| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Stéphane Nicolet
Date: Sat Jul 1 13:01:28 2023 +0200 Timestamp: 1688209288 Document the LEB128 patch Add some comments and harmonize style for the LEB128 patch. closes https://github.com/official-stockfish/Stockfish/pull/4642 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Daniel Monroe
Date: Sat Jul 1 12:59:28 2023 +0200 Timestamp: 1688209168 Update default net to nn-a3d1bfca1672.nnue faster permutation of master net weights Activation data taken from https://drive.google.com/drive/folders/1Ec9YuuRx4N03GPnVPoQOW70eucOKngQe?usp=sharing Permutation found using https://github.com/Ergodice/nnue-pytorch/blob/836387a0e5e690431d404158c46648710f13904d/ftperm.py See also https://github.com/glinscott/nnue-pytorch/pull/254 The algorithm greedily selects 2- and 3-cycles that can be permuted to increase the number of runs of zeroes. The percent of zero runs from the master net increased from 68.46 to 70.11 from 2-cycles and only increased to 70.32 when considering 3-cycles. Interestingly, allowing both halves of L1 to intermix when creating zero runs can give another 0.5% zero-run density increase with this method. Measured speedup: ``` CPU: 16 x AMD Ryzen 9 3950X 16-Core Processor Result of 50 runs base (./stockfish.master ) = 1561556 +/- 5439 test (./stockfish.patch ) = 1575788 +/- 5427 diff = +14231 +/- 2636 speedup = +0.0091 P(speedup > 0) = 1.0000 ``` closes https://github.com/official-stockfish/Stockfish/pull/4640 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joost VandeVondele
Date: Sat Jul 1 12:52:31 2023 +0200 Timestamp: 1688208751 Restore development closes https://github.com/official-stockfish/Stockfish/pull/4651 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joost VandeVondele
Date: Thu Jun 29 08:00:10 2023 +0200 Timestamp: 1688018410 Stockfish 16 Official release version of Stockfish 16 Bench: 2593605 --- Stockfish 16 A new major release of Stockfish is now available at https://stockfishchess.org/download/ *Quality of chess play* Stockfish continues to demonstrate its ability to discover superior moves with remarkable speed. In self-play against Stockfish 15, this new release gains up to 50 Elo[1] and wins up to 12 times more game pairs[2] than it loses. In major chess engine tournaments, Stockfish reliably tops the rankings[3] winning the TCEC season 24 Superfinal, Swiss, Fischer Random, and Double Random Chess tournaments and the CCC 19 Bullet, 20 Blitz, and 20 Rapid competitions. Leela Chess Zero[4] was the challenger in most finals, putting top-engine chess now firmly in the hands of teams embracing free and open-source software. *Progress made* This updated version of Stockfish introduces several enhancements, including an upgraded neural net architecture (SFNNv6)[5], improved implementation, and refined parameterization. The ongoing utilization of Leela’s data combined with a novel inference approach exploiting sparsity[6], and network compression[7] ensure a speedy evaluation and modest binary sizes while allowing for more weights and higher accuracy. The search has undergone more optimization, leading to improved performance, particularly in longer analyses[8]. Additionally, the Fishtest framework has been improved and is now able to run the tests needed to validate new ideas with 10000s of CPU cores. *Usability improvements* Stockfish now comes with documentation, found in the wiki folder when downloading it or on GitHub[9]. Additionally, Stockfish now includes a clear and consistent forced tablebase win score, displaying a value of 200 minus the number of plies required to reach a tablebase win[10]. Furthermore, the UCI_Elo option, to reduce its strength, has been calibrated[11]. It is worth noting that the evaluation system remains consistent with Stockfish 15.1[12], maintaining the choice that 100cp means a 50% chance of winning the game against an equal opponent[13]. Finally, binaries of our latest development version are now provided continuously as pre-releases on GitHub making it easier for enthusiasts to download the latest and strongest version of the program[14], we thank Roman Korba for having provided a similar service for a long time. *Thank you* The success of the Stockfish project relies on the vibrant community of passionate enthusiasts (we appreciate each and every one of you!) who generously contribute their knowledge, time, and resources. Together, this dedicated community works towards the common goal of developing a powerful, freely accessible, and open-source chess engine. We invite all chess enthusiasts to join the Fishtest testing framework and contribute to the project[15]. The Stockfish team [1] https://tests.stockfishchess.org/tests/view/649409f0dc7002ce609c99cc [2] https://tests.stockfishchess.org/tests/view/649409d7dc7002ce609c99c6 [3] https://en.wikipedia.org/wiki/Stockfish_(chess)#Competition_results [4] https://lczero.org/ [5] https://github.com/official-stockfish/Stockfish/commit/c1fff71 [6] https://github.com/official-stockfish/Stockfish/commit/38e6166 [7] https://github.com/official-stockfish/Stockfish/commit/a46087e [8] https://github.com/official-stockfish/Stockfish/commit/472e726 [9] https://github.com/official-stockfish/Stockfish/wiki/ [10] https://github.com/official-stockfish/Stockfish/commit/def2966 [11] https://github.com/official-stockfish/Stockfish/commit/a08b8d4 [12] https://github.com/official-stockfish/Stockfish/commit/52e84e4 [13] https://github.com/official-stockfish/Stockfish/wiki/Stockfish-FAQ#interpretation-of-the-stockfish-evaluation [14] https://github.com/official-stockfish/Stockfish/releases?q=prerelease%3Atrue [15] https://stockfishchess.org/get-involved/ see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Linmiao Xu
Date: Thu Jun 22 10:33:19 2023 +0200 Timestamp: 1687422799 Update default net to nn-5af11540bbfe.nnue Created by retraining the sparsified master net (nn-cd2ff4716c34.nnue) on a 100% minified dataset including Leela transformers data from T80 may2023. Weights permuted with the exact methods and code in: https://github.com/official-stockfish/Stockfish/pull/4620 LEB128 compression done with the new serialize.py param in: https://github.com/glinscott/nnue-pytorch/pull/251 Initially trained with max epoch 800. Around epoch 780, training was paused and max epoch raised to 960. python3 easy_train.py \ --experiment-name L1-1536-sparse-master-retrain \ --training-dataset /data/leela96-dfrc99-v2-T60novdecT77decT78jantosepT79aprmayT80juntonovjan-v6dd-T80febtomay2023.min.binpack \ --early-fen-skipping 27 \ --start-from-engine-test-net True \ --max_epoch 960 \ --lr 4.375e-4 \ --gamma 0.995 \ --start-lambda 1.0 \ --end-lambda 0.7 \ --tui False \ --seed $RANDOM \ --gpus 0 For preparing the training dataset (interleaved size 328G): python3 interleave_binpacks.py \ leela96-filt-v2.min.binpack \ dfrc99-16tb7p-eval-filt-v2.min.binpack \ filt-v6-dd-min/test60-novdec2021-12tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test77-dec2021-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test78-jantomay2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test78-juntosep2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test79-apr2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test79-may2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-jun2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-jul2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-aug2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-sep2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-oct2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-nov2022-16tb7p-filter-v6-dd.min.binpack \ filt-v6-dd-min/test80-jan2023-16tb7p-filter-v6-dd.min.binpack \ test80-2023/test80-feb2023-16tb7p-no-db.min.binpack \ test80-2023/test80-mar2023-2tb7p-no-db.min.binpack \ test80-2023/test80-apr2023-2tb7p-no-db.min.binpack \ test80-2023/test80-may2023-2tb7p-no-db.min.binpack \ /data/leela96-dfrc99-v2-T60novdecT77decT78jantosepT79aprmayT80juntonovjan-v6dd-T80febtomay2023.min.binpack Minified binpacks and Leela T80 training data from 2023 available at: https://robotmoon.com/nnue-training-data/ Local elo at 25k nodes per move: nn-epoch879.nnue : 3.9 +/- 5.7 Passed STC: https://tests.stockfishchess.org/tests/view/64928c1bdc7002ce609c7690 LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 72000 W: 19242 L: 18889 D: 33869 Elo +1.70 Ptnml(0-2): 182, 7787, 19716, 8126, 189 Passed LTC: https://tests.stockfishchess.org/tests/view/64930a37dc7002ce609c82e3 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 54552 W: 14978 L: 14647 D: 24927 Elo +2.11 Ptnml(0-2): 23, 5123, 16650, 5460, 20 closes https://github.com/official-stockfish/Stockfish/pull/4635 bench 2593605 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: peregrineshahin
Date: Thu Jun 22 10:26:17 2023 +0200 Timestamp: 1687422377 Fix Potential in TB cutoffs for NMP. Removes the second dependency on beta and caps the return value to VALUE_TB_WIN_IN_MAX_PLY - 1 Earlier tests: STC: LLR: 2.96 (-2.94,2.94) <-1.75,0.25> Total: 193632 W: 51372 L: 51326 D: 90934 Elo +0.08 Ptnml(0-2): 447, 20111, 55687, 20091, 480 https://tests.stockfishchess.org/tests/view/6486ee4465ffe077ca125bc1 LTC: LLR: 2.97 (-2.94,2.94) <-1.75,0.25> Total: 331758 W: 89538 L: 89624 D: 152596 Elo -0.09 Ptnml(0-2): 114, 30121, 105516, 29993, 135 https://tests.stockfishchess.org/tests/view/6489401af42a44347ed7be42 updated constant: LTC: LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 100260 W: 27143 L: 27017 D: 46100 Elo +0.44 Ptnml(0-2): 34, 8842, 32248, 8976, 30 https://tests.stockfishchess.org/tests/view/6492fcafdc7002ce609c818c closes: https://github.com/official-stockfish/Stockfish/pull/4632 fixes: https://github.com/official-stockfish/Stockfish/issues/4598 bench: 2370027 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joost VandeVondele
Date: Thu Jun 22 10:17:44 2023 +0200 Timestamp: 1687421864 Update winrate model with June data Retained 748191776 scored positions for analysis const int NormalizeToPawnValue = 328; Corresponding spread = 60; Corresponding normalized spread = 0.18337766691628035; Draw rate at 0.0 eval at move 32 = 0.9914715947898592; closes https://github.com/official-stockfish/Stockfish/pull/4636 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joost VandeVondele
Date: Thu Jun 22 10:15:51 2023 +0200 Timestamp: 1687421751 Update top CPU contributors closes https://github.com/official-stockfish/Stockfish/pull/4629 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: disservin
Date: Tue Jun 20 18:50:12 2023 +0200 Timestamp: 1687279812 Fix failing CI of pull requests adds a guard to prevent pull requests from trying to delete the previous pre-release closing https://github.com/official-stockfish/Stockfish/pull/4631 No functional change. see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joerg Oster
Date: Tue Jun 20 10:47:07 2023 +0200 Timestamp: 1687250827 Fix indentation in qsearch. https://github.com/official-stockfish/Stockfish/pull/4630 No functional change. see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: disservin
Date: Tue Jun 20 08:55:54 2023 +0200 Timestamp: 1687244154 create prereleases upon push to master using github actions, create a prerelease for the latest commit to master. As such a development version will be available on github, in addition to the latest release. closes https://github.com/official-stockfish/Stockfish/pull/4622 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: maxim
Date: Mon Jun 19 21:37:23 2023 +0200 Timestamp: 1687203443 Compressed network parameters Implemented LEB128 (de)compression for the feature transformer. Reduces embedded network size from 70 MiB to 39 Mib. The new nn-78bacfcee510.nnue corresponds to the master net compressed. closes https://github.com/official-stockfish/Stockfish/pull/4617 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: cj5716
Date: Fri Jun 16 19:14:58 2023 +0200 Timestamp: 1686935698 Small cleanup This non-functional change keeps formatting consistent. closes https://github.com/official-stockfish/Stockfish/pull/4623 Bench 2370027 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: AndrovT
Date: Wed Jun 14 18:36:39 2023 +0200 Timestamp: 1686760599 Permute master net weights to increase sparsity Activation data collection using https://github.com/AndrovT/Stockfish/commit/ac468039ab544b03ad9a22c859a4217729c10a77 run as bench 16 1 13 varied_1000.epd depth NNUE log.bin on FENs from https://gist.github.com/AndrovT/7eae6918eb50764227e2bafe7938953c. Permutation found using https://gist.github.com/AndrovT/359c831b7223c637e9156b01eb96949e. Uses a greedy algorithm that goes sequentially through the output positions and chooses a neuron for that position such that the number of nonzero quartets is the smallest. Net weights permuted using https://gist.github.com/AndrovT/9e3fbaebb7082734dc84d27e02094cb3. Benchmark: Result of 100 runs of 'bench 16 1 13 default depth NNUE' ======================================================== base (...kfish-master) = 885869 +/- 7395 test (./stockfish ) = 895885 +/- 7368 diff = +10016 +/- 2984 speedup = +0.0113 P(speedup > 0) = 1.0000 Passed STC: https://tests.stockfishchess.org/tests/view/648866c4713491385c804728 LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 126784 W: 34003 L: 33586 D: 59195 Elo +1.14 Ptnml(0-2): 283, 13001, 36437, 13358, 313 closes https://github.com/official-stockfish/Stockfish/pull/4620 No functional change. see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: peregrineshahin
Date: Wed Jun 14 18:34:57 2023 +0200 Timestamp: 1686760497 Consistent bench extraction with fishtest. Consistent with recent fishtest commit https://github.com/glinscott/fishtest/commit/c0d174396f7fb1c0b3243aaa6cc73769079f3ff9 closes https://github.com/official-stockfish/Stockfish/pull/4619 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Viren6
Date: Wed Jun 14 18:33:56 2023 +0200 Timestamp: 1686760436 Remove setting of static to none if in check in qsearch Small simplification Passed non-regression STC: https://tests.stockfishchess.org/tests/view/6487924d713491385c8034ae LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 59616 W: 15885 L: 15703 D: 28028 Elo +1.06 Ptnml(0-2): 144, 6130, 17086, 6296, 152 closes https://github.com/official-stockfish/Stockfish/pull/4618 No functional change. see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Andreas Matthies
Date: Tue Jun 13 08:45:25 2023 +0200 Timestamp: 1686638725 Fix for MSVC compilation. MSVC needs two more explicit casts to compile new affine_transform_sparse_input. See https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm256_castsi256_ps and https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm_castsi128_ps closes https://github.com/official-stockfish/Stockfish/pull/4616 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Stéphane Nicolet
Date: Tue Jun 13 08:42:55 2023 +0200 Timestamp: 1686638575 Clean-up code indentation in qsearch closes https://github.com/official-stockfish/Stockfish/pull/4615 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Michael Chaly
Date: Mon Jun 12 21:17:31 2023 +0200 Timestamp: 1686597451 Improve comments Fix comments for IIR, also document non-linear scaling in extensions. Also add explicitly the bench, to fix an issue after the last commit. closes https://github.com/official-stockfish/Stockfish/pull/4614 bench 2370027 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: AndrovT
Date: Mon Jun 12 20:41:27 2023 +0200 Timestamp: 1686595287 Use block sparse input for the first layer. Use block sparse input for the first fully connected layer on architectures with at least SSSE3. Depending on the CPU architecture, this yields a speedup of up to 10%, e.g. ``` Result of 100 runs of 'bench 16 1 13 default depth NNUE' base (...ockfish-base) = 959345 +/- 7477 test (...ckfish-patch) = 1054340 +/- 9640 diff = +94995 +/- 3999 speedup = +0.0990 P(speedup > 0) = 1.0000 CPU: 8 x AMD Ryzen 7 5700U with Radeon Graphics Hyperthreading: on ``` Passed STC: https://tests.stockfishchess.org/tests/view/6485aa0965ffe077ca12409c LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 8864 W: 2479 L: 2223 D: 4162 Elo +10.04 Ptnml(0-2): 13, 829, 2504, 1061, 25 This commit includes a net with reordered weights, to increase the likelihood of block sparse inputs, but otherwise equivalent to the previous master net (nn-ea57bea57e32.nnue). Activation data collected with https://github.com/AndrovT/Stockfish/tree/log-activations, running bench 16 1 13 varied_1000.epd depth NNUE on this data. Net parameters permuted with https://gist.github.com/AndrovT/9e3fbaebb7082734dc84d27e02094cb3. closes https://github.com/official-stockfish/Stockfish/pull/4612 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Joost VandeVondele
Date: Mon Jun 12 20:35:44 2023 +0200 Timestamp: 1686594944 Add network export to CI verify the network written by export_net matches the original closes https://github.com/official-stockfish/Stockfish/pull/4613 No functional change see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Linmiao Xu
Date: Sun Jun 11 15:23:52 2023 +0200 Timestamp: 1686489832 Update default net to nn-ea57bea57e32.nnue Created by retraining an earlier epoch (ep659) of the experiment that led to the first SFNNv6 net: - First retrained on the nn-0dd1cebea573 dataset - Then retrained with skip 20 on a smaller dataset containing unfiltered Leela data - And then retrained again with skip 27 on the nn-0dd1cebea573 dataset The equivalent 7-step training sequence from scratch that led here was: 1. max-epoch 400, lambda 1.0, constant LR 9.75e-4, T79T77-filter-v6-dd.min.binpack ep379 chosen for retraining in step2 2. max-epoch 800, end-lambda 0.75, T60T70wIsRightFarseerT60T74T75T76.binpack ep679 chosen for retraining in step3 3. max-epoch 800, end-lambda 0.75, skip 28, nn-e1fb1ade4432 dataset ep799 chosen for retraining in step4 4. max-epoch 800, end-lambda 0.7, skip 28, nn-e1fb1ade4432 dataset ep759 became nn-8d69132723e2.nnue (first SFNNv6 net) ep659 chosen for retraining in step5 5. max-epoch 800, end-lambda 0.7, skip 28, nn-0dd1cebea573 dataset ep759 chosen for retraining in step6 6. max-epoch 800, end-lambda 0.7, skip 20, leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack ep639 chosen for retraining in step7 7. max-epoch 800, end-lambda 0.7, skip 27, nn-0dd1cebea573 dataset ep619 became nn-ea57bea57e32.nnue For the last retraining (step7): python3 easy_train.py --experiment-name L1-1536-Re6-masterShuffled-ep639-sk27-Re5-leela-dfrc-v2-T77toT80small-Re4-masterShuffled-ep659-Re3-sameAs-Re2-leela96-dfrc99-16t-v2-T60novdecT80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-Re1-LeelaFarseer-new-T77T79 \ --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \ --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \ --early-fen-skipping 27 \ --start-lambda 1.0 \ --end-lambda 0.7 \ --max_epoch 800 \ --start-from-engine-test-net False \ --start-from-model /data/L1-1536-Re5-leela-dfrc-v2-T77toT80small-epoch639.nnue \ --lr 4.375e-4 \ --gamma 0.995 \ --tui False \ --seed $RANDOM \ --gpus "0," For preparing the step6 leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack dataset: python3 interleave_binpacks.py \ leela96-filt-v2.binpack \ dfrc99-16tb7p-eval-filt-v2.binpack \ test77-dec2021-16tb7p.no-db.min-mar2023.binpack \ test78-janfeb2022-16tb7p.no-db.min-mar2023.binpack \ test79-apr2022-16tb7p-filter-v6-dd.binpack \ test80-apr2022-16tb7p.no-db.min-mar2023.binpack \ /data/leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack The unfiltered Leela data used for the step6 dataset can be found at: https://robotmoon.com/nnue-training-data Local elo at 25k nodes per move: nn-epoch619.nnue : 2.3 +/- 1.9 Passed STC: https://tests.stockfishchess.org/tests/view/6480d43c6e6ce8d9fc6d7cc8 LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 40992 W: 11017 L: 10706 D: 19269 Elo +2.64 Ptnml(0-2): 113, 4400, 11170, 4689, 124 Passed LTC: https://tests.stockfishchess.org/tests/view/648119ac6e6ce8d9fc6d8208 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 129174 W: 35059 L: 34579 D: 59536 Elo +1.29 Ptnml(0-2): 66, 12548, 38868, 13050, 55 closes https://github.com/official-stockfish/Stockfish/pull/4611 bench: 2370027 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Guenther Demetz
Date: Sun Jun 11 15:13:57 2023 +0200 Timestamp: 1686489237 Reintroduce SEE verification against discovered attacks Reintroduces https://github.com/official-stockfish/Stockfish/pull/4453 along with https://github.com/official-stockfish/Stockfish/pull/4469 Leaving out https://github.com/official-stockfish/Stockfish/pull/4533 https://github.com/official-stockfish/Stockfish/pull/4572 Passed STC: https://tests.stockfishchess.org/tests/view/647d8c37726f6b400e408a0a LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 143168 W: 38346 L: 37892 D: 66930 Elo +1.10 Ptnml(0-2): 352, 15672, 39164, 15962, 434 Passed LTC: https://tests.stockfishchess.org/tests/view/647ee8c528c4431bcb58e432 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 71538 W: 19560 L: 19190 D: 32788 Elo +1.80 Ptnml(0-2): 49, 6905, 21499, 7259, 57 closes https://github.com/official-stockfish/Stockfish/pull/4609 bench: 2595430 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: cj5716
Date: Sun Jun 11 15:05:54 2023 +0200 Timestamp: 1686488754 Simplify multiplier for improvement This simplifies a `* 99 / 1300` term into just `/ 13`. Passed non-regression STC: LLR: 2.92 (-2.94,2.94) <-1.75,0.25> Total: 58816 W: 15727 L: 15540 D: 27549 Elo +1.10 Ptnml(0-2): 149, 6370, 16203, 6517, 169 https://tests.stockfishchess.org/tests/view/647d25e4726f6b400e408165 Passed non-regression LTC: LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 154386 W: 41749 L: 41669 D: 70968 Elo +0.18 Ptnml(0-2): 94, 14992, 46956, 15042, 109 https://tests.stockfishchess.org/tests/view/647d9b3c726f6b400e408b2a closes https://github.com/official-stockfish/Stockfish/pull/4608 Bench: 2511327 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Linmiao Xu
Date: Tue Jun 6 21:21:56 2023 +0200 Timestamp: 1686079316 Remove optimism multiplier in nnue eval calculation The same formula had passed SPRT against an earlier version of master. Passed non-regression STC vs. d99942f: https://tests.stockfishchess.org/tests/view/6478e76654dd118e1d98f72e LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 118720 W: 31402 L: 31277 D: 56041 Elo +0.37 Ptnml(0-2): 301, 13148, 32344, 13259, 308 Passed non-regression LTC vs. d99942f: https://tests.stockfishchess.org/tests/view/647a22c154dd118e1d991146 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 74286 W: 20019 L: 19863 D: 34404 Elo +0.73 Ptnml(0-2): 31, 7189, 22540, 7359, 24 The earlier patch had conflicted with a faster SPRT passer, so this was tested again after rebasing on latest master. Passed non-regression STC: https://tests.stockfishchess.org/tests/view/647d6e46726f6b400e408790 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 166176 W: 44309 L: 44234 D: 77633 Elo +0.16 Ptnml(0-2): 461, 18252, 45557, 18387, 431 Passed non-regression LTC: https://tests.stockfishchess.org/tests/view/647eb00ba268d1bc11255e7b LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 28170 W: 7713 L: 7513 D: 12944 Elo +2.47 Ptnml(0-2): 14, 2609, 8635, 2817, 10 closes https://github.com/official-stockfish/Stockfish/pull/4607 bench 2503095 see source |