language_modeling.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
import pickle
import random
import time
import warnings
from typing import Dict, List, Optional

import torch
from torch.utils.data import Dataset

from filelock import FileLock

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)


DEPRECATION_WARNING = (
    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
    "library. You can have a look at this example script for pointers: {0}"
)


class TextDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach soon.
    """

    def __init__(
        self,
        tokenizer: PreTrainedTokenizer,
        file_path: str,
        block_size: int,
        overwrite_cache=False,
        cache_dir: Optional[str] = None,
    ):
        warnings.warn(
            DEPRECATION_WARNING.format(
                "https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py"
            ),
            FutureWarning,
        )
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"

        block_size = block_size - tokenizer.num_special_tokens_to_add(pair=False)

        directory, filename = os.path.split(file_path)
        cached_features_file = os.path.join(
            cache_dir if cache_dir is not None else directory,
            f"cached_lm_{tokenizer.__class__.__name__}_{block_size}_{filename}",
        )

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        lock_path = cached_features_file + ".lock"
        with FileLock(lock_path):

            if os.path.exists(cached_features_file) and not overwrite_cache:
                start = time.time()
                with open(cached_features_file, "rb") as handle:
                    self.examples = pickle.load(handle)
                logger.info(
                    f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                )

            else:
                logger.info(f"Creating features from dataset file at {directory}")

                self.examples = []
                with open(file_path, encoding="utf-8") as f:
                    text = f.read()

                tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))

                for i in range(0, len(tokenized_text) - block_size + 1, block_size):  # Truncate in block of block_size
                    self.examples.append(
                        tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])
                    )
                # Note that we are losing the last truncated example here for the sake of simplicity (no padding)
                # If your dataset is small, first you should look for a bigger one :-) and second you
                # can change this behavior by adding (model specific) padding.

                start = time.time()
                with open(cached_features_file, "wb") as handle:
                    pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
                logger.info(
                    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
                )

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> torch.Tensor:
        return torch.tensor(self.examples[i], dtype=torch.long)


class LineByLineTextDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach soon.
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
        warnings.warn(
            DEPRECATION_WARNING.format(
                "https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py"
            ),
            FutureWarning,
        )
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"
        # Here, we do not cache the features, operating under the assumption
        # that we will soon use fast multithreaded tokenizers from the
        # `tokenizers` repo everywhere =)
        logger.info(f"Creating features from dataset file at {file_path}")

        with open(file_path, encoding="utf-8") as f:
            lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]

        batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)
        self.examples = batch_encoding["input_ids"]
        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


class LineByLineWithRefDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach soon.
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
        warnings.warn(
            DEPRECATION_WARNING.format(
                "https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm_wwm.py"
            ),
            FutureWarning,
        )
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"
        assert os.path.isfile(ref_path), f"Ref file path {file_path} not found"
        # Here, we do not cache the features, operating under the assumption
        # that we will soon use fast multithreaded tokenizers from the
        # `tokenizers` repo everywhere =)
        logger.info(f"Creating features from dataset file at {file_path}")
        logger.info(f"Use ref segment results at {ref_path}")
        with open(file_path, encoding="utf-8") as f:
            data = f.readlines()  # use this method to avoid delimiter '\u2029' to split a line
        data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]
        # Get ref inf from file
        with open(ref_path, encoding="utf-8") as f:
            ref = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
        assert len(data) == len(ref)

        batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size)
        self.examples = batch_encoding["input_ids"]
        self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples]

        n = len(self.examples)
        for i in range(n):
            self.examples[i]["chinese_ref"] = torch.tensor(ref[i], dtype=torch.long)

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


class LineByLineWithSOPTextDataset(Dataset):
    """
    Dataset for sentence order prediction task, prepare sentence pairs for SOP task
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int):
        warnings.warn(
            DEPRECATION_WARNING.format(
                "https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py"
            ),
            FutureWarning,
        )
        assert os.path.isdir(file_dir)
        logger.info(f"Creating features from dataset file folder at {file_dir}")
        self.examples = []
        # TODO: randomness could apply a random seed, ex. rng = random.Random(random_seed)
        # file path looks like ./dataset/wiki_1, ./dataset/wiki_2
        for file_name in os.listdir(file_dir):
            file_path = os.path.join(file_dir, file_name)
            assert os.path.isfile(file_path)
            article_open = False
            with open(file_path, encoding="utf-8") as f:
                original_lines = f.readlines()
                article_lines = []
                for line in original_lines:
                    if "<doc id=" in line:
                        article_open = True
                    elif "</doc>" in line:
                        article_open = False
                        document = [
                            tokenizer.convert_tokens_to_ids(tokenizer.tokenize(line))
                            for line in article_lines[1:]
                            if (len(line) > 0 and not line.isspace())
                        ]

                        examples = self.create_examples_from_document(document, block_size, tokenizer)
                        self.examples.extend(examples)
                        article_lines = []
                    else:
                        if article_open:
                            article_lines.append(line)

        logger.info("Dataset parse finished.")

    def create_examples_from_document(self, document, block_size, tokenizer, short_seq_prob=0.1):
        """Creates examples for a single document."""

        # Account for special tokens
        max_num_tokens = block_size - tokenizer.num_special_tokens_to_add(pair=True)

        # We *usually* want to fill up the entire sequence since we are padding
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pretraining and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
        if random.random() < short_seq_prob:
            target_seq_length = random.randint(2, max_num_tokens)

        # We DON'T just concatenate all of the tokens from a document into a long
        # sequence and choose an arbitrary split point because this would make the
        # next sentence prediction task too easy. Instead, we split the input into
        # segments "A" and "B" based on the actual "sentences" provided by the user
        # input.
        examples = []
        current_chunk = []  # a buffer stored current working segments
        current_length = 0
        i = 0
        while i < len(document):
            segment = document[i]  # get a segment
            if not segment:
                i += 1
                continue
            current_chunk.append(segment)  # add a segment to current chunk
            current_length += len(segment)  # overall token length
            # if current length goes to the target length or reaches the end of file, start building token a and b
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A` (first) sentence.
                    a_end = 1
                    # if current chunk has more than 2 sentences, pick part of it `A` (first) sentence
                    if len(current_chunk) >= 2:
                        a_end = random.randint(1, len(current_chunk) - 1)
                    # token a
                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    # token b
                    tokens_b = []
                    for j in range(a_end, len(current_chunk)):
                        tokens_b.extend(current_chunk[j])

                    if len(tokens_a) == 0 or len(tokens_b) == 0:
                        continue

                    # switch tokens_a and tokens_b randomly
                    if random.random() < 0.5:
                        is_next = False
                        tokens_a, tokens_b = tokens_b, tokens_a
                    else:
                        is_next = True

                    def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens):
                        """Truncates a pair of sequences to a maximum sequence length."""
                        while True:
                            total_length = len(tokens_a) + len(tokens_b)
                            if total_length <= max_num_tokens:
                                break
                            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
                            assert len(trunc_tokens) >= 1
                            # We want to sometimes truncate from the front and sometimes from the
                            # back to add more randomness and avoid biases.
                            if random.random() < 0.5:
                                del trunc_tokens[0]
                            else:
                                trunc_tokens.pop()

                    truncate_seq_pair(tokens_a, tokens_b, max_num_tokens)
                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    # add special tokens
                    input_ids = tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
                    # add token type ids, 0 for sentence a, 1 for sentence b
                    token_type_ids = tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)

                    example = {
                        "input_ids": torch.tensor(input_ids, dtype=torch.long),
                        "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
                        "sentence_order_label": torch.tensor(0 if is_next else 1, dtype=torch.long),
                    }
                    examples.append(example)
                current_chunk = []  # clear current chunk
                current_length = 0  # reset current text length
            i += 1  # go to next line
        return examples

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i) -> Dict[str, torch.tensor]:
        return self.examples[i]


class TextDatasetForNextSentencePrediction(Dataset):
    """
    This will be superseded by a framework-agnostic approach soon.
    """

    def __init__(
        self,
        tokenizer: PreTrainedTokenizer,
        file_path: str,
        block_size: int,
        overwrite_cache=False,
        short_seq_probability=0.1,
        nsp_probability=0.5,
    ):
        warnings.warn(
            DEPRECATION_WARNING.format(
                "https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py"
            ),
            FutureWarning,
        )
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"

        self.short_seq_probability = short_seq_probability
        self.nsp_probability = nsp_probability

        directory, filename = os.path.split(file_path)
        cached_features_file = os.path.join(
            directory,
            f"cached_nsp_{tokenizer.__class__.__name__}_{block_size}_{filename}",
        )

        self.tokenizer = tokenizer

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        lock_path = cached_features_file + ".lock"

        # Input file format:
        # (1) One sentence per line. These should ideally be actual sentences, not
        # entire paragraphs or arbitrary spans of text. (Because we use the
        # sentence boundaries for the "next sentence prediction" task).
        # (2) Blank lines between documents. Document boundaries are needed so
        # that the "next sentence prediction" task doesn't span between documents.
        #
        # Example:
        # I am very happy.
        # Here is the second sentence.
        #
        # A new document.

        with FileLock(lock_path):
            if os.path.exists(cached_features_file) and not overwrite_cache:
                start = time.time()
                with open(cached_features_file, "rb") as handle:
                    self.examples = pickle.load(handle)
                logger.info(
                    f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                )
            else:
                logger.info(f"Creating features from dataset file at {directory}")

                self.documents = [[]]
                with open(file_path, encoding="utf-8") as f:
                    while True:
                        line = f.readline()
                        if not line:
                            break
                        line = line.strip()

                        # Empty lines are used as document delimiters
                        if not line and len(self.documents[-1]) != 0:
                            self.documents.append([])
                        tokens = tokenizer.tokenize(line)
                        tokens = tokenizer.convert_tokens_to_ids(tokens)
                        if tokens:
                            self.documents[-1].append(tokens)

                logger.info(f"Creating examples from {len(self.documents)} documents.")
                self.examples = []
                for doc_index, document in enumerate(self.documents):
                    self.create_examples_from_document(document, doc_index, block_size)

                start = time.time()
                with open(cached_features_file, "wb") as handle:
                    pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
                logger.info(
                    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
                )

    def create_examples_from_document(self, document: List[List[int]], doc_index: int, block_size: int):
        """Creates examples for a single document."""

        max_num_tokens = block_size - self.tokenizer.num_special_tokens_to_add(pair=True)

        # We *usually* want to fill up the entire sequence since we are padding
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pretraining and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
        if random.random() < self.short_seq_probability:
            target_seq_length = random.randint(2, max_num_tokens)

        current_chunk = []  # a buffer stored current working segments
        current_length = 0
        i = 0

        while i < len(document):
            segment = document[i]
            current_chunk.append(segment)
            current_length += len(segment)
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A`
                    # (first) sentence.
                    a_end = 1
                    if len(current_chunk) >= 2:
                        a_end = random.randint(1, len(current_chunk) - 1)

                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    tokens_b = []

                    if len(current_chunk) == 1 or random.random() < self.nsp_probability:
                        is_random_next = True
                        target_b_length = target_seq_length - len(tokens_a)

                        # This should rarely go for more than one iteration for large
                        # corpora. However, just to be careful, we try to make sure that
                        # the random document is not the same as the document
                        # we're processing.
                        for _ in range(10):
                            random_document_index = random.randint(0, len(self.documents) - 1)
                            if random_document_index != doc_index:
                                break

                        random_document = self.documents[random_document_index]
                        random_start = random.randint(0, len(random_document) - 1)
                        for j in range(random_start, len(random_document)):
                            tokens_b.extend(random_document[j])
                            if len(tokens_b) >= target_b_length:
                                break
                        # We didn't actually use these segments so we "put them back" so
                        # they don't go to waste.
                        num_unused_segments = len(current_chunk) - a_end
                        i -= num_unused_segments
                    # Actual next
                    else:
                        is_random_next = False
                        for j in range(a_end, len(current_chunk)):
                            tokens_b.extend(current_chunk[j])

                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    # add special tokens
                    input_ids = self.tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b)
                    # add token type ids, 0 for sentence a, 1 for sentence b
                    token_type_ids = self.tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b)

                    example = {
                        "input_ids": torch.tensor(input_ids, dtype=torch.long),
                        "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
                        "next_sentence_label": torch.tensor(1 if is_random_next else 0, dtype=torch.long),
                    }

                    self.examples.append(example)

                current_chunk = []
                current_length = 0

            i += 1

    def __len__(self):
        return len(self.examples)

    def __getitem__(self, i):
        return self.examples[i]