How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention

The xFormers toolkit offers a set of optimization techniques designed to reduce the memory footprint and increase the processing speed of Transformer models on GPUs. By implementing methods like grouped query attention and packed sequence handling, developers can train or run large models more efficiently within existing hardware constraints.