/* * Copyright (c) 2018 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. */ #include "GEMMReshapeLHSMatrix.h" #include "arm_compute/core/Types.h" #include "tests/validation/Helpers.h" #include #include #include namespace arm_compute { namespace test { namespace validation { namespace reference { template SimpleTensor gemm_reshape_lhs_matrix(const SimpleTensor &in, const TensorShape &output_shape, const GEMMLHSMatrixInfo &lhs_info) { ARM_COMPUTE_ERROR_ON(in.shape().num_dimensions() > 3); SimpleTensor out{ output_shape, in.data_type() }; // Initialize the output tensor with zero std::memset(&out[0], 0, out.num_elements() * sizeof(T)); const unsigned int K = in.shape()[0]; const unsigned int M = in.shape()[1]; const unsigned int B = in.shape()[2]; const unsigned int num_tiles_x = std::ceil(K / static_cast(lhs_info.k0)); const unsigned int num_tiles_y = std::ceil(M / static_cast(lhs_info.m0)); const TensorShape tile_dims(lhs_info.k0, lhs_info.m0); const TensorShape tile_dims_transposed(lhs_info.m0, lhs_info.k0); // Simple tensor for the input tile SimpleTensor src_tile{ tile_dims, in.data_type() }; // Simple tensor for the input tile SimpleTensor src_tile_transposed{ tile_dims_transposed, in.data_type() }; // Simple tensor to use when storing the values SimpleTensor *tile_to_use = lhs_info.transpose ? &src_tile_transposed : &src_tile; const unsigned int offset_output_x = lhs_info.interleave ? tile_to_use->shape()[0] : tile_to_use->shape()[0] * tile_to_use->shape()[1]; const unsigned int step_output_x = lhs_info.interleave ? tile_to_use->shape()[0] * lhs_info.v0 : tile_to_use->shape()[0]; for(unsigned int z = 0; z < B; ++z) { for(unsigned int y = 0; y < num_tiles_y; ++y) { for(unsigned int x = 0; x < num_tiles_x; ++x) { // Get the tile from the input tensor get_tile(in, src_tile, Coordinates(x * lhs_info.k0, y * lhs_info.m0, z, 0)); if(lhs_info.transpose) { // Transpose matrix transpose_matrix(src_tile, src_tile_transposed); } // Store const unsigned int offset_output = (x * lhs_info.k0 * lhs_info.m0 * lhs_info.v0) + ((y % lhs_info.v0) * offset_output_x) + ((y / lhs_info.v0) * out.shape()[0]) + (z * out.shape()[0] * out.shape()[1]); for(unsigned int i = 0; i < tile_to_use->shape()[1]; ++i) { const unsigned int offset_tile = i * tile_to_use->shape()[0]; // Copy per row std::copy(&(*tile_to_use)[offset_tile], &(*tile_to_use)[offset_tile + tile_to_use->shape()[0]], &out[offset_output + i * step_output_x]); } } } } return out; } template SimpleTensor gemm_reshape_lhs_matrix(const SimpleTensor &in, const TensorShape &output_shape, const GEMMLHSMatrixInfo &lhs_info); template SimpleTensor gemm_reshape_lhs_matrix(const SimpleTensor &in, const TensorShape &output_shape, const GEMMLHSMatrixInfo &lhs_info); template SimpleTensor gemm_reshape_lhs_matrix(const SimpleTensor &in, const TensorShape &output_shape, const GEMMLHSMatrixInfo &lhs_info); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute