modules/voice_conversion/fairseq/tasks/denoising.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 os
from dataclasses import dataclass, field
from typing import Any, Optional

import numpy as np
from omegaconf import II, MISSING

from fairseq import utils
from fairseq.data import (
    AppendTokenDataset,
    DenoisingDataset,
    Dictionary,
    IdDataset,
    NestedDictionaryDataset,
    NumelDataset,
    PadDataset,
    PrependTokenDataset,
    StripTokenDataset,
    TokenBlockDataset,
    data_utils,
)
from fairseq.data.encoders.utils import get_whole_word_mask
from fairseq.data.shorten_dataset import maybe_shorten_dataset
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.tasks import FairseqTask, register_task

from ..data.indexed_dataset import get_available_dataset_impl

logger = logging.getLogger(__name__)

SAMPLE_BREAK_MODE_CHOICES = ChoiceEnum(["none", "complete", "complete_doc", "eos"])
SHORTEN_METHOD_CHOICES = ChoiceEnum(["none", "truncate", "random_crop"])
MASK_LENGTH_CHOICES = ChoiceEnum(["subword", "word", "span-poisson"])


@dataclass
class DenoisingConfig(FairseqDataclass):
    data: str = field(
        default=MISSING,
        metadata={"help": "path to data directory"},
    )
    bpe: Optional[str] = field(
        default=None,
        metadata={"help": "TODO"},
    )
    tokens_per_sample: int = field(
        default=512,
        metadata={
            "help": "max number of total tokens over all segments "
            "per sample for dataset"
        },
    )
    sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field(
        default="complete_doc",
        metadata={
            "help": 'If omitted or "none", fills each sample with tokens-per-sample '
            'tokens. If set to "complete", splits samples only at the end '
            "of sentence, but may include multiple sentences per sample. "
            '"complete_doc" is similar but respects doc boundaries. '
            'If set to "eos", includes only one sentence per sample.'
        },
    )
    replace_length: int = field(
        default=0,
        metadata={"help": "TODO, should only allow -1, 0 and 1"},
    )
    mask: float = field(
        default=0.0,
        metadata={"help": "fraction of words/subwords that will be masked"},
    )
    mask_random: float = field(
        default=0.0,
        metadata={"help": "instead of using [MASK], use random token this often"},
    )
    insert: float = field(
        default=0.0,
        metadata={"help": "insert this percentage of additional random tokens"},
    )
    permute: float = field(
        default=0.0,
        metadata={"help": "take this proportion of subwords and permute them"},
    )
    rotate: float = field(
        default=0.5,
        metadata={"help": "rotate this proportion of inputs"},
    )
    poisson_lambda: float = field(
        default=3.0,
        metadata={"help": "randomly shuffle sentences for this proportion of inputs"},
    )
    shuffle_instance: float = field(
        default=0.0,
        metadata={"help": "shuffle this proportion of sentences in all inputs"},
    )
    mask_length: MASK_LENGTH_CHOICES = field(
        default="subword",
        metadata={"help": "mask length to choose"},
    )
    permute_sentences: int = field(
        default=-1,
        metadata={
            "help": "when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)"
        },
    )
    seed: int = II("common.seed")
    shorten_method: SHORTEN_METHOD_CHOICES = field(
        default="none",
        metadata={
            "help": "if not none, shorten sequences that exceed --tokens-per-sample"
        },
    )
    shorten_data_split_list: str = field(
        default="",
        metadata={
            "help": "comma-separated list of dataset splits to apply shortening to, "
            'e.g., "train,valid" (default: all dataset splits)'
        },
    )
    max_source_positions: int = field(
        default=1024,
        metadata={"help": "max number of tokens in the source sequence"},
    )
    max_target_positions: int = field(
        default=1024,
        metadata={"help": "max number of tokens in the target sequence"},
    )
    dataset_impl: Optional[ChoiceEnum(get_available_dataset_impl())] = II(
        "dataset.dataset_impl"
    )


@register_task("denoising", dataclass=DenoisingConfig)
class DenoisingTask(FairseqTask):
    """
    Denoising task for applying sequence to sequence denoising. (ie. BART)
    """

    cfg: DenoisingConfig

    def __init__(self, cfg, dictionary):
        super().__init__(cfg)
        self.dictionary = dictionary

        # add mask token
        self.mask_idx = self.dictionary.add_symbol("<mask>")

    @classmethod
    def setup_task(cls, cfg: DenoisingConfig, **kwargs):
        """Setup the task."""
        paths = utils.split_paths(cfg.data)
        assert len(paths) > 0
        dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
        logger.info("dictionary: {} types".format(len(dictionary)))
        if not hasattr(cfg, "shuffle_instance"):
            cfg.shuffle_instance = False
        return cls(cfg, dictionary)

    def _load_dataset_split(self, split, epoch, combine):
        paths = utils.split_paths(self.cfg.data)
        assert len(paths) > 0
        data_path = paths[(epoch - 1) % len(paths)]
        split_path = os.path.join(data_path, split)

        dataset = data_utils.load_indexed_dataset(
            split_path,
            self.dictionary,
            self.cfg.dataset_impl,
            combine=combine,
        )
        if dataset is None:
            raise FileNotFoundError(
                "Dataset not found: {} ({})".format(split, split_path)
            )

        dataset = StripTokenDataset(dataset, self.dictionary.eos())

        dataset = maybe_shorten_dataset(
            dataset,
            split,
            self.cfg.shorten_data_split_list,
            self.cfg.shorten_method,
            self.cfg.tokens_per_sample,
            self.cfg.seed,
        )

        # create continuous blocks of tokens
        dataset = TokenBlockDataset(
            dataset,
            dataset.sizes,
            self.cfg.tokens_per_sample - 2,
            # one less for <s> and one for </s>
            pad=self.dictionary.pad(),
            eos=self.dictionary.eos(),
            break_mode=self.cfg.sample_break_mode,
            document_sep_len=0,
        )
        logger.info("loaded {} blocks from: {}".format(len(dataset), split_path))

        # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
        dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
        dataset = AppendTokenDataset(dataset, self.source_dictionary.eos())
        return dataset

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

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        dataset = self._load_dataset_split(split, epoch, combine)

        mask_whole_words = (
            get_whole_word_mask(self.cfg.bpe, self.source_dictionary)
            if self.cfg.mask_length != "subword"
            else None
        )

        self.datasets[split] = DenoisingDataset(
            dataset,
            dataset.sizes,
            self.dictionary,
            self.mask_idx,
            mask_whole_words,
            shuffle=self.cfg.shuffle_instance,
            seed=self.cfg.seed,
            mask=self.cfg.mask,
            mask_random=self.cfg.mask_random,
            insert=self.cfg.insert,
            rotate=self.cfg.rotate,
            permute_sentences=self.cfg.permute_sentences,
            bpe=self.cfg.bpe,
            replace_length=self.cfg.replace_length,
            mask_length=self.cfg.mask_length,
            poisson_lambda=self.cfg.poisson_lambda,
        )
        logger.info(
            "Split: {0}, Loaded {1} samples of denoising_dataset".format(
                split,
                len(self.datasets[split]),
            )
        )

    def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
        """
        Generate batches for inference. We assume that the input begins with a
        bos symbol (`<s>`) and ends with an eos symbol (`</s>`).
        """
        pad = self.source_dictionary.pad()
        eos = self.source_dictionary.eos()
        src_dataset = TokenBlockDataset(
            src_tokens,
            src_lengths,
            block_size=self.cfg.tokens_per_sample - 2,  # for <s> and </s>
            pad=pad,
            eos=eos,
            break_mode=self.cfg.sample_break_mode,
            document_sep_len=0,
        )
        prev_output_tokens = PrependTokenDataset(
            StripTokenDataset(src_dataset, eos), eos
        )
        src_dataset = PadDataset(src_dataset, pad_idx=pad, left_pad=False)
        return NestedDictionaryDataset(
            {
                "id": IdDataset(),
                "net_input": {
                    "src_tokens": src_dataset,
                    "src_lengths": NumelDataset(src_dataset, reduce=False),
                    "prev_output_tokens": PadDataset(
                        prev_output_tokens, pad_idx=pad, left_pad=False
                    ),
                },
                "target": src_dataset,
            },
            sizes=[np.array(src_lengths)],
        )

    def max_positions(self):
        """Return the max sentence length allowed by the task."""
        return (self.cfg.max_source_positions, self.cfg.max_target_positions)

    @property
    def source_dictionary(self):
        """Return the source :class:`~fairseq.data.Dictionary`."""
        return self.dictionary

    @property
    def target_dictionary(self):
        """Return the target :class:`~fairseq.data.Dictionary`."""
        return self.dictionary