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stockfish/src/movepick.h

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/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Add NNUE evaluation This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish. Both the NNUE and the classical evaluations are available, and can be used to assign a value to a position that is later used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs. The network is optimized and trained on the evalutions of millions of positions at moderate search depth. The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. [The nodchip repository](https://github.com/nodchip/Stockfish) provides additional tools to train and develop the NNUE networks. This patch is the result of contributions of various authors, from various communities, including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather, rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler, dorzechowski, and vondele. This new evaluation needed various changes to fishtest and the corresponding infrastructure, for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged. The first networks have been provided by gekkehenker and sergiovieri, with the latter net (nn-97f742aaefcd.nnue) being the current default. The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option, provided the `EvalFile` option points the the network file (depending on the GUI, with full path). The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest: 60000 @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c ELO: 92.77 +-2.1 (95%) LOS: 100.0% Total: 60000 W: 24193 L: 8543 D: 27264 Ptnml(0-2): 609, 3850, 9708, 10948, 4885 40000 @ 20+0.2 th 8 https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58 ELO: 89.47 +-2.0 (95%) LOS: 100.0% Total: 40000 W: 12756 L: 2677 D: 24567 Ptnml(0-2): 74, 1583, 8550, 7776, 2017 At the same time, the impact on the classical evaluation remains minimal, causing no significant regression: sprt @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b LLR: 2.94 (-2.94,2.94) {-6.00,-4.00} Total: 34936 W: 6502 L: 6825 D: 21609 Ptnml(0-2): 571, 4082, 8434, 3861, 520 sprt @ 60+0.6 th 1 https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d LLR: 2.93 (-2.94,2.94) {-6.00,-4.00} Total: 10088 W: 1232 L: 1265 D: 7591 Ptnml(0-2): 49, 914, 3170, 843, 68 The needed networks can be found at https://tests.stockfishchess.org/nns It is recommended to use the default one as indicated by the `EvalFile` UCI option. Guidelines for testing new nets can be found at https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests Integration has been discussed in various issues: https://github.com/official-stockfish/Stockfish/issues/2823 https://github.com/official-stockfish/Stockfish/issues/2728 The integration branch will be closed after the merge: https://github.com/official-stockfish/Stockfish/pull/2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes https://github.com/official-stockfish/Stockfish/pull/2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
2020-08-05 09:11:15 -06:00
Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
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
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,
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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.
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You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef MOVEPICK_H_INCLUDED
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#define MOVEPICK_H_INCLUDED
#include <array>
#include <limits>
#include <type_traits>
#include "movegen.h"
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#include "position.h"
#include "types.h"
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/// StatsEntry stores the stat table value. It is usually a number but could
/// be a move or even a nested history. We use a class instead of naked value
/// to directly call history update operator<<() on the entry so to use stats
/// tables at caller sites as simple multi-dim arrays.
template<typename T, int D>
class StatsEntry {
T entry;
public:
void operator=(const T& v) { entry = v; }
T* operator&() { return &entry; }
T* operator->() { return &entry; }
operator const T&() const { return entry; }
void operator<<(int bonus) {
assert(abs(bonus) <= D); // Ensure range is [-D, D]
static_assert(D <= std::numeric_limits<T>::max(), "D overflows T");
entry += bonus - entry * abs(bonus) / D;
assert(abs(entry) <= D);
}
};
/// Stats is a generic N-dimensional array used to store various statistics.
/// The first template parameter T is the base type of the array, the second
/// template parameter D limits the range of updates in [-D, D] when we update
/// values with the << operator, while the last parameters (Size and Sizes)
/// encode the dimensions of the array.
template <typename T, int D, int Size, int... Sizes>
struct Stats : public std::array<Stats<T, D, Sizes...>, Size>
{
typedef Stats<T, D, Size, Sizes...> stats;
void fill(const T& v) {
// For standard-layout 'this' points to first struct member
assert(std::is_standard_layout<stats>::value);
typedef StatsEntry<T, D> entry;
entry* p = reinterpret_cast<entry*>(this);
std::fill(p, p + sizeof(*this) / sizeof(entry), v);
}
};
template <typename T, int D, int Size>
struct Stats<T, D, Size> : public std::array<StatsEntry<T, D>, Size> {};
/// In stats table, D=0 means that the template parameter is not used
enum StatsParams { NOT_USED = 0 };
enum StatsType { NoCaptures, Captures };
/// ButterflyHistory records how often quiet moves have been successful or
/// unsuccessful during the current search, and is used for reduction and move
/// ordering decisions. It uses 2 tables (one for each color) indexed by
/// the move's from and to squares, see www.chessprogramming.org/Butterfly_Boards
typedef Stats<int16_t, 10692, COLOR_NB, int(SQUARE_NB) * int(SQUARE_NB)> ButterflyHistory;
/// At higher depths LowPlyHistory records successful quiet moves near the root
/// and quiet moves which are/were in the PV (ttPv). It is cleared with each new
/// search and filled during iterative deepening.
constexpr int MAX_LPH = 4;
typedef Stats<int16_t, 10692, MAX_LPH, int(SQUARE_NB) * int(SQUARE_NB)> LowPlyHistory;
/// CounterMoveHistory stores counter moves indexed by [piece][to] of the previous
/// move, see www.chessprogramming.org/Countermove_Heuristic
typedef Stats<Move, NOT_USED, PIECE_NB, SQUARE_NB> CounterMoveHistory;
/// CapturePieceToHistory is addressed by a move's [piece][to][captured piece type]
typedef Stats<int16_t, 10692, PIECE_NB, SQUARE_NB, PIECE_TYPE_NB> CapturePieceToHistory;
/// PieceToHistory is like ButterflyHistory but is addressed by a move's [piece][to]
typedef Stats<int16_t, 29952, PIECE_NB, SQUARE_NB> PieceToHistory;
/// ContinuationHistory is the combined history of a given pair of moves, usually
/// the current one given a previous one. The nested history table is based on
/// PieceToHistory instead of ButterflyBoards.
typedef Stats<PieceToHistory, NOT_USED, PIECE_NB, SQUARE_NB> ContinuationHistory;
/// MovePicker class is used to pick one pseudo legal move at a time from the
/// current position. The most important method is next_move(), which returns a
/// new pseudo legal move each time it is called, until there are no moves left,
/// when MOVE_NONE is returned. In order to improve the efficiency of the alpha
/// beta algorithm, MovePicker attempts to return the moves which are most likely
/// to get a cut-off first.
class MovePicker {
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enum PickType { Next, Best };
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public:
MovePicker(const MovePicker&) = delete;
MovePicker& operator=(const MovePicker&) = delete;
MovePicker(const Position&, Move, Value, const CapturePieceToHistory*);
MovePicker(const Position&, Move, Depth, const ButterflyHistory*,
const CapturePieceToHistory*,
const PieceToHistory**,
Square);
MovePicker(const Position&, Move, Depth, const ButterflyHistory*,
const LowPlyHistory*,
const CapturePieceToHistory*,
const PieceToHistory**,
Move,
const Move*,
int);
Move next_move(bool skipQuiets = false);
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private:
template<PickType T, typename Pred> Move select(Pred);
template<GenType> void score();
ExtMove* begin() { return cur; }
ExtMove* end() { return endMoves; }
const Position& pos;
const ButterflyHistory* mainHistory;
const LowPlyHistory* lowPlyHistory;
const CapturePieceToHistory* captureHistory;
const PieceToHistory** continuationHistory;
Move ttMove;
ExtMove refutations[3], *cur, *endMoves, *endBadCaptures;
int stage;
Square recaptureSquare;
Value threshold;
Depth depth;
int ply;
ExtMove moves[MAX_MOVES];
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};
#endif // #ifndef MOVEPICK_H_INCLUDED