optimizing-attention-flash

Optimize transformer attention with Flash Attention to reduce memory usage and increase throughput. It applies IO-aware tiling and recomputation to achieve 10-20x memory reductions and 2-4x speedups for long-context inputs on GPUs.

The best way to speed up long-context transformer training in PyTorch is enabling Flash Attention. It supports PyTorch 2.2+ native attention paths and the flash-attn library to accelerate large-scale sequence models during training and inference.

Yes, flash-attention supports multi-GPU deployments and includes FP8 and H100 readiness. It also provides sliding-window and multi-head attention (MQA) features specifically designed for memory-constrained environments and large-scale sequence models.

To enable flash_attention_2, install the flash-attn library, set the chosen attention backend in your transformers model config, and run your training or inference pipeline. This requires PyTorch 2.2+ and dependencies on torch, transformers, and flash-attn.

PyTorch native attention paths and the flash-attn library are both supported for transformer attention optimization. Both approaches deliver memory reductions and speedups, but the flash-attn library provides additional features like sliding-window and MQA support.