302 lines
26 KiB
C++
302 lines
26 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_CLLSTMLAYER_H
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#define ARM_COMPUTE_CLLSTMLAYER_H
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/CL/CLTensor.h"
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#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
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#include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
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#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
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#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
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#include "arm_compute/runtime/CL/functions/CLGEMM.h"
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#include "arm_compute/runtime/CL/functions/CLMeanStdDevNormalizationLayer.h"
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#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
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#include "arm_compute/runtime/IMemoryManager.h"
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#include "arm_compute/runtime/MemoryGroup.h"
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#include "arm_compute/runtime/common/LSTMParams.h"
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#include <memory>
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namespace arm_compute
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{
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class CLCompileContext;
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class CLCopyKernel;
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class CLMemsetKernel;
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class CLTransposeKernel;
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class ICLTensor;
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/** This function performs a single time step in a Long Short-Term Memory (LSTM) layer.
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*
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*/
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class CLLSTMLayer : public IFunction
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{
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public:
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/** Default constructor */
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CLLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
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/** Prevent instances of this class from being copied */
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CLLSTMLayer(const CLLSTMLayer &) = delete;
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/** Prevent instances of this class from being copied */
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CLLSTMLayer &operator=(const CLLSTMLayer &) = delete;
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/** Prevent instances of this class to be moved */
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CLLSTMLayer(CLLSTMLayer &&) = delete;
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/** Prevent instances of this class to be moved */
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CLLSTMLayer &operator=(CLLSTMLayer &&) = delete;
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/** Default destructor */
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~CLLSTMLayer();
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/** Initialize function's tensors.
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*
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* @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
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* @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
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* @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
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* Data types supported: Same as @p input.
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* @param[in] lstm_params Weights tensors used in peephole optimization:
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* input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
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* cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
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* projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
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* input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
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* @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
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* If set to 0.0f then clipping is disabled.
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* @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
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* If set to 0.0f then clipping is disabled.
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*/
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void configure(const ICLTensor *input,
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const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
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const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
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const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
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const ICLTensor *output_state_in, ICLTensor *cell_state_in,
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ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
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const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
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/** Initialize function's tensors.
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*
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* @param[in] compile_context The compile context to be used.
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* @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
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* @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
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* @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].
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* Data types supported: Same as @p input.
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* @param[in] lstm_params Weights tensors used in peephole optimization:
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* input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
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* cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
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* projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
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* input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
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* @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
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* If set to 0.0f then clipping is disabled.
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* @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
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* If set to 0.0f then clipping is disabled.
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*/
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void configure(const CLCompileContext &compile_context, const ICLTensor *input,
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const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
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const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
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const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
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const ICLTensor *output_state_in, ICLTensor *cell_state_in,
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ICLTensor *scratch_buffer, ICLTensor *output_state_out, ICLTensor *cell_state_out, ICLTensor *output,
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const LSTMParams<ICLTensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
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/** Static function to check if given info will lead to a valid configuration of @ref CLLSTMLayer
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*
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* @param[in] input Source tensor info. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
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* @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] output_state_in 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[in] scratch_buffer 2D tensor info with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF.
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* Data type supported: Same as @p input.
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* @param[in] output_state_out 2D weights tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
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* @param[in] cell_state_out 2D tensor info with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
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* @param[in] output Destination tensor info. Output is a 2D tensor with dimensions [output_size, batch_size]. Data types supported: Same as @p input.
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* @param[in] lstm_params Weights tensors info used in peephole optimization:
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* input_to_input_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: Same as @p input.
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* recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* cell_to_input_weights 1D weights tensor info with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
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* cell_to_forget_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_to_output_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* input_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input
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* projection_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: Same as @p input.
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* projection_bias 1D weights tensor info with dimensions [output_size]. Data type supported: Same as @p input.
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* input_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* forget_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* cell_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* output_layer_norm_weights 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
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* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
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* @param[in] cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
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* If set to 0.0f then clipping is disabled.
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* @param[in] projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within [-proj_clip, proj_clip].
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* If set to 0.0f then clipping is disabled.
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input,
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const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
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const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
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const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
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const ITensorInfo *output_state_in, const ITensorInfo *cell_state_in,
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const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output,
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const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
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// Inherited methods overridden:
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void run() override;
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void prepare() override;
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private:
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MemoryGroup _memory_group;
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CLFullyConnectedLayer _fully_connected_input_gate;
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CLArithmeticAddition _accum_input_gate1;
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CLArithmeticSubtraction _subtract_input_gate;
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CLPixelWiseMultiplication _pixelwise_mul_input_gate;
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CLActivationLayer _activation_input_gate;
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CLFullyConnectedLayer _fully_connected_forget_gate;
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CLArithmeticAddition _accum_forget_gate1;
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CLPixelWiseMultiplication _pixelwise_mul_forget_gate;
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CLActivationLayer _activation_forget_gate;
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CLFullyConnectedLayer _fully_connected_cell_state;
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CLGEMM _gemm_cell_state1;
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std::unique_ptr<CLTransposeKernel> _transpose_cell_state;
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CLArithmeticAddition _accum_cell_state1;
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CLArithmeticAddition _accum_cell_state2;
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CLPixelWiseMultiplication _pixelwise_mul_cell_state1;
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CLActivationLayer _activation_cell_state;
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CLActivationLayer _cell_clip;
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CLPixelWiseMultiplication _pixelwise_mul_cell_state2;
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CLFullyConnectedLayer _fully_connected_output;
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CLPixelWiseMultiplication _pixelwise_mul_output_state1;
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CLArithmeticAddition _accum_output1;
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CLActivationLayer _activation_output;
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CLActivationLayer _activation_output_state;
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CLPixelWiseMultiplication _pixelwise_mul_output_state2;
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CLFullyConnectedLayer _fully_connected_output_state;
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CLActivationLayer _projection_clip;
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std::unique_ptr<CLCopyKernel> _copy_cell_state;
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std::unique_ptr<CLCopyKernel> _copy_output;
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CLConcatenateLayer _concat_scratch_buffer;
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CLConcatenateLayer _concat_inputs_forget_gate;
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CLConcatenateLayer _concat_weights_forget_gate;
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CLConcatenateLayer _concat_weights_input_gate;
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CLConcatenateLayer _concat_weights_output;
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std::unique_ptr<CLMemsetKernel> _ones_memset_kernel;
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CLMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
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CLPixelWiseMultiplication _pixelwise_mul_input_gate_coeff;
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CLArithmeticAddition _accum_input_gate_bias;
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CLMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
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CLPixelWiseMultiplication _pixelwise_mul_forget_gate_coeff;
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CLArithmeticAddition _accum_forget_gate_bias;
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CLMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
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CLPixelWiseMultiplication _pixelwise_mul_cell_gate_coeff;
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CLArithmeticAddition _accum_cell_gate_bias;
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CLMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
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CLPixelWiseMultiplication _pixelwise_mul_output_gate_coeff;
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CLArithmeticAddition _accum_output_gate_bias;
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CLTensor _input_gate_out1;
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CLTensor _input_gate_out2;
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CLTensor _input_gate_out3;
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CLTensor _input_gate_out4;
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CLTensor _forget_gate_out1;
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CLTensor _forget_gate_out2;
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CLTensor _forget_gate_out3;
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CLTensor _forget_gate_out4;
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CLTensor _forget_gate_out5;
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CLTensor _forget_gate_out6;
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CLTensor _cell_state_out1;
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CLTensor _cell_state_out2;
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CLTensor _cell_state_out3;
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CLTensor _cell_state_out4;
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CLTensor _cell_state_out5;
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CLTensor _output1;
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CLTensor _output2;
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CLTensor _output3;
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CLTensor _output4;
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CLTensor _cell_state_activation;
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CLTensor _output_state1;
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CLTensor _ones;
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CLTensor _input_layer_norm_out1;
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CLTensor _input_layer_norm_out2;
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CLTensor _forget_layer_norm_out1;
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CLTensor _forget_layer_norm_out2;
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CLTensor _cell_layer_norm_out1;
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CLTensor _cell_layer_norm_out2;
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CLTensor _output_layer_norm_out1;
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CLTensor _output_layer_norm_out2;
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bool _run_peephole_opt;
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bool _run_cifg_opt;
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bool _perform_cell_clipping;
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bool _has_projection_weights;
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bool _perform_projection_clipping;
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bool _is_prepared;
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bool _is_layer_norm_lstm;
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};
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_CLLSTMLAYER_H */
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