We reverse-engineered Flash Attention 4
8 hours ago
- #GPU Programming
- #AI Acceleration
- #Transformer Models
- Flash Attention 4 (FA4) is the latest CUDA kernel optimized for Nvidia's Blackwell architecture, offering a ~20% speedup over previous state-of-the-art attention kernels.
- FA4 introduces advanced asynchronous programming techniques, including a complex pipeline of operations managed via warp specialization, improving concurrency and performance.
- Key innovations in FA4 include faster approximate exponentials using a cubic polynomial and a more efficient online softmax algorithm that reduces rescaling operations by a factor of 10.
- The kernel is structured into five specialized warps: Load, MMA (Matrix Multiply-Accumulate), Softmax, Correction, and Epilogue, each handling specific tasks in the attention computation pipeline.
- FA4 leverages Tensor Cores and Tensor Memory for efficient matrix multiplications and intermediate storage, while also utilizing shared memory for data buffering and synchronization.
- The implementation details of FA4, including its use of warpgroups and TMA (Tensor Memory Accelerator), highlight the increasing complexity and sophistication of GPU programming for AI workloads.
- The article provides a detailed breakdown of FA4's architecture, from high-level tile processing to low-level warp operations, making it accessible to both general software engineers and GPU specialists.
- The future of GPU programming is moving towards tile-based, warp-specialized models, with Nvidia investing in new languages and libraries to simplify this paradigm, such as CuTe, CUTLASS, and CuTile.