Tags: MendezV/Stockfish
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Stockfish 14 Official release version of Stockfish 14 Bench: 4770936 --- Today, we have the pleasure to announce Stockfish 14. As usual, downloads will be freely available at https://stockfishchess.org The engine is now significantly stronger than just a few months ago, and wins four times more game pairs than it loses against the previous release version [0]. Stockfish 14 is now at least 400 Elo ahead of Stockfish 7, a top engine in 2016 [1]. During the last five years, Stockfish has thus gained about 80 Elo per year. Stockfish 14 evaluates positions more accurately than Stockfish 13 as a result of two major steps forward in defining and training the efficiently updatable neural network (NNUE) that provides the evaluation for positions. First, the collaboration with the Leela Chess Zero team - announced previously [2] - has come to fruition. The LCZero team has provided a collection of billions of positions evaluated by Leela that we have combined with billions of positions evaluated by Stockfish to train the NNUE net that powers Stockfish 14. The fact that we could use and combine these datasets freely was essential for the progress made and demonstrates the power of open source and open data [3]. Second, the architecture of the NNUE network was significantly updated: the new network is not only larger, but more importantly, it deals better with large material imbalances and can specialize for multiple phases of the game [4]. A new project, kick-started by Gary Linscott and Tomasz Sobczyk, led to a GPU accelerated net trainer written in pytorch.[5] This tool allows for training high-quality nets in a couple of hours. Finally, this release features some search refinements, minor bug fixes and additional improvements. For example, Stockfish is now about 90 Elo stronger for chess960 (Fischer random chess) at short time control. The Stockfish project builds on a thriving community of enthusiasts (thanks everybody!) that contribute their expertise, time, and resources to build a free and open-source chess engine that is robust, widely available, and very strong. We invite our chess fans to join the fishtest testing framework and programmers to contribute to the project on github [6]. Stay safe and enjoy chess! The Stockfish team [0] https://tests.stockfishchess.org/tests/view/60dae5363beab81350aca077 [1] https://nextchessmove.com/dev-builds [2] https://stockfishchess.org/blog/2021/stockfish-13/ [3] https://lczero.org/blog/2021/06/the-importance-of-open-data/ [4] official-stockfish@e8d64af1 [5] https://github.com/glinscott/nnue-pytorch/ [6] https://stockfishchess.org/get-involved/
Stockfish 12 Official release version of Stockfish 12 Bench: 3624569 ----------------------- It is our pleasure to release Stockfish 12 to users world-wide Downloads will be freely available at https://stockfishchess.org/download/ This version 12 of Stockfish plays significantly stronger than any of its predecessors. In a match against Stockfish 11, Stockfish 12 will typically win at least ten times more game pairs than it loses. This jump in strength, visible in regular progression tests during development[1], results from the introduction of an efficiently updatable neural network (NNUE) for the evaluation in Stockfish[2], and associated tuning of the engine as a whole. The concept of the NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. Stockfish remains a CPU-only engine, since the NNUE networks can be very efficiently evaluated on CPUs. The recommended parameters of the NNUE network are embedded in distributed binaries, and Stockfish will use NNUE by default. Both the NNUE and the classical evaluations are available, and can be used to assign values to positions that are 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 evaluations of millions of positions. The Stockfish project builds on a thriving community of enthusiasts that contribute their expertise, time, and resources to build a free and open source chess engine that is robust, widely available, and very strong. We invite chess fans to join the fishtest testing framework and programmers to contribute on github[3]. Stay safe and enjoy chess! The Stockfish team [1] https://github.com/glinscott/fishtest/wiki/Regression-Tests [2] official-stockfish@84f3e86 [3] https://stockfishchess.org/get-involved/
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: official-stockfish#2823 official-stockfish#2728 The integration branch will be closed after the merge: official-stockfish#2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes official-stockfish#2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
Tweak cutnode reduction
Less reduction for second move at non-check CUT node with depth <= 10.
STC:
LLR: 2.94 (-2.94,2.94) {-0.50,1.50}
Total: 38680 W: 7490 L: 7245 D: 23945
Ptnml(0-2): 643, 4441, 8967, 4606, 683
https://tests.stockfishchess.org/tests/view/5f21e1782f7e63962b99f451
LTC:
LLR: 2.95 (-2.94,2.94) {0.25,1.75}
Total: 71976 W: 9003 L: 8636 D: 54337
Ptnml(0-2): 440, 6414, 21972, 6663, 499
https://tests.stockfishchess.org/tests/view/5f2245762f7e63962b99f4bd
closes official-stockfish#2868
Bench: 4746616
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