| /* |
| * 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_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/NEGEMMLowpMatrixMultiplyCore.h" |
| #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" |
| #include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h" |
| #include "arm_compute/runtime/NEON/functions/NETranspose.h" |
| #include "arm_compute/runtime/common/LSTMParams.h" |
| |
| #include <memory> |
| |
| 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<IMemoryManager> 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<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: |
| /** 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 |
| 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<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _input_to_input_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _recurrent_to_input_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _input_to_forget_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _recurrent_to_forget_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _input_to_cell_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _recurrent_to_cell_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _input_to_output_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _recurrent_to_output_reduction; |
| std::unique_ptr<cpu::kernels::CpuGemmLowpMatrixAReductionKernel> _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<std::unique_ptr<NEQLSTMLayerNormalizationKernel>, _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<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 std::unique_ptr<NEQLSTMLayerNormalizationKernel> &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_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 */ |
