使用 cProfile 对其他完美工作的多处理 python 脚本进行分析时出错 [重复]

我写了一个小的 python 脚本，使用multiprocessing (See https://stackoverflow.com/a/41875711/1878788 https://stackoverflow.com/a/41875711/1878788）。当我测试它时它有效：

$ ./forkiter.py
0
1
2
3
4
sum of x+1: 15
sum of 2*x: 20
sum of x*x: 30

但是当我尝试用它来分析它时cProfile，我得到以下信息：

$ python3.6 -m cProfile -o forkiter.prof ./forkiter.py
0
1
2
3
4
Traceback (most recent call last):
  File "/home/bli/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/bli/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/bli/lib/python3.6/cProfile.py", line 160, in <module>
    main()
  File "/home/bli/lib/python3.6/cProfile.py", line 153, in main
    runctx(code, globs, None, options.outfile, options.sort)
  File "/home/bli/lib/python3.6/cProfile.py", line 20, in runctx
    filename, sort)
  File "/home/bli/lib/python3.6/profile.py", line 64, in runctx
    prof.runctx(statement, globals, locals)
  File "/home/bli/lib/python3.6/cProfile.py", line 100, in runctx
    exec(cmd, globals, locals)
  File "./forkiter.py", line 71, in <module>
    exit(main())
  File "./forkiter.py", line 67, in main
    sum_tuples, results_generator))
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 699, in next
    raise value
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 385, in _handle_tasks
    put(task)
  File "/home/bli/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/bli/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed

会发生什么？

这是脚本：

#!/usr/bin/env python3
"""This script tries to work around some limitations of multiprocessing."""

from itertools import repeat, starmap
from multiprocessing import Pool
from functools import reduce
from operator import add
from time import sleep

# Doesn't work because local functions can't be pickled:
# def make_tuple_func(funcs):
#     def tuple_func(args_list):
#         return tuple(func(args) for func, args in zip(funcs, args_list))
#     return tuple_func
#
# test_tuple_func = make_tuple_func((plus_one, double, square))

class FuncApplier(object):
    """This kind of object can be used to group functions and call them on a
    tuple of arguments."""
    __slots__ = ("funcs", )

    def __init__(self, funcs):
        self.funcs = funcs

    def __len__(self):
        return len(self.funcs)

    def __call__(self, args_list):
        return tuple(func(args) for func, args in zip(self.funcs, args_list))

    def fork_args(self, args_list):
        """Takes an arguments list and repeat them in a n-tuple."""
        return tuple(repeat(args_list, len(self)))


def sum_tuples(*tuples):
    """Element-wise sum of tuple items."""
    return tuple(starmap(add, zip(*tuples)))


# Can't define these functions in main:
# They wouldn't be pickleable.
def plus_one(x):
    return x + 1

def double(x):
    return 2 * x

def square(x):
    return x * x

def main():
    def my_generator():
        for i in range(5):
            print(i)
            yield i


    test_tuple_func = FuncApplier((plus_one, double, square))

    with Pool(processes=5) as pool:
        results_generator = pool.imap_unordered(
            test_tuple_func,
            (test_tuple_func.fork_args(args_list) for args_list in my_generator()))
        print("sum of x+1:\t%s\nsum of 2*x:\t%s\nsum of x*x:\t%s" % reduce(
            sum_tuples, results_generator))
    exit(0)

if __name__ == "__main__":
    exit(main())

一些酸洗测试

一些研究表明，有时物体需要一个__setstate__ and __getstate__可腌制的方法。这对某些酸洗协议有帮助，但这似乎并不能解决问题cProfile案件。 请参阅下面的测试。

更新后的脚本：

#!/usr/bin/env python3
"""This script tries to work around some limitations of multiprocessing."""

from itertools import repeat, starmap
from multiprocessing import Pool
from functools import reduce
from operator import add
from time import sleep
import pickle

# Doesn't work because local functions can't be pickled:
# def make_tuple_func(funcs):
#     def tuple_func(args_list):
#         return tuple(func(args) for func, args in zip(funcs, args_list))
#     return tuple_func
#
# test_tuple_func = make_tuple_func((plus_one, double, square))

class FuncApplier(object):
    """This kind of object can be used to group functions and call them on a
    tuple of arguments."""
    __slots__ = ("funcs", )

    def __init__(self, funcs):
        self.funcs = funcs

    def __len__(self):
        return len(self.funcs)

    def __call__(self, args_list):
        return tuple(func(args) for func, args in zip(self.funcs, args_list))

    # Attempt to make it pickleable when under cProfile (doesn't help)
    def __getstate__(self):
        return self.funcs

    def __setstate__(self, state):
        self.funcs = state

    def fork_args(self, args_list):
        """Takes an arguments list and repeat them in a n-tuple."""
        return tuple(repeat(args_list, len(self)))


def sum_tuples(*tuples):
    """Element-wise sum of tuple items."""
    return tuple(starmap(add, zip(*tuples)))


# Can't define these functions in main:
# They wouldn't be pickleable.
def plus_one(x):
    return x + 1

def double(x):
    return 2 * x

def square(x):
    return x * x

def main():
    def my_generator():
        for i in range(5):
            print(i)
            yield i


    test_tuple_func = FuncApplier((plus_one, double, square))

    print("protocol 0")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 1")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 2")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 3")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)
    print("protocol 4")
    try:
        print(pickle.dumps(test_tuple_func, 0))
    except pickle.PicklingError as err:
        print("failed with the following error:\n%s" % err)

    with Pool(processes=5) as pool:
        results_generator = pool.imap_unordered(
            test_tuple_func,
            (test_tuple_func.fork_args(args_list) for args_list in my_generator()))
        print("sum of x+1:\t%s\nsum of 2*x:\t%s\nsum of x*x:\t%s" % reduce(
            sum_tuples, results_generator))
    exit(0)

if __name__ == "__main__":
    exit(main())

测试无cProfile看起来不错：

$ ./forkiter.py
protocol 0
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 1
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 2
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 3
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
protocol 4
b'ccopy_reg\n_reconstructor\np0\n(c__main__\nFuncApplier\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\n(c__main__\nplus_one\np5\nc__main__\ndouble\np6\nc__main__\nsquare\np7\ntp8\nb.'
0
1
2
3
4
sum of x+1: 15
sum of 2*x: 20
sum of x*x: 30

cProfile 下的测试在每个酸洗协议中都失败（因此在多处理中也是如此）：

$ python3.6 -m cProfile -o forkiter.prof ./forkiter.py
protocol 0
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 1
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 2
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 3
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
protocol 4
failed with the following error:
Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed
0
1
2
3
4
Traceback (most recent call last):
  File "/home/bli/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/bli/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/bli/lib/python3.6/cProfile.py", line 160, in <module>
    main()
  File "/home/bli/lib/python3.6/cProfile.py", line 153, in main
    runctx(code, globs, None, options.outfile, options.sort)
  File "/home/bli/lib/python3.6/cProfile.py", line 20, in runctx
    filename, sort)
  File "/home/bli/lib/python3.6/profile.py", line 64, in runctx
    prof.runctx(statement, globals, locals)
  File "/home/bli/lib/python3.6/cProfile.py", line 100, in runctx
    exec(cmd, globals, locals)
  File "./forkiter.py", line 105, in <module>
    exit(main())
  File "./forkiter.py", line 101, in main
    sum_tuples, results_generator))
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 699, in next
    raise value
  File "/home/bli/lib/python3.6/multiprocessing/pool.py", line 385, in _handle_tasks
    put(task)
  File "/home/bli/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/home/bli/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '__main__.FuncApplier'>: attribute lookup FuncApplier on __main__ failed

它似乎cProfile根本不适用于多处理。

如果您愿意修改代码，仅分析主流程（或为子流程添加特定分析），cProfile.run()似乎在一定程度上起作用。

在您的示例中，替换

exit(main())

with

exit(cProfile.run('main()')

如果并行函数是全局作用域函数，那么这至少有效，但不确定对于像您这样的类来说也是如此。