mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
ed19580dc4
Summary:
Otherwise you may see errors like
```
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x000001F99F5CB9D8>
Traceback (most recent call last):
File "C:\Users\Divyansh J\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 883, in __del__
self._shutdown_workers()
File "C:\Users\Divyansh J\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 860, in _shutdown_workers
if self.workers_status[worker_id]:
IndexError: list index out of range
```
e.g. https://discuss.pytorch.org/t/how-to-construct-dataset-with-iterator-for-multi-process-dataloader/49612/5
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23761
Differential Revision: D16644687
Pulled By: soumith
fbshipit-source-id: a60e847431264525079456ff422317af1ac2be4b
927 lines
45 KiB
Python
927 lines
45 KiB
Python
r"""Definition of the DataLoader and it's iterator _DataLoaderIter classes.
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To support these two classes, in `./_utils` we define many utility methods and
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functions to be run in multiprocessing. E.g., the data loading worker loop is
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in `./_utils/worker.py`.
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"""
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import torch
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import multiprocessing as python_multiprocessing
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import torch.multiprocessing as multiprocessing
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from . import IterableDataset, Sampler, SequentialSampler, RandomSampler, BatchSampler
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from . import _utils
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from torch._utils import ExceptionWrapper
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import threading
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import itertools
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from torch._six import queue, string_classes
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get_worker_info = _utils.worker.get_worker_info
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# This function used to be defined in this file. However, it was moved to
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# _utils/collate.py. Although it is rather hard to access this from user land
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# (one has to explicitly directly `import torch.utils.data.dataloader`), there
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# probably is user code out there using it. This aliasing maintains BC in this
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# aspect.
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default_collate = _utils.collate.default_collate
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class _DatasetKind(object):
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Map = 0
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Iterable = 1
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@staticmethod
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def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
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if kind == _DatasetKind.Map:
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return _utils.fetch._MapDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
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else:
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return _utils.fetch._IterableDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
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class _InfiniteConstantSampler(Sampler):
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r"""Analogous to ``itertools.repeat(None, None)``.
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Used as sampler for :class:`~torch.utils.data.IterableDataset`.
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"""
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def __init__(self):
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super(_InfiniteConstantSampler, self).__init__(None)
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def __iter__(self):
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while True:
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yield None
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def __len__(self):
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# This has to be a TypeError, otherwise, since this is used in
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# `len(dataloader)`, `list(dataloader)` will fail.
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# see NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
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raise TypeError('Cannot determine the DataLoader length of a IterableDataset')
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class DataLoader(object):
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r"""
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Data loader. Combines a dataset and a sampler, and provides an iterable over
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the given dataset.
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The :class:`~torch.utils.data.DataLoader` supports both map-style and
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iterable-style datasets with single- or multi-process loading, customizing
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loading order and optional automatic batching (collation) and memory pinning.
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See :py:mod:`torch.utils.data` documentation page for more details.
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Arguments:
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dataset (Dataset): dataset from which to load the data.
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batch_size (int, optional): how many samples per batch to load
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(default: ``1``).
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shuffle (bool, optional): set to ``True`` to have the data reshuffled
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at every epoch (default: ``False``).
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sampler (Sampler, optional): defines the strategy to draw samples from
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the dataset. If specified, :attr:`shuffle` must be ``False``.
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batch_sampler (Sampler, optional): like :attr:`sampler`, but returns a batch of
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indices at a time. Mutually exclusive with :attr:`batch_size`,
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:attr:`shuffle`, :attr:`sampler`, and :attr:`drop_last`.
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num_workers (int, optional): how many subprocesses to use for data
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loading. ``0`` means that the data will be loaded in the main process.
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(default: ``0``)
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collate_fn (callable, optional): merges a list of samples to form a
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mini-batch of Tensor(s). Used when using batched loading from a
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map-style dataset.
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pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
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into CUDA pinned memory before returning them. If your data elements
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are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
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see the example below.
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drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
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if the dataset size is not divisible by the batch size. If ``False`` and
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the size of dataset is not divisible by the batch size, then the last batch
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will be smaller. (default: ``False``)
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timeout (numeric, optional): if positive, the timeout value for collecting a batch
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from workers. Should always be non-negative. (default: ``0``)
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worker_init_fn (callable, optional): If not ``None``, this will be called on each
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worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
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input, after seeding and before data loading. (default: ``None``)
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.. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
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cannot be an unpicklable object, e.g., a lambda function. See
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:ref:`multiprocessing-best-practices` on more details related
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to multiprocessing in PyTorch.
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.. note:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
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When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
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an infinite sampler is used, whose :meth:`__len__` is not
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implemented, because the actual length depends on both the
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iterable as well as multi-process loading configurations. So one
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should not query this method unless they work with a map-style
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dataset. See `Dataset Types`_ for more details on these two types
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of datasets.
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"""
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__initialized = False
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def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None,
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batch_sampler=None, num_workers=0, collate_fn=None,
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pin_memory=False, drop_last=False, timeout=0,
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worker_init_fn=None, multiprocessing_context=None):
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torch._C._log_api_usage_once("python.data_loader")
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if num_workers < 0:
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raise ValueError('num_workers option should be non-negative; '
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'use num_workers=0 to disable multiprocessing.')
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if timeout < 0:
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raise ValueError('timeout option should be non-negative')
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self.dataset = dataset
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self.num_workers = num_workers
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self.pin_memory = pin_memory
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self.timeout = timeout
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self.worker_init_fn = worker_init_fn
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self.multiprocessing_context = multiprocessing_context
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# Arg-check dataset related before checking samplers because we want to
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# tell users that iterable-style datasets are incompatible with custom
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# samplers first, so that they don't learn that this combo doesn't work
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# after spending time fixing the custom sampler errors.
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if isinstance(dataset, IterableDataset):
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self.dataset_kind = _DatasetKind.Iterable
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# NOTE [ Custom Samplers and `IterableDataset` ]
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#
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# `IterableDataset` does not support custom `batch_sampler` or
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# `sampler` since the key is irrelevant (unless we support
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# generator-style dataset one day...).
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#
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# For `sampler`, we always create a dummy sampler. This is an
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# infinite sampler even when the dataset may have an implemented
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# finite `__len__` because in multi-process data loading, naive
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# settings will return duplicated data (which may be desired), and
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# thus using a sampler with length matching that of dataset will
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# cause data lost (you may have duplicates of the first couple
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# batches, but never see anything afterwards). Therefore,
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# `Iterabledataset` always uses an infinite sampler, an instance of
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# `_InfiniteConstantSampler` defined above.
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#
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# A custom `batch_sampler` essentially only controls the batch size.
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# However, it is unclear how useful it would be since an iterable-style
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# dataset can handle that within itself. Moreover, it is pointless
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# in multi-process data loading as the assignment order of batches
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# to workers is an implementation detail so users can not control
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# how to batchify each worker's iterable. Thus, we disable this
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# option. If this turns out to be useful in future, we can re-enable
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# this, and support custom samplers that specify the assignments to
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# specific workers.
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if shuffle is not False:
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raise ValueError(
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"DataLoader with IterableDataset: expected unspecified "
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"shuffle option, but got shuffle={}".format(shuffle))
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elif sampler is not None:
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# See NOTE [ Custom Samplers and IterableDataset ]
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raise ValueError(
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"DataLoader with IterableDataset: expected unspecified "
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"sampler option, but got sampler={}".format(sampler))
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elif batch_sampler is not None:
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# See NOTE [ Custom Samplers and IterableDataset ]
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raise ValueError(
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"DataLoader with IterableDataset: expected unspecified "
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"batch_sampler option, but got batch_sampler={}".format(batch_sampler))
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else:
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self.dataset_kind = _DatasetKind.Map
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if sampler is not None and shuffle:
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raise ValueError('sampler option is mutually exclusive with '
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'shuffle')
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if batch_sampler is not None:
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# auto_collation with custom batch_sampler
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if batch_size != 1 or shuffle or sampler is not None or drop_last:
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raise ValueError('batch_sampler option is mutually exclusive '
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'with batch_size, shuffle, sampler, and '
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'drop_last')
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batch_size = None
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drop_last = False
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elif batch_size is None:
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# no auto_collation
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if shuffle or sampler is not None or drop_last:
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raise ValueError('batch_size=None option disables auto-batching '
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'and is mutually exclusive with '
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'shuffle, sampler, and drop_last')
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if sampler is None: # give default samplers
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if self.dataset_kind == _DatasetKind.Iterable:
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# See NOTE [ Custom Samplers and IterableDataset ]
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sampler = _InfiniteConstantSampler()
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else: # map-style
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if shuffle:
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sampler = RandomSampler(dataset)
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else:
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sampler = SequentialSampler(dataset)
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if batch_size is not None and batch_sampler is None:
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# auto_collation without custom batch_sampler
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batch_sampler = BatchSampler(sampler, batch_size, drop_last)
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self.batch_size = batch_size
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self.drop_last = drop_last
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self.sampler = sampler
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self.batch_sampler = batch_sampler
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if collate_fn is None:
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if self._auto_collation:
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collate_fn = _utils.collate.default_collate
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else:
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collate_fn = _utils.collate.default_convert
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self.collate_fn = collate_fn
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self.__initialized = True
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@property
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def multiprocessing_context(self):
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return self.__multiprocessing_context
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@multiprocessing_context.setter
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def multiprocessing_context(self, multiprocessing_context):
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if multiprocessing_context is not None:
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if self.num_workers > 0:
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if not multiprocessing._supports_context:
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raise ValueError('multiprocessing_context relies on Python >= 3.4, with '
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'support for different start methods')
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if isinstance(multiprocessing_context, string_classes):
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valid_start_methods = multiprocessing.get_all_start_methods()
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if multiprocessing_context not in valid_start_methods:
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raise ValueError(
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('multiprocessing_context option '
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'should specify a valid start method in {}, but got '
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'multiprocessing_context={}').format(valid_start_methods, multiprocessing_context))
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multiprocessing_context = multiprocessing.get_context(multiprocessing_context)
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if not isinstance(multiprocessing_context, python_multiprocessing.context.BaseContext):
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raise ValueError(('multiprocessing_context option should be a valid context '
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'object or a string specifying the start method, but got '
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'multiprocessing_context={}').format(multiprocessing_context))
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else:
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raise ValueError(('multiprocessing_context can only be used with '
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'multi-process loading (num_workers > 0), but got '
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'num_workers={}').format(self.num_workers))
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self.__multiprocessing_context = multiprocessing_context
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def __setattr__(self, attr, val):
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if self.__initialized and attr in ('batch_size', 'sampler', 'drop_last'):
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raise ValueError('{} attribute should not be set after {} is '
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'initialized'.format(attr, self.__class__.__name__))
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super(DataLoader, self).__setattr__(attr, val)
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def __iter__(self):
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if self.num_workers == 0:
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return _SingleProcessDataLoaderIter(self)
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else:
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return _MultiProcessingDataLoaderIter(self)
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@property
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def _auto_collation(self):
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return self.batch_sampler is not None
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@property
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def _index_sampler(self):
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# The actual sampler used for generating indices for `_DatasetFetcher`
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# (see _utils/fetch.py) to read data at each time. This would be
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# `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
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# We can't change `.sampler` and `.batch_sampler` attributes for BC
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# reasons.
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if self._auto_collation:
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return self.batch_sampler
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else:
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return self.sampler
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def __len__(self):
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return len(self._index_sampler) # with iterable-style dataset, this will error
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class _BaseDataLoaderIter(object):
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def __init__(self, loader):
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self.dataset = loader.dataset
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self.dataset_kind = loader.dataset_kind
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self.auto_collation = loader._auto_collation
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self.drop_last = loader.drop_last
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self.index_sampler = loader._index_sampler
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self.num_workers = loader.num_workers
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self.pin_memory = loader.pin_memory and torch.cuda.is_available()
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self.timeout = loader.timeout
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self.collate_fn = loader.collate_fn
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self.sampler_iter = iter(self.index_sampler)
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self.base_seed = torch.empty((), dtype=torch.int64).random_().item()
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def __iter__(self):
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return self
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def _next_index(self):
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return next(self.sampler_iter) # may raise StopIteration
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def __next__(self):
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raise NotImplementedError
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def __len__(self):
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return len(self.index_sampler)
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def __getstate__(self):
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# TODO: add limited pickling support for sharing an iterator
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# across multiple threads for HOGWILD.
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# Probably the best way to do this is by moving the sample pushing
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# to a separate thread and then just sharing the data queue
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# but signalling the end is tricky without a non-blocking API
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raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
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class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
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def __init__(self, loader):
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super(_SingleProcessDataLoaderIter, self).__init__(loader)
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assert self.timeout == 0
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assert self.num_workers == 0
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self.dataset_fetcher = _DatasetKind.create_fetcher(
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self.dataset_kind, self.dataset, self.auto_collation, self.collate_fn, self.drop_last)
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def __next__(self):
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index = self._next_index() # may raise StopIteration
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data = self.dataset_fetcher.fetch(index) # may raise StopIteration
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if self.pin_memory:
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data = _utils.pin_memory.pin_memory(data)
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return data
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next = __next__ # Python 2 compatibility
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class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
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r"""Iterates once over the DataLoader's dataset, as specified by the sampler"""
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# NOTE [ Data Loader Multiprocessing Shutdown Logic ]
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#
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# Preliminary:
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#
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# Our data model looks like this (queues are indicated with curly brackets):
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#
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# main process ||
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# | ||
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# {index_queue} ||
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# | ||
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# worker processes || DATA
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# | ||
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# {worker_result_queue} || FLOW
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# | ||
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# pin_memory_thread of main process || DIRECTION
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# | ||
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# {data_queue} ||
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# | ||
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# data output \/
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#
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# P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
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# `pin_memory=False`.
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#
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#
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# Terminating multiprocessing logic requires very careful design. In
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# particular, we need to make sure that
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#
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# 1. The iterator gracefully exits the workers when its last reference is
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# gone or it is depleted.
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#
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# In this case, the workers should be gracefully exited because the
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# main process may still need to continue to run, and we want cleaning
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# up code in the workers to be executed (e.g., releasing GPU memory).
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# Naturally, we implement the shutdown logic in `__del__` of
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# DataLoaderIterator.
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#
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# We delay the discussion on the logic in this case until later.
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#
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# 2. The iterator exits the workers when the loader process and/or worker
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# processes exits normally or with error.
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#
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# We set all workers and `pin_memory_thread` to have `daemon=True`.
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#
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# You may ask, why can't we make the workers non-daemonic, and
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# gracefully exit using the same logic as we have in `__del__` when the
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# iterator gets deleted (see 1 above)?
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#
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# First of all, `__del__` is **not** guaranteed to be called when
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# interpreter exits. Even if it is called, by the time it executes,
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# many Python core library resources may alreay be freed, and even
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# simple things like acquiring an internal lock of a queue may hang.
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# Therefore, in this case, we actually need to prevent `__del__` from
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# being executed, and rely on the automatic termination of daemonic
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# children. Thus, we register an `atexit` hook that sets a global flag
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# `_utils.python_exit_status`. Since `atexit` hooks are executed in the
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# reverse order of registration, we are guaranteed that this flag is
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# set before library resources we use are freed. (Hooks freeing those
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# resources are registered at importing the Python core libraries at
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# the top of this file.) So in `__del__`, we check if
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# `_utils.python_exit_status` is set or `None` (freed), and perform
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# no-op if so.
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#
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# Another problem with `__del__` is also related to the library cleanup
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# calls. When a process ends, it shuts the all its daemonic children
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# down with a SIGTERM (instead of joining them without a timeout).
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# Simiarly for threads, but by a different mechanism. This fact,
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# together with a few implementation details of multiprocessing, forces
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# us to make workers daemonic. All of our problems arise when a
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# DataLoader is used in a subprocess, and are caused by multiprocessing
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# code which looks more or less like this:
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#
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# try:
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# your_function_using_a_dataloader()
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# finally:
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# multiprocessing.util._exit_function()
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#
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# The joining/termination mentioned above happens inside
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# `_exit_function()`. Now, if `your_function_using_a_dataloader()`
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# throws, the stack trace stored in the exception will prevent the
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# frame which uses `DataLoaderIter` to be freed. If the frame has any
|
|
# reference to the `DataLoaderIter` (e.g., in a method of the iter),
|
|
# its `__del__`, which starts the shutdown procedure, will not be
|
|
# called. That, in turn, means that workers aren't notified. Attempting
|
|
# to join in `_exit_function` will then result in a hang.
|
|
#
|
|
# For context, `_exit_function` is also registered as an `atexit` call.
|
|
# So it is unclear to me (@ssnl) why this is needed in a finally block.
|
|
# The code dates back to 2008 and there is no comment on the original
|
|
# PEP 371 or patch https://bugs.python.org/issue3050 (containing both
|
|
# the finally block and the `atexit` registration) that explains this.
|
|
#
|
|
# Another choice is to just shutdown workers with logic in 1 above
|
|
# whenever we see an error in `next`. This isn't ideal because
|
|
# a. It prevents users from using try-catch to resume data loading.
|
|
# b. It doesn't prevent hanging if users have references to the
|
|
# iterator.
|
|
#
|
|
# 3. All processes exit if any of them die unexpectedly by fatal signals.
|
|
#
|
|
# As shown above, the workers are set as daemonic children of the main
|
|
# process. However, automatic cleaning-up of such child processes only
|
|
# happens if the parent process exits gracefully (e.g., not via fatal
|
|
# signals like SIGKILL). So we must ensure that each process will exit
|
|
# even the process that should send/receive data to/from it were
|
|
# killed, i.e.,
|
|
#
|
|
# a. A process won't hang when getting from a queue.
|
|
#
|
|
# Even with carefully designed data dependencies (i.e., a `put()`
|
|
# always corresponding to a `get()`), hanging on `get()` can still
|
|
# happen when data in queue is corrupted (e.g., due to
|
|
# `cancel_join_thread` or unexpected exit).
|
|
#
|
|
# For child exit, we set a timeout whenever we try to get data
|
|
# from `data_queue`, and check the workers' status on each timeout
|
|
# and error.
|
|
# See `_DataLoaderiter._get_batch()` and
|
|
# `_DataLoaderiter._try_get_data()` for details.
|
|
#
|
|
# Additionally, for child exit on non-Windows platforms, we also
|
|
# register a SIGCHLD handler (which is supported on Windows) on
|
|
# the main process, which checks if any of the workers fail in the
|
|
# (Python) handler. This is more efficient and faster in detecting
|
|
# worker failures, compared to only using the above mechanism.
|
|
# See `DataLoader.cpp` and `_utils/signal_handling.py` for details.
|
|
#
|
|
# For `.get()` calls where the sender(s) is not the workers, we
|
|
# guard them with timeouts, and check the status of the sender
|
|
# when timeout happens:
|
|
# + in the workers, the `_utils.worker.ManagerWatchdog` class
|
|
# checks the status of the main process.
|
|
# + if `pin_memory=True`, when getting from `pin_memory_thread`,
|
|
# check `pin_memory_thread` status periodically until `.get()`
|
|
# returns or see that `pin_memory_thread` died.
|
|
#
|
|
# b. A process won't hang when putting into a queue;
|
|
#
|
|
# We use `mp.Queue` which has a separate background thread to put
|
|
# objects from an unbounded buffer array. The background thread is
|
|
# daemonic and usually automatically joined when the process
|
|
# exits.
|
|
#
|
|
# However, in case that the receiver has ended abruptly while
|
|
# reading from the pipe, the join will hang forever. Therefore,
|
|
# for both `worker_result_queue` (worker -> main process/pin_memory_thread)
|
|
# and each `index_queue` (main process -> worker), we use
|
|
# `q.cancel_join_thread()` in sender process before any `q.put` to
|
|
# prevent this automatic join.
|
|
#
|
|
# Moreover, having all queues called `cancel_join_thread` makes
|
|
# implementing graceful shutdown logic in `__del__` much easier.
|
|
# It won't need to get from any queue, which would also need to be
|
|
# guarded by periodic status checks.
|
|
#
|
|
# Nonetheless, `cancel_join_thread` must only be called when the
|
|
# queue is **not** going to be read from or write into by another
|
|
# process, because it may hold onto a lock or leave corrupted data
|
|
# in the queue, leading other readers/writers to hang.
|
|
#
|
|
# `pin_memory_thread`'s `data_queue` is a `queue.Queue` that does
|
|
# a blocking `put` if the queue is full. So there is no above
|
|
# problem, but we do need to wrap the `put` in a loop that breaks
|
|
# not only upon success, but also when the main process stops
|
|
# reading, i.e., is shutting down.
|
|
#
|
|
#
|
|
# Now let's get back to 1:
|
|
# how we gracefully exit the workers when the last reference to the
|
|
# iterator is gone.
|
|
#
|
|
# To achieve this, we implement the following logic along with the design
|
|
# choices mentioned above:
|
|
#
|
|
# `workers_done_event`:
|
|
# A `multiprocessing.Event` shared among the main process and all worker
|
|
# processes. This is used to signal the workers that the iterator is
|
|
# shutting down. After it is set, they will not send processed data to
|
|
# queues anymore, and only wait for the final `None` before exiting.
|
|
# `done_event` isn't strictly needed. I.e., we can just check for `None`
|
|
# from the input queue, but it allows us to skip wasting resources
|
|
# processing data if we are already shutting down.
|
|
#
|
|
# `pin_memory_thread_done_event`:
|
|
# A `threading.Event` for a similar purpose to that of
|
|
# `workers_done_event`, but is for the `pin_memory_thread`. The reason
|
|
# that separate events are neede is that `pin_memory_thread` reads from
|
|
# the output queue of the workers. But the workers, upon seeing that
|
|
# `workers_done_event` is set, only wants to see the final `None`, and is
|
|
# not required to flush all data in the output queue (e.g., it may call
|
|
# `cancel_join_thread` on that queue if its `IterableDataset` iterator
|
|
# happens to exhaust coincidentally, which is out of the control of the
|
|
# main process). Thus, since we will exit `pin_memory_thread` before the
|
|
# workers (see below), two separete events are used.
|
|
#
|
|
# NOTE: In short, the protocol is that the main process will set these
|
|
# `done_event`s and then the corresponding processes/threads a `None`,
|
|
# and that they may exit at any time after receiving the `None`.
|
|
#
|
|
# NOTE: Using `None` as the final signal is valid, since normal data will
|
|
# always be a 2-tuple with the 1st element being the index of the data
|
|
# transferred (different from dataset index/key), and the 2nd being
|
|
# either the dataset key or the data sample (depending on which part
|
|
# of the data model the queue is at).
|
|
#
|
|
# [ worker processes ]
|
|
# While loader process is alive:
|
|
# Get from `index_queue`.
|
|
# If get anything else,
|
|
# Check `workers_done_event`.
|
|
# If set, continue to next iteration
|
|
# i.e., keep getting until see the `None`, then exit.
|
|
# Otherwise, process data:
|
|
# If is fetching from an `IterableDataset` and the iterator
|
|
# is exhausted, send an `_IterableDatasetStopIteration`
|
|
# object to signal iteration end. The main process, upon
|
|
# receiving such an object, will send `None` to this
|
|
# worker and not use the corresponding `index_queue`
|
|
# anymore.
|
|
# If timed out,
|
|
# No matter `workers_done_event` is set (still need to see `None`)
|
|
# or not, must continue to next iteration.
|
|
# (outside loop)
|
|
# If `workers_done_event` is set, (this can be False with `IterableDataset`)
|
|
# `data_queue.cancel_join_thread()`. (Everything is ending here:
|
|
# main process won't read from it;
|
|
# other workers will also call
|
|
# `cancel_join_thread`.)
|
|
#
|
|
# [ pin_memory_thread ]
|
|
# # No need to check main thread. If this thread is alive, the main loader
|
|
# # thread must be alive, because this thread is set as daemonic.
|
|
# While `pin_memory_thread_done_event` is not set:
|
|
# Get from `index_queue`.
|
|
# If timed out, continue to get in the next iteration.
|
|
# Otherwise, process data.
|
|
# While `pin_memory_thread_done_event` is not set:
|
|
# Put processed data to `data_queue` (a `queue.Queue` with blocking put)
|
|
# If timed out, continue to put in the next iteration.
|
|
# Otherwise, break, i.e., continuing to the out loop.
|
|
#
|
|
# NOTE: we don't check the status of the main thread because
|
|
# 1. if the process is killed by fatal signal, `pin_memory_thread`
|
|
# ends.
|
|
# 2. in other cases, either the cleaning-up in __del__ or the
|
|
# automatic exit of daemonic thread will take care of it.
|
|
# This won't busy-wait either because `.get(timeout)` does not
|
|
# busy-wait.
|
|
#
|
|
# [ main process ]
|
|
# In the DataLoader Iter's `__del__`
|
|
# b. Exit `pin_memory_thread`
|
|
# i. Set `pin_memory_thread_done_event`.
|
|
# ii Put `None` in `worker_result_queue`.
|
|
# iii. Join the `pin_memory_thread`.
|
|
# iv. `worker_result_queue.cancel_join_thread()`.
|
|
#
|
|
# c. Exit the workers.
|
|
# i. Set `workers_done_event`.
|
|
# ii. Put `None` in each worker's `index_queue`.
|
|
# iii. Join the workers.
|
|
# iv. Call `.cancel_join_thread()` on each worker's `index_queue`.
|
|
#
|
|
# NOTE: (c) is better placed after (b) because it may leave corrupted
|
|
# data in `worker_result_queue`, which `pin_memory_thread`
|
|
# reads from, in which case the `pin_memory_thread` can only
|
|
# happen at timeing out, which is slow. Nonetheless, same thing
|
|
# happens if a worker is killed by signal at unfortunate times,
|
|
# but in other cases, we are better off having a non-corrupted
|
|
# `worker_result_queue` for `pin_memory_thread`.
|
|
#
|
|
# NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)
|
|
# can be omitted
|
|
#
|
|
# NB: `done_event`s isn't strictly needed. E.g., we can just check for
|
|
# `None` from `index_queue`, but it allows us to skip wasting resources
|
|
# processing indices already in `index_queue` if we are already shutting
|
|
# down.
|
|
|
|
def __init__(self, loader):
|
|
super(_MultiProcessingDataLoaderIter, self).__init__(loader)
|
|
|
|
assert self.num_workers > 0
|
|
|
|
if loader.multiprocessing_context is None:
|
|
multiprocessing_context = multiprocessing
|
|
else:
|
|
multiprocessing_context = loader.multiprocessing_context
|
|
|
|
self.worker_init_fn = loader.worker_init_fn
|
|
self.worker_queue_idx_cycle = itertools.cycle(range(self.num_workers))
|
|
self.worker_result_queue = multiprocessing_context.Queue()
|
|
self.worker_pids_set = False
|
|
self.shutdown = False
|
|
self.send_idx = 0 # idx of the next task to be sent to workers
|
|
self.rcvd_idx = 0 # idx of the next task to be returned in __next__
|
|
# information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).
|
|
# map: task idx => - (worker_id,) if data isn't fetched (outstanding)
|
|
# \ (worker_id, data) if data is already fetched (out-of-order)
|
|
self.task_info = {}
|
|
self.tasks_outstanding = 0 # always equal to count(v for v in task_info.values() if len(v) == 1)
|
|
self.workers_done_event = multiprocessing_context.Event()
|
|
|
|
self.index_queues = []
|
|
self.workers = []
|
|
# A list of booleans representing whether each worker still has work to
|
|
# do, i.e., not having exhausted its iterable dataset object. It always
|
|
# contains all `True`s if not using an iterable-style dataset
|
|
# (i.e., if kind != Iterable).
|
|
self.workers_status = []
|
|
for i in range(self.num_workers):
|
|
index_queue = multiprocessing_context.Queue()
|
|
# index_queue.cancel_join_thread()
|
|
w = multiprocessing_context.Process(
|
|
target=_utils.worker._worker_loop,
|
|
args=(self.dataset_kind, self.dataset, index_queue,
|
|
self.worker_result_queue, self.workers_done_event,
|
|
self.auto_collation, self.collate_fn, self.drop_last,
|
|
self.base_seed + i, self.worker_init_fn, i, self.num_workers))
|
|
w.daemon = True
|
|
# NB: Process.start() actually take some time as it needs to
|
|
# start a process and pass the arguments over via a pipe.
|
|
# Therefore, we only add a worker to self.workers list after
|
|
# it started, so that we do not call .join() if program dies
|
|
# before it starts, and __del__ tries to join but will get:
|
|
# AssertionError: can only join a started process.
|
|
w.start()
|
|
self.index_queues.append(index_queue)
|
|
self.workers.append(w)
|
|
self.workers_status.append(True)
|
|
|
|
if self.pin_memory:
|
|
self.pin_memory_thread_done_event = threading.Event()
|
|
self.data_queue = queue.Queue()
|
|
pin_memory_thread = threading.Thread(
|
|
target=_utils.pin_memory._pin_memory_loop,
|
|
args=(self.worker_result_queue, self.data_queue,
|
|
torch.cuda.current_device(),
|
|
self.pin_memory_thread_done_event))
|
|
pin_memory_thread.daemon = True
|
|
pin_memory_thread.start()
|
|
# Similar to workers (see comment above), we only register
|
|
# pin_memory_thread once it is started.
|
|
self.pin_memory_thread = pin_memory_thread
|
|
else:
|
|
self.data_queue = self.worker_result_queue
|
|
|
|
_utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self.workers))
|
|
_utils.signal_handling._set_SIGCHLD_handler()
|
|
self.worker_pids_set = True
|
|
|
|
# prime the prefetch loop
|
|
for _ in range(2 * self.num_workers):
|
|
self._try_put_index()
|
|
|
|
def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):
|
|
# Tries to fetch data from `self.data_queue` once for a given timeout.
|
|
# This can also be used as inner loop of fetching without timeout, with
|
|
# the sender status as the loop condition.
|
|
#
|
|
# This raises a `RuntimeError` if any worker died expectedly. This error
|
|
# can come from either the SIGCHLD handler in `_utils/signal_handling.py`
|
|
# (only for non-Windows platforms), or the manual check below on errors
|
|
# and timeouts.
|
|
#
|
|
# Returns a 2-tuple:
|
|
# (bool: whether successfully get data, any: data if successful else None)
|
|
try:
|
|
data = self.data_queue.get(timeout=timeout)
|
|
return (True, data)
|
|
except Exception as e:
|
|
# At timeout and error, we manually check whether any worker has
|
|
# failed. Note that this is the only mechanism for Windows to detect
|
|
# worker failures.
|
|
failed_workers = []
|
|
for worker_id, w in enumerate(self.workers):
|
|
if self.workers_status[worker_id] and not w.is_alive():
|
|
failed_workers.append(w)
|
|
self._shutdown_worker(worker_id)
|
|
if len(failed_workers) > 0:
|
|
pids_str = ', '.join(str(w.pid) for w in failed_workers)
|
|
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
|
|
if isinstance(e, queue.Empty):
|
|
return (False, None)
|
|
raise
|
|
|
|
def _get_data(self):
|
|
# Fetches data from `self.data_queue`.
|
|
#
|
|
# We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,
|
|
# which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`
|
|
# in a loop. This is the only mechanism to detect worker failures for
|
|
# Windows. For other platforms, a SIGCHLD handler is also used for
|
|
# worker failure detection.
|
|
#
|
|
# If `pin_memory=True`, we also need check if `pin_memory_thread` had
|
|
# died at timeouts.
|
|
if self.timeout > 0:
|
|
success, data = self._try_get_data(self.timeout)
|
|
if success:
|
|
return data
|
|
else:
|
|
raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
|
|
elif self.pin_memory:
|
|
while self.pin_memory_thread.is_alive():
|
|
success, data = self._try_get_data()
|
|
if success:
|
|
return data
|
|
else:
|
|
# while condition is false, i.e., pin_memory_thread died.
|
|
raise RuntimeError('Pin memory thread exited unexpectedly')
|
|
# In this case, `self.data_queue` is a `queue.Queue`,. But we don't
|
|
# need to call `.task_done()` because we don't use `.join()`.
|
|
else:
|
|
while True:
|
|
success, data = self._try_get_data()
|
|
if success:
|
|
return data
|
|
|
|
def __next__(self):
|
|
while True:
|
|
# If the worker responsible for `self.rcvd_idx` has already ended
|
|
# and was unable to fulfill this task (due to exhausting an `IterableDataset`),
|
|
# we try to advance `self.rcvd_idx` to find the next valid index.
|
|
#
|
|
# This part needs to run in the loop because both the `self._get_data()`
|
|
# call and `_IterableDatasetStopIteration` check below can mark
|
|
# extra worker(s) as dead.
|
|
while self.rcvd_idx < self.send_idx:
|
|
info = self.task_info[self.rcvd_idx]
|
|
worker_id = info[0]
|
|
if len(info) == 2 or self.workers_status[worker_id]: # has data or is still active
|
|
break
|
|
del self.task_info[self.rcvd_idx]
|
|
self.rcvd_idx += 1
|
|
else:
|
|
# no valid `self.rcvd_idx` is found (i.e., didn't break)
|
|
self._shutdown_workers()
|
|
raise StopIteration
|
|
|
|
# Now `self.rcvd_idx` is the batch index we want to fetch
|
|
|
|
# Check if the next sample has already been generated
|
|
if len(self.task_info[self.rcvd_idx]) == 2:
|
|
data = self.task_info.pop(self.rcvd_idx)[1]
|
|
return self._process_data(data)
|
|
|
|
assert not self.shutdown and self.tasks_outstanding > 0
|
|
idx, data = self._get_data()
|
|
self.tasks_outstanding -= 1
|
|
|
|
if self.dataset_kind == _DatasetKind.Iterable:
|
|
# Check for _IterableDatasetStopIteration
|
|
if isinstance(data, _utils.worker._IterableDatasetStopIteration):
|
|
self._shutdown_worker(data.worker_id)
|
|
self._try_put_index()
|
|
continue
|
|
|
|
if idx != self.rcvd_idx:
|
|
# store out-of-order samples
|
|
self.task_info[idx] += (data,)
|
|
else:
|
|
del self.task_info[idx]
|
|
return self._process_data(data)
|
|
|
|
next = __next__ # Python 2 compatibility
|
|
|
|
def _try_put_index(self):
|
|
assert self.tasks_outstanding < 2 * self.num_workers
|
|
try:
|
|
index = self._next_index()
|
|
except StopIteration:
|
|
return
|
|
for _ in range(self.num_workers): # find the next active worker, if any
|
|
worker_queue_idx = next(self.worker_queue_idx_cycle)
|
|
if self.workers_status[worker_queue_idx]:
|
|
break
|
|
else:
|
|
# not found (i.e., didn't break)
|
|
return
|
|
|
|
self.index_queues[worker_queue_idx].put((self.send_idx, index))
|
|
self.task_info[self.send_idx] = (worker_queue_idx,)
|
|
self.tasks_outstanding += 1
|
|
self.send_idx += 1
|
|
|
|
def _process_data(self, data):
|
|
self.rcvd_idx += 1
|
|
self._try_put_index()
|
|
if isinstance(data, ExceptionWrapper):
|
|
data.reraise()
|
|
return data
|
|
|
|
def _shutdown_worker(self, worker_id):
|
|
# Mark a worker as having finished its work and dead, e.g., due to
|
|
# exhausting an `IterableDataset`. This should be used only when this
|
|
# `_MultiProcessingDataLoaderIter` is going to continue running.
|
|
|
|
assert self.workers_status[worker_id]
|
|
|
|
# Signal termination to that specific worker.
|
|
q = self.index_queues[worker_id]
|
|
# Indicate that no more data will be put on this queue by the current
|
|
# process.
|
|
q.put(None)
|
|
|
|
# Note that we don't actually join the worker here, nor do we remove the
|
|
# worker's pid from C side struct because (1) joining may be slow, and
|
|
# (2) since we don't join, the worker may still raise error, and we
|
|
# prefer capturing those, rather than ignoring them, even though they
|
|
# are raised after the worker has finished its job.
|
|
# Joinning is deferred to `_shutdown_workers`, which it is called when
|
|
# all workers finish their jobs (e.g., `IterableDataset` replicas) or
|
|
# when this iterator is garbage collected.
|
|
self.workers_status[worker_id] = False
|
|
|
|
def _shutdown_workers(self):
|
|
# Called when shutting down this `_MultiProcessingDataLoaderIter`.
|
|
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on
|
|
# the logic of this function.
|
|
python_exit_status = _utils.python_exit_status
|
|
if python_exit_status is True or python_exit_status is None:
|
|
# See (2) of the note. If Python is shutting down, do no-op.
|
|
return
|
|
# Normal exit when last reference is gone / iterator is depleted.
|
|
# See (1) and the second half of the note.
|
|
if not self.shutdown:
|
|
self.shutdown = True
|
|
try:
|
|
# Exit `pin_memory_thread` first because exiting workers may leave
|
|
# corrupted data in `worker_result_queue` which `pin_memory_thread`
|
|
# reads from.
|
|
if hasattr(self, 'pin_memory_thread'):
|
|
# Use hasattr in case error happens before we set the attribute.
|
|
self.pin_memory_thread_done_event.set()
|
|
# Send something to pin_memory_thread in case it is waiting
|
|
# so that it can wake up and check `pin_memory_thread_done_event`
|
|
self.worker_result_queue.put((None, None))
|
|
self.pin_memory_thread.join()
|
|
self.worker_result_queue.close()
|
|
|
|
# Exit workers now.
|
|
self.workers_done_event.set()
|
|
for worker_id in range(len(self.workers)):
|
|
# Get number of workers from `len(self.workers)` instead of
|
|
# `self.num_workers` in case we error before starting all
|
|
# workers.
|
|
if self.workers_status[worker_id]:
|
|
self._shutdown_worker(worker_id)
|
|
for w in self.workers:
|
|
w.join()
|
|
for q in self.index_queues:
|
|
q.cancel_join_thread()
|
|
q.close()
|
|
finally:
|
|
# Even though all this function does is putting into queues that
|
|
# we have called `cancel_join_thread` on, weird things can
|
|
# happen when a worker is killed by a signal, e.g., hanging in
|
|
# `Event.set()`. So we need to guard this with SIGCHLD handler,
|
|
# and remove pids from the C side data structure only at the
|
|
# end.
|
|
#
|
|
# FIXME: Unfortunately, for Windows, we are missing a worker
|
|
# error detection mechanism here in this function, as it
|
|
# doesn't provide a SIGCHLD handler.
|
|
if self.worker_pids_set:
|
|
_utils.signal_handling._remove_worker_pids(id(self))
|
|
self.worker_pids_set = False
|
|
|
|
def __del__(self):
|
|
self._shutdown_workers()
|