Skip to content

C
cython
Commit 3c2dd5a8 authored by scoder's avatar scoder Committed by GitHub
Browse files
Options

Merge pull request #2162 from gabrieldemarmiesse/cython_numpy_users 

Cython numpy users
parents d5d6508c a171e51a
Show whitespace changes
Inline Side-by-side
Showing with 845 additions and 666 deletions
docs/examples/memoryviews/convolve_fused_types.pyx 0 → 100644
View file @ 3c2dd5a8
# cython: infer_types=True
import numpy as np
cimport cython
ctypedef fused my_type:
int
double
long
@cython.boundscheck(False)
@cython.wraparound(False)
cpdef naive_convolve(my_type [:,:] f, my_type [:,:] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
vmax = f.shape[0]
wmax = f.shape[1]
smax = g.shape[0]
tmax = g.shape[1]
smid = smax // 2
tmid = tmax // 2
xmax = vmax + 2*smid
ymax = wmax + 2*tmid
if my_type is int:
dtype = np.intc
elif my_type is double:
dtype = np.double
else:
dtype = np.long
h_np = np.zeros([xmax, ymax], dtype=dtype)
cdef my_type [:,:] h = h_np
cdef my_type value
for x in range(xmax):
for y in range(ymax):
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h_np
\ No newline at end of file
docs/examples/memoryviews/convolve_infer_types.pyx 0 → 100644
View file @ 3c2dd5a8
# cython: infer_types=True
import numpy as np
cimport cython
DTYPE = np.intc
@cython.boundscheck(False)
@cython.wraparound(False)
def naive_convolve(int [:,::1] f, int [:,::1] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
vmax = f.shape[0]
wmax = f.shape[1]
smax = g.shape[0]
tmax = g.shape[1]
smid = smax // 2
tmid = tmax // 2
xmax = vmax + 2*smid
ymax = wmax + 2*tmid
h_np = np.zeros([xmax, ymax], dtype=DTYPE)
cdef int [:,::1] h = h_np
cdef int value
for x in range(xmax):
for y in range(ymax):
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h_np
\ No newline at end of file
docs/examples/memoryviews/convolve_memview.pyx 0 → 100644
View file @ 3c2dd5a8
import numpy as np
DTYPE = np.intc
# It is possible to declare types in the function declaration.
def naive_convolve(int [:,:] f, int [:,:] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
# We don't need to check for the type of NumPy array here because
# a check is already performed when calling the function.
cdef Py_ssize_t x, y, s, t, v, w, s_from, s_to, t_from, t_to
cdef Py_ssize_t vmax = f.shape[0]
cdef Py_ssize_t wmax = f.shape[1]
cdef Py_ssize_t smax = g.shape[0]
cdef Py_ssize_t tmax = g.shape[1]
cdef Py_ssize_t smid = smax // 2
cdef Py_ssize_t tmid = tmax // 2
cdef Py_ssize_t xmax = vmax + 2*smid
cdef Py_ssize_t ymax = wmax + 2*tmid
h_np = np.zeros([xmax, ymax], dtype=DTYPE)
cdef int [:,:] h = h_np
cdef int value
for x in range(xmax):
for y in range(ymax):
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h_np
\ No newline at end of file
docs/examples/memoryviews/convolve_py.py 0 → 100644
View file @ 3c2dd5a8
from __future__ import division
import numpy as np
def naive_convolve(f, g):
# f is an image and is indexed by (v, w)
# g is a filter kernel and is indexed by (s, t),
# it needs odd dimensions
# h is the output image and is indexed by (x, y),
# it is not cropped
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
# smid and tmid are number of pixels between the center pixel
# and the edge, ie for a 5x5 filter they will be 2.
#
# The output size is calculated by adding smid, tmid to each
# side of the dimensions of the input image.
vmax = f.shape[0]
wmax = f.shape[1]
smax = g.shape[0]
tmax = g.shape[1]
smid = smax // 2
tmid = tmax // 2
xmax = vmax + 2*smid
ymax = wmax + 2*tmid
# Allocate result image.
h = np.zeros([xmax, ymax], dtype=f.dtype)
# Do convolution
for x in range(xmax):
for y in range(ymax):
# Calculate pixel value for h at (x,y). Sum one component
# for each pixel (s, t) of the filter g.
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h
docs/examples/memoryviews/convolve_typed.pyx 0 → 100644
View file @ 3c2dd5a8
import numpy as np
# We now need to fix a datatype for our arrays. I've used the variable
# DTYPE for this, which is assigned to the usual NumPy runtime
# type info object.
DTYPE = np.intc
def naive_convolve(f, g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
assert f.dtype == DTYPE and g.dtype == DTYPE
# The "cdef" keyword is also used within functions to type variables. It
# can only be used at the top indentation level (there are non-trivial
# problems with allowing them in other places, though we'd love to see
# good and thought out proposals for it).
# Py_ssize_t is the proper C type for Python array indices.
cdef Py_ssize_t x, y, s, t, v, w, s_from, s_to, t_from, t_to
cdef Py_ssize_t vmax = f.shape[0]
cdef Py_ssize_t wmax = f.shape[1]
cdef Py_ssize_t smax = g.shape[0]
cdef Py_ssize_t tmax = g.shape[1]
cdef Py_ssize_t smid = smax // 2
cdef Py_ssize_t tmid = tmax // 2
cdef Py_ssize_t xmax = vmax + 2*smid
cdef Py_ssize_t ymax = wmax + 2*tmid
h = np.zeros([xmax, ymax], dtype=DTYPE)
# It is very important to type ALL your variables. You do not get any
# warnings if not, only much slower code (they are implicitly typed as
# Python objects).
# For the value variable, we want to use the same data type as is
# stored in the array, so we use int because it correspond to np.intc.
# NB! An important side-effect of this is that if "value" overflows its
# datatype size, it will simply wrap around like in C, rather than raise
# an error like in Python.
cdef int value
for x in range(xmax):
for y in range(ymax):
# Cython has built-in C functions for min and max.
# This makes the following lines very fast.
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h
\ No newline at end of file
docs/src/quickstart/build.rst
View file @ 3c2dd5a8
......@@ -56,6 +56,7 @@ To build, run ``python setup.py build_ext --inplace``. Then simply
start a Python session and do ``from hello import say_hello_to`` and
use the imported function as you see fit.
.. _jupyter-notebook:
Using the Jupyter notebook
--------------------------
......
docs/src/reference/compilation.rst
View file @ 3c2dd5a8
......@@ -59,7 +59,7 @@ that CPython generates for disambiguation, such as
``yourmod.cpython-35m-x86_64-linux-gnu.so`` on a regular 64bit Linux installation
of CPython 3.5.
.. _compiling-distutils:
Compiling with ``distutils``
============================
......
docs/src/tutorial/numpy.rst
View file @ 3c2dd5a8
.. _working-numpy:
=======================
Working with NumPy
=======================
......@@ -6,7 +8,7 @@ Working with NumPy
integration described here. They are easier to use than the buffer syntax
below, have less overhead, and can be passed around without requiring the GIL.
They should be preferred to the syntax presented in this page.
See :ref:`Typed Memoryviews <memoryviews>`.
See :ref:`Cython for NumPy users <numpy_tutorial>`.
You can use NumPy from Cython exactly the same as in regular Python, but by
doing so you are losing potentially high speedups because Cython has support
......
docs/src/userguide/numpy_tutorial.rst
View file @ 3c2dd5a8
......@@ -6,35 +6,28 @@
Cython for NumPy users
**************************
.. NOTE:: Cython 0.16 introduced typed memoryviews as a successor to the NumPy
integration described here. They are easier to use than the buffer syntax
below, have less overhead, and can be passed around without requiring the GIL.
They should be preferred to the syntax presented in this page.
See :ref:`Typed Memoryviews <memoryviews>`.
This tutorial is aimed at NumPy users who have no experience with Cython at
all. If you have some knowledge of Cython you may want to skip to the
''Efficient indexing'' section which explains the new improvements made in
summer 2008.
''Efficient indexing'' section.
The main scenario considered is NumPy end-use rather than NumPy/SciPy
development. The reason is that Cython is not (yet) able to support functions
that are generic with respect to datatype and the number of dimensions in a
that are generic with respect to the number of dimensions in a
high-level fashion. This restriction is much more severe for SciPy development
than more specific, "end-user" functions. See the last section for more
information on this.
The style of this tutorial will not fit everybody, so you can also consider:
* Robert Bradshaw's `slides on cython for SciPy2008
<http://wiki.sagemath.org/scipy08?action=AttachFile&do=get&target=scipy-cython.tgz>`_
(a higher-level and quicker introduction)
* Basic Cython documentation (see `Cython front page <http://cython.org>`_).
* ``[:enhancements/buffer:Spec for the efficient indexing]``
* Kurt Smith's `video tutorial of Cython at SciPy 2015
<https://www.youtube.com/watch?v=gMvkiQ-gOW8&t=4730s&ab_channel=Enthought>`_.
The slides and notebooks of this talk are `on github
<https://github.com/kwmsmith/scipy-2015-cython-tutorial>`_.
* Basic Cython documentation (see `Cython front page
<https://cython.readthedocs.io/en/latest/index.html>`_).
Cython at a glance
====================
==================
Cython is a compiler which compiles Python-like code files to C code. Still,
''Cython is not a Python to C translator''. That is, it doesn't take your full
......@@ -52,12 +45,12 @@ This has two important consequences:
of C libraries. When writing code in Cython you can call into C code as
easily as into Python code.
Some Python constructs are not yet supported, though making Cython compile all
Python code is a stated goal (among the more important omissions are inner
functions and generator functions).
Very few Python constructs are not yet supported, though making Cython compile all
Python code is a stated goal, you can see the differences with Python in
:ref:`limitations <cython-limitations>`.
Your Cython environment
========================
=======================
Using Cython consists of these steps:
......@@ -70,15 +63,21 @@ However there are several options to automate these steps:
1. The `SAGE <http://sagemath.org>`_ mathematics software system provides
excellent support for using Cython and NumPy from an interactive command
line (like IPython) or through a notebook interface (like
line or through a notebook interface (like
Maple/Mathematica). See `this documentation
<http://www.sagemath.org/doc/prog/node40.html>`_.
2. A version of `pyximport <http://www.prescod.net/pyximport/>`_ is shipped
with Cython, so that you can import pyx-files dynamically into Python and
<http://doc.sagemath.org/html/en/developer/coding_in_cython.html>`_.
2. Cython can be used as an extension within a Jupyter notebook,
making it easy to compile and use Cython code with just a ``%%cython``
at the top of a cell. For more information see
:ref:`Using the Jupyter Notebook <jupyter-notebook>`.
3. A version of pyximport is shipped with Cython,
so that you can import pyx-files dynamically into Python and
have them compiled automatically (See :ref:`pyximport`).
3. Cython supports distutils so that you can very easily create build scripts
which automate the process, this is the preferred method for full programs.
4. Manual compilation (see below)
4. Cython supports distutils so that you can very easily create build scripts
which automate the process, this is the preferred method for
Cython implemented libraries and packages.
See :ref:`Compiling with distutils <compiling-distutils>`.
5. Manual compilation (see below)
.. Note::
If using another interactive command line environment than SAGE, like
......@@ -89,12 +88,12 @@ However there are several options to automate these steps:
Installation
=============
Unless you are used to some other automatic method:
`download Cython <http://cython.org/#download>`_ (0.9.8.1.1 or later), unpack it,
and run the usual ```python setup.py install``. This will install a
``cython`` executable on your system. It is also possible to use Cython from
the source directory without installing (simply launch :file:`cython.py` in the
root directory).
If you already have a C compiler, just do::
pip install Cython
otherwise, see :ref:`the installation page <install>`.
As of this writing SAGE comes with an older release of Cython than required
for this tutorial. So if using SAGE you should download the newest Cython and
......@@ -127,7 +126,11 @@ like::
``gcc`` should have access to the NumPy C header files so if they are not
installed at :file:`/usr/include/numpy` or similar you may need to pass another
option for those.
option for those. You only need to provide the NumPy headers if you write::
cimport numpy
in your Cython code.
This creates :file:`yourmod.so` in the same directory, which is importable by
Python by using a normal ``import yourmod`` statement.
......@@ -138,55 +141,13 @@ The first Cython program
The code below does 2D discrete convolution of an image with a filter (and I'm
sure you can do better!, let it serve for demonstration purposes). It is both
valid Python and valid Cython code. I'll refer to it as both
:file:`convolve_py.py` for the Python version and :file:`convolve1.pyx` for the
:file:`convolve_py.py` for the Python version and :file:`convolve_cy.pyx` for the
Cython version -- Cython uses ".pyx" as its file suffix.
.. code-block:: python
from __future__ import division
import numpy as np
def naive_convolve(f, g):
# f is an image and is indexed by (v, w)
# g is a filter kernel and is indexed by (s, t),
# it needs odd dimensions
# h is the output image and is indexed by (x, y),
# it is not cropped
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
# smid and tmid are number of pixels between the center pixel
# and the edge, ie for a 5x5 filter they will be 2.
#
# The output size is calculated by adding smid, tmid to each
# side of the dimensions of the input image.
vmax = f.shape[0]
wmax = f.shape[1]
smax = g.shape[0]
tmax = g.shape[1]
smid = smax // 2
tmid = tmax // 2
xmax = vmax + 2*smid
ymax = wmax + 2*tmid
# Allocate result image.
h = np.zeros([xmax, ymax], dtype=f.dtype)
# Do convolution
for x in range(xmax):
for y in range(ymax):
# Calculate pixel value for h at (x,y). Sum one component
# for each pixel (s, t) of the filter g.
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h
.. literalinclude:: ../../examples/memoryviews/convolve_py.py
:linenos:
This should be compiled to produce :file:`yourmod.so` (for Linux systems). We
This should be compiled to produce :file:`convolve_cy.so` (for Linux systems). We
run a Python session to test both the Python version (imported from
``.py``-file) and the compiled Cython module.
......@@ -200,171 +161,130 @@ run a Python session to test both the Python version (imported from
array([[1, 1, 1],
[2, 2, 2],
[1, 1, 1]])
In [4]: import convolve1
In [4]: convolve1.naive_convolve(np.array([[1, 1, 1]], dtype=np.int),
In [4]: import convolve_cy
In [4]: convolve_cy.naive_convolve(np.array([[1, 1, 1]], dtype=np.int),
... np.array([[1],[2],[1]], dtype=np.int))
Out [4]:
array([[1, 1, 1],
[2, 2, 2],
[1, 1, 1]])
In [11]: N = 100
In [11]: N = 600
In [12]: f = np.arange(N*N, dtype=np.int).reshape((N,N))
In [13]: g = np.arange(81, dtype=np.int).reshape((9, 9))
In [19]: %timeit -n2 -r3 convolve_py.naive_convolve(f, g)
2 loops, best of 3: 1.86 s per loop
In [20]: %timeit -n2 -r3 convolve1.naive_convolve(f, g)
2 loops, best of 3: 1.41 s per loop
In [19]: %timeit convolve_py.naive_convolve(f, g)
16 s ± 70.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [20]: %timeit convolve_cy.naive_convolve(f, g)
13.5 s ± 99.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
There's not such a huge difference yet; because the C code still does exactly
what the Python interpreter does (meaning, for instance, that a new object is
allocated for each number used). Look at the generated html file and see what
is needed for even the simplest statements you get the point quickly. We need
allocated for each number used).
You can look at the Python interaction and the generated C
code by using ``-a`` when calling Cython from the command
line, ``%%cython -a`` when using a Jupyter Notebook, or by using
``cythonize('convolve_cy.pyx', annotate=True)`` when using a ``setup.py``.
Look at the generated html file and see what
is needed for even the simplest statements. You get the point quickly. We need
to give Cython more information; we need to add types.
Adding types
=============
To add types we use custom Cython syntax, so we are now breaking Python source
compatibility. Here's :file:`convolve2.pyx`. *Read the comments!* ::
from __future__ import division
import numpy as np
# "cimport" is used to import special compile-time information
# about the numpy module (this is stored in a file numpy.pxd which is
# currently part of the Cython distribution).
cimport numpy as np
# We now need to fix a datatype for our arrays. I've used the variable
# DTYPE for this, which is assigned to the usual NumPy runtime
# type info object.
DTYPE = np.int
# "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
# every type in the numpy module there's a corresponding compile-time
# type with a _t-suffix.
ctypedef np.int_t DTYPE_t
# The builtin min and max functions works with Python objects, and are
# so very slow. So we create our own.
# - "cdef" declares a function which has much less overhead than a normal
# def function (but it is not Python-callable)
# - "inline" is passed on to the C compiler which may inline the functions
# - The C type "int" is chosen as return type and argument types
# - Cython allows some newer Python constructs like "a if x else b", but
# the resulting C file compiles with Python 2.3 through to Python 3.0 beta.
cdef inline int int_max(int a, int b): return a if a >= b else b
cdef inline int int_min(int a, int b): return a if a <= b else b
# "def" can type its arguments but not have a return type. The type of the
# arguments for a "def" function is checked at run-time when entering the
# function.
#
# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
# this has is to a) insert checks that the function arguments really are
# NumPy arrays, and b) make some attribute access like f.shape[0] much
# more efficient. (In this example this doesn't matter though.)
def naive_convolve(np.ndarray f, np.ndarray g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Only odd dimensions on filter supported")
assert f.dtype == DTYPE and g.dtype == DTYPE
# The "cdef" keyword is also used within functions to type variables. It
# can only be used at the top indentation level (there are non-trivial
# problems with allowing them in other places, though we'd love to see
# good and thought out proposals for it).
#
# For the indices, the "int" type is used. This corresponds to a C int,
# other C types (like "unsigned int") could have been used instead.
# Purists could use "Py_ssize_t" which is the proper Python type for
# array indices.
cdef int vmax = f.shape[0]
cdef int wmax = f.shape[1]
cdef int smax = g.shape[0]
cdef int tmax = g.shape[1]
cdef int smid = smax // 2
cdef int tmid = tmax // 2
cdef int xmax = vmax + 2*smid
cdef int ymax = wmax + 2*tmid
cdef np.ndarray h = np.zeros([xmax, ymax], dtype=DTYPE)
cdef int x, y, s, t, v, w
# It is very important to type ALL your variables. You do not get any
# warnings if not, only much slower code (they are implicitly typed as
# Python objects).
cdef int s_from, s_to, t_from, t_to
# For the value variable, we want to use the same data type as is
# stored in the array, so we use "DTYPE_t" as defined above.
# NB! An important side-effect of this is that if "value" overflows its
# datatype size, it will simply wrap around like in C, rather than raise
# an error like in Python.
cdef DTYPE_t value
for x in range(xmax):
for y in range(ymax):
s_from = int_max(smid - x, -smid)
s_to = int_min((xmax - x) - smid, smid + 1)
t_from = int_max(tmid - y, -tmid)
t_to = int_min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = x - smid + s
w = y - tmid + t
value += g[smid - s, tmid - t] * f[v, w]
h[x, y] = value
return h
compatibility. Here's :file:`convolve_typed.pyx`. *Read the comments!*
At this point, have a look at the generated C code for :file:`convolve1.pyx` and
:file:`convolve2.pyx`. Click on the lines to expand them and see corresponding C.
(Note that this code annotation is currently experimental and especially
"trailing" cleanup code for a block may stick to the last expression in the
block and make it look worse than it is -- use some common sense).
.. literalinclude:: ../../examples/memoryviews/convolve_typed.pyx
:linenos:
* .. literalinclude: convolve1.html
* .. literalinclude: convolve2.html
.. figure:: convolve_types_html.png
Especially have a look at the for loops: In :file:`convolve1.c`, these are ~20 lines
of C code to set up while in :file:`convolve2.c` a normal C for loop is used.
At this point, have a look at the generated C code for :file:`convolve_cy.pyx` and
:file:`convolve_typed.pyx`. Click on the lines to expand them and see corresponding C.
Especially have a look at the ``for-loops``: In :file:`convolve_cy.c`, these are ~20 lines
of C code to set up while in :file:`convolve_typed.c` a normal C for loop is used.
After building this and continuing my (very informal) benchmarks, I get:
.. sourcecode:: ipython
In [21]: import convolve2
In [22]: %timeit -n2 -r3 convolve2.naive_convolve(f, g)
2 loops, best of 3: 828 ms per loop
In [22]: %timeit convolve_typed.naive_convolve(f, g)
55.8 s ± 199 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Efficient indexing
====================
So in the end, adding types make the Cython code slower?
What happend is that most of the time spend in this code is spent on line
54. ::
value += g[smid - s, tmid - t] * f[v, w]
So what made this line so much slower than in the pure Python version?
``g`` and ``f`` are still NumPy arrays, so Python objects, and expect
Python integers as indexes. Here we pass C int values. So every time
Cython reaches this line, it has to convert all the C integers to Python
int objects. Since this line is called very often, it outweighs the speed
benefits of the pure C loops that were created from the ``range()`` earlier.
Furthermore, ``g[smid - s, tmid - t] * f[v, w]`` returns a Python integer
and ``value`` is a C integer, so Cython has to do type conversions again.
In the end those types conversions add up. And made our convolution really
slow. But this problem can be solved easily by using memoryviews.
There's still a bottleneck killing performance, and that is the array lookups
and assignments. The ``[]``-operator still uses full Python operations --
Efficient indexing with memoryviews
===================================
There are still two bottlenecks that degrade the performance, and that is the array lookups
and assignments, as well as C/Python types conversion.
The ``[]``-operator still uses full Python operations --
what we would like to do instead is to access the data buffer directly at C
speed.
What we need to do then is to type the contents of the :obj:`ndarray` objects.
We do this with a special "buffer" syntax which must be told the datatype
(first argument) and number of dimensions ("ndim" keyword-only argument, if
not provided then one-dimensional is assumed).
We do this with a memoryview. There is :ref:`a page in the Cython documentation
<memoryviews>` dedicated to it.
More information on this syntax [:enhancements/buffer:can be found here].
In short, memoryviews are C structures that can hold a pointer to the data
of a NumPy array and all the necessary buffer metadata to provide efficient
and safe access: dimensions, strides, item size, item type information, etc...
They also support slices, so they work even if
the NumPy array isn't contiguous in memory.
They can be indexed by C integers, thus allowing fast access to the
NumPy array data.
Showing the changes needed to produce :file:`convolve3.pyx` only::
Here is how to declare a memoryview of integers::
...
def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
...
cdef np.ndarray[DTYPE_t, ndim=2] h = ...
cdef int [:] foo # 1D memoryview
cdef int [:, :] foo # 2D memoryview
cdef int [:, :, :] foo # 3D memoryview
... # You get the idea.
No data is copied from the NumPy array to the memoryview in our example.
As the name implies, it is only a "view" of the memory. So we can use the
view ``h`` for efficient indexing and at the end return the real NumPy
array ``h_np`` that holds the data that we operated on.
Usage:
Here is how to use them in our code:
:file:`convolve_memview.pyx`
.. literalinclude:: ../../examples/memoryviews/convolve_memview.pyx
:linenos:
Let's see how much faster accessing is now.
.. sourcecode:: ipython
In [18]: import convolve3
In [19]: %timeit -n3 -r100 convolve3.naive_convolve(f, g)
3 loops, best of 100: 11.6 ms per loop
In [22]: %timeit convolve_memview.naive_convolve(f, g)
57.1 ms ± 268 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
Note the importance of this change.
We're now 280 times faster than an interpreted version of Python.
*Gotcha*: This efficient indexing only affects certain index operations,
namely those with exactly ``ndim`` number of typed integer indices. So if
``v`` for instance isn't typed, then the lookup ``f[v, w]`` isn't
optimized. On the other hand this means that you can continue using Python
objects for sophisticated dynamic slicing etc. just as when the array is not
typed.
Memoryviews can be used with slices too, or even
with Python arrays. Check out the :ref:`memoryview page <memoryviews>` to
see what they can do for you.
Tuning indexing further
========================
......@@ -374,13 +294,15 @@ The array lookups are still slowed down by two factors:
1. Bounds checking is performed.
2. Negative indices are checked for and handled correctly. The code above is
explicitly coded so that it doesn't use negative indices, and it
(hopefully) always access within bounds. We can add a decorator to disable
bounds checking::
(hopefully) always access within bounds.
With decorators, we can deactivate those checks::
...
cimport cython
@cython.boundscheck(False) # turn off bounds-checking for entire function
def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
@cython.boundscheck(False) # Deactivate bounds checking
@cython.wraparound(False) # Deactivate negative indexing.
def naive_convolve(int [:, :] f, int [:, :] g):
...
Now bounds checking is not performed (and, as a side-effect, if you ''do''
......@@ -389,54 +311,13 @@ and in the worst case corrupt data). It is possible to switch bounds-checking
mode in many ways, see :ref:`compiler-directives` for more
information.
Negative indices are dealt with by ensuring Cython that the indices will be
positive, by casting the variables to unsigned integer types (if you do have
negative values, then this casting will create a very large positive value
instead and you will attempt to access out-of-bounds values). Casting is done
with a special ``<>``-syntax. The code below is changed to use either
unsigned ints or casting as appropriate::
...
cdef int s, t # changed
cdef unsigned int x, y, v, w # changed
cdef int s_from, s_to, t_from, t_to
cdef DTYPE_t value
for x in range(xmax):
for y in range(ymax):
s_from = max(smid - x, -smid)
s_to = min((xmax - x) - smid, smid + 1)
t_from = max(tmid - y, -tmid)
t_to = min((ymax - y) - tmid, tmid + 1)
value = 0
for s in range(s_from, s_to):
for t in range(t_from, t_to):
v = <unsigned int>(x - smid + s) # changed
w = <unsigned int>(y - tmid + t) # changed
value += g[<unsigned int>(smid - s), <unsigned int>(tmid - t)] * f[v, w] # changed
h[x, y] = value
...
(In the next Cython release we will likely add a compiler directive or
argument to the ``np.ndarray[]``-type specifier to disable negative indexing
so that casting so much isn't necessary; feedback on this is welcome.)
The function call overhead now starts to play a role, so we compare the latter
two examples with larger N:
.. sourcecode:: ipython
In [11]: %timeit -n3 -r100 convolve4.naive_convolve(f, g)
3 loops, best of 100: 5.97 ms per loop
In [12]: N = 1000
In [13]: f = np.arange(N*N, dtype=np.int).reshape((N,N))
In [14]: g = np.arange(81, dtype=np.int).reshape((9, 9))
In [17]: %timeit -n1 -r10 convolve3.naive_convolve(f, g)
1 loops, best of 10: 1.16 s per loop
In [18]: %timeit -n1 -r10 convolve4.naive_convolve(f, g)
1 loops, best of 10: 597 ms per loop
In [23]: %timeit convolve_index.naive_convolve(f, g)
19.8 ms ± 299 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
(Also this is a mixed benchmark as the result array is allocated within the
function call.)
We're now 800 times faster than the interpreted Python version.
.. Warning::
......@@ -450,48 +331,121 @@ function call.)
The actual rules are a bit more complicated but the main message is clear: Do
not use typed objects without knowing that they are not set to ``None``.
Declaring the NumPy arrays as contiguous
========================================
For extra speed gains, if you know that the NumPy arrays you are
providing are contiguous in memory, you can declare the
memoryview as contiguous.
We give an example on an array that has 3 dimensions.
If you want to give Cython the information that the data is C-contiguous
you have to declare the memoryview like this::
cdef int [:,:,::1] a
If you want to give Cython the information that the data is Fortran-contiguous
you have to declare the memoryview like this::
cdef int [::1, :, :] a
If all this makes no sense to you, you can skip this part, the performance gains are
not that important. If you still want to understand what contiguous arrays are
all about, you can see `this answer on StackOverflow
<https://stackoverflow.com/questions/26998223/what-is-the-difference-between-contiguous-and-non-contiguous-arrays>`_.
For the sake of giving numbers, here are the speed gains that you should
get by declaring the memoryviews as contiguous:
.. sourcecode:: ipython
In [23]: %timeit convolve_contiguous.naive_convolve(f, g)
21.3 ms ± 489 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
We're still around 800 times faster than the interpreted Python version.
Making the function cleaner
===========================
Declaring types can make your code quite verbose. If you don't mind
Cython inferring the C types of your variables, you can use
the ``infer_types=True`` compiler directive at the top of the file.
It will save you quite a bit of typing.
Note that since type declarations must happen at the top indentation level,
Cython won't infer the type of variables declared for the first time
in other indentation levels. It would change too much the meaning of
our code. This is why, we must still declare manually the type of the
``value`` variable.
And actually, manually giving the type of the ``value`` variable will
be useful when using fused types.
.. literalinclude:: ../../examples/memoryviews/convolve_infer_types.pyx
:linenos:
We now do a speed test:
.. sourcecode:: ipython
In [24]: %timeit convolve_infer_types.naive_convolve(f, g)
21.3 ms ± 344 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
We're still around 800 times faster than the interpreted Python version.
More generic code
==================
It would be possible to do::
def naive_convolve(object[DTYPE_t, ndim=2] f, ...):
i.e. use :obj:`object` rather than :obj:`np.ndarray`. Under Python 3.0 this
can allow your algorithm to work with any libraries supporting the buffer
interface; and support for e.g. the Python Imaging Library may easily be added
if someone is interested also under Python 2.x.
There is some speed penalty to this though (as one makes more assumptions
compile-time if the type is set to :obj:`np.ndarray`, specifically it is
assumed that the data is stored in pure strided mode and not in indirect
mode).
[:enhancements/buffer:More information]
The future
============
These are some points to consider for further development. All points listed
here has gone through a lot of thinking and planning already; still they may
or may not happen depending on available developer time and resources for
Cython.
1. Support for efficient access to structs/records stored in arrays; currently
only primitive types are allowed.
2. Support for efficient access to complex floating point types in arrays. The
main obstacle here is getting support for efficient complex datatypes in
Cython.
3. Calling NumPy/SciPy functions currently has a Python call overhead; it
would be possible to take a short-cut from Cython directly to C. (This does
however require some isolated and incremental changes to those libraries;
mail the Cython mailing list for details).
4. Efficient code that is generic with respect to the number of dimensions.
This can probably be done today by calling the NumPy C multi-dimensional
iterator API directly; however it would be nice to have for-loops over
:func:`enumerate` and :func:`ndenumerate` on NumPy arrays create efficient
code.
5. A high-level construct for writing type-generic code, so that one can write
functions that work simultaneously with many datatypes. Note however that a
macro preprocessor language can help with doing this for now.
All those speed gains are nice, but adding types constrains our code.
At the moment, it would mean that our function can only work with
NumPy arrays with the ``np.intc`` type. Is it possible to make our
code work for multiple NumPy data types?
Yes, with the help of a new feature called fused types.
You can learn more about it at :ref:`this section of the documentation
<fusedtypes>`.
It is similar to C++ 's templates. It generates mutiple function declarations
at compile time, and then chooses the right one at run-time based on the
types of the arguments provided. By comparing types in if-conditions, it
is also possible to execute entirely different code paths depending
on the specific data type.
In our example, since we don't have access anymore to the NumPy's dtype
of our input arrays, we use those ``if-else`` statements to
know what NumPy data type we should use for our output array.
In this case, our function now works for ints, doubles and floats.
.. literalinclude:: ../../examples/memoryviews/convolve_fused_types.pyx
:linenos:
We can check that the output type is the right one::
>>>naive_convolve_fused_types(f, g).dtype
dtype('int32')
>>>naive_convolve_fused_types(f.astype(np.double), g.astype(np.double)).dtype
dtype('float64')
We now do a speed test:
.. sourcecode:: ipython
In [25]: %timeit convolve_fused_types.naive_convolve(f, g)
20 ms ± 392 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
We're still around 800 times faster than the interpreted Python version.
Where to go from here?
======================
* Since there is no Python interaction in the loops, it is possible with Cython
to release the GIL and use multiple cores easily. To learn how to do that,
you can see :ref:`using parallelism in Cython <parallel>`.
* If you want to learn how to make use of `BLAS <http://www.netlib.org/blas/>`_
or `LAPACK <http://www.netlib.org/lapack/>`_ with Cython, you can watch
`the presentation of Ian Henriksen at SciPy 2015
<https://www.youtube.com/watch?v=R4yB-8tB0J0&t=693s&ab_channel=Enthought>`_.
* If you want to learn how to use Pythran as backend in Cython, you
can see how in :ref:`Pythran as a NumPy backend <numpy-pythran>`.
Note that using Pythran only works with the
:ref:`old buffer syntax <working-numpy>` and not yet with memoryviews.
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment
GitLab Nexedi Edition | About GitLab | About Nexedi | 沪ICP备2021021310号-2 | 沪ICP备2021021310号-7