"""Module that contain iterator used for dynamic data."""
import torch
from itertools import cycle
from onmt.constants import CorpusTask
from onmt.inputters.text_corpus import get_corpora, build_corpora_iters
from onmt.inputters.text_utils import (
text_sort_key,
process,
numericalize,
tensorify,
_addcopykeys,
)
from onmt.transforms import make_transforms
from onmt.utils.logging import init_logger, logger
from onmt.utils.misc import RandomShuffler
from torch.utils.data import DataLoader
class MixingStrategy(object):
"""Mixing strategy that should be used in Data Iterator."""
def __init__(self, iterables, weights):
"""Initilize neccessary attr."""
self._valid_iterable(iterables, weights)
self.iterables = iterables
self.weights = weights
def _valid_iterable(self, iterables, weights):
iter_keys = iterables.keys()
weight_keys = weights.keys()
if iter_keys != weight_keys:
raise ValueError(f"keys in {iterables} & {weights} should be equal.")
def __iter__(self):
raise NotImplementedError
class SequentialMixer(MixingStrategy):
"""Generate data sequentially from `iterables` which is exhaustible."""
def _iter_datasets(self):
for ds_name, ds_weight in self.weights.items():
for _ in range(ds_weight):
yield ds_name
def __iter__(self):
for ds_name in self._iter_datasets():
iterable = self.iterables[ds_name]
yield from iterable
class WeightedMixer(MixingStrategy):
"""A mixing strategy that mix data weightedly and iterate infinitely."""
def __init__(self, iterables, weights):
super().__init__(iterables, weights)
self._iterators = {}
self._counts = {}
for ds_name in self.iterables.keys():
self._reset_iter(ds_name)
def _logging(self):
"""Report corpora loading statistics."""
msgs = []
# patch to log stdout spawned processes of dataloader
logger = init_logger()
for ds_name, ds_count in self._counts.items():
msgs.append(f"\t\t\t* {ds_name}: {ds_count}")
logger.info("Weighted corpora loaded so far:\n" + "\n".join(msgs))
def _reset_iter(self, ds_name):
self._iterators[ds_name] = iter(self.iterables[ds_name])
self._counts[ds_name] = self._counts.get(ds_name, 0) + 1
self._logging()
def _iter_datasets(self):
for ds_name, ds_weight in self.weights.items():
for _ in range(ds_weight):
yield ds_name
def __iter__(self):
for ds_name in cycle(self._iter_datasets()):
iterator = self._iterators[ds_name]
try:
item = next(iterator)
except StopIteration:
self._reset_iter(ds_name)
iterator = self._iterators[ds_name]
item = next(iterator)
finally:
yield item
class DynamicDatasetIter(torch.utils.data.IterableDataset):
"""Yield batch from (multiple) plain text corpus.
Args:
corpora (dict[str, ParallelCorpus]): collections of corpora to iterate;
corpora_info (dict[str, dict]): corpora infos correspond to corpora;
transforms (dict[str, Transform]): transforms may be used by corpora;
vocabs (dict[str, Vocab]): vocab dict for convert corpora into Tensor;
task (str): CorpusTask.TRAIN/VALID/INFER;
batch_type (str): batching type to count on, choices=[tokens, sents];
batch_size (int): numbers of examples in a batch;
batch_size_multiple (int): make batch size multiply of this;
data_type (str): input data type, currently only text;
bucket_size (int): accum this number of examples in a dynamic dataset;
bucket_size_init (int): initialize the bucket with this
amount of examples;
bucket_size_increment (int): increment the bucket
size with this amount of examples;
copy (Bool): if True, will add specific items for copy_attn
skip_empty_level (str): security level when encouter empty line;
stride (int): iterate data files with this stride;
offset (int): iterate data files with this offset.
Attributes:
sort_key (function): functions define how to sort examples;
mixer (MixingStrategy): the strategy to iterate corpora.
"""
def __init__(
self,
corpora,
corpora_info,
transforms,
vocabs,
task,
batch_type,
batch_size,
batch_size_multiple,
data_type="text",
bucket_size=2048,
bucket_size_init=-1,
bucket_size_increment=0,
copy=False,
skip_empty_level="warning",
stride=1,
offset=0,
):
super(DynamicDatasetIter).__init__()
self.corpora = corpora
self.transforms = transforms
self.vocabs = vocabs
self.corpora_info = corpora_info
self.task = task
self.init_iterators = False
self.batch_size = batch_size
self.batch_type = batch_type
self.batch_size_multiple = batch_size_multiple
self.device = "cpu"
self.sort_key = text_sort_key
self.bucket_size = bucket_size
self.bucket_size_init = bucket_size_init
self.bucket_size_increment = bucket_size_increment
self.copy = copy
if stride <= 0:
raise ValueError(f"Invalid argument for stride={stride}.")
self.stride = stride
self.offset = offset
if skip_empty_level not in ["silent", "warning", "error"]:
raise ValueError(f"Invalid argument skip_empty_level={skip_empty_level}")
self.skip_empty_level = skip_empty_level
self.random_shuffler = RandomShuffler()
@classmethod
def from_opt(cls, corpora, transforms, vocabs, opt, task, copy, stride=1, offset=0):
"""Initilize `DynamicDatasetIter` with options parsed from `opt`."""
corpora_info = {}
batch_size = (
opt.valid_batch_size if (task == CorpusTask.VALID) else opt.batch_size
)
if task != CorpusTask.INFER:
if opt.batch_size_multiple is not None:
batch_size_multiple = opt.batch_size_multiple
else:
batch_size_multiple = 8 if opt.model_dtype == "fp16" else 1
corpora_info = opt.data
bucket_size = opt.bucket_size
bucket_size_init = opt.bucket_size_init
bucket_size_increment = opt.bucket_size_increment
skip_empty_level = opt.skip_empty_level
else:
batch_size_multiple = 1
corpora_info[CorpusTask.INFER] = {"transforms": opt.transforms}
corpora_info[CorpusTask.INFER]["weight"] = 1
# bucket_size = batch_size
bucket_size = 16384
bucket_size_init = -1
bucket_size_increment = 0
skip_empty_level = "warning"
return cls(
corpora,
corpora_info,
transforms,
vocabs,
task,
opt.batch_type,
batch_size,
batch_size_multiple,
data_type=opt.data_type,
bucket_size=bucket_size,
bucket_size_init=bucket_size_init,
bucket_size_increment=bucket_size_increment,
copy=copy,
skip_empty_level=skip_empty_level,
stride=stride,
offset=offset,
)
def _init_datasets(self, worker_id):
if self.num_workers > 0:
stride = self.stride * self.num_workers
offset = self.offset * self.num_workers + worker_id
else:
stride = self.stride
offset = self.offset
datasets_iterables = build_corpora_iters(
self.corpora,
self.transforms,
self.corpora_info,
skip_empty_level=self.skip_empty_level,
stride=stride,
offset=offset,
)
# import pdb
# pdb.set_trace()
datasets_weights = {
ds_name: int(self.corpora_info[ds_name]["weight"])
for ds_name in datasets_iterables.keys()
}
if self.task == CorpusTask.TRAIN:
self.mixer = WeightedMixer(datasets_iterables, datasets_weights)
else:
self.mixer = SequentialMixer(datasets_iterables, datasets_weights)
self.init_iterators = True
def _tuple_to_json_with_tokIDs(self, tuple_bucket):
bucket = []
tuple_bucket = process(self.task, tuple_bucket)
for example in tuple_bucket:
if example is not None:
if self.copy:
example = _addcopykeys(self.vocabs, example)
bucket.append(numericalize(self.vocabs, example))
return bucket
def _bucketing(self):
"""
Add up to bucket_size examples from the mixed corpora according
to the above strategy. example tuple is converted to json and
tokens numericalized.
"""
bucket = []
if self.bucket_size_init > 0:
_bucket_size = self.bucket_size_init
else:
_bucket_size = self.bucket_size
for ex in self.mixer:
bucket.append(ex)
if len(bucket) == _bucket_size:
yield self._tuple_to_json_with_tokIDs(bucket)
bucket = []
if _bucket_size < self.bucket_size:
_bucket_size += self.bucket_size_increment
else:
_bucket_size = self.bucket_size
if bucket:
yield self._tuple_to_json_with_tokIDs(bucket)
def batch_iter(self, data, batch_size, batch_type="sents", batch_size_multiple=1):
"""Yield elements from data in chunks of batch_size,
where each chunk size is a multiple of batch_size_multiple.
"""
def batch_size_fn(nbsents, maxlen):
if batch_type == "sents":
return nbsents
elif batch_type == "tokens":
return nbsents * maxlen
else:
raise ValueError(f"Invalid argument batch_type={batch_type}")
def max_src_tgt(ex):
"""return the max tokens btw src and tgt in the sequence."""
if ex["tgt"]:
return max(len(ex["src"]["src_ids"]), len(ex["tgt"]["tgt_ids"]))
return len(ex["src"]["src_ids"])
minibatch, maxlen, size_so_far, seen = [], 0, 0, set()
for ex in data:
src = ex["src"]["src"]
if src not in seen or (self.task != CorpusTask.TRAIN):
seen.add(src)
minibatch.append(ex)
nbsents = len(minibatch)
maxlen = max(max_src_tgt(ex), maxlen)
size_so_far = batch_size_fn(nbsents, maxlen)
if size_so_far >= batch_size:
overflowed = 1 if size_so_far > batch_size else 0
if batch_size_multiple > 1:
overflowed += (nbsents - overflowed) % batch_size_multiple
if overflowed == 0:
yield minibatch
minibatch, maxlen, size_so_far, seen = [], 0, 0, set()
else:
if overflowed == nbsents:
logger.warning(
"The batch will be filled until we reach"
" %d, its size may exceed %d tokens"
% (batch_size_multiple, batch_size)
)
else:
yield minibatch[:-overflowed]
minibatch = minibatch[-overflowed:]
maxlen = max([max_src_tgt(ex) for ex in minibatch])
size_so_far = batch_size_fn(len(minibatch), maxlen)
seen = set()
if minibatch:
yield minibatch
def __iter__(self):
for bucket in self._bucketing():
# For TRAIN we need to group examples by length
# for faster performance, but otherwise, sequential.
if self.task == CorpusTask.TRAIN:
bucket = sorted(bucket, key=self.sort_key)
p_batch = list(
self.batch_iter(
bucket,
self.batch_size,
batch_type=self.batch_type,
batch_size_multiple=self.batch_size_multiple,
)
)
# For TRAIN we shuffle batches within the bucket
# otherwise sequential
if self.task == CorpusTask.TRAIN:
p_batch = self.random_shuffler(p_batch)
for minibatch in p_batch:
# for specific case of rnn_packed need to be sorted
# within the batch
minibatch.sort(key=self.sort_key, reverse=True)
tensor_batch = tensorify(self.vocabs, minibatch)
yield tensor_batch
def build_dynamic_dataset_iter(
opt,
transforms_cls,
vocabs,
copy=False,
task=CorpusTask.TRAIN,
stride=1,
offset=0,
src=None,
tgt=None,
align=None,
):
"""
Build `DynamicDatasetIter` from opt.
if src, tgt,align are passed then dataset is built from those lists
instead of opt.[src, tgt, align]
Typically this function is called for CorpusTask.[TRAIN,VALID,INFER]
from the main tain / translate scripts
We disable automatic batching in the DataLoader.
The custom optimized batching is performed by the
custom class DynamicDatasetIter inherited from IterableDataset
(and not by a custom collate function).
We load opt.bucket_size examples, sort them and yield
mini-batchs of size opt.batch_size.
The bucket_size must be large enough to ensure homogeneous batches.
Each worker will load opt.prefetch_factor mini-batches in
advance to avoid the GPU waiting during the refilling of the bucket.
"""
transforms = make_transforms(opt, transforms_cls, vocabs)
corpora = get_corpora(opt, task, src=src, tgt=tgt, align=align)
if corpora is None:
assert task != CorpusTask.TRAIN, "only valid corpus is ignorable."
return None
data_iter = DynamicDatasetIter.from_opt(
corpora, transforms, vocabs, opt, task, copy=copy, stride=stride, offset=offset
)
data_iter.num_workers = opt.num_workers if hasattr(opt, "num_workers") else 0
if data_iter.num_workers == 0 or task == CorpusTask.INFER:
data_iter._init_datasets(0) # when workers=0 init_fn not called
data_loader = data_iter
else:
data_loader = DataLoader(
data_iter,
batch_size=None,
pin_memory=True,
multiprocessing_context="spawn",
num_workers=data_iter.num_workers,
worker_init_fn=data_iter._init_datasets,
prefetch_factor=opt.prefetch_factor,
)
return data_loader