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/*
* Copyright (c) 2020-2022 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_NEQLSTMLAYER_H
#define ARM_COMPUTE_NEQLSTMLAYER_H
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/common/LSTMParams.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h"
#include "arm_compute/runtime/NEON/functions/NEArithmeticSubtraction.h"
#include "arm_compute/runtime/NEON/functions/NECopy.h"
#include "arm_compute/runtime/NEON/functions/NEDequantizationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h"
#include "arm_compute/runtime/NEON/functions/NEQuantizationLayer.h"
#include "arm_compute/runtime/NEON/functions/NETranspose.h"
#include <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
NEDequantizationLayer _dequantize_input_to_forget_weights;
NEQuantizationLayer _quantize_input_to_forget_weights;
NETranspose _transpose_input_to_forget_weights;
NETranspose _transpose_input_to_cell_weights;
NETranspose _transpose_input_to_output_weights;
NETranspose _transpose_input_to_input_weights;
NETranspose _transpose_recurrent_to_forget_weights;
NETranspose _transpose_recurrent_to_cell_weights;
NETranspose _transpose_recurrent_to_output_weights;
NETranspose _transpose_recurrent_to_input_weights;
NETranspose _transpose_projection_weights;
std::unique_ptr<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_f32{nullptr};
Tensor _input_to_forget_weights_symm8{nullptr};
Tensor _input_to_forget_weights_transposed{nullptr};
Tensor _input_to_cell_weights_transposed{nullptr};
Tensor _input_to_output_weights_transposed{nullptr};
Tensor _input_to_input_weights_transposed{nullptr};
Tensor _recurrent_to_forget_weights_transposed{nullptr};
Tensor _recurrent_to_cell_weights_transposed{nullptr};
Tensor _recurrent_to_output_weights_transposed{nullptr};
Tensor _recurrent_to_input_weights_transposed{nullptr};
Tensor _projection_weights_transposed{nullptr};
Tensor _input_to_input_eff_bias{nullptr};
Tensor _recurrent_to_input_eff_bias{nullptr};
Tensor _input_to_forget_eff_bias{nullptr};
Tensor _recurrent_to_forget_eff_bias{nullptr};
Tensor _input_to_cell_eff_bias{nullptr};
Tensor _recurrent_to_cell_eff_bias{nullptr};
Tensor _input_to_output_eff_bias{nullptr};
Tensor _recurrent_to_output_eff_bias{nullptr};
Tensor _projection_reduction_res{nullptr};
Tensor _projection_eff_bias{nullptr};
Tensor _mm_input_to_forget_res{nullptr};
Tensor _mm_recurrent_to_forget_res{nullptr};
Tensor _mul_cell_to_forget_res{nullptr};
Tensor _input_to_forget_outstage_res{nullptr};
Tensor _cell_to_forget_outstage_res{nullptr};
Tensor _recurrent_to_forget_outstage_res{nullptr};
Tensor _forget_gate{nullptr};
Tensor _mm_input_to_cell_res{nullptr};
Tensor _input_to_cell_outstage_res{nullptr};
Tensor _mm_recurrent_to_cell_res{nullptr};
Tensor _recurrent_to_cell_outstage_res{nullptr};
Tensor _cell_gate{nullptr};
Tensor _mul_input_cell_res{nullptr};
Tensor _mm_input_to_input_res{nullptr};
Tensor _input_to_input_outstage_res{nullptr};
Tensor _mm_recurrent_to_input_res{nullptr};
Tensor _mul_cell_to_input_res{nullptr};
Tensor _cell_to_input_outstage_res{nullptr};
Tensor _recurrent_to_input_outstage_res{nullptr};
Tensor _input_gate{nullptr};
Tensor _mm_input_to_output_res{nullptr};
Tensor _input_to_output_outstage_res{nullptr};
Tensor _mm_recurrent_to_output_res{nullptr};
Tensor _mul_cell_to_output_res{nullptr};
Tensor _cell_to_output_outstage_res{nullptr};
Tensor _recurrent_to_output_outstage_res{nullptr};
Tensor _output_gate{nullptr};
Tensor _hidden_mul_res{nullptr};
Tensor _hidden_gate{nullptr};
Tensor _mm_projection_res{nullptr};
Tensor _projection_outstage_res{nullptr};
Tensor _projection_out_res{nullptr};
Tensor _projection_accumulate_res{nullptr};
Tensor _ones{nullptr};
std::array<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};
bool _convert_input_to_forget_weights_to_qsymm8{false};
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEQLSTMLAYER_H */
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