使用line_profiler对python代码性能进行评估优化

性能测试的意义

在做完一个python项目之后,我们经常要考虑对软件的性能进行优化。那么我们需要一个软件优化的思路,首先我们需要明确软件本身代码以及函数的瓶颈,最理想的情况就是有这样一个工具,能够将一个目标函数的代码每一行的性能都评估出来,这样我们可以针对所有代码中性能最差的那一部分,来进行针对性的优化。开源库line_profiler就做了一个这样的工作,开源地址:github.com/rkern/line_profiler。下面让我们一起看下该工具的安装和使用详情。

line_profiler的安装

line_profiler的安装支持源码安装和pip的安装,这里我们仅介绍pip形式的安装,也比较容易,源码安装方式请参考官方开源地址。

[dechin@dechin-manjaro line_profiler]$ python3 -m pip install line_profiler
Collecting line_profiler
  Downloading line_profiler-3.1.0-cp38-cp38-manylinux2010_x86_64.whl (65 kB)
     |████████████████████████████████| 65 kB 221 kB/s 
Requirement already satisfied: IPython in /home/dechin/anaconda3/lib/python3.8/site-packages (from line_profiler) (7.19.0)
Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (3.0.8)
Requirement already satisfied: backcall in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (0.2.0)
Requirement already satisfied: pexpect>4.3; sys_platform != "win32" in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (4.8.0)
Requirement already satisfied: setuptools>=18.5 in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (50.3.1.post20201107)
Requirement already satisfied: jedi>=0.10 in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (0.17.1)
Requirement already satisfied: decorator in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (4.4.2)
Requirement already satisfied: traitlets>=4.2 in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (5.0.5)
Requirement already satisfied: pygments in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (2.7.2)
Requirement already satisfied: pickleshare in /home/dechin/anaconda3/lib/python3.8/site-packages (from IPython->line_profiler) (0.7.5)
Requirement already satisfied: wcwidth in /home/dechin/anaconda3/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->IPython->line_profiler) (0.2.5)
Requirement already satisfied: ptyprocess>=0.5 in /home/dechin/anaconda3/lib/python3.8/site-packages (from pexpect>4.3; sys_platform != "win32"->IPython->line_profiler) (0.6.0)
Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/dechin/anaconda3/lib/python3.8/site-packages (from jedi>=0.10->IPython->line_profiler) (0.7.0)
Requirement already satisfied: ipython-genutils in /home/dechin/anaconda3/lib/python3.8/site-packages (from traitlets>=4.2->IPython->line_profiler) (0.2.0)
Installing collected packages: line-profiler
Successfully installed line-profiler-3.1.0

这里额外介绍一种临时使用pip的源进行安装的方案,这里用到的是腾讯所提供的pypi源:

python3 -m pip install -i https://mirrors.cloud.tencent.com/pypi/simple line_profiler

如果需要永久保存源可以修改~/.pip/pip.conf文件,一个参考示例如下(采用华为云的镜像源):

[global]
index-url = https://mirrors.huaweicloud.com/repository/pypi/simple
trusted-host = mirrors.huaweicloud.com
timeout = 120

在需要调试优化的代码中引用line_profiler

让我们直接来看一个案例:

# line_profiler_test.py
from line_profiler import LineProfiler
import numpy as np

@profile
def test_profiler():
    for i in range(100):
        a = np.random.randn(100)
        b = np.random.randn(1000)
        c = np.random.randn(10000)
    return None

if __name__ == '__main__':
    test_profiler()

在这个案例中,我们定义了一个需要测试的函数test_profiler,在这个函数中有几行待分析性能的模块numpy.random.randn。使用的方式就是先import进来LineProfiler函数,然后在需要逐行进行性能分析的函数上方引用名为profile的装饰器,就完成了line_profiler性能分析的配置。关于python装饰器的使用和原理,可以参考这篇博客的内容介绍。还有一点需要注意的是,line_profiler所能够分析的范围仅限于加了装饰器的函数内容,如果函数内有其他的调用之类的,不会再进入其他的函数进行分析,除了内嵌的嵌套函数。

使用line_profiler进行简单性能分析

line_profiler的使用方法也较为简单,主要就是两步:先用kernprof解析,再采用python执行得到分析结果。

  1. 在定义好需要分析的函数模块之后,用kernprof解析成二进制lprof文件:
[dechin-manjaro line_profiler]# kernprof -l line_profiler_test.py 
Wrote profile results to line_profiler_test.py.lprof

该命令执行结束后,会在当前目录下产生一个lprof文件:

[dechin-manjaro line_profiler]# ll
总用量 8
-rw-r--r-- 1 dechin dechin 304  1月 20 16:00 line_profiler_test.py
-rw-r--r-- 1 root   root   185  1月 20 16:00 line_profiler_test.py.lprof
  1. 使用python3运行lprof二进制文件:
[dechin-manjaro line_profiler]# python3 -m line_profiler line_profiler_test.py.lprof 
Timer unit: 1e-06 s

Total time: 0.022633 s
File: line_profiler_test.py
Function: test_profiler at line 5

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     5                                           @profile
     6                                           def test_profiler():
     7       101         40.0      0.4      0.2      for i in range(100):
     8       100        332.0      3.3      1.5          a = np.random.randn(100)
     9       100       2092.0     20.9      9.2          b = np.random.randn(1000)
    10       100      20169.0    201.7     89.1          c = np.random.randn(10000)
    11         1          0.0      0.0      0.0      return None

这里我们就直接得到了逐行的性能分析结论。简单介绍一下每一列的含义:代码在代码文件中对应的行号、被调用的次数、该行的总共执行时间、单次执行所消耗的时间、执行时间在该函数下的占比,最后一列是具体的代码内容。其实,关于line_profiler的使用介绍到这里就可以结束了,但是我们希望通过另外一个实际案例来分析line_profiler的功能,感兴趣的读者可以继续往下阅读。

使用line_profiler分析不同函数库计算正弦函数sin的效率

我们这里需要测试多个库中所实现的正弦函数,其中包含我们自己使用的fortran内置的SIN函数。

在演示line_profiler的性能测试之前,让我们先看看如何将一个fortran的f90文件转换成python可调用的动态链接库文件。

  1. 首先在Manjaro Linux平台上安装gfotran
[dechin-manjaro line_profiler]# pacman -S gcc-fortran
正在解析依赖关系...
正在查找软件包冲突...

软件包 (1) gcc-fortran-10.2.0-4

下载大小:   9.44 MiB
全部安装大小:  31.01 MiB

:: 进行安装吗? [Y/n] Y
:: 正在获取软件包......
 gcc-fortran-10.2.0-4-x86_64                                                                                        9.4 MiB  6.70 MiB/s 00:01 [#######################################################################################] 100%
(1/1) 正在检查密钥环里的密钥                                                                                                                  [#######################################################################################] 100%
(1/1) 正在检查软件包完整性                                                                                                                    [#######################################################################################] 100%
(1/1) 正在加载软件包文件                                                                                                                      [#######################################################################################] 100%
(1/1) 正在检查文件冲突                                                                                                                        [#######################################################################################] 100%
(1/1) 正在检查可用存储空间                                                                                                                    [#######################################################################################] 100%
:: 正在处理软件包的变化...
(1/1) 正在安装 gcc-fortran                                                                                                                    [#######################################################################################] 100%
:: 正在运行事务后钩子函数...
(1/2) Arming ConditionNeedsUpdate...
(2/2) Updating the info directory file...
  1. 创建一个简单的fortran文件fmath.f90,功能为返回正弦函数的值:
subroutine fsin(theta,result)
        implicit none
        real*8::theta
        real*8,intent(out)::result
        result=SIN(theta)
end subroutine
  1. 用f2py将该fortran文件编译成名为fmath的动态链接库:
[dechin-manjaro line_profiler]# f2py -c -m fmath fmath.f90 
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
build_src
building extension "fmath" sources
f2py options: []
f2py:> /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fmathmodule.c
creating /tmp/tmpup5ia9lf/src.linux-x86_64-3.8
Reading fortran codes...
        Reading file 'fmath.f90' (format:free)
Post-processing...
        Block: fmath
                        Block: fsin
Post-processing (stage 2)...
Building modules...
        Building module "fmath"...
                Constructing wrapper function "fsin"...
                  result = fsin(theta)
        Wrote C/API module "fmath" to file "/tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fmathmodule.c"
  adding '/tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.c' to sources.
  adding '/tmp/tmpup5ia9lf/src.linux-x86_64-3.8' to include_dirs.
copying /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/f2py/src/fortranobject.c -> /tmp/tmpup5ia9lf/src.linux-x86_64-3.8
copying /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/f2py/src/fortranobject.h -> /tmp/tmpup5ia9lf/src.linux-x86_64-3.8
build_src: building npy-pkg config files
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
get_default_fcompiler: matching types: '['gnu95', 'intel', 'lahey', 'pg', 'absoft', 'nag', 'vast', 'compaq', 'intele', 'intelem', 'gnu', 'g95', 'pathf95', 'nagfor']'
customize Gnu95FCompiler
Found executable /usr/bin/gfortran
customize Gnu95FCompiler
customize Gnu95FCompiler using build_ext
building 'fmath' extension
compiling C sources
C compiler: gcc -pthread -B /home/dechin/anaconda3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating /tmp/tmpup5ia9lf/tmp
creating /tmp/tmpup5ia9lf/tmp/tmpup5ia9lf
creating /tmp/tmpup5ia9lf/tmp/tmpup5ia9lf/src.linux-x86_64-3.8
compile options: '-I/tmp/tmpup5ia9lf/src.linux-x86_64-3.8 -I/home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include -I/home/dechin/anaconda3/include/python3.8 -c'
gcc: /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fmathmodule.c
gcc: /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.c
In file included from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1822,
                 from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.h:13,
                 from /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fmathmodule.c:15:
/home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: 警告:#warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   17 | #warning "Using deprecated NumPy API, disable it with " \
      |  ^~~~~~~
In file included from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1822,
                 from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.h:13,
                 from /tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.c:2:
/home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: 警告:#warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
   17 | #warning "Using deprecated NumPy API, disable it with " \
      |  ^~~~~~~
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-I/tmp/tmpup5ia9lf/src.linux-x86_64-3.8 -I/home/dechin/anaconda3/lib/python3.8/site-packages/numpy/core/include -I/home/dechin/anaconda3/include/python3.8 -c'
gfortran:f90: fmath.f90
/usr/bin/gfortran -Wall -g -Wall -g -shared /tmp/tmpup5ia9lf/tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fmathmodule.o /tmp/tmpup5ia9lf/tmp/tmpup5ia9lf/src.linux-x86_64-3.8/fortranobject.o /tmp/tmpup5ia9lf/fmath.o -L/usr/lib/gcc/x86_64-pc-linux-gnu/10.2.0/../../../../lib -L/usr/lib/gcc/x86_64-pc-linux-gnu/10.2.0/../../../../lib -lgfortran -o ./fmath.cpython-38-x86_64-linux-gnu.so
Removing build directory /tmp/tmpup5ia9lf

这中间会有一些告警,但是并不影响我们的正常使用,编译好之后,可以在当前目录下看到一个so文件(如果是windows平台可能是其他类型的动态链接库文件):

[dechin-manjaro line_profiler]# ll
总用量 120
-rwxr-xr-x 1 root   root   107256  1月 20 16:40 fmath.cpython-38-x86_64-linux-gnu.so
-rw-r--r-- 1 root   root      150  1月 20 16:40 fmath.f90
-rw-r--r-- 1 dechin dechin    304  1月 20 16:00 line_profiler_test.py
-rw-r--r-- 1 root   root      185  1月 20 16:00 line_profiler_test.py.lprof
  1. 用ipython测试该动态链接库的功能是否正常:
[dechin-manjaro line_profiler]# ipython
Python 3.8.5 (default, Sep  4 2020, 07:30:14) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from fmath import fsin

In [2]: print (fsin(3.14))
0.0015926529164868282

In [3]: print (fsin(3.1415926))
5.3589793170057245e-08

这里我们可以看到基于fortran的正弦函数的功能已经完成实现了,接下来让我们正式对比几种正弦函数实现的性能(底层的实现有可能重复,这里作为黑盒来进行性能测试)。

首先,我们还是需要创建好待测试的python文件sin_profiler_test.py

# sin_profiler_test.py
from line_profiler import LineProfiler
import random
from numpy import sin as numpy_sin
from math import sin as math_sin
# from cupy import sin as cupy_sin
from cmath import sin as cmath_sin
from fmath import fsin as fortran_sin

@profile
def test_profiler():
    for i in range(100000):
        r = random.random()
        a = numpy_sin(r)
        b = math_sin(r)
        # c = cupy_sin(r)
        d = cmath_sin(r)
        e = fortran_sin(r)
    return None

if __name__ == '__main__':
    test_profiler()

这里line_profiler的定义跟前面定义的例子一致,我们主要测试的对象为numpy,math,cmath四个开源库的正弦函数实现以及自己实现的一个fortran的正弦函数,通过上面介绍的f2py构造的动态链接库跟python实现无缝对接。由于这里的cupy库没有安装成功,所以这里暂时没办法测试而注释掉了。接下来还是一样的,通过kernprof进行编译构建:

[dechin-manjaro line_profiler]# kernprof -l sin_profiler_test.py 
Wrote profile results to sin_profiler_test.py.lprof

最后通过python3来执行:

[dechin-manjaro line_profiler]# python3 -m line_profiler sin_profiler_test.py.lprof 
Timer unit: 1e-06 s

Total time: 0.261304 s
File: sin_profiler_test.py
Function: test_profiler at line 10

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    10                                           @profile
    11                                           def test_profiler():
    12    100001      28032.0      0.3     10.7      for i in range(100000):
    13    100000      33995.0      0.3     13.0          r = random.random()
    14    100000      86870.0      0.9     33.2          a = numpy_sin(r)
    15    100000      33374.0      0.3     12.8          b = math_sin(r)
    16                                                   # c = cupy_sin(r)
    17    100000      40179.0      0.4     15.4          d = cmath_sin(r)
    18    100000      38854.0      0.4     14.9          e = fortran_sin(r)
    19         1          0.0      0.0      0.0      return None

从这个结果上我们可以看出,在这测试的四个库中,math的计算效率是最高的,numpy的计算效率是最低的,而我们自己编写的fortran接口函数甚至都比numpy的实现快了一倍,仅次于math的实现。其实,这里值涉及到了单个函数的性能测试,我们还可以通过ipython中自带的timeit来进行测试:

[dechin-manjaro line_profiler]# ipython
Python 3.8.5 (default, Sep  4 2020, 07:30:14) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from fmath import fsin

In [2]: import random

In [3]: %timeit fsin(random.random())
145 ns ± 2.38 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [4]: from math import sin as math_sin

In [5]: %timeit math_sin(random.random())
107 ns ± 0.116 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [6]: from numpy import sin as numpy_sin

In [7]: %timeit numpy_sin(random.random())
611 ns ± 4.28 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [8]: from cmath import sin as cmath_sin

In [9]: %timeit cmath_sin(random.random())
151 ns ± 1.01 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

在这个结果中我们看到排名的趋势依然跟之前的保持一致,但是由于将random模块和计算模块放在一起,在给出的时间数值上有些差异。

总结概要

本文重点介绍了python的一款逐行性能分析的工具line_profiler,通过简单的装饰器的调用就可以分析出程序的性能瓶颈,从而进行针对性的优化。另外,在测试的过程中我们还可以发现,不同形式的正弦三角函数实现,性能是存在差异的,只是在日常使用频率较低的情况下是不感知的。需要了解的是,即使是正弦函数也有很多不同的实现方案,比如各种级数展开,而目前最流行、性能最高的计算方式,其实还是通过查表法。因此,不同的算法实现、不同的语言实现,都会导致完全不一样的结果。就测试情况而言,已知的性能排名为:math<fortran<cmath<numpy从左到右运行时长逐步增加。

版权声明

本文首发链接为:https://www.cnblogs.com/dechinphy/p/line-profiler.html
作者ID:DechinPhy
更多原著文章请参考:https://www.cnblogs.com/dechinphy/

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posted @ 2021-01-20 19:36  DECHIN  阅读(519)  评论(0编辑  收藏  举报