arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h

/*
 * Copyright (c) 2017-2019 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
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 * 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
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#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/CLTransposeKernel.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h"
#include "arm_compute/runtime/CL/functions/CLFlattenLayer.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: QASYMM8/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: QASYMM8/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: QASYMM8/F16/F32.
     * @param[in]  weights Weights tensor. The weights must be 2 dimensional.
     *                     If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
     *                     If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
     *                     Data type supported: Same as @p input.
     * @param[in]  biases  Bias tensor. Can be nullptr. Data type supported:Same as @p input.
     * @param[out] output  Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
     *                     - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
     *                     - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
     *                     Data type supported: Same as @p input.
     * @param[in]  fc_info (Optional) Fully connected layer additional info
     */
    void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
                   FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
    /** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayer
     *
     * @param[in]  input   Source tensor info. Data type supported: QASYMM8/F16/F32.
     * @param[in]  weights Weights tensor info. The weights must be 2 dimensional.
     *                     If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
     *                     If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
     *                     Data type supported: Same as @p input.
     * @param[in]  biases  Bias tensor info. Can be nullptr. Data type supported:Same as @p input.
     * @param[out] output  Destination tensor info. Its shape should be equal to the output of a matrix multiplication between:
     *                     - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
     *                     - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
     *                     Data type supported: Same as @p input.
     * @param[in]  fc_info (Optional) Fully connected layer additional info
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
                           FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());

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

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

    CLMemoryGroup                                       _memory_group;
    CLConvertFullyConnectedWeights                      _convert_weights;
    CLFlattenLayer                                      _flatten_layer;
    CLFullyConnectedLayerReshapeWeights                 _reshape_weights_kernel;
    CLGEMM                                              _mm_gemm;
    CLGEMMLowpMatrixMultiplyCore                        _mm_gemmlowp;
    CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
    CLGEMMMatrixAccumulateBiasesKernel                  _accumulate_biases_kernel; // TODO(COMPMID-1889): Use CLGEMM to add bias in CLFullyConnectedLayer
    CLTensor                                            _flatten_output;
    CLTensor                                            _gemmlowp_output;
    CLTensor                                            _converted_weights_output;
    CLTensor                                            _reshape_weights_output;
    bool                                                _are_weights_converted;
    bool                                                _are_weights_reshaped;
    bool                                                _is_fc_after_conv;
    bool                                                _accumulate_biases;
    bool                                                _is_quantized;
    bool                                                _is_prepared;
    const ICLTensor                                    *_original_weights;
};
}
#endif /* __ARM_COMPUTE_CLFULLYCONNECTEDLAYER_H__ */