fire self-play nnue
- fire without NNUE can be found here: https://github.com/FireFather/fire-HCE (archived, no longer actively maintained)
- fire w/ NNUE from shared SF & LC0 data: https://github.com/FireFather/fire-NN (archived, no longer actively maintained)
- c++20
- windows & linux
- uci
- 64-bit
- smp (to 256 threads)
- configurable hash (to 1024 GB)
- ponder
- multiPV
- analysis (infinite) mode
- chess960 (Fischer Random)
- syzygy tablebases
- adjustable contempt setting
- fast perft & divide
- bench (includes ttd time-to-depth calculation)
- timestamped bench, perft, & divide log files
- asychronous cout (acout) class using std::unique_lockstd::mutex
- uci option EngineMode random w/ uniform_real_distribution & mesenne_twister_engine
- uses a unique NNUE (halfkp_256x2-32-32) evaluation
- fast alpha-beta search
- optional experimental MCTS-UCT search (Monte Carlo Tree Search w/ Upper Confidence Bounds Applied to Trees) pure/no minmax
fire-9 is now available
| games | (+win, =draw, -loss) | (%) | ||
| fire-9_x64_bmi2 | 3460 | 16384 | (+5968,=4937,-5479) | 51.5 % |
| vs. | ||||
| stockfish-15 | 3861 | 863 | (+715,=135,-13) | 90.7 % |
| komodo-dragon-3.1 | 3838 | 863 | (+702,=141,-20) | 89.5 % |
| berserk-9 | 3754 | 863 | (+631,=190,-42) | 84.1 % |
| koivisto_8.0 | 3637 | 863 | (+501,=261,-101) | 73.2 % |
| rubiChess-20220813 | 3564 | 863 | (+386,=338,-139) | 64.3 % |
| seer-v2.5 | 3504 | 862 | (+322,=324,-216) | 56.1 % |
| rofChade-3.0 | 3492 | 862 | (+302,=336,-224) | 54.5 % |
| slow64-2.9 | 3452 | 863 | (+254,=336,-273) | 48.9 % |
| rebel-15.1 | 3450 | 862 | (+245,=348,-269) | 48.6 % |
| wasp-6.00 | 3395 | 862 | (+207,=291,-364) | 40.9 % |
| fire-8242022 | 3384 | 862 | (+195,=290,-377) | 39.4 % |
| nemorino-6.00 | 3374 | 862 | (+156,=344,-362) | 38.1 % |
| igel-3.1.0 | 3334 | 862 | (+122,=323,-417) | 32.9 % |
| ethereal-12.75 | 3331 | 862 | (+158,=244,-460) | 32.5 % |
| clover-3.1 | 3296 | 862 | (+147,=194,-521) | 28.3 % |
| minic-3.24 | 3290 | 862 | (+106,=264,-492) | 27.6 % |
| xiphos-0.6 | 3266 | 862 | (+124,=184,-554) | 25.1 % |
| tucano-10.00 | 3266 | 862 | (+99,=234,-529) | 25.1 % |
| marvin-6.0.0 | 3233 | 862 | (+107,=160,-595) | 21.7 % |
Games Completed = 16384 of 16384 (Avg game length = 12.627 sec)
Settings = Gauntlet/32MB/1000ms+100ms/M 500cp for 6 moves, D 120 moves/EPD:book.epd(31526)
- engine for generation of self-play games
- application to run self-play games
- cutechess-cli https://github.com/cutechess/cutechess
- application to obtain fen positions and evaluation data from pgn files & convert to plain text
- nnue-extract.exe from deeds tools https://outskirts.altervista.org/forum/viewtopic.php?t=2009
- windows application for nnue training session management
- optimized port of the original nodchip shogi neural network implementation
- application used by nnue-extract to extract fen positions and info from pgns
- application for testing nnue vs nnue, etc.
- little blitzer http://www.kimiensoftware.com/software/chess/littleblitzer
- utility for removing duplicate games from pgn files
- chessbase 16 64-bit https://shop.chessbase.com/en/products/chessbase_16_fritz18_bundle
- misc utilities: truncate.exe (opening book ply limitation) & gameSplit.exe (break up pgns to multiple parts)
- 40H-pgn tools http://40hchess.epizy.com/
- setting up a suitable Windows environment
- creating an efficient opening book using cutechess-cli
- running selfplay games for a 'zero' nnue approach
- using eval-extract to obtain fens & data from pgn files
- converting plain text data files to nnue .bin training format
- training & command-line management using nnue-gui
- testing the generated nnues
- improving the NNUE with supervised & reinforcement learning
fire-9 has undergone months of meticulous analysis and refactoring using many of the most modern C++ tools available today, including Clang, ReSharper C++, and Visual Studio Code Analysis, ensuring the production of extremely fast highly optimized and stable executables.
-
x64 bmi2 = fast pgo binary (for modern 64-bit systems w/ BMI2 instruction set) if you own a Intel Haswell or newer cpu, this compile should be faster.
-
x64 avx2 = fast pgo binary (for modern 64-bit systems w/ AVX2 instruction set) if you own a modern AMD cpu, this compile should be the fastest.
-
x64 popc = fast pgo binary (for older 64-bit systems w/ SSE41 & POPCNT instruction sets)
-
windows : fire-9_x64_x64_bmi2.exe, fire-9_x64_avx2.exe, fire-9_x64_popc.exe
run 'bench' at command line to determine which binary runs best/fastest on your system. for greater accuracy, run it twice and calculate the average of both results.
please see http://chesslogik.wix.com/fire for more info
- windows (visual studio) use included project files: Fire.vcxproj or Fire.sln
- minGW run one of the included shell scripts: make_bmi2.sh, make_avx2.sh, make_popc.sh, or make_all.sh
- ubuntu type 'make profile-build ARCH=x86-64-bmi2', 'make profile-build ARCH=x86-64-avx2', etc.
- Hash size of the hash table. default is 64 MB.
- Threads number of processor threads to use. default is 1, max = 128.
- MultiPV number of pv's/principal variations (lines of play) to be output. default is 1.
- Contempt higher contempt resists draws.
- MoveOverhead Adjust this to compensate for network and GUI latency. This is useful to avoid losses on time.
- MinimumTime Absolute minimum time (in ms) to spend on a single move. Also useful to avoid losses on time.
- Ponder also think during opponent's time. default is false.
- UCI_Chess960 play chess960 (often called FRC or Fischer Random Chess). default is false.
- Clear Hash clear the hash table. delete allocated memory and re-initialize.
- SyzygyProbeDepth engine begins probing at specified depth. increasing this option makes the engine probe less.
- EngineMode choose NNUE (default), or random.
- MCTS enable Monte Carlo Tree Search w/UCT (Upper Confidence Bounds Applied to Trees)
- SyzygyProbeLimit number of pieces that have to be on the board in the endgame before the table-bases are probed.
- Syzygy50MoveRule set to false, tablebase positions that are drawn by the 50-move rule will count as a win or loss.
- SyzygyPath path to the syzygy tablebase files.
many of the ideas & techiques incorporated into Fire are documented in detail here
and some have been adapted from the super strong open-source chess engine
and others
the endgame table bases are implemented using code adapted from Ronald de Man's
The NNUE implementation utilizes a modified version of Daniel Shawul's/Cfish excellent nnue probe code:
the MCTS implementation is derived from Stephane Nicolet's work
if you are interested in learning about my particular ultra-fast testing methodology, I've explained it in some detail here: http://www.chessdom.com/fire-the-chess-engine-releases-a-new-version/
Fire is distributed under the GNU General Public License. Please read LICENSE for more information.
please see http://chesslogik.wix.com/fire for more info
No. Here are the results of Don Daily's SIM program (default values) today to measure Fire 8.NN move selection vs the last 4 versions of Stockfish:
As you can see Stockfish 14.1 and Stockfish 15 make the same moves ~64% of the time (see row 4, column 5)
Fire 8.NN has an excellent score of ~43.34 % (see row 1), making different moves than SF almost 60% of the time.
The Sim tool can be downloaded here: https://komodochess.com/downloads.htm
If you're interested in similarity testing results, see also: https://github.com/FireFather/sim03
best regards-
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