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330 lines
13 KiB
330 lines
13 KiB
6 years ago
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"""Custom implementation of multiprocessing.Pool with custom pickler.
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This module provides efficient ways of working with data stored in
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shared memory with numpy.memmap arrays without inducing any memory
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copy between the parent and child processes.
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This module should not be imported if multiprocessing is not
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available as it implements subclasses of multiprocessing Pool
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that uses a custom alternative to SimpleQueue.
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"""
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# Author: Olivier Grisel <olivier.grisel@ensta.org>
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# Copyright: 2012, Olivier Grisel
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# License: BSD 3 clause
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import sys
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import warnings
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from time import sleep
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try:
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WindowsError
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except NameError:
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WindowsError = type(None)
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# Customizable pure Python pickler in Python 2
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# customizable C-optimized pickler under Python 3.3+
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from pickle import Pickler
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from pickle import HIGHEST_PROTOCOL
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from io import BytesIO
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from .disk import delete_folder
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from ._memmapping_reducer import get_memmapping_reducers
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from ._multiprocessing_helpers import mp, assert_spawning
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# We need the class definition to derive from it, not the multiprocessing.Pool
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# factory function
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from multiprocessing.pool import Pool
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try:
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import numpy as np
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except ImportError:
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np = None
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if sys.version_info[:2] > (2, 7):
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import copyreg
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###############################################################################
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# Enable custom pickling in Pool queues
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class CustomizablePickler(Pickler):
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"""Pickler that accepts custom reducers.
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HIGHEST_PROTOCOL is selected by default as this pickler is used
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to pickle ephemeral datastructures for interprocess communication
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hence no backward compatibility is required.
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`reducers` is expected to be a dictionary with key/values
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being `(type, callable)` pairs where `callable` is a function that
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give an instance of `type` will return a tuple `(constructor,
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tuple_of_objects)` to rebuild an instance out of the pickled
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`tuple_of_objects` as would return a `__reduce__` method. See the
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standard library documentation on pickling for more details.
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"""
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# We override the pure Python pickler as its the only way to be able to
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# customize the dispatch table without side effects in Python 2.7
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# to 3.2. For Python 3.3+ leverage the new dispatch_table
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# feature from https://bugs.python.org/issue14166 that makes it possible
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# to use the C implementation of the Pickler which is faster.
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def __init__(self, writer, reducers=None, protocol=HIGHEST_PROTOCOL):
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Pickler.__init__(self, writer, protocol=protocol)
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if reducers is None:
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reducers = {}
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if hasattr(Pickler, 'dispatch'):
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# Make the dispatch registry an instance level attribute instead of
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# a reference to the class dictionary under Python 2
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self.dispatch = Pickler.dispatch.copy()
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else:
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# Under Python 3 initialize the dispatch table with a copy of the
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# default registry
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self.dispatch_table = copyreg.dispatch_table.copy()
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for type, reduce_func in reducers.items():
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self.register(type, reduce_func)
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def register(self, type, reduce_func):
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"""Attach a reducer function to a given type in the dispatch table."""
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if hasattr(Pickler, 'dispatch'):
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# Python 2 pickler dispatching is not explicitly customizable.
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# Let us use a closure to workaround this limitation.
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def dispatcher(self, obj):
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reduced = reduce_func(obj)
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self.save_reduce(obj=obj, *reduced)
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self.dispatch[type] = dispatcher
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else:
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self.dispatch_table[type] = reduce_func
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class CustomizablePicklingQueue(object):
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"""Locked Pipe implementation that uses a customizable pickler.
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This class is an alternative to the multiprocessing implementation
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of SimpleQueue in order to make it possible to pass custom
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pickling reducers, for instance to avoid memory copy when passing
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memory mapped datastructures.
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`reducers` is expected to be a dict with key / values being
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`(type, callable)` pairs where `callable` is a function that, given an
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instance of `type`, will return a tuple `(constructor, tuple_of_objects)`
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to rebuild an instance out of the pickled `tuple_of_objects` as would
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return a `__reduce__` method.
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See the standard library documentation on pickling for more details.
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"""
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def __init__(self, context, reducers=None):
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self._reducers = reducers
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self._reader, self._writer = context.Pipe(duplex=False)
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self._rlock = context.Lock()
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if sys.platform == 'win32':
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self._wlock = None
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else:
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self._wlock = context.Lock()
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self._make_methods()
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def __getstate__(self):
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assert_spawning(self)
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return (self._reader, self._writer, self._rlock, self._wlock,
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self._reducers)
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def __setstate__(self, state):
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(self._reader, self._writer, self._rlock, self._wlock,
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self._reducers) = state
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self._make_methods()
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def empty(self):
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return not self._reader.poll()
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def _make_methods(self):
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self._recv = recv = self._reader.recv
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racquire, rrelease = self._rlock.acquire, self._rlock.release
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def get():
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racquire()
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try:
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return recv()
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finally:
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rrelease()
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self.get = get
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if self._reducers:
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def send(obj):
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buffer = BytesIO()
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CustomizablePickler(buffer, self._reducers).dump(obj)
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self._writer.send_bytes(buffer.getvalue())
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self._send = send
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else:
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self._send = send = self._writer.send
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if self._wlock is None:
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# writes to a message oriented win32 pipe are atomic
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self.put = send
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else:
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wlock_acquire, wlock_release = (
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self._wlock.acquire, self._wlock.release)
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def put(obj):
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wlock_acquire()
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try:
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return send(obj)
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finally:
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wlock_release()
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self.put = put
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class PicklingPool(Pool):
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"""Pool implementation with customizable pickling reducers.
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This is useful to control how data is shipped between processes
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and makes it possible to use shared memory without useless
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copies induces by the default pickling methods of the original
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objects passed as arguments to dispatch.
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`forward_reducers` and `backward_reducers` are expected to be
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dictionaries with key/values being `(type, callable)` pairs where
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`callable` is a function that, given an instance of `type`, will return a
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tuple `(constructor, tuple_of_objects)` to rebuild an instance out of the
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pickled `tuple_of_objects` as would return a `__reduce__` method.
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See the standard library documentation about pickling for more details.
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"""
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def __init__(self, processes=None, forward_reducers=None,
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backward_reducers=None, **kwargs):
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if forward_reducers is None:
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forward_reducers = dict()
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if backward_reducers is None:
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backward_reducers = dict()
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self._forward_reducers = forward_reducers
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self._backward_reducers = backward_reducers
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poolargs = dict(processes=processes)
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poolargs.update(kwargs)
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super(PicklingPool, self).__init__(**poolargs)
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def _setup_queues(self):
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context = getattr(self, '_ctx', mp)
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self._inqueue = CustomizablePicklingQueue(context,
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self._forward_reducers)
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self._outqueue = CustomizablePicklingQueue(context,
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self._backward_reducers)
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self._quick_put = self._inqueue._send
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self._quick_get = self._outqueue._recv
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class MemmappingPool(PicklingPool):
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"""Process pool that shares large arrays to avoid memory copy.
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This drop-in replacement for `multiprocessing.pool.Pool` makes
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it possible to work efficiently with shared memory in a numpy
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context.
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Existing instances of numpy.memmap are preserved: the child
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suprocesses will have access to the same shared memory in the
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original mode except for the 'w+' mode that is automatically
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transformed as 'r+' to avoid zeroing the original data upon
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instantiation.
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Furthermore large arrays from the parent process are automatically
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dumped to a temporary folder on the filesystem such as child
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processes to access their content via memmapping (file system
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backed shared memory).
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Note: it is important to call the terminate method to collect
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the temporary folder used by the pool.
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Parameters
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----------
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processes: int, optional
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Number of worker processes running concurrently in the pool.
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initializer: callable, optional
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Callable executed on worker process creation.
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initargs: tuple, optional
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Arguments passed to the initializer callable.
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temp_folder: str, optional
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Folder to be used by the pool for memmapping large arrays
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for sharing memory with worker processes. If None, this will try in
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order:
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- a folder pointed by the JOBLIB_TEMP_FOLDER environment variable,
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- /dev/shm if the folder exists and is writable: this is a RAMdisk
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filesystem available by default on modern Linux distributions,
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- the default system temporary folder that can be overridden
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with TMP, TMPDIR or TEMP environment variables, typically /tmp
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under Unix operating systems.
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max_nbytes int or None, optional, 1e6 by default
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Threshold on the size of arrays passed to the workers that
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triggers automated memory mapping in temp_folder.
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Use None to disable memmapping of large arrays.
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mmap_mode: {'r+', 'r', 'w+', 'c'}
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Memmapping mode for numpy arrays passed to workers.
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See 'max_nbytes' parameter documentation for more details.
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forward_reducers: dictionary, optional
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Reducers used to pickle objects passed from master to worker
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processes: see below.
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backward_reducers: dictionary, optional
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Reducers used to pickle return values from workers back to the
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master process.
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verbose: int, optional
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Make it possible to monitor how the communication of numpy arrays
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with the subprocess is handled (pickling or memmapping)
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prewarm: bool or str, optional, "auto" by default.
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If True, force a read on newly memmapped array to make sure that OS
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pre-cache it in memory. This can be useful to avoid concurrent disk
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access when the same data array is passed to different worker
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processes. If "auto" (by default), prewarm is set to True, unless the
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Linux shared memory partition /dev/shm is available and used as temp
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folder.
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`forward_reducers` and `backward_reducers` are expected to be
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dictionaries with key/values being `(type, callable)` pairs where
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`callable` is a function that give an instance of `type` will return
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a tuple `(constructor, tuple_of_objects)` to rebuild an instance out
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of the pickled `tuple_of_objects` as would return a `__reduce__`
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method. See the standard library documentation on pickling for more
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details.
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"""
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def __init__(self, processes=None, temp_folder=None, max_nbytes=1e6,
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mmap_mode='r', forward_reducers=None, backward_reducers=None,
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verbose=0, context_id=None, prewarm=False, **kwargs):
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if context_id is not None:
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warnings.warn('context_id is deprecated and ignored in joblib'
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' 0.9.4 and will be removed in 0.11',
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DeprecationWarning)
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forward_reducers, backward_reducers, self._temp_folder = \
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get_memmapping_reducers(
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id(self), temp_folder=temp_folder, max_nbytes=max_nbytes,
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mmap_mode=mmap_mode, forward_reducers=forward_reducers,
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backward_reducers=backward_reducers, verbose=verbose,
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prewarm=prewarm)
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poolargs = dict(
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processes=processes,
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forward_reducers=forward_reducers,
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backward_reducers=backward_reducers)
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poolargs.update(kwargs)
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super(MemmappingPool, self).__init__(**poolargs)
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def terminate(self):
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n_retries = 10
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for i in range(n_retries):
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try:
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super(MemmappingPool, self).terminate()
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break
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except OSError as e:
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if isinstance(e, WindowsError):
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# Workaround occasional "[Error 5] Access is denied" issue
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# when trying to terminate a process under windows.
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sleep(0.1)
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if i + 1 == n_retries:
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warnings.warn("Failed to terminate worker processes in"
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" multiprocessing pool: %r" % e)
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delete_folder(self._temp_folder)
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