TiledAttention: a CUDA Tile SDPA Kernel for PyTorch

1. 1Install the TiledAttention kernel using cuTile Python
2. 2Implement the TiledAttention forward operator in your PyTorch model
3. 3Configure the kernel to use online softmax and tiled $K,V$ streaming
4. 4Test the performance of your model using the TiledAttention kernel
5. 5Compare the results with other attention mechanisms to evaluate the benefits of TiledAttention

Machine learning engineers and researchers working with PyTorch and NVIDIA GPUs can benefit from this kernel to improve the performance of their models

💡 TiledAttention provides a scalable and efficient way to perform scaled dot-product attention on NVIDIA GPUs, making it a valuable tool for machine learning research

🚀 Accelerate your PyTorch models with TiledAttention, a CUDA tile SDPA kernel for NVIDIA GPUs! 🚀