Package: haste-pytorch-cpu
Haste is a CUDA implementation of fused RNN layers with built-inDropConnectandZoneoutregularization. These layers are exposed through C++ and Python APIs for easy integration into your own projects or machine learning frameworks.Which RNN types are supported?GRUIndRNNLSTMLayer Normalized GRULayer Normalized LSTMWhat's included in this project?a standalone C++ API (libhaste)a TensorFlow Python API (haste_tf)a PyTorch API (haste_pytorch)examples for writing your own custom C++ inference / training code usinglibhastebenchmarking programs to evaluate the performance of RNN implementationsFor questions or feedback about Haste, please open an issue on GitHub or send us an email athaste@lmnt.com.InstallHere's what you'll need to get started:aCUDA Compute Capability3.7+ GPU (required)CUDA Toolkit10.0+ (required)TensorFlow GPU1.14+ or 2.0+ for TensorFlow integration (optional)PyTorch1.3+ for PyTorch integration (optional)Eigen 3to build the C++ examples (optional)cuDNN Developer Libraryto build benchmarking programs (optional)Once you have the prerequisites, you can install with pip or by building the source code.Using pippip install haste_pytorch
pip install haste_tfBuilding from sourcemake               # Build everything
make haste         # ;) Build C++ API
make haste_tf      # Build TensorFlow API
make haste_pytorch # Build PyTorch API
make examples
make benchmarksIf you built the TensorFlow or PyTorch API, install it withpip:pip install haste_tf-*.whl
pip install haste_pytorch-*.whlIf the CUDA Toolkit that you're building against is not in/usr/local/cuda, you must specify the$CUDA_HOMEenvironment variable before running make:CUDA_HOME=/usr/local/cuda-10.2 makePerformanceOur LSTM and GRU benchmarks indicate that Haste has the fastest publicly available implementation for nearly all problem sizes. The following charts show our LSTM results, but the GRU results are qualitatively similar.Here is our complete LSTM benchmark result grid:N=1 C=64N=1 C=128N=1 C=256N=1 C=512N=32 C=64N=32 C=128N=32 C=256N=32 C=512N=64 C=64N=64 C=128N=64 C=256N=64 C=512N=128 C=64N=128 C=128N=128 C=256N=128 C=512DocumentationTensorFlow APIimporthaste_tfashastegru_layer=haste.GRU(num_units=256,direction='bidirectional',zoneout=0.1,dropout=0.05)indrnn_layer=haste.IndRNN(num_units=256,direction='bidirectional',zoneout=0.1)lstm_layer=haste.LSTM(num_units=256,direction='bidirectional',zoneout=0.1,dropout=0.05)norm_gru_layer=haste.LayerNormGRU(num_units=256,direction='bidirectional',zoneout=0.1,dropout=0.05)norm_lstm_layer=haste.LayerNormLSTM(num_units=256,direction='bidirectional',zoneout=0.1,dropout=0.05)# `x` is a tensor with shape [N,T,C]x=tf.random.normal([5,25,128])y,state=gru_layer(x,training=True)y,state=indrnn_layer(x,training=True)y,state=lstm_layer(x,training=True)y,state=norm_gru_layer(x,training=True)y,state=norm_lstm_layer(x,training=True)The TensorFlow Python API is documented indocs/tf/haste_tf.md.PyTorch APIimporttorchimporthaste_pytorchashastegru_layer=haste.GRU(input_size=128,hidden_size=256,zoneout=0.1,dropout=0.05)indrnn_layer=haste.IndRNN(input_size=128,hidden_size=256,zoneout=0.1)lstm_layer=haste.LSTM(input_size=128,hidden_size=256,zoneout=0.1,dropout=0.05)norm_gru_layer=haste.LayerNormGRU(input_size=128,hidden_size=256,zoneout=0.1,dropout=0.05)norm_lstm_layer=haste.LayerNormLSTM(input_size=128,hidden_size=256,zoneout=0.1,dropout=0.05)gru_layer.cuda()indrnn_layer.cuda()lstm_layer.cuda()norm_gru_layer.cuda()norm_lstm_layer.cuda()# `x` is a CUDA tensor with shape [T,N,C]x=torch.rand([25,5,128]).cuda()y,state=gru_layer(x)y,state=indrnn_layer(x)y,state=lstm_layer(x)y,state=norm_gru_layer(x)y,state=norm_lstm_layer(x)The PyTorch API is documented indocs/pytorch/haste_pytorch.md.C++ APIThe C++ API is documented inlib/haste/*.hand there are code samples inexamples/.Code layoutbenchmarks/: programs to evaluate performance of RNN implementationsdocs/tf/: API reference documentation forhaste_tfdocs/pytorch/: API reference documentation forhaste_pytorchexamples/: examples for writing your own C++ inference / training code usinglibhasteframeworks/tf/: TensorFlow Python API and custom op codeframeworks/pytorch/: PyTorch API and custom op codelib/: CUDA kernels and C++ APIvalidation/: scripts to validate output and gradients of RNN layersImplementation notesthe GRU implementation is based on1406.1078v1(same as cuDNN) rather than1406.1078v3Zoneout on LSTM cells is applied to the hidden state only, and not the cell statethe layer normalized LSTM implementation usesthese equationsReferencesHochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory.Neural Computation,9(8), 1735â€“1780.https://doi.org/10.1162/neco.1997.9.8.1735Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.arXiv:1406.1078 [cs, stat].http://arxiv.org/abs/1406.1078.Wan, L., Zeiler, M., Zhang, S., Cun, Y. L., & Fergus, R. (2013). Regularization of Neural Networks using DropConnect. InInternational Conference on Machine Learning(pp. 1058â€“1066). Presented at the International Conference on Machine Learning.http://proceedings.mlr.press/v28/wan13.html.Krueger, D., Maharaj, T., KramÃ¡r, J., Pezeshki, M., Ballas, N., Ke, N. R., et al. (2017). Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.arXiv:1606.01305 [cs].http://arxiv.org/abs/1606.01305.Ba, J., Kiros, J.R., & Hinton, G.E. (2016). Layer Normalization.arXiv:1607.06450 [cs, stat].https://arxiv.org/abs/1607.06450.Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018). Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN.arXiv:1803.04831 [cs].http://arxiv.org/abs/1803.04831.Citing this workTo cite this work, please use the following BibTeX entry:@misc{haste2020,
  title  = {Haste: a fast, simple, and open RNN library},
  author = {Sharvil Nanavati},
  year   = 2020,
  month  = "Jan",
  howpublished = {\url{https://github.com/lmnt-com/haste/}},
}LicenseApache 2.0