Repository navigation
Expand file tree
/
Copy pathevaluate.cpp
More file actions
124 lines (96 loc) · 4.74 KB
/
Copy pathevaluate.cpp
File metadata and controls
124 lines (96 loc) · 4.74 KB
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
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2025 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.hpp"
#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.b.inCheck());
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) < 236))
{
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 / 468;
nnue -= nnue * nnueComplexity / 18000;
int material = 535 * pos.count<PAWN>() + pos.non_pawn_material();
int v = (nnue * (77777 + material) + optimism * (7777 + material)) / 77777;
// Damp down the evaluation linearly when shuffling
v -= v * pos.rule50_count() / 212;
// 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.b.inCheck())
return "Final evaluation: none (in check)";
Eval::NNUE::AccumulatorStack accumulators;
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 * 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 * to_cp(v, pos) << " (white side)";
ss << " [with scaled NNUE, ...]";
ss << "\n";
return ss.str();
}
} // namespace Stockfish