diff options
Diffstat (limited to 'arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h')
| -rw-r--r-- | arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h | 272 |
1 files changed, 34 insertions, 238 deletions
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h index a60992a0f4..ff9c872896 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h @@ -45,247 +45,28 @@ 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 + * -# @ref opencl::kernels::ClGemmLowpQuantizeDownInt32ScaleKernel + * -# @ref opencl::kernels::ClGemmLowpQuantizeDownInt32ScaleByFloatKernel + * -# @ref opencl::kernels::ClGemmLowpQuantizeDownInt32ScaleByFixedPointKernel */ -class CLGEMMLowpOutputStage : public ICLSimpleFunction +class CLGEMMLowpOutputStage : public IFunction { public: + CLGEMMLowpOutputStage(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLGEMMLowpOutputStage(const CLGEMMLowpOutputStage &) = delete; + /** Default move constructor */ + CLGEMMLowpOutputStage(CLGEMMLowpOutputStage &&); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLGEMMLowpOutputStage &operator=(const CLGEMMLowpOutputStage &) = delete; + /** Default move assignment operator */ + CLGEMMLowpOutputStage &operator=(CLGEMMLowpOutputStage &&); + /** Default destructor */ + ~CLGEMMLowpOutputStage(); /** Initialise the kernel's inputs, output * * Valid data layouts: @@ -304,7 +85,8 @@ public: * @param[out] output Output tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM16 * @param[in] info GEMMLowp output stage metadata. */ - void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const GEMMLowpOutputStageInfo &info); + 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. @@ -314,8 +96,12 @@ public: * @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 + 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 opencl::kernels::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. @@ -325,7 +111,17 @@ public: * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo &info); + static Status validate(const ITensorInfo *input, + const ITensorInfo *bias, + const ITensorInfo *output, + const GEMMLowpOutputStageInfo &info); + + // Inherited methods overridden: + void run() override; + +private: + struct Impl; + std::unique_ptr<Impl> _impl; }; } // namespace arm_compute #endif /*ARM_COMPUTE_CLGEMMLOWPOUTPUTSTAGE_H */ |