Merge branch 'hepj-test' into 'main'

更新transformer代码

See merge request dcutoolkit/deeplearing/dlexamples_new!47

PyTorch/NLP/new-Transformer/fairseq/data/iterators.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+import logging
+import math
+import operator
+import os
+import queue
+import time
+from threading import Thread
+
+import numpy as np
+import torch
+from fairseq.data import data_utils
+
+
+logger = logging.getLogger(__name__)
+
+# Object used by _background_consumer to signal the source is exhausted
+# to the main thread.
+_sentinel = object()
+
+
+class CountingIterator(object):
+    """Wrapper around an iterable that maintains the iteration count.
+
+    Args:
+        iterable (iterable): iterable to wrap
+        start (int): starting iteration count. Note that this doesn't
+            actually advance the iterator.
+        total (int): override the iterator length returned by ``__len``.
+            This can be used to truncate *iterator*.
+
+    Attributes:
+        n (int): number of elements consumed from this iterator
+    """
+
+    def __init__(self, iterable, start=None, total=None):
+        self._itr = iter(iterable)
+        self.n = start or getattr(iterable, "n", 0)
+        self.total = total if total is not None else self.n + len(iterable)
+
+    def __len__(self):
+        return self.total
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if not self.has_next():
+            raise StopIteration
+        try:
+            x = next(self._itr)
+        except StopIteration:
+            raise IndexError(
+                f"Iterator expected to have length {self.total}, "
+                f"but exhausted at position {self.n}."
+            )
+        self.n += 1
+        return x
+
+    def has_next(self):
+        """Whether the iterator has been exhausted."""
+        return self.n < self.total
+
+    def skip(self, n):
+        """Fast-forward the iterator by skipping n elements."""
+        for _ in range(n):
+            next(self)
+        return self
+
+    def take(self, n):
+        """Truncate the iterator to n elements at most."""
+        self.total = min(self.total, n)
+        # Propagate this change to the underlying iterator
+        if hasattr(self._itr, "take"):
+            self._itr.take(max(n - self.n, 0))
+        return self
+
+
+class EpochBatchIterating(object):
+    def __len__(self) -> int:
+        raise NotImplementedError
+
+    @property
+    def next_epoch_idx(self):
+        raise NotImplementedError
+
+    def next_epoch_itr(
+        self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
+    ):
+        """Return a new iterator over the dataset.
+
+        Args:
+            shuffle (bool, optional): shuffle batches before returning the
+                iterator (default: True).
+            fix_batches_to_gpus (bool, optional): ensure that batches are always
+                allocated to the same shards across epochs. Requires
+                that :attr:`dataset` supports prefetching (default: False).
+            set_dataset_epoch (bool, optional): update the wrapped Dataset with
+                the new epoch number (default: True).
+        """
+        raise NotImplementedError
+
+    def end_of_epoch(self) -> bool:
+        """Returns whether the most recent epoch iterator has been exhausted"""
+        raise NotImplementedError
+
+    @property
+    def iterations_in_epoch(self) -> int:
+        """The number of consumed batches in the current epoch."""
+        raise NotImplementedError
+
+    def state_dict(self):
+        """Returns a dictionary containing a whole state of the iterator."""
+        raise NotImplementedError
+
+    def load_state_dict(self, state_dict):
+        """Copies the state of the iterator from the given *state_dict*."""
+        raise NotImplementedError
+
+    @property
+    def first_batch(self):
+        return "DUMMY"
+
+
+class StreamingEpochBatchIterator(EpochBatchIterating):
+    """A steaming-style iterator over a :class:`torch.utils.data.IterableDataset`.
+
+    Args:
+        dataset (~torch.utils.data.Dataset): dataset from which to load the data
+        max_sentences: batch size
+        collate_fn (callable): merges a list of samples to form a mini-batch
+        num_workers (int, optional): how many subprocesses to use for data
+            loading. 0 means the data will be loaded in the main process
+            (default: 0).
+        epoch (int, optional): the epoch to start the iterator from
+            (default: 1).
+        buffer_size (int, optional): the number of batches to keep ready in the
+            queue. Helps speeding up dataloading. When buffer_size is zero, the
+            default torch.utils.data.DataLoader preloading is used.
+        timeout (int, optional): if positive, the timeout value for collecting a batch
+            from workers. Should always be non-negative (default: ``0``).
+    """
+
+    def __init__(
+        self,
+        dataset,
+        max_sentences=1,
+        collate_fn=None,
+        epoch=1,
+        num_workers=0,
+        buffer_size=0,
+        timeout=0,
+    ):
+        assert isinstance(dataset, torch.utils.data.IterableDataset)
+        self.dataset = dataset
+        self.max_sentences = max_sentences
+        self.collate_fn = collate_fn
+        self.epoch = max(epoch, 1)  # we use 1-based indexing for epochs
+        self.num_workers = num_workers
+        # This upper limit here is to prevent people from abusing this feature
+        # in a shared computing environment.
+        self.buffer_size = min(buffer_size, 20)
+        self.timeout = timeout
+
+        self._current_epoch_iterator = None
+
+    @property
+    def next_epoch_idx(self):
+        """Return the epoch index after *next_epoch_itr* is called."""
+        if self._current_epoch_iterator is not None and self.end_of_epoch():
+            return self.epoch + 1
+        else:
+            return self.epoch
+
+    def next_epoch_itr(
+        self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
+    ):
+        self.epoch = self.next_epoch_idx
+        if set_dataset_epoch and hasattr(self.dataset, "set_epoch"):
+            self.dataset.set_epoch(self.epoch)
+        self._current_epoch_iterator = self._get_iterator_for_epoch(self.epoch, shuffle)
+        return self._current_epoch_iterator
+
+    def end_of_epoch(self) -> bool:
+        return not self._current_epoch_iterator.has_next()
+
+    @property
+    def iterations_in_epoch(self) -> int:
+        if self._current_epoch_iterator is not None:
+            return self._current_epoch_iterator.n
+        return 0
+
+    def state_dict(self):
+        return {
+            "epoch": self.epoch,
+        }
+
+    def load_state_dict(self, state_dict):
+        self.epoch = state_dict["epoch"]
+
+    def _get_iterator_for_epoch(self, epoch, shuffle, offset=0):
+        if self.num_workers > 0:
+            os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
+
+        # Create data loader
+        worker_init_fn = getattr(self.dataset, "worker_init_fn", None)
+        itr = torch.utils.data.DataLoader(
+            self.dataset,
+            batch_size=self.max_sentences,
+            collate_fn=self.collate_fn,
+            num_workers=self.num_workers,
+            timeout=self.timeout,
+            worker_init_fn=worker_init_fn,
+            pin_memory=True,
+        )
+
+        # Wrap with a BufferedIterator if needed
+        if self.buffer_size > 0:
+            itr = BufferedIterator(self.buffer_size, itr)
+
+        # Wrap with CountingIterator
+        itr = CountingIterator(itr, start=offset)
+
+        return itr
+
+
+class EpochBatchIterator(EpochBatchIterating):
+    """A multi-epoch iterator over a :class:`torch.utils.data.Dataset`.
+
+    Compared to :class:`torch.utils.data.DataLoader`, this iterator:
+
+    - can be reused across multiple epochs with the :func:`next_epoch_itr`
+      method (optionally shuffled between epochs)
+    - can be serialized/deserialized with the :func:`state_dict` and
+      :func:`load_state_dict` methods
+    - supports sharding with the *num_shards* and *shard_id* arguments
+
+    Args:
+        dataset (~torch.utils.data.Dataset): dataset from which to load the data
+        collate_fn (callable): merges a list of samples to form a mini-batch
+        batch_sampler (~torch.utils.data.Sampler or a callable): an iterator over batches of
+            indices, or a callable to create such an iterator (~torch.utils.data.Sampler).
+            A callable batch_sampler will be called for each epoch to enable per epoch dynamic
+            batch iterators defined by this callable batch_sampler.
+        seed (int, optional): seed for random number generator for
+            reproducibility (default: 1).
+        num_shards (int, optional): shard the data iterator into N
+            shards (default: 1).
+        shard_id (int, optional): which shard of the data iterator to
+            return (default: 0).
+        num_workers (int, optional): how many subprocesses to use for data
+            loading. 0 means the data will be loaded in the main process
+            (default: 0).
+        epoch (int, optional): the epoch to start the iterator from
+            (default: 1).
+        buffer_size (int, optional): the number of batches to keep ready in the
+            queue. Helps speeding up dataloading. When buffer_size is zero, the
+            default torch.utils.data.DataLoader preloading is used.
+        timeout (int, optional): if positive, the timeout value for collecting a batch
+            from workers. Should always be non-negative (default: ``0``).
+        disable_shuffling (bool, optional): force disable shuffling
+            (default: ``False``).
+        skip_remainder_batch (bool, optional): if set, discard the last batch in an epoch
+            for the sake of training stability, as the last batch is usually smaller than
+                local_batch_size * distributed_word_size (default: ``False``).
+        grouped_shuffling (bool, optional): enable shuffling batches in groups
+            of num_shards. Ensures that each GPU receives similar length sequences when
+            batches are sorted by length.
+    """
+
+    def __init__(
+        self,
+        dataset,
+        collate_fn,
+        batch_sampler,
+        seed=1,
+        num_shards=1,
+        shard_id=0,
+        num_workers=0,
+        epoch=1,
+        buffer_size=0,
+        timeout=0,
+        disable_shuffling=False,
+        skip_remainder_batch=False,
+        grouped_shuffling=False,
+    ):
+        assert isinstance(dataset, torch.utils.data.Dataset)
+        self.dataset = dataset
+        self.collate_fn = collate_fn
+        self.batch_sampler = batch_sampler
+        self._frozen_batches = (
+            tuple(batch_sampler) if not callable(batch_sampler) else None
+        )
+        self.seed = seed
+        self.num_shards = num_shards
+        self.shard_id = shard_id
+        self.num_workers = num_workers
+        # This upper limit here is to prevent people from abusing this feature
+        # in a shared computing environment.
+        self.buffer_size = min(buffer_size, 20)
+        self.timeout = timeout
+        self.disable_shuffling = disable_shuffling
+        self.skip_remainder_batch = skip_remainder_batch
+        self.grouped_shuffling = grouped_shuffling
+
+        self.epoch = max(epoch, 1)  # we use 1-based indexing for epochs
+        self.shuffle = not disable_shuffling
+        self._cur_epoch_itr = None
+        self._next_epoch_itr = None
+        self._supports_prefetch = getattr(dataset, "supports_prefetch", False)
+
+    @property
+    def frozen_batches(self):
+        if self._frozen_batches is None:
+            self._frozen_batches = tuple(self.batch_sampler(self.dataset, self.epoch))
+        return self._frozen_batches
+
+    @property
+    def first_batch(self):
+        if len(self.frozen_batches) == 0:
+            raise Exception(
+                "The dataset is empty. This could indicate "
+                "that all elements in the dataset have been skipped. "
+                "Try increasing the max number of allowed tokens or using "
+                "a larger dataset."
+            )
+
+        if getattr(self.dataset, "supports_fetch_outside_dataloader", True):
+            return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0]])
+        else:
+            return "DUMMY"
+
+    def __len__(self):
+        return int(math.ceil(len(self.frozen_batches) / float(self.num_shards)))
+
+    @property
+    def n(self):
+        return self.iterations_in_epoch
+
+    @property
+    def next_epoch_idx(self):
+        """Return the epoch index after *next_epoch_itr* is called."""
+        if self._next_epoch_itr is not None:
+            return self.epoch
+        elif self._cur_epoch_itr is not None and self.end_of_epoch():
+            return self.epoch + 1
+        else:
+            return self.epoch
+
+    def next_epoch_itr(
+        self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
+    ):
+        """Return a new iterator over the dataset.
+
+        Args:
+            shuffle (bool, optional): shuffle batches before returning the
+                iterator (default: True).
+            fix_batches_to_gpus (bool, optional): ensure that batches are always
+                allocated to the same shards across epochs. Requires
+                that :attr:`dataset` supports prefetching (default: False).
+            set_dataset_epoch (bool, optional): update the wrapped Dataset with
+                the new epoch number (default: True).
+        """
+        if self.disable_shuffling:
+            shuffle = False
+        prev_epoch = self.epoch
+        self.epoch = self.next_epoch_idx
+        if set_dataset_epoch and hasattr(self.dataset, "set_epoch"):
+            self.dataset.set_epoch(self.epoch)
+        if self._next_epoch_itr is not None:
+            self._cur_epoch_itr = self._next_epoch_itr
+            self._next_epoch_itr = None
+        else:
+            if callable(self.batch_sampler) and prev_epoch != self.epoch:
+                # reset _frozen_batches to refresh the next epoch
+                self._frozen_batches = None
+            self._cur_epoch_itr = self._get_iterator_for_epoch(
+                self.epoch,
+                shuffle,
+                fix_batches_to_gpus=fix_batches_to_gpus,
+            )
+        self.shuffle = shuffle
+        return self._cur_epoch_itr
+
+    def end_of_epoch(self) -> bool:
+        """Returns whether the most recent epoch iterator has been exhausted"""
+        return not self._cur_epoch_itr.has_next()
+
+    @property
+    def iterations_in_epoch(self):
+        """The number of consumed batches in the current epoch."""
+        if self._cur_epoch_itr is not None:
+            return self._cur_epoch_itr.n
+        elif self._next_epoch_itr is not None:
+            return self._next_epoch_itr.n
+        return 0
+
+    def state_dict(self):
+        """Returns a dictionary containing a whole state of the iterator."""
+        if self.end_of_epoch():
+            epoch = self.epoch + 1
+            iter_in_epoch = 0
+        else:
+            epoch = self.epoch
+            iter_in_epoch = self.iterations_in_epoch
+        return {
+            "version": 2,
+            "epoch": epoch,
+            "iterations_in_epoch": iter_in_epoch,
+            "shuffle": self.shuffle,
+        }
+
+    def load_state_dict(self, state_dict):
+        """Copies the state of the iterator from the given *state_dict*."""
+        self.epoch = state_dict["epoch"]
+        itr_pos = state_dict.get("iterations_in_epoch", 0)
+        version = state_dict.get("version", 1)
+        if itr_pos > 0:
+            # fast-forward epoch iterator
+            self._next_epoch_itr = self._get_iterator_for_epoch(
+                self.epoch,
+                shuffle=state_dict.get("shuffle", True),
+                offset=itr_pos,
+            )
+            if self._next_epoch_itr is None:
+                if version == 1:
+                    # legacy behavior: we finished the epoch, increment epoch counter
+                    self.epoch += 1
+                else:
+                    raise RuntimeError(
+                        "Cannot resume training due to dataloader mismatch, please "
+                        "report this to the fairseq developers. You can relaunch "
+                        "training with `--reset-dataloader` and it should work."
+                    )
+        else:
+            self._next_epoch_itr = None
+
+    def _get_iterator_for_epoch(
+        self, epoch, shuffle, fix_batches_to_gpus=False, offset=0
+    ):
+        def shuffle_batches(batches, seed):
+            with data_utils.numpy_seed(seed):
+
+                if self.grouped_shuffling:
+                    grouped_batches = [
+                        batches[(i * self.num_shards) : ((i + 1) * self.num_shards)]
+                        for i in range((len(batches) // self.num_shards))
+                    ]
+                    np.random.shuffle(grouped_batches)
+                    batches = list(itertools.chain(*grouped_batches))
+                else:
+                    np.random.shuffle(batches)
+
+            return batches
+
+        if self._supports_prefetch:
+            batches = self.frozen_batches
+
+            if shuffle and not fix_batches_to_gpus:
+                batches = shuffle_batches(list(batches), self.seed + epoch)
+
+            batches = list(
+                ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
+            )
+            self.dataset.prefetch([i for s in batches for i in s])
+
+            if shuffle and fix_batches_to_gpus:
+                batches = shuffle_batches(batches, self.seed + epoch + self.shard_id)
+        else:
+            if shuffle:
+                batches = shuffle_batches(list(self.frozen_batches), self.seed + epoch)
+            else:
+                batches = self.frozen_batches
+            batches = list(
+                ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
+            )
+
+        if offset > 0 and offset >= len(batches):
+            return None
+
+        if self.num_workers > 0:
+            os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
+
+        # Create data loader
+        itr = torch.utils.data.DataLoader(
+            self.dataset,
+            collate_fn=self.collate_fn,
+            batch_sampler=batches[offset:],
+            num_workers=self.num_workers,
+            timeout=self.timeout,
+            pin_memory=True,
+        )
+
+        # Wrap with a BufferedIterator if needed
+        if self.buffer_size > 0:
+            itr = BufferedIterator(self.buffer_size, itr)
+
+        # Wrap with CountingIterator
+        itr = CountingIterator(itr, start=offset)
+
+        if self.skip_remainder_batch:
+            # TODO: Below is a lazy implementation which discard the final batch regardless
+            # of whether it is a full batch or not.
+            total_num_itrs = len(batches) - 1
+            itr.take(total_num_itrs)
+            logger.info(f"skip final residual batch, total_num_itrs = {total_num_itrs}")
+
+        return itr
+
+
+class GroupedIterator(CountingIterator):
+    """Wrapper around an iterable that returns groups (chunks) of items.
+
+    Args:
+        iterable (iterable): iterable to wrap
+        chunk_size (int): size of each chunk
+        skip_remainder_batch (bool, optional): if set, discard the last grouped batch in
+          each training epoch, as the last grouped batch is usually smaller than
+                local_batch_size * distributed_word_size * chunk_size (default: ``False``).
+    Attributes:
+        n (int): number of elements consumed from this iterator
+    """
+
+    def __init__(self, iterable, chunk_size, skip_remainder_batch=False):
+        if skip_remainder_batch:
+            total_num_itrs = int(math.floor(len(iterable) / float(chunk_size)))
+            logger.info(
+                f"skip final residual batch, grouped total_num_itrs = {total_num_itrs}"
+            )
+        else:
+            total_num_itrs = int(math.ceil(len(iterable) / float(chunk_size)))
+            logger.info(f"grouped total_num_itrs = {total_num_itrs}")
+
+        itr = _chunk_iterator(iterable, chunk_size, skip_remainder_batch)
+        super().__init__(
+            itr,
+            start=int(math.ceil(getattr(iterable, "n", 0) / float(chunk_size))),
+            total=total_num_itrs,
+        )
+        self.chunk_size = chunk_size
+
+        if skip_remainder_batch:
+            self.take(total_num_itrs)
+            # TODO: [Hack] Here the grouped iterator modifies the base iterator size so that
+            # training can move into the next epoch once the grouped iterator is exhausted.
+            # Double-check this implementation in case unexpected behavior occurs.
+            iterable.take(total_num_itrs * chunk_size)
+
+
+def _chunk_iterator(itr, chunk_size, skip_remainder_batch=False):
+    chunk = []
+    for x in itr:
+        chunk.append(x)
+        if len(chunk) == chunk_size:
+            yield chunk
+            chunk = []
+    if not skip_remainder_batch and len(chunk) > 0:
+        yield chunk
+
+
+class ShardedIterator(CountingIterator):
+    """A sharded wrapper around an iterable, padded to length.
+
+    Args:
+        iterable (iterable): iterable to wrap
+        num_shards (int): number of shards to split the iterable into
+        shard_id (int): which shard to iterator over
+        fill_value (Any, optional): padding value when the iterable doesn't
+            evenly divide *num_shards* (default: None).
+
+    Attributes:
+        n (int): number of elements consumed from this iterator
+    """
+
+    def __init__(
+        self, iterable, num_shards, shard_id, fill_value=None, skip_remainder_batch=None
+    ):
+        """
+        Args:
+            skip_remainder_batch: ignored"""
+        if shard_id < 0 or shard_id >= num_shards:
+            raise ValueError("shard_id must be between 0 and num_shards")
+        sharded_len = int(math.ceil(len(iterable) / float(num_shards)))
+        itr = map(
+            operator.itemgetter(1),
+            itertools.zip_longest(
+                range(sharded_len),
+                itertools.islice(iterable, shard_id, len(iterable), num_shards),
+                fillvalue=fill_value,
+            ),
+        )
+        super().__init__(
+            itr,
+            start=int(math.ceil(getattr(iterable, "n", 0) / float(num_shards))),
+            total=sharded_len,
+        )
+
+
+class BackgroundConsumer(Thread):
+    def __init__(self, queue, source, max_len, cuda_device):
+        Thread.__init__(self)
+
+        self._queue = queue
+        self._source = source
+        self._max_len = max_len
+        self.count = 0
+        self.cuda_device = cuda_device
+
+    def run(self):
+        # set_device to avoid creation of GPU0 context when using pin_memory
+        if self.cuda_device is not None:
+            torch.cuda.set_device(self.cuda_device)
+
+        try:
+            for item in self._source:
+                self._queue.put(item)
+
+                # Stop if we reached the maximum length
+                self.count += 1
+                if self._max_len is not None and self.count >= self._max_len:
+                    break
+
+            # Signal the consumer we are done.
+            self._queue.put(_sentinel)
+        except Exception as e:
+            self._queue.put(e)
+
+
+class BufferedIterator(object):
+    def __init__(self, size, iterable):
+        self._queue = queue.Queue(size)
+        self._iterable = iterable
+        self._consumer = None
+
+        self.start_time = time.time()
+        self.warning_time = None
+
+        self.total = len(iterable)
+
+    def _create_consumer(self):
+        self._consumer = BackgroundConsumer(
+            self._queue,
+            self._iterable,
+            self.total,
+            torch.cuda.current_device() if torch.cuda.is_available() else None,
+        )
+        self._consumer.daemon = True
+        self._consumer.start()
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return self.total
+
+    def take(self, n):
+        self.total = min(self.total, n)
+        # Propagate this change to the underlying iterator
+        if hasattr(self._iterable, "take"):
+            self._iterable.take(n)
+        return self
+
+    def __next__(self):
+        # Create consumer if not created yet
+        if self._consumer is None:
+            self._create_consumer()
+
+        # Notify the user if there is a data loading bottleneck
+        if self._queue.qsize() < min(2, max(1, self._queue.maxsize // 2)):
+            if time.time() - self.start_time > 5 * 60:
+                if (
+                    self.warning_time is None
+                    or time.time() - self.warning_time > 15 * 60
+                ):
+                    logger.debug(
+                        "Data loading buffer is empty or nearly empty. This may "
+                        "indicate a data loading bottleneck, and increasing the "
+                        "number of workers (--num-workers) may help."
+                    )
+                    self.warning_time = time.time()
+
+        # Get next example
+        item = self._queue.get(True)
+        if isinstance(item, Exception):
+            raise item
+        if item is _sentinel:
+            raise StopIteration()
+        return item
+
+
+class GroupedEpochBatchIterator(EpochBatchIterator):
+    """Grouped version of EpochBatchIterator
+    It takes several samplers from different datasets.
+    Each epoch shuffle the dataset wise sampler individually with different
+    random seed. The those sub samplers are combined with into
+    one big samplers with deterministic permutation to mix batches from
+    different datasets. It will act like EpochBatchIterator but make sure
+    1) data from one data set each time
+    2) for different workers, they use the same order to fetch the data
+    so they will use data from the same dataset everytime
+    mult_rate is used for update_freq > 1 case where we want to make sure update_freq
+    mini-batches come from same source
+    """
+
+    def __init__(
+        self,
+        dataset,
+        collate_fn,
+        batch_samplers,
+        seed=1,
+        num_shards=1,
+        shard_id=0,
+        num_workers=0,
+        epoch=0,
+        mult_rate=1,
+        buffer_size=0,
+        skip_remainder_batch=False,
+    ):
+        super().__init__(
+            dataset,
+            collate_fn,
+            batch_samplers,
+            seed,
+            num_shards,
+            shard_id,
+            num_workers,
+            epoch,
+            buffer_size,
+            skip_remainder_batch=skip_remainder_batch,
+        )
+        # level 0: sub-samplers 1: batch_idx 2: batches
+        self._frozen_batches = tuple([tuple(sub_batch) for sub_batch in batch_samplers])
+        self.step_size = mult_rate * num_shards
+
+        self.lengths = [
+            (len(x) // self.step_size) * self.step_size for x in self.frozen_batches
+        ]
+
+    def __len__(self):
+        return sum(self.lengths)
+
+    @property
+    def first_batch(self):
+        if len(self.frozen_batches) == 0:
+            raise Exception(
+                "The dataset is empty. This could indicate "
+                "that all elements in the dataset have been skipped. "
+                "Try increasing the max number of allowed tokens or using "
+                "a larger dataset."
+            )
+
+        if self.dataset.supports_fetch_outside_dataloader:
+            return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0][0]])
+        else:
+            return "DUMMY"
+
+    def _get_iterator_for_epoch(
+        self, epoch, shuffle, fix_batches_to_gpus=False, offset=0
+    ):
+        def shuffle_batches(batches, seed):
+            with data_utils.numpy_seed(seed):
+                np.random.shuffle(batches)
+            return batches
+
+        def return_full_batches(batch_sets, seed, shuffle):
+            if shuffle:
+                batch_sets = [shuffle_batches(list(x), seed) for x in batch_sets]
+
+            batch_sets = [
+                batch_sets[i][: self.lengths[i]] for i in range(len(batch_sets))
+            ]
+            batches = list(itertools.chain.from_iterable(batch_sets))
+
+            if shuffle:
+                with data_utils.numpy_seed(seed):
+                    idx = np.random.permutation(len(batches) // self.step_size)
+                    if len(idx) * self.step_size != len(batches):
+                        raise ValueError(
+                            "ERROR: %d %d %d %d"
+                            % (len(idx), self.step_size, len(batches), self.shard_id),
+                            ":".join(["%d" % x for x in self.lengths]),
+                        )
+                    mini_shards = [
+                        batches[i * self.step_size : (i + 1) * self.step_size]
+                        for i in idx
+                    ]
+                    batches = list(itertools.chain.from_iterable(mini_shards))
+
+            return batches
+
+        if self._supports_prefetch:
+            raise NotImplementedError("To be implemented")
+        else:
+            batches = return_full_batches(
+                self.frozen_batches, self.seed + epoch, shuffle
+            )
+            batches = list(
+                ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
+            )
+
+        if offset > 0 and offset >= len(batches):
+            return None
+
+        if self.num_workers > 0:
+            os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
+
+        itr = torch.utils.data.DataLoader(
+            self.dataset,
+            collate_fn=self.collate_fn,
+            batch_sampler=batches[offset:],
+            num_workers=self.num_workers,
+        )
+        if self.buffer_size > 0:
+            itr = BufferedIterator(self.buffer_size, itr)
+
+        return CountingIterator(itr, start=offset)

PyTorch/NLP/new-Transformer/fairseq/data/language_pair_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+import numpy as np
+import torch
+from fairseq.data import FairseqDataset, data_utils
+
+
+logger = logging.getLogger(__name__)
+
+
+def collate(
+    samples,
+    pad_idx,
+    eos_idx,
+    left_pad_source=True,
+    left_pad_target=False,
+    input_feeding=True,
+    pad_to_length=None,
+    pad_to_multiple=1,
+):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx,
+            left_pad,
+            move_eos_to_beginning,
+            pad_to_length=pad_to_length,
+            pad_to_multiple=pad_to_multiple,
+        )
+
+    def check_alignment(alignment, src_len, tgt_len):
+        if alignment is None or len(alignment) == 0:
+            return False
+        if (
+            alignment[:, 0].max().item() >= src_len - 1
+            or alignment[:, 1].max().item() >= tgt_len - 1
+        ):
+            logger.warning("alignment size mismatch found, skipping alignment!")
+            return False
+        return True
+
+    def compute_alignment_weights(alignments):
+        """
+        Given a tensor of shape [:, 2] containing the source-target indices
+        corresponding to the alignments, a weight vector containing the
+        inverse frequency of each target index is computed.
+        For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then
+        a tensor containing [1., 0.5, 0.5, 1] should be returned (since target
+        index 3 is repeated twice)
+        """
+        align_tgt = alignments[:, 1]
+        _, align_tgt_i, align_tgt_c = torch.unique(
+            align_tgt, return_inverse=True, return_counts=True
+        )
+        align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]]
+        return 1.0 / align_weights.float()
+
+    id = torch.LongTensor([s["id"] for s in samples])
+    src_tokens = merge(
+        "source",
+        left_pad=left_pad_source,
+        pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
+    )
+    # sort by descending source length
+    src_lengths = torch.LongTensor(
+        [s["source"].ne(pad_idx).long().sum() for s in samples]
+    )
+    src_lengths, sort_order = src_lengths.sort(descending=True)
+    id = id.index_select(0, sort_order)
+    src_tokens = src_tokens.index_select(0, sort_order)
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge(
+            "target",
+            left_pad=left_pad_target,
+            pad_to_length=pad_to_length["target"]
+            if pad_to_length is not None
+            else None,
+        )
+        target = target.index_select(0, sort_order)
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        ).index_select(0, sort_order)
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
+        elif input_feeding:
+            # we create a shifted version of targets for feeding the
+            # previous output token(s) into the next decoder step
+            prev_output_tokens = merge(
+                "target",
+                left_pad=left_pad_target,
+                move_eos_to_beginning=True,
+                pad_to_length=pad_to_length["target"]
+                if pad_to_length is not None
+                else None,
+            )
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+        },
+        "target": target,
+    }
+    if prev_output_tokens is not None:
+        batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select(
+            0, sort_order
+        )
+
+    if samples[0].get("alignment", None) is not None:
+        bsz, tgt_sz = batch["target"].shape
+        src_sz = batch["net_input"]["src_tokens"].shape[1]
+
+        offsets = torch.zeros((len(sort_order), 2), dtype=torch.long)
+        offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz
+        if left_pad_source:
+            offsets[:, 0] += src_sz - src_lengths
+        if left_pad_target:
+            offsets[:, 1] += tgt_sz - tgt_lengths
+
+        alignments = [
+            alignment + offset
+            for align_idx, offset, src_len, tgt_len in zip(
+                sort_order, offsets, src_lengths, tgt_lengths
+            )
+            for alignment in [samples[align_idx]["alignment"].view(-1, 2)]
+            if check_alignment(alignment, src_len, tgt_len)
+        ]
+
+        if len(alignments) > 0:
+            alignments = torch.cat(alignments, dim=0)
+            align_weights = compute_alignment_weights(alignments)
+
+            batch["alignments"] = alignments
+            batch["align_weights"] = align_weights
+
+    if samples[0].get("constraints", None) is not None:
+        # Collate the packed constraints across the samples, padding to
+        # the length of the longest sample.
+        lens = [sample.get("constraints").size(0) for sample in samples]
+        max_len = max(lens)
+        constraints = torch.zeros((len(samples), max(lens))).long()
+        for i, sample in enumerate(samples):
+            constraints[i, 0 : lens[i]] = samples[i].get("constraints")
+        batch["constraints"] = constraints.index_select(0, sort_order)
+
+    return batch
+
+
+class LanguagePairDataset(FairseqDataset):
+    """
+    A pair of torch.utils.data.Datasets.
+
+    Args:
+        src (torch.utils.data.Dataset): source dataset to wrap
+        src_sizes (List[int]): source sentence lengths
+        src_dict (~fairseq.data.Dictionary): source vocabulary
+        tgt (torch.utils.data.Dataset, optional): target dataset to wrap
+        tgt_sizes (List[int], optional): target sentence lengths
+        tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary
+        left_pad_source (bool, optional): pad source tensors on the left side
+            (default: True).
+        left_pad_target (bool, optional): pad target tensors on the left side
+            (default: False).
+        shuffle (bool, optional): shuffle dataset elements before batching
+            (default: True).
+        input_feeding (bool, optional): create a shifted version of the targets
+            to be passed into the model for teacher forcing (default: True).
+        remove_eos_from_source (bool, optional): if set, removes eos from end
+            of source if it's present (default: False).
+        append_eos_to_target (bool, optional): if set, appends eos to end of
+            target if it's absent (default: False).
+        align_dataset (torch.utils.data.Dataset, optional): dataset
+            containing alignments.
+        constraints (Tensor, optional): 2d tensor with a concatenated, zero-
+            delimited list of constraints for each sentence.
+        append_bos (bool, optional): if set, appends bos to the beginning of
+            source/target sentence.
+        num_buckets (int, optional): if set to a value greater than 0, then
+            batches will be bucketed into the given number of batch shapes.
+        src_lang_id (int, optional): source language ID, if set, the collated batch
+            will contain a field 'src_lang_id' in 'net_input' which indicates the
+            source language of the samples.
+        tgt_lang_id (int, optional): target language ID, if set, the collated batch
+            will contain a field 'tgt_lang_id' which indicates the target language
+             of the samples.
+    """
+
+    def __init__(
+        self,
+        src,
+        src_sizes,
+        src_dict,
+        tgt=None,
+        tgt_sizes=None,
+        tgt_dict=None,
+        left_pad_source=True,
+        left_pad_target=False,
+        shuffle=True,
+        input_feeding=True,
+        remove_eos_from_source=False,
+        append_eos_to_target=False,
+        align_dataset=None,
+        constraints=None,
+        append_bos=False,
+        eos=None,
+        num_buckets=0,
+        src_lang_id=None,
+        tgt_lang_id=None,
+        pad_to_multiple=1,
+    ):
+        if tgt_dict is not None:
+            assert src_dict.pad() == tgt_dict.pad()
+            assert src_dict.eos() == tgt_dict.eos()
+            assert src_dict.unk() == tgt_dict.unk()
+        if tgt is not None:
+            assert len(src) == len(
+                tgt
+            ), "Source and target must contain the same number of examples"
+        self.src = src
+        self.tgt = tgt
+        self.src_sizes = np.array(src_sizes)
+        self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None
+        self.sizes = (
+            np.vstack((self.src_sizes, self.tgt_sizes)).T
+            if self.tgt_sizes is not None
+            else self.src_sizes
+        )
+        self.src_dict = src_dict
+        self.tgt_dict = tgt_dict
+        self.left_pad_source = left_pad_source
+        self.left_pad_target = left_pad_target
+        self.shuffle = shuffle
+        self.input_feeding = input_feeding
+        self.remove_eos_from_source = remove_eos_from_source
+        self.append_eos_to_target = append_eos_to_target
+        self.align_dataset = align_dataset
+        if self.align_dataset is not None:
+            assert (
+                self.tgt_sizes is not None
+            ), "Both source and target needed when alignments are provided"
+        self.constraints = constraints
+        self.append_bos = append_bos
+        self.eos = eos if eos is not None else src_dict.eos()
+        self.src_lang_id = src_lang_id
+        self.tgt_lang_id = tgt_lang_id
+        if num_buckets > 0:
+            from fairseq.data import BucketPadLengthDataset
+
+            self.src = BucketPadLengthDataset(
+                self.src,
+                sizes=self.src_sizes,
+                num_buckets=num_buckets,
+                pad_idx=self.src_dict.pad(),
+                left_pad=self.left_pad_source,
+            )
+            self.src_sizes = self.src.sizes
+            logger.info("bucketing source lengths: {}".format(list(self.src.buckets)))
+            if self.tgt is not None:
+                self.tgt = BucketPadLengthDataset(
+                    self.tgt,
+                    sizes=self.tgt_sizes,
+                    num_buckets=num_buckets,
+                    pad_idx=self.tgt_dict.pad(),
+                    left_pad=self.left_pad_target,
+                )
+                self.tgt_sizes = self.tgt.sizes
+                logger.info(
+                    "bucketing target lengths: {}".format(list(self.tgt.buckets))
+                )
+
+            # determine bucket sizes using self.num_tokens, which will return
+            # the padded lengths (thanks to BucketPadLengthDataset)
+            num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long])
+            self.bucketed_num_tokens = num_tokens(np.arange(len(self.src)))
+            self.buckets = [
+                (None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens)
+            ]
+        else:
+            self.buckets = None
+        self.pad_to_multiple = pad_to_multiple
+
+    def get_batch_shapes(self):
+        return self.buckets
+
+    def __getitem__(self, index):
+        tgt_item = self.tgt[index] if self.tgt is not None else None
+        src_item = self.src[index]
+        # Append EOS to end of tgt sentence if it does not have an EOS and remove
+        # EOS from end of src sentence if it exists. This is useful when we use
+        # use existing datasets for opposite directions i.e., when we want to
+        # use tgt_dataset as src_dataset and vice versa
+        if self.append_eos_to_target:
+            eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos()
+            if self.tgt and self.tgt[index][-1] != eos:
+                tgt_item = torch.cat([self.tgt[index], torch.LongTensor([eos])])
+
+        if self.append_bos:
+            bos = self.tgt_dict.bos() if self.tgt_dict else self.src_dict.bos()
+            if self.tgt and self.tgt[index][0] != bos:
+                tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]])
+
+            bos = self.src_dict.bos()
+            if self.src[index][0] != bos:
+                src_item = torch.cat([torch.LongTensor([bos]), self.src[index]])
+
+        if self.remove_eos_from_source:
+            eos = self.src_dict.eos()
+            if self.src[index][-1] == eos:
+                src_item = self.src[index][:-1]
+
+        example = {
+            "id": index,
+            "source": src_item,
+            "target": tgt_item,
+        }
+        if self.align_dataset is not None:
+            example["alignment"] = self.align_dataset[index]
+        if self.constraints is not None:
+            example["constraints"] = self.constraints[index]
+        return example
+
+    def __len__(self):
+        return len(self.src)
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+
+        Args:
+            samples (List[dict]): samples to collate
+            pad_to_length (dict, optional): a dictionary of
+                {'source': source_pad_to_length, 'target': target_pad_to_length}
+                to indicate the max length to pad to in source and target respectively.
+
+        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 left if *left_pad_source* is ``True``.
+                  - `src_lengths` (LongTensor): 1D Tensor of the unpadded
+                    lengths of each source sentence of shape `(bsz)`
+                  - `prev_output_tokens` (LongTensor): a padded 2D Tensor of
+                    tokens in the target sentence, shifted right by one
+                    position for teacher forcing, of shape `(bsz, tgt_len)`.
+                    This key will not be present if *input_feeding* is
+                    ``False``.  Padding will appear on the left if
+                    *left_pad_target* is ``True``.
+                  - `src_lang_id` (LongTensor): a long Tensor which contains source
+                    language IDs of each sample in the batch
+
+                - `target` (LongTensor): a padded 2D Tensor of tokens in the
+                  target sentence of shape `(bsz, tgt_len)`. Padding will appear
+                  on the left if *left_pad_target* is ``True``.
+                - `tgt_lang_id` (LongTensor): a long Tensor which contains target language
+                   IDs of each sample in the batch
+        """
+        res = collate(
+            samples,
+            pad_idx=self.src_dict.pad(),
+            eos_idx=self.eos,
+            left_pad_source=self.left_pad_source,
+            left_pad_target=self.left_pad_target,
+            input_feeding=self.input_feeding,
+            pad_to_length=pad_to_length,
+            pad_to_multiple=self.pad_to_multiple,
+        )
+        if self.src_lang_id is not None or self.tgt_lang_id is not None:
+            src_tokens = res["net_input"]["src_tokens"]
+            bsz = src_tokens.size(0)
+            if self.src_lang_id is not None:
+                res["net_input"]["src_lang_id"] = (
+                    torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
+                )
+            if self.tgt_lang_id is not None:
+                res["tgt_lang_id"] = (
+                    torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
+                )
+        return res
+
+    def num_tokens(self, index):
+        """Return the number of tokens in a sample. This value is used to
+        enforce ``--max-tokens`` during batching."""
+        return max(
+            self.src_sizes[index],
+            self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
+        )
+
+    def num_tokens_vec(self, indices):
+        """Return the number of tokens for a set of positions defined by indices.
+        This value is used to enforce ``--max-tokens`` during batching."""
+        sizes = self.src_sizes[indices]
+        if self.tgt_sizes is not None:
+            sizes = np.maximum(sizes, self.tgt_sizes[indices])
+        return sizes
+
+    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.src_sizes[index],
+            self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
+        )
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.shuffle:
+            indices = np.random.permutation(len(self)).astype(np.int64)
+        else:
+            indices = np.arange(len(self), dtype=np.int64)
+        if self.buckets is None:
+            # sort by target length, then source length
+            if self.tgt_sizes is not None:
+                indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")]
+            return indices[np.argsort(self.src_sizes[indices], kind="mergesort")]
+        else:
+            # sort by bucketed_num_tokens, which is:
+            #   max(padded_src_len, padded_tgt_len)
+            return indices[
+                np.argsort(self.bucketed_num_tokens[indices], kind="mergesort")
+            ]
+
+    @property
+    def supports_prefetch(self):
+        return getattr(self.src, "supports_prefetch", False) and (
+            getattr(self.tgt, "supports_prefetch", False) or self.tgt is None
+        )
+
+    def prefetch(self, indices):
+        self.src.prefetch(indices)
+        if self.tgt is not None:
+            self.tgt.prefetch(indices)
+        if self.align_dataset is not None:
+            self.align_dataset.prefetch(indices)
+
+    def filter_indices_by_size(self, indices, max_sizes):
+        """Filter a list of sample indices. Remove those that are longer
+            than specified in max_sizes.
+
+        Args:
+            indices (np.array): original array of sample indices
+            max_sizes (int or list[int] or tuple[int]): max sample size,
+                can be defined separately for src and tgt (then list or tuple)
+
+        Returns:
+            np.array: filtered sample array
+            list: list of removed indices
+        """
+        return data_utils.filter_paired_dataset_indices_by_size(
+            self.src_sizes,
+            self.tgt_sizes,
+            indices,
+            max_sizes,
+        )

PyTorch/NLP/new-Transformer/fairseq/data/legacy/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .block_pair_dataset import BlockPairDataset
+from .masked_lm_dataset import MaskedLMDataset
+from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary
+
+
+__all__ = [
+    "BertDictionary",
+    "BlockPairDataset",
+    "MaskedLMDataset",
+    "MaskedLMDictionary",
+]

PyTorch/NLP/new-Transformer/fairseq/data/legacy/block_pair_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import numpy as np
+import torch
+from fairseq.data import FairseqDataset
+
+
+class BlockPairDataset(FairseqDataset):
+    """Break a Dataset of tokens into sentence pair blocks for next sentence
+       prediction as well as masked language model.
+
+       High-level logics are:
+       1. break input tensor to tensor blocks
+       2. pair the blocks with 50% next sentence and 50% random sentence
+       3. return paired blocks as well as related segment labels
+
+    Args:
+        dataset (~torch.utils.data.Dataset): dataset to break into blocks
+        sizes: array of sentence lengths
+        dictionary: dictionary for the task
+        block_size: maximum block size
+        break_mode: mode for breaking copurs into block pairs. currently we support
+            2 modes
+            doc: respect document boundaries and each part of the pair should belong to on document
+            none: don't respect any boundary and cut tokens evenly
+        short_seq_prob: probability for generating shorter block pairs
+        doc_break_size: Size for empty line separating documents. Typically 1 if
+                        the sentences have eos, 0 otherwise.
+    """
+
+    def __init__(
+        self,
+        dataset,
+        dictionary,
+        sizes,
+        block_size,
+        break_mode="doc",
+        short_seq_prob=0.1,
+        doc_break_size=1,
+    ):
+        super().__init__()
+        self.dataset = dataset
+        self.pad = dictionary.pad()
+        self.eos = dictionary.eos()
+        self.cls = dictionary.cls()
+        self.mask = dictionary.mask()
+        self.sep = dictionary.sep()
+        self.break_mode = break_mode
+        self.dictionary = dictionary
+        self.short_seq_prob = short_seq_prob
+        self.block_indices = []
+
+        assert len(dataset) == len(sizes)
+
+        if break_mode == "doc":
+            cur_doc = []
+            for sent_id, sz in enumerate(sizes):
+                assert doc_break_size == 0 or sz != 0, (
+                    "when doc_break_size is non-zero, we expect documents to be"
+                    "separated by a blank line with a single eos."
+                )
+                # empty line as document separator
+                if sz == doc_break_size:
+                    if len(cur_doc) == 0:
+                        continue
+                    self.block_indices.append(cur_doc)
+                    cur_doc = []
+                else:
+                    cur_doc.append(sent_id)
+            max_num_tokens = block_size - 3  # Account for [CLS], [SEP], [SEP]
+            self.sent_pairs = []
+            self.sizes = []
+            for doc_id, doc in enumerate(self.block_indices):
+                self._generate_sentence_pair(doc, doc_id, max_num_tokens, sizes)
+        elif break_mode is None or break_mode == "none":
+            # each block should have half of the block size since we are constructing block pair
+            sent_length = (block_size - 3) // 2
+            total_len = sum(dataset.sizes)
+            length = math.ceil(total_len / sent_length)
+
+            def block_at(i):
+                start = i * sent_length
+                end = min(start + sent_length, total_len)
+                return (start, end)
+
+            sent_indices = np.array([block_at(i) for i in range(length)])
+            sent_sizes = np.array([e - s for s, e in sent_indices])
+            dataset_index = self._sent_to_dataset_index(sent_sizes)
+
+            # pair sentences
+            self._pair_sentences(dataset_index)
+        else:
+            raise ValueError("Invalid break_mode: " + break_mode)
+
+    def _pair_sentences(self, dataset_index):
+        """
+        Give a list of evenly cut blocks/sentences, pair these sentences with 50%
+        consecutive sentences and 50% random sentences.
+        This is used for none break mode
+        """
+        # pair sentences
+        for sent_id, sent in enumerate(dataset_index):
+            next_sent_label = (
+                1 if np.random.rand() > 0.5 and sent_id != len(dataset_index) - 1 else 0
+            )
+            if next_sent_label:
+                next_sent = dataset_index[sent_id + 1]
+            else:
+                next_sent = dataset_index[
+                    self._skip_sampling(len(dataset_index), [sent_id, sent_id + 1])
+                ]
+            self.sent_pairs.append((sent, next_sent, next_sent_label))
+
+            # The current blocks don't include the special tokens but the
+            # sizes already account for this
+            self.sizes.append(3 + sent[3] + next_sent[3])
+
+    def _sent_to_dataset_index(self, sent_sizes):
+        """
+        Build index mapping block indices to the underlying dataset indices
+        """
+        dataset_index = []
+        ds_idx, ds_remaining = -1, 0
+        for to_consume in sent_sizes:
+            sent_size = to_consume
+            if ds_remaining == 0:
+                ds_idx += 1
+                ds_remaining = sent_sizes[ds_idx]
+            start_ds_idx = ds_idx
+            start_offset = sent_sizes[ds_idx] - ds_remaining
+            while to_consume > ds_remaining:
+                to_consume -= ds_remaining
+                ds_idx += 1
+                ds_remaining = sent_sizes[ds_idx]
+            ds_remaining -= to_consume
+            dataset_index.append(
+                (
+                    start_ds_idx,  # starting index in dataset
+                    start_offset,  # starting offset within starting index
+                    ds_idx,  # ending index in dataset
+                    sent_size,  # sentence length
+                )
+            )
+        assert ds_remaining == 0
+        assert ds_idx == len(self.dataset) - 1
+        return dataset_index
+
+    def _generate_sentence_pair(self, doc, doc_id, max_num_tokens, sizes):
+        """
+        Go through a single document and genrate sentence paris from it
+        """
+        current_chunk = []
+        current_length = 0
+        curr = 0
+        # To provide more randomness, we decrease target seq length for parts of
+        # samples (10% by default). Note that max_num_tokens is the hard threshold
+        # for batching and will never be changed.
+        target_seq_length = max_num_tokens
+        if np.random.random() < self.short_seq_prob:
+            target_seq_length = np.random.randint(2, max_num_tokens)
+        # loop through all sentences in document
+        while curr < len(doc):
+            sent_id = doc[curr]
+            current_chunk.append(sent_id)
+            current_length = sum(sizes[current_chunk])
+            # split chunk and generate pair when exceed target_seq_length or
+            # finish the loop
+            if curr == len(doc) - 1 or current_length >= target_seq_length:
+                # split the chunk into 2 parts
+                a_end = 1
+                if len(current_chunk) > 2:
+                    a_end = np.random.randint(1, len(current_chunk) - 1)
+                sent_a = current_chunk[:a_end]
+                len_a = sum(sizes[sent_a])
+                # generate next sentence label, note that if there is only 1 sentence
+                # in current chunk, label is always 0
+                next_sent_label = (
+                    1 if np.random.rand() > 0.5 and len(current_chunk) != 1 else 0
+                )
+                if not next_sent_label:
+                    # if next sentence label is 0, sample sent_b from a random doc
+                    target_b_length = target_seq_length - len_a
+                    rand_doc_id = self._skip_sampling(len(self.block_indices), [doc_id])
+                    random_doc = self.block_indices[rand_doc_id]
+                    random_start = np.random.randint(0, len(random_doc))
+                    sent_b = []
+                    len_b = 0
+                    for j in range(random_start, len(random_doc)):
+                        sent_b.append(random_doc[j])
+                        len_b = sum(sizes[sent_b])
+                        if len_b >= target_b_length:
+                            break
+                    # return the second part of the chunk since it's not used
+                    num_unused_segments = len(current_chunk) - a_end
+                    curr -= num_unused_segments
+                else:
+                    # if next sentence label is 1, use the second part of chunk as sent_B
+                    sent_b = current_chunk[a_end:]
+                    len_b = sum(sizes[sent_b])
+                # currently sent_a and sent_B may be longer than max_num_tokens,
+                # truncate them and return block idx and offsets for them
+                sent_a, sent_b = self._truncate_sentences(
+                    sent_a, sent_b, max_num_tokens
+                )
+                self.sent_pairs.append((sent_a, sent_b, next_sent_label))
+                self.sizes.append(3 + sent_a[3] + sent_b[3])
+                current_chunk = []
+            curr += 1
+
+    def _skip_sampling(self, total, skip_ids):
+        """
+        Generate a random integer which is not in skip_ids. Sample range is [0, total)
+        TODO: ids in skip_ids should be consecutive, we can extend it to more generic version later
+        """
+        rand_id = np.random.randint(total - len(skip_ids))
+        return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)
+
+    def _truncate_sentences(self, sent_a, sent_b, max_num_tokens):
+        """
+        Trancate a pair of sentence to limit total length under max_num_tokens
+        Logics:
+            1. Truncate longer sentence
+            2. Tokens to be truncated could be at the beginning or the end of the sentnce
+        Returns:
+            Truncated sentences represented by dataset idx
+        """
+        len_a, len_b = sum(self.dataset.sizes[sent_a]), sum(self.dataset.sizes[sent_b])
+        front_cut_a = front_cut_b = end_cut_a = end_cut_b = 0
+
+        while True:
+            total_length = (
+                len_a + len_b - front_cut_a - front_cut_b - end_cut_a - end_cut_b
+            )
+            if total_length <= max_num_tokens:
+                break
+
+            if len_a - front_cut_a - end_cut_a > len_b - front_cut_b - end_cut_b:
+                if np.random.rand() < 0.5:
+                    front_cut_a += 1
+                else:
+                    end_cut_a += 1
+            else:
+                if np.random.rand() < 0.5:
+                    front_cut_b += 1
+                else:
+                    end_cut_b += 1
+
+        # calculate ds indices as well as offsets and return
+        truncated_sent_a = self._cut_sentence(sent_a, front_cut_a, end_cut_a)
+        truncated_sent_b = self._cut_sentence(sent_b, front_cut_b, end_cut_b)
+        return truncated_sent_a, truncated_sent_b
+
+    def _cut_sentence(self, sent, front_cut, end_cut):
+        """
+        Cut a sentence based on the numbers of tokens to be cut from beginning and end
+        Represent the sentence as dataset idx and return
+        """
+        start_ds_idx, end_ds_idx, offset = sent[0], sent[-1], 0
+        target_len = sum(self.dataset.sizes[sent]) - front_cut - end_cut
+        while front_cut > 0:
+            if self.dataset.sizes[start_ds_idx] > front_cut:
+                offset += front_cut
+                break
+            else:
+                front_cut -= self.dataset.sizes[start_ds_idx]
+                start_ds_idx += 1
+        while end_cut > 0:
+            if self.dataset.sizes[end_ds_idx] > end_cut:
+                break
+            else:
+                end_cut -= self.dataset.sizes[end_ds_idx]
+                end_ds_idx -= 1
+        return start_ds_idx, offset, end_ds_idx, target_len
+
+    def _fetch_block(self, start_ds_idx, offset, end_ds_idx, length):
+        """
+        Fetch a block of tokens based on its dataset idx
+        """
+        buffer = torch.cat(
+            [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
+        )
+        s, e = offset, offset + length
+        return buffer[s:e]
+
+    def __getitem__(self, index):
+        block1, block2, next_sent_label = self.sent_pairs[index]
+        block1 = self._fetch_block(*block1)
+        block2 = self._fetch_block(*block2)
+        return block1, block2, next_sent_label
+
+    def __len__(self):
+        return len(self.sizes)
+
+    @property
+    def supports_prefetch(self):
+        return getattr(self.dataset, "supports_prefetch", False)
+
+    def prefetch(self, indices):
+        prefetch_idx = set()
+        for index in indices:
+            for block1, block2, _ in [self.sent_pairs[index]]:
+                for ds_idx in range(block1[0], block1[2] + 1):
+                    prefetch_idx.add(ds_idx)
+                for ds_idx in range(block2[0], block2[2] + 1):
+                    prefetch_idx.add(ds_idx)
+        self.dataset.prefetch(prefetch_idx)

PyTorch/NLP/new-Transformer/fairseq/data/legacy/masked_lm_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch
+from fairseq.data import Dictionary, FairseqDataset, data_utils
+from fairseq.data.concat_dataset import ConcatDataset
+from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
+from fairseq.data.token_block_dataset import TokenBlockDataset
+
+
+class MaskedLMDataset(FairseqDataset):
+    """
+    A wrapper Dataset for masked language modelling. The dataset
+    wraps around TokenBlockDataset or BlockedPairDataset and creates a batch
+    where the input blocks are masked according to the specified masking
+    probability. Additionally the batch can also contain sentence level targets
+    if this is specified.
+
+    Args:
+        dataset: Dataset which generates blocks of data. Only BlockPairDataset
+            and TokenBlockDataset are supported.
+        sizes: Sentence lengths
+        vocab: Dictionary with the vocabulary and special tokens.
+        pad_idx: Id of padding token in dictionary
+        mask_idx: Id of mask token in dictionary
+        classif_token_idx: Id of classification token in dictionary. This is the
+            token associated with the sentence embedding (Eg: CLS for BERT)
+        sep_token_idx: Id of separator token in dictionary
+            (Eg: SEP in BERT)
+        seed: Seed for random number generator for reproducibility.
+        shuffle: Shuffle the elements before batching.
+        has_pairs: Specifies whether the underlying dataset
+            generates a pair of blocks along with a sentence_target or not.
+            Setting it to True assumes that the underlying dataset generates a
+            label for the pair of sentences which is surfaced as
+            sentence_target. The default value assumes a single block with no
+            sentence target.
+        segment_id: An optional segment id for filling in the segment labels
+            when we are in the single block setting (Eg: XLM). Default is 0.
+        masking_ratio: specifies what percentage of the blocks should be masked.
+        masking_prob: specifies the probability of a given token being
+            replaced with the "MASK" token.
+        random_token_prob: specifies the probability of a given token being
+            replaced by a random token from the vocabulary.
+    """
+
+    def __init__(
+        self,
+        dataset: FairseqDataset,
+        sizes: np.ndarray,
+        vocab: Dictionary,
+        pad_idx: int,
+        mask_idx: int,
+        classif_token_idx: int,
+        sep_token_idx: int,
+        seed: int = 1,
+        shuffle: bool = True,
+        has_pairs: bool = True,
+        segment_id: int = 0,
+        masking_ratio: float = 0.15,
+        masking_prob: float = 0.8,
+        random_token_prob: float = 0.1,
+    ):
+        # Make sure the input datasets are the ones supported
+        assert (
+            isinstance(dataset, TokenBlockDataset)
+            or isinstance(dataset, BlockPairDataset)
+            or isinstance(dataset, ConcatDataset)
+        ), (
+            "MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or "
+            "ConcatDataset"
+        )
+
+        self.dataset = dataset
+        self.sizes = np.array(sizes)
+        self.vocab = vocab
+        self.pad_idx = pad_idx
+        self.mask_idx = mask_idx
+        self.classif_token_idx = classif_token_idx
+        self.sep_token_idx = sep_token_idx
+        self.shuffle = shuffle
+        self.seed = seed
+        self.has_pairs = has_pairs
+        self.segment_id = segment_id
+        self.masking_ratio = masking_ratio
+        self.masking_prob = masking_prob
+        self.random_token_prob = random_token_prob
+
+        # If we have only one block then sizes needs to be updated to include
+        # the classification token
+        if not has_pairs:
+            self.sizes = self.sizes + 1
+
+    def __getitem__(self, index: int):
+        # if has_pairs, then expect 2 blocks and a sentence target
+        if self.has_pairs:
+            (block_one, block_two, sentence_target) = self.dataset[index]
+        else:
+            block_one = self.dataset[index]
+
+        return {
+            "id": index,
+            "block_one": block_one,
+            "block_two": block_two if self.has_pairs else None,
+            "sentence_target": sentence_target if self.has_pairs else None,
+        }
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def _mask_block(
+        self,
+        sentence: np.ndarray,
+        mask_idx: int,
+        pad_idx: int,
+        dictionary_token_range: Tuple,
+    ):
+        """
+        Mask tokens for Masked Language Model training
+        Samples mask_ratio tokens that will be predicted by LM.
+
+        Note:This function may not be efficient enough since we had multiple
+        conversions between np and torch, we can replace them with torch
+        operators later.
+
+        Args:
+            sentence: 1d tensor to be masked
+            mask_idx: index to use for masking the sentence
+            pad_idx: index to use for masking the target for tokens we aren't
+                predicting
+            dictionary_token_range: range of indices in dictionary which can
+                be used for random word replacement
+                (e.g. without special characters)
+        Return:
+            masked_sent: masked sentence
+            target: target with words which we are not predicting replaced
+                by pad_idx
+        """
+        masked_sent = np.copy(sentence)
+        sent_length = len(sentence)
+        mask_num = math.ceil(sent_length * self.masking_ratio)
+        mask = np.random.choice(sent_length, mask_num, replace=False)
+        target = np.copy(sentence)
+
+        for i in range(sent_length):
+            if i in mask:
+                rand = np.random.random()
+
+                # replace with mask if probability is less than masking_prob
+                # (Eg: 0.8)
+                if rand < self.masking_prob:
+                    masked_sent[i] = mask_idx
+
+                # replace with random token if probability is less than
+                # masking_prob + random_token_prob (Eg: 0.9)
+                elif rand < (self.masking_prob + self.random_token_prob):
+                    # sample random token from dictionary
+                    masked_sent[i] = np.random.randint(
+                        dictionary_token_range[0], dictionary_token_range[1]
+                    )
+            else:
+                target[i] = pad_idx
+
+        return masked_sent, target
+
+    def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int):
+        """
+        Does the heavy lifting for creating a batch from the input list of
+        examples. The logic is as follows:
+            1. Mask the input blocks. In case has_pair is True then we have 2
+               blocks to mask.
+            2. Prepend the first masked block tensor with the special token
+               used as sentence embedding. Eg: CLS in BERT. This happens
+               irrespective of the value of has_pair.
+            3. If has_pair is True, then append the first masked block with the
+               special separator token (eg: SEP for BERT) and compute segment
+               label accordingly. In this case, also append the second masked
+               block with this special separator token and compute its segment
+               label.
+            4. For the targets tensor, prepend and append with padding index
+               accordingly.
+            5. Concatenate all tensors.
+        """
+        if len(samples) == 0:
+            return {}
+        # To ensure determinism, we reset the state of the PRNG after every
+        # batch based on the seed and the first id of the batch. This ensures
+        # that across epochs we get the same mask for the same example. This
+        # is needed for reproducibility and is how BERT does masking
+        # TODO: Can we add deteminism without this constraint?
+        with data_utils.numpy_seed(self.seed + samples[0]["id"]):
+            for s in samples:
+
+                # token range is needed for replacing with random token during
+                # masking
+                token_range = (self.vocab.nspecial, len(self.vocab))
+
+                # mask according to specified probabilities.
+                masked_blk_one, masked_tgt_one = self._mask_block(
+                    s["block_one"],
+                    self.mask_idx,
+                    self.pad_idx,
+                    token_range,
+                )
+
+                tokens = np.concatenate([[self.classif_token_idx], masked_blk_one])
+                targets = np.concatenate([[self.pad_idx], masked_tgt_one])
+                segments = np.ones(len(tokens)) * self.segment_id
+
+                # if has_pairs is True then we need to add the SEP token to both
+                # the blocks after masking and re-compute segments based on the new
+                # lengths.
+                if self.has_pairs:
+                    tokens_one = np.concatenate([tokens, [self.sep_token_idx]])
+                    targets_one = np.concatenate([targets, [self.pad_idx]])
+
+                    masked_blk_two, masked_tgt_two = self._mask_block(
+                        s["block_two"], self.mask_idx, self.pad_idx, token_range
+                    )
+                    tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]])
+                    targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]])
+
+                    # block + 1 sep + 1 special (CLS)
+                    segments_one = np.zeros(len(tokens_one))
+                    # block + 1 sep
+                    segments_two = np.ones(len(tokens_two))
+
+                    tokens = np.concatenate([tokens_one, tokens_two])
+                    targets = np.concatenate([targets_one, targets_two])
+                    segments = np.concatenate([segments_one, segments_two])
+
+                s["source"] = torch.LongTensor(tokens)
+                s["segment_labels"] = torch.LongTensor(segments)
+                s["lm_target"] = torch.LongTensor(targets)
+
+        def merge(key):
+            return data_utils.collate_tokens(
+                [s[key] for s in samples], pad_idx, eos_idx, left_pad=False
+            )
+
+        return {
+            "id": torch.LongTensor([s["id"] for s in samples]),
+            "ntokens": sum(len(s["source"]) for s in samples),
+            "net_input": {
+                "src_tokens": merge("source"),
+                "segment_labels": merge("segment_labels"),
+            },
+            "lm_target": merge("lm_target"),
+            "sentence_target": torch.LongTensor([s["sentence_target"] for s in samples])
+            if self.has_pairs
+            else None,
+            "nsentences": len(samples),
+        }
+
+    def collater(self, samples: List[Dict]):
+        """Merge a list of samples to form a mini-batch.
+
+        Args:
+            samples (List[dict]): samples to collate
+
+        Returns:
+            dict: a mini-batch of data
+        """
+        return self._collate(samples, self.vocab.pad(), self.vocab.eos())
+
+    def num_tokens(self, index: int):
+        """
+        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: int):
+        """
+        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:
+            return 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)

PyTorch/NLP/new-Transformer/fairseq/data/legacy/masked_lm_dictionary.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from fairseq.data import Dictionary
+
+
+class MaskedLMDictionary(Dictionary):
+    """
+    Dictionary for Masked Language Modelling tasks. This extends Dictionary by
+    adding the mask symbol.
+    """
+
+    def __init__(
+        self,
+        pad="<pad>",
+        eos="</s>",
+        unk="<unk>",
+        mask="<mask>",
+    ):
+        super().__init__(pad=pad, eos=eos, unk=unk)
+        self.mask_word = mask
+        self.mask_index = self.add_symbol(mask)
+        self.nspecial = len(self.symbols)
+
+    def mask(self):
+        """Helper to get index of mask symbol"""
+        return self.mask_index
+
+
+class BertDictionary(MaskedLMDictionary):
+    """
+    Dictionary for BERT task. This extends MaskedLMDictionary by adding support
+    for cls and sep symbols.
+    """
+
+    def __init__(
+        self,
+        pad="<pad>",
+        eos="</s>",
+        unk="<unk>",
+        mask="<mask>",
+        cls="<cls>",
+        sep="<sep>",
+    ):
+        super().__init__(pad=pad, eos=eos, unk=unk, mask=mask)
+        self.cls_word = cls
+        self.sep_word = sep
+        self.cls_index = self.add_symbol(cls)
+        self.sep_index = self.add_symbol(sep)
+        self.nspecial = len(self.symbols)
+
+    def cls(self):
+        """Helper to get index of cls symbol"""
+        return self.cls_index
+
+    def sep(self):
+        """Helper to get index of sep symbol"""
+        return self.sep_index

PyTorch/NLP/new-Transformer/fairseq/data/list_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from . import BaseWrapperDataset
+
+
+class ListDataset(BaseWrapperDataset):
+    def __init__(self, dataset, sizes=None):
+        super().__init__(dataset)
+        self._sizes = sizes
+
+    def __iter__(self):
+        for x in self.dataset:
+            yield x
+
+    def collater(self, samples):
+        return samples
+
+    @property
+    def sizes(self):
+        return self._sizes
+
+    def num_tokens(self, index):
+        return self.sizes[index]
+
+    def size(self, index):
+        return self.sizes[index]
+
+    def set_epoch(self, epoch):
+        pass

PyTorch/NLP/new-Transformer/fairseq/data/lm_context_window_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from typing import Dict
+
+from fairseq.data.monolingual_dataset import MonolingualDataset
+
+from . import FairseqDataset
+
+
+class LMContextWindowDataset(FairseqDataset):
+    """
+    Wraps a MonolingualDataset and provides more context for evaluation.
+
+    Each item in the new dataset will have a maximum size of
+    ``tokens_per_sample + context_window``.
+
+    Args:
+        dataset: dataset to wrap
+        tokens_per_sample (int): the max number of tokens in each dataset item
+        context_window (int): the number of accumulated tokens to add to each
+            dataset item
+        pad_idx (int): padding symbol
+    """
+
+    def __init__(
+        self,
+        dataset: MonolingualDataset,
+        tokens_per_sample: int,
+        context_window: int,
+        pad_idx: int,
+    ):
+        assert context_window > 0
+        self.dataset = dataset
+        self.tokens_per_sample = tokens_per_sample
+        self.context_window = context_window
+        self.pad_idx = pad_idx
+        self.prev_tokens = np.empty([0])
+
+    def __getitem__(self, index):
+        return self.dataset[index]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def collater(self, samples) -> Dict:
+        sample = self.dataset.collater(samples)
+
+        pad = self.pad_idx
+        max_sample_len = self.tokens_per_sample + self.context_window
+
+        bsz, tsz = sample["net_input"]["src_tokens"].shape
+        start_idxs = [0] * bsz
+        toks = sample["net_input"]["src_tokens"]
+        lengths = sample["net_input"]["src_lengths"]
+        tgt = sample["target"]
+        new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64)
+        new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64)
+        sample_lens = toks.ne(pad).long().sum(dim=1).cpu()
+        for i in range(bsz):
+            sample_len = sample_lens[i]
+            extra = len(self.prev_tokens) + sample_len - max_sample_len
+            if extra > 0:
+                self.prev_tokens = self.prev_tokens[extra:]
+            pads = np.full(self.context_window - len(self.prev_tokens), pad)
+            new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads])
+            new_tgt[
+                i, len(self.prev_tokens) : len(self.prev_tokens) + len(tgt[i])
+            ] = tgt[i]
+            start_idxs[i] = len(self.prev_tokens)
+            lengths[i] += len(self.prev_tokens)
+            self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window :]
+        sample["net_input"]["src_tokens"] = torch.from_numpy(new_toks)
+        sample["target"] = torch.from_numpy(new_tgt)
+        sample["start_indices"] = start_idxs
+        return sample
+
+    def num_tokens(self, index):
+        return self.dataset.num_tokens(index)
+
+    def size(self, index):
+        return self.dataset.size(index)
+
+    def ordered_indices(self):
+        # NOTE we don't shuffle the data to retain access to the previous dataset elements
+        return np.arange(len(self.dataset))
+
+    @property
+    def supports_prefetch(self):
+        return getattr(self.dataset, "supports_prefetch", False)
+
+    def prefetch(self, indices):
+        return self.dataset.prefetch(indices)

PyTorch/NLP/new-Transformer/fairseq/data/lru_cache_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+
+from . import BaseWrapperDataset
+
+
+class LRUCacheDataset(BaseWrapperDataset):
+    def __init__(self, dataset, token=None):
+        super().__init__(dataset)
+
+    @lru_cache(maxsize=8)
+    def __getitem__(self, index):
+        return self.dataset[index]
+
+    @lru_cache(maxsize=8)
+    def collater(self, samples):
+        return self.dataset.collater(samples)

PyTorch/NLP/new-Transformer/fairseq/data/mask_tokens_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+
+import numpy as np
+import torch
+from fairseq.data import Dictionary, data_utils
+
+from . import BaseWrapperDataset, LRUCacheDataset
+
+
+class MaskTokensDataset(BaseWrapperDataset):
+    """
+    A wrapper Dataset for masked language modeling.
+
+    Input items are masked according to the specified masking probability.
+
+    Args:
+        dataset: Dataset to wrap.
+        sizes: Sentence lengths
+        vocab: Dictionary with the vocabulary and special tokens.
+        pad_idx: Id of pad token in vocab
+        mask_idx: Id of mask token in vocab
+        return_masked_tokens: controls whether to return the non-masked tokens
+            (the default) or to return a tensor with the original masked token
+            IDs (and *pad_idx* elsewhere). The latter is useful as targets for
+            masked LM training.
+        seed: Seed for random number generator for reproducibility.
+        mask_prob: probability of replacing a token with *mask_idx*.
+        leave_unmasked_prob: probability that a masked token is unmasked.
+        random_token_prob: probability of replacing a masked token with a
+            random token from the vocabulary.
+        freq_weighted_replacement: sample random replacement words based on
+            word frequencies in the vocab.
+        mask_whole_words: only mask whole words. This should be a byte mask
+            over vocab indices, indicating whether it is the beginning of a
+            word. We will extend any mask to encompass the whole word.
+        bpe: BPE to use for whole-word masking.
+        mask_multiple_length : repeat each mask index multiple times. Default
+            value is 1.
+        mask_stdev : standard deviation of masks distribution in case of
+            multiple masking. Default value is 0.
+    """
+
+    @classmethod
+    def apply_mask(cls, dataset: torch.utils.data.Dataset, *args, **kwargs):
+        """Return the source and target datasets for masked LM training."""
+        dataset = LRUCacheDataset(dataset)
+        return (
+            LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=False)),
+            LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=True)),
+        )
+
+    def __init__(
+        self,
+        dataset: torch.utils.data.Dataset,
+        vocab: Dictionary,
+        pad_idx: int,
+        mask_idx: int,
+        return_masked_tokens: bool = False,
+        seed: int = 1,
+        mask_prob: float = 0.15,
+        leave_unmasked_prob: float = 0.1,
+        random_token_prob: float = 0.1,
+        freq_weighted_replacement: bool = False,
+        mask_whole_words: torch.Tensor = None,
+        mask_multiple_length: int = 1,
+        mask_stdev: float = 0.0,
+    ):
+        assert 0.0 < mask_prob < 1.0
+        assert 0.0 <= random_token_prob <= 1.0
+        assert 0.0 <= leave_unmasked_prob <= 1.0
+        assert random_token_prob + leave_unmasked_prob <= 1.0
+        assert mask_multiple_length >= 1
+        assert mask_stdev >= 0.0
+
+        self.dataset = dataset
+        self.vocab = vocab
+        self.pad_idx = pad_idx
+        self.mask_idx = mask_idx
+        self.return_masked_tokens = return_masked_tokens
+        self.seed = seed
+        self.mask_prob = mask_prob
+        self.leave_unmasked_prob = leave_unmasked_prob
+        self.random_token_prob = random_token_prob
+        self.mask_whole_words = mask_whole_words
+        self.mask_multiple_length = mask_multiple_length
+        self.mask_stdev = mask_stdev
+
+        if random_token_prob > 0.0:
+            if freq_weighted_replacement:
+                weights = np.array(self.vocab.count)
+            else:
+                weights = np.ones(len(self.vocab))
+            weights[: self.vocab.nspecial] = 0
+            self.weights = weights / weights.sum()
+
+        self.epoch = 0
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return True  # only the noise changes, not item sizes
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    def __getitem__(self, index: int):
+        return self.__getitem_cached__(self.seed, self.epoch, index)
+
+    @lru_cache(maxsize=8)
+    def __getitem_cached__(self, seed: int, epoch: int, index: int):
+        with data_utils.numpy_seed(self.seed, self.epoch, index):
+            item = self.dataset[index]
+            sz = len(item)
+
+            assert (
+                self.mask_idx not in item
+            ), "Dataset contains mask_idx (={}), this is not expected!".format(
+                self.mask_idx,
+            )
+
+            if self.mask_whole_words is not None:
+                word_begins_mask = self.mask_whole_words.gather(0, item)
+                word_begins_idx = word_begins_mask.nonzero().view(-1)
+                sz = len(word_begins_idx)
+                words = np.split(word_begins_mask, word_begins_idx)[1:]
+                assert len(words) == sz
+                word_lens = list(map(len, words))
+
+            # decide elements to mask
+            mask = np.full(sz, False)
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                self.mask_prob * sz / float(self.mask_multiple_length)
+                + np.random.rand()
+            )
+
+            # multiple masking as described in the vq-wav2vec paper (https://arxiv.org/abs/1910.05453)
+            mask_idc = np.random.choice(sz, num_mask, replace=False)
+            if self.mask_stdev > 0.0:
+                lengths = np.random.normal(
+                    self.mask_multiple_length, self.mask_stdev, size=num_mask
+                )
+                lengths = [max(0, int(round(x))) for x in lengths]
+                mask_idc = np.asarray(
+                    [
+                        mask_idc[j] + offset
+                        for j in range(len(mask_idc))
+                        for offset in range(lengths[j])
+                    ],
+                    dtype=np.int64,
+                )
+            else:
+                mask_idc = np.concatenate(
+                    [mask_idc + i for i in range(self.mask_multiple_length)]
+                )
+            mask_idc = mask_idc[mask_idc < len(mask)]
+            try:
+                mask[mask_idc] = True
+            except:  # something wrong
+                print(
+                    "Assigning mask indexes {} to mask {} failed!".format(
+                        mask_idc, mask
+                    )
+                )
+                raise
+
+            if self.return_masked_tokens:
+                # exit early if we're just returning the masked tokens
+                # (i.e., the targets for masked LM training)
+                if self.mask_whole_words is not None:
+                    mask = np.repeat(mask, word_lens)
+                new_item = np.full(len(mask), self.pad_idx)
+                new_item[mask] = item[torch.from_numpy(mask.astype(np.uint8)) == 1]
+                return torch.from_numpy(new_item)
+
+            # decide unmasking and random replacement
+            rand_or_unmask_prob = self.random_token_prob + self.leave_unmasked_prob
+            if rand_or_unmask_prob > 0.0:
+                rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob)
+                if self.random_token_prob == 0.0:
+                    unmask = rand_or_unmask
+                    rand_mask = None
+                elif self.leave_unmasked_prob == 0.0:
+                    unmask = None
+                    rand_mask = rand_or_unmask
+                else:
+                    unmask_prob = self.leave_unmasked_prob / rand_or_unmask_prob
+                    decision = np.random.rand(sz) < unmask_prob
+                    unmask = rand_or_unmask & decision
+                    rand_mask = rand_or_unmask & (~decision)
+            else:
+                unmask = rand_mask = None
+
+            if unmask is not None:
+                mask = mask ^ unmask
+
+            if self.mask_whole_words is not None:
+                mask = np.repeat(mask, word_lens)
+
+            new_item = np.copy(item)
+            new_item[mask] = self.mask_idx
+            if rand_mask is not None:
+                num_rand = rand_mask.sum()
+                if num_rand > 0:
+                    if self.mask_whole_words is not None:
+                        rand_mask = np.repeat(rand_mask, word_lens)
+                        num_rand = rand_mask.sum()
+
+                    new_item[rand_mask] = np.random.choice(
+                        len(self.vocab),
+                        num_rand,
+                        p=self.weights,
+                    )
+
+            return torch.from_numpy(new_item)

PyTorch/NLP/new-Transformer/fairseq/data/monolingual_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+from . import FairseqDataset, data_utils
+
+
+def collate(samples, pad_idx, eos_idx, fixed_pad_length=None, pad_to_bsz=None):
+    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,
+                        pad_to_length=fixed_pad_length,
+                        pad_to_bsz=pad_to_bsz,
+                    )
+                )
+            return res
+        else:
+            return data_utils.collate_tokens(
+                [s[key] for s in samples],
+                pad_idx,
+                eos_idx,
+                left_pad=False,
+                pad_to_length=fixed_pad_length,
+                pad_to_bsz=pad_to_bsz,
+            )
+
+    src_tokens = merge("source")
+    if samples[0]["target"] is not None:
+        is_target_list = isinstance(samples[0]["target"], list)
+        target = merge("target", is_target_list)
+    else:
+        target = src_tokens
+
+    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": src_tokens,
+            "src_lengths": torch.LongTensor([s["source"].numel() for s in samples]),
+        },
+        "target": target,
+    }
+
+
+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=None,
+        add_eos_for_other_targets=False,
+        shuffle=False,
+        targets=None,
+        add_bos_token=False,
+        fixed_pad_length=None,
+        pad_to_bsz=None,
+        src_lang_idx=None,
+        tgt_lang_idx=None,
+    ):
+        self.dataset = dataset
+        self.sizes = np.array(sizes)
+        self.vocab = src_vocab
+        self.tgt_vocab = tgt_vocab or src_vocab
+        self.add_eos_for_other_targets = add_eos_for_other_targets
+        self.shuffle = shuffle
+        self.add_bos_token = add_bos_token
+        self.fixed_pad_length = fixed_pad_length
+        self.pad_to_bsz = pad_to_bsz
+        self.src_lang_idx = src_lang_idx
+        self.tgt_lang_idx = tgt_lang_idx
+
+        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):
+        if self.targets is not None:
+            # *future_target* is the original sentence
+            # *source* is shifted right by 1 (maybe left-padded with eos)
+            # *past_target* is shifted right by 2 (left-padded as needed)
+            #
+            # Left-to-right language models should condition on *source* and
+            # predict *future_target*.
+            # Right-to-left language models should condition on *source* and
+            # predict *past_target*.
+            source, future_target, past_target = self.dataset[index]
+            source, target = self._make_source_target(
+                source, future_target, past_target
+            )
+        else:
+            source = self.dataset[index]
+            target = None
+        source, target = self._maybe_add_bos(source, 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 _maybe_add_bos(self, source, target):
+        if self.add_bos_token:
+            source = torch.cat([source.new([self.vocab.bos()]), source])
+            if target is not None:
+                target = torch.cat([target.new([self.tgt_vocab.bos()]), target])
+        return source, target
+
+    def num_tokens_vec(self, indices):
+        """Return the number of tokens for a set of positions defined by indices.
+        This value is used to enforce ``--max-tokens`` during batching."""
+        return self.sizes[indices]
+
+    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(),
+            self.fixed_pad_length,
+            self.pad_to_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)

PyTorch/NLP/new-Transformer/fairseq/data/multi_corpus_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import time
+from collections import OrderedDict
+from typing import Dict, List, Optional
+
+import numpy as np
+from fairseq.data import data_utils
+
+from . import FairseqDataset
+
+logger = logging.getLogger(__name__)
+
+
+class MultiCorpusDataset(FairseqDataset):
+    """
+    Stores multiple instances of FairseqDataset together.
+    Unless batch_sample=True, requires each instance
+    to be the same dataset, as the collate method needs to work on batches with
+    samples from each dataset.
+
+    Allows specifying a distribution over the datasets to use. Note that unlike
+    MultiCorpusSampledDataset, this distribution allows sampling for each item,
+    rather than on a batch level. Note that datasets with sampling probabilty
+    of 0 will be skipped.
+
+    Each time ordered_indices() is called, a new sample is generated with
+    the specified distribution.
+
+    Args:
+        datasets: a OrderedDict of FairseqDataset instances.
+        distribution: a List containing the probability of getting an utterance from
+                        corresponding dataset
+        seed: random seed for sampling the datsets
+        sort_indices: if true, will sort the ordered indices by size
+        batch_sample: if true, will ensure each batch is from a single dataset
+    """
+
+    def __init__(
+        self,
+        datasets: Dict[str, FairseqDataset],
+        distribution: List[float],
+        seed: int,
+        sort_indices: bool = False,
+        batch_sample: bool = False,
+        distributed_rank: Optional[int] = None,
+    ):
+        super().__init__()
+        assert isinstance(datasets, OrderedDict)
+        assert len(datasets) == len(distribution)
+        assert sum(distribution) == 1
+        self.datasets = datasets
+        self.distribution = distribution
+        self.seed = seed
+        self.sort_indices = sort_indices
+        self.batch_sample = batch_sample
+        self.distributed_rank = distributed_rank
+
+        # Avoid repeated conversions to list later
+        self.dataset_list = list(datasets.values())
+        self.total_num_instances = 0
+
+        first_dataset = self.dataset_list[0]
+
+        self.num_instances_per_dataset = []
+        self.dataset_offsets = []
+        for i, dataset in enumerate(self.dataset_list):
+            assert isinstance(dataset, FairseqDataset)
+            assert type(dataset) is type(first_dataset)
+            self.num_instances_per_dataset.append(
+                0 if self.distribution[i] == 0 else len(dataset)
+            )
+            self.dataset_offsets.append(self.total_num_instances)
+            self.total_num_instances += self.num_instances_per_dataset[i]
+
+    def ordered_indices(self):
+        start = time.time()
+        with data_utils.numpy_seed(self.seed, self.epoch):
+            logger.info(
+                f"sampling new dataset with seed {self.seed} epoch {self.epoch}"
+            )
+            sampled_indices = []
+            num_selected_instances = 0
+
+            # For each dataset i, sample self.distribution[i] * self.total_num_instances
+            for i, key in enumerate(self.datasets):
+                if self.distribution[i] == 0:
+                    # skip dataset if sampling probability is 0
+                    continue
+
+                if i < len(self.datasets) - 1:
+                    num_instances = int(self.distribution[i] * self.total_num_instances)
+                    high = self.dataset_offsets[i + 1]
+                else:
+                    num_instances = self.total_num_instances - num_selected_instances
+                    high = self.total_num_instances
+
+                logger.info(f"sampling {num_instances} from {key} dataset")
+                num_selected_instances += num_instances
+
+                # First, add k copies of the dataset where k = num_instances // len(dataset).
+                # This ensures an equal distribution of the data points as much as possible.
+                # For the remaining entries randomly sample them
+                dataset_size = len(self.datasets[key])
+                num_copies = num_instances // dataset_size
+                dataset_indices = (
+                    np.random.permutation(high - self.dataset_offsets[i])
+                    + self.dataset_offsets[i]
+                )[: num_instances - num_copies * dataset_size]
+                if num_copies > 0:
+                    sampled_indices += list(
+                        np.concatenate(
+                            (
+                                np.repeat(
+                                    np.arange(self.dataset_offsets[i], high), num_copies
+                                ),
+                                dataset_indices,
+                            )
+                        )
+                    )
+                else:
+                    sampled_indices += list(dataset_indices)
+
+            assert (
+                len(sampled_indices) == self.total_num_instances
+            ), f"{len(sampled_indices)} vs {self.total_num_instances}"
+
+            np.random.shuffle(sampled_indices)
+            if self.sort_indices:
+                sampled_indices.sort(key=lambda i: self.num_tokens(i))
+
+            logger.info(
+                "multi_corpus_dataset ordered_indices took {}s".format(
+                    time.time() - start
+                )
+            )
+            return np.array(sampled_indices, dtype=np.int64)
+
+    def _map_index(self, index: int):
+        """
+        If dataset A has length N and dataset B has length M
+        then index 1 maps to index 1 of dataset A, and index N + 1
+        maps to index 1 of B.
+        """
+        counter = 0
+        for num_instances, key in zip(self.num_instances_per_dataset, self.datasets):
+            if index < counter + num_instances:
+                return index - counter, key
+            counter += num_instances
+        raise ValueError(
+            "Invalid index: {}, max: {}".format(index, self.total_num_instances)
+        )
+
+    def __len__(self):
+        """
+        Length of this dataset is the sum of individual datasets
+        """
+        return self.total_num_instances
+
+    def __getitem__(self, index):
+        new_index, key = self._map_index(index)
+        try:
+            item = self.datasets[key][new_index]
+            item["full_id"] = index
+            return item
+        except Exception as e:
+            e.args = (f"Error from {key} dataset", *e.args)
+            raise
+
+    def collater(self, samples):
+        """
+        If we are doing batch sampling, then pick the right collater to use.
+
+        Otherwise we assume all collaters are the same.
+        """
+        if len(samples) == 0:
+            return None
+        if "full_id" in samples[0]:
+            _, key = self._map_index(samples[0]["full_id"])
+            try:
+                batch = self.datasets[key].collater(samples)
+            except Exception:
+                print(f"Collating failed for key {key}", flush=True)
+                raise
+            return batch
+        else:
+            # Subclasses may override __getitem__ to not specify full_id
+            return list(self.datasets.values())[0].collater(samples)
+
+    def num_tokens(self, index: int):
+        index, key = self._map_index(index)
+        return self.datasets[key].num_tokens(index)
+
+    def size(self, index: int):
+        index, key = self._map_index(index)
+        return self.datasets[key].size(index)
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return False
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        logger.info(f"setting epoch of multi_corpus_dataset to {epoch}")
+        self.epoch = epoch
+
+    @property
+    def supports_prefetch(self):
+        return False
+
+    @property
+    def supports_fetch_outside_dataloader(self):
+        return all(
+            self.datasets[key].supports_fetch_outside_dataloader
+            for key in self.datasets
+        )
+
+    def batch_by_size(
+        self,
+        indices,
+        max_tokens=None,
+        max_sentences=None,
+        required_batch_size_multiple=1,
+    ):
+        if not self.batch_sample:
+            return super().batch_by_size(
+                indices, max_tokens, max_sentences, required_batch_size_multiple
+            )
+
+        dataset_indices = {key: [] for key in self.datasets}
+        for i in indices:
+            _, key = self._map_index(i)
+            dataset_indices[key].append(i)
+
+        batches = []
+        for key in dataset_indices:
+            cur_batches = super().batch_by_size(
+                np.array(dataset_indices[key], dtype=np.int64),
+                max_tokens,
+                max_sentences,
+                required_batch_size_multiple,
+            )
+            logger.info(f"Created {len(cur_batches)} batches for dataset {key}")
+            batches += cur_batches
+
+        # If this dataset is used in a distributed training setup,
+        # then shuffle such that the order is seeded by the distributed rank
+        # as well
+        if self.distributed_rank is not None:
+            with data_utils.numpy_seed(self.seed, self.epoch, self.distributed_rank):
+                np.random.shuffle(batches)
+        return batches

PyTorch/NLP/new-Transformer/fairseq/data/multi_corpus_sampled_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import OrderedDict
+from typing import Callable, Dict, List
+
+import numpy as np
+
+from . import FairseqDataset
+
+
+def uniform_sampler(x):
+    # Sample from uniform distribution
+    return np.random.choice(x, 1).item()
+
+
+class MultiCorpusSampledDataset(FairseqDataset):
+    """
+    Stores multiple instances of FairseqDataset together and in every iteration
+    creates a batch by first sampling a dataset according to a specified
+    probability distribution and then getting instances from that dataset.
+
+    Args:
+        datasets: an OrderedDict of FairseqDataset instances.
+        sampling_func: A function for sampling over list of dataset keys.
+            The default strategy is to sample uniformly.
+    """
+
+    def __init__(
+        self,
+        datasets: Dict[str, FairseqDataset],
+        sampling_func: Callable[[List], int] = None,
+    ):
+        super().__init__()
+        assert isinstance(datasets, OrderedDict)
+        self.datasets = datasets
+        if sampling_func is None:
+            sampling_func = uniform_sampler
+        self.sampling_func = sampling_func
+
+        self.total_num_instances = 0
+        for _, dataset in datasets.items():
+            assert isinstance(dataset, FairseqDataset)
+            self.total_num_instances += len(dataset)
+
+        self._ordered_indices = None
+
+    def __len__(self):
+        """
+        Length of this dataset is the sum of individual datasets
+        """
+        return self.total_num_instances
+
+    def ordered_indices(self):
+        """
+        Ordered indices for batching. Here we call the underlying
+        dataset's ordered_indices() so that we get the same random ordering
+        as we would have from using the underlying dataset directly.
+        """
+        if self._ordered_indices is None:
+            self._ordered_indices = OrderedDict(
+                [
+                    (key, dataset.ordered_indices())
+                    for key, dataset in self.datasets.items()
+                ]
+            )
+        return np.arange(len(self))
+
+    def _map_index_to_dataset(self, key: int, index: int):
+        """
+        Different underlying datasets have different lengths. In order to ensure
+        we are not accessing an index outside the range of the current dataset
+        size, we wrap around. This function should be called after we have
+        created an ordering for this and all underlying datasets.
+        """
+        assert (
+            self._ordered_indices is not None
+        ), "Must call MultiCorpusSampledDataset.ordered_indices() first"
+        mapped_index = index % len(self.datasets[key])
+        return self._ordered_indices[key][mapped_index]
+
+    def __getitem__(self, index: int):
+        """
+        Get the item associated with index from each underlying dataset.
+        Since index is in the range of [0, TotalNumInstances], we need to
+        map the index to the dataset before retrieving the item.
+        """
+        return OrderedDict(
+            [
+                (key, dataset[self._map_index_to_dataset(key, index)])
+                for key, dataset in self.datasets.items()
+            ]
+        )
+
+    def collater(self, samples: List[Dict]):
+        """
+        Generate a mini-batch for this dataset.
+        To convert this into a regular mini-batch we use the following
+        logic:
+            1. Select a dataset using the specified probability distribution.
+            2. Call the collater function of the selected dataset.
+        """
+        if len(samples) == 0:
+            return None
+
+        selected_key = self.sampling_func(list(self.datasets.keys()))
+        selected_samples = [sample[selected_key] for sample in samples]
+        return self.datasets[selected_key].collater(selected_samples)
+
+    def num_tokens(self, index: int):
+        """
+        Return an example's length (number of tokens), used for batching. Here
+        we return the max across all examples at index across all underlying
+        datasets.
+        """
+        return max(
+            dataset.num_tokens(self._map_index_to_dataset(key, index))
+            for key, dataset in self.datasets.items()
+        )
+
+    def size(self, index: int):
+        """
+        Return an example's size as a float or tuple. Here we return the max
+        across all underlying datasets. This value is used when filtering a
+        dataset with max-positions.
+        """
+        return max(
+            dataset.size(self._map_index_to_dataset(key, index))
+            for key, dataset in self.datasets.items()
+        )
+
+    @property
+    def supports_prefetch(self):
+        return all(
+            getattr(dataset, "supports_prefetch", False)
+            for dataset in self.datasets.values()
+        )
+
+    def prefetch(self, indices):
+        for key, dataset in self.datasets.items():
+            dataset.prefetch(
+                [self._map_index_to_dataset(key, index) for index in indices]
+            )
+
+    @property
+    def supports_fetch_outside_dataloader(self):
+        return all(
+            self.datasets[key].supports_fetch_outside_dataloader
+            for key in self.datasets
+        )

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/__init__.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/multilingual_data_manager.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+import json
+import logging
+import math
+import os
+from collections import OrderedDict, defaultdict
+from argparse import ArgumentError
+
+from fairseq import utils
+from fairseq.data import (
+    AppendTokenDataset,
+    ConcatDataset,
+    Dictionary,
+    LanguagePairDataset,
+    PrependTokenDataset,
+    SampledMultiDataset,
+    SampledMultiEpochDataset,
+    StripTokenDataset,
+    TransformEosLangPairDataset,
+    TruncateDataset,
+    data_utils,
+    indexed_dataset,
+)
+from fairseq.data.multilingual.multilingual_utils import (
+    EncoderLangtok,
+    LangTokSpec,
+    LangTokStyle,
+    augment_dictionary,
+    get_lang_tok,
+)
+from fairseq.data.multilingual.sampled_multi_dataset import CollateFormat
+from fairseq.file_io import PathManager
+from fairseq.utils import FileContentsAction, csv_str_list, eval_str_dict
+
+
+logger = logging.getLogger(__name__)
+
+SRC_DICT_NAME = "src"
+TGT_DICT_NAME = "tgt"
+
+
+def _lang_id(dic: Dictionary, lang: str):
+    """Return language ID index."""
+    idx = dic.index(lang)
+    assert idx != dic.unk_index, "cannot find language ID for lang {}".format(lang)
+    return idx
+
+
+def load_sampling_weights(from_file):
+    with open(from_file) as f:
+        weights = json.load(f)
+    return weights
+
+
+class MultilingualDatasetManager(object):
+    def __init__(self, args, lang_pairs, langs, dicts, sampling_method):
+        super().__init__()
+        self.args = args
+        self.seed = args.seed
+        self.lang_pairs = lang_pairs
+        self.extra_lang_pairs = (
+            list({p for _, v in args.extra_lang_pairs.items() for p in v.split(",")})
+            if args.extra_lang_pairs
+            else []
+        )
+        self.src_langs = {
+            p.split("-")[0] for p in args.lang_pairs + self.extra_lang_pairs
+        }
+        self.tgt_langs = {
+            p.split("-")[1] for p in args.lang_pairs + self.extra_lang_pairs
+        }
+        self.langs = langs
+        self.dicts = dicts
+        self.lang_dict = self.create_lang_dictionary(self.langs)
+        self.sampling_method = sampling_method
+        self.sampling_scheduler = None
+        self._has_sharded_data = False
+        self._num_shards_dict = {}
+        self._training_data_sizes = defaultdict(lambda: {})
+
+    @classmethod
+    def setup_data_manager(cls, args, lang_pairs, langs, dicts, sampling_method):
+        return MultilingualDatasetManager(
+            args, lang_pairs, langs, dicts, sampling_method
+        )
+
+    @staticmethod
+    def add_args(parser):
+        parser.add_argument(
+            "data",
+            help="colon separated path to data directories list, \
+                            will be iterated upon during epochs in round-robin manner",
+            action=FileContentsAction,
+        )
+        parser.add_argument(
+            "--langs",
+            default=None,
+            type=csv_str_list,
+            help="a list of languages comma sperated languages which can appear in lang-pairs; "
+            "note that the ordering determines language token IDs",
+        )
+        parser.add_argument(
+            "--lang-dict",
+            default=None,
+            type=str,
+            help="an external file which contains a list of "
+            "languages which can appear in lang-pairs; "
+            "note that the ordering determines language token IDs; "
+            "--langs and --lang-dict are two exclusive options",
+        )
+        parser.add_argument(
+            "--source-dict",
+            default=None,
+            type=str,
+            help="path to source dictionary; if specified it will override per language dictionary loading",
+        )
+        parser.add_argument(
+            "--target-dict",
+            default=None,
+            type=str,
+            help="path to target dictionary; if specified it will override per language dictionary loading",
+        )
+        parser.add_argument(
+            "--lang-tok-style",
+            default=LangTokStyle.multilingual.value,
+            type=str,
+            choices=[LangTokStyle.multilingual.value, LangTokStyle.mbart.value],
+            help="language token styles",
+        )
+
+        parser.add_argument(
+            "--load-alignments",
+            action="store_true",
+            help="load the binarized alignments",
+        )
+        parser.add_argument(
+            "--left-pad-source",
+            default="True",
+            type=str,
+            metavar="BOOL",
+            help="pad the source on the left",
+        )
+        parser.add_argument(
+            "--left-pad-target",
+            default="False",
+            type=str,
+            metavar="BOOL",
+            help="pad the target on the left",
+        )
+        try:
+            parser.add_argument(
+                "--max-source-positions",
+                default=1024,
+                type=int,
+                metavar="N",
+                help="max number of tokens in the source sequence",
+            )
+            parser.add_argument(
+                "--max-target-positions",
+                default=1024,
+                type=int,
+                metavar="N",
+                help="max number of tokens in the target sequence",
+            )
+        except ArgumentError:
+            # this might have already been defined. Once we transition this to hydra it should be fine to add it here.
+            pass
+        parser.add_argument(
+            "--upsample-primary",
+            default=1,
+            type=int,
+            help="amount to upsample primary dataset",
+        )
+        parser.add_argument(
+            "--truncate-source",
+            action="store_true",
+            default=False,
+            help="truncate source to max-source-positions",
+        )
+        parser.add_argument(
+            "--encoder-langtok",
+            default=None,
+            type=str,
+            choices=[EncoderLangtok.src.value, EncoderLangtok.tgt.value],
+            metavar="SRCTGT",
+            help="prepend to the beginning of source sentence the source or target "
+            "language token. (src/tgt)",
+        )
+        parser.add_argument(
+            "--decoder-langtok",
+            action="store_true",
+            help="prepend to the beginning of target sentence the target language token",
+        )
+        parser.add_argument(
+            "--lang-tok-replacing-bos-eos", action="store_true", default=False
+        )
+        parser.add_argument(
+            "--enable-lang-ids",
+            default=False,
+            action="store_true",
+            help="whether to include language IDs in samples",
+        )
+        parser.add_argument(
+            "--enable-reservsed-directions-shared-datasets",
+            default=False,
+            action="store_true",
+            help="whether to allow datasets be used in reversed directions",
+        )
+
+        parser.add_argument(
+            "--extra-data",
+            help='a dictionary of data name to this path, \
+                            e.g. {"mined", path_to_mined_data, "denoised": path_to_denoised_data}',
+            type=lambda uf: eval_str_dict(uf, type=str),
+            default=None,
+        )
+        parser.add_argument(
+            "--extra-lang-pairs",
+            help='a dictionary of data name to the language pairs they serve, \
+                            e.g. {"mined": comma-separated-lang-pairs, "denoised":  comma-separated-lang-pairs}',
+            type=lambda uf: eval_str_dict(uf, type=str),
+            default=None,
+        )
+        parser.add_argument(
+            "--fixed-dictionary",
+            help="Fixed dictionary to use with model path",
+            default=None,
+            type=str,
+        )
+        parser.add_argument(
+            "--langtoks-specs",
+            help='a list of comma separated data types that a set of language tokens to be specialized for, \
+                            e.g. "main,dae,mined". There will be a set of language tokens added to the vocab to \
+                            distinguish languages in different training data types. If not specified, default language \
+                            tokens per languages will be added',
+            default=LangTokSpec.main.value,
+            type=csv_str_list,
+        )
+        parser.add_argument(
+            "--langtoks",
+            help='a dictionary of how to add language tokens, \
+                            e.g. {"mined": (None, "tgt"), "mono_dae": ("src.dae", "tgt"), "main": \
+                            ("src", "tgt")}, or {"mined": ("src.mined", "tgt")}',
+            default=None,
+            type=lambda uf: eval_str_dict(uf, type=str),
+        )
+        parser.add_argument(
+            "--sampling-weights-from-file",
+            help='a file contain a python dictionary of how to sample data sets, \
+                                e.g. { "main:en_XX-es_XX": 0.2, "mined:en_XX-pt_XX": 0.5, \
+                                    "mono_dae:es_XX-es_XX: 0.3, "main:en_xx-fr_XX": 0.8 }',
+            default=None,
+            type=str,
+        )
+        parser.add_argument(
+            "--sampling-weights",
+            help='a dictionary of how to sample data sets, \
+                            e.g. { "main:en_XX-es_XX": 0.2, "mined:en_XX-pt_XX": 0.5, \
+                                   "mono_dae:es_XX-es_XX: 0.3, "main:en_xx-fr_XX": 0.8 }',
+            default=None,
+            type=lambda uf: eval_str_dict(uf, type=str),
+        )
+        parser.add_argument(
+            "--virtual-epoch-size",
+            default=None,
+            type=int,
+            help="virtual epoch size to speed up data loading",
+        )
+        parser.add_argument(
+            "--virtual-data-size",
+            default=None,
+            type=int,
+            help="virtual data size of the whole joint dataset to speed"
+            "up data loading and have specific dynamic sampling strategy interval",
+        )
+
+    @classmethod
+    def load_langs(cls, args, **kwargs):
+        if args.lang_dict and args.langs:
+            raise ValueError("--langs and --lang-dict can not both be specified")
+        if args.lang_dict is None and args.langs is None:
+            logger.warning(
+                "External language dictionary is not provided; "
+                "use lang-pairs to infer the set of supported languages. "
+                "The language ordering is not stable which might cause "
+                "misalignment in pretraining and finetuning."
+            )
+            # infer from lang_pairs as it is
+            langs = list(
+                {x for lang_pair in args.lang_pairs for x in lang_pair.split("-")}
+            )
+            langs = sorted(langs)
+            logger.info(f"inferred language list: {langs}")
+        elif args.lang_dict:
+            with open(
+                PathManager.get_local_path(args.lang_dict), "r", encoding="utf-8"
+            ) as f:
+                langs = [lang.strip() for lang in f.readlines() if lang.strip()]
+                logger.info(
+                    f"loaded language list from {args.lang_dict} as they are ordered in file"
+                )
+        elif args.langs:
+            langs = args.langs
+            logger.info(
+                f"parsed the language list as they are ordered in the option: {langs}"
+            )
+        return langs
+
+    def has_sharded_data(self, split):
+        return self._has_sharded_data and split == getattr(
+            self.args, "train_subset", None
+        )
+
+    def _shared_collater(self):
+        return not (self.args.extra_data and "mono_dae" in self.args.extra_data) and (
+            not self.args.lang_tok_replacing_bos_eos
+        )
+
+    def estimate_global_pass_epoch(self, epoch):
+        if self.args.virtual_epoch_size is None or self.args.virtual_data_size is None:
+            return None
+        # one epoch more for remaining data in each shard
+        virtual_epochs_per_shard = math.ceil(
+            self.args.virtual_data_size / self.args.virtual_epoch_size
+        )
+        # note that fairseq epoch / shard_epoch starts from 1
+        shard_epoch = (epoch - 1) // virtual_epochs_per_shard + 1
+        return shard_epoch
+
+    @classmethod
+    def prepare(cls, load_dictionary, args, **kargs):
+        args.left_pad_source = utils.eval_bool(args.left_pad_source)
+        args.left_pad_target = utils.eval_bool(args.left_pad_target)
+
+        if not hasattr(args, "shuffle_instance"):
+            args.shuffle_instance = False
+        if args.langtoks is None:
+            args.langtoks = {}
+        if "main" not in args.langtoks:
+            src_langtok_spec = args.encoder_langtok if args.encoder_langtok else None
+            tgt_langtok_spec = "tgt" if args.decoder_langtok else None
+            args.langtoks["main"] = (src_langtok_spec, tgt_langtok_spec)
+
+        def check_langs(langs, pairs):
+            messages = []
+            for src, tgt in pairs:
+                if src not in langs or tgt not in langs:
+                    messages.append(
+                        f"language pair {src}-{tgt} contains languages "
+                        "that are not in the language dictionary"
+                    )
+            if len(messages) > 0:
+                raise ValueError(" ".join(messages) + f"; langs: {langs}")
+
+        if args.lang_pairs is None:
+            raise ValueError(
+                "--lang-pairs is required. List all the language pairs in the training objective."
+            )
+        if isinstance(args.lang_pairs, str):
+            args.lang_pairs = args.lang_pairs.split(",")
+        if args.source_lang is not None or args.target_lang is not None:
+            training = False
+        else:
+            training = True
+        language_list = cls.load_langs(args, **kargs)
+        check_langs(
+            language_list,
+            (
+                [p.split("-") for p in args.lang_pairs]
+                if training
+                else [(args.source_lang, args.target_lang)]
+            ),
+        )
+
+        def load_dictionary_and_postproc(path):
+            d = load_dictionary(path)
+            augment_dictionary(
+                dictionary=d,
+                language_list=language_list,
+                lang_tok_style=args.lang_tok_style,
+                langtoks_specs=args.langtoks_specs,
+                extra_data=args.extra_data,
+            )
+            return d
+
+        dicts = cls.load_all_dictionaries(
+            args, language_list, load_dictionary_and_postproc, training
+        )
+        return language_list, dicts, training
+
+    @classmethod
+    def load_all_dictionaries(cls, args, language_list, load_dictionary, training):
+        dicts = OrderedDict()
+        if args.source_dict is not None:
+            dicts[SRC_DICT_NAME] = load_dictionary(args.source_dict)
+        if args.target_dict is not None:
+            dicts[TGT_DICT_NAME] = load_dictionary(args.target_dict)
+
+        if training:
+            extra_lang_pairs = (
+                list(
+                    {p for _, v in args.extra_lang_pairs.items() for p in v.split(",")}
+                )
+                if args.extra_lang_pairs
+                else []
+            )
+            src_langs_to_load_dicts = sorted(
+                {p.split("-")[0] for p in (args.lang_pairs + extra_lang_pairs)}
+            )
+            tgt_langs_to_load_dicts = sorted(
+                {p.split("-")[1] for p in (args.lang_pairs + extra_lang_pairs)}
+            )
+        else:
+            src_langs_to_load_dicts = [args.source_lang]
+            tgt_langs_to_load_dicts = [args.target_lang]
+
+        paths = utils.split_paths(args.data)
+        assert len(paths) > 0
+
+        def load_dicts(langs_to_load_dicts):
+            for lang in langs_to_load_dicts:
+                dicts[lang] = load_dictionary(
+                    os.path.join(paths[0], "dict.{}.txt".format(lang))
+                )
+            if len(dicts) > 0:
+                dict0 = next(iter(dicts.values()))
+                assert dicts[lang].pad() == dict0.pad()
+                assert dicts[lang].eos() == dict0.eos()
+                assert dicts[lang].unk() == dict0.unk()
+            logger.info("[{}] dictionary: {} types".format(lang, len(dicts[lang])))
+
+        if args.fixed_dictionary is not None:
+            fixed_dict = load_dictionary(args.fixed_dictionary)
+            dicts = {
+                lang: fixed_dict
+                for lang in src_langs_to_load_dicts + tgt_langs_to_load_dicts
+            }
+        else:
+            if args.source_dict is None:
+                load_dicts(src_langs_to_load_dicts)
+            if args.target_dict is None:
+                load_dicts(tgt_langs_to_load_dicts)
+        return dicts
+
+    def get_source_dictionary(self, lang):
+        if self.args.source_dict is not None:
+            return self.dicts[SRC_DICT_NAME]
+        else:
+            return self.dicts[lang]
+
+    def get_target_dictionary(self, lang):
+        if self.args.target_dict is not None:
+            return self.dicts[TGT_DICT_NAME]
+        else:
+            return self.dicts[lang]
+
+    @classmethod
+    def create_lang_dictionary(cls, langs):
+        unk = "<unk>"
+        # hack to remove symbols other than unk as they are not needed by lang dict
+        lang_dict = Dictionary(pad=unk, eos=unk, unk=unk, bos=unk)
+        for lang in langs:
+            lang_dict.add_symbol(lang)
+        return lang_dict
+
+    @classmethod
+    def get_langtok_index(cls, lang_tok, dic):
+        idx = dic.index(lang_tok)
+        assert (
+            idx != dic.unk_index
+        ), "cannot find language token {} in the dictionary".format(lang_tok)
+        return idx
+
+    def get_encoder_langtok(self, src_lang, tgt_lang, spec=None):
+        if spec is None:
+            return None
+        if spec and spec.startswith("src"):
+            if src_lang is None:
+                return None
+            langtok = get_lang_tok(
+                lang=src_lang, lang_tok_style=self.args.lang_tok_style, spec=spec
+            )
+        else:
+            if tgt_lang is None:
+                return None
+            langtok = get_lang_tok(
+                lang=tgt_lang, lang_tok_style=self.args.lang_tok_style, spec=spec
+            )
+        return self.get_langtok_index(
+            langtok,
+            self.get_source_dictionary(src_lang)
+            if src_lang
+            else self.get_target_dictionary(tgt_lang),
+        )
+
+    def get_decoder_langtok(self, tgt_lang, spec=None):
+        if spec is None:
+            return None
+        langtok = get_lang_tok(
+            lang=tgt_lang, lang_tok_style=self.args.lang_tok_style, spec=spec
+        )
+        return self.get_langtok_index(langtok, self.get_target_dictionary(tgt_lang))
+
+    @classmethod
+    def load_data(cls, path, vdict, impl):
+        dataset = data_utils.load_indexed_dataset(path, vdict, impl)
+        return dataset
+
+    @classmethod
+    def split_exists(cls, split, src, tgt, lang, data_path, dataset_impl):
+        filename = os.path.join(data_path, "{}.{}-{}.{}".format(split, src, tgt, lang))
+        return indexed_dataset.dataset_exists(filename, impl=dataset_impl)
+
+    def load_lang_dataset(
+        self,
+        data_path,
+        split,
+        src,
+        src_dict,
+        tgt,
+        tgt_dict,
+        combine,
+        dataset_impl,
+        upsample_primary,
+        max_source_positions,
+        prepend_bos=False,
+        load_alignments=False,
+        truncate_source=False,
+    ):
+
+        src_datasets = []
+        tgt_datasets = []
+
+        for k in itertools.count():
+            split_k = split + (str(k) if k > 0 else "")
+
+            # infer langcode
+            if self.split_exists(split_k, src, tgt, src, data_path, dataset_impl):
+                prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, src, tgt))
+            elif self.split_exists(split_k, tgt, src, src, data_path, dataset_impl):
+                prefix = os.path.join(data_path, "{}.{}-{}.".format(split_k, tgt, src))
+            else:
+                if k > 0:
+                    break
+                else:
+                    logger.error(
+                        f"Dataset not found: {data_path}, {split_k}, {src}, {tgt}"
+                    )
+                    raise FileNotFoundError(
+                        "Dataset not found: {} ({})".format(split, data_path)
+                    )
+
+            src_dataset = self.load_data(prefix + src, src_dict, dataset_impl)
+            if truncate_source:
+                src_dataset = AppendTokenDataset(
+                    TruncateDataset(
+                        StripTokenDataset(src_dataset, src_dict.eos()),
+                        max_source_positions - 1,
+                    ),
+                    src_dict.eos(),
+                )
+            src_datasets.append(src_dataset)
+            tgt_datasets.append(self.load_data(prefix + tgt, tgt_dict, dataset_impl))
+
+            logger.info(
+                "{} {} {}-{} {} examples".format(
+                    data_path, split_k, src, tgt, len(src_datasets[-1])
+                )
+            )
+
+            if not combine:
+                break
+
+        assert len(src_datasets) == len(tgt_datasets)
+
+        if len(src_datasets) == 1:
+            src_dataset, tgt_dataset = src_datasets[0], tgt_datasets[0]
+        else:
+            sample_ratios = [1] * len(src_datasets)
+            sample_ratios[0] = upsample_primary
+            src_dataset = ConcatDataset(src_datasets, sample_ratios)
+            tgt_dataset = ConcatDataset(tgt_datasets, sample_ratios)
+
+        if prepend_bos:
+            assert hasattr(src_dict, "bos_index") and hasattr(tgt_dict, "bos_index")
+            src_dataset = PrependTokenDataset(src_dataset, src_dict.bos())
+            tgt_dataset = PrependTokenDataset(tgt_dataset, tgt_dict.bos())
+
+        align_dataset = None
+        if load_alignments:
+            align_path = os.path.join(
+                data_path, "{}.align.{}-{}".format(split, src, tgt)
+            )
+            if indexed_dataset.dataset_exists(align_path, impl=dataset_impl):
+                align_dataset = data_utils.load_indexed_dataset(
+                    align_path, None, dataset_impl
+                )
+
+        return src_dataset, tgt_dataset, align_dataset
+
+    def load_langpair_dataset(
+        self,
+        data_path,
+        split,
+        src,
+        src_dict,
+        tgt,
+        tgt_dict,
+        combine,
+        dataset_impl,
+        upsample_primary,
+        left_pad_source,
+        left_pad_target,
+        max_source_positions,
+        max_target_positions,
+        prepend_bos=False,
+        load_alignments=False,
+        truncate_source=False,
+        src_dataset_transform_func=lambda dataset: dataset,
+        tgt_dataset_transform_func=lambda dataset: dataset,
+        src_lang_id=None,
+        tgt_lang_id=None,
+        langpairs_sharing_datasets=None,
+    ):
+        norm_direction = "-".join(sorted([src, tgt]))
+        if langpairs_sharing_datasets is not None:
+            src_dataset = langpairs_sharing_datasets.get(
+                (data_path, split, norm_direction, src), "NotInCache"
+            )
+            tgt_dataset = langpairs_sharing_datasets.get(
+                (data_path, split, norm_direction, tgt), "NotInCache"
+            )
+            align_dataset = langpairs_sharing_datasets.get(
+                (data_path, split, norm_direction, src, tgt), "NotInCache"
+            )
+
+        # a hack: any one is not in cache, we need to reload them
+        if (
+            langpairs_sharing_datasets is None
+            or src_dataset == "NotInCache"
+            or tgt_dataset == "NotInCache"
+            or align_dataset == "NotInCache"
+            or split != getattr(self.args, "train_subset", None)
+        ):
+            # source and target datasets can be reused in reversed directions to save memory
+            # reversed directions of valid and test data will not share source and target datasets
+            src_dataset, tgt_dataset, align_dataset = self.load_lang_dataset(
+                data_path,
+                split,
+                src,
+                src_dict,
+                tgt,
+                tgt_dict,
+                combine,
+                dataset_impl,
+                upsample_primary,
+                max_source_positions=max_source_positions,
+                prepend_bos=prepend_bos,
+                load_alignments=load_alignments,
+                truncate_source=truncate_source,
+            )
+            src_dataset = src_dataset_transform_func(src_dataset)
+            tgt_dataset = tgt_dataset_transform_func(tgt_dataset)
+            if langpairs_sharing_datasets is not None:
+                langpairs_sharing_datasets[
+                    (data_path, split, norm_direction, src)
+                ] = src_dataset
+                langpairs_sharing_datasets[
+                    (data_path, split, norm_direction, tgt)
+                ] = tgt_dataset
+                langpairs_sharing_datasets[
+                    (data_path, split, norm_direction, src, tgt)
+                ] = align_dataset
+                if align_dataset is None:
+                    # no align data so flag the reverse direction as well in sharing
+                    langpairs_sharing_datasets[
+                        (data_path, split, norm_direction, tgt, src)
+                    ] = align_dataset
+        else:
+            logger.info(
+                f"Reusing source and target datasets of [{split}] {tgt}-{src} for reversed direction: "
+                f"[{split}] {src}-{tgt}: src length={len(src_dataset)}; tgt length={len(tgt_dataset)}"
+            )
+
+        return LanguagePairDataset(
+            src_dataset,
+            src_dataset.sizes,
+            src_dict,
+            tgt_dataset,
+            tgt_dataset.sizes if tgt_dataset is not None else None,
+            tgt_dict,
+            left_pad_source=left_pad_source,
+            left_pad_target=left_pad_target,
+            align_dataset=align_dataset,
+            src_lang_id=src_lang_id,
+            tgt_lang_id=tgt_lang_id,
+        )
+
+    def src_dataset_tranform_func(self, src_lang, tgt_lang, dataset, spec=None):
+        if self.args.lang_tok_replacing_bos_eos:
+            # it is handled by self.alter_dataset_langtok
+            # TODO: Unifiy with alter_dataset_langtok
+            return dataset
+        if spec is None:
+            return dataset
+        tok = self.get_encoder_langtok(src_lang, tgt_lang, spec)
+        if tok:
+            return PrependTokenDataset(dataset, tok)
+        return dataset
+
+    def tgt_dataset_tranform_func(self, source_lang, target_lang, dataset, spec=None):
+        if dataset is None:
+            # note that target dataset can be None during inference time
+            return None
+        if self.args.lang_tok_replacing_bos_eos:
+            # TODO: Unifiy with alter_dataset_langtok
+            # It is handled by self.alter_dataset_langtok.
+            # The complication in self.alter_dataset_langtok
+            # makes a unified framework difficult.
+            return dataset
+        # if not self.args.decoder_langtok:
+        if not spec:
+            return dataset
+        tok = self.get_decoder_langtok(target_lang, spec)
+        if tok:
+            return PrependTokenDataset(dataset, tok)
+        return dataset
+
+    def alter_dataset_langtok(
+        self,
+        lang_pair_dataset,
+        src_eos=None,
+        src_lang=None,
+        tgt_eos=None,
+        tgt_lang=None,
+        src_langtok_spec=None,
+        tgt_langtok_spec=None,
+    ):
+        if src_langtok_spec is None and tgt_langtok_spec is None:
+            return lang_pair_dataset
+
+        new_src_eos = None
+        if (
+            src_langtok_spec is not None
+            and src_eos is not None
+            and (src_lang is not None or tgt_lang is not None)
+        ):
+            new_src_eos = self.get_encoder_langtok(src_lang, tgt_lang, src_langtok_spec)
+        else:
+            src_eos = None
+
+        new_tgt_bos = None
+        if tgt_langtok_spec and tgt_eos is not None and tgt_lang is not None:
+            new_tgt_bos = self.get_decoder_langtok(tgt_lang, tgt_langtok_spec)
+        else:
+            tgt_eos = None
+
+        return TransformEosLangPairDataset(
+            lang_pair_dataset,
+            src_eos=src_eos,
+            new_src_eos=new_src_eos,
+            tgt_bos=tgt_eos,
+            new_tgt_bos=new_tgt_bos,
+        )
+
+    def load_a_dataset(
+        self,
+        split,
+        data_path,
+        src,
+        src_dict,
+        tgt,
+        tgt_dict,
+        combine,
+        prepend_bos=False,
+        langpairs_sharing_datasets=None,
+        data_category=None,
+        **extra_kwargs,
+    ):
+        dataset_impl = self.args.dataset_impl
+        upsample_primary = self.args.upsample_primary
+        left_pad_source = self.args.left_pad_source
+        left_pad_target = self.args.left_pad_target
+        max_source_positions = self.args.max_source_positions
+        max_target_positions = self.args.max_target_positions
+        load_alignments = self.args.load_alignments
+        truncate_source = self.args.truncate_source
+        src_dataset_transform_func = self.src_dataset_tranform_func
+        tgt_dataset_transform_func = self.tgt_dataset_tranform_func
+        enable_lang_ids = self.args.enable_lang_ids
+        lang_dictionary = self.lang_dict
+        src_langtok_spec, tgt_langtok_spec = extra_kwargs["langtok_spec"]
+
+        src_langtok = self.get_encoder_langtok(src, tgt, src_langtok_spec)
+        tgt_langtok = self.get_decoder_langtok(tgt, tgt_langtok_spec)
+        logger.info(
+            f"{data_category}:{src}-{tgt} src_langtok: {src_langtok}; tgt_langtok: {tgt_langtok}"
+        )
+
+        langpair_ds = self.load_langpair_dataset(
+            data_path,
+            split,
+            src,
+            src_dict,
+            tgt,
+            tgt_dict,
+            combine,
+            dataset_impl,
+            upsample_primary,
+            left_pad_source,
+            left_pad_target,
+            max_source_positions,
+            max_target_positions,
+            prepend_bos,
+            load_alignments,
+            truncate_source,
+            src_dataset_transform_func=lambda dataset: src_dataset_transform_func(
+                src, tgt, dataset, src_langtok_spec
+            ),
+            tgt_dataset_transform_func=lambda dataset: tgt_dataset_transform_func(
+                src, tgt, dataset, tgt_langtok_spec
+            ),
+            src_lang_id=_lang_id(lang_dictionary, src)
+            if enable_lang_ids and lang_dictionary is not None
+            else None,
+            tgt_lang_id=_lang_id(lang_dictionary, tgt)
+            if enable_lang_ids and lang_dictionary is not None
+            else None,
+            langpairs_sharing_datasets=langpairs_sharing_datasets,
+        )
+        # TODO: handle modified lang toks for mined data and dae data
+        if self.args.lang_tok_replacing_bos_eos:
+            ds = self.alter_dataset_langtok(
+                langpair_ds,
+                src_eos=self.get_source_dictionary(src).eos()
+                if src
+                else self.get_target_dictionary(tgt).eos(),
+                src_lang=src,
+                tgt_eos=self.get_target_dictionary(tgt).eos(),
+                tgt_lang=tgt,
+                src_langtok_spec=src_langtok_spec,
+                tgt_langtok_spec=tgt_langtok_spec,
+            )
+        else:
+            ds = langpair_ds
+        return ds
+
+    def load_split_langpair_datasets(self, split, data_param_list):
+        datasets = []
+        langpairs_sharing_datasets = (
+            {} if self.args.enable_reservsed_directions_shared_datasets else None
+        )
+        for param in data_param_list:
+            ds = self.load_a_dataset(
+                split=split,
+                langpairs_sharing_datasets=langpairs_sharing_datasets,
+                **param,
+            )
+            datasets.append(ds)
+        return datasets
+
+    def get_data_paths_and_lang_pairs(self, split):
+        datapaths = {"main": self.args.data}
+        lang_pairs = {"main": self.lang_pairs}
+        if split == getattr(self.args, "train_subset", None):
+            # only training data can have extra data and extra language pairs
+            if self.args.extra_data:
+                extra_datapaths = self.args.extra_data
+                datapaths.update(extra_datapaths)
+            if self.args.extra_lang_pairs:
+                extra_lang_pairs = {
+                    k: v.split(",") for k, v in self.args.extra_lang_pairs.items()
+                }
+                lang_pairs.update(extra_lang_pairs)
+        return datapaths, lang_pairs
+
+    @classmethod
+    def get_dataset_key(cls, data_category, src, tgt):
+        return f"{data_category}:{src}-{tgt}"
+
+    @classmethod
+    def _get_shard_num_dict(cls, split, paths):
+        shards = defaultdict(int)
+        for path in paths:
+            files = PathManager.ls(path)
+            directions = set()
+            for f in files:
+                if f.startswith(split) and f.endswith(".idx"):
+                    # idx files of the form "{split}.{src}-{tgt}.{lang}.idx"
+                    direction = f.split(".")[-3]
+                    directions.add(direction)
+            for direction in directions:
+                shards[direction] += 1
+        return shards
+
+    def get_split_num_data_shards(self, split):
+        if split in self._num_shards_dict:
+            return self._num_shards_dict[split]
+        num_shards_dict = {}
+        data_paths, lang_pairs = self.get_data_paths_and_lang_pairs(split)
+
+        for data_category, paths in data_paths.items():
+            if data_category not in lang_pairs:
+                continue
+            paths = utils.split_paths(paths)
+            shards_dict = self._get_shard_num_dict(split, paths)
+            lang_dirs = [
+                lang_pair.split("-") for lang_pair in lang_pairs[data_category]
+            ]
+            lang_dirs = [x if len(x) > 1 else (x[0], x[0]) for x in lang_dirs]
+            for src, tgt in lang_dirs:
+                key = self.get_dataset_key(data_category, src, tgt)
+                if "mono_" in data_category:
+                    # monolingual data requires tgt only
+                    assert src is None or src == tgt, (
+                        f"error: src={src}, "
+                        f"tgt={tgt} for data_category={data_category}"
+                    )
+                    num_shards_dict[key] = shards_dict[tgt]
+                else:
+                    if f"{src}-{tgt}" in shards_dict:
+                        num_shards_dict[key] = shards_dict[f"{src}-{tgt}"]
+                    elif f"{tgt}-{src}" in shards_dict:
+                        # follow the fairseq tradition to use reversed direction data if it is not available
+                        num_shards_dict[key] = shards_dict[f"{tgt}-{src}"]
+        self._num_shards_dict[split] = num_shards_dict
+        logger.info(f"[{split}] num of shards: {num_shards_dict}")
+        return num_shards_dict
+
+    @classmethod
+    def get_shard_id(cls, num_shards, epoch, shard_epoch=None):
+        shard = epoch if shard_epoch is None else shard_epoch
+        shard = (shard - 1) % num_shards
+        return shard
+
+    def get_split_data_path(self, paths, epoch, shard_epoch, num_shards):
+        path = paths[self.get_shard_id(num_shards, epoch, shard_epoch)]
+        return path
+
+    def get_split_data_param_list(self, split, epoch, shard_epoch=None):
+        # TODO: to extend with extra datasets and keys and loop over different shard data paths
+        param_list = []
+        data_paths, lang_pairs = self.get_data_paths_and_lang_pairs(split)
+        logger.info(f"langtoks settings: {self.args.langtoks}")
+        split_num_shards_dict = self.get_split_num_data_shards(split)
+        for data_category, paths in data_paths.items():
+            if data_category not in lang_pairs:
+                continue
+            paths = utils.split_paths(paths)
+            assert len(paths) > 0
+            if len(paths) > 1:
+                self._has_sharded_data = True
+            if split != getattr(self.args, "train_subset", None):
+                # if not training data set, use the first shard for valid and test
+                paths = paths[:1]
+
+            if data_category in self.args.langtoks:
+                lang_tok_spec = self.args.langtoks[data_category]
+            else:
+                # default to None
+                lang_tok_spec = (None, None)
+
+            # infer langcode
+            lang_dirs = [
+                lang_pair.split("-") for lang_pair in lang_pairs[data_category]
+            ]
+            lang_dirs = [x if len(x) > 1 else (x[0], x[0]) for x in lang_dirs]
+            for src, tgt in lang_dirs:
+                assert src is not None or data_category == "mono_dae", (
+                    f"error: src={src}, " f"tgt={tgt} for data_category={data_category}"
+                )
+                # logger.info(f"preparing param for {data_category}: {src} - {tgt}")
+                key = self.get_dataset_key(data_category, src, tgt)
+                data_path = self.get_split_data_path(
+                    paths, epoch, shard_epoch, split_num_shards_dict[key]
+                )
+                param_list.append(
+                    {
+                        "key": key,
+                        "data_path": data_path,
+                        "split": split,
+                        "src": src,
+                        "src_dict": self.get_source_dictionary(src)
+                        if src and data_category != "mono_dae"
+                        else None,
+                        "tgt": tgt,
+                        "tgt_dict": self.get_target_dictionary(tgt),
+                        "data_category": data_category,
+                        "langtok_spec": lang_tok_spec,
+                    }
+                )
+        return param_list
+
+    def get_train_dataset_sizes(
+        self, data_param_list, datasets, epoch, shard_epoch=None
+    ):
+        num_shards = [
+            self.get_split_num_data_shards(param["split"])[param["key"]]
+            for param in data_param_list
+        ]
+        data_sizes = []
+        for (key, d), num_shard in zip(datasets, num_shards):
+            my_data_sizes = self._training_data_sizes[key]
+            shard_ind = self.get_shard_id(num_shard, epoch, shard_epoch)
+            if shard_ind not in my_data_sizes:
+                my_data_sizes[shard_ind] = len(d)
+            known_size = max(my_data_sizes.values())
+            data_sizes.append(
+                # If we don't know the data size of the shard yet,
+                # use the the max known data size to approximate.
+                # Note that we preprocess shards by a designated shard size
+                # and put any remaining data at the end into the last shard so
+                # the max shard size approximation is almost correct before loading
+                # the last shard; after loading the last shard, it will have the
+                # exact data sizes of the whole data size.
+                (key, sum(my_data_sizes.get(i, known_size) for i in range(num_shard)))
+            )
+        logger.info(
+            f"estimated total data sizes of all shards used in sampling ratios: {data_sizes}. "
+            "Note that if the data a shard has not been loaded yet, use the max known data size to approximate"
+        )
+        return [s for _, s in data_sizes]
+
+    def get_train_sampling_ratios(
+        self, data_param_list, datasets, epoch=1, shard_epoch=None
+    ):
+        data_sizes = self.get_train_dataset_sizes(
+            data_param_list, datasets, epoch, shard_epoch
+        )
+        sampling_func = self.sampling_method.sampling_method_selector()
+        sample_ratios = sampling_func(data_sizes) if sampling_func is not None else None
+        return sample_ratios
+
+    def get_sampling_ratios(self, data_param_list, datasets, epoch, shard_epoch=None):
+        if self.args.sampling_weights_from_file:
+            weights = load_sampling_weights(self.args.sampling_weights_from_file)
+            sample_ratios = [weights[k] for k, _ in datasets]
+            logger.info(
+                "| ignoring --sampling-weights when loadding sampling weights "
+                f"from file {self.args.sampling_weights_from_file}"
+            )
+        elif self.args.sampling_weights:
+            sample_ratios = [self.args.sampling_weights[k] for k, _ in datasets]
+        else:
+            sample_ratios = self.get_train_sampling_ratios(
+                data_param_list, datasets, epoch, shard_epoch
+            )
+
+        if sample_ratios is not None:
+            logger.info(
+                "| Upsample ratios: {}".format(
+                    list(zip(map(lambda x: x["key"], data_param_list), sample_ratios))
+                )
+            )
+            assert len(sample_ratios) == len(datasets)
+        return sample_ratios
+
+    def load_split_datasets(
+        self, split, training, epoch=1, combine=False, shard_epoch=None, **kwargs
+    ):
+        data_param_list = self.get_split_data_param_list(
+            split, epoch, shard_epoch=shard_epoch
+        )
+        langpairs_sharing_datasets = (
+            {} if self.args.enable_reservsed_directions_shared_datasets else None
+        )
+        datasets = [
+            (
+                param["key"],
+                self.load_a_dataset(
+                    combine=combine,
+                    langpairs_sharing_datasets=langpairs_sharing_datasets,
+                    **param,
+                ),
+            )
+            for param in data_param_list
+        ]
+        return datasets, data_param_list
+
+    def load_into_concat_dataset(self, split, datasets, data_param_list):
+        if self.args.lang_tok_replacing_bos_eos:
+            # TODO: to investigate why TransformEosLangPairDataset doesn't work with ConcatDataset
+            return SampledMultiDataset(
+                OrderedDict(datasets),
+                sampling_ratios=None,
+                eval_key=None,
+                collate_format=CollateFormat.single,
+                virtual_size=None,
+                split=split,
+            )
+        return ConcatDataset([d for _, d in datasets])
+
+    def load_sampled_multi_epoch_dataset(
+        self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs
+    ):
+        datasets, data_param_list = self.load_split_datasets(
+            split, training, epoch, combine, shard_epoch=shard_epoch, **kwargs
+        )
+        if training and split == getattr(self.args, "train_subset", None):
+            sample_ratios = self.get_sampling_ratios(data_param_list, datasets, epoch)
+            return SampledMultiEpochDataset(
+                OrderedDict(datasets),
+                epoch=epoch,
+                shard_epoch=shard_epoch,
+                # valid and test datasets will be degenerate to concating datasets:
+                sampling_ratios=sample_ratios,
+                eval_key=None,
+                collate_format=CollateFormat.single,
+                virtual_size=self.args.virtual_data_size,
+                split=split,
+                virtual_epoch_size=self.args.virtual_epoch_size,
+                # if not using lang_tok altering, simplified to use the same collater
+                shared_collater=self._shared_collater(),
+            )
+        else:
+            return self.load_into_concat_dataset(split, datasets, data_param_list)
+
+    def load_sampled_multi_dataset(
+        self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs
+    ):
+        datasets, data_param_list = self.load_split_datasets(
+            split, training, epoch, combine, shard_epoch=shard_epoch, **kwargs
+        )
+        if training and split == getattr(self.args, "train_subset", None):
+            sample_ratios = self.get_sampling_ratios(data_param_list, datasets, epoch)
+            return SampledMultiDataset(
+                OrderedDict(datasets),
+                epoch=epoch,
+                # valid and test datasets will be degerate to concating datasets:
+                sampling_ratios=sample_ratios,
+                eval_key=None,
+                collate_format=CollateFormat.single,
+                virtual_size=self.args.virtual_data_size,
+                split=split,
+                # if not using lang_tok altering, simplified to use the same collater
+                shared_collater=self._shared_collater(),
+            )
+        else:
+            return self.load_into_concat_dataset(split, datasets, data_param_list)
+
+    def load_dataset(
+        self, split, training, epoch=0, combine=False, shard_epoch=None, **kwargs
+    ):
+        if self.args.virtual_epoch_size is None:
+            return self.load_sampled_multi_dataset(
+                split, training, epoch, combine, shard_epoch, **kwargs
+            )
+        else:
+            return self.load_sampled_multi_epoch_dataset(
+                split, training, epoch, combine, shard_epoch, **kwargs
+            )

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/multilingual_utils.py

+from enum import Enum
+from typing import Dict, List, Optional, Sequence
+
+import torch
+from fairseq.data import Dictionary
+
+
+class EncoderLangtok(Enum):
+    """
+    Prepend to the beginning of source sentence either the
+    source or target language token. (src/tgt).
+    """
+
+    src = "src"
+    tgt = "tgt"
+
+
+class LangTokSpec(Enum):
+    main = "main"
+    mono_dae = "mono_dae"
+
+
+class LangTokStyle(Enum):
+    multilingual = "multilingual"
+    mbart = "mbart"
+
+
+@torch.jit.export
+def get_lang_tok(
+    lang: str, lang_tok_style: str, spec: str = LangTokSpec.main.value
+) -> str:
+    # TOKEN_STYLES can't be defined outside this fn since it needs to be
+    # TorchScriptable.
+    TOKEN_STYLES: Dict[str, str] = {
+        LangTokStyle.mbart.value: "[{}]",
+        LangTokStyle.multilingual.value: "__{}__",
+    }
+
+    if spec.endswith("dae"):
+        lang = f"{lang}_dae"
+    elif spec.endswith("mined"):
+        lang = f"{lang}_mined"
+    style = TOKEN_STYLES[lang_tok_style]
+    return style.format(lang)
+
+
+def augment_dictionary(
+    dictionary: Dictionary,
+    language_list: List[str],
+    lang_tok_style: str,
+    langtoks_specs: Sequence[str] = (LangTokSpec.main.value,),
+    extra_data: Optional[Dict[str, str]] = None,
+) -> None:
+    for spec in langtoks_specs:
+        for language in language_list:
+            dictionary.add_symbol(
+                get_lang_tok(lang=language, lang_tok_style=lang_tok_style, spec=spec)
+            )
+
+    if lang_tok_style == LangTokStyle.mbart.value or (
+        extra_data is not None and LangTokSpec.mono_dae.value in extra_data
+    ):
+        dictionary.add_symbol("<mask>")

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/sampled_multi_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import datetime
+import hashlib
+import logging
+import time
+from bisect import bisect_right
+from collections import OrderedDict, defaultdict
+from enum import Enum
+from typing import List
+
+import numpy as np
+import torch
+
+from fairseq.data import FairseqDataset, data_utils
+from fairseq.distributed import utils as distributed_utils
+
+
+def get_time_gap(s, e):
+    return (
+        datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s)
+    ).__str__()
+
+
+logger = logging.getLogger(__name__)
+
+
+def default_virtual_size_func(datasets, ratios, max_scale_up=1.5):
+    sizes = [len(d) for d in datasets]
+    if ratios is None:
+        return sum(sizes)
+    largest_idx = np.argmax(sizes)
+    largest_r = ratios[largest_idx]
+    largest_s = sizes[largest_idx]
+    # set virtual sizes relative to the largest dataset
+    virtual_sizes = [(r / largest_r) * largest_s for r in ratios]
+    vsize = sum(virtual_sizes)
+    max_size = sum(sizes) * max_scale_up
+    return int(vsize if vsize < max_size else max_size)
+
+
+class CollateFormat(Enum):
+    single = 1
+    ordered_dict = 2
+
+
+class SampledMultiDataset(FairseqDataset):
+    """Samples from multiple sub-datasets according to given sampling ratios.
+    Args:
+        datasets (
+            List[~torch.utils.data.Dataset]
+            or OrderedDict[str, ~torch.utils.data.Dataset]
+        ): datasets
+        sampling_ratios (List[float]): list of probability of each dataset to be sampled
+            (default: None, which corresponds to concatenating all dataset together).
+        seed (int): RNG seed to use (default: 2).
+        epoch (int): starting epoch number (default: 1).
+        eval_key (str, optional): a key used at evaluation time that causes
+            this instance to pass-through batches from *datasets[eval_key]*.
+        collate_format (CollateFormat):  collater output format, either CollateFormat.ordered_dict or
+            CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
+            the collater to output batches of data mixed from all sub-datasets,
+            and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
+            of sub-datasets.
+            Note that not all sub-datasets will present in a single batch in both formats.
+        virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
+        split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
+        shared_collater (bool): whether or not to all sub-datasets have the same collater.
+        shuffle (bool): whether or not to shuffle data (default: True).
+    """
+
+    def __init__(
+        self,
+        datasets,
+        sampling_ratios=None,
+        seed=2,
+        epoch=1,
+        eval_key=None,
+        collate_format=CollateFormat.single,
+        virtual_size=default_virtual_size_func,
+        split="",
+        shared_collater=False,
+        shuffle=True,
+    ):
+        super().__init__()
+        self.shared_collater = shared_collater
+        self.shuffle = shuffle
+
+        if isinstance(datasets, OrderedDict):
+            self.keys = list(datasets.keys())
+            datasets = list(datasets.values())
+        elif isinstance(datasets, List):
+            self.keys = list(range(len(datasets)))
+        else:
+            raise AssertionError()
+        self.datasets = datasets
+        self.split = split
+
+        self.eval_key = eval_key
+        if self.eval_key is not None:
+            self.collate_format = CollateFormat.single
+        else:
+            self.collate_format = collate_format
+
+        self.seed = seed
+        self._cur_epoch = None
+
+        self.cumulated_sizes = None
+        # self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset
+        # namely, data item i is sampled from the kth sub-dataset self.datasets[k]
+        # where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k]
+        self._cur_indices = None
+
+        self._sizes = None
+        self.virtual_size_per_dataset = None
+        # caching properties
+        self._reset_cached_properties()
+        self.setup_sampling(sampling_ratios, virtual_size)
+        self.set_epoch(epoch)
+
+    def _clean_if_not_none(self, var_list):
+        for v in var_list:
+            if v is not None:
+                del v
+
+    def _reset_cached_properties(self):
+        self._clean_if_not_none([self._sizes, self._cur_indices])
+        self._sizes = None
+        self._cur_indices = None
+
+    def setup_sampling(self, sample_ratios, virtual_size):
+        sizes = [len(d) for d in self.datasets]
+        if sample_ratios is None:
+            # default back to concating datasets
+            self.sample_ratios = None
+            self.virtual_size = sum(sizes)
+        else:
+            if not isinstance(sample_ratios, np.ndarray):
+                sample_ratios = np.array(sample_ratios)
+            self.sample_ratios = sample_ratios
+            virtual_size = (
+                default_virtual_size_func if virtual_size is None else virtual_size
+            )
+            self.virtual_size = (
+                virtual_size(self.datasets, self.sample_ratios)
+                if callable(virtual_size)
+                else virtual_size
+            )
+
+    def adjust_sampling(self, epoch, sampling_ratios, virtual_size):
+        if sampling_ratios is not None:
+            sampling_ratios = self._sync_sample_ratios(sampling_ratios)
+            self.setup_sampling(sampling_ratios, virtual_size)
+
+    def _sync_sample_ratios(self, ratios):
+        # in case the ratios are not precisely the same across processes
+        # also to ensure every procresses update the ratios in the same pace
+        ratios = torch.DoubleTensor(ratios)
+        if torch.distributed.is_initialized():
+            if torch.cuda.is_available():
+                distributed_utils.all_reduce(
+                    ratios.cuda(), group=distributed_utils.get_data_parallel_group()
+                )
+            else:
+                distributed_utils.all_reduce(
+                    ratios, group=distributed_utils.get_data_parallel_group()
+                )
+            ret = ratios.cpu()
+            ret = ret.numpy()
+        return ret
+
+    def random_choice_in_dataset(self, rng, dataset, choice_size):
+        if hasattr(dataset, "random_choice_in_dataset"):
+            return dataset.random_choice_in_dataset(rng, choice_size)
+        dataset_size = len(dataset)
+        return rng.choice(
+            dataset_size, choice_size, replace=(choice_size > dataset_size)
+        )
+
+    def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size):
+        def get_counts(sample_ratios):
+            counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64)
+            diff = virtual_size - counts.sum()
+            assert diff >= 0
+            # due to round-offs, the size might not match the desired sizes
+            if diff > 0:
+                dataset_indices = rng.choice(
+                    len(sample_ratios), size=diff, p=sample_ratios
+                )
+                for i in dataset_indices:
+                    counts[i] += 1
+            return counts
+
+        def get_in_dataset_indices(datasets, sizes, sample_ratios):
+            counts = get_counts(sample_ratios)
+            # uniformally sample desired counts for each dataset
+            # if the desired counts are large, sample with replacement:
+            indices = [
+                self.random_choice_in_dataset(rng, d, c)
+                for c, d in zip(counts, datasets)
+            ]
+            return indices
+
+        sizes = [len(d) for d in datasets]
+        if sample_ratios is None:
+            # default back to concating datasets
+            in_dataset_indices = [list(range(s)) for s in sizes]
+            virtual_sizes_per_dataset = sizes
+        else:
+            ratios = sample_ratios / sample_ratios.sum()
+            in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios)
+            virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices]
+        virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64)
+        cumulative_sizes = np.cumsum(virtual_sizes_per_dataset)
+        assert sum(virtual_sizes_per_dataset) == virtual_size
+        assert cumulative_sizes[-1] == virtual_size
+        if virtual_size < sum(sizes):
+            logger.warning(
+                f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})."
+                " If virtual size << real data size, there could be data coverage issue."
+            )
+        in_dataset_indices = np.hstack(in_dataset_indices)
+        return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset
+
+    def _get_dataset_and_index(self, index):
+        i = bisect_right(self.cumulated_sizes, index)
+        return i, self._cur_indices[index]
+
+    def __getitem__(self, index):
+        # self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]]
+        # where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k]
+        ds_idx, ds_sample_idx = self._get_dataset_and_index(index)
+        ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx])
+        return ret
+
+    def num_tokens(self, index):
+        return self.sizes[index].max()
+
+    def num_tokens_vec(self, indices):
+        sizes_vec = self.sizes[np.array(indices)]
+        # max across all dimensions but first one
+        return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape))))
+
+    def size(self, index):
+        return self.sizes[index]
+
+    def __len__(self):
+        return self.virtual_size
+
+    def collater(self, samples, **extra_args):
+        """Merge a list of samples to form a mini-batch."""
+        if len(samples) == 0:
+            return None
+        if self.collate_format == "ordered_dict":
+            collect_samples = [[] for _ in range(len(self.datasets))]
+            for (i, sample) in samples:
+                collect_samples[i].append(sample)
+            batch = OrderedDict(
+                [
+                    (self.keys[i], dataset.collater(collect_samples[i]))
+                    for i, (key, dataset) in enumerate(zip(self.keys, self.datasets))
+                    if len(collect_samples[i]) > 0
+                ]
+            )
+        elif self.shared_collater:
+            batch = self.datasets[0].collater([s for _, s in samples])
+        else:
+            samples_dict = defaultdict(list)
+            pad_to_length = (
+                defaultdict(int)
+                if "pad_to_length" not in extra_args
+                else extra_args["pad_to_length"]
+            )
+            for ds_idx, s in samples:
+                pad_to_length["source"] = max(
+                    pad_to_length["source"], s["source"].size(0)
+                )
+                if s["target"] is not None:
+                    pad_to_length["target"] = max(
+                        pad_to_length["target"], s["target"].size(0)
+                    )
+                samples_dict[ds_idx].append(s)
+            batches = [
+                self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length)
+                for i in range(len(self.datasets))
+                if len(samples_dict[i]) > 0
+            ]
+
+            def straight_data(tensors):
+                batch = torch.cat(tensors, dim=0)
+                return batch
+
+            src_lengths = straight_data(
+                [b["net_input"]["src_lengths"] for b in batches]
+            )
+            src_lengths, sort_order = src_lengths.sort(descending=True)
+
+            def straight_order(tensors):
+                batch = straight_data(tensors)
+                return batch.index_select(0, sort_order)
+
+            batch = {
+                "id": straight_order([b["id"] for b in batches]),
+                "nsentences": sum(b["nsentences"] for b in batches),
+                "ntokens": sum(b["ntokens"] for b in batches),
+                "net_input": {
+                    "src_tokens": straight_order(
+                        [b["net_input"]["src_tokens"] for b in batches]
+                    ),
+                    "src_lengths": src_lengths,
+                },
+                "target": straight_order([b["target"] for b in batches])
+                if batches[0]["target"] is not None
+                else None,
+            }
+            if "prev_output_tokens" in batches[0]["net_input"]:
+                batch["net_input"]["prev_output_tokens"] = straight_order(
+                    [b["net_input"]["prev_output_tokens"] for b in batches]
+                )
+            if "src_lang_id" in batches[0]["net_input"]:
+                batch["net_input"]["src_lang_id"] = straight_order(
+                    [b["net_input"]["src_lang_id"] for b in batches]
+                )
+            if "tgt_lang_id" in batches[0]:
+                batch["tgt_lang_id"] = straight_order(
+                    [b["tgt_lang_id"] for b in batches]
+                )
+        return batch
+
+    @property
+    def sizes(self):
+        if self._sizes is not None:
+            return self._sizes
+        start_time = time.time()
+        in_sub_dataset_indices = [
+            self._cur_indices[
+                0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i]
+            ]
+            for i in range(len(self.datasets))
+        ]
+        sub_dataset_sizes = [
+            d.sizes[indices]
+            for d, indices in zip(self.datasets, in_sub_dataset_indices)
+        ]
+        self._sizes = np.vstack(sub_dataset_sizes)
+        logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}")
+        return self._sizes
+
+    def ordered_indices(self):
+        if self.shuffle:
+            indices = np.random.permutation(len(self))
+        else:
+            indices = np.arange(len(self))
+
+        sizes = self.sizes
+        tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
+        src_sizes = (
+            sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
+        )
+
+        # sort by target length, then source length
+        if tgt_sizes is not None:
+            indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
+        sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")]
+        return sort_indices
+
+    def prefetch(self, indices):
+        prefetch_indices = [[] for _ in range(len(self.datasets))]
+        for i in indices:
+            ds_idx, ds_sample_idx = self._get_dataset_and_index(i)
+            prefetch_indices[ds_idx].append(ds_sample_idx)
+        for i in range(len(prefetch_indices)):
+            self.datasets[i].prefetch(prefetch_indices[i])
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return False
+
+    def set_epoch(self, epoch):
+        super().set_epoch(epoch)
+        if epoch == self._cur_epoch:
+            # re-enter so return
+            return
+        for d in self.datasets:
+            if hasattr(d, "set_epoch"):
+                d.set_epoch(epoch)
+        self._cur_epoch = epoch
+        self._establish_virtual_datasets()
+
+    def _establish_virtual_datasets(self):
+        if self.sample_ratios is None and self._cur_indices is not None:
+            # not a samping dataset, no need to resample if indices are already established
+            return
+        self._reset_cached_properties()
+
+        start_time = time.time()
+        # Generate a weighted sample of indices as a function of the
+        # random seed and the current epoch.
+        rng = np.random.RandomState(
+            [
+                int(
+                    hashlib.sha1(
+                        str(self.__class__.__name__).encode("utf-8")
+                    ).hexdigest(),
+                    16,
+                )
+                % (2**32),
+                self.seed % (2**32),  # global seed
+                self._cur_epoch,  # epoch index,
+            ]
+        )
+        self._clean_if_not_none(
+            [self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes]
+        )
+        self._sizes = None
+
+        indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices(
+            rng, self.datasets, self.sample_ratios, self.virtual_size
+        )
+        self._cur_indices = indices
+        self.cumulated_sizes = cumulated_sizes
+        self.virtual_size_per_dataset = virtual_size_per_dataset
+
+        raw_sizes = [len(d) for d in self.datasets]
+        sampled_sizes = self.virtual_size_per_dataset
+        logger.info(
+            f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; "
+            f"raw total size: {sum(raw_sizes)}"
+        )
+        logger.info(
+            f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; "
+            f"resampled total size: {sum(sampled_sizes)}"
+        )
+        if self.sample_ratios is not None:
+            logger.info(
+                f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}"
+            )
+        else:
+            logger.info(f"[{self.split}] A concat dataset")
+        logger.info(
+            f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}"
+        )
+
+    def filter_indices_by_size(self, indices, max_sizes):
+        """Filter a list of sample indices. Remove those that are longer
+            than specified in max_sizes.
+
+        Args:
+            indices (np.array): original array of sample indices
+            max_sizes (int or list[int] or tuple[int]): max sample size,
+                can be defined separately for src and tgt (then list or tuple)
+
+        Returns:
+            np.array: filtered sample array
+            list: list of removed indices
+        """
+        sizes = self.sizes
+        tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
+        src_sizes = (
+            sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
+        )
+
+        return data_utils.filter_paired_dataset_indices_by_size(
+            src_sizes, tgt_sizes, indices, max_sizes
+        )

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/sampled_multi_epoch_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import hashlib
+import logging
+import math
+
+import numpy as np
+
+from fairseq.data import SampledMultiDataset
+
+from .sampled_multi_dataset import CollateFormat, default_virtual_size_func
+
+logger = logging.getLogger(__name__)
+
+
+class SampledMultiEpochDataset(SampledMultiDataset):
+    """Samples from multiple sub-datasets according to sampling ratios
+       using virtual epoch sizes to speed up dataloading.
+    Args:
+        datasets (
+            List[~torch.utils.data.Dataset]
+            or OrderedDict[str, ~torch.utils.data.Dataset]
+        ): datasets
+        sampling_ratios (List[float]): list of probability of each dataset to be sampled
+            (default: None, which corresponds to concating all dataset together).
+        seed (int): RNG seed to use (default: 2).
+        epoch (int): starting epoch number (default: 1).
+        eval_key (str, optional): a key used at evaluation time that causes
+            this instance to pass-through batches from *datasets[eval_key]*.
+        collate_format (CollateFormat):  collater output format, either CollateFormat.ordered_dict or
+            CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
+            the collater to output batches of data mixed from all sub-datasets,
+            and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
+            of sub-datasets.
+            Note that not all sub-datasets will present in a single batch in both formats.
+        virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
+        split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
+        virtual_epoch_size (int): virtual epoch size, the dataset will go through the data by
+            this virtual epoch size one by one to speed up data loading, e.g. indicing and filtering
+            can be performed whenever a virtual epoch is loaded without waiting for the whole dataset to be loaded.
+        shared_collater (bool): whether or not to all sub-datasets have the same collater.
+        shard_epoch (int): the real epoch number for shard selection.
+        shuffle (bool): whether or not to shuffle data (default: True).
+    """
+
+    def __init__(
+        self,
+        datasets,
+        sampling_ratios=None,
+        seed=2,
+        epoch=1,
+        eval_key=None,
+        collate_format=CollateFormat.single,
+        virtual_size=default_virtual_size_func,
+        split="",
+        virtual_epoch_size=None,
+        shared_collater=False,
+        shard_epoch=1,
+        shuffle=True,
+    ):
+        self.virtual_epoch_size = virtual_epoch_size
+        self._current_epoch_start_index = None
+        self._random_global_indices = None
+        self.shard_epoch = shard_epoch if shard_epoch is not None else 1
+        self.load_next_shard = None
+        self._epoch_sizes = None
+        super().__init__(
+            datasets=datasets,
+            sampling_ratios=sampling_ratios,
+            seed=seed,
+            epoch=epoch,
+            eval_key=eval_key,
+            collate_format=collate_format,
+            virtual_size=virtual_size,
+            split=split,
+            shared_collater=shared_collater,
+            shuffle=shuffle,
+        )
+
+    def _setup(self, epoch):
+        self.virtual_epoch_size = (
+            self.virtual_epoch_size
+            if self.virtual_epoch_size is not None
+            else self.virtual_size
+        )
+        if self.virtual_epoch_size > self.virtual_size:
+            logger.warning(
+                f"virtual epoch size {self.virtual_epoch_size} "
+                f"is greater than virtual dataset size {self.virtual_size}"
+            )
+            self.virtual_epoch_size = self.virtual_size
+        self.num_virtual_epochs = math.ceil(self.virtual_size / self.virtual_epoch_size)
+        self._current_epoch_start_index = self._get_epoch_start_index(epoch)
+        logger.info(
+            f"virtual epoch size {self.virtual_epoch_size}; virtual dataset size {self.virtual_size}"
+        )
+
+    def _map_epoch_index_to_global(self, index):
+        index = self._current_epoch_start_index + index
+        # add randomness
+        return self._random_global_indices[index]
+
+    @property
+    def sizes(self):
+        if self._epoch_sizes is not None:
+            return self._epoch_sizes
+        _sizes = super().sizes
+        indices = self._random_global_indices[
+            self._current_epoch_start_index : self._current_epoch_start_index
+            + len(self)
+        ]
+        self._epoch_sizes = _sizes[indices]
+        # del super()._sizes to save memory
+        del self._sizes
+        self._sizes = None
+        return self._epoch_sizes
+
+    def _get_dataset_and_index(self, index):
+        i = self._map_epoch_index_to_global(index)
+        return super()._get_dataset_and_index(i)
+
+    def __len__(self):
+        return (
+            self.virtual_epoch_size
+            if self._current_epoch_start_index + self.virtual_epoch_size
+            < self.virtual_size
+            else self.virtual_size - self._current_epoch_start_index
+        )
+
+    def set_epoch(self, epoch):
+        if self._current_epoch_start_index is None:
+            # initializing epoch idnices of a virtual dataset
+            self._setup(epoch)
+            self._next_virtual_epoch(epoch)
+        else:
+            # working on already intialized epoch indices
+            if epoch == self._cur_epoch:
+                # re-enter so return
+                return
+            self._next_virtual_epoch(epoch)
+
+    def _get_epoch_start_index(self, epoch):
+        assert epoch >= 1  # fairseq is using 1-based epoch everywhere
+        return ((epoch - 1) % self.num_virtual_epochs) * self.virtual_epoch_size
+
+    def _next_global_indices(self, epoch):
+        rng = np.random.RandomState(
+            [
+                int(
+                    hashlib.sha1(
+                        str(self.__class__.__name__).encode("utf-8")
+                    ).hexdigest(),
+                    16,
+                )
+                % (2**32),
+                self.seed % (2**32),  # global seed
+                epoch,  # epoch index,
+            ]
+        )
+        del self._random_global_indices
+        self._random_global_indices = rng.choice(
+            self.virtual_size, self.virtual_size, replace=False
+        )
+        if self.load_next_shard is None:
+            self.load_next_shard = False
+        else:
+            # increase shard epoch for next loading
+            self.shard_epoch += 1
+            self.load_next_shard = True
+            logger.info(
+                "to load next epoch/shard in next load_dataset: "
+                f"epoch={epoch}/shard_epoch={self.shard_epoch}"
+            )
+
+    def _next_virtual_epoch(self, epoch):
+        index = self._get_epoch_start_index(epoch)
+        if index == 0 or self._random_global_indices is None:
+            # need to start from the beginning,
+            # so call super().set_epoch(epoch) to establish the global virtual indices
+            logger.info(
+                "establishing a new set of global virtual indices for "
+                f"epoch={epoch}/shard_epoch={self.shard_epoch}"
+            )
+            super().set_epoch(epoch)
+            self._next_global_indices(epoch)
+        else:
+            self._cur_epoch = epoch
+
+        # reset cache sizes and ordered_indices for the epoch after moving to a new epoch
+        self._clean_if_not_none(
+            [
+                self._epoch_sizes,
+            ]
+        )
+        self._epoch_sizes = None
+        self._current_epoch_start_index = index

PyTorch/NLP/new-Transformer/fairseq/data/multilingual/sampling_method.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import List
+
+
+logger = logging.getLogger(__name__)
+
+
+def uniform(dataset_sizes: List[int]):
+    return [1.0] * len(dataset_sizes)
+
+
+def temperature_sampling(dataset_sizes, temp):
+    total_size = sum(dataset_sizes)
+    return [(size / total_size) ** (1.0 / temp) for size in dataset_sizes]
+
+
+def make_temperature_sampling(temp=1.0):
+    def sampling_func(dataset_sizes):
+        return temperature_sampling(dataset_sizes, temp)
+
+    return sampling_func
+
+
+def make_ratio_sampling(ratios):
+    def sampling_func(dataset_sizes):
+        return ratios
+
+    return sampling_func
+
+
+class SamplingMethod:
+    @staticmethod
+    def add_arguments(parser):
+        parser.add_argument(
+            "--sampling-method",
+            choices=[
+                "uniform",
+                "temperature",
+                "concat",
+                "RoundRobin",
+            ],
+            type=str,
+            default="concat",
+            help="The method to sample data per language pairs",
+        )
+        parser.add_argument(
+            "--sampling-temperature",
+            default=1.5,
+            type=float,
+            help="only work with --sampling-method temperature",
+        )
+
+    @staticmethod
+    def build_sampler(args, task):
+        return SamplingMethod(args, task)
+
+    def __init__(self, args, task):
+        self.args = args
+        self.task = task
+
+    def is_adaptive(self):
+        return False
+
+    def sampling_method_selector(self):
+        args = self.args
+        logger.info(f"selected sampler: {args.sampling_method}")
+        if args.sampling_method == "uniform":
+            return uniform
+        elif args.sampling_method == "temperature" or self.is_adaptive():
+            return make_temperature_sampling(float(args.sampling_temperature))
+        else:
+            # default to concating all data set together
+            return None

PyTorch/NLP/new-Transformer/fairseq/data/nested_dictionary_dataset.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import OrderedDict
+
+import torch
+from torch.utils.data.dataloader import default_collate
+
+from . import FairseqDataset
+
+
+def _flatten(dico, prefix=None):
+    """Flatten a nested dictionary."""
+    new_dico = OrderedDict()
+    if isinstance(dico, dict):
+        prefix = prefix + "." if prefix is not None else ""
+        for k, v in dico.items():
+            if v is None:
+                continue
+            new_dico.update(_flatten(v, prefix + k))
+    elif isinstance(dico, list):
+        for i, v in enumerate(dico):
+            new_dico.update(_flatten(v, prefix + ".[" + str(i) + "]"))
+    else:
+        new_dico = OrderedDict({prefix: dico})
+    return new_dico
+
+
+def _unflatten(dico):
+    """Unflatten a flattened dictionary into a nested dictionary."""
+    new_dico = OrderedDict()
+    for full_k, v in dico.items():
+        full_k = full_k.split(".")
+        node = new_dico
+        for k in full_k[:-1]:
+            if k.startswith("[") and k.endswith("]"):
+                k = int(k[1:-1])
+            if k not in node:
+                node[k] = OrderedDict()
+            node = node[k]
+        node[full_k[-1]] = v
+    return new_dico
+
+
+class NestedDictionaryDataset(FairseqDataset):
+    def __init__(self, defn, sizes=None):
+        super().__init__()
+        self.defn = _flatten(defn)
+        self.sizes = [sizes] if not isinstance(sizes, (list, tuple)) else sizes
+
+        first = None
+        for v in self.defn.values():
+            if not isinstance(
+                v,
+                (
+                    FairseqDataset,
+                    torch.utils.data.Dataset,
+                ),
+            ):
+                raise ValueError("Expected Dataset but found: {}".format(v.__class__))
+            first = first or v
+            if len(v) > 0:
+                assert len(v) == len(first), "dataset lengths must match"
+
+        self._len = len(first)
+
+    def __getitem__(self, index):
+        return OrderedDict((k, ds[index]) for k, ds in self.defn.items())
+
+    def __len__(self):
+        return self._len
+
+    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 suitable for forwarding with a Model
+        """
+        if len(samples) == 0:
+            return {}
+        sample = OrderedDict()
+        for k, ds in self.defn.items():
+            try:
+                sample[k] = ds.collater([s[k] for s in samples])
+            except NotImplementedError:
+                sample[k] = default_collate([s[k] for s in samples])
+        return _unflatten(sample)
+
+    def num_tokens(self, index):
+        """Return the number of tokens in a sample. This value is used to
+        enforce ``--max-tokens`` during batching."""
+        return max(s[index] for s in self.sizes)
+
+    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``."""
+        if len(self.sizes) == 1:
+            return self.sizes[0][index]
+        else:
+            return (s[index] for s in self.sizes)
+
+    @property
+    def supports_prefetch(self):
+        """Whether this dataset supports prefetching."""
+        return any(ds.supports_prefetch for ds in self.defn.values())
+
+    def prefetch(self, indices):
+        """Prefetch the data required for this epoch."""
+        for ds in self.defn.values():
+            if getattr(ds, "supports_prefetch", False):
+                ds.prefetch(indices)
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return all(ds.can_reuse_epoch_itr_across_epochs for ds in self.defn.values())
+
+    def set_epoch(self, epoch):
+        super().set_epoch(epoch)
+        for ds in self.defn.values():
+            ds.set_epoch(epoch)