Stephen Touset [Mon, 16 Jan 2023 22:25:47 +0000 (14:25 -0800)]
Remove precomputed SquareBB
Bit-shifting is a single instruction, and should be faster than an array lookup
on supported architectures. Besides (ever so slightly) speeding up the
conversion of a square into a bitboard, we may see minor general performance
improvements due to preserving more of the CPU's existing cache.
The old parameterization (https://github.com/official-stockfish/Stockfish/pull/2225/files) has now become quite inaccurate.
This updates the formula based on updated results with master. The formula is based on a fit of the Elo results for games
played between master at various skill levels, and various versions of the Stash engine, which have been ranked at CCRL.
Skill 0 .. 19, now covers CCRL Blitz Elo from 1320 to 3190, approximately.
Indeed, the Elo of stash in this analysis is only to within +- 100 Elo of CCRL,
probably because it depends quite a bit on the opponent pool.
To obtain a skill level for a given Elo number, the above data is fit as a 3rd
degree polynomial Skill(Elo). A quick test confirms the correspondence to the above table:
```
Score of master-elo-2721 vs stash-bot-v21.0: 51 - 16 - 19 [0.703] 86
Elo difference: 150.1 +/- 70.2, LOS: 100.0 %, DrawRatio: 22.1 %
```
Fix asm modifiers in add_dpbusd_epi32x2 implementations
The accumulator should be an earlyclobber because it is written before
all input operands are read. Otherwise, the asm code computes a wrong
result if the accumulator shares a register with one of the other input
operands (which happens if we pass in the same expression for the
accumulator and the operand).
Created by retraining the master net on a dataset composed of:
* The Leela-dfrc_n5000.binpack dataset filtered with depth6 multipv2 search to remove positions with only one good move, in addition to removing positions where either of the two best moves are captures
* The same Leela T80 oct+nov 2022 training data used in recent best datasets
* Additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022
Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
Around epoch 780, training was manually paused and max epoch increased to 920 before resuming.
During depth6 multipv2 data filtering, positions were considered to have only one good move if the score of the best move was significantly better than the 2nd best move in a way that changes the outcome of the game:
* the best move leads to a significant advantage while the 2nd best move equalizes or loses
* the best move is about equal while the 2nd best move loses
The modified stockfish branch and exact score thresholds used for filtering are at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff/src/filter
About 95% of the Leela portion and 96% of the DFRC portion of the Leela-dfrc_n5000.binpack dataset was filtered. Unfiltered parts of the dataset were left out.
The additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022 was WDL-rescored with about 12TB of syzygy 7-piece tablebases where the material difference is less than around 6 pawns. Best moves were exported to .plain data files during data conversion with the lc0 rescorer.
The exact training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move
experiment_leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7
run_0/nn-epoch899.nnue : 3.8 +/- 1.6
Removed sprintf() which generated a warning, because of security reasons.
Replace NULL with nullptr
Replace typedef with using
Do not inherit from std::vector. Use composition instead.
optimize mutex-unlocking
Warn if a global function has no previous declaration
If a global function has no previous declaration, either the declaration
is missing in the corresponding header file or the function should be
declared static. Static functions are local to the translation unit,
which allows the compiler to apply some optimizations earlier (when
compiling the translation unit rather than during link-time
optimization).
The commit enables the warning for gcc, clang, and mingw. It also fixes
the reported warnings by declaring the functions static or by adding a
header file (benchmark.h).
mstembera [Wed, 28 Dec 2022 00:44:32 +0000 (16:44 -0800)]
Fix stack initialization
This fixes a bug where on line 278 the Stack::staticEvals are
initialized to 0. However VALUE_NONE is defined to be 32002 so
this is a bug in master. It is probably due to the calculation
of improvement, where staticEval prior to rootPos can be accessed.
This is a later epoch (epoch 859) from the same experiment run that trained yesterday's master net nn-60fa44e376d9.nnue (epoch 779). The experiment was manually paused around epoch 790 and unpaused with max epoch increased to 900 mainly to get more local elo data without letting the GPU idle.
Local elo vs. nn-335a9b2d8a80.nnue at 25k nodes per move:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
run_0/nn-epoch779.nnue (nn-60fa44e376d9.nnue) : 5.0 +/- 1.2
run_0/nn-epoch859.nnue (nn-a3dc078bafc7.nnue) : 5.6 +/- 1.6
Created by retraining the master net on the previous best dataset with additional filtering. No new data was added.
More of the Leela-dfrc_n5000.binpack part of the dataset was pre-filtered with depth6 multipv2 search to remove bestmove captures. About 93% of the previous Leela/SF data and 99% of the SF dfrc data was filtered. Unfiltered parts of the dataset were left out. The new Leela T80 oct+nov data is the same as before. All early game positions with ply count <= 28 were skipped during training by modifying the training data loader in nnue-pytorch.
Trained in a similar way as recent master nets, with a different nnue-pytorch branch for early ply skipping:
In both modified methods, the variable 'result' is checked to detect
whether the probe operation failed. However, the variable is not
initialized on all paths, so the check might test an uninitialized
value.
A test position (with TB) is given by:
position fen 3K1k2/R7/8/8/8/8/8/R6Q w - - 0 1 moves a1b1 f8g8 b1a1 g8f8 a1b1 f8g8 b1a1
This is now fixed by always initializing the variable.
Created by retraining the master net with a combination of:
the previous best dataset (Leela-dfrc_n5000.binpack), with about half the dataset filtered using depth6 multipv2 search to throw away positions where either of the 2 best moves are captures
Leela T80 Oct and Nov training data rescored with best moves, adding ~9.5 billion positions
Trained effectively the same way as the previous master net:
Local testing at a fixed 25k nodes:
experiments/experiment_leela-dfrc-filtered-T80-oct-nov/training/run_0/nn-epoch779.nnue
localElo: run_0/nn-epoch779.nnue : 4.7 +/- 3.1
The new Leela T80 part of the dataset was prepared by downloading test80 training data from all of Oct 2022 and Nov 2022, rescoring with syzygy 6-piece tablebases and ~600 GB of 7-piece tablebases, saving best moves to exported .plain files, removing all positions with castling flags, then converting to binpacks and using interleave_binpacks.py to merge them together. Scripts used in this data conversion process are available at:
https://github.com/linrock/lc0-data-converter
Filtering binpack data using depth6 multipv2 search was done by modifying transform.cpp in the tools branch:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-no-rescore
Links for downloading the training data (total size: 338 GB) are available at:
https://robotmoon.com/nnue-training-data/
MinetaS [Tue, 20 Dec 2022 07:01:05 +0000 (16:01 +0900)]
Fix a dependency bug
Instead of allowing .depend for specific build-related targets, filter
non-build-related targets (i.e. help, clean) so that other targets can
normally execute .depend target.
mstembera [Tue, 13 Dec 2022 07:22:02 +0000 (23:22 -0800)]
Don't reset increaseDepth back to true after it has been set to false
Resetting increaseDepth back to true each time on the very next iteration was not intended so this is a bug fix and a simplification.
See more discussion here #2482 (comment) Thanks to xoto10
Michael Chaly [Sat, 17 Dec 2022 09:48:03 +0000 (12:48 +0300)]
Reintroduce doEvenDeeperSearch
This patch is basically the same as a reverted patch
but now has some guarding against search being stuck - the same
way as we do with double extensions. This should help with
search explosions - albeit slowly but they eventually should be resolved.
Alfredo Menezes [Fri, 9 Dec 2022 15:11:43 +0000 (12:11 -0300)]
Extend all moves at low depth if ttMove is doubly extended
If ttMove is doubly extended, we allow a depth growth of the remaining moves.
The idea is to get a more realistic score comparison, because of the depth
difference. We take some care to avoid this extension for high depths,
in order to avoid the cost, since the search result is supposed
to be more accurate in this case.
Guenther Demetz [Tue, 6 Dec 2022 18:09:33 +0000 (19:09 +0100)]
Correctly output lowerbound/upperbound scores
fixes the lowerbound/upperbound output by avoiding
scores outside the alpha,beta bracket. Since SF search
uses fail-soft we can't simply take the returned value
as score.
FauziAkram [Fri, 2 Dec 2022 15:23:28 +0000 (18:23 +0300)]
doEvenDeeperSearch + tuning
Credit for the main idea of doEvenDeeperSearch goes to Vizvezdenec,
tuning by FauziAkram: Expansion of existing logic of doDeeperSearch -
if value from LMR is really really good do full depth search not
1 ply deeper but rather 2 instead.
ppigazzini [Thu, 8 Dec 2022 17:32:30 +0000 (18:32 +0100)]
Set the right PATH for ARM compiler and build tests in CI
Fix for the GitHub upgrade:
https://github.com/actions/runner-images/issues/5879
that broke our ARM workflows because it changed the value of
the ANDROID_NDK_HOME variable referenced in our PATH.
Today, we have the pleasure to announce Stockfish 15.1.
As usual, downloads will be freely available at stockfishchess.org/download
*Elo gain and competition results*
With this release, version 5 of the NNUE neural net architecture has
been introduced, and the training data has been extended to include
Fischer random chess (FRC) positions. As a result, Elo gains are largest
for FRC, reaching up to 50 Elo for doubly randomized FRC[1] (DFRC).
More importantly, also for standard chess this release progressed and
will win two times more game pairs than it loses[2] against
Stockfish 15. Stockfish continues to win in a dominating way[3] all
chess engine tournaments, including the TCEC Superfinal, Cup, FRC, DFRC,
and Swiss as well as the CCC Bullet, Blitz, and Rapid events.
*New evaluation*
This release also introduces a new convention for the evaluation that
is reported by search. An evaluation of +1 is now no longer tied to the
value of one pawn, but to the likelihood of winning the game. With
a +1 evaluation, Stockfish has now a 50% chance of winning the game
against an equally strong opponent. This convention scales down
evaluations a bit compared to Stockfish 15 and allows for consistent
evaluations in the future.
*ChessBase settlement*
In this release period, the Stockfish team has successfully enforced
its GPL license against ChessBase. This has been an intense process that
included filing a lawsuit[4], a court hearing[5], and finally
negotiating a settlement[6] that established that ChessBase infringed on
the license by not distributing the Stockfish derivatives Fat Fritz 2
and Houdini 6 as free software, and that ensures ChessBase will respect
the Free Software principles in the future. This settlement has been
covered by major chess sites (see e.g. lichess.org[7] and chess.com[8]),
and we are proud that it has been hailed as a ‘historic violation
settlement[9]’ by the Software Freedom Conservancy.
*Thank you*
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[10].
If multiple threads have the same best move,
pick the thread with the largest contribution to the confidence vote.
This thread will later be used to display PV, so this patch is
about user-friendliness and/or least surprises, it non-functional for playing strenght.
Guenther Demetz [Tue, 22 Nov 2022 10:07:18 +0000 (11:07 +0100)]
Correctly output lowerbound/upperbound in threaded searches
fixes the lowerbound/upperbound output by taking the alpha,beta bracket
into account also if a bestThread is selected that is different from the master thread.
Instead of keeping track which bounds where used in the specific search,
in this version we simply store the quality (exact, upperbound,
lowerbound) of the score along with the actual score as information on
rootMove.
VoyagerOne [Tue, 22 Nov 2022 17:07:33 +0000 (20:07 +0300)]
Skip full depth search in LMR depending on depth
dynamically adjust newDepth, and skip full depth search if newDepth doesn't exceed the previous search depth.
This affects the used newDepth for future searches, and influences the stat bonus for the move.
disservin [Mon, 7 Nov 2022 17:15:42 +0000 (18:15 +0100)]
speedup CI
Github Actions allows us to use up to 20 workers.
This way we can launch multiple different checks
at the same time and optimize the overall time
the CI takes a bit.
disservin [Sun, 6 Nov 2022 15:17:17 +0000 (16:17 +0100)]
Change versioning and save binaries as CI artifacts
For development versions of Stockfish, the version will now look like
dev-20221107-dca9a0533
indicating a development version, the date of the last commit,
and the git SHA of that commit. If git is not available,
the fallback is the date of compilation. Releases will continue to be
versioned as before.
Additionally, this PR extends the CI to create binary artifacts,
i.e. pushes to master will automatically build Stockfish and upload
the binaries to github.
Normalizes the internal value as reported by evaluate or search
to the UCI centipawn result used in output. This value is derived from
the win_rate_model() such that Stockfish outputs an advantage of
"100 centipawns" for a position if the engine has a 50% probability to win
from this position in selfplay at fishtest LTC time control.
The reason to introduce this normalization is that our evaluation is, since NNUE,
no longer related to the classical parameter PawnValueEg (=208). This leads to
the current evaluation changing quite a bit from release to release, for example,
the eval needed to have 50% win probability at fishtest LTC (in cp and internal Value):
June 2020 : 113cp (237)
June 2021 : 115cp (240)
April 2022 : 134cp (279)
July 2022 : 167cp (348)
With this patch, a 100cp advantage will have a fixed interpretation,
i.e. a 50% win chance. To keep this value steady, it will be needed to update the win_rate_model()
from time to time, based on fishtest data. This analysis can be performed with
a set of scripts currently available at https://github.com/vondele/WLD_model
kurt22i [Sun, 30 Oct 2022 13:02:48 +0000 (09:02 -0400)]
Adjust reduction less at medium depths
This patch dampens the reduction increase/decrease from statScore at mid-range depths.
Inspired by patterns noticed in this tune: https://tests.stockfishchess.org/tests/view/635188930e5f47a8d0ffe8f5
dav1312 [Tue, 25 Oct 2022 09:15:00 +0000 (11:15 +0200)]
Add issue template
Add an issue template using GitHub's form schema
https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema
Michael Chaly [Fri, 21 Oct 2022 14:10:45 +0000 (17:10 +0300)]
Use TT moves more often in qsearch
During the recapture phase of quiescence search (where we limit the generated moves to recaptures on the last seen capture square),
the move picker will now emit the tt move, even if the tt move is not a recapture.
Rodrigo Roim [Fri, 7 Oct 2022 23:55:07 +0000 (16:55 -0700)]
Fix tablebase probe for dtz >1000 w/o 50 move rule
For qn4N1/6R1/3K4/8/B2k4/8/8/8 w - - 0 1, white loses with DTZ 1034.
See https://syzygy-tables.info/?fen=qn4N1/6R1/3K4/8/B2k4/8/8/8_w_-_-_0_1
Prior to this fix, due to a too small hard-coded value, Stockfish interpreted this as winning.
The new value picked (1<<18) is large enough to deal with the largest DTZ values that can be stored in the current syzygy format.
If Stockfish is winning at root, we have optimism > 0 for all leaves in
the search tree where Stockfish is to move. There, if (psq - nnue) > 0
(ie if the advantage is more materialistic than positional), then the
product D = optimism * (psq - nnue) will be positive, nnueComplexity will
increase, and the eval will increase from SF point of view.
So the effect A is that if Stockfish is winning at root, she will slightly
favor in the search tree (in other words, search more) the positions where
she can convert her advantage via materialist means.
effect B :
If Stockfish is losing at root, we have optimism > 0 for all leaves in
the search tree where the opponent is to move. There, if (psq - nnue) < 0
(ie if the opponent advantage is more positional than materialistic), then
the product D = optimism * (psq-nnue) will be negative, nnueComplexity will
decrease, and the eval will decrease from the opponent point of view.
So the effect B is that Stockfish will slightly favor in the search tree
(search more) the branches where she can defend by slowly reducing the
opponent positional advantage.
this also introduces `-flto-partition=one` as suggested by MinetaS (Syine Mineta)
to avoid linking errors due to LTO on 32 bit mingw. This change was tested in isolation as well
Remove old line in "Futility pruning for captures"
The line is no longer needed after https://github.com/official-stockfish/Stockfish/commit/910cf8b21839eb9f1991934a5436eea112021723.
This patch incidentally applies "Futility Pruning for Captures" also in case of en-passant, changing the bench signature.
This patch reuses the threatenedPieces variable (which is calculated in movepicker)
to reduce less in the search tree the moves which escape a capture.
This removes the restriction that no hashfull information is printed within the first second of a search.
On modern systems, a non-zero value is returned within 6 ms with default settings.
This patch simplifies the formulas used to compute the trend and optimism values before each search iteration.
As a side effect, this removes the parameters which make the relationship between the displayed evaluation value
and the expected game result asymmetric.
I've also provided links to the results of isotonic regression analysis of the relationship between the evaluation and game result (statistical data and a graph) for both tests, which demonstrate that the new version has a more symmetric relationship:
STC: [Data and graph](https://github.com/official-stockfish/Stockfish/discussions/4150#discussioncomment-3548954)
LTC: [Data and graph](https://github.com/official-stockfish/Stockfish/discussions/4150#discussioncomment-3626311)
See also https://github.com/official-stockfish/Stockfish/issues/4142
Michael Chaly [Sat, 10 Sep 2022 09:30:25 +0000 (12:30 +0300)]
Do less singular extensions for former PVnode
Patch is a reintroduction of logic what was simplified a while ago
in a slightly different form. Do bigger extension offset in
case of non-pv node having a pv.
relatively soon servers with 512 threads will be available 'quite commonly',
anticipate even more threads, and increase our current maximum from 512 to 1024.
A bug fix plus non functional speed optimization. Position::key_after(Move m) is now
consistent with Position::key() thus prefetching correct TT entries which speeds things up.
Related PR #3759
projects / stockfish / log