/* * Copyright (c) 2017 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/CLGEMMMatrixMultiplyKernel.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/FixedPoint.h" #include "arm_compute/core/Helpers.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 #include #include using namespace arm_compute; CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel() : _input0(nullptr), _input1(nullptr), _output(nullptr) { } void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, bool is_interleaved_transposed) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output); if(!is_interleaved_transposed) { ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1)); } _input0 = input0; _input1 = input1; _output = output; if(output->info()->dimension(1) == 196) { _lws_hint = cl::NDRange(1, 7); } else { _lws_hint = cl::NDRange(8, 8); } std::set build_opts; build_opts.emplace(("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0)))); build_opts.emplace(("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0)))); if(is_data_type_fixed_point(input0->info()->data_type())) { build_opts.emplace(("-DALPHA=" + support::cpp11::to_string((input0->info()->data_type() == DataType::QS8 ? sqcvt_qs8_f32(alpha, input0->info()->fixed_point_position()) : sqcvt_qs16_f32(alpha, input0->info()->fixed_point_position()))))); build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input0->info()->fixed_point_position()))); } else { build_opts.emplace(("-DALPHA=" + float_to_string_with_full_precision(alpha))); } if(is_interleaved_transposed) { // Create kernel std::string data_type_name = lower_string(string_from_data_type(input0->info()->data_type())); if(data_type_name == "f32") { GPUTarget arch_target = get_arch_from_target(get_target()); _kernel = static_cast(CLKernelLibrary::get().create_kernel("gemm_mm_interleaved_transposed_f32_" + string_from_target(arch_target), build_opts)); } else { _kernel = static_cast(CLKernelLibrary::get().create_kernel("gemm_mm_interleaved_transposed_" + data_type_name, build_opts)); } // Configure window kernel const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type()); constexpr unsigned int num_elems_processed_per_iteration_y = 4; Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f); AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f); AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); update_window_and_padding(win, input0_access, input1_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape())); ICLKernel::configure(win); } else // The input tensors have not been reshaped { ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1)); // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor const unsigned int num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(input0->info()->data_type()); const unsigned int num_elems_processed_per_iteration_y = std::min(static_cast(output->info()->dimension(1)), 4); build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()))); build_opts.emplace(("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elems_processed_per_iteration_x))); build_opts.emplace(("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elems_processed_per_iteration_y))); // Create kernel if(is_data_type_fixed_point(input0->info()->data_type())) { std::string kernel_name = "gemm_mm_" + lower_string(string_from_data_type(input0->info()->data_type())); _kernel = static_cast(CLKernelLibrary::get().create_kernel((kernel_name), build_opts)); } else { std::string kernel_name = "gemm_mm_floating_point"; _kernel = static_cast(CLKernelLibrary::get().create_kernel((kernel_name), build_opts)); } Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic input0_access(input0->info(), 0, 0, input0->info()->dimension(0), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y)); AccessWindowStatic input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1)); AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); update_window_and_padding(win, input0_access, input1_access, output_access); Coordinates coord; coord.set_num_dimensions(output->info()->num_dimensions()); output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape())); ICLKernel::configure(win); // Set config_id for enabling LWS tuning _config_id = "gemm_"; _config_id += (is_interleaved_transposed ? "reshaped_" : ""); _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)); _config_id += "_"; _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1))); } } void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); Window slice = window.first_slice_window_2D(); 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)); 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 the matrix multiplication is used to perform a convolution operation if(_input1->info()->num_dimensions() < 3) { 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); enqueue(queue, *this, slice, _lws_hint); } while(window.slide_window_slice_2D(slice)); }