[P] A library for linear RNNs

2026-02-12 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

A new open-source PyTorch library, lrnnx, has been developed to implement several popular Linear Recurrent Neural Networks (RNNs). This library includes accelerated kernels designed to enhance both inference and training performance, drawing inspiration from techniques used in models like Mamba. The entire codebase is available under an MIT license, and its technical report has been accepted for presentation at EACL SRW 2026. The developers are actively seeking feedback and contributions from the community to further improve the library.

Key takeaway

For AI scientists and researchers working with recurrent neural networks, the lrnnx library offers a significant performance boost for Linear RNNs within PyTorch. You should consider integrating this open-source library into your current projects to leverage its accelerated kernels for both training and inference, potentially reducing computational overhead and speeding up experimentation cycles.

Key insights

The lrnnx library provides accelerated PyTorch implementations of popular Linear RNNs.

Principles

• Accelerated kernels improve RNN performance.
• Open-source development fosters community contributions.

In practice

• Integrate lrnnx for faster Linear RNNs.
• Explore lrnnx for PyTorch-based RNN projects.

Topics

• Linear RNNs
• PyTorch Library
• Accelerated Kernels
• Machine Learning Research
• Open-Source Software

Code references

• SforAiDl/lrnnx

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.