train.py

#!/usr/bin/env python3
"""
Fine-tuning script for SmolLM2-135M model using Unsloth.

This script demonstrates how to:
1. Install and configure Unsloth
2. Prepare and format training data
3. Configure and run the training process
4. Save and evaluate the model

To run this script:
1. Install dependencies: pip install -r requirements.txt
2. Run: python train.py
"""

import logging
import os
from datetime import datetime
from pathlib import Path
from typing import Union

# isort: off
from unsloth import FastLanguageModel, is_bfloat16_supported  # noqa: E402
from unsloth.chat_templates import get_chat_template  # noqa: E402

# isort: on

from datasets import (
    Dataset,
    DatasetDict,
    IterableDataset,
    IterableDatasetDict,
    load_dataset,
)
from transformers import AutoTokenizer, Trainer, TrainingArguments
from trl import SFTTrainer

# Configuration
max_seq_length = 2048  # Auto supports RoPE Scaling internally
dtype = (
    None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
)
load_in_4bit = True  # Use 4bit quantization to reduce memory usage
validation_split = 0.1  # 10% of data for validation


# Setup logging
def setup_logging():
    """Configure logging for the training process."""
    # Create logs directory if it doesn't exist
    log_dir = Path("logs")
    log_dir.mkdir(exist_ok=True)

    # Create a unique log file name with timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    log_file = log_dir / f"training_{timestamp}.log"

    # Configure logging
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
        handlers=[logging.FileHandler(log_file), logging.StreamHandler()],
    )

    logger = logging.getLogger(__name__)
    logger.info(f"Logging initialized. Log file: {log_file}")
    return logger


logger = setup_logging()


def install_dependencies():
    """Install required dependencies."""
    logger.info("Installing dependencies...")
    try:
        os.system(
            'pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"'
        )
        os.system("pip install --no-deps xformers trl peft accelerate bitsandbytes")
        logger.info("Dependencies installed successfully")
    except Exception as e:
        logger.error(f"Error installing dependencies: {e}")
        raise


def load_model() -> tuple[FastLanguageModel, AutoTokenizer]:
    """Load and configure the model."""
    logger.info("Loading model and tokenizer...")
    try:
        model, tokenizer = FastLanguageModel.from_pretrained(
            model_name="unsloth/SmolLM2-135M-Instruct-bnb-4bit",
            max_seq_length=max_seq_length,
            dtype=dtype,
            load_in_4bit=load_in_4bit,
        )
        logger.info("Base model loaded successfully")

        # Configure LoRA
        model = FastLanguageModel.get_peft_model(
            model,
            r=64,
            target_modules=[
                "q_proj",
                "k_proj",
                "v_proj",
                "o_proj",
                "gate_proj",
                "up_proj",
                "down_proj",
            ],
            lora_alpha=128,
            lora_dropout=0.05,
            bias="none",
            use_gradient_checkpointing="unsloth",
            random_state=3407,
            use_rslora=True,
            loftq_config=None,
        )
        logger.info("LoRA configuration applied successfully")

        return model, tokenizer
    except Exception as e:
        logger.error(f"Error loading model: {e}")
        raise


def load_and_format_dataset(
    tokenizer: AutoTokenizer,
) -> tuple[
    Union[DatasetDict, Dataset, IterableDatasetDict, IterableDataset], AutoTokenizer
]:
    """Load and format the training dataset."""
    logger.info("Loading and formatting dataset...")
    try:
        # Load the code-act dataset
        dataset = load_dataset("xingyaoww/code-act", split="codeact")
        logger.info(f"Dataset loaded successfully. Size: {len(dataset)} examples")

        # Split into train and validation sets
        dataset = dataset.train_test_split(test_size=validation_split, seed=3407)
        logger.info(
            f"Dataset split into train ({len(dataset['train'])} examples) and validation ({len(dataset['test'])} examples) sets"
        )

        # Configure chat template
        tokenizer = get_chat_template(
            tokenizer,
            chat_template="chatml",  # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth
            mapping={
                "role": "from",
                "content": "value",
                "user": "human",
                "assistant": "gpt",
            },  # ShareGPT style
            map_eos_token=True,  # Maps <|im_end|> to </s> instead
        )
        logger.info("Chat template configured successfully")

        def formatting_prompts_func(examples):
            convos = examples["conversations"]
            texts = [
                tokenizer.apply_chat_template(
                    convo, tokenize=False, add_generation_prompt=False
                )
                for convo in convos
            ]
            return {"text": texts}

        # Apply formatting to both train and validation sets
        dataset = DatasetDict(
            {
                "train": dataset["train"].map(formatting_prompts_func, batched=True),
                "validation": dataset["test"].map(
                    formatting_prompts_func, batched=True
                ),
            }
        )
        logger.info("Dataset formatting completed successfully")

        return dataset, tokenizer
    except Exception as e:
        logger.error(f"Error loading/formatting dataset: {e}")
        raise


def create_trainer(
    model: FastLanguageModel,
    tokenizer: AutoTokenizer,
    dataset: Union[DatasetDict, Dataset, IterableDatasetDict, IterableDataset],
) -> Trainer:
    """Create and configure the SFTTrainer."""
    logger.info("Creating trainer...")
    try:
        trainer = SFTTrainer(
            model=model,
            tokenizer=tokenizer,
            train_dataset=dataset["train"],
            eval_dataset=dataset["validation"],
            dataset_num_proc=2,
            packing=False,
            args=TrainingArguments(
                per_device_train_batch_size=2,
                per_device_eval_batch_size=2,
                gradient_accumulation_steps=16,
                warmup_steps=100,
                max_steps=120,
                learning_rate=5e-5,
                fp16=not is_bfloat16_supported(),
                bf16=is_bfloat16_supported(),
                logging_steps=1,
                save_strategy="steps",
                save_steps=30,
                eval_strategy="steps",  # Match save_strategy
                eval_steps=30,  # Match save_steps
                save_total_limit=2,
                optim="adamw_8bit",
                weight_decay=0.01,
                lr_scheduler_type="cosine_with_restarts",
                seed=3407,
                output_dir="outputs",
                gradient_checkpointing=True,
                load_best_model_at_end=True,
                metric_for_best_model="eval_loss",
                greater_is_better=False,
            ),
        )
        logger.info("Trainer created successfully")
        return trainer
    except Exception as e:
        logger.error(f"Error creating trainer: {e}")
        raise


def main():
    """Main training function."""
    try:
        logger.info("Starting training process...")

        # Install dependencies
        install_dependencies()

        # Load model and tokenizer
        model, tokenizer = load_model()

        # Load and prepare dataset
        dataset, tokenizer = load_and_format_dataset(tokenizer)

        # Create trainer
        trainer: Trainer = create_trainer(model, tokenizer, dataset)

        # Train
        logger.info("Starting training...")
        trainer.train()

        # Save model
        logger.info("Saving final model...")
        trainer.save_model("final_model")

        # Print final metrics
        final_metrics = trainer.state.log_history[-1]
        logger.info("\nTraining completed!")
        logger.info(f"Final training loss: {final_metrics.get('loss', 'N/A')}")
        logger.info(f"Final validation loss: {final_metrics.get('eval_loss', 'N/A')}")

    except Exception as e:
        logger.error(f"Error in main training process: {e}")
        raise


if __name__ == "__main__":
    main()