asappresearch/sru

Health v2 · maintenance only

Adoption not part of the score

2106 stars · 304 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SRU is a PyTorch library implementing the Simple Recurrent Unit, a highly parallelizable RNN that runs 10-16x faster than cuDNN LSTM with comparable accuracy. It also includes SRU++, a variant combining fast recurrence with attention for efficient language modeling.

Use cases

• train RNNs as fast as CNNs on GPU
• replace LSTM with a faster recurrent unit in PyTorch models
• train language models with reduced compute
• speed up sequence modeling for NLP tasks
• build bidirectional recurrent layers with layer norm and highway connections

When to choose

• you need recurrent sequence models but LSTM training speed is a bottleneck
• you want a drop-in PyTorch alternative to nn.LSTM with GPU acceleration
• you are training language models on limited compute budgets

When to avoid

• you need transformer-only architectures or the latest attention variants
• you require active community development and frequent updates
• you work outside PyTorch or without CUDA GPUs

Facets

library · maturity maintenance

machine-learning deep-learning nlp machine-learning deep-learning python cross-platform pytorch recurrent-neural-networks rnn lstm-alternative gpu-accelerated language-modeling sru natural-language-processing gpu linux

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