OptimizedRNNStack

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Implements the optimized CuDNN5 RNN stack of one or more recurrent network layers.

OptimizedRNNStack (weights, input,
                   hiddenDims, numLayers = 1,
                   bidirectional = false,
                   recurrentOp='lstm')

Parameters

• weights: one weight matrix containing all model parameters as a single matrix. Use dimension inference, cf. description below.
• input: data to apply the stack of one or more recurrent networks to. Must be a sequence, and must not be sparse.
• hiddenDims: dimension of the hidden state in each layer and, if bidirectional, of each of the two directions
• numLayers (default: 1): number of layers
• bidirectional (default: false): if true, the model is bidirectional
• recurrentOp (default: lstm): select the RNN type. Allowed values: lstm, gru, rnnTanh, rnnReLU

Description

This function gives access to the CuDNN5 RNN, a highly efficient implementation of a stack of one or more layers of recurrent networks. We have observed speed-ups on the order of 5, compared to an explicit implementation as a computation network in BrainScript. Although it is not as flexible as a BrainScript implementation, the speed-up of training time may be worth the compromise (note, however, that such models can only be deployed on machines with a GPU).

The networks can be uni- or bidirectional, and be of the following kind (recurrentOp parameter):

• lstm: Long Short Term Memory (Hochreiter and Schmidhuber)
• gru: Gated Recurrent Unit
• rnnTanh: plain RNN with a tanh non-linearity
• rnnReLU: plain RNN with a rectified linear non-linearity

All weights are contained in a single matrix that should have hiddenDims rows and as many columns as needed to hold all parameters. Since this can be cumbersome to determine, you can have the dimension inferred automatically. To make sure that random initialization uses the correct fan-in, specify initOutputRank=-1:

W = ParameterTensor {(Inferred:Inferred), initOutputRank=-1}

If you use the lstm operation, we recommend to use this primitive through RecurrentLSTMLayerStack{}, which will take care of creating the weights.

Training on GPU, deploy on CPU

Currently, it is not possible to deploy an RNN trained as an OptimizedRNNStack() on systems without GPUs. We believe it is possible to perform a post-training model-editing action that replaces the OptimizedRNNStack() nodes by CPU-compatible native BrainScript expressions that precisely emulate the CuDNN5 RNN implementation.

Example

Speech recognition model that consists of a 3-hidden layer a bidirectional LSTM with a hidden-state dimension per layer and direction of 512:

features = Input {40}
W = ParameterTensor {(Inferred:Inferred), initOutputRank=-1, initValueScale=1/10}
h = OptimizedRNNStack (W, features, 512, numLayers=3, bidirectional=true)
p = DenseLayer {9000, activation=Softmax, init='heUniform', initValueScale=1/3}