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- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- import numpy as np
- import torch
- from . import data_utils, FairseqDataset
- def collate(samples, pad_idx, eos_idx):
- if len(samples) == 0:
- return {}
- def merge(key, is_list=False):
- if is_list:
- res = []
- for i in range(len(samples[0][key])):
- res.append(data_utils.collate_tokens(
- [s[key][i] for s in samples], pad_idx, eos_idx, left_pad=False,
- ))
- return res
- else:
- return data_utils.collate_tokens(
- [s[key] for s in samples], pad_idx, eos_idx, left_pad=False,
- )
- is_target_list = isinstance(samples[0]['target'], list)
- return {
- 'id': torch.LongTensor([s['id'] for s in samples]),
- 'nsentences': len(samples),
- 'ntokens': sum(len(s['source']) for s in samples),
- 'net_input': {
- 'src_tokens': merge('source'),
- 'src_lengths': torch.LongTensor([
- s['source'].numel() for s in samples
- ]),
- },
- 'target': merge('target', is_target_list),
- }
- class MonolingualDataset(FairseqDataset):
- """
- A wrapper around torch.utils.data.Dataset for monolingual data.
- Args:
- dataset (torch.utils.data.Dataset): dataset to wrap
- sizes (List[int]): sentence lengths
- vocab (~fairseq.data.Dictionary): vocabulary
- shuffle (bool, optional): shuffle the elements before batching
- (default: True).
- """
- def __init__(self, dataset, sizes, src_vocab, tgt_vocab, add_eos_for_other_targets, shuffle,
- targets=None):
- self.dataset = dataset
- self.sizes = np.array(sizes)
- self.vocab = src_vocab
- self.tgt_vocab = tgt_vocab
- self.add_eos_for_other_targets = add_eos_for_other_targets
- self.shuffle = shuffle
- assert targets is None or all(t in {'self', 'future', 'past'} for t in targets), \
- "targets must be none or one of 'self', 'future', 'past'"
- if targets is not None and len(targets) == 0:
- targets = None
- self.targets = targets
- def __getitem__(self, index):
- source, future_target, past_target = self.dataset[index]
- source, target = self._make_source_target(source, future_target, past_target)
- return {'id': index, 'source': source, 'target': target}
- def __len__(self):
- return len(self.dataset)
- def _make_source_target(self, source, future_target, past_target):
- if self.targets is not None:
- target = []
- if self.add_eos_for_other_targets and (('self' in self.targets) or ('past' in self.targets)) \
- and source[-1] != self.vocab.eos():
- # append eos at the end of source
- source = torch.cat([source, source.new([self.vocab.eos()])])
- if 'future' in self.targets:
- future_target = torch.cat([future_target, future_target.new([self.vocab.pad()])])
- if 'past' in self.targets:
- # first token is before the start of sentence which is only used in "none" break mode when
- # add_eos_for_other_targets is False
- past_target = torch.cat([past_target.new([self.vocab.pad()]), past_target[1:], source[-2, None]])
- for t in self.targets:
- if t == 'self':
- target.append(source)
- elif t == 'future':
- target.append(future_target)
- elif t == 'past':
- target.append(past_target)
- else:
- raise Exception('invalid target ' + t)
- if len(target) == 1:
- target = target[0]
- else:
- target = future_target
- return source, self._filter_vocab(target)
- def _filter_vocab(self, target):
- if len(self.tgt_vocab) != len(self.vocab):
- def _filter(target):
- mask = target.ge(len(self.tgt_vocab))
- if mask.any():
- target[mask] = self.tgt_vocab.unk()
- return target
- if isinstance(target, list):
- return [_filter(t) for t in target]
- return _filter(target)
- return target
- def collater(self, samples):
- """Merge a list of samples to form a mini-batch.
- Args:
- samples (List[dict]): samples to collate
- Returns:
- dict: a mini-batch with the following keys:
- - `id` (LongTensor): example IDs in the original input order
- - `ntokens` (int): total number of tokens in the batch
- - `net_input` (dict): the input to the Model, containing keys:
- - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
- the source sentence of shape `(bsz, src_len)`. Padding will
- appear on the right.
- - `target` (LongTensor): a padded 2D Tensor of tokens in the
- target sentence of shape `(bsz, tgt_len)`. Padding will appear
- on the right.
- """
- return collate(samples, self.vocab.pad(), self.vocab.eos())
- def get_dummy_batch(self, num_tokens, max_positions, tgt_len=128):
- """Return a dummy batch with a given number of tokens."""
- if isinstance(max_positions, float) or isinstance(max_positions, int):
- tgt_len = min(tgt_len, max_positions)
- bsz = max(num_tokens // tgt_len, 1)
- target = self.vocab.dummy_sentence(tgt_len + 2)
- source, past_target, future_target = target[1:-1], target[2:], target[:-2]
- source, target = self._make_source_target(source, past_target, future_target)
- return self.collater([
- {'id': i, 'source': source, 'target': target}
- for i in range(bsz)
- ])
- def num_tokens(self, index):
- """Return the number of tokens in a sample. This value is used to
- enforce ``--max-tokens`` during batching."""
- return self.sizes[index]
- def size(self, index):
- """Return an example's size as a float or tuple. This value is used when
- filtering a dataset with ``--max-positions``."""
- return self.sizes[index]
- def ordered_indices(self):
- """Return an ordered list of indices. Batches will be constructed based
- on this order."""
- if self.shuffle:
- order = [np.random.permutation(len(self))]
- else:
- order = [np.arange(len(self))]
- order.append(self.sizes)
- return np.lexsort(order)
- @property
- def supports_prefetch(self):
- return getattr(self.dataset, 'supports_prefetch', False)
- def prefetch(self, indices):
- self.dataset.prefetch(indices)
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