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Tune Search Parameters

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Parameters Tune, adding also another tunable parameter (npmDiv) to be
variable for different nets (bignet, smallnet, psqtOnly smallnet). P.s:
The changed values are only the parameters where there is agreement
among the different time controls, so in other words, the tunings are
telling us that changing these specific values to this specific
direction is good in all time controls, so there shouldn't be a high
risk of regressing at longer time controls.

Passed STC:
LLR: 2.97 (-2.94,2.94) <0.00,2.00>
Total: 39552 W: 10329 L: 9999 D: 19224
Ptnml(0-2): 156, 4592, 9989, 4844, 195
https://tests.stockfishchess.org/tests/view/661be9a0bd68065432a088c0

Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 56394 W: 14439 L: 14078 D: 27877
Ptnml(0-2): 30, 6152, 15480, 6497, 38
https://tests.stockfishchess.org/tests/view/661c746296961e72eb565406

closes https://github.com/official-stockfish/Stockfish/pull/5187

Bench: 1836777
This commit is contained in:
FauziAkram 2024-04-22 14:34:22 +03:00 • committed by Disservin
parent ddd250b9d6
commit fcba524793
3 changed files with 35 additions and 35 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

14
src/evaluate.cpp
View file

@ -58,14 +58,14 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks, const Position& pos,
Value nnue = smallNet ? networks.small.evaluate(pos, true, &nnueComplexity, psqtOnly)
: networks.big.evaluate(pos, true, &nnueComplexity, false);
const auto adjustEval = [&](int optDiv, int nnueDiv, int pawnCountConstant, int pawnCountMul,
int npmConstant, int evalDiv, int shufflingConstant,
int shufflingDiv) {
const auto adjustEval = [&](int optDiv, int nnueDiv, int npmDiv, int pawnCountConstant,
int pawnCountMul, int npmConstant, int evalDiv,
int shufflingConstant, int shufflingDiv) {
// Blend optimism and eval with nnue complexity and material imbalance
optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / optDiv;
nnue -= nnue * (nnueComplexity * 5 / 3) / nnueDiv;
int npm = pos.non_pawn_material() / 64;
int npm = pos.non_pawn_material() / npmDiv;
v = (nnue * (npm + pawnCountConstant + pawnCountMul * pos.count<PAWN>())
+ optimism * (npmConstant + npm))
/ evalDiv;
@ -76,11 +76,11 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks, const Position& pos,
};
if (!smallNet)
adjustEval(513, 32395, 919, 11, 145, 1036, 178, 204);
adjustEval(524, 32395, 66, 942, 11, 139, 1058, 178, 204);
else if (psqtOnly)
adjustEval(517, 32857, 908, 7, 155, 1019, 224, 238);
adjustEval(517, 32857, 65, 908, 7, 155, 1006, 224, 238);
else
adjustEval(499, 32793, 903, 9, 147, 1067, 208, 211);
adjustEval(515, 32793, 63, 944, 9, 140, 1067, 206, 206);
// Guarantee evaluation does not hit the tablebase range
v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);

10
src/movepick.cpp
View file

@ -190,8 +190,8 @@ void MovePicker::score() {
m.value += bool(pos.check_squares(pt) & to) * 16384;
// bonus for escaping from capture
m.value += threatenedPieces & from ? (pt == QUEEN && !(to & threatenedByRook) ? 51000
: pt == ROOK && !(to & threatenedByMinor) ? 24950
m.value += threatenedPieces & from ? (pt == QUEEN && !(to & threatenedByRook) ? 51700
: pt == ROOK && !(to & threatenedByMinor) ? 25600
: !(to & threatenedByPawn) ? 14450
: 0)
: 0;
@ -200,7 +200,7 @@ void MovePicker::score() {
m.value -= !(threatenedPieces & from)
? (pt == QUEEN ? bool(to & threatenedByRook) * 48150
+ bool(to & threatenedByMinor) * 10650
: pt == ROOK ? bool(to & threatenedByMinor) * 24500
: pt == ROOK ? bool(to & threatenedByMinor) * 24335
: pt != PAWN ? bool(to & threatenedByPawn) * 14950
: 0)
: 0;
@ -241,7 +241,7 @@ Move MovePicker::select(Pred filter) {
// moves left, picking the move with the highest score from a list of generated moves.
Move MovePicker::next_move(bool skipQuiets) {
auto quiet_threshold = [](Depth d) { return -3550 * d; };
auto quiet_threshold = [](Depth d) { return -3560 * d; };
top:
switch (stage)
@ -310,7 +310,7 @@ top:
return *cur != refutations[0] && *cur != refutations[1] && *cur != refutations[2];
}))
{
if ((cur - 1)->value > -8000 || (cur - 1)->value <= quiet_threshold(depth))
if ((cur - 1)->value > -7998 || (cur - 1)->value <= quiet_threshold(depth))
return *(cur - 1);
// Remaining quiets are bad

46
src/search.cpp
View file

@ -57,9 +57,9 @@ static constexpr double EvalLevel[10] = {1.043, 1.017, 0.952, 1.009, 0.971,
// Futility margin
Value futility_margin(Depth d, bool noTtCutNode, bool improving, bool oppWorsening) {
Value futilityMult = 118 - 44 * noTtCutNode;
Value futilityMult = 118 - 45 * noTtCutNode;
Value improvingDeduction = 52 * improving * futilityMult / 32;
Value worseningDeduction = (310 + 48 * improving) * oppWorsening * futilityMult / 1024;
Value worseningDeduction = (316 + 48 * improving) * oppWorsening * futilityMult / 1024;
return futilityMult * d - improvingDeduction - worseningDeduction;
}
@ -76,10 +76,10 @@ Value to_corrected_static_eval(Value v, const Worker& w, const Position& pos) {
}
// History and stats update bonus, based on depth
int stat_bonus(Depth d) { return std::clamp(211 * d - 315, 0, 1291); }
int stat_bonus(Depth d) { return std::clamp(214 * d - 318, 16, 1304); }
// History and stats update malus, based on depth
int stat_malus(Depth d) { return (d < 4 ? 572 * d - 285 : 1372); }
int stat_malus(Depth d) { return (d < 4 ? 572 * d - 284 : 1355); }
// Add a small random component to draw evaluations to avoid 3-fold blindness
Value value_draw(size_t nodes) { return VALUE_DRAW - 1 + Value(nodes & 0x2); }
@ -303,12 +303,12 @@ void Search::Worker::iterative_deepening() {
// Reset aspiration window starting size
Value avg = rootMoves[pvIdx].averageScore;
delta = 11 + avg * avg / 11254;
delta = 10 + avg * avg / 11480;
alpha = std::max(avg - delta, -VALUE_INFINITE);
beta = std::min(avg + delta, VALUE_INFINITE);
// Adjust optimism based on root move's averageScore (~4 Elo)
optimism[us] = 125 * avg / (std::abs(avg) + 91);
optimism[us] = 122 * avg / (std::abs(avg) + 92);
optimism[~us] = -optimism[us];
// Start with a small aspiration window and, in the case of a fail
@ -752,7 +752,7 @@ Value Search::Worker::search(
// If eval is really low check with qsearch if it can exceed alpha, if it can't,
// return a fail low.
// Adjust razor margin according to cutoffCnt. (~1 Elo)
if (eval < alpha - 471 - (276 - 148 * ((ss + 1)->cutoffCnt > 3)) * depth * depth)
if (eval < alpha - 471 - (275 - 148 * ((ss + 1)->cutoffCnt > 3)) * depth * depth)
{
value = qsearch<NonPV>(pos, ss, alpha - 1, alpha);
if (value < alpha)
@ -763,14 +763,14 @@ Value Search::Worker::search(
// The depth condition is important for mate finding.
if (!ss->ttPv && depth < 12
&& eval - futility_margin(depth, cutNode && !ss->ttHit, improving, opponentWorsening)
- (ss - 1)->statScore / 284
- (ss - 1)->statScore / 286
>= beta
&& eval >= beta && eval < VALUE_TB_WIN_IN_MAX_PLY && (!ttMove || ttCapture))
return beta > VALUE_TB_LOSS_IN_MAX_PLY ? (eval + beta) / 2 : eval;
// Step 9. Null move search with verification search (~35 Elo)
if (!PvNode && (ss - 1)->currentMove != Move::null() && (ss - 1)->statScore < 18001
&& eval >= beta && ss->staticEval >= beta - 21 * depth + 315 && !excludedMove
&& eval >= beta && ss->staticEval >= beta - 21 * depth + 312 && !excludedMove
&& pos.non_pawn_material(us) && ss->ply >= thisThread->nmpMinPly
&& beta > VALUE_TB_LOSS_IN_MAX_PLY)
{
@ -881,7 +881,7 @@ Value Search::Worker::search(
moves_loop: // When in check, search starts here
// Step 12. A small Probcut idea, when we are in check (~4 Elo)
probCutBeta = beta + 436;
probCutBeta = beta + 452;
if (ss->inCheck && !PvNode && ttCapture && (tte->bound() & BOUND_LOWER)
&& tte->depth() >= depth - 4 && ttValue >= probCutBeta
&& std::abs(ttValue) < VALUE_TB_WIN_IN_MAX_PLY && std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY)
@ -964,7 +964,7 @@ moves_loop: // When in check, search starts here
{
Piece capturedPiece = pos.piece_on(move.to_sq());
Value futilityValue =
ss->staticEval + 288 + 277 * lmrDepth + PieceValue[capturedPiece]
ss->staticEval + 285 + 277 * lmrDepth + PieceValue[capturedPiece]
+ thisThread->captureHistory[movedPiece][move.to_sq()][type_of(capturedPiece)]
/ 7;
if (futilityValue <= alpha)
@ -972,7 +972,7 @@ moves_loop: // When in check, search starts here
}
// SEE based pruning for captures and checks (~11 Elo)
if (!pos.see_ge(move, -199 * depth))
if (!pos.see_ge(move, -203 * depth))
continue;
}
else
@ -992,10 +992,10 @@ moves_loop: // When in check, search starts here
lmrDepth += history / 5285;
Value futilityValue =
ss->staticEval + (bestValue < ss->staticEval - 54 ? 128 : 58) + 131 * lmrDepth;
ss->staticEval + (bestValue < ss->staticEval - 54 ? 128 : 57) + 131 * lmrDepth;
// Futility pruning: parent node (~13 Elo)
if (!ss->inCheck && lmrDepth < 15 && futilityValue <= alpha)
if (!ss->inCheck && lmrDepth < 14 && futilityValue <= alpha)
{
if (bestValue <= futilityValue && std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY
&& futilityValue < VALUE_TB_WIN_IN_MAX_PLY)
@ -1006,7 +1006,7 @@ moves_loop: // When in check, search starts here
lmrDepth = std::max(lmrDepth, 0);
// Prune moves with negative SEE (~4 Elo)
if (!pos.see_ge(move, -26 * lmrDepth * lmrDepth))
if (!pos.see_ge(move, -27 * lmrDepth * lmrDepth))
continue;
}
}
@ -1026,11 +1026,11 @@ moves_loop: // When in check, search starts here
// so changing them requires tests at these types of time controls.
// Recursive singular search is avoided.
if (!rootNode && move == ttMove && !excludedMove
&& depth >= 4 - (thisThread->completedDepth > 32) + ss->ttPv
&& depth >= 4 - (thisThread->completedDepth > 33) + ss->ttPv
&& std::abs(ttValue) < VALUE_TB_WIN_IN_MAX_PLY && (tte->bound() & BOUND_LOWER)
&& tte->depth() >= depth - 3)
{
Value singularBeta = ttValue - (64 + 59 * (ss->ttPv && !PvNode)) * depth / 64;
Value singularBeta = ttValue - (65 + 59 * (ss->ttPv && !PvNode)) * depth / 63;
Depth singularDepth = newDepth / 2;
ss->excludedMove = move;
@ -1134,10 +1134,10 @@ moves_loop: // When in check, search starts here
ss->statScore = 2 * thisThread->mainHistory[us][move.from_to()]
+ (*contHist[0])[movedPiece][move.to_sq()]
+ (*contHist[1])[movedPiece][move.to_sq()]
+ (*contHist[3])[movedPiece][move.to_sq()] - 5007;
+ (*contHist[3])[movedPiece][move.to_sq()] - 5024;
// Decrease/increase reduction for moves with a good/bad history (~8 Elo)
r -= ss->statScore / 12901;
r -= ss->statScore / 13182;
// Step 17. Late moves reduction / extension (LMR, ~117 Elo)
if (depth >= 2 && moveCount > 1 + rootNode)
@ -1274,7 +1274,7 @@ moves_loop: // When in check, search starts here
else
{
// Reduce other moves if we have found at least one score improvement (~2 Elo)
if (depth > 2 && depth < 12 && beta < 13132 && value > -13295)
if (depth > 2 && depth < 12 && beta < 13546 && value > -13478)
depth -= 2;
assert(depth > 0);
@ -1319,7 +1319,7 @@ moves_loop: // When in check, search starts here
{
int bonus = (depth > 5) + (PvNode || cutNode) + ((ss - 1)->statScore < -14761)
+ ((ss - 1)->moveCount > 11)
+ (!ss->inCheck && bestValue <= ss->staticEval - 144);
+ (!ss->inCheck && bestValue <= ss->staticEval - 142);
update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
stat_bonus(depth) * bonus);
thisThread->mainHistory[~us][((ss - 1)->currentMove).from_to()]
@ -1477,7 +1477,7 @@ Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta,
if (bestValue > alpha)
alpha = bestValue;
futilityBase = ss->staticEval + 246;
futilityBase = ss->staticEval + 250;
}
const PieceToHistory* contHist[] = {(ss - 1)->continuationHistory,
@ -1625,7 +1625,7 @@ Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta,
Depth Search::Worker::reduction(bool i, Depth d, int mn, int delta) {
int reductionScale = reductions[d] * reductions[mn];
return (reductionScale + 1123 - delta * 832 / rootDelta) / 1024 + (!i && reductionScale > 1025);
return (reductionScale + 1150 - delta * 832 / rootDelta) / 1024 + (!i && reductionScale > 1025);
}
TimePoint Search::Worker::elapsed() const {