| /* |
| * Copyright (c) 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/helpers/MatMulKernelHelpers.h" |
| |
| #include "arm_compute/core/Coordinates.h" |
| #include "arm_compute/core/utils/helpers/AdjustVecSize.h" |
| #include "arm_compute/core/utils/math/Math.h" |
| |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| Status validate_matmul_input_shapes(const TensorShape &lhs_shape, |
| const TensorShape &rhs_shape, |
| const MatMulKernelInfo &matmul_kernel_info) |
| { |
| const size_t lhs_k = matmul_kernel_info.adj_lhs ? lhs_shape.y() : lhs_shape.x(); |
| const size_t rhs_k = matmul_kernel_info.adj_rhs ? rhs_shape.x() : rhs_shape.y(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_k != rhs_k, "K dimension in Lhs and Rhs matrices must match."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape.total_size() == 0, "Lhs tensor can't be empty"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_shape.total_size() == 0, "Rhs tensor can't be empty"); |
| |
| constexpr size_t batch_dim_start = 2; |
| for (size_t i = batch_dim_start; i < Coordinates::num_max_dimensions; ++i) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape[i] != rhs_shape[i], "Batch dimension broadcasting is not supported"); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window_for_mmul_kernels(const ITensorInfo *lhs, |
| const ITensorInfo *rhs, |
| const ITensorInfo *dst, |
| const MatMulKernelInfo &matmul_kernel_info, |
| int mmul_m0, |
| int mmul_n0) |
| { |
| ARM_COMPUTE_UNUSED(lhs, rhs); |
| |
| const Window win = calculate_max_window(*dst, Steps(1, 1)); |
| |
| // Collapse along the Z direction |
| // This collapse needs to be here in order to tune the Z dimension of LWS |
| Window collapsed = win.collapse(win, Window::DimZ); |
| |
| // Reconfigure window size, one arm_matrix_multiply call needs 16 threads to finish. |
| Window::Dimension x_dimension = collapsed.x(); |
| Window::Dimension y_dimension = collapsed.y(); |
| |
| const int m = dst->dimension(1); |
| const int n = dst->dimension(0); |
| |
| const int m0 = std::min(matmul_kernel_info.m0, m); |
| const int n0 = adjust_vec_size(matmul_kernel_info.n0, n); |
| |
| // Make M and N multiple of M0 and N0 respectively |
| const unsigned int ceil_to_multiple_n_n0 = ceil_to_multiple(n, n0); |
| const unsigned int ceil_to_multiple_m_m0 = ceil_to_multiple(m, m0); |
| |
| // Divide M and N by M0 and N0 respectively |
| const unsigned int n_div_n0 = ceil_to_multiple_n_n0 / n0; |
| const unsigned int m_div_m0 = ceil_to_multiple_m_m0 / m0; |
| |
| // Make n_div_n0 and m_div_m0 multiple of mmul_n0 and mmul_m0 respectively |
| const unsigned int ceil_to_multiple_n_div_n0_mmul_n0 = ceil_to_multiple(n_div_n0, mmul_n0); |
| const unsigned int ceil_to_multiple_m_div_m0_mmul_m0 = ceil_to_multiple(m_div_m0, mmul_m0); |
| |
| // Ensure x_dimension is multiple of MMUL block size (mmul_m0 * mmul_n0) |
| x_dimension.set_end(ceil_to_multiple_n_div_n0_mmul_n0 * mmul_m0); |
| y_dimension.set_end(ceil_to_multiple_m_div_m0_mmul_m0 / mmul_m0); |
| |
| collapsed.set(Window::DimX, x_dimension); |
| collapsed.set(Window::DimY, y_dimension); |
| |
| return std::make_pair(Status{}, collapsed); |
| } |
| |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |