Overview
Flash Attention is an open-source project that provides a fast, memory-efficient exact attention implementation. It reduces peak memory usage for Transformer attention while maintaining numerical precision, making it suitable for large-scale model training and inference.
Key Features
- Memory-friendly attention implementation to reduce peak GPU memory.
- High-throughput GPU kernels with support for multiple numeric formats.
- Community-maintained open-source code with integration paths into common deep learning frameworks.
Use Cases
- Replace standard attention in large-scale language model training to lower memory use and increase batch sizes.
- Improve inference throughput and latency on memory-constrained devices.
- Serve as a baseline and reference for research and engineering efforts on attention performance.
Technical Details
- Optimized data access and tiling strategies to reduce memory traffic.
- CUDA-based high-performance kernels focusing on parallelism and bandwidth utilization.
- Support for multiple precisions and integration workflows for training and inference.