streaming/text/pile.py

# Copyright 2022-2024 MosaicML Streaming authors
# SPDX-License-Identifier: Apache-2.0

"""The Pile.

The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller,
high-quality datasets combined together.
"""

from typing import Any, Optional

from transformers.models.auto.tokenization_auto import AutoTokenizer

from streaming.base import StreamingDataset

__all__ = ['StreamingPile']


class StreamingPile(StreamingDataset):
    """Implementation of the the Pile using StreamingDataset.

    Args:
        remote (str, optional): Remote path or directory to download the dataset from. If ``None``,
            its data must exist locally. StreamingDataset uses either ``streams`` or
            ``remote``/``local``. Defaults to ``None``.
        local (str, optional): Local working directory to download shards to. This is where shards
            are cached while they are being used. Uses a temp directory if not set.
            StreamingDataset uses either ``streams`` or ``remote``/``local``. Defaults to ``None``.
        split (str, optional): Which dataset split to use, if any. If provided, we stream from/to
            the ``split`` subdirs of  ``remote`` and ``local``. Defaults to ``None``.
        download_retry (int): Number of download re-attempts before giving up. Defaults to ``2``.
        download_timeout (float): Number of seconds to wait for a shard to download before raising
            an exception. Defaults to ``60``.
        validate_hash (str, optional): Optional hash or checksum algorithm to use to validate
            shards. Defaults to ``None``.
        keep_zip (bool): Whether to keep or delete the compressed form when decompressing
            downloaded shards. If ``False``, keep iff remote is local or no remote. Defaults to
            ``False``.
        epoch_size (int, optional): Number of samples to draw per epoch balanced across all
            streams. If ``None``, takes its value from the total number of underlying samples.
            Provide this field if you are weighting streams relatively to target a larger or
            smaller epoch size. Defaults to ``None``.
        predownload (int, optional): Target number of samples to download per worker in advance
            of current sample. Workers will attempt to download ahead by this many samples during,
            but not before, training. Recommendation is to provide a value greater than per device
            batch size to ensure at-least per device batch size number of samples cached locally.
            If ``None``, its value gets derived using per device batch size and number of
            canonical nodes ``max(batch_size, 256 * batch_size // num_canonical_nodes)``.
            Defaults to ``None``.
        cache_limit (int, optional): Maximum size in bytes of this StreamingDataset's shard cache.
            Before downloading a shard, the least recently used resident shard(s) may be evicted
            (deleted from the local cache) in order to stay under the limit. Set to ``None`` to
            disable shard eviction. Defaults to ``None``.
        partition_algo (str): Which partitioning algorithm to use. Defaults to ``orig``.
        num_canonical_nodes (int, optional): Canonical number of nodes for shuffling with
            resumption. The sample space is divided evenly according to the number of canonical
            nodes. The higher the value, the more independent non-overlapping paths the
            StreamingDataset replicas take through the shards per model replica (increasing data
            source diversity). Defaults to ``None``, which is interpreted as 64 times the number
            of nodes of the initial run.

            .. note::

                For sequential sample ordering, set ``shuffle`` to ``False`` and
                ``num_canonical_nodes`` to the number of physical nodes of the initial run.
        batch_size (int, optional): Per-device batch size, the same as what is passed to the
            DataLoader. This affects how the dataset is partitioned over the workers and is
            necessary for deterministic resumption and optimal performance. Defaults to ``None``.
        shuffle (bool): Whether to iterate over the samples in randomized order. Defaults to
            ``False``.
        shuffle_algo (str): Which shuffling algorithm to use. Defaults to ``py1s``.
        shuffle_seed (int): Seed for Deterministic data shuffling. Defaults to ``9176``.
        shuffle_block_size (int): Unit of shuffle. Defaults to ``1 << 18``.
        tokenizer_name (str): The name of the HuggingFace tokenizer to use to tokenize samples.
        max_seq_len (int): The max sequence length of each token sample.
        group_method (str): How to group text samples into token samples. Currently only supporting
            ``'truncate'``.
    """

    def __init__(self,
                 *,
                 remote: Optional[str] = None,
                 local: Optional[str] = None,
                 split: Optional[str] = None,
                 download_retry: int = 2,
                 download_timeout: float = 60,
                 validate_hash: Optional[str] = None,
                 keep_zip: bool = False,
                 epoch_size: Optional[int] = None,
                 predownload: Optional[int] = None,
                 cache_limit: Optional[int] = None,
                 partition_algo: str = 'orig',
                 num_canonical_nodes: Optional[int] = None,
                 batch_size: Optional[int] = None,
                 shuffle: bool = False,
                 shuffle_algo: str = 'py1s',
                 shuffle_seed: int = 9176,
                 shuffle_block_size: int = 1 << 18,
                 tokenizer_name: str,
                 max_seq_len: int,
                 group_method: str) -> None:
        if group_method not in ['truncate']:
            raise ValueError(f'Only group_method="truncate" is supported at this time.')

        super().__init__(remote=remote,
                         local=local,
                         split=split,
                         download_retry=download_retry,
                         download_timeout=download_timeout,
                         validate_hash=validate_hash,
                         keep_zip=keep_zip,
                         epoch_size=epoch_size,
                         predownload=predownload,
                         cache_limit=cache_limit,
                         partition_algo=partition_algo,
                         num_canonical_nodes=num_canonical_nodes,
                         batch_size=batch_size,
                         shuffle=shuffle,
                         shuffle_algo=shuffle_algo,
                         shuffle_seed=shuffle_seed,
                         shuffle_block_size=shuffle_block_size)

        self.tokenizer_name = tokenizer_name
        self.max_seq_len = max_seq_len
        self.group_method = group_method

        # Build tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name)
        if self.tokenizer.pad_token is None:
            # Some tokenizers (e.g. GPT2 tokenizer) have no padding token which causes bugs
            self.tokenizer.pad_token = self.tokenizer.eos_token

    def _tokenize(self, text_sample: dict[str, Any]):
        """Apply the tokenizer to a sample.

        Args:
            text_sample (Dict[str, Any]): Sample to tokenize.
        """
        if self.group_method == 'truncate':
            truncation = True
            padding = 'max_length'
            max_length = self.max_seq_len
        else:
            truncation = False
            padding = False
            max_length = None
        return self.tokenizer(text_sample['text'],
                              truncation=truncation,
                              padding=padding,
                              max_length=max_length)

    def get_item(self, idx: int) -> Any:
        """Get sample by global index, blocking to load its shard if missing.

        Args:
            idx (int): Sample index.

        Returns:
            Any: Sample data.
        """
        text_sample = super().get_item(idx)
        token_sample = self._tokenize(text_sample)
        # Skip any token grouping, currently only supporting group_method='truncate'
        return token_sample