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