From bf288536f3e86602db4cce83ecc6839fcfb02b7b Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 15:15:09 +0200
Subject: [PATCH 1/9] Update grid-like functions

---
 dpnp/dpnp_iface_arraycreation.py | 34 ++++++++++++++++++++++----------
 1 file changed, 24 insertions(+), 10 deletions(-)

diff --git a/dpnp/dpnp_iface_arraycreation.py b/dpnp/dpnp_iface_arraycreation.py
index 5a245c69037..560ad16e1d5 100644
--- a/dpnp/dpnp_iface_arraycreation.py
+++ b/dpnp/dpnp_iface_arraycreation.py
@@ -3036,7 +3036,7 @@ def logspace(
 # pylint: disable=redefined-outer-name
 def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
     """
-    Return coordinate matrices from coordinate vectors.
+    Return a tuple of coordinate matrices from coordinate vectors.
 
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
@@ -3069,6 +3069,13 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
         the elements of `xi` repeated to fill the matrix along the first
         dimension for `x1`, the second for `x2` and so on.
 
+    See Also
+    --------
+    :obj:`dpnp.mgrid` : Construct a multi-dimensional "meshgrid" using
+        indexing notation.
+    :obj:`dpnp.ogrid` : Construct an open multi-dimensional "meshgrid" using
+        indexing notation.
+
     Examples
     --------
     >>> import dpnp as np
@@ -3164,9 +3171,9 @@ class MGridClass:
 
     Returns
     -------
-    out : one dpnp.ndarray or tuple of dpnp.ndarray
-        Returns one array of grid indices,
-        ``grid.shape = (len(dimensions),) + tuple(dimensions)``.
+    out : dpnp.ndarray
+        A single array, containing a set of arrays all of the same dimensions,
+        stacked along the first axis.
 
     Examples
     --------
@@ -3183,6 +3190,13 @@ class MGridClass:
             [0, 1, 2, 3, 4],
             [0, 1, 2, 3, 4]]])
 
+    >>> np.mgrid[0:4].shape
+    (4,)
+    >>> np.mgrid[0:4, 0:5].shape
+    (2, 4, 5)
+    >>> np.mgrid[0:4, 0:5, 0:6].shape
+    (3, 4, 5, 6)
+
     Creating an array on a different device or with a specified usm_type
 
     >>> x = np.mgrid[-1:1:5j] # default case
@@ -3243,20 +3257,20 @@ class OGridClass:
 
     Returns
     -------
-    out : one dpnp.ndarray or tuple of dpnp.ndarray
-        Returns a tuple of arrays,
-        with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)
-        with dimensions[i] in the i-th place.
+    out : dpnp.ndarray or tuple of dpnp.ndarray
+        If the input is a single slice, returns an array.
+        If the input is multiple slices, returns a tuple of arrays, with
+        only one dimension not equal to 1.
 
     Examples
     --------
     >>> import dpnp as np
     >>> np.ogrid[0:5, 0:5]
-    [array([[0],
+    (array([[0],
             [1],
             [2],
             [3],
-            [4]]), array([[0, 1, 2, 3, 4]])]
+            [4]]), array([[0, 1, 2, 3, 4]]))
 
     Creating an array on a different device or with a specified usm_type
 

From 6ea41a5e0d2663c141e3ee26a8a3a6ccef4051c8 Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 15:16:17 +0200
Subject: [PATCH 2/9] Update histogramdd function

---
 dpnp/dpnp_iface_histograms.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/dpnp/dpnp_iface_histograms.py b/dpnp/dpnp_iface_histograms.py
index 7e91968926f..bb1dd2291b0 100644
--- a/dpnp/dpnp_iface_histograms.py
+++ b/dpnp/dpnp_iface_histograms.py
@@ -1151,8 +1151,8 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
     H : dpnp.ndarray
         The multidimensional histogram of sample x. See density and weights
         for the different possible semantics.
-    edges : list of {dpnp.ndarray or usm_ndarray}
-        A list of D arrays describing the bin edges for each dimension.
+    edges : tuple of dpnp.ndarray
+        A tuple of D arrays describing the bin edges for each dimension.
 
     See Also
     --------
@@ -1228,4 +1228,4 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
         if dpnp.is_supported_array_type(b):
             bin_edges_view_list[i] = b
 
-    return n, bin_edges_view_list
+    return n, tuple(bin_edges_view_list)

From 1dbe497feacab3d480961b8330fb84c5df65faf8 Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 15:17:22 +0200
Subject: [PATCH 3/9] Update manipulation functions

---
 dpnp/dpnp_algo/dpnp_arraycreation.py |  3 +-
 dpnp/dpnp_iface_manipulation.py      | 92 ++++++++++++++--------------
 2 files changed, 48 insertions(+), 47 deletions(-)

diff --git a/dpnp/dpnp_algo/dpnp_arraycreation.py b/dpnp/dpnp_algo/dpnp_arraycreation.py
index 9c9110b8538..6052ad46727 100644
--- a/dpnp/dpnp_algo/dpnp_arraycreation.py
+++ b/dpnp/dpnp_algo/dpnp_arraycreation.py
@@ -439,4 +439,5 @@ def __getitem__(self, key):
                 slobj[k] = slice(None, None)
                 nn[k] = nn[k][tuple(slobj)]
                 slobj[k] = dpnp.newaxis
-        return nn
+            return tuple(nn)  # ogrid -> tuple of arrays
+        return nn  # mgrid -> ndarray
diff --git a/dpnp/dpnp_iface_manipulation.py b/dpnp/dpnp_iface_manipulation.py
index 6ef4400562f..e613089b8ee 100644
--- a/dpnp/dpnp_iface_manipulation.py
+++ b/dpnp/dpnp_iface_manipulation.py
@@ -612,8 +612,8 @@ def array_split(ary, indices_or_sections, axis=0):
 
     Returns
     -------
-    sub-arrays : list of dpnp.ndarray
-        A list of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : tuple of dpnp.ndarray
+        A tuple of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -625,11 +625,11 @@ def array_split(ary, indices_or_sections, axis=0):
     >>> import dpnp as np
     >>> x = np.arange(8.0)
     >>> np.array_split(x, 3)
-    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7.])]
+    (array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7.]))
 
     >>> x = np.arange(9)
     >>> np.array_split(x, 4)
-    [array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]
+    (array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8]))
 
     """
 
@@ -662,7 +662,7 @@ def array_split(ary, indices_or_sections, axis=0):
         end = div_points[i + 1]
         sub_arys.append(dpnp.swapaxes(sary[st:end], axis, 0))
 
-    return sub_arys
+    return tuple(sub_arys)
 
 
 def asarray_chkfinite(
@@ -881,7 +881,7 @@ def atleast_1d(*arys):
     Returns
     -------
     out : dpnp.ndarray
-        An array, or list of arrays, each with ``a.ndim >= 1``.
+        An array, or tuple of arrays, each with ``a.ndim >= 1``.
         Copies are made only if necessary.
 
     See Also
@@ -899,7 +899,7 @@ def atleast_1d(*arys):
 
     >>> y = np.array([3, 4])
     >>> np.atleast_1d(x, y)
-    [array([1.]), array([3, 4])]
+    (array([1.]), array([3, 4]))
 
     >>> x = np.arange(9.0).reshape(3, 3)
     >>> np.atleast_1d(x)
@@ -926,7 +926,7 @@ def atleast_1d(*arys):
         res.append(result)
     if len(res) == 1:
         return res[0]
-    return res
+    return tuple(res)
 
 
 def atleast_2d(*arys):
@@ -944,7 +944,7 @@ def atleast_2d(*arys):
     Returns
     -------
     out : dpnp.ndarray
-        An array, or list of arrays, each with ``a.ndim >= 2``.
+        An array, or tuple of arrays, each with ``a.ndim >= 2``.
         Copies are avoided where possible, and views with two or more
         dimensions are returned.
 
@@ -982,7 +982,7 @@ def atleast_2d(*arys):
         res.append(result)
     if len(res) == 1:
         return res[0]
-    return res
+    return tuple(res)
 
 
 def atleast_3d(*arys):
@@ -1000,7 +1000,7 @@ def atleast_3d(*arys):
     Returns
     -------
     out : dpnp.ndarray
-        An array, or list of arrays, each with ``a.ndim >= 3``. Copies are
+        An array, or tuple of arrays, each with ``a.ndim >= 3``. Copies are
         avoided where possible, and views with three or more dimensions are
         returned.
 
@@ -1044,7 +1044,7 @@ def atleast_3d(*arys):
         res.append(result)
     if len(res) == 1:
         return res[0]
-    return res
+    return tuple(res)
 
 
 def broadcast_arrays(*args, subok=False):
@@ -1653,8 +1653,8 @@ def dsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : list of dpnp.ndarray
-        A list of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : tuple of dpnp.ndarray
+        A tuple of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -1671,16 +1671,16 @@ def dsplit(ary, indices_or_sections):
            [[ 8.,  9., 10., 11.],
             [12., 13., 14., 15.]]])
     >>> np.dsplit(x, 2)
-    [array([[[ 0.,  1.],
+    (array([[[ 0.,  1.],
              [ 4.,  5.]],
             [[ 8.,  9.],
              [12., 13.]]]),
      array([[[ 2.,  3.],
              [ 6.,  7.]],
             [[10., 11.],
-             [14., 15.]]])]
+             [14., 15.]]]))
     >>> np.dsplit(x, np.array([3, 6]))
-    [array([[[ 0.,  1.,  2.],
+    (array([[[ 0.,  1.,  2.],
              [ 4.,  5.,  6.]],
             [[ 8.,  9., 10.],
              [12., 13., 14.]]]),
@@ -1688,7 +1688,7 @@ def dsplit(ary, indices_or_sections):
              [ 7.]],
             [[11.],
              [15.]]]),
-     array([])]
+     array([]))
 
     """
 
@@ -1753,8 +1753,8 @@ def dstack(tup):
     _check_stack_arrays(tup)
 
     arrs = atleast_3d(*tup)
-    if not isinstance(arrs, list):
-        arrs = [arrs]
+    if not isinstance(arrs, tuple):
+        arrs = (arrs,)
     return dpnp.concatenate(arrs, axis=2)
 
 
@@ -2052,8 +2052,8 @@ def hsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : list of dpnp.ndarray
-        A list of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : tuple of dpnp.ndarray
+        A tuple of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -2070,16 +2070,16 @@ def hsplit(ary, indices_or_sections):
            [ 8.,  9., 10., 11.],
            [12., 13., 14., 15.]])
     >>> np.hsplit(x, 2)
-    [array([[ 0.,  1.],
+    (array([[ 0.,  1.],
             [ 4.,  5.],
             [ 8.,  9.],
             [12., 13.]]),
      array([[ 2.,  3.],
             [ 6.,  7.],
             [10., 11.],
-            [14., 15.]])]
+            [14., 15.]]))
     >>> np.hsplit(x, np.array([3, 6]))
-    [array([[ 0.,  1.,  2.],
+    (array([[ 0.,  1.,  2.],
             [ 4.,  5.,  6.],
             [ 8.,  9., 10.],
             [12., 13., 14.]]),
@@ -2087,7 +2087,7 @@ def hsplit(ary, indices_or_sections):
             [ 7.],
             [11.],
             [15.]]),
-     array([])]
+     array([]))
 
     With a higher dimensional array the split is still along the second axis.
 
@@ -2098,16 +2098,16 @@ def hsplit(ary, indices_or_sections):
            [[4., 5.],
             [6., 7.]]])
     >>> np.hsplit(x, 2)
-    [array([[[0., 1.]],
+    (array([[[0., 1.]],
             [[4., 5.]]]),
      array([[[2., 3.]],
-            [[6., 7.]]])]
+            [[6., 7.]]]))
 
     With a 1-D array, the split is along axis 0.
 
     >>> x = np.array([0, 1, 2, 3, 4, 5])
     >>> np.hsplit(x, 2)
-    [array([0, 1, 2]), array([3, 4, 5])]
+    (array([0, 1, 2]), array([3, 4, 5]))
 
     """
 
@@ -2178,8 +2178,8 @@ def hstack(tup, *, dtype=None, casting="same_kind"):
     _check_stack_arrays(tup)
 
     arrs = dpnp.atleast_1d(*tup)
-    if not isinstance(arrs, list):
-        arrs = [arrs]
+    if not isinstance(arrs, tuple):
+        arrs = (arrs,)
 
     # As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
     if arrs and arrs[0].ndim == 1:
@@ -3555,8 +3555,8 @@ def split(ary, indices_or_sections, axis=0):
 
     Returns
     -------
-    sub-arrays : list of dpnp.ndarray
-        A list of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : tuple of dpnp.ndarray
+        A tuple of sub arrays. Each array is a view of the corresponding input
         array.
 
     Raises
@@ -3588,12 +3588,12 @@ def split(ary, indices_or_sections, axis=0):
     >>> import dpnp as np
     >>> x = np.arange(9.0)
     >>> np.split(x, 3)
-    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
+    (array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.]))
 
     >>> x = np.arange(8.0)
     >>> np.split(x, [3, 5, 6, 10])
-    [array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), \
-    array([])]
+    (array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), \
+    array([]))
 
     """
 
@@ -4551,8 +4551,8 @@ def vsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : list of dpnp.ndarray
-        A list of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : tuple of dpnp.ndarray
+        A tuple of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -4569,16 +4569,16 @@ def vsplit(ary, indices_or_sections):
            [ 8.,  9., 10., 11.],
            [12., 13., 14., 15.]])
     >>> np.vsplit(x, 2)
-    [array([[0., 1., 2., 3.],
+    (array([[0., 1., 2., 3.],
             [4., 5., 6., 7.]]),
      array([[ 8.,  9., 10., 11.],
-            [12., 13., 14., 15.]])]
+            [12., 13., 14., 15.]]))
     >>> np.vsplit(x, np.array([3, 6]))
-    [array([[ 0.,  1.,  2.,  3.],
+    (array([[ 0.,  1.,  2.,  3.],
             [ 4.,  5.,  6.,  7.],
             [ 8.,  9., 10., 11.]]),
      array([[12., 13., 14., 15.]]),
-     array([], shape=(0, 4), dtype=float64)]
+     array([], shape=(0, 4), dtype=float64))
 
     With a higher dimensional array the split is still along the first axis.
 
@@ -4589,10 +4589,10 @@ def vsplit(ary, indices_or_sections):
            [[4., 5.],
             [6., 7.]]])
     >>> np.vsplit(x, 2)
-    [array([[[0., 1.],
+    (array([[[0., 1.],
              [2., 3.]]]),
      array([[[4., 5.],
-             [6., 7.]]])]
+             [6., 7.]]]))
 
     """
 
@@ -4664,6 +4664,6 @@ def vstack(tup, *, dtype=None, casting="same_kind"):
     _check_stack_arrays(tup)
 
     arrs = dpnp.atleast_2d(*tup)
-    if not isinstance(arrs, list):
-        arrs = [arrs]
+    if not isinstance(arrs, tuple):
+        arrs = (arrs,)
     return dpnp.concatenate(arrs, axis=0, dtype=dtype, casting=casting)

From 47309d615d3d2a6842e0c80816f3e0aecaa34a3b Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 15:19:33 +0200
Subject: [PATCH 4/9] Update tests to improve coverage and check the result is
 a tuple for the affected functions

---
 dpnp/tests/test_arraycreation.py     | 22 ++++++++++++++--------
 dpnp/tests/test_arraymanipulation.py |  6 ++++++
 dpnp/tests/test_histogram.py         |  3 +++
 dpnp/tests/test_manipulation.py      |  3 ++-
 4 files changed, 25 insertions(+), 9 deletions(-)

diff --git a/dpnp/tests/test_arraycreation.py b/dpnp/tests/test_arraycreation.py
index 5a9a1c73c6e..d6e72672636 100644
--- a/dpnp/tests/test_arraycreation.py
+++ b/dpnp/tests/test_arraycreation.py
@@ -1050,13 +1050,19 @@ def test_meshgrid_raise_error():
         dpnp.meshgrid(b, indexing="ab")
 
 
-class TestMgrid:
+@pytest.mark.parametrize("grid", ["mgrid", "ogrid"])
+class TestGrid:
     def check_results(self, result, expected):
-        if isinstance(result, (list, tuple)):
+        # mgrid always returns a single array; ogrid returns a single array for
+        # a single slice and a tuple of arrays for multiple slices. In every
+        # case the container kind (tuple vs. bare array) must match NumPy.
+        if isinstance(expected, tuple):
+            assert isinstance(result, tuple)
             assert len(result) == len(expected)
             for dp_arr, np_arr in zip(result, expected):
                 assert_allclose(dp_arr, np_arr)
         else:
+            assert not isinstance(result, tuple)
             assert_allclose(result, expected)
 
     @pytest.mark.parametrize(
@@ -1069,9 +1075,9 @@ def check_results(self, result, expected):
             slice(0, 5, None),  # no step
         ],
     )
-    def test_single_slice(self, slice):
-        dpnp_result = dpnp.mgrid[slice]
-        numpy_result = numpy.mgrid[slice]
+    def test_single_slice(self, grid, slice):
+        dpnp_result = getattr(dpnp, grid)[slice]
+        numpy_result = getattr(numpy, grid)[slice]
         self.check_results(dpnp_result, numpy_result)
 
     @pytest.mark.parametrize(
@@ -1086,9 +1092,9 @@ def test_single_slice(self, slice):
             ),  # float start and complex step
         ],
     )
-    def test_md_slice(self, slices):
-        dpnp_result = dpnp.mgrid[slices]
-        numpy_result = numpy.mgrid[slices]
+    def test_md_slice(self, grid, slices):
+        dpnp_result = getattr(dpnp, grid)[slices]
+        numpy_result = getattr(numpy, grid)[slices]
         self.check_results(dpnp_result, numpy_result)
 
 
diff --git a/dpnp/tests/test_arraymanipulation.py b/dpnp/tests/test_arraymanipulation.py
index 25c454b9761..70ce4e440d1 100644
--- a/dpnp/tests/test_arraymanipulation.py
+++ b/dpnp/tests/test_arraymanipulation.py
@@ -78,6 +78,8 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_1d(a, b)
+        # multiple inputs are returned as a tuple of arrays
+        assert isinstance(res, tuple)
         desired = [dpnp.array([1, 2]), dpnp.array([2, 3])]
         assert_array_equal(res, desired)
 
@@ -117,6 +119,8 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_2d(a, b)
+        # multiple inputs are returned as a tuple of arrays
+        assert isinstance(res, tuple)
         desired = [dpnp.array([[1, 2]]), dpnp.array([[2, 3]])]
         assert_array_equal(res, desired)
 
@@ -156,6 +160,8 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_3d(a, b)
+        # multiple inputs are returned as a tuple of arrays
+        assert isinstance(res, tuple)
         desired = [dpnp.array([[[1], [2]]]), dpnp.array([[[2], [3]]])]
         assert_array_equal(res, desired)
 
diff --git a/dpnp/tests/test_histogram.py b/dpnp/tests/test_histogram.py
index b0988609d2c..9c915e10a24 100644
--- a/dpnp/tests/test_histogram.py
+++ b/dpnp/tests/test_histogram.py
@@ -738,6 +738,9 @@ def test_bins(self, bins):
         expected_hist, expected_edges = numpy.histogramdd(v, bins)
         result_hist, result_edges = dpnp.histogramdd(iv, bins_dpnp)
         assert_allclose(result_hist, expected_hist)
+
+        # bin edges are returned as a tuple of arrays
+        assert isinstance(result_edges, tuple)
         for x, y in zip(result_edges, expected_edges):
             assert_allclose(x, y)
 
diff --git a/dpnp/tests/test_manipulation.py b/dpnp/tests/test_manipulation.py
index d09f19f1d77..67099cc2c38 100644
--- a/dpnp/tests/test_manipulation.py
+++ b/dpnp/tests/test_manipulation.py
@@ -28,7 +28,8 @@
 
 
 def _compare_results(result, expected):
-    """Compare lists of arrays."""
+    """Compare tuples of arrays returned by the split family of functions."""
+    assert isinstance(result, tuple)
     if len(result) != len(expected):
         raise ValueError("Iterables have different lengths")
 

From 35e949cd1bd6876f40c5ec58791cdfdbf1bbe8f2 Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 15:21:13 +0200
Subject: [PATCH 5/9] Update changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 885cc73bab4..37340547f14 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -26,6 +26,7 @@ This release is compatible with NumPy 2.5.
 * Clarified support for negative axes in `dpnp.transpose`/`dpnp.permute_dims` documentation [#2940](https://github.com/IntelPython/dpnp/pull/2940)
 * Allowed `dpnp.take` and `dpnp.compress` to cast the result into an `out` array of a different but same-kind dtype [#2959](https://github.com/IntelPython/dpnp/pull/2959)
 * Clarified the summary in `dpnp.reshape` and `dpnp.ndarray.reshape` docstrings [#2964](https://github.com/IntelPython/dpnp/pull/2964)
+* Changed `dpnp.atleast_1d`, `dpnp.atleast_2d`, `dpnp.atleast_3d`, `dpnp.array_split`, `dpnp.split`, `dpnp.hsplit`, `dpnp.vsplit`, `dpnp.dsplit`, `dpnp.ogrid`, and `dpnp.histogramdd` to return a tuple of arrays instead of a list [#2965](https://github.com/IntelPython/dpnp/pull/2965)
 
 ### Deprecated
 

From fed9658d9101ee51a98c33efe791c0f0948f6569 Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 16:12:51 +0200
Subject: [PATCH 6/9] Reverting changes for histogramm and split functions,
 since latest NumPy still use list for the result from them

---
 dpnp/dpnp_iface_histograms.py   |  6 +--
 dpnp/dpnp_iface_manipulation.py | 66 ++++++++++++++++-----------------
 2 files changed, 36 insertions(+), 36 deletions(-)

diff --git a/dpnp/dpnp_iface_histograms.py b/dpnp/dpnp_iface_histograms.py
index bb1dd2291b0..b60ec12cc48 100644
--- a/dpnp/dpnp_iface_histograms.py
+++ b/dpnp/dpnp_iface_histograms.py
@@ -1151,8 +1151,8 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
     H : dpnp.ndarray
         The multidimensional histogram of sample x. See density and weights
         for the different possible semantics.
-    edges : tuple of dpnp.ndarray
-        A tuple of D arrays describing the bin edges for each dimension.
+    edges : list of dpnp.ndarray
+        A list of D arrays describing the bin edges for each dimension.
 
     See Also
     --------
@@ -1228,4 +1228,4 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
         if dpnp.is_supported_array_type(b):
             bin_edges_view_list[i] = b
 
-    return n, tuple(bin_edges_view_list)
+    return n, bin_edges_view_list
diff --git a/dpnp/dpnp_iface_manipulation.py b/dpnp/dpnp_iface_manipulation.py
index e613089b8ee..a8b2fc931de 100644
--- a/dpnp/dpnp_iface_manipulation.py
+++ b/dpnp/dpnp_iface_manipulation.py
@@ -612,8 +612,8 @@ def array_split(ary, indices_or_sections, axis=0):
 
     Returns
     -------
-    sub-arrays : tuple of dpnp.ndarray
-        A tuple of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : list of dpnp.ndarray
+        A list of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -625,11 +625,11 @@ def array_split(ary, indices_or_sections, axis=0):
     >>> import dpnp as np
     >>> x = np.arange(8.0)
     >>> np.array_split(x, 3)
-    (array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7.]))
+    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7.])]
 
     >>> x = np.arange(9)
     >>> np.array_split(x, 4)
-    (array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8]))
+    [array([0, 1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]
 
     """
 
@@ -662,7 +662,7 @@ def array_split(ary, indices_or_sections, axis=0):
         end = div_points[i + 1]
         sub_arys.append(dpnp.swapaxes(sary[st:end], axis, 0))
 
-    return tuple(sub_arys)
+    return sub_arys
 
 
 def asarray_chkfinite(
@@ -1653,8 +1653,8 @@ def dsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : tuple of dpnp.ndarray
-        A tuple of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : list of dpnp.ndarray
+        A list of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -1671,16 +1671,16 @@ def dsplit(ary, indices_or_sections):
            [[ 8.,  9., 10., 11.],
             [12., 13., 14., 15.]]])
     >>> np.dsplit(x, 2)
-    (array([[[ 0.,  1.],
+    [array([[[ 0.,  1.],
              [ 4.,  5.]],
             [[ 8.,  9.],
              [12., 13.]]]),
      array([[[ 2.,  3.],
              [ 6.,  7.]],
             [[10., 11.],
-             [14., 15.]]]))
+             [14., 15.]]])]
     >>> np.dsplit(x, np.array([3, 6]))
-    (array([[[ 0.,  1.,  2.],
+    [array([[[ 0.,  1.,  2.],
              [ 4.,  5.,  6.]],
             [[ 8.,  9., 10.],
              [12., 13., 14.]]]),
@@ -1688,7 +1688,7 @@ def dsplit(ary, indices_or_sections):
              [ 7.]],
             [[11.],
              [15.]]]),
-     array([]))
+     array([])]
 
     """
 
@@ -2052,8 +2052,8 @@ def hsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : tuple of dpnp.ndarray
-        A tuple of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : list of dpnp.ndarray
+        A list of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -2070,16 +2070,16 @@ def hsplit(ary, indices_or_sections):
            [ 8.,  9., 10., 11.],
            [12., 13., 14., 15.]])
     >>> np.hsplit(x, 2)
-    (array([[ 0.,  1.],
+    [array([[ 0.,  1.],
             [ 4.,  5.],
             [ 8.,  9.],
             [12., 13.]]),
      array([[ 2.,  3.],
             [ 6.,  7.],
             [10., 11.],
-            [14., 15.]]))
+            [14., 15.]])]
     >>> np.hsplit(x, np.array([3, 6]))
-    (array([[ 0.,  1.,  2.],
+    [array([[ 0.,  1.,  2.],
             [ 4.,  5.,  6.],
             [ 8.,  9., 10.],
             [12., 13., 14.]]),
@@ -2087,7 +2087,7 @@ def hsplit(ary, indices_or_sections):
             [ 7.],
             [11.],
             [15.]]),
-     array([]))
+     array([])]
 
     With a higher dimensional array the split is still along the second axis.
 
@@ -2098,16 +2098,16 @@ def hsplit(ary, indices_or_sections):
            [[4., 5.],
             [6., 7.]]])
     >>> np.hsplit(x, 2)
-    (array([[[0., 1.]],
+    [array([[[0., 1.]],
             [[4., 5.]]]),
      array([[[2., 3.]],
-            [[6., 7.]]]))
+            [[6., 7.]]])]
 
     With a 1-D array, the split is along axis 0.
 
     >>> x = np.array([0, 1, 2, 3, 4, 5])
     >>> np.hsplit(x, 2)
-    (array([0, 1, 2]), array([3, 4, 5]))
+    [array([0, 1, 2]), array([3, 4, 5])]
 
     """
 
@@ -3555,8 +3555,8 @@ def split(ary, indices_or_sections, axis=0):
 
     Returns
     -------
-    sub-arrays : tuple of dpnp.ndarray
-        A tuple of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : list of dpnp.ndarray
+        A list of sub arrays. Each array is a view of the corresponding input
         array.
 
     Raises
@@ -3588,12 +3588,12 @@ def split(ary, indices_or_sections, axis=0):
     >>> import dpnp as np
     >>> x = np.arange(9.0)
     >>> np.split(x, 3)
-    (array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.]))
+    [array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]
 
     >>> x = np.arange(8.0)
     >>> np.split(x, [3, 5, 6, 10])
-    (array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), \
-    array([]))
+    [array([0., 1., 2.]), array([3., 4.]), array([5.]), array([6., 7.]), \
+    array([])]
 
     """
 
@@ -4551,8 +4551,8 @@ def vsplit(ary, indices_or_sections):
 
     Returns
     -------
-    sub-arrays : tuple of dpnp.ndarray
-        A tuple of sub arrays. Each array is a view of the corresponding input
+    sub-arrays : list of dpnp.ndarray
+        A list of sub arrays. Each array is a view of the corresponding input
         array.
 
     See Also
@@ -4569,16 +4569,16 @@ def vsplit(ary, indices_or_sections):
            [ 8.,  9., 10., 11.],
            [12., 13., 14., 15.]])
     >>> np.vsplit(x, 2)
-    (array([[0., 1., 2., 3.],
+    [array([[0., 1., 2., 3.],
             [4., 5., 6., 7.]]),
      array([[ 8.,  9., 10., 11.],
-            [12., 13., 14., 15.]]))
+            [12., 13., 14., 15.]])]
     >>> np.vsplit(x, np.array([3, 6]))
-    (array([[ 0.,  1.,  2.,  3.],
+    [array([[ 0.,  1.,  2.,  3.],
             [ 4.,  5.,  6.,  7.],
             [ 8.,  9., 10., 11.]]),
      array([[12., 13., 14., 15.]]),
-     array([], shape=(0, 4), dtype=float64))
+     array([], shape=(0, 4), dtype=float64)]
 
     With a higher dimensional array the split is still along the first axis.
 
@@ -4589,10 +4589,10 @@ def vsplit(ary, indices_or_sections):
            [[4., 5.],
             [6., 7.]]])
     >>> np.vsplit(x, 2)
-    (array([[[0., 1.],
+    [array([[[0., 1.],
              [2., 3.]]]),
      array([[[4., 5.],
-             [6., 7.]]]))
+             [6., 7.]]])]
 
     """
 

From 00ae59bcee0f58810e8ccc6bce0499fc9256f85e Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 16:16:51 +0200
Subject: [PATCH 7/9] Update the check in tests to validate result type matches
 NumPy

---
 dpnp/tests/test_arraymanipulation.py | 20 +++++++++++---------
 dpnp/tests/test_histogram.py         |  4 ++--
 dpnp/tests/test_manipulation.py      |  5 +++--
 3 files changed, 16 insertions(+), 13 deletions(-)

diff --git a/dpnp/tests/test_arraymanipulation.py b/dpnp/tests/test_arraymanipulation.py
index 70ce4e440d1..d1587b9a50c 100644
--- a/dpnp/tests/test_arraymanipulation.py
+++ b/dpnp/tests/test_arraymanipulation.py
@@ -78,9 +78,9 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_1d(a, b)
-        # multiple inputs are returned as a tuple of arrays
-        assert isinstance(res, tuple)
-        desired = [dpnp.array([1, 2]), dpnp.array([2, 3])]
+        desired = numpy.atleast_1d(numpy.array([1, 2]), numpy.array([2, 3]))
+        # multiple inputs are returned as a tuple of arrays, matching NumPy
+        assert type(res) is type(desired) is tuple
         assert_array_equal(res, desired)
 
 
@@ -119,9 +119,9 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_2d(a, b)
-        # multiple inputs are returned as a tuple of arrays
-        assert isinstance(res, tuple)
-        desired = [dpnp.array([[1, 2]]), dpnp.array([[2, 3]])]
+        desired = numpy.atleast_2d(numpy.array([1, 2]), numpy.array([2, 3]))
+        # multiple inputs are returned as a tuple of arrays, matching NumPy
+        assert type(res) is type(desired) is tuple
         assert_array_equal(res, desired)
 
 
@@ -160,9 +160,9 @@ def test_dpnp_dpt_array(self):
         a = dpnp.array([1, 2])
         b = dpt.asarray([2, 3])
         res = dpnp.atleast_3d(a, b)
-        # multiple inputs are returned as a tuple of arrays
-        assert isinstance(res, tuple)
-        desired = [dpnp.array([[[1], [2]]]), dpnp.array([[[2], [3]]])]
+        desired = numpy.atleast_3d(numpy.array([1, 2]), numpy.array([2, 3]))
+        # multiple inputs are returned as a tuple of arrays, matching NumPy
+        assert type(res) is type(desired) is tuple
         assert_array_equal(res, desired)
 
 
@@ -1073,6 +1073,8 @@ def test_1d_array(self, dtype):
 
         np_res = numpy.unstack(np_a)
         dp_res = dpnp.unstack(dp_a)
+        # the result is a tuple of arrays, matching NumPy
+        assert type(dp_res) is type(np_res) is tuple
         assert len(dp_res) == len(np_res)
         for dp_arr, np_arr in zip(dp_res, np_res):
             assert_array_equal(dp_arr, np_arr)
diff --git a/dpnp/tests/test_histogram.py b/dpnp/tests/test_histogram.py
index 9c915e10a24..9fbf18bdc32 100644
--- a/dpnp/tests/test_histogram.py
+++ b/dpnp/tests/test_histogram.py
@@ -739,8 +739,8 @@ def test_bins(self, bins):
         result_hist, result_edges = dpnp.histogramdd(iv, bins_dpnp)
         assert_allclose(result_hist, expected_hist)
 
-        # bin edges are returned as a tuple of arrays
-        assert isinstance(result_edges, tuple)
+        assert isinstance(result_edges, list)
+        assert isinstance(expected_edges, list)
         for x, y in zip(result_edges, expected_edges):
             assert_allclose(x, y)
 
diff --git a/dpnp/tests/test_manipulation.py b/dpnp/tests/test_manipulation.py
index 67099cc2c38..9eedcfe275d 100644
--- a/dpnp/tests/test_manipulation.py
+++ b/dpnp/tests/test_manipulation.py
@@ -28,8 +28,9 @@
 
 
 def _compare_results(result, expected):
-    """Compare tuples of arrays returned by the split family of functions."""
-    assert isinstance(result, tuple)
+    """Compare lists of arrays returned by the split family of functions."""
+    # split/array_split/{h,v,d}split return a list of arrays
+    assert type(result) is type(expected) is list
     if len(result) != len(expected):
         raise ValueError("Iterables have different lengths")
 

From d98fa28ac0e552193d6bd5e6f95980586e71ed16 Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 16:20:26 +0200
Subject: [PATCH 8/9] Update tests for other functions returning a collection

---
 dpnp/tests/test_indexing.py | 5 +++++
 dpnp/tests/test_search.py   | 8 ++++++--
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/dpnp/tests/test_indexing.py b/dpnp/tests/test_indexing.py
index b7dc5f791b7..84bf62d0356 100644
--- a/dpnp/tests/test_indexing.py
+++ b/dpnp/tests/test_indexing.py
@@ -480,6 +480,7 @@ def test_ix(self, x0, x1):
         expected = dpnp.ix_(dpnp.array(x0), dpnp.array(x1))
         result = numpy.ix_(numpy.array(x0), numpy.array(x1))
 
+        assert type(expected) is type(result) is tuple
         assert_array_equal(result[0], expected[0])
         assert_array_equal(result[1], expected[1])
 
@@ -571,6 +572,7 @@ def test_1d(self, dtype):
 
         np_res = numpy.nonzero(a)
         dpnp_res = dpnp.nonzero(ia)
+        assert type(dpnp_res) is type(np_res) is tuple
         assert_array_equal(np_res, dpnp_res)
 
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
@@ -1295,6 +1297,7 @@ def test_putmask3(arr, mask, vals):
 def test_tril_indices(n, k, m):
     result = dpnp.tril_indices(n, k, m)
     expected = numpy.tril_indices(n, k, m)
+    assert type(result) is type(expected) is tuple
     assert_array_equal(expected, result)
 
 
@@ -1322,6 +1325,7 @@ def test_tril_indices_from(array, k):
 def test_triu_indices(n, k, m):
     result = dpnp.triu_indices(n, k, m)
     expected = numpy.triu_indices(n, k, m)
+    assert type(result) is type(expected) is tuple
     assert_array_equal(expected, result)
 
 
@@ -1451,6 +1455,7 @@ def test_basic(self):
 
         expected = numpy.unravel_index(x_np, (7, 6))
         result = dpnp.unravel_index(x_dp, (7, 6))
+        assert type(result) is type(expected) is tuple
         assert_equal(expected, result)
 
     def test_order_f(self):
diff --git a/dpnp/tests/test_search.py b/dpnp/tests/test_search.py
index 493c1edce2d..4c6e3fd6595 100644
--- a/dpnp/tests/test_search.py
+++ b/dpnp/tests/test_search.py
@@ -320,8 +320,12 @@ def test_empty_result(self):
         a = numpy.zeros((1, 1))
         ia = dpnp.array(a)
 
-        expected = numpy.vstack(numpy.where(a == 99.0))
-        result = dpnp.vstack(dpnp.where(ia == 99.0))
+        np_res = numpy.where(a == 99.0)
+        dp_res = dpnp.where(ia == 99.0)
+        assert type(dp_res) is type(np_res) is tuple
+
+        expected = numpy.vstack(np_res)
+        result = dpnp.vstack(dp_res)
         assert_array_equal(result, expected)
 
     @testing.with_requires("numpy>=2.5")

From 5378d28b875ca156208ca155650c776b12a4016e Mon Sep 17 00:00:00 2001
From: Anton Volkov <antonwolfy@gmail.com>
Date: Mon, 22 Jun 2026 16:25:30 +0200
Subject: [PATCH 9/9] Revert changes in CHANGELOG.md lost after the rebase

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 37340547f14..616f10e16b8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -26,7 +26,7 @@ This release is compatible with NumPy 2.5.
 * Clarified support for negative axes in `dpnp.transpose`/`dpnp.permute_dims` documentation [#2940](https://github.com/IntelPython/dpnp/pull/2940)
 * Allowed `dpnp.take` and `dpnp.compress` to cast the result into an `out` array of a different but same-kind dtype [#2959](https://github.com/IntelPython/dpnp/pull/2959)
 * Clarified the summary in `dpnp.reshape` and `dpnp.ndarray.reshape` docstrings [#2964](https://github.com/IntelPython/dpnp/pull/2964)
-* Changed `dpnp.atleast_1d`, `dpnp.atleast_2d`, `dpnp.atleast_3d`, `dpnp.array_split`, `dpnp.split`, `dpnp.hsplit`, `dpnp.vsplit`, `dpnp.dsplit`, `dpnp.ogrid`, and `dpnp.histogramdd` to return a tuple of arrays instead of a list [#2965](https://github.com/IntelPython/dpnp/pull/2965)
+* Changed `dpnp.atleast_1d`, `dpnp.atleast_2d`, `dpnp.atleast_3d`, and `dpnp.ogrid` to return a tuple of arrays instead of a list [#2965](https://github.com/IntelPython/dpnp/pull/2965)
 
 ### Deprecated