Merge branch 'master' into impl-piecewise

Author: vtavana

CHANGELOG.md

@@ -51,6 +51,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Avoided negating unsigned integers in ceil division used in `dpnp.resize` implementation [#2508](https://github.com/IntelPython/dpnp/pull/2508)
 * Fixed `dpnp.unique` with 1d input array and `axis=0`, `equal_nan=True` keywords passed where the produced result doesn't collapse the NaNs [#2530](https://github.com/IntelPython/dpnp/pull/2530)
 * Resolved issue when `dpnp.ndarray` constructor is called with `dpnp.ndarray.data` as `buffer` keyword [#2533](https://github.com/IntelPython/dpnp/pull/2533)
+* Fixed `dpnp.linalg.cond` to always return a real dtype [#2547](https://github.com/IntelPython/dpnp/pull/2547)
 
 ### Security
 

dpnp/linalg/dpnp_utils_linalg.py

@@ -1976,13 +1976,15 @@ def dpnp_cond(x, p=None):
         else:
             r = s[..., 0] / s[..., -1]
     else:
-        result_t = _common_type(x)
         # The result array will contain nans in the entries
         # where inversion failed
         invx = dpnp.linalg.inv(x)
         r = dpnp.linalg.norm(x, p, axis=(-2, -1)) * dpnp.linalg.norm(
             invx, p, axis=(-2, -1)
         )
+
+        # condition number is always real
+        result_t = _real_type(_common_type(x), device=x.sycl_queue)
         r = r.astype(result_t, copy=False)
 
     # Convert nans to infs unless the original array had nan entries

dpnp/tests/test_linalg.py

@@ -278,15 +278,12 @@ def test_cholesky_errors(self):
 
 
 class TestCond:
-    def setup_method(self):
-        numpy.random.seed(70)
+    _norms = [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
 
     @pytest.mark.parametrize(
-        "shape", [(0, 4, 4), (4, 0, 3, 3)], ids=["(0, 5, 3)", "(4, 0, 2, 3)"]
-    )
-    @pytest.mark.parametrize(
-        "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
+        "shape", [(0, 4, 4), (4, 0, 3, 3)], ids=["(0, 4, 4)", "(4, 0, 3, 3)"]
     )
+    @pytest.mark.parametrize("p", _norms)
     def test_empty(self, shape, p):
         a = numpy.empty(shape)
         ia = dpnp.array(a)
@@ -295,26 +292,27 @@ def test_empty(self, shape, p):
         expected = numpy.linalg.cond(a, p=p)
         assert_dtype_allclose(result, expected)
 
+    # TODO: uncomment once numpy 2.3.3 release is published
+    # @testing.with_requires("numpy>=2.3.3")
     @pytest.mark.parametrize(
         "dtype", get_all_dtypes(no_none=True, no_bool=True)
     )
     @pytest.mark.parametrize(
         "shape", [(4, 4), (2, 4, 3, 3)], ids=["(4, 4)", "(2, 4, 3, 3)"]
     )
-    @pytest.mark.parametrize(
-        "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
-    )
+    @pytest.mark.parametrize("p", _norms)
     def test_basic(self, dtype, shape, p):
         a = generate_random_numpy_array(shape, dtype)
         ia = dpnp.array(a)
 
         result = dpnp.linalg.cond(ia, p=p)
         expected = numpy.linalg.cond(a, p=p)
+        # TODO: remove when numpy#29333 is released
+        if numpy_version() < "2.3.3":
+            expected = expected.real
         assert_dtype_allclose(result, expected, factor=16)
 
-    @pytest.mark.parametrize(
-        "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
-    )
+    @pytest.mark.parametrize("p", _norms)
     def test_bool(self, p):
         a = numpy.array([[True, True], [True, False]])
         ia = dpnp.array(a)
@@ -323,9 +321,7 @@ def test_bool(self, p):
         expected = numpy.linalg.cond(a, p=p)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
-    )
+    @pytest.mark.parametrize("p", _norms)
     def test_nan_to_inf(self, p):
         a = numpy.zeros((2, 2))
         ia = dpnp.array(a)
@@ -343,9 +339,7 @@ def test_nan_to_inf(self, p):
         else:
             assert_raises(dpnp.linalg.LinAlgError, dpnp.linalg.cond, ia, p=p)
 
-    @pytest.mark.parametrize(
-        "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
-    )
+    @pytest.mark.parametrize("p", _norms)
     @pytest.mark.parametrize(
         "stride",
         [(-2, -3, 2, -2), (-2, 4, -4, -4), (2, 3, 4, 4), (-1, 3, 3, -3)],
@@ -357,21 +351,23 @@ def test_nan_to_inf(self, p):
         ],
     )
     def test_strided(self, p, stride):
-        A = numpy.random.rand(6, 8, 10, 10)
-        B = dpnp.asarray(A)
+        A = generate_random_numpy_array(
+            (6, 8, 10, 10), seed_value=70, low=0, high=1
+        )
+        iA = dpnp.array(A)
         slices = tuple(slice(None, None, stride[i]) for i in range(A.ndim))
-        a = A[slices]
-        b = B[slices]
+        a, ia = A[slices], iA[slices]
 
-        result = dpnp.linalg.cond(b, p=p)
+        result = dpnp.linalg.cond(ia, p=p)
         expected = numpy.linalg.cond(a, p=p)
         assert_dtype_allclose(result, expected, factor=24)
 
-    def test_error(self):
+    @pytest.mark.parametrize("xp", [dpnp, numpy])
+    def test_error(self, xp):
         # cond is not defined on empty arrays
-        ia = dpnp.empty((2, 0))
+        a = xp.empty((2, 0))
         with pytest.raises(ValueError):
-            dpnp.linalg.cond(ia, p=1)
+            xp.linalg.cond(a, p=1)
 
 
 class TestDet:

environments/build_conda_pkg.yml

@@ -3,4 +3,4 @@ channels:
   - conda-forge
 dependencies:
   - python=3.13
-  - conda-build=25.5.0
+  - conda-build=25.7.0