initial version of fft backend for scipy 1.4

Author: oleksandr-pavlyk

conda-recipe/meta.yaml

@@ -1,4 +1,4 @@
-{% set version = "1.0dev" %}
+{% set version = "1.1.0" %}
 {% set buildnumber = 0 %}
 
 
@@ -19,6 +19,7 @@ build:
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/_pydfti.*
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/_numpy_fft.py
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/_scipy_fft.py
+      - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/_scipy_fft_backend.py
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/setup.py
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/tests/test_fft1d.py
       - {{ SP_DIR.replace('\\', '/') if win else SP_DIR }}/mkl_fft/__init__.pyc         [py27]
@@ -36,12 +37,13 @@ requirements:
       - python
       - setuptools
       - intelpython
-      - mkl-devel               [not nomkl]
+      - mkl-devel
       - cython
       - numpy x.x
     run:
       - python
-      - mkl                     [not nomkl]
+      - mkl
+      - mkl-service
       - intelpython
       - numpy x.x
 

mkl_fft/_scipy_fft_backend.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+# Copyright (c) 2019, Intel Corporation
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#     * 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.
+#
+# 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.
+
+from . import _pydfti
+import mkl
+
+import scipy.fft as _fft
+
+# Complete the namespace (these are not actually used in this module)
+from scipy.fft import (
+    dct, idct, dst, idst, dctn, idctn, dstn, idstn,
+    hfft2, ihfft2, hfftn, ihfftn,
+    fftshift, ifftshift, fftfreq, rfftfreq,
+    get_workers, set_workers
+)
+
+__all__ = ['fft', 'ifft', 'fft2', 'ifft2', 'fftn', 'ifftn',
+           'rfft', 'irfft', 'rfft2', 'irfft2', 'rfftn', 'irfftn',
+           'hfft', 'ihfft', 'hfft2', 'ihfft2', 'hfftn', 'ihfftn',
+           'dct', 'idct', 'dst', 'idst', 'dctn', 'idctn', 'dstn', 'idstn',
+           'fftshift', 'ifftshift', 'fftfreq', 'rfftfreq', 'get_workers',
+           'set_workers', 'next_fast_len']
+
+__ua_domain__ = 'numpy.scipy.fft'
+__implemented = dict()
+
+def __ua_function__(method, args, kwargs):
+    """Fetch registered UA function."""
+    fn = _implemented.get(method, None)
+    if fn is None:
+        return NotImplemented
+    return fn(*args, **kwargs)
+
+def _implements(scipy_func):
+    """Decorator adds function to the dictionary of implemented UA functions"""
+    def inner(func):
+        _implemented[scipy_func] = func
+        return func
+
+    return inner
+
+
+def _unitary(norm):
+    if norm not in (None, "ortho"):
+        raise ValueError("Invalid norm value %s, should be None or \"ortho\"."
+                         % norm)
+    return norm is not None
+
+
+@_implements(_fft.fft)
+def fft(x, n=None, axis=-1, norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.fft(x, n=n, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        output *= 1 / sqrt(output.shape[axis])
+    return output
+
+
+@_implements(_fft.ifft)
+def ifft(x, n=None, axis=-1, norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.ifft(x, n=n, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        output *= sqrt(output.shape[axis])
+    return output
+
+
+@_implements(_fft.fft2)
+def fft2(x, s=None, axes=(-2,-1), norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.fftn(x, s=s, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        factor = 1
+        for axis in axes:
+            factor *= 1 / sqrt(output.shape[axis])
+        output *= factor
+    return output
+
+
+@_implements(_fft.ifft2)
+def ifft2(x, s=None, axes=(-2,-1), norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.ifftn(x, s=s, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        factor = 1
+        _axes = range(output.ndim) if axes is None else axes
+        for axis in _axes:
+            factor *= sqrt(output.shape[axis])
+        output *= factor
+    return output
+
+
+@_implements(_fft.fftn)
+def fftn(x, s=None, axes=None, norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.fftn(x, s=s, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        factor = 1
+        _axes = range(output.ndim) if axes is None else axes
+        for axis in _axes:
+            factor *= 1 / sqrt(output.shape[axis])
+        output *= factor
+    return output
+
+
+@_implements(_fft.ifftn)
+def ifftn(x, s=None, axes=None, norm=None, overwrite_x=False, workers=None):
+    output = _pydfti.ifftn(x, s=s, axis=axis, overwrite_x=overwrite_x)
+    if _unitary(norm):
+        factor = 1
+        _axes = range(output.ndim) if axes is None else axes
+        for axis in _axes:
+            factor *= sqrt(output.shape[axis])
+        output *= factor
+    return output