modules/voice_conversion/fairseq/tasks/sentence_ranking.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

import numpy as np
from fairseq import utils
from fairseq.data import (
    ConcatSentencesDataset,
    Dictionary,
    IdDataset,
    NestedDictionaryDataset,
    NumelDataset,
    NumSamplesDataset,
    PrependTokenDataset,
    RawLabelDataset,
    RightPadDataset,
    SortDataset,
    TruncateDataset,
    data_utils,
)
from fairseq.data.shorten_dataset import maybe_shorten_dataset
from fairseq.tasks import LegacyFairseqTask, register_task


logger = logging.getLogger(__name__)


@register_task("sentence_ranking")
class SentenceRankingTask(LegacyFairseqTask):
    """
    Ranking task on multiple sentences.

    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", metavar="FILE", help="file prefix for data")
        parser.add_argument(
            "--num-classes", type=int, help="number of sentences to be ranked"
        )
        parser.add_argument(
            "--init-token",
            type=int,
            help="add token at the beginning of each batch item",
        )
        parser.add_argument(
            "--separator-token", type=int, help="add separator token between inputs"
        )
        parser.add_argument("--no-shuffle", action="store_true")
        parser.add_argument(
            "--shorten-method",
            default="none",
            choices=["none", "truncate", "random_crop"],
            help="if not none, shorten sequences that exceed --tokens-per-sample",
        )
        parser.add_argument(
            "--shorten-data-split-list",
            default="",
            help="comma-separated list of dataset splits to apply shortening to, "
            'e.g., "train,valid" (default: all dataset splits)',
        )
        parser.add_argument(
            "--max-option-length", type=int, help="max length for each option"
        )

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

    @classmethod
    def load_dictionary(cls, args, filename, source=True):
        """Load the dictionary from the filename

        Args:
            filename (str): the filename
        """
        dictionary = Dictionary.load(filename)
        dictionary.add_symbol("<mask>")
        return dictionary

    @classmethod
    def setup_task(cls, args, **kwargs):
        assert (
            args.criterion == "sentence_ranking"
        ), "Must set --criterion=sentence_ranking"

        # load data dictionary
        data_dict = cls.load_dictionary(
            args,
            os.path.join(args.data, "input0", "dict.txt"),
            source=True,
        )
        logger.info("[input] dictionary: {} types".format(len(data_dict)))
        return SentenceRankingTask(args, data_dict)

    def load_dataset(self, split, combine=False, **kwargs):
        """Load a given dataset split (e.g., train, valid, test)."""

        def get_path(type, split):
            return os.path.join(self.args.data, type, split)

        def make_dataset(type, dictionary):
            split_path = get_path(type, split)

            dataset = data_utils.load_indexed_dataset(
                split_path,
                self.source_dictionary,
                self.args.dataset_impl,
                combine=combine,
            )
            return dataset

        input0 = make_dataset("input0", self.source_dictionary)
        input_options = [
            make_dataset("input{idx}".format(idx=idx + 1), self.source_dictionary)
            for idx in range(self.args.num_classes)
        ]

        if self.args.separator_token is not None:
            input0 = PrependTokenDataset(input0, self.args.separator_token)

        src_tokens = []
        for input_option in input_options:
            if self.args.init_token is not None:
                input_option = PrependTokenDataset(input_option, self.args.init_token)
            if self.args.max_option_length is not None:
                input_option = TruncateDataset(
                    input_option, self.args.max_option_length
                )
            src_token = ConcatSentencesDataset(input_option, input0)
            src_token = maybe_shorten_dataset(
                src_token,
                split,
                self.args.shorten_data_split_list,
                self.args.shorten_method,
                self.args.max_positions,
                self.args.seed,
            )
            src_tokens.append(src_token)

        with data_utils.numpy_seed(self.args.seed):
            shuffle = np.random.permutation(len(src_tokens[0]))

        dataset = {
            "id": IdDataset(),
            "nsentences": NumSamplesDataset(),
            "ntokens": NumelDataset(src_tokens[0], reduce=True),
        }

        for src_token_idx in range(len(src_tokens)):
            dataset.update(
                {
                    "net_input{idx}".format(idx=src_token_idx + 1): {
                        "src_tokens": RightPadDataset(
                            src_tokens[src_token_idx],
                            pad_idx=self.source_dictionary.pad(),
                        ),
                        "src_lengths": NumelDataset(
                            src_tokens[src_token_idx], reduce=False
                        ),
                    }
                }
            )

        label_path = "{}.label".format(get_path("label", split))
        if os.path.exists(label_path):
            with open(label_path) as h:
                dataset.update(
                    target=RawLabelDataset([int(x.strip()) for x in h.readlines()])
                )

        nested_dataset = NestedDictionaryDataset(
            dataset,
            sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])],
        )

        if self.args.no_shuffle:
            dataset = nested_dataset
        else:
            dataset = SortDataset(
                nested_dataset,
                # shuffle
                sort_order=[shuffle],
            )

        logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset)))

        self.datasets[split] = dataset
        return self.datasets[split]

    def build_model(self, args, from_checkpoint=False):
        from fairseq import models

        model = models.build_model(args, self, from_checkpoint)

        model.register_classification_head(
            getattr(args, "ranking_head_name", "sentence_classification_head"),
            num_classes=1,
        )

        return model

    def max_positions(self):
        return self.args.max_positions

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

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