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/*
* Copyright (c) 2018-2019 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_NELSTMLAYER_H
#define ARM_COMPUTE_NELSTMLAYER_H
#include "arm_compute/core/NEON/kernels/NEActivationLayerKernel.h"
#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
#include "arm_compute/core/NEON/kernels/NEArithmeticSubtractionKernel.h"
#include "arm_compute/core/NEON/kernels/NECopyKernel.h"
#include "arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h"
#include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
#include "arm_compute/runtime/NEON/functions/NEMeanStdDevNormalizationLayer.h"
#include "arm_compute/runtime/common/LSTMParams.h"
namespace arm_compute
{
// Forward declarations
class ITensor;
/** Basic function to run @ref NELSTMLayer */
class NELSTMLayer : public IFunction
{
public:
/** Default constructor */
NELSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Initialize function's tensors.
*
* @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
* @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
* @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
* @param[out] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
* @param[out] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
* @param[out] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type 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 (Optional) Weights tensors used in peephole optimization:
* input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
* cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
* projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
* input_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* forget_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* cell_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* output_layer_norm_coefficients 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
* @param[in] cell_threshold 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.
* @param[in] projection_threshold 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 *output_state_in, const ITensor *cell_state_in,
ITensor *scratch_buffer, ITensor *output_state_out, ITensor *cell_state_out, ITensor *output,
const LSTMParams<ITensor> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
/** Static function to check if given info will lead to a valid configuration of @ref NELSTMLayer
*
* @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: F16/F32.
* @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] output_state_in 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
* @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
* @param[in] scratch_buffer 2D tensor with dimensions [num_units * 4, batch_size] with CIFG or [num_units * 3, batch_size] without CIGF. Data type supported: Same as @p input.
* @param[in] output_state_out 2D weights tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
* @param[in] cell_state_out 2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
* @param[in] 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 (Optional) Weights tensors used in peephole optimization:
* input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: Same as @p input.
* recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input.
* cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input.
* input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input
* projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input.
* projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p input.
* input_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
* forget_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
* cell_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
* output_layer_norm_coefficients 1D weights tensor info with dimensions [num_units]. Data type supported: Same as @p input.
* @param[in] activation_info Contains activation information described in @ref ActivationLayerInfo.
* @param[in] cell_threshold 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.
* @param[in] projection_threshold 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 *output_state_in, const ITensorInfo *cell_state_in,
const ITensorInfo *scratch_buffer, const ITensorInfo *output_state_out, const ITensorInfo *cell_state_out, const ITensorInfo *output,
const LSTMParams<ITensorInfo> &lstm_params, const ActivationLayerInfo &activation_info, float cell_threshold = 0.f, float projection_threshold = 0.f);
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
NEFullyConnectedLayer _fully_connected_input_gate;
NEArithmeticAddition _accum_input_gate1;
NEArithmeticSubtractionKernel _subtract_input_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_input_gate;
NEActivationLayerKernel _activation_input_gate;
NEFullyConnectedLayer _fully_connected_forget_gate;
NEArithmeticAddition _accum_forget_gate1;
NEPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate;
NEActivationLayerKernel _activation_forget_gate;
NEFullyConnectedLayer _fully_connected_cell_state;
NEGEMM _gemm_cell_state1;
NETransposeKernel _transpose_cell_state;
NEArithmeticAdditionKernel _accum_cell_state1;
NEArithmeticAdditionKernel _accum_cell_state2;
NEPixelWiseMultiplicationKernel _pixelwise_mul_cell_state1;
NEActivationLayerKernel _activation_cell_state;
NEActivationLayerKernel _cell_clip;
NEPixelWiseMultiplicationKernel _pixelwise_mul_cell_state2;
NEFullyConnectedLayer _fully_connected_output;
NEPixelWiseMultiplicationKernel _pixelwise_mul_output_state1;
NEArithmeticAddition _accum_output1;
NEActivationLayerKernel _activation_output;
NEActivationLayerKernel _activation_output_state;
NEPixelWiseMultiplicationKernel _pixelwise_mul_output_state2;
NEFullyConnectedLayer _fully_connected_output_state;
NEActivationLayerKernel _projection_clip;
NECopyKernel _copy_cell_state;
NECopyKernel _copy_output;
NEConcatenateLayer _concat_scratch_buffer;
NEConcatenateLayer _concat_inputs_forget_gate;
NEConcatenateLayer _concat_weights_forget_gate;
NEConcatenateLayer _concat_weights_input_gate;
NEConcatenateLayer _concat_weights_output;
NEMeanStdDevNormalizationLayer _mean_std_norm_input_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_input_gate_coeff;
NEArithmeticAdditionKernel _accum_input_gate_bias;
NEMeanStdDevNormalizationLayer _mean_std_norm_forget_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_forget_gate_coeff;
NEArithmeticAdditionKernel _accum_forget_gate_bias;
NEMeanStdDevNormalizationLayer _mean_std_norm_cell_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_cell_gate_coeff;
NEArithmeticAdditionKernel _accum_cell_gate_bias;
NEMeanStdDevNormalizationLayer _mean_std_norm_output_gate;
NEPixelWiseMultiplicationKernel _pixelwise_mul_output_gate_coeff;
NEArithmeticAdditionKernel _accum_output_gate_bias;
Tensor _input_gate_out1;
Tensor _input_gate_out2;
Tensor _input_gate_out3;
Tensor _input_gate_out4;
Tensor _forget_gate_out1;
Tensor _forget_gate_out2;
Tensor _forget_gate_out3;
Tensor _forget_gate_out4;
Tensor _forget_gate_out5;
Tensor _forget_gate_out6;
Tensor _cell_state_out1;
Tensor _cell_state_out2;
Tensor _cell_state_out3;
Tensor _cell_state_out4;
Tensor _cell_state_out5;
Tensor _output1;
Tensor _output2;
Tensor _output3;
Tensor _output4;
Tensor _cell_state_activation;
Tensor _output_state1;
Tensor _ones;
Tensor _input_layer_norm_out1;
Tensor _input_layer_norm_out2;
Tensor _forget_layer_norm_out1;
Tensor _forget_layer_norm_out2;
Tensor _cell_layer_norm_out1;
Tensor _cell_layer_norm_out2;
Tensor _output_layer_norm_out1;
Tensor _output_layer_norm_out2;
bool _run_peephole_opt;
bool _run_cifg_opt;
bool _perform_cell_clipping;
bool _has_projection_weights;
bool _perform_projection_clipping;
bool _is_prepared;
bool _is_layer_norm_lstm;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NELSTMLAYER_H */
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