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Lev Zlotnik authored
* Introduce a modular, Python-level implementation of LSTM/LSTMCell using existing PyTorch nn.Modules as building blocks * This allows quantization of weights and internal activations of LSTM layers using the existing Quantizer. (In the PyTorch implementation of RNN/LSTM only the weights are exposed at the Python level, whereas the internal activations are "hidden" in C++ code.) * Supports stacked (multi-layer) and bi-directional LSTM * Implemented conversion functions from PyTorch LSTM module to our LSTM module and vice-versa * Tests for modular implementation correctness and for conversions * Jupyter notebook showing post-training quantization of a language model
Lev Zlotnik authored* Introduce a modular, Python-level implementation of LSTM/LSTMCell using existing PyTorch nn.Modules as building blocks * This allows quantization of weights and internal activations of LSTM layers using the existing Quantizer. (In the PyTorch implementation of RNN/LSTM only the weights are exposed at the Python level, whereas the internal activations are "hidden" in C++ code.) * Supports stacked (multi-layer) and bi-directional LSTM * Implemented conversion functions from PyTorch LSTM module to our LSTM module and vice-versa * Tests for modular implementation correctness and for conversions * Jupyter notebook showing post-training quantization of a language model