Add implementation of dpnp.matrix_transpose() and .mT attribute for dpnp.array (#2095)

* Add implementaion of dpnp.matrix_transpose()

* Add TestMatrixtranspose to dpnp tests

* Add .mT attribute for ndarray

* Add tests for .mT arrribute

* Add matrix_transpose() for dpnp.linalg module

* Add test for dpnp.linalg.matrix_transpose()

* Expand docs for .mT attribute

* Reuse dpctl for matrix_transpose

* Update dpnp.matrix_transpose()

---------

Co-authored-by: Anton <[email protected]>

Author: vlad-perevezentsev

doc/reference/linalg.rst

@@ -69,7 +69,6 @@ Norms and other numbers
    dpnp.trace
    dpnp.linalg.trace (Array API compatible)
 
-
 Solving linear equations
 --------------------------
 

doc/reference/ndarray.rst

@@ -92,6 +92,7 @@ Other attributes
    :nosignatures:
 
    dpnp.ndarray.T
+   dpnp.ndarray.mT
    dpnp.ndarray.real
    dpnp.ndarray.imag
    dpnp.ndarray.flat

dpnp/dpnp_array.py

@@ -108,6 +108,49 @@ def T(self):
         """View of the transposed array."""
         return self.transpose()
 
+    @property
+    def mT(self):
+        """
+        View of the matrix transposed array.
+
+        The matrix transpose is the transpose of the last two dimensions, even
+        if the array is of higher dimension.
+
+        Raises
+        ------
+        ValueError
+            If the array is of dimension less than 2.
+
+        Examples
+        --------
+        >>> import dpnp as np
+        >>> a = np.array([[1, 2], [3, 4]])
+        >>> a
+        array([[1, 2],
+               [3, 4]])
+        >>> a.mT
+        array([[1, 3],
+               [2, 4]])
+
+        >>> a = np.arange(8).reshape((2, 2, 2))
+        >>> a
+        array([[[0, 1],
+                [2, 3]],
+               [[4, 5],
+                [6, 7]]])
+        >>> a.mT
+        array([[[0, 2],
+                [1, 3]],
+               [[4, 6],
+                [5, 7]]])
+
+        """
+
+        if self.ndim < 2:
+            raise ValueError("matrix transpose with ndim < 2 is undefined")
+
+        return self._array_obj.mT
+
     def to_device(self, target_device):
         """Transfer array to target device."""
 

dpnp/dpnp_iface_manipulation.py

@@ -73,6 +73,7 @@
     "flipud",
     "hsplit",
     "hstack",
+    "matrix_transpose",
     "moveaxis",
     "ndim",
     "permute_dims",
@@ -1752,6 +1753,58 @@ def hstack(tup, *, dtype=None, casting="same_kind"):
     return dpnp.concatenate(arrs, axis=1, dtype=dtype, casting=casting)
 
 
+def matrix_transpose(x, /):
+    """
+    Transposes a matrix (or a stack of matrices) `x`.
+
+    For full documentation refer to :obj:`numpy.matrix_transpose`.
+
+    Parameters
+    ----------
+    x : (..., M, N) {dpnp.ndarray, usm_ndarray}
+        Input array with ``x.ndim >= 2`` and whose two innermost
+        dimensions form ``MxN`` matrices.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        An array containing the transpose for each matrix and having shape
+        (..., N, M).
+
+    See Also
+    --------
+    :obj:`dpnp.transpose` : Returns an array with axes transposed.
+    :obj:`dpnp.linalg.matrix_transpose` : Equivalent function.
+    :obj:`dpnp.ndarray.mT` : Equivalent method.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.matrix_transpose(a)
+    array([[1, 3],
+           [2, 4]])
+
+    >>> b = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+    >>> np.matrix_transpose(b)
+    array([[[1, 3],
+            [2, 4]],
+           [[5, 7],
+            [6, 8]]])
+
+    """
+
+    usm_x = dpnp.get_usm_ndarray(x)
+    if usm_x.ndim < 2:
+        raise ValueError(
+            "Input array must be at least 2-dimensional, "
+            f"but it is {usm_x.ndim}"
+        )
+
+    usm_res = dpt.matrix_transpose(usm_x)
+    return dpnp_array._create_from_usm_ndarray(usm_res)
+
+
 def moveaxis(a, source, destination):
     """
     Move axes of an array to new positions. Other axes remain in their original

dpnp/linalg/dpnp_iface_linalg.py

@@ -81,6 +81,7 @@
     "matrix_norm",
     "matrix_power",
     "matrix_rank",
+    "matrix_transpose",
     "multi_dot",
     "norm",
     "outer",
@@ -1117,6 +1118,50 @@ def matrix_rank(A, tol=None, hermitian=False):
     return dpnp_matrix_rank(A, tol=tol, hermitian=hermitian)
 
 
+def matrix_transpose(x, /):
+    """
+    Transposes a matrix (or a stack of matrices) `x`.
+
+    For full documentation refer to :obj:`numpy.linalg.matrix_transpose`.
+
+    Parameters
+    ----------
+    x : (..., M, N) {dpnp.ndarray, usm_ndarray}
+        Input array with ``x.ndim >= 2`` and whose two innermost
+        dimensions form ``MxN`` matrices.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        An array containing the transpose for each matrix and having shape
+        (..., N, M).
+
+    See Also
+    --------
+    :obj:`dpnp.transpose` : Returns an array with axes transposed.
+    :obj:`dpnp.matrix_transpose` : Equivalent function.
+    :obj:`dpnp.ndarray.mT` : Equivalent method.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> np.linalg.matrix_transpose(a)
+    array([[1, 3],
+           [2, 4]])
+
+    >>> b = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+    >>> np.linalg.matrix_transpose(b)
+    array([[[1, 3],
+            [2, 4]],
+           [[5, 7],
+            [6, 8]]])
+
+    """
+
+    return dpnp.matrix_transpose(x)
+
+
 def multi_dot(arrays, *, out=None):
     """
     Compute the dot product of two or more arrays in a single function call.

tests/test_arraymanipulation.py

@@ -556,6 +556,50 @@ def test_one_element(self):
         assert_array_equal(res, a)
 
 
+# numpy.matrix_transpose() is available since numpy >= 2.0
+@testing.with_requires("numpy>=2.0")
+class TestMatrixtranspose:
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
+    @pytest.mark.parametrize(
+        "shape",
+        [(3, 5), (4, 2), (2, 5, 2), (2, 3, 3, 6)],
+        ids=["(3,5)", "(4,2)", "(2,5,2)", "(2,3,3,6)"],
+    )
+    def test_matrix_transpose(self, dtype, shape):
+        a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape)
+        dp_a = dpnp.array(a)
+
+        expected = numpy.matrix_transpose(a)
+        result = dpnp.matrix_transpose(dp_a)
+
+        assert_allclose(result, expected)
+
+    @pytest.mark.parametrize(
+        "shape",
+        [(0, 0), (1, 0, 0), (0, 2, 2), (0, 1, 0, 4)],
+        ids=["(0,0)", "(1,0,0)", "(0,2,2)", "(0, 1, 0, 4)"],
+    )
+    def test_matrix_transpose_empty(self, shape):
+        a = numpy.empty(shape, dtype=dpnp.default_float_type())
+        dp_a = dpnp.array(a)
+
+        expected = numpy.matrix_transpose(a)
+        result = dpnp.matrix_transpose(dp_a)
+
+        assert_allclose(result, expected)
+
+    def test_matrix_transpose_errors(self):
+        a_dp = dpnp.array([[1, 2], [3, 4]], dtype="float32")
+
+        # unsupported type
+        a_np = dpnp.asnumpy(a_dp)
+        assert_raises(TypeError, dpnp.matrix_transpose, a_np)
+
+        # a.ndim < 2
+        a_dp_ndim_1 = a_dp.flatten()
+        assert_raises(ValueError, dpnp.matrix_transpose, a_dp_ndim_1)
+
+
 class TestRollaxis:
     data = [
         (0, 0),

tests/test_linalg.py

@@ -2124,6 +2124,25 @@ def test_matrix_rank_errors(self):
         )
 
 
+# numpy.linalg.matrix_transpose() is available since numpy >= 2.0
+@testing.with_requires("numpy>=2.0")
+# dpnp.linalg.matrix_transpose() calls dpnp.matrix_transpose()
+# 1 test to increase code coverage
+def test_matrix_transpose():
+    a = numpy.arange(6).reshape((2, 3))
+    a_dp = inp.array(a)
+
+    expected = numpy.linalg.matrix_transpose(a)
+    result = inp.linalg.matrix_transpose(a_dp)
+
+    assert_allclose(expected, result)
+
+    with assert_raises_regex(
+        ValueError, "array must be at least 2-dimensional"
+    ):
+        inp.linalg.matrix_transpose(a_dp[:, 0])
+
+
 class TestNorm:
     def setup_method(self):
         numpy.random.seed(42)

tests/test_ndarray.py

@@ -4,6 +4,7 @@
 from numpy.testing import assert_allclose, assert_array_equal
 
 import dpnp
+from tests.third_party.cupy import testing
 
 from .helper import (
     get_all_dtypes,
@@ -258,6 +259,38 @@ def test_array_as_index(shape, index_dtype):
     assert a[tuple(ind_arr)] == a[1]
 
 
+# numpy.ndarray.mT is available since numpy >= 2.0
+@testing.with_requires("numpy>=2.0")
+@pytest.mark.parametrize(
+    "shape",
+    [(3, 5), (2, 5, 2), (2, 3, 3, 6)],
+    ids=["(3,5)", "(2,5,2)", "(2,3,3,6)"],
+)
+def test_matrix_transpose(shape):
+    a = numpy.arange(numpy.prod(shape)).reshape(shape)
+    dp_a = dpnp.array(a)
+
+    expected = a.mT
+    result = dp_a.mT
+
+    assert_allclose(result, expected)
+
+    # result is a view of dp_a:
+    # changing result, modifies dp_a
+    first_elem = (0,) * dp_a.ndim
+
+    result[first_elem] = -1.0
+    assert dp_a[first_elem] == -1.0
+
+
+@testing.with_requires("numpy>=2.0")
+def test_matrix_transpose_error():
+    # 1D array
+    dp_a = dpnp.arange(6)
+    with pytest.raises(ValueError):
+        dp_a.mT
+
+
 def test_ravel():
     a = dpnp.ones((2, 2))
     b = a.ravel()