preprocess.py

#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Data pre-processing: build vocabularies and binarize training data.
"""

from collections import Counter, defaultdict
from itertools import zip_longest

from fairseq import options, tasks
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
from fairseq.utils import import_user_module
from multiprocessing import Pool

import os
import shutil


def main(args):
    import_user_module(args)

    print(args)

    os.makedirs(args.destdir, exist_ok=True)
    target = not args.only_source

    task = tasks.get_task(args.task)

    def train_path(lang):
        return "{}{}".format(args.trainpref, ("." + lang) if lang else "")

    def file_name(prefix, lang):
        fname = prefix
        if lang is not None:
            fname += ".{lang}".format(lang=lang)
        return fname

    def dest_path(prefix, lang):
        return os.path.join(args.destdir, file_name(prefix, lang))

    def dict_path(lang):
        return dest_path("dict", lang) + ".txt"

    def build_dictionary(filenames, src=False, tgt=False):
        assert src ^ tgt
        return task.build_dictionary(
            filenames,
            workers=args.workers,
            threshold=args.thresholdsrc if src else args.thresholdtgt,
            nwords=args.nwordssrc if src else args.nwordstgt,
            padding_factor=args.padding_factor,
        )

    if not args.srcdict and os.path.exists(dict_path(args.source_lang)):
        raise FileExistsError(dict_path(args.source_lang))
    if target and not args.tgtdict and os.path.exists(dict_path(args.target_lang)):
        raise FileExistsError(dict_path(args.target_lang))

    if args.copy_ext_dict:
        assert args.joined_dictionary, \
            "--joined-dictionary must be set if --copy-extended-dictionary is specified"
        assert args.workers == 1, \
            "--workers must be set to 1 if --copy-extended-dictionary is specified"

    if args.joined_dictionary:
        assert not args.srcdict or not args.tgtdict, \
            "cannot use both --srcdict and --tgtdict with --joined-dictionary"

        if args.srcdict:
            src_dict = task.load_dictionary(args.srcdict)
        elif args.tgtdict:
            src_dict = task.load_dictionary(args.tgtdict)
        else:
            assert args.trainpref, "--trainpref must be set if --srcdict is not specified"
            src_dict = build_dictionary(
                {train_path(lang) for lang in [args.source_lang, args.target_lang]}, src=True
            )
        tgt_dict = src_dict
    else:
        if args.srcdict:
            src_dict = task.load_dictionary(args.srcdict)
        else:
            assert args.trainpref, "--trainpref must be set if --srcdict is not specified"
            src_dict = build_dictionary([train_path(args.source_lang)], src=True)

        if target:
            if args.tgtdict:
                tgt_dict = task.load_dictionary(args.tgtdict)
            else:
                assert args.trainpref, "--trainpref must be set if --tgtdict is not specified"
                tgt_dict = build_dictionary([train_path(args.target_lang)], tgt=True)
        else:
            tgt_dict = None

    src_dict.save(dict_path(args.source_lang))
    if target and tgt_dict is not None:
        tgt_dict.save(dict_path(args.target_lang))

    def make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers, copy_src_words=None):
        print("| [{}] Dictionary: {} types".format(lang, len(vocab) - 1))
        n_seq_tok = [0, 0]
        replaced = Counter()
        copyied = Counter()

        def merge_result(worker_result):
            replaced.update(worker_result["replaced"])
            copyied.update(worker_result["copied"])
            n_seq_tok[0] += worker_result["nseq"]
            n_seq_tok[1] += worker_result["ntok"]

        input_file = "{}{}".format(
            input_prefix, ("." + lang) if lang is not None else ""
        )
        offsets = Binarizer.find_offsets(input_file, num_workers)
        pool = None
        if num_workers > 1:  # todo: not support copy 
            pool = Pool(processes=num_workers - 1)
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                pool.apply_async(
                    binarize,
                    (
                        args,
                        input_file,
                        vocab,
                        prefix,
                        lang,
                        offsets[worker_id],
                        offsets[worker_id + 1]
                    ),
                    callback=merge_result  
                )
            pool.close()

        ds = indexed_dataset.IndexedDatasetBuilder(
            dataset_dest_file(args, output_prefix, lang, "bin")
        )
        words_list = []

        def binarize_consumer(ids, words):
            ds.add_item(ids)
            words_list.append(words)

        merge_result(
            Binarizer.binarize(
                input_file, vocab, binarize_consumer,
                offset=0, end=offsets[1], copy_ext_dict=args.copy_ext_dict, copy_src_words=copy_src_words
            )
        )
        if num_workers > 1:
            pool.join()
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                temp_file_path = dataset_dest_prefix(args, prefix, lang)
                ds.merge_file_(temp_file_path)
                os.remove(indexed_dataset.data_file_path(temp_file_path))
                os.remove(indexed_dataset.index_file_path(temp_file_path))

        ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))

        print(
            "| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}, {:.3}% <unk> copied from src".format(
                lang,
                input_file,
                n_seq_tok[0],
                n_seq_tok[1],
                100 * sum(replaced.values()) / n_seq_tok[1],
                vocab.unk_word,
                100 * sum(copyied.values()) / n_seq_tok[1]
            )
        )

        return words_list

    def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1, copy_src_words=None):
        if args.output_format == "binary":
            return make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers, copy_src_words)
        elif args.output_format == "raw":
            # Copy original text file to destination folder
            output_text_file = dest_path(
                output_prefix + ".{}-{}".format(args.source_lang, args.target_lang),
                lang,
            )
            shutil.copyfile(file_name(input_prefix, lang), output_text_file)

            return None

    def make_all(lang, vocab, source_words_list_dict=defaultdict(lambda: None)):
        words_list_dict = defaultdict(lambda: None)

        if args.trainpref:
            words_list_dict["train"] = \
                make_dataset(vocab, args.trainpref, "train", lang, num_workers=args.workers,
                             copy_src_words=source_words_list_dict['train'])
        if args.validpref:
            for k, validpref in enumerate(args.validpref.split(",")):
                outprefix = "valid{}".format(k) if k > 0 else "valid"
                words_list_dict["valid"] = \
                    make_dataset(vocab, validpref, outprefix, lang, copy_src_words=source_words_list_dict['valid'])
        if args.testpref:
            for k, testpref in enumerate(args.testpref.split(",")):
                outprefix = "test{}".format(k) if k > 0 else "test"
                words_list_dict["test"] = \
                    make_dataset(vocab, testpref, outprefix, lang, copy_src_words=source_words_list_dict['test'])

        return words_list_dict

    source_words_list_dict = make_all(args.source_lang, src_dict)
    if target:
        target_words_list_dict = make_all(args.target_lang, tgt_dict, source_words_list_dict)

    print("| Wrote preprocessed data to {}".format(args.destdir))

    if False: #args.alignfile:
        assert args.trainpref, "--trainpref must be set if --alignfile is specified"
        src_file_name = train_path(args.source_lang)
        tgt_file_name = train_path(args.target_lang)
        freq_map = {}
        with open(args.alignfile, "r", encoding='utf-8') as align_file:
            with open(src_file_name, "r", encoding='utf-8') as src_file:
                with open(tgt_file_name, "r", encoding='utf-8') as tgt_file:
                    for a, s, t in zip_longest(align_file, src_file, tgt_file):
                        si = src_dict.encode_line(s, add_if_not_exist=False)
                        ti = tgt_dict.encode_line(t, add_if_not_exist=False)
                        ai = list(map(lambda x: tuple(x.split("-")), a.split()))
                        for sai, tai in ai:
                            srcidx = si[int(sai)]
                            tgtidx = ti[int(tai)]
                            if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
                                assert srcidx != src_dict.pad()
                                assert srcidx != src_dict.eos()
                                assert tgtidx != tgt_dict.pad()
                                assert tgtidx != tgt_dict.eos()

                                if srcidx not in freq_map:
                                    freq_map[srcidx] = {}
                                if tgtidx not in freq_map[srcidx]:
                                    freq_map[srcidx][tgtidx] = 1
                                else:
                                    freq_map[srcidx][tgtidx] += 1

        align_dict = {}
        for srcidx in freq_map.keys():
            align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)

        with open(
                os.path.join(
                    args.destdir,
                    "alignment.{}-{}.txt".format(args.source_lang, args.target_lang),
                ),
                "w", encoding='utf-8'
        ) as f:
            for k, v in align_dict.items():
                print("{} {}".format(src_dict[k], tgt_dict[v]), file=f)


    if args.alignfile:
        from fairseq.tokenizer import tokenize_line
        import numpy as np
        assert args.trainpref, "--trainpref must be set if --alignfile is specified"
        src_file_name = train_path(args.source_lang)
        tgt_file_name = train_path(args.target_lang)
        src_labels_list = []
        tgt_labels_list = []
        with open(args.alignfile, "r", encoding='utf-8') as align_file:
            with open(src_file_name, "r", encoding='utf-8') as src_file:
                with open(tgt_file_name, "r", encoding='utf-8') as tgt_file:
                    for a, s, t in zip_longest(align_file, src_file, tgt_file):
                        src_words = tokenize_line(s)
                        tgt_words = tokenize_line(t)
                        ai = list(map(lambda x: tuple(x.split("-")), a.split()))
                        src_labels = np.ones(len(src_words), int)
                        tgt_labels = np.ones(len(tgt_words), int)
                        for sai, tai in ai:
                            if int(tai) >= len(tgt_words):
                                print('Bad case:')
                                print(tgt_words)
                                print(ai)
                                continue
                            src_word = src_words[int(sai)]
                            tgt_word = tgt_words[int(tai)]
                            if src_word == tgt_word:
                                src_labels[int(sai)] = 0
                                tgt_labels[int(tai)] = 0
                        src_labels_list.append(src_labels)
                        tgt_labels_list.append(tgt_labels)

        save_label_file(os.path.join(args.destdir, "train.label.{}.txt".format(args.source_lang)), src_labels_list)
        save_label_file(os.path.join(args.destdir, "train.label.{}.txt".format(args.target_lang)), tgt_labels_list)

def save_label_file(path, label_list):
    with open(path, 'w', encoding='utf-8') as ofile:
        for src_labes in label_list:
            ofile.write(' '.join([str(l) for l in src_labes]) + os.linesep)

def binarize(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True, copy_from=None):
    ds = indexed_dataset.IndexedDatasetBuilder(
        dataset_dest_file(args, output_prefix, lang, "bin")
    )
    words_list = [] # todo: 目前传不出去

    def consumer(ids, words):
        ds.add_item(ids)
        words_list.append(words)

    res = Binarizer.binarize(filename, vocab, consumer, append_eos=append_eos,
                             offset=offset, end=end)
    ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
    return res


def dataset_dest_prefix(args, output_prefix, lang):
    base = "{}/{}".format(args.destdir, output_prefix)
    lang_part = (
        ".{}-{}.{}".format(args.source_lang, args.target_lang, lang) if lang is not None else ""
    )
    return "{}{}".format(base, lang_part)


def dataset_dest_file(args, output_prefix, lang, extension):
    base = dataset_dest_prefix(args, output_prefix, lang)
    return "{}.{}".format(base, extension)


def get_offsets(input_file, num_workers):
    return Binarizer.find_offsets(input_file, num_workers)


def merge_files(files, outpath):
    ds = indexed_dataset.IndexedDatasetBuilder("{}.bin".format(outpath))
    for file in files:
        ds.merge_file_(file)
        os.remove(indexed_dataset.data_file_path(file))
        os.remove(indexed_dataset.index_file_path(file))
    ds.finalize("{}.idx".format(outpath))


def cli_main():
    parser = options.get_preprocessing_parser()
    args = parser.parse_args()
    main(args)


if __name__ == "__main__":
    cli_main()