PyTorch 的 DataLoader 类介绍
DataLoader 类
功能与作用
-
PyTorch 是一个流行的开源机器学习库，它提供了一个名为 DataLoader 的类，用于加载数据集并将其封装成一个可迭代的对象。DataLoader 可以自动地将数据集划分为多个批次，并在训练过程中迭代地返回这些批次。是用于加载数据集的重要组件。DataLoader 结合了数据集和采样器，提供了一个可迭代的数据加载器，支持多进程数据加载、自定义加载顺序和可选的自动批处理（collation）以及内存锁定。
-
DataLoader 类的一些关键特性：
- 批量加载（Batching）：可以指定每个批次的大小。
- 多进程加载（Multiprocessing）：可以利用多个进程来加速数据的加载。
- 数据打乱（Shuffling）：可以在每个epoch开始时随机打乱数据，以提高模型训练的泛化能力。
- 数据采样（Sampling）：可以自定义数据采样方式，例如，可以定义一个采样器来选择数据集中的特定样本。
- 数据预处理（Data Preprocessing）：可以在加载数据时应用预处理操作，例如数据增强。
基本结构介绍
-
参数介绍：
dataset(Dataset): 要加载数据的数据集。batch_size(int, optional): 每个批次加载的样本数，默认为1。shuffle(bool, optional): 是否在每个 epoch 打乱数据，默认为False。sampler(Sampler or Iterable, optional): 定义从数据集中抽取样本的策略。batch_sampler(Sampler or Iterable, optional): 类似于sampler，但每次返回一批索引。num_workers(int, optional): 用于数据加载的子进程数，默认为0，即在主进程中加载数据。collate_fn(Callable, optional): 合并样本以形成 mini-batch 的函数。pin_memory(bool, optional): 是否将数据复制到 CUDA 固定内存中，默认为False。drop_last(bool, optional): 是否丢弃最后一个不完整的批次，默认为False。timeout(numeric, optional): 从工作进程收集一批数据的超时时间，默认为0。worker_init_fn(Callable, optional): 每个工作进程启动时调用的函数。multiprocessing_context(str or multiprocessing.context.BaseContext, optional): 多进程上下文。generator(torch.Generator, optional): 用于生成随机索引的随机数生成器。prefetch_factor(int, optional): 每个工作进程提前加载的批次数。persistent_workers(bool, optional): 是否在数据集消费完毕后保持工作进程活动，默认为False。pin_memory_device(str, optional): 将数据固定到的设备。
-
方法介绍：
__init__: 初始化 DataLoader 实例。_get_iterator: 返回一个迭代器，用于从 DataLoader 中获取数据。multiprocessing_context: 获取或设置多进程上下文。__setattr__: 设置实例属性，如果 DataLoader 已经初始化，则不能修改某些属性。__iter__: 返回一个迭代器，用于遍历 DataLoader。_auto_collation: 属性，指示是否启用自动批处理。_index_sampler: 属性，获取用于生成索引的实际采样器。__len__: 返回 DataLoader 的长度，即可以生成的批次总数。check_worker_number_rationality: 检查工作进程数是否合理，基于系统资源。
-
其他相关类：
- _BaseDataLoaderIter 类：这是 DataLoader 的基类迭代器，提供了多进程数据加载的底层逻辑。
- _SingleProcessDataLoaderIter 类：这是 DataLoader 的单进程迭代器，用于在主进程中加载数据，不涉及多进程通信。
- _MultiProcessingDataLoaderIter 类：这是 DataLoader 的多进程迭代器，用于在多个进程中加载数据，提高了数据加载的效率。
- 辅助函数和类：例如 default_collate、default_convert、get_worker_info 等，这些函数和类提供了数据加载过程中需要的一些辅助功能。
DataLoader 和 Dataset
DataLoader 和 Dataset 是 PyTorch 中用于数据加载和预处理的两个非常重要的类，它们在数据加载流程中扮演着不同的角色，但又紧密相关。
-
Dataset：
Dataset是 PyTorch 中的一个抽象类，它定义了数据集的接口规范。具体来说，Dataset类需要实现以下两个方法：__len__: 返回数据集中样本的总数。__getitem__: 接受一个索引值，并返回对应的样本。
Dataset的子类可以是任何类型的数据集，比如图像、文本或音频数据集。开发者需要根据具体的数据类型和格式，继承Dataset类并实现这两个方法。这样，Dataset的实例就可以被看作是一个包含所有数据的容器。
-
DataLoader
DataLoader是 PyTorch 中用于加载数据集的工具。它提供了一种简便的方式来迭代地访问Dataset中的数据。DataLoader支持多进程数据加载，可以显著提高数据读取和预处理的效率。DataLoader的主要特点包括：- 多进程数据加载：通过
num_workers参数，可以指定多个子进程来并行加载数据，这样可以有效地利用多核 CPU 资源，加速数据的准备过程。 - 批处理：
DataLoader可以将多个样本组合成一个批次（batch），并自动处理数据的打乱、采样等操作。 - 可定制的数据预处理：通过
collate_fn参数，用户可以定义自己的数据预处理函数，以适应特定的需求。
- 多进程数据加载：通过
-
区别与联系：
-
区别：
Dataset是一个抽象类，定义了数据集的结构和访问方式，而DataLoader是一个具体类，用于从Dataset中加载和迭代数据。Dataset负责存储数据和提供单个样本的访问，DataLoader负责按批次加载数据，并支持多进程加载和批处理。Dataset的实现侧重于数据的组织和读取，而DataLoader的实现侧重于数据加载的效率和灵活性。
-
联系：
DataLoader依赖于Dataset，它需要一个Dataset的实例作为数据源。DataLoader通过Dataset提供的样本来创建批次数据，它封装了Dataset的访问方式，使得数据的迭代更加高效和方便。- 在使用时，通常先定义一个
Dataset的子类来管理特定的数据集，然后创建DataLoader的实例来加载和迭代这个数据集。
-
总的来说，Dataset 和 DataLoader 是 PyTorch 数据处理流程中不可或缺的两个组件，它们共同工作，为模型训练提供了高效、灵活的数据支持。
示例说明
import torch
from torch.utils.data import Dataset, DataLoader
# 定义一个简单的数据集
class SimpleDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.data[index], self.labels[index]
# 创建数据和标签
data = torch.randn(100, 10) # 100个样本，每个样本10个特征
labels = torch.randint(0, 2, (100,)) # 100个样本的二分类标签
# 实例化数据集
dataset = SimpleDataset(data, labels)
# 实例化DataLoader
data_loader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=2)
# 遍历DataLoader
for batch_idx, (inputs, targets) in enumerate(data_loader):
print(f"Batch {batch_idx+1}:")
print(f"Inputs: {inputs}")
print(f"Targets: {targets}")
# 这里可以加入模型训练的代码
相关源码
- PyTorch源码地址：https://github.com/pytorch/pytorch/blob/main/torch/utils/data/dataloader.py
# mypy: allow-untyped-defs
r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter.
To support these two classes, in `./_utils` we define many utility methods and
functions to be run in multiprocessing. E.g., the data loading worker loop is
in `./_utils/worker.py`.
"""
import functools
import itertools
import logging
import multiprocessing as python_multiprocessing
import os
import queue
import threading
import warnings
from typing import Any, Callable, Generic, Iterable, List, Optional, TypeVar, Union
import torch
import torch.distributed as dist
import torch.utils.data.graph_settings
from torch._utils import ExceptionWrapper
from torch.utils.data import _utils
from torch.utils.data.datapipes.datapipe import (
_IterDataPipeSerializationWrapper,
_MapDataPipeSerializationWrapper,
IterDataPipe,
MapDataPipe,
)
from torch.utils.data.dataset import Dataset, IterableDataset
from torch.utils.data.sampler import (
BatchSampler,
RandomSampler,
Sampler,
SequentialSampler,
)
__all__ = [
"DataLoader",
"get_worker_info",
"default_collate",
"default_convert",
]
_T = TypeVar("_T")
_T_co = TypeVar("_T_co", covariant=True)
_worker_init_fn_t = Callable[[int], None]
# Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that
# type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.
# See https://github.com/python/mypy/issues/3737.
_collate_fn_t = Callable[[List[_T]], Any]
# These functions used to be defined in this file. However, it was moved to
# _utils/collate.py. Although it is rather hard to access this from user land
# (one has to explicitly directly `import torch.utils.data.dataloader`), there
# probably is user code out there using it. This aliasing maintains BC in this
# aspect.
default_collate: _collate_fn_t = _utils.collate.default_collate
default_convert = _utils.collate.default_convert
get_worker_info = _utils.worker.get_worker_info
logger = logging.getLogger(__name__)
class _DatasetKind:
Map = 0
Iterable = 1
@staticmethod
def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):
if kind == _DatasetKind.Map:
return _utils.fetch._MapDatasetFetcher(
dataset, auto_collation, collate_fn, drop_last
)
else:
return _utils.fetch._IterableDatasetFetcher(
dataset, auto_collation, collate_fn, drop_last
)
class _InfiniteConstantSampler(Sampler):
r"""Analogous to ``itertools.repeat(None, None)``.
Used as sampler for :class:`~torch.utils.data.IterableDataset`.
"""
def __iter__(self):
while True:
yield None
def _get_distributed_settings():
if dist.is_available() and dist.is_initialized():
return dist.get_world_size(), dist.get_rank()
else:
return 1, 0
def _sharding_worker_init_fn(worker_init_fn, world_size, rank_id, worker_id):
global_worker_id = worker_id
info = torch.utils.data.get_worker_info()
assert info is not None
total_workers = info.num_workers
datapipe = info.dataset
assert isinstance(datapipe, (IterDataPipe, MapDataPipe))
# To distribute elements across distributed process evenly, we should shard data on distributed
# processes first then shard on worker processes
total_workers *= world_size
global_worker_id = global_worker_id * world_size + rank_id
# For BC, use default SHARDING_PRIORITIES
torch.utils.data.graph_settings.apply_sharding(
datapipe, total_workers, global_worker_id
)
if worker_init_fn is not None:
worker_init_fn(worker_id)
def _share_dist_seed(generator, pg):
_shared_seed = torch.empty((), dtype=torch.int64).random_(generator=generator)
if isinstance(pg, dist.ProcessGroup):
dist.broadcast(_shared_seed, src=0, group=pg)
return _shared_seed.item()
class DataLoader(Generic[_T_co]):
r"""
Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.
The :class:`~torch.utils.data.DataLoader` supports both map-style and
iterable-style datasets with single- or multi-process loading, customizing
loading order and optional automatic batching (collation) and memory pinning.
See :py:mod:`torch.utils.data` documentation page for more details.
Args:
dataset (Dataset): dataset from which to load the data.
batch_size (int, optional): how many samples per batch to load
(default: ``1``).
shuffle (bool, optional): set to ``True`` to have the data reshuffled
at every epoch (default: ``False``).
sampler (Sampler or Iterable, optional): defines the strategy to draw
samples from the dataset. Can be any ``Iterable`` with ``__len__``
implemented. If specified, :attr:`shuffle` must not be specified.
batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but
returns a batch of indices at a time. Mutually exclusive with
:attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,
and :attr:`drop_last`.
num_workers (int, optional): how many subprocesses to use for data
loading. ``0`` means that the data will be loaded in the main process.
(default: ``0``)
collate_fn (Callable, optional): merges a list of samples to form a
mini-batch of Tensor(s). Used when using batched loading from a
map-style dataset.
pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
into device/CUDA pinned memory before returning them. If your data elements
are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
see the example below.
drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If ``False`` and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: ``False``)
timeout (numeric, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative. (default: ``0``)
worker_init_fn (Callable, optional): If not ``None``, this will be called on each
worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
input, after seeding and before data loading. (default: ``None``)
multiprocessing_context (str or multiprocessing.context.BaseContext, optional): If
``None``, the default `multiprocessing context`_ of your operating system will
be used. (default: ``None``)
generator (torch.Generator, optional): If not ``None``, this RNG will be used
by RandomSampler to generate random indexes and multiprocessing to generate
``base_seed`` for workers. (default: ``None``)
prefetch_factor (int, optional, keyword-only arg): Number of batches loaded
in advance by each worker. ``2`` means there will be a total of
2 * num_workers batches prefetched across all workers. (default value depends
on the set value for num_workers. If value of num_workers=0 default is ``None``.
Otherwise, if value of ``num_workers > 0`` default is ``2``).
persistent_workers (bool, optional): If ``True``, the data loader will not shut down
the worker processes after a dataset has been consumed once. This allows to
maintain the workers `Dataset` instances alive. (default: ``False``)
pin_memory_device (str, optional): the device to :attr:`pin_memory` to if ``pin_memory`` is
``True``.
.. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`
cannot be an unpicklable object, e.g., a lambda function. See
:ref:`multiprocessing-best-practices` on more details related
to multiprocessing in PyTorch.
.. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.
When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,
it instead returns an estimate based on ``len(dataset) / batch_size``, with proper
rounding depending on :attr:`drop_last`, regardless of multi-process loading
configurations. This represents the best guess PyTorch can make because PyTorch
trusts user :attr:`dataset` code in correctly handling multi-process
loading to avoid duplicate data.
However, if sharding results in multiple workers having incomplete last batches,
this estimate can still be inaccurate, because (1) an otherwise complete batch can
be broken into multiple ones and (2) more than one batch worth of samples can be
dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such
cases in general.
See `Dataset Types`_ for more details on these two types of datasets and how
:class:`~torch.utils.data.IterableDataset` interacts with
`Multi-process data loading`_.
.. warning:: See :ref:`reproducibility`, and :ref:`dataloader-workers-random-seed`, and
:ref:`data-loading-randomness` notes for random seed related questions.
.. _multiprocessing context:
https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
"""
dataset: Dataset[_T_co]
batch_size: Optional[int]
num_workers: int
pin_memory: bool
drop_last: bool
timeout: float
sampler: Union[Sampler, Iterable]
pin_memory_device: str
prefetch_factor: Optional[int]
_iterator: Optional["_BaseDataLoaderIter"]
__initialized = False
def __init__(
self,
dataset: Dataset[_T_co],
batch_size: Optional[int] = 1,
shuffle: Optional[bool] = None,
sampler: Union[Sampler, Iterable, None] = None,
batch_sampler: Union[Sampler[List], Iterable[List], None] = None,
num_workers: int = 0,
collate_fn: Optional[_collate_fn_t] = None,
pin_memory: bool = False,
drop_last: bool = False,
timeout: float = 0,
worker_init_fn: Optional[_worker_init_fn_t] = None,
multiprocessing_context=None,
generator=None,
*,
prefetch_factor: Optional[int] = None,
persistent_workers: bool = False,
pin_memory_device: str = "",
):
torch._C._log_api_usage_once("python.data_loader")
if num_workers < 0:
raise ValueError(
"num_workers option should be non-negative; "
"use num_workers=0 to disable multiprocessing."
)
if timeout < 0:
raise ValueError("timeout option should be non-negative")
if num_workers == 0 and prefetch_factor is not None:
raise ValueError(
"prefetch_factor option could only be specified in multiprocessing."
"let num_workers > 0 to enable multiprocessing, otherwise set prefetch_factor to None."
)
elif num_workers > 0 and prefetch_factor is None:
prefetch_factor = 2
elif prefetch_factor is not None and prefetch_factor < 0:
raise ValueError("prefetch_factor option should be non-negative")
if persistent_workers and num_workers == 0:
raise ValueError("persistent_workers option needs num_workers > 0")
self.dataset = dataset
self.num_workers = num_workers
self.prefetch_factor = prefetch_factor
self.pin_memory = pin_memory
self.pin_memory_device = pin_memory_device
self.timeout = timeout
self.worker_init_fn = worker_init_fn
self.multiprocessing_context = multiprocessing_context
# Adds forward compatibilities so classic DataLoader can work with DataPipes:
# _DataPipeSerializationWrapper container makes it easier to serialize without redefining pickler
if isinstance(self.dataset, IterDataPipe):
self.dataset = _IterDataPipeSerializationWrapper(self.dataset)
elif isinstance(self.dataset, MapDataPipe):
self.dataset = _MapDataPipeSerializationWrapper(self.dataset)
# Arg-check dataset related before checking samplers because we want to
# tell users that iterable-style datasets are incompatible with custom
# samplers first, so that they don't learn that this combo doesn't work
# after spending time fixing the custom sampler errors.
if isinstance(dataset, IterableDataset):
self._dataset_kind = _DatasetKind.Iterable
# NOTE [ Custom Samplers and IterableDataset ]
#
# `IterableDataset` does not support custom `batch_sampler` or
# `sampler` since the key is irrelevant (unless we support
# generator-style dataset one day...).
#
# For `sampler`, we always create a dummy sampler. This is an
# infinite sampler even when the dataset may have an implemented
# finite `__len__` because in multi-process data loading, naive
# settings will return duplicated data (which may be desired), and
# thus using a sampler with length matching that of dataset will
# cause data lost (you may have duplicates of the first couple
# batches, but never see anything afterwards). Therefore,
# `Iterabledataset` always uses an infinite sampler, an instance of
# `_InfiniteConstantSampler` defined above.
#
# A custom `batch_sampler` essentially only controls the batch size.
# However, it is unclear how useful it would be since an iterable-style
# dataset can handle that within itself. Moreover, it is pointless
# in multi-process data loading as the assignment order of batches
# to workers is an implementation detail so users can not control
# how to batchify each worker's iterable. Thus, we disable this
# option. If this turns out to be useful in future, we can re-enable
# this, and support custom samplers that specify the assignments to
# specific workers.
if isinstance(dataset, IterDataPipe):
if shuffle is not None:
dataset = torch.utils.data.graph_settings.apply_shuffle_settings(
dataset, shuffle=shuffle
)
# We cannot check `shuffle is not None` here, since previously `shuffle=False` was the default.
elif shuffle not in {False, None}:
raise ValueError(
f"DataLoader with IterableDataset: expected unspecified shuffle option, but got shuffle={shuffle}"
)
if sampler is not None:
# See NOTE [ Custom Samplers and IterableDataset ]
raise ValueError(
f"DataLoader with IterableDataset: expected unspecified sampler option, but got sampler={sampler}"
)
elif batch_sampler is not None:
# See NOTE [ Custom Samplers and IterableDataset ]
raise ValueError(
"DataLoader with IterableDataset: expected unspecified "
f"batch_sampler option, but got batch_sampler={batch_sampler}"
)
else:
shuffle = bool(shuffle)
self._dataset_kind = _DatasetKind.Map
if sampler is not None and shuffle:
raise ValueError("sampler option is mutually exclusive with " "shuffle")
if batch_sampler is not None:
# auto_collation with custom batch_sampler
if batch_size != 1 or shuffle or sampler is not None or drop_last:
raise ValueError(
"batch_sampler option is mutually exclusive "
"with batch_size, shuffle, sampler, and "
"drop_last"
)
batch_size = None
drop_last = False
elif batch_size is None:
# no auto_collation
if drop_last:
raise ValueError(
"batch_size=None option disables auto-batching "
"and is mutually exclusive with drop_last"
)
if sampler is None: # give default samplers
if self._dataset_kind == _DatasetKind.Iterable:
# See NOTE [ Custom Samplers and IterableDataset ]
sampler = _InfiniteConstantSampler()
else: # map-style
if shuffle:
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
else:
sampler = SequentialSampler(dataset) # type: ignore[arg-type]
if batch_size is not None and batch_sampler is None:
# auto_collation without custom batch_sampler
batch_sampler = BatchSampler(sampler, batch_size, drop_last)
self.batch_size = batch_size
self.drop_last = drop_last
self.sampler = sampler
self.batch_sampler = batch_sampler
self.generator = generator
if collate_fn is None:
if self._auto_collation:
collate_fn = _utils.collate.default_collate
else:
collate_fn = _utils.collate.default_convert
self.collate_fn = collate_fn
self.persistent_workers = persistent_workers
self.__initialized = True
self._IterableDataset_len_called = (
None # See NOTE [ IterableDataset and __len__ ]
)
self._iterator = None
self.check_worker_number_rationality()
torch.set_vital("Dataloader", "enabled", "True") # type: ignore[attr-defined]
def _get_iterator(self) -> "_BaseDataLoaderIter":
if self.num_workers == 0:
return _SingleProcessDataLoaderIter(self)
else:
self.check_worker_number_rationality()
return _MultiProcessingDataLoaderIter(self)
@property
def multiprocessing_context(self):
return self.__multiprocessing_context
@multiprocessing_context.setter
def multiprocessing_context(self, multiprocessing_context):
if multiprocessing_context is not None:
if self.num_workers > 0:
if isinstance(multiprocessing_context, str):
valid_start_methods = torch.multiprocessing.get_all_start_methods()
if multiprocessing_context not in valid_start_methods:
raise ValueError(
"multiprocessing_context option "
f"should specify a valid start method in {valid_start_methods!r}, but got "
f"multiprocessing_context={multiprocessing_context!r}"
)
multiprocessing_context = torch.multiprocessing.get_context(
multiprocessing_context
)
if not isinstance(
multiprocessing_context, python_multiprocessing.context.BaseContext
):
raise TypeError(
"multiprocessing_context option should be a valid context "
"object or a string specifying the start method, but got "
f"multiprocessing_context={multiprocessing_context}"
)
else:
raise ValueError(
"multiprocessing_context can only be used with "
"multi-process loading (num_workers > 0), but got "
f"num_workers={self.num_workers}"
)
self.__multiprocessing_context = multiprocessing_context
def __setattr__(self, attr, val):
if self.__initialized and attr in (
"batch_size",
"batch_sampler",
"sampler",
"drop_last",
"dataset",
"persistent_workers",
):
raise ValueError(
f"{attr} attribute should not be set after {self.__class__.__name__} is initialized"
)
super().__setattr__(attr, val)
# We quote '_BaseDataLoaderIter' since it isn't defined yet and the definition can't be moved up
# since '_BaseDataLoaderIter' references 'DataLoader'.
def __iter__(self) -> "_BaseDataLoaderIter":
# When using a single worker the returned iterator should be
# created everytime to avoid resetting its state
# However, in the case of a multiple workers iterator
# the iterator is only created once in the lifetime of the
# DataLoader object so that workers can be reused
if self.persistent_workers and self.num_workers > 0:
if self._iterator is None:
self._iterator = self._get_iterator()
else:
self._iterator._reset(self)
return self._iterator
else:
return self._get_iterator()
@property
def _auto_collation(self):
return self.batch_sampler is not None
@property
def _index_sampler(self):
# The actual sampler used for generating indices for `_DatasetFetcher`
# (see _utils/fetch.py) to read data at each time. This would be
# `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.
# We can't change `.sampler` and `.batch_sampler` attributes for BC
# reasons.
if self._auto_collation:
return self.batch_sampler
else:
return self.sampler
def __len__(self) -> int:
if self._dataset_kind == _DatasetKind.Iterable:
# NOTE [ IterableDataset and __len__ ]
#
# For `IterableDataset`, `__len__` could be inaccurate when one naively
# does multi-processing data loading, since the samples will be duplicated.
# However, no real use case should be actually using that behavior, so
# it should count as a user error. We should generally trust user
# code to do the proper thing (e.g., configure each replica differently
# in `__iter__`), and give us the correct `__len__` if they choose to
# implement it (this will still throw if the dataset does not implement
# a `__len__`).
#
# To provide a further warning, we track if `__len__` was called on the
# `DataLoader`, save the returned value in `self._len_called`, and warn
# if the iterator ends up yielding more than this number of samples.
# Cannot statically verify that dataset is Sized
length = self._IterableDataset_len_called = len(self.dataset) # type: ignore[assignment, arg-type]
if (
self.batch_size is not None
): # IterableDataset doesn't allow custom sampler or batch_sampler
from math import ceil
if self.drop_last:
length = length // self.batch_size
else:
length = ceil(length / self.batch_size)
return length
else:
return len(self._index_sampler)
def check_worker_number_rationality(self):
# This function check whether the dataloader's worker number is rational based on
# current system's resource. Current rule is that if the number of workers this
# Dataloader will create is bigger than the number of logical cpus that is allowed to
# use, than we will pop up a warning to let user pay attention.
#
# eg. If current system has 2 physical CPUs with 16 cores each. And each core support 2
# threads, then the total logical cpus here is 2 * 16 * 2 = 64. Let's say current
# DataLoader process can use half of them which is 32, then the rational max number of
# worker that initiated from this process is 32.
# Now, let's say the created DataLoader has num_works = 40, which is bigger than 32.
# So the warning message is triggered to notify the user to lower the worker number if
# necessary.
#
#
# [Note] Please note that this function repects `cpuset` only when os.sched_getaffinity is
# available (available in most of Linux system, but not OSX and Windows).
# When os.sched_getaffinity is not available, os.cpu_count() is called instead, but
# it doesn't repect cpuset.
# We don't take threading into account since each worker process is single threaded
# at this time.
#
# We don't set any threading flags (eg. OMP_NUM_THREADS, MKL_NUM_THREADS, etc)
# other than `torch.set_num_threads` to 1 in the worker process, if the passing
# in functions use 3rd party modules that rely on those threading flags to determine
# how many thread to create (eg. numpy, etc), then it is caller's responsibility to
# set those flags correctly.
def _create_warning_msg(num_worker_suggest, num_worker_created, cpuset_checked):
suggested_max_worker_msg = (
(
(
"Our suggested max number of worker in current system is {}{}, which is smaller "
"than what this DataLoader is going to create."
).format(
num_worker_suggest,
(
""
if cpuset_checked
else " (`cpuset` is not taken into account)"
),
)
)
if num_worker_suggest is not None
else (
"DataLoader is not able to compute a suggested max number of worker in current system."
)
)
warn_msg = (
f"This DataLoader will create {num_worker_created} worker processes in total. {suggested_max_worker_msg} "
"Please be aware that excessive worker creation might get DataLoader running slow or even freeze, "
"lower the worker number to avoid potential slowness/freeze if necessary."
)
return warn_msg
if not self.num_workers or self.num_workers == 0:
return
# try to compute a suggested max number of worker based on system's resource
max_num_worker_suggest = None
cpuset_checked = False
if hasattr(os, "sched_getaffinity"):
try:
max_num_worker_suggest = len(os.sched_getaffinity(0))
cpuset_checked = True
except Exception:
pass
if max_num_worker_suggest is None:
# os.cpu_count() could return Optional[int]
# get cpu count first and check None in order to satisfy mypy check
cpu_count = os.cpu_count()
if cpu_count is not None:
max_num_worker_suggest = cpu_count
if max_num_worker_suggest is None:
warnings.warn(
_create_warning_msg(
max_num_worker_suggest, self.num_workers, cpuset_checked
)
)
return
if self.num_workers > max_num_worker_suggest:
warnings.warn(
_create_warning_msg(
max_num_worker_suggest, self.num_workers, cpuset_checked
)
)
class _BaseDataLoaderIter:
def __init__(self, loader: DataLoader) -> None:
self._dataset = loader.dataset
self._shared_seed = None
self._pg = None
if isinstance(self._dataset, IterDataPipe):
if dist.is_available() and dist.is_initialized():
self._pg = dist.new_group(backend="gloo")
self._shared_seed = _share_dist_seed(loader.generator, self._pg)
shared_rng = torch.Generator()
shared_rng.manual_seed(self._shared_seed)
self._dataset = torch.utils.data.graph_settings.apply_random_seed(
self._dataset, shared_rng
)
self._dataset_kind = loader._dataset_kind
self._IterableDataset_len_called = loader._IterableDataset_len_called
self._auto_collation = loader._auto_collation
self._drop_last = loader.drop_last
self._index_sampler = loader._index_sampler
self._num_workers = loader.num_workers
ws, rank = _get_distributed_settings()
self._world_size = ws
self._rank = rank
# for other backends, pin_memory_device need to set. if not set
# default behaviour is CUDA device. if pin_memory_device is selected
# and pin_memory is not set, the default behaviour false.
if len(loader.pin_memory_device) == 0:
self._pin_memory = loader.pin_memory and torch.cuda.is_available()
self._pin_memory_device = None
else:
if not loader.pin_memory:
warn_msg = (
"pin memory device is set and pin_memory flag is not used then device pinned memory won't be used"
"please set pin_memory to true, if you need to use the device pin memory"
)
warnings.warn(warn_msg)
self._pin_memory = loader.pin_memory
self._pin_memory_device = loader.pin_memory_device
self._timeout = loader.timeout
self._collate_fn = loader.collate_fn
self._sampler_iter = iter(self._index_sampler)
self._base_seed = (
torch.empty((), dtype=torch.int64)
.random_(generator=loader.generator)
.item()
)
self._persistent_workers = loader.persistent_workers
self._num_yielded = 0
self._profile_name = f"enumerate(DataLoader)#{self.__class__.__name__}.__next__"
def __iter__(self) -> "_BaseDataLoaderIter":
return self
def _reset(self, loader, first_iter=False):
self._sampler_iter = iter(self._index_sampler)
self._num_yielded = 0
self._IterableDataset_len_called = loader._IterableDataset_len_called
if isinstance(self._dataset, IterDataPipe):
self._shared_seed = _share_dist_seed(loader.generator, self._pg)
shared_rng = torch.Generator()
shared_rng.manual_seed(self._shared_seed)
self._dataset = torch.utils.data.graph_settings.apply_random_seed(
self._dataset, shared_rng
)
def _next_index(self):
return next(self._sampler_iter) # may raise StopIteration
def _next_data(self):
raise NotImplementedError
def __next__(self) -> Any:
with torch.autograd.profiler.record_function(self._profile_name):
if self._sampler_iter is None:
# TODO(https://github.com/pytorch/pytorch/issues/76750)
self._reset() # type: ignore[call-arg]
data = self._next_data()
self._num_yielded += 1
if (
self._dataset_kind == _DatasetKind.Iterable
and self._IterableDataset_len_called is not None
and self._num_yielded > self._IterableDataset_len_called
):
warn_msg = (
f"Length of IterableDataset {self._dataset} was reported to be {self._IterableDataset_len_called}"
f"(when accessing len(dataloader)), but {self._num_yielded} samples have been fetched. "
)
if self._num_workers > 0:
warn_msg += (
"For multiprocessing data-loading, this could be caused by not properly configuring the "
"IterableDataset replica at each worker. Please see "
"https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples."
)
warnings.warn(warn_msg)
return data
def __len__(self) -> int:
return len(self._index_sampler)
def __getstate__(self):
# TODO: add limited pickling support for sharing an iterator
# across multiple threads for HOGWILD.
# Probably the best way to do this is by moving the sample pushing
# to a separate thread and then just sharing the data queue
# but signalling the end is tricky without a non-blocking API
raise NotImplementedError("{} cannot be pickled", self.__class__.__name__)
class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
def __init__(self, loader):
super().__init__(loader)
assert self._timeout == 0
assert self._num_workers == 0
# Adds forward compatibilities so classic DataLoader can work with DataPipes:
# Taking care of distributed sharding
if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
# For BC, use default SHARDING_PRIORITIES
torch.utils.data.graph_settings.apply_sharding(
self._dataset, self._world_size, self._rank
)
self._dataset_fetcher = _DatasetKind.create_fetcher(
self._dataset_kind,
self._dataset,
self._auto_collation,
self._collate_fn,
self._drop_last,
)
def _next_data(self):
index = self._next_index() # may raise StopIteration
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
if self._pin_memory:
data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)
return data
class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):
r"""Iterates once over the DataLoader's dataset, as specified by the sampler."""
# NOTE [ Data Loader Multiprocessing Shutdown Logic ]
#
# Preliminary:
#
# Our data model looks like this (queues are indicated with curly brackets):
#
# main process ||
# | ||
# {index_queue} ||
# | ||
# worker processes || DATA
# | ||
# {worker_result_queue} || FLOW
# | ||
# pin_memory_thread of main process || DIRECTION
# | ||
# {data_queue} ||
# | ||
# data output \/
#
# P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if
# `pin_memory=False`.
#
#
# Terminating multiprocessing logic requires very careful design. In
# particular, we need to make sure that
#
# 1. The iterator gracefully exits the workers when its last reference is
# gone or it is depleted.
#
# In this case, the workers should be gracefully exited because the
# main process may still need to continue to run, and we want cleaning
# up code in the workers to be executed (e.g., releasing GPU memory).
# Naturally, we implement the shutdown logic in `__del__` of
# DataLoaderIterator.
#
# We delay the discussion on the logic in this case until later.
#
# 2. The iterator exits the workers when the loader process and/or worker
# processes exits normally or with error.
#
# We set all workers and `pin_memory_thread` to have `daemon=True`.
#
# You may ask, why can't we make the workers non-daemonic, and
# gracefully exit using the same logic as we have in `__del__` when the
# iterator gets deleted (see 1 above)?
#
# First of all, `__del__` is **not** guaranteed to be called when
# interpreter exits. Even if it is called, by the time it executes,
# many Python core library resources may already be freed, and even
# simple things like acquiring an internal lock of a queue may hang.
# Therefore, in this case, we actually need to prevent `__del__` from
# being executed, and rely on the automatic termination of daemonic
# children.
#
# Thus, we register an `atexit` hook that sets a global flag
# `_utils.python_exit_status`. Since `atexit` hooks are executed in the
# reverse order of registration, we are guaranteed that this flag is
# set before library resources we use are freed (which, at least in
# CPython, is done via an `atexit` handler defined in
# `multiprocessing/util.py`
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/util.py#L320-L362
# registered when an object requiring this mechanism is first
# created, e.g., `mp.Queue`
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/context.py#L100-L103
# https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/queues.py#L29
# )
#
# So in `__del__`, we check if `_utils.python_exit_status` is set or
# `None` (freed), and perform no-op if so.
#
# However, simply letting library clean-up codes run can also be bad,
# because such codes (i.e., `multiprocessing.util._exit_function()`)
# include join putting threads for `mp.Queue`, which can be blocking.
# Hence, the main process putting threads are called with
# `cancel_join_thread` at creation. See later section
# [ 3b. A process won't hang when putting into a queue; ]
# for more details.
#
# Here are two example cases where library clean-up codes can run
# before `__del__` is called:
#
# 1. If we hold onto a reference to the iterator, it more often
# than not tries to do `multiprocessing` library cleaning before
# clearing the alive referenced objects (https://github.com/pytorch/pytorch/issues/48666)
# and thus prevents our cleaning-up code to run first.
#
# 2. A similar issue araises when a `DataLoader` is used in a subprocess.
# When a process ends, it shuts the all its daemonic children
# down with a SIGTERM (instead of joining them without a timeout).
# Simiarly for threads, but by a different mechanism. This fact,
# together with a few implementation details of multiprocessing, forces
# us to make workers daemonic. All of our problems arise when a
# DataLoader is used in a subprocess, and are caused by multiprocessing
# code which looks more or less like this:
#
# try:
# your_function_using_a_dataloader()
# finally:
# multiprocessing.util._exit_function()
#
# The joining/termination mentioned above happens inside
# `_exit_function()`. Now, if `your_function_using_a_dataloader()`
# throws, the stack trace stored in the exception will prevent the
# 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.
#
#
# Finally, 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*.
#
# In case that the receiver has ended abruptly while
# reading from the pipe, the join will hang forever. The usual
# solution for this in Python is calling `q.cancel_join_thread`,
# which prevents automatically joining it when finalizing
# (exiting).
#
# 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.
#
# Hence,
# + For worker processes, we only do so (for their output
# queues, i.e., `worker_result_queue`) before exiting.
# + For `pin_memory_thread`, its output queue `data_queue` is a
# `queue.Queue` that does blocking `put` if the queue is full.
# So there is no above problem, but as a result, in
# `_pin_memory_loop`, 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.
# + For loader process, we `cancel_join_thread()` for all
# `_index_queues` because the whole purpose of workers and
# `pin_memory_thread` is to serve the loader process. If
# loader process is already exiting, we don't really care if
# the queues are corrupted.
#
#
# 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 needed 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 `worker_result_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 timing 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().__init__(loader)
self._prefetch_factor = loader.prefetch_factor
assert self._num_workers > 0
assert self._prefetch_factor > 0
if loader.multiprocessing_context is None:
multiprocessing_context = torch.multiprocessing
else:
multiprocessing_context = loader.multiprocessing_context
self._worker_init_fn = loader.worker_init_fn
# Adds forward compatibilities so classic DataLoader can work with DataPipes:
# Additional worker init function will take care of sharding in MP and Distributed
if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):
self._worker_init_fn = functools.partial(
_sharding_worker_init_fn,
self._worker_init_fn,
self._world_size,
self._rank,
)
# No certainty which module multiprocessing_context is
self._worker_result_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]
self._worker_pids_set = False
self._shutdown = False
self._workers_done_event = multiprocessing_context.Event()
self._index_queues = []
self._workers = []
for i in range(self._num_workers):
# No certainty which module multiprocessing_context is
index_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]
# Need to `cancel_join_thread` here!
# See sections (2) and (3b) above.
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,
self._worker_init_fn,
i,
self._num_workers,
self._persistent_workers,
self._shared_seed,
),
)
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)
if self._pin_memory:
self._pin_memory_thread_done_event = threading.Event()
# Queue is not type-annotated
self._data_queue = queue.Queue() # type: ignore[var-annotated]
if self._pin_memory_device == "xpu":
current_device = torch.xpu.current_device() # type: ignore[attr-defined]
elif self._pin_memory_device == torch._C._get_privateuse1_backend_name():
custom_device_mod = getattr(
torch, torch._C._get_privateuse1_backend_name()
)
current_device = custom_device_mod.current_device()
else:
current_device = torch.cuda.current_device() # choose cuda for default
pin_memory_thread = threading.Thread(
target=_utils.pin_memory._pin_memory_loop,
args=(
self._worker_result_queue,
self._data_queue,
current_device,
self._pin_memory_thread_done_event,
self._pin_memory_device,
),
)
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 # type: ignore[assignment]
# In some rare cases, persistent workers (daemonic processes)
# would be terminated before `__del__` of iterator is invoked
# when main process exits
# It would cause failure when pin_memory_thread tries to read
# corrupted data from worker_result_queue
# atexit is used to shutdown thread and child processes in the
# right sequence before main process exits
if self._persistent_workers and self._pin_memory:
import atexit
for w in self._workers:
atexit.register(_MultiProcessingDataLoaderIter._clean_up_worker, w)
# .pid can be None only before process is spawned (not the case, so ignore)
_utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers)) # type: ignore[misc]
_utils.signal_handling._set_SIGCHLD_handler()
self._worker_pids_set = True
self._reset(loader, first_iter=True)
def _reset(self, loader, first_iter=False):
super()._reset(loader, first_iter)
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)
)
# 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).
# Not that this indicates that a worker still has work to do *for this epoch*.
# It does not mean that a worker is dead. In case of `_persistent_workers`,
# the worker will be reset to available in the next epoch.
self._workers_status = [True for i in range(self._num_workers)]
# Reset the worker queue cycle so it resumes next epoch at worker 0
self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))
# We resume the prefetching in case it was enabled
if not first_iter:
for idx in range(self._num_workers):
self._index_queues[idx].put(
_utils.worker._ResumeIteration(self._shared_seed)
)
resume_iteration_cnt = self._num_workers
while resume_iteration_cnt > 0:
return_idx, return_data = self._get_data()
if isinstance(return_idx, _utils.worker._ResumeIteration):
assert return_data is None
resume_iteration_cnt -= 1
# prime the prefetch loop
for _ in range(self._prefetch_factor * 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._mark_worker_as_unavailable(worker_id)
if len(failed_workers) > 0:
pids_str = ", ".join(str(w.pid) for w in failed_workers)
raise RuntimeError(
f"DataLoader worker (pid(s) {pids_str}) exited unexpectedly"
) from e
if isinstance(e, queue.Empty):
return (False, None)
import errno
import tempfile
try:
# Raise an exception if we are this close to the FDs limit.
# Apparently, trying to open only one file is not a sufficient
# test.
# See NOTE [ DataLoader on Linux and open files limit ]
fds_limit_margin = 10
fs = [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]
except OSError as e:
if e.errno == errno.EMFILE:
raise RuntimeError(
"Too many open files. Communication with the"
" workers is no longer possible. Please increase the"
" limit using `ulimit -n` in the shell or change the"
" sharing strategy by calling"
" `torch.multiprocessing.set_sharing_strategy('file_system')`"
" at the beginning of your code"
) from None
raise
# NOTE [ DataLoader on Linux and open files limit ]
#
# On Linux when DataLoader is used with multiprocessing we pass the data between
# the root process and the workers through SHM files. We remove those files from
# the filesystem as soon as they are created and keep them alive by
# passing around their file descriptors through AF_UNIX sockets. (See
# docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in
# the wiki (https://github.com/pytorch/pytorch/wiki).)
#
# This sometimes leads us to exceeding the open files limit. When that happens,
# and the offending file descriptor is coming over a socket, the `socket` Python
# package silently strips the file descriptor from the message, setting only the
# `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that
# it _indicates that some control data were discarded due to lack of space in
# the buffer for ancillary data_). This might reflect the C implementation of
# AF_UNIX sockets.
#
# This behaviour can be reproduced with the script and instructions at the
# bottom of this note.
#
# When that happens, the standard Python `multiprocessing` (and not
# `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`
#
# Sometimes, instead of the FD being stripped, you may get an `OSError:
# Too many open files`, both in the script below and in DataLoader. However,
# this is rare and seems to be nondeterministic.
#
#
# #!/usr/bin/env python3
# import sys
# import socket
# import os
# import array
# import shutil
# import socket
#
#
# if len(sys.argv) != 4:
# print("Usage: ", sys.argv[0], " tmp_dirname iteration (send|recv)")
# sys.exit(1)
#
# if __name__ == '__main__':
# dirname = sys.argv[1]
# sock_path = dirname + "/sock"
# iterations = int(sys.argv[2])
# def dummy_path(i):
# return dirname + "/" + str(i) + ".dummy"
#
#
# if sys.argv[3] == 'send':
# while not os.path.exists(sock_path):
# pass
# client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
# client.connect(sock_path)
# for i in range(iterations):
# fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)
# ancdata = array.array('i', [fd])
# msg = bytes([i % 256])
# print("Sending fd ", fd, " (iteration #", i, ")")
# client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])
#
#
# else:
# assert sys.argv[3] == 'recv'
#
# if os.path.exists(dirname):
# raise Exception("Directory exists")
#
# os.mkdir(dirname)
#
# print("Opening socket...")
# server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)
# server.bind(sock_path)
#
# print("Listening...")
# for i in range(iterations):
# a = array.array('i')
# msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))
# assert(len(ancdata) == 1)
# cmsg_level, cmsg_type, cmsg_data = ancdata[0]
# a.frombytes(cmsg_data)
# print("Received fd ", a[0], " (iteration #", i, ")")
#
# shutil.rmtree(dirname)
#
# Steps to reproduce:
#
# 1. Run two shells and set lower file descriptor limit in the receiving one:
# (shell1) ulimit -n 1020
# (shell2) ulimit -n 1022
#
# 2. Run the script above with the `recv` option in the first shell
# (shell1) ./test_socket.py sock_tmp 1017 recv
#
# 3. Run the script with the `send` option in the second shell:
# (shell2) ./test_socket.py sock_tmp 1017 send
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(
f"DataLoader timed out after {self._timeout} seconds"
)
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_data(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)
if not self._persistent_workers:
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):
if self._persistent_workers:
self._workers_status[data.worker_id] = False
else:
self._mark_worker_as_unavailable(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)
def _try_put_index(self):
assert self._tasks_outstanding < self._prefetch_factor * 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)) # type: ignore[possibly-undefined]
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 _mark_worker_as_unavailable(self, worker_id, shutdown=False):
# Mark a worker as having finished its work 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] or (
self._persistent_workers and shutdown
)
# 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
assert self._workers_done_event.is_set() == shutdown
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.
if (
_utils is None
or _utils.python_exit_status is True
or _utils.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:
# Normal exit when last reference is gone / iterator is depleted.
# See (1) and the second half of the note.
# 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.cancel_join_thread()
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 we are using workers_status with persistent_workers
# we have to shut it down because the worker is paused
if self._persistent_workers or self._workers_status[worker_id]:
self._mark_worker_as_unavailable(worker_id, shutdown=True)
for w in self._workers:
# We should be able to join here, but in case anything went
# wrong, we set a timeout and if the workers fail to join,
# they are killed in the `finally` block.
w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
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
for w in self._workers:
if w.is_alive():
# Existing mechanisms try to make the workers exit
# peacefully, but in case that we unfortunately reach
# here, which we shouldn't, (e.g., pytorch/pytorch#39570),
# we kill the worker.
w.terminate()
# staticmethod is used to remove reference to `_MultiProcessingDataLoaderIter`
@staticmethod
def _clean_up_worker(w):
try:
w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)
finally:
if w.is_alive():
w.terminate()
def __del__(self):
self._shutdown_workers()