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

In this tutorial, we implement xFormers: a practical toolkit for building fast, memory-efficient Transformer models on GPUs. We begin by validating memory-efficient attention against a standard attention implementation, then compare their speed and memory consumption across different sequence lengths. We then examine causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi positional biases. Finally, we combine these techniques into a trainable GPT-style model that uses xFormers attention, SwiGLU feed-forward layers, and automatic mixed-precision training.

Setting Up xFormers and Validating Memory-Efficient Attention

import subprocess, sys

def _pip(*a): subprocess.run([sys.executable, "-m", "pip", "install", *a], check=False)

try: