example_add_method.py

"""
Example: How to Add a New Optimization Method

This script demonstrates how to add a new optimization method to the fine-tuning pipeline.
Run this script to see how to extend the system with new methods.
"""

from optimization_methods import OptimizationMethod, add_method, get_available_methods
from peft import LoraConfig
import torch

class AdaLoRA(OptimizationMethod):
    """
    Example: AdaLoRA with adaptive rank allocation.
    This is a demonstration of how to implement a new optimization method.
    """
    
    def __init__(self):
        super().__init__("adalora", "AdaLoRA with adaptive rank allocation and pruning")
    
    def load_model(self, pretrained_model: str, trust_remote_code: bool = True):
        """
        Load model with AdaLoRA-specific settings.
        In a real implementation, you might use specific AdaLoRA configurations.
        """
        from transformers import AutoModelForCausalLM, AutoTokenizer
        
        use_cuda = torch.cuda.is_available()
        
        tokenizer = AutoTokenizer.from_pretrained(pretrained_model, trust_remote_code=trust_remote_code)
        tokenizer.pad_token = tokenizer.eos_token or tokenizer.pad_token or "[PAD]"
        
        # For AdaLoRA, we might want to use different precision settings
        model = AutoModelForCausalLM.from_pretrained(
            pretrained_model,
            torch_dtype=torch.float16 if use_cuda else torch.float32,
            device_map="auto" if use_cuda else "cpu",
            trust_remote_code=trust_remote_code
        )
        
        return model, tokenizer, "adalora"
    
    def setup_lora_config(self, model_config: dict) -> LoraConfig:
        """
        Set up AdaLoRA-specific configuration.
        AdaLoRA typically uses different target modules and settings.
        """
        return LoraConfig(
            r=model_config['lora_rank'],
            lora_alpha=model_config['lora_alpha'],
            lora_dropout=model_config['lora_dropout'],
            target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],  # More target modules
            task_type="CAUSAL_LM",
            # AdaLoRA-specific settings would go here
            # target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
        )
    
    def get_training_config(self, use_cuda: bool) -> dict:
        """
        Get AdaLoRA-specific training configuration.
        """
        return {
            "fp16": use_cuda,
            "bf16": False,
            "dataloader_pin_memory": use_cuda,
            "gradient_checkpointing": True,  # AdaLoRA often benefits from this
            "max_grad_norm": 1.0  # Different gradient clipping for AdaLoRA
        }

def demonstrate_adding_method():
    """Demonstrate how to add a new optimization method."""
    
    print("Available methods before adding AdaLoRA:")
    print(get_available_methods())
    
    # Add the new method
    add_method("adalora", AdaLoRA())
    
    print("\nAvailable methods after adding AdaLoRA:")
    print(get_available_methods())
    
    # Test the new method
    try:
        from optimization_methods import get_method
        adalora_method = get_method("adalora")
        print(f"\nSuccessfully added method: {adalora_method.name}")
        print(f"Description: {adalora_method.description}")
        
        # Show how it would be used in training
        print("\nExample usage in training:")
        print("python finetune.py --method adalora")
        
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    print("=== Adding New Optimization Method Example ===\n")
    demonstrate_adding_method()
    
    print("\n=== How to Use in Your Code ===")
    print("1. Create a new class inheriting from OptimizationMethod")
    print("2. Implement the required methods: load_model, setup_lora_config, get_training_config")
    print("3. Use add_method() to register it")
    print("4. Run fine-tuning with: python finetune.py --method your_method_name")
    
    print("\n=== Example Implementation ===")
    print("""
class YourNewMethod(OptimizationMethod):
    def __init__(self):
        super().__init__("your_method", "Description of your method")
    
    def load_model(self, pretrained_model: str, trust_remote_code: bool = True):
        # Your model loading logic
        pass
    
    def setup_lora_config(self, model_config: dict) -> LoraConfig:
        # Your LoRA configuration
        pass
    
    def get_training_config(self, use_cuda: bool) -> dict:
        # Your training configuration
        pass

# Register it
add_method("your_method", YourNewMethod())
    """)