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Merge pull request IntelPython#1428 from IntelPython/changelog/0.23
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Changelog for 0.23 223df44
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github-actions[bot] committed Apr 4, 2024
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2 changes: 1 addition & 1 deletion dev/.buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: 0ddf752fefb2c1b5c368616a1552a600
config: 0d9537beee7f5571dedc6534f308f4ed
tags: 645f666f9bcd5a90fca523b33c5a78b7
2 changes: 1 addition & 1 deletion dev/_sources/autoapi/numba_dpex/core/boxing/index.rst.txt
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.. py:class:: USMNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL)
.. py:class:: USMNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL.value)
Bases: :py:obj:`numba.core.types.npytypes.Array`

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42 changes: 3 additions & 39 deletions dev/_sources/autoapi/numba_dpex/core/index.rst.txt
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boxing/index.rst
datamodel/index.rst
kernel_interface/index.rst
overloads/index.rst
parfors/index.rst
passes/index.rst
pipelines/index.rst
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:widths: auto
:class: summarytable

* - :py:obj:`Array <numba_dpex.core.Array>`
- An array type for use inside our compiler pipeline.
* - :py:obj:`DpctlSyclQueue <numba_dpex.core.DpctlSyclQueue>`
- A Numba type to represent a dpctl.SyclQueue PyObject.
* - :py:obj:`DpctlSyclEvent <numba_dpex.core.DpctlSyclEvent>`
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Classes
-------

.. py:class:: Array(dtype, ndim, layout, readonly=False, name=None, aligned=True, addrspace=None)
Bases: :py:obj:`Array`

An array type for use inside our compiler pipeline.


.. rubric:: Overview


.. list-table:: Methods
:header-rows: 0
:widths: auto
:class: summarytable

* - :py:obj:`copy <numba_dpex.core.Array.copy>`\ (dtype, ndim, layout, readonly, addrspace)
- \-
* - :py:obj:`is_precise <numba_dpex.core.Array.is_precise>`\ ()
- Whether this type is precise, i.e. can be part of a successful


.. rubric:: Members

.. py:method:: copy(dtype=None, ndim=None, layout=None, readonly=None, addrspace=None)
.. py:method:: is_precise()
Whether this type is precise, i.e. can be part of a successful
type inference. Default implementation returns True.




.. py:class:: DpctlSyclQueue(sycl_queue)
Bases: :py:obj:`numba.types.Type`
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.. py:class:: DpnpNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL)
.. py:class:: DpnpNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL.value)
Bases: :py:obj:`numba_dpex.core.types.usm_ndarray_type.USMNdArray`

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.. py:class:: USMNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL)
.. py:class:: USMNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL.value)
Bases: :py:obj:`numba.core.types.npytypes.Array`

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7 changes: 7 additions & 0 deletions dev/_sources/autoapi/numba_dpex/core/overloads/index.rst.txt
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numba_dpex.core.overloads
=========================

.. py:module:: numba_dpex.core.overloads
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:orphan:

numba_dpex.core.overloads.ranges_overloads
==========================================

.. py:module:: numba_dpex.core.overloads.ranges_overloads
Attributes
----------
.. py:data:: DPEX_TARGET_NAME
:value: 'dpex'





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Classes
-------

.. py:class:: ParforKernel(name, kernel, signature, kernel_args, kernel_arg_types, queue: dpctl.SyclQueue)
.. py:class:: ParforKernel(name, kernel, signature, kernel_args, kernel_arg_types, queue: dpctl.SyclQueue, local_accessors=None, work_group_size=None)
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Classes
-------

.. py:class:: TreeReduceIntermediateKernelTemplate(kernel_name, kernel_params, ivar_names, sentinel_name, loop_ranges, param_dict, parfor_dim, redvars, parfor_args, parfor_reddict, redvars_dict, typemap, work_group_size)
.. py:class:: TreeReduceIntermediateKernelTemplate(kernel_name, kernel_params, ivar_names, sentinel_name, loop_ranges, param_dict, parfor_dim, redvars, parfor_args, parfor_reddict, redvars_dict, local_accessors_dict, typemap)
Bases: :py:obj:`numba_dpex.core.parfors.kernel_templates.kernel_template_iface.KernelTemplateInterface`

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:orphan:

numba_dpex.core.parfors.parfor_diagnostics
==========================================

.. py:module:: numba_dpex.core.parfors.parfor_diagnostics
Overview
--------
.. list-table:: Classes
:header-rows: 0
:widths: auto
:class: summarytable

* - :py:obj:`ExtendedParforDiagnostics <numba_dpex.core.parfors.parfor_diagnostics.ExtendedParforDiagnostics>`
- Holds parfor diagnostic info, this is accumulated throughout the




Classes
-------

.. py:class:: ExtendedParforDiagnostics
Bases: :py:obj:`numba.parfors.parfor.ParforDiagnostics`

Holds parfor diagnostic info, this is accumulated throughout the
PreParforPass and ParforPass, also in the closure inlining!


.. rubric:: Overview


.. list-table:: Methods
:header-rows: 0
:widths: auto
:class: summarytable

* - :py:obj:`dump <numba_dpex.core.parfors.parfor_diagnostics.ExtendedParforDiagnostics.dump>`\ (level)
- \-
* - :py:obj:`print_auto_offloading <numba_dpex.core.parfors.parfor_diagnostics.ExtendedParforDiagnostics.print_auto_offloading>`\ (lines)
- \-


.. rubric:: Members

.. py:method:: dump(level=1)
.. py:method:: print_auto_offloading(lines)
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Functions
---------
.. py:function:: create_reduction_main_kernel_for_parfor(loop_ranges, parfor_node, typemap, flags, has_aliases, reductionKernelVar, parfor_reddict=None)
.. py:function:: create_reduction_main_kernel_for_parfor(loop_ranges, parfor_node, typemap, flags, has_aliases, reductionKernelVar: numba_dpex.core.parfors.reduction_helper.ReductionKernelVariables, parfor_reddict=None)
Creates a numba_dpex.kernel function for reduction main kernel.

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Classes
-------

.. py:class:: DpnpNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL)
.. py:class:: DpnpNdArray(ndim, layout='C', dtype=None, usm_type='device', device=None, queue=None, readonly=False, name=None, aligned=True, addrspace=address_space.GLOBAL.value)
Bases: :py:obj:`numba_dpex.core.types.usm_ndarray_type.USMNdArray`

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