LLM Micro Optimizer

What It Does

Installation

pip install torch transformers scipy

Usage

python llm_micro_optimizer.py --model_path <path_to_model> \
                              --optimization_level <low|high> \
                              --save_path <path_to_save_model> \
                              [--distillation_dataset <path_to_dataset>]

Source Code

import argparse
import torch
from transformers import AutoModel
from scipy.sparse import csr_matrix
import os

def prune_weights(model, level):
    """Prunes redundant weights based on the optimization level."""
    for name, param in model.named_parameters():
        if 'weight' in name and param.requires_grad:
            threshold = 0.1 if level == 'high' else 0.05
            mask = torch.abs(param) > threshold
            param.data *= mask.float()
    return model

def compress_embeddings(model):
    """Compresses embeddings to reduce memory footprint."""
    for name, param in model.named_parameters():
        if 'embedding' in name:
            sparse_matrix = csr_matrix(param.detach().numpy())
            param.data = torch.tensor(sparse_matrix.toarray())
    return model

def apply_distillation(model, dataset_path):
    """Applies distillation techniques using a dataset."""
    if not dataset_path or not os.path.exists(dataset_path):
        raise ValueError("Distillation dataset path is invalid or does not exist.")
    # Placeholder for distillation logic (e.g., teacher-student training)
    print("Distillation applied using dataset at", dataset_path)
    return model

def optimize_model(model_path, optimization_level, save_path, distillation_dataset=None):
    """Main function to optimize the model."""
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"Model file not found: {model_path}")

    model = torch.load(model_path)
    if not isinstance(model, torch.nn.Module):
        raise ValueError("The loaded file is not a valid PyTorch model.")

    model = prune_weights(model, optimization_level)
    model = compress_embeddings(model)

    if distillation_dataset:
        model = apply_distillation(model, distillation_dataset)

    torch.save(model, save_path)
    print(f"Optimized model saved to {save_path}")

def main():
    parser = argparse.ArgumentParser(description="LLM Micro Optimizer")
    parser.add_argument("--model_path", required=True, help="Path to the model file to optimize.")
    parser.add_argument("--optimization_level", required=True, choices=['low', 'high'], help="Level of optimization to apply.")
    parser.add_argument("--save_path", required=True, help="Path to save the optimized model.")
    parser.add_argument("--distillation_dataset", help="Path to the distillation dataset (optional).")

    args = parser.parse_args()

    try:
        optimize_model(args.model_path, args.optimization_level, args.save_path, args.distillation_dataset)
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()