/* * Copyright (c) 2016-2021 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 "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/NEON/INEKernel.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include #include #include #include using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); //Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input) is not needed here as this kernel doesn't use CPU FP16 instructions. ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::UNKNOWN); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); if(output->total_size() != 0) { TensorShape output_shape = input->tensor_shape(); output_shape.set(0, input->dimension(0) * 4); output_shape.set(1, std::ceil(input->dimension(1) / 4.0f)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } return Status{}; } } // namespace NEGEMMInterleave4x4Kernel::NEGEMMInterleave4x4Kernel() : _func(nullptr) { } void NEGEMMInterleave4x4Kernel::configure(const ITensor *input, ITensor *output) { 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()))); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); _input = input; _output = output; switch(input->info()->element_size()) { case 1: _func = &NEGEMMInterleave4x4Kernel::gemm_interleave4x4; break; case 2: _func = &NEGEMMInterleave4x4Kernel::gemm_interleave4x4; break; case 4: _func = &NEGEMMInterleave4x4Kernel::gemm_interleave4x4; break; default: ARM_COMPUTE_ERROR_ON("Element size not supported"); break; } Window win = calculate_max_window(*input->info(), Steps(1, 4)); INEKernel::configure(win); } Status NEGEMMInterleave4x4Kernel::validate(const ITensorInfo *input, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); return Status{}; } template void NEGEMMInterleave4x4Kernel::gemm_interleave4x4(const ITensor *input, ITensor *output, const Window &window) { const size_t window_start_x = window.x().start(); const size_t window_end_x = window.x().end(); const size_t in_height = input->info()->dimension(1); const size_t in_stride = input->info()->strides_in_bytes()[1]; const size_t partial_y = in_height % 4; // Set window for the input tensor Window win = window; win.set(Window::DimX, Window::Dimension(0, 1, 1)); // Set window for the output tensor Window win_out(window); win_out.set(Window::DimX, Window::Dimension(0, 1, 1)); win_out.scale(Window::DimY, 0.25f); Iterator in(input, win); Iterator out(output, win_out); execute_window_loop(win, [&](const Coordinates & id) { if(id.y() + 4 <= static_cast(in_height)) { for(size_t x = window_start_x; x < window_end_x; ++x) { const ScalarType data[4] = { *(reinterpret_cast(in.ptr() + 0 * in_stride) + x), *(reinterpret_cast(in.ptr() + 1 * in_stride) + x), *(reinterpret_cast(in.ptr() + 2 * in_stride) + x), *(reinterpret_cast(in.ptr() + 3 * in_stride) + x), }; std::memcpy(out.ptr() + x * 4 * sizeof(ScalarType), data, 4 * sizeof(ScalarType)); } } else { for(size_t x = window_start_x; x < window_end_x; ++x) { ScalarType data[4] = { 0, 0, 0, 0 }; for(size_t y = 0; y < partial_y; ++y) { data[y] = *(reinterpret_cast(in.ptr() + y * in_stride) + x); } std::memcpy(out.ptr() + x * 4 * sizeof(ScalarType), data, 4 * sizeof(ScalarType)); } } }, in, out); } void NEGEMMInterleave4x4Kernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); ARM_COMPUTE_ERROR_ON(_func == nullptr); /* * 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, ceil(width / 4.0f) ] */ (this->*_func)(_input, _output, window); }