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
Summary:
If torch.multiprocessing.spawn is used to launch non-daemonic
processes (the default since #14391), the spawned children won't be
automatically terminated when the parent terminates.
On Linux, we can address this by setting PR_SET_PDEATHSIG, which
delivers a configurable signal to child processes when their parent
terminates.
Fixes#14394.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14491
Differential Revision: D13270374
Pulled By: pietern
fbshipit-source-id: 092c9d3c3cea2622c3766b467957bc27a1bd500c
Summary:
This will be super helpful to the user
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14039
Differential Revision: D13089200
Pulled By: teng-li
fbshipit-source-id: 29e7507bd8fe5a0c58a85c52f976bfca282b4c1b
Summary:
This helper addresses a common pattern where one spawns N processes to
work on some common task (e.g. parallel preprocessing or multiple
training loops).
A straightforward approach is to use the multiprocessing API directly
and then consecutively call join on the resulting processes.
This pattern breaks down in the face of errors. If one of the
processes terminates with an exception or via some signal, and it is
not the first process that was launched, the join call on the first
process won't be affected. This helper seeks to solve this by waiting
on termination from any of the spawned processes. When any process
terminates with a non-zero exit status, it terminates the remaining
processes, and raises an exception in the parent process. If the
process terminated with an exception, it is propagated to the parent.
If the process terminated via a signal (e.g. SIGINT, SIGSEGV), this is
mentioned in the exception as well.
Requires Python >= 3.4.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13518
Reviewed By: orionr
Differential Revision: D12929045
Pulled By: pietern
fbshipit-source-id: 00df19fa16a568d1e22f37a2ba65677ab0cce3fd
This hooks into the (internal) ForkingPickler class in multiprocessing
to reduce tensors, storages, and CUDA events instead of our queue from
joblib. This makes it easier to use the standard multiprocessing classes
in later versions of Python.
This also exposes:
- Tensor/Storage.share_memory_()
- Module.share_memory()
These methods move the CPU tensors and storages to shared memory. If
you're using the "fork" method of multiprocessing, these objects can be
directly inherited instead of serialized through a queue.
CUDA IPC only works with Python 3 using the "spawn" start method. You
can select the start method using the get_context method:
import torch.multiprocessing as mp
ctx = mp.get_context('spawn')
queue = ctx.Queue()
event = ctx.Event()
