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Merge branch 'master' into update_norm_FFT
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‎.github/workflows/check-mkl-interfaces.yaml‎

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- name: Merge conda env files
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run: |
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conda-merge ${{ env.build-with-oneapi-env }} ${{ env.dpctl-pkg-env }} ${{ env.oneapi-pkgs-env }} > ${{ env.environment-file }}
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conda-merge ${{ env.dpctl-pkg-env }} ${{ env.oneapi-pkgs-env }} ${{ env.build-with-oneapi-env }} > ${{ env.environment-file }}
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cat ${{ env.environment-file }}
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- name: Upload artifact

‎.github/workflows/conda-package.yml‎

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continue-on-error: true
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run: conda build --no-test --python ${{ matrix.python }} --numpy 2.0 ${{ env.channels-list }} conda-recipe
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env:
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MAX_BUILD_CMPL_MKL_VERSION: '2025.1a0'
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MAX_BUILD_CMPL_MKL_VERSION: '2025.2a0'
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- name: ReBuild conda package
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if: steps.build_conda_pkg.outcome == 'failure'
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run: conda build --no-test --python ${{ matrix.python }} --numpy 2.0 ${{ env.channels-list }} conda-recipe
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env:
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MAX_BUILD_CMPL_MKL_VERSION: '2025.1a0'
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MAX_BUILD_CMPL_MKL_VERSION: '2025.2a0'
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- name: Upload artifact
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uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2

‎CHANGELOG.md‎

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### Changed
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* Extended `dpnp.fft.fftfreq` and `dpnp.fft.rfftfreq` functions to support `dtype` keyword per Python Array API spec 2024.12 [#2384](https://github.com/IntelPython/dpnp/pull/2384)
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* Improved performance of `dpnp.nansum`, `dpnp.nanprod`, `dpnp.nancumsum`, and `dpnp.nancumprod` by reusing `dpnp.nan_to_num` function in implementation of the functions [#2339](https://github.com/IntelPython/dpnp/pull/2339)
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* Allowed input array of `uint64` dtype in `dpnp.bincount` [#2361](https://github.com/IntelPython/dpnp/pull/2361)
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* The vector norms `ord={None, 1, 2, inf}` and the matrix norms `ord={None, 1, 2, inf, "fro", "nuc"}` now consistently return zero for empty arrays, which are arrays with at least one axis of size zero. This change affects `dpnp.linalg.norm`, `dpnp.linalg.vector_norm`, and `dpnp.linalg.matrix_norm`. Previously, dpnp would either raise errors or return zero depending on the parameters provided [#2371](https://github.com/IntelPython/dpnp/pull/2371)
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* Improved performance of `dpnp.nansum`, `dpnp.nanprod`, `dpnp.nancumsum`, and `dpnp.nancumprod` by reusing `dpnp.nan_to_num` function in implementation of the functions [#2339](https://github.com/IntelPython/dpnp/pull/2339)
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* Extended `dpnp.fft.fftfreq` and `dpnp.fft.rfftfreq` functions to support `dtype` keyword per Python Array API spec 2024.12 [#2384](https://github.com/IntelPython/dpnp/pull/2384)
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### Fixed
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* Resolved an issue with an incorrect result returned due to missing dependency from the strided kernel on a copy event in `dpnp.erf` [#2378](https://github.com/IntelPython/dpnp/pull/2378)
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* Updated `conda create` commands build and install instructions of `Quick start guide` to avoid a compilation error [#2395](https://github.com/IntelPython/dpnp/pull/2395)
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## [0.17.0] - 02/26/2025

‎doc/quick_start_guide.rst‎

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.. code-block:: bash
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conda create -n build-env dpctl cython dpcpp_linux-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c https://software.repos.intel.com/python/conda/ -c conda-forge
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conda create -n build-env dpctl cython dpcpp_linux-64 mkl-devel-dpcpp tbb-devel \
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onedpl-devel cmake scikit-build ninja pytest intel-gpu-ocl-icd-system \
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-c dppy/label/dev -c https://software.repos.intel.com/python/conda/ -c conda-forge
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conda activate build-env
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On Windows:
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.. code-block:: bash
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conda create -n build-env dpctl cython dpcpp_win-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c https://software.repos.intel.com/python/conda/ -c conda-forge
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conda create -n build-env dpctl cython dpcpp_win-64 mkl-devel-dpcpp tbb-devel \
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onedpl-devel cmake scikit-build ninja pytest intel-gpu-ocl-icd-system \
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-c dppy/label/dev -c https://software.repos.intel.com/python/conda/ -c conda-forge
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conda activate build-env
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To build and install the package on Linux OS, run:

‎dpnp/dpnp_iface_types.py‎

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This module provides public type interface file for the library
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"""
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import functools
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import dpctl
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import dpctl.tensor as dpt
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import numpy
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import dpnp
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from .dpnp_array import dpnp_array
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# pylint: disable=no-name-in-module
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from .dpnp_utils import get_usm_allocations
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__all__ = [
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"bool",
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"bool_",
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"byte",
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"cdouble",
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"common_type",
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"complex128",
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"complex64",
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"complexfloating",
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pi = numpy.pi
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def common_type(*arrays):
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"""
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Return a scalar type which is common to the input arrays.
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The return type will always be an inexact (i.e. floating point or complex)
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scalar type, even if all the arrays are integer arrays.
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If one of the inputs is an integer array, the minimum precision type
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that is returned is the default floating point data type for the device
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where the input arrays are allocated.
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For full documentation refer to :obj:`numpy.common_type`.
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Parameters
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----------
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arrays: {dpnp.ndarray, usm_ndarray}
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Input arrays.
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Returns
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-------
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out: data type
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Data type object.
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See Also
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--------
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:obj:`dpnp.dtype` : Create a data type object.
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Examples
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--------
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>>> import dpnp as np
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>>> np.common_type(np.arange(2, dtype=np.float32))
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numpy.float32
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>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
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numpy.float64 # may vary
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>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
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numpy.complex128 # may vary
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"""
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if len(arrays) == 0:
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return (
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dpnp.float16
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if dpctl.select_default_device().has_aspect_fp16
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else dpnp.float32
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)
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dpnp.check_supported_arrays_type(*arrays)
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_, exec_q = get_usm_allocations(arrays)
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default_float_dtype = dpnp.default_float_type(sycl_queue=exec_q)
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dtypes = []
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for a in arrays:
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if not dpnp.issubdtype(a.dtype, dpnp.number):
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raise TypeError("can't get common type for non-numeric array")
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if dpnp.issubdtype(a.dtype, dpnp.integer):
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dtypes.append(default_float_dtype)
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else:
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dtypes.append(a.dtype)
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return functools.reduce(numpy.promote_types, dtypes).type
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# pylint: disable=redefined-outer-name
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def finfo(dtype):
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"""

‎dpnp/tests/helper.py‎

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return dev.backend == dpctl.backend_type.cuda
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def is_gpu_device(device=None):
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"""
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Return True if a test is running on GPU device, False otherwise.
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"""
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dev = dpctl.select_default_device() if device is None else device
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return dev.has_aspect_gpu
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def is_win_platform():
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"""
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Return True if a test is running on Windows OS, False otherwise.

‎dpnp/tests/skipped_tests.tbl‎

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tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)]
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tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpctl_memory
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tests/test_umath.py::test_umaths[('divmod', 'ii')]
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tests/test_umath.py::test_umaths[('divmod', 'll')]
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tests/test_umath.py::test_umaths[('divmod', 'ff')]
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tests/test_umath.py::test_umaths[('divmod', 'dd')]
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tests/test_umath.py::test_umaths[('frexp', 'f')]
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tests/test_umath.py::test_umaths[('frexp', 'd')]

‎dpnp/tests/skipped_tests_cuda.tbl‎

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tests/test_arithmetic.py::TestArithmetic::test_modf_part1
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tests/test_arithmetic.py::TestArithmetic::test_modf_part2
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tests/test_sycl_queue.py::test_modf[cuda:gpu:0]
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tests/test_umath.py::test_umaths[('modf', 'f')]
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tests/test_umath.py::test_umaths[('modf', 'd')]
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tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticModf::test_modf
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# random

‎dpnp/tests/skipped_tests_gpu.tbl‎

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tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]]
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tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)]
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tests/test_umath.py::test_umaths[('divmod', 'ii')]
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tests/test_umath.py::test_umaths[('divmod', 'll')]
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tests/test_umath.py::test_umaths[('divmod', 'ff')]
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tests/test_umath.py::test_umaths[('divmod', 'dd')]
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tests/test_umath.py::test_umaths[('floor_divide', 'ff')]
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tests/test_umath.py::test_umaths[('frexp', 'f')]
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tests/test_umath.py::test_umaths[('frexp', 'd')]
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tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpctl_memory

‎dpnp/tests/skipped_tests_gpu_no_fp64.tbl‎

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