Build Memory-Efficient Transformers with xFormers

A tutorial implements memory-efficient Transformer models on GPUs using xFormers. It validates attention speed and memory across sequence lengths, then covers causal masking, packed sequences, grouped-query attention, ALiBi biases, and SwiGLU layers. The techniques combine into a trainable GPT-style model with automatic mixed-precision training.