/* * 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 * 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/runtime/CL/ICLSimpleFunction.h" /** 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 ITensor; /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8Scale on OpenCL. * * CLGEMMLowpQuantizeDownInt32ToUint8Scale depends on 3 parameters: result_offset, result_mult_int, result_shift * The final result is: * * ((input[i][k] + result_offset) * result_mult_int) >> result_shift * * In case the bias tensor is provided, the final result is: * * ((input[i][k] + bias[k] + result_offset) * result_mult_int) >> result_shift * * This function calls the following OpenCL kernels: * * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel * * @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 CLGEMMLowpQuantizeDownInt32ToUint8Scale : public ICLSimpleFunction { public: /** Initialise the kernel's inputs, output * * @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[out] output Output tensor. Data type supported: Data type supported: QASYMM8 * @param[in] result_offset Offset to be added to each element of the input matrix * @param[in] result_mult_int 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 before converting back to QASYMM8 * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 * @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 */ void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min = 0, int max = 0); /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8Scale * * @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: Data type supported: QASYMM8 * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 * @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 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; /** 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 CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel * * @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: 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 * @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 */ void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0); /** 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: Data type supported: QASYMM8 * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 * @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 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; /** 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 CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel * * @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: 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 0 * @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 */ void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0); /** 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: 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 0 * @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 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; /** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat on OpenCL. * * This function calls the following OpenCL kernels: * * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel * * @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 CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat : 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: Data type supported: QASYMM8 * @param[in] multiplier Float multiplier to be multiplied to each element of the input matrix * @param[in] offset 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 * @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 */ void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, float multiplier, int offset, int min = 0, int max = 0); /** 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: Data type supported: QASYMM8 * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 * @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 * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); }; /** 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 NEON kernels: * * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel * * @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: 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 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 ICLTensor *input, const ICLTensor *bias, ICLTensor *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 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: 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); }; } // namespace arm_compute #endif /*ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H */