/* * 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/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/AccessWindowTranspose.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h" #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" #include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 using namespace arm_compute; GCGEMMMatrixMultiplyKernel::GCGEMMMatrixMultiplyKernel() : _input0(nullptr), _input1(nullptr), _output(nullptr) { } void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(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; std::set build_opts; Window win; build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1)); build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1)); build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1)); build_opts.emplace("#define COLS_A " + support::cpp11::to_string(input0->info()->dimension(0))); build_opts.emplace("#define COLS_B " + support::cpp11::to_string(input1->info()->dimension(0))); build_opts.emplace("#define ALPHA " + float_to_string_with_full_precision(alpha)); // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication if(is_interleaved_transposed) { switch(input0->info()->data_type()) { case DataType::F16: build_opts.emplace("#define DATA_TYPE_FP16"); break; case DataType::F32: build_opts.emplace("#define DATA_TYPE_FP32"); break; default: ARM_COMPUTE_ERROR("Current data type is not supported"); break; } build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED"); // Create kernel _kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts); // Configure window kernel const unsigned int num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type()); constexpr unsigned int num_elems_processed_per_iteration_y = 4; 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())); } else { ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1)); // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor unsigned int num_elems_processed_per_iteration_x; unsigned int num_elems_processed_per_iteration_y; switch(input0->info()->data_type()) { case DataType::F16: num_elems_processed_per_iteration_x = 4; num_elems_processed_per_iteration_y = 1; build_opts.emplace("#define DATA_TYPE_FP16"); break; case DataType::F32: num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type()); num_elems_processed_per_iteration_y = std::min(static_cast(output->info()->dimension(1)), 4); build_opts.emplace("#define DATA_TYPE_FP32"); break; default: ARM_COMPUTE_ERROR("Current data type is not supported"); break; } build_opts.emplace("#define GEMM_MM_FLOATING_POINT"); build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x)); build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y)); // Create kernel _kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts); 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, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), 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())); } IGCKernel::configure(win); } void GCGEMMMatrixMultiplyKernel::run(const Window &window) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IGCKernel::window(), window); _kernel.use(); 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; switch(_input0->info()->data_type()) { case DataType::F16: add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice); add_2D_tensor_argument(idx, _input1, BufferParam(2, 3), slice_b); add_2D_tensor_argument(idx, _output, BufferParam(3, 3), slice); break; case DataType::F32: add_2D_tensor_argument(idx, _input0, BufferParam(1, 2), slice); add_2D_tensor_argument(idx, _input1, BufferParam(2, 2), slice_b); add_2D_tensor_argument(idx, _output, BufferParam(3, 2), slice); break; default: ARM_COMPUTE_ERROR("Current data type is not supported"); break; } _kernel.update_shader_params(); enqueue(*this, slice); } while(window.slide_window_slice_2D(slice)); }