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/*
* Copyright (c) 2019 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_NELSTMLAYERQUANTIZED_H
#define ARM_COMPUTE_NELSTMLAYERQUANTIZED_H
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h"
#include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h"
#include "arm_compute/runtime/NEON/functions/NEDequantizationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEElementwiseOperations.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.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/NESlice.h"
#include "arm_compute/runtime/NEON/functions/NETranspose.h"
#include "arm_compute/runtime/common/LSTMParams.h"
namespace arm_compute
{
// Forward declarations
class ITensor;
/** Basic function to run @ref NELSTMLayerQuantized
*
* This function calls the following NEON functions/kernels:
*
* -# @ref NEGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers
* -# @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint Convert 32-bit integers into QSYMM16
* -# @ref NETranspose Matrix transpose
* -# @ref NEConcatenateLayer Tensor concatenation
* -# @ref NEActivationLayer Activation functions (tanh and logistic)
* -# @ref NEArithmeticAddition Elementwise addition
* -# @ref NEPixelWiseMultiplication Elementwise multiplication
* -# @ref NESlice Tensor slicing
* -# @ref NEDequantizationLayer Dequantize into float
* -# @ref NEQuantizationLayer Quantize from float
* */
class NELSTMLayerQuantized : public IFunction
{
public:
/** Default constructor */
NELSTMLayerQuantized(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NELSTMLayerQuantized(const NELSTMLayerQuantized &) = delete;
/** Default move constructor */
NELSTMLayerQuantized(NELSTMLayerQuantized &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NELSTMLayerQuantized &operator=(const NELSTMLayerQuantized &) = delete;
/** Default move assignment operator */
NELSTMLayerQuantized &operator=(NELSTMLayerQuantized &&) = default;
/** Initialize function's tensors.
*
* @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8.
* @param[in] input_to_input_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_input_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32.
* @param[in] forget_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32.
* @param[in] cell_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32.
* @param[in] output_gate_bias 1D weights tensor with dimensions [output_size]. Data type supported: S32.
* @param[in] cell_state_in 2D tensor with dimensions [output_size, 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 [output_size, 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.
*/
void configure(const ITensor *input,
const ITensor *input_to_input_weights, const ITensor *input_to_forget_weights, const ITensor *input_to_cell_weights, const ITensor *input_to_output_weights,
const ITensor *recurrent_to_input_weights, const ITensor *recurrent_to_forget_weights, const ITensor *recurrent_to_cell_weights, const ITensor *recurrent_to_output_weights,
const ITensor *input_gate_bias, const ITensor *forget_gate_bias, const ITensor *cell_bias, const ITensor *output_gate_bias,
ITensor *cell_state_in, const ITensor *output_state_in,
ITensor *cell_state_out, ITensor *output_state_out);
/** Static function to check if given info will lead to a valid configuration of @ref NELSTMLayer
*
* @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8.
* @param[in] input_to_input_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_input_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, output_size]. Data type supported: Same as @p input.
* @param[in] input_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32.
* @param[in] forget_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32.
* @param[in] cell_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32.
* @param[in] output_gate_bias 1D weights tensor info with dimensions [output_size]. Data type supported: S32.
* @param[in] cell_state_in 2D tensor info with dimensions [output_size, 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[out] cell_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size]. Data type supported: QSYMM16.
* @param[out] 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.
*
* @return a status
*/
static Status validate(const ITensorInfo *input,
const ITensorInfo *input_to_input_weights, const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
const ITensorInfo *recurrent_to_input_weights, const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
const ITensorInfo *input_gate_bias, 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);
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
// Functions used
NEGEMMLowpMatrixMultiplyCore _gemmlowp;
NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint _output_stage;
NETranspose _transpose_weights;
NEConcatenateLayer _concat_input_weights;
NEConcatenateLayer _concat_recurrent_weights;
NEConcatenateLayer _concat_weights;
NEConcatenateLayer _concat_inputs;
NEConcatenateLayer _concat_bias;
NEActivationLayer _sigmoid_forget_gate;
NEActivationLayer _sigmoid_input_gate;
NEActivationLayer _sigmoid_output_gate;
NEActivationLayer _tanh_modulation_gate;
NEActivationLayer _tanh_output_state;
NEArithmeticAddition _add1;
NEArithmeticAddition _add2;
NEPixelWiseMultiplication _mul1;
NEPixelWiseMultiplication _mul2;
NEPixelWiseMultiplication _mul3;
NESlice _slice_input_tensor;
NESlice _slice_forget_tensor;
NESlice _slice_cell_tensor;
NESlice _slice_output_tensor;
NEDequantizationLayer _dequantize;
NEQuantizationLayer _quantize;
// Tensor pointers
const ITensor *_input_to_input_weights;
const ITensor *_input_to_forget_weights;
const ITensor *_input_to_cell_weights;
const ITensor *_input_to_output_weights;
const ITensor *_recurrent_to_input_weights;
const ITensor *_recurrent_to_forget_weights;
const ITensor *_recurrent_to_cell_weights;
const ITensor *_recurrent_to_output_weights;
const ITensor *_input_gate_bias;
const ITensor *_forget_gate_bias;
const ITensor *_cell_bias;
const ITensor *_output_gate_bias;
// Temporary tensors
Tensor _recurrent_weights;
Tensor _input_weights;
Tensor _weights;
Tensor _input;
Tensor _weights_transposed;
Tensor _output_highp;
Tensor _output_lowp;
Tensor _bias;
Tensor _forget_gate_input;
Tensor _input_gate_input;
Tensor _output_gate_input;
Tensor _input_modulation_gate_input;
Tensor _forget_gate_output;
Tensor _input_gate_output;
Tensor _output_gate_output;
Tensor _input_modulation_gate_output;
Tensor _cell_state1;
Tensor _cell_state2;
Tensor _output_state_tmp;
Tensor _output_state_out_symm;
Tensor _output_state_out_f32;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NELSTMLAYERQUANTIZED_H */
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