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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
123 lines
3.3 KiB
C++
123 lines
3.3 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2020 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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#ifndef ENDGAME_H_INCLUDED
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#define ENDGAME_H_INCLUDED
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#include <memory>
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#include <string>
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#include <type_traits>
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#include <unordered_map>
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#include <utility>
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#include "position.h"
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#include "types.h"
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/// EndgameCode lists all supported endgame functions by corresponding codes
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enum EndgameCode {
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EVALUATION_FUNCTIONS,
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KNNK, // KNN vs K
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KNNKP, // KNN vs KP
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KXK, // Generic "mate lone king" eval
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KBNK, // KBN vs K
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KPK, // KP vs K
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KRKP, // KR vs KP
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KRKB, // KR vs KB
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KRKN, // KR vs KN
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KQKP, // KQ vs KP
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KQKR, // KQ vs KR
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SCALING_FUNCTIONS,
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KBPsK, // KB and pawns vs K
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KQKRPs, // KQ vs KR and pawns
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KRPKR, // KRP vs KR
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KRPKB, // KRP vs KB
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KRPPKRP, // KRPP vs KRP
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KPsK, // K and pawns vs K
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KBPKB, // KBP vs KB
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KBPPKB, // KBPP vs KB
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KBPKN, // KBP vs KN
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KPKP // KP vs KP
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};
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/// Endgame functions can be of two types depending on whether they return a
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/// Value or a ScaleFactor.
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template<EndgameCode E> using
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eg_type = typename std::conditional<(E < SCALING_FUNCTIONS), Value, ScaleFactor>::type;
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/// Base and derived functors for endgame evaluation and scaling functions
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template<typename T>
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struct EndgameBase {
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explicit EndgameBase(Color c) : strongSide(c), weakSide(~c) {}
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virtual ~EndgameBase() = default;
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virtual T operator()(const Position&) const = 0;
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const Color strongSide, weakSide;
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};
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template<EndgameCode E, typename T = eg_type<E>>
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struct Endgame : public EndgameBase<T> {
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explicit Endgame(Color c) : EndgameBase<T>(c) {}
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T operator()(const Position&) const override;
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};
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/// The Endgames namespace handles the pointers to endgame evaluation and scaling
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/// base objects in two std::map. We use polymorphism to invoke the actual
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/// endgame function by calling its virtual operator().
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namespace Endgames {
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template<typename T> using Ptr = std::unique_ptr<EndgameBase<T>>;
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template<typename T> using Map = std::unordered_map<Key, Ptr<T>>;
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extern std::pair<Map<Value>, Map<ScaleFactor>> maps;
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void init();
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template<typename T>
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Map<T>& map() {
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return std::get<std::is_same<T, ScaleFactor>::value>(maps);
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}
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template<EndgameCode E, typename T = eg_type<E>>
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void add(const std::string& code) {
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StateInfo st;
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map<T>()[Position().set(code, WHITE, &st).material_key()] = Ptr<T>(new Endgame<E>(WHITE));
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map<T>()[Position().set(code, BLACK, &st).material_key()] = Ptr<T>(new Endgame<E>(BLACK));
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}
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template<typename T>
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const EndgameBase<T>* probe(Key key) {
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auto it = map<T>().find(key);
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return it != map<T>().end() ? it->second.get() : nullptr;
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}
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}
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#endif // #ifndef ENDGAME_H_INCLUDED
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