# -*- coding: utf-8 -*-
import collections
import gc
import itertools
import multiprocessing
import os
import platform
import queue
import random
import threading
import time
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")
[文档]class DataLoader:
r"""Provides a convenient way to iterate on a given dataset.
The process is as follows:
.. mermaid::
:align: center
flowchart LR
Dataset.__len__ -- Sampler --> Indices
batch_size -- Sampler --> Indices
Indices -- Dataset.__getitem__ --> Samples
Samples -- Transform + Collator --> mini-batch
DataLoader combines a :class:`~.Dataset` with
:class:`~.Sampler`, :class:`~.Transform` and :class:`~.Collator`,
make it flexible to get minibatch continually from a dataset.
See :ref:`data-guide` for more details.
Args:
dataset: dataset from which to load the minibatch.
sampler: defines the strategy to sample data from the dataset.
If ``None``, it will sequentially sample from the dataset one by one.
transform: defined the transforming strategy for a sampled batch.
collator: defined the merging strategy for a transformed batch.
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
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.
parallel_stream: whether to splitting workload across all workers when dataset is streamdataset and num_workers > 0.
When enabling, each worker will collect data from different dataset in order to speed up the whole loading process.
See ref:`streamdataset-example` for more details
.. admonition:: The effect of enabling preload
:class: warning
* All elements in :class:`map`, :class:`list`, and :class:`tuple` will be converted to :class:`~.Tensor` by preloading,
and you will get :class:`~.Tensor` instead of the original Numpy array or Python built-in data structrure.
* 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.
"""
def __init__(
self,
dataset: Dataset,
sampler: Sampler = None,
transform: Transform = None,
collator: Collator = None,
num_workers: int = 0,
timeout: int = 0,
preload: bool = False,
parallel_stream: bool = False,
):
if num_workers < 0:
raise ValueError("num_workers should not be negative")
if timeout < 0:
raise ValueError("timeout should not be negative")
self.dataset = dataset
self.num_workers = num_workers
self.timeout = timeout
self.preload = preload
self.parallel_stream = parallel_stream
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"
if parallel_stream is False and self.num_workers > 1:
logger.warning(
"Data time will be affected by getting origin-data, please set parallel_stream in order to speed up dataloader!"
)
self.datakind = "stream"
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"
self.datakind = "map"
if transform is None:
self.transform = PseudoTransform()
else:
self.transform = transform
if collator is None:
self.collator = Collator()
else:
self.collator = collator
if platform.system() == "Linux" and self.num_workers > 0:
self.check_memory_rationality()
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)
def check_memory_rationality(self):
import psutil
main_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024
total_memory = (self.num_workers + 1) * main_memory
current_memory = (
int(os.popen("cat /sys/fs/cgroup/memory/memory.limit_in_bytes").read())
/ 1024
/ 1024
/ 1024
)
if current_memory < total_memory:
logger.warning(
"Each worker need to read the shared meta-data, which will be increasing the reference count."
"Copy-On-Write propety will lead to 'memory leak', the memory usage will end up being "
+ str(total_memory)
+ " GB, "
"However the current requested memory is "
+ str(current_memory)
+ " GB, "
"Maybe you can request more memory or uesd np-array to save meta-data rather than List or Tuple"
)
class PreLoader:
def __init__(self, loader, 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.num_processed = 0
self.datakind = loader.datakind
self.parallel_stream = loader.parallel_stream
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
def __iter__(self):
return self
"""
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 _ParallelDataLoaderIter:
def __init__(self):
self._worker_queue_idx_cycle = itertools.cycle(range(self.num_workers))
from .tools._queue import PlasmaShmQueue
self._worker_result_queue = PlasmaShmQueue()
self._shutdown = False
self._workers_done_event = multiprocessing.Event()
self._index_queues = []
self._workers = []
for i in range(self.num_workers):
index_queue = multiprocessing.Queue()
index_queue.cancel_join_thread()
w = multiprocessing.Process(
target=_worker_loop,
args=(
self.dataset,
index_queue,
self._worker_result_queue,
self._workers_done_event,
self.transform,
self.collator,
self.sampler.batch_size,
self.seed + i,
i,
self.num_workers,
self.datakind,
self.parallel_stream,
),
daemon=True,
)
gc.collect()
w.start()
self._index_queues.append(index_queue)
self._workers.append(w)
self._data_queue = self._worker_result_queue
self._reset()
def _try_put_index(self):
raise NotImplementedError
def _reset(self):
self._sampler_iter = iter(self.sampler)
self._send_idx = 0
self._rcvd_idx = 0
self._task_info = {}
self._workers_status = [True for _ in range(self.num_workers)]
for _ in range(2 * self.num_workers):
self._try_put_index()
def _process_data(self, data):
self._rcvd_idx += 1
self._try_put_index()
return data
def _get_data(self):
if self.timeout > 0:
success, data = self._try_get_data(self.timeout)
if success:
return data
else:
raise_timeout_error()
else:
while True:
success, data = self._try_get_data()
if success:
return data
def _get_next_batch(self):
while True:
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 work is still active
break
del self._task_info[self._rcvd_idx]
self._rcvd_idx += 1
else:
self._shutdown_workers()
raise StopIteration
if len(self._task_info[self._rcvd_idx]) == 2:
data = self._task_info.pop(self._rcvd_idx)[1]
return self._process_data(data)
idx, data = self._get_data()
if isinstance(data, int): # Check if StopIteration in StreamDataset
self._mark_worker_as_unavailable(data)
self._try_put_index()
continue
if idx != self._rcvd_idx:
self._task_info[idx] += (data,)
else:
del self._task_info[idx]
return self._process_data(data)
def _try_get_data(self, timeout=GLOBAL_TIMEOUT):
try:
data = self._data_queue.get(timeout=timeout)
return (True, data)
except Exception as e:
failed_workers = []
for worker_id, w in enumerate(self._workers):
if self._workers_status[worker_id] and not w.is_alive():
failed_workers.append((worker_id, w))
self._mark_worker_as_unavailable(worker_id)
if w.exitcode == -9:
logger.debug(
"Maybe memory is not enough, please request for more memory!"
)
if len(failed_workers) > 0:
pids_str = ", ".join(str(w_info[1].pid) for w_info in failed_workers)
w_ids_str = ", ".join(str(w_info[0]) for w_info in failed_workers)
exitcode_str = ", ".join(
str(w_info[1].exitcode) for w_info in failed_workers
)
raise RuntimeError(
"DataLoader worker (worker(s): {} , pid(s): {}) exited unexpectedly, exitcode(s): {}".format(
w_ids_str, pids_str, exitcode_str
)
)
if isinstance(e, queue.Empty):
return (False, None)
def _mark_worker_as_unavailable(self, worker_id, shutdown=False):
q = self._index_queues[worker_id]
q.put(None)
self._workers_status[worker_id] = False
assert self._workers_done_event.is_set() == shutdown
def _shutdown_workers(self):
if not self._shutdown:
self._shutdown = True
try:
self._workers_done_event.set()
for worker_id in range(len(self._workers)):
if self._workers_status[worker_id]:
self._mark_worker_as_unavailable(worker_id, shutdown=True)
for w in self._workers:
w.join(timeout=GLOBAL_TIMEOUT)
for q in self._index_queues:
q.cancel_join_thread()
q.close()
self._data_queue.cancel_join_thread()
self._data_queue.close()
finally:
for w in self._workers:
if w.is_alive():
w.terminate()
def __del__(self):
self._shutdown_workers()
class _BaseMapDataLoaderIter(PreLoader):
def __init__(self, loader, preload):
super().__init__(loader, preload)
def __len__(self):
return len(self.sampler)
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:
data = self._get_next_batch()
return data
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._sampler_iter = iter(self.sampler)
def _get_next_batch(self):
indices = next(self._sampler_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, _ParallelDataLoaderIter):
def __init__(self, loader, preload):
_BaseMapDataLoaderIter.__init__(self, loader, preload)
_ParallelDataLoaderIter.__init__(self)
def _try_put_index(self):
try:
index = next(self._sampler_iter)
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
self._index_queues[worker_queue_idx].put((self._send_idx, index))
self._task_info[self._send_idx] = (worker_queue_idx,)
self._send_idx += 1
_worker_info = None
class WorkerInfo(object):
__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(
"Cannot assign attributes to {} objects".format(self.__class__.__name__)
)
return super(WorkerInfo, self).__setattr__(key, val)
def __repr__(self):
items = []
for k in self.__keys:
items.append("{}={}".format(k, getattr(self, k)))
return "{}({})".format(self.__class__.__name__, ", ".join(items))
def get_worker_info():
return _worker_info
class _BaseStreamDataLoaderIter(PreLoader):
def __init__(self, loader, preload):
super().__init__(loader, preload)
self.dataset_iter = iter(self.dataset)
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)
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 AttributeError as error:
raise error
except:
if self.timeout > 0:
timer.cancel()
waited_time = time.time() - start_time
if waited_time > self.timeout:
raise_timeout_error()
return raw_data
def _get_next_batch(self):
ret = []
start_time = time.time()
while len(ret) < self.sampler.batch_size:
raw_data = self._try_get_raw_data(start_time)
ret.append(self.transform.apply(raw_data))
return self.collator.apply(ret)
class _ParallelStreamDataLoaderIter(_BaseStreamDataLoaderIter, _ParallelDataLoaderIter):
def __init__(self, loader, preload):
_BaseStreamDataLoaderIter.__init__(self, loader, preload)
_ParallelDataLoaderIter.__init__(self)
def _get_remaind_data(self, place_holder):
num = self.sampler.batch_size
for _ in range(num - 1):
place_holder.append(next(self.dataset_iter))
return place_holder
def _try_put_index(self):
try:
if self.parallel_stream is False:
start_time = time.time()
place_holder = [next(self.dataset_iter)]
waited_time = time.time() - start_time
if self.timeout > 0 and waited_time > self.timeout:
raise_timeout_error()
place_holder = self._get_remaind_data(place_holder)
else:
place_holder = next(self._sampler_iter)
except StopIteration:
return
for _ in range(self.num_workers):
worker_queue_idx = next(self._worker_queue_idx_cycle)
if self._workers_status[worker_queue_idx]:
break
else:
return
self._index_queues[worker_queue_idx].put((self._send_idx, place_holder))
self._task_info[self._send_idx] = (worker_queue_idx,)
self._send_idx += 1
class ManagerWatchdog(object):
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
def stream_fetcher(
dataset_iter, place_holder, transform, collate, parallel_stream, batch_size
):
data = []
for idx in place_holder:
try:
if parallel_stream is False:
raw_data = idx
else:
raw_data = next(dataset_iter)
trans_items = transform.apply(raw_data)
data.append(trans_items)
except StopIteration:
break
if len(data) == 0:
raise StopIteration
data = collate.apply(data)
return data
def map_fetcher(dataset, place_holder, transform, collate, parallel_stream, batch_size):
items = [dataset[idx] for idx in place_holder]
trans_items = transform.apply_batch(items)
data = collate.apply(trans_items)
return data
def _worker_loop(
dataset,
index_queue,
data_queue,
done_event,
transform,
collate,
batch_size,
seed,
worker_id,
num_workers,
datakind,
parallel_stream,
):
random.seed(seed)
np.random.seed(seed)
watchdog = ManagerWatchdog()
iteration_end = False
fetcher = map_fetcher
if datakind == "stream":
global _worker_info
_worker_info = WorkerInfo(idx=worker_id, worker=num_workers, seed=seed)
dataset = iter(dataset)
fetcher = stream_fetcher
while watchdog.is_alive():
try:
r = index_queue.get(timeout=GLOBAL_TIMEOUT)
except queue.Empty:
continue
if r is None:
assert done_event.is_set() or iteration_end
break
elif done_event.is_set() or iteration_end:
continue
idx, place_holder = r
try:
data = fetcher(
dataset, place_holder, transform, collate, parallel_stream, batch_size
)
except Exception as e:
if isinstance(e, StopIteration) and datakind == "stream":
data = worker_id
iteration_end = True
else:
raise e
data_queue.put((idx, data))
del data, idx, place_holder, r
if done_event.is_set():
data_queue.disconnect_client()
data_queue.close()