| 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: rn5f107s2
Date: Sun Jan 21 12:42:07 2024 +0100 Timestamp: 1705837327 Reduce futility_margin further when improving The idea of this is to unroll the futility_margin calculation to allow for the improving flag to have a greater effect on the futility margin. The current factor is 1.5 instead of the previous 1 resulting in a deduction of an extra margin/2 from futilit_margin if improving. The chosen value was not tuned, meaning that there is room for tweaking it. This patch is partially inspired by @Vizvezdenec, who, although quite different in execution, tested another idea where the futility_margin is lowered further when improving [1]. [1]: (first take) https://tests.stockfishchess.org/tests/view/65a56b1879aa8af82b97164b Passed STC: https://tests.stockfishchess.org/tests/live_elo/65a8bfc179aa8af82b974e3c LLR: 2.95 (-2.94,2.94) <0.00,2.00> Total: 161152 W: 41321 L: 40816 D: 79015 Elo +1.09 Ptnml(0-2): 559, 19030, 40921, 19479, 587 Passed rebased LTC: https://tests.stockfishchess.org/tests/live_elo/65a8b9ef79aa8af82b974dc0 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 96024 W: 24172 L: 23728 D: 48124 Elo +1.61 Ptnml(0-2): 56, 10598, 26275, 11012, 71 closes https://github.com/official-stockfish/Stockfish/pull/5000 Bench: 1281703 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: Sun Jan 21 12:33:08 2024 +0100 Timestamp: 1705836788 Refactor ttPv reduction conditions closes https://github.com/official-stockfish/Stockfish/pull/4999 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: Sun Jan 21 12:21:01 2024 +0100 Timestamp: 1705836061 Refactor NativeThread start_routine Removes the free function and fixes the formatting for the function call. closes https://github.com/official-stockfish/Stockfish/pull/4995 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 Jan 17 18:56:37 2024 +0100 Timestamp: 1705514197 Improve ttPv reduction This patch allows a partial reduction decrease when a node is likely to fail low, and increases the reduction decrease when a node has failed high. Passed STC: https://tests.stockfishchess.org/tests/view/65a626e779aa8af82b9722bc LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 157824 W: 40332 L: 39835 D: 77657 Elo +1.09 Ptnml(0-2): 543, 18617, 40098, 19108, 546 Passed LTC: https://tests.stockfishchess.org/tests/view/65a7290279aa8af82b97328a LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 57228 W: 14475 L: 14111 D: 28642 Elo +2.21 Ptnml(0-2): 34, 6278, 15633, 6628, 41 closes https://github.com/official-stockfish/Stockfish/pull/4994 Bench: 1364759 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: FauziAkram
Date: Wed Jan 17 18:55:44 2024 +0100 Timestamp: 1705514144 Remove threatenedByPawn from rook threat Can be simplified away. Passed STC: https://tests.stockfishchess.org/tests/view/65a3fa4179aa8af82b96face LLR: 2.92 (-2.94,2.94) <-1.75,0.25> Total: 30592 W: 7903 L: 7674 D: 15015 Elo +2.60 Ptnml(0-2): 96, 3590, 7711, 3787, 112 Passed LTC: https://tests.stockfishchess.org/tests/view/65a42b9a79aa8af82b96fe88 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 73656 W: 18382 L: 18212 D: 37062 Elo +0.80 Ptnml(0-2): 47, 8287, 19981, 8475, 38 closes https://github.com/official-stockfish/Stockfish/pull/4993 Bench: 1430061 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: pb00067
Date: Wed Jan 17 18:51:03 2024 +0100 Timestamp: 1705513863 Refactor code for correcting unadjustedStaticEval Passed non-regression STC: https://tests.stockfishchess.org/tests/live_elo/65a4df6a79aa8af82b970ca0 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 43328 W: 11103 L: 10892 D: 21333 Elo +1.69 Ptnml(0-2): 120, 4920, 11407, 5063, 154 https://github.com/official-stockfish/Stockfish/pull/4992 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: Torsten Hellwig
Date: Wed Jan 17 18:32:20 2024 +0100 Timestamp: 1705512740 Fix dotprod detection This fixes the detection of dotprod capable CPUs. Previously it looked for the `dotprod` flag, but this does not exist (https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git/tree/arch/arm64/kernel/cpuinfo.c#n50). The correct flag that specifies the dotprod capability is the `asimddp` flag. fixes #4931 closes https://github.com/official-stockfish/Stockfish/pull/4991 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: Shahin M. Shahin
Date: Wed Jan 17 18:12:16 2024 +0100 Timestamp: 1705511536 Fix mated-in behaviour This addresses the issue where Stockfish may output non-proven checkmate scores if the search is prematurely halted, either due to a time control or node limit, before it explores other possibilities where the checkmate score could have been delayed or refuted. The fix also replaces staving off from proven mated scores in a multithread environment making use of the threads instead of a negative effect with multithreads (1t was better in proving mated in scores than more threads). Issue reported on mate tracker repo by and this PR is co-authored with @robertnurnberg Special thanks to @AndyGrant for outlining that a fix is eventually possible. Passed Adj off SMP STC: https://tests.stockfishchess.org/tests/view/65a125d779aa8af82b96c3eb LLR: 2.96 (-2.94,2.94) <-1.75,0.25> Total: 303256 W: 75823 L: 75892 D: 151541 Elo -0.08 Ptnml(0-2): 406, 35269, 80395, 35104, 454 Passed Adj off SMP LTC: https://tests.stockfishchess.org/tests/view/65a37add79aa8af82b96f0f7 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 56056 W: 13951 L: 13770 D: 28335 Elo +1.12 Ptnml(0-2): 11, 5910, 16002, 6097, 8 Passed all tests in matetrack without any better mate for opponent found in 1t and multithreads. Fixed bugs in https://github.com/official-stockfish/Stockfish/pull/4976 closes https://github.com/official-stockfish/Stockfish/pull/4990 Bench: 1308279 Co-Authored-By: Robert Nürnberg <28635489+> 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: Wed Jan 17 18:06:20 2024 +0100 Timestamp: 1705511180 Update installation guide links in CONTRIBUTING.md Link to more user friendly installation guides, these are shorter and easier to follow. closes https://github.com/official-stockfish/Stockfish/pull/4985 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: Wed Jan 17 18:05:00 2024 +0100 Timestamp: 1705511100 Remove global TB variables from search.cpp Follow up cleanup of #4968, removes the global variables from search and instead uses a dedicated tb config struct. closes https://github.com/official-stockfish/Stockfish/pull/4982 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: mstembera
Date: Wed Jan 17 18:04:29 2024 +0100 Timestamp: 1705511069 Remove some outdated SIMD functions Since https://github.com/official-stockfish/Stockfish/pull/4391 the x2 SIMD functions no longer serve any useful purpose. Passed non-regression STC: https://tests.stockfishchess.org/tests/view/659cf42579aa8af82b966d55 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 67392 W: 17222 L: 17037 D: 33133 Elo +0.95 Ptnml(0-2): 207, 7668, 17762, 7851, 208 closes https://github.com/official-stockfish/Stockfish/pull/4974 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: Sun Jan 14 10:46:13 2024 +0100 Timestamp: 1705225573 Add ignoreRevsFile to CONTRIBUTING.md closes https://github.com/official-stockfish/Stockfish/pull/4980 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: Sun Jan 14 10:46:13 2024 +0100 Timestamp: 1705225573 Remove the dependency on a Worker from evaluate Also remove dead code, `rootSimpleEval` is no longer used since the introduction of dual net. `iterBestValue` is also no longer used in evaluate and can be reduced to a local variable. closes https://github.com/official-stockfish/Stockfish/pull/4979 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: Sun Jan 14 10:46:13 2024 +0100 Timestamp: 1705225573 Fix UCI options Fixes the type for 'Clear Hash' and uses MAX_MOVES for 'MultiPV' as we had before. 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: Sun Jan 14 00:30:06 2024 +0100 Timestamp: 1705188606 Remove unused method init() is no longer used, and was previously replaced by the clear function. fixes https://github.com/official-stockfish/Stockfish/issues/4981 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: mstembera
Date: Sat Jan 13 19:40:53 2024 +0100 Timestamp: 1705171253 Simplify bad quiets The main difference is that instead of returning the first bad quiet as a good one we fall through. This is actually more correct and simpler to implement. Non regression STC: https://tests.stockfishchess.org/tests/view/659bbb3479aa8af82b964ec7 LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 150944 W: 38399 L: 38305 D: 74240 Elo +0.22 Ptnml(0-2): 485, 18042, 38298, 18188, 459 Non regression LTC: https://tests.stockfishchess.org/tests/view/659c6e6279aa8af82b9660eb LLR: 2.96 (-2.94,2.94) <-1.75,0.25> Total: 192060 W: 47871 L: 47823 D: 96366 Elo +0.09 Ptnml(0-2): 144, 21912, 51845, 22010, 119 The cutoff is now -8K instead of -7.5K. -7.5K failed. https://tests.stockfishchess.org/tests/view/659a1f4b79aa8af82b962a0e This was likely a false negative. closes https://github.com/official-stockfish/Stockfish/pull/4975 Bench: 1308279 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: FauziAkram
Date: Sat Jan 13 19:40:53 2024 +0100 Timestamp: 1705171253 Remove threatenedByPawn term for queen threats Passed STC: https://tests.stockfishchess.org/tests/view/659d614c79aa8af82b9677d0 LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 151776 W: 38690 L: 38597 D: 74489 Elo +0.21 Ptnml(0-2): 522, 17841, 39015, 18042, 468 Passed LTC: https://tests.stockfishchess.org/tests/view/659d94d379aa8af82b967cb2 LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 91908 W: 23075 L: 22924 D: 45909 Elo +0.57 Ptnml(0-2): 70, 10311, 25037, 10470, 66 closes https://github.com/official-stockfish/Stockfish/pull/4977 Bench: 1266493 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: Sat Jan 13 19:40:53 2024 +0100 Timestamp: 1705171253 Refactor global variables This aims to remove some of the annoying global structure which Stockfish has. Overall there is no major elo regression to be expected. Non regression SMP STC (paused, early version): https://tests.stockfishchess.org/tests/view/65983d7979aa8af82b9608f1 LLR: 0.23 (-2.94,2.94) <-1.75,0.25> Total: 76232 W: 19035 L: 19096 D: 38101 Elo -0.28 Ptnml(0-2): 92, 8735, 20515, 8690, 84 Non regression STC (early version): https://tests.stockfishchess.org/tests/view/6595b3a479aa8af82b95da7f LLR: 2.93 (-2.94,2.94) <-1.75,0.25> Total: 185344 W: 47027 L: 46972 D: 91345 Elo +0.10 Ptnml(0-2): 571, 21285, 48943, 21264, 609 Non regression SMP STC: https://tests.stockfishchess.org/tests/view/65a0715c79aa8af82b96b7e4 LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 142936 W: 35761 L: 35662 D: 71513 Elo +0.24 Ptnml(0-2): 209, 16400, 38135, 16531, 193 These global structures/variables add hidden dependencies and allow data to be mutable from where it shouldn't it be (i.e. options). They also prevent Stockfish from internal selfplay, which would be a nice thing to be able to do, i.e. instantiate two Stockfish instances and let them play against each other. It will also allow us to make Stockfish a library, which can be easier used on other platforms. For consistency with the old search code, `thisThread` has been kept, even though it is not strictly necessary anymore. This the first major refactor of this kind (in recent time), and future changes are required, to achieve the previously described goals. This includes cleaning up the dependencies, transforming the network to be self contained and coming up with a plan to deal with proper tablebase memory management (see comments for more information on this). The removal of these global structures has been discussed in parts with Vondele and Sopel. closes https://github.com/official-stockfish/Stockfish/pull/4968 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: Mon Jan 8 18:34:36 2024 +0100 Timestamp: 1704735276 Update default main net to nn-baff1edbea57.nnue Created by retraining the previous main net nn-b1e55edbea57.nnue with: - some of the same options as before: ranger21 optimizer, more WDL skipping - adding T80 aug filter-v6, sep, and oct 2023 data to the previous best dataset - increasing training loss for positions where predicted win rates were higher than estimated match results from training data position scores ```yaml experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28 training-dataset: # https://github.com/official-stockfish/Stockfish/pull/4782 - /data/S6-1ee1aba5ed.binpack - /data/test80-aug2023-2tb7p.v6.min.binpack - /data/test80-sep2023-2tb7p.binpack - /data/test80-oct2023-2tb7p.binpack early-fen-skipping: 28 start-from-engine-test-net: True nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q num-epochs: 1000 lr: 4.375e-4 gamma: 0.995 start-lambda: 1.0 end-lambda: 0.7 ``` Training data can be found at: https://robotmoon.com/nnue-training-data/ Training loss was increased by 10% for positions where predicted win rates were higher than suggested by the win rate model based on the training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a variant of experiments from Sopel's NNUE training & experimentation log: https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY Experiment 302 - increase loss when prediction too high, vondele’s idea Experiment 309 - increase loss when prediction too high, normalize in a batch Passed STC: https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 148320 W: 37960 L: 37475 D: 72885 Elo +1.14 Ptnml(0-2): 542, 17565, 37383, 18206, 464 Passed LTC: https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 55188 W: 13955 L: 13592 D: 27641 Elo +2.29 Ptnml(0-2): 34, 6162, 14834, 6535, 29 closes https://github.com/official-stockfish/Stockfish/pull/4972 Bench: 1219824 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 Jan 8 18:33:38 2024 +0100 Timestamp: 1704735218 Cleanup Evalfile handling This cleans up the EvalFile handling after the merge of #4915, which has become a bit confusing on what it is actually doing. closes https://github.com/official-stockfish/Stockfish/pull/4971 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: Sun Jan 7 21:41:52 2024 +0100 Timestamp: 1704660112 Prefix abs with std:: 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 Jan 7 21:20:15 2024 +0100 Timestamp: 1704658815 Update smallnet to nn-baff1ede1f90.nnue with wider eval range Created by training an L1-128 net from scratch with a wider range of evals in the training data and wld-fen-skipping disabled during training. The differences in this training data compared to the first dual nnue PR are: - removal of all positions with 3 pieces - when piece count >= 16, keep positions with simple eval above 750 - when piece count < 16, remove positions with simple eval above 3000 The asymmetric data filtering was meant to flatten the training data piece count distribution, which was previously heavily skewed towards positions with low piece counts. Additionally, the simple eval range where the smallnet is used was widened to cover more positions previously evaluated by the big net and simple eval. ```yaml experiment-name: 128--S1-hse-S7-v4-S3-v1-no-wld-skip training-dataset: - /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack - /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-v4.binpack - /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-v4.binpack - /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-v4.binpack - /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-v4.binpack - /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-v4.binpack - /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack - /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-v4.binpack - /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-v4.binpack - /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-v4.binpack wld-fen-skipping: False start-from-engine-test-net: False nnue-pytorch-branch: linrock/nnue-pytorch/L1-128 engine-test-branch: linrock/Stockfish/L1-128-nolazy engine-base-branch: linrock/Stockfish/L1-128 num-epochs: 500 start-lambda: 1.0 end-lambda: 1.0 ``` Experiment yaml configs converted to easy_train.sh commands with: https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py Binpacks interleaved at training time with: https://github.com/official-stockfish/nnue-pytorch/pull/259 FT weights permuted with 10k positions from fishpack32.binpack with: https://github.com/official-stockfish/nnue-pytorch/pull/254 Data filtered for high simple eval positions (v4) with: https://github.com/linrock/Stockfish/blob/b9c8440/src/tools/transform.cpp#L640-L675 Training data can be found at: https://robotmoon.com/nnue-training-data/ Local elo at 25k nodes per move of L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data: nn-epoch319.nnue : -241.7 +/- 3.2 Passed STC vs. 36db936: https://tests.stockfishchess.org/tests/view/6576b3484d789acf40aabbfe LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 21920 W: 5680 L: 5381 D: 10859 Elo +4.74 Ptnml(0-2): 82, 2488, 5520, 2789, 81 Passed LTC vs. DualNNUE #4915: https://tests.stockfishchess.org/tests/view/65775c034d789acf40aac7e3 LLR: 2.95 (-2.94,2.94) <0.50,2.50> Total: 147606 W: 36619 L: 36063 D: 74924 Elo +1.31 Ptnml(0-2): 98, 16591, 39891, 17103, 120 closes https://github.com/official-stockfish/Stockfish/pull/4919 Bench: 1438336 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 Jan 7 21:15:52 2024 +0100 Timestamp: 1704658552 Dual NNUE with L1-128 smallnet Credit goes to @mstembera for: - writing the code enabling dual NNUE: https://github.com/official-stockfish/Stockfish/pull/4898 - the idea of trying L1-128 trained exclusively on high simple eval positions The L1-128 smallnet is: - epoch 399 of a single-stage training from scratch - trained only on positions from filtered data with high material difference - defined by abs(simple_eval) > 1000 ```yaml experiment-name: 128--S1-only-hse-v2 training-dataset: - /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack - /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-1k.binpack - /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack # T80 2022 - /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-1k.binpack - /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-1k.binpack # T80 2023 - /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-1k.binpack start-from-engine-test-net: False nnue-pytorch-branch: linrock/nnue-pytorch/L1-128 engine-test-branch: linrock/Stockfish/L1-128-nolazy engine-base-branch: linrock/Stockfish/L1-128 num-epochs: 500 lambda: 1.0 ``` Experiment yaml configs converted to easy_train.sh commands with: https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py Binpacks interleaved at training time with: https://github.com/official-stockfish/nnue-pytorch/pull/259 Data filtered for high simple eval positions with: https://github.com/linrock/nnue-data/blob/32d6a68/filter_high_simple_eval_plain.py https://github.com/linrock/Stockfish/blob/61dbfe/src/tools/transform.cpp#L626-L655 Training data can be found at: https://robotmoon.com/nnue-training-data/ Local elo at 25k nodes per move of L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data: nn-epoch399.nnue : -318.1 +/- 2.1 Passed STC: https://tests.stockfishchess.org/tests/view/6574cb9d95ea6ba1fcd49e3b LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 62432 W: 15875 L: 15521 D: 31036 Elo +1.97 Ptnml(0-2): 177, 7331, 15872, 7633, 203 Passed LTC: https://tests.stockfishchess.org/tests/view/6575da2d4d789acf40aaac6e LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 64830 W: 16118 L: 15738 D: 32974 Elo +2.04 Ptnml(0-2): 43, 7129, 17697, 7497, 49 closes https://github.com/official-stockfish/Stockfish/pulls Bench: 1330050 Co-Authored-By: mstembera <5421953+> 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: Sun Jan 7 13:41:50 2024 +0100 Timestamp: 1704631310 Introduce BAD_QUIET movepicker stage Split quiets into good and bad as we do with captures. When we find the first quiet move below a certain threshold that has been sorted we consider all subsequent quiets bad. Inspired by @locutus2 idea to skip bad captures. Passed STC: https://tests.stockfishchess.org/tests/view/6597759f79aa8af82b95fa17 LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 138688 W: 35566 L: 35096 D: 68026 Elo +1.18 Ptnml(0-2): 476, 16367, 35183, 16847, 471 Passed LTC: https://tests.stockfishchess.org/tests/view/6598583c79aa8af82b960ad0 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 84108 W: 21468 L: 21048 D: 41592 Elo +1.73 Ptnml(0-2): 38, 9355, 22858, 9755, 48 closes https://github.com/official-stockfish/Stockfish/pull/4970 Bench: 1336907 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: Sun Jan 7 13:38:55 2024 +0100 Timestamp: 1704631135 Add .git-blame-ignore-revs Add a `.git-blame-ignore-revs` file which can be used to skip specified commits when blaming, this is useful to ignore formatting commits, like clang-format #4790. Github blame automatically supports this file format, as well as other third party tools. Git itself needs to be told about the file name to work, the following command will add it to the current git repo. `git config blame.ignoreRevsFile .git-blame-ignore-revs`, alternatively one has to specify it with every blame. `git blame --ignore-revs-file .git-blame-ignore-revs search.cpp` Supported since git 2.23. closes https://github.com/official-stockfish/Stockfish/pull/4969 No functional change see source |