File size: 4,108 Bytes
0cd5102 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Processing nmt data for finetuning."""
import argparse
import json
import multiprocessing
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir)))
import time
import torch
from megatron.tokenizer import build_tokenizer
from megatron.data import indexed_dataset
class Encoder(object):
def __init__(self, args):
self.args = args
def initializer(self):
# Use Encoder class as a container for global data
Encoder.tokenizer = build_tokenizer(self.args)
def encode(self, text):
ids = {}
ids = Encoder.tokenizer.tokenize(text)
assert len(ids) > 0
return ids, len(text)
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True,
help='Path to input JSON')
group = parser.add_argument_group(title='tokenizer')
group.add_argument('--tokenizer-type', type=str, default='YTTMTokenizer',
choices=['BertWordPieceLowerCase','BertWordPieceCase',
'GPT2BPETokenizer', 'SentencePieceTokenizer'],
help='What type of tokenizer to use.')
group.add_argument('--vocab-file', type=str, default=None,
help='Path to the vocab file')
group.add_argument('--merge-file', type=str, default=None,
help='Path to the BPE merge file (if necessary).')
group = parser.add_argument_group(title='output data')
group.add_argument('--output-prefix', type=str, required=True,
help='Path to binary output file without suffix')
group.add_argument('--dataset-impl', type=str, default='mmap',
choices=['lazy', 'cached', 'mmap'])
group = parser.add_argument_group(title='runtime')
group.add_argument('--workers', type=int, default=1,
help='Number of worker processes to launch')
group.add_argument('--log-interval', type=int, default=100,
help='Interval between progress updates')
args = parser.parse_args()
args.keep_empty = False
# some default/dummy values for the tokenizer
args.rank = 0
args.make_vocab_size_divisible_by = 128
args.tensor_model_parallel_size = 1
args.vocab_extra_ids = 0
return args
def main():
args = get_args()
startup_start = time.time()
print("Opening", args.input)
fin = open(args.input, 'r', encoding='utf-8')
encoder = Encoder(args)
tokenizer = build_tokenizer(args)
pool = multiprocessing.Pool(args.workers, initializer=encoder.initializer)
encoded_sentences = pool.imap(encoder.encode, fin, 25)
print(f"Vocab size: {tokenizer.vocab_size}")
print(f"Output prefix: {args.output_prefix}")
output_bin_file = "{}.bin".format(args.output_prefix)
output_idx_file = "{}.idx".format(args.output_prefix)
builder = indexed_dataset.make_builder(output_bin_file,
impl=args.dataset_impl,
vocab_size=tokenizer.vocab_size)
startup_end = time.time()
proc_start = time.time()
total_bytes_processed = 0
print("Time to startup:", startup_end - startup_start)
for i, (sentence, bytes_processed) in enumerate(encoded_sentences, start=1):
total_bytes_processed += bytes_processed
builder.add_item(torch.IntTensor(sentence))
# documents contain only one sentence.
builder.end_document()
if i % args.log_interval == 0:
current = time.time()
elapsed = current - proc_start
mbs = total_bytes_processed/elapsed/1024/1024
print(f"Processed {i} sentences",
f"({i/elapsed} sentences/s, {mbs} MB/s).",
file=sys.stderr)
builder.finalize(output_idx_file)
if __name__ == '__main__':
main()
|