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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
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()- 1
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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()- 1
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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- 1
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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)- 1
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输出：
6.9873279229996115 [6.382431660999828, 6.248147055000118, 6.325469069000064]- 1
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pytest、pytest-benchmark
pip install pytest pip install pytest-benchmark- 1
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$ pytest test_simul.py::test_evolve =================== test session starts ====================platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0 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- 1
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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- 1
$ 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) 。。。- 1
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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}- 1
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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}- 1
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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}- 1
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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-cProfilesudo apt install kcachegrind pip install pyprof2calltree- 1
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python -m cProfile -o prof.out taylor.py pyprof2calltree -i prof.out -o prof.calltree- 1
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kcachegrind prof.calltree- 1
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
@profilepip install line_profiler- 1
https://github.com/rkern/line_profiler
kernprof -l -v simul.py- 1
$ 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_y- 1
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python -m line_profiler simul.py.lprof- 1
$ 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_y- 1
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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
import dis dis.dis(函数名)- 1
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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- 1
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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- 1
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也需要对监视的函数 加装饰器
@profilepython -m memory_profiler simul.py- 1
$ 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)- 1
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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...- 1
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绘制曲线
$ mprof plot- 1
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【oncmdmsg 鼠标】2023/8/19 上午9:50:14
- 原文地址：https://blog.csdn.net/qq_21201267/article/details/126241080
