from __future__ import division
import time
import math
import random
from contextlib import contextmanager
from copy import deepcopy
import torch
from torch.distributed import get_world_size, get_rank
from .batch import Batch
from .dataset import Dataset
class RandomShuffler(object):
"""Use random functions while keeping track of the random state to make it
reproducible and deterministic."""
def __init__(self, random_state=None):
self._random_state = random_state
self.extra = 0
if self._random_state is None:
self._random_state = random.getstate()
@contextmanager
def use_internal_state(self):
"""Use a specific RNG state."""
old_state = random.getstate()
random.setstate(self._random_state)
yield
self._random_state = random.getstate()
random.setstate(old_state)
@property
def random_state(self):
return deepcopy(self._random_state)
@random_state.setter
def random_state(self, s):
self._random_state = s
def __call__(self, data, subsample=None):
"""Shuffle and return a new list."""
with self.use_internal_state():
return random.sample(data, len(data))
def set_epoch(self, epoch):
self.epoch = epoch
class DistributedShuffler:
def __init__(self, num_replicas=None, rank=None):
if num_replicas is None:
num_replicas = get_world_size()
if rank is None:
rank = get_rank()
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.extra = 0
def __call__(self, data, subsample=True):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = list(torch.randperm(len(data), generator=g))
if not subsample:
return [data[i] for i in indices]
return [data[i] for i in self.subsample(indices)]
def subsample(self, indices):
# add extra samples to make it evenly divisible
num_samples = int(math.ceil(len(indices) * 1.0 / self.num_replicas))
total_size = num_samples * self.num_replicas
extras = indices[:(total_size - len(indices))]
self.extra = len(extras)
indices += extras
assert len(indices) == total_size
# subsample
offset = num_samples * self.rank
indices = indices[offset:offset + num_samples]
assert len(indices) == num_samples
return indices
def set_epoch(self, epoch):
self.epoch = epoch
class Iterator(object):
"""Defines an iterator that loads batches of data from a Dataset.
Attributes:
dataset: The Dataset object to load Examples from.
batch_size: Batch size.
batch_size_fn: Function of three arguments (new example to add, current
count of examples in the batch, and current effective batch size)
that returns the new effective batch size resulting from adding
that example to a batch. This is useful for dynamic batching, where
this function would add to the current effective batch size the
number of tokens in the new example.
sort_key: A key to use for sorting examples in order to batch together
examples with similar lengths and minimize padding. The sort_key
provided to the Iterator constructor overrides the sort_key
attribute of the Dataset, or defers to it if None.
train: Whether the iterator represents a train set.
repeat: Whether to repeat the iterator for multiple epochs.
shuffle: Whether to shuffle examples between epochs.
sort: Whether to sort examples according to self.sort_key.
Note that repeat, shuffle, and sort default to train, train, and
(not train).
sort_within_batch: Whether to sort (in descending order according to
self.sort_key) within each batch. If None, defaults to self.sort.
If self.sort is True and this is False, the batch is left in the
original (ascending) sorted order.
device: Device to create batches on. Use -1 for CPU and None for the
currently active GPU device.
"""
def __init__(self, dataset, batch_size, sort_key=None, device=None,
batch_size_fn=None, train=True,
repeat=None, shuffle=None, sort=None, reverse=False,
sort_within_batch=None, distributed=False, num_replicas=None, rank=None):
self.batch_size, self.train, self.dataset = batch_size, train, dataset
self.batch_size_fn = batch_size_fn
self.iterations = 0
self.epoch = 0
self.reverse = reverse
self.repeat = train if repeat is None else repeat
self.shuffle = train if shuffle is None else shuffle
self.sort = not train if sort is None else sort
if sort_within_batch is None:
self.sort_within_batch = self.sort
else:
self.sort_within_batch = sort_within_batch
if sort_key is None:
self.sort_key = dataset.sort_key
else:
self.sort_key = sort_key
self.device = device
self.distributed = distributed
if distributed:
self.random_shuffler = DistributedShuffler(num_replicas=num_replicas, rank=rank)
else:
self.random_shuffler = RandomShuffler()
# For state loading/saving only
self._iterations_this_epoch = 0
self._random_state_this_epoch = None
self._restored_from_state = False
@classmethod
def splits(cls, datasets, batch_sizes=None, **kwargs):
"""Create Iterator objects for multiple splits of a dataset.
Arguments:
datasets: Tuple of Dataset objects corresponding to the splits. The
first such object should be the train set.
batch_sizes: Tuple of batch sizes to use for the different splits,
or None to use the same batch_size for all splits.
Remaining keyword arguments: Passed to the constructor of the
iterator class being used.
"""
if batch_sizes is None:
batch_sizes = [kwargs.pop('batch_size')] * len(datasets)
ret = []
for i in range(len(datasets)):
train = i == 0
ret.append(cls(
datasets[i], batch_size=batch_sizes[i], train=train, **kwargs))
return tuple(ret)
def data(self):
"""Return the examples in the dataset in order, sorted, or shuffled."""
if self.sort:
xs = sorted(self.dataset, key=self.sort_key, reverse=self.reverse)
if self.distributed:
xs = [xs[i] for i in self.random_shuffler.subsample(list(range(len(xs))))]
elif self.shuffle:
xs = self.random_shuffler(list(self.dataset))
else:
xs = self.dataset
self.extra = self.random_shuffler.extra
return xs
def init_epoch(self):
"""Set up the batch generator for a new epoch."""
if not self.distributed:
if self._restored_from_state:
self.random_shuffler.random_state = self._random_state_this_epoch
else:
self._random_state_this_epoch = self.random_shuffler.random_state
self.create_batches()
if not self.distributed:
if self._restored_from_state:
self._restored_from_state = False
else:
self._iterations_this_epoch = 0
else:
self._iterations_this_epoch = 0
if not self.repeat:
self.iterations = 0
self.epoch += 1
if self.distributed:
self.random_shuffler.set_epoch(self.epoch)
def create_batches(self):
self.batches = batch(self.data(), self.batch_size, self.batch_size_fn)
def __len__(self):
if self.batch_size_fn is not None:
raise NotImplementedError
return math.ceil(len(self.dataset) / self.batch_size)
def __iter__(self):
while True:
self.init_epoch()
for idx, minibatch in enumerate(self.batches):
# fast-forward if loaded from state
if self._iterations_this_epoch > idx:
continue
self.iterations += 1
self._iterations_this_epoch += 1
if self.sort_within_batch:
# NOTE: `rnn.pack_padded_sequence` requires that a minibatch
# be sorted by decreasing order, which requires reversing
# relative to typical sort keys
if self.sort:
minibatch.reverse()
else:
minibatch.sort(key=self.sort_key, reverse=self.reverse)
b = Batch(minibatch, self.dataset, self.device,
self.train)
yield b
if not self.repeat:
return
def state_dict(self):
d = {"iterations": self.iterations}
if not self.distributed:
d.update({
"iterations_this_epoch": self._iterations_this_epoch,
"random_state_this_epoch": self._random_state_this_epoch
})
def load_state_dict(self, state_dict):
self.iterations = state_dict["iterations"]
self._iterations_this_epoch = state_dict["iterations_this_epoch"]
if not self.distributed:
self._random_state_this_epoch = state_dict["random_state_this_epoch"]
self._restored_from_state = True
class BPTTIterator(Iterator):
"""Defines an iterator for language modeling tasks that use BPTT.
Provides contiguous streams of examples together with targets that are
one timestep further forward, for language modeling training with
backpropagation through time (BPTT). Expects a Dataset with a single
example and a single field called 'text' and produces Batches with text and
target attributes.
Attributes:
dataset: The Dataset object to load Examples from.
batch_size: Batch size.
bptt_len: Length of sequences for backpropagation through time.
sort_key: A key to use for sorting examples in order to batch together
examples with similar lengths and minimize padding. The sort_key
provided to the Iterator constructor overrides the sort_key
attribute of the Dataset, or defers to it if None.
train: Whether the iterator represents a train set.
repeat: Whether to repeat the iterator for multiple epochs.
shuffle: Whether to shuffle examples between epochs.
sort: Whether to sort examples according to self.sort_key.
Note that repeat, shuffle, and sort default to train, train, and
(not train).
device: Device to create batches on. Use -1 for CPU and None for the
currently active GPU device.
"""
def __init__(self, dataset, batch_size, bptt_len, **kwargs):
self.bptt_len = bptt_len
super(BPTTIterator, self).__init__(dataset, batch_size, **kwargs)
def __len__(self):
return math.ceil((len(self.dataset[0].text) / self.batch_size - 1) /
self.bptt_len)
def __iter__(self):
text = self.dataset[0].text
TEXT = self.dataset.fields['text']
TEXT.eos_token = None
text = text + ([TEXT.pad_token] * int(math.ceil(len(text) / self.batch_size) *
self.batch_size - len(text)))
data = TEXT.numericalize(
[text], device=self.device, train=self.train)
data = data.view(self.batch_size, -1).t().contiguous()
dataset = Dataset(examples=self.dataset.examples, fields=[
('text', TEXT), ('target', TEXT)])
while True:
for i in range(0, len(self) * self.bptt_len, self.bptt_len):
seq_len = min(self.bptt_len, len(data) - i - 1)
yield Batch.fromvars(
dataset, self.batch_size, train=self.train,
text=data[i:i + seq_len],
target=data[i + 1:i + 1 + seq_len])
if not self.repeat:
raise StopIteration
class BucketIterator(Iterator):
"""Defines an iterator that batches examples of similar lengths together.
Minimizes amount of padding needed while producing freshly shuffled
batches for each new epoch. See pool for the bucketing procedure used.
"""
def create_batches(self):
if self.sort:
self.batches = batch(self.data(), self.batch_size,
self.batch_size_fn, repeat=self.repeat)
else:
self.batches = pool(self.data(), self.batch_size,
self.sort_key, self.batch_size_fn,
random_shuffler=self.random_shuffler, repeat=self.repeat,
reverse=self.reverse, shuffle=self.shuffle)
def batch(data, batch_size, batch_size_fn=None, repeat=False):
"""Yield elements from data in chunks of batch_size."""
if batch_size_fn is None:
def batch_size_fn(new, count, sofar):
return count
minibatch = []
size_so_far = 0
for ex in data:
minibatch.append(ex)
size_so_far = batch_size_fn(ex, len(minibatch), size_so_far)
if size_so_far == batch_size:
yield minibatch
minibatch, size_so_far = [], 0
elif size_so_far > batch_size:
if len(minibatch) == 1: # if we only have one really big example
yield minibatch
minibatch, size_so_far = [], 0
else:
yield minibatch[:-1]
minibatch, size_so_far = minibatch[-1:], batch_size_fn(ex, 1, 0)
if size_so_far > batch_size: # if we add a really big example that needs to be on its own to a batch
yield minibatch
minibatch, size_so_far = [], 0
if minibatch:
yield minibatch
def pool(data, batch_size, key, batch_size_fn=lambda new, count, sofar: count,
random_shuffler=None, reverse=False, shuffle=False, repeat=False, leftovers=None):
"""Sort within buckets, then batch, then shuffle batches.
Partitions data into chunks of size 100*batch_size, sorts examples within
each chunk using sort_key, then batch these examples and shuffle the
batches.
"""
if random_shuffler is None:
random_shuffler = random.shuffle
for p in batch(data, batch_size * 100, batch_size_fn):
p_batch = batch(sorted(p, key=key, reverse=reverse), batch_size, batch_size_fn, repeat=repeat)
if shuffle:
for b in random_shuffler(list(p_batch), subsample=False):
yield b
else:
for b in list(p_batch):
yield b
