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/*
* 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
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