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import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset, PrependTokenDataset, AppendTokenDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoTokenizer
from infoxlm.data.tlm_dataset import TLMDataset
class IndexDataset(FairseqDataset):
def __init__(self, indices):
self.indices = indices
self._sizes = [len(i) for i in indices]
@property
def sizes(self):
return self._sizes
def size(self, index):
item = self.__getitem__(index)
return len(item)
def __getitem__(self, index):
item = self.indices[index]
item = torch.LongTensor(item)
return item
def __len__(self):
return len(self.indices)
def collater(self, samples):
raise NotImplementedError
def build_tokenizer(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
return tokenizer
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="CZWin32768/xlm-align")
parser.add_argument("--input_src", type=str, default="")
parser.add_argument("--input_trg", type=str, default="")
parser.add_argument("--output", type=str, default="")
parser.add_argument("--max_pos", type=int, default=256)
args = parser.parse_args()
return args
def save_items(items, prefix, vocab_size):
bin_fn = "%s.bin" % prefix
idx_fn = "%s.idx" % prefix
builder = make_builder(bin_fn, "mmap", vocab_size=vocab_size)
print("builder: " + str(builder))
for item in items: builder.add_item(item)
builder.finalize(idx_fn)
def get_indices(input_fn, tokenizer):
indices = []
with open(input_fn) as fp:
for lid, line in tqdm(enumerate(fp)):
# DEBUG
# if lid > 500: break
line = line.strip()
indices.append(tokenizer.encode(line))
print("tokenize finished.")
return indices
def main(args):
tokenizer = build_tokenizer(args)
src_indices = get_indices(args.input_src, tokenizer)
trg_indices = get_indices(args.input_trg, tokenizer)
src_dataset = IndexDataset(src_indices)
trg_dataset = IndexDataset(trg_indices)
eos = tokenizer.sep_token_id
bos = tokenizer.cls_token_id
max_pos = args.max_pos
datasets = []
src_dataset = TruncateDataset(
StripTokenDataset(src_dataset, eos), max_pos - 2,)
trg_dataset = TruncateDataset(
StripTokenDataset(trg_dataset, eos), max_pos - 2,)
src_dataset = PrependTokenDataset(src_dataset, bos)
trg_dataset = PrependTokenDataset(trg_dataset, bos)
src_dataset = AppendTokenDataset(src_dataset, eos)
trg_dataset = AppendTokenDataset(trg_dataset, eos)
print("| get all items ...")
# items = [i for i in tqdm(dataset)]
items = []
for t1, t2 in tqdm(zip(src_dataset, trg_dataset)):
items.append(t1)
items.append(t2)
print("| writing binary file ...")
prefix = os.path.join(args.output, "train.0")
save_items(items, prefix, len(tokenizer))
if __name__ == "__main__":
args = get_args()
main(args)
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