python 基准测试（cProfile \ kcachegrind \ line_profiler \ memory_profiler）
python 基准测试（cProfile \ kcachegrind \ line_profiler \ memory_profiler）
Michael阿明
发布于 2022-09-21 10:29:39
发布于 2022-09-21 10:29:39
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
from matplotlib import pyplot as plt
from matplotlib import animation
from random import uniform
import timeit
class 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_speed
class 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_y
def 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()
在这里插入图片描述
2. 运行耗时测试
linux time 命令
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：
time python my.py
real 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
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：
6.9873279229996115
[6.382431660999828, 6.248147055000118, 6.325469069000064]pytest、pytest-benchmark
pip install pytest
pip install pytest-benchmark$ pytest test_simul.py::test_evolve
=================== test session starts ====================platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
benchmark: 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/Chapter01
plugins: benchmark-3.4.1
collected 1 item
test_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
python -m cProfile simul.py$ 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 <__array_function__ internals>:177(any)
160 0.000 0.000 0.002 0.000 <__array_function__ internals>:177(column_stack)
161 0.000 0.000 0.004 0.000 <__array_function__ internals>:177(concatenate)
34 0.000 0.000 0.000 0.000 <__array_function__ internals>:177(copyto)
30 0.000 0.000 0.002 0.000 <__array_function__ internals>:177(linspace)
30 0.000 0.000 0.000 0.000 <__array_function__ internals>:177(ndim)
30 0.000 0.000 0.000 0.000 <__array_function__ internals>:177(result_type)
5 0.000 0.000 0.116 0.023 <frozen importlib._bootstrap>:1002(_gcd_import)
485/33 0.001 0.000 6.807 0.206 <frozen importlib._bootstrap>: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
$ 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 <frozen importlib._bootstrap_external>: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
>>> 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 <string>:1(<module>)
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(<listcomp>)
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
>>> 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 <stdin>:1(<module>)
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(<listcomp>)
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
sudo apt install kcachegrind
pip install pyprof2calltreepython -m cProfile -o prof.out taylor.py
pyprof2calltree -i prof.out -o prof.calltreekcachegrind 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
pip install line_profilerhttps://github.com/rkern/line_profiler
kernprof -l -v simul.py$ kernprof -l -v simul.py
Wrote profile results to simul.py.lprof
Timer unit: 1e-06 s
Total time: 4.39747 s
File: simul.py
Function: evolve at line 21
Line # 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_ypython -m line_profiler simul.py.lprof$ python -m line_profiler simul.py.lprof
Timer unit: 1e-06 s
Total time: 5.34553 s
File: simul.py
Function: evolve at line 21
Line # 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_y5. 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
import dis
dis.dis(函数名)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/
pip install memory_profiler
pip install psutil也需要对监视的函数 加装饰器 @profile
python -m memory_profiler simul.py$ python -m memory_profiler simul.py
Filename: simul.py
Line # 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
$ mprof run simul.py
mprof: Sampling memory every 0.1s
running new process
running as a Python program...绘制曲线
$ mprof plot
在这里插入图片描述
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原始发表：2022-08-10，如有侵权请联系 cloudcommunity@tencent.com 删除
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