Source code for gluonnlp.data.dataloader
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"""DataLoader. An extension of Gluon data loader that allows multi-shard sampling."""
__all__ = ['ShardedDataLoader']
import io
import pickle
import multiprocessing
from multiprocessing.pool import ThreadPool
from mxnet import context
from mxnet.gluon.data.dataloader import ForkingPickler, _as_in_context
from mxnet.gluon.data.dataloader import default_mp_batchify_fn, default_batchify_fn
from mxnet.gluon.data import sampler as _sampler
_worker_dataset = None
def _worker_initializer(dataset):
"""Initializer for processing pool."""
# global dataset is per-process based and only available in worker processes
# this is only necessary to handle MXIndexedRecordIO because otherwise dataset
# can be passed as argument
global _worker_dataset
_worker_dataset = dataset
def _worker_fn(samples, batchify_fn, dataset=None):
"""Function for processing data in worker process."""
# pylint: disable=unused-argument
# it is required that each worker process has to fork a new MXIndexedRecordIO handle
# preserving dataset as global variable can save tons of overhead and is safe in new process
global _worker_dataset
if isinstance(samples[0], (list, tuple)):
batch = [batchify_fn([_worker_dataset[i] for i in shard]) for shard in samples]
else:
batch = batchify_fn([_worker_dataset[i] for i in samples])
buf = io.BytesIO()
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(batch)
return buf.getvalue()
def _thread_worker_fn(samples, batchify_fn, dataset):
"""Threadpool worker function for processing data."""
if isinstance(samples[0], (list, tuple)):
batch = [batchify_fn([dataset[i] for i in shard]) for shard in samples]
else:
batch = batchify_fn([dataset[i] for i in samples])
return batch
class _MultiWorkerIter:
"""Internal multi-worker iterator for DataLoader."""
def __init__(self, worker_pool, batchify_fn, batch_sampler, pin_memory=False,
worker_fn=_worker_fn, prefetch=0, dataset=None):
self._worker_pool = worker_pool
self._batchify_fn = batchify_fn
self._batch_sampler = batch_sampler
self._data_buffer = {}
self._rcvd_idx = 0
self._sent_idx = 0
self._iter = iter(self._batch_sampler)
self._worker_fn = worker_fn
self._pin_memory = pin_memory
self._dataset = dataset
# pre-fetch
for _ in range(prefetch):
self._push_next()
def __len__(self):
return len(self._batch_sampler)
def _push_next(self):
"""Assign next batch workload to workers."""
r = next(self._iter, None)
if r is None:
return
async_ret = self._worker_pool.apply_async(
self._worker_fn, (r, self._batchify_fn, self._dataset))
self._data_buffer[self._sent_idx] = async_ret
self._sent_idx += 1
def __next__(self):
self._push_next()
if self._rcvd_idx == self._sent_idx:
assert not self._data_buffer, 'Data buffer should be empty at this moment'
raise StopIteration
assert self._rcvd_idx < self._sent_idx, 'rcvd_idx must be smaller than sent_idx'
assert self._rcvd_idx in self._data_buffer, 'fatal error with _push_next, rcvd_idx missing'
ret = self._data_buffer.pop(self._rcvd_idx)
batch = pickle.loads(ret.get()) if self._dataset is None else ret.get()
if self._pin_memory:
batch = _as_in_context(batch, context.cpu_pinned())
self._rcvd_idx += 1
return batch
def next(self):
return self.__next__()
def __iter__(self):
return self
[docs]class ShardedDataLoader:
"""Loads data from a dataset and returns mini-batches of data.
Parameters
----------
dataset : Dataset
Source dataset. Note that numpy and mxnet arrays can be directly used
as a Dataset.
batch_size : int
Size of mini-batch.
shuffle : bool
Whether to shuffle the samples.
sampler : Sampler
The sampler to use. Either specify sampler or shuffle, not both.
last_batch : {'keep', 'discard', 'rollover'}
How to handle the last batch if batch_size does not evenly divide
`len(dataset)`.
keep - A batch with less samples than previous batches is returned.
discard - The last batch is discarded if its incomplete.
rollover - The remaining samples are rolled over to the next epoch.
batch_sampler : Sampler
A sampler that returns mini-batches. Do not specify batch_size,
shuffle, sampler, and last_batch if batch_sampler is specified.
batchify_fn : callable
Callback function to allow users to specify how to merge samples
into a batch. Defaults to `default_batchify_fn`::
def default_batchify_fn(data):
if isinstance(data[0], nd.NDArray):
return nd.stack(*data)
elif isinstance(data[0], tuple):
data = zip(*data)
return [default_batchify_fn(i) for i in data]
else:
data = np.asarray(data)
return nd.array(data, dtype=data.dtype)
num_workers : int, default 0
The number of multiprocessing workers to use for data preprocessing.
`num_workers > 0` is not supported on Windows yet.
pin_memory : boolean, default False
If ``True``, the dataloader will copy NDArrays into pinned memory
before returning them. Copying from CPU pinned memory to GPU is faster
than from normal CPU memory.
prefetch : int, default is `num_workers * 2`
The number of prefetching batches only works if `num_workers` > 0.
If `prefetch` > 0, it allow worker process to prefetch certain batches before
acquiring data from iterators.
Note that using large prefetching batch will provide smoother bootstrapping performance,
but will consume more shared_memory. Using smaller number may forfeit the purpose of using
multiple worker processes, try reduce `num_workers` in this case.
By default it defaults to `num_workers * 2`.
thread_pool : bool, default False
If ``True``, use threading pool instead of multiprocessing pool. Using threadpool
can avoid shared memory usage. If `DataLoader` is more IO bounded or GIL is not a killing
problem, threadpool version may achieve better performance than multiprocessing.
"""
def __init__(self, dataset, batch_size=None, shuffle=False, sampler=None,
last_batch=None, batch_sampler=None, batchify_fn=None,
num_workers=0, pin_memory=False, prefetch=None, thread_pool=False):
self._dataset = dataset
self._pin_memory = pin_memory
self._thread_pool = thread_pool
if batch_sampler is None:
if batch_size is None:
raise ValueError('batch_size must be specified unless ' \
'batch_sampler is specified')
if sampler is None:
if shuffle:
sampler = _sampler.RandomSampler(len(dataset))
else:
sampler = _sampler.SequentialSampler(len(dataset))
elif shuffle:
raise ValueError('shuffle must not be specified if sampler is specified')
batch_sampler = _sampler.BatchSampler(
sampler, batch_size, last_batch if last_batch else 'keep')
elif batch_size is not None or shuffle or sampler is not None or \
last_batch is not None:
raise ValueError('batch_size, shuffle, sampler and last_batch must ' \
'not be specified if batch_sampler is specified.')
self._batch_sampler = batch_sampler
self._num_workers = num_workers if num_workers >= 0 else 0
self._worker_pool = None
self._prefetch = max(0, int(prefetch) if prefetch is not None else 2 * self._num_workers)
if self._num_workers > 0:
if self._thread_pool:
self._worker_pool = ThreadPool(self._num_workers)
else:
self._worker_pool = multiprocessing.Pool(
self._num_workers, initializer=_worker_initializer, initargs=[self._dataset])
if batchify_fn is None:
if num_workers > 0:
self._batchify_fn = default_mp_batchify_fn
else:
self._batchify_fn = default_batchify_fn
else:
self._batchify_fn = batchify_fn
def __iter__(self):
if self._num_workers == 0:
def _same_process_iter():
for batch in self._batch_sampler:
if isinstance(batch[0], (list, tuple)):
rets = [self._batchify_fn([self._dataset[idx] for idx in shard])
for shard in batch]
if self._pin_memory:
rets = [_as_in_context(ret, context.cpu_pinned()) for ret in rets]
yield rets
else:
ret = self._batchify_fn([self._dataset[idx] for idx in batch])
if self._pin_memory:
ret = _as_in_context(ret, context.cpu_pinned())
yield ret
return _same_process_iter()
# multi-worker
return _MultiWorkerIter(self._worker_pool, self._batchify_fn, self._batch_sampler,
pin_memory=self._pin_memory,
worker_fn=_thread_worker_fn if self._thread_pool else _worker_fn,
prefetch=self._prefetch,
dataset=self._dataset if self._thread_pool else None)
def __len__(self):
return len(self._batch_sampler)
def __del__(self):
if self._worker_pool:
# manually terminate due to a bug that pool is not automatically terminated
# https://bugs.python.org/issue34172
assert isinstance(self._worker_pool, multiprocessing.pool.Pool)
self._worker_pool.terminate()