/* * Copyright (c) 2017 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_GCGEMMMATRIXMULTIPLYKERNEL_H__ #define __ARM_COMPUTE_GCGEMMMATRIXMULTIPLYKERNEL_H__ #include "arm_compute/core/GLES_COMPUTE/IGCKernel.h" namespace arm_compute { class IGCTensor; /** GLES Compute kernel to multiply two input matrices "A" and "B" or to multiply a vector "A" by a matrix "B". All elements of the output matrix/vector will be multiplied by alpha * * @note If the output tensor is a matrix, the implementation assumes that the input tensors @p input0 and @p input1 are both matrices and reshaped respectively with @ref GCGEMMInterleave4x4Kernel" and @ref GCGEMMTranspose1xWKernel * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p input0 is a vector and the second input tensor @p input1 a matrix. The implementation also assumes that both tensors have not been reshaped * * @attention The second input tensor must have at least 2 dimensions (matrix) * */ class GCGEMMMatrixMultiplyKernel : public IGCKernel { public: /** Default constructor */ GCGEMMMatrixMultiplyKernel(); /** Prevent instances of this class from being copied (As this class contains pointers) */ GCGEMMMatrixMultiplyKernel(const GCGEMMMatrixMultiplyKernel &) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ GCGEMMMatrixMultiplyKernel &operator=(const GCGEMMMatrixMultiplyKernel &) = delete; /** Allow instances of this class to be moved */ GCGEMMMatrixMultiplyKernel(GCGEMMMatrixMultiplyKernel &&) = default; /** Allow instances of this class to be moved */ GCGEMMMatrixMultiplyKernel &operator=(GCGEMMMatrixMultiplyKernel &&) = default; /** Initialise the kernel's input, output and alpha * * @param[in] input0 Input tensor containing the interleaved Matrix A or the vector A. Data types supported: F16/F32 * @param[in] input1 Input tensor containing the transposed Matrix B if the first input tensor A is not a vector. * If the output tensor is a vector, input1 must contain the matrix B not reshaped. Data type supported: same as @p input0 * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 * @param[in] alpha Weight of the matrix product * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref GCGEMMInterleave4x4Kernel and @ref GCGEMMTranspose1xWKernel */ void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed = true); // Inherited methods overridden: void run(const Window &window) override; private: const IGCTensor *_input0; const IGCTensor *_input1; IGCTensor *_output; }; } #endif /* __ARM_COMPUTE_GCGEMMMATRIXMULTIPLYKERNEL_H__ */