ssm-bridge 0.0.2
SSM-As-VLM-Bridge: State Space Models as Vision-Language Bridge Layers
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, vision-language, state-space-models, computer-vision, natural-language-processing

# SSM-As-VLM-Bridge: State Space Models as Vision-Language Bridge Layers

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## 📖 Abstract

This repository presents an exploration into leveraging State Space Models (SSMs) as bridge/adapter layers for Vision-Language Models (VLMs). The project introduces a novel architecture that uses SSMs to facilitate cross-modal understanding between visual and textual representations, potentially offering more efficient and interpretable vision-language fusion compared to traditional attention-based approaches.

## 🏗️ Model Architecture

### Overview

The SSM-As-VLM-Bridge architecture consists of three main components:

1. **Vision Encoder**: Enhanced Vision Transformer (ViT) for image feature extraction
2. **SSM Bridge**: Multi-layer State Space Model for cross-modal fusion
3. **Language Model**: Transformer-based decoder for text generation

### Detailed Architecture

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Vision Input  │    │   Text Input    │    │   Generated     │
│   (224×224×3)   │    │   (Tokens)      │    │   Text Output   │
└─────────┬───────┘    └─────────┬───────┘    └─────────┬───────┘
          │                      │                      │
          ▼                      ▼                      ▲
┌─────────────────┐    ┌─────────────────┐              │
│  Vision Encoder │    │  Token Embedding│              │
│  (Enhanced ViT) │    │  + Positional   │              │
└─────────┬───────┘    └─────────┬───────┘              │
          │                      │                      │
          ▼                      │                      │
┌─────────────────┐              │                      │
│   SSM Bridge    │              │                      │
│  (Multi-layer   │              │                      │
│   State Space   │              │                      │
│   Model)        │              │                      │
└─────────┬───────┘              │                      │
          │                      │                      │
          └──────────┬───────────┘                      │
                     │                                  │
                     ▼                                  │
            ┌─────────────────┐                         │
            │ Cross-Modal     │                         │
            │ Attention       │                         │
            └─────────┬───────┘                         │
                      │                                 │
                      ▼                                 │
            ┌─────────────────┐                         │
            │ Language        │                         │
            │ Transformer     │                         │
            │ (Decoder)       │                         │
            └─────────┬───────┘                         │
                      │                                 │
                      └─────────────────────────────────┘
```

### Key Components

#### 1. Enhanced Vision Transformer (ViT)
- **Patch Size**: 16×16 pixels
- **Embedding Dimension**: 768
- **Number of Layers**: 12
- **Number of Heads**: 12
- **Input Resolution**: 224×224×3

#### 2. SSM Bridge Layer
- **State Dimension**: 64
- **Hidden Dimension**: 256
- **Number of Layers**: 4
- **Dropout**: 0.1
- **Activation**: Enhanced Swish with numerical stability

#### 3. Cross-Modal Attention
- **Number of Layers**: 2
- **Number of Heads**: 8
- **Multi-Query Attention**: Efficient attention with shared key-value heads

#### 4. Language Model
- **Vocabulary Size**: 32,000
- **Embedding Dimension**: 768
- **Number of Layers**: 12
- **Number of Heads**: 12
- **KV Heads**: 1 (Multi-Query)
- **Max Sequence Length**: 2,048

## 🚀 Installation

### Prerequisites
- Python 3.10+
- PyTorch 2.0+
- CUDA (optional, for GPU acceleration)

### Install from PyPI (Recommended)

```bash
pip install ssm-bridge
```

### Install from Source

```bash
# Clone the repository
git clone https://github.com/kyegomez/SSM-As-VLM-Bridge.git
cd SSM-As-VLM-Bridge

# Install in development mode
pip install -e .
```

### Install Dependencies

```bash
pip install -r requirements.txt
```

## 💻 Usage

### Basic Usage

```python
import torch
from ssm_bridge.model import EnhancedVLM, VLMConfig

# Create configuration
config = VLMConfig(
    img_size=224,
    patch_size=16,
    vision_embed_dim=768,
    vision_num_layers=12,
    vision_num_heads=12,
    vocab_size=32000,
    text_embed_dim=768,
    text_num_layers=12,
    text_num_heads=12,
    text_kv_heads=1,
    max_seq_length=2048,
    ssm_state_dim=64,
    ssm_hidden_dim=256,
    ssm_num_layers=4,
    ssm_dropout=0.1,
    cross_attn_layers=2,
    cross_attn_heads=8,
    dropout=0.1,
    layer_norm_eps=1e-5,
)

# Initialize model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = EnhancedVLM(config).to(device)

# Prepare input data
images = torch.randn(2, 3, 224, 224).to(device)  # Batch of images
tokens = torch.randint(0, 32000, (2, 10)).to(device)  # Text tokens
targets = torch.randint(0, 32000, (2, 10)).to(device)  # Target tokens

# Forward pass
logits, loss = model(images, tokens, targets)
print(f"Logits shape: {logits.shape}, Loss: {loss.item():.4f}")

# Text generation
generated_tokens = model.generate(
    images, 
    max_length=50, 
    temperature=0.8, 
    top_k=50,
    top_p=0.9
)
print(f"Generated tokens shape: {generated_tokens.shape}")
```

### Advanced Configuration

```python
# Custom SSM configuration for different use cases
config = VLMConfig(
    # Vision settings
    img_size=384,  # Higher resolution
    patch_size=16,
    vision_embed_dim=1024,  # Larger embeddings
    vision_num_layers=24,   # Deeper vision encoder
    
    # SSM Bridge settings
    ssm_state_dim=128,      # Larger state dimension
    ssm_hidden_dim=512,     # Larger hidden dimension
    ssm_num_layers=6,       # More SSM layers
    
    # Language settings
    text_embed_dim=1024,    # Match vision embedding
    text_num_layers=24,     # Deeper language model
    max_seq_length=4096,    # Longer sequences
)
```

## 🔬 Research Contributions

### Novel SSM Bridge Design

The SSM bridge introduces several innovations:

1. **Multi-layer SSM Architecture**: Stacked SSM layers with residual connections
2. **Enhanced Numerical Stability**: Clamped activations and proper initialization
3. **Gating Mechanisms**: Adaptive gating for better information flow
4. **Cross-modal State Management**: Efficient state transitions between modalities

### Key Features

- **Efficient Attention**: Multi-Query Attention reduces computational complexity
- **Numerical Stability**: Enhanced Swish activation and proper gradient flow
- **Modular Design**: Easy to modify and extend components
- **Memory Efficient**: Optimized for large-scale training

## 📊 Model Specifications

| Component | Parameter Count | Memory Usage | FLOPs |
|-----------|----------------|--------------|-------|
| Vision Encoder | ~86M | ~2GB | ~15G |
| SSM Bridge | ~2M | ~0.5GB | ~5G |
| Language Model | ~86M | ~2GB | ~20G |
| **Total** | **~174M** | **~4.5GB** | **~40G** |

*Estimates for batch size 1, sequence length 2048, image size 224×224*

## 🎯 TODO & Roadmap

### Immediate Tasks (Next 2-4 weeks)
- [ ] **Train Base Model**: Implement training pipeline with COCO/CC3M datasets
- [ ] **Benchmark Performance**: Compare against CLIP, LLaVA, and other VLMs
- [ ] **Ablation Studies**: Analyze SSM bridge effectiveness
- [ ] **Memory Optimization**: Implement gradient checkpointing and mixed precision

### Medium-term Goals (1-3 months)
- [ ] **All-SSM VLM**: Explore replacing attention with SSMs throughout
- [ ] **Multi-modal SSM**: Extend SSM to handle multiple modalities
- [ ] **Efficient Inference**: Optimize for real-time applications
- [ ] **Pre-trained Models**: Release checkpoints for various scales

### Long-term Vision (3-6 months)
- [ ] **Large-scale Training**: Train on web-scale datasets
- [ ] **Zero-shot Evaluation**: Comprehensive evaluation on VQA, captioning, etc.
- [ ] **Deployment Pipeline**: Easy deployment for production use
- [ ] **Community Models**: Open-source ecosystem for SSM-based VLMs

## 🧪 Experiments & Benchmarks

### Planned Evaluations

1. **Image Captioning**
   - COCO Captions
   - Flickr30k
   - NoCaps

2. **Visual Question Answering**
   - VQA v2.0
   - GQA
   - OK-VQA

3. **Zero-shot Classification**
   - ImageNet-1k
   - ImageNet-21k
   - CIFAR-100

4. **Cross-modal Retrieval**
   - MS-COCO retrieval
   - Flickr30k retrieval

### Baseline Comparisons

- **CLIP**: OpenAI's contrastive learning approach
- **LLaVA**: Large Language and Vision Assistant
- **Flamingo**: DeepMind's few-shot learning model
- **BLIP-2**: Bootstrapping Language-Image Pre-training

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.

### Development Setup

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
# Code formatting
black .
ruff check . --fix
```

## 📚 Citation

If you use this code in your research, please cite:

```bibtex
@misc{gomez2024ssmvlm,
  title={SSM-As-VLM-Bridge: State Space Models as Vision-Language Bridge Layers},
  author={Kye Gomez},
  year={2024},
  howpublished={\url{https://github.com/kyegomez/SSM-As-VLM-Bridge}},
  note={An exploration into leveraging SSMs as bridge/adapter layers for VLMs}
}
```

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- **Kye Gomez** - Original research and implementation
- **PyTorch Team** - Excellent deep learning framework
- **Open Source Community** - Inspiration and collaboration

## 📞 Contact

- **Author**: Kye Gomez
- **Email**: kye@swarms.world
- **GitHub**: [@kyegomez](https://github.com/kyegomez)
- **Discord**: [Join our community](https://discord.gg/jM3Z6M9uMq)

## 🔗 Related Work

- [Mamba: Linear-Time Sequence Modeling](https://arxiv.org/abs/2312.00752)
- [Vision Mamba: Efficient Visual Representation Learning](https://arxiv.org/abs/2401.09417)
- [CLIP: Learning Transferable Visual Representations](https://arxiv.org/abs/2103.00020)
- [LLaVA: Large Language and Vision Assistant](https://arxiv.org/abs/2304.08485)

---

**Note**: This is a research project. The model architecture and implementation are subject to ongoing development and improvement. Please check the [Issues](https://github.com/kyegomez/SSM-As-VLM-Bridge/issues) page for known limitations and planned features.