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Use one nnue pawn count multiplier

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Switch to the value used by the main net.

Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/6647e8096dcff0d1d6b05e96
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 51040 W: 13249 L: 13044 D: 24747
Ptnml(0-2): 139, 6029, 13016, 6160, 176

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/6647f4a46dcff0d1d6b05eea
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 20460 W: 5195 L: 4972 D: 10293
Ptnml(0-2): 8, 2178, 5637, 2397, 10

https://github.com/official-stockfish/Stockfish/pull/5258

bench 1887462
This commit is contained in:
Linmiao Xu 2024-05-17 19:27:20 -04:00 • committed by Joost VandeVondele
parent 285f1d2a66
commit 99dfc63e03
1 changed files with 4 additions and 4 deletions
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8
src/evaluate.cpp
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@ -72,13 +72,13 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
smallNet = false;
}
const auto adjustEval = [&](int pawnCountMul, int shufflingConstant) {
const auto adjustEval = [&](int shufflingConstant) {
// Blend optimism and eval with nnue complexity and material imbalance
optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / 584;
nnue -= nnue * (nnueComplexity * 5 / 3) / 32395;
int npm = pos.non_pawn_material() / 64;
v = (nnue * (npm + 943 + pawnCountMul * pos.count<PAWN>()) + optimism * (npm + 140)) / 1058;
v = (nnue * (npm + 943 + 11 * pos.count<PAWN>()) + optimism * (npm + 140)) / 1058;
// Damp down the evaluation linearly when shuffling
int shuffling = pos.rule50_count();
@ -86,9 +86,9 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
};
if (!smallNet)
adjustEval(11, 178);
adjustEval(178);
else
adjustEval(9, 206);
adjustEval(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);