Spaces:
Running
Running
File size: 4,872 Bytes
dc7e9f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | /*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2026 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "evaluate.h"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <memory>
#include <sstream>
#include <tuple>
#include "nnue/network.h"
#include "nnue/nnue_misc.h"
#include "position.h"
#include "types.h"
#include "uci.h"
#include "nnue/nnue_accumulator.h"
namespace Stockfish {
// Returns a static, purely materialistic evaluation of the position from
// the point of view of the side to move. It can be divided by PawnValue to get
// an approximation of the material advantage on the board in terms of pawns.
int Eval::simple_eval(const Position& pos) {
Color c = pos.side_to_move();
return PawnValue * (pos.count<PAWN>(c) - pos.count<PAWN>(~c)) + pos.non_pawn_material(c)
- pos.non_pawn_material(~c);
}
bool Eval::use_smallnet(const Position& pos) { return std::abs(simple_eval(pos)) > 962; }
// Evaluate is the evaluator for the outer world. It returns a static evaluation
// of the position from the point of view of the side to move.
Value Eval::evaluate(const Eval::NNUE::Networks& networks,
const Position& pos,
Eval::NNUE::AccumulatorStack& accumulators,
Eval::NNUE::AccumulatorCaches& caches,
int optimism) {
assert(!pos.checkers());
bool smallNet = use_smallnet(pos);
auto [psqt, positional] = smallNet ? networks.small.evaluate(pos, accumulators, caches.small)
: networks.big.evaluate(pos, accumulators, caches.big);
Value nnue = (125 * psqt + 131 * positional) / 128;
// Re-evaluate the position when higher eval accuracy is worth the time spent
if (smallNet && (std::abs(nnue) < 277))
{
std::tie(psqt, positional) = networks.big.evaluate(pos, accumulators, caches.big);
nnue = (125 * psqt + 131 * positional) / 128;
smallNet = false;
}
// Blend optimism and eval with nnue complexity
int nnueComplexity = std::abs(psqt - positional);
optimism += optimism * nnueComplexity / 476;
nnue -= nnue * nnueComplexity / 18236;
int material = 534 * pos.count<PAWN>() + pos.non_pawn_material();
int v = (nnue * (77871 + material) + optimism * (7191 + material)) / 77871;
// Damp down the evaluation linearly when shuffling
v -= v * pos.rule50_count() / 199;
// 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);
return v;
}
// Like evaluate(), but instead of returning a value, it returns
// a string (suitable for outputting to stdout) that contains the detailed
// descriptions and values of each evaluation term. Useful for debugging.
// Trace scores are from white's point of view
std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {
if (pos.checkers())
return "Final evaluation: none (in check)";
auto accumulators = std::make_unique<Eval::NNUE::AccumulatorStack>();
auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
std::stringstream ss;
ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';
ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
auto [psqt, positional] = networks.big.evaluate(pos, *accumulators, caches->big);
Value v = psqt + positional;
v = pos.side_to_move() == WHITE ? v : -v;
ss << "NNUE evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)\n";
v = evaluate(networks, pos, *accumulators, *caches, VALUE_ZERO);
v = pos.side_to_move() == WHITE ? v : -v;
ss << "Final evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)";
ss << " [with scaled NNUE, ...]";
ss << "\n";
return ss.str();
}
} // namespace Stockfish
|