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Merge branch 'master' into impl_histogram
2 parents ebe1f18 + 859aa9a commit ddd622c

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‎doc/dpctl.rst

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.. include:: ./ext_links.txt
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Interplay with the Data Parallel Control Library
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===============================================
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================================================
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`Data Parallel Control Library`_ provides API to manage specific
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`SYCL*`_ resources for SYCL-based Python packages.

‎doc/index.rst

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==================================
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.. module:: dpnp
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:noindex:
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.. toctree::
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:maxdepth: 2

‎doc/quick_start_guide.rst

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Python Interpreter
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=================
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==================
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You will need Python 3.8, 3.9, or 3.10 installed on your system. If you
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do not have one yet the easiest way to do that is to install
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============
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Install Package from Anaconda
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---------------------
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-----------------------------
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It is recommended to use conda packages from the ``anaconda.org/intel``
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channel. You will need one of the commands below:

‎doc/reference/creation.rst

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Building matrices
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---------------
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-----------------
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.. autosummary::
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:toctree: generated/

‎doc/reference/dtypes_table.rst

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Available array data types
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==========================
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.. _Data types:
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Available array data types
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==========================
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Table below shows a list of all supported data types (dtypes) and constants of the Data Parallel Extension for NumPy*.
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‎doc/reference/linalg.rst

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dpnp.linalg.eigvalsh
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Norms and other numbers
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----------
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-----------------------
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.. autosummary::
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:toctree: generated/

‎doc/reference/ndarray.rst

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:func:`diagonal <dpnp.diagonal>`, :func:`imag <dpnp.imag>`,
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:func:`max <dpnp.max>`, :func:`mean <dpnp.mean>`, :func:`min <dpnp.min>`,
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:func:`nonzero <dpnp.nonzero>`, :func:`partition <dpnp.partition>`,
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:func:`prod <dpnp.prod>`, :func:`ptp <dpnp.ptp>`, :func:`put <dpnp.put>`,
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:func:`prod <dpnp.prod>`, :func:`put <dpnp.put>`,
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:func:`ravel <dpnp.ravel>`, :func:`real <dpnp.real>`, :func:`repeat <dpnp.repeat>`,
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:func:`reshape <dpnp.reshape>`, :func:`round <dpnp.around>`,
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:func:`searchsorted <dpnp.searchsorted>`, :func:`sort <dpnp.sort>`,
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dpnp.ndarray.argmax
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dpnp.ndarray.min
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dpnp.ndarray.argmin
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dpnp.ndarray.ptp
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dpnp.ndarray.clip
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dpnp.ndarray.conj
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dpnp.ndarray.conjugate

‎doc/reference/statistics.rst

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Averages and variances
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-------------------
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----------------------
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.. autosummary::
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:toctree: generated/

‎dpnp/backend/CMakeLists.txt

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# endif()
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target_link_options(${_trgt} PUBLIC -fsycl-device-code-split=per_kernel)
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if(UNIX)
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# this option is support on Linux only
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target_link_options(${_trgt} PUBLIC -flink-huge-device-code)
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endif()
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if(DPNP_GENERATE_COVERAGE)
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target_link_options(${_trgt} PRIVATE -fprofile-instr-generate -fcoverage-mapping)

‎dpnp/backend/extensions/blas/CMakeLists.txt

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endif()
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target_link_options(${python_module_name} PUBLIC -fsycl-device-code-split=per_kernel)
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if (UNIX)
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# this option is support on Linux only
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target_link_options(${python_module_name} PUBLIC -flink-huge-device-code)
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endif()
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if (DPNP_GENERATE_COVERAGE)
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target_link_options(${python_module_name} PRIVATE -fprofile-instr-generate -fcoverage-mapping)

‎dpnp/backend/extensions/lapack/CMakeLists.txt

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endif()
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target_link_options(${python_module_name} PUBLIC -fsycl-device-code-split=per_kernel)
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if (UNIX)
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# this option is support on Linux only
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target_link_options(${python_module_name} PUBLIC -flink-huge-device-code)
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endif()
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if (DPNP_GENERATE_COVERAGE)
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target_link_options(${python_module_name} PRIVATE -fprofile-instr-generate -fcoverage-mapping)

‎dpnp/backend/extensions/sycl_ext/CMakeLists.txt

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endif()
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target_link_options(${python_module_name} PUBLIC -fsycl-device-code-split=per_kernel)
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if (UNIX)
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# this option is support on Linux only
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target_link_options(${python_module_name} PUBLIC -flink-huge-device-code)
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endif()
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if (DPNP_GENERATE_COVERAGE)
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target_link_options(${python_module_name} PRIVATE -fprofile-instr-generate -fcoverage-mapping)

‎dpnp/backend/extensions/vm/CMakeLists.txt

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target_link_options(${python_module_name} PUBLIC -fsycl-device-code-split=per_kernel)
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if (UNIX)
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# this option is support on Linux only
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target_link_options(${python_module_name} PUBLIC -flink-huge-device-code)
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endif()
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if (DPNP_GENERATE_COVERAGE)
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target_link_options(${python_module_name} PRIVATE -fprofile-instr-generate -fcoverage-mapping)

‎dpnp/dpnp_array.py

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Otherwise, a copy is returned.
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casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
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Controls what kind of data casting may occur. Defaults to 'unsafe' for backwards compatibility.
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'no' means the data types should not be cast at all.
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'equiv' means only byte-order changes are allowed.
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'safe' means only casts which can preserve values are allowed.
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'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed.
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'unsafe' means any data conversions may be done.
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- 'no' means the data types should not be cast at all.
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- 'equiv' means only byte-order changes are allowed.
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- 'safe' means only casts which can preserve values are allowed.
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- 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed.
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- 'unsafe' means any data conversions may be done.
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By default, astype always returns a newly allocated array. If this is set to false, and the dtype,
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order, and subok requirements are satisfied, the input array is returned instead of a copy.

‎dpnp/dpnp_iface.py

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casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
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for backwards compatibility.
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- 'no' means the data types should not be cast at all.
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- 'equiv' means only byte-order changes are allowed.
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- 'safe' means only casts which can preserve values are allowed.
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- 'same_kind' means only safe casts or casts within a kind, like
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float64 to float32, are allowed.
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float64 to float32, are allowed.
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is set to ``False``, and the dtype, order, and subok requirements

‎dpnp/dpnp_iface_types.py

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-------
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An object have the following attributes
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number of bits occupied by dtype.
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* dtype: dtype
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An object with the following attributes
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number of bits occupied by the data type
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* dtype: dtype

‎dpnp/linalg/dpnp_iface_linalg.py

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array([ True])
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Inverses of several matrices can be computed at once:
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>>> np.linalg.inv(a)
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array([[[-2. , 1. ],
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[ 1.5 , -0.5 ]],
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[[-1.25, 0.75],
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[[-1.25, 0.75],
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[ 0.75, -0.25]]])
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"""

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