Extend ._tensor_impl with remaining functions used by dpnp

dpctl_ext/tensor/CMakeLists.txt

@@ -52,12 +52,12 @@ set(_tensor_impl_sources
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_for_reshape.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_for_roll.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/linear_sequences.cpp
-    # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/integer_advanced_indexing.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/integer_advanced_indexing.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/boolean_advanced_indexing.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/eye_ctor.cpp
-    # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/full_ctor.cpp
-    # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/zeros_ctor.cpp
-    # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/triul_ctor.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/full_ctor.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/zeros_ctor.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/triul_ctor.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/where.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/device_support_queries.cpp
     # ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/repeat.cpp

dpctl_ext/tensor/__init__.py

@@ -25,3 +25,22 @@
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 # THE POSSIBILITY OF SUCH DAMAGE.
 # *****************************************************************************
+
+
+from dpctl_ext.tensor._ctors import (
+    full,
+    tril,
+    triu,
+)
+from dpctl_ext.tensor._indexing_functions import (
+    put,
+    take,
+)
+
+__all__ = [
+    "full",
+    "put",
+    "take",
+    "tril",
+    "triu",
+]

dpctl_ext/tensor/_ctors.py

@@ -0,0 +1,326 @@
+# *****************************************************************************
+# 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.
+# *****************************************************************************
+
+import operator
+from numbers import Number
+
+import dpctl
+import dpctl.tensor as dpt
+import dpctl.utils
+import numpy as np
+from dpctl.tensor._data_types import _get_dtype
+from dpctl.tensor._device import normalize_queue_device
+
+import dpctl_ext.tensor._tensor_impl as ti
+
+
+def _cast_fill_val(fill_val, dt):
+    """
+    Casts the Python scalar `fill_val` to another Python type coercible to the
+    requested data type `dt`, if necessary.
+    """
+    val_type = type(fill_val)
+    if val_type in [float, complex] and np.issubdtype(dt, np.integer):
+        return int(fill_val.real)
+    elif val_type is complex and np.issubdtype(dt, np.floating):
+        return fill_val.real
+    elif val_type is int and np.issubdtype(dt, np.integer):
+        return _to_scalar(fill_val, dt)
+    else:
+        return fill_val
+
+
+def _to_scalar(obj, sc_ty):
+    """A way to convert object to NumPy scalar type.
+    Raises OverflowError if obj can not be represented
+    using the requested scalar type.
+    """
+    zd_arr = np.asarray(obj, dtype=sc_ty)
+    return zd_arr[()]
+
+
+def _validate_fill_value(fill_val):
+    """Validates that `fill_val` is a numeric or boolean scalar."""
+    # TODO: verify if `np.True_` and `np.False_` should be instances of
+    # Number in NumPy, like other NumPy scalars and like Python bools
+    # check for `np.bool_` separately as NumPy<2 has no `np.bool`
+    if not isinstance(fill_val, Number) and not isinstance(fill_val, np.bool_):
+        raise TypeError(
+            f"array cannot be filled with scalar of type {type(fill_val)}"
+        )
+
+
+def full(
+    shape,
+    fill_value,
+    *,
+    dtype=None,
+    order="C",
+    device=None,
+    usm_type=None,
+    sycl_queue=None,
+):
+    """
+    Returns a new :class:`dpctl.tensor.usm_ndarray` having a specified
+    shape and filled with `fill_value`.
+
+    Args:
+        shape (tuple):
+            Dimensions of the array to be created.
+        fill_value (int,float,complex,usm_ndarray):
+            fill value
+        dtype (optional): data type of the array. Can be typestring,
+            a :class:`numpy.dtype` object, :mod:`numpy` char string,
+            or a NumPy scalar type. Default: ``None``
+        order ("C", or "F"):
+            memory layout for the array. Default: ``"C"``
+        device (optional): array API concept of device where the output array
+            is created. ``device`` can be ``None``, a oneAPI filter selector
+            string, an instance of :class:`dpctl.SyclDevice` corresponding to
+            a non-partitioned SYCL device, an instance of
+            :class:`dpctl.SyclQueue`, or a :class:`dpctl.tensor.Device` object
+            returned by :attr:`dpctl.tensor.usm_ndarray.device`.
+            Default: ``None``
+        usm_type (``"device"``, ``"shared"``, ``"host"``, optional):
+            The type of SYCL USM allocation for the output array.
+            Default: ``"device"``
+        sycl_queue (:class:`dpctl.SyclQueue`, optional):
+            The SYCL queue to use
+            for output array allocation and copying. ``sycl_queue`` and
+            ``device`` are complementary arguments, i.e. use one or another.
+            If both are specified, a :exc:`TypeError` is raised unless both
+            imply the same underlying SYCL queue to be used. If both are
+            ``None``, a cached queue targeting default-selected device is
+            used for allocation and population. Default: ``None``
+
+    Returns:
+        usm_ndarray:
+            New array initialized with given value.
+    """
+    if not isinstance(order, str) or len(order) == 0 or order[0] not in "CcFf":
+        raise ValueError(
+            "Unrecognized order keyword value, expecting 'F' or 'C'."
+        )
+    order = order[0].upper()
+    dpctl.utils.validate_usm_type(usm_type, allow_none=True)
+
+    if isinstance(fill_value, (dpt.usm_ndarray, np.ndarray, tuple, list)):
+        if (
+            isinstance(fill_value, dpt.usm_ndarray)
+            and sycl_queue is None
+            and device is None
+        ):
+            sycl_queue = fill_value.sycl_queue
+        else:
+            sycl_queue = normalize_queue_device(
+                sycl_queue=sycl_queue, device=device
+            )
+        X = dpt.asarray(
+            fill_value,
+            dtype=dtype,
+            order=order,
+            usm_type=usm_type,
+            sycl_queue=sycl_queue,
+        )
+        return dpt.copy(dpt.broadcast_to(X, shape), order=order)
+    else:
+        _validate_fill_value(fill_value)
+
+    sycl_queue = normalize_queue_device(sycl_queue=sycl_queue, device=device)
+    usm_type = usm_type if usm_type is not None else "device"
+    dtype = _get_dtype(dtype, sycl_queue, ref_type=type(fill_value))
+    res = dpt.usm_ndarray(
+        shape,
+        dtype=dtype,
+        buffer=usm_type,
+        order=order,
+        buffer_ctor_kwargs={"queue": sycl_queue},
+    )
+    fill_value = _cast_fill_val(fill_value, dtype)
+
+    _manager = dpctl.utils.SequentialOrderManager[sycl_queue]
+    # populating new allocation, no dependent events
+    hev, full_ev = ti._full_usm_ndarray(fill_value, res, sycl_queue)
+    _manager.add_event_pair(hev, full_ev)
+    return res
+
+
+def tril(x, /, *, k=0):
+    """
+    Returns the lower triangular part of a matrix (or a stack of matrices)
+    ``x``.
+
+    The lower triangular part of the matrix is defined as the elements on and
+    below the specified diagonal ``k``.
+
+    Args:
+        x (usm_ndarray):
+            Input array
+        k (int, optional):
+            Specifies the diagonal above which to set
+            elements to zero. If ``k = 0``, the diagonal is the main diagonal.
+            If ``k < 0``, the diagonal is below the main diagonal.
+            If ``k > 0``, the diagonal is above the main diagonal.
+            Default: ``0``
+
+    Returns:
+        usm_ndarray:
+            A lower-triangular array or a stack of lower-triangular arrays.
+    """
+    if not isinstance(x, dpt.usm_ndarray):
+        raise TypeError(
+            "Expected argument of type dpctl.tensor.usm_ndarray, "
+            f"got {type(x)}."
+        )
+
+    k = operator.index(k)
+
+    order = "F" if (x.flags.f_contiguous) else "C"
+
+    shape = x.shape
+    nd = x.ndim
+    if nd < 2:
+        raise ValueError("Array dimensions less than 2.")
+
+    q = x.sycl_queue
+    if k >= shape[nd - 1] - 1:
+        res = dpt.empty(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+        _manager = dpctl.utils.SequentialOrderManager[q]
+        dep_evs = _manager.submitted_events
+        hev, cpy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
+            src=x, dst=res, sycl_queue=q, depends=dep_evs
+        )
+        _manager.add_event_pair(hev, cpy_ev)
+    elif k < -shape[nd - 2]:
+        res = dpt.zeros(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+    else:
+        res = dpt.empty(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+        _manager = dpctl.utils.SequentialOrderManager[q]
+        dep_evs = _manager.submitted_events
+        hev, tril_ev = ti._tril(
+            src=x, dst=res, k=k, sycl_queue=q, depends=dep_evs
+        )
+        _manager.add_event_pair(hev, tril_ev)
+
+    return res
+
+
+def triu(x, /, *, k=0):
+    """
+    Returns the upper triangular part of a matrix (or a stack of matrices)
+    ``x``.
+
+    The upper triangular part of the matrix is defined as the elements on and
+    above the specified diagonal ``k``.
+
+    Args:
+        x (usm_ndarray):
+            Input array
+        k (int, optional):
+            Specifies the diagonal below which to set
+            elements to zero. If ``k = 0``, the diagonal is the main diagonal.
+            If ``k < 0``, the diagonal is below the main diagonal.
+            If ``k > 0``, the diagonal is above the main diagonal.
+            Default: ``0``
+
+    Returns:
+        usm_ndarray:
+            An upper-triangular array or a stack of upper-triangular arrays.
+    """
+    if not isinstance(x, dpt.usm_ndarray):
+        raise TypeError(
+            "Expected argument of type dpctl.tensor.usm_ndarray, "
+            f"got {type(x)}."
+        )
+
+    k = operator.index(k)
+
+    order = "F" if (x.flags.f_contiguous) else "C"
+
+    shape = x.shape
+    nd = x.ndim
+    if nd < 2:
+        raise ValueError("Array dimensions less than 2.")
+
+    q = x.sycl_queue
+    if k > shape[nd - 1]:
+        res = dpt.zeros(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+    elif k <= -shape[nd - 2] + 1:
+        res = dpt.empty(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+        _manager = dpctl.utils.SequentialOrderManager[q]
+        dep_evs = _manager.submitted_events
+        hev, cpy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
+            src=x, dst=res, sycl_queue=q, depends=dep_evs
+        )
+        _manager.add_event_pair(hev, cpy_ev)
+    else:
+        res = dpt.empty(
+            x.shape,
+            dtype=x.dtype,
+            order=order,
+            usm_type=x.usm_type,
+            sycl_queue=q,
+        )
+        _manager = dpctl.utils.SequentialOrderManager[q]
+        dep_evs = _manager.submitted_events
+        hev, triu_ev = ti._triu(
+            src=x, dst=res, k=k, sycl_queue=q, depends=dep_evs
+        )
+        _manager.add_event_pair(hev, triu_ev)
+
+    return res