487 lines
36 KiB
C++
487 lines
36 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_CLQLSTMLAYER_H
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#define ARM_COMPUTE_CLQLSTMLAYER_H
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
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#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
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#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
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#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
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#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
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#include "arm_compute/runtime/CL/functions/CLTranspose.h"
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#include "arm_compute/runtime/common/LSTMParams.h"
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namespace arm_compute
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{
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// Forward declarations
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class CLCompileContext;
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class CLCopyKernel;
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class ICLTensor;
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class CLGEMMLowpMatrixAReductionKernel;
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class CLQLSTMLayerNormalizationKernel;
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class ITensorInfo;
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/** Basic function to run @ref CLQLSTMLayer
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*
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* This function calls the following CL functions/kernels:
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*
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* -# @ref CLActivationLayer Activation functions (tanh and logistic)
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* -# @ref CLCopyKernel Copy kernel for copying output_state_out to output
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* -# @ref CLArithmeticAddition Elementwise addition and subtraction
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* -# @ref CLGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers
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* -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint Convert 32-bit integers into QSYMM16
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* -# @ref CLGEMMLowpMatrixAReductionKernel For precomputing effective biases to use
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* -# @ref CLPixelWiseMultiplication Elementwise multiplication
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* -# @ref CLTranspose Transpose function for reshaping the weights
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* */
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class CLQLSTMLayer : public IFunction
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{
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public:
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/** Default constructor */
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CLQLSTMLayer(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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CLQLSTMLayer(const CLQLSTMLayer &) = delete;
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/** Default move constructor */
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CLQLSTMLayer(CLQLSTMLayer &&) = default;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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CLQLSTMLayer &operator=(const CLQLSTMLayer &) = delete;
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/** Default move assignment operator */
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CLQLSTMLayer &operator=(CLQLSTMLayer &&) = default;
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/** Default destructor */
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~CLQLSTMLayer();
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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 ICLTensor *input,
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const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
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const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
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const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
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ICLTensor *cell_state_in, ICLTensor *output_state_in,
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ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output,
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const LSTMParams<ICLTensor> &lstm_params);
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/** Initialize function's tensors.
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*
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* @param[in] compile_context The compile context to be used.
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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 CLCompileContext &compile_context, const ICLTensor *input,
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const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
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const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
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const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
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ICLTensor *cell_state_in, ICLTensor *output_state_in,
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ICLTensor *cell_state_out, ICLTensor *output_state_out, ICLTensor *output,
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const LSTMParams<ICLTensor> &lstm_params);
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/** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer
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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] compile_context The compile context to be used.
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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(const CLCompileContext &compile_context, CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
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const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias, CLTensor *mm_res,
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CLTensor *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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ICLTensor *_src{ nullptr };
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ICLTensor *_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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/** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer::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(ICLTensor &src, ICLTensor &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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CLTranspose _transpose_input_to_forget_weights{};
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CLTranspose _transpose_input_to_cell_weights{};
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CLTranspose _transpose_input_to_output_weights{};
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CLTranspose _transpose_input_to_input_weights{};
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CLTranspose _transpose_recurrent_to_forget_weights{};
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CLTranspose _transpose_recurrent_to_cell_weights{};
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CLTranspose _transpose_recurrent_to_output_weights{};
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CLTranspose _transpose_recurrent_to_input_weights{};
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CLTranspose _transpose_projection_weights{};
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _input_to_input_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _recurrent_to_input_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _input_to_forget_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _recurrent_to_forget_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _input_to_cell_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _recurrent_to_cell_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _input_to_output_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _recurrent_to_output_reduction;
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std::unique_ptr<CLGEMMLowpMatrixAReductionKernel> _projection_reduction;
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CLArithmeticAddition _projection_bias_add{};
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CLGEMMLowpMatrixMultiplyCore _mm_input_to_forget{};
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CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_forget{};
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CLPixelWiseMultiplication _pixelwise_mul_cell_to_forget{};
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CLGEMMLowpOutputStage _input_to_forget_outstage{};
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CLGEMMLowpOutputStage _recurrent_to_forget_outstage{};
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CLGEMMLowpOutputStage _cell_to_forget_outstage{};
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CLArithmeticAddition _accumulate_input_recurrent_forget{};
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CLArithmeticAddition _accumulate_cell_forget{};
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CLActivationLayer _forget_gate_sigmoid{};
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CLGEMMLowpMatrixMultiplyCore _mm_input_to_cell{};
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CLGEMMLowpOutputStage _input_to_cell_outstage{};
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CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_cell{};
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CLGEMMLowpOutputStage _recurrent_to_cell_outstage{};
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CLArithmeticAddition _accumulate_input_recurrent_modulation{};
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CLActivationLayer _cell_gate_tanh{};
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CLArithmeticSubtraction _input_gate_sub{};
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CLGEMMLowpMatrixMultiplyCore _mm_input_to_input{};
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CLGEMMLowpOutputStage _input_to_input_outstage{};
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CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_input{};
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CLGEMMLowpOutputStage _recurrent_to_input_outstage{};
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CLArithmeticAddition _accumulate_input_recurrent_input{};
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CLPixelWiseMultiplication _pixelwise_mul_cell_to_input{};
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CLGEMMLowpOutputStage _cell_to_input_outstage{};
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CLArithmeticAddition _accumulate_cell_input{};
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CLActivationLayer _input_gate_sigmoid{};
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CLPixelWiseMultiplication _pixelwise_mul_forget_cell{};
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CLPixelWiseMultiplication _pixelwise_mul_input_cell{};
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CLArithmeticAddition _add_forget_cell{};
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CLActivationLayer _cell_clip{};
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CLGEMMLowpMatrixMultiplyCore _mm_input_to_output{};
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CLGEMMLowpOutputStage _input_to_output_outstage{};
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CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_output{};
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CLGEMMLowpOutputStage _recurrent_to_output_outstage{};
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CLArithmeticAddition _accumulate_input_recurrent_output{};
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CLPixelWiseMultiplication _pixelwise_mul_cell_to_output{};
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CLGEMMLowpOutputStage _cell_to_output_outstage{};
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CLArithmeticAddition _accumulate_cell_to_output{};
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CLActivationLayer _output_gate_sigmoid{};
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CLActivationLayer _hidden_tanh{};
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CLPixelWiseMultiplication _pixelwise_mul_hidden{};
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CLGEMMLowpOutputStage _hidden_outstage{};
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CLGEMMLowpMatrixMultiplyCore _mm_projection{};
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CLGEMMLowpOutputStage _projection_outstage{};
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CLArithmeticAddition _accumulate_projection{};
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CLActivationLayer _projection_clip{};
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std::array<std::unique_ptr<CLQLSTMLayerNormalizationKernel>, _layer_norm_count> _layer_norms;
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std::unique_ptr<CLCopyKernel> _copy_output;
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|
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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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|
|
|
// Tensor pointers
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const ICLTensor *_input_to_input_weights
|
|
{
|
|
nullptr
|
|
};
|
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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<const ICLTensor *, _layer_norm_count> _layer_norm_weights{ {} };
|
|
std::array<const ICLTensor *, _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 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<CLTensor, _layer_norm_count> _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 */
|