/* * Copyright (c) 2017-2018 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 "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLValidate.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/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, int mult_interleave4x4_height, bool reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(mult_interleave4x4_height < 1); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32); if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_interleaved_shape(*input, mult_interleave4x4_height, reinterpret_input_as_3d)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, int mult_interleave4x4_height, bool reinterpret_input_as_3d) { constexpr unsigned int num_elems_processed_per_iteration_x = 4; constexpr unsigned int num_elems_processed_per_iteration_y = 4; const unsigned int num_elems_written_per_iteration = num_elems_processed_per_iteration_x * num_elems_processed_per_iteration_y * mult_interleave4x4_height; bool window_changed = false; TensorInfo tmp_info(*input); if(reinterpret_input_as_3d) { // Since the input tensor has to be reinterpreted as 3D and the execute window is based on a 2D interleave, // the window needs to be constructed on the 2D collapsed version of the tensor TensorShape tmp_shape(input->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_interleaved_shape(*input, mult_interleave4x4_height))); // Configure window const float scale_x = 4.0f * static_cast(mult_interleave4x4_height); const float scale_y = 1.0f / (scale_x); // Note: bottom paddings are calculated manually as the input 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 ? input->tensor_shape()[1] * input->tensor_shape()[2] : input->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; Window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); Window win_in = calculate_max_window(*input, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic input_access(input, 0, 0, ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration_x), input->dimension(1) + bottom_pad); AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration, 1, scale_x, scale_y); window_changed = update_window_and_padding(win_in, input_access) || // window used by the execute_window_loop update_window_and_padding(win, output_access); // window used to update the padding requirements of output tensor output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), 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.collapse(win, Window::DimZ); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, collapsed); } } // namespace CLGEMMInterleave4x4Kernel::CLGEMMInterleave4x4Kernel() : _input(nullptr), _output(nullptr), _reinterpret_input_as_3d(false) { } void CLGEMMInterleave4x4Kernel::configure(const ICLTensor *input, ICLTensor *output, int mult_interleave4x4_height, bool reinterpret_input_as_3d, bool unroll_block) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_interleaved_shape(*input->info(), mult_interleave4x4_height, reinterpret_input_as_3d))); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), mult_interleave4x4_height, reinterpret_input_as_3d)); _input = input; _output = output; _reinterpret_input_as_3d = reinterpret_input_as_3d; // Create build options CLBuildOptions build_opts; build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height)); build_opts.add_option_if(unroll_block, "-DUNROLL_BLOCK"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(1))); build_opts.add_option_if(_reinterpret_input_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(input->info()->dimension(2))); switch(input->info()->element_size()) { case 1: build_opts.add_option("-DDATA_TYPE=uchar"); break; case 2: build_opts.add_option("-DDATA_TYPE=ushort"); break; case 4: build_opts.add_option("-DDATA_TYPE=uint"); break; default: ARM_COMPUTE_ERROR("Data type not supported"); } // Create kernel _kernel = static_cast(CLKernelLibrary::get().create_kernel("gemm_interleave4x4", build_opts.options())); // Configure kernel window auto win_config = validate_and_configure_window(input->info(), output->info(), mult_interleave4x4_height, reinterpret_input_as_3d); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Set config_id for enabling LWS tuning _config_id = "interleave4x4_"; _config_id += (_reinterpret_input_as_3d ? "3d_" : ""); _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(3)); } Status CLGEMMInterleave4x4Kernel::validate(const ITensorInfo *input, const ITensorInfo *output, int mult_interleave4x4_height, bool reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mult_interleave4x4_height, reinterpret_input_as_3d)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), mult_interleave4x4_height, reinterpret_input_as_3d).first); return Status{}; } void CLGEMMInterleave4x4Kernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); /* * This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values) * |a00 a01 a02 a03| * |a10 a11 a12 a13| * |a20 a21 a22 a23| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | * |a30 a31 a32 a33| * * After this operation, the output matrix will have the following shape: [ height * 4, width / 4 ] */ Window slice = window.first_slice_window_3D(); 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 = 2 * num_arguments_per_3D_tensor(); const unsigned int total_cross_plane_pad = _input->info()->padding().top + _input->info()->padding().bottom; _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } do { unsigned int idx = 0; add_3D_tensor_argument(idx, _input, slice); add_3D_tensor_argument(idx, _output, slice); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice)); }