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Sru

Sru is an open-source Python library that implements the Simple Recurrent Unit (SRU) for recurrent sequence processing in PyTorch. It includes SRU and SRUCell and supports stacked layers, bidirectionality, dropout and layer normalization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Sru — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use SRU when implementing or adapting a PyTorch sequence model and you want to try a parallelizable recurrent unit. The repository's usage example is similar to nn.LSTM and recommends starting with two SRU layers.

Who it is for

  • PyTorch developers building recurrent sequence models.
  • NLP and sequence-modeling practitioners comparing recurrent-layer designs.

Documented uses

  • Replace an LSTM layer with SRU in a PyTorch sequence model.
  • Build a stacked recurrent encoder with configurable dropout or layer normalization.
  • Experiment with bidirectional recurrent processing.
Before you choose

Requirements & limits

The public repository lists an MIT license. Installation options documented in the README include PyPI and installation from source.

  • PyTorch 1.6 or later is recommended, and ninja is listed as a requirement.
  • The example uses CUDA tensors and calls .cuda(), so that demonstrated workflow needs CUDA available to the system.
  • The README advises that SRU may require more stacked layers than LSTM.
  • Published speed comparisons describe specific tests and hardware; they do not predict performance on every workload.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install SRU from PyPI

Install the SRU package using the documented PyPI route.

Before you start

  • PyTorch 1.6 or later is recommended.
  • ninja is listed as a requirement.

Steps

  1. Install the listed requirements using the repository's requirements file.

  2. Install SRU with the command below.

    Documented command
    pip install sru

Expected outcome

The SRU package is installed through the documented PyPI option.

Guide limitations

  • This guide covers installation only, not configuring a model or running a forward pass.
  • The README lists PyTorch and ninja requirements; it does not establish compatibility for every environment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
16 October 2026
Original website title
asappresearch/sru · Official homepage title · retained in its source language
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