modules/voice_conversion/fairseq/binarizer.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 typing as tp
from abc import ABC, abstractmethod
from collections import Counter
from dataclasses import dataclass
from multiprocessing import Pool

import torch

from fairseq.data import Dictionary, indexed_dataset
from fairseq.file_chunker_utils import Chunker, find_offsets
from fairseq.file_io import PathManager
from fairseq.tokenizer import tokenize_line

logger = logging.getLogger("binarizer")


@dataclass
class BinarizeSummary:
    """
    Keep track of what's going on in the binarizer
    """

    num_seq: int = 0
    replaced: tp.Optional[Counter] = None
    num_tok: int = 0

    @property
    def num_replaced(self) -> int:
        if self.replaced is None:
            return 0
        return sum(self.replaced.values())

    @property
    def replaced_percent(self) -> float:
        return 100 * self.num_replaced / self.num_tok

    def __str__(self) -> str:
        base = f"{self.num_seq} sents, {self.num_tok} tokens"
        if self.replaced is None:
            return base

        return f"{base}, {self.replaced_percent:.3}% replaced"

    def merge(self, other: "BinarizeSummary"):
        replaced = None
        if self.replaced is not None:
            replaced = self.replaced
        if other.replaced is not None:
            if replaced is None:
                replaced = other.replaced
            else:
                replaced += other.replaced
        self.replaced = replaced
        self.num_seq += other.num_seq
        self.num_tok += other.num_tok


class Binarizer(ABC):
    """
    a binarizer describes how to take a string and build a tensor out of it
    """

    @abstractmethod
    def binarize_line(
        self,
        line: str,
        summary: BinarizeSummary,
    ) -> torch.IntTensor:
        ...


def _worker_prefix(output_prefix: str, worker_id: int):
    return f"{output_prefix}.pt{worker_id}"


class FileBinarizer:
    """
    An file binarizer can take a file, tokenize it, and binarize each line to a tensor
    """

    @classmethod
    def multiprocess_dataset(
        cls,
        input_file: str,
        dataset_impl: str,
        binarizer: Binarizer,
        output_prefix: str,
        vocab_size=None,
        num_workers=1,
    ) -> BinarizeSummary:
        final_summary = BinarizeSummary()

        offsets = find_offsets(input_file, num_workers)
        # find_offsets returns a list of position [pos1, pos2, pos3, pos4] but we would want pairs:
        # [(pos1, pos2), (pos2, pos3), (pos3, pos4)] to process the chunks with start/end info
        # we zip the list with itself shifted by one to get all the pairs.
        (first_chunk, *more_chunks) = zip(offsets, offsets[1:])
        pool = None
        if num_workers > 1:
            pool = Pool(processes=num_workers - 1)
            worker_results = [
                pool.apply_async(
                    cls._binarize_chunk_and_finalize,
                    args=(
                        binarizer,
                        input_file,
                        start_offset,
                        end_offset,
                        _worker_prefix(
                            output_prefix,
                            worker_id,
                        ),
                        dataset_impl,
                    ),
                    kwds={
                        "vocab_size": vocab_size,
                    }
                    if vocab_size is not None
                    else {},
                )
                for worker_id, (start_offset, end_offset) in enumerate(
                    more_chunks, start=1
                )
            ]

            pool.close()
            pool.join()
            for r in worker_results:
                summ = r.get()
                final_summary.merge(summ)

        # do not close the bin file as we need to merge the worker results in
        final_ds, summ = cls._binarize_file_chunk(
            binarizer,
            input_file,
            offset_start=first_chunk[0],
            offset_end=first_chunk[1],
            output_prefix=output_prefix,
            dataset_impl=dataset_impl,
            vocab_size=vocab_size if vocab_size is not None else None,
        )
        final_summary.merge(summ)

        if num_workers > 1:
            for worker_id in range(1, num_workers):
                # merge the worker outputs
                worker_output_prefix = _worker_prefix(
                    output_prefix,
                    worker_id,
                )
                final_ds.merge_file_(worker_output_prefix)
                try:
                    os.remove(indexed_dataset.data_file_path(worker_output_prefix))
                    os.remove(indexed_dataset.index_file_path(worker_output_prefix))
                except Exception as e:
                    logger.error(
                        f"couldn't remove {worker_output_prefix}.*", exc_info=e
                    )

        #  now we can close the file
        idx_file = indexed_dataset.index_file_path(output_prefix)
        final_ds.finalize(idx_file)
        return final_summary

    @staticmethod
    def _binarize_file_chunk(
        binarizer: Binarizer,
        filename: str,
        offset_start: int,
        offset_end: int,
        output_prefix: str,
        dataset_impl: str,
        vocab_size=None,
    ) -> tp.Tuple[tp.Any, BinarizeSummary]:  # (dataset builder, BinarizeSummary)
        """
        creates a dataset builder and append binarized items to it. This function does not
        finalize the builder, this is useful if you want to do other things with your bin file
        like appending/merging other files
        """
        bin_file = indexed_dataset.data_file_path(output_prefix)
        ds = indexed_dataset.make_builder(
            bin_file,
            impl=dataset_impl,
            vocab_size=vocab_size,
        )
        summary = BinarizeSummary()

        with Chunker(
            PathManager.get_local_path(filename), offset_start, offset_end
        ) as line_iterator:
            for line in line_iterator:
                ds.add_item(binarizer.binarize_line(line, summary))

        return ds, summary

    @classmethod
    def _binarize_chunk_and_finalize(
        cls,
        binarizer: Binarizer,
        filename: str,
        offset_start: int,
        offset_end: int,
        output_prefix: str,
        dataset_impl: str,
        vocab_size=None,
    ):
        """
        same as above, but also finalizes the builder
        """
        ds, summ = cls._binarize_file_chunk(
            binarizer,
            filename,
            offset_start,
            offset_end,
            output_prefix,
            dataset_impl,
            vocab_size=vocab_size,
        )

        idx_file = indexed_dataset.index_file_path(output_prefix)
        ds.finalize(idx_file)

        return summ


class VocabularyDatasetBinarizer(Binarizer):
    """
    Takes a Dictionary/Vocabulary, assign ids to each
    token using the dictionary encode_line function.
    """

    def __init__(
        self,
        dict: Dictionary,
        tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line,
        append_eos: bool = True,
        reverse_order: bool = False,
        already_numberized: bool = False,
    ) -> None:
        self.dict = dict
        self.tokenize = tokenize
        self.append_eos = append_eos
        self.reverse_order = reverse_order
        self.already_numberized = already_numberized
        super().__init__()

    def binarize_line(
        self,
        line: str,
        summary: BinarizeSummary,
    ):
        if summary.replaced is None:
            summary.replaced = Counter()

        def replaced_consumer(word, idx):
            if idx == self.dict.unk_index and word != self.dict.unk_word:
                summary.replaced.update([word])

        if self.already_numberized:
            id_strings = line.strip().split()
            id_list = [int(id_string) for id_string in id_strings]
            if self.reverse_order:
                id_list.reverse()
            if self.append_eos:
                id_list.append(self.dict.eos())
            ids = torch.IntTensor(id_list)
        else:
            ids = self.dict.encode_line(
                line=line,
                line_tokenizer=self.tokenize,
                add_if_not_exist=False,
                consumer=replaced_consumer,
                append_eos=self.append_eos,
                reverse_order=self.reverse_order,
            )

        summary.num_seq += 1
        summary.num_tok += len(ids)
        return ids


class AlignmentDatasetBinarizer(Binarizer):
    """
    binarize by parsing a set of alignments and packing
    them in a tensor (see utils.parse_alignment)
    """

    def __init__(
        self,
        alignment_parser: tp.Callable[[str], torch.IntTensor],
    ) -> None:
        super().__init__()
        self.alignment_parser = alignment_parser

    def binarize_line(
        self,
        line: str,
        summary: BinarizeSummary,
    ):
        ids = self.alignment_parser(line)
        summary.num_seq += 1
        summary.num_tok += len(ids)
        return ids


class LegacyBinarizer:
    @classmethod
    def binarize(
        cls,
        filename: str,
        dico: Dictionary,
        consumer: tp.Callable[[torch.IntTensor], None],
        tokenize: tp.Callable[[str], tp.List[str]] = tokenize_line,
        append_eos: bool = True,
        reverse_order: bool = False,
        offset: int = 0,
        end: int = -1,
        already_numberized: bool = False,
    ) -> tp.Dict[str, int]:
        binarizer = VocabularyDatasetBinarizer(
            dict=dico,
            tokenize=tokenize,
            append_eos=append_eos,
            reverse_order=reverse_order,
            already_numberized=already_numberized,
        )
        return cls._consume_file(
            filename,
            binarizer,
            consumer,
            offset_start=offset,
            offset_end=end,
        )

    @classmethod
    def binarize_alignments(
        cls,
        filename: str,
        alignment_parser: tp.Callable[[str], torch.IntTensor],
        consumer: tp.Callable[[torch.IntTensor], None],
        offset: int = 0,
        end: int = -1,
    ) -> tp.Dict[str, int]:
        binarizer = AlignmentDatasetBinarizer(alignment_parser)
        return cls._consume_file(
            filename,
            binarizer,
            consumer,
            offset_start=offset,
            offset_end=end,
        )

    @staticmethod
    def _consume_file(
        filename: str,
        binarizer: Binarizer,
        consumer: tp.Callable[[torch.IntTensor], None],
        offset_start: int,
        offset_end: int,
    ) -> tp.Dict[str, int]:
        summary = BinarizeSummary()

        with Chunker(
            PathManager.get_local_path(filename), offset_start, offset_end
        ) as line_iterator:
            for line in line_iterator:
                consumer(binarizer.binarize_line(line, summary))

        return {
            "nseq": summary.num_seq,
            "nunk": summary.num_replaced,
            "ntok": summary.num_tok,
            "replaced": summary.replaced,
        }