sho/demo_integration.py

#!/usr/bin/env python
"""
Example script demonstrating how to integrate Genjo with Enso MoE architecture.
"""

import os
import argparse
import torch
from transformers import AutoTokenizer, AutoModel

def create_directory_if_not_exists(path):
    """Create directory if it doesn't exist."""
    if not os.path.exists(path):
        os.makedirs(path)
        print(f"Created directory: {path}")

def main():
    parser = argparse.ArgumentParser(description='Demonstrate Genjo-Enso integration')
    parser.add_argument('--base-model', type=str, default='GSAI-ML/Genjo-8B-Base',
                        help='Base Genjo model to start from')
    parser.add_argument('--output-dir', type=str, default='./genjo-moe',
                        help='Output directory for the converted model')
    parser.add_argument('--num-experts', type=int, default=8,
                        help='Number of experts for the MoE model')
    parser.add_argument('--experts-per-token', type=int, default=2,
                        help='Number of experts to use per token')
    parser.add_argument('--only-demo', action='store_true',
                        help='Only run the demo without conversion')
    args = parser.parse_args()
    
    # Create output directory
    create_directory_if_not_exists(args.output_dir)
    
    if not args.only_demo:
        print(f"This script would convert {args.base_model} to a Genjo-MoE model")
        print(f"Using {args.num_experts} experts with {args.experts_per_token} experts per token")
        print(f"The converted model would be saved to {args.output_dir}")
        print("""
        The conversion would involve:
        1. Loading the base Genjo model
        2. Replacing the standard MLP layers with MoE layers
        3. Initializing experts weights and routing networks
        4. Saving the converted model
        
        This functionality will be implemented as part of the integration
        """)
    
    # Demo of Genjo-Enso integration concept
    print("\nDemonstrating the potential Genjo-Enso integration concept:")
    
    # Sample Genjo generation
    print("\nStandard Genjo generation approach:")
    print("""
    # Genjo generation (diffusion-based)
    x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long)
    x[:, :prompt.shape[1]] = prompt.clone()
    
    # Iterative denoising (as in current Genjo implementation)
    for num_block in range(num_blocks):
        block_mask_index = (x == mask_id)
        num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
        for i in range(steps):
            mask_index = (x == mask_id)
            logits = model(x).logits
            x0 = torch.argmax(add_gumbel_noise(logits), dim=-1)
            # Select tokens to unmask based on confidence
            # Update sequence with new tokens
    """)
    
    # Sample Genjo-MoE integration
    print("\nEnso MoE integration approach:")
    print("""
    # Genjo-MoE generation
    x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long)
    x[:, :prompt.shape[1]] = prompt.clone()
    
    # Iterative denoising with MoE optimization
    for num_block in range(num_blocks):
        block_mask_index = (x == mask_id)
        num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
        for i in range(steps):
            mask_index = (x == mask_id)
            
            # MoE routing optimization
            feature_map = model.encoder(x)
            topk_idx, topk_weight, _ = model.gate(feature_map)
            
            # Batch tokens by expert for parallel processing
            expert_outputs = []
            for expert_idx in range(num_experts):
                tokens_for_expert = (topk_idx == expert_idx)
                if tokens_for_expert.any():
                    expert_output = model.experts[expert_idx](feature_map[tokens_for_expert])
                    expert_outputs.append((expert_output, tokens_for_expert, topk_weight))
            
            # Combine expert outputs and continue with denoising
            # ...
    """)
    
    # Explain benefits
    print("\nKey benefits of Genjo-Enso integration:")
    print("1. Parameter Efficiency: MoE architecture allows scaling to 16B+ parameters with similar compute")
    print("2. Expert Specialization: Different experts can specialize in different language tasks")
    print("3. Optimized Inference: Batch processing of tokens by expert reduces computation")
    print("4. Scalability: Model can scale to larger sizes while maintaining efficient inference")
    
    print("\nThis integration script demonstrates the concept, but actual implementation")
    print("would require deeper integration between the Genjo and Enso codebases.")

if __name__ == "__main__":
    main()