/* * Copyright (c) 2019-2023 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 ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS_H #define ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS_H #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Types.h" #include "arm_compute/function_info/ActivationLayerInfo.h" namespace arm_compute { /** Descriptor for FFT scale kernels */ struct FFTScaleKernelInfo { float scale{0.f}; /**< Axis to perform the kernel on. */ bool conjugate{true}; /**< Flag to conjugate the output/ */ }; /** Descriptor for FFT digit reverse kernels */ struct FFTDigitReverseKernelInfo { unsigned int axis{0}; /**< Axis to perform the kernel on. */ bool conjugate{false}; /**< Flag to conjugate the output/ */ }; /** Descriptor used by the FFT core kernels */ struct FFTRadixStageKernelInfo { unsigned int axis{0}; /**< Axis to run the kernel on. */ unsigned int radix{0}; /**< Radix to use. */ unsigned int Nx{0}; /**< Nx coefficient. */ bool is_first_stage{false}; /**< Flags if the FFT kernels is the first stage of a decomposed FFT. */ }; class ITensorInfo; /** Descriptor used by the GEMM kernels */ struct GEMMKernelInfo { GEMMKernelInfo() = default; GEMMKernelInfo(unsigned int im, unsigned int in, unsigned int ik, unsigned int idepth_output_gemm3d, bool ireinterpret_input_as_3d, bool ibroadcast_bias, bool ifp_mixed_precision, bool ihas_pad_y, ActivationLayerInfo iactivation_info, int inmult_transpose1xW_width, int imult_interleave4x4_height, GEMMLHSMatrixInfo ilhs_info, GEMMRHSMatrixInfo irhs_info, int32_t ina_offset, int32_t inb_offset) : m(im), n(in), k(ik), depth_output_gemm3d(idepth_output_gemm3d), reinterpret_input_as_3d(ireinterpret_input_as_3d), broadcast_bias(ibroadcast_bias), fp_mixed_precision(ifp_mixed_precision), has_pad_y(ihas_pad_y), activation_info(iactivation_info), mult_transpose1xW_width(inmult_transpose1xW_width), mult_interleave4x4_height(imult_interleave4x4_height), lhs_info(ilhs_info), rhs_info(irhs_info), a_offset(ina_offset), b_offset(inb_offset) { } unsigned int m{0}; /**< Number of LHS rows*/ unsigned int n{0}; /**< Number of RHS columns*/ unsigned int k{0}; /**< Number of LHS columns or RHS rows */ unsigned int depth_output_gemm3d{0}; /**< Depth of the output tensor in case is reinterpreted as 3D */ bool reinterpret_input_as_3d{false}; /**< Flag used to reinterpret the input as 3D */ bool broadcast_bias{false}; /**< Flag used to broadcast the bias addition */ bool fp_mixed_precision{false}; /**< Flag used to indicate wider accumulators (32 bit instead of 16 for FP16). */ bool has_pad_y{ false}; /**< Flag used to indicate if the input/output tensors have internal pad on the y direction */ ActivationLayerInfo activation_info{}; /**< Activation function to perform after the matrix multiplication */ int mult_transpose1xW_width{1}; /**< Multiplication factor for the width of the 1xW transposed block */ int mult_interleave4x4_height{1}; /**< Multiplication factor for the height of the 4x4 interleaved block */ GEMMLHSMatrixInfo lhs_info{}; /**< LHS matrix information used to retrieve the number of rows processed by each thread */ GEMMRHSMatrixInfo rhs_info{}; /**< RHS matrix information used for reshaping the RHS matrix */ int32_t a_offset{0}; /**< Offset to be added to each element of the matrix A */ int32_t b_offset{0}; /**< Offset to be added to each element of the matrix B */ GEMMLowpOutputStageInfo output_stage{}; /**< GEMMLowp output stage information */ }; /** Compute descriptor used by the depthwise convolution native kernel */ struct DWCComputeKernelInfo { unsigned int n0{1}; /**< Number of columns processed by each thread */ unsigned int m0{1}; /**< Number of rows processed by each thread */ bool export_input_to_cl_image{false}; /**< Export input to cl_image */ bool export_weights_to_cl_image{false}; /**< Export the weights to cl_image */ }; /** Compute descriptor used by the direct convolution kernel */ struct DirectConvComputeKernelInfo { int32_t m0{1}; /**< Number of rows to be processed by the kernel */ int32_t n0{1}; /**< Number of columns to be processed by the kernel */ int32_t k0{1}; /**< Number of partial accumulations to be processed in a single iteration by the kernel */ bool export_weights_to_cl_image{false}; /**< Flag to export the weights to cl_image */ bool export_output_to_cl_image{false}; /**< Flag to export the output to cl_image */ bool export_input_to_cl_image{false}; /**< Flag to export the input to cl_image */ }; /** Descriptor used by the softmax kernels */ struct SoftmaxKernelInfo { float beta{1.f}; /**< A scaling factor for the exponent with default value 1.0 */ bool is_log{false}; /**< Flag used to perform Log Softmax operation */ DataType input_data_type{DataType::UNKNOWN}; /**< Input tensor data type */ int32_t axis{0}; /**< The dimension in which to apply softmax. */ }; /** Descriptor used by the direct convolution layer output stage kernels */ struct DirectConvolutionLayerOutputStageKernelInfo { int32_t result_fixedpoint_multiplier{0}; /**< Result output stage multiplier used for quantizing */ int32_t result_shift{0}; /**< Result output stage shift used for quantizing */ int32_t result_offset_after_shift{0}; /**< Result offset used for quantizing */ DataType output_data_type{ DataType::UNKNOWN}; /**< Output tensor data type to use if the output is not initialized */ }; struct InstanceNormalizationLayerKernelInfo { /** Default constructor */ InstanceNormalizationLayerKernelInfo() : InstanceNormalizationLayerKernelInfo(1.f, 0.f, 1e-12, true) { } /** Constructor * * @param[in] gamma The scale scalar value applied to the normalized tensor. * @param[in] beta The offset scalar value applied to the normalized tensor * @param[in] epsilon Lower bound value for the normalization. * @param[in] use_mixed_precision Use mixed precision in case of FP16 execution. */ InstanceNormalizationLayerKernelInfo(float gamma, float beta, float epsilon, bool use_mixed_precision) : gamma(gamma), beta(beta), epsilon(epsilon), use_mixed_precision(use_mixed_precision) { } float gamma; /**< The scale scalar value applied to the normalized tensor. Defaults to 1.0 */ float beta; /**< The offset scalar value applied to the normalized tensor. Defaults to 0.0 */ float epsilon; /**< Lower bound value for the normalization. Defaults to 1e-12 */ bool use_mixed_precision; /**< Use mixed precision in case of FP16 execution. Defaults to true */ }; struct GEMMLowpReductionKernelInfo { /** Default constructor */ GEMMLowpReductionKernelInfo() = default; /** Constructor * * @param[in] k Number of matrix columns/rows. * @param[in] is_reshaped True if the input tensor has been reshaped. * @param[in] scalar Scalar value to multiply each reduced column/row by. * @param[in] mul_by_scalar True if each column/row reduction has to be multiplied by a scalar value. */ GEMMLowpReductionKernelInfo(int32_t k, bool is_reshaped, int32_t scalar, bool mul_by_scalar) : k(k), is_reshaped(is_reshaped), scalar(scalar), mul_by_scalar(mul_by_scalar) { } int32_t k{0}; /**< Number of matrix columns/rows */ bool is_reshaped{false}; /**< True if the input tensor has been reshaped */ int32_t scalar{0}; /**< Scalar value to multiply each reduced column/row by */ bool mul_by_scalar{false}; /**< True if each column/row reduction has to be multiplied by a scalar value */ }; struct ScaleKernelInfo { /** Constructor * * @param[in] interpolation_policy Interpolation type to use * @param[in] border_mode Border mode policy * @param[in] constant_border_value (Optional) Constant value to use for borders if border_mode is set to CONSTANT and use_padding is set to false. Defaults to default @ref PixelValue * @param[in] sampling_policy (Optional) Sampling policy used by the interpolation. Defaults to @ref SamplingPolicy::CENTER * @param[in] use_padding (Optional) Is padding in use or not. Defaults to true. * @param[in] align_corners (Optional) Align corners of input and output, only affecting bilinear policy with TOP_LEFT sampling policy. Defaults to false. * @param[in] data_layout (Optional) Data layout used by the layer. Defaults to @ref DataLayout::UNKNOWN */ ScaleKernelInfo(InterpolationPolicy interpolation_policy, BorderMode border_mode, PixelValue constant_border_value = PixelValue(), SamplingPolicy sampling_policy = SamplingPolicy::CENTER, bool use_padding = true, bool align_corners = false, DataLayout data_layout = DataLayout::UNKNOWN) noexcept : interpolation_policy{interpolation_policy}, border_mode{border_mode}, constant_border_value{constant_border_value}, sampling_policy{sampling_policy}, use_padding{use_padding}, align_corners{align_corners}, data_layout{data_layout} { } InterpolationPolicy interpolation_policy; /**< Interpolation type to use */ BorderMode border_mode; /**< Border mode policy */ PixelValue constant_border_value; /**< Constant value to use for constant border mode policy */ SamplingPolicy sampling_policy; /**< Sampling policy used by the interpolation. */ bool use_padding; /**< Indication of using padding */ bool align_corners; /**< Align corners of input and output */ DataLayout data_layout; /**< Data layout to use */ }; struct MatMulKernelInfo { MatMulKernelInfo() = default; MatMulKernelInfo( bool adj_lhs, bool adj_rhs, int m0 = 1, int n0 = 1, int k0 = 1, bool export_rhs_to_cl_image = false) : adj_lhs{adj_lhs}, adj_rhs{adj_rhs}, m0{m0}, n0{n0}, k0{k0}, export_rhs_to_cl_image{export_rhs_to_cl_image} { } bool adj_lhs{false}; /**< Get Adjoint LHS flag value */ bool adj_rhs{false}; /**< Get Adjoint RHS flag value */ int m0{1}; /**< Number of output rows processed by each work-item*/ int n0{1}; /**< Number of output columns processed by each work-item*/ int k0{1}; /**< Number of inner accumulations */ bool export_rhs_to_cl_image{false}; /**< Flag to know whether the RHS tensor should be exported to cl_image*/ }; } // namespace arm_compute #endif // ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS_H