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SRU++, a new SRU variant, is released. [tech report] [blog]

The experimental code and SRU++ implementation are available on the dev branch which will be merged into master later.

About

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

<p align="center"> <img width=620 src="https://raw.githubusercontent.com/taolei87/sru/master/imgs/speed.png"><br> <i>Average processing time of LSTM, conv2d and SRU, tested on GTX 1070</i><br> </p> For example, the figure above presents the processing time of a single mini-batch of 32 samples. SRU achieves 10 to 16 times speed-up compared to LSTM, and operates as fast as (or faster than) word-level convolution using conv2d.

Reference:

Simple Recurrent Units for Highly Parallelizable Recurrence [paper]

@inproceedings{lei2018sru,
  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)},
  year={2018}
}

When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute [paper]

@article{lei2021srupp,
  title={When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute},
  author={Tao Lei},
  journal={arXiv preprint arXiv:2102.12459},
  year={2021}
}
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Requirements

Install requirements via pip install -r requirements.txt.

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Installation

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
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Examples

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 sru import SRU, SRUCell

# input has length 20, batch size 32 and dimension 128
x = 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 = -2,        # initial bias of highway gate (<= 0)
)
rnn.cuda()

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)
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Contributing

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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