/* * Copyright (c) 2016-2019 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/NEON/kernels/NEGEMMTranspose1xWKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/INEKernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include #include #include using namespace arm_compute; namespace { TensorShape get_output_shape(const ITensorInfo *input) { TensorShape output_shape{ input->tensor_shape() }; const size_t transpose_w = 16 / input->element_size(); output_shape.set(0, input->dimension(1) * transpose_w); output_shape.set(1, static_cast(std::ceil((input->dimension(0) / static_cast(transpose_w))))); return output_shape; } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) { //Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input) is not needed here as this kernel doesn't use NEON FP16 instructions. 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); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) { const unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); // Configure kernel window Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); // Configure window in case of configured output if(output->total_size() != 0) { AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } const bool window_changed = update_window_and_padding(win, input_access); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, win); } } // namespace void NEGEMMTranspose1xWKernel::configure(const ITensor *input, ITensor *output) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); // Output tensor auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), get_output_shape(input->info()), 1, input->info()->data_type()); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); _input = input; _output = output; // Configure kernel window auto win_config = validate_and_configure_window(input->info(), output->info()); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } Status NEGEMMTranspose1xWKernel::validate(const ITensorInfo *input, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); return Status{}; } void NEGEMMTranspose1xWKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INESimpleKernel::window(), window); /* * Following an example of how the transposition1xW works when the input data type is F32 * * |a00 a01 a02 a03| * |a10 a11 a12 a13| * |a20 a21 a22 a23| = | a00 a01 a02 a03 || a10 a11 a12 a13 || a20 a21 a22 a23 || a30 a31 a32 a33 | * |a30 a31 a32 a33| * * The output matrix will have the following shape: [ height * W, ceil(width / W) ], where W = (16 / element size of the tensor) */ // Set window for output tensor. Set to 0 the X and Y dimensions in order to allow multi-threading implementation and future batched matrix multiplications Window win_out(window); win_out.set(Window::DimX, Window::Dimension(0, 0, 0)); win_out.set(Window::DimY, Window::Dimension(0, 0, 0)); Iterator in(_input, window); Iterator out(_output, win_out); switch(_input->info()->element_size()) { case 1: { const size_t out_stride = _output->info()->strides_in_bytes()[1]; execute_window_loop(window, [&](const Coordinates & id) { // Output address = base addr + (y * 16) + (x / 16 ) * stride const uint8_t *in_ptr = in.ptr(); uint8_t *const out_ptr = out.ptr() + (id.y() << 4) + (id.x() >> 4) * out_stride; vst1q_u8(out_ptr, vld1q_u8(in_ptr)); }, in, out); break; } case 2: { const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(int16_t); execute_window_loop(window, [&](const Coordinates & id) { // Output address = base addr + (y * 8) + (x / 8 ) * stride const auto in_ptr = reinterpret_cast(in.ptr()); const auto out_ptr = reinterpret_cast(out.ptr()) + (id.y() << 3) + (id.x() >> 3) * out_stride; vst1q_u16(out_ptr, vld1q_u16(in_ptr)); }, in, out); break; } case 4: { const size_t out_stride = _output->info()->strides_in_bytes()[1] / sizeof(float); execute_window_loop(window, [&](const Coordinates & id) { // Output address = base addr + (y * 4) + (x / 4 ) * stride const auto in_ptr = reinterpret_cast(in.ptr()); const auto out_ptr = reinterpret_cast(out.ptr()) + (id.y() << 2) + (id.x() >> 2) * out_stride; vst1q_u32(out_ptr, vld1q_u32(in_ptr)); }, in, out); break; } default: { ARM_COMPUTE_ERROR("Element size not supported"); break; } } }