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Fix formatting and license #205

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7b64b0a
use the correct license for init_helper.py file
vtavana Jul 16, 2025
8fbe03a
use oneMKL instead OneMKL
vtavana Jul 16, 2025
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19 changes: 9 additions & 10 deletions README.md
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## `mkl_fft` -- a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality
[![Conda package](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package.yml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package.yml)
[![Editable build using pip and pre-release NumPy](https://github.com/IntelPython/mkl_fft/actions/workflows/build_pip.yaml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/build_pip.yaml)
[![Conda package with conda-forge channel only](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package-cf.yml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package-cf.yml)
[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/IntelPython/mkl_fft/badge)](https://securityscorecards.dev/viewer/?uri=github.com/IntelPython/mkl_fft)

## `mkl_fft` -- a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (oneMKL) Fourier Transform Functions

# Introduction
`mkl_fft` started as a part of Intel® Distribution for Python* optimizations to NumPy, and is now being released
as a stand-alone package. It offers a thin layered interface for the Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality that allows efficient access to native FFT optimizations from a range of NumPy and SciPy functions. As a result, its performance is close to the performance of native C/Intel® OneMKL. The optimizations are provided for real and complex data types in both single and double precisions for in-place and out-of-place modes of operation. For analyzing the performance use [FFT benchmarks](https://github.com/intelpython/fft_benchmark).
`mkl_fft` started as a part of [Intel® Distribution for Python*](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html) optimizations to NumPy, and is now being released as a stand-alone package.
It offers a thin layered python interface to the [Intel® oneAPI Math Kernel Library (oneMKL) Fourier Transform Functions](https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2025-2/fourier-transform-functions.html) that allows efficient access to computing a discrete Fourier transform through the fast Fourier transform (FFT) algorithm. As a result, its performance is close to the performance of native C/Intel® oneMKL. The optimizations are provided for real and complex data types in both single and double precisions for in-place and out-of-place modes of operation. For analyzing the performance use [FFT benchmarks](https://github.com/intelpython/fft_benchmark).

Thanks to Intel® OneMKL’s flexibility in its supports for arbitrarily strided input and output arrays both one-dimensional and multi-dimensional Fast Fourier Transforms along distinct axes can be performed directly, without the need to copy the input into a contiguous array first. Furthermore, input strides can be arbitrary, including negative or zero, as long as strides remain an integer multiple of array’s item size, otherwise a copy will be made.
Thanks to Intel® oneMKL’s flexibility in its supports for arbitrarily strided input and output arrays both one-dimensional and multi-dimensional FFTs along distinct axes can be performed directly, without the need to copy the input into a contiguous array first. Furthermore, input strides can be arbitrary, including negative or zero, as long as strides remain an integer multiple of array’s item size, otherwise a copy will be made.

More details can be found in ["Accelerating Scientific Python with Intel Optimizations"](https://proceedings.scipy.org/articles/shinma-7f4c6e7-00f) from Proceedings of the 16th Python in Science Conference (SciPy 2017).

Expand All @@ -26,9 +27,7 @@ or from conda-forge channel:
conda install -c conda-forge mkl_fft
```

---

To install `mkl_fft` PyPI package please use following command:
To install `mkl_fft` PyPI package please use the following command:

```
python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft
Expand All @@ -40,7 +39,7 @@ If command above installs NumPy package from the PyPI, please use following comm
python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft numpy==<numpy_version>
```

Where `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/
where `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/.

---
# How to use?
Expand Down Expand Up @@ -85,10 +84,10 @@ numpy.allclose(mkl_res, np_res)
---
# Building from source

To build `mkl_fft` from sources on Linux with Intel® OneMKL:
To build `mkl_fft` from sources on Linux with Intel® oneMKL:
- create a virtual environment: `python3 -m venv fft_env`
- activate the environment: `source fft_env/bin/activate`
- install a recent version of OneMKL, if necessary
- install a recent version of oneMKL, if necessary
- execute `source /path_to_oneapi/mkl/latest/env/vars.sh`
- `git clone https://github.com/IntelPython/mkl_fft.git mkl_fft`
- `cd mkl_fft`
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31 changes: 21 additions & 10 deletions mkl_fft/_init_helper.py
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# Copyright 2025 Intel Corporation
# Copyright (c) 2025, Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# http://www.apache.org/licenses/LICENSE-2.0
# * 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.
# * Neither the name of Intel Corporation nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# 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 OWNER 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.

import os
import os.path
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3 changes: 1 addition & 2 deletions mkl_fft/interfaces/README.md
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Expand Up @@ -101,7 +101,7 @@ print(f"Time with scipy.fft default backend: {t1:.1f} seconds")
with scipy.fft.set_backend(mkl_backend, only=True):
t2 = timeit.timeit(lambda: scipy.signal.fftconvolve(a, b), number=10)

print(f"Time with OneMKL FFT backend installed: {t2:.1f} seconds")
print(f"Time with oneMKL FFT backend installed: {t2:.1f} seconds")
# Time with MKL FFT backend installed: 9.1 seconds
```

Expand All @@ -110,7 +110,6 @@ In the following example, we use `set_worker` to control the number of threads w
```python
import numpy, mkl, scipy
import mkl_fft.interfaces.scipy_fft as mkl_fft
import scipy
a = numpy.random.randn(128, 64) + 1j*numpy.random.randn(128, 64)
scipy.fft.set_global_backend(mkl_fft) # set mkl_fft as global backend

Expand Down
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