/* * Copyright (c) 2020-2022 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/Types.h" #include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" #include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h" #include "arm_compute/runtime/NEON/functions/NEArithmeticSubtraction.h" #include "arm_compute/runtime/NEON/functions/NECopy.h" #include "arm_compute/runtime/NEON/functions/NEDequantizationLayer.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h" #include "arm_compute/runtime/NEON/functions/NEQuantizationLayer.h" #include "arm_compute/runtime/NEON/functions/NETranspose.h" #include "arm_compute/runtime/common/LSTMParams.h" #include namespace arm_compute { // Forward declarations class ITensor; class ITensorInfo; class NEQLSTMLayerNormalizationKernel; namespace cpu { namespace kernels { class CpuGemmLowpMatrixAReductionKernel; } // namespace kernels } // namespace cpu /** Basic function to run @ref NEQLSTMLayer * * This function calls the following kernels: * * -# @ref NEActivationLayer Activation functions (tanh and logistic) * -# @ref NEArithmeticAddition Elementwise addition * -# @ref NEArithmeticSubtraction Elementwise subtraction * -# @ref NECopy Copy kernel for copying output_state_out to output * -# @ref NEGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers * -# @ref NEGEMMLowpOutputStage Convert 32-bit integers into QSYMM16 * -# @ref cpu::kernels::CpuGemmLowpMatrixAReductionKernel For precomputing effective biases to use * -# @ref NEPixelWiseMultiplication Elementwise multiplication * -# @ref NETranspose Transpose function for reshaping the weights * */ class NEQLSTMLayer : public IFunction { public: /** Default constructor */ NEQLSTMLayer(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ NEQLSTMLayer(const NEQLSTMLayer &) = delete; /** Prevent instances of this class from being moved (As this class contains pointers) */ NEQLSTMLayer(NEQLSTMLayer &&) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ NEQLSTMLayer &operator=(const NEQLSTMLayer &) = delete; /** Prevent instances of this class from being moved (As this class contains pointers) */ NEQLSTMLayer &operator=(NEQLSTMLayer &&) = delete; /** Default destructor */ ~NEQLSTMLayer(); /** Initialize function's tensors. * * Valid data layouts: * - All * * Valid data type configurations: * |src0 |src1 - src6 |src7 -src9 |src10 |src11 |dst0 |dst1 - dst2 | * |:-------------|:------------|:------------|:------|:-------------|:------|:-----------------| * |QASYMM8_SIGNED|QASYMM8 |S32 |QSYMM16|QASYMM8_SIGNED|QSYMM16|QASYMM8_SIGNED | * * @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, ITensor *output_state_in, ITensor *cell_state_out, ITensor *output_state_out, ITensor *output, const LSTMParams &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 &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(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: /** Destructor */ ~TensorCopyKernel(); /** 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 NEDequantizationLayer _dequantize_input_to_forget_weights; NEQuantizationLayer _quantize_input_to_forget_weights; 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; std::unique_ptr _input_to_input_reduction; std::unique_ptr _recurrent_to_input_reduction; std::unique_ptr _input_to_forget_reduction; std::unique_ptr _recurrent_to_forget_reduction; std::unique_ptr _input_to_cell_reduction; std::unique_ptr _recurrent_to_cell_reduction; std::unique_ptr _input_to_output_reduction; std::unique_ptr _recurrent_to_output_reduction; std::unique_ptr _projection_reduction; NEArithmeticAddition _projection_bias_add; NEGEMMLowpMatrixMultiplyCore _mm_input_to_forget; NEGEMMLowpMatrixMultiplyCore _mm_recurrent_to_forget; NEPixelWiseMultiplication _pixelwise_mul_cell_to_forget; NEGEMMLowpOutputStage _input_to_forget_outstage; NEGEMMLowpOutputStage _recurrent_to_forget_outstage; NEGEMMLowpOutputStage _cell_to_forget_outstage; NEArithmeticAddition _accumulate_input_recurrent_forget; NEArithmeticAddition _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; NEArithmeticAddition _accumulate_input_recurrent_modulation; NEActivationLayer _cell_gate_tanh; NEArithmeticSubtraction _input_gate_sub; NEGEMMLowpMatrixMultiplyCore _mm_input_to_input; NEGEMMLowpOutputStage _input_to_input_outstage; NEGEMMLowpMatrixMultiplyCore _mm_recurrent_to_input; NEGEMMLowpOutputStage _recurrent_to_input_outstage; NEArithmeticAddition _accumulate_input_recurrent_input; NEPixelWiseMultiplication _pixelwise_mul_cell_to_input; NEGEMMLowpOutputStage _cell_to_input_outstage; NEArithmeticAddition _accumulate_cell_input; NEActivationLayer _input_gate_sigmoid; NEPixelWiseMultiplication _pixelwise_mul_forget_cell; NEPixelWiseMultiplication _pixelwise_mul_input_cell; NEArithmeticAddition _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; NEArithmeticAddition _accumulate_input_recurrent_output; NEPixelWiseMultiplication _pixelwise_mul_cell_to_output; NEGEMMLowpOutputStage _cell_to_output_outstage; NEArithmeticAddition _accumulate_cell_to_output; NEActivationLayer _output_gate_sigmoid; NEActivationLayer _hidden_tanh; NEPixelWiseMultiplication _pixelwise_mul_hidden; NEGEMMLowpOutputStage _hidden_outstage; NEGEMMLowpMatrixMultiplyCore _mm_projection; NEGEMMLowpOutputStage _projection_outstage; NEArithmeticAddition _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, _layer_norm_count> _layer_norms; NECopy _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 _layer_norm_weights{}; std::array _layer_norm_bias{}; using LayerNormIndexType = typename std::underlying_type::type; inline LayerNormIndexType getGateIndex(LayerNormGate g) { return static_cast(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 std::unique_ptr &get_layer_norm(LayerNormGate g) { return _layer_norms[getGateIndex(g)]; } void configure_layer_norm(LayerNormGate g, const ITensor *in); static Status validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias); // Temporary tensors Tensor _input_to_forget_weights_f32{ nullptr }; Tensor _input_to_forget_weights_symm8{ nullptr }; 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 _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 }; bool _convert_input_to_forget_weights_to_qsymm8{ false }; }; } // namespace arm_compute #endif /* ARM_COMPUTE_NEQLSTMLAYER_H */