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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_LSTMPARAMS_H__
#define __ARM_COMPUTE_LSTMPARAMS_H__
#include "arm_compute/core/IPyramid.h"
#include "arm_compute/core/PyramidInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/Tensor.h"
#include <cstddef>
#include <memory>
namespace arm_compute
{
template <typename T>
class LSTMParams
{
public:
/** Constructor */
LSTMParams()
: _input_to_input_weights(nullptr), _recurrent_to_input_weights(nullptr), _cell_to_input_weights(nullptr), _input_gate_bias(nullptr), _cell_to_forget_weights(nullptr),
_cell_to_output_weights(nullptr), _projection_weights(nullptr), _projection_bias(nullptr), _input_layer_norm_weights(nullptr), _forget_layer_norm_weights(nullptr), _cell_layer_norm_weights(nullptr),
_output_layer_norm_weights(nullptr), _has_peephole_opt(false), _has_projection(false), _has_cifg_opt(true), _use_layer_norm(false)
{
}
/** Prevent instances of this class from being copied (As this class contains pointers) */
LSTMParams(const LSTMParams &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
LSTMParams &operator=(const LSTMParams &) = delete;
/** Default destructor */
~LSTMParams() = default;
/** Set CIFG tensor parameters.
*
* @param[in] input_to_input_weights 2D weights tensor with dimensions [input_size, num_units]. Data types supported: F16/F32.
* @param[in] recurrent_to_input_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Same as @p input_to_input_weights.
* @param[in] cell_to_input_weights 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: Same as @p input_to_input_weights.
* @param[in] input_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input_to_input_weights
*
* @return Reference to this LSTMParams object
*/
LSTMParams &set_cifg_params(const T *input_to_input_weights, const T *recurrent_to_input_weights, const T *cell_to_input_weights, const T *input_gate_bias)
{
_input_to_input_weights = input_to_input_weights;
_recurrent_to_input_weights = recurrent_to_input_weights;
_cell_to_input_weights = cell_to_input_weights;
_input_gate_bias = input_gate_bias;
_has_cifg_opt = false;
return *this;
}
/** Set projection tensor parameters.
*
* @param[in] projection_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: Data types supported: F16/F32.
* @param[in] projection_bias 1D weights tensor with dimensions [output_size]. Data type supported: Same as @p projection_weights.
*
* @return Reference to this LSTMParams object
*/
LSTMParams &set_projection_params(const T *projection_weights, const T *projection_bias)
{
_projection_weights = projection_weights;
_projection_bias = projection_bias;
_has_projection = true;
return *this;
}
/** Set peephole tensor parameters.
*
* @param[in] cell_to_forget_weights 1D weights tensor with dimensions [num_units]. Data type supported: Data types supported: F16/F32.
* @param[in] cell_to_output_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p cell_to_input_weights.
*
* @return Reference to this LSTMParams object
*/
LSTMParams &set_peephole_params(const T *cell_to_forget_weights, const T *cell_to_output_weights)
{
_cell_to_forget_weights = cell_to_forget_weights;
_cell_to_output_weights = cell_to_output_weights;
_has_peephole_opt = true;
return *this;
}
/** Set layer normalization tensor parameters.
*
* @param[in] input_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Data types supported: F16/F32.
* @param[in] forget_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input_layer_norm_weights.
* @param[in] cell_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input_layer_norm_weights.
* @param[in] output_layer_norm_weights 1D weights tensor with dimensions [num_units]. Data type supported: Same as @p input_layer_norm_weights.
*
* @return Reference to this LSTMParams object
*/
LSTMParams &set_layer_normalization_params(const T *input_layer_norm_weights, const T *forget_layer_norm_weights,
const T *cell_layer_norm_weights, const T *output_layer_norm_weights)
{
_input_layer_norm_weights = input_layer_norm_weights;
_forget_layer_norm_weights = forget_layer_norm_weights;
_cell_layer_norm_weights = cell_layer_norm_weights;
_output_layer_norm_weights = output_layer_norm_weights;
_use_layer_norm = true;
return *this;
}
const T *input_to_input_weights() const
{
return _input_to_input_weights;
}
const T *recurrent_to_input_weights() const
{
return _recurrent_to_input_weights;
}
const T *cell_to_input_weights() const
{
return _cell_to_input_weights;
}
const T *input_gate_bias() const
{
return _input_gate_bias;
}
const T *cell_to_forget_weights() const
{
return _cell_to_forget_weights;
}
const T *cell_to_output_weights() const
{
return _cell_to_output_weights;
}
const T *projection_weights() const
{
return _projection_weights;
}
const T *projection_bias() const
{
return _projection_bias;
}
const T *input_layer_norm_weights() const
{
return _input_layer_norm_weights;
}
const T *forget_layer_norm_weights() const
{
return _forget_layer_norm_weights;
}
const T *cell_layer_norm_weights() const
{
return _cell_layer_norm_weights;
}
const T *output_layer_norm_weights() const
{
return _output_layer_norm_weights;
}
bool has_peephole_opt() const
{
return _has_peephole_opt;
}
bool has_projection() const
{
return _has_projection;
}
bool has_cifg_opt() const
{
return _has_cifg_opt;
}
bool use_layer_norm() const
{
return _use_layer_norm;
}
private:
const T *_input_to_input_weights;
const T *_recurrent_to_input_weights;
const T *_cell_to_input_weights;
const T *_input_gate_bias;
const T *_cell_to_forget_weights;
const T *_cell_to_output_weights;
const T *_projection_weights;
const T *_projection_bias;
const T *_input_layer_norm_weights;
const T *_forget_layer_norm_weights;
const T *_cell_layer_norm_weights;
const T *_output_layer_norm_weights;
bool _has_peephole_opt;
bool _has_projection;
bool _has_cifg_opt;
bool _use_layer_norm;
};
}
#endif /*__ARM_COMPUTE_LSTMPARAMS_H__ */
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