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Quasi-Recurrent Neural Network (QRNN) for PyTorch

Updated to support multi-GPU environments via DataParallel - see the the multigpu_dataparallel.py example.

This repository contains a PyTorch implementation of Salesforce Research's Quasi-Recurrent Neural Networks paper.

The QRNN provides similar accuracy to the LSTM but can be betwen 2 and 17 times faster than the highly optimized NVIDIA cuDNN LSTM implementation depending on the use case.

To install, simply run:

pip install cupy pynvrtc git+https://github.com/salesforce/pytorch-qrnn

If you use this code or our results in your research, please cite:

@article{bradbury2016quasi,
  title={{Quasi-Recurrent Neural Networks}},
  author={Bradbury, James and Merity, Stephen and Xiong, Caiming and Socher, Richard},
  journal={International Conference on Learning Representations (ICLR 2017)},
  year={2017}
}

Software Requirements

This codebase requires Python 3, PyTorch, pynvrtc (NVIDIA's Python Bindings to NVRTC), and CuPy. While the codebase contains a CPU implementation of the QRNN, the GPU QRNN implementation is used by default if possible. Requirements are provided in requirements.txt.

Example Usage

We've updated the previously released Salesforce Research AWD-LSTM language modeling codebase to support use of the AWD-QRNN. With the same number of parameters as the LSTM and less well tuned hyper parameters, the QRNN model trains over twice as quickly and achieves nearly equivalent state-of-the-art language modeling results. For full details, refer to the AWD-LSTM-LM repository.

Usage

The QRNN API is meant to be drop-in compatible with the LSTM for many standard use cases. As such, the easiest thing to do is replace any GRU or LSTM module with the QRNN.

Note: bidirectional QRNN is not yet supported though will be in the near future.

import torch
from torchqrnn import QRNN

seq_len, batch_size, hidden_size = 7, 20, 256
size = (seq_len, batch_size, hidden_size)
X = torch.autograd.Variable(torch.rand(size), requires_grad=True).cuda()

qrnn = QRNN(hidden_size, hidden_size, num_layers=2, dropout=0.4)
qrnn.cuda()
output, hidden = qrnn(X)

print(output.size(), hidden.size())

The full documentation for the QRNN is listed below:

QRNN(input_size, hidden_size, num_layers, dropout=0):
    Applies a multiple layer Quasi-Recurrent Neural Network (QRNN) to an input sequence.

    Args:
        input_size: The number of expected features in the input x.
        hidden_size: The number of features in the hidden state h. If not specified, the input size is used.
        num_layers: The number of QRNN layers to produce.
        layers: List of preconstructed QRNN layers to use for the QRNN module (optional).
        save_prev_x: Whether to store previous inputs for use in future convolutional windows (i.e. for a continuing sequence such as in language modeling). If true, you must call reset to remove cached previous values of x. Default: False.
        window: Defines the size of the convolutional window (how many previous tokens to look when computing the QRNN values). Supports 1 and 2. Default: 1.
        zoneout: Whether to apply zoneout (i.e. failing to update elements in the hidden state) to the hidden state updates. Default: 0.
        output_gate: If True, performs QRNN-fo (applying an output gate to the output). If False, performs QRNN-f. Default: True.
        use_cuda: If True, uses fast custom CUDA kernel. If False, uses naive for loop. Default: True.

    Inputs: X, hidden
        - X (seq_len, batch, input_size): tensor containing the features of the input sequence.
        - hidden (layers, batch, hidden_size): tensor containing the initial hidden state for the QRNN.

    Outputs: output, h_n
        - output (seq_len, batch, hidden_size): tensor containing the output of the QRNN for each timestep.
        - h_n (layers, batch, hidden_size): tensor containing the hidden state for t=seq_len

The included QRNN layer supports convolutional windows of size 1 or 2 but will be extended in the future to support arbitrary convolutions.

If you are using convolutional windows of size 2 (i.e. looking at the inputs from two previous timesteps to compute the input) and want to run over a long sequence in batches, such as when using BPTT, you can set save_prev_x=True and call reset when you wish to reset the cached previous inputs.

If you want flexibility in the definition of each QRNN layer, you can construct individual QRNNLayer modules and pass them to the QRNN module using the layer argument.

Speed

Speeds are between 2 and 17 times faster than NVIDIA's cuDNN LSTM, with the difference as a result of varying batch size and sequence length. The largest gains are for small batch sizes or long sequence lengths, both highlighting the LSTMs parallelization difficulty due to forced sequentiality. For full information, refer to the Quasi-Recurrent Neural Networks paper.

Figure 4 from QRNN paper

Pictured above is Figure 4 from the QRNN paper:
Left: Training speed for two-layer 640-unit PTB LM on a batch of 20 examples of 105 timesteps. “RNN” and “softmax” include the forward and backward times, while “optimization overhead” includes gradient clipping, L2 regularization, and SGD computations.
Right: Inference speed advantage of a 320-unit QRNN layer alone over an equal-sized cuDNN LSTM layer for data with the given batch size and sequence length. Training results are similar.

Extending the QRNN speed advantage to other recurrent architectures with ForgetMult

The QRNN architecture's speed advantage comes from two primary sources: the ability to batch all computations into a few large matrix multiplications and the use of a fast element-wise recurrence function. This recurrence function, named ForgetMult, is general and can be used in other scenarios. The ForgetMult takes two arguments - the candidate input x and forget gates f - and computes h = f * x + (1 - f) * hm1 where hm1 is the previous hidden state output.

The QRNN class is a thin wrapper around this that performs the large matrix multiplications for the candidate x, the forget gates f, and the output gates o. Any other operation which requires recurrence and can have precomputed values for the candidate x and forget gates f can use this fast form of recurrence.

Example usage of the ForgetMult module: output = ForgetMult()(f, x, hidden).

    ForgetMult computes a simple recurrent equation:
    h_t = f_t * x_t + (1 - f_t) * h_{t-1}

    This equation is equivalent to dynamic weighted averaging.

    Inputs: X, hidden
        - X (seq_len, batch, input_size): tensor containing the features of the input sequence.
        - F (seq_len, batch, input_size): tensor containing the forget gate values, assumed in range [0, 1].
        - hidden_init (batch, input_size): tensor containing the initial hidden state for the recurrence (h_{t-1}).
        - cuda: If True, use the fast element-wise CUDA kernel for recurrence. If False, uses naive for loop. Default: True.

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