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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* Copyright (c) 2018 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_NERNNLAYER_EX_H__
#define __ARM_COMPUTE_NERNNLAYER_EX_H__
#include "arm_compute/core/NEON/kernels/NEActivationLayerKernel.h"
#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
#include "arm_compute/core/NEON/kernels/NECopyKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
namespace arm_compute
{
// Forward declarations
class ITensor;
/** Basic function to run @ref NERNNLayerEx */
class NERNNLayerEx : public IFunction
{
public:
/** Default constructor */
NERNNLayerEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NERNNLayerEx(const NERNNLayerEx &) = delete;
/** Default move constructor */
NERNNLayerEx(NERNNLayerEx &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NERNNLayerEx &operator=(const NERNNLayerEx &) = delete;
/** Default move assignment operator */
NERNNLayerEx &operator=(NERNNLayerEx &&) = default;
/** Initialize the function
*
* @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data
* types supported: F16/F32
* @param[in] weights Weights tensor of shape [input_size, num_units] that
* multiplies the input. Data types supported: Same as @p input
* @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies
* the current 'state'. Data types supported: Same as @p input
* @param[in] bias Bias vector of shape [num_units]. Data types supported: Same
* as @p input
* @param[out] output Output tensor of shape [num_units, batch_size]. Data types
* supported: Same as @p input
* @param[in,out] hidden_state Output tensor of shape [num_units, batch_size]. Data types
* supported: Same as @p input
* @param[in] info Activation layer parameter.
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *recurrent_weights,
const ITensor *bias, ITensor *hidden_state, ITensor *output,
ActivationLayerInfo &info);
/** Initialize the function
*
* @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data
* types supported: F16/F32
* @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies
* the input. Data types supported: Same as @p input
* @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the
* current 'state'. Data types supported: Same as @p input
* @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p
* input
* @param[in] output Output tensor of shape [num_units, batch_size]. Data types
* supported: Same as @p input
* @param[in] hidden_state Output tensor of shape [num_units, batch_size]. Data types
* supported: Same as @p input
* @param[in] info Activation layer parameter.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights,
const ITensorInfo *recurrent_weights, const ITensorInfo *bias,
const ITensorInfo *hidden_state, const ITensorInfo *output,
const ActivationLayerInfo &info);
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
NEGEMM _gemm_state_f;
NEArithmeticAdditionKernel _add_kernel;
NEActivationLayerKernel _activation_kernel;
NEFullyConnectedLayer _fully_connected_kernel;
NECopyKernel _copy_kernel;
Tensor _fully_connected_out;
Tensor _gemm_output;
Tensor _add_output;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NERNNLAYER_EX_H__ */
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