sft.py

import time
import evaluate
import numpy as np

from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
from transformers import TrainingArguments, Trainer

from utils import (
    get_dataset,
    get_tok_and_model,
    get_open_prompt_data,
    get_dict_dataset,
    get_advance_dataset,)

base_model = "distilgpt2"
tokenizer, model = get_tok_and_model(f"./models/{base_model}")
tokenizer.pad_token = tokenizer.eos_token
rouge = evaluate.load("rouge")

# train_data, test_data = get_open_prompt_data("./data")
# train_dataset, test_dataset = get_dataset(train_data, test_data)
dict_data = get_dict_dataset("./data")
dataset = get_advance_dataset(dict_data)
dataset = dataset.train_test_split(test_size=0.2)

def preprocess_function(examples):
    x_inputs = [x for x in examples["x"]]
    y_inputs = examples["y"]
    model_inputs = tokenizer(x_inputs, max_length=128, truncation=True)

    labels = tokenizer(text_target=y_inputs, max_length=128, truncation=True)

    model_inputs["labels"] = model_inputs["input_ids"]
    return model_inputs

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)

    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
    result["gen_len"] = np.mean(prediction_lens)

    return {k: round(v, 4) for k, v in result.items()}

# data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)


print("tokenize data...")
t1 = time.time()
tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=["x", "y"])
t2 = time.time()
print(f"data tokenize done. process time : {t2 - t1}")


training_args = TrainingArguments(
    output_dir=f"./output/{base_model}_openprpmpt",
    evaluation_strategy="steps",
    eval_steps=20000,
    learning_rate=2e-5,
    lr_scheduler_type="constant",
    report_to="tensorboard",
    per_device_train_batch_size=64,
    per_device_eval_batch_size=32,
    adam_beta1=0.9,
    adam_beta2=0.98,
    save_total_limit=1,
    num_train_epochs=100,
    fp16=True,
    push_to_hub=False,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
)

trainer.train()

import math

eval_results = trainer.evaluate()
print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")