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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""
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torch.multiprocessing is a wrapper around the native :mod:`multiprocessing`
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module. It registers custom reducers, that use shared memory to provide shared
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views on the same data in different processes. Once the tensor/storage is moved
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to shared_memory (see :func:`~torch.Tensor.share_memory_`), it will be possible
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to send it to other processes without making any copies.
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The API is 100% compatible with the original module - it's enough to change
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``import multiprocessing`` to ``import torch.multiprocessing`` to have all the
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tensors sent through the queues or shared via other mechanisms, moved to shared
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memory.
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Because of the similarity of APIs we do not document most of this package
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contents, and we recommend referring to very good docs of the original module.
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"""
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import torch
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import sys
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from .reductions import init_reductions
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import multiprocessing
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__all__ = ['set_sharing_strategy', 'get_sharing_strategy',
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'get_all_sharing_strategies']
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from multiprocessing import * # noqa: F401
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__all__ += multiprocessing.__all__
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# This call adds a Linux specific prctl(2) wrapper function to this module.
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# See https://github.com/pytorch/pytorch/pull/14391 for more information.
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torch._C._multiprocessing_init()
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if sys.version_info < (3, 3):
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"""Override basic classes in Python 2.7 and Python 3.3 to use ForkingPickler
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for serialization. Later versions of Python already use ForkingPickler."""
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from .queue import Queue, SimpleQueue # noqa: F401
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from .pool import Pool # noqa: F401
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"""Add helper function to spawn N processes and wait for completion of any of
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them. This depends `mp.get_context` which was added in Python 3.4."""
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from .spawn import spawn, SpawnContext # noqa: F401
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if sys.platform == 'darwin' or sys.platform == 'win32':
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_sharing_strategy = 'file_system'
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_all_sharing_strategies = {'file_system'}
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else:
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_sharing_strategy = 'file_descriptor'
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_all_sharing_strategies = {'file_descriptor', 'file_system'}
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def set_sharing_strategy(new_strategy):
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"""Sets the strategy for sharing CPU tensors.
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Arguments:
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new_strategy (str): Name of the selected strategy. Should be one of
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the values returned by :func:`get_all_sharing_strategies()`.
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"""
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global _sharing_strategy
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assert new_strategy in _all_sharing_strategies
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_sharing_strategy = new_strategy
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def get_sharing_strategy():
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"""Returns the current strategy for sharing CPU tensors."""
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return _sharing_strategy
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def get_all_sharing_strategies():
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"""Returns a set of sharing strategies supported on a current system."""
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return _all_sharing_strategies
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init_reductions()
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