| /* |
| * Copyright (c) 2020 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. |
| */ |
| #pragma once |
| |
| #include "convolution_parameters.hpp" |
| |
| #include <algorithm> |
| #include <cstddef> |
| #include <tuple> |
| #include <vector> |
| |
| namespace arm_gemm { |
| |
| // Class to assist with convolution calculations. |
| // |
| // This is framed as a hierarchy of objects: |
| // |
| // - Top level object which depends only on convolution parameters. This sets up std::vectors for the padding and |
| // kernel offset arrays. From this you can request: |
| // |
| // - Mid level object (e.g. instantiated at start of 'ConvolutionInterleave'). This holds specifics about the |
| // input tensor, and the desired column range. Calculations specific to this can be done once when this is set |
| // up. From this you can request: |
| // |
| // - Low level object (instantiated for each range of rows). This contains methods to actually populate a row |
| // pointer array. |
| |
| |
| template<typename T> |
| class convolver { |
| private: |
| const ConvolutionParameters m_params; |
| |
| // Vector of padding data |
| const std::vector<T> m_pad_row; |
| |
| // X/Y offsets for each kernel position |
| std::vector<int> m_kernel_y; |
| std::vector<int> m_kernel_x; |
| |
| class column_handler { |
| private: |
| const convolver<T> &m_parent; |
| |
| // Base/stride of input image |
| const T * const m_input_base; |
| const size_t m_input_stride; |
| |
| // Starting kernel point and channel offset within that point |
| const unsigned int m_start_pos; |
| const unsigned int m_start_offset; |
| |
| // Total length to process, rounded length of each input channel block. |
| const unsigned int m_length; |
| const unsigned int m_rounded_stringlen; |
| |
| class row_handler { |
| private: |
| const convolver<T> &m_convolver; |
| const column_handler &m_parent; |
| |
| // These variables track progress through the current block of rows |
| unsigned int m_start_output_y=0; |
| unsigned int m_start_output_x=0; |
| |
| unsigned int m_length_remaining=0; |
| unsigned int m_current_pos=0; |
| |
| unsigned int m_active_height=0; |
| |
| public: |
| row_handler(const column_handler &parent, unsigned int start_row, unsigned int active_height) : |
| m_convolver(parent.m_parent), |
| m_parent(parent), |
| m_start_output_y(start_row / m_convolver.m_params.output_width), |
| m_start_output_x(start_row % m_convolver.m_params.output_width), |
| m_length_remaining(m_parent.m_length), |
| m_current_pos(m_parent.m_start_pos), |
| m_active_height(active_height) { } |
| |
| bool finished() const { |
| return (m_length_remaining == 0); |
| } |
| |
| std::tuple<unsigned int, unsigned int> next_block(const T ** const row_ptr) { |
| if (finished()) { |
| return std::make_tuple(0, 0); |
| } |
| |
| // "in_width" in the amount of data that will be read in (copied) |
| // "out_width" is the total amount of data that will be produced (including padding) |
| unsigned int offset = (m_current_pos == m_parent.m_start_pos) ? m_parent.m_start_offset : 0; |
| unsigned int in_width = std::min(m_length_remaining, static_cast<unsigned int>(m_convolver.m_params.input_channels) - offset); |
| unsigned int out_width = std::min(m_length_remaining, m_parent.m_rounded_stringlen - offset); |
| |
| unsigned int output_y = m_start_output_y; |
| unsigned int output_x = m_start_output_x; |
| |
| for (unsigned int row=0; row<m_active_height; row++) { |
| int input_y = (output_y * m_convolver.m_params.output_stride_h) + m_convolver.m_kernel_y[m_current_pos]; |
| int input_x = (output_x * m_convolver.m_params.output_stride_w) + m_convolver.m_kernel_x[m_current_pos]; |
| |
| // Out-of-bounds points will read the padding data, |
| // otherwise find the correct address in the input image. |
| if (input_y < 0 || input_y >= m_convolver.m_params.input_height || input_x < 0 || input_x >= m_convolver.m_params.input_width) { |
| row_ptr[row] = m_convolver.m_pad_row.data(); |
| } else { |
| row_ptr[row] = m_parent.m_input_base + ((input_y * m_convolver.m_params.input_width) + input_x) * m_parent.m_input_stride; |
| } |
| |
| output_x++; |
| if (output_x == m_convolver.m_params.output_width) { |
| output_y++; |
| output_x=0; |
| } |
| } |
| |
| m_current_pos++; |
| m_length_remaining-=out_width; |
| |
| return std::make_tuple(in_width, offset); |
| } |
| }; // end of "row handler" class |
| |
| public: |
| column_handler(const convolver<T> &parent, const T *input_base, size_t input_stride, |
| unsigned int k_start, unsigned int k_end, unsigned int rounded_stringlen) |
| : m_parent(parent), m_input_base(input_base), m_input_stride(input_stride), |
| m_start_pos(k_start / rounded_stringlen), |
| m_start_offset(k_start % rounded_stringlen), |
| m_length(k_end - k_start), |
| m_rounded_stringlen(rounded_stringlen) { } |
| |
| row_handler process_rows(unsigned int start_row, unsigned int active_height) const { |
| return row_handler(*this, start_row, active_height); |
| } |
| }; // end of "column handler" class |
| |
| public: |
| convolver(ConvolutionParameters params) : |
| m_params (params), m_pad_row(params.input_channels, static_cast<T>(params.padding_value)), |
| m_kernel_y(params.kernel_width * params.kernel_height, 0), |
| m_kernel_x(params.kernel_width * params.kernel_height, 0) { |
| |
| // Kernel points are addressed across, then down (assumed weight layout is WHIO) |
| for (unsigned int ky=0; ky<params.kernel_height; ky++) { |
| for (unsigned int kx=0; kx<params.kernel_width; kx++) { |
| unsigned int n = (ky * params.kernel_width) + kx; |
| m_kernel_y[n] = ky - params.padding_top; |
| m_kernel_x[n] = kx - params.padding_left; |
| } |
| } |
| } |
| |
| column_handler process_columns(const T *input_base, size_t input_stride, |
| unsigned int k_start, unsigned int k_end, unsigned int rounded_stringlen) const { |
| return column_handler(*this, input_base, input_stride, k_start, k_end, rounded_stringlen); |
| } |
| }; |
| |
| } // namespace arm_gemm |