text: /*
   * Copyright (c) 2020 ARM Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #ifndef ARM_COMPUTE_NEQLSTMLAYER_H
  #define ARM_COMPUTE_NEQLSTMLAYER_H
  
  #include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
  #include "arm_compute/core/NEON/kernels/NEArithmeticSubtractionKernel.h"
  #include "arm_compute/core/NEON/kernels/NECopyKernel.h"
  #include "arm_compute/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
  #include "arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h"
  #include "arm_compute/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h"
  #include "arm_compute/core/Types.h"
  #include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
  #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
  #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
  #include "arm_compute/runtime/NEON/functions/NETranspose.h"
  
  #include "arm_compute/runtime/common/LSTMParams.h"
  
  namespace arm_compute
  {
  // Forward declarations
  class ITensor;
  
  /** Basic function to run @ref NEQLSTMLayer
   *
   * This function calls the following NEON functions/kernels:
   *
   * -# @ref NEActivationLayer                                     Activation functions (tanh and logistic)
   * -# @ref NEArithmeticAdditionKernel                            Elementwise addition
   * -# @ref NEArithmeticSubtractionKernel                         Elementwise subtraction
   * -# @ref NECopyKernel                                          Copy kernel for copying output_state_out to output
   * -# @ref NEGEMMLowpMatrixMultiplyCore                          Quantized matrix multiplication core. Accumulators are 32-bit integers
   * -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint   Convert 32-bit integers into QSYMM16
   * -# @ref NEGEMMLowpMatrixAReductionKernel                      For precomputing effective biases to use
   * -# @ref NEPixelWiseMultiplicationKernel                       Elementwise multiplication
   * -# @ref NETranspose                                           Transpose function for reshaping the weights
   * */
  class NEQLSTMLayer : public IFunction
  {
  public:
      /** Default constructor */
      NEQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEQLSTMLayer(const NEQLSTMLayer &) = delete;
      /** Default move constructor */
      NEQLSTMLayer(NEQLSTMLayer &&) = default;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      NEQLSTMLayer &operator=(const NEQLSTMLayer &) = delete;
      /** Default move assignment operator */
      NEQLSTMLayer &operator=(NEQLSTMLayer &&) = default;
      /** Initialize function's tensors.
       *
       * @param[in]  input                       Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
       * @param[in]  input_to_forget_weights     2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  input_to_cell_weights       2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  input_to_output_weights     2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  recurrent_to_cell_weights   2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in]  forget_gate_bias            1D weights tensor with dimensions [num_units]. Data type supported: S32.
       * @param[in]  cell_bias                   1D weights tensor with dimensions [num_units]. Data type supported: S32.
       * @param[in]  output_gate_bias            1D weights tensor with dimensions [num_units]. Data type supported: S32.
       * @param[in]  cell_state_in               2D tensor with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
       * @param[in]  output_state_in             2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
       * @param[out] cell_state_out              Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
       * @param[out] output_state_out            Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
       * @param[out] output                      Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
       * @param[in]  lstm_params                 Weights tensors used in peephole, CIFG and layer normalization optimizations:
       *                                         input_intermediate_scale   Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
       *                                         forget_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
       *                                         cell_intermediate_scale    Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
       *                                         output_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
       *                                         hidden_state_zero          The zero point of the hidden state.
       *                                         hidden_state_scale         The scale of the hidden state.
       *                                         input_to_input_weights     (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       *                                         recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       *                                         cell_to_input_weights      (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
       *                                         cell_to_forget_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         cell_to_output_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         input_gate_bias            (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
       *                                         projection_weights         (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       *                                         projection_bias            (Optional) 1D weights tensor with dimensions [output_size]. S32.
       *                                         input_layer_norm_weights   (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         forget_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         cell_layer_norm_weights    (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         output_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                         cell_threshold             (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
       *                                                                               If set to 0.0 then clipping is disabled.
       *                                         projection_threshold       (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
       *                                                                               [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
       */
      void configure(const ITensor *input,
                     const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights,
                     const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights,
                     const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias,
                     const ITensor *cell_state_in, const ITensor *output_state_in,
                     ITensor *cell_state_out, ITensor *output_state_out, ITensor *output,
                     const LSTMParams<ITensor> &lstm_params);
  
      /** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayer
       *
       * @param[in] input                       Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
       * @param[in] input_to_forget_weights     2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in] input_to_cell_weights       2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in] input_to_output_weights     2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in] recurrent_to_cell_weights   2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       * @param[in] forget_gate_bias            1D weights tensor info with dimensions [num_units]. Data type supported: S32.
       * @param[in] cell_bias                   1D weights tensor info with dimensions [num_units]. Data type supported: S32.
       * @param[in] output_gate_bias            1D weights tensor info with dimensions [num_units]. Data type supported: S32.
       * @param[in] cell_state_in               2D tensor info with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
       * @param[in] output_state_in             2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
       * @param[in] cell_state_out              Destination tensor info. Output is a 2D tensor info with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
       * @param[in] output_state_out            Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
       * @param[in] output                      Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
       * @param[in] lstm_params                 Weights tensors info used in peephole, CIFG and layer normalization optimizations:
       *                                        input_intermediate_scale   Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
       *                                        forget_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
       *                                        cell_intermediate_scale    Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
       *                                        output_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
       *                                        hidden_state_zero          The zero point of the hidden state.
       *                                        hidden_state_scale         The scale of the hidden state.
       *                                        input_to_input_weights     (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
       *                                        recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       *                                        cell_to_input_weights      (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
       *                                        cell_to_forget_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        cell_to_output_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        input_gate_bias            (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
       *                                        projection_weights         (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
       *                                        projection_bias            (Optional) 1D weights tensor with dimensions [output_size]. S32.
       *                                        input_layer_norm_weights   (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        forget_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        cell_layer_norm_weights    (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        output_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
       *                                        cell_threshold             (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
       *                                                                              If set to 0.0 then clipping is disabled.
       *                                        projection_threshold       (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
       *                                                                              [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
       * @return a status
       */
      static Status validate(const ITensorInfo *input,
                             const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
                             const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
                             const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
                             const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
                             const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out, const ITensorInfo *output,
                             const LSTMParams<ITensorInfo> &lstm_params);
  
      // Inherited methods overridden:
      void run() override;
      void prepare() override;
  
  private:
      enum class LayerNormGate : uint8_t
      {
          Forget,
          Cell,
          Input,
          Output,
          Count
      };
      static constexpr uint8_t  _layer_norm_count                    = static_cast<uint8_t>(LayerNormGate::Count);
      static constexpr uint32_t _out_state_output_size_dimension_idx = 0;
  
      /** Internal method to configure matrix multiplication plus output stage of each gate.
       *
       * @param[in] mm             Matrix multiplication function to use.
       * @param[in] outstage       Output stage function to use.
       * @param[in] gemmlowp_info  GEMMLowp metadata to be used by the output stage.
       * @param[in] mm_input       Input tensor to matrix multiplication function.
       * @param[in] mm_weights     Weights tensor to matrix multiplication function.
       * @param[in] bias           Bias tensor to matrix multiplication function.
       * @param[in] outstage_res   Tensor to be used for storing the result of the output stage.
       * @param[in] gemmlowp_scale Real multiplier to be used computing multiplier and shift for requantization.
       * @param[in] mm_res_info    Tensor info to be used to initialize matrix multiplication result tensor.
       * @param[in] mm_res_info    Tensor info to be used to initialize output stage result tensor.
       *
       */
      void configure_mm(NEGEMMLowpMatrixMultiplyCore &mm, NEGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
                        const ITensor *mm_input, const ITensor *mm_weights, const ITensor *bias, Tensor *mm_res,
                        Tensor *outstage_res, float gemmlowp_scale,
                        const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info);
  
      MemoryGroup _memory_group{};
  
      /** A small internel kernel do the copy between two tensors */
      class TensorCopyKernel
      {
          static constexpr uint32_t max_dimension_supported = 2;
  
          ITensor *_src{ nullptr };
          ITensor *_dst{ nullptr };
          size_t   _row_size{};
          Window   _window{};
  
      public:
          /** Static function to check if given info will lead to a valid configuration of @ref NEQLSTMLayer::TensorCopyKernel
           *
           * @param[in] src Source tensor info.
           * @param[in] dst Destination tensor info
           *
           * @return a status
           */
          static Status validate(const ITensorInfo &src, const ITensorInfo &dst);
          /** Set the input and output tensors.
           *
           * @param[in]  src Source tensor
           * @param[out] dst Destination tensor
           */
          void configure(ITensor &src, ITensor &dst);
          /** run the kernel */
          void run();
      };
  
      // Functions used
      NETranspose                      _transpose_input_to_forget_weights{};
      NETranspose                      _transpose_input_to_cell_weights{};
      NETranspose                      _transpose_input_to_output_weights{};
      NETranspose                      _transpose_input_to_input_weights{};
      NETranspose                      _transpose_recurrent_to_forget_weights{};
      NETranspose                      _transpose_recurrent_to_cell_weights{};
      NETranspose                      _transpose_recurrent_to_output_weights{};
      NETranspose                      _transpose_recurrent_to_input_weights{};
      NETranspose                      _transpose_projection_weights{};
      NEGEMMLowpMatrixAReductionKernel _input_to_input_reduction{};
      NEGEMMLowpMatrixAReductionKernel _recurrent_to_input_reduction{};
      NEGEMMLowpMatrixAReductionKernel _input_to_forget_reduction{};
      NEGEMMLowpMatrixAReductionKernel _recurrent_to_forget_reduction{};
      NEGEMMLowpMatrixAReductionKernel _input_to_cell_reduction{};
      NEGEMMLowpMatrixAReductionKernel _recurrent_to_cell_reduction{};
      NEGEMMLowpMatrixAReductionKernel _input_to_output_reduction{};
      NEGEMMLowpMatrixAReductionKernel _recurrent_to_output_reduction{};
      NEGEMMLowpMatrixAReductionKernel _projection_reduction{};
      NEArithmeticAdditionKernel       _projection_bias_add{};
      NEGEMMLowpMatrixMultiplyCore     _mm_input_to_forget{};
      NEGEMMLowpMatrixMultiplyCore     _mm_recurrent_to_forget{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_cell_to_forget{};
      NEGEMMLowpOutputStage            _input_to_forget_outstage{};
      NEGEMMLowpOutputStage            _recurrent_to_forget_outstage{};
      NEGEMMLowpOutputStage            _cell_to_forget_outstage{};
      NEArithmeticAdditionKernel       _accumulate_input_recurrent_forget{};
      NEArithmeticAdditionKernel       _accumulate_cell_forget{};
      NEActivationLayer                _forget_gate_sigmoid{};
      NEGEMMLowpMatrixMultiplyCore     _mm_input_to_cell{};
      NEGEMMLowpOutputStage            _input_to_cell_outstage{};
      NEGEMMLowpMatrixMultiplyCore     _mm_recurrent_to_cell{};
      NEGEMMLowpOutputStage            _recurrent_to_cell_outstage{};
      NEArithmeticAdditionKernel       _accumulate_input_recurrent_modulation{};
      NEActivationLayer                _cell_gate_tanh{};
      NEArithmeticSubtractionKernel    _input_gate_sub{};
      NEGEMMLowpMatrixMultiplyCore     _mm_input_to_input{};
      NEGEMMLowpOutputStage            _input_to_input_outstage{};
      NEGEMMLowpMatrixMultiplyCore     _mm_recurrent_to_input{};
      NEGEMMLowpOutputStage            _recurrent_to_input_outstage{};
      NEArithmeticAdditionKernel       _accumulate_input_recurrent_input{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_cell_to_input{};
      NEGEMMLowpOutputStage            _cell_to_input_outstage{};
      NEArithmeticAdditionKernel       _accumulate_cell_input{};
      NEActivationLayer                _input_gate_sigmoid{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_forget_cell{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_input_cell{};
      NEArithmeticAdditionKernel       _add_forget_cell{};
      NEActivationLayer                _cell_clip{};
      NEGEMMLowpMatrixMultiplyCore     _mm_input_to_output{};
      NEGEMMLowpOutputStage            _input_to_output_outstage{};
      NEGEMMLowpMatrixMultiplyCore     _mm_recurrent_to_output{};
      NEGEMMLowpOutputStage            _recurrent_to_output_outstage{};
      NEArithmeticAdditionKernel       _accumulate_input_recurrent_output{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_cell_to_output{};
      NEGEMMLowpOutputStage            _cell_to_output_outstage{};
      NEArithmeticAdditionKernel       _accumulate_cell_to_output{};
      NEActivationLayer                _output_gate_sigmoid{};
      NEActivationLayer                _hidden_tanh{};
      NEPixelWiseMultiplicationKernel  _pixelwise_mul_hidden{};
      NEGEMMLowpOutputStage            _hidden_outstage{};
      NEGEMMLowpMatrixMultiplyCore     _mm_projection{};
      NEGEMMLowpOutputStage            _projection_outstage{};
      NEArithmeticAdditionKernel       _accumulate_projection{};
      NEActivationLayer                _projection_clip{};
  
      TensorCopyKernel _projection_bias_copy{};
      TensorCopyKernel _projection_output_to_accumulate_copy{};
      TensorCopyKernel _projection_accumulate_to_output_copy{};
      TensorCopyKernel _hidden_to_output_copy{};
  
      std::array<NEQLSTMLayerNormalizationKernel, _layer_norm_count> _layer_norms{ {} };
  
      NECopyKernel _copy_output{};
  
      // Tensor pointers
      const ITensor *_input_to_input_weights{ nullptr };
      const ITensor *_recurrent_to_input_weights{ nullptr };
      const ITensor *_projection_bias{ nullptr };
      const ITensor *_input_to_forget_weights{ nullptr };
      const ITensor *_input_to_cell_weights{ nullptr };
      const ITensor *_input_to_output_weights{ nullptr };
      const ITensor *_recurrent_to_forget_weights{ nullptr };
      const ITensor *_recurrent_to_cell_weights{ nullptr };
      const ITensor *_recurrent_to_output_weights{ nullptr };
      const ITensor *_projection_weights{ nullptr };
      std::array<const ITensor *, _layer_norm_count> _layer_norm_weights{ {} };
      std::array<const ITensor *, _layer_norm_count> _layer_norm_bias{ {} };
  
      using LayerNormIndexType = typename std::underlying_type<LayerNormGate>::type;
      inline LayerNormIndexType getGateIndex(LayerNormGate g)
      {
          return static_cast<LayerNormIndexType>(g);
      }
  
      inline void set_layer_norm_weight(const ITensor *t, LayerNormGate g)
      {
          _layer_norm_weights[getGateIndex(g)] = t;
      }
  
      inline void set_layer_norm_bias(const ITensor *t, LayerNormGate g)
      {
          _layer_norm_bias[getGateIndex(g)] = t;
      }
  
      inline const ITensor *get_layer_norm_weight(LayerNormGate g)
      {
          return _layer_norm_weights[getGateIndex(g)];
      }
  
      inline const ITensor *get_layer_norm_bias(LayerNormGate g)
      {
          return _layer_norm_bias[getGateIndex(g)];
      }
  
      inline NEQLSTMLayerNormalizationKernel &get_layer_norm(LayerNormGate g)
      {
          return _layer_norms[getGateIndex(g)];
      }
  
      inline void configure_layer_norm(LayerNormGate g, const ITensor *in)
      {
          ARM_COMPUTE_ERROR_ON(!_has_layer_norm);
  
          Tensor &out = get_layer_norm_output(g);
          _memory_group.manage(&out);
          out.allocator()->init(*(in->info()));
  
          get_layer_norm(g).configure(in, &out, get_layer_norm_weight(g), get_layer_norm_bias(g));
      }
  
      inline static Status validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias)
      {
          // Output quantization scale will be different, but ignored here
          // since it will be configured at configure() stage.
          const TensorInfo out{ in };
          return NEQLSTMLayerNormalizationKernel::validate(&in, &out, &weight, &bias);
      }
  
      // Temporary tensors
      Tensor _input_to_forget_weights_transposed{ nullptr };
      Tensor _input_to_cell_weights_transposed{ nullptr };
      Tensor _input_to_output_weights_transposed{ nullptr };
      Tensor _input_to_input_weights_transposed{ nullptr };
      Tensor _recurrent_to_forget_weights_transposed{ nullptr };
      Tensor _recurrent_to_cell_weights_transposed{ nullptr };
      Tensor _recurrent_to_output_weights_transposed{ nullptr };
      Tensor _recurrent_to_input_weights_transposed{ nullptr };
      Tensor _projection_weights_transposed{ nullptr };
      Tensor _input_to_input_eff_bias{ nullptr };
      Tensor _recurrent_to_input_eff_bias{ nullptr };
      Tensor _input_to_forget_eff_bias{ nullptr };
      Tensor _recurrent_to_forget_eff_bias{ nullptr };
      Tensor _input_to_cell_eff_bias{ nullptr };
      Tensor _recurrent_to_cell_eff_bias{ nullptr };
      Tensor _input_to_output_eff_bias{ nullptr };
      Tensor _recurrent_to_output_eff_bias{ nullptr };
      Tensor _projection_reduction_res{ nullptr };
      Tensor _projection_eff_bias{ nullptr };
      Tensor _mm_input_to_forget_res{ nullptr };
      Tensor _mm_recurrent_to_forget_res{ nullptr };
      Tensor _mul_cell_to_forget_res{ nullptr };
      Tensor _input_to_forget_outstage_res{ nullptr };
      Tensor _cell_to_forget_outstage_res{ nullptr };
      Tensor _recurrent_to_forget_outstage_res{ nullptr };
      Tensor _forget_gate{ nullptr };
      Tensor _mm_input_to_cell_res{ nullptr };
      Tensor _input_to_cell_outstage_res{ nullptr };
      Tensor _mm_recurrent_to_cell_res{ nullptr };
      Tensor _recurrent_to_cell_outstage_res{ nullptr };
      Tensor _cell_gate{ nullptr };
      Tensor _mul_input_cell_res{ nullptr };
      Tensor _mm_input_to_input_res{ nullptr };
      Tensor _input_to_input_outstage_res{ nullptr };
      Tensor _mm_recurrent_to_input_res{ nullptr };
      Tensor _mul_cell_to_input_res{ nullptr };
      Tensor _cell_to_input_outstage_res{ nullptr };
      Tensor _recurrent_to_input_outstage_res{ nullptr };
      Tensor _input_gate{ nullptr };
      Tensor _mm_input_to_output_res{ nullptr };
      Tensor _input_to_output_outstage_res{ nullptr };
      Tensor _mm_recurrent_to_output_res{ nullptr };
      Tensor _mul_cell_to_output_res{ nullptr };
      Tensor _cell_to_output_outstage_res{ nullptr };
      Tensor _recurrent_to_output_outstage_res{ nullptr };
      Tensor _output_gate{ nullptr };
      Tensor _hidden_mul_res{ nullptr };
      Tensor _hidden_gate{ nullptr };
      Tensor _mm_projection_res{ nullptr };
      Tensor _projection_outstage_res{ nullptr };
      Tensor _projection_out_res{ nullptr };
      Tensor _projection_accumulate_res{ nullptr };
      Tensor _ones{ nullptr };
      std::array<Tensor, _layer_norm_count> _layer_norm_output{ {} };
  
      inline Tensor &get_layer_norm_output(LayerNormGate g)
      {
          return _layer_norm_output[getGateIndex(g)];
      }
  
      bool _is_prepared{ false };
      bool _has_cifg{ false };
      bool _has_cell_clipping{ false };
      bool _has_projection{ false };
      bool _has_projection_clipping{ false };
      bool _has_peephole{ false };
      bool _has_layer_norm{ false };
      bool _projection_tensor_copy_required{ false };
  };
  } // namespace arm_compute
  #endif /* ARM_COMPUTE_NEQLSTMLAYER_H */
  