text: /*
   * Copyright (c) 2017-2021 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_CLGEMMLOWPOUTPUTSTAGE_H
  #define ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H
  
  #include "arm_compute/core/Error.h"
  #include "arm_compute/runtime/CL/ICLSimpleFunction.h"
  
  #include <limits>
  
  /** This file contains all available output stages for GEMMLowp on OpenCL.
   *
   *  In gemmlowp, the "output stage" is the process that takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyCore),
   *  and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value.
   *
   *  More information about the GEMMLowp output stage can be found at https://github.com/google/gemmlowp/blob/master/doc/output.md
   */
  
  namespace arm_compute
  {
  class CLCompileContext;
  class ITensor;
  class ICLTensor;
  class ITensorInfo;
  struct GEMMLowpOutputStageInfo;
  
  /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint on OpenCL.
   *
   *  CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint depends on 3 parameters:
   *
   *  result_fixedpoint_multiplier, result_shift, result_offset_after_shift
   *
   * The final result is:
   *
   * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift
   *
   * where FixedPointMul(x, y) is the nearest integer to the following
   * mathematical expression, evaluated without overflow or intermediate rounding:
   *
   * (x * y) / 2^31
   *
   * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
   *
   * In case the bias tensor is provided, the final result is:
   *
   * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
   *
   *  This function calls the following OpenCL kernels:
   *
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
   *
   * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
   *       after the result is shifted right by result_shift
  */
  class CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint : public ICLSimpleFunction
  {
  public:
      /** Initialise the kernel's inputs, 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: QASYMM8
       * @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                 Number of bits to shift right the result after the fixed point multiplication
       * @param[in]  result_offset_after_shift    Offset to be applied to result before converting it back to QASYMM8
       * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
       * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
       *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       */
      void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
                     int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
      /** Initialise the kernel's inputs, output
       *
       * @param[in]  compile_context              The compile context to be used.
       * @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: QASYMM8
       * @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                 Number of bits to shift right the result after the fixed point multiplication
       * @param[in]  result_offset_after_shift    Offset to be applied to result before converting it back to QASYMM8
       * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
       * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
       *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       */
      void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift,
                     int result_offset_after_shift,
                     int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
      /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
       *
       * @param[in] input  Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
       * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
       *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
       * @param[in] output Output tensor. Data type supported: QASYMM8
       * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8. Defaults to the minimum possible 32-bit signed integer.
       * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
       *                            Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
  };
  
  /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint on OpenCL.
   *
   *  CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint depends on 3 parameters:
   *
   *  result_fixedpoint_multiplier, result_shift, result_offset_after_shift
   *
   * The final result is:
   *
   * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift
   *
   * where FixedPointMul(x, y) is the nearest integer to the following
   * mathematical expression, evaluated without overflow or intermediate rounding:
   *
   * (x * y) / 2^31
   *
   * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
   *
   * In case the bias tensor is provided, the final result is:
   *
   * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
   *
   *  This function calls the following OpenCL kernels:
   *
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
   *
   * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
   *       after the result is shifted right by result_shift
  */
  class CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint : public ICLSimpleFunction
  {
  public:
      /** Initialise the kernel's inputs, 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: QASYMM8_SIGNED
       * @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                 Number of bits to shift right the result after the fixed point multiplication
       * @param[in]  result_offset_after_shift    Offset to be applied to result before converting it back to QASYMM8_SIGNED
       * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED. Defaults to the minimum possible 32-bit signed integer.
       * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED. Defaults to 0
       *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       */
      void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
                     int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
      /** Initialise the kernel's inputs, output
       *
       * @param[in]  compile_context              The compile context to be used.
       * @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: QASYMM8_SIGNED
       * @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                 Number of bits to shift right the result after the fixed point multiplication
       * @param[in]  result_offset_after_shift    Offset to be applied to result before converting it back to QASYMM8_SIGNED
       * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED. Defaults to the minimum possible 32-bit signed integer.
       * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED. Defaults to 0
       *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       */
      void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift,
                     int result_offset_after_shift,
                     int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
      /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint
       *
       * @param[in] input  Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
       * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
       *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
       * @param[in] output Output tensor. Data type supported: QASYMM8_SIGNED
       * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED. Defaults to the minimum possible 32-bit signed integer.
       * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED. Defaults to 0
       *                            Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to the maximum possible 32-bit signed integer.
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
  };
  
  /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint on OpenCL.
   *
   *  CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint depends on 2 parameters:
   *
   *  result_fixedpoint_multiplier, result_shift
   *
   * The final result is:
   *
   * (FixedPointMul(input[i][k], result_fixedpoint_multiplier) >> result_shift)
   *
   * where FixedPointMul(x, y) is the nearest integer to the following
   * mathematical expression, evaluated without overflow or intermediate rounding:
   *
   * (x * y) / 2^31
   *
   * For more information: https://github.com/google/gemmlowp/blob/master/public/output_stages.h#L68
   *
   * In case the bias tensor is provided, the final result is:
   *
   * ((FixedPointMul(input[i][k] + bias[k], result_fixedpoint_multiplier)) >> result_shift) + result_offset_after_shift
   *
   *  This function calls the following CL kernels:
   *
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
   *
   * @note The function accepts also 2 optional input arguments (min and max) which can be used to implement "rectified linear unit" activation functions
   *       after the result is shifted right by result_shift
  */
  class CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint : public ICLSimpleFunction
  {
  public:
      /** Initialise the kernel's inputs, 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: 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                 Number of bits to shift right 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 the minimum possible 32-bit signed integer.
       * @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 the maximum possible 32-bit signed integer.
       */
      void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int min = std::numeric_limits<int32_t>::lowest(),
                     int max = std::numeric_limits<int32_t>::max());
      /** Initialise the kernel's inputs, output
       *
       * @param[in]  compile_context              The compile context to be used.
       * @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: 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                 Number of bits to shift right 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 the minimum possible 32-bit signed integer.
       * @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 the maximum possible 32-bit signed integer.
       */
      void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift,
                     int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
      /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint
       *
       * @param[in] input  Input tensor info. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
       * @param[in] bias   Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
       *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
       * @param[in] output Output tensor info. Data type supported: QSYMM16
       * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to the minimum possible 32-bit signed integer.
       * @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 the maximum possible 32-bit signed integer.
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = std::numeric_limits<int32_t>::lowest(), int max = std::numeric_limits<int32_t>::max());
  };
  /** Basic function to execute GEMMLowpQuantizeDown kernels on CL.
   *
   *  This function calls the following CL kernels:
   *
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleKernel
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
   * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
  */
  class CLGEMMLowpOutputStage : public ICLSimpleFunction
  {
  public:
      /** Initialise the kernel's inputs, 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: QASYMM8/QASYMM8_SIGNED
       * @param[in]  info   GEMMLowp output stage metadata.
       */
      void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info);
      /** Initialise the kernel's inputs, output
       *
       * @param[in]  compile_context The compile context to be used.
       * @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: QASYMM8/QASYMM8_SIGNED
       * @param[in]  info            GEMMLowp output stage metadata.
       */
      void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info);
      /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ScaleByFixedPointKernel
       *
       * @param[in] input  Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
       * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
       *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
       * @param[in] output Output tensor. Data type supported: QASYMM8/QASYMM8_SIGNED
       * @param[in] info   GEMMLowp output stage metadata.
       *
       * @return a status
       */
      static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info);
  };
  } // namespace arm_compute
  #endif /*ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H */
  