| !! latest version !! Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: SFisGOD
Date: Mon Jul 26 07:52:59 2021 +0200 Timestamp: 1627278779 Update default net to nn-26abeed38351.nnue SPSA: https://tests.stockfishchess.org/tests/view/60fba335d8a6b65b2f3a7891 New best values: Half of the changes from the tuning run. Setting: nodestime=300 with 10+0.1 (approximate real TC is 2.5 seconds) The rest is the same as described in #3593 The change from nodestime=600 to 300 was suggested by gekkehenker to prevent time losses for some slow workers SFisGOD@94cd757#commitcomment-53324840 STC: LLR: 2.96 (-2.94,2.94) <-0.50,2.50> Total: 67448 W: 5241 L: 5036 D: 57171 Elo +1.06 Ptnml(0-2): 151, 4198, 24827, 4391, 157 https://tests.stockfishchess.org/tests/view/60fd50f2d8a6b65b2f3a798e LTC: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 48752 W: 1504 L: 1358 D: 45890 Elo +1.04 Ptnml(0-2): 13, 1226, 21754, 1368, 15 https://tests.stockfishchess.org/tests/view/60fd7bb2d8a6b65b2f3a79a9 Closes https://github.com/official-stockfish/Stockfish/pull/3630 Bench: 5124774 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Giacomo Lorenzetti
Date: Mon Jul 26 07:48:58 2021 +0200 Timestamp: 1627278538 Simplification in LMR This commit removes the `!captureOrPromotion` condition from ttCapture reduction and from good/bad history reduction (similar to #3619). passed STC: https://tests.stockfishchess.org/tests/view/60fc734ad8a6b65b2f3a7922 LLR: 2.97 (-2.94,2.94) <-2.50,0.50> Total: 48680 W: 3855 L: 3776 D: 41049 Elo +0.56 Ptnml(0-2): 118, 3145, 17744, 3206, 127 passed LTC: https://tests.stockfishchess.org/tests/view/60fce7d5d8a6b65b2f3a794c LLR: 2.93 (-2.94,2.94) <-2.50,0.50> Total: 86528 W: 2471 L: 2450 D: 81607 Elo +0.08 Ptnml(0-2): 28, 2203, 38777, 2232, 24 closes https://github.com/official-stockfish/Stockfish/pull/3629 Bench: 4951406 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: MichaelB7
Date: Sat Jul 24 18:04:59 2021 +0200 Timestamp: 1627142699 Update the default net to nn-76a8a7ffb820.nnue. combined work by Serio Vieri, Michael Byrne, and Jonathan D (aka SFisGod) based on top of previous developments, by restarts from good nets. Sergio generated the net https://tests.stockfishchess.org/api/nn/nn-d8609abe8caf.nnue: The initial net nn-d8609abe8caf.nnue is trained by generating around 16B of training data from the last master net nn-9e3c6298299a.nnue, then trained, continuing from the master net, with lambda=0.2 and sampling ratio of 1. Starting with LR=2e-3, dropping LR with a factor of 0.5 until it reaches LR=5e-4. in_scaling is set to 361. No other significant changes made to the pytorch trainer. Training data gen command (generates in chunks of 200k positions): generate_training_data min_depth 9 max_depth 11 count 200000 random_move_count 10 random_move_max_ply 80 random_multi_pv 12 random_multi_pv_diff 100 random_multi_pv_depth 8 write_min_ply 10 eval_limit 1500 book noob_3moves.epd output_file_name gendata/$(date +"%Y%m%d-%H%M")_${HOSTNAME}.binpack PyTorch trainer command (Note that this only trains for 20 epochs, repeatedly train until convergence): python train.py --features "HalfKAv2^" --max_epochs 20 --smart-fen-skipping --random-fen-skipping 500 --batch-size 8192 --default_root_dir $dir --seed $RANDOM --threads 4 --num-workers 32 --gpus $gpuids --track_grad_norm 2 --gradient_clip_val 0.05 --lambda 0.2 --log_every_n_steps 50 $resumeopt $data $val See https://github.com/sergiovieri/Stockfish/tree/tools_mod/rl for the scripts used to generate data. Based on that Michael generated nn-76a8a7ffb820.nnue in the following way: The net being submitted was trained with the pytorch trainer: https://github.com/glinscott/nnue-pytorch python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 30 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --auto_lr_find True --lambda=1.0 --max_epochs=240 --seed %random%%random% --default_root_dir exp/run_109 --resume-from-model ./pt/nn-d8609abe8caf.pt This run is thus started from Segio Vieri's net nn-d8609abe8caf.nnue all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing Each set was concatenated together - making one large Wrong_NNUE 2 binpack and one large Training so the were approximately equal in size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training_Data binpack model.py modifications: loss = torch.pow(torch.abs(p - q), 2.6).mean() LR = 8.0e-5 calculated as follows: 1.5e-3*(.992^360) - the idea here was to take a highly trained net and just use all.binpack as a finishing micro refinement touch for the last 2 Elo or so. This net was discovered on the 59th epoch. optimizer = ranger.Ranger(train_params, betas=(.90, 0.999), eps=1.0e-7, gc_loc=False, use_gc=False) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.992) For this micro optimization, I had set the period to "5" in train.py. This changes the checkpoint output so that every 5th checkpoint file is created The final touches were to adjust the NNUE scale, as was done by Jonathan in tests running at the same time. passed LTC https://tests.stockfishchess.org/tests/view/60fa45aed8a6b65b2f3a77a4 LLR: 2.94 (-2.94,2.94) <0.50,3.50> Total: 53040 W: 1732 L: 1575 D: 49733 Elo +1.03 Ptnml(0-2): 14, 1432, 23474, 1583, 17 passed STC https://tests.stockfishchess.org/tests/view/60f9fee2d8a6b65b2f3a7775 LLR: 2.94 (-2.94,2.94) <-0.50,2.50> Total: 37928 W: 3178 L: 3001 D: 31749 Elo +1.62 Ptnml(0-2): 100, 2446, 13695, 2623, 100. closes https://github.com/official-stockfish/Stockfish/pull/3626 Bench: 5169957 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Giacomo Lorenzetti
Date: Fri Jul 23 19:02:58 2021 +0200 Timestamp: 1627059778 Apply good/bad history reduction also when inCheck Main idea is that, in some cases, 'in check' situations are not so different from 'not in check' ones. Trying to use piece count in order to select only a few 'in check' situations have failed LTC testing. It could be interesting to apply one of those ideas in other parts of the search function. passed STC: https://tests.stockfishchess.org/tests/view/60f1b68dd1189bed71812d40 LLR: 2.93 (-2.94,2.94) <-2.50,0.50> Total: 53472 W: 4078 L: 4008 D: 45386 Elo +0.45 Ptnml(0-2): 127, 3297, 19795, 3413, 104 passed LTC: https://tests.stockfishchess.org/tests/view/60f291e6d1189bed71812de3 LLR: 2.92 (-2.94,2.94) <-2.50,0.50> Total: 89712 W: 2651 L: 2632 D: 84429 Elo +0.07 Ptnml(0-2): 60, 2261, 40188, 2294, 53 closes https://github.com/official-stockfish/Stockfish/pull/3619 Bench: 5185789 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: pb00067
Date: Fri Jul 23 18:53:03 2021 +0200 Timestamp: 1627059183 Simplify lowply-history scoring logic STC: https://tests.stockfishchess.org/tests/view/60eee559d1189bed71812b16 LLR: 2.97 (-2.94,2.94) <-2.50,0.50> Total: 33976 W: 2523 L: 2431 D: 29022 Elo +0.94 Ptnml(0-2): 66, 2030, 12730, 2070, 92 LTC: https://tests.stockfishchess.org/tests/view/60eefa12d1189bed71812b24 LLR: 2.93 (-2.94,2.94) <-2.50,0.50> Total: 107240 W: 3053 L: 3046 D: 101141 Elo +0.02 Ptnml(0-2): 56, 2668, 48154, 2697, 45 closes https://github.com/official-stockfish/Stockfish/pull/3616 bench: 5199177 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Vizvezdenec
Date: Fri Jul 23 18:47:30 2021 +0200 Timestamp: 1627058850 Prune illegal moves in qsearch earlier The main idea is that illegal moves influencing search or qsearch obviously can't be any sort of good. The only reason why initially legality checks for search and qsearch were done after they actually can influence some heuristics is because legality check is expensive computationally. Eventually in search it was moved to the place where it makes sure that illegal moves can't influence search. This patch shows that the same can be done for qsearch + it passed STC with elo-gaining bounds + it removes 3 lines of code because one no longer needs to increment/decrement movecount on illegal moves. passed STC with elo-gaining bounds https://tests.stockfishchess.org/tests/view/60f20aefd1189bed71812da0 LLR: 2.94 (-2.94,2.94) <-0.50,2.50> Total: 61512 W: 4688 L: 4492 D: 52332 Elo +1.11 Ptnml(0-2): 139, 3730, 22848, 3874, 165 The same version functionally but with moving condition ever earlier passed LTC with simplification bounds. https://tests.stockfishchess.org/tests/view/60f292cad1189bed71812de9 LLR: 2.98 (-2.94,2.94) <-2.50,0.50> Total: 60944 W: 1724 L: 1685 D: 57535 Elo +0.22 Ptnml(0-2): 11, 1556, 27298, 1597, 10 closes https://github.com/official-stockfish/Stockfish/pull/3618 bench 4709569 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Liam Keegan
Date: Fri Jul 23 18:16:05 2021 +0200 Timestamp: 1627056965 Add macOS and windows to CI - macOS - system clang - gcc - windows / msys2 - mingw 64-bit gcc - mingw 32-bit gcc - minor code fixes to get new CI jobs to pass - code: suppress unused-parameter warning on 32-bit windows - Makefile: if arch=any on macos, don't specify arch at all fixes https://github.com/official-stockfish/Stockfish/issues/2958 closes https://github.com/official-stockfish/Stockfish/pull/3623 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: VoyagerOne
Date: Tue Jul 13 17:35:20 2021 +0200 Timestamp: 1626190520 Don't save excluded move eval in TT STC: LLR: 2.93 (-2.94,2.94) <-0.50,2.50> Total: 17544 W: 1384 L: 1236 D: 14924 Elo +2.93 Ptnml(0-2): 37, 1031, 6499, 1157, 48 https://tests.stockfishchess.org/tests/view/60ec8d9bd1189bed71812999 LTC: LLR: 2.95 (-2.94,2.94) <0.50,3.50> Total: 26136 W: 823 L: 707 D: 24606 Elo +1.54 Ptnml(0-2): 6, 643, 11656, 755, 8 https://tests.stockfishchess.org/tests/view/60ecb11ed1189bed718129ba closes https://github.com/official-stockfish/Stockfish/pull/3614 Bench: 5505251 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Vizvezdenec
Date: Tue Jul 13 17:33:20 2021 +0200 Timestamp: 1626190400 Remove second futility pruning depth limit This patch removes futility pruning lmrDepth limit for futility pruning at parent nodes. Since it's already capped by margin that is a function of lmrDepth there is no need to extra cap it with lmrDepth. passed STC https://tests.stockfishchess.org/tests/view/60e9b5dfd1189bed71812777 LLR: 2.97 (-2.94,2.94) <-2.50,0.50> Total: 14872 W: 1264 L: 1145 D: 12463 Elo +2.78 Ptnml(0-2): 37, 942, 5369, 1041, 47 passed LTC https://tests.stockfishchess.org/tests/view/60e9c635d1189bed71812790 LLR: 2.96 (-2.94,2.94) <-2.50,0.50> Total: 40336 W: 1280 L: 1225 D: 37831 Elo +0.47 Ptnml(0-2): 24, 1057, 17960, 1094, 33 closes https://github.com/official-stockfish/Stockfish/pull/3612 bench: 5064969 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: pb00067
Date: Tue Jul 13 17:31:15 2021 +0200 Timestamp: 1626190275 SEE: simplify stm variable initialization Pull #3458 removed the only usage of pos.see_ge() moving pieces that don't belong to the side to move, so we can simplify this, adding an assert. closes https://github.com/official-stockfish/Stockfish/pull/3607 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Vizvezdenec
Date: Tue Jul 13 17:23:30 2021 +0200 Timestamp: 1626189810 Remove futility pruning depth limit This patch removes futility pruning depth limit for child node futility pruning. In current master it was double capped by depth and by futility margin, which is also a function of depth, which didn't make much sense. passed STC https://tests.stockfishchess.org/tests/view/60e2418f9ea99d7c2d693e64 LLR: 2.95 (-2.94,2.94) <-2.50,0.50> Total: 116168 W: 9100 L: 9097 D: 97971 Elo +0.01 Ptnml(0-2): 319, 7496, 42476, 7449, 344 passed LTC https://tests.stockfishchess.org/tests/view/60e3374f9ea99d7c2d693f20 LLR: 2.96 (-2.94,2.94) <-2.50,0.50> Total: 43304 W: 1282 L: 1231 D: 40791 Elo +0.41 Ptnml(0-2): 8, 1126, 19335, 1173, 10 closes https://github.com/official-stockfish/Stockfish/pull/3606 bench 4965493 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: SFisGOD
Date: Sat Jul 3 10:03:32 2021 +0200 Timestamp: 1625299412 Update default net to nn-9e3c6298299a.nnue Optimization of nn-956480d8378f.nnue using SPSA https://tests.stockfishchess.org/tests/view/60da2bf63beab81350ac9fe7 Same method as described in PR #3593 STC: LLR: 2.93 (-2.94,2.94) <-0.50,2.50> Total: 17792 W: 1525 L: 1372 D: 14895 Elo +2.99 Ptnml(0-2): 28, 1156, 6401, 1257, 54 https://tests.stockfishchess.org/tests/view/60deffc59ea99d7c2d693c19 LTC: LLR: 2.96 (-2.94,2.94) <0.50,3.50> Total: 36544 W: 1245 L: 1109 D: 34190 Elo +1.29 Ptnml(0-2): 12, 988, 16139, 1118, 15 https://tests.stockfishchess.org/tests/view/60df11339ea99d7c2d693c22 closes https://github.com/official-stockfish/Stockfish/pull/3601 Bench: 4687476 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Paul Mulders
Date: Sat Jul 3 09:51:03 2021 +0200 Timestamp: 1625298663 Allow passing RTLIB=compiler-rt to make Not all linux users will have libatomic installed. When using clang as the system compiler with compiler-rt as the default runtime library instead of libgcc, atomic builtins may be provided by compiler-rt. This change allows such users to pass RTLIB=compiler-rt to make sure the build doesn't error out on the missing (unnecessary) libatomic. closes https://github.com/official-stockfish/Stockfish/pull/3597 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: candirufish
Date: Sat Jul 3 09:44:05 2021 +0200 Timestamp: 1625298245 no cut node reduction for killer moves. stc: LLR: 2.95 (-2.94,2.94) <-0.50,2.50> Total: 44344 W: 3474 L: 3294 D: 37576 Elo +1.41 Ptnml(0-2): 117, 2710, 16338, 2890, 117 https://tests.stockfishchess.org/tests/view/60d8ea673beab81350ac9eb8 ltc: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 82600 W: 2638 L: 2441 D: 77521 Elo +0.83 Ptnml(0-2): 38, 2147, 36749, 2312, 54 https://tests.stockfishchess.org/tests/view/60d9048f3beab81350ac9eed closes https://github.com/official-stockfish/Stockfish/pull/3600 Bench: 5160239 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: xoto10
Date: Sat Jul 3 09:26:58 2021 +0200 Timestamp: 1625297218 Simplify lazy_skip. Small speedup by removing operations in lazy_skip. STC 10+0.1 : LLR: 2.93 (-2.94,2.94) <-2.50,0.50> Total: 55088 W: 4553 L: 4482 D: 46053 Elo +0.45 Ptnml(0-2): 163, 3546, 20045, 3637, 153 https://tests.stockfishchess.org/tests/view/60daa2cb3beab81350aca04d LTC 60+0.6 : LLR: 2.93 (-2.94,2.94) <-2.50,0.50> Total: 46136 W: 1457 L: 1407 D: 43272 Elo +0.38 Ptnml(0-2): 10, 1282, 20442, 1316, 18 https://tests.stockfishchess.org/tests/view/60db0e753beab81350aca08e closes https://github.com/official-stockfish/Stockfish/pull/3599 Bench 5122403 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 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 3 09:25:16 2021 +0200 Timestamp: 1625297116 Simplify format_cp_aligned_dot() closes https://github.com/official-stockfish/Stockfish/pull/3583 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 Windows 32 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 3 09:20:06 2021 +0200 Timestamp: 1625296806 Restore development version 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 Windows 32 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: Fri Jul 2 14:53:30 2021 +0200 Timestamp: 1625230410 Stockfish 14 Official release version of Stockfish 14 Bench: 4770936 --- Today, we have the pleasure to announce Stockfish 14. As usual, downloads will be freely available at https://stockfishchess.org The engine is now significantly stronger than just a few months ago, and wins four times more game pairs than it loses against the previous release version [0]. Stockfish 14 is now at least 400 Elo ahead of Stockfish 7, a top engine in 2016 [1]. During the last five years, Stockfish has thus gained about 80 Elo per year. Stockfish 14 evaluates positions more accurately than Stockfish 13 as a result of two major steps forward in defining and training the efficiently updatable neural network (NNUE) that provides the evaluation for positions. First, the collaboration with the Leela Chess Zero team - announced previously [2] - has come to fruition. The LCZero team has provided a collection of billions of positions evaluated by Leela that we have combined with billions of positions evaluated by Stockfish to train the NNUE net that powers Stockfish 14. The fact that we could use and combine these datasets freely was essential for the progress made and demonstrates the power of open source and open data [3]. Second, the architecture of the NNUE network was significantly updated: the new network is not only larger, but more importantly, it deals better with large material imbalances and can specialize for multiple phases of the game [4]. A new project, kick-started by Gary Linscott and Tomasz Sobczyk, led to a GPU accelerated net trainer written in pytorch.[5] This tool allows for training high-quality nets in a couple of hours. Finally, this release features some search refinements, minor bug fixes and additional improvements. For example, Stockfish is now about 90 Elo stronger for chess960 (Fischer random chess) at short time control. The Stockfish project builds on a thriving community of enthusiasts (thanks everybody!) that contribute their expertise, time, and resources to build a free and open-source chess engine that is robust, widely available, and very strong. We invite our chess fans to join the fishtest testing framework and programmers to contribute to the project on github [6]. Stay safe and enjoy chess! The Stockfish team [0] https://tests.stockfishchess.org/tests/view/60dae5363beab81350aca077 [1] https://nextchessmove.com/dev-builds [2] https://stockfishchess.org/blog/2021/stockfish-13/ [3] https://lczero.org/blog/2021/06/the-importance-of-open-data/ [4] https://github.com/official-stockfish/Stockfish/commit/e8d64af1 [5] https://github.com/glinscott/nnue-pytorch/ [6] 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: Brad Knox
Date: Tue Jun 29 10:24:54 2021 +0200 Timestamp: 1624955094 Update Top CPU Contributors closes https://github.com/official-stockfish/Stockfish/pull/3595 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: SFisGOD
Date: Mon Jun 28 21:31:58 2021 +0200 Timestamp: 1624908718 Update default net to nn-3475407dc199.nnue Optimization of eight subnetwork output layers of Michael's nn-190f102a22c3.nnue using SPSA https://tests.stockfishchess.org/tests/view/60d5510642a522cc50282ef3 Parameters: A total of 256 net weights and 8 net biases were tuned New best values: The raw values at the end of the tuning run were used (800k games, 5 seconds TC) Settings: default ck value and SPSA A is 30,000 (3.75% of the total number of games) STC: LLR: 2.94 (-2.94,2.94) <-0.50,2.50> Total: 29064 W: 2435 L: 2269 D: 24360 Elo +1.98 Ptnml(0-2): 72, 1857, 10505, 2029, 69 https://tests.stockfishchess.org/tests/view/60d8ea123beab81350ac9eb6 LTC: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 61848 W: 2055 L: 1884 D: 57909 Elo +0.96 Ptnml(0-2): 18, 1708, 27310, 1861, 27 https://tests.stockfishchess.org/tests/view/60d8f0393beab81350ac9ec6 closes https://github.com/official-stockfish/Stockfish/pull/3593 Bench: 4770936 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: MichaelB7
Date: Mon Jun 28 21:20:05 2021 +0200 Timestamp: 1624908005 Make net nn-956480d8378f.nnue the default Trained with the pytorch trainer: https://github.com/glinscott/nnue-pytorch python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --lambda=1.0 --max_epochs=440 --seed %random%%random% --default_root_dir exp/run_18 --resume-from-model ./pt/nn-75980ca503c6.pt This run is thus started from a previous master net. all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing Each set was concatenated together - making one large Wrong_NNUE 2 binpack and one large Training so the were approximately equal in size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training_Data binpack passed STC: https://tests.stockfishchess.org/tests/view/60d0c0a7a8ec07dc34c072b2 LLR: 2.93 (-2.94,2.94) <-0.50,2.50> Total: 18440 W: 1693 L: 1531 D: 15216 Elo +3.05 Ptnml(0-2): 67, 1225, 6464, 1407, 57 passed LTC: https://tests.stockfishchess.org/tests/view/60d762793beab81350ac9d72 LLR: 2.98 (-2.94,2.94) <0.50,3.50> Total: 93120 W: 3152 L: 2933 D: 87035 Elo +0.82 Ptnml(0-2): 48, 2581, 41076, 2814, 41 passed LTC (rebased branch to current master): https://tests.stockfishchess.org/tests/view/60d85eeb3beab81350ac9e2b LLR: 2.96 (-2.94,2.94) <0.50,3.50> Total: 42688 W: 1347 L: 1206 D: 40135 Elo +1.15 Ptnml(0-2): 14, 1097, 18981, 1238, 14. closes https://github.com/official-stockfish/Stockfish/pull/3592 Bench: 4906727 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 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 28 21:13:30 2021 +0200 Timestamp: 1624907610 Update WDL model for NNUE This updates the WDL model based on the LTC statistics in June this year (10M games), so from pre-NNUE to NNUE based results. (for old results see, https://github.com/official-stockfish/Stockfish/pull/2778) As before the fit by the model to the data is quite good. closes https://github.com/official-stockfish/Stockfish/pull/3582 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 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: bmc4
Date: Mon Jun 28 21:12:04 2021 +0200 Timestamp: 1624907524 Simplify Reductions Initialization passed STC: LLR: 2.94 (-2.94,2.94) <-2.50,0.50> Total: 45032 W: 3600 L: 3518 D: 37914 Elo +0.63 Ptnml(0-2): 111, 2893, 16435, 2957, 120 https://tests.stockfishchess.org/tests/view/60d2655d40925195e7a6c527 LTC: LLR: 3.00 (-2.94,2.94) <-2.50,0.50> Total: 25728 W: 786 L: 722 D: 24220 Elo +0.86 Ptnml(0-2): 5, 650, 11494, 706, 9 https://tests.stockfishchess.org/tests/view/60d2b14240925195e7a6c577 closes https://github.com/official-stockfish/Stockfish/pull/3584 bench: 4602977 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 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 22 11:51:03 2021 +0200 Timestamp: 1624355463 Detect fortresses a little bit quicker In the so-called "hybrid" method of evaluation of current master, we use the classical eval (because of its speed) instead of the NNUE eval when the classical material balance approximation hints that the position is "winning enough" to rely on the classical eval. This trade-off idea between speed and accuracy works well in general, but in some fortress positions the classical eval is just bad. So in shuffling branches of the search tree, we (slowly) increase the thresehold so that eventually we don't trust classical anymore and switch to NNUE evaluation. This patch increases that threshold faster, so that we switch to NNUE quicker in shuffling branches. Idea is to incite Stockfish to spend less time in fortresses lines in the search tree, and spend more time searching the critical lines. passed STC: LLR: 2.96 (-2.94,2.94) <-0.50,2.50> Total: 47872 W: 3908 L: 3720 D: 40244 Elo +1.36 Ptnml(0-2): 122, 3053, 17419, 3199, 143 https://tests.stockfishchess.org/tests/view/60cef34b457376eb8bcab79d passed LTC: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 73616 W: 2326 L: 2143 D: 69147 Elo +0.86 Ptnml(0-2): 21, 1940, 32705, 2119, 23 https://tests.stockfishchess.org/tests/view/60cf6d842114332881e73528 Retested at LTC against lastest master: LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 18264 W: 642 L: 532 D: 17090 Elo +2.09 Ptnml(0-2): 6, 479, 8055, 583, 9 https://tests.stockfishchess.org/tests/view/60d18cd540925195e7a6c351 closes https://github.com/official-stockfish/Stockfish/pull/3578 Bench: 5139233 see source |
| Windows x64 for Haswell CPUs Windows x64 for modern computers + AVX2 Windows x64 for modern computers Windows x64 + SSSE3 Windows x64 Windows 32 Linux x64 for Haswell CPUs Linux x64 for modern computers + AVX2 Linux x64 for modern computers Linux x64 + SSSE3 Linux x64 | Author: MichaelB7
Date: Mon Jun 21 23:16:55 2021 +0200 Timestamp: 1624310215 Make net nn-190f102a22c3.nnue the default net. Trained with the pytorch trainer: https://github.com/glinscott/nnue-pytorch python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --lambda=1.0 --max_epochs=440 --seed %random%%random% --default_root_dir exp/run_17 --resume-from-model ./pt/nn-75980ca503c6.pt This run is thus started from the previous master net. all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing Each set was concatenated together - making one large Wrong_NNUE 2 binpack and one large Training so the were approximately equal in size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training_Data binpack passed LTC https://tests.stockfishchess.org/tests/view/60d09f52b4c17000d679517f LLR: 2.93 (-2.94,2.94) <0.50,3.50> Total: 32184 W: 1100 L: 970 D: 30114 Elo +1.40 Ptnml(0-2): 10, 878, 14193, 994, 17 passed STC https://tests.stockfishchess.org/tests/view/60d086c02114332881e7368e LLR: 2.93 (-2.94,2.94) <-0.50,2.50> Total: 11360 W: 1056 L: 906 D: 9398 Elo +4.59 Ptnml(0-2): 25, 735, 4026, 853, 41 closes https://github.com/official-stockfish/Stockfish/pull/3576 Bench: 4631244 see source |