| /* |
| * Copyright (c) 2017-2019 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 "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/AccessWindowTranspose.h" |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include <cstddef> |
| #include <cstdint> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| using ElementsProcessed = Steps; |
| |
| Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMReshapeInfo &gemm_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && gemm_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D"); |
| |
| const int m = gemm_info.m(); |
| const int n = gemm_info.n(); |
| const int k = gemm_info.k(); |
| |
| ARM_COMPUTE_UNUSED(m); |
| ARM_COMPUTE_UNUSED(n); |
| ARM_COMPUTE_UNUSED(k); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != static_cast<unsigned int>(n)); |
| ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != static_cast<unsigned int>(k)); |
| if(gemm_info.reinterpret_input_as_3d()) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m)); |
| } |
| |
| if(output->total_size() != 0) |
| { |
| const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, false, gemm_info)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed) |
| { |
| unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; |
| unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); |
| |
| Window win{}; |
| Window win_out{}; |
| bool window_changed = false; |
| |
| // In case both input and output have to be reinterpreted as 3D tensors, |
| // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. |
| if(reinterpret_input_as_3d == reinterpret_output_as_3d) |
| { |
| reinterpret_input_as_3d = false; |
| reinterpret_output_as_3d = false; |
| } |
| |
| // Output tensor auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, false, gemm_info)).set_data_type(DataType::S32)); |
| |
| TensorInfo tmp_info(*output); |
| |
| if(reinterpret_output_as_3d) |
| { |
| // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, |
| // the window needs to be constructed on the 2D collapsed version of the tensor |
| TensorShape tmp_shape(output->tensor_shape()); |
| tmp_shape.collapse(2U, 1U); |
| tmp_info.set_tensor_shape(tmp_shape); |
| } |
| |
| // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x |
| // Note: if the dot product instruction is available, the 8x2 tile has to be used |
| num_elems_processed_per_iteration_x = 4; |
| num_elems_processed_per_iteration_y = std::min(static_cast<int>(output->dimension(1)), 4); |
| |
| // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor |
| // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic |
| const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1]; |
| const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; |
| |
| // Configure window |
| win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); |
| |
| AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad); |
| AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); |
| AccessWindowStatic output_access(output, 0, 0, |
| ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), |
| output->dimension(1) + bottom_pad); |
| |
| window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop |
| update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor |
| |
| Coordinates coord; |
| coord.set_num_dimensions(output->num_dimensions()); |
| output_access.set_valid_region(win_out, ValidRegion(coord, output->tensor_shape())); |
| |
| // Collapse along the Z direction |
| // This collapse needs to be here in order to tune the Z dimension of LWS |
| Window collapsed = win; |
| const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u); |
| collapsed = win.collapse(win, dimension_to_collapse); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, collapsed); |
| } |
| } // namespace |
| |
| CLGEMMLowpMatrixMultiplyKernel::CLGEMMLowpMatrixMultiplyKernel() |
| : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false) |
| { |
| } |
| |
| void CLGEMMLowpMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMReshapeInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), gemm_info)); |
| |
| _input0 = input0; |
| _input1 = input1; |
| _output = output; |
| _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); |
| |
| // In case both input and output have to be reinterpreted as 3D tensors, |
| // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. |
| if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) |
| { |
| _reinterpret_input_as_3d = false; |
| _reinterpret_output_as_3d = false; |
| } |
| |
| // Check if we need to slide the matrix B |
| const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions(); |
| _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); |
| |
| ElementsProcessed num_elements_processed{}; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), gemm_info, num_elements_processed); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure_internal(win_config.second); |
| |
| // Create build options |
| std::string kernel_name(" "); |
| CLBuildOptions build_opts; |
| build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); |
| build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); |
| build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1))); |
| build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2))); |
| build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); |
| build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))); |
| build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x())); |
| build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y())); |
| |
| kernel_name = "gemmlowp_mm_midgard"; |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = kernel_name; |
| _config_id += "_"; |
| _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); |
| _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); |
| _config_id += lower_string(string_from_data_type(input0->info()->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(0)); |
| } |
| |
| Status CLGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMReshapeInfo &gemm_info) |
| { |
| ElementsProcessed num_elements_processed{}; |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, gemm_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), |
| input1->clone().get(), |
| output->clone().get(), |
| gemm_info, |
| num_elements_processed) |
| .first); |
| |
| return Status{}; |
| } |
| |
| void CLGEMMLowpMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); |
| |
| if(_input1->info()->num_dimensions() < 3) |
| { |
| // The stride_z for matrix B must be zero if we do not slice |
| ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0); |
| } |
| |
| Window slice = window.first_slice_window_3D(); |
| Window slice_matrix_b = slice; |
| |
| slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| if(_reinterpret_input_as_3d) |
| { |
| // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor |
| const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3; |
| const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; |
| _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); |
| } |
| |
| if(_reinterpret_output_as_3d) |
| { |
| // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor |
| const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); |
| const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; |
| _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); |
| } |
| |
| do |
| { |
| Window slice_b = slice; |
| // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 |
| // This scenario can happen when the matrix multiplication is used to perform a convolution operation |
| if(!_slide_matrix_b) |
| { |
| slice_b = slice_matrix_b; |
| } |
| |
| unsigned int idx = 0; |
| add_2D_tensor_argument(idx, _input0, slice); |
| add_2D_tensor_argument(idx, _input1, slice_b); |
| add_2D_tensor_argument(idx, _output, slice); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_3D(slice)); |
| } |
