exclude-protected = ["_create_from_usm_ndarray"]
 
 [tool.pylint.design]
 max-args = 11
-max-branches = 16
+max-branches = 17
 max-locals = 30
 max-positional-arguments = 9
 max-returns = 8