task: add broadcast class implementation

CHANGELOG.md

@@ -7,6 +7,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [0.21.0] - MM/DD/2026
 
 ### Added
+* Added `dpnp.broadcast` class implementation [#2901](https://github.com/IntelPython/dpnp/pull/2901)
 
 ### Changed
 

dpnp/__init__.py

@@ -304,6 +304,7 @@
     unravel_index,
 )
 from .dpnp_flatiter import flatiter
+from .dpnp_broadcast import broadcast
 
 # -----------------------------------------------------------------------------
 # Linear algebra
@@ -691,6 +692,7 @@
     "atleast_1d",
     "atleast_2d",
     "atleast_3d",
+    "broadcast",
     "broadcast_arrays",
     "broadcast_to",
     "column_stack",

dpnp/dpnp_broadcast.py

@@ -0,0 +1,183 @@
+# *****************************************************************************
+# Copyright (c) 2026, Intel Corporation
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# - Redistributions of source code must retain the above copyright notice,
+#   this list of conditions and the following disclaimer.
+# - Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+# - Neither the name of the copyright holder nor the names of its contributors
+#   may be used to endorse or promote products derived from this software
+#   without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+"""Implementation of broadcast class."""
+
+import dpnp
+import dpnp.tensor as dpt
+from dpnp.tensor._manipulation_functions import _broadcast_shapes
+
+
+class broadcast:
+    """
+    Produce an object that mimics broadcasting.
+
+    For full documentation refer to :obj:`numpy.broadcast`.
+
+    Parameters
+    ----------
+    *args : object
+        Input parameters. Every argument must define ``shape`` attribute.
+
+    Returns
+    -------
+    broadcast : broadcast object
+        Broadcast the input parameters against one another, and
+        return an object that encapsulates the result.
+        Amongst others, it has ``shape`` and ``nd`` properties, and
+        may be used as an iterator.
+
+    See Also
+    --------
+    :obj:`dpnp.broadcast_arrays` : Broadcast any number of arrays against
+        each other.
+    :obj:`dpnp.broadcast_to` : Broadcast an array to a new shape.
+    :obj:`dpnp.broadcast_shapes` : Broadcast the input shapes into a single
+        shape.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> x = np.array([[1], [2], [3]])
+    >>> y = np.array([4, 5, 6])
+    >>> b = np.broadcast(x, y)
+    >>> b.shape
+    (3, 3)
+    >>> b.nd
+    2
+    >>> b.size
+    9
+
+    Notes
+    -----
+    Iterator functionality is not supported.
+
+    """
+
+    def __init__(self, *args):
+        for i, arg in enumerate(args):
+            if not hasattr(arg, "shape"):
+                raise TypeError(
+                    f"Argument at position {i} must define shape attribute"
+                )
+
+        self._arrays = tuple(args)
+
+        dpnp_arrays = [arg for arg in self._arrays if isinstance(arg, dpnp.ndarray)]
+        if len(dpnp_arrays) > 1:
+            exec_q = dpt.get_execution_queue(
+                tuple(array.sycl_queue for array in dpnp_arrays)
+            )
+            if exec_q is None:
+                raise dpt.ExecutionPlacementError(
+                    "Execution placement can not be unambiguously inferred "
+                    "from input arguments."
+                )
+
+        if len(self._arrays) == 0:
+            self._shape = ()
+            self._size = 1
+            self._nd = 0
+            return
+
+        # Compute the broadcasted shape using _broadcast_shapes
+        self._shape = _broadcast_shapes(*self._arrays)
+
+        # Calculate size and ndim
+        self._size = 1
+        for dim in self._shape:
+            self._size *= dim
+        self._nd = len(self._shape)
+
+    @property
+    def shape(self):
+        """
+        Shape of the broadcasted result.
+
+        Returns
+        -------
+        out : tuple
+            A tuple containing the shape of the broadcasted result.
+
+        """
+        return self._shape
+
+    @property
+    def size(self):
+        """
+        Total size of the broadcasted result.
+
+        Returns
+        -------
+        out : int
+            The total size (number of elements) of the broadcasted result.
+
+        """
+        return self._size
+
+    @property
+    def nd(self):
+        """
+        Number of dimensions of the broadcasted result.
+
+        Returns
+        -------
+        out : int
+            The number of dimensions of the broadcasted result.
+
+        """
+        return self._nd
+
+    @property
+    def ndim(self):
+        """
+        Number of dimensions of the broadcasted result.
+
+        Returns
+        -------
+        out : int
+            The number of dimensions of the broadcasted result.
+
+        """
+        return self._nd
+
+    @property
+    def numiter(self):
+        """
+        Number of iterators possessed by the broadcast object.
+
+        Returns
+        -------
+        out : int
+            The number of iterators.
+
+        """
+        return len(self._arrays)
+
+    def __repr__(self):
+        return f"<broadcast shape={self.shape}, nd={self.nd}, size={self.size}>"