| /* |
| * 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. |
| */ |
| #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include <limits> |
| #include <utility> |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace gemm |
| { |
| std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> configure_lhs_rhs_info(unsigned int m, |
| unsigned int n, |
| unsigned int m0, |
| unsigned int n0, |
| unsigned int k0, |
| unsigned int v0, |
| unsigned int h0, |
| bool lhs_interleave, |
| bool rhs_interleave, |
| bool lhs_transpose, |
| bool rhs_transpose, |
| bool export_to_cl_image) |
| { |
| ARM_COMPUTE_ERROR_ON(m0 == 0 || n0 == 0); |
| ARM_COMPUTE_ERROR_ON(v0 == 0); |
| v0 = std::max(std::min(static_cast<int>(m / m0), static_cast<int>(v0)), static_cast<int>(1)); |
| |
| if (h0 == 0) |
| { |
| // When h0 is 0, we should take the maximum H0 possible |
| h0 = std::max(n / n0, 1U); |
| } |
| else |
| { |
| h0 = std::max(std::min(static_cast<int>(n / n0), static_cast<int>(h0)), static_cast<int>(1)); |
| } |
| |
| const GEMMLHSMatrixInfo lhs_info(m0, k0, v0, lhs_transpose, lhs_interleave); |
| const GEMMRHSMatrixInfo rhs_info(n0, k0, h0, rhs_transpose, rhs_interleave, export_to_cl_image); |
| |
| return std::make_pair(lhs_info, rhs_info); |
| } |
| |
| std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> |
| select_lhs_rhs_info(std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> info_img, |
| std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> info_buf, |
| unsigned int n, |
| unsigned int k, |
| unsigned int b, |
| DataType data_type) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(info_buf.second.export_to_cl_image == true, |
| "The fallback GeMM configuration cannot have export_to_cl_image = true"); |
| |
| const TensorInfo tensor_rhs_info(TensorShape(n, k, b), 1, data_type); |
| const TensorShape shape = misc::shape_calculator::compute_rhs_reshaped_shape(tensor_rhs_info, info_img.second); |
| const TensorInfo tensor_reshaped_info(shape, 1, data_type); |
| |
| if (bool(validate_image2d_support_on_rhs(tensor_reshaped_info, info_img.second))) |
| { |
| return info_img; |
| } |
| else |
| { |
| return info_buf; |
| } |
| } |
| |
| void update_padding_for_cl_image(ITensorInfo *tensor) |
| { |
| constexpr unsigned int num_floats_per_pixel = 4; |
| |
| const unsigned int stride_y_in_elements = tensor->strides_in_bytes()[1] / tensor->element_size(); |
| const unsigned int pixel_alignment = get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(pixel_alignment == 0, "Cannot retrieve cl_image pitch alignment"); |
| if (pixel_alignment == 0) |
| { |
| return; |
| } |
| |
| const unsigned int row_pitch_alignment = pixel_alignment * num_floats_per_pixel; |
| const unsigned int round_up_width = |
| ((stride_y_in_elements + row_pitch_alignment - 1) / row_pitch_alignment) * row_pitch_alignment; |
| const unsigned int padding = round_up_width - stride_y_in_elements; |
| |
| tensor->extend_padding(PaddingSize(0, tensor->padding().right + padding, 0, 0)); |
| } |
| |
| Status validate_image2d_support_on_rhs(const ITensorInfo &tensor_reshaped_info, const GEMMRHSMatrixInfo &rhs_info) |
| { |
| if (rhs_info.export_to_cl_image) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 == 2) || (rhs_info.n0 == 3)) && rhs_info.transpose == false, |
| "Export to cl_image only supported with n0 = 4, 8 or 16"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 == 2) || (rhs_info.k0 == 3)) && rhs_info.transpose == true, |
| "Export to cl_image only supported with k0 = 4, 8 or 16"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(&tensor_reshaped_info, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| !image2d_from_buffer_supported(CLKernelLibrary::get().get_device()), |
| "The extension cl_khr_image2d_from_buffer is not supported on the target platform"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0, |
| "Impossible to retrieve the cl_image pitch alignment"); |
| |
| // Check the width and height of the output tensor. |
| // Since we cannot create a 3d image from a buffer, the third dimension is collapsed on the second dimension |
| const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); |
| const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(tensor_reshaped_info.tensor_shape()[0] > max_image_w * 4, |
| "Not supported width for cl_image"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG( |
| tensor_reshaped_info.tensor_shape()[1] * tensor_reshaped_info.tensor_shape()[2] > max_image_h, |
| "Not supported height for cl_image"); |
| } |
| |
| return Status{}; |
| } |
| |
| bool is_mmul_kernel_preferred(const unsigned int m, |
| const unsigned int n, |
| const unsigned int k, |
| const unsigned int b, |
| const DataType data_type, |
| unsigned int &best_m0, |
| unsigned int &best_n0) |
| { |
| ARM_COMPUTE_UNUSED(n, k, b, data_type); |
| |
| const unsigned int mmul_k0 = 4; |
| best_m0 = 4; |
| best_n0 = 4; |
| |
| const unsigned int ceil_to_multiple_m_m0 = ceil_to_multiple(m, best_m0); |
| const unsigned int m_div_m0 = ceil_to_multiple_m_m0 / best_m0; |
| const unsigned int ceil_to_multiple_m_div_m0_mmul_k0 = ceil_to_multiple(m_div_m0, mmul_k0); |
| const unsigned int gws_y = ceil_to_multiple_m_div_m0_mmul_k0 / mmul_k0; |
| |
| return ((k % mmul_k0) == 0) && (gws_y > 4); |
| } |
| |
| std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> |
| find_lhs_rhs_info(const GeMMConfigsMatrix &configs, unsigned int m, unsigned int n, unsigned int k, unsigned int b) |
| { |
| size_t min_acc = std::numeric_limits<size_t>::max(); |
| size_t min_idx = 0; |
| |
| ARM_COMPUTE_ERROR_ON(configs.size() == 0); |
| const size_t num_rows = configs.size(); |
| const size_t num_cols = configs[0].size(); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(num_cols != 14U, "The entry should have 14 integer values representing: M, N, K, B, M0, " |
| "N0. K0, V0, H0, INT_LHS, INT_RHS, TRA_LHS, TRA_RHS, IMG_RHS"); |
| ARM_COMPUTE_UNUSED(num_cols); |
| |
| // Find nearest GeMM workload |
| // Note: the workload does not depend on the K dimension |
| for (size_t y = 0; y < num_rows; ++y) |
| { |
| size_t mc0 = static_cast<size_t>(configs[y][0]); |
| size_t nc0 = static_cast<size_t>(configs[y][1]); |
| size_t kc0 = static_cast<size_t>(configs[y][2]); |
| size_t bc0 = static_cast<size_t>(configs[y][3]); |
| |
| size_t acc = 0; |
| acc += (m - mc0) * (m - mc0); |
| acc += (n - nc0) * (n - nc0); |
| acc += (k - kc0) * (k - kc0); |
| acc += (b - bc0) * (b - bc0); |
| acc = std::sqrt(acc); |
| if (acc < min_acc) |
| { |
| min_acc = acc; |
| min_idx = y; |
| } |
| } |
| |
| // Get the configuration from the nearest GeMM shape |
| const int m0 = configs[min_idx][4]; |
| const int n0 = configs[min_idx][5]; |
| const int k0 = configs[min_idx][6]; |
| const int v0 = configs[min_idx][7]; |
| const int h0 = configs[min_idx][8]; |
| const int i_lhs = configs[min_idx][9]; |
| const int i_rhs = configs[min_idx][10]; |
| const int t_lhs = configs[min_idx][11]; |
| const int t_rhs = configs[min_idx][12]; |
| const int im_rhs = configs[min_idx][13]; |
| |
| return configure_lhs_rhs_info(m, n, m0, n0, k0, v0, h0, i_lhs, i_rhs, t_lhs, t_rhs, im_rhs); |
| } |
| } // namespace gemm |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |