Override populate_array for kernel targets

LICENSES.third-party

@@ -28,3 +28,30 @@ SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
 FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
 ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 DEALINGS IN THE SOFTWARE.
+
+numba
+------------------------
+Copyright (c) 2012, Anaconda, Inc.
+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.
+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.

numba_dpex/core/kernel_interface/arrayobj.py

@@ -0,0 +1,81 @@
+# SPDX-FileCopyrightText: 2012 - 2024 Anaconda Inc.
+# SPDX-FileCopyrightText: 2024 Intel Corporation
+#
+# SPDX-License-Identifier: BSD-2-Clause
+
+"""
+This package contains implementation of some numpy.np.arrayobj functions without
+parent and meminfo fields required, because they don't make sense on device.
+These functions intended to be used only in kernel targets like local/private or
+usm array view.
+"""
+
+
+from numba.core import cgutils, types
+
+from numba_dpex.core import types as dpex_types
+
+
+def populate_array(array, data, shape, strides, itemsize):
+    """
+    Helper function for populating array structures.
+    This avoids forgetting to set fields.
+
+    *shape* and *strides* can be Python tuples or LLVM arrays.
+
+    This is analog of numpy.np.arrayobj.populate_array without parent and
+    meminfo fields, because they don't make sense on device. This function
+    intended to be used only in kernel targets.
+    """
+    context = array._context
+    builder = array._builder
+    datamodel = array._datamodel
+    # doesn't matter what this array type instance is, it's just to get the
+    # fields for the datamodel of the standard array type in this context
+    standard_array = dpex_types.Array(types.float64, 1, "C")
+    standard_array_type_datamodel = context.data_model_manager[standard_array]
+    required_fields = set(standard_array_type_datamodel._fields)
+    datamodel_fields = set(datamodel._fields)
+    # Make sure that the presented array object has a data model that is close
+    # enough to an array for this function to proceed.
+    if (required_fields & datamodel_fields) != required_fields:
+        missing = required_fields - datamodel_fields
+        msg = (
+            f"The datamodel for type {array._fe_type} is missing "
+            f"field{'s' if len(missing) > 1 else ''} {missing}."
+        )
+        raise ValueError(msg)
+
+    intp_t = context.get_value_type(types.intp)
+    if isinstance(shape, (tuple, list)):
+        shape = cgutils.pack_array(builder, shape, intp_t)
+    if isinstance(strides, (tuple, list)):
+        strides = cgutils.pack_array(builder, strides, intp_t)
+    if isinstance(itemsize, int):
+        itemsize = intp_t(itemsize)
+
+    attrs = dict(
+        shape=shape,
+        strides=strides,
+        data=data,
+        itemsize=itemsize,
+    )
+
+    # Calc num of items from shape
+    nitems = context.get_constant(types.intp, 1)
+    unpacked_shape = cgutils.unpack_tuple(builder, shape, shape.type.count)
+    # (note empty shape => 0d array therefore nitems = 1)
+    for axlen in unpacked_shape:
+        nitems = builder.mul(nitems, axlen, flags=["nsw"])
+    attrs["nitems"] = nitems
+
+    # Make sure that we have all the fields
+    got_fields = set(attrs.keys())
+    if got_fields != required_fields:
+        raise ValueError("missing {0}".format(required_fields - got_fields))
+
+    # Set field value
+    for k, v in attrs.items():
+        setattr(array, k, v)
+
+    return array

numba_dpex/core/targets/kernel_target.py

@@ -435,6 +435,14 @@ def addrspacecast(self, builder, src, addrspace):
     def get_ufunc_info(self, ufunc_key):
         return self.ufunc_db[ufunc_key]
 
+    def populate_array(self, arr, **kwargs):
+        """
+        Populate array structure.
+        """
+        from numba_dpex.core.kernel_interface import arrayobj
+
+        return arrayobj.populate_array(arr, **kwargs)
+
 
 class DpexCallConv(MinimalCallConv):
     """Custom calling convention class used by numba-dpex.

numba_dpex/ocl/oclimpl.py

@@ -421,7 +421,6 @@ def _make_array(
         shape=cgutils.pack_array(builder, kshape),
         strides=cgutils.pack_array(builder, kstrides),
         itemsize=context.get_constant(types.intp, itemsize),
-        meminfo=None,
     )
 
     return ary._getvalue()