/*
 * Copyright (c) 2017-2018 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_CLFULLYCONNECTEDLAYER_H__
#define __ARM_COMPUTE_CLFULLYCONNECTEDLAYER_H__

#include "arm_compute/runtime/CL/ICLSimpleFunction.h"

#include "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
#include "arm_compute/core/CL/kernels/CLTransposeKernel.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLGEMM.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"

namespace arm_compute
{
/** Basic function to reshape the weights of Fully Connected layer with OpenCL. This function calls the following kernels:
 *
 *  -# @ref CLTransposeKernel
 *
 * @note  The fully connected layer accepts "weights" tensors only with 2 dimensions.
 */
class CLFullyConnectedLayerReshapeWeights : public ICLSimpleFunction
{
public:
    /** Set the input and output tensors.
     *
     * @param[in]  input  Weights tensor. The weights must be 2 dimensional. Data types supported: QS8/QASYMM8/QS16/F16/F32.
     * @param[out] output Destination tensor which stores the transposed input tensor. Data type supported: Same as @p input.
     */
    void configure(const ICLTensor *input, ICLTensor *output);
    /** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayerReshapeWeights
     *
     * @param[in] input  Weights tensor. The weights must be 2 dimensional. Data types supported: QS8/QASYMM8/QS16/F16/F32.
     * @param[in] output Destination tensor which stores the transposed input tensor. Data type supported: Same as @p input.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *output);
};

/** Basic function to compute a Fully Connected layer on OpenCL. This function calls the following OpenCL kernels:
 *
 *  -# @ref CLIm2ColKernel (called when the input comes from a convolutional layer)
 *  -# @ref CLFullyConnectedLayerReshapeWeights (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
 *  -# @ref CLGEMMMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
 *  -# @ref CLGEMMMatrixAccumulateBiasesKernel or @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is not equal to nullptr)
 *
 * @note  The fully connected layer accepts "weights" tensors only with 2 dimensions.
 */
class CLFullyConnectedLayer : public IFunction
{
public:
    /** Constructor */
    CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLFullyConnectedLayer(const CLFullyConnectedLayer &) = delete;
    /** Default move constructor */
    CLFullyConnectedLayer(CLFullyConnectedLayer &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLFullyConnectedLayer &operator=(const CLFullyConnectedLayer &) = delete;
    /** Default move assignment operator */
    CLFullyConnectedLayer &operator=(CLFullyConnectedLayer &&) = default;
    /** Set the input and output tensors.
     *
     * @param[in]  input                   Source tensor. Data type supported: QS8/QASYMM8/QS16/F16/F32.
     * @param[in]  weights                 Weights tensor. The weights must be 2 dimensional. Data type supported: Same as @p input
     * @param[in]  biases                  Bias tensor. It can be nullptr. Data type supported:Same as @p input.
     * @param[out] output                  Destination tensor. Data type supported: Same as @p input.
     * @param[in]  transpose_weights       (Optional) Transpose weights if true. Defaults to true.
     * @param[in]  are_weights_reshaped    (Optional) Reshape the weights tensor if false. Defaults to false.
     * @param[in]  retain_internal_weights (Optional) Retain internal reshaped weights. Defaults to false.
     *                                     Used for reconfiguration purposes.
     */
    void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights = true, bool are_weights_reshaped = false,
                   bool retain_internal_weights = false);
    /** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayer
     *
     * @param[in] input                   Source tensor. Data type supported: QS8/QASYMM8/QS16/F16/F32.
     * @param[in] weights                 Weights tensor. The weights must be 2 dimensional. Data type supported: Same as @p input
     * @param[in] biases                  Bias tensor. It can be nullptr. Data type supported:Same as @p input.
     * @param[in] output                  Destination tensor. Data type supported: Same as @p input.
     * @param[in] transpose_weights       (Optional) Transpose weights if true. Defaults to true.
     * @param[in] are_weights_reshaped    (Optional) Reshape the weights tensor if false. Defaults to false.
     * @param[in] retain_internal_weights (Optional) Retain internal reshaped weights. Defaults to false.
     *                                    Used for reconfiguration purposes.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights = true, bool are_weights_reshaped = false,
                           bool retain_internal_weights = false);

    //Inherited methods override
    void run() override;
    void prepare() override;

private:
    void configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);
    void configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);
    void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output);

    CLMemoryGroup                                       _memory_group;
    CLIm2ColKernel                                      _im2col_kernel;
    CLFullyConnectedLayerReshapeWeights                 _reshape_weights_kernel;
    CLGEMM                                              _mm_gemm;
    CLGEMMLowpMatrixMultiplyCore                        _mm_gemmlowp;
    CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
    CLGEMMMatrixAccumulateBiasesKernel                  _accumulate_biases_kernel;
    CLTensor                                            _im2col_output;
    CLTensor                                            _gemmlowp_output;
    CLTensor                                            _reshape_weights_output;
    bool                                                _are_weights_reshaped;
    bool                                                _is_fc_after_conv;
    bool                                                _accumulate_biases;
    bool                                                _is_quantized;
    const ICLTensor                                    *_original_weights;
};
}
#endif /* __ARM_COMPUTE_CLFULLYCONNECTEDLAYER_H__ */