src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h

/*
 * Copyright (c) 2019-2020 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
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#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H
#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H

#include "src/core/NEON/INEKernel.h"

namespace arm_compute
{
class ITensor;

/** NEON kernel used to quantize down the int32 accumulator values of GEMMLowp to QSYMM16
 *
 * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 value.
 * The following computations will be performed by the kernel:
 *
 *  -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier
 *  -# Add bias to final result if bias tensor is not a nullptr
 *  -# Round to nearest division by a power-of-two using result_shift
 *  -# Clamp the value between the specified min and max bounds
 *  -# Clamp the resulting int32 values to the [-32768, 32767] range and cast to QSYMM16.
 *
 */
class NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel : public INEKernel
{
public:
    const char *name() const override
    {
        return "NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel";
    }
    /** Constructor */
    NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel();
    /** Prevent instances of this class from being copied (As this class contains pointers)*/
    NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete;
    /** Prevent instances of this class from being copied (As this class contains pointers)*/
    NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete;
    /** Allow instances of this class to be moved */
    NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default;
    /** Allow instances of this class to be moved */
    NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default;
    /** Default destructor */
    ~NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel() = default;
    /** Initialise the kernel's input and output.
     *
     * @param[in]  input                        Input tensor. Data type supported: S32
     * @param[in]  bias                         Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
     *                                          Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
     * @param[out] output                       Output tensor. Data type supported: Data type supported: QSYMM16
     * @param[in]  result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add
     * @param[in]  result_shift                 Integer value used to round to nearest division by a power-of-two the result after the fixed point multiplication
     * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0.
     * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QSYMM16.
     *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0.
     */
    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0);
    /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
     *
     * @param[in] input  Input tensor info. Data type supported: S32
     * @param[in] bias   Biases tensor info. Only shared biases supported and it can be a nullptr if the biases addition is not required.
     *                   Biases are 1D tensor info with dimensions [OFM]. Data type supported: Same as @p input.
     * @param[in] output Output tensor info. Data type supported: Data type supported: QSYMM16
     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0.
     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QSYMM16,
     *                            Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);

    // Inherited methods overridden:
    void run(const Window &window, const ThreadInfo &info) override;

private:
    /** Template function to run the NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
     *
     * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
     */
    template <bool is_bounded_relu>
    void run(const Window &window);

    /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel functions
     *
     * @param[in] window Region on which to execute the kernel.
     */
    using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::*)(const Window &window);

    QuantizeDownFunctionPtr _func;
    const ITensor          *_input;
    const ITensor          *_bias;
    ITensor                *_output;
    int                     _result_fixedpoint_multiplier;
    int                     _result_shift;
    int                     _min;
    int                     _max;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H */