| /* |
| * Copyright (c) 2016, 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/NEON/kernels/NEGEMMTranspose1xWKernel.h" |
| |
| #include "arm_compute/core/AccessWindowTranspose.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 <arm_neon.h> |
| #include <cstddef> |
| #include <cstring> |
| |
| 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<size_t>(std::ceil((input->dimension(0) / static_cast<float>(transpose_w))))); |
| return output_shape; |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8, |
| DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32, |
| DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(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_FIXED_POINT(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| const unsigned int num_elems_processed_per_iteration = 16 / input->element_size(); |
| const int scale_x = num_elems_processed_per_iteration; |
| bool window_changed = false; |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| |
| ARM_COMPUTE_ERROR_ON_MSG((win.x().end() / scale_x) == 0, "Transposed shape would be 0 in the second dimension"); |
| |
| AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, input_access); |
| |
| // Configure window in case of configured output |
| if(output->total_size() != 0) |
| { |
| AccessWindowTranspose output_access(output, 0, 0, num_elems_processed_per_iteration, 1, scale_x, 1.f / scale_x); |
| window_changed = window_changed || update_window_and_padding(win, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), input->tensor_shape())); |
| } |
| |
| 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(), input->info()->fixed_point_position()); |
| |
| // 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<const uint16_t *>(in.ptr()); |
| const auto out_ptr = reinterpret_cast<uint16_t *>(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<const uint32_t *>(in.ptr()); |
| const auto out_ptr = reinterpret_cast<uint32_t *>(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; |
| } |
| } |
| } |