dataloader_iter.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import six
import sys
import time
import signal
import numbers
import logging
import itertools
import threading
import numpy as np
import multiprocessing
from collections import namedtuple
from paddle.fluid.framework import _set_expected_place, _current_expected_place

# NOTE: queue has a different name in python2 and python3
if six.PY2:
    import Queue as queue
else:
    import queue

import paddle
from .. import core, layers
from ..framework import in_dygraph_mode
from ..multiprocess_utils import _set_SIGCHLD_handler, MP_STATUS_CHECK_INTERVAL, CleanupFuncRegistrar
from .fetcher import _IterableDatasetFetcher, _MapDatasetFetcher
from .batch_sampler import _InfiniteIterableSampler
from .collate import default_collate_fn, default_convert_fn
from .worker import ParentWatchDog, get_worker_info, _worker_loop, \
        _DatasetKind, _IterableDatasetStopIteration, _WorkerException
from .flat import _flatten_batch, _restore_batch

__all__ = ['get_worker_info']


class _DataLoaderIterBase(object):
    """
    Iterator implement of DataLoader, will load and feed mini-batch
    data by setting in given dataloader.

    Args:
        loader(instance of DataLoader): instance of `fluid.io.DataLoader`
    """

    def __init__(self, loader):
        self._dataset = loader.dataset
        self._feed_list = loader.feed_list or []
        self._places = loader.places
        self._return_list = loader.return_list
        self._batch_sampler = loader.batch_sampler
        self._auto_collate_batch = loader.auto_collate_batch
        self._num_workers = loader.num_workers
        self._use_buffer_reader = loader.use_buffer_reader
        self._use_shared_memory = loader.use_shared_memory
        self._timeout = loader.timeout if loader.timeout > 0 else MP_STATUS_CHECK_INTERVAL
        self._worker_init_fn = loader.worker_init_fn
        self._dataset_kind = loader.dataset_kind
        self._pin_memory = loader.pin_memory

        if self._auto_collate_batch:
            self._sampler_iter = iter(loader.batch_sampler)
            self._collate_fn = loader.collate_fn or default_collate_fn
        else:
            if self._dataset_kind == _DatasetKind.MAP:
                self._sampler_iter = iter(list(range(len(self._dataset))))
            else:
                self._sampler_iter = iter(
                    _InfiniteIterableSampler(self._dataset, 1))
            self._collate_fn = loader.collate_fn or default_convert_fn

        # LoDTensorBlockingQueue instance for create_py_reader and a thread
        # to put mini-batch data to self._blocking_queue, mini-batch data
        # will be get from:
        # 1. multi-process mode: get data from workers' result queue
        # 2. single-process mode: read mini-batch data in main process
        self._blocking_queue = None
        self._thread = None
        self._thread_done_event = threading.Event()

    def __iter__(self):
        return self

    def __len__(self):
        return len(self._batch_sampler)


class _DataLoaderIterSingleProcess(_DataLoaderIterBase):
    """
    Single process implement of DataLoaderIter, loading data from
    loader.data in main process
    """

    def __init__(self, loader):
        super(_DataLoaderIterSingleProcess, self).__init__(loader)

        self._dataset_fetcher = _DatasetKind.create_fetcher(
            self._dataset_kind, self._dataset, self._auto_collate_batch,
            self._collate_fn, True)

        # NOTE: _structrue_infos used to record the data structure of
        # batch to restore batch structure after reading Tensor
        # from blocking_queue in single-process mode. Note that
        # only single process is used in single-process mode, we
        # can record the data structure sequencely in a list without
        # recording the send and recv index
        self._structure_infos = []

        # NOTE: len(self._places) batch data compose as an output
        # iteration, set blocking_queue can cache 2 iteration datas
        # at most here
        self._blocking_queue_capacity = 2 * len(self._places)

        self._init_thread()

        # if user exit python program when dataloader is still
        # iterating, resource may no release safely, so we
        # add __del__ function to to CleanupFuncRegistrar
        # to make sure __del__ is always called when program
        # exit for resoure releasing safely
        CleanupFuncRegistrar.register(self.__del__)

    def _init_thread(self):
        self._var_names = [v.name for v in self._feed_list]
        self._shapes = [v.shape for v in self._feed_list]
        self._dtypes = [v.dtype for v in self._feed_list]
        self._need_check_feed = [
            v.desc.need_check_feed() for v in self._feed_list
        ]
        # if only 1 place, do not need to keep order
        self._blocking_queue = core.init_lod_tensor_blocking_queue(
            core.Variable(), self._blocking_queue_capacity,
            len(self._places) > 1)
        self._reader = core.create_py_reader(
            self._blocking_queue, self._var_names, self._shapes, self._dtypes,
            self._need_check_feed, self._places, self._use_buffer_reader, True,
            self._pin_memory)

        self._thread = threading.Thread(
            target=self._thread_loop, args=(_current_expected_place(), ))
        self._thread.daemon = True
        self._thread.start()

    def _thread_loop(self, legacy_expected_place):
        try:
            #NOTE(zhiqiu): Set the expected place for new thread as the same as father thread,
            # and it will call platform::SetDeviceId() in c++ internally.
            # If we do not set cudaDeviceId in new thread, the default cudaDeviceId will be 0,
            # Which may cost hundreds of MB of GPU memory on CUDAPlace(0) if calling some cuda 
            # APIs in this thread.
            _set_expected_place(legacy_expected_place)

            for indices in self._sampler_iter:
                # read data from dataset in mini-batch
                batch = self._dataset_fetcher.fetch(indices)

                # flat batch and record structure infos
                batch, structure = _flatten_batch(batch)
                self._structure_infos.append(structure)

                # pack as LoDTensorArray
                array = core.LoDTensorArray()
                for slot in batch:
                    if isinstance(slot, paddle.Tensor):
                        slot = slot.value().get_tensor()
                    elif not isinstance(slot, core.LoDTensor):
                        tmp = core.LoDTensor()
                        tmp.set(slot, core.CPUPlace())
                        slot = tmp

                    array.append(slot)

                if not self._blocking_queue.push(array):
                    break

                if self._thread_done_event.is_set():
                    break

            self._blocking_queue.close()
            self._shutdown_thread()
        except StopIteration:
            self._blocking_queue.close()
        except Exception:
            self._blocking_queue.kill()
            self._shutdown_thread()
            logging.warning("DataLoader reader thread raised an exception.")
            six.reraise(*sys.exc_info())

    def __next__(self):
        try:
            if in_dygraph_mode():
                data = self._reader.read_next_var_list()
                data = _restore_batch(data, self._structure_infos.pop(0))
            else:
                if self._return_list:
                    data = self._reader.read_next_list()
                    data = [
                        _restore_batch(d, s)
                        for d, s in zip(data, self._structure_infos[:len(
                            self._places)])
                    ]
                    self._structure_infos = self._structure_infos[len(
                        self._places):]
                    # static graph organized data on multi-device with list, if
                    # place number is 1, there is only 1 device, extra the data
                    # from list for devices to be compatible with dygraph mode
                    if len(self._places) == 1:
                        data = data[0]
                else:
                    data = self._reader.read_next()

            return data
        except StopIteration:
            self._reader.shutdown()
            six.reraise(*sys.exc_info())

    def _shutdown_thread(self):
        if self._thread:
            self._thread_done_event.set()
            if self._thread is not threading.current_thread():
                self._thread.join()
                self._thread = None

    # python2 compatibility
    def next(self):
        return self.__next__()

    def __del__(self):
        # _blocking_queue in keep order mode holds sub-threads
        # need to release thread resources on unexpected exit
        if self._blocking_queue:
            self._blocking_queue.close()
        # NOTE: blocking queue should be closed firstly for
        # blocking queue read may hang and _thread_done_event
        # cannot be checked
        self._shutdown_thread()


class _DataLoaderIterMultiProcess(_DataLoaderIterBase):
    def __init__(self, loader):
        super(_DataLoaderIterMultiProcess, self).__init__(loader)

        assert self._num_workers > 0,  "Multi-process DataLoader " \
                    "invalid num_workers({})".format(self._num_workers)

        # subprocess wrokers' result queue
        self._data_queue = None

        # data get from _data_queue will be reordered by _rcvd_idx
        # for data order keeping, data index not equal _rcvd_idx 
        # will be cached in _task_infos
        self._send_idx = 0
        self._rcvd_idx = 0
        self._batches_outstanding = 0
        self._task_infos = {}
        self._structure_infos = []

        # indices outstand as _outstanding_capacity at first, and
        # blocking_queue capacity is also _outstanding_capacity.
        # _outstanding_capacity here to make sure each indices_queue
        # has at least 2 indices, and outstanding batch cached
        # output data for at least 2 iterations(Note that len(_places)
        # batches will be composed as an iteration output)
        self._outstanding_capacity = 2 * max(self._num_workers,
                                             len(self._places))

        # see _try_put_indices
        self._thread_lock = threading.Lock()

        # init workers and indices queues and put 2 indices in each indices queue
        self._init_workers()
        for _ in range(self._outstanding_capacity):
            self._try_put_indices()

        self._init_thread()
        self._shutdown = False

        # if user exit python program when dataloader is still
        # iterating, resource may no release safely, so we
        # add _shutdown_on_exit function to to CleanupFuncRegistrar
        # to make sure _try_shutdown_all is always called when program
        # exit for resoure releasing safely
        # worker join may hang for in _try_shutdown_all call in atexit
        # for main process is in atexit state in some OS, so we add
        # timeout=1 for shutdown function call in atexit, for shutdown
        # function call in __del__, we keep it as it is
        CleanupFuncRegistrar.register(self._shutdown_on_exit)

    def _init_workers(self):
        # multiprocess worker and indice queue list initial as empty
        self._workers = []
        self._worker_status = []
        self._indices_queues = []
        self._workers_idx_cycle = itertools.cycle(range(self._num_workers))

        # create data_queue for workers
        self._data_queue = multiprocessing.Queue()

        # event for workers and thread, thread event is only need 
        # in multi-processing mode
        self._workers_done_event = multiprocessing.Event()
        self._thread_done_event = threading.Event()

        for i in range(self._num_workers):
            indices_queue = multiprocessing.Queue()
            self._indices_queues.append(indices_queue)
            worker = multiprocessing.Process(
                target=_worker_loop,
                args=(self._dataset, self._dataset_kind, indices_queue,
                      self._data_queue, self._workers_done_event,
                      self._auto_collate_batch, self._collate_fn,
                      self._worker_init_fn, i, self._num_workers,
                      self._use_shared_memory))
            worker.daemon = True
            worker.start()
            self._workers.append(worker)
            self._worker_status.append(True)

        core._set_process_pids(id(self), tuple(w.pid for w in self._workers))
        _set_SIGCHLD_handler()

    def _clear_and_remove_data_queue(self):
        if self._data_queue is not None:
            while True:
                try:
                    self._data_queue.get_nowait()
                except:
                    self._data_queue.cancel_join_thread()
                    self._data_queue.close()
                    break

    def _init_thread(self):
        self._var_names = [v.name for v in self._feed_list]
        self._shapes = [v.shape for v in self._feed_list]
        self._dtypes = [v.dtype for v in self._feed_list]
        self._need_check_feed = [
            v.desc.need_check_feed() for v in self._feed_list
        ]
        # if only 1 place, do not need to keep order
        self._blocking_queue = core.init_lod_tensor_blocking_queue(
            core.Variable(), self._outstanding_capacity, len(self._places) > 1)
        self._reader = core.create_py_reader(
            self._blocking_queue, self._var_names, self._shapes, self._dtypes,
            self._need_check_feed, self._places, self._use_buffer_reader, True,
            self._pin_memory)

        self._thread_done_event = threading.Event()
        self._thread = threading.Thread(
            target=self._thread_loop, args=(_current_expected_place(), ))
        self._thread.daemon = True
        self._thread.start()

    def _shutdown_worker(self, worker_id):
        if self._worker_status[worker_id]:
            self._indices_queues[worker_id].put(None)
            self._worker_status[worker_id] = False

    def _try_shutdown_all(self, timeout=None):
        if not self._shutdown:
            try:
                self._exit_thread_expectedly()
                self._clear_and_remove_data_queue()

                # set _workers_done_event should be set before put None
                # to indices_queue, workers wll exit on reading None from
                # indices_queue
                self._workers_done_event.set()
                for i in range(self._num_workers):
                    self._shutdown_worker(i)

                if not self._shutdown:
                    for w in self._workers:
                        w.join(timeout)
                    for q in self._indices_queues:
                        q.cancel_join_thread()
                        q.close()
            finally:
                core._erase_process_pids(id(self))
                self._shutdown = True

    def _exit_thread_expectedly(self):
        self._thread_done_event.set()
        self._blocking_queue.close()

    def _exit_thread_unexpectedly(self):
        self._thread_done_event.set()
        self._blocking_queue.kill()
        logging.error("DataLoader reader thread raised an exception!")

    def _thread_loop(self, legacy_expected_place):
        #NOTE(zhiqiu): Set the expected place for new thread as the same as father thread,
        # and it will call platform::SetDeviceId() in c++ internally.
        # If we do not set cudaDeviceId in new thread, the default cudaDeviceId will be 0,
        # Which may cost hundreds of MB of GPU memory on CUDAPlace(0) if calling some cuda 
        # APIs in this thread.
        _set_expected_place(legacy_expected_place)

        while not self._thread_done_event.is_set():
            batch = self._get_data()
            if not self._thread_done_event.is_set():
                if batch is None:
                    self._exit_thread_expectedly()
                else:
                    try:
                        # pack as LoDTensorArray
                        array = core.LoDTensorArray()
                        if self._use_shared_memory:
                            for tensor in batch:
                                array.append(tensor)
                        else:
                            # LoDTensor not in shared memory is not
                            # serializable, cannot be create in workers
                            for slot in batch:
                                if isinstance(slot, paddle.Tensor):
                                    slot = slot.value().get_tensor()
                                elif not isinstance(slot, core.LoDTensor):
                                    tmp = core.LoDTensor()
                                    tmp.set(slot, core.CPUPlace())
                                    slot = tmp
                                array.append(slot)

                        if not self._blocking_queue.push(array):
                            self._blocking_queue.close()
                    except:
                        self._exit_thread_unexpectedly()
                        six.reraise(*sys.exc_info())
                    finally:
                        self._rcvd_idx += 1

    def _get_data(self):
        while not self._thread_done_event.is_set():
            # For IterableDataset, batch indices is generated infinitely
            # for each worker to raise StopIteration, but a StopIteration
            # raising process will discard a batch indices which is count
            # in _send_idx but will not increase _rcvd_idx, so we check 
            # whether the worker is still alive here to skip the discarded
            # batch indices and increase _rcvd_idx
            if self._dataset_kind == _DatasetKind.ITER:
                while self._rcvd_idx < self._send_idx:
                    sys.stdout.flush()
                    info = self._task_infos[self._rcvd_idx]
                    if len(info) == 3 or self._worker_status[info[0]]:
                        break
                    del self._task_infos[self._rcvd_idx]
                    self._rcvd_idx += 1
                    self._batches_outstanding -= 1
                else:
                    # NOTE: _rcvd_idx and _send_idx only record batches among
                    #       workers, if batches among workers drained, there
                    #       may also be data in blocking queue
                    if self._batches_outstanding < len(self._places):
                        return None
                    continue

            if self._rcvd_idx in self._task_infos and \
                    len(self._task_infos[self._rcvd_idx]) == 3:
                info = self._task_infos.pop(self._rcvd_idx)
                self._structure_infos.append(info[2])
                return info[1]

            try:
                # [ avoid hang ]: main process may blocking at _reader.read_next when
                # KeyboardInterrupt, we do following tradeoff:
                # 1. get data with timeout, MP_STATUS_CHECK_INTERVAL(5s) as timeout
                #    default, if KeyboardInterrupt blocking, failed workers will be
                #    checked and raise RuntimeError to quit DataLoader in timeout
                #    exception handling.
                # 2. if get data timeout and check workers all alive, continue to
                #    get data again
                data = self._data_queue.get(timeout=self._timeout)
            except Exception as e:
                # check if thread done event set when waiting data
                if self._thread_done_event.is_set():
                    continue

                # check failed workers
                failed_workers = []
                for i, w in enumerate(self._workers):
                    if self._worker_status[i] and not w.is_alive():
                        failed_workers.append(w)
                        self._shutdown_worker(i)
                if len(failed_workers) > 0:
                    self._exit_thread_unexpectedly()
                    pids = ', '.join(str(w.pid) for w in failed_workers)
                    raise RuntimeError("DataLoader {} workers exit unexpectedly, " \
                                "pids: {}".format(len(failed_workers), pids))

                # get(timeout) will call _poll(timeout) and may raise IOError
                if isinstance(e, queue.Empty) or isinstance(e, IOError):
                    # continue on timeout to keep getting data from queue
                    continue

                self._exit_thread_unexpectedly()
                logging.error("DataLoader reader thread failed({}) to read data from " \
                              "workers' result queue.".format(e))
                six.reraise(*sys.exc_info())
            else:
                if self._dataset_kind == _DatasetKind.ITER and isinstance(
                        data, _IterableDatasetStopIteration):
                    # if a worker get StopIteraion, we shutdown this worker,
                    # note that this batch indices to trigger StopIteration
                    # is discard, outstanding batch number should be decrease
                    # and another indices should be put for other workers
                    # may still working.
                    self._shutdown_worker(data.worker_id)
                    self._batches_outstanding -= 1
                    self._try_put_indices()
                    continue

                idx, batch, structure = data
                if isinstance(batch, _WorkerException):
                    self._exit_thread_unexpectedly()
                    batch.reraise()

                if idx == self._rcvd_idx:
                    del self._task_infos[idx]
                    self._structure_infos.append(structure)
                    return batch
                else:
                    self._task_infos[idx] += (batch, structure)
                    continue

    def _try_put_indices(self):
        assert self._batches_outstanding <= self._outstanding_capacity, \
                    "too many indices have been put to queue"
        # In multi-process mode for IterableDataset, _try_put_indices will
        # be called both in main process(for our implement has blocking queue,
        # and blocking queue read is in main process) and thread, which may
        # cause error following error
        #   1. "ValueError: generator already executing" in next(self._sampler_iter)
        #   2. re-enter in increase _send_idx
        # add a lock for threading save, for _try_put_indices is only a slight
        # function which is not in data reading pipeline, this lock almost no
        # influence on performance
        with self._thread_lock:
            try:
                indices = next(self._sampler_iter)
            except StopIteration:
                return

            for i in range(self._num_workers):
                worker_idx = next(self._workers_idx_cycle)
                if self._worker_status[worker_idx]:
                    break
            else:
                return

            self._indices_queues[worker_idx].put((self._send_idx, indices))
            self._task_infos[self._send_idx] = (worker_idx, )
            self._batches_outstanding += 1
            self._send_idx += 1

    def __del__(self):
        self._try_shutdown_all()

    def _shutdown_on_exit(self):
        self._try_shutdown_all(1)

    def __next__(self):
        try:
            # _batches_outstanding here record the total batch data number
            # in 'from after _try_put_indices to beforeoutput data', this
            # value should be _outstanding_capacity if data is not drained,
            # if _batches_outstanding is less than _places number, there are
            # no enough data to generate next output, close blocking_queue and
            # set _thread_done_event here, py_reader will raise StopIteration,
            # end workers and indices_queues in StopIteration handling
            if self._batches_outstanding < len(self._places):
                self._thread_done_event.set()
                self._blocking_queue.close()

            if in_dygraph_mode():
                data = self._reader.read_next_var_list()
                data = _restore_batch(data, self._structure_infos.pop(0))
            else:
                if self._return_list:
                    data = self._reader.read_next_list()
                    data = [
                        _restore_batch(d, s)
                        for d, s in zip(data, self._structure_infos[:len(
                            self._places)])
                    ]
                    self._structure_infos = self._structure_infos[len(
                        self._places):]
                    # static graph organized data on multi-device with list, if
                    # place number is 1, there is only 1 device, extra the data
                    # from list for devices to be compatible with dygraph mode
                    if len(self._places) == 1:
                        data = data[0]
                else:
                    data = self._reader.read_next()
            self._on_output_batch()
            return data
        except StopIteration:
            self._reader.shutdown()
            self._try_shutdown_all()
            six.reraise(*sys.exc_info())

    # python2 compatibility
    def next(self):
        return self.__next__()

    def _on_output_batch(self):
        for _ in range(len(self._places)):
            self._batches_outstanding -= 1
            self._try_put_indices()