mirror of
https://github.com/sockspls/badfish
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e046c4ef0d
Set digits in adjusted eval params all to 7. Passed non-regression STC: https://tests.stockfishchess.org/tests/view/66fc493d86d5ee47d953b94c LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 57696 W: 15098 L: 14898 D: 27700 Ptnml(0-2): 205, 6784, 14678, 6968, 213 Passed non-regression LTC: https://tests.stockfishchess.org/tests/view/66fd4b9386d5ee47d953b9d5 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 93786 W: 23868 L: 23721 D: 46197 Ptnml(0-2): 55, 10322, 25993, 10467, 56 closes https://github.com/official-stockfish/Stockfish/pull/5618 Bench: 1277182
124 lines
4.7 KiB
C++
124 lines
4.7 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2024 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish 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 General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "evaluate.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <memory>
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#include <sstream>
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#include <tuple>
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#include "nnue/network.h"
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#include "nnue/nnue_misc.h"
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#include "position.h"
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#include "types.h"
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#include "uci.h"
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#include "nnue/nnue_accumulator.h"
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namespace Stockfish {
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// Returns a static, purely materialistic evaluation of the position from
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// the point of view of the given color. It can be divided by PawnValue to get
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// an approximation of the material advantage on the board in terms of pawns.
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int Eval::simple_eval(const Position& pos, Color c) {
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return PawnValue * (pos.count<PAWN>(c) - pos.count<PAWN>(~c))
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+ (pos.non_pawn_material(c) - pos.non_pawn_material(~c));
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}
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bool Eval::use_smallnet(const Position& pos) {
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int simpleEval = simple_eval(pos, pos.side_to_move());
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return std::abs(simpleEval) > 962;
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}
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// Evaluate is the evaluator for the outer world. It returns a static evaluation
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// of the position from the point of view of the side to move.
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Value Eval::evaluate(const Eval::NNUE::Networks& networks,
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const Position& pos,
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Eval::NNUE::AccumulatorCaches& caches,
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int optimism) {
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assert(!pos.checkers());
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bool smallNet = use_smallnet(pos);
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auto [psqt, positional] = smallNet ? networks.small.evaluate(pos, &caches.small)
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: networks.big.evaluate(pos, &caches.big);
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Value nnue = (125 * psqt + 131 * positional) / 128;
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// Re-evaluate the position when higher eval accuracy is worth the time spent
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if (smallNet && (nnue * psqt < 0 || std::abs(nnue) < 227))
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{
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std::tie(psqt, positional) = networks.big.evaluate(pos, &caches.big);
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nnue = (125 * psqt + 131 * positional) / 128;
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smallNet = false;
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}
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// Blend optimism and eval with nnue complexity
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int nnueComplexity = std::abs(psqt - positional);
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optimism += optimism * nnueComplexity / (smallNet ? 430 : 474);
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nnue -= nnue * nnueComplexity / (smallNet ? 20233 : 17879);
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int material = (smallNet ? 553 : 532) * pos.count<PAWN>() + pos.non_pawn_material();
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int v = (nnue * (77777 + material) + optimism * (7777 + material)) / 77777;
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// Damp down the evaluation linearly when shuffling
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v -= v * pos.rule50_count() / 212;
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// Guarantee evaluation does not hit the tablebase range
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v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
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return v;
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}
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// Like evaluate(), but instead of returning a value, it returns
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// a string (suitable for outputting to stdout) that contains the detailed
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// descriptions and values of each evaluation term. Useful for debugging.
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// Trace scores are from white's point of view
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std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {
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if (pos.checkers())
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return "Final evaluation: none (in check)";
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auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
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std::stringstream ss;
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ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
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ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';
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ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
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auto [psqt, positional] = networks.big.evaluate(pos, &caches->big);
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Value v = psqt + positional;
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "NNUE evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)\n";
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v = evaluate(networks, pos, *caches, VALUE_ZERO);
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "Final evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)";
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ss << " [with scaled NNUE, ...]";
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ss << "\n";
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return ss.str();
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}
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} // namespace Stockfish
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