# 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()