Add dpnp.tensor.broadcast_shapes to align with the 2025.12 Python array API

CHANGELOG.md

@@ -16,6 +16,7 @@ This release is compatible with NumPy 2.5.
 * Added `--includes` and `--include-dir` options to the `dpnp` CLI [#2916](https://github.com/IntelPython/dpnp/pull/2916)
 * Added `dpnp-config.cmake` to make `find_package(Dpnp)` work out of the box, and an example which uses it [#2941](https://github.com/IntelPython/dpnp/pull/2941)
 * Added implementation of `dpnp.lib.stride_tricks.as_strided` [#2991](https://github.com/IntelPython/dpnp/pull/2991)
+* Added `dpnp.tensor.broadcast_shapes` to align with the 2025.12 version of the Python array API [#3009](https://github.com/IntelPython/dpnp/pull/3009)
 
 ### Changed
 

dpnp/dpnp_iface_manipulation.py

@@ -1126,7 +1126,25 @@ def broadcast_shapes(*args):
 
     """
 
-    return numpy.broadcast_shapes(*args)
+    shapes = []
+    for sh in args:
+        # a bare integer is treated as a one-dimensional shape
+        if not isinstance(sh, (tuple, list)):
+            sh = (sh,)
+
+        new_sh = []
+        for dim in sh:
+            if isinstance(dim, bool):
+                raise TypeError(
+                    "'bool' object cannot be interpreted as an integer"
+                )
+
+            dim = operator.index(dim)
+            if dim < 0:
+                raise ValueError("negative dimensions are not allowed")
+            new_sh.append(dim)
+        shapes.append(tuple(new_sh))
+    return dpt.broadcast_shapes(*shapes)
 
 
 # pylint: disable=redefined-outer-name

dpnp/tensor/__init__.py

@@ -174,6 +174,7 @@
 )
 from ._manipulation_functions import (
     broadcast_arrays,
+    broadcast_shapes,
     broadcast_to,
     concat,
     expand_dims,
@@ -282,6 +283,7 @@
     "bitwise_right_shift",
     "bitwise_xor",
     "broadcast_arrays",
+    "broadcast_shapes",
     "broadcast_to",
     "can_cast",
     "cbrt",

dpnp/tensor/_manipulation_functions.py

@@ -248,6 +248,32 @@ def broadcast_arrays(*args):
     return [broadcast_to(X, shape) for X in args]
 
 
+def broadcast_shapes(*shapes):
+    """broadcast_shapes(*shapes)
+
+    Broadcasts one or more shapes against one another.
+
+    Args:
+        shapes (Tuple[int, ...]): an arbitrary number of shapes to be
+            broadcasted against one another. Each shape must be a tuple of
+            integers.
+
+    Returns:
+        Tuple[int, ...]:
+            The broadcasted shape resulting from broadcasting the input
+            shapes against one another.
+
+    Raises:
+        TypeError: if a shape contains a non-integer dimension.
+        ValueError: if the input shapes are not broadcast-compatible.
+    """
+    if len(shapes) == 0:
+        return ()
+
+    normalized = [tuple(map(operator.index, sh)) for sh in shapes]
+    return _broadcast_shape_impl(normalized)
+
+
 def broadcast_to(X, /, shape):
     """broadcast_to(x, shape)
 

dpnp/tests/tensor/test_usm_ndarray_manipulation.py

@@ -469,6 +469,42 @@ def test_broadcast_arrays_no_args():
         dpt.broadcast_arrays()
 
 
+@pytest.mark.parametrize(
+    "shapes",
+    [
+        [(1,), (3,)],
+        [(1, 3), (3, 3)],
+        [(3, 1), (3, 3)],
+        [(1, 3), (3, 1)],
+        [(6, 7), (5, 6, 1), (7,), (5, 1, 7)],
+        [(1, 2), (3, 1), (3, 2)],
+        [(1, 0), (0, 1)],
+        [()],
+        [(5,)],
+    ],
+)
+def test_broadcast_shapes(shapes):
+    expected = np.broadcast_shapes(*shapes)
+    result = dpt.broadcast_shapes(*shapes)
+    assert result == expected
+
+
+def test_broadcast_shapes_no_args():
+    # matches numpy.broadcast_shapes() returning an empty shape
+    assert dpt.broadcast_shapes() == ()
+
+
+def test_broadcast_shapes_raises():
+    with pytest.raises(ValueError):
+        dpt.broadcast_shapes((2, 3), (4, 5))
+
+
+@pytest.mark.parametrize("shapes", [[(2.0,)], [(1.5,), (2,)], [(2,), (3.0, 1)]])
+def test_broadcast_shapes_non_integer_dim(shapes):
+    with pytest.raises(TypeError):
+        dpt.broadcast_shapes(*shapes)
+
+
 def test_flip_axis_incorrect():
     q = get_queue_or_skip()
 

dpnp/tests/test_manipulation.py

@@ -308,7 +308,7 @@ def test_no_copy(self):
         assert_array_equal(b, a)
 
 
-class TestBroadcast:
+class TestBroadcastShapes:
     @pytest.mark.parametrize(
         "shape",
         [
@@ -332,6 +332,31 @@ def test_broadcast_shapes(self, shape):
         result = dpnp.broadcast_shapes(*shape)
         assert_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "shape",
+        [
+            [1, 2],
+            [(3, 1), 3],
+            [1, (5, 1), 5],
+        ],
+    )
+    def test_scalar(self, shape):
+        expected = numpy.broadcast_shapes(*shape)
+        result = dpnp.broadcast_shapes(*shape)
+        assert_equal(result, expected)
+
+    @pytest.mark.parametrize("xp", [dpnp, numpy])
+    @pytest.mark.parametrize("shape", [[(-1,)], [(2, -3), (2, 3)], [-1, 2]])
+    def test_negative_dim(self, xp, shape):
+        with pytest.raises(ValueError, match="negative dimensions"):
+            xp.broadcast_shapes(*shape)
+
+    @pytest.mark.parametrize("xp", [dpnp, numpy])
+    @pytest.mark.parametrize("shape", [[(2.0,)], [(True, 2)], [2.5]])
+    def test_non_integer_dim(self, xp, shape):
+        with pytest.raises(TypeError, match="integer"):
+            xp.broadcast_shapes(*shape)
+
 
 class TestCopyTo:
     testdata = []