OFA-OCR / fairseq /fairseq_cli /preprocess.py
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#!/usr/bin/env python3
# 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.
"""
Data pre-processing: build vocabularies and binarize training data.
"""
import logging
import os
import shutil
import sys
from collections import Counter
from itertools import zip_longest
from multiprocessing import Pool
from fairseq import options, tasks, utils
from fairseq.binarizer import Binarizer
from fairseq.data import indexed_dataset
from fairseq.file_chunker_utils import find_offsets
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.preprocess")
def main(args):
utils.import_user_module(args)
os.makedirs(args.destdir, exist_ok=True)
logger.addHandler(
logging.FileHandler(
filename=os.path.join(args.destdir, "preprocess.log"),
)
)
logger.info(args)
assert args.dataset_impl != "huffman", "preprocessing.py doesn't support Huffman yet, use HuffmanCodeBuilder directly."
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,
)
target = not args.only_source
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.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))
if args.dict_only:
return
def make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers):
logger.info("[{}] Dictionary: {} types".format(lang, len(vocab)))
n_seq_tok = [0, 0]
replaced = Counter()
def merge_result(worker_result):
replaced.update(worker_result["replaced"])
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 = find_offsets(input_file, num_workers)
(first_chunk, *more_chunks) = zip(offsets, offsets[1:])
pool = None
if num_workers > 1:
pool = Pool(processes=num_workers - 1)
for worker_id, (start_offset, end_offset) in enumerate(
more_chunks, start=1
):
prefix = "{}{}".format(output_prefix, worker_id)
pool.apply_async(
binarize,
(
args,
input_file,
vocab,
prefix,
lang,
start_offset,
end_offset,
),
callback=merge_result,
)
pool.close()
ds = indexed_dataset.make_builder(
dataset_dest_file(args, output_prefix, lang, "bin"),
impl=args.dataset_impl,
vocab_size=len(vocab),
)
merge_result(
Binarizer.binarize(
input_file,
vocab,
lambda t: ds.add_item(t),
offset=first_chunk[0],
end=first_chunk[1],
)
)
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"))
logger.info(
"[{}] {}: {} sents, {} tokens, {:.3}% replaced by {}".format(
lang,
input_file,
n_seq_tok[0],
n_seq_tok[1],
100 * sum(replaced.values()) / n_seq_tok[1],
vocab.unk_word,
)
)
def make_binary_alignment_dataset(input_prefix, output_prefix, num_workers):
nseq = [0]
def merge_result(worker_result):
nseq[0] += worker_result["nseq"]
input_file = input_prefix
offsets = find_offsets(input_file, num_workers)
(first_chunk, *more_chunks) = zip(offsets, offsets[1:])
pool = None
if num_workers > 1:
pool = Pool(processes=num_workers - 1)
for worker_id, (start_offset, end_offset) in enumerate(
more_chunks, start=1
):
prefix = "{}{}".format(output_prefix, worker_id)
pool.apply_async(
binarize_alignments,
(
args,
input_file,
utils.parse_alignment,
prefix,
start_offset,
end_offset,
),
callback=merge_result,
)
pool.close()
ds = indexed_dataset.make_builder(
dataset_dest_file(args, output_prefix, None, "bin"), impl=args.dataset_impl
)
merge_result(
Binarizer.binarize_alignments(
input_file,
utils.parse_alignment,
lambda t: ds.add_item(t),
offset=first_chunk[0],
end=first_chunk[1],
)
)
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, None)
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, None, "idx"))
logger.info("[alignments] {}: parsed {} alignments".format(input_file, nseq[0]))
def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1):
if args.dataset_impl == "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)
else:
make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers)
def make_all(lang, vocab):
if args.trainpref:
make_dataset(vocab, args.trainpref, "train", lang, num_workers=args.workers)
if args.validpref:
for k, validpref in enumerate(args.validpref.split(",")):
outprefix = "valid{}".format(k) if k > 0 else "valid"
make_dataset(
vocab, validpref, outprefix, lang, num_workers=args.workers
)
if args.testpref:
for k, testpref in enumerate(args.testpref.split(",")):
outprefix = "test{}".format(k) if k > 0 else "test"
make_dataset(vocab, testpref, outprefix, lang, num_workers=args.workers)
def make_all_alignments():
if args.trainpref and os.path.exists(args.trainpref + "." + args.align_suffix):
make_binary_alignment_dataset(
args.trainpref + "." + args.align_suffix,
"train.align",
num_workers=args.workers,
)
if args.validpref and os.path.exists(args.validpref + "." + args.align_suffix):
make_binary_alignment_dataset(
args.validpref + "." + args.align_suffix,
"valid.align",
num_workers=args.workers,
)
if args.testpref and os.path.exists(args.testpref + "." + args.align_suffix):
make_binary_alignment_dataset(
args.testpref + "." + args.align_suffix,
"test.align",
num_workers=args.workers,
)
make_all(args.source_lang, src_dict)
if target:
make_all(args.target_lang, tgt_dict)
if args.align_suffix:
make_all_alignments()
logger.info("Wrote preprocessed data to {}".format(args.destdir))
if 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)
def binarize(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True):
ds = indexed_dataset.make_builder(
dataset_dest_file(args, output_prefix, lang, "bin"),
impl=args.dataset_impl,
vocab_size=len(vocab),
)
def consumer(tensor):
ds.add_item(tensor)
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 binarize_alignments(args, filename, parse_alignment, output_prefix, offset, end):
ds = indexed_dataset.make_builder(
dataset_dest_file(args, output_prefix, None, "bin"),
impl=args.dataset_impl,
vocab_size=None,
)
def consumer(tensor):
ds.add_item(tensor)
res = Binarizer.binarize_alignments(
filename, parse_alignment, consumer, offset=offset, end=end
)
ds.finalize(dataset_dest_file(args, output_prefix, None, "idx"))
return res
def dataset_dest_prefix(args, output_prefix, lang):
base = "{}/{}".format(args.destdir, output_prefix)
if lang is not None:
lang_part = ".{}-{}.{}".format(args.source_lang, args.target_lang, lang)
elif args.only_source:
lang_part = ""
else:
lang_part = ".{}-{}".format(args.source_lang, args.target_lang)
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 cli_main():
parser = options.get_preprocessing_parser()
args = parser.parse_args()
main(args)
if __name__ == "__main__":
cli_main()