Source code for torch.utils.data._utils.worker
r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
import random
import os
from dataclasses import dataclass
from torch._six import queue
from torch._utils import ExceptionWrapper
from typing import Union
from . import signal_handling, MP_STATUS_CHECK_INTERVAL, IS_WINDOWS
if IS_WINDOWS:
import ctypes
from ctypes.wintypes import DWORD, BOOL, HANDLE
# On Windows, the parent ID of the worker process remains unchanged when the manager process
# is gone, and the only way to check it through OS is to let the worker have a process handle
# of the manager and ask if the process status has changed.
class ManagerWatchdog(object):
def __init__(self):
self.manager_pid = os.getppid()
# mypy cannot detect this code is windows only
self.kernel32 = ctypes.WinDLL('kernel32', use_last_error=True) # type: ignore
self.kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
self.kernel32.OpenProcess.restype = HANDLE
self.kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
self.kernel32.WaitForSingleObject.restype = DWORD
# Value obtained from https://msdn.microsoft.com/en-us/library/ms684880.aspx
SYNCHRONIZE = 0x00100000
self.manager_handle = self.kernel32.OpenProcess(SYNCHRONIZE, 0, self.manager_pid)
if not self.manager_handle:
raise ctypes.WinError(ctypes.get_last_error()) # type: ignore
self.manager_dead = False
def is_alive(self):
if not self.manager_dead:
# Value obtained from https://msdn.microsoft.com/en-us/library/windows/desktop/ms687032.aspx
self.manager_dead = self.kernel32.WaitForSingleObject(self.manager_handle, 0) == 0
return not self.manager_dead
else:
class ManagerWatchdog(object): # type: ignore[no-redef]
def __init__(self):
self.manager_pid = os.getppid()
self.manager_dead = False
def is_alive(self):
if not self.manager_dead:
self.manager_dead = os.getppid() != self.manager_pid
return not self.manager_dead
_worker_info = None
class WorkerInfo(object):
__initialized = False
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
self.__keys = tuple(kwargs.keys())
self.__initialized = True
def __setattr__(self, key, val):
if self.__initialized:
raise RuntimeError("Cannot assign attributes to {} objects".format(self.__class__.__name__))
return super(WorkerInfo, self).__setattr__(key, val)
def __repr__(self):
items = []
for k in self.__keys:
items.append('{}={}'.format(k, getattr(self, k)))
return '{}({})'.format(self.__class__.__name__, ', '.join(items))
[docs]def get_worker_info():
r"""Returns the information about the current
:class:`~torch.utils.data.DataLoader` iterator worker process.
When called in a worker, this returns an object guaranteed to have the
following attributes:
* :attr:`id`: the current worker id.
* :attr:`num_workers`: the total number of workers.
* :attr:`seed`: the random seed set for the current worker. This value is
determined by main process RNG and the worker id. See
:class:`~torch.utils.data.DataLoader`'s documentation for more details.
* :attr:`dataset`: the copy of the dataset object in **this** process. Note
that this will be a different object in a different process than the one
in the main process.
When called in the main process, this returns ``None``.
.. note::
When used in a :attr:`worker_init_fn` passed over to
:class:`~torch.utils.data.DataLoader`, this method can be useful to
set up each worker process differently, for instance, using ``worker_id``
to configure the ``dataset`` object to only read a specific fraction of a
sharded dataset, or use ``seed`` to seed other libraries used in dataset
code (e.g., NumPy).
"""
return _worker_info
r"""Dummy class used to signal the end of an IterableDataset"""
@dataclass(frozen=True)
class _IterableDatasetStopIteration(object):
worker_id: int
r"""Dummy class used to resume the fetching when worker reuse is enabled"""
@dataclass(frozen=True)
class _ResumeIteration(object):
pass
def _worker_loop(dataset_kind, dataset, index_queue, data_queue, done_event,
auto_collation, collate_fn, drop_last, seed, init_fn, worker_id,
num_workers, persistent_workers):
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
# logic of this function.
try:
# Initialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal had already happened
# again.
# https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
global _worker_info
_worker_info = WorkerInfo(id=worker_id, num_workers=num_workers,
seed=seed, dataset=dataset)
from torch.utils.data import _DatasetKind
init_exception = None
try:
if init_fn is not None:
init_fn(worker_id)
fetcher = _DatasetKind.create_fetcher(dataset_kind, dataset, auto_collation, collate_fn, drop_last)
except Exception:
init_exception = ExceptionWrapper(
where="in DataLoader worker process {}".format(worker_id))
# When using Iterable mode, some worker can exit earlier than others due
# to the IterableDataset behaving differently for different workers.
# When such things happen, an `_IterableDatasetStopIteration` object is
# sent over to the main process with the ID of this worker, so that the
# main process won't send more tasks to this worker, and will send
# `None` to this worker to properly exit it.
#
# Note that we cannot set `done_event` from a worker as it is shared
# among all processes. Instead, we set the `iteration_end` flag to
# signify that the iterator is exhausted. When either `done_event` or
# `iteration_end` is set, we skip all processing step and just wait for
# `None`.
iteration_end = False
watchdog = ManagerWatchdog()
while watchdog.is_alive():
try:
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if isinstance(r, _ResumeIteration):
# Acknowledge the main process
data_queue.put((r, None))
iteration_end = False
# Recreate the fetcher for worker-reuse policy
fetcher = _DatasetKind.create_fetcher(
dataset_kind, dataset, auto_collation, collate_fn, drop_last)
continue
elif r is None:
# Received the final signal
assert done_event.is_set() or iteration_end
break
elif done_event.is_set() or iteration_end:
# `done_event` is set. But I haven't received the final signal
# (None) yet. I will keep continuing until get it, and skip the
# processing steps.
continue
idx, index = r
data: Union[_IterableDatasetStopIteration, ExceptionWrapper]
if init_exception is not None:
data = init_exception
init_exception = None
else:
try:
data = fetcher.fetch(index)
except Exception as e:
if isinstance(e, StopIteration) and dataset_kind == _DatasetKind.Iterable:
data = _IterableDatasetStopIteration(worker_id)
# Set `iteration_end`
# (1) to save future `next(...)` calls, and
# (2) to avoid sending multiple `_IterableDatasetStopIteration`s.
iteration_end = True
else:
# It is important that we don't store exc_info in a variable.
# `ExceptionWrapper` does the correct thing.
# See NOTE [ Python Traceback Reference Cycle Problem ]
data = ExceptionWrapper(
where="in DataLoader worker process {}".format(worker_id))
data_queue.put((idx, data))
del data, idx, index, r # save memory
except KeyboardInterrupt:
# Main process will raise KeyboardInterrupt anyways.
pass
if done_event.is_set():
data_queue.cancel_join_thread()
data_queue.close()
