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from datasets import concatenate_datasets, load_dataset, load_from_disk
import argparse
from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers
from transformers import GPT2TokenizerFast, AutoTokenizer
from datasets import config
from datasets import DatasetDict, Dataset
import logging
def initialize_logger(log_file):
logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s')
def log_parameters(vocab_size, batch_size, fertility_score, proportion_continued_words, log_file='parameters.log'):
initialize_logger(log_file)
logging.info(f"Vocabulary Size: {vocab_size}, Batch Size: {batch_size}, Fertility Score: {fertility_score}, Proportion of Continued word: {proportion_continued_words}")
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
"--batch_size",
type=int,
required=True,
help="Batch size to use for training"
)
parser.add_argument(
"--vocab_size",
type=int,
required=True,
help="Vocabulary size to use for tokenizer"
)
parser.add_argument(
"--use_config",
choices=['xlm-roberta', 'vanilla','llama'],
required=True,
help="Use XLM-RoBERTa config or Vanilla BPE"
)
parser.add_argument(
"--do_evaluate",
action='store_true',
help="Enable evaluation."
)
args = parser.parse_known_args()
return args
def train_tokenizer(args):
code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code']
indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:6000000]
indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:6000000]
wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000]
combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi+indic_datasets_bn+wikipedia_en
data = {
"train":{"text": combined_train_set},
"validation": {"text": []},
"test": {"text": []},
}
# print(data)
custom_dataset = DatasetDict()
for split in data:
custom_dataset[split] = Dataset.from_dict(data[split])
custom_dataset=custom_dataset["train"]
def batch_iterator():
for idx in range(0, len(custom_dataset), args.batch_size):
yield custom_dataset[idx: idx + args.batch_size]['text']
new_line="\n"
replacing_dict={}
for i in range(5,25):
replacecable_token="<|reserved_special_token_"+str(i)+"|>"
replacing_dict[replacecable_token]=new_line
new_line+="\n"
if args.use_config == 'vanilla':
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
print(f"[INFO] The brown fox jumped over the lazy dog\n{tokenizer.pre_tokenizer.pre_tokenize_str('The brown fox jumped over the lazy dog')}")
print(f"[INFO] Training...")
trainer = trainers.BpeTrainer(vocab_size=args.vocab_size, special_tokens=["<|endoftext|>"])
tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
tokenizer.decoder = decoders.ByteLevel()
tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)
elif args.use_config == 'xlm-roberta':
tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')
trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size)
elif args.use_config == 'llama':
print("skipped")
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')
print(tokenizer)
trained_tokenizer = tokenizer.train_new_from_iterator(batch_iterator(), vocab_size=args.vocab_size, new_special_tokens=["<unk>","<pad>","<mask>"],special_tokens_map=replacing_dict)
trained_tokenizer.save_pretrained('hi-sanghara-xlmr-bgpt-bpe-tokenizer1')
print(f"[INFO] Tokenizer saved to disk")
def main():
args, _ = parse_arguments()
train_tokenizer(args)
if __name__ == "__main__":
main()
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