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/*
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2008-10-19 10:56:28 -06:00
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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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
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Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
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2008-10-19 10:56:28 -06:00
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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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2008-10-19 10:56:28 -06:00
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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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2009-01-07 06:28:04 -07:00
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2008-08-31 23:59:13 -06:00
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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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2013-07-23 07:31:57 -06:00
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#ifndef MATERIAL_H_INCLUDED
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#define MATERIAL_H_INCLUDED
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#include "endgame.h"
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#include "misc.h"
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#include "position.h"
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#include "types.h"
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namespace Material {
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/// Material::Entry contains various information about a material configuration.
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/// It contains a material imbalance evaluation, a function pointer to a special
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/// endgame evaluation function (which in most cases is NULL, meaning that the
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/// standard evaluation function will be used), and scale factors.
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///
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/// The scale factors are used to scale the evaluation score up or down. For
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/// instance, in KRB vs KR endgames, the score is scaled down by a factor of 4,
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/// which will result in scores of absolute value less than one pawn.
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struct Entry {
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Score imbalance() const { return make_score(value, value); }
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Phase game_phase() const { return gamePhase; }
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bool specialized_eval_exists() const { return evaluationFunction != nullptr; }
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Value evaluate(const Position& pos) const { return (*evaluationFunction)(pos); }
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// scale_factor() takes a position and a color as input and returns a scale factor
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// for the given color. We have to provide the position in addition to the color
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// because the scale factor may also be a function which should be applied to
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// the position. For instance, in KBP vs K endgames, the scaling function looks
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// for rook pawns and wrong-colored bishops.
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ScaleFactor scale_factor(const Position& pos, Color c) const {
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ScaleFactor sf = scalingFunction[c] ? (*scalingFunction[c])(pos)
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: SCALE_FACTOR_NONE;
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return sf != SCALE_FACTOR_NONE ? sf : ScaleFactor(factor[c]);
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}
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Key key;
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const EndgameBase<Value>* evaluationFunction;
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const EndgameBase<ScaleFactor>* scalingFunction[COLOR_NB]; // Could be one for each
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// side (e.g. KPKP, KBPsK)
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int16_t value;
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uint8_t factor[COLOR_NB];
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Phase gamePhase;
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};
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typedef HashTable<Entry, 8192> Table;
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Entry* probe(const Position& pos);
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2014-04-27 01:31:22 -06:00
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} // namespace Material
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#endif // #ifndef MATERIAL_H_INCLUDED
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