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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
%{
#include "armnn/LstmParams.hpp"
%}
namespace armnn
{
%feature("docstring",
"
Long Short-Term Memory layer input parameters.
See `INetwork.AddLstmLayer()`.
Operation described by the following equations:
\[i_t=\sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) \\\\
f_t=\sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) \\\\
C_t=clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) \\\\
o_t = \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) \\\\
h_t = clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})\ if\ there\ is\ a\ projection; \\\\
h_t = o_t \odot g(C_t)\ otherwise. \]
Where:
\(x_t\) - input;
\(i_t\) - input gate;
\(f_t\) - forget gate;
\(C_t\) - cell state;
\(o_t\) - output;
\(h_t\) - output state;
\(\sigma\) - logistic sigmoid function;
\(g\) - cell input and cell output activation function, see `LstmDescriptor.m_ActivationFunc`;
\(t_{cell}\) - threshold for clipping the cell state, see `LstmDescriptor.m_ClippingThresCell`;
\(t_{proj}\) - threshold for clipping the projected output, see `LstmDescriptor.m_ClippingThresProj`;
Contains:
m_InputToInputWeights (ConstTensor): \(W_{xi}\), input-to-input weight matrix.
m_InputToForgetWeights (ConstTensor): \(W_{xf}\), input-to-forget weight matrix.
m_InputToCellWeights (ConstTensor): \(W_{xc}\), input-to-cell weight matrix.
m_InputToOutputWeights (ConstTensor): \(W_{xo}\), input-to-output weight matrix.
m_RecurrentToInputWeights (ConstTensor): \(W_{hi}\), recurrent-to-input weight matrix.
m_RecurrentToForgetWeights (ConstTensor): \(W_{hf}\), recurrent-to-forget weight matrix.
m_RecurrentToCellWeights (ConstTensor): \(W_{hc}\), recurrent-to-cell weight matrix.
m_RecurrentToOutputWeights (ConstTensor): \(W_{ho}\), recurrent-to-output weight matrix.
m_CellToInputWeights (ConstTensor): \(W_{ci}\), cell-to-input weight matrix. Has effect if `LstmDescriptor.m_PeepholeEnabled`.
m_CellToForgetWeights (ConstTensor): \(W_{cf}\), cell-to-forget weight matrix. Has effect if `LstmDescriptor.m_PeepholeEnabled`.
m_CellToOutputWeights (ConstTensor): \(W_{co}\), cell-to-output weight matrix. Has effect if `LstmDescriptor.m_PeepholeEnabled`.
m_InputGateBias (ConstTensor): \(b_i\), input gate bias.
m_ForgetGateBias (ConstTensor): \(b_f\), forget gate bias.
m_CellBias (ConstTensor): \(b_c\), cell bias.
m_OutputGateBias (ConstTensor): \(b_o\), output gate bias.
m_ProjectionWeights (ConstTensor): \(W_{proj}\), projection weight matrix.
Has effect if `LstmDescriptor.m_ProjectionEnabled` is set to True.
m_ProjectionBias (ConstTensor): \(b_{proj}\), projection bias.
Has effect if `LstmDescriptor.m_ProjectionEnabled` is set to True.
m_InputLayerNormWeights (ConstTensor): normalisation weights for input,
has effect if `LstmDescriptor.m_LayerNormEnabled` set to True.
m_ForgetLayerNormWeights (ConstTensor): normalisation weights for forget gate,
has effect if `LstmDescriptor.m_LayerNormEnabled` set to True.
m_CellLayerNormWeights (ConstTensor): normalisation weights for current cell,
has effect if `LstmDescriptor.m_LayerNormEnabled` set to True.
m_OutputLayerNormWeights (ConstTensor): normalisation weights for output gate,
has effect if `LstmDescriptor.m_LayerNormEnabled` set to True.
") LstmInputParams;
struct LstmInputParams
{
LstmInputParams();
const armnn::ConstTensor* m_InputToInputWeights;
const armnn::ConstTensor* m_InputToForgetWeights;
const armnn::ConstTensor* m_InputToCellWeights;
const armnn::ConstTensor* m_InputToOutputWeights;
const armnn::ConstTensor* m_RecurrentToInputWeights;
const armnn::ConstTensor* m_RecurrentToForgetWeights;
const armnn::ConstTensor* m_RecurrentToCellWeights;
const armnn::ConstTensor* m_RecurrentToOutputWeights;
const armnn::ConstTensor* m_CellToInputWeights;
const armnn::ConstTensor* m_CellToForgetWeights;
const armnn::ConstTensor* m_CellToOutputWeights;
const armnn::ConstTensor* m_InputGateBias;
const armnn::ConstTensor* m_ForgetGateBias;
const armnn::ConstTensor* m_CellBias;
const armnn::ConstTensor* m_OutputGateBias;
const armnn::ConstTensor* m_ProjectionWeights;
const armnn::ConstTensor* m_ProjectionBias;
const armnn::ConstTensor* m_InputLayerNormWeights;
const armnn::ConstTensor* m_ForgetLayerNormWeights;
const armnn::ConstTensor* m_CellLayerNormWeights;
const armnn::ConstTensor* m_OutputLayerNormWeights;
};
}
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