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Martina Burlando 2025-08-02 02:00:33 +02:00
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venv/Lib/site-packages/joblib/pool.py Normal file
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"""Custom implementation of multiprocessing.Pool with custom pickler.
This module provides efficient ways of working with data stored in
shared memory with numpy.memmap arrays without inducing any memory
copy between the parent and child processes.
This module should not be imported if multiprocessing is not
available as it implements subclasses of multiprocessing Pool
that uses a custom alternative to SimpleQueue.
"""
# Author: Olivier Grisel <olivier.grisel@ensta.org>
# Copyright: 2012, Olivier Grisel
# License: BSD 3 clause
import copyreg
import sys
import warnings
from time import sleep
try:
WindowsError
except NameError:
WindowsError = type(None)
from io import BytesIO
# We need the class definition to derive from it, not the multiprocessing.Pool
# factory function
from multiprocessing.pool import Pool
from pickle import HIGHEST_PROTOCOL, Pickler
from ._memmapping_reducer import TemporaryResourcesManager, get_memmapping_reducers
from ._multiprocessing_helpers import assert_spawning, mp
try:
import numpy as np
except ImportError:
np = None
###############################################################################
# Enable custom pickling in Pool queues
class CustomizablePickler(Pickler):
"""Pickler that accepts custom reducers.
TODO python2_drop : can this be simplified ?
HIGHEST_PROTOCOL is selected by default as this pickler is used
to pickle ephemeral datastructures for interprocess communication
hence no backward compatibility is required.
`reducers` is expected to be a dictionary with key/values
being `(type, callable)` pairs where `callable` is a function that
give an instance of `type` will return a tuple `(constructor,
tuple_of_objects)` to rebuild an instance out of the pickled
`tuple_of_objects` as would return a `__reduce__` method. See the
standard library documentation on pickling for more details.
"""
# We override the pure Python pickler as its the only way to be able to
# customize the dispatch table without side effects in Python 2.7
# to 3.2. For Python 3.3+ leverage the new dispatch_table
# feature from https://bugs.python.org/issue14166 that makes it possible
# to use the C implementation of the Pickler which is faster.
def __init__(self, writer, reducers=None, protocol=HIGHEST_PROTOCOL):
Pickler.__init__(self, writer, protocol=protocol)
if reducers is None:
reducers = {}
if hasattr(Pickler, "dispatch"):
# Make the dispatch registry an instance level attribute instead of
# a reference to the class dictionary under Python 2
self.dispatch = Pickler.dispatch.copy()
else:
# Under Python 3 initialize the dispatch table with a copy of the
# default registry
self.dispatch_table = copyreg.dispatch_table.copy()
for type, reduce_func in reducers.items():
self.register(type, reduce_func)
def register(self, type, reduce_func):
"""Attach a reducer function to a given type in the dispatch table."""
if hasattr(Pickler, "dispatch"):
# Python 2 pickler dispatching is not explicitly customizable.
# Let us use a closure to workaround this limitation.
def dispatcher(self, obj):
reduced = reduce_func(obj)
self.save_reduce(obj=obj, *reduced)
self.dispatch[type] = dispatcher
else:
self.dispatch_table[type] = reduce_func
class CustomizablePicklingQueue(object):
"""Locked Pipe implementation that uses a customizable pickler.
This class is an alternative to the multiprocessing implementation
of SimpleQueue in order to make it possible to pass custom
pickling reducers, for instance to avoid memory copy when passing
memory mapped datastructures.
`reducers` is expected to be a dict with key / values being
`(type, callable)` pairs where `callable` is a function that, given an
instance of `type`, will return a tuple `(constructor, tuple_of_objects)`
to rebuild an instance out of the pickled `tuple_of_objects` as would
return a `__reduce__` method.
See the standard library documentation on pickling for more details.
"""
def __init__(self, context, reducers=None):
self._reducers = reducers
self._reader, self._writer = context.Pipe(duplex=False)
self._rlock = context.Lock()
if sys.platform == "win32":
self._wlock = None
else:
self._wlock = context.Lock()
self._make_methods()
def __getstate__(self):
assert_spawning(self)
return (self._reader, self._writer, self._rlock, self._wlock, self._reducers)
def __setstate__(self, state):
(self._reader, self._writer, self._rlock, self._wlock, self._reducers) = state
self._make_methods()
def empty(self):
return not self._reader.poll()
def _make_methods(self):
self._recv = recv = self._reader.recv
racquire, rrelease = self._rlock.acquire, self._rlock.release
def get():
racquire()
try:
return recv()
finally:
rrelease()
self.get = get
if self._reducers:
def send(obj):
buffer = BytesIO()
CustomizablePickler(buffer, self._reducers).dump(obj)
self._writer.send_bytes(buffer.getvalue())
self._send = send
else:
self._send = send = self._writer.send
if self._wlock is None:
# writes to a message oriented win32 pipe are atomic
self.put = send
else:
wlock_acquire, wlock_release = (self._wlock.acquire, self._wlock.release)
def put(obj):
wlock_acquire()
try:
return send(obj)
finally:
wlock_release()
self.put = put
class PicklingPool(Pool):
"""Pool implementation with customizable pickling reducers.
This is useful to control how data is shipped between processes
and makes it possible to use shared memory without useless
copies induces by the default pickling methods of the original
objects passed as arguments to dispatch.
`forward_reducers` and `backward_reducers` are expected to be
dictionaries with key/values being `(type, callable)` pairs where
`callable` is a function that, given an instance of `type`, will return a
tuple `(constructor, tuple_of_objects)` to rebuild an instance out of the
pickled `tuple_of_objects` as would return a `__reduce__` method.
See the standard library documentation about pickling for more details.
"""
def __init__(
self, processes=None, forward_reducers=None, backward_reducers=None, **kwargs
):
if forward_reducers is None:
forward_reducers = dict()
if backward_reducers is None:
backward_reducers = dict()
self._forward_reducers = forward_reducers
self._backward_reducers = backward_reducers
poolargs = dict(processes=processes)
poolargs.update(kwargs)
super(PicklingPool, self).__init__(**poolargs)
def _setup_queues(self):
context = getattr(self, "_ctx", mp)
self._inqueue = CustomizablePicklingQueue(context, self._forward_reducers)
self._outqueue = CustomizablePicklingQueue(context, self._backward_reducers)
self._quick_put = self._inqueue._send
self._quick_get = self._outqueue._recv
class MemmappingPool(PicklingPool):
"""Process pool that shares large arrays to avoid memory copy.
This drop-in replacement for `multiprocessing.pool.Pool` makes
it possible to work efficiently with shared memory in a numpy
context.
Existing instances of numpy.memmap are preserved: the child
suprocesses will have access to the same shared memory in the
original mode except for the 'w+' mode that is automatically
transformed as 'r+' to avoid zeroing the original data upon
instantiation.
Furthermore large arrays from the parent process are automatically
dumped to a temporary folder on the filesystem such as child
processes to access their content via memmapping (file system
backed shared memory).
Note: it is important to call the terminate method to collect
the temporary folder used by the pool.
Parameters
----------
processes: int, optional
Number of worker processes running concurrently in the pool.
initializer: callable, optional
Callable executed on worker process creation.
initargs: tuple, optional
Arguments passed to the initializer callable.
temp_folder: (str, callable) optional
If str:
Folder to be used by the pool for memmapping large arrays
for sharing memory with worker processes. If None, this will try in
order:
- a folder pointed by the JOBLIB_TEMP_FOLDER environment variable,
- /dev/shm if the folder exists and is writable: this is a RAMdisk
filesystem available by default on modern Linux distributions,
- the default system temporary folder that can be overridden
with TMP, TMPDIR or TEMP environment variables, typically /tmp
under Unix operating systems.
if callable:
An callable in charge of dynamically resolving a temporary folder
for memmapping large arrays.
max_nbytes int or None, optional, 1e6 by default
Threshold on the size of arrays passed to the workers that
triggers automated memory mapping in temp_folder.
Use None to disable memmapping of large arrays.
mmap_mode: {'r+', 'r', 'w+', 'c'}
Memmapping mode for numpy arrays passed to workers.
See 'max_nbytes' parameter documentation for more details.
forward_reducers: dictionary, optional
Reducers used to pickle objects passed from main process to worker
processes: see below.
backward_reducers: dictionary, optional
Reducers used to pickle return values from workers back to the
main process.
verbose: int, optional
Make it possible to monitor how the communication of numpy arrays
with the subprocess is handled (pickling or memmapping)
prewarm: bool or str, optional, "auto" by default.
If True, force a read on newly memmapped array to make sure that OS
pre-cache it in memory. This can be useful to avoid concurrent disk
access when the same data array is passed to different worker
processes. If "auto" (by default), prewarm is set to True, unless the
Linux shared memory partition /dev/shm is available and used as temp
folder.
`forward_reducers` and `backward_reducers` are expected to be
dictionaries with key/values being `(type, callable)` pairs where
`callable` is a function that give an instance of `type` will return
a tuple `(constructor, tuple_of_objects)` to rebuild an instance out
of the pickled `tuple_of_objects` as would return a `__reduce__`
method. See the standard library documentation on pickling for more
details.
"""
def __init__(
self,
processes=None,
temp_folder=None,
max_nbytes=1e6,
mmap_mode="r",
forward_reducers=None,
backward_reducers=None,
verbose=0,
prewarm=False,
**kwargs,
):
manager = TemporaryResourcesManager(temp_folder)
self._temp_folder_manager = manager
# The usage of a temp_folder_resolver over a simple temp_folder is
# superfluous for multiprocessing pools, as they don't get reused, see
# get_memmapping_executor for more details. We still use it for code
# simplicity.
forward_reducers, backward_reducers = get_memmapping_reducers(
temp_folder_resolver=manager.resolve_temp_folder_name,
max_nbytes=max_nbytes,
mmap_mode=mmap_mode,
forward_reducers=forward_reducers,
backward_reducers=backward_reducers,
verbose=verbose,
unlink_on_gc_collect=False,
prewarm=prewarm,
)
poolargs = dict(
processes=processes,
forward_reducers=forward_reducers,
backward_reducers=backward_reducers,
)
poolargs.update(kwargs)
super(MemmappingPool, self).__init__(**poolargs)
def terminate(self):
n_retries = 10
for i in range(n_retries):
try:
super(MemmappingPool, self).terminate()
break
except OSError as e:
if isinstance(e, WindowsError):
# Workaround occasional "[Error 5] Access is denied" issue
# when trying to terminate a process under windows.
sleep(0.1)
if i + 1 == n_retries:
warnings.warn(
"Failed to terminate worker processes in"
" multiprocessing pool: %r" % e
)
# Clean up the temporary resources as the workers should now be off.
self._temp_folder_manager._clean_temporary_resources()
@property
def _temp_folder(self):
# Legacy property in tests. could be removed if we refactored the
# memmapping tests. SHOULD ONLY BE USED IN TESTS!
# We cache this property because it is called late in the tests - at
# this point, all context have been unregistered, and
# resolve_temp_folder_name raises an error.
if getattr(self, "_cached_temp_folder", None) is not None:
return self._cached_temp_folder
else:
self._cached_temp_folder = (
self._temp_folder_manager.resolve_temp_folder_name()
) # noqa
return self._cached_temp_folder