/* * Copyright (c) 2020-2021 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_CLQLSTMLAYER_H #define ARM_COMPUTE_CLQLSTMLAYER_H #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/functions/CLActivationLayer.h" #include "arm_compute/runtime/CL/functions/CLCopy.h" #include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h" #include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h" #include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h" #include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h" #include "arm_compute/runtime/CL/functions/CLTranspose.h" #include "arm_compute/runtime/common/LSTMParams.h" namespace arm_compute { // Forward declarations class CLCompileContext; class ICLTensor; class CLQLSTMLayerNormalizationKernel; class ITensorInfo; namespace opencl { namespace kernels { class ClGemmLowpMatrixAReductionKernel; } // namespace kernels } // namespace opencl /** Basic function to run @ref CLQLSTMLayer * * This function calls the following CL functions/kernels: * * -# @ref CLActivationLayer Activation functions (tanh and logistic) * -# @ref CLCopy Copy function for copying output_state_out to output * -# @ref CLArithmeticAddition Elementwise addition and subtraction * -# @ref CLGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers * -# @ref CLGEMMLowpOutputStage Convert 32-bit integers into QSYMM16 * -# @ref opencl::kernels::ClGemmLowpMatrixAReductionKernel For precomputing effective biases to use * -# @ref CLPixelWiseMultiplication Elementwise multiplication * -# @ref CLTranspose Transpose function for reshaping the weights * */ class CLQLSTMLayer : public IFunction { public: /** Default constructor */ CLQLSTMLayer(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ CLQLSTMLayer(const CLQLSTMLayer &) = delete; /** Default move constructor */ CLQLSTMLayer(CLQLSTMLayer &&) = default; /** Prevent instances of this class from being copied (As this class contains pointers) */ CLQLSTMLayer &operator=(const CLQLSTMLayer &) = delete; /** Default move assignment operator */ CLQLSTMLayer &operator=(CLQLSTMLayer &&) = default; /** Default destructor */ ~CLQLSTMLayer(); /** 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 ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *cell_state_in, ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output, const LSTMParams &lstm_params); /** Initialize function's tensors. * * @param[in] compile_context The compile context to be used. * @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 CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights, const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights, const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias, ICLTensor *cell_state_in, ICLTensor *output_state_in, ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output, const LSTMParams &lstm_params); /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer * * @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] compile_context The compile context to be used. * @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(const CLCompileContext &compile_context, CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info, const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias, CLTensor *mm_res, CLTensor *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; ICLTensor *_src{ nullptr }; ICLTensor *_dst{ nullptr }; size_t _row_size{}; Window _window{}; public: /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer::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(ICLTensor &src, ICLTensor &dst); /** run the kernel */ void run(); }; // Functions used CLTranspose _transpose_input_to_forget_weights{}; CLTranspose _transpose_input_to_cell_weights{}; CLTranspose _transpose_input_to_output_weights{}; CLTranspose _transpose_input_to_input_weights{}; CLTranspose _transpose_recurrent_to_forget_weights{}; CLTranspose _transpose_recurrent_to_cell_weights{}; CLTranspose _transpose_recurrent_to_output_weights{}; CLTranspose _transpose_recurrent_to_input_weights{}; CLTranspose _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; CLArithmeticAddition _projection_bias_add{}; CLGEMMLowpMatrixMultiplyCore _mm_input_to_forget{}; CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_forget{}; CLPixelWiseMultiplication _pixelwise_mul_cell_to_forget{}; CLGEMMLowpOutputStage _input_to_forget_outstage{}; CLGEMMLowpOutputStage _recurrent_to_forget_outstage{}; CLGEMMLowpOutputStage _cell_to_forget_outstage{}; CLArithmeticAddition _accumulate_input_recurrent_forget{}; CLArithmeticAddition _accumulate_cell_forget{}; CLActivationLayer _forget_gate_sigmoid{}; CLGEMMLowpMatrixMultiplyCore _mm_input_to_cell{}; CLGEMMLowpOutputStage _input_to_cell_outstage{}; CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_cell{}; CLGEMMLowpOutputStage _recurrent_to_cell_outstage{}; CLArithmeticAddition _accumulate_input_recurrent_modulation{}; CLActivationLayer _cell_gate_tanh{}; CLArithmeticSubtraction _input_gate_sub{}; CLGEMMLowpMatrixMultiplyCore _mm_input_to_input{}; CLGEMMLowpOutputStage _input_to_input_outstage{}; CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_input{}; CLGEMMLowpOutputStage _recurrent_to_input_outstage{}; CLArithmeticAddition _accumulate_input_recurrent_input{}; CLPixelWiseMultiplication _pixelwise_mul_cell_to_input{}; CLGEMMLowpOutputStage _cell_to_input_outstage{}; CLArithmeticAddition _accumulate_cell_input{}; CLActivationLayer _input_gate_sigmoid{}; CLPixelWiseMultiplication _pixelwise_mul_forget_cell{}; CLPixelWiseMultiplication _pixelwise_mul_input_cell{}; CLArithmeticAddition _add_forget_cell{}; CLActivationLayer _cell_clip{}; CLGEMMLowpMatrixMultiplyCore _mm_input_to_output{}; CLGEMMLowpOutputStage _input_to_output_outstage{}; CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_output{}; CLGEMMLowpOutputStage _recurrent_to_output_outstage{}; CLArithmeticAddition _accumulate_input_recurrent_output{}; CLPixelWiseMultiplication _pixelwise_mul_cell_to_output{}; CLGEMMLowpOutputStage _cell_to_output_outstage{}; CLArithmeticAddition _accumulate_cell_to_output{}; CLActivationLayer _output_gate_sigmoid{}; CLActivationLayer _hidden_tanh{}; CLPixelWiseMultiplication _pixelwise_mul_hidden{}; CLGEMMLowpOutputStage _hidden_outstage{}; CLGEMMLowpMatrixMultiplyCore _mm_projection{}; CLGEMMLowpOutputStage _projection_outstage{}; CLArithmeticAddition _accumulate_projection{}; CLActivationLayer _projection_clip{}; std::array, _layer_norm_count> _layer_norms; CLCopy _copy_output; TensorCopyKernel _projection_bias_copy{}; TensorCopyKernel _projection_output_to_accumulate_copy{}; TensorCopyKernel _projection_accumulate_to_output_copy{}; TensorCopyKernel _hidden_to_output_copy{}; // Tensor pointers const ICLTensor *_input_to_input_weights { nullptr }; const ICLTensor *_recurrent_to_input_weights{ nullptr }; const ICLTensor *_projection_bias{ nullptr }; const ICLTensor *_input_to_forget_weights{ nullptr }; const ICLTensor *_input_to_cell_weights{ nullptr }; const ICLTensor *_input_to_output_weights{ nullptr }; const ICLTensor *_recurrent_to_forget_weights{ nullptr }; const ICLTensor *_recurrent_to_cell_weights{ nullptr }; const ICLTensor *_recurrent_to_output_weights{ nullptr }; const ICLTensor *_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 ICLTensor *t, LayerNormGate g) { _layer_norm_weights[getGateIndex(g)] = t; } inline void set_layer_norm_bias(const ICLTensor *t, LayerNormGate g) { _layer_norm_bias[getGateIndex(g)] = t; } inline const ICLTensor *get_layer_norm_weight(LayerNormGate g) { return _layer_norm_weights[getGateIndex(g)]; } inline const ICLTensor *get_layer_norm_bias(LayerNormGate g) { return _layer_norm_bias[getGateIndex(g)]; } inline CLQLSTMLayerNormalizationKernel &get_layer_norm(LayerNormGate g) { return *_layer_norms[getGateIndex(g)]; } inline void configure_layer_norm(LayerNormGate g, const ICLTensor *in); inline static Status validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias); // Temporary tensors CLTensor _input_to_forget_weights_transposed{ nullptr }; CLTensor _input_to_cell_weights_transposed{ nullptr }; CLTensor _input_to_output_weights_transposed{ nullptr }; CLTensor _input_to_input_weights_transposed{ nullptr }; CLTensor _recurrent_to_forget_weights_transposed{ nullptr }; CLTensor _recurrent_to_cell_weights_transposed{ nullptr }; CLTensor _recurrent_to_output_weights_transposed{ nullptr }; CLTensor _recurrent_to_input_weights_transposed{ nullptr }; CLTensor _projection_weights_transposed{ nullptr }; CLTensor _input_to_input_eff_bias{ nullptr }; CLTensor _recurrent_to_input_eff_bias{ nullptr }; CLTensor _input_to_forget_eff_bias{ nullptr }; CLTensor _recurrent_to_forget_eff_bias{ nullptr }; CLTensor _input_to_cell_eff_bias{ nullptr }; CLTensor _recurrent_to_cell_eff_bias{ nullptr }; CLTensor _input_to_output_eff_bias{ nullptr }; CLTensor _recurrent_to_output_eff_bias{ nullptr }; CLTensor _projection_reduction_res{ nullptr }; CLTensor _projection_eff_bias{ nullptr }; CLTensor _mm_input_to_forget_res{ nullptr }; CLTensor _mm_recurrent_to_forget_res{ nullptr }; CLTensor _mul_cell_to_forget_res{ nullptr }; CLTensor _input_to_forget_outstage_res{ nullptr }; CLTensor _cell_to_forget_outstage_res{ nullptr }; CLTensor _recurrent_to_forget_outstage_res{ nullptr }; CLTensor _forget_gate{ nullptr }; CLTensor _mm_input_to_cell_res{ nullptr }; CLTensor _input_to_cell_outstage_res{ nullptr }; CLTensor _mm_recurrent_to_cell_res{ nullptr }; CLTensor _recurrent_to_cell_outstage_res{ nullptr }; CLTensor _cell_gate{ nullptr }; CLTensor _mul_input_cell_res{ nullptr }; CLTensor _mm_input_to_input_res{ nullptr }; CLTensor _input_to_input_outstage_res{ nullptr }; CLTensor _mm_recurrent_to_input_res{ nullptr }; CLTensor _mul_cell_to_input_res{ nullptr }; CLTensor _cell_to_input_outstage_res{ nullptr }; CLTensor _recurrent_to_input_outstage_res{ nullptr }; CLTensor _input_gate{ nullptr }; CLTensor _mm_input_to_output_res{ nullptr }; CLTensor _input_to_output_outstage_res{ nullptr }; CLTensor _mm_recurrent_to_output_res{ nullptr }; CLTensor _mul_cell_to_output_res{ nullptr }; CLTensor _cell_to_output_outstage_res{ nullptr }; CLTensor _recurrent_to_output_outstage_res{ nullptr }; CLTensor _output_gate{ nullptr }; CLTensor _hidden_mul_res{ nullptr }; CLTensor _hidden_gate{ nullptr }; CLTensor _mm_projection_res{ nullptr }; CLTensor _projection_outstage_res{ nullptr }; CLTensor _projection_out_res{ nullptr }; CLTensor _projection_accumulate_res{ nullptr }; CLTensor _ones{ nullptr }; std::array _layer_norm_output{ {} }; inline CLTensor &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_CLQLSTMLAYER_H */