Georgios Pinitas | c0b6f76 | 2020-11-02 01:37:17 +0000 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2020 Arm Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #pragma once |
| 25 | |
| 26 | #include "convolution_parameters.hpp" |
| 27 | |
| 28 | #include <algorithm> |
| 29 | #include <cstddef> |
| 30 | #include <tuple> |
| 31 | #include <vector> |
| 32 | |
| 33 | namespace arm_gemm { |
| 34 | |
| 35 | // Class to assist with convolution calculations. |
| 36 | // |
| 37 | // This is framed as a hierarchy of objects: |
| 38 | // |
| 39 | // - Top level object which depends only on convolution parameters. This sets up std::vectors for the padding and |
| 40 | // kernel offset arrays. From this you can request: |
| 41 | // |
| 42 | // - Mid level object (e.g. instantiated at start of 'ConvolutionInterleave'). This holds specifics about the |
| 43 | // input tensor, and the desired column range. Calculations specific to this can be done once when this is set |
| 44 | // up. From this you can request: |
| 45 | // |
| 46 | // - Low level object (instantiated for each range of rows). This contains methods to actually populate a row |
| 47 | // pointer array. |
| 48 | |
| 49 | |
| 50 | template<typename T> |
| 51 | class convolver { |
| 52 | private: |
| 53 | const ConvolutionParameters m_params; |
| 54 | |
| 55 | // Vector of padding data |
| 56 | const std::vector<T> m_pad_row; |
| 57 | |
| 58 | // X/Y offsets for each kernel position |
| 59 | std::vector<int> m_kernel_y; |
| 60 | std::vector<int> m_kernel_x; |
| 61 | |
| 62 | class column_handler { |
| 63 | private: |
| 64 | const convolver<T> &m_parent; |
| 65 | |
| 66 | // Base/stride of input image |
| 67 | const T * const m_input_base; |
| 68 | const size_t m_input_stride; |
| 69 | |
| 70 | // Starting kernel point and channel offset within that point |
| 71 | const unsigned int m_start_pos; |
| 72 | const unsigned int m_start_offset; |
| 73 | |
| 74 | // Total length to process, rounded length of each input channel block. |
| 75 | const unsigned int m_length; |
| 76 | const unsigned int m_rounded_stringlen; |
| 77 | |
| 78 | class row_handler { |
| 79 | private: |
| 80 | const convolver<T> &m_convolver; |
| 81 | const column_handler &m_parent; |
| 82 | |
| 83 | // These variables track progress through the current block of rows |
| 84 | unsigned int m_start_output_y=0; |
| 85 | unsigned int m_start_output_x=0; |
| 86 | |
| 87 | unsigned int m_length_remaining=0; |
| 88 | unsigned int m_current_pos=0; |
| 89 | |
| 90 | unsigned int m_active_height=0; |
| 91 | |
| 92 | public: |
| 93 | row_handler(const column_handler &parent, unsigned int start_row, unsigned int active_height) : |
| 94 | m_convolver(parent.m_parent), |
| 95 | m_parent(parent), |
| 96 | m_start_output_y(start_row / m_convolver.m_params.output_width), |
| 97 | m_start_output_x(start_row % m_convolver.m_params.output_width), |
| 98 | m_length_remaining(m_parent.m_length), |
| 99 | m_current_pos(m_parent.m_start_pos), |
| 100 | m_active_height(active_height) { } |
| 101 | |
| 102 | bool finished() const { |
| 103 | return (m_length_remaining == 0); |
| 104 | } |
| 105 | |
| 106 | std::tuple<unsigned int, unsigned int> next_block(const T ** const row_ptr) { |
| 107 | if (finished()) { |
Georgios Pinitas | 6c62d7a | 2020-11-16 16:34:06 +0000 | [diff] [blame^] | 108 | return std::make_tuple(0, 0); |
Georgios Pinitas | c0b6f76 | 2020-11-02 01:37:17 +0000 | [diff] [blame] | 109 | } |
| 110 | |
| 111 | // "in_width" in the amount of data that will be read in (copied) |
| 112 | // "out_width" is the total amount of data that will be produced (including padding) |
| 113 | unsigned int offset = (m_current_pos == m_parent.m_start_pos) ? m_parent.m_start_offset : 0; |
| 114 | unsigned int in_width = std::min(m_length_remaining, static_cast<unsigned int>(m_convolver.m_params.input_channels) - offset); |
| 115 | unsigned int out_width = std::min(m_length_remaining, m_parent.m_rounded_stringlen - offset); |
| 116 | |
| 117 | unsigned int output_y = m_start_output_y; |
| 118 | unsigned int output_x = m_start_output_x; |
| 119 | |
| 120 | for (unsigned int row=0; row<m_active_height; row++) { |
| 121 | int input_y = (output_y * m_convolver.m_params.output_stride_h) + m_convolver.m_kernel_y[m_current_pos]; |
| 122 | int input_x = (output_x * m_convolver.m_params.output_stride_w) + m_convolver.m_kernel_x[m_current_pos]; |
| 123 | |
| 124 | // Out-of-bounds points will read the padding data, |
| 125 | // otherwise find the correct address in the input image. |
| 126 | if (input_y < 0 || input_y >= m_convolver.m_params.input_height || input_x < 0 || input_x >= m_convolver.m_params.input_width) { |
| 127 | row_ptr[row] = m_convolver.m_pad_row.data(); |
| 128 | } else { |
| 129 | row_ptr[row] = m_parent.m_input_base + ((input_y * m_convolver.m_params.input_width) + input_x) * m_parent.m_input_stride; |
| 130 | } |
| 131 | |
| 132 | output_x++; |
| 133 | if (output_x == m_convolver.m_params.output_width) { |
| 134 | output_y++; |
| 135 | output_x=0; |
| 136 | } |
| 137 | } |
| 138 | |
| 139 | m_current_pos++; |
| 140 | m_length_remaining-=out_width; |
| 141 | |
Georgios Pinitas | 6c62d7a | 2020-11-16 16:34:06 +0000 | [diff] [blame^] | 142 | return std::make_tuple(in_width, offset); |
Georgios Pinitas | c0b6f76 | 2020-11-02 01:37:17 +0000 | [diff] [blame] | 143 | } |
| 144 | }; // end of "row handler" class |
| 145 | |
| 146 | public: |
| 147 | column_handler(const convolver<T> &parent, const T *input_base, size_t input_stride, |
| 148 | unsigned int k_start, unsigned int k_end, unsigned int rounded_stringlen) |
| 149 | : m_parent(parent), m_input_base(input_base), m_input_stride(input_stride), |
| 150 | m_start_pos(k_start / rounded_stringlen), |
| 151 | m_start_offset(k_start % rounded_stringlen), |
| 152 | m_length(k_end - k_start), |
| 153 | m_rounded_stringlen(rounded_stringlen) { } |
| 154 | |
| 155 | row_handler process_rows(unsigned int start_row, unsigned int active_height) const { |
| 156 | return row_handler(*this, start_row, active_height); |
| 157 | } |
| 158 | }; // end of "column handler" class |
| 159 | |
| 160 | public: |
| 161 | convolver(ConvolutionParameters params) : |
| 162 | m_params (params), m_pad_row(params.input_channels, static_cast<T>(params.padding_value)), |
| 163 | m_kernel_y(params.kernel_width * params.kernel_height, 0), |
| 164 | m_kernel_x(params.kernel_width * params.kernel_height, 0) { |
| 165 | |
| 166 | // Kernel points are addressed across, then down (assumed weight layout is WHIO) |
| 167 | for (unsigned int ky=0; ky<params.kernel_height; ky++) { |
| 168 | for (unsigned int kx=0; kx<params.kernel_width; kx++) { |
| 169 | unsigned int n = (ky * params.kernel_width) + kx; |
| 170 | m_kernel_y[n] = ky - params.padding_top; |
| 171 | m_kernel_x[n] = kx - params.padding_left; |
| 172 | } |
| 173 | } |
| 174 | } |
| 175 | |
| 176 | column_handler process_columns(const T *input_base, size_t input_stride, |
| 177 | unsigned int k_start, unsigned int k_end, unsigned int rounded_stringlen) const { |
| 178 | return column_handler(*this, input_base, input_stride, k_start, k_end, rounded_stringlen); |
| 179 | } |
| 180 | }; |
| 181 | |
| 182 | } // namespace arm_gemm |