python 基准测试（cProfile kcachegrind line_profiler memory_profiler）

learn from 《python高性能（第2版）》

类似工具：pycharm profile对函数调用效率进行测试

1. 例子

一个圆周运动的动画

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from matplotlib import pyplot as pltfrom matplotlib import animationfrom random import uniformimport timeitclass Particle:    __slots__ = ('x', 'y', 'ang_speed')    # 声明成员只允许这么多，不能动态添加，当生成大量实例时，可以减少内存占用    def __init__(self, x, y, ang_speed):        self.x = x        self.y = y        self.ang_speed = ang_speedclass ParticleSimulator:    def __init__(self, particles):        self.particles = particles    def evolve(self, dt):        timestep = 0.00001        nsteps = int(dt / timestep)        for i in range(nsteps):            for p in self.particles:                norm = (p.x ** 2 + p.y ** 2) ** 0.5                v_x = (-p.y) / norm                v_y = p.x / norm                d_x = timestep * p.ang_speed * v_x                d_y = timestep * p.ang_speed * v_y                p.x += d_x                p.y += d_ydef visualize(simulator):    X = [p.x for p in simulator.particles]    Y = [p.y for p in simulator.particles]    fig = plt.figure()    ax = plt.subplot(111, aspect='equal')    line, = ax.plot(X, Y, 'ro')    # Axis limits    plt.xlim(-1, 1)    plt.ylim(-1, 1)    # It will be run when the animation starts    def init():        line.set_data([], [])        return line,    def animate(i):        # We let the particle evolve for 0.1 time units        simulator.evolve(0.01)        X = [p.x for p in simulator.particles]        Y = [p.y for p in simulator.particles]        line.set_data(X, Y)        return line,    # Call the animate function each 10 ms    anim = animation.FuncAnimation(fig,                                   animate,                                   init_func=init,                                   blit=True,                                   interval=10)    plt.show()def test_visualize():    particles = [Particle(0.3, 0.5, +1),                 Particle(0.0, -0.5, -1),                 Particle(-0.1, -0.4, +3),                 Particle(-0.2, -0.8, +3),]    simulator = ParticleSimulator(particles)    visualize(simulator)if __name__ == '__main__':    test_visualize()

2. 运行耗时测试linux time 命令代码语言：javascript代码运行次数：0运行复制

def benchmark():    particles = [Particle(uniform(-1.0, 1.0),                          uniform(-1.0, 1.0),                          uniform(-1.0, 1.0))                  for i in range(100)]    simulator = ParticleSimulator(particles)    # visualize(simulator)    simulator.evolve(0.1)if __name__ == '__main__':    benchmark()

生成100个实例，模拟 0.1 秒

在 linux 中进行测试耗时：

立即学习“Python免费学习笔记（深入）”；

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time python my.pyreal    0m10.435s  # 进程实际花费时间user    0m2.078s  # 计算期间 所有CPU花费总时间sys     0m1.412s  #  执行系统相关任务（内存分配）期间，所有CPU花费总时间

python timeit包指定 循环次数、重复次数代码语言：javascript代码运行次数：0运行复制

def timing():    result = timeit.timeit('benchmark()',                           setup='from __main__ import benchmark',                           number=10)    # Result is the time it takes to run the whole loop    print(result)    result = timeit.repeat('benchmark()',                           setup='from __main__ import benchmark',                           number=10,                           repeat=3)    # Result is a list of times    print(result)

输出：

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6.9873279229996115[6.382431660999828, 6.248147055000118, 6.325469069000064]

pytest、pytest-benchmark代码语言：javascript代码运行次数：0运行复制

pip install pytestpip install pytest-benchmark

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$ pytest test_simul.py::test_evolve=================== test session starts ====================platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)rootdir: /mnt/d/gitcode/Python_learning/Python-High-Performance-Second-Edition-master/Chapter01plugins: benchmark-3.4.1collected 1 itemtest_simul.py .                                      [100%]---------------------------------------------- benchmark: 1 tests ---------------------------------------------Name (time in ms)         Min      Max     Mean  StdDev   Median     IQR  Outliers      OPS  Rounds  Iterations---------------------------------------------------------------------------------------------------------------test_evolve           15.9304  42.7975  20.1502  5.6825  18.2795  3.7249       5;5  49.6274      58           1---------------------------------------------------------------------------------------------------------------Legend:  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.  OPS: Operations Per Second, computed as 1 / Mean

上面显示，测了58次，用时的最小、最大、均值、方差、中位数等

3. cProfile 找出瓶颈profile包是 python写的开销比较大，cProfile 是C语言编写的，开销小代码语言：javascript代码运行次数：0运行复制

python -m cProfile simul.py

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$ python -m cProfile simul.py         2272804 function calls (2258641 primitive calls) in 8.209 seconds   Ordered by: standard name   ncalls  tottime  percall  cumtime  percall filename:lineno(function)       30    0.000    0.000    0.001    0.000 :177(any)      160    0.000    0.000    0.002    0.000 :177(column_stack)      161    0.000    0.000    0.004    0.000 :177(concatenate)       34    0.000    0.000    0.000    0.000 :177(copyto)       30    0.000    0.000    0.002    0.000 :177(linspace)       30    0.000    0.000    0.000    0.000 :177(ndim)       30    0.000    0.000    0.000    0.000 :177(result_type)        5    0.000    0.000    0.116    0.023 :1002(_gcd_import)   485/33    0.001    0.000    6.807    0.206 :1017(_handle_fromlist)   。。。

输出结果非常长

按 tottime 排序 -s tottime，看前几个就是耗时最多的几个

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$ python -m cProfile -s tottime simul.py         2272784 function calls (2258621 primitive calls) in 7.866 seconds   Ordered by: internal time   ncalls  tottime  percall  cumtime  percall filename:lineno(function)     1258    2.498    0.002    2.498    0.002 {built-in method posix.stat}      273    1.057    0.004    1.057    0.004 {built-in method io.open_code}       27    0.874    0.032    0.879    0.033 {built-in method _imp.create_dynamic}        1    0.691    0.691    0.691    0.691 simul.py:21(evolve)      273    0.464    0.002    0.464    0.002 {method 'read' of '_io.BufferedReader' objects}      273    0.432    0.002    1.953    0.007 :1034(get_data)    32045    0.245    0.000    0.411    0.000 inspect.py:625(cleandoc)       30    0.171    0.006    0.171    0.006 {built-in method posix.listdir}       33    0.151    0.005    0.151    0.005 {built-in method io.open}

或者使用代码

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>>> from simul import benchmark>>> import cProfile>>> cProfile.run('benchmark()')                  707 function calls in 0.733 seconds   Ordered by: standard name   ncalls  tottime  percall  cumtime  percall filename:lineno(function)        1    0.000    0.000    0.733    0.733 :1()      300    0.000    0.000    0.000    0.000 random.py:415(uniform)      100    0.000    0.000    0.000    0.000 simul.py:10(__init__)        1    0.000    0.000    0.733    0.733 simul.py:117(benchmark)        1    0.000    0.000    0.000    0.000 simul.py:118()        1    0.000    0.000    0.000    0.000 simul.py:18(__init__)        1    0.733    0.733    0.733    0.733 simul.py:21(evolve)        1    0.000    0.000    0.733    0.733 {built-in method builtins.exec}        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}      300    0.000    0.000    0.000    0.000 {method 'random' of '_random.Random' objects}

profile 对象开启和关闭之间可以包含任意代码

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>>> from simul import benchmark>>> import cProfile>>>>>> pr = cProfile.Profile()>>> pr.enable()>>> benchmark()>>> pr.disable()>>> pr.print_stats()         706 function calls in 0.599 seconds   Ordered by: standard name   ncalls  tottime  percall  cumtime  percall filename:lineno(function)        1    0.000    0.000    0.000    0.000 :1()      300    0.000    0.000    0.000    0.000 random.py:415(uniform)      100    0.000    0.000    0.000    0.000 simul.py:10(__init__)        1    0.000    0.000    0.599    0.599 simul.py:117(benchmark)        1    0.000    0.000    0.000    0.000 simul.py:118()        1    0.000    0.000    0.000    0.000 simul.py:18(__init__)        1    0.599    0.599    0.599    0.599 simul.py:21(evolve)        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}      300    0.000    0.000    0.000    0.000 {method 'random' of '_random.Random' objects}

tottime 不含调用其他函数的时间，cumtime 执行函数（包含调用其他函数的时间）的总时间KCachegrind 图形化分析

KCachegrind – pyprof2calltree – cProfile

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sudo apt install kcachegrindpip install pyprof2calltree

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python -m cProfile -o prof.out taylor.pypyprof2calltree -i prof.out -o prof.calltree

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kcachegrind prof.calltree

安装 kcachegrind 失败，没有运行截图

还有其他工具 Gprof2Dot 可以生成调用图

4. line_profiler

它是一个 py 包，安装后，对要监视的函数应用 装饰器 @profile

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pip install line_profiler

https://github.com/rkern/line_profiler

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kernprof -l -v simul.py

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$ kernprof -l -v simul.pyWrote profile results to simul.py.lprofTimer unit: 1e-06 sTotal time: 4.39747 sFile: simul.pyFunction: evolve at line 21Line #      Hits         Time  Per Hit   % Time  Line Contents==============================================================    21                                               @profile    22                                               def evolve(self, dt):    23         1          5.0      5.0      0.0          timestep = 0.00001    24         1          5.0      5.0      0.0          nsteps = int(dt/timestep)    25    26     10001       5419.0      0.5      0.1          for i in range(nsteps):    27   1010000     454924.0      0.5     10.3              for p in self.particles:    28    29   1000000     791441.0      0.8     18.0                  norm = (p.x**2 + p.y**2)**0.5    30   1000000     537019.0      0.5     12.2                  v_x = (-p.y)/norm    31   1000000     492304.0      0.5     11.2                  v_y = p.x/norm    32    33   1000000     525471.0      0.5     11.9                  d_x = timestep * p.ang_speed * v_x    34   1000000     521829.0      0.5     11.9                  d_y = timestep * p.ang_speed * v_y    35    36   1000000     537637.0      0.5     12.2                  p.x += d_x    37   1000000     531418.0      0.5     12.1                  p.y += d_y

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python -m line_profiler simul.py.lprof

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$ python -m line_profiler simul.py.lprofTimer unit: 1e-06 sTotal time: 5.34553 sFile: simul.pyFunction: evolve at line 21Line #      Hits         Time  Per Hit   % Time  Line Contents==============================================================    21                                               @profile    22                                               def evolve(self, dt):    23         1          3.0      3.0      0.0          timestep = 0.00001    24         1          3.0      3.0      0.0          nsteps = int(dt/timestep)    25    26     10001       6837.0      0.7      0.1          for i in range(nsteps):    27   1010000     567894.0      0.6     10.6              for p in self.particles:    28    29   1000000     953363.0      1.0     17.8                  norm = (p.x**2 + p.y**2)**0.5    30   1000000     656821.0      0.7     12.3                  v_x = (-p.y)/norm    31   1000000     601929.0      0.6     11.3                  v_y = p.x/norm    32    33   1000000     635255.0      0.6     11.9                  d_x = timestep * p.ang_speed * v_x    34   1000000     636091.0      0.6     11.9                  d_y = timestep * p.ang_speed * v_y    35    36   1000000     651873.0      0.7     12.2                  p.x += d_x    37   1000000     635462.0      0.6     11.9                  p.y += d_y

5. 性能优化用更简洁的计算公式预计算不变量减少赋值语句，消除中间变量

注意：细微的优化，速度有所提高，但可能并不显著，还需要保证算法正确

6. dis 模块

该包可以了解代码是如何转换为字节码的， dis 表示 disassemble 反汇编

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import disdis.dis(函数名)

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dis.dis(ParticleSimulator.evolve) 22           0 LOAD_CONST               1 (1e-05)              2 STORE_FAST               2 (timestep) 23           4 LOAD_GLOBAL              0 (int)              6 LOAD_FAST                1 (dt)              8 LOAD_FAST                2 (timestep)             10 BINARY_TRUE_DIVIDE             12 CALL_FUNCTION            1             14 STORE_FAST               3 (nsteps) 25          16 LOAD_GLOBAL              1 (range)             18 LOAD_FAST                3 (nsteps)             20 CALL_FUNCTION            1             22 GET_ITER        >>   24 FOR_ITER               118 (to 144)             26 STORE_FAST               4 (i) 26          28 LOAD_FAST                0 (self)             30 LOAD_ATTR                2 (particles)             32 GET_ITER        >>   34 FOR_ITER               106 (to 142)             36 STORE_FAST               5 (p) 28          38 LOAD_FAST                5 (p)             40 LOAD_ATTR                3 (x)             42 LOAD_CONST               2 (2)             44 BINARY_POWER             46 LOAD_FAST                5 (p)             48 LOAD_ATTR                4 (y)             50 LOAD_CONST               2 (2)             52 BINARY_POWER             54 BINARY_ADD             56 LOAD_CONST               3 (0.5)             58 BINARY_POWER             60 STORE_FAST               6 (norm) 29          62 LOAD_FAST                5 (p)             64 LOAD_ATTR                4 (y)             66 UNARY_NEGATIVE             68 LOAD_FAST                6 (norm)             70 BINARY_TRUE_DIVIDE             72 STORE_FAST               7 (v_x) 30          74 LOAD_FAST                5 (p)             76 LOAD_ATTR                3 (x)             78 LOAD_FAST                6 (norm)             80 BINARY_TRUE_DIVIDE             82 STORE_FAST               8 (v_y) 32          84 LOAD_FAST                2 (timestep)             86 LOAD_FAST                5 (p)             88 LOAD_ATTR                5 (ang_speed)             90 BINARY_MULTIPLY             92 LOAD_FAST                7 (v_x)             94 BINARY_MULTIPLY             96 STORE_FAST               9 (d_x) 33          98 LOAD_FAST                2 (timestep)            100 LOAD_FAST                5 (p)            102 LOAD_ATTR                5 (ang_speed)            104 BINARY_MULTIPLY            106 LOAD_FAST                8 (v_y)            108 BINARY_MULTIPLY            110 STORE_FAST              10 (d_y) 35         112 LOAD_FAST                5 (p)            114 DUP_TOP            116 LOAD_ATTR                3 (x)            118 LOAD_FAST                9 (d_x)            120 INPLACE_ADD            122 ROT_TWO            124 STORE_ATTR               3 (x) 36         126 LOAD_FAST                5 (p)            128 DUP_TOP            130 LOAD_ATTR                4 (y)            132 LOAD_FAST               10 (d_y)            134 INPLACE_ADD            136 ROT_TWO            138 STORE_ATTR               4 (y)            140 JUMP_ABSOLUTE           34        >>  142 JUMP_ABSOLUTE           24        >>  144 LOAD_CONST               0 (None)            146 RETURN_VALUE

可以是用该工具了解指令的多少和代码是如何转换的

7. memory_profiler

https://pypi.org/project/memory-profiler/

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pip install memory_profilerpip install psutil

psutil说明

也需要对监视的函数 加装饰器 @profile

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python -m memory_profiler simul.py

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$ python -m memory_profiler simul.pyFilename: simul.pyLine #    Mem usage    Increment  Occurrences   Line Contents=============================================================   141   67.465 MiB   67.465 MiB           1   @profile   142                                         def benchmark_memory():   143   84.023 MiB   16.559 MiB      300004       particles = [Particle(uniform(-1.0, 1.0),   144   84.023 MiB    0.000 MiB      100000                             uniform(-1.0, 1.0),   145   84.023 MiB    0.000 MiB      100000                             uniform(-1.0, 1.0))   146   84.023 MiB    0.000 MiB      100001                     for i in range(100000)]   147   148   84.023 MiB    0.000 MiB           1       simulator = ParticleSimulator(particles)   149   84.023 MiB    0.000 MiB           1       simulator.evolve(0.001)

内存使用随时间的变化

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$ mprof run simul.pymprof: Sampling memory every 0.1srunning new processrunning as a Python program...

绘制曲线

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$ mprof plot

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