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https://github.com/saymrwulf/pytorch.git
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4753100a3b
Summary: Generally wildcard imports are bad for the reasons described here: https://www.flake8rules.com/rules/F403.html This PR replaces wildcard imports with an explicit list of imported items where possible, and adds a `# noqa: F403` comment in the other cases (mostly re-exports in `__init__.py` files). This is a prerequisite for https://github.com/pytorch/pytorch/issues/55816, because currently [`tools/codegen/dest/register_dispatch_key.py` simply fails if you sort its imports](https://github.com/pytorch/pytorch/actions/runs/742505908). Pull Request resolved: https://github.com/pytorch/pytorch/pull/55838 Test Plan: CI. You can also run `flake8` locally. Reviewed By: jbschlosser Differential Revision: D27724232 Pulled By: samestep fbshipit-source-id: 269fb09cb4168f8a51fd65bfaacc6cda7fb87c34
72 lines
2.4 KiB
Python
72 lines
2.4 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: F403
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__all__ += multiprocessing.__all__ # type: ignore[attr-defined]
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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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"""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, start_processes, ProcessContext, \
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ProcessRaisedException, ProcessExitedException
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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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Args:
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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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