modules/voice_conversion/fairseq/tasks/legacy_masked_lm.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 os

import numpy as np
from fairseq import tokenizer, utils
from fairseq.data import ConcatDataset, Dictionary, data_utils, indexed_dataset
from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
from fairseq.data.legacy.masked_lm_dataset import MaskedLMDataset
from fairseq.data.legacy.masked_lm_dictionary import BertDictionary
from fairseq.tasks import LegacyFairseqTask, register_task


logger = logging.getLogger(__name__)


@register_task("legacy_masked_lm")
class LegacyMaskedLMTask(LegacyFairseqTask):
    """
    Task for training Masked LM (BERT) model.
    Args:
        dictionary (Dictionary): the dictionary for the input of the task
    """

    @staticmethod
    def add_args(parser):
        """Add task-specific arguments to the parser."""
        parser.add_argument(
            "data",
            help="colon separated path to data directories list, \
                            will be iterated upon during epochs in round-robin manner",
        )
        parser.add_argument(
            "--tokens-per-sample",
            default=512,
            type=int,
            help="max number of total tokens over all segments"
            " per sample for BERT dataset",
        )
        parser.add_argument(
            "--break-mode", default="doc", type=str, help="mode for breaking sentence"
        )
        parser.add_argument("--shuffle-dataset", action="store_true", default=False)

    def __init__(self, args, dictionary):
        super().__init__(args)
        self.dictionary = dictionary
        self.seed = args.seed

    @classmethod
    def load_dictionary(cls, filename):
        return BertDictionary.load(filename)

    @classmethod
    def build_dictionary(
        cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8
    ):
        d = BertDictionary()
        for filename in filenames:
            Dictionary.add_file_to_dictionary(
                filename, d, tokenizer.tokenize_line, workers
            )
        d.finalize(threshold=threshold, nwords=nwords, padding_factor=padding_factor)
        return d

    @property
    def target_dictionary(self):
        return self.dictionary

    @classmethod
    def setup_task(cls, args, **kwargs):
        """Setup the task."""
        paths = utils.split_paths(args.data)
        assert len(paths) > 0
        dictionary = BertDictionary.load(os.path.join(paths[0], "dict.txt"))
        logger.info("dictionary: {} types".format(len(dictionary)))

        return cls(args, dictionary)

    def load_dataset(self, split, epoch=1, combine=False):
        """Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        loaded_datasets = []

        paths = utils.split_paths(self.args.data)
        assert len(paths) > 0
        data_path = paths[(epoch - 1) % len(paths)]
        logger.info("data_path", data_path)

        for k in itertools.count():
            split_k = split + (str(k) if k > 0 else "")
            path = os.path.join(data_path, split_k)
            ds = indexed_dataset.make_dataset(
                path,
                impl=self.args.dataset_impl,
                fix_lua_indexing=True,
                dictionary=self.dictionary,
            )

            if ds is None:
                if k > 0:
                    break
                else:
                    raise FileNotFoundError(
                        "Dataset not found: {} ({})".format(split, data_path)
                    )

            with data_utils.numpy_seed(self.seed + k):
                loaded_datasets.append(
                    BlockPairDataset(
                        ds,
                        self.dictionary,
                        ds.sizes,
                        self.args.tokens_per_sample,
                        break_mode=self.args.break_mode,
                        doc_break_size=1,
                    )
                )

            logger.info(
                "{} {} {} examples".format(data_path, split_k, len(loaded_datasets[-1]))
            )

            if not combine:
                break

        if len(loaded_datasets) == 1:
            dataset = loaded_datasets[0]
            sizes = dataset.sizes
        else:
            dataset = ConcatDataset(loaded_datasets)
            sizes = np.concatenate([ds.sizes for ds in loaded_datasets])

        self.datasets[split] = MaskedLMDataset(
            dataset=dataset,
            sizes=sizes,
            vocab=self.dictionary,
            pad_idx=self.dictionary.pad(),
            mask_idx=self.dictionary.mask(),
            classif_token_idx=self.dictionary.cls(),
            sep_token_idx=self.dictionary.sep(),
            shuffle=self.args.shuffle_dataset,
            seed=self.seed,
        )