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)})"


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()