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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
98 lines
2.8 KiB
C++
98 lines
2.8 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 TT_H_INCLUDED
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#define TT_H_INCLUDED
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#include "misc.h"
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#include "types.h"
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/// TTEntry struct is the 10 bytes transposition table entry, defined as below:
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///
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/// key 16 bit
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/// move 16 bit
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/// value 16 bit
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/// eval value 16 bit
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/// generation 5 bit
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/// pv node 1 bit
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/// bound type 2 bit
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/// depth 8 bit
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struct TTEntry {
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Move move() const { return (Move )move16; }
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Value value() const { return (Value)value16; }
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Value eval() const { return (Value)eval16; }
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Depth depth() const { return (Depth)depth8 + DEPTH_OFFSET; }
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bool is_pv() const { return (bool)(genBound8 & 0x4); }
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Bound bound() const { return (Bound)(genBound8 & 0x3); }
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void save(Key k, Value v, bool pv, Bound b, Depth d, Move m, Value ev);
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private:
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friend class TranspositionTable;
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uint16_t key16;
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uint16_t move16;
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int16_t value16;
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int16_t eval16;
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uint8_t genBound8;
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uint8_t depth8;
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};
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/// A TranspositionTable is an array of Cluster, of size clusterCount. Each
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/// cluster consists of ClusterSize number of TTEntry. Each non-empty TTEntry
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/// contains information on exactly one position. The size of a Cluster should
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/// divide the size of a cache line for best performance, as the cacheline is
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/// prefetched when possible.
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class TranspositionTable {
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static constexpr int ClusterSize = 3;
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struct Cluster {
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TTEntry entry[ClusterSize];
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char padding[2]; // Pad to 32 bytes
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};
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static_assert(sizeof(Cluster) == 32, "Unexpected Cluster size");
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public:
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~TranspositionTable() { aligned_ttmem_free(mem); }
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void new_search() { generation8 += 8; } // Lower 3 bits are used by PV flag and Bound
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TTEntry* probe(const Key key, bool& found) const;
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int hashfull() const;
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void resize(size_t mbSize);
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void clear();
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TTEntry* first_entry(const Key key) const {
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return &table[mul_hi64(key, clusterCount)].entry[0];
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}
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private:
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friend struct TTEntry;
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size_t clusterCount;
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Cluster* table;
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void* mem;
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uint8_t generation8; // Size must be not bigger than TTEntry::genBound8
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};
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extern TranspositionTable TT;
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#endif // #ifndef TT_H_INCLUDED
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