Implementing the Reformer Transformer from Scratch: LSH Attention, Reversible Layers, and What the…

2026-06-13 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, medium

Summary

The Reformer Transformer, published by Kitaev, Kaiser, and Levskaya at ICLR 2020, directly addresses the quadratic memory scaling problem inherent in standard transformer architectures by introducing four key changes: LSH Attention, Reversible Layers, Chunked Feed-Forward Networks, and Axial Positional Encoding. This implementation from scratch in PyTorch highlights the practical challenges of translating abstract paper descriptions into working code, such as managing sequence padding for LSH attention and leveraging `torch.utils.checkpoint` for reversible layers. The project demonstrates how these techniques enable processing long sequences, up to 64,000 tokens, on memory-constrained hardware like a 4GB GTX 1050 Ti, using small hyperparameters (e.g., `dim=64`, 2 layers, 4 heads, `bucket_size=16`). A comprehensive test suite covers 24 cases, ensuring correctness across all architectural components.

Key takeaway

For AI Engineers developing models for long sequence processing, the Reformer's techniques offer a viable path to overcome quadratic memory scaling. You should consider implementing LSH Attention, Reversible Layers, and Axial Positional Encoding to handle sequences up to 64,000 tokens on memory-constrained hardware. Be aware that LSH attention is an approximation, which might impact tasks requiring precise long-range dependencies. This approach allows for deeper understanding and fine-grained control over memory optimization.

Key insights

The Reformer architecture mitigates transformer memory issues through LSH attention, reversible layers, and axial positional encoding, enabling longer sequence processing.

Principles

• Attention can be approximated by hashing similar tokens.
• Activations can be recomputed to save memory.
• Positional embeddings can be factored for scalability.

Method

Implement LSH attention by projecting queries onto random directions, sorting by hash, computing attention within buckets, and unsorting. Use `torch.utils.checkpoint` for reversible layers.

In practice

• Pad sequences to `bucket_size` multiples for LSH attention.
• Use `use_reentrant=False` with `torch.utils.checkpoint`.
• Factor `max_seq_len` for axial positional encoding.

Topics

• Reformer Transformer
• LSH Attention
• Reversible Layers
• Axial Positional Encoding
• Memory Optimization
• PyTorch Implementation

Code references

• aieng-abdullah/reformer-transformer-from-scratch

Best for: Machine Learning Engineer, AI Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.