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# List of authors for Stockfish
2014-06-20 19:35:09 -06:00
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
# Founders of the Stockfish project and fishtest infrastructure
Tord Romstad (romstad)
Marco Costalba (mcostalba)
Joona Kiiski (zamar)
Gary Linscott (glinscott)
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
# Authors and inventors of NNUE, training, NNUE port
Yu Nasu (ynasu87)
Motohiro Isozaki (yaneurao)
Hisayori Noda (nodchip)
# all other authors of the code in alphabetical order
Aditya (absimaldata)
Adrian Petrescu (apetresc)
Ajith Chandy Jose (ajithcj)
Alain Savard (Rocky640)
Adjust aspiration window with eval This patch changes the base aspiration window size depending on the absolute value of the previous iteration score, increasing it away from zero. This stems from the observation that the further away from zero, the more likely the evaluation is to change significantly with more depth. Conversely, a tighter aspiration window is more efficient when close to zero. A beneficial side-effect is that analysis of won positions without a quick mate is less prone to waste nodes in repeated fail-high that change the eval by tiny steps. STC: LLR: 2.96 (-2.94,2.94) [0.50,4.50] Total: 60102 W: 13327 L: 12868 D: 33907 http://tests.stockfishchess.org/tests/view/5d9a70d40ebc5902b6cf39ba LTC: LLR: 2.95 (-2.94,2.94) [0.00,3.50] Total: 155553 W: 25745 L: 25141 D: 104667 http://tests.stockfishchess.org/tests/view/5d9a7ca30ebc5902b6cf4028 Future work : the values used in this patch were only a reasonable guess. Further testing should unveil more optimal values. However, the aspiration window is rather tight with a minimum of 21 internal units, so discrete integers put a practical limitation to such tweaking. More exotic experiments around the aspiration window parameters could also be tried, but efficient conditions to adjust the base aspiration window size or allow it to not be centered on the current evaluation are not obvious. The aspiration window increases after a fail-high or a fail-low is another avenue to explore for potential enhancements. Bench: 4043748
2019-10-07 11:02:33 -06:00
Alayan Feh (Alayan-stk-2)
2014-06-20 19:35:09 -06:00
Alexander Kure
Alexander Pagel (Lolligerhans)
Alfredo Menezes (lonfom169)
Ali AlZhrani (Cooffe)
Andrei Vetrov (proukornew)
Andrew Grant (AndyGrant)
Andrey Neporada (nepal)
2014-06-20 19:35:09 -06:00
Andy Duplain
Antoine Champion (antoinechampion)
Aram Tumanian (atumanian)
Arjun Temurnikar
Artem Solopiy (EntityFX)
2014-06-20 19:35:09 -06:00
Auguste Pop
Balint Pfliegel
Ben Koshy (BKSpurgeon)
Bill Henry (VoyagerOne)
Bojun Guo (noobpwnftw, Nooby)
braich
Brian Sheppard (SapphireBrand, briansheppard-toast)
Bruno de Melo Costa (BM123499)
Bryan Cross (crossbr)
candirufish
Chess13234
Chris Cain (ceebo)
Dale Weiler (graphitemaster)
Dan Schmidt (dfannius)
Daniel Axtens (daxtens)
Daniel Dugovic (ddugovic)
Dariusz Orzechowski (dorzechowski)
David Zar
Daylen Yang (daylen)
2020-11-12 06:05:28 -07:00
Deshawn Mohan-Smith (GoldenRare)
Dieter Dobbelaere (ddobbelaere)
DiscanX
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
Dominik Schlösser (domschl)
double-beep
Douglas Matos Gomes (dsmsgms)
Eduardo Cáceres (eduherminio)
Eelco de Groot (KingDefender)
Elvin Liu (solarlight2)
erbsenzaehler
Ernesto Gatti
Linmiao Xu (linrock)
Fabian Beuke (madnight)
Fabian Fichter (ianfab)
Fanael Linithien (Fanael)
fanon
Fauzi Akram Dabat (FauziAkram)
Felix Wittmann
gamander
Gary Heckman (gheckman)
George Sobala (gsobala)
gguliash
Giacomo Lorenzetti (G-Lorenz)
Gian-Carlo Pascutto (gcp)
Gontran Lemaire (gonlem)
Goodkov Vasiliy Aleksandrovich (goodkov)
2014-06-20 19:35:09 -06:00
Gregor Cramer
GuardianRM
Günther Demetz (pb00067, pb00068)
Guy Vreuls (gvreuls)
Henri Wiechers
Hiraoka Takuya (HiraokaTakuya)
homoSapiensSapiens
2014-06-20 19:35:09 -06:00
Hongzhi Cheng
Ivan Ivec (IIvec)
Jacques B. (Timshel)
Jan Ondruš (hxim)
Jared Kish (Kurtbusch)
Jarrod Torriero (DU-jdto)
Jean Gauthier (OuaisBla)
Jean-Francois Romang (jromang)
Jekaa
Jerry Donald Watson (jerrydonaldwatson)
jjoshua2
Jonathan Calovski (Mysseno)
Jonathan Buladas Dumale (SFisGOD)
Joost VandeVondele (vondele)
Jörg Oster (joergoster)
Using a S-curve for the optimism measure Add a logarithmic term in the optimism computation, increase the maximal optimism and lower the contempt offset. This increases the dynamics of the optimism aspects, giving a boost for balanced positions without skewing too much on unbalanced positions (but this version will enter panic mode faster than previous master when behind, trying to draw faster when slightly behind). This helps, since optimism is in general a good thing, for instance at LTC, but too high optimism rapidly contaminates play. passed STC: LLR: 2.96 (-2.94,2.94) [0.00,5.00] Total: 159343 W: 34489 L: 33588 D: 91266 http://tests.stockfishchess.org/tests/view/5a8db9340ebc590297cc85b6 passed LTC: LLR: 2.97 (-2.94,2.94) [0.00,5.00] Total: 47491 W: 7825 L: 7517 D: 32149 http://tests.stockfishchess.org/tests/view/5a9456a80ebc590297cc8a89 It must be mentioned that a version of the PR with contempt 0 did not pass STC [0,5]. The version in the patch, which uses default contempt 12, was found to be as strong as current master on different matches against SF7 and SF8, both at STC and LTC. One drawback maybe is that it raises the draw rate in self-play from 56% to 59%, giving a little bit less sensitivity for SF developpers to find evaluation improvements by selfplay tests in fishtest. Possible further work: • tune the values accurately, while keeping in mind the drawrate issue • check whether it is possible to remove linear and offset term • try to simplify the S-shape curve Bench: 5934644
2018-03-04 08:50:19 -07:00
Joseph Ellis (jhellis3)
Joseph R. Prostko
Update default net to nn-8e47cf062333.nnue This net is the result of training on data used by the Leela project. More precisely, we shuffled T60 and T74 data kindly provided by borg (for different Tnn, the data is a result of Leela selfplay with differently sized Leela nets). The data is available at vondele's google drive: https://drive.google.com/drive/folders/1mftuzYdl9o6tBaceR3d_VBQIrgKJsFpl. The Leela data comes in small chunks of .binpack files. To shuffle them, we simply used a small python script to randomly rename the files, and then concatenated them using `cat`. As validation data we picked a file of T60 data. We will further investigate T74 data. The training for the NNUE architecture used 200 epochs with the Python trainer from the Stockfish project. Unlike the previous run we tried with this data, this run does not have adjusted scaling — not because we didn't want to, but because we forgot. However, this training randomly skips 40% more positions than previous run. The loss was very spiky and decreased slower than it does usually. Training loss: https://github.com/official-stockfish/images/blob/main/training-loss-8e47cf062333.png Validation loss: https://github.com/official-stockfish/images/blob/main/validation-loss-8e47cf062333.png This is the exact training command: python train.py --smart-fen-skipping --random-fen-skipping 14 --batch-size 16384 --threads 4 --num-workers 4 --gpus 1 trainingdata\training_data.binpack validationdata\val.binpack --- 10k STC result: ELO: 3.61 +-3.3 (95%) LOS: 98.4% Total: 10000 W: 1241 L: 1137 D: 7622 Ptnml(0-2): 68, 841, 3086, 929, 76 https://tests.stockfishchess.org/tests/view/60c67e50457376eb8bcaae70 10k LTC result: ELO: 2.71 +-2.4 (95%) LOS: 98.8% Total: 10000 W: 659 L: 581 D: 8760 Ptnml(0-2): 22, 485, 3900, 579, 14 https://tests.stockfishchess.org/tests/view/60c69deb457376eb8bcaae98 Passed LTC: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 9648 W: 685 L: 545 D: 8418 Ptnml(0-2): 22, 448, 3740, 596, 18 https://tests.stockfishchess.org/tests/view/60c6d41c457376eb8bcaaecf --- closes https://github.com/official-stockfish/Stockfish/pull/3550 Bench: 4877339
2021-06-13 15:48:32 -06:00
Julian Willemer (NightlyKing)
jundery
Justin Blanchard (UncombedCoconut)
2014-06-20 19:35:09 -06:00
Kelly Wilson
Ken Takusagawa
kinderchocolate
Kiran Panditrao (Krgp)
2014-06-20 19:35:09 -06:00
Kojirion
Krystian Kuzniarek (kuzkry)
Leonardo Ljubičić (ICCF World Champion)
Leonid Pechenik (lp--)
Liam Keegan (lkeegan)
Linus Arver (listx)
loco-loco
Lub van den Berg (ElbertoOne)
Luca Brivio (lucabrivio)
Lucas Braesch (lucasart)
Lyudmil Antonov (lantonov)
Maciej Żenczykowski (zenczykowski)
Smarter time management near stop limit This patch makes Stockfish search same depth again if > 60% of optimum time is already used, instead of trying the next iteration. The idea is that the next iteration will generally take about the same amount of time as has already been used in total. When we are likely to begin the last iteration, as judged by total time taken so far > 0.6 * optimum time, searching the last depth again instead of increasing the depth still helps the other threads in lazy SMP and prepares better move ordering for the next moves. STC : LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 13436 W: 2695 L: 2558 D: 8183 Ptnml(0-2): 222, 1538, 3087, 1611, 253 https://tests.stockfishchess.org/tests/view/5e1618a761fe5f83a67dd964 LTC : LLR: 2.94 (-2.94,2.94) {0.00,2.00} Total: 32160 W: 4261 L: 4047 D: 23852 Ptnml(0-2): 211, 2988, 9448, 3135, 247 https://tests.stockfishchess.org/tests/view/5e162ca061fe5f83a67dd96d The code was revised as suggested by @vondele for multithreading: STC (8 threads): LLR: 2.95 (-2.94,2.94) {0.00,2.00} Total: 16640 W: 2049 L: 1885 D: 12706 Ptnml(0-2): 119, 1369, 5158, 1557, 108 https://tests.stockfishchess.org/tests/view/5e19826a2cc590e03c3c2f52 LTC (8 threads): LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 16536 W: 2758 L: 2629 D: 11149 Ptnml(0-2): 182, 1758, 4296, 1802, 224 https://tests.stockfishchess.org/tests/view/5e18b91a27dab692fcf9a140 Thanks to those discussing Stockfish lazy SMP on fishcooking which made me try this, and to @vondele for suggestions and doing related tests. See full discussion in the pull request thread: https://github.com/official-stockfish/Stockfish/pull/2482 Bench: 4586187
2020-01-11 15:10:22 -07:00
Malcolm Campbell (xoto10)
Introduce Overload This patch applies a S(10, 5) bonus for every square that is: - Occupied by an enemy piece which is not a pawn - Attacked exactly once by our pieces - Defended exactly once by enemy pieces The idea is that these pieces must be defended. Their defenders have dramatically limited mobility, and they are vulnerable to our future attack. As with connectivity, there are probably many more tests to be run in this area. In particular: - I believe @snicolet's queen overload tests have demonstrated a potential need for a queen overload bonus above and beyond this one; however, the conditions for "overload" in this patch are different (excluding pieces we attack twice). My next test after this is (hopefully) merged will be to intersect the Bitboard I define here with the enemy's queen attacks and attempt to give additional bonus. - Perhaps we should exclude pieces attacked by pawns--can pawns really be overloaded? Should they have the same weight, or less? This didn't work with a previous version, but it could work with this one. - More generally, different pieces may need more or less bonus. We could change bonuses based on what type of enemy piece is being overloaded, what type of friendly piece is attacking, and/or what type of piece is being defended by the overloaded piece and attacked by us, or any intersection of these three. For example, here attacked/defended pawns are excluded, but they're not totally worthless targets, and could be added again with a smaller bonus. - This list is by no means exhaustive. STC: LLR: 2.96 (-2.94,2.94) [0.00,5.00] Total: 17439 W: 3599 L: 3390 D: 10450 http://tests.stockfishchess.org/tests/view/5ac78a2e0ebc59435923735e LTC: LLR: 2.95 (-2.94,2.94) [0.00,5.00] Total: 43304 W: 6533 L: 6256 D: 30515 http://tests.stockfishchess.org/tests/view/5ac7a1d80ebc59435923736f Closes https://github.com/official-stockfish/Stockfish/pull/1533 Bench: 5248871 ---------------- This is my first time opening a PR, so I apologize if there are errors. There are too many people to thank since I submitted my first test just over a month ago. Thank you all for the warm welcome and here is to more green patches! In particular, I would like to thank: - @crossbr, whose comment in a FishCooking thread first inspired me to consider the overloading of pieces other than queens, - @snicolet, whose queen overload tests inspired this one and served as the base of my first overload attempts, - @protonspring, whose connectivity tests inspired this one and who provided much of the feedback needed to take this from red to green, - @vondele, who kindly corrected me when I submitted a bad LTC test, - @Rocky640, who has helped me over and over again in the past month. Thank you all!
2018-04-06 17:20:48 -06:00
Mark Tenzer (31m059)
marotear
Matt Ginsberg (mattginsberg)
Matthew Lai (matthewlai)
Matthew Sullivan (Matt14916)
Maxim Molchanov (Maxim)
Michael An (man)
Michael Byrne (MichaelB7)
Michael Chaly (Vizvezdenec)
Michael Stembera (mstembera)
Michael Whiteley (protonspring)
Michel Van den Bergh (vdbergh)
Miguel Lahoz (miguel-l)
Mikael Bäckman (mbootsector)
Mira
Miroslav Fontán (Hexik)
Moez Jellouli (MJZ1977)
Mohammed Li (tthsqe12)
Nathan Rugg (nmrugg)
Nick Pelling (nickpelling)
Nicklas Persson (NicklasPersson)
Niklas Fiekas (niklasf)
Nikolay Kostov (NikolayIT)
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
Nguyen Pham (nguyenpham)
Norman Schmidt (FireFather)
Support building for Android using NDK The easiest way to use the NDK in conjunction with this Makefile (tested on linux-x86_64): 1. Download the latest NDK (r21d) from Google from https://developer.android.com/ndk/downloads 2. Place and unzip the NDK in $HOME/ndk folder 3. Export the path variable e.g., `export PATH=$PATH:$HOME/ndk/android-ndk-r21d/toolchains/llvm/prebuilt/linux-x86_64/bin` 4. cd to your Stockfish/src dir 5. Issue `make -j ARCH=armv8 COMP=ndk build` (use `ARCH=armv7` or `ARCH=armv7-neon` for older CPUs) 6. Optionally `make -j ARCH=armv8 COMP=ndk strip` 7. That's all. Enjoy! Improves support from Raspberry Pi (incomplete?) and compiling on arm in general closes https://github.com/official-stockfish/Stockfish/pull/3015 fixes https://github.com/official-stockfish/Stockfish/issues/2860 fixes https://github.com/official-stockfish/Stockfish/issues/2641 Support is still fragile as we're missing CI on these targets. Nevertheless tested with: ```bash # build crosses from ubuntu 20.04 on x86 to various arch/OS combos # tested with suitable packages installed # (build-essentials, mingw-w64, g++-arm-linux-gnueabihf, NDK (r21d) from google) # cross to Android export PATH=$HOME/ndk/android-ndk-r21d/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH make clean && make -j build ARCH=armv7 COMP=ndk && make -j build ARCH=armv7 COMP=ndk strip make clean && make -j build ARCH=armv7-neon COMP=ndk && make -j build ARCH=armv7-neon COMP=ndk strip make clean && make -j build ARCH=armv8 COMP=ndk && make -j build ARCH=armv8 COMP=ndk strip # cross to Raspberry Pi make clean && make -j build ARCH=armv7 COMP=gcc COMPILER=arm-linux-gnueabihf-g++ make clean && make -j build ARCH=armv7-neon COMP=gcc COMPILER=arm-linux-gnueabihf-g++ # cross to Windows make clean && make -j build ARCH=x86-64-modern COMP=mingw ``` No functional change
2020-08-16 09:59:13 -06:00
notruck
Ofek Shochat (OfekShochat, ghostway)
Ondrej Mosnáček (WOnder93)
Oskar Werkelin Ahlin
2014-06-20 19:35:09 -06:00
Pablo Vazquez
Panthee
Pascal Romaret
Pasquale Pigazzini (ppigazzini)
Patrick Jansen (mibere)
pellanda
Peter Zsifkovits (CoffeeOne)
Praveen Kumar Tummala (praveentml)
Rahul Dsilva (silversolver1)
Ralph Stößer (Ralph Stoesser)
2014-06-20 19:35:09 -06:00
Raminder Singh
renouve
Reuven Peleg
2014-06-20 19:35:09 -06:00
Richard Lloyd
Rodrigo Exterckötter Tjäder
Ron Britvich (Britvich)
Ronald de Man (syzygy1, syzygy)
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
rqs
Ryan Schmitt
2014-06-20 19:35:09 -06:00
Ryan Takker
Sami Kiminki (skiminki)
Sebastian Buchwald (UniQP)
Sergei Antonov (saproj)
Sergei Ivanov (svivanov72)
Sergio Vieri (sergiovieri)
sf-x
Shane Booth (shane31)
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
Shawn Varghese (xXH4CKST3RXx)
Siad Daboul (Topologist)
Stefan Geschwentner (locutus2)
Stefano Cardanobile (Stefano80)
Steinar Gunderson (sesse)
Stéphane Nicolet (snicolet)
Prokop Randáček (ProkopRandacek)
Thanar2
2014-06-20 19:35:09 -06:00
thaspel
theo77186
Tom Truscott
Tom Vijlbrief (tomtor)
Tomasz Sobczyk (Sopel97)
Torsten Franz (torfranz, tfranzer)
Update default net to nn-ac5605a608d6.nnue This net was created with the nnue-pytorch trainer, it used the previous master net as a starting point. The training data includes all T60 data (https://drive.google.com/drive/folders/1rzZkgIgw7G5vQMLr2hZNiUXOp7z80613), all T74 data (https://drive.google.com/drive/folders/1aFUv3Ih3-A8Vxw9064Kw_FU4sNhMHZU-) and the wrongNNUE_02_d9.binpack (https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq). The Leela data were randomly named and then concatenated. All data was merged into one binpack using interleave_binpacks.py. python3 train.py \ ../data/t60_t74_wrong.binpack \ ../data/t60_t74_wrong.binpack \ --resume-from-model ../data/nn-e8321e467bf6.pt \ --gpus 1 \ --threads 4 \ --num-workers 1 \ --batch-size 16384 \ --progress_bar_refresh_rate 300 \ --random-fen-skipping 3 \ --features=HalfKAv2_hm^ \ --lambda=1.0 \ --max_epochs=600 \ --seed $RANDOM \ --default_root_dir ../output/exp_24 STC: LLR: 2.95 (-2.94,2.94) <-0.50,2.50> Total: 15320 W: 1415 L: 1257 D: 12648 Ptnml(0-2): 50, 1002, 5402, 1152, 54 https://tests.stockfishchess.org/tests/view/611c404a4977aa1525c9c97f LTC: LLR: 2.94 (-2.94,2.94) <0.50,3.50> Total: 9440 W: 345 L: 248 D: 8847 Ptnml(0-2): 3, 222, 4175, 315, 5 https://tests.stockfishchess.org/tests/view/611c6c7d4977aa1525c9c996 LTC with UHO_XXL_+0.90_+1.19.epd: LLR: 2.94 (-2.94,2.94) <0.50,3.50> Total: 6232 W: 1638 L: 1459 D: 3135 Ptnml(0-2): 5, 592, 1744, 769, 6 https://tests.stockfishchess.org/tests/view/611c9b214977aa1525c9c9cb closes https://github.com/official-stockfish/Stockfish/pull/3664 Bench: 5375286
2021-08-18 01:12:14 -06:00
Torsten Hellwig (Torom)
Tracey Emery (basepr1me)
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
tttak
Unai Corzo (unaiic)
Uri Blass (uriblass)
Vince Negri (cuddlestmonkey)
xefoci7612
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
zz4032
Smarter time management near stop limit This patch makes Stockfish search same depth again if > 60% of optimum time is already used, instead of trying the next iteration. The idea is that the next iteration will generally take about the same amount of time as has already been used in total. When we are likely to begin the last iteration, as judged by total time taken so far > 0.6 * optimum time, searching the last depth again instead of increasing the depth still helps the other threads in lazy SMP and prepares better move ordering for the next moves. STC : LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 13436 W: 2695 L: 2558 D: 8183 Ptnml(0-2): 222, 1538, 3087, 1611, 253 https://tests.stockfishchess.org/tests/view/5e1618a761fe5f83a67dd964 LTC : LLR: 2.94 (-2.94,2.94) {0.00,2.00} Total: 32160 W: 4261 L: 4047 D: 23852 Ptnml(0-2): 211, 2988, 9448, 3135, 247 https://tests.stockfishchess.org/tests/view/5e162ca061fe5f83a67dd96d The code was revised as suggested by @vondele for multithreading: STC (8 threads): LLR: 2.95 (-2.94,2.94) {0.00,2.00} Total: 16640 W: 2049 L: 1885 D: 12706 Ptnml(0-2): 119, 1369, 5158, 1557, 108 https://tests.stockfishchess.org/tests/view/5e19826a2cc590e03c3c2f52 LTC (8 threads): LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 16536 W: 2758 L: 2629 D: 11149 Ptnml(0-2): 182, 1758, 4296, 1802, 224 https://tests.stockfishchess.org/tests/view/5e18b91a27dab692fcf9a140 Thanks to those discussing Stockfish lazy SMP on fishcooking which made me try this, and to @vondele for suggestions and doing related tests. See full discussion in the pull request thread: https://github.com/official-stockfish/Stockfish/pull/2482 Bench: 4586187
2020-01-11 15:10:22 -07:00
# Additionally, we acknowledge the authors and maintainers of fishtest,
Smarter time management near stop limit This patch makes Stockfish search same depth again if > 60% of optimum time is already used, instead of trying the next iteration. The idea is that the next iteration will generally take about the same amount of time as has already been used in total. When we are likely to begin the last iteration, as judged by total time taken so far > 0.6 * optimum time, searching the last depth again instead of increasing the depth still helps the other threads in lazy SMP and prepares better move ordering for the next moves. STC : LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 13436 W: 2695 L: 2558 D: 8183 Ptnml(0-2): 222, 1538, 3087, 1611, 253 https://tests.stockfishchess.org/tests/view/5e1618a761fe5f83a67dd964 LTC : LLR: 2.94 (-2.94,2.94) {0.00,2.00} Total: 32160 W: 4261 L: 4047 D: 23852 Ptnml(0-2): 211, 2988, 9448, 3135, 247 https://tests.stockfishchess.org/tests/view/5e162ca061fe5f83a67dd96d The code was revised as suggested by @vondele for multithreading: STC (8 threads): LLR: 2.95 (-2.94,2.94) {0.00,2.00} Total: 16640 W: 2049 L: 1885 D: 12706 Ptnml(0-2): 119, 1369, 5158, 1557, 108 https://tests.stockfishchess.org/tests/view/5e19826a2cc590e03c3c2f52 LTC (8 threads): LLR: 2.95 (-2.94,2.94) {-1.00,3.00} Total: 16536 W: 2758 L: 2629 D: 11149 Ptnml(0-2): 182, 1758, 4296, 1802, 224 https://tests.stockfishchess.org/tests/view/5e18b91a27dab692fcf9a140 Thanks to those discussing Stockfish lazy SMP on fishcooking which made me try this, and to @vondele for suggestions and doing related tests. See full discussion in the pull request thread: https://github.com/official-stockfish/Stockfish/pull/2482 Bench: 4586187
2020-01-11 15:10:22 -07:00
# an amazing and essential framework for the development of Stockfish!
#
# https://github.com/glinscott/fishtest/blob/master/AUTHORS