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

We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training.

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