Shortcuts

Source code for torch.utils.data._utils.worker

r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.

These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""

import torch
import random
import os
import queue
from dataclasses import dataclass
from torch._utils import ExceptionWrapper
from typing import Optional, Union, TYPE_CHECKING
from . import signal_handling, MP_STATUS_CHECK_INTERVAL, IS_WINDOWS, HAS_NUMPY
if TYPE_CHECKING:
    from torch.utils.data import Dataset

if IS_WINDOWS:
    import ctypes
    from ctypes.wintypes import DWORD, BOOL, HANDLE

    # On Windows, the parent ID of the worker process remains unchanged when the manager process
    # is gone, and the only way to check it through OS is to let the worker have a process handle
    # of the manager and ask if the process status has changed.
    class ManagerWatchdog:
        def __init__(self):
            self.manager_pid = os.getppid()

            # mypy cannot detect this code is windows only
            self.kernel32 = ctypes.WinDLL('kernel32', use_last_error=True)  # type: ignore[attr-defined]
            self.kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
            self.kernel32.OpenProcess.restype = HANDLE
            self.kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
            self.kernel32.WaitForSingleObject.restype = DWORD

            # Value obtained from https://msdn.microsoft.com/en-us/library/ms684880.aspx
            SYNCHRONIZE = 0x00100000
            self.manager_handle = self.kernel32.OpenProcess(SYNCHRONIZE, 0, self.manager_pid)

            if not self.manager_handle:
                raise ctypes.WinError(ctypes.get_last_error())  # type: ignore[attr-defined]

            self.manager_dead = False

        def is_alive(self):
            if not self.manager_dead:
                # Value obtained from https://msdn.microsoft.com/en-us/library/windows/desktop/ms687032.aspx
                self.manager_dead = self.kernel32.WaitForSingleObject(self.manager_handle, 0) == 0
            return not self.manager_dead
else:
    class ManagerWatchdog:  # type: ignore[no-redef]
        def __init__(self):
            self.manager_pid = os.getppid()
            self.manager_dead = False

        def is_alive(self):
            if not self.manager_dead:
                self.manager_dead = os.getppid() != self.manager_pid
            return not self.manager_dead

_worker_info: Optional["WorkerInfo"] = None


class WorkerInfo:
    id: int
    num_workers: int
    seed: int
    dataset: 'Dataset'
    __initialized = False

    def __init__(self, **kwargs):
        for k, v in kwargs.items():
            setattr(self, k, v)
        self.__keys = tuple(kwargs.keys())
        self.__initialized = True

    def __setattr__(self, key, val):
        if self.__initialized:
            raise RuntimeError(f"Cannot assign attributes to {self.__class__.__name__} objects")
        return super().__setattr__(key, val)

    def __repr__(self):
        items = []
        for k in self.__keys:
            items.append(f'{k}={getattr(self, k)}')
        return f"{self.__class__.__name__}({', '.join(items)})"


[docs]def get_worker_info() -> Optional[WorkerInfo]: r"""Returns the information about the current :class:`~torch.utils.data.DataLoader` iterator worker process. When called in a worker, this returns an object guaranteed to have the following attributes: * :attr:`id`: the current worker id. * :attr:`num_workers`: the total number of workers. * :attr:`seed`: the random seed set for the current worker. This value is determined by main process RNG and the worker id. See :class:`~torch.utils.data.DataLoader`'s documentation for more details. * :attr:`dataset`: the copy of the dataset object in **this** process. Note that this will be a different object in a different process than the one in the main process. When called in the main process, this returns ``None``. .. note:: When used in a :attr:`worker_init_fn` passed over to :class:`~torch.utils.data.DataLoader`, this method can be useful to set up each worker process differently, for instance, using ``worker_id`` to configure the ``dataset`` object to only read a specific fraction of a sharded dataset, or use ``seed`` to seed other libraries used in dataset code. """ return _worker_info
r"""Dummy class used to signal the end of an IterableDataset""" @dataclass(frozen=True) class _IterableDatasetStopIteration: worker_id: int r"""Dummy class used to resume the fetching when worker reuse is enabled""" @dataclass(frozen=True) class _ResumeIteration: seed: Optional[int] = None # The function `_generate_state` is adapted from `numpy.random.SeedSequence` # from https://github.com/numpy/numpy/blob/main/numpy/random/bit_generator.pyx # It's MIT licensed, here is the copyright: # Copyright (c) 2015 Melissa E. O'Neill # Copyright (c) 2019 NumPy Developers # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # This function generates an array of int32 as the seed for # `numpy.random`, in order to prevent state collision due to same # seed and algorithm for `numpy.random` and `random` modules. # TODO: Implement `SeedSequence` like object for `torch.random` def _generate_state(base_seed, worker_id): INIT_A = 0x43b0d7e5 MULT_A = 0x931e8875 INIT_B = 0x8b51f9dd MULT_B = 0x58f38ded MIX_MULT_L = 0xca01f9dd MIX_MULT_R = 0x4973f715 XSHIFT = 4 * 8 // 2 MASK32 = 0xFFFFFFFF entropy = [worker_id, base_seed & MASK32, base_seed >> 32, 0] pool = [0] * 4 hash_const_A = INIT_A def hash(value): nonlocal hash_const_A value = (value ^ hash_const_A) & MASK32 hash_const_A = (hash_const_A * MULT_A) & MASK32 value = (value * hash_const_A) & MASK32 value = (value ^ (value >> XSHIFT)) & MASK32 return value def mix(x, y): result_x = (MIX_MULT_L * x) & MASK32 result_y = (MIX_MULT_R * y) & MASK32 result = (result_x - result_y) & MASK32 result = (result ^ (result >> XSHIFT)) & MASK32 return result # Add in the entropy to the pool. for i in range(len(pool)): pool[i] = hash(entropy[i]) # Mix all bits together so late bits can affect earlier bits. for i_src in range(len(pool)): for i_dst in range(len(pool)): if i_src != i_dst: pool[i_dst] = mix(pool[i_dst], hash(pool[i_src])) hash_const_B = INIT_B state = [] for i_dst in range(4): data_val = pool[i_dst] data_val = (data_val ^ hash_const_B) & MASK32 hash_const_B = (hash_const_B * MULT_B) & MASK32 data_val = (data_val * hash_const_B) & MASK32 data_val = (data_val ^ (data_val >> XSHIFT)) & MASK32 state.append(data_val) return state def _worker_loop(dataset_kind, dataset, index_queue, data_queue, done_event, auto_collation, collate_fn, drop_last, base_seed, init_fn, worker_id, num_workers, persistent_workers, shared_seed): # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the # logic of this function. try: # Initialize C side signal handlers for SIGBUS and SIGSEGV. Python signal # module's handlers are executed after Python returns from C low-level # handlers, likely when the same fatal signal had already happened # again. # https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers signal_handling._set_worker_signal_handlers() torch.set_num_threads(1) seed = base_seed + worker_id random.seed(seed) torch.manual_seed(seed) if HAS_NUMPY: np_seed = _generate_state(base_seed, worker_id) import numpy as np np.random.seed(np_seed) from torch.utils.data import IterDataPipe from torch.utils.data.graph_settings import apply_random_seed shared_rng = torch.Generator() if isinstance(dataset, IterDataPipe): assert shared_seed is not None shared_rng.manual_seed(shared_seed) dataset = apply_random_seed(dataset, shared_rng) global _worker_info _worker_info = WorkerInfo(id=worker_id, num_workers=num_workers, seed=seed, dataset=dataset) from torch.utils.data import _DatasetKind init_exception = None try: if init_fn is not None: init_fn(worker_id) fetcher = _DatasetKind.create_fetcher(dataset_kind, dataset, auto_collation, collate_fn, drop_last) except Exception: init_exception = ExceptionWrapper( where=f"in DataLoader worker process {worker_id}") # When using Iterable mode, some worker can exit earlier than others due # to the IterableDataset behaving differently for different workers. # When such things happen, an `_IterableDatasetStopIteration` object is # sent over to the main process with the ID of this worker, so that the # main process won't send more tasks to this worker, and will send # `None` to this worker to properly exit it. # # Note that we cannot set `done_event` from a worker as it is shared # among all processes. Instead, we set the `iteration_end` flag to # signify that the iterator is exhausted. When either `done_event` or # `iteration_end` is set, we skip all processing step and just wait for # `None`. iteration_end = False watchdog = ManagerWatchdog() while watchdog.is_alive(): try: r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL) except queue.Empty: continue if isinstance(r, _ResumeIteration): # Acknowledge the main process data_queue.put((r, None)) iteration_end = False if isinstance(dataset, IterDataPipe): assert r.seed is not None shared_rng.manual_seed(r.seed) dataset = apply_random_seed(dataset, shared_rng) # Recreate the fetcher for worker-reuse policy fetcher = _DatasetKind.create_fetcher( dataset_kind, dataset, auto_collation, collate_fn, drop_last) continue elif r is None: # Received the final signal assert done_event.is_set() or iteration_end break elif done_event.is_set() or iteration_end: # `done_event` is set. But I haven't received the final signal # (None) yet. I will keep continuing until get it, and skip the # processing steps. continue idx, index = r data: Union[_IterableDatasetStopIteration, ExceptionWrapper] if init_exception is not None: data = init_exception init_exception = None else: try: data = fetcher.fetch(index) # type: ignore[possibly-undefined] except Exception as e: if isinstance(e, StopIteration) and dataset_kind == _DatasetKind.Iterable: data = _IterableDatasetStopIteration(worker_id) # Set `iteration_end` # (1) to save future `next(...)` calls, and # (2) to avoid sending multiple `_IterableDatasetStopIteration`s. iteration_end = True else: # It is important that we don't store exc_info in a variable. # `ExceptionWrapper` does the correct thing. # See NOTE [ Python Traceback Reference Cycle Problem ] data = ExceptionWrapper( where=f"in DataLoader worker process {worker_id}") data_queue.put((idx, data)) del data, idx, index, r # save memory except KeyboardInterrupt: # Main process will raise KeyboardInterrupt anyways. pass if done_event.is_set(): data_queue.cancel_join_thread() data_queue.close()

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources