Traktor/myenv/Lib/site-packages/torch/multiprocessing/pool.py
2024-05-26 05:12:46 +02:00

53 lines
1.7 KiB
Python

import multiprocessing.pool
import multiprocessing.util as util
from .queue import SimpleQueue
def clean_worker(*args, **kwargs):
import gc
multiprocessing.pool.worker(*args, **kwargs)
# Regular multiprocessing workers don't fully clean up after themselves,
# so we have to explicitly trigger garbage collection to make sure that all
# destructors are called...
gc.collect()
class Pool(multiprocessing.pool.Pool):
"""Pool implementation which uses our version of SimpleQueue.
This lets us pass tensors in shared memory across processes instead of
serializing the underlying data.
"""
def _setup_queues(self):
self._inqueue = SimpleQueue()
self._outqueue = SimpleQueue()
self._quick_put = self._inqueue._writer.send
self._quick_get = self._outqueue._reader.recv
def _repopulate_pool(self):
"""Increase the number of pool processes to the specified number.
Bring the number of pool processes up to the specified number, for use after
reaping workers which have exited.
"""
for i in range(self._processes - len(self._pool)):
# changed worker -> clean_worker
args = (
self._inqueue,
self._outqueue,
self._initializer,
self._initargs,
self._maxtasksperchild,
)
if hasattr(self, "_wrap_exception"):
args += (self._wrap_exception,)
w = self.Process(target=clean_worker, args=args)
self._pool.append(w)
w.name = w.name.replace("Process", "PoolWorker")
w.daemon = True
w.start()
util.debug("added worker")