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https://github.com/danog/ton.git
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200 lines
5.3 KiB
C++
200 lines
5.3 KiB
C++
/*
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This file is part of TON Blockchain Library.
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TON Blockchain Library is free software: you can redistribute it and/or modify
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it under the terms of the GNU Lesser General Public License as published by
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the Free Software Foundation, either version 2 of the License, or
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(at your option) any later version.
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TON Blockchain Library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public License
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along with TON Blockchain Library. If not, see <http://www.gnu.org/licenses/>.
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Copyright 2017-2019 Telegram Systems LLP
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*/
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#pragma once
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#include "td/fec/algebra/Octet.h"
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#include "td/fec/algebra/Simd.h"
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#include "td/utils/Span.h"
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#include "td/utils/format.h"
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namespace td {
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class MatrixGF256 {
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public:
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MatrixGF256(size_t rows, size_t cols) : rows_(rows), cols_(cols) {
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stride_ = (cols_ + Simd::alignment() - 1) / Simd::alignment() * Simd::alignment();
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storage_ = std::make_unique<uint8[]>(stride_ * rows + Simd::alignment() - 1);
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matrix_ = storage_.get();
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while (!Simd::is_aligned_pointer(matrix_)) {
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matrix_++;
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}
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CHECK(Simd::is_aligned_pointer(matrix_));
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CHECK(Simd::is_aligned_pointer(matrix_ + stride_));
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CHECK(static_cast<size_t>(matrix_ - storage_.get()) < Simd::alignment());
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}
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void set_zero() {
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std::fill(matrix_, matrix_ + stride_ * rows_, 0);
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}
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size_t rows() const {
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return rows_;
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}
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size_t cols() const {
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return cols_;
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}
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MatrixGF256 apply_row_permutation(Span<uint32> permutation) {
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MatrixGF256 res(rows_, cols_);
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for (size_t row = 0; row < rows_; row++) {
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res.row(row).copy_from(this->row(permutation[row]));
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}
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return res;
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}
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Octet get(size_t row, size_t col) const {
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DCHECK(row < rows_ && col < cols_);
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return Octet(matrix_[row * stride_ + col]);
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}
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void set(size_t row, size_t col, Octet o) {
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DCHECK(row < rows_ && col < cols_);
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matrix_[row * stride_ + col] = o.value();
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}
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void row_multiply(size_t row, Octet o) {
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uint8* p = row_ptr(row);
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Simd::gf256_mul(p, o.value(), stride_);
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}
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Slice row(size_t row) const {
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return Slice(row_ptr(row), cols());
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}
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MutableSlice row(size_t row) {
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return MutableSlice(row_ptr(row), cols());
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}
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template <class M>
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void set_from(const M& m, size_t row_offset, size_t col_offset) {
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auto to = block_view(row_offset, col_offset, rows() - row_offset, cols() - col_offset);
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for (size_t i = 0; i < m.rows(); i++) {
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to.row(i).copy_from(m.row(i));
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}
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}
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MatrixGF256 copy() {
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MatrixGF256 res(rows(), cols());
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res.set_from(*this, 0, 0);
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return res;
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}
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void add(const MatrixGF256& m) {
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CHECK(m.rows() == rows());
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CHECK(m.cols() == cols());
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for (size_t i = 0; i < m.rows(); i++) {
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auto* to = row_ptr(i);
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auto* from = m.row_ptr(i);
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row_add(to, from);
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}
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}
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// row(a) += row(b) * m
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void row_add_mul(size_t a, size_t b, Octet m) {
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row_add_mul(row_ptr(a), row_ptr(b), m);
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}
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void row_add_mul(size_t a, Slice b, Octet m) {
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row_add_mul(row_ptr(a), b.ubegin(), m);
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}
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// row(a) += row(b)
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void row_add(size_t a, size_t b) {
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row_add(row_ptr(a), row_ptr(b));
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}
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void row_add(size_t a, Slice b) {
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row_add(row_ptr(a), b.ubegin());
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}
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void row_set(size_t a, Slice b) {
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row(a).copy_from(b);
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}
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class BlockView {
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public:
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BlockView(size_t row_offset, size_t col_offset, size_t row_size, size_t col_size, MatrixGF256& m)
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: row_offset_(row_offset), col_offset_(col_offset), row_size_(row_size), col_size_(col_size), m_(m) {
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}
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size_t cols() const {
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return col_size_;
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}
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size_t rows() const {
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return row_size_;
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}
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Slice row(size_t row) const {
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return m_.row(row_offset_ + row).remove_prefix(col_offset_);
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}
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MutableSlice row(size_t row) {
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return m_.row(row_offset_ + row).remove_prefix(col_offset_);
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}
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private:
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size_t row_offset_;
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size_t col_offset_;
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size_t row_size_;
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size_t col_size_;
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MatrixGF256& m_;
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};
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BlockView block_view(size_t row_offset, size_t col_offset, size_t row_size, size_t col_size) {
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return BlockView(row_offset, col_offset, row_size, col_size, *this);
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}
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private:
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uint8* matrix_;
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size_t rows_;
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size_t cols_;
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size_t stride_;
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std::unique_ptr<uint8[]> storage_;
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uint8* row_ptr(size_t row) {
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return matrix_ + stride_ * row;
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}
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const uint8* row_ptr(size_t row) const {
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return matrix_ + stride_ * row;
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}
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void row_add_mul(uint8* ap, const uint8* bp, Octet m) {
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uint8 u = m.value();
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if (u == 0) {
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return;
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}
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if (u == 1) {
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return row_add(ap, bp);
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}
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Simd::gf256_add_mul(ap, bp, u, stride_);
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}
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void row_add(uint8* ap, const uint8* bp) {
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Simd::gf256_add(ap, bp, stride_);
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}
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};
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inline StringBuilder& operator<<(StringBuilder& sb, const MatrixGF256& m) {
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sb << "\n";
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for (uint32 i = 0; i < m.rows(); i++) {
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auto row = m.row(i);
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for (uint32 j = 0; j < m.cols(); j++) {
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uint8 x = row[j];
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sb << " " << format::hex_digit(x / 16) << format::hex_digit(x % 16);
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}
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sb << "\n";
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}
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return sb;
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}
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} // namespace td
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