megenginelite-sys 1.8.2

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import collections
import gc
import math
import multiprocessing
import os
import platform
import queue
import random
import threading
import time
from typing import Callable, Union

import numpy as np

from ..device import _sh, get_default_device
from ..functional.tensor import copy
from ..logger import get_logger
from ..random.rng import _random_seed_generator
from ..tensor import Tensor
from .collator import Collator
from .dataset import Dataset, StreamDataset
from .sampler import MapSampler, Sampler, SequentialSampler, StreamSampler
from .transform import PseudoTransform, Transform

try:
    import thread
except:
    import _thread as thread


logger = get_logger(__name__)


GLOBAL_TIMEOUT = 5


def raise_timeout_error():
    raise RuntimeError("dataloader timeout")


class DataLoader:
    r"""Provides a convenient way to iterate on a given dataset.

    DataLoader combines a dataset with
    :class:`~.Sampler`, :class:`~.Transform` and :class:`~.Collator`,
    make it flexible to get minibatch continually from a dataset.

    Args:
        dataset: dataset from which to load the minibatch.
        sampler: defines the strategy to sample data from the dataset.
        transform: defined the transforming strategy for a sampled batch.
            Default: None
        collator: defined the merging strategy for a transformed batch.
            Default: None
        num_workers: the number of sub-process to load, transform and collate
            the batch. ``0`` means using single-process. Default: 0
        timeout: if positive, means the timeout value(second) for collecting a
            batch from workers. Default: 0
        timeout_event: callback function triggered by timeout, default to raise
            runtime error.
        divide: define the paralleling strategy in multi-processing mode.
            ``True`` means one batch is divided into :attr:`num_workers` pieces, and
            the workers will process these pieces parallelly. ``False`` means
            different sub-process will process different batch. Default: False
        preload: whether to enable the preloading strategy of the dataloader. When enabling, the dataloader will preload one batch to the device memory to speed up the whole training process.
            All values in the map, list, and tuple will be converted to :class:`~.Tensor` by preloading, and you will get :class:`~.Tensor` instead of the original Numpy array or Python number.


    .. note::

        By enabling preload, tensors' host2device copy and device kernel execution will be overlapped, which will improve the training speed at the cost of higher device memory usage (due to one more batch data on device memory).
        This feature saves more time when your NN training time is short or your machine's host PCIe bandwidth for each device is low.
    """
    __initialized = False

    def __init__(
        self,
        dataset: Dataset,
        sampler: Sampler = None,
        transform: Transform = None,
        collator: Collator = None,
        num_workers: int = 0,
        timeout: int = 0,
        timeout_event: Callable = raise_timeout_error,
        divide: bool = False,
        preload: bool = False,
    ):
        if num_workers < 0:
            raise ValueError("num_workers should not be negative")

        if timeout < 0:
            raise ValueError("timeout should not be negative")

        if divide and num_workers <= 1:
            raise ValueError("divide should not be set to True when num_workers <= 1")

        self.dataset = dataset

        self.num_workers = num_workers
        self.timeout = timeout
        self.timeout_event = timeout_event

        self.divide = divide
        self.preload = preload

        if isinstance(dataset, StreamDataset):
            self.sampler = sampler if sampler else StreamSampler(batch_size=1)
            assert isinstance(
                self.sampler, StreamSampler
            ), "types of dataset and sampler do not match"
        else:
            assert isinstance(
                dataset, Dataset
            ), "Can not recognize this kind of dataset: %s" % type(dataset)
            self.sampler = (
                sampler
                if sampler
                else SequentialSampler(dataset, batch_size=1, drop_last=False)
            )
            assert isinstance(
                self.sampler, MapSampler
            ), "types of dataset and sampler do not match"

        if divide:
            if self.sampler.batch_size <= self.num_workers:
                raise ValueError(
                    "batch size must not smaller than num_workers in divide mode."
                )
            elif self.sampler.batch_size % self.num_workers:
                logger.warning(
                    "batch size is not divisible by num_workers, may lose performance in divide mode."
                )

        if transform is None:
            self.transform = PseudoTransform()
        else:
            self.transform = transform

        if collator is None:
            self.collator = Collator()
        else:
            self.collator = collator

        self.__initialized = True

    def __iter__(self):
        if platform.system() == "Windows" and self.num_workers > 0:
            print(
                "pyarrow.plasma does not support ParallelDataLoader on windows, changing num_workers to be zero"
            )
            self.num_workers = 0
        if os.getenv("TERMUX_VERSION"):
            # FIXME: termux install pyarrow will build error now
            # remove this logic after pyarrow fix this issue
            print(
                "pyarrow do not support on termux env now, changing num_workers to be zero"
            )
            self.num_workers = 0
        if isinstance(self.dataset, StreamDataset):
            if not self.num_workers:
                return _SerialStreamDataLoaderIter(self, self.preload)
            else:
                return _ParallelStreamDataLoaderIter(self, self.preload)
        else:
            assert isinstance(
                self.dataset, Dataset
            ), "Can not recognize this kind of dataset: %s" % type(self.dataset)
            if not self.num_workers:
                return _SerialMapDataLoaderIter(self, self.preload)
            else:
                return _ParallelMapDataLoaderIter(self, self.preload)

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


class PreLoader:
    def __init__(self, preload):
        if preload:
            self.default_device = get_default_device()
            self.pre_load_device = self.default_device + ":" + str(_sh.get_next())
            self.pre_load_device_cache = None
        self.preload = preload

    """
    strategy one: load from numpy data, and generate dtype tensor
    """

    def _load_tensor(self, batch, cached=True):
        if isinstance(batch, np.ndarray):
            device = self.pre_load_device if cached else self.default_device
            return Tensor(batch, device=device)
        elif isinstance(batch, collections.abc.Mapping):
            return {k: self._load_tensor(v, cached) for k, v in batch.items()}
        elif isinstance(batch, tuple) and hasattr(batch, "_fields"):  # namedtuple
            return type(batch)(*(self._load_tensor(value, cached) for value in batch))
        elif isinstance(batch, collections.abc.Sequence):
            return [self._load_tensor(value, cached) for value in batch]
        else:
            return batch

    """
    strategy two: load from cache that is already tensor just do d2d copy
    """

    def _load_cache(self, data):
        if isinstance(data, Tensor):
            if data.device == self.default_device:
                return data
            return copy(data, device=self.default_device)
        elif isinstance(data, collections.abc.Mapping):
            return {k: self._load_cache(v) for k, v in data.items()}
        elif isinstance(data, tuple) and hasattr(data, "_fields"):  # namedtuple
            return type(data)(*(self._load_cache(value) for value in data))
        elif isinstance(data, collections.abc.Sequence):
            return [self._load_cache(value) for value in data]
        else:
            return data

    def _swap_out_cache(self):
        out = self._load_cache(self.pre_load_device_cache)
        self.pre_load_device_cache = None  # clean cache
        return out


class _BaseMapDataLoaderIter(PreLoader):
    def __init__(self, loader, preload):
        super().__init__(preload)
        self.dataset = loader.dataset
        self.sampler = loader.sampler
        self.seed = _random_seed_generator().__next__()
        self.transform = loader.transform
        self.collator = loader.collator
        self.num_workers = loader.num_workers
        self.timeout = loader.timeout
        self.timeout_event = loader.timeout_event
        self.divide = loader.divide
        self.num_processed = 0

    def _get_next_batch(self):
        raise NotImplementedError

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

    def __iter__(self):
        return self

    def __next__(self):
        if self.preload:
            cached = self.pre_load_device_cache
            if cached is None:  # first and last
                if self.num_processed >= len(self):  # last
                    raise StopIteration
                elif self.num_processed == 0:  # first
                    self._try_load_tensor(cached=False)  # first do the h2d
            out = self._swap_out_cache()
            self._try_load_tensor()
            return out
        else:
            if self.num_processed >= len(self):
                raise StopIteration
            minibatch = self._get_next_batch()
            self.num_processed += 1
            return minibatch

    def _try_load_tensor(self, cached=True):
        if self.num_processed >= len(self):
            return
        else:
            self.num_processed += 1
            batch = self._get_next_batch()
            self.pre_load_device_cache = self._load_tensor(batch, cached)


class _SerialMapDataLoaderIter(_BaseMapDataLoaderIter):
    def __init__(self, loader, preload):
        super(_SerialMapDataLoaderIter, self).__init__(loader, preload)
        self.indices_iter = iter(self.sampler)

    def _get_next_batch(self):
        indices = next(self.indices_iter)
        items = [self.dataset[idx] for idx in indices]
        trans_items = self.transform.apply_batch(items)
        return self.collator.apply(trans_items)


class _ParallelMapDataLoaderIter(_BaseMapDataLoaderIter):
    __initialized = False

    def __init__(self, loader, preload):
        super(_ParallelMapDataLoaderIter, self).__init__(loader, preload)

        self.task_queues = [
            multiprocessing.Queue(maxsize=2) for _ in range(self.num_workers)
        ]

        self.feed_batch_idx = multiprocessing.Value("i", 0)
        self.target_batch_idx = multiprocessing.Value("i", 0)
        self.shutdown_flag = multiprocessing.Value("i", 0)

        self.trans_data_queues = [
            multiprocessing.Queue(maxsize=1) for _ in range(self.num_workers)
        ]

        # use shared-memory queue implemented by pyarrow plasma store.
        from .tools._queue import PlasmaShmQueue

        self.batch_queue = PlasmaShmQueue(maxsize=2)

        self.task_feeding_worker = multiprocessing.Process(
            target=_task_feeding_loop,
            args=(
                iter(self.sampler),
                self.task_queues,
                self.num_workers,
                self.divide,
                self.shutdown_flag,
                self.feed_batch_idx,
            ),
            daemon=True,
        )
        gc.collect()
        self.task_feeding_worker.start()

        self.workers = []
        for worker_id in range(self.num_workers):
            worker = multiprocessing.Process(
                target=_worker_loop,
                args=(
                    self.dataset,
                    self.task_queues[worker_id],
                    self.trans_data_queues[worker_id],
                    self.transform,
                    self.seed + worker_id + 1,
                    self.shutdown_flag,
                ),
                daemon=True,
            )
            gc.collect()
            worker.start()
            self.workers.append(worker)

        if self.divide:
            self.data_collecting_worker = multiprocessing.Process(
                target=_data_gathering_loop,
                args=(
                    self.trans_data_queues,
                    self.batch_queue,
                    self.collator,
                    len(self),
                    self.num_workers,
                    self.shutdown_flag,
                    self.target_batch_idx,
                ),
                daemon=True,
            )
        else:
            self.data_collecting_worker = multiprocessing.Process(
                target=_data_selecting_loop,
                args=(
                    self.trans_data_queues,
                    self.batch_queue,
                    self.collator,
                    len(self),
                    self.num_workers,
                    self.shutdown_flag,
                    self.target_batch_idx,
                ),
                daemon=True,
            )
        gc.collect()
        self.data_collecting_worker.start()

        self.__initialized = True

    def _check_workers(self):
        # Check the status of each worker.
        if not self.data_collecting_worker.is_alive():
            exitcode = self.data_collecting_worker.exitcode
            if exitcode != 0:
                raise RuntimeError("data collecting worker died. {}".format(exitcode))

        if not self.task_feeding_worker.is_alive():
            exitcode = self.task_feeding_worker.exitcode
            if exitcode != 0:
                raise RuntimeError("task feeding worker died. {}".format(exitcode))

        for worker_id, worker in enumerate(self.workers):
            if not worker.is_alive():
                exitcode = worker.exitcode
                if exitcode != 0:
                    raise RuntimeError("worker:{} died. {}".format(worker_id, exitcode))

        logger.debug("all workers are alive.")

    def _get_next_batch(self):
        start_time = time.time()
        while True:
            self._check_workers()
            try:
                return self.batch_queue.get(timeout=1)
            except queue.Empty:
                logger.debug("batch queue empty!")
            waited_time = time.time() - start_time
            if self.timeout > 0:
                if waited_time > self.timeout:
                    raise RuntimeError("get_next_batch timeout!")

    def _shutdown(self):
        with self.shutdown_flag.get_lock():
            self.shutdown_flag.value = 1

        if self.task_feeding_worker.is_alive():
            self.task_feeding_worker.terminate()
        self.task_feeding_worker.join()

        if self.data_collecting_worker.is_alive():
            self.data_collecting_worker.terminate()
        self.data_collecting_worker.join()

        for worker in self.workers:
            if worker.is_alive():
                worker.terminate()
            worker.join()

        for q in self.trans_data_queues:
            q.cancel_join_thread()
            q.close()

        for q in self.task_queues:
            q.cancel_join_thread()
            q.close()

        self.batch_queue.cancel_join_thread()
        self.batch_queue.close()

    def __del__(self):
        if self.__initialized:
            self._shutdown()


class _BaseStreamDataLoaderIter(PreLoader):
    def __init__(self, loader, preload):
        super().__init__(preload)
        self.dataset = loader.dataset
        self.sampler = loader.sampler
        self.transform = loader.transform
        self.collator = loader.collator
        self.num_workers = loader.num_workers
        self.timeout = loader.timeout
        self.timeout_event = loader.timeout_event

    def _get_next_batch(self):
        raise NotImplementedError

    def _process_raw_data(self, raw_data):
        assert len(raw_data) == 2 and isinstance(
            raw_data[0], bool
        ), "StreamDataset should provide a binary tuple, the first item indicates whether the data was batched."
        if not raw_data[0]:
            data = list((x,) for x in raw_data[1])
        else:
            data = raw_data[1]
        ret = []
        for idx in range(len(data[0])):
            ret.append(tuple(e[idx] for e in data))
        return ret

    def __iter__(self):
        return self

    def __next__(self):
        if self.preload:
            if self.pre_load_device_cache is None:
                self._try_load_tensor(cached=False)  # load in current
            out = self._swap_out_cache()
            self._try_load_tensor()  # load in cached
            return out
        else:
            return self._get_next_batch()

    def _try_load_tensor(self, cached=True):
        batch = self._get_next_batch()
        self.pre_load_device_cache = self._load_tensor(batch, cached)


class _SerialStreamDataLoaderIter(_BaseStreamDataLoaderIter):
    def __init__(self, loader, preload):
        super().__init__(loader, preload)
        self.dataset_iter = iter(self.dataset)
        self.idx = 0
        self.unused = []

    def _try_get_raw_data(self, start_time):
        raw_data = None
        while not raw_data:
            try:
                if self.timeout > 0:
                    timer = threading.Timer(self.timeout, thread.interrupt_main)
                    timer.start()
                raw_data = next(self.dataset_iter)
                if self.timeout > 0:
                    timer.cancel()
            except KeyboardInterrupt:
                raw_data = self.timeout_event()
            except:
                if self.timeout > 0:
                    timer.cancel()
                    waited_time = time.time() - start_time
                    if waited_time > self.timeout:
                        raw_data = self.timeout_event()
        return raw_data

    def _get_next_batch(self):
        ret = []
        start_time = time.time()
        while len(ret) < self.sampler.batch_size:
            if len(self.unused) != 0:
                batch_data = self.unused
            else:
                raw_data = self._try_get_raw_data(start_time)
                batch_data = self._process_raw_data(raw_data)

            while len(batch_data) != 0 and len(ret) < self.sampler.batch_size:
                data = batch_data.pop()
                ret.append(self.transform.apply(data))
            self.unused = batch_data

        return self.collator.apply(ret)


class _ParallelStreamDataLoaderIter(_BaseStreamDataLoaderIter):
    __initialized = False

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

        self.shutdown_flag = multiprocessing.Value("i", 0)

        self.raw_data_queues = [
            multiprocessing.Queue(maxsize=1) for _ in range(self.num_workers)
        ]

        self.trans_data_queues = [
            multiprocessing.Queue(maxsize=1) for _ in range(self.num_workers)
        ]

        # shared-memory queue implemented by pyarrow plasma store
        from .tools._queue import PlasmaShmQueue

        self.batch_queue = PlasmaShmQueue(maxsize=2)

        self.recieve_worker = multiprocessing.Process(
            target=self._worker_to_raw_data_queues, daemon=True
        )
        gc.collect()
        self.recieve_worker.start()

        self.transform_workers = []
        for worker_id in range(self.num_workers):
            worker = multiprocessing.Process(
                target=self._worker_to_trans_data_queues, args=(worker_id,), daemon=True
            )
            gc.collect()
            worker.start()
            self.transform_workers.append(worker)

        self.collect_worker = multiprocessing.Process(
            target=self._worker_to_batch_queue, daemon=True
        )
        gc.collect()
        self.collect_worker.start()

        self.__initialized = True

    def _put_raw_data_queues(self, raw_data, qidx):
        batch_data = self._process_raw_data(raw_data)
        for data in batch_data:
            while True:
                qidx = qidx % self.num_workers
                try:
                    self.raw_data_queues[qidx].put(data)
                    break
                except queue.Full:
                    if self.shutdown_flag.value == 1:
                        break
                    logger.debug("raw data queue %d is full" % qidx)
                finally:
                    qidx += 1
        return qidx

    def _worker_to_raw_data_queues(self):
        dataset_iter = iter(self.dataset)
        qidx = 0
        while True:
            if self.shutdown_flag.value == 1:
                break
            raw_data = next(dataset_iter)
            qidx = self._put_raw_data_queues(raw_data, qidx)

    def _worker_to_trans_data_queues(self, worker_id):
        while True:
            if self.shutdown_flag.value == 1:
                break
            try:
                data = self.raw_data_queues[worker_id].get(timeout=GLOBAL_TIMEOUT)
            except queue.Empty:
                continue
            trans_data = self.transform.apply(data)
            while True:
                try:
                    self.trans_data_queues[worker_id].put(trans_data)
                    break
                except queue.Full:
                    if self.shutdown_flag.value == 1:
                        break
                    logger.debug("batch queue if full")

    def _worker_to_batch_queue(self):
        cnt = -1
        trans_items = []
        while True:
            if self.shutdown_flag.value == 1:
                break
            cnt += 1
            queue_id = cnt % self.num_workers
            try:
                trans_item = self.trans_data_queues[queue_id].get(
                    timeout=GLOBAL_TIMEOUT
                )
            except queue.Empty:
                continue
            trans_items.append(trans_item)
            if len(trans_items) == self.sampler.batch_size:
                batch_data = self.collator.apply(trans_items)
                while True:
                    try:
                        self.batch_queue.put(batch_data, timeout=1)
                        break
                    except queue.Full:
                        if self.shutdown_flag.value == 1:
                            break
                        logger.debug("batch queue is full")
                trans_items = []

    def _check_workers(self):
        if not self.collect_worker.is_alive():
            exitcode = self.collect_worker.exitcode
            if exitcode != 0:
                raise RuntimeError("collator worker died. {}".format(exitcode))

        for worker_id, worker in enumerate(self.transform_workers):
            if not worker.is_alive():
                exitcode = worker.exitcode
                if exitcode != 0:
                    raise RuntimeError(
                        "worker: {} died. {}".format(worker_id, exitcode)
                    )

    def _get_next_batch(self):
        start_time = time.time()
        while True:
            self._check_workers()
            try:
                return self.batch_queue.get(timeout=1)
            except queue.Empty:
                logger.debug("batch queue empty!")
            waited_time = time.time() - start_time
            if self.timeout > 0 and waited_time > self.timeout:
                self._put_raw_data_queues(self.timeout_event(), 0)

    def _shutdown(self):
        with self.shutdown_flag.get_lock():
            self.shutdown_flag.value = 1

        if self.recieve_worker.is_alive():
            self.recieve_worker.terminate()
        self.recieve_worker.join()

        if self.collect_worker.is_alive():
            self.collect_worker.terminate()
        self.collect_worker.join()

        for worker in self.transform_workers:
            if worker.is_alive():
                worker.terminate()
            worker.join()

        for q in self.raw_data_queues:
            q.cancel_join_thread()
            q.close()

        for q in self.trans_data_queues:
            q.cancel_join_thread()
            q.close()

        self.batch_queue.cancel_join_thread()
        self.batch_queue.close()

    def __del__(self):
        if self.__initialized:
            self._shutdown()


def _task_feeding_loop(
    indices_iter, task_queues, num_workers, divide, shutdown_flag, feed_batch_idx
):
    # Feed the indices into the task queues
    while True:
        if shutdown_flag.value == 1:
            break
        batch_idx = feed_batch_idx.value
        try:
            indices = next(indices_iter)
        except StopIteration:
            break
        if divide:
            # make sure all task_queues is ready for put
            while any([q.full() for q in task_queues]):
                if shutdown_flag.value == 1:
                    return
            # divide into small pieces, feed to different workers.
            sub_num = math.ceil(len(indices) / num_workers)
            for worker_id in range(num_workers):
                sub_indices = indices[worker_id * sub_num : (worker_id + 1) * sub_num]
                task_queues[worker_id].put((batch_idx, sub_indices))
        else:
            # distribute tasks to different workers uniformly.
            target_id = batch_idx % num_workers
            while task_queues[target_id].full():
                if shutdown_flag.value == 1:
                    return
            task_queues[target_id].put((batch_idx, indices))
        with feed_batch_idx.get_lock():
            feed_batch_idx.value += 1


def _worker_loop(dataset, task_queue, trans_data_queue, transform, seed, shutdown_flag):
    # Get dataset items and do the transform
    random.seed(seed)
    np.random.seed(seed)
    while True:
        if shutdown_flag.value == 1:
            break
        try:
            batch_idx, indices = task_queue.get(timeout=GLOBAL_TIMEOUT)
        except queue.Empty:
            continue
        if len(indices) > 0:
            items = [dataset[idx] for idx in indices]
            trans_items = transform.apply_batch(items)
        else:
            # in case of incomplete last batch
            trans_items = ()
        while True:
            try:
                trans_data_queue.put((batch_idx, trans_items), timeout=1)
                break
            except queue.Full:
                if shutdown_flag.value == 1:
                    break
                logger.debug("batch part queue is full!")


def _data_gathering_loop(
    trans_data_queues,
    batch_queue,
    collator,
    length,
    num_workers,
    shutdown_flag,
    target_idx,
):
    # Gathering the small pieces of batch data into full batch data
    while True:
        if shutdown_flag.value == 1:
            break

        target_batch_idx = target_idx.value

        if target_batch_idx >= length:
            break

        full_trans_items = []
        for worker_id in range(num_workers):
            while True:
                try:
                    batch_idx, trans_items = trans_data_queues[worker_id].get(
                        timeout=GLOBAL_TIMEOUT
                    )
                    break
                except queue.Empty:
                    if shutdown_flag.value == 1:
                        break
                    logger.debug(
                        "worker:{} data queue get timeout! target batch idx:{}".format(
                            worker_id, target_batch_idx
                        )
                    )
            if batch_idx != target_batch_idx:
                raise RuntimeError(
                    "Unexperted batch_idx in data gathering loop. worker_id:{}.".format(
                        worker_id
                    )
                )
            else:
                full_trans_items.extend(trans_items)

        # Merge different parts into a batch.
        full_batch = collator.apply(full_trans_items)

        while True:
            try:
                batch_queue.put(full_batch, timeout=1)
                break
            except queue.Full:
                if shutdown_flag.value == 1:
                    break
                logger.debug("batch queue is full!")

        with target_idx.get_lock():
            target_idx.value += 1

    batch_queue.disconnect_client()


def _data_selecting_loop(
    trans_data_queues,
    batch_queue,
    collator,
    length,
    num_workers,
    shutdown_flag,
    target_idx,
):
    # Make sure that batch is generated exactly with the same order as generated indices
    while True:
        if shutdown_flag.value == 1:
            break

        target_batch_idx = target_idx.value

        if target_batch_idx >= length:
            break

        target_worker_id = target_batch_idx % num_workers
        while True:
            try:
                batch_idx, trans_items = trans_data_queues[target_worker_id].get(
                    timeout=GLOBAL_TIMEOUT
                )
                batch_data = collator.apply(trans_items)
                break
            except queue.Empty:
                if shutdown_flag.value == 1:
                    break
                logger.debug(
                    "worker:{} data queue get timeout! target batch idx:{}".format(
                        target_worker_id, target_batch_idx
                    )
                )

        if batch_idx != target_batch_idx:
            raise RuntimeError(
                "batch_idx {} mismatch the target_batch_idx {}".format(
                    batch_idx, target_batch_idx
                )
            )

        while True:
            try:
                batch_queue.put(batch_data, timeout=1)
                break
            except queue.Full:
                if shutdown_flag.value == 1:
                    break
                logger.debug("batch queue is full!")

        with target_idx.get_lock():
            target_idx.value += 1

    batch_queue.disconnect_client()