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/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 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
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 TYPES_H_INCLUDED
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#define TYPES_H_INCLUDED
/// When compiling with provided Makefile (e.g. for Linux and OSX), configuration
/// is done automatically. To get started type 'make help'.
///
/// When Makefile is not used (e.g. with Microsoft Visual Studio) some switches
/// need to be set manually:
///
/// -DNDEBUG | Disable debugging mode. Always use this for release.
///
/// -DNO_PREFETCH | Disable use of prefetch asm-instruction. You may need this to
/// | run on some very old machines.
///
/// -DUSE_POPCNT | Add runtime support for use of popcnt asm-instruction. Works
/// | only in 64-bit mode and requires hardware with popcnt support.
///
/// -DUSE_PEXT | Add runtime support for use of pext asm-instruction. Works
/// | only in 64-bit mode and requires hardware with pext support.
#include <cassert>
#include <cctype>
#include <cstdint>
#include <cstdlib>
#include <algorithm>
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#if defined(_MSC_VER)
// Disable some silly and noisy warning from MSVC compiler
#pragma warning(disable: 4127) // Conditional expression is constant
#pragma warning(disable: 4146) // Unary minus operator applied to unsigned type
#pragma warning(disable: 4800) // Forcing value to bool 'true' or 'false'
#endif
/// Predefined macros hell:
///
/// __GNUC__ Compiler is gcc, Clang or Intel on Linux
/// __INTEL_COMPILER Compiler is Intel
/// _MSC_VER Compiler is MSVC or Intel on Windows
/// _WIN32 Building on Windows (any)
/// _WIN64 Building on Windows 64 bit
#if defined(__GNUC__ ) && (__GNUC__ < 9 || (__GNUC__ == 9 && __GNUC_MINOR__ <= 2)) && defined(_WIN32) && !defined(__clang__)
#define ALIGNAS_ON_STACK_VARIABLES_BROKEN
#endif
#define ASSERT_ALIGNED(ptr, alignment) assert(reinterpret_cast<uintptr_t>(ptr) % alignment == 0)
#if defined(_WIN64) && defined(_MSC_VER) // No Makefile used
# include <intrin.h> // Microsoft header for _BitScanForward64()
# define IS_64BIT
#endif
#if defined(USE_POPCNT) && (defined(__INTEL_COMPILER) || defined(_MSC_VER))
# include <nmmintrin.h> // Intel and Microsoft header for _mm_popcnt_u64()
#endif
#if !defined(NO_PREFETCH) && (defined(__INTEL_COMPILER) || defined(_MSC_VER))
# include <xmmintrin.h> // Intel and Microsoft header for _mm_prefetch()
#endif
#if defined(USE_PEXT)
# include <immintrin.h> // Header for _pext_u64() intrinsic
# define pext(b, m) _pext_u64(b, m)
#else
# define pext(b, m) 0
#endif
namespace Stockfish {
#ifdef USE_POPCNT
constexpr bool HasPopCnt = true;
#else
constexpr bool HasPopCnt = false;
#endif
#ifdef USE_PEXT
constexpr bool HasPext = true;
#else
constexpr bool HasPext = false;
#endif
#ifdef IS_64BIT
constexpr bool Is64Bit = true;
#else
constexpr bool Is64Bit = false;
#endif
typedef uint64_t Key;
typedef uint64_t Bitboard;
constexpr int MAX_MOVES = 256;
constexpr int MAX_PLY = 246;
/// A move needs 16 bits to be stored
///
/// bit 0- 5: destination square (from 0 to 63)
/// bit 6-11: origin square (from 0 to 63)
/// bit 12-13: promotion piece type - 2 (from KNIGHT-2 to QUEEN-2)
/// bit 14-15: special move flag: promotion (1), en passant (2), castling (3)
/// NOTE: en passant bit is set only when a pawn can be captured
///
/// Special cases are MOVE_NONE and MOVE_NULL. We can sneak these in because in
/// any normal move destination square is always different from origin square
/// while MOVE_NONE and MOVE_NULL have the same origin and destination square.
enum Move : int {
MOVE_NONE,
MOVE_NULL = 65
};
enum MoveType {
NORMAL,
PROMOTION = 1 << 14,
EN_PASSANT = 2 << 14,
CASTLING = 3 << 14
};
enum Color {
WHITE, BLACK, COLOR_NB = 2
};
enum CastlingRights {
NO_CASTLING,
WHITE_OO,
WHITE_OOO = WHITE_OO << 1,
BLACK_OO = WHITE_OO << 2,
BLACK_OOO = WHITE_OO << 3,
KING_SIDE = WHITE_OO | BLACK_OO,
QUEEN_SIDE = WHITE_OOO | BLACK_OOO,
WHITE_CASTLING = WHITE_OO | WHITE_OOO,
BLACK_CASTLING = BLACK_OO | BLACK_OOO,
ANY_CASTLING = WHITE_CASTLING | BLACK_CASTLING,
CASTLING_RIGHT_NB = 16
};
enum Phase {
PHASE_ENDGAME,
PHASE_MIDGAME = 128,
MG = 0, EG = 1, PHASE_NB = 2
};
enum ScaleFactor {
SCALE_FACTOR_DRAW = 0,
SCALE_FACTOR_NORMAL = 64,
SCALE_FACTOR_MAX = 128,
SCALE_FACTOR_NONE = 255
};
enum Bound {
BOUND_NONE,
BOUND_UPPER,
BOUND_LOWER,
BOUND_EXACT = BOUND_UPPER | BOUND_LOWER
};
Detect search explosions This patch detects some search explosions (due to double extensions in search.cpp) which can happen in some pathological positions, and takes measures to ensure progress in search even for these pathological situations. While a small number of double extensions can be useful during search (for example to resolve a tactical sequence), a sustained regime of double extensions leads to search explosion and a non-finishing search. See the discussion in https://github.com/official-stockfish/Stockfish/pull/3544 and the issue https://github.com/official-stockfish/Stockfish/issues/3532 . The implemented algorithm is the following: a) at each node during search, store the current depth in the stack. Double extensions are by definition levels of the stack where the depth at ply N is strictly higher than depth at ply N-1. b) during search, calculate for each thread a running average of the number of double extensions in the last 4096 visited nodes. c) if one thread has more than 2% of double extensions for a sustained period of time (6 millions consecutive nodes, or about 4 seconds on my iMac), we decide that this thread is in an explosion state and we calm down this thread by preventing it to do any double extension for the next 6 millions nodes. To calculate the running averages, we also introduced a auxiliary class generalizing the computations of ttHitAverage variable we already had in code. The implementation uses an exponential moving average of period 4096 and resolution 1/1024, and all computations are done with integers for efficiency. ----------- Example where the patch solves a search explosion: ``` ./stockfish ucinewgame position fen 8/Pk6/8/1p6/8/P1K5/8/6B1 w - - 37 130 go infinite ``` This algorithm does not affect search in normal, non-pathological positions. We verified, for instance, that the usual bench is unchanged up to depth 20 at least, and that the node numbers are unchanged for a search of the starting position at depth 32. ------------- See https://github.com/official-stockfish/Stockfish/pull/3714 Bench: 5575265
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enum ExplosionState {
EXPLOSION_NONE,
MUST_CALM_DOWN
};
enum Value : int {
VALUE_ZERO = 0,
VALUE_DRAW = 0,
VALUE_KNOWN_WIN = 10000,
VALUE_MATE = 32000,
VALUE_INFINITE = 32001,
VALUE_NONE = 32002,
Fix for incorrect VALUE_MATE_IN_MAX_PLY usage. Fixes #2533, fixes #2543, fixes #2423. the code that prevents false mate announcements depending on the TT state (GHI), incorrectly used VALUE_MATE_IN_MAX_PLY. The latter constant, however, also includes, counterintuitively, the TB win range. This patch fixes that, by restoring the behavior for TB win scores, while retaining the false mate correctness, and improving the mate finding ability. In particular no alse mates are announced with the poisened hash testcase ``` position fen 8/8/8/3k4/8/8/6K1/7R w - - 0 1 go depth 40 position fen 8/8/8/3k4/8/8/6K1/7R w - - 76 1 go depth 20 ucinewgame ``` mates are found with the testcases reported in #2543 ``` position fen 4k3/3pp3/8/8/8/8/2PPP3/4K3 w - - 0 1 setoption name Hash value 1024 go depth 55 ucinewgame ``` and ``` position fen 4k3/4p3/8/8/8/8/3PP3/4K3 w - - 0 1 setoption name Hash value 1024 go depth 45 ucinewgame ``` furthermore, on the mate finding benchmark (ChestUCI_23102018.epd), performance improves over master, roughly reaching performance with the false mate protection reverted ``` Analyzing 6566 mate positions for best and found mates: ----------------best ---------------found nodes master revert fixed master revert fixed 16000000 4233 4236 4235 5200 5201 5199 32000000 4583 4585 4585 5417 5424 5418 64000000 4852 4853 4855 5575 5584 5579 128000000 5071 5068 5066 5710 5720 5716 256000000 5280 5282 5279 5819 5827 5826 512000000 5471 5468 5468 5919 5935 5932 ``` On a testcase with TB enabled, progress is made consistently, contrary to master ``` setoption name SyzygyPath value ../../../syzygy/3-4-5/ setoption name Hash value 2048 position fen 1R6/3k4/8/K2p4/4n3/2P5/8/8 w - - 0 1 go depth 58 ucinewgame ``` The PR (prior to a rewrite for clarity) passed STC: LLR: 2.94 (-2.94,2.94) {-1.50,0.50} Total: 65405 W: 12454 L: 12384 D: 40567 Ptnml(0-2): 920, 7256, 16285, 7286, 944 http://tests.stockfishchess.org/tests/view/5e441a3be70d848499f63d15 passed LTC: LLR: 2.94 (-2.94,2.94) {-1.50,0.50} Total: 27096 W: 3477 L: 3413 D: 20206 Ptnml(0-2): 128, 2215, 8776, 2292, 122 http://tests.stockfishchess.org/tests/view/5e44e277e70d848499f63d63 The incorrectly named VALUE_MATE_IN_MAX_PLY and VALUE_MATED_IN_MAX_PLY were renamed into VALUE_TB_WIN_IN_MAX_PLY and VALUE_TB_LOSS_IN_MAX_PLY, and correclty defined VALUE_MATE_IN_MAX_PLY and VALUE_MATED_IN_MAX_PLY were introduced. One further (corner case) mistake using these constants was fixed (go mate X), which could lead to a premature return if X > MAX_PLY / 2, but TB were present. Thanks to @svivanov72 for one of the reports and help fixing the issue. closes https://github.com/official-stockfish/Stockfish/pull/2552 Bench: 4932981
2020-02-11 12:42:32 -07:00
VALUE_TB_WIN_IN_MAX_PLY = VALUE_MATE - 2 * MAX_PLY,
VALUE_TB_LOSS_IN_MAX_PLY = -VALUE_TB_WIN_IN_MAX_PLY,
Fix for incorrect VALUE_MATE_IN_MAX_PLY usage. Fixes #2533, fixes #2543, fixes #2423. the code that prevents false mate announcements depending on the TT state (GHI), incorrectly used VALUE_MATE_IN_MAX_PLY. The latter constant, however, also includes, counterintuitively, the TB win range. This patch fixes that, by restoring the behavior for TB win scores, while retaining the false mate correctness, and improving the mate finding ability. In particular no alse mates are announced with the poisened hash testcase ``` position fen 8/8/8/3k4/8/8/6K1/7R w - - 0 1 go depth 40 position fen 8/8/8/3k4/8/8/6K1/7R w - - 76 1 go depth 20 ucinewgame ``` mates are found with the testcases reported in #2543 ``` position fen 4k3/3pp3/8/8/8/8/2PPP3/4K3 w - - 0 1 setoption name Hash value 1024 go depth 55 ucinewgame ``` and ``` position fen 4k3/4p3/8/8/8/8/3PP3/4K3 w - - 0 1 setoption name Hash value 1024 go depth 45 ucinewgame ``` furthermore, on the mate finding benchmark (ChestUCI_23102018.epd), performance improves over master, roughly reaching performance with the false mate protection reverted ``` Analyzing 6566 mate positions for best and found mates: ----------------best ---------------found nodes master revert fixed master revert fixed 16000000 4233 4236 4235 5200 5201 5199 32000000 4583 4585 4585 5417 5424 5418 64000000 4852 4853 4855 5575 5584 5579 128000000 5071 5068 5066 5710 5720 5716 256000000 5280 5282 5279 5819 5827 5826 512000000 5471 5468 5468 5919 5935 5932 ``` On a testcase with TB enabled, progress is made consistently, contrary to master ``` setoption name SyzygyPath value ../../../syzygy/3-4-5/ setoption name Hash value 2048 position fen 1R6/3k4/8/K2p4/4n3/2P5/8/8 w - - 0 1 go depth 58 ucinewgame ``` The PR (prior to a rewrite for clarity) passed STC: LLR: 2.94 (-2.94,2.94) {-1.50,0.50} Total: 65405 W: 12454 L: 12384 D: 40567 Ptnml(0-2): 920, 7256, 16285, 7286, 944 http://tests.stockfishchess.org/tests/view/5e441a3be70d848499f63d15 passed LTC: LLR: 2.94 (-2.94,2.94) {-1.50,0.50} Total: 27096 W: 3477 L: 3413 D: 20206 Ptnml(0-2): 128, 2215, 8776, 2292, 122 http://tests.stockfishchess.org/tests/view/5e44e277e70d848499f63d63 The incorrectly named VALUE_MATE_IN_MAX_PLY and VALUE_MATED_IN_MAX_PLY were renamed into VALUE_TB_WIN_IN_MAX_PLY and VALUE_TB_LOSS_IN_MAX_PLY, and correclty defined VALUE_MATE_IN_MAX_PLY and VALUE_MATED_IN_MAX_PLY were introduced. One further (corner case) mistake using these constants was fixed (go mate X), which could lead to a premature return if X > MAX_PLY / 2, but TB were present. Thanks to @svivanov72 for one of the reports and help fixing the issue. closes https://github.com/official-stockfish/Stockfish/pull/2552 Bench: 4932981
2020-02-11 12:42:32 -07:00
VALUE_MATE_IN_MAX_PLY = VALUE_MATE - MAX_PLY,
VALUE_MATED_IN_MAX_PLY = -VALUE_MATE_IN_MAX_PLY,
PawnValueMg = 126, PawnValueEg = 208,
KnightValueMg = 781, KnightValueEg = 854,
BishopValueMg = 825, BishopValueEg = 915,
RookValueMg = 1276, RookValueEg = 1380,
QueenValueMg = 2538, QueenValueEg = 2682,
MidgameLimit = 15258, EndgameLimit = 3915
};
enum PieceType {
NO_PIECE_TYPE, PAWN, KNIGHT, BISHOP, ROOK, QUEEN, KING,
ALL_PIECES = 0,
PIECE_TYPE_NB = 8
};
enum Piece {
NO_PIECE,
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W_PAWN = PAWN, W_KNIGHT, W_BISHOP, W_ROOK, W_QUEEN, W_KING,
B_PAWN = PAWN + 8, B_KNIGHT, B_BISHOP, B_ROOK, B_QUEEN, B_KING,
PIECE_NB = 16
};
constexpr Value PieceValue[PHASE_NB][PIECE_NB] = {
{ VALUE_ZERO, PawnValueMg, KnightValueMg, BishopValueMg, RookValueMg, QueenValueMg, VALUE_ZERO, VALUE_ZERO,
VALUE_ZERO, PawnValueMg, KnightValueMg, BishopValueMg, RookValueMg, QueenValueMg, VALUE_ZERO, VALUE_ZERO },
{ VALUE_ZERO, PawnValueEg, KnightValueEg, BishopValueEg, RookValueEg, QueenValueEg, VALUE_ZERO, VALUE_ZERO,
VALUE_ZERO, PawnValueEg, KnightValueEg, BishopValueEg, RookValueEg, QueenValueEg, VALUE_ZERO, VALUE_ZERO }
};
Eliminate ONE_PLY Simplification that eliminates ONE_PLY, based on a suggestion in the forum that support for fractional plies has never been used, and @mcostalba's openness to the idea of eliminating it. We lose a little bit of type safety by making Depth an integer, but in return we simplify the code in search.cpp quite significantly. No functional change ------------------------------------------ The argument favoring eliminating ONE_PLY: * The term “ONE_PLY” comes up in a lot of forum posts (474 to date) https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:relevance * There is occasionally a commit that breaks invariance of the code with respect to ONE_PLY https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:date/fishcooking/ZIPdYj6k0fk/KdNGcPWeBgAJ * To prevent such commits, there is a Travis CI hack that doubles ONE_PLY and rechecks bench * Sustaining ONE_PLY has, alas, not resulted in any improvements to the engine, despite many individuals testing many experiments over 5 years. The strongest argument in favor of preserving ONE_PLY comes from @locutus: “If we use par example ONE_PLY=256 the parameter space is increases by the factor 256. So it seems very unlikely that the optimal setting is in the subspace of ONE_PLY=1.” There is a strong theoretical impediment to fractional depth systems: the transposition table uses depth to determine when a stored result is good enough to supply an answer for a current search. If you have fractional depths, then different pathways to the position can be at fractionally different depths. In the end, there are three separate times when a proposal to remove ONE_PLY was defeated by the suggestion to “give it a few more months.” So… it seems like time to remove this distraction from the community. See the pull request here: https://github.com/official-stockfish/Stockfish/pull/2289
2019-09-28 14:27:23 -06:00
typedef int Depth;
Eliminate ONE_PLY Simplification that eliminates ONE_PLY, based on a suggestion in the forum that support for fractional plies has never been used, and @mcostalba's openness to the idea of eliminating it. We lose a little bit of type safety by making Depth an integer, but in return we simplify the code in search.cpp quite significantly. No functional change ------------------------------------------ The argument favoring eliminating ONE_PLY: * The term “ONE_PLY” comes up in a lot of forum posts (474 to date) https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:relevance * There is occasionally a commit that breaks invariance of the code with respect to ONE_PLY https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:date/fishcooking/ZIPdYj6k0fk/KdNGcPWeBgAJ * To prevent such commits, there is a Travis CI hack that doubles ONE_PLY and rechecks bench * Sustaining ONE_PLY has, alas, not resulted in any improvements to the engine, despite many individuals testing many experiments over 5 years. The strongest argument in favor of preserving ONE_PLY comes from @locutus: “If we use par example ONE_PLY=256 the parameter space is increases by the factor 256. So it seems very unlikely that the optimal setting is in the subspace of ONE_PLY=1.” There is a strong theoretical impediment to fractional depth systems: the transposition table uses depth to determine when a stored result is good enough to supply an answer for a current search. If you have fractional depths, then different pathways to the position can be at fractionally different depths. In the end, there are three separate times when a proposal to remove ONE_PLY was defeated by the suggestion to “give it a few more months.” So… it seems like time to remove this distraction from the community. See the pull request here: https://github.com/official-stockfish/Stockfish/pull/2289
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enum : int {
DEPTH_QS_CHECKS = 0,
DEPTH_QS_NO_CHECKS = -1,
DEPTH_QS_RECAPTURES = -5,
Eliminate ONE_PLY Simplification that eliminates ONE_PLY, based on a suggestion in the forum that support for fractional plies has never been used, and @mcostalba's openness to the idea of eliminating it. We lose a little bit of type safety by making Depth an integer, but in return we simplify the code in search.cpp quite significantly. No functional change ------------------------------------------ The argument favoring eliminating ONE_PLY: * The term “ONE_PLY” comes up in a lot of forum posts (474 to date) https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:relevance * There is occasionally a commit that breaks invariance of the code with respect to ONE_PLY https://groups.google.com/forum/?fromgroups=#!searchin/fishcooking/ONE_PLY%7Csort:date/fishcooking/ZIPdYj6k0fk/KdNGcPWeBgAJ * To prevent such commits, there is a Travis CI hack that doubles ONE_PLY and rechecks bench * Sustaining ONE_PLY has, alas, not resulted in any improvements to the engine, despite many individuals testing many experiments over 5 years. The strongest argument in favor of preserving ONE_PLY comes from @locutus: “If we use par example ONE_PLY=256 the parameter space is increases by the factor 256. So it seems very unlikely that the optimal setting is in the subspace of ONE_PLY=1.” There is a strong theoretical impediment to fractional depth systems: the transposition table uses depth to determine when a stored result is good enough to supply an answer for a current search. If you have fractional depths, then different pathways to the position can be at fractionally different depths. In the end, there are three separate times when a proposal to remove ONE_PLY was defeated by the suggestion to “give it a few more months.” So… it seems like time to remove this distraction from the community. See the pull request here: https://github.com/official-stockfish/Stockfish/pull/2289
2019-09-28 14:27:23 -06:00
DEPTH_NONE = -6,
Allow TT entries with key16==0 to be fetched Fix the issue where a TT entry with key16==0 would always be reported as a miss. Instead, we'll use depth8 to detect whether the TT entry is occupied. In order to do that, we'll change DEPTH_OFFSET to -7 (depth8==0) to distinguish between an unoccupied entry and the otherwise lowest possible depth, i.e., DEPTH_NONE (depth8==1). To prevent a performance regression, we'll reorder the TT entry fields by the access order of TranspositionTable::probe(). Memory in general works fastest when accessed in sequential order. We'll also match the store order in TTEntry::save() with the entry field order, and re-order the 'if-or' expressions in TTEntry::save() from the cheapest to the most expensive. Finally, as we now have a proper TT entry occupancy test, we'll fix a minor corner case with hashfull reporting. To reproduce: - Use a big hash - Either: a. Start 31 very quick searches (this wraparounds generation to 0); or b. Force generation of the first search to 0. - go depth infinite Before the fix, hashfull would incorrectly report nearly full hash immediately after the search start, since TranspositionTable::hashfull() used to consider only the entry generation and not whether the entry was actually occupied. STC: LLR: 2.95 (-2.94,2.94) {-0.25,1.25} Total: 36848 W: 4091 L: 3898 D: 28859 Ptnml(0-2): 158, 2996, 11972, 3091, 207 https://tests.stockfishchess.org/tests/view/5f3f98d5dc02a01a0c2881f7 LTC: LLR: 2.95 (-2.94,2.94) {0.25,1.25} Total: 32280 W: 1828 L: 1653 D: 28799 Ptnml(0-2): 34, 1428, 13051, 1583, 44 https://tests.stockfishchess.org/tests/view/5f3fe77a87a5c3c63d8f5332 closes https://github.com/official-stockfish/Stockfish/pull/3048 Bench: 3760677
2020-08-21 03:12:39 -06:00
DEPTH_OFFSET = -7 // value used only for TT entry occupancy check
};
enum Square : int {
SQ_A1, SQ_B1, SQ_C1, SQ_D1, SQ_E1, SQ_F1, SQ_G1, SQ_H1,
SQ_A2, SQ_B2, SQ_C2, SQ_D2, SQ_E2, SQ_F2, SQ_G2, SQ_H2,
SQ_A3, SQ_B3, SQ_C3, SQ_D3, SQ_E3, SQ_F3, SQ_G3, SQ_H3,
SQ_A4, SQ_B4, SQ_C4, SQ_D4, SQ_E4, SQ_F4, SQ_G4, SQ_H4,
SQ_A5, SQ_B5, SQ_C5, SQ_D5, SQ_E5, SQ_F5, SQ_G5, SQ_H5,
SQ_A6, SQ_B6, SQ_C6, SQ_D6, SQ_E6, SQ_F6, SQ_G6, SQ_H6,
SQ_A7, SQ_B7, SQ_C7, SQ_D7, SQ_E7, SQ_F7, SQ_G7, SQ_H7,
SQ_A8, SQ_B8, SQ_C8, SQ_D8, SQ_E8, SQ_F8, SQ_G8, SQ_H8,
SQ_NONE,
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
SQUARE_ZERO = 0,
SQUARE_NB = 64
};
enum Direction : int {
NORTH = 8,
EAST = 1,
SOUTH = -NORTH,
WEST = -EAST,
NORTH_EAST = NORTH + EAST,
SOUTH_EAST = SOUTH + EAST,
SOUTH_WEST = SOUTH + WEST,
NORTH_WEST = NORTH + WEST
};
enum File : int {
FILE_A, FILE_B, FILE_C, FILE_D, FILE_E, FILE_F, FILE_G, FILE_H, FILE_NB
};
enum Rank : int {
RANK_1, RANK_2, RANK_3, RANK_4, RANK_5, RANK_6, RANK_7, RANK_8, RANK_NB
};
Remove EvalList This patch removes the EvalList structure from the Position object and generally simplifies the interface between do_move() and the NNUE code. The NNUE evaluation function first calculates the "accumulator". The accumulator consists of two halves: one for white's perspective, one for black's perspective. If the "friendly king" has moved or the accumulator for the parent position is not available, the accumulator for this half has to be calculated from scratch. To do this, the NNUE node needs to know the positions and types of all non-king pieces and the position of the friendly king. This information can easily be obtained from the Position object. If the "friendly king" has not moved, its half of the accumulator can be calculated by incrementally updating the accumulator for the previous position. For this, the NNUE code needs to know which pieces have been added to which squares and which pieces have been removed from which squares. In principle this information can be derived from the Position object and StateInfo struct (in the same way as undo_move() does this). However, it is probably a bit faster to prepare this information in do_move(), so I have kept the DirtyPiece struct. Since the DirtyPiece struct now stores the squares rather than "PieceSquare" indices, there are now at most three "dirty pieces" (previously two). A promotion move that captures a piece removes the capturing pawn and the captured piece from the board (to SQ_NONE) and moves the promoted piece to the promotion square (from SQ_NONE). An STC test has confirmed a small speedup: https://tests.stockfishchess.org/tests/view/5f43f06b5089a564a10d850a LLR: 2.94 (-2.94,2.94) {-0.25,1.25} Total: 87704 W: 9763 L: 9500 D: 68441 Ptnml(0-2): 426, 6950, 28845, 7197, 434 closes https://github.com/official-stockfish/Stockfish/pull/3068 No functional change
2020-08-23 18:29:38 -06:00
// Keep track of what a move changes on the board (used by NNUE)
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
struct DirtyPiece {
// Number of changed pieces
int dirty_num;
Remove EvalList This patch removes the EvalList structure from the Position object and generally simplifies the interface between do_move() and the NNUE code. The NNUE evaluation function first calculates the "accumulator". The accumulator consists of two halves: one for white's perspective, one for black's perspective. If the "friendly king" has moved or the accumulator for the parent position is not available, the accumulator for this half has to be calculated from scratch. To do this, the NNUE node needs to know the positions and types of all non-king pieces and the position of the friendly king. This information can easily be obtained from the Position object. If the "friendly king" has not moved, its half of the accumulator can be calculated by incrementally updating the accumulator for the previous position. For this, the NNUE code needs to know which pieces have been added to which squares and which pieces have been removed from which squares. In principle this information can be derived from the Position object and StateInfo struct (in the same way as undo_move() does this). However, it is probably a bit faster to prepare this information in do_move(), so I have kept the DirtyPiece struct. Since the DirtyPiece struct now stores the squares rather than "PieceSquare" indices, there are now at most three "dirty pieces" (previously two). A promotion move that captures a piece removes the capturing pawn and the captured piece from the board (to SQ_NONE) and moves the promoted piece to the promotion square (from SQ_NONE). An STC test has confirmed a small speedup: https://tests.stockfishchess.org/tests/view/5f43f06b5089a564a10d850a LLR: 2.94 (-2.94,2.94) {-0.25,1.25} Total: 87704 W: 9763 L: 9500 D: 68441 Ptnml(0-2): 426, 6950, 28845, 7197, 434 closes https://github.com/official-stockfish/Stockfish/pull/3068 No functional change
2020-08-23 18:29:38 -06:00
// Max 3 pieces can change in one move. A promotion with capture moves
// both the pawn and the captured piece to SQ_NONE and the piece promoted
// to from SQ_NONE to the capture square.
Piece piece[3];
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
Remove EvalList This patch removes the EvalList structure from the Position object and generally simplifies the interface between do_move() and the NNUE code. The NNUE evaluation function first calculates the "accumulator". The accumulator consists of two halves: one for white's perspective, one for black's perspective. If the "friendly king" has moved or the accumulator for the parent position is not available, the accumulator for this half has to be calculated from scratch. To do this, the NNUE node needs to know the positions and types of all non-king pieces and the position of the friendly king. This information can easily be obtained from the Position object. If the "friendly king" has not moved, its half of the accumulator can be calculated by incrementally updating the accumulator for the previous position. For this, the NNUE code needs to know which pieces have been added to which squares and which pieces have been removed from which squares. In principle this information can be derived from the Position object and StateInfo struct (in the same way as undo_move() does this). However, it is probably a bit faster to prepare this information in do_move(), so I have kept the DirtyPiece struct. Since the DirtyPiece struct now stores the squares rather than "PieceSquare" indices, there are now at most three "dirty pieces" (previously two). A promotion move that captures a piece removes the capturing pawn and the captured piece from the board (to SQ_NONE) and moves the promoted piece to the promotion square (from SQ_NONE). An STC test has confirmed a small speedup: https://tests.stockfishchess.org/tests/view/5f43f06b5089a564a10d850a LLR: 2.94 (-2.94,2.94) {-0.25,1.25} Total: 87704 W: 9763 L: 9500 D: 68441 Ptnml(0-2): 426, 6950, 28845, 7197, 434 closes https://github.com/official-stockfish/Stockfish/pull/3068 No functional change
2020-08-23 18:29:38 -06:00
// From and to squares, which may be SQ_NONE
Square from[3];
Square to[3];
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
};
/// Score enum stores a middlegame and an endgame value in a single integer (enum).
/// The least significant 16 bits are used to store the middlegame value and the
/// upper 16 bits are used to store the endgame value. We have to take care to
/// avoid left-shifting a signed int to avoid undefined behavior.
enum Score : int { SCORE_ZERO };
constexpr Score make_score(int mg, int eg) {
return Score((int)((unsigned int)eg << 16) + mg);
}
/// Extracting the signed lower and upper 16 bits is not so trivial because
/// according to the standard a simple cast to short is implementation defined
/// and so is a right shift of a signed integer.
inline Value eg_value(Score s) {
union { uint16_t u; int16_t s; } eg = { uint16_t(unsigned(s + 0x8000) >> 16) };
return Value(eg.s);
}
inline Value mg_value(Score s) {
union { uint16_t u; int16_t s; } mg = { uint16_t(unsigned(s)) };
return Value(mg.s);
}
#define ENABLE_BASE_OPERATORS_ON(T) \
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
constexpr T operator+(T d1, int d2) { return T(int(d1) + d2); } \
constexpr T operator-(T d1, int d2) { return T(int(d1) - d2); } \
constexpr T operator-(T d) { return T(-int(d)); } \
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
inline T& operator+=(T& d1, int d2) { return d1 = d1 + d2; } \
inline T& operator-=(T& d1, int d2) { return d1 = d1 - d2; }
#define ENABLE_INCR_OPERATORS_ON(T) \
inline T& operator++(T& d) { return d = T(int(d) + 1); } \
inline T& operator--(T& d) { return d = T(int(d) - 1); }
#define ENABLE_FULL_OPERATORS_ON(T) \
ENABLE_BASE_OPERATORS_ON(T) \
constexpr T operator*(int i, T d) { return T(i * int(d)); } \
constexpr T operator*(T d, int i) { return T(int(d) * i); } \
constexpr T operator/(T d, int i) { return T(int(d) / i); } \
constexpr int operator/(T d1, T d2) { return int(d1) / int(d2); } \
inline T& operator*=(T& d, int i) { return d = T(int(d) * i); } \
inline T& operator/=(T& d, int i) { return d = T(int(d) / i); }
ENABLE_FULL_OPERATORS_ON(Value)
ENABLE_FULL_OPERATORS_ON(Direction)
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
ENABLE_INCR_OPERATORS_ON(Piece)
ENABLE_INCR_OPERATORS_ON(PieceType)
ENABLE_INCR_OPERATORS_ON(Square)
ENABLE_INCR_OPERATORS_ON(File)
ENABLE_INCR_OPERATORS_ON(Rank)
ENABLE_BASE_OPERATORS_ON(Score)
#undef ENABLE_FULL_OPERATORS_ON
#undef ENABLE_INCR_OPERATORS_ON
#undef ENABLE_BASE_OPERATORS_ON
/// Additional operators to add a Direction to a Square
constexpr Square operator+(Square s, Direction d) { return Square(int(s) + int(d)); }
constexpr Square operator-(Square s, Direction d) { return Square(int(s) - int(d)); }
inline Square& operator+=(Square& s, Direction d) { return s = s + d; }
inline Square& operator-=(Square& s, Direction d) { return s = s - d; }
/// Only declared but not defined. We don't want to multiply two scores due to
/// a very high risk of overflow. So user should explicitly convert to integer.
Score operator*(Score, Score) = delete;
/// Division of a Score must be handled separately for each term
inline Score operator/(Score s, int i) {
return make_score(mg_value(s) / i, eg_value(s) / i);
}
/// Multiplication of a Score by an integer. We check for overflow in debug mode.
inline Score operator*(Score s, int i) {
Score result = Score(int(s) * i);
assert(eg_value(result) == (i * eg_value(s)));
assert(mg_value(result) == (i * mg_value(s)));
assert((i == 0) || (result / i) == s);
return result;
}
/// Multiplication of a Score by a boolean
inline Score operator*(Score s, bool b) {
return b ? s : SCORE_ZERO;
}
constexpr Color operator~(Color c) {
return Color(c ^ BLACK); // Toggle color
}
constexpr Square flip_rank(Square s) { // Swap A1 <-> A8
return Square(s ^ SQ_A8);
}
constexpr Square flip_file(Square s) { // Swap A1 <-> H1
return Square(s ^ SQ_H1);
}
constexpr Piece operator~(Piece pc) {
return Piece(pc ^ 8); // Swap color of piece B_KNIGHT <-> W_KNIGHT
}
constexpr CastlingRights operator&(Color c, CastlingRights cr) {
return CastlingRights((c == WHITE ? WHITE_CASTLING : BLACK_CASTLING) & cr);
}
constexpr Value mate_in(int ply) {
return VALUE_MATE - ply;
}
constexpr Value mated_in(int ply) {
return -VALUE_MATE + ply;
}
constexpr Square make_square(File f, Rank r) {
return Square((r << 3) + f);
}
constexpr Piece make_piece(Color c, PieceType pt) {
return Piece((c << 3) + pt);
}
constexpr PieceType type_of(Piece pc) {
return PieceType(pc & 7);
}
inline Color color_of(Piece pc) {
assert(pc != NO_PIECE);
return Color(pc >> 3);
}
constexpr bool is_ok(Square s) {
return s >= SQ_A1 && s <= SQ_H8;
}
constexpr File file_of(Square s) {
return File(s & 7);
}
constexpr Rank rank_of(Square s) {
return Rank(s >> 3);
}
constexpr Square relative_square(Color c, Square s) {
return Square(s ^ (c * 56));
}
constexpr Rank relative_rank(Color c, Rank r) {
return Rank(r ^ (c * 7));
}
constexpr Rank relative_rank(Color c, Square s) {
return relative_rank(c, rank_of(s));
}
constexpr Direction pawn_push(Color c) {
return c == WHITE ? NORTH : SOUTH;
}
constexpr Square from_sq(Move m) {
return Square((m >> 6) & 0x3F);
}
constexpr Square to_sq(Move m) {
return Square(m & 0x3F);
}
constexpr int from_to(Move m) {
return m & 0xFFF;
}
constexpr MoveType type_of(Move m) {
return MoveType(m & (3 << 14));
}
constexpr PieceType promotion_type(Move m) {
return PieceType(((m >> 12) & 3) + KNIGHT);
}
constexpr Move make_move(Square from, Square to) {
return Move((from << 6) + to);
}
template<MoveType T>
constexpr Move make(Square from, Square to, PieceType pt = KNIGHT) {
return Move(T + ((pt - KNIGHT) << 12) + (from << 6) + to);
}
constexpr bool is_ok(Move m) {
return from_sq(m) != to_sq(m); // Catch MOVE_NULL and MOVE_NONE
}
/// Based on a congruential pseudo random number generator
constexpr Key make_key(uint64_t seed) {
return seed * 6364136223846793005ULL + 1442695040888963407ULL;
}
} // namespace Stockfish
#endif // #ifndef TYPES_H_INCLUDED
#include "tune.h" // Global visibility to tuning setup