January 21, 2019

350 words 2 mins read



Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

repo name asappresearch/sru
repo link https://github.com/asappresearch/sru
language Python
size (curr.) 679 kB
stars (curr.) 1763
created 2017-08-28
license MIT License


SRU is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks.

The paper has multiple versions, please check the latest one.


Simple Recurrent Units for Highly Parallelizable Recurrence

  title={Simple Recurrent Units for Highly Parallelizable Recurrence},
  author={Tao Lei and Yu Zhang and Sida I. Wang and Hui Dai and Yoav Artzi},
  booktitle={Empirical Methods in Natural Language Processing (EMNLP)},


Install requirements via pip install -r requirements.txt.


From source:

SRU can be installed as a regular package via python setup.py install or pip install ..

From PyPi:

pip install sru

Directly use the source without installation:

Make sure this repo and CUDA library can be found by the system, e.g.

export PYTHONPATH=path_to_repo/sru
export LD_LIBRARY_PATH=/usr/local/cuda/lib64


The usage of SRU is similar to nn.LSTM. SRU likely requires more stacking layers than LSTM. We recommend starting by 2 layers and use more if necessary (see our report for more experimental details).

import torch
from torch.autograd import Variable
from sru import SRU, SRUCell

# input has length 20, batch size 32 and dimension 128
x = Variable(torch.FloatTensor(20, 32, 128).cuda())

input_size, hidden_size = 128, 128

rnn = SRU(input_size, hidden_size,
    num_layers = 2,          # number of stacking RNN layers
    dropout = 0.0,           # dropout applied between RNN layers
    bidirectional = False,   # bidirectional RNN
    layer_norm = False,      # apply layer normalization on the output of each layer
    highway_bias = 0,        # initial bias of highway gate (<= 0)
    rescale = True,          # whether to use scaling correction

output_states, c_states = rnn(x)      # forward pass

# output_states is (length, batch size, number of directions * hidden size)
# c_states is (layers, batch size, number of directions * hidden size)


Please read and follow the guidelines.

Other Implementations

@musyoku had a very nice SRU implementaion in chainer.

@adrianbg implemented the first CPU version.

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