理解多处理：Python中的共享内存管理、锁和队列

Question

Multiprocessing是python中一个强大的工具，我想更深入地理解它。 我想知道何时使用regularLocks和Queues以及何时使用多处理Manager在所有进程之间共享这些。

我提出了以下测试场景，其中有四种不同的多处理条件：

1. 使用游泳池和否经理

2. 使用游泳池和管理器

3. 使用个别流程和否经理

4. 使用单个流程和管理器

工作

所有条件都执行作业函数the_job。the_job包含一些由锁保护的打印。此外，函数的输入只是放入一个队列中（看看它是否可以从队列中恢复）。这个输入只是在名为start_scenario的主脚本中创建的idx的索引（显示在底部）。

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)

条件的成功被定义为完全调用输入 从队列中，查看底部的函数read_queue。

条件

条件1和条件2相当不言自明。 条件1包括创建锁和队列，并将它们传递给流程池：

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.imap(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)

（helper函数make_iterator在本文的底部给出。） 条件1失败，出现RuntimeError: Lock objects should only be shared between processes through inheritance。

条件2非常相似，但现在锁和队列由管理器管理：

def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.imap(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)

在条件3中，手动启动新进程，并且在没有管理器的情况下创建锁和队列：

def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

条件4类似，但现在再次使用管理器：

def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)

在两种情况下-3和4-我开始一个新的 处理the_job的10个任务中的每一个，最多有ncores个进程 同时操作。这是通过以下助手函数实现的：

def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)

结果

只有条件1失败（RuntimeError: Lock objects should only be shared between processes through inheritance），而其他3个条件成功。我试着用我的头脑来思考这个结果。

为什么池需要在所有进程之间共享锁和队列，但条件3中的各个进程不需要？

我知道的是，对于池条件（1和2），来自迭代器的所有数据都是通过pickling传递的，而在单进程条件（3和4）中，来自迭代器的所有数据都是通过主进程的继承传递的（我使用的是Linux）。 我想，在从子进程中更改内存之前，将访问父进程使用的相同内存（写时复制）。但是只要有人说lock.acquire()，就应该更改它，并且子进程确实在内存中的其他地方使用不同的锁，不是吗？一个子进程如何知道一个兄弟激活了一个不通过管理器共享的锁？

最后，有点相关的是我的问题，条件3和条件4有多大的不同。两者都有单独的过程，但它们在管理器的使用上有所不同。两者都被认为是有效的代码吗？或者，如果实际上不需要经理，那么应该避免使用经理吗？

---

完整脚本

对于那些只想复制和粘贴所有内容来执行代码的人，下面是完整的脚本：

__author__ = 'Me and myself'

import multiprocessing as mp
import time

def the_job(args):
    """The job for multiprocessing.

    Prints some stuff secured by a lock and 
    finally puts the input into a queue.

    """
    idx = args[0]
    lock = args[1]
    queue=args[2]

    lock.acquire()
    print 'I'
    print 'was '
    print 'here '
    print '!!!!'
    print '1111'
    print 'einhundertelfzigelf\n'
    who= ' By run %d \n' % idx
    print who
    lock.release()

    queue.put(idx)


def read_queue(queue):
    """Turns a qeue into a normal python list."""
    results = []
    while not queue.empty():
        result = queue.get()
        results.append(result)
    return results


def make_iterator(args, lock, queue):
    """Makes an iterator over args and passes the lock an queue to each element."""
    return ((arg, lock, queue) for arg in args)


def start_scenario(scenario_number = 1):
    """Starts one of four multiprocessing scenarios.

    :param scenario_number: Index of scenario, 1 to 4

    """
    args = range(10)
    ncores = 3
    if scenario_number==1:
        result =  scenario_1_pool_no_manager(the_job, args, ncores)

    elif scenario_number==2:
        result =  scenario_2_pool_manager(the_job, args, ncores)

    elif scenario_number==3:
        result =  scenario_3_single_processes_no_manager(the_job, args, ncores)

    elif scenario_number==4:
        result =  scenario_4_single_processes_manager(the_job, args, ncores)

    if result != args:
        print 'Scenario %d fails: %s != %s' % (scenario_number, args, result)
    else:
        print 'Scenario %d successful!' % scenario_number


def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    FAILS!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    mypool.map(jobfunc, iterator)

    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_2_pool_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITH a Manager for the lock and queue.

    SUCCESSFUL!

    """
    mypool = mp.Pool(ncores)
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)
    mypool.map(jobfunc, iterator)
    mypool.close()
    mypool.join()

    return read_queue(queue)


def scenario_3_single_processes_no_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITHOUT a Manager,

    SUCCESSFUL!

    """
    lock = mp.Lock()
    queue = mp.Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def scenario_4_single_processes_manager(jobfunc, args, ncores):
    """Runs an individual process for every task WITH a Manager,

    SUCCESSFUL!

    """
    lock = mp.Manager().Lock()
    queue = mp.Manager().Queue()

    iterator = make_iterator(args, lock, queue)

    do_job_single_processes(jobfunc, iterator, ncores)

    return read_queue(queue)


def do_job_single_processes(jobfunc, iterator, ncores):
    """Runs a job function by starting individual processes for every task.

    At most `ncores` processes operate at the same time

    :param jobfunc: Job to do

    :param iterator:

        Iterator over different parameter settings,
        contains a lock and a queue

    :param ncores:

        Number of processes operating at the same time

    """
    keep_running=True
    process_dict = {} # Dict containing all subprocees

    while len(process_dict)>0 or keep_running:

        terminated_procs_pids = []
        # First check if some processes did finish their job
        for pid, proc in process_dict.iteritems():

            # Remember the terminated processes
            if not proc.is_alive():
                terminated_procs_pids.append(pid)

        # And delete these from the process dict
        for terminated_proc in terminated_procs_pids:
            process_dict.pop(terminated_proc)

        # If we have less active processes than ncores and there is still
        # a job to do, add another process
        if len(process_dict) < ncores and keep_running:
            try:
                task = iterator.next()
                proc = mp.Process(target=jobfunc,
                                                   args=(task,))
                proc.start()
                process_dict[proc.pid]=proc
            except StopIteration:
                # All tasks have been started
                keep_running=False

        time.sleep(0.1)


def main():
    """Runs 1 out of 4 different multiprocessing scenarios"""
    start_scenario(1)


if __name__ == '__main__':
    main()

Answer 1

multiprocessing.Lock是使用操作系统提供的信号量对象实现的。在Linux上，子对象只是通过os.fork从父对象继承信号量的句柄。这不是信号量的副本；它实际上继承了父进程拥有的相同句柄，继承文件描述符的方式也一样。另一方面，Windows不支持os.fork，因此它必须对Lock进行pickle。它通过使用Windows ^{}API为multiprocessing.Lock对象在内部使用的Windows信号量创建一个重复的句柄来实现，该API声明：

The duplicate handle refers to the same object as the original handle. Therefore, any changes to the object are reflected through both handles

通过DuplicateHandleAPI，您可以将复制句柄的所有权授予子进程，以便子进程在取消对其的绑定后可以实际使用它。通过创建子对象拥有的重复句柄，可以有效地“共享”锁对象。

这里是multiprocessing/synchronize.py中的信号量对象

class SemLock(object):

    def __init__(self, kind, value, maxvalue):
        sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue)
        debug('created semlock with handle %s' % sl.handle)
        self._make_methods()

        if sys.platform != 'win32':
            def _after_fork(obj):
                obj._semlock._after_fork()
            register_after_fork(self, _after_fork)

    def _make_methods(self):
        self.acquire = self._semlock.acquire
        self.release = self._semlock.release
        self.__enter__ = self._semlock.__enter__
        self.__exit__ = self._semlock.__exit__

    def __getstate__(self):  # This is called when you try to pickle the `Lock`.
        assert_spawning(self)
        sl = self._semlock
        return (Popen.duplicate_for_child(sl.handle), sl.kind, sl.maxvalue)

    def __setstate__(self, state): # This is called when unpickling a `Lock`
        self._semlock = _multiprocessing.SemLock._rebuild(*state)
        debug('recreated blocker with handle %r' % state[0])
        self._make_methods()

注意assert_spawning调用中的__getstate__，它在拾取对象时被调用。这是如何实现的：

#
# Check that the current thread is spawning a child process
#

def assert_spawning(self):
    if not Popen.thread_is_spawning():
        raise RuntimeError(
            '%s objects should only be shared between processes'
            ' through inheritance' % type(self).__name__
            )

这个函数通过调用thread_is_spawning，确保您“继承”了Lock。在Linux上，该方法只返回False：

@staticmethod
def thread_is_spawning():
    return False

这是因为Linux不需要pickle来继承Lock，所以如果__getstate__实际上是在Linux上调用的，我们就不能继承。在Windows上，还有更多：

def dump(obj, file, protocol=None):
    ForkingPickler(file, protocol).dump(obj)

class Popen(object):
    '''
    Start a subprocess to run the code of a process object
    '''
    _tls = thread._local()

    def __init__(self, process_obj):
        ...
        # send information to child
        prep_data = get_preparation_data(process_obj._name)
        to_child = os.fdopen(wfd, 'wb')
        Popen._tls.process_handle = int(hp)
        try:
            dump(prep_data, to_child, HIGHEST_PROTOCOL)
            dump(process_obj, to_child, HIGHEST_PROTOCOL)
        finally:
            del Popen._tls.process_handle
            to_child.close()


    @staticmethod
    def thread_is_spawning():
        return getattr(Popen._tls, 'process_handle', None) is not None

这里，thread_is_spawning如果Popen._tls对象具有process_handle属性，则返回True。我们可以看到在__init__中创建了process_handle属性，然后使用dump将要继承的数据从父级传递到子级，然后删除该属性。因此thread_is_spawning将只在__init__期间是True。根据this python-ideas mailing list thread，这实际上是一个人为的限制，用于模拟Linux上的os.fork行为。Windows实际上可以在任何时候支持传递Lock，因为DuplicateHandle可以在任何时候运行。

以上所有内容都适用于Queue对象，因为它在内部使用Lock。

我想说，继承Lock对象比使用Manager.Lock()对象更好，因为当您使用Manager.Lock时，您对Lock的每个调用都必须通过IPC发送到Manager进程，这将比使用位于调用进程内的共享Lock进程慢得多。不过，这两种方法都是完全有效的。

最后，可以不使用Manager，使用initializer/initargs关键字参数，将Lock传递给Pool的所有成员：

lock = None
def initialize_lock(l):
   global lock
   lock = l

def scenario_1_pool_no_manager(jobfunc, args, ncores):
    """Runs a pool of processes WITHOUT a Manager for the lock and queue.

    """
    lock = mp.Lock()
    mypool = mp.Pool(ncores, initializer=initialize_lock, initargs=(lock,))
    queue = mp.Queue()

    iterator = make_iterator(args, queue)

    mypool.imap(jobfunc, iterator) # Don't pass lock. It has to be used as a global in the child. (This means `jobfunc` would need to be re-written slightly.

    mypool.close()
    mypool.join()

return read_queue(queue)

这是因为传递给initargs的参数被传递给在Pool内运行的Process对象的__init__方法，所以它们最终被继承，而不是被pickle。