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Reformer

Paid

Train and deploy models efficiently, adapt to new tasks easily, and code with minimal effort.

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About Reformer

Reformer is an efficient and powerful deep learning library for Natural Language Processing (NLP). The library is designed for fast development and experimentation, allowing developers to quickly train models and deploy them in production. Reformer provides an array of features that make it an ideal choice for NLP applications. It has a flexible architecture that allows for quick adaptation to new tasks, a lightweight memory footprint, and a high-performance backend that enables faster model training. Additionally, Reformer features various tools and methods, such as Transformer and RNN architectures, that help developers build and deploy models quickly and easily. Reformer is easy to use and requires minimal coding, making it an excellent choice for both novice and experienced developers alike. With its robust features and capabilities, Reformer is the perfect tool for building and deploying powerful NLP models with optimal speed and accuracy.

Key Features

Quickly train models and deploy them in production.
Flexible architecture enables fast adaptation to new tasks.
Easy to use with minimal coding for developers of all levels.

Pros & Cons

Pros
  • Drastically reduces memory footprint compared to standard Transformers via LSH attention and reversible layers
  • Enables training on much longer sequences (e.g., 8k+ tokens) on consumer GPUs
  • Open-source with permissive license (MIT), easy to integrate into any PyTorch project
  • Highly configurable: numerous hyperparameters to tune performance and memory trade-offs
  • Includes both Reformer stack and ReformerLM (language model) for convenience
Cons
  • Requires understanding of LSH attention and reversible layers for effective use
  • Approximate attention via LSH may be less accurate than full attention for some tasks
  • Hyperparameter tuning (bucket_size, n_hashes) is needed to balance speed and quality
  • Limited to PyTorch; not a multi-framework solution
  • Documentation is limited to README and code comments; lacks extensive tutorials

Best For

Quickly train models and deploy them in production.Flexible architecture enables fast adaptation to new tasks.Easy to use with minimal coding for developers of all levels.

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FAQ

What is Reformer?
Reformer is an efficient Transformer architecture proposed by Kitaev et al. (2020) that uses Locality-Sensitive Hashing (LSH) attention, reversible layers, and chunking to drastically reduce memory and time complexity, enabling processing of long sequences.
How do I install the library?
Install via pip: pip install reformer_pytorch
What are the key components of this implementation?
It includes LSH attention, reversible network, chunked feedforward and attention layers, and supports options like causal masking, bucket size customization, persistent memory keys, and more.
Is this implementation validated?
Yes, it has been validated on the enwik8 auto-regressive task (32k tokens with full precision, 81k tokens with half precision).
Can I use this for non-autoregressive tasks?
Yes, you can set causal=False for bidirectional (non-autoregressive) applications.