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

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.

We install and import xFormers, verify GPU availability, and inspect the attention kernels supported by the environment. We define helper functions for measuring CUDA execution time and peak memory consumption. We then validate memory-efficient attention against standard attention to confirm that both produce results that closely match each other.

We benchmark naive attention and xFormers attention across progressively longer sequences using forward and backward passes. We compare their execution times and peak GPU memory usage to observe how xFormers avoids quadratic memory growth. We also apply an implicit lower-triangular mask and verify causal attention against the reference implementation.

We concatenate variable-length sequences and use BlockDiagonalMask to prevent attention from crossing sequence boundaries without padding. We recover the individual outputs and also perform packed causal attention for decoder-style workloads. We then demonstrate grouped-query attention, where multiple query heads share fewer key-value heads to reduce KV-cache requirements.

We construct a custom ALiBi tensor that applies a different linear positional penalty to each attention head. We combine this additive bias with a causal mask so that tokens attend only to valid previous positions. We pass the resulting bias directly to xFormers attention and verify the shape of its output.

We build a compact GPT-style Transformer using causal xFormers attention, residual connections, normalization, and SwiGLU feed-forward layers. We train the model with automatic mixed precision on a synthetic next-token prediction task that counts upward modulo the vocabulary size. We monitor its loss and accuracy to confirm that the complete memory-efficient Transformer learns successfully end-to-end.

In conclusion, we developed a practical understanding of how xFormers improves Transformer efficiency without changing the fundamental attention calculation. We saw how memory-efficient kernels reduce the cost of long sequences, while causal masks, packed sequences, grouped-query attention, and additive biases support realistic training and inference workflows. We concluded by integrating these capabilities into a compact GPT model and training it end-to-end, giving us a strong foundation for applying xFormers to larger language models and more demanding datasets.