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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
78 lines
2.4 KiB
C++
78 lines
2.4 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 TBPROBE_H
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#define TBPROBE_H
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#include <ostream>
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#include "../search.h"
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namespace Tablebases {
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enum WDLScore {
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WDLLoss = -2, // Loss
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WDLBlessedLoss = -1, // Loss, but draw under 50-move rule
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WDLDraw = 0, // Draw
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WDLCursedWin = 1, // Win, but draw under 50-move rule
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WDLWin = 2, // Win
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WDLScoreNone = -1000
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};
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// Possible states after a probing operation
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enum ProbeState {
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FAIL = 0, // Probe failed (missing file table)
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OK = 1, // Probe succesful
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CHANGE_STM = -1, // DTZ should check the other side
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ZEROING_BEST_MOVE = 2 // Best move zeroes DTZ (capture or pawn move)
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};
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extern int MaxCardinality;
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void init(const std::string& paths);
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WDLScore probe_wdl(Position& pos, ProbeState* result);
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int probe_dtz(Position& pos, ProbeState* result);
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bool root_probe(Position& pos, Search::RootMoves& rootMoves);
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bool root_probe_wdl(Position& pos, Search::RootMoves& rootMoves);
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void rank_root_moves(Position& pos, Search::RootMoves& rootMoves);
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inline std::ostream& operator<<(std::ostream& os, const WDLScore v) {
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os << (v == WDLLoss ? "Loss" :
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v == WDLBlessedLoss ? "Blessed loss" :
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v == WDLDraw ? "Draw" :
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v == WDLCursedWin ? "Cursed win" :
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v == WDLWin ? "Win" : "None");
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return os;
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}
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inline std::ostream& operator<<(std::ostream& os, const ProbeState v) {
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os << (v == FAIL ? "Failed" :
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v == OK ? "Success" :
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v == CHANGE_STM ? "Probed opponent side" :
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v == ZEROING_BEST_MOVE ? "Best move zeroes DTZ" : "None");
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return os;
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}
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}
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#endif
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