alpaca-lora/finetune.py

import os

# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
import torch.nn as nn
import bitsandbytes as bnb
from datasets import load_dataset
import transformers
from transformers import AutoTokenizer, AutoConfig, LLaMAForCausalLM, LLaMATokenizer
from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model

model = LLaMAForCausalLM.from_pretrained(
    "./7B/llama-7b",
    load_in_8bit=True,
    max_sequence_length=128,  # data length
    device_map="auto",
)


tokenizer = LLaMATokenizer.from_pretrained("./7B/tokenizer")


def print_trainable_parameters(model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
    )


print_trainable_parameters(model)
model = prepare_model_for_int8_training(model)

config = LoraConfig(
    r=4,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)

print_trainable_parameters(model)

tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id

data = load_dataset("json", data_files="alpaca_data.json")


def generate_prompt(data_point):
    # sorry about the formatting disaster gotta move fast
    if data_point["instruction"]:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{data_point["instruction"]}

### Input:
{data_point["input"]}

### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{data_point["instruction"]}

### Response:"""


data = data.map(
    lambda data_point: tokenizer(
        generate_prompt(data_point),
        truncation=True,
        max_length=128,
        padding="max_length",
    )
)

DATA_SIZE = 51368
MICRO_BATCH_SIZE = 12
BATCH_SIZE = 36
GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
EPOCHS = 3
LEARNING_RATE = 2e-5


trainer = transformers.Trainer(
    model=model,
    train_dataset=data["train"],
    args=transformers.TrainingArguments(
        per_device_train_batch_size=MICRO_BATCH_SIZE,
        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
        warmup_steps=100,
        num_train_epochs=EPOCHS,
        learning_rate=LEARNING_RATE,
        fp16=True,
        logging_steps=1,
        output_dir="lora-alpaca",
        save_total_limit=3,
    ),
    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False
trainer.train(resume_from_checkpoint=False)

model.save_pretrained("lora-alpaca")