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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Processing data for pretraining."""
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
try:
import nltk
nltk_available = True
except ImportError:
nltk_available = False
from megatron.data import indexed_dataset
from megatron.tokenizer import build_tokenizer
# https://stackoverflow.com/questions/33139531/preserve-empty-lines-with-nltks-punkt-tokenizer
class CustomLanguageVars(nltk.tokenize.punkt.PunktLanguageVars):
_period_context_fmt = r"""
\S* # some word material
%(SentEndChars)s # a potential sentence ending
\s* # <-- THIS is what I changed
(?=(?P<after_tok>
%(NonWord)s # either other punctuation
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(?P<next_tok>\S+) # <-- Normally you would have \s+ here
))"""
class IdentitySplitter(object):
def tokenize(self, *text):
return text
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)
if self.args.split_sentences:
if not nltk_available:
print("NLTK is not available to split sentences.")
exit()
library = "tokenizers/punkt/{}.pickle".format(self.args.lang)
print("loading: " + library)
splitter = nltk.load(library)
if self.args.keep_newlines:
# this prevents punkt from eating newlines after sentences
Encoder.splitter = nltk.tokenize.punkt.PunktSentenceTokenizer(
train_text=splitter._params, lang_vars=CustomLanguageVars()
)
else:
Encoder.splitter = splitter
else:
Encoder.splitter = IdentitySplitter()
def encode(self, json_line):
data = json.loads(json_line)
ids = {}
for key in self.args.json_keys:
text = data[key]
doc_ids = []
for sentence in Encoder.splitter.tokenize(text):
sentence_ids = Encoder.tokenizer.tokenize(sentence)
if len(sentence_ids) > 0:
doc_ids.append(sentence_ids)
if len(doc_ids) > 0 and self.args.append_eod:
doc_ids[-1].append(Encoder.tokenizer.eos)
ids[key] = doc_ids
return ids, len(json_line)
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.add_argument(
"--json_keys",
nargs="+",
default=["text"],
help="space separate listed of keys to extract from json",
)
group.add_argument(
"--split_sentences", action="store_true", help="Split documents into sentences."
)
group.add_argument(
"--keep_newlines",
action="store_true",
help="Keep newlines between sentences when splitting.",
)
group = parser.add_argument_group(title="tokenizer")
group.add_argument(
"--tokenizer_type",
type=str,
required=True,
choices=[
"BertWordPieceLowerCase",
"BertWordPieceCase",
"GPT2BPETokenizer",
"SentencePieceTokenizer",
"PretrainedFromHF",
"FalconTokenizer",
],
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.add_argument(
"--append_eod",
action="store_true",
help="Append an <eod> token to the end of a document.",
)
group.add_argument(
"--lang",
type=str,
default="english",
help="Language to use for NLTK-powered sentence splitting.",
)
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,
required=True,
help="Number of worker processes to launch",
)
group.add_argument(
"--chunk_size",
type=int,
required=True,
help="Chunk size assigned to each worker process",
)
group.add_argument(
"--log_interval",
type=int,
default=100,
help="Interval between progress updates",
)
group.add_argument("--vocab_extra_ids", type=int, default=0)
group.add_argument(
"--vocab_extra_ids_list",
type=str,
default=None,
help="comma separated list of special vocab ids to add to the tokenizer",
)
group.add_argument(
"--no_new_tokens",
action="store_false",
dest="new_tokens",
help=(
"Whether to add special tokens (e.g. CLS, MASK, etc) "
"in the sentenciepiece tokenizer or not"
),
)
args = parser.parse_args()
args.keep_empty = False
if args.tokenizer_type.lower().startswith("bert"):
if not args.split_sentences:
print(
"Bert tokenizer detected, are you sure you don't want to split sentences?"
)
# some default/dummy values for the tokenizer
args.rank = 0
args.make_vocab_size_divisible_by = 128
args.tensor_model_parallel_size = 1
return args
def main():
args = get_args()
startup_start = time.time()
print("Opening", args.input)
fin = open(args.input, "r", encoding="utf-8")
if nltk_available and args.split_sentences:
nltk.download("punkt", quiet=True)
encoder = Encoder(args)
tokenizer = build_tokenizer(args)
pool = multiprocessing.Pool(args.workers, initializer=encoder.initializer)
encoded_docs = pool.imap(encoder.encode, fin, args.chunk_size)
# encoded_docs = map(encoder.encode, fin)
level = "document"
if args.split_sentences:
level = "sentence"
print(f"Vocab size: {tokenizer.vocab_size}")
print(f"Output prefix: {args.output_prefix}")
output_bin_files = {}
output_idx_files = {}
builders = {}
for key in args.json_keys:
output_bin_files[key] = "{}_{}_{}.bin".format(args.output_prefix, key, level)
output_idx_files[key] = "{}_{}_{}.idx".format(args.output_prefix, key, level)
builders[key] = indexed_dataset.make_builder(
output_bin_files[key],
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, (doc, bytes_processed) in enumerate(encoded_docs, start=1):
total_bytes_processed += bytes_processed
for key, sentences in doc.items():
if len(sentences) == 0:
continue
for sentence in sentences:
builders[key].add_item(torch.IntTensor(sentence))
builders[key].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} documents",
f"({i/elapsed} docs/s, {mbs} MB/s).",
file=sys.stderr,
)
print("Done! Now finalizing.")
for key in args.json_keys:
builders[key].finalize(output_idx_files[key])
if __name__ == "__main__":
main()
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