The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
-[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
-tools to train and develop the NNUE networks. On CPUs supporting modern vector instructions
+[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first version of
+the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available
+in [the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/), while data generation tools
+are available in [a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
+
+On CPUs supporting modern vector instructions
(avx2 and similar), the NNUE evaluation results in much stronger playing strength, even
if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps
is typical).
goto encode_remaining; // With pawns we have finished special treatments
}
- // In positions withouth pawns, we further flip the squares to ensure leading
+ // In positions without pawns, we further flip the squares to ensure leading
// piece is below RANK_5.
if (rank_of(squares[0]) > RANK_4)
for (int i = 0; i < size; ++i)
// Rs "together" in 62 * 61 / 2 ways (we divide by 2 because rooks can be
// swapped and still get the same position.)
//
- // In case we have at least 3 unique pieces (inlcuded kings) we encode them
+ // In case we have at least 3 unique pieces (included kings) we encode them
// together.
if (entry->hasUniquePieces) {
+ (squares[1] - adjust1)) * 62
+ squares[2] - adjust2;
- // First piece is on a1-h8 diagonal, second below: map this occurence to
+ // First piece is on a1-h8 diagonal, second below: map this occurrence to
// 6 to differentiate from the above case, rank_of() maps a1-d4 diagonal
// to 0...3 and finally MapB1H1H7[] maps the b1-h1-h7 triangle to 0..27.
else if (off_A1H8(squares[1]))
idx *= d->groupIdx[0];
Square* groupSq = squares + d->groupLen[0];
- // Encode remainig pawns then pieces according to square, in ascending order
+ // Encode remaining pawns then pieces according to square, in ascending order
bool remainingPawns = entry->hasPawns && entry->pawnCount[1];
while (d->groupLen[++next])
// Group together pieces that will be encoded together. The general rule is that
// a group contains pieces of same type and color. The exception is the leading
-// group that, in case of positions withouth pawns, can be formed by 3 different
+// group that, in case of positions without pawns, can be formed by 3 different
// pieces (default) or by the king pair when there is not a unique piece apart
// from the kings. When there are pawns, pawns are always first in pieces[].
//
//
// This ensures unique encoding for the whole position. The order of the
// groups is a per-table parameter and could not follow the canonical leading
- // pawns/pieces -> remainig pawns -> remaining pieces. In particular the
+ // pawns/pieces -> remaining pawns -> remaining pieces. In particular the
// first group is at order[0] position and the remaining pawns, when present,
// are at order[1] position.
bool pp = e.hasPawns && e.pawnCount[1]; // Pawns on both sides
d->groupIdx[1] = idx;
idx *= Binomial[d->groupLen[1]][48 - d->groupLen[0]];
}
- else // Remainig pieces
+ else // Remaining pieces
{
d->groupIdx[next] = idx;
idx *= Binomial[d->groupLen[next]][freeSquares];
d->groupIdx[n] = idx;
}
-// In Recursive Pairing each symbol represents a pair of childern symbols. So
+// In Recursive Pairing each symbol represents a pair of children symbols. So
// read d->btree[] symbols data and expand each one in his left and right child
// symbol until reaching the leafs that represent the symbol value.
uint8_t set_symlen(PairsData* d, Sym s, std::vector<bool>& visited) {
for (auto p : bothOnDiagonal)
MapKK[p.first][p.second] = code++;
- // Binomial[] stores the Binomial Coefficents using Pascal rule. There
+ // Binomial[] stores the Binomial Coefficients using Pascal rule. There
// are Binomial[k][n] ways to choose k elements from a set of n elements.
Binomial[0][0] = 1;
for (int leadPawnsCnt = 1; leadPawnsCnt <= 5; ++leadPawnsCnt)
for (File f = FILE_A; f <= FILE_D; ++f)
{
- // Restart the index at every file because TB table is splitted
+ // Restart the index at every file because TB table is split
// by file, so we can reuse the same index for different files.
int idx = 0;
projects / stockfish / commitdiff