fine-tuning.py

# start with torchrun --nproc-per-node <n-gpu's> fine-tuning.py
import os

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    TrainingArguments,
    Trainer,
    BitsAndBytesConfig,
    TrainerCallback,
)
from datasets import load_from_disk
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from peft.tuners.lora import LoraLayer
from accelerate import Accelerator


batch_size = 2

checkpoint = "google/gemma-2b"
data_dir = "dataset_ro_small_v1/"
save_dir = "gemma-2b-romanian-1.6gb-finetuned-qlora"
log_dir = "training_logs/"

# load dataset
tokenized_datasets = load_from_disk(f'tokenized_{data_dir}')

tokenized_datasets = tokenized_datasets.shuffle(seed=42)

print(tokenized_datasets)

# load quantized model
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_quant_dtype=torch.float16,
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    load_in_8bit=False,
    quantization_config=bnb_config,
    device_map={ "": Accelerator().process_index }, # see https://github.com/huggingface/trl/issues/1348
    torch_dtype=torch.float16,
    trust_remote_code=True,
    attn_implementation='sdpa',#'flash_attention_2',
    use_cache=False,
)
model = prepare_model_for_kbit_training(model)

# load qlora config
lora_config = LoraConfig(
    lora_alpha=32,
    lora_dropout=0.1,
    r=8,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
)
model = get_peft_model(model, lora_config)

model.print_trainable_parameters()

# load tokenizer from checkpoint
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

# training args
args = TrainingArguments(
    output_dir='training_checkpoints/',
    logging_dir=log_dir,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    evaluation_strategy='no',
    logging_steps=100,
    save_strategy='steps',
    save_steps=100,
    save_total_limit=10,
    gradient_accumulation_steps=4,
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={ "use_reentrant": False },
    num_train_epochs=1,
    warmup_steps=1_000,
    weight_decay=0.001,
    lr_scheduler_type='cosine',
    learning_rate=1e-4,
    max_grad_norm=0.3,
    fp16=True,
    ddp_find_unused_parameters=False,
)

# stop the training loop after 1000 updates
class StopCallback(TrainerCallback):
    def on_step_end(self, args, state, control, **kwargs):
        if state.global_step != 0 and state.global_step % 1000 == 0:
            # stop training
            control.should_training_stop = True

# train as usual
trainer = Trainer(
    model=model,
    args=args,
    data_collator=data_collator,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'],
    tokenizer=tokenizer,
)
trainer.add_callback(StopCallback)

print("Starting training...")

train_checkpoint = os.getenv("TRAIN_CHECKPOINT")
if train_checkpoint is not None:
    trainer.train(train_checkpoint) # resume training from checkpoint dir
else:
    trainer.train()

# save trainer state at end
torch.save(trainer.state.log_history, "trainer_log_history.pth")

model.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)