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Diffstat (limited to 'python/pyarmnn/src/pyarmnn/swig/modules/armnn_lstmparam.i')
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diff --git a/python/pyarmnn/src/pyarmnn/swig/modules/armnn_lstmparam.i b/python/pyarmnn/src/pyarmnn/swig/modules/armnn_lstmparam.i new file mode 100644 index 0000000000..a0e993c7ac --- /dev/null +++ b/python/pyarmnn/src/pyarmnn/swig/modules/armnn_lstmparam.i @@ -0,0 +1,97 @@ +// +// 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; +}; +} |