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from argparse import ArgumentParser
from itertools import chain
from torch import float16
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, default_data_collator
parser = ArgumentParser()
parser.add_argument('-i', '--input', default='data',
help='training data input file')
parser.add_argument('-o', '--output', default='model',
help='output directory for trained model')
args = parser.parse_args()
# Load and tokenize dataset
raw_dataset = load_dataset('text', data_files={'train': args.input}, keep_linebreaks=True)
tokenizer = AutoTokenizer.from_pretrained('gpt2-large', use_fast=True)
tokenized_dataset = raw_dataset.map(lambda examples: tokenizer(examples['text']),
batched=True, remove_columns='text')
# Generate chunks of block_size
block_size = 256 # tokenizer.model_max_length
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
lm_dataset = tokenized_dataset.map(group_texts, batched=True)
# Create and train the model
model = AutoModelForCausalLM.from_pretrained('gpt2-large',
torch_dtype=float16, low_cpu_mem_usage=True).to('cuda')
trainer = Trainer(model, TrainingArguments(output_dir=args.output, per_device_train_batch_size=1),
default_data_collator, lm_dataset['train'])
trainer.train()
trainer.save_model()
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