How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention
This tutorial demonstrates how to build memory-efficient Transformer models using the xFormers library on GPUs. It covers implementing and comparing memory-efficient attention with standard attention, analyzing techniques like causal masking, packed sequences, grouped-query attention (GQA), and ALiBi positional biases. The guide also shows how to combine these methods into a trainable GPT-style model utilizing xFormers attention and SwiGLU feed-forward layers with automatic mixed-precision training. AI
IMPACT Provides practical guidance for optimizing Transformer models, potentially reducing computational costs and improving inference speed.