| 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: FauziAkram
Date: Mon Jul 3 18:38:41 2023 +0200 Timestamp: 1688402321 Improving grammar and readability of comments closes https://github.com/official-stockfish/Stockfish/pull/4643 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: Muzhen Gaming
Date: Mon Jul 3 18:33:27 2023 +0200 Timestamp: 1688402007 Simplify score improvement reduction Reduce depth by 2 based on score improvement, only for depths 3 to 11. Simplification STC: https://tests.stockfishchess.org/tests/view/64929a53dc7002ce609c7807 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 238912 W: 63466 L: 63468 D: 111978 Elo -0.00 Ptnml(0-2): 564, 26262, 65805, 26262, 563 Simplification LTC: https://tests.stockfishchess.org/tests/view/64942e47dc7002ce609c9e07 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 64452 W: 17485 L: 17320 D: 29647 Elo +0.89 Ptnml(0-2): 19, 6161, 19706, 6316, 24 closes https://github.com/official-stockfish/Stockfish/pull/4637 Bench: 2740142 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: Muzhen Gaming
Date: Mon Jul 3 18:27:33 2023 +0200 Timestamp: 1688401653 Simplify away improvement term in null move search passed STC: https://tests.stockfishchess.org/tests/view/649c0d2edc7002ce609d33b5 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 271104 W: 72181 L: 72217 D: 126706 Elo -0.05 Ptnml(0-2): 691, 30042, 74129, 29992, 698 passed LTC: https://tests.stockfishchess.org/tests/view/649d0dd7dc7002ce609d4efa LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 183120 W: 49469 L: 49418 D: 84233 Elo +0.10 Ptnml(0-2): 84, 17636, 56063, 17699, 78 closes https://github.com/official-stockfish/Stockfish/pull/4650 Bench: 2642851 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: Mon Jul 3 18:24:41 2023 +0200 Timestamp: 1688401481 Fix pruning to (in TB loss) in Null move pruning. Current logic can apply Null move pruning on a dead-lost position returning an unproven loss (i.e. in TB loss score or mated in losing score) on nonPv nodes. on a default bench, this can be observed by adding this debugging line: ``` if (nullValue >= beta) { // Do not return unproven mate or TB scores nullValue = std::min(nullValue, VALUE_TB_WIN_IN_MAX_PLY-1); dbg_hit_on(nullValue <= VALUE_TB_LOSS_IN_MAX_PLY); // Hit #0: Total 73983 Hits 1 Hit Rate (%) 0.00135166 if (thisThread->nmpMinPly || depth < 14) return nullValue; ``` This fixes this very rare issue (happens at ~0.00135166% of the time) by eliminating the need to try Null Move Pruning with dead-lost positions and leaving it to be determined by a normal searching flow. The previous try to fix was not as safe enough because it was capping the returned value to (out of TB range) thus reviving the dead-lost position based on an artificial clamp (i.e. the in TB score/mate score can be lost on that nonPv node): https://tests.stockfishchess.org/tests/view/649756d5dc7002ce609cd794 Final fix: Passed STC: https://tests.stockfishchess.org/tests/view/649a5446dc7002ce609d1049 LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 577280 W: 153613 L: 153965 D: 269702 Elo -0.21 Ptnml(0-2): 1320, 60594, 165190, 60190, 1346 Passed LTC: https://tests.stockfishchess.org/tests/view/649cd048dc7002ce609d4801 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 246432 W: 66769 L: 66778 D: 112885 Elo -0.01 Ptnml(0-2): 83, 22105, 78847, 22100, 81 closes https://github.com/official-stockfish/Stockfish/pull/4649 Bench: 2425978 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: mstembera
Date: Mon Jul 3 18:20:10 2023 +0200 Timestamp: 1688401210 Simplify lookup_count and clean up pieces(). https://github.com/official-stockfish/Stockfish/pull/4656 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: Mon Jul 3 18:17:20 2023 +0200 Timestamp: 1688401040 Add bmi2 to CI generated binaries verify bench for avx2 and bmi2 as well closes https://github.com/official-stockfish/Stockfish/pull/4658 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: ppigazzini
Date: Sun Jul 2 10:32:36 2023 +0200 Timestamp: 1688286756 Make posix and msys2 shells consistent in CI In CI, it is typical for the process to halt immediately when an error is encountered. However, with our `shell: bash {0}` configuration, the process continues despite errors for posix shells. This commit updates the behavior of posix and msys2 shells to ensure consistency in terms of pipeline exit codes and stop conditions. We adopt the most appropriate default behavior as recommended by the GitHub documentation. Update the code that searches for the bench value in the git log: - to be compatible with the new shell settings - to retry the value from the first line that contains only the template and spaces/tabs/newlines see also https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsshell https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#exit-codes-and-error-action-preference https://github.com/msys2/setup-msys2/blob/main/main.js closes https://github.com/official-stockfish/Stockfish/pull/4653 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: Sat Jul 1 13:34:30 2023 +0200 Timestamp: 1688211270 Update NNUE architecture to SFNNv7 with larger L1 size of 2048 Creating this net involved: - a 5-step training process from scratch - greedy permuting L1 weights with https://github.com/official-stockfish/Stockfish/pull/4620 - leb128 compression with https://github.com/glinscott/nnue-pytorch/pull/251 - greedy 2- and 3- cycle permuting with https://github.com/official-stockfish/Stockfish/pull/4640 The 5 training steps were: 1. 400 epochs, lambda 1.0, lr 9.75e-4 UHOx2-wIsRight-multinet-dfrc-n5000-largeGensfen-d9.binpack (178G) nodes5000pv2_UHO.binpack data_pv-2_diff-100_nodes-5000.binpack wrongIsRight_nodes5000pv2.binpack multinet_pv-2_diff-100_nodes-5000.binpack dfrc_n5000.binpack large_gensfen_multipvdiff_100_d9.binpack ep399 chosen as start model for step2 2. 800 epochs, end-lambda 0.75, skip 16 LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G) T60T70wIsRightFarseerT60T74T75T76.binpack test78-junjulaug2022-16tb7p.no-db.min.binpack test79-mar2022-16tb7p.no-db.min.binpack test80-dec2022-16tb7p.no-db.min.binpack ep559 chosen as start model for step3 3. 800 epochs, end-lambda 0.725, skip 20 leela96-dfrc99-v2-T80dectofeb-sk20-mar-v6-T77decT78janfebT79apr.binpack (223G) leela96-filt-v2.min.binpack dfrc99-16tb7p-eval-filt-v2.min.binpack test80-dec2022-16tb7p-filter-v6-sk20.min-mar2023.binpack test80-jan2023-16tb7p-filter-v6-sk20.min-mar2023.binpack test80-feb2023-16tb7p-filter-v6-sk20.min-mar2023.binpack test80-mar2023-2tb7p-filter-v6.min.binpack test77-dec2021-16tb7p.no-db.min.binpack test78-janfeb2022-16tb7p.no-db.min.binpack test79-apr2022-16tb7p.no-db.min.binpack ep499 chosen as start model for step4 4. 800 epochs, end-lambda 0.7, skip 24 0dd1cebea57 dataset https://github.com/official-stockfish/Stockfish/pull/4606 ep599 chosen as start model for step5 5. 800 epochs, end-lambda 0.7, skip 28 same dataset as step4 ep619 became nn-1b951f8b449d.nnue For the final step5 training: python3 easy_train.py \ --experiment-name L1-2048-S5-sameData-sk28-S4-0dd1cebea57-shuffled-S3-leela96-dfrc99-v2-T80dectofeb-sk20-mar-v6-T77decT78janfebT79apr-sk20-S2-LeelaFarseerT78T79T80-ep399-S1-UHOx2-wIsRight-multinet-dfrc-n5000-largeGensfen-d9 \ --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \ --early-fen-skipping 28 \ --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-2048 \ --engine-test-branch linrock/Stockfish/L1-2048 \ --start-from-engine-test-net False \ --start-from-model /data/experiments/experiment_L1-2048-S4-0dd1cebea57-shuffled-S3-leela96-dfrc99-v2-T80dectofeb-sk20-mar-v6-T77decT78janfebT79apr-sk20-S2-LeelaFarseerT78T79T80-ep399-S1-UHOx2-wIsRight-multinet-dfrc-n5000-largeGensfen-d9/training/run_0/nn-epoch599.nnue --max_epoch 800 \ --lr 4.375e-4 \ --gamma 0.995 \ --start-lambda 1.0 \ --end-lambda 0.7 \ --tui False \ --seed $RANDOM \ --gpus 0 SF training data components for the step1 dataset: https://drive.google.com/drive/folders/1yLCEmioC3Xx9KQr4T7uB6GnLm5icAYGU Leela training data for steps 2-5 can be found at: https://robotmoon.com/nnue-training-data/ Due to larger L1 size and slower inference, the speed penalty loses elo at STC. Measurements from 100 bench runs at depth 13 with x86-64-modern on Intel Core i5-1038NG7 2.00GHz: sf_base = 1240730 +/- 3443 (95%) sf_test = 1153341 +/- 2832 (95%) diff = -87388 +/- 1616 (95%) speedup = -7.04330% +/- 0.130% (95%) Local elo at 25k nodes per move (vs. L1-1536 nn-fdc1d0fe6455.nnue): nn-epoch619.nnue : 21.1 +/- 3.2 Failed STC: https://tests.stockfishchess.org/tests/view/6498ee93dc7002ce609cf979 LLR: -2.95 (-2.94,2.94) <0.00,2.00> Total: 11680 W: 3058 L: 3299 D: 5323 Elo -7.17 Ptnml(0-2): 44, 1422, 3149, 1181, 44 LTC: https://tests.stockfishchess.org/tests/view/649b32f5dc7002ce609d20cf Elo: 0.68 ± 1.5 (95%) LOS: 80.5% Total: 40000 W: 10887 L: 10809 D: 18304 Elo +0.68 Ptnml(0-2): 36, 3938, 11958, 4048, 20 nElo: 1.50 ± 3.4 (95%) PairsRatio: 1.02 Passed VLTC 180+1.8: https://tests.stockfishchess.org/tests/view/64992b43dc7002ce609cfd20 LLR: 3.06 (-2.94,2.94) <0.00,2.00> Total: 38086 W: 10612 L: 10338 D: 17136 Elo +2.50 Ptnml(0-2): 9, 3316, 12115, 3598, 5 Passed VLTC SMP 60+0.6 th 8: https://tests.stockfishchess.org/tests/view/649a21fedc7002ce609d0c7d LLR: 2.95 (-2.94,2.94) <0.50,2.50> Total: 38936 W: 11091 L: 10820 D: 17025 Elo +2.42 Ptnml(0-2): 1, 2948, 13305, 3207, 7 closes https://github.com/official-stockfish/Stockfish/pull/4646 Bench: 2505168 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: Sat Jul 1 13:06:49 2023 +0200 Timestamp: 1688209609 Negative extension on cutNodes based on depth This patch was inspired by candirufish original attempt at negative extensions here that failed yellow: https://tests.stockfishchess.org/tests/view/6486529065ffe077ca124f32 I tested some variations of the idea and tuned a depth condition for a modified version of it here https://tests.stockfishchess.org/tests/view/648db80a91c58631ce31fe00 after noticing abnormal scaling (ie many passed STC but not LTC) After some small tweaks I got the final version here Passed STC: LLR: 2.98 (-2.94,2.94) <0.00,2.00> Total: 122208 W: 32776 L: 32350 D: 57082 Elo +1.21 Ptnml(0-2): 310, 13250, 33553, 13686, 305 https://tests.stockfishchess.org/tests/view/64997934dc7002ce609d01e3 Passed LTC: LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 145092 W: 39617 L: 39115 D: 66360 Elo +1.20 Ptnml(0-2): 54, 13691, 44552, 14197, 52 https://tests.stockfishchess.org/tests/view/649a1c5ddc7002ce609d0bff closes https://github.com/official-stockfish/Stockfish/pull/4644 Bench: 2637784 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: 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 |