/* * Copyright (c) 2018-2019 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/NEPermuteKernel.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" namespace { #include "arm_compute/core/NEON/kernels/convolution/common/shims.hpp" } // namespace #include #include using namespace arm_compute; namespace { inline bool is_permutation_supported(const PermutationVector &v) { static const std::array permutations3 = { PermutationVector(2U, 0U, 1U), PermutationVector(1U, 2U, 0U), PermutationVector(0U, 1U, 2U), PermutationVector(0U, 2U, 1U), PermutationVector(1U, 0U, 2U), PermutationVector(2U, 1U, 0U), }; static const std::array permutations4 = { PermutationVector(0U, 1U, 2U, 3U), PermutationVector(1U, 0U, 2U, 3U), PermutationVector(2U, 0U, 1U, 3U), PermutationVector(0U, 2U, 1U, 3U), PermutationVector(1U, 2U, 0U, 3U), PermutationVector(2U, 1U, 0U, 3U), PermutationVector(2U, 1U, 3U, 0U), PermutationVector(1U, 2U, 3U, 0U), PermutationVector(3U, 2U, 1U, 0U), PermutationVector(2U, 3U, 1U, 0U), PermutationVector(1U, 3U, 2U, 0U), PermutationVector(3U, 1U, 2U, 0U), PermutationVector(3U, 0U, 2U, 1U), PermutationVector(0U, 3U, 2U, 1U), PermutationVector(2U, 3U, 0U, 1U), PermutationVector(3U, 2U, 0U, 1U), PermutationVector(0U, 2U, 3U, 1U), PermutationVector(2U, 0U, 3U, 1U), PermutationVector(1U, 0U, 3U, 2U), PermutationVector(0U, 1U, 3U, 2U), PermutationVector(3U, 1U, 0U, 2U), PermutationVector(1U, 3U, 0U, 2U), PermutationVector(0U, 3U, 1U, 2U), PermutationVector(3U, 0U, 1U, 2U) }; return (permutations3.end() != std::find(permutations3.begin(), permutations3.end(), v)) || (permutations4.end() != std::find(permutations4.begin(), permutations4.end(), v)); } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm) { //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::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::U32, DataType::S32, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_permutation_supported(perm), "PermutationVector not supported."); const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input, perm); // Validate configured output if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } return Status{}; } } // namespace template void NEPermuteKernel::run_permute(const Window &window) { const DataLayout input_layout = _input->info()->data_layout(); // Input window Window window_in = window; // we only support these two configs in arm_compute/core/NEON/kernels/convolution/common/shims.hpp, for all others // we have to fall back to C++ if((input_layout == DataLayout::NCHW && _perm == PermutationVector{ 2U, 0U, 1U }) || (input_layout == DataLayout::NHWC && _perm == PermutationVector{ 1U, 2U, 0U })) { window_in.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().end() - window.x().start())); window_in.set(Window::DimY, Window::Dimension(window.y().start(), window.y().end(), window.y().end() - window.y().start())); window_in.set(Window::DimZ, Window::Dimension(window.z().start(), window.z().end(), window.z().end() - window.z().start())); window_in.set(3, Window::Dimension(window[3].start(), window[3].end(), window[3].end() - window[3].start())); } // Output window Window window_out(window); const Window::Dimension zero_window = Window::Dimension(0, 0, 0); for(size_t d = 0; d <= _perm.num_dimensions(); ++d) { window_out.set(d, zero_window); } // Create iterators Iterator in(_input, window_in); Iterator out(_output, window_out); int in_row_stride = 0; int in_col_stride = 0; int in_channel_stride = 0; int in_batch_stride = 0; int n_cols = 0; int n_rows = 0; int n_channels = 0; int n_batches = 0; switch(input_layout) { case DataLayout::NCHW: { in_row_stride = _input->info()->strides_in_bytes().y() / sizeof(T); in_channel_stride = _input->info()->strides_in_bytes().z() / sizeof(T); in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T); n_cols = _input->info()->tensor_shape().x(); n_rows = window_in.y().step(); n_channels = _input->info()->tensor_shape().z(); n_batches = _input->info()->tensor_shape()[3]; break; } case DataLayout::NHWC: { in_col_stride = _input->info()->strides_in_bytes().y() / sizeof(T); in_row_stride = _input->info()->strides_in_bytes().z() / sizeof(T); in_batch_stride = _input->info()->strides_in_bytes()[3] / sizeof(T); n_channels = _input->info()->tensor_shape().x(); n_cols = window_in.y().step(); n_rows = _input->info()->tensor_shape().z(); n_batches = _input->info()->tensor_shape()[3]; break; } default: { ARM_COMPUTE_ERROR("Invalid input data layout."); break; } } // CHW -> HWC if(input_layout == DataLayout::NCHW && _perm == PermutationVector{ 2U, 0U, 1U }) { const int out_channel_stride = _output->info()->strides_in_bytes().x() / sizeof(T); const int out_col_stride = _output->info()->strides_in_bytes().y() / sizeof(T); const int out_row_stride = _output->info()->strides_in_bytes().z() / sizeof(T); const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T); execute_window_loop(window_in, [&](const Coordinates & id) { const int idx = id[0] * out_col_stride + id[1] * out_row_stride + id[2] * out_channel_stride; reorder::nchw_to_nhwc(reinterpret_cast(in.ptr()), reinterpret_cast(out.ptr()) + idx, n_batches, n_channels, n_rows, n_cols, in_batch_stride, in_channel_stride, in_row_stride, out_batch_stride, out_row_stride, out_col_stride); }, in, out); } // HWC -> CHW else if(input_layout == DataLayout::NHWC && _perm == PermutationVector{ 1U, 2U, 0U }) { const int out_col_stride = _output->info()->strides_in_bytes().x() / sizeof(T); const int out_row_stride = _output->info()->strides_in_bytes().y() / sizeof(T); const int out_channel_stride = _output->info()->strides_in_bytes().z() / sizeof(T); const int out_batch_stride = _output->info()->strides_in_bytes()[3] / sizeof(T); execute_window_loop(window_in, [&](const Coordinates & id) { const int idx = id[0] * out_channel_stride + id[1] * out_col_stride + id[2] * out_row_stride; reorder::nhwc_to_nchw(reinterpret_cast(in.ptr()), reinterpret_cast(out.ptr()) + idx, n_batches, n_rows, n_cols, n_channels, in_batch_stride, in_row_stride, in_col_stride, out_batch_stride, out_channel_stride, out_row_stride); }, in, out); } else { // All other cases fall back to C++ // Permute strides Strides strides = _output->info()->strides_in_bytes(); Strides perm_strides = strides; permute_strides(perm_strides, _perm); const int perm_stride_3 = _input->info()->num_dimensions() >= 4 ? perm_strides[3] : 0; execute_window_loop(window, [&](const Coordinates & id) { const int idx = id[0] * perm_strides[0] + id[1] * perm_strides[1] + id[2] * perm_strides[2] + id[3] * perm_stride_3; *(reinterpret_cast(out.ptr() + idx)) = *(reinterpret_cast(in.ptr())); }, in, out); } } NEPermuteKernel::NEPermuteKernel() : _func(), _input(nullptr), _output(nullptr), _perm() { } void NEPermuteKernel::configure(const ITensor *input, ITensor *output, const PermutationVector &perm) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); const TensorShape output_shape = misc::shape_calculator::compute_permutation_output_shape(*input->info(), perm); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); // Perform validation step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), perm)); _input = input; _output = output; _perm = perm; switch(input->info()->element_size()) { case 1: _func = &NEPermuteKernel::run_permute; break; case 2: _func = &NEPermuteKernel::run_permute; break; case 4: _func = &NEPermuteKernel::run_permute; break; default: ARM_COMPUTE_ERROR("Element size not supported"); break; } // Configure kernel window Window win = calculate_max_window(*input->info(), Steps()); // The NEPermute doesn't need padding so update_window_and_padding() can be skipped Coordinates coord; coord.set_num_dimensions(output->info()->num_dimensions()); output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); ICPPKernel::configure(win); } Status NEPermuteKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, perm)); return Status{}; } void NEPermuteKernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window); if(_func != nullptr) { (this->*_func)(window); } }