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Merge master to gold/2021 (#1441)
* Add a copy for strided arrays in dpnp.dot() * update versions * Rework transpose methods to use dpctl.tensor functions (#1389) * Rework transpose methods to call dpctl.tensor functions * Applied review comments & added more tests * Obtain a proper result type on device without fp64 (#1429) * Obtain a proper result type on device without fp64 * remove excess defenitions * Reuse dpctl.tensor.reshape (#1391) * Rework transpose methods to call dpctl.tensor functions * Reuse dpctl.tensor.reshape * added dpnp.shape() and unmuted more tests * fixed compiling issue & unmuted reshaper tests with order param * Resolve merge issues * resolve type mismatch on Win * Use MCG59 engine on GPU device (#1423) * Use MCG59 engine on GPU device * Fix issue for Windows * reduce precision in tests * move w/a before import dpctl * Reuse add(), multiply() and subtract() from dpctl (#1430) * Reuse add(), multiply() and subtract() from dpctl * add in-place support * Reuse dpctl.tensor.sum for dpnp.sum (#1426) * Reuse dpctl.tensor.sun for dpnp.sum * Update tests for dpnp.sum * Fix remarks * Update tests/third_party/cupy/testing/helper.py --------- Co-authored-by: Anton <[email protected]> * Workaround to Klocwork (#1433) * Add inplace support of divide (#1434) * Add dpnp.result_type() support (#1435) * Add dpnp.result_type() support * Update dpnp/dpnp_iface_manipulation.py Co-authored-by: Natalia Polina <[email protected]> --------- Co-authored-by: Natalia Polina <[email protected]> * Implementation of dpnp.mean() (#1431) * Reuse dpctl.tensor.sun for dpnp.sum * Update tests for dpnp.sum * Fix remarks * Implementation of dpnp.mean * Update logic for dpnp.mean function * add normalize_axis_tuple * Additional tests for dpnp.mean * Fix minor remarks * Add inplace support of divide * Use inplace divide only for dpnp.inexact types * Update tests for dpnp.mean * Skip test_sample.py::TestRandint2::test_bound_float1 * Remove unused import * Update dtype check * Update dpnp/dpnp_iface_statistics.py * Return deleted skips --------- Co-authored-by: Anton Volkov <[email protected]> Co-authored-by: Anton <[email protected]> * New implementation of dpnp.outer (#1436) * Add a new implementation of dpnp.outer * Update dpnp.outer implementation --------- Co-authored-by: Anton <[email protected]> * Set dpnp dependency on dpctl>=0.14.3 (#1437) * Implement dpnp.cov() though existing dpnp methods (#1396) * Implement dpnp.cov() though existing dpnp methods * Applied review comments * Clean up the code to get rid of todo * use dpnp.mean() * Added ChangeLog.md (#1439) * Added ChangeLog.md * exclude PR for comprasion ops * Update version to 0.12.0 (#1440) --------- Co-authored-by: vlad-perevezentsev <[email protected]> Co-authored-by: Natalia Polina <[email protected]>
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‎.github/workflows/conda-package.yml

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@@ -196,8 +196,6 @@ jobs:
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run: |
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python -m pytest -q -ra --disable-warnings -vv ${{ env.TEST_SCOPE }}
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working-directory: ${{ env.tests-path }}
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env:
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SYCL_QUEUE_THREAD_POOL_SIZE: 6
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test_windows:
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name: Test ['windows-latest', python='${{ matrix.python }}']
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run: |
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python -m pytest -q -ra --disable-warnings -vv ${{ env.TEST_SCOPE }}
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working-directory: ${{ env.tests-path }}
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env:
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SYCL_QUEUE_THREAD_POOL_SIZE: 6
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upload:
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name: Upload ['${{ matrix.os }}', python='${{ matrix.python }}']

‎.github/workflows/generate_coverage.yaml

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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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COVERALLS_PARALLEL: true
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SYCL_QUEUE_THREAD_POOL_SIZE: 6
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coveralls:
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name: Indicate completion to coveralls.io

‎CHANGELOG.md

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# Changelog
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All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.12.0] - 06/15/2023
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### Added
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* Implemented `dpnp.broadcast_to` function [#1333](https://github.com/IntelPython/dpnp/pull/1333)
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* Implemented `dpnp.extract` function [#1340](https://github.com/IntelPython/dpnp/pull/1340)
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* Implemented `dpnp.linalg.eigh` function through pybind11 extension of OneMKL call [#1383](https://github.com/IntelPython/dpnp/pull/1383)
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* Implemented `dpnp.mean` function [#1431](https://github.com/IntelPython/dpnp/pull/1431)
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* Added support of bool types in bitwise operations [#1334](https://github.com/IntelPython/dpnp/pull/1334)
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* Added `out` parameter in `dpnp.add` function [#1329](https://github.com/IntelPython/dpnp/pull/1329)
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* Added `out` parameter in `dpnp.multiply` function [#1365](https://github.com/IntelPython/dpnp/pull/1365)
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* Added `out` parameter in `dpnp.sqrt` function [#1332](https://github.com/IntelPython/dpnp/pull/1332)
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* Added `rowvar` parameter in `dpnp.cov` function [#1371](https://github.com/IntelPython/dpnp/pull/1371)
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* Added `nbytes` property to dpnp array [#1359](https://github.com/IntelPython/dpnp/pull/1359)
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* Introduced a new github Action to control code coverage [#1373](https://github.com/IntelPython/dpnp/pull/1373)
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* Added change log [#1439](https://github.com/IntelPython/dpnp/pull/1439)
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### Changed
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* Leveraged `dpctl.tensor` implementation for `dpnp.place` function [#1337](https://github.com/IntelPython/dpnp/pull/1337)
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* Leveraged `dpctl.tensor` implementation for `dpnp.moveaxis` function [#1382](https://github.com/IntelPython/dpnp/pull/1382)
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* Leveraged `dpctl.tensor` implementation for `dpnp.squeeze` function [#1381](https://github.com/IntelPython/dpnp/pull/1381)
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* Leveraged `dpctl.tensor` implementation for `dpnp.where` function [#1380](https://github.com/IntelPython/dpnp/pull/1380)
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* Leveraged `dpctl.tensor` implementation for `dpnp.transpose` function [#1389](https://github.com/IntelPython/dpnp/pull/1389)
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* Leveraged `dpctl.tensor` implementation for `dpnp.reshape` function [#1391](https://github.com/IntelPython/dpnp/pull/1391)
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* Leveraged `dpctl.tensor` implementation for `dpnp.add`, `dpnp.multiply` and `dpnp.subtract` functions [#1430](https://github.com/IntelPython/dpnp/pull/1430)
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* Leveraged `dpctl.tensor` implementation for `dpnp.sum` function [#1426](https://github.com/IntelPython/dpnp/pull/1426)
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* Leveraged `dpctl.tensor` implementation for `dpnp.result_type` function [#1435](https://github.com/IntelPython/dpnp/pull/1435)
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* Reused OneDPL `std::nth_element` function in `dpnp.partition` with 1d array [#1406](https://github.com/IntelPython/dpnp/pull/1406)
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* Transitioned dpnp build system to use scikit-build [#1349](https://github.com/IntelPython/dpnp/pull/1349)
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* Renamed included dpnp_algo_*.pyx files to *.pxi [#1356](https://github.com/IntelPython/dpnp/pull/1356)
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* Implemented support of key as a tuple in `dpnp.__getitem__()` and `dpnp.__setitem__()` functions [#1362](https://github.com/IntelPython/dpnp/pull/1362)
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* Selected dpnp own kernels for elementwise functions instead of OneMKL VM calls on a device without fp64 aspect [#1386](https://github.com/IntelPython/dpnp/pull/1386)
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* Pinned to `sysroot>=2.28` and transitioned to `conda-forge` channel [#1408](https://github.com/IntelPython/dpnp/pull/1408)
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* Redesigned `dpnp.divide` implementation to call `div` from OneMKL for C-contiguous data or to use `dpctl.tensor` library otherwise [#1418](https://github.com/IntelPython/dpnp/pull/1418)
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* Changed an engine used for random generated array on GPU device from MT19937 to MCG59 [#1423](https://github.com/IntelPython/dpnp/pull/1423)
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* Implemented in-place support of `dpnp.divide` [#1434](https://github.com/IntelPython/dpnp/pull/1434)
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* Redesigned `dpnp.outer` implementation through `dpnp.multiply` with broadcasted arrays [#1436](https://github.com/IntelPython/dpnp/pull/1436)
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* Pinned to `dpctl>=0.14.3` as host and run dependencies [#1437](https://github.com/IntelPython/dpnp/pull/1437)
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* Reimplemented `dpnp.cov` through existing dpnp function instead of a separate kernel [#1396](https://github.com/IntelPython/dpnp/pull/1396)
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### Fixed
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* Fixed `dpnp.asarray` function to accept a sequence of dpnp arrays [#1355](https://github.com/IntelPython/dpnp/pull/1355)
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* Fixed crash in `dpnp.sum` with an empty array [#1369](https://github.com/IntelPython/dpnp/pull/1369)
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* Fixed compilation error around `sycl::abs` with DPC++ 2023.2.0 [#1393](https://github.com/IntelPython/dpnp/pull/1393)
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* Fixed Klockwork run and enabled cmake verbose mode for conda build [#1433](https://github.com/IntelPython/dpnp/pull/1433)

‎conda-recipe/bld.bat

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%PYTHON% -m dpctl --cmakedir > Output
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set /p DPCTL_CMAKE_DIR= < Output
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set "SKBUILD_ARGS=-G Ninja -- -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx"
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set "SKBUILD_ARGS=-G Ninja -- -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON"
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set "SKBUILD_ARGS=%SKBUILD_ARGS% -DDPCTL_MODULE_PATH:PATH=%DPCTL_CMAKE_DIR% "
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set "SKBUILD_ARGS=%SKBUILD_ARGS% -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON"
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‎conda-recipe/build.sh

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#!/bin/bash
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# Workaround to Klocwork overwriting LD_LIBRARY_PATH that was modified
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# by DPC++ compiler conda packages. Will need to be added to DPC++ compiler
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# activation scripts.
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export LDFLAGS="$LDFLAGS -Wl,-rpath,$PREFIX/lib"
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export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PREFIX/compiler/lib/intel64_lin:$PREFIX/compiler/lib:$PREFIX/lib"
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# Intel LLVM must cooperate with compiler and sysroot from conda
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echo "--gcc-toolchain=${BUILD_PREFIX} --sysroot=${BUILD_PREFIX}/${HOST}/sysroot -target ${HOST}" > icpx_for_conda.cfg
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export ICPXCFG="$(pwd)/icpx_for_conda.cfg"
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export DPL_ROOT_HINT=$PREFIX
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SKBUILD_ARGS="-- -DDPCTL_MODULE_PATH=$($PYTHON -m dpctl --cmakedir) "
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SKBUILD_ARGS="${SKBUILD_ARGS} -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx"
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SKBUILD_ARGS="${SKBUILD_ARGS} -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON"
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SKBUILD_ARGS="${SKBUILD_ARGS} -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON"
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# Build wheel package

‎conda-recipe/meta.yaml

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- cmake >=3.21
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- ninja
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- git
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- dpctl >=0.14.2
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- dpctl >=0.14.3
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- mkl-devel-dpcpp {{ environ.get('MKL_VER', '>=2023.1.0') }}
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- onedpl-devel
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- tbb-devel
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- sysroot_linux-64 >=2.28 # [linux]
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run:
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- python
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- dpctl >=0.14.2
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- dpctl >=0.14.3
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- {{ pin_compatible('dpcpp-cpp-rt', min_pin='x.x', max_pin='x') }}
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- {{ pin_compatible('mkl-dpcpp', min_pin='x.x', max_pin='x') }}
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- {{ pin_compatible('numpy', min_pin='x.x', max_pin='x') }}

‎doc/conf.py

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author = 'Intel'
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# The short X.Y version
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version = '0.11'
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version = '0.12'
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# The full version, including alpha/beta/rc tags
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release = '0.11.2dev1'
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release = '0.12.0'
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# -- General configuration ---------------------------------------------------

‎dpnp/__init__.py

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import os
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mypath = os.path.dirname(os.path.realpath(__file__))
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# workaround against hanging in OneMKL calls and in DPCTL
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os.environ.setdefault('SYCL_QUEUE_THREAD_POOL_SIZE', '6')
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import dpctl
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os.add_dll_directory(dpctlpath)
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os.environ["PATH"] = os.pathsep.join([os.getenv("PATH", ""), mypath, dpctlpath])
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# workaround against hanging in OneMKL calls
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from dpnp.dpnp_array import dpnp_array as ndarray
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‎dpnp/backend/doc/Doxyfile

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# could be handy for archiving the generated documentation or if some version
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# control system is used.
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PROJECT_NUMBER = 0.11.2dev1
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PROJECT_NUMBER = 0.12.0
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# Using the PROJECT_BRIEF tag one can provide an optional one line description
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# for a project that appears at the top of each page and should give viewer a

‎dpnp/backend/include/dpnp_iface_fptr.hpp

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DPNP_FN_COUNT_NONZERO, /**< Used in numpy.count_nonzero() impl */
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DPNP_FN_COUNT_NONZERO_EXT, /**< Used in numpy.count_nonzero() impl, requires extra parameters */
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DPNP_FN_COV, /**< Used in numpy.cov() impl */
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DPNP_FN_COV_EXT, /**< Used in numpy.cov() impl, requires extra parameters */
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DPNP_FN_CROSS, /**< Used in numpy.cross() impl */
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DPNP_FN_CROSS_EXT, /**< Used in numpy.cross() impl, requires extra parameters */
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DPNP_FN_CUMPROD, /**< Used in numpy.cumprod() impl */
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DPNP_FN_MAXIMUM, /**< Used in numpy.maximum() impl */
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DPNP_FN_MAXIMUM_EXT, /**< Used in numpy.maximum() impl , requires extra parameters */
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DPNP_FN_MEAN, /**< Used in numpy.mean() impl */
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DPNP_FN_MEAN_EXT, /**< Used in numpy.mean() impl, requires extra parameters */
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DPNP_FN_MEDIAN, /**< Used in numpy.median() impl */
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DPNP_FN_MIN, /**< Used in numpy.min() impl */
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DPNP_FN_TANH, /**< Used in numpy.tanh() impl */
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DPNP_FN_TANH_EXT, /**< Used in numpy.tanh() impl, requires extra parameters */
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DPNP_FN_TRANSPOSE, /**< Used in numpy.transpose() impl */
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DPNP_FN_TRANSPOSE_EXT, /**< Used in numpy.transpose() impl, requires extra parameters */
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DPNP_FN_TRACE, /**< Used in numpy.trace() impl */
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DPNP_FN_TRACE_EXT, /**< Used in numpy.trace() impl, requires extra parameters */
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DPNP_FN_TRAPZ, /**< Used in numpy.trapz() impl */

‎dpnp/backend/kernels/dpnp_krnl_manipulation.cpp

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//*****************************************************************************
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// Copyright (c) 2016-2020, Intel Corporation
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// Copyright (c) 2016-2023, Intel Corporation
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// All rights reserved.
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//
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// Redistribution and use in source and binary forms, with or without
@@ -211,6 +211,7 @@ void dpnp_elemwise_transpose_c(void* array1_in,
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size,
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dep_event_vec_ref);
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DPCTLEvent_WaitAndThrow(event_ref);
214+
DPCTLEvent_Delete(event_ref);
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}
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template <typename _DataType>
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void*,
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size_t) = dpnp_elemwise_transpose_c<_DataType>;
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template <typename _DataType>
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DPCTLSyclEventRef (*dpnp_elemwise_transpose_ext_c)(DPCTLSyclQueueRef,
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void*,
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const shape_elem_type*,
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const shape_elem_type*,
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const shape_elem_type*,
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size_t,
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void*,
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size_t,
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const DPCTLEventVectorRef) = dpnp_elemwise_transpose_c<_DataType>;
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void func_map_init_manipulation(func_map_t& fmap)
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{
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fmap[DPNPFuncName::DPNP_FN_REPEAT][eft_INT][eft_INT] = {eft_INT, (void*)dpnp_repeat_default_c<int32_t>};
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(void*)dpnp_elemwise_transpose_default_c<float>};
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fmap[DPNPFuncName::DPNP_FN_TRANSPOSE][eft_DBL][eft_DBL] = {eft_DBL,
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(void*)dpnp_elemwise_transpose_default_c<double>};
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fmap[DPNPFuncName::DPNP_FN_TRANSPOSE_EXT][eft_INT][eft_INT] = {eft_INT,
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(void*)dpnp_elemwise_transpose_ext_c<int32_t>};
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fmap[DPNPFuncName::DPNP_FN_TRANSPOSE_EXT][eft_LNG][eft_LNG] = {eft_LNG,
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(void*)dpnp_elemwise_transpose_ext_c<int64_t>};
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fmap[DPNPFuncName::DPNP_FN_TRANSPOSE_EXT][eft_FLT][eft_FLT] = {eft_FLT,
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(void*)dpnp_elemwise_transpose_ext_c<float>};
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fmap[DPNPFuncName::DPNP_FN_TRANSPOSE_EXT][eft_DBL][eft_DBL] = {eft_DBL,
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(void*)dpnp_elemwise_transpose_ext_c<double>};
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return;
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}

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