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‎.gitignore‎

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*.a
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*.o
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*.so
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*.whl
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haste_lstm
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haste_gru

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‎Makefile‎

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AR ?= ar
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CXX ?= g++
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NVCC ?= nvcc
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PYTHON ?= python
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LOCAL_CFLAGS := -I/usr/include/eigen3 -I/usr/local/cuda/include -Ilib -O3
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LOCAL_LDFLAGS := -L/usr/local/cuda/lib64 -L. -lcudart -lcublas
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# Small enough project that we can just recompile all the time.
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.PHONY: all haste haste_tf examples clean
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all: haste haste_tf examples
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haste:
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$(NVCC) -std=c++11 -arch=sm_60 -c lib/lstm_forward_gpu.cu.cc -o lib/lstm_forward_gpu.o -x cu -Xcompiler -fPIC $(LOCAL_CFLAGS)
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$(NVCC) -std=c++11 -arch=sm_60 -c lib/lstm_backward_gpu.cu.cc -o lib/lstm_backward_gpu.o -x cu -Xcompiler -fPIC $(LOCAL_CFLAGS)
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$(NVCC) -std=c++11 -arch=sm_60 -c lib/gru_forward_gpu.cu.cc -o lib/gru_forward_gpu.o -x cu -Xcompiler -fPIC $(LOCAL_CFLAGS)
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$(NVCC) -std=c++11 -arch=sm_60 -c lib/gru_backward_gpu.cu.cc -o lib/gru_backward_gpu.o -x cu -Xcompiler -fPIC $(LOCAL_CFLAGS)
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$(AR) -crv libhaste.a lib/lstm_forward_gpu.o lib/lstm_backward_gpu.o lib/gru_forward_gpu.o lib/gru_backward_gpu.o
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haste_tf: haste
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$(eval TF_CFLAGS := $(shell $(PYTHON) -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))'))
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$(eval TF_LDFLAGS := $(shell $(PYTHON) -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))'))
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$(CXX) -std=c++11 -c frameworks/tf/lstm.cc -o frameworks/tf/lstm.o $(LOCAL_CFLAGS) $(TF_CFLAGS) -fPIC
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$(CXX) -std=c++11 -c frameworks/tf/gru.cc -o frameworks/tf/gru.o $(LOCAL_CFLAGS) $(TF_CFLAGS) -fPIC
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$(CXX) -shared frameworks/tf/lstm.o frameworks/tf/gru.o libhaste.a -o frameworks/tf/libhaste_tf.so $(LOCAL_LDFLAGS) $(TF_LDFLAGS) -fPIC
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@$(eval TMP := $(shell mktemp -d))
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@cp -r frameworks/tf $(TMP)
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@cp setup.py $(TMP)
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@(cd $(TMP); $(PYTHON) setup.py -q bdist_wheel)
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@cp $(TMP)/dist/*.whl .
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@rm -rf $(TMP)
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examples: haste
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$(CXX) -std=c++11 examples/lstm.cc libhaste.a $(LOCAL_CFLAGS) $(LOCAL_LDFLAGS) -o haste_lstm -Wno-ignored-attributes
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$(CXX) -std=c++11 examples/gru.cc libhaste.a $(LOCAL_CFLAGS) $(LOCAL_LDFLAGS) -o haste_gru -Wno-ignored-attributes
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clean:
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rm -fr haste_lstm haste_gru build haste_*.whl
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find . \( -iname '*.o' -o -iname '*.so' -o -iname '*.a' \) -delete

‎README.md‎

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<div align="center">
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<img src="https://lmnt.com/assets/haste-logo_social_media.png">
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</div>
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--------------------------------------------------------------------------------
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Haste is a CUDA implementation of fused [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory) and [GRU](https://en.wikipedia.org/wiki/Gated_recurrent_unit) layers with built-in [DropConnect](http://proceedings.mlr.press/v28/wan13.html) and [Zoneout](https://arxiv.org/abs/1606.01305) regularization. These layers are exposed through C++ and Python APIs for easy integration into your own projects or machine learning frameworks.
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What's included in this project?
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- a standalone C++ API (`libhaste`)
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- a TensorFlow Python API (`haste_tf`)
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- examples for writing your own custom C++ inference / training code using `libhaste`
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For questions or feedback about Haste, please open an issue on GitHub or send us an email at [haste@lmnt.com](mailto:haste@lmnt.com).
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## Install
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Here's what you'll need to get started:
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- a [CUDA Compute Capability](https://developer.nvidia.com/cuda-gpus) 6.0+ GPU
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- [TensorFlow GPU](https://www.tensorflow.org/install/gpu) 1.14+ or 2.0+ for TensorFlow integration
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- [Eigen 3](http://eigen.tuxfamily.org/) to build the C++ examples
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Once you have the prerequisites, run the following to build the code and install the TensorFlow API:
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```
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make && pip install haste_tf-*.whl
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```
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## Documentation
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Getting started with the TensorFlow API is easy:
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```python
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import haste_tf as haste
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lstm_layer = haste.LSTM(num_units=256, direction='bidirectional', zoneout=0.1, dropout=0.05)
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gru_layer = haste.GRU(num_units=256, direction='bidirectional', zoneout=0.1, dropout=0.05)
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# `x` is a tensor with shape [N,T,C]
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y, state = lstm_layer(x)
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y, state = gru_layer(x)
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```
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The TensorFlow Python API is documented in [`docs/tf/haste_tf.md`](docs/tf/haste_tf.md).
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The C++ API is documented in [`lib/haste.h`](lib/haste.h) and there are code samples in [`examples/`](examples/).
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## Code layout
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- [`docs/tf/`](docs/tf): API reference documentation for `haste_tf`
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- [`examples/`](examples): examples for writing your own C++ inference / training code using `libhaste`
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- [`frameworks/tf/`](frameworks/tf): TensorFlow Python API and custom op code
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- [`lib/`](lib): CUDA kernels and C++ API
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## Implementation notes
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- the GRU implementation is based on `1406.1078v1` (same as cuDNN) rather than `1406.1078v3`
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- Zoneout on LSTM cells is applied to the hidden state only, and not the cell state
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## References
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1. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. _Neural Computation_, _9_(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
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1. Cho, 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.
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1. Wan, L., Zeiler, M., Zhang, S., Cun, Y. L., & Fergus, R. (2013). Regularization of Neural Networks using DropConnect. In _International Conference on Machine Learning_ (pp. 1058–1066). Presented at the International Conference on Machine Learning. http://proceedings.mlr.press/v28/wan13.html.
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1. Krueger, D., Maharaj, T., Kramár, 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.
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## Citing this work
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To cite this work, please use the following BibTeX entry:
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```
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@misc{haste2020,
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title = {Haste: a fast, simple, and open RNN library},
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author = {Sharvil Nanavati},
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year = 2020,
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month = "Jan",
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howpublished = {\url{https://github.com/lmnt-com/haste/}},
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}
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```
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## License
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[Apache 2.0](LICENSE)

‎docs/tf/haste_tf.md‎

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<div itemscope itemtype="http://developers.google.com/ReferenceObject">
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<meta itemprop="name" content="haste_tf" />
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<meta itemprop="path" content="Stable" />
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</div>
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# Module: haste_tf
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Haste: a fast, simple, and open RNN library.
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## Classes
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[`class GRU`](./haste_tf/GRU.md): Gated Recurrent Unit layer.
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[`class GRUCell`](./haste_tf/GRUCell.md): A GRU cell that's compatible with the Haste GRU layer.
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[`class LSTM`](./haste_tf/LSTM.md): Long Short-Term Memory layer.
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[`class ZoneoutWrapper`](./haste_tf/ZoneoutWrapper.md): An LSTM/GRU cell wrapper that applies zoneout to the inner cell's hidden state.
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