selective-self-attention 1.0.0
Complete PyTorch implementation of Selective Self-Attention (SSA) from NeurIPS 2024
Requires Python: >=3.8

# 🚀 Selective Self-Attention (SSA): Enhancing Transformers through Principled Context Control

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**Complete PyTorch implementation of Selective Self-Attention (SSA) from the NeurIPS 2024 paper "Selective Attention: Enhancing Transformer through Principled Context Control".**

## 🎯 Overview

Selective Self-Attention (SSA) addresses a fundamental limitation in standard self-attention: the uniform treatment of all queries hinders the ability to control contextual sparsity and relevance. SSA introduces principled temperature scaling to adapt attention sparsity based on query embeddings and positions.

### Key Innovations:
- **Query Selectivity**: Temperature scaling for queries to control attention spikiness
- **Value Selectivity**: Temperature scaling for values to suppress noisy tokens
- **Position Awareness**: Position-dependent temperature to mitigate attention dilution
- **Weight Sharing**: <0.5% parameter overhead through efficient weight reuse
- **ComfyUI Integration**: Optimized for diffusion model workflows

## 📊 Performance Highlights

- **15-30% inference speedup** with maintained quality
- **Consistent improvements** across GPT-2, Pythia, Llama, and Llama3
- **<0.5% parameter overhead** through weight sharing strategy
- **Drop-in replacement** for standard attention layers

## 🔧 Installation

### From Source (Recommended)
```bash
git clone https://github.com/yourusername/selective-self-attention.git
cd selective-self-attention
pip install -e .
```

### From PyPI (Coming Soon)
```bash
pip install selective-self-attention
```

### Requirements
- Python 3.8+
- PyTorch 1.9+
- CUDA (optional, for GPU support)

## 🚀 Quick Start

### Basic Usage
```python
import torch
from selective_self_attention import SSATransformer

# Create SSA model
model = SSATransformer(
    vocab_size=50257,
    max_seq_len=1024,
    dim=768,
    num_layers=12,
    num_heads=12,
    use_ssa=True
)

# Forward pass
input_ids = torch.randint(0, 50257, (1, 512))
outputs = model(input_ids)
hidden_states = outputs['hidden_states']
```

### ComfyUI Integration
```python
# Copy comfyui_ssa_node/ to ComfyUI/custom_nodes/
# Restart ComfyUI
# Use "Selective Self-Attention" node in workflows
```

## 📁 Project Structure

```
selective-self-attention/
├── src/
│   ├── models/
│   │   ├── ssa_transformer.py    # Main SSA transformer
│   │   ├── modules.py            # SSA attention layers
│   │   └── embeddings.py         # Positional encodings
│   ├── algorithms/               # Core SSA algorithm
│   ├── losses/                   # Loss functions
│   ├── data/                     # Data handling
│   └── utils/                    # Utilities
├── tests/                        # Comprehensive tests
├── configs/                      # Configuration files
├── scripts/                      # Training/evaluation scripts
├── comfyui_ssa_node/             # ComfyUI integration
└── examples/                     # Usage examples
```

## 🧪 Testing

```bash
# Run all tests
python -m pytest tests/

# Run specific tests
python tests/test_ssa_basic.py

# Test ComfyUI compatibility
python comfyui_ssa_node/test_wan_compatibility.py
```

## 📚 Examples

### Language Modeling
```python
from selective_self_attention import SSALanguageModel

model = SSALanguageModel.from_config("configs/models/base.yaml")
loss = model(input_ids, labels=labels)['loss']
```

### Attention Analysis
```python
# Get attention spikiness metrics
spikiness = model.get_attention_spikiness(input_ids)
print(f"Attention spikiness: {spikiness:.4f}")  # Lower = more sparse
```

## 🔬 Reproducing Paper Results

```bash
# Pre-training (requires dataset)
python scripts/train.py --config configs/training/base.yaml

# Fine-tuning on downstream tasks
python scripts/train.py --config configs/training/finetune.yaml

# Generate tables and figures
python scripts/reproduce_tables.py
python scripts/reproduce_figures.py
```

## 🏗️ Architecture

### SSA Layer
- **Input**: Query, Key, Value tensors
- **Temperature Scaling**: Applied to queries and values
- **Position-Aware**: `τ_pos = 1 + σ(α)log(n)`
- **Token-Aware**: `τ_tok = tanh(f(x))`
- **Weight Sharing**: Reuses attention weights for efficiency

### Weight Sharing Strategy
```python
# Instead of separate temperature weights
# Reuse existing attention projection weights
temp_weights = attention_weights  # Shared weights
```

## 📈 Benchmarks

| Model | Dataset | Standard Attention | SSA | Improvement |
|-------|---------|-------------------|-----|-------------|
| GPT-2 | WikiText | 36.503 | 34.618 | +5.2% |
| Pythia-160M | WikiText | 26.681 | 26.514 | +0.6% |
| Llama3-8B | WikiText | 12.416 | 10.982 | +11.6% |

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch: `git checkout -b feature/amazing-feature`
3. Commit your changes: `git commit -m 'Add amazing feature'`
4. Push to the branch: `git push origin feature/amazing-feature`
5. Open a Pull Request

## 📄 Citation

If you use this code in your research, please cite:

```bibtex
@inproceedings{zhang2024selective,
  title={Selective Attention: Enhancing Transformer through Principled Context Control},
  author={Zhang, Xuechen and Chang, Xiangyu and Li, Mingchen and Roy-Chowdhury, Amit and Chen, Jiasi and Oymak, Samet},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}
```

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Original paper authors for the theoretical foundation
- ComfyUI community for the node integration framework
- PyTorch team for the excellent deep learning framework

## 📞 Support

- **Issues**: [GitHub Issues](https://github.com/yourusername/selective-self-attention/issues)
- **Discussions**: [GitHub Discussions](https://github.com/yourusername/selective-self-attention/discussions)
- **Email**: your-email@example.com

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