/* * 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/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 "arm_compute/core/utils/misc/ShapeCalculator.h" #include #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; namespace { using ElementsProcessed = Steps; inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1); if(!is_interleaved_transposed) { ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0)); ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output); } } else { const int m = reshape_info.m(); const int n = reshape_info.n(); const int k = reshape_info.k(); const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); TensorShape tensor_shape0{ input0->tensor_shape() }; tensor_shape0.set(0, k); tensor_shape0.set(1, m); TensorShape tensor_shape1{ input1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height)); const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast(n)); ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast(m)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output); } } return Status{}; } inline std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, bool is_interleaved_transposed, GPUTarget gpu_target, ElementsProcessed &num_elements_processed) { bool window_changed = false; Window win{}; const DataType data_type = input0->data_type(); unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; if(is_interleaved_transposed) { // Configure kernel window num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); num_elems_processed_per_iteration_y = 4; win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f); AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f); AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->tensor_shape())); } else // The input tensors have not been reshaped { // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case. num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); num_elems_processed_per_iteration_y = std::min(static_cast(output->dimension(1)), 4); // Create kernels according to the architecture, data type and input size. if(gpu_target == GPUTarget::BIFROST && data_type == DataType::F32) { num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4; } // Configure window win = 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), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y)); AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); window_changed = update_window_and_padding(win, input0_access, input1_access, output_access); Coordinates coord; coord.set_num_dimensions(output->num_dimensions()); output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape())); } Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, win); } } // namespace 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, const GEMMReshapeInfo &reshape_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); // Output tensor auto inizialitation if not yet initialized TensorShape tensor_shape{ input0->info()->tensor_shape() }; tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->info()->dimension(0)); tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->info()->dimension(1)); auto_init_if_empty(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape)); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info)); _input0 = input0; _input1 = input1; _output = output; const DataType data_type = input0->info()->data_type(); const int fp_pos = input0->info()->fixed_point_position(); // Get target architecture GPUTarget arch_target = get_arch_from_target(get_target()); // Configure LWS hint if(arch_target == GPUTarget::BIFROST && input1->info()->dimension(1) == 24) { // LWS optimized for the 11x11 AlexNet convolution on Bifrost. _lws_hint = cl::NDRange(2, 2); } else if(output->info()->dimension(1) == 196) { _lws_hint = cl::NDRange(1, 7); } else { _lws_hint = cl::NDRange(8, 8); } ElementsProcessed num_elements_processed{}; // Configure kernel window auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, arch_target, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure(win_config.second); // Create build options CLBuildOptions build_opts; build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(fp_pos)); // Only define ALPHA when alpha is not 1.0f. This avoids performing unnecessary multiplications. if(std::abs(1.0f - alpha) > 0.00001f) { build_opts.add_option_if_else(is_data_type_fixed_point(data_type), "-DALPHA=" + support::cpp11::to_string((data_type == DataType::QS8 ? sqcvt_qs8_f32(alpha, fp_pos) : sqcvt_qs16_f32(alpha, fp_pos))), "-DALPHA=" + float_to_string_with_full_precision(alpha)); } std::string kernel_name; if(is_interleaved_transposed) { const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0))); build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width)); build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height)); if(data_type == DataType::F32) { kernel_name = "gemm_mm_interleaved_transposed_f32_" + string_from_target(arch_target); } else { kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)); } } else // The input tensors have not been reshaped { build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))); // Create kernels according to the architecture, data type and input size. if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32) { // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. // FC6 and FC7 of AlexNet and VGG-16). kernel_name = (input1->info()->dimension(0) <= 1000 && input0->info()->num_dimensions() == 1) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost"; // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels // via exhaustive autotuning over a range of representative layer configurations. _lws_hint = cl::NDRange(4); } else if(is_data_type_fixed_point(data_type)) { kernel_name = "gemm_mm_" + lower_string(string_from_data_type(data_type)); } else // (MIDGARD and F32) or (F16) { build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); kernel_name = "gemm_mm_floating_point"; } build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y())); build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x())); } // Create kernel _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // 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))); } Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target) { // Note: num_elements_processed will be set in validate_and_configure_window() ElementsProcessed num_elements_processed{}; ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get(), is_interleaved_transposed, gpu_target, num_elements_processed) .first); return Status{}; } 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)); }