Takes advantage of the sample skipping rules that are used during training (capture, check, or VALUE_NONE).
Adds positions to keep continuity, which improves compression.
Append hash of first master net trained with filter method
Hardcode depth 6 and remove option to set depth
Underscores for consistency
Filter out standard startpos positions too
Allows UCI_Chess960 to be true during data generation.
If UCI_Chess960 is true then strips castling rights from all saved
positions and skips saving positions with castling move.
UCI_Chess960 is respected in transforms.
The speedup is around 0.25% using gcc 11.3.1 (bmi2, nnue bench, depth 16
and 23) while it is neutral using clang (same conditions).
According to `perf` that integer division was one of the most time-consuming
instructions in search (gcc disassembly).
Passed STC:
https://tests.stockfishchess.org/tests/view/628a17fe24a074e5cd59b3aa
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 22232 W: 5992 L: 5751 D: 10489
Ptnml(0-2): 88, 2235, 6218, 2498, 77
yellow LTC:
https://tests.stockfishchess.org/tests/view/628a35d7ccae0450e35106f7
LLR: -2.95 (-2.94,2.94) <0.50,3.00>
Total: 320168 W: 85853 L: 85326 D: 148989
Ptnml(0-2): 185, 29698, 99821, 30165, 215
This patch also suggests that UHO STC is sensible to small speedups (< 0.50%).
closes https://github.com/official-stockfish/Stockfish/pull/4032
No functional change
This patch provides command line flags `--help` and `--license` as well as the corresponding `help` and `license` commands.
```
$ ./stockfish --help
Stockfish 200522 by the Stockfish developers (see AUTHORS file)
Stockfish is a powerful chess engine and free software licensed under the GNU GPLv3.
Stockfish is normally used with a separate graphical user interface (GUI).
Stockfish implements the universal chess interface (UCI) to exchange information.
For further information see https://github.com/official-stockfish/Stockfish#readme
or the corresponding README.md and Copying.txt files distributed with this program.
```
The idea is to provide a minimal help that links to the README.md file,
not replicating information that is already available elsewhere.
We use this opportunity to explicitly report the license as well.
closes https://github.com/official-stockfish/Stockfish/pull/4027
No functional change.
train a net using training data with a
heavier weight on positions having 16 pieces on the board. More specifically,
with a relative weight of `i * (32-i)/(16 * 16)+1` (where i is the number of pieces on the board).
This is done with the trainer branch https://github.com/glinscott/nnue-pytorch/pull/173
The command used is:
```
python train.py $datafile $datafile $restarttype $restartfile --gpus 1 --threads 4 --num-workers 12 --random-fen-skipping=3 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --features=HalfKAv2_hm^ --lambda=1.00 --max_epochs=$epochs --seed $RANDOM --default_root_dir exp/run_$i
```
The datafile is T60T70wIsRightFarseerT60T74T75T76.binpack, the restart is from the master net.
passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 22728 W: 6197 L: 5945 D: 10586
Ptnml(0-2): 105, 2453, 6001, 2695, 110
https://tests.stockfishchess.org/tests/view/625cf944ff677a888877cd90
passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 35664 W: 9535 L: 9264 D: 16865
Ptnml(0-2): 30, 3524, 10455, 3791, 32
https://tests.stockfishchess.org/tests/view/625d3c32ff677a888877d7ca
closes https://github.com/official-stockfish/Stockfish/pull/3989
Bench: 7269563
Official release version of Stockfish 15
Bench: 8129754
---
A new major release of Stockfish is now available at https://stockfishchess.org
Stockfish 15 continues to push the boundaries of chess, providing unrivalled
analysis and playing strength. In our testing, Stockfish 15 is ahead of
Stockfish 14 by 36 Elo points and wins nine times more game pairs than it
loses[1].
Improvements to the engine have made it possible for Stockfish to end up
victorious in tournaments at all sorts of time controls ranging from bullet to
classical and even at Fischer random chess[2]. At CCC, Stockfish won all of
the latest tournaments: CCC 16 Bullet, Blitz and Rapid, CCC 960 championship,
and the CCC 17 Rapid. At TCEC, Stockfish won the Season 21, Cup 9, FRC 4 and
in the current Season 22 superfinal, at the time of writing, has won 16 game
pairs and not yet lost a single one.
This progress is the result of a dedicated team of developers that comes up
with new ideas and improvements. For Stockfish 15, we tested nearly 13000
different changes and retained the best 200. These include the fourth
generation of our NNUE network architecture, as well as various search
improvements. To perform these tests, contributors provide CPU time for
testing, and in the last year, they have collectively played roughly a
billion chess games. In the last few years, our distributed testing
framework, Fishtest, has been operated superbly and has been developed and
improved extensively. This work by Pasquale Pigazzini, Tom Vijlbrief, Michel
Van den Bergh, and various other developers[3] is an essential part of the
success of the Stockfish project.
Indeed, the Stockfish project builds on a thriving community of enthusiasts
to offer 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[4].
The Stockfish team
[1] https://tests.stockfishchess.org/tests/view/625d156dff677a888877d1be
[2] https://en.wikipedia.org/wiki/Stockfish_(chess)#Competition_results
[3] https://github.com/glinscott/fishtest/blob/master/AUTHORS
[4] https://stockfishchess.org/get-involved/
This patch chooses the delta value (which skews the nnue evaluation between positional and materialistic)
depending on the material: If the material is low, delta will be higher and the evaluation is shifted
to the positional value. If the material is high, the evaluation will be shifted to the psqt value.
I don't think slightly negative values of delta should be a concern.
Passed STC:
https://tests.stockfishchess.org/tests/view/62418513b3b383e86185766f
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 28808 W: 7832 L: 7564 D: 13412
Ptnml(0-2): 147, 3186, 7505, 3384, 182
Passed LTC:
https://tests.stockfishchess.org/tests/view/62419137b3b383e861857842
LLR: 2.96 (-2.94,2.94) <0.50,3.00>
Total: 58632 W: 15776 L: 15450 D: 27406
Ptnml(0-2): 42, 5889, 17149, 6173, 63
closes https://github.com/official-stockfish/Stockfish/pull/3971
Bench: 7588855
This idea is a mix of koivisto idea of threat history and heuristic that
was simplified some time ago in LMR - decreasing reduction for moves that evade a capture.
Instead of doing so in LMR this patch does it in movepicker - to do this it
calculates squares that are attacked by different piece types and pieces that are located
on this squares and boosts up weight of moves that make this pieces land on a square that is not under threat.
Boost is greater for pieces with bigger material values.
Special thanks to koivisto and seer authors for explaining me ideas behind threat history.
Passed STC:
https://tests.stockfishchess.org/tests/view/62406e473b32264b9aa1478b
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 19816 W: 5320 L: 5072 D: 9424
Ptnml(0-2): 86, 2165, 5172, 2385, 100
Passed LTC:
https://tests.stockfishchess.org/tests/view/62407f2e3b32264b9aa149c8
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 51200 W: 13805 L: 13500 D: 23895
Ptnml(0-2): 44, 5023, 15164, 5322, 47
closes https://github.com/official-stockfish/Stockfish/pull/3970
bench 7736491
Via the ttPv flag an implicit tree of current and former PV nodes is maintained. In addition this tree is grown or shrinked at the leafs dependant on the search results. But now the shrinking step has been removed.
As the frequency of ttPv nodes decreases with depth the shown scaling behavior (STC barely passed but LTC scales well) of the tests was expected.
STC:
LLR: 2.93 (-2.94,2.94) <-2.25,0.25>
Total: 270408 W: 71593 L: 71785 D: 127030
Ptnml(0-2): 1339, 31024, 70630, 30912, 1299
https://tests.stockfishchess.org/tests/view/622fbf9dc9e950cbfc2376d6
LTC:
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 34368 W: 9135 L: 8992 D: 16241
Ptnml(0-2): 28, 3423, 10135, 3574, 24
https://tests.stockfishchess.org/tests/view/62305257c9e950cbfc238964
closes https://github.com/official-stockfish/Stockfish/pull/3963
Bench: 7044203
This commit generalizes the feature transform to use vec_t macros
that are architecture defined instead of using a seperate code path for each one.
It should make some old architectures (MMX, including improvements by Fanael) faster
and make further such improvements easier in the future.
Includes some corrections to CI for mingw.
closes https://github.com/official-stockfish/Stockfish/pull/3955
closes https://github.com/official-stockfish/Stockfish/pull/3928
No functional change
- set the variable only for the required tests to keep simple the yml file
- use NDK 21.x until will be fixed the Stockfish static build problem
with NDK 23.x
- set the test for armv7, armv7-neon, armv8 builds:
- use armv7a-linux-androideabi21-clang++ compiler for armv7 armv7-neon
- enforce a static build
- silence the Warning for the unused compilation flag "-pie" with
the static build, otherwise the Github workflow stops
- use qemu to bench the build and get the signature
Many thanks to @pschneider1968 that made all the hard work with NDK :)
closes https://github.com/official-stockfish/Stockfish/pull/3924
No functional change
This patch is a result of tuning done by user @candirufish after 150k games.
Since the tuned values were really interesting and touched heuristics
that are known for their non-linear scaling I decided to run limited
games LTC match, even if the STC test was really bad (which was expected).
After seeing the results of the LTC match, I also run a VLTC (very long
time control) SPRTtest, which passed.
The main difference is in extensions: this patch allows much more
singular/double extensions, both in terms of allowing them at lower
depths and with lesser margins.
Failed STC:
https://tests.stockfishchess.org/tests/view/620d66643ec80158c0cd3b46
LLR: -2.94 (-2.94,2.94) <0.00,2.50>
Total: 4968 W: 1194 L: 1398 D: 2376
Ptnml(0-2): 47, 633, 1294, 497, 13
Performed well at LTC in a fixed-length match:
https://tests.stockfishchess.org/tests/view/620d66823ec80158c0cd3b4a
ELO: 3.36 +-1.8 (95%) LOS: 100.0%
Total: 30000 W: 7966 L: 7676 D: 14358
Ptnml(0-2): 36, 2936, 8755, 3248, 25
Passed VLTC SPRT test:
https://tests.stockfishchess.org/tests/view/620da11a26f5b17ec884f939
LLR: 2.96 (-2.94,2.94) <0.50,3.00>
Total: 4400 W: 1326 L: 1127 D: 1947
Ptnml(0-2): 13, 309, 1348, 526, 4
closes https://github.com/official-stockfish/Stockfish/pull/3937
Bench: 6318903
Architecture:
The diagram of the "SFNNv4" architecture:
https://user-images.githubusercontent.com/8037982/153455685-cbe3a038-e158-4481-844d-9d5fccf5c33a.png
The most important architectural changes are the following:
* 1024x2 [activated] neurons are pairwise, elementwise multiplied (not quite pairwise due to implementation details, see diagram), which introduces a non-linearity that exhibits similar benefits to previously tested sigmoid activation (quantmoid4), while being slightly faster.
* The following layer has therefore 2x less inputs, which we compensate by having 2 more outputs. It is possible that reducing the number of outputs might be beneficial (as we had it as low as 8 before). The layer is now 1024->16.
* The 16 outputs are split into 15 and 1. The 1-wide output is added to the network output (after some necessary scaling due to quantization differences). The 15-wide is activated and follows the usual path through a set of linear layers. The additional 1-wide output is at least neutral, but has shown a slightly positive trend in training compared to networks without it (all 16 outputs through the usual path), and allows possibly an additional stage of lazy evaluation to be introduced in the future.
Additionally, the inference code was rewritten and no longer uses a recursive implementation. This was necessitated by the splitting of the 16-wide intermediate result into two, which was impossible to do with the old implementation with ugly hacks. This is hopefully overall for the better.
First session:
The first session was training a network from scratch (random initialization). The exact trainer used was slightly different (older) from the one used in the second session, but it should not have a measurable effect. The purpose of this session is to establish a strong network base for the second session. Small deviations in strength do not harm the learnability in the second session.
The training was done using the following command:
python3 train.py \
/home/sopel/nnue/nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
/home/sopel/nnue/nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
--gpus "$3," \
--threads 4 \
--num-workers 4 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--lambda=1.0 \
--gamma=0.992 \
--lr=8.75e-4 \
--max_epochs=400 \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2
Every 20th net was saved and its playing strength measured against some baseline at 25k nodes per move with pure NNUE evaluation (modified binary). The exact setup is not important as long as it's consistent. The purpose is to sift good candidates from bad ones.
The dataset can be found https://drive.google.com/file/d/1UQdZN_LWQ265spwTBwDKo0t1WjSJKvWY/view
Second session:
The second training session was done starting from the best network (as determined by strength testing) from the first session. It is important that it's resumed from a .pt model and NOT a .ckpt model. The conversion can be performed directly using serialize.py
The LR schedule was modified to use gamma=0.995 instead of gamma=0.992 and LR=4.375e-4 instead of LR=8.75e-4 to flatten the LR curve and allow for longer training. The training was then running for 800 epochs instead of 400 (though it's possibly mostly noise after around epoch 600).
The training was done using the following command:
The training was done using the following command:
python3 train.py \
/data/sopel/nnue/nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
/data/sopel/nnue/nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
--gpus "$3," \
--threads 4 \
--num-workers 4 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--lambda=1.0 \
--gamma=0.995 \
--lr=4.375e-4 \
--max_epochs=800 \
--resume-from-model /data/sopel/nnue/nnue-pytorch-training/data/exp295/nn-epoch399.pt \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$run_id
In particular note that we now use lambda=1.0 instead of lambda=0.8 (previous nets), because tests show that WDL-skipping introduced by vondele performs better with lambda=1.0. Nets were being saved every 20th epoch. In total 16 runs were made with these settings and the best nets chosen according to playing strength at 25k nodes per move with pure NNUE evaluation - these are the 4 nets that have been put on fishtest.
The dataset can be found either at ftp://ftp.chessdb.cn/pub/sopel/data_sf/T60T70wIsRightFarseerT60T74T75T76.binpack in its entirety (download might be painfully slow because hosted in China) or can be assembled in the following way:
Get the https://github.com/official-stockfish/Stockfish/blob/5640ad48ae5881223b868362c1cbeb042947f7b4/script/interleave_binpacks.py script.
Download T60T70wIsRightFarseer.binpack https://drive.google.com/file/d/1_sQoWBl31WAxNXma2v45004CIVltytP8/view
Download farseerT74.binpack http://trainingdata.farseer.org/T74-May13-End.7z
Download farseerT75.binpack http://trainingdata.farseer.org/T75-June3rd-End.7z
Download farseerT76.binpack http://trainingdata.farseer.org/T76-Nov10th-End.7z
Run python3 interleave_binpacks.py T60T70wIsRightFarseer.binpack farseerT74.binpack farseerT75.binpack farseerT76.binpack T60T70wIsRightFarseerT60T74T75T76.binpack
Tests:
STC: https://tests.stockfishchess.org/tests/view/6203fb85d71106ed12a407b7
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 16952 W: 4775 L: 4521 D: 7656
Ptnml(0-2): 133, 1818, 4318, 2076, 131
LTC: https://tests.stockfishchess.org/tests/view/62041e68d71106ed12a40e85
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 14944 W: 4138 L: 3907 D: 6899
Ptnml(0-2): 21, 1499, 4202, 1728, 22
closes https://github.com/official-stockfish/Stockfish/pull/3927
Bench: 4919707
Most credits for this patch should go to @candirufish.
Based on his big search tuning (1M games at 20+0.1s)
https://tests.stockfishchess.org/tests/view/61fc7a6ed508ec6a1c9f4b7d
with some hand polishing on top of it, which includes :
a) correcting trend sigmoid - for some reason original tuning resulted in it being negative. This heuristic was proven to be worth some elo for years so reversing it sign is probably some random artefact;
b) remove changes to continuation history based pruning - this heuristic historically was really good at providing green STCs and then failing at LTC miserably if we tried to make it more strict, original tuning was done at short time control and thus it became more strict - which doesn't scale to longer time controls;
c) remove changes to improvement - not really indended :).
passed STC
https://tests.stockfishchess.org/tests/view/6203526e88ae2c84271c2ee2
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 16840 W: 4604 L: 4363 D: 7873
Ptnml(0-2): 82, 1780, 4449, 2033, 76
passed LTC
https://tests.stockfishchess.org/tests/view/620376e888ae2c84271c35d4
LLR: 2.96 (-2.94,2.94) <0.50,3.00>
Total: 17232 W: 4771 L: 4542 D: 7919
Ptnml(0-2): 14, 1655, 5048, 1886, 13
closes https://github.com/official-stockfish/Stockfish/pull/3926
bench 5030992
have maximal compatibility on legacy target arch, now supporting AMD Athlon
The old behavior can anyway be selected by the user if needed, for example
make -j profile-build ARCH=x86-32 sse=yes
fixes#3904
closes https://github.com/official-stockfish/Stockfish/pull/3918
No functional change
For cross-compiling to Android on windows, the Makefile needs some tweaks.
Tested with Android NDK 23.1.7779620 and 21.4.7075529, using
Windows 10 with clean MSYS2 environment (i.e. no MINGW/GCC/Clang
toolchain in PATH) and Fedora 35, with build target:
build ARCH=armv8 COMP=ndk
The resulting binary runs fine inside Droidfish on my Samsung
Galaxy Note20 Ultra and Samsung Galaxy Tab S7+
Other builds tested to exclude regressions: MINGW64/Clang64 build
on Windows; MINGW64 cross build, native Clang and GCC builds on Fedora.
wiki docs https://github.com/glinscott/fishtest/wiki/Cross-compiling-Stockfish-for-Android-on-Windows-and-Linux
closes https://github.com/official-stockfish/Stockfish/pull/3901
No functional change
A Windows Native Build (WNB) can be done:
- on Windows, using a recent mingw-w64 g++/clang compiler
distributed by msys2, cygwin and others
- on Linux, using mingw-w64 g++ to cross compile
Improvements:
- check for a WNB in a proper way and set a variable to simplify the code
- set the proper EXE for a WNB
- use the proper name for the mingw-w64 clang compiler
- use the static linking for a WNB
- use wine to make a PGO cross compile on Linux (also with Intel SDE)
- enable the LTO build for mingw-w64 g++ compiler
- set `lto=auto` to use the make's job server, if available, or otherwise
to fall back to autodetection of the number of CPU threads
- clean up all the temporary LTO files saved in the local directory
Tested on:
- msys2 MINGW64 (g++), UCRT64 (g++), MINGW32 (g++), CLANG64 (clang)
environments
- cygwin mingw-w64 g++
- Ubuntu 18.04 & 21.10 mingw-w64 PGO cross compile (also with Intel SDE)
closes#3891
No functional change
This updates estimates from 2yr ago #2401, and adds missing terms.
All tests run at 10+0.1 (STC), 20000 games, error bars +- 1.8 Elo, book 8moves_v3.png.
A table of Elo values with the links to the corresponding tests can be found at the PR
closes https://github.com/official-stockfish/Stockfish/pull/3868
Non-functional Change
This is a reintroduction of an idea that was simplified away approximately 1 year ago.
There are some tweaks to it :
a) exclude promotions;
b) exclude Pv Nodes from it - Pv Nodes logic for captures is really different from non Pv nodes so it makes a lot of sense;
c) use a big grain of capture history - idea is taken from my recent patches in futility pruning.
passed STC
https://tests.stockfishchess.org/tests/view/61bd90f857a0d0f327c373b7
LLR: 2.96 (-2.94,2.94) <0.00,2.50>
Total: 86640 W: 22474 L: 22110 D: 42056
Ptnml(0-2): 268, 9732, 22963, 10082, 275
passed LTC
https://tests.stockfishchess.org/tests/view/61be094457a0d0f327c38aa3
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 23240 W: 6079 L: 5838 D: 11323
Ptnml(0-2): 14, 2261, 6824, 2512, 9
https://github.com/official-stockfish/Stockfish/pull/3864
bench 4493723
This patch is a follow up of previous 2 patches that introduced more reductions for PV nodes with low delta and more pruning for nodes with low delta. Instead of writing separate heuristics now it adjust reductions based on delta / rootDelta - it allows to remove 3 separate adjustements of pruning/LMR in different places and also makes reduction dependence on delta and rootDelta smoother. Also now it works for all pruning heuristics and not just 2.
Passed STC
https://tests.stockfishchess.org/tests/view/61ba9b6c57a0d0f327c2d48b
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 79192 W: 20513 L: 20163 D: 38516
Ptnml(0-2): 238, 8900, 21024, 9142, 292
passed LTC
https://tests.stockfishchess.org/tests/view/61baf77557a0d0f327c2eb8e
LLR: 2.96 (-2.94,2.94) <0.50,3.00>
Total: 158400 W: 41134 L: 40572 D: 76694
Ptnml(0-2): 101, 16372, 45745, 16828, 154
closes https://github.com/official-stockfish/Stockfish/pull/3862
bench 4651538
This idea is somewhat similar to extentions in LMR but has a different flavour.
If result of LMR was really good - thus exceeded alpha by some pretty
big given margin, we can extend move after LMR in full depth search with 0 window.
The idea is that this move is probably a fail high with somewhat of a big
probability so extending it makes a lot of sense
passed STC
https://tests.stockfishchess.org/tests/view/61ad45ea56fcf33bce7d74b7
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 59680 W: 15531 L: 15215 D: 28934
Ptnml(0-2): 193, 6711, 15734, 6991, 211
passed LTC
https://tests.stockfishchess.org/tests/view/61ad9ff356fcf33bce7d8646
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 59104 W: 15321 L: 14992 D: 28791
Ptnml(0-2): 53, 6023, 17065, 6364, 47
closes https://github.com/official-stockfish/Stockfish/pull/3838
bench 4881329
This patch optimizes the NEON implementation in two ways.
The activation layer after the feature transformer is rewritten to make it easier for the compiler to see through dependencies and unroll. This in itself is a minimal, but a positive improvement. Other architectures could benefit from this too in the future. This is not an algorithmic change.
The affine transform for large matrices (first layer after FT) on NEON now utilizes the same optimized code path as >=SSSE3, which makes the memory accesses more sequential and makes better use of the available registers, which allows for code that has longer dependency chains.
Benchmarks from Redshift#161, profile-build with apple clang
george@Georges-MacBook-Air nets % ./stockfish-b82d93 bench 2>&1 | tail -4 (current master)
===========================
Total time (ms) : 2167
Nodes searched : 4667742
Nodes/second : 2154011
george@Georges-MacBook-Air nets % ./stockfish-7377b8 bench 2>&1 | tail -4 (this patch)
===========================
Total time (ms) : 1842
Nodes searched : 4667742
Nodes/second : 2534061
This is a solid 18% improvement overall, larger in a bench with NNUE-only, not mixed.
Improvement is also observed on armv7-neon (Raspberry Pi, and older phones), around 5% speedup.
No changes for architectures other than NEON.
closes https://github.com/official-stockfish/Stockfish/pull/3837
No functional changes.
Initialize continuation history with a slighlty negative value -71 instead of zero.
The idea is, because the most history entries will be later negative anyway, to shift
the starting values a little bit in the "correct" direction. Of course the effect of
initialization dimishes with greater depth so I had the apprehension that the LTC test
would be difficult to pass, but it passed.
STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 34520 W: 9076 L: 8803 D: 16641
Ptnml(0-2): 136, 3837, 9047, 4098, 142
https://tests.stockfishchess.org/tests/view/61aa52e39e8855bba1a3776b
LTC:
LLR: 2.93 (-2.94,2.94) <0.50,3.00>
Total: 75568 W: 19620 L: 19254 D: 36694
Ptnml(0-2): 44, 7773, 21796, 8115, 56
https://tests.stockfishchess.org/tests/view/61aa87d39e8855bba1a383a5
closes https://github.com/official-stockfish/Stockfish/pull/3834
Bench: 4674029
In their infinite wisdom, Intel axed AVX512 from Alder Lake
chips (well, not entirely, but we kind of want to use the Gracemont
cores for chess!) but still added VNNI support.
Confusingly enough, this is not the same as VNNI256 support.
This adds a specific AVX-VNNI target that will use this AVX-VNNI
mode, by prefixing the VNNI instructions with the appropriate VEX
prefix, and avoiding AVX512 usage.
This is about 1% faster on P cores:
Result of 20 runs
==================
base (./clang-bmi2 ) = 3306337 +/- 7519
test (./clang-vnni ) = 3344226 +/- 7388
diff = +37889 +/- 4153
speedup = +0.0115
P(speedup > 0) = 1.0000
But a nice 3% faster on E cores:
Result of 20 runs
==================
base (./clang-bmi2 ) = 1938054 +/- 28257
test (./clang-vnni ) = 1994606 +/- 31756
diff = +56552 +/- 3735
speedup = +0.0292
P(speedup > 0) = 1.0000
This was measured on Clang 13. GCC 11.2 appears to generate
worse code for Alder Lake, though the speedup on the E cores
is similar.
It is possible to run the engine specifically on the P or E using binding,
for example in linux it is possible to use (for an 8 P + 8 E setup like i9-12900K):
taskset -c 0-15 ./stockfish
taskset -c 16-23 ./stockfish
where the first call binds to the P-cores and the second to the E-cores.
closes https://github.com/official-stockfish/Stockfish/pull/3824
No functional change
This patch is a result of refining of tuning vondele did after
new net passed and some hand-made values adjustements - excluding
changes in other pruning heuristics and rounding value of history
divisor to the nearest power of 2.
With this patch futility pruning becomes more aggressive and
history influence on it is doubled again.
passed STC
https://tests.stockfishchess.org/tests/view/61a2c4c1a26505c2278c150d
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 33848 W: 8841 L: 8574 D: 16433
Ptnml(0-2): 100, 3745, 8988, 3970, 121
passed LTC
https://tests.stockfishchess.org/tests/view/61a327ffa26505c2278c26d9
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 22272 W: 5856 L: 5614 D: 10802
Ptnml(0-2): 12, 2230, 6412, 2468, 14
closes https://github.com/official-stockfish/Stockfish/pull/3814
bench 6302543
This idea is somewhat of a respin of smth we had in futility pruning and that was simplified away - dependence of it not only on static evaluation of position but also on move history heuristics.
Instead of aborting it when they are high there we use fraction of their sum to adjust static eval pruning criteria.
passed STC
https://tests.stockfishchess.org/tests/view/619bd438c0a4ea18ba95a27d
LLR: 2.93 (-2.94,2.94) <0.00,2.50>
Total: 113704 W: 29284 L: 28870 D: 55550
Ptnml(0-2): 357, 12884, 30044, 13122, 445
passed LTC
https://tests.stockfishchess.org/tests/view/619cb8f0c0a4ea18ba95a334
LLR: 2.96 (-2.94,2.94) <0.50,3.00>
Total: 147136 W: 37307 L: 36770 D: 73059
Ptnml(0-2): 107, 15279, 42265, 15804, 113
closes https://github.com/official-stockfish/Stockfish/pull/3805
bench 6777918
retire msvc support and corresponding CI. No active development happens on msvc,
and build is much slower or wrong.
gcc (mingw) is our toolchain of choice also on windows, and the latter is tested.
No functional change
Current master implements a scaling of the raw NNUE output value with a formula
equivalent to 'eval = alpha * NNUE_output', where the scale factor alpha varies
between 1.8 (for early middle game) and 0.9 (for pure endgames). This feature
allows Stockfish to keep material on the board when she thinks she has the advantage,
and to seek exchanges and simplifications when she thinks she has to defend.
This patch slightly offsets the turning point between these two strategies, by adding
to Stockfish's evaluation a small "optimism" value before actually doing the scaling.
The effect is that SF will play a little bit more risky, trying to keep the tension a
little bit longer when she is defending, and keeping even more material on the board
when she has an advantage.
We note that this patch is similar in spirit to the old "Contempt" idea we used to have
in classical Stockfish, but this implementation differs in two key points:
a) it has been tested as an Elo-gainer against master;
b) the values output by the search are not changed on average by the implementation
(in other words, the optimism value changes the tension/exchange strategy, but a
displayed value of 1.0 pawn has the same signification before and after the patch).
See the old comment https://github.com/official-stockfish/Stockfish/pull/1361#issuecomment-359165141
for some images illustrating the ideas.
-------
finished yellow at STC:
LLR: -2.94 (-2.94,2.94) <0.00,2.50>
Total: 165048 W: 41705 L: 41611 D: 81732
Ptnml(0-2): 565, 18959, 43245, 19327, 428
https://tests.stockfishchess.org/tests/view/61942a3dcd645dc8291c876b
passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 121656 W: 30762 L: 30287 D: 60607
Ptnml(0-2): 87, 12558, 35032, 13095, 56
https://tests.stockfishchess.org/tests/view/61962c58cd645dc8291c8877
-------
How to continue from there?
a) the shape (slope and amplitude) of the sigmoid used to compute the optimism value
could be tweaked to try to gain more Elo, so the parameters of the sigmoid function
in line 391 of search.cpp could be tuned with SPSA. Manual tweaking is also possible
using this Desmos page: https://www.desmos.com/calculator/jhh83sqq92
b) in a similar vein, with two recents patches affecting the scaling of the NNUE
evaluation in evaluate.cpp, now could be a good time to try a round of SPSA tuning
of the NNUE network;
c) this patch will tend to keep tension in middlegame a little bit longer, so any
patch improving the defensive aspect of play via search extensions in risky,
tactical positions would be welcome.
-------
closes https://github.com/official-stockfish/Stockfish/pull/3797
Bench: 6184852
Starting with Windows Build 20348 the behavior of the numa API has been changed:
https://docs.microsoft.com/en-us/windows/win32/procthread/numa-support
Old code only worked because there was probably a limit on how many
cores/threads can reside within one NUMA node, and the OS creates extra NUMA
nodes when necessary, however the actual mechanism of core binding is
done by "Processor Groups"(https://docs.microsoft.com/en-us/windows/win32/procthread/processor-groups). With a newer OS, one NUMA node can have many
such "Processor Groups" and we should just consistently use the number
of groups to bind the threads instead of deriving the topology from
the number of NUMA nodes.
This change is required to spread threads on all cores on Windows 11 with
a 3990X CPU. It has only 1 NUMA node with 2 groups of 64 threads each.
closes https://github.com/official-stockfish/Stockfish/pull/3787
No functional change.
In case the evaluation at root is large, discourage the use of lazyEval.
This fixes https://github.com/official-stockfish/Stockfish/issues/3772
or at least improves it significantly. In this case, poor play with large
odds can be observed, in extreme cases leading to a loss despite large
advantage:
r1bq1b1r/ppp3p1/3p1nkp/n3p3/2B1P2N/2NPB3/PPP2PPP/R3K2R b KQ - 5 9
With this patch the poor move is only considered up to depth 13, in master
up to depth 28.
The patch did not pass at LTC with Elo gainer bounds, but with slightly
positive Elo nevertheless (95% LOS).
STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 40368 W: 10318 L: 10041 D: 20009
Ptnml(0-2): 103, 4493, 10725, 4750, 113
https://tests.stockfishchess.org/tests/view/61800ad259e71df00dcc420d
LTC:
LLR: -2.94 (-2.94,2.94) <0.50,3.00>
Total: 212288 W: 52997 L: 52692 D: 106599
Ptnml(0-2): 112, 22038, 61549, 22323, 122
https://tests.stockfishchess.org/tests/view/618050d959e71df00dcc426d
closes https://github.com/official-stockfish/Stockfish/pull/3780
Bench: 7127040
Maintain for each root move an exponential average of the search value with a weight ratio of 2:1 (new value vs old values). Then the average score is used as the center of the initial aspiration window instead of the previous score.
Stats indicate (see PR) that the deviation for previous score is in general greater than using average score, so later seems a better estimation of the next search value. This is probably the reason this patch succeded besides smoothing the sometimes wild swings in search score. An additional observation is that at higher depth previous score is above but average score below zero. So for average score more/less fail/low highs should be occur than previous score.
STC:
LLR: 2.97 (-2.94,2.94) <0.00,2.50>
Total: 59792 W: 15106 L: 14792 D: 29894
Ptnml(0-2): 144, 6718, 15869, 7010, 155
https://tests.stockfishchess.org/tests/view/61841612d7a085ad008eef06
LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 46448 W: 11835 L: 11537 D: 23076
Ptnml(0-2): 21, 4756, 13374, 5050, 23
https://tests.stockfishchess.org/tests/view/618463abd7a085ad008eef3e
closes https://github.com/official-stockfish/Stockfish/pull/3776
Bench: 6719976
Currently we handle the UCI_Elo with a double randomization. This
seems not necessary and a bit involuted.
This patch removes the first randomization and unifies the 2 cases.
closes https://github.com/official-stockfish/Stockfish/pull/3769
No functional change.
To help with debugging, the worker sends the output of
stderr (suitable truncated) to the action log on the
server, in case a build fails. For this to work it is
important that there is no spurious output to stderr.
closes https://github.com/official-stockfish/Stockfish/pull/3773
No functional change
Official release version of Stockfish 14.1
Bench: 6334068
---
Today, we have the pleasure to announce Stockfish 14.1.
As usual, downloads will be freely available at stockfishchess.org/download [1].
With Stockfish 14.1 our users get access to the strongest chess engine
available today. In the period leading up to this release, Stockfish
convincingly won several chess engine tournaments, including the TCEC 21
superfinal, the TCEC Cup 9, and the Computer Chess Championship for
Fischer Random Chess (Chess960). In the latter tournament, Stockfish
was undefeated in 599 out of 600 games played.
Compared to Stockfish 14, this release introduces a more advanced NNUE
architecture and various search improvements. In self play testing, using
a book of balanced openings, Stockfish 14.1 wins three times more game
pairs than it loses [2]. At this high level, draws are very common, so the
Elo difference to Stockfish 14 is about 17 Elo. The NNUE evaluation method,
introduced to top level chess with Stockfish 12 about one year ago [3],
has now been adopted by several other strong CPU based chess engines.
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 [4].
Stay safe and enjoy chess!
The Stockfish team
[1] https://stockfishchess.org/download/
[2] https://tests.stockfishchess.org/tests/view/6175c320af70c2be1788fa2b
[3] https://github.com/official-stockfish/Stockfish/discussions/3628
[4] https://stockfishchess.org/get-involved/
Idea of this patch is the following: in case we already have four moves that
exceeded alpha in the current node, the probability of finding fifth should
be reasonably low. Note that four is completely arbitrary - there could and
probably should be some tweaks, both in tweaking best move count threshold
for more reductions and tweaking how they work - for example making more
reductions with best move count linearly.
passed STC:
https://tests.stockfishchess.org/tests/view/615f614783dd501a05b0aee2
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 141816 W: 36056 L: 35686 D: 70074
Ptnml(0-2): 499, 15131, 39273, 15511, 494
passed LTC:
https://tests.stockfishchess.org/tests/view/615fdff683dd501a05b0af35
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 68536 W: 17221 L: 16891 D: 34424
Ptnml(0-2): 38, 6573, 20725, 6885, 47
closes https://github.com/official-stockfish/Stockfish/pull/3736
Bench: 6131513
This patch updates the stat_bonus() function (used in the history tables to
help move ordering), keeping the same quadratic for small depths but changing
the values for depth >= 9:
The old bonus formula was increasing from zero at depth 1 to 4100 at depth 14,
then used the strange, small value of 73 for all depths >= 15.
The new bonus formula increases from 0 at depth 1 to 2000 at depth 8, then
keeps 2000 for all depths >= 8.
passed STC:
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 169624 W: 42875 L: 42454 D: 84295
Ptnml(0-2): 585, 19340, 44557, 19729, 601
https://tests.stockfishchess.org/tests/view/615bd69e9d256038a969b97c
passed LTC:
LLR: 3.07 (-2.94,2.94) <0.50,3.50>
Total: 37336 W: 9456 L: 9191 D: 18689
Ptnml(0-2): 20, 3810, 10747, 4067, 24
https://tests.stockfishchess.org/tests/view/615c75d99d256038a969b9b2
closes https://github.com/official-stockfish/Stockfish/pull/3731
Bench: 6261865
When playing games in MultiPV mode we must take care to only track the
best move changing for the first PV line. Otherwise, SF will spend most
of its time for the initial moves after the book exit.
This has been observed and reported on Discord, but can also be seen in
games played in Stefan Pohl's MultiPV experiment.
Tested with MultiPV=4.
STC:
https://tests.stockfishchess.org/tests/view/615c24b59d256038a969b990
LLR: 2.95 (-2.94,2.94) <-0.50,2.50>
Total: 1744 W: 694 L: 447 D: 603
Ptnml(0-2): 32, 125, 358, 278, 79
LTC:
https://tests.stockfishchess.org/tests/view/615c31769d256038a969b993
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 2048 W: 723 L: 525 D: 800
Ptnml(0-2): 10, 158, 511, 314, 31
closes https://github.com/official-stockfish/Stockfish/pull/3729
Bench: 5714575
Respin of multi-thread idea that was simplified away recently: basically doing
more reductions with thread count since Lazy SMP naturally widens search. With
drawish book this idea got simplified away but with less drawish book it again
gains elo, maybe trying to reinstall other ideas that were simplified away
previously can be beneficial.
passed STC
LLR: 2.96 (-2.94,2.94) <-0.50,2.50>
Total: 39736 W: 10205 L: 9986 D: 19545
Ptnml(0-2): 45, 4254, 11064, 4447, 58
https://tests.stockfishchess.org/tests/view/615750702d02f48db3961b00
passed LTC
LLR: 2.97 (-2.94,2.94) <0.50,3.50>
Total: 60352 W: 15530 L: 15218 D: 29604
Ptnml(0-2): 24, 5900, 18016, 6212, 24
https://tests.stockfishchess.org/tests/view/6157d8935488e26ea5eace7f
closes https://github.com/official-stockfish/Stockfish/pull/3724
Bench 5714575
Idea is to extend some quiet ttMoves if a lot of things indicate that
the transposition table move is going to be a good move:
1) move being a killer - so being the best move in nearby node;
2) reply continuation history is really good.
This is basically saying that move is good "in general" in this position,
that it is a good reply to the opponent move and that it was the best in
this position somewhere in search - so extending it makes a lot of sense.
In general in past year we had a lot of extensions of different types,
maybe there is something more in it :)
passed STC
LLR: 2.96 (-2.94,2.94) <-0.50,2.50>
Total: 42944 W: 10932 L: 10695 D: 21317
Ptnml(0-2): 141, 4869, 11210, 5116, 136
https://tests.stockfishchess.org/tests/view/614cca8e7bdc23e77ceb89f0
passed LTC
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 156848 W: 39473 L: 38893 D: 78482
Ptnml(0-2): 125, 16327, 44913, 16961, 98
https://tests.stockfishchess.org/tests/view/614cf93d7bdc23e77ceb8a13
closes https://github.com/official-stockfish/Stockfish/pull/3719
Bench: 5714575
In master, during singular move analysis, when both the transposition value
and a reduced search for the other moves seem to indicate a fail high, we
heuristically prune the whole subtree and return an fail high score.
This patch is a little bit more cautious in this case, and instead of the
risky cutoff, we now search the ttMove with a reduced depth (by two plies).
STC:
https://tests.stockfishchess.org/tests/view/614dafe07bdc23e77ceb8a89
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 46728 W: 11909 L: 11666 D: 23153
Ptnml(0-2): 181, 5288, 12168, 5561, 166
LTC:
https://tests.stockfishchess.org/tests/view/614dc84abe4c07e0ecac3c95
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 74520 W: 18809 L: 18450 D: 37261
Ptnml(0-2): 45, 7735, 21346, 8084, 50
closes https://github.com/official-stockfish/Stockfish/pull/3718
Bench: 5499262
This patch relax a little bit the condition for doubly singular moves
(ie moves that are so forced that we think that they deserve a local
double extension of the search). We lower the margin and allow up to
six such double extensions in the path between the root and the critical
node.
Original idea by Siad Daboul (@TopoIogist) in PR #3709
Tested with the previous commit:
passed STC:
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 33048 W: 8458 L: 8236 D: 16354
Ptnml(0-2): 120, 3701, 8660, 3923, 120
https://tests.stockfishchess.org/tests/view/614b24347bdc23e77ceb88fe
passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,3.50>
Total: 54176 W: 13712 L: 13406 D: 27058
Ptnml(0-2): 36, 5653, 15399, 5969, 31
https://tests.stockfishchess.org/tests/view/614b3b727bdc23e77ceb8911
closes https://github.com/official-stockfish/Stockfish/pull/3714
Bench: 5792377
This patch detects some search explosions (due to double extensions in
search.cpp) which can happen in some pathological positions, and takes
measures to ensure progress in search even for these pathological situations.
While a small number of double extensions can be useful during search
(for example to resolve a tactical sequence), a sustained regime of
double extensions leads to search explosion and a non-finishing search.
See the discussion in https://github.com/official-stockfish/Stockfish/pull/3544
and the issue https://github.com/official-stockfish/Stockfish/issues/3532 .
The implemented algorithm is the following:
a) at each node during search, store the current depth in the stack.
Double extensions are by definition levels of the stack where the
depth at ply N is strictly higher than depth at ply N-1.
b) during search, calculate for each thread a running average of the
number of double extensions in the last 4096 visited nodes.
c) if one thread has more than 2% of double extensions for a sustained
period of time (6 millions consecutive nodes, or about 4 seconds on
my iMac), we decide that this thread is in an explosion state and
we calm down this thread by preventing it to do any double extension
for the next 6 millions nodes.
To calculate the running averages, we also introduced a auxiliary class
generalizing the computations of ttHitAverage variable we already had in
code. The implementation uses an exponential moving average of period 4096
and resolution 1/1024, and all computations are done with integers for
efficiency.
-----------
Example where the patch solves a search explosion:
```
./stockfish
ucinewgame
position fen 8/Pk6/8/1p6/8/P1K5/8/6B1 w - - 37 130
go infinite
```
This algorithm does not affect search in normal, non-pathological positions.
We verified, for instance, that the usual bench is unchanged up to depth 20
at least, and that the node numbers are unchanged for a search of the starting
position at depth 32.
-------------
See https://github.com/official-stockfish/Stockfish/pull/3714
Bench: 5575265
This patch introduces extension for captures and promotions. Every capture or
promotion that is not the first move in the list gets extended at PvNodes and
cutNodes. Special thanks to @locutus2 - all my previous attepmts that failed
on this idea were done only for PvNodes - idea to include also cutNodes was
based on his latest passed patch.
STC
https://tests.stockfishchess.org/tests/view/6134abf325b9b35584838574
LLR: 2.95 (-2.94,2.94) <-0.50,2.50>
Total: 188920 W: 47754 L: 47304 D: 93862
Ptnml(0-2): 595, 21754, 49344, 22140, 627
LTC
https://tests.stockfishchess.org/tests/view/613521de25b9b355848385d7
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 8768 W: 2283 L: 2098 D: 4387
Ptnml(0-2): 7, 866, 2452, 1053, 6
closes https://github.com/official-stockfish/Stockfish/pull/3692
bench: 5564555
The new network caused some issues initially due to the very narrow neuron set between the first two FC layers. Necessary changes were hacked together to make it work. This patch is a mature approach to make the affine transform code faster, more readable, and easier to maintain should the layer sizes change again.
The following changes were made:
* ClippedReLU always produces a multiple of 32 outputs. This is about as good of a solution for AffineTransform's SIMD requirements as it can get without a bigger rewrite.
* All self-contained simd helpers are moved to a separate file (simd.h). Inline asm is utilized to work around GCC's issues with code generation and register assignment. See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=101693, https://godbolt.org/z/da76fY1n7
* AffineTransform has 2 specializations. While it's more lines of code due to the boilerplate, the logic in both is significantly reduced, as these two are impossible to nicely combine into one.
1) The first specialization is for cases when there's >=128 inputs. It uses a different approach to perform the affine transform and can make full use of AVX512 without any edge cases. Furthermore, it has higher theoretical throughput because less loads are needed in the hot path, requiring only a fixed amount of instructions for horizontal additions at the end, which are amortized by the large number of inputs.
2) The second specialization is made to handle smaller layers where performance is still necessary but edge cases need to be handled. AVX512 implementation for this was ommited by mistake, a remnant from the temporary implementation for the new... This could be easily reintroduced if needed. A slightly more detailed description of both implementations is in the code.
Overall it should be a minor speedup, as shown on fishtest:
passed STC:
LLR: 2.96 (-2.94,2.94) <-0.50,2.50>
Total: 51520 W: 4074 L: 3888 D: 43558
Ptnml(0-2): 111, 3136, 19097, 3288, 128
and various tests shown in the pull request
closes https://github.com/official-stockfish/Stockfish/pull/3663
No functional change
Introduces a new NNUE network architecture and associated network parameters
The summary of the changes:
* Position for each perspective mirrored such that the king is on e..h files. Cuts the feature transformer size in half, while preserving enough knowledge to be good. See https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY/edit#heading=h.b40q4rb1w7on.
* The number of neurons after the feature transformer increased two-fold, to 1024x2. This is possibly mostly due to the now very optimized feature transformer update code.
* The number of neurons after the second layer is reduced from 16 to 8, to reduce the speed impact. This, perhaps surprisingly, doesn't harm the strength much. See https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY/edit#heading=h.6qkocr97fezq
The AffineTransform code did not work out-of-the box with the smaller number of neurons after the second layer, so some temporary changes have been made to add a special case for InputDimensions == 8. Also additional 0 padding is added to the output for some archs that cannot process inputs by <=8 (SSE2, NEON). VNNI uses an implementation that can keep all outputs in the registers while reducing the number of loads by 3 for each 16 inputs, thanks to the reduced number of output neurons. However GCC is particularily bad at optimization here (and perhaps why the current way the affine transform is done even passed sprt) (see https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY/edit# for details) and more work will be done on this in the following days. I expect the current VNNI implementation to be improved and extended to other architectures.
The network was trained with a slightly modified version of the pytorch trainer (https://github.com/glinscott/nnue-pytorch); the changes are in https://github.com/glinscott/nnue-pytorch/pull/143
The training utilized 2 datasets.
dataset A - https://drive.google.com/file/d/1VlhnHL8f-20AXhGkILujnNXHwy9T-MQw/view?usp=sharing
dataset B - as described in https://github.com/official-stockfish/Stockfish/commit/ba01f4b95448bcb324755f4dd2a632a57c6e67bc
The training process was as following:
train on dataset A for 350 epochs, take the best net in terms of elo at 20k nodes per move (it's fine to take anything from later stages of training).
convert the .ckpt to .pt
--resume-from-model from the .pt file, train on dataset B for <600 epochs, take the best net. Lambda=0.8, applied before the loss function.
The first training command:
python3 train.py \
../nnue-pytorch-training/data/large_gensfen_multipvdiff_100_d9.binpack \
../nnue-pytorch-training/data/large_gensfen_multipvdiff_100_d9.binpack \
--gpus "$3," \
--threads 1 \
--num-workers 1 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--smart-fen-skipping \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--lambda=1.0 \
--max_epochs=600 \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2
The second training command:
python3 serialize.py \
--features=HalfKAv2_hm^ \
../nnue-pytorch-training/experiment_131/run_6/default/version_0/checkpoints/epoch-499.ckpt \
../nnue-pytorch-training/experiment_$1/base/base.pt
python3 train.py \
../nnue-pytorch-training/data/michael_commit_b94a65.binpack \
../nnue-pytorch-training/data/michael_commit_b94a65.binpack \
--gpus "$3," \
--threads 1 \
--num-workers 1 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--smart-fen-skipping \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--lambda=0.8 \
--max_epochs=600 \
--resume-from-model ../nnue-pytorch-training/experiment_$1/base/base.pt \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2
STC: https://tests.stockfishchess.org/tests/view/611120b32a8a49ac5be798c4
LLR: 2.97 (-2.94,2.94) <-0.50,2.50>
Total: 22480 W: 2434 L: 2251 D: 17795
Ptnml(0-2): 101, 1736, 7410, 1865, 128
LTC: https://tests.stockfishchess.org/tests/view/611152b32a8a49ac5be798ea
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 9776 W: 442 L: 333 D: 9001
Ptnml(0-2): 5, 295, 4180, 402, 6
closes https://github.com/official-stockfish/Stockfish/pull/3646
bench: 5189338
This patch improves the codegen in the AffineTransform::forward function for architectures >=SSSE3. Current code works directly on memory and the compiler cannot see that the stores through outptr do not alias the loads through weights and input32. The solution implemented is to perform the affine transform with local variables as accumulators and only store the result to memory at the end. The number of accumulators required is OutputDimensions / OutputSimdWidth, which means that for the 1024->16 affine transform it requires 4 registers with SSSE3, 2 with AVX2, 1 with AVX512. It also cuts the number of stores required by NumRegs * 256 for each node evaluated. The local accumulators are expected to be assigned to registers, but even if this cannot be done in some case due to register pressure it will help the compiler to see that there is no aliasing between the loads and stores and may still result in better codegen.
See https://godbolt.org/z/59aTKbbYc for codegen comparison.
passed STC:
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 140328 W: 10635 L: 10358 D: 119335
Ptnml(0-2): 302, 8339, 52636, 8554, 333
closes https://github.com/official-stockfish/Stockfish/pull/3634
No functional change
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
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
Ptnml(0-2): 100, 2446, 13695, 2623, 100.
closes https://github.com/official-stockfish/Stockfish/pull/3626
Bench: 5169957
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
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
Ptnml(0-2): 11, 1556, 27298, 1597, 10
closes https://github.com/official-stockfish/Stockfish/pull/3618
bench 4709569
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
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/
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
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
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
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
This patch removes the UCI option for setting Contempt in classical evaluation.
It is exactly equivalent to using Contempt=0 for the UCI contempt value and keeping
the dynamic part in the algo (renaming this dynamic part `trend` to better describe
what it does). We have tried quite hard to implement a working Contempt feature for
NNUE but nothing really worked, so it is probably time to give up.
Interested chess fans wishing to keep playing with the UCI option for Contempt and
use it with the classical eval are urged to download the version tagged "SF_Classical"
of Stockfish (dated 31 July 2020), as it was the last version where our search
algorithm was tuned for the classical eval and is probably our strongest classical
player ever: https://github.com/official-stockfish/Stockfish/tags
Passed STC:
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 72904 W: 6228 L: 6175 D: 60501
Ptnml(0-2): 221, 5006, 25971, 5007, 247
https://tests.stockfishchess.org/tests/view/60c98bf9457376eb8bcab18d
Passed LTC:
LLR: 2.93 (-2.94,2.94) <-2.50,0.50>
Total: 45168 W: 1601 L: 1547 D: 42020
Ptnml(0-2): 38, 1331, 19786, 1397, 32
https://tests.stockfishchess.org/tests/view/60c9c7fa457376eb8bcab1bb
closes https://github.com/official-stockfish/Stockfish/pull/3575
Bench: 4947716
This patch increase the weight of pawns and pieces from 28 to 32
in the scaling formula we apply to the output of the NNUE pure eval.
Increasing this gradient for pawns and pieces means that Stockfish
will try a little harder to keep material when she has the advantage,
and try a little bit harder to escape into an endgame when she is
under pressure.
STC:
LLR: 2.93 (-2.94,2.94) <-0.50,2.50>
Total: 53168 W: 4371 L: 4177 D: 44620
Ptnml(0-2): 160, 3389, 19283, 3601, 151
https://tests.stockfishchess.org/tests/view/60cefd1d457376eb8bcab7ab
LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 10888 W: 386 L: 288 D: 10214
Ptnml(0-2): 3, 260, 4821, 356, 4
https://tests.stockfishchess.org/tests/view/60cf709d2114332881e7352b
closes https://github.com/official-stockfish/Stockfish/pull/3571
Bench: 4965430
trained with the Python command
c:\nnue>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_10 --resume-from-model ./pt/nn-3b20abec10c1.pt
`
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 .
Net nn-3b20abec10c1.nnue was chosen as the --resume-from-model with the idea that through learning, the manually hex edited values will be learned and will not need to be manually adjusted going forward. They would also be fine tuned by the learning process.
passed STC:
https://tests.stockfishchess.org/tests/view/60cdf91e457376eb8bcab66f
LLR: 2.95 (-2.94,2.94) <-0.50,2.50>
Total: 18256 W: 1639 L: 1479 D: 15138
Ptnml(0-2): 59, 1179, 6505, 1313, 72
passed LTC:
https://tests.stockfishchess.org/tests/view/60ce2166457376eb8bcab6e1
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 18792 W: 654 L: 542 D: 17596
Ptnml(0-2): 9, 490, 8291, 592, 14
closes https://github.com/official-stockfish/Stockfish/pull/3570
Bench: 5020972
The Cygwin environment has two g++ compilers, each with a different problem
for compiling Stockfish at the moment:
(a) g++.exe : full posix build compiler, linked to cygwin dll.
=> This one has a problem embedding the net.
(b) x86_64-w64-mingw32-g++.exe : native Windows build compiler.
=> This one manages to embed the net, but has a problem related to libgcov
when we use the profile-build target of Stockfish.
This patch solves the problem for compiler (b), so that our recommended command line
if you want to build an optimized version of Stockfish on Cygwin becomes something
like the following (you can change the ARCH value to whatever you want, but note
the COMP and CXX variables pointing at the right compiler):
```
make -j profile-build ARCH=x86-64-modern COMP=mingw CXX=x86_64-w64-mingw32-c++.exe
```
closes https://github.com/official-stockfish/Stockfish/pull/3569
No functional change
move to github actions to replace travis CI.
First version, testing on linux using gcc and clang.
gcc build with sanitizers and valgrind.
No functional change
Optimization of vondele's nn-33c9d39e5eb6.nnue using SPSA
https://tests.stockfishchess.org/tests/view/60ca68be457376eb8bcab28b
Setting: ck values are default based on how large the parameters are
The new values for this net are the raw values at the end of the tuning (80k games)
The significant changes are in buckets 1 and 2 (5-12 pieces) so the main difference is in playing endgames if we compare it to nn-33c9. There is also change in bucket 7 (29-32 pieces) but not as substantial as the changes in buckets 1 and 2. If we interpret the changes based on an experiment a few months ago, this new net plays more optimistically during endgames and less optimistically during openings.
STC:
LLR: 2.93 (-2.94,2.94) <-0.50,2.50>
Total: 49504 W: 4246 L: 4053 D: 41205
Ptnml(0-2): 140, 3282, 17749, 3407, 174
https://tests.stockfishchess.org/tests/view/60cbd752457376eb8bcab478
LTC:
LLR: 2.95 (-2.94,2.94) <0.50,3.50>
Total: 88720 W: 4926 L: 4651 D: 79143
Ptnml(0-2): 105, 4048, 35793, 4295, 119
https://tests.stockfishchess.org/tests/view/60cc7828457376eb8bcab4fa
closes https://github.com/official-stockfish/Stockfish/pull/3566
Bench: 4758885
This net was created by @pleomati, who manually edited with an hex editor
10 values randomly chosen in the LCSFNet10 net (nn-6ad41a9207d0.nnue) to
create this one. The LCSFNet10 net was trained by Joost VandeVondele from
a dataset combining Stockfish games and Leela games (16x10^9 positions from
SF self-play at depth 9, and 6.3x10^9 positions from Leela games, so overall
72% of Stockfish positions and 28% of Leela positions).
passed STC 10+0.1:
LLR: 2.94 (-2.94,2.94) <-0.50,2.50>
Total: 50888 W: 5881 L: 5654 D: 39353
Ptnml(0-2): 281, 4290, 16085, 4497, 291
https://tests.stockfishchess.org/tests/view/60cbfa68457376eb8bcab49a
passed LTC 60+0.6:
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 25480 W: 1498 L: 1338 D: 22644
Ptnml(0-2): 36, 1155, 10193, 1325, 31
https://tests.stockfishchess.org/tests/view/60cc4af8457376eb8bcab4d4
closes https://github.com/official-stockfish/Stockfish/pull/3564
Bench: 4904930
This reverts commit "Fix for Cygwin's environment build-profile", as it was
giving errors for "make clean" on some Windows environments. See comments in
https://github.com/official-stockfish/Stockfish/commit/68bf362ea2385a641be9f5ed9ce2acdf55a1ecf1
Possibly somebody can propose a solution that would fix Cygwin builds and
not break on other system too, stay tuned! :-)
No functional change
The Cygwin environment has two g++ compilers, each with a different problem
for compiling Stockfish at the moment:
(a) g++.exe : full posix build compiler, linked to cygwin dll.
=> This one has a problem embedding the net.
(b) x86_64-w64-mingw32-g++.exe : native Windows build compiler.
=> This one manages to embed the net, but has a problem related to libgcov
when we use the profile-build target of Stockfish.
This patch solves the problem for compiler (b), so that our recommended command line
if you want to build an optimized version of Stockfish on Cygwin becomes something
like the following (you can change the ARCH value to whatever you want, but note
the COMP and CXX variables pointing at the right compiler):
```
make -j profile-build ARCH=x86-64-modern COMP=mingw CXX=x86_64-w64-mingw32-c++.exe
```
closes https://github.com/official-stockfish/Stockfish/pull/3463
No functional change
of a root move leading to a 3-fold repetition.
With this small fix a draw ranking and thus a draw score is being applied.
This works for both, ranking by dtz or wdl tables.
Fixes https://github.com/official-stockfish/Stockfish/issues/3542
(No functional change without TBs.)
Bench: 4877339
This net is the result of training on data used by the Leela project. More precisely,
we shuffled T60 and T74 data kindly provided by borg (for different Tnn, the data is
a result of Leela selfplay with differently sized Leela nets).
The data is available at vondele's google drive:
https://drive.google.com/drive/folders/1mftuzYdl9o6tBaceR3d_VBQIrgKJsFpl.
The Leela data comes in small chunks of .binpack files. To shuffle them, we simply
used a small python script to randomly rename the files, and then concatenated them
using `cat`. As validation data we picked a file of T60 data. We will further investigate
T74 data.
The training for the NNUE architecture used 200 epochs with the Python trainer from
the Stockfish project. Unlike the previous run we tried with this data, this run does
not have adjusted scaling — not because we didn't want to, but because we forgot.
However, this training randomly skips 40% more positions than previous run. The loss
was very spiky and decreased slower than it does usually.
Training loss: https://github.com/official-stockfish/images/blob/main/training-loss-8e47cf062333.png
Validation loss: https://github.com/official-stockfish/images/blob/main/validation-loss-8e47cf062333.png
This is the exact training command:
python train.py --smart-fen-skipping --random-fen-skipping 14 --batch-size 16384 --threads 4 --num-workers 4 --gpus 1 trainingdata\training_data.binpack validationdata\val.binpack
---
10k STC result:
ELO: 3.61 +-3.3 (95%) LOS: 98.4%
Total: 10000 W: 1241 L: 1137 D: 7622
Ptnml(0-2): 68, 841, 3086, 929, 76
https://tests.stockfishchess.org/tests/view/60c67e50457376eb8bcaae70
10k LTC result:
ELO: 2.71 +-2.4 (95%) LOS: 98.8%
Total: 10000 W: 659 L: 581 D: 8760
Ptnml(0-2): 22, 485, 3900, 579, 14
https://tests.stockfishchess.org/tests/view/60c69deb457376eb8bcaae98
Passed LTC:
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 9648 W: 685 L: 545 D: 8418
Ptnml(0-2): 22, 448, 3740, 596, 18
https://tests.stockfishchess.org/tests/view/60c6d41c457376eb8bcaaecf
---
closes https://github.com/official-stockfish/Stockfish/pull/3550
Bench: 4877339
Compute optimal register count for feature transformer accumulation dynamically.
This also introduces a change where AVX512 would only use 8 registers instead of 16
(now possible due to a 2x increase in feature transformer size).
closes https://github.com/official-stockfish/Stockfish/pull/3543
No functional change
This patch restricts LMR extensions (of non-transposition table moves) from being
used when the transposition table move was extended by two plies via singular
extension. This may serve to limit search explosions in certain positions.
This makes a lot of sense because the precondition for the tt-move to have been
singular extended by two plies is that the result of the alternate search (with
excluded the tt-move) has been a hard fail low: it is natural to later search less
for non tt-moves in this situation.
The current state of depth/extensions/reductions management is getting quite tricky
in our search algo, see https://github.com/official-stockfish/Stockfish/pull/3546#issuecomment-860174549
for some discussion. Suggestions welcome!
Passed STC
https://tests.stockfishchess.org/tests/view/60c3f293457376eb8bcaac8d
LLR: 2.95 (-2.94,2.94) <-0.50,2.50>
Total: 117984 W: 9698 L: 9430 D: 98856
Ptnml(0-2): 315, 7708, 42703, 7926, 340
passed LTC
https://tests.stockfishchess.org/tests/view/60c46ea5457376eb8bcaacc7
LLR: 2.97 (-2.94,2.94) <0.50,3.50>
Total: 11280 W: 401 L: 302 D: 10577
Ptnml(0-2): 2, 271, 4998, 364, 5
closes https://github.com/official-stockfish/Stockfish/pull/3546
Bench: 4709974
Load feature transformer weights in bulk on little-endian machines.
This is in particular useful to test new nets with c-chess-cli,
see https://github.com/lucasart/c-chess-cli/issues/44
```
$ time ./stockfish.exe uci
Before : 0m0.914s
After : 0m0.483s
```
No functional change
Cleaner vector code structure in feature transformer. This patch just
regroups the parts of the inner loop for each SIMD instruction set.
Tested for non-regression:
LLR: 2.96 (-2.94,2.94) <-2.50,0.50>
Total: 115760 W: 9835 L: 9831 D: 96094
Ptnml(0-2): 326, 7776, 41715, 7694, 369
https://tests.stockfishchess.org/tests/view/60b96b39457376eb8bcaa26e
It would be nice if a future patch could use some of the macros at
the top of the file to unify the code between the distincts SIMD
instruction sets (of course, unifying the Relu will be the challenge).
closes https://github.com/official-stockfish/Stockfish/pull/3506
No functional change
This simplification patch implements two changes:
1. it simplifies away the so-called "lazy" path in the NNUE evaluation internals,
where we trusted the psqt head alone to avoid the costly "positional" head in
some cases;
2. it raises a little bit the NNUEThreshold1 in evaluate.cpp (from 682 to 800),
which increases the limit where we switched from NNUE eval to Classical eval.
Both effects increase the number of positional evaluations done by our new net
architecture, but the results of our tests below seem to indicate that the loss
of speed will be compensated by the gain of eval quality.
STC:
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 26280 W: 2244 L: 2137 D: 21899
Ptnml(0-2): 72, 1755, 9405, 1810, 98
https://tests.stockfishchess.org/tests/view/60ae73f112066fd299795a51
LTC:
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 20592 W: 750 L: 677 D: 19165
Ptnml(0-2): 9, 614, 8980, 681, 12
https://tests.stockfishchess.org/tests/view/60ae88e812066fd299795a82
closes https://github.com/official-stockfish/Stockfish/pull/3503
Bench: 3817907
this tool with take N binpacks as input to produce 1 binpack as output.
The input binpacks are read in random order, with a probability related to their size,
but each input file is read sequentially. The output is thus an appropriately shuffled binpack.
The tool is much faster than cat'ing the files together followed by a shuffle.
It assumes that the input binpacks themselves have no particular internal ordering.
Definition of the lazy threshold moved to evaluate.cpp where all others are.
Lazy threshold only used for real searches, not used for the "eval" call.
This preserves the purity of NNUE evaluation, which is useful to verify
consistency between the engine and the NNUE trainer.
closes https://github.com/official-stockfish/Stockfish/pull/3499
No functional change
basically, the binpack lib doesn't reset the epsquare after f7f5 in this 5kb1/5p2/2B3p1/1N1KP2p/3p1P2/2bP2P1/5r2/8 b - - 0 1 position, but it does reset it when passed the fen 5kb1/8/2B3p1/1N1KPp1p/3p1P2/2bP2P1/5r2/8 w - f6 0 50. Potentially creating a discrepancy based on whether the position was set directly or arrived at by a move
Our new nets output two values for the side to move in the last layer.
We can interpret the first value as a material evaluation of the
position, and the second one as the dynamic, positional value of the
location of pieces.
This patch changes the balance for the (materialist, positional) parts
of the score from (128, 128) to (121, 135) when the piece material is
equal between the two players, but keeps the standard (128, 128) balance
when one player is at least an exchange up.
Passed STC:
LLR: 2.93 (-2.94,2.94) <-0.50,2.50>
Total: 15936 W: 1421 L: 1266 D: 13249
Ptnml(0-2): 37, 1037, 5694, 1134, 66
https://tests.stockfishchess.org/tests/view/60a82df9ce8ea25a3ef0408f
Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 13904 W: 516 L: 410 D: 12978
Ptnml(0-2): 4, 374, 6088, 484, 2
https://tests.stockfishchess.org/tests/view/60a8bbf9ce8ea25a3ef04101
closes https://github.com/official-stockfish/Stockfish/pull/3492
Bench: 3856635
The Tempo variable was introduced 10 years ago in our search because the
classical evaluation function was antisymmetrical in White and Black by design
to gain speed:
Eval(White to play) = -Eval(Black to play)
Nowadays our neural networks know which side is to play in a position when
they evaluate a position and are trained on real games, so the neural network
encodes the advantage of moving as an output of search. This patch shows that
the Tempo variable is not necessary anymore.
STC:
LLR: 2.94 (-2.94,2.94) <-2.50,0.50>
Total: 33512 W: 2805 L: 2709 D: 27998
Ptnml(0-2): 80, 2209, 12095, 2279, 93
https://tests.stockfishchess.org/tests/view/60a44ceace8ea25a3ef03d30
LTC:
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 53920 W: 1807 L: 1760 D: 50353
Ptnml(0-2): 16, 1617, 23650, 1658, 19
https://tests.stockfishchess.org/tests/view/60a477f0ce8ea25a3ef03d49
We also tried a match (20000 games) at STC using purely classical, result was neutral:
https://tests.stockfishchess.org/tests/view/60a4eebcce8ea25a3ef03db5
Note: there are two locations left in search.cpp where we assume antisymmetry
of evaluation (in relation with a speed optimization for null moves in lines
770 and 1439), but as the values are just used for heuristic pruning this
approximation should not hurt too much because the order of magnitude is still
true most of the time.
closes https://github.com/official-stockfish/Stockfish/pull/3481
Bench: 4015864
This improves the speed of NNUE by a bit on old hardware that code path
is intended for, like a Pentium III 1.13 GHz:
10 repeats of "./stockfish bench 16 1 13 default depth NNUE":
Before:
54 642 504 897 cycles (± 0.12%)
62 301 937 829 instructions (± 0.03%)
After:
54 320 821 928 cycles (± 0.13%)
62 084 742 699 instructions (± 0.02%)
Speed of go depth 20 from startpos:
Before: 53103 nps
After: 53856 nps
closes https://github.com/official-stockfish/Stockfish/pull/3476
No functional change.
This patch increases lmrDepth threshold for continuation history based pruning in search.
This part of code for a long time was known to be really TC sensitive - decreasing
this threshold easily passed lower time controls but failed badly at LTC,
on the other hand it increase was part of a tuning that resulted
in being negative at STC but was +12 elo at 180+1.8.
After recent simplification of special conditions that sometimes
increase it from 4 to 5 it was logical to overall test at longer
time controls if 5 is better than 4 with deeper searches.
reduces strenght on STC
https://tests.stockfishchess.org/tests/view/60a3a8bbce8ea25a3ef03c74
ELO: -2.57 +-2.0 (95%) LOS: 0.6%
Total: 20000 W: 1820 L: 1968 D: 16212
Ptnml(0-2): 68, 1582, 6836, 1458, 56
Passed LTC with STC bounds
https://tests.stockfishchess.org/tests/view/60a027395085663412d090ce
LLR: 2.93 (-2.94,2.94) <-0.50,2.50>
Total: 175256 W: 6774 L: 6548 D: 161934
Ptnml(0-2): 91, 5808, 75604, 6034, 91
Passed VLTC with LTC bounds
https://tests.stockfishchess.org/tests/view/60a2bccce229097940a037a7
LLR: 2.96 (-2.94,2.94) <0.50,3.50>
Total: 65736 W: 1224 L: 1092 D: 63420
Ptnml(0-2): 5, 1012, 30706, 1136, 9
closes https://github.com/official-stockfish/Stockfish/pull/3473
bench 3689330
- Comment for Countemove pruning -> Continuation history
- Fix comment in input_slice.h
- Shorter lines in Makefile
- Comment for scale factor
- Fix comment for pinners in see_ge()
- Change Thread.id() signature to size_t
- Trailing space in reprosearch.sh
- Add Douglas Matos Gomes to the AUTHORS file
- Introduce comment for undo_null_move()
- Use Stockfish coding style for export_net()
- Change date in AUTHORS file
closes https://github.com/official-stockfish/Stockfish/pull/3416
No functional change
e2k (Elbrus 2000) - this is a VLIW/EPIC architecture,
the like Intel Itanium (IA-64) architecture.
The architecture has half native / half software support
for most Intel/AMD SIMD (e.g. MMX/SSE/SSE2/SSE3/SSSE3/SSE4.1/SSE4.2/AES/AVX/AVX2 & 3DNow!/SSE4a/XOP/FMA4) via intrinsics.
https://en.wikipedia.org/wiki/Elbrus_2000
closes https://github.com/official-stockfish/Stockfish/pull/3425
No functional change
This PR adds an ability to export any currently loaded network.
The export_net command now takes an optional filename parameter.
If the loaded net is not the embedded net the filename parameter is required.
Two changes were required to support this:
* the "architecture" string, which is really just a some kind of description in the net, is now saved into netDescription on load and correctly saved on export.
* the AffineTransform scrambles weights for some architectures and sparsifies them, such that retrieving the index is hard. This is solved by having a temporary scrambled<->unscrambled index lookup table when loading the network, and the actual index is saved for each individual weight that makes it to canSaturate16. This increases the size of the canSaturate16 entries by 6 bytes.
closes https://github.com/official-stockfish/Stockfish/pull/3456
No functional change
This patch broadens and simplifies definition of PvNode that is likely to fail low.
New definition can be described as following "If node was already researched
at depth >= current depth and failed low there" which is more logical than the
previous version and takes less space + allows to not recompute it every time during move loop.
Passed simplification STC
https://tests.stockfishchess.org/tests/view/609148bf95e7f1852abd2e82
LLR: 2.93 (-2.94,2.94) <-2.50,0.50>
Total: 20128 W: 1865 L: 1751 D: 16512
Ptnml(0-2): 63, 1334, 7165, 1430, 72
Passed simplification LTC
https://tests.stockfishchess.org/tests/view/6091691295e7f1852abd2e8b
LLR: 2.94 (-2.94,2.94) <-2.50,0.50>
Total: 95128 W: 3498 L: 3481 D: 88149
Ptnml(0-2): 41, 2956, 41549, 2981, 37
closes https://github.com/official-stockfish/Stockfish/pull/3455
Bench: 3933037
Introduce variable tempo for nnue depending on logarithm of estimated
strength, where strength is the product of time and number of threads.
The original idea here was that NNUE is best with a slightly different
tempo value to classical, since its style of play is slightly different.
It turns out that the best tempo for NNUE varies with strength of play,
so a formula is used which gives about 19 for STC and 24 for LTC under
current fishtest settings.
STC 10+0.1:
LLR: 2.94 (-2.94,2.94) {-0.20,1.10}
Total: 120816 W: 11155 L: 10861 D: 98800
Ptnml(0-2): 406, 8728, 41933, 8848, 493
https://tests.stockfishchess.org/tests/view/60735b3a8141753378960534
LTC 60+0.6:
LLR: 2.94 (-2.94,2.94) {0.20,0.90}
Total: 35688 W: 1392 L: 1234 D: 33062
Ptnml(0-2): 23, 1079, 15473, 1255, 14
https://tests.stockfishchess.org/tests/view/6073ffbc814175337896057f
Passed non-regression SMP test at LTC 20+0.2 (8 threads):
LLR: 2.95 (-2.94,2.94) {-0.70,0.20}
Total: 11008 W: 317 L: 267 D: 10424
Ptnml(0-2): 2, 245, 4962, 291, 4
https://tests.stockfishchess.org/tests/view/60749ea881417533789605a4
closes https://github.com/official-stockfish/Stockfish/pull/3426
Bench 4075325
A lot of optimizations happend since the NNUE was introduced
and since then some parts of the code were left unused. This
got to the point where asserts were have to be made just to
let people know that modifying something will not have any
effects or may even break everything due to the assumptions
being made. Removing these parts removes those inexisting
"false dependencies". Additionally:
* append_changed_indices now takes the king pos and stateinfo
explicitly, no more misleading pos parameter
* IndexList is removed in favor of a generic ValueList.
Feature transformer just instantiates the type it needs.
* The update cost and refresh requirement is deferred to the
feature set once again, but now doesn't go through the whole
FeatureSet machinery and just calls HalfKP directly.
* accumulator no longer has a singular dimension.
* The PS constants and the PieceSquareIndex array are made local
to the HalfKP feature set because they are specific to it and
DO differ for other feature sets.
* A few names are changed to more descriptive
Passed STC non-regression:
https://tests.stockfishchess.org/tests/view/608421dd95e7f1852abd2790
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 180008 W: 16186 L: 16258 D: 147564
Ptnml(0-2): 587, 12593, 63725, 12503, 596
closes https://github.com/official-stockfish/Stockfish/pull/3441
No functional change
NNUE evaluation is incapable of recognizing trivially drawn bishop endgames
(the wrong-colored rook pawn), which are in fact ubiquitous and stock standard
in chess analysis. Switching off NNUE evaluation in KBPs vs KPs endgames is
a measure that stops Stockfish from trading down to a drawn version of these
endings when we presumably have advantage. The patch is able to edge over master
in endgame positions.
Patch tested for Elo gain with the "endgame.epd" book, and verified for
non-regression with our usual book (see the pull request for details).
STC:
LLR: 2.93 (-2.94,2.94) {-0.20,1.10}
Total: 33232 W: 6655 L: 6497 D: 20080
Ptnml(0-2): 4, 2342, 11769, 2494, 7
https://tests.stockfishchess.org/tests/view/6074a52981417533789605b8
LTC:
LLR: 2.93 (-2.94,2.94) {0.20,0.90}
Total: 159056 W: 29799 L: 29378 D: 99879
Ptnml(0-2): 7, 9004, 61085, 9425, 7
https://tests.stockfishchess.org/tests/view/6074c39a81417533789605ca
Closes https://github.com/official-stockfish/Stockfish/pull/3427
Bench: 4503918
blah
Specified with `warmup_epochs`, uses `warmup_lr`.
The purpose is to put the net into a somewhat stable state so that the gradients are not as high during the early stages of the training and don't "accidentally" break the net.
This patch changes the pop_lsb() signature from Square pop_lsb(Bitboard*) to
Square pop_lsb(Bitboard&). This is more idomatic for C++ style signatures.
Passed a non-regression STC test:
LLR: 2.93 (-2.94,2.94) {-1.25,0.25}
Total: 21280 W: 1928 L: 1847 D: 17505
Ptnml(0-2): 71, 1427, 7558, 1518, 66
https://tests.stockfishchess.org/tests/view/6053a1e22433018de7a38e2f
We have verified that the generated binary is identical on gcc-10.
Closes https://github.com/official-stockfish/Stockfish/pull/3404
No functional change.
our net currently is not trained on FRC games, and so doesn't know about the important pattern of a bishop that is cornered in FRC.
This patch introduces a term we have in the classical evaluation for this case, and adds it to the NNUE eval.
Since fishtest doesn't support FRC right now, the patch was tested locally at STC conditions,
starting from the book of FRC starting positions.
Score of master vs patch: 993 - 2226 - 6781 [0.438] 10000
Which corresponds to approximately 40 Elo
The patch passes non-regression testing for traditional chess (where it adds one branch).
passed STC:
https://tests.stockfishchess.org/tests/view/604fa2532433018de7a38b67
LLR: 2.95 (-2.94,2.94) {-1.25,0.25}
Total: 30560 W: 2701 L: 2636 D: 25223
Ptnml(0-2): 88, 2056, 10921, 2133, 82
passed STC also in an earlier version:
https://tests.stockfishchess.org/tests/view/604f61282433018de7a38b4d
closes https://github.com/official-stockfish/Stockfish/pull/3398
No functional change
We remark that in current master, most of our use cases for between_bb() can be
optimized if the second parameter of the function is added to the segment. So we
change the definition of between_bb(s1, s2) such that it excludes s1 but includes s2.
We also use a precomputed array for between_bb() for another small speed gain
(see https://tests.stockfishchess.org/tests/view/604d09f72433018de7a389fb).
Passed STC:
LLR: 2.96 (-2.94,2.94) {-0.25,1.25}
Total: 18736 W: 1746 L: 1607 D: 15383
Ptnml(0-2): 61, 1226, 6644, 1387, 50
https://tests.stockfishchess.org/tests/view/60428c84ddcba5f0627bb6e4
Yellow LTC:
LTC:
LLR: -3.00 (-2.94,2.94) {0.25,1.25}
Total: 39144 W: 1431 L: 1413 D: 36300
Ptnml(0-2): 13, 1176, 17184, 1178, 21
https://tests.stockfishchess.org/tests/view/605128702433018de7a38ca1
Closes https://github.com/official-stockfish/Stockfish/pull/3397
---------
Verified for correctness by running perft on the following position:
./stockfish
position fen 4rrk1/1p1nq3/p7/2p1P1pp/3P2bp/3Q1Bn1/PPPB4/1K2R1NR w - - 40 21
go perft 6
Nodes searched: 6136386434
--------
No functional change
The codebase contains multiple functions returning by const-value.
This patch is a small cleanup making those function returns
by value instead, removing the const specifier.
closes https://github.com/official-stockfish/Stockfish/pull/3328
No functional change
We introduce a metric for each internal node in search, called DistanceFromPV.
This distance indicated how far the current node is from the principal variation.
We then use this distance to search the nodes which are close to the PV a little
deeper (up to 4 plies deeper than the PV): this improves the quality of the search
at these nodes and bring better updates for the PV during search.
STC:
LLR: 2.96 (-2.94,2.94) {-0.25,1.25}
Total: 54936 W: 5047 L: 4850 D: 45039
Ptnml(0-2): 183, 3907, 19075, 4136, 167
https://tests.stockfishchess.org/tests/view/6037b88e7f517a561bc4a392
LTC:
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 49608 W: 1880 L: 1703 D: 46025
Ptnml(0-2): 22, 1514, 21555, 1691, 22
https://tests.stockfishchess.org/tests/view/6038271b7f517a561bc4a3cb
Closes https://github.com/official-stockfish/Stockfish/pull/3369
Bench: 5037279
The idea of this patch can be described as follows: if we are in check
and the transposition table move is a capture that returns a value
far above beta, we can assume that the opponent just blundered a piece
by giving check, and we return the transposition table value. This is
similar to the usual probCut logic for quiet moves, but with a different
threshold.
Passed STC
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 33440 W: 3056 L: 2891 D: 27493
Ptnml(0-2): 110, 2338, 11672, 2477, 123
https://tests.stockfishchess.org/tests/view/602cd1087f517a561bc49bda
Passed LTC
LLR: 2.98 (-2.94,2.94) {0.25,1.25}
Total: 10072 W: 401 L: 309 D: 9362
Ptnml(0-2): 2, 288, 4365, 378, 3
https://tests.stockfishchess.org/tests/view/602ceea57f517a561bc49bf0
The committed version has an additional fix to never return unproven wins
in the tablebase range or the mate range. This fix passed tests for non-
regression at STC and LTC:
STC:
LLR: 2.93 (-2.94,2.94) {-1.25,0.25}
Total: 26240 W: 2354 L: 2280 D: 21606
Ptnml(0-2): 85, 1763, 9372, 1793, 107
https://tests.stockfishchess.org/tests/view/602d86a87f517a561bc49c7a
LTC:
LLR: 2.95 (-2.94,2.94) {-0.75,0.25}
Total: 35304 W: 1299 L: 1256 D: 32749
Ptnml(0-2): 14, 1095, 15395, 1130, 18
https://tests.stockfishchess.org/tests/view/602d98d17f517a561bc49c83
Closes https://github.com/official-stockfish/Stockfish/pull/3362
Bench: 3830215
Official release version of Stockfish 13
Bench: 3766422
-----
It is our pleasure to release Stockfish 13 to chess fans worldwide.
As usual, downloads are freely available at
https://stockfishchess.org
The Stockfish project builds on a thriving community of enthusiasts
who contribute their expertise, time, and resources to build a free
and open-source chess engine that is robust, widely available, and
very strong. We would like to thank them all!
The good news first: from now on, our users can expect more frequent
high-quality releases of Stockfish! Sadly, this decision has been
triggered by the start of sales of the Fat Fritz 2 engine by ChessBase,
which is a copy of a very recent development version of Stockfish
with minor modifications. We refer to our statement on Fat Fritz 2[1]
and a community blog[2] for further information.
This version of Stockfish is significantly stronger than any of its
predecessors. Stockfish 13 outperforms Stockfish 12 by at least
35 Elo[3]. When playing against a one-year-old Stockfish, it wins 60
times more game pairs than it loses[4]. This release features an NNUE
network retrained on billions of positions, much faster network
evaluation code, and significantly improved search heuristics, as
well as additional evaluation tweaks. In the course of its development,
this version has won the superfinals of the TCEC Season 19 and
TCEC Season 20.
Going forward, the Leela Chess Zero and Stockfish teams will join
forces to demonstrate our commitment to open source chess engines and
training tools, and open data. We are convinced that our free and
open-source chess engines serve the chess community very well.
Stay safe and enjoy chess!
The Stockfish team
[1] https://blog.stockfishchess.org/post/643239805544792064/statement-on-fat-fritz-2
[2] https://lichess.org/blog/YCvy7xMAACIA8007/fat-fritz-2-is-a-rip-off
[3] https://tests.stockfishchess.org/tests/view/602bcccf7f517a561bc49b11
[4] https://tests.stockfishchess.org/tests/view/600fbb9c735dd7f0f0352d59
• reorder some sections of the README file
• add reference to the AUTHORS file
• rename Syzygybases to Syzygy tablebases
• add pointer to the Discord channel
• more precise info about the GPLv3 licence
No functional change
Do not decrease reduction at pv-nodes which are likely to fail low.
The idea of this patch can be described as following: during the search, if a node
on the principal variation was re-searched in non-pv search and this re-search got
a value which was much lower than alpha, then we can assume that this pv-node is
likely to fail low again, and we can reduce more aggressively at this node.
Passed STC
https://tests.stockfishchess.org/tests/view/6023a5fa7f517a561bc49638
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 70288 W: 6443 L: 6223 D: 57622
Ptnml(0-2): 239, 5022, 24436, 5174, 273
Passed LTC
https://tests.stockfishchess.org/tests/view/6023f2617f517a561bc49661
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 105656 W: 4048 L: 3748 D: 97860
Ptnml(0-2): 67, 3312, 45761, 3630, 58
Closes https://github.com/official-stockfish/Stockfish/pull/3349
Bench: 3766422
This patch removes some magic numbers in TT bit management and introduce proper
constants in the code, to improve documentation and ease further modifications.
No function change
It's about 1% speedup for Stockfish.
Result of 100 runs
==================
base (...fish_clang12) = 1946851 +/- 3717
test (./stockfish ) = 1967276 +/- 3408
diff = +20425 +/- 2438
speedup = +0.0105
P(speedup > 0) = 1.0000
Thanks to David Major for making me aware of this part
of LLVM development.
closes https://github.com/official-stockfish/Stockfish/pull/3346
No functional change
This patch give a small bonus to incite the attacking side to keep more
pawns on the board.
A consequence of this bonus is that Stockfish will tend to play positions
slightly more closed on average than master, especially when it believes
that it has an advantage.
To lower the risk of blockades where Stockfish start shuffling without
progress, we also implement a progressive decrease of the evaluation
value with the 50 moves counter (along with the necessary aging of the
transposition table to reduce the impact of the Graph History Interaction
problem): since the evaluation decreases during shuffling phases, the
engine will tend to examine the consequences of pawn breaks faster during
the search.
Passed STC:
LLR: 2.96 (-2.94,2.94) {-0.25,1.25}
Total: 26184 W: 2406 L: 2252 D: 21526
Ptnml(0-2): 85, 1784, 9223, 1892, 108
https://tests.stockfishchess.org/tests/view/600cc08b735dd7f0f0352c06
Passed LCT:
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 199768 W: 7695 L: 7191 D: 184882
Ptnml(0-2): 85, 6478, 86269, 6952, 100
https://tests.stockfishchess.org/tests/view/600ccd28735dd7f0f0352c10
Closes https://github.com/official-stockfish/Stockfish/pull/3321
Bench: 3988915
Give an additional penalty of S(20, 10) for any doubled pawn if none of
the opponent's pawns is facing any of our
- pawns or
- pawn attacks;
that means, each of their pawns can push at least one square without
being captured.
This ignores their non-pawns pieces and attacks.
One possible justification: Their pawns' ability to push freely provides
options to react to our threats by changing their pawn structure. Our
doubled pawns however will likely lead to an exploitable weakness, even
if the pawn structure is not yet fixed.
Note that the notion of "their pawns not being fixed" is symmetric for
both players: If all of their pawns can push freely so can ours. All
pawns being freely pushable might just be an early-game-indicator.
However, it can trigger during endgame pawns races, where doubled pawns
are especially hindering, too.
LTC
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 134976 W: 17964 L: 17415 D: 99597
Ptnml(0-2): 998, 12702, 39619, 13091, 1078
https://tests.stockfishchess.org/tests/view/5ffdd5316019e097de3ef281
STC
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 35640 W: 7219 L: 6904 D: 21517
Ptnml(0-2): 645, 4096, 8084, 4289, 706
https://tests.stockfishchess.org/tests/view/5ffda4a16019e097de3ef265
closes https://github.com/official-stockfish/Stockfish/pull/3302
Bench: 4363873
This change simplifies control flow in the generate_moves function which ensures the compiler doesn't duplicate work due to possibly not resolving pureness of the function calls. Also the biggest change is the removal of the unnecessary condition checking for empty b in a convoluted way. The rationale for removal of this condition is that computing attacks_bb with occupancy is not much more costly than computing pseudo attacks and overall the condition (also being likely unpredictable) is a pessimisation.
This is inspired by previous changes by @BM123499.
Passed STC:
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 88040 W: 8172 L: 7931 D: 71937
Ptnml(0-2): 285, 6128, 30957, 6361, 289
https://tests.stockfishchess.org/tests/view/5ffc28386019e097de3ef1c7
closes https://github.com/official-stockfish/Stockfish/pull/3300
No functional change.
Changed name from Bad Outpost to Uncontested Outpost
Scale Uncontested Outpost with number of pawns + Decrease Bishop PSQT values and general tuning
Credits for the decrease of the Bishop PSQT values: Fauzi
Credits for scaling Uncontested Outpost with number of pawns: Lolligerhans
Credits for the tunings: Fauzi
Passed STC:
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 32040 W: 6593 L: 6281 D: 19166
Ptnml(0-2): 596, 3713, 7095, 4015, 601
https://tests.stockfishchess.org/tests/view/5ffa43026019e097de3ef0f2
Passed LTC:
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 84376 W: 11395 L: 10950 D: 62031
Ptnml(0-2): 652, 7930, 24623, 8287, 696
https://tests.stockfishchess.org/tests/view/5ffa6e7b6019e097de3ef0fd
closes https://github.com/official-stockfish/Stockfish/pull/3296
Bench: 4287509
- "discovered check" (instead of "discovery check")
- "en passant" (instead of "en-passant")
- "pseudo-legal" before a noun (instead of "pseudo legal")
- "3-fold" (instead of "3fold")
closes https://github.com/official-stockfish/Stockfish/pull/3294
No functional change.
In a recent patch we added comparing capture history to a number for LMR of captures.
Calling it via thisThread-> is not needed since capture history was already declared by this time -
so removing makes code slightly shorter and easier to follow.
closes https://github.com/official-stockfish/Stockfish/pull/3297
No functional change.
Size of the weights in the last layer is less than 512 bits. It leads to wrong data access for AVX512. There is no error because in current implementation it is guaranteed that there is an array of zeros after weights so zero multiplied by something is returned and sum is correct. It is a mistake that can lead to unexpected bugs in the future. Used AVX2 instructions for smaller input size.
No measurable slowdown on avx512.
closes https://github.com/official-stockfish/Stockfish/pull/3298
No functional change.
Reordered weights in such a way that accumulated sum fits to output.
Weights are grouped in blocks of four elements because four
int8 (weight type) corresponds to one int32 (output type).
No horizontal additions.
Grouped AVX512, AVX2 and SSSE3 implementations.
Repeated code was removed.
An earlier version passed STC:
LLR: 2.97 (-2.94,2.94) {-0.25,1.25}
Total: 15336 W: 1495 L: 1355 D: 12486
Ptnml(0-2): 44, 1054, 5350, 1158, 62
https://tests.stockfishchess.org/tests/view/5ff60e106019e097de3eefd5
Speedup depends on the architecture, up to 4% measured on a NNUE only bench.
closes https://github.com/official-stockfish/Stockfish/pull/3287
No functional change
allows for repeating a depth N search K times.
Repeated searches improve the quality of eval, but don't bring in higher depth info.
Might allow for removing some of the noise in low depth scoring.
The idea of this patch is pretty simple - we already do more reductions
for non-PV and root nodes in case of stable best move for depth > 10.
This patch makes us do so if root depth if > 10 instead, which
is logical since best move changes (thus instability of it) is
counted at root, so it makes a lot of sense to use depth of the root.
passed STC
https://tests.stockfishchess.org/tests/view/5fd643271ac16912018885c5
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 13232 W: 1308 L: 1169 D: 10755
Ptnml(0-2): 39, 935, 4535, 1062, 45
passed LTC
https://tests.stockfishchess.org/tests/view/5fd68db11ac16912018885f0
LLR: 2.96 (-2.94,2.94) {0.25,1.25}
Total: 14024 W: 565 L: 463 D: 12996
Ptnml(0-2): 3, 423, 6062, 517, 7
closes https://github.com/official-stockfish/Stockfish/pull/3263
Bench: 4050630
Improves throughput by summing 2 intermediate dot products using 16 bit addition before upconverting to 32 bit.
Potential saturation is detected and the code-path is avoided in this case.
The saturation can't happen with the current nets,
but nets can be constructed that trigger this check.
STC https://tests.stockfishchess.org/tests/view/5fd40a861ac1691201888479
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 25544 W: 2451 L: 2296 D: 20797
Ptnml(0-2): 92, 1761, 8925, 1888, 106
about 5% speedup
closes https://github.com/official-stockfish/Stockfish/pull/3261
No functional change
Imbalance tables tweaked to contain MiddleGame and Endgame values, instead of a single value.
The idea started from Fisherman, which requested my help to tune the values back in June/July,
so I tuned the values back then, and we were able to accomplish good results,
but not enough to pass both STC and LTC tests.
So after the recent changes, I decided to give it another shot, and I am glad that it was a successful attempt.
A special thanks goes also to mstembera, which notified me a simple way to let the patch perform a little better.
Passed STC:
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 115976 W: 23124 L: 22695 D: 70157
Ptnml(0-2): 2074, 13652, 26285, 13725, 2252
https://tests.stockfishchess.org/tests/view/5fc92d2d42a050a89f02ccc8
Passed LTC:
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 156304 W: 20617 L: 20024 D: 115663
Ptnml(0-2): 1138, 14647, 46084, 15050, 1233
https://tests.stockfishchess.org/tests/view/5fc9fee142a050a89f02cd3e
closes https://github.com/official-stockfish/Stockfish/pull/3255
Bench: 4278746
This appears to be slightly faster than using a comparison against zero
to compute the high bits, on both old (like Pentium III) and new (like
Zen 2) hardware.
closes https://github.com/official-stockfish/Stockfish/pull/3254
No functional change.
in case a score can be parsed from the comment field in the pgn, add it to the output.
This form works for the fishtest pgns, and is quite common (cutechess-cli among others).
older versions of gcc (<8.1) need this, even if they accept -std=c++17
with this patch, the code can be run on fishtest again,
at least by the majority of workers (fishtest doesn't require c++17 to be available)
See e.g.
https://tests.stockfishchess.org/tests/view/5fcfbf801ac1691201888235
Bench: 3820648
The idea of this patch can be described as following: we update static
history stats based on comparison of the static evaluations of the
position before and after the move. If the move increases static evaluation
it's assigned positive bonus, if it decreases static evaluation
it's assigned negative bonus. These stats are used in movepicker
to sort quiet moves.
passed STC
https://tests.stockfishchess.org/tests/view/5fca4c0842a050a89f02cd66
LLR: 3.00 (-2.94,2.94) {-0.25,1.25}
Total: 78152 W: 7409 L: 7171 D: 63572
Ptnml(0-2): 303, 5695, 26873, 5871, 334
passed LTC
https://tests.stockfishchess.org/tests/view/5fca6be442a050a89f02cd75
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 40240 W: 1602 L: 1441 D: 37197
Ptnml(0-2): 19, 1306, 17305, 1475, 15
closes https://github.com/official-stockfish/Stockfish/pull/3253
bench 3845156
Include scaling change as suggested by Dietrich Kappe,
the one who trained net for Komodo. According to him,
some nets may require different scaling in order to utilize its full strength.
STC:
LLR: 2.93 (-2.94,2.94) {-0.25,1.25}
Total: 99856 W: 9669 L: 9401 D: 80786
Ptnml(0-2): 374, 7468, 34037, 7614, 435
https://tests.stockfishchess.org/tests/view/5fc2697642a050a89f02c8ec
LTC:
LLR: 2.96 (-2.94,2.94) {0.25,1.25}
Total: 29840 W: 1220 L: 1081 D: 27539
Ptnml(0-2): 10, 969, 12827, 1100, 14
https://tests.stockfishchess.org/tests/view/5fc2ea5142a050a89f02c957
Bench: 3561701
This patch removes the incrementally updated piece lists from the Position object.
This has been tried before but always failed. My reasons for trying again are:
* 32-bit systems (including phones) are now much less important than they were some years ago (and are absent from fishtest);
* NNUE may have made SF less finely tuned to the order in which moves were generated.
STC:
LLR: 2.94 (-2.94,2.94) {-1.25,0.25}
Total: 55272 W: 5260 L: 5216 D: 44796
Ptnml(0-2): 208, 4147, 18898, 4159, 224
https://tests.stockfishchess.org/tests/view/5fc2986a42a050a89f02c926
LTC:
LLR: 2.96 (-2.94,2.94) {-0.75,0.25}
Total: 16600 W: 673 L: 608 D: 15319
Ptnml(0-2): 14, 533, 7138, 604, 11
https://tests.stockfishchess.org/tests/view/5fc2f98342a050a89f02c95c
closes https://github.com/official-stockfish/Stockfish/pull/3247
Bench: 3940967
+-----------------+
| . . . . . . . . | All files are closed. Some files are
| . . . . . o o . | more valuable for rooks, because
| . . . . o . . o | they might open in the future.
| . . . o x . . x |
| o . o x . x x . |
| x o x . . . . . | x our pawns
| . x . . . . . . | o their pawns
| . . . . . . . . | ^ rooks are scored higher on these files
+-----------------+
^ ^
Files containing none of our own pawns are open or half-open (otherwise
they are closed). Rooks on (half-)open files recieve a bonus for the
future potential to act along all ranks.
This commit refines the (relative) penalty of rooks on closed files.
Files that contain one of our blocked pawns are considered less likely
to open in the future; rooks on these files are now penalized stronger.
This bonus does not generally correlate with mobility. If the condition
is sufficiently refined in the future, it may be beneficial to adjust or
override mobility scores in some cases.
LTC
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 494384 W: 71565 L: 70231 D: 352588
Ptnml(0-2): 3907, 48050, 142118, 49036, 4081
https://tests.stockfishchess.org/tests/view/5fb9312e67cbf42301d6afb9
LTC (non-regression w/ book noob_3moves.epd)
LLR: 2.95 (-2.94,2.94) {-0.75,0.25}
Total: 208520 W: 27044 L: 26937 D: 154539
Ptnml(0-2): 1557, 19850, 61391, 19853, 1609
https://tests.stockfishchess.org/tests/view/5fc01ced67cbf42301d6b3df
STC
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 98392 W: 20269 L: 19868 D: 58255
Ptnml(0-2): 1804, 11297, 22589, 11706, 1800
https://tests.stockfishchess.org/tests/view/5fb7f88a67cbf42301d6af10
closes https://github.com/official-stockfish/Stockfish/pull/3242
Bench: 3682630
in affine transform for AVX512/AVX2/SSSE3
The idea is to initialize sum with the first element instead of zero.
Reduce one add_epi32 and one set_zero SIMD instructions for each output dimension.
sum = 0; for i = 1 to n sum += a[i] ->
sum = a[1]; for i = 2 to n sum += a[i]
STC:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 69048 W: 7024 L: 6799 D: 55225
Ptnml(0-2): 260, 5175, 23458, 5342, 289
https://tests.stockfishchess.org/tests/view/5faf2cf467cbf42301d6aa06
closes https://github.com/official-stockfish/Stockfish/pull/3227
No functional change.
For the feature transformer the code is analogical to AVX2 since there was room for easy adaptation of wider simd registers.
For the smaller affine transforms that have 32 byte stride we keep 2 columns in one zmm register. We also unroll more aggressively so that in the end we have to do 16 parallel horizontal additions on ymm slices each consisting of 4 32-bit integers. The slices are embedded in 8 zmm registers.
These changes provide about 1.5% speedup for AVX-512 builds.
Closes https://github.com/official-stockfish/Stockfish/pull/3218
No functional change.
Using no searching time in case of a single legal move is not beneficial from
a strength point of view, and this special case can be easily removed:
STC:
LLR: 2.93 (-2.94,2.94) {-1.25,0.25}
Total: 22472 W: 2458 L: 2357 D: 17657
Ptnml(0-2): 106, 1733, 7453, 1842, 102
https://tests.stockfishchess.org/tests/view/5f926cbc81eda81bd78cb6df
LTC:
LLR: 2.94 (-2.94,2.94) {-0.75,0.25}
Total: 37880 W: 1736 L: 1682 D: 34462
Ptnml(0-2): 22, 1392, 16057, 1448, 21
https://tests.stockfishchess.org/tests/view/5f92a26081eda81bd78cb6fe
The advantage of using the normal time management for a single legal move is that scores
reported for that move are reasonable, not searching leads to artifacts during games
(see e.g. https://tcec-chess.com/#div=sf&game=96&season=19)
The disadvantage of using normal time management of a single legal move is that thinking
times can be unnaturally long, making it 'painful to watch' in online tournaments.
This patch uses normal time management, but caps the used time to 500ms.
This should lead to reasonable scores, and be hardly perceptible.
closes https://github.com/official-stockfish/Stockfish/pull/3195
closes https://github.com/official-stockfish/Stockfish/pull/3183
variant of a patch suggested by SFisGOD
No functional change.
Only do countermove based pruning in qsearch if we already have a move with a better score than a TB loss.
This patch fixes a bug (started as 843a961) that incorrectly prunes moves if in check,
and adds an assert to make sure no wrong mate scores are given in the future.
It replaces a no-op moveCount check with a check for bestValue.
Initially discussed in #3171 and later in #3199, #3198 and #3210.
This PR effectively closes#3171
It also likely fixes#3196 where this causes user visible incorrect TB scores,
which probably result from these incorrect mate scores.
Passed STC and LTC non-regression tests.
https://tests.stockfishchess.org/tests/view/5f9ef8dabca9bf35bae7f648
LLR: 2.93 (-2.94,2.94) {-1.25,0.25}
Total: 21672 W: 2339 L: 2230 D: 17103
Ptnml(0-2): 126, 1689, 7083, 1826, 112
https://tests.stockfishchess.org/tests/view/5f9f0caebca9bf35bae7f666
LLR: 2.97 (-2.94,2.94) {-0.75,0.25}
Total: 33152 W: 1551 L: 1485 D: 30116
Ptnml(0-2): 27, 1308, 13832, 1390, 19
closes https://github.com/official-stockfish/Stockfish/pull/3214
Bench: 3625915
A non-functional speedup. Unroll the loops going over
the output dimensions in the affine transform layers by
a factor of 4 and perform 4 horizontal additions at a time.
Instead of doing naive horizontal additions on each vector
separately use hadd and shuffling between vectors to reduce
the number of instructions by using all lanes for all stages
of the horizontal adds.
passed STC of the initial version:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 17808 W: 1914 L: 1756 D: 14138
Ptnml(0-2): 76, 1330, 5948, 1460, 90
https://tests.stockfishchess.org/tests/view/5f9d516f6a2c112b60691da3
passed STC of the final version after cleanup:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 16296 W: 1750 L: 1595 D: 12951
Ptnml(0-2): 72, 1192, 5479, 1319, 86
https://tests.stockfishchess.org/tests/view/5f9df5776a2c112b60691de3
closes https://github.com/official-stockfish/Stockfish/pull/3203
No functional change
This patch was inspired by c065abd which updates the accumulator,
if possible, based on the accumulator of two plies back if
the accumulator of the preceding ply is not available.
With this patch we look back even further in the position history
in an attempt to reduce the number of complete recomputations.
When we find a usable accumulator for the position N plies back,
we also update the accumulator of the position N-1 plies back
because that accumulator is most likely to be helpful later
when evaluating positions in sibling branches.
By not updating all intermediate accumulators immediately,
we avoid doing too much work that is not certain to be useful.
Overall, roughly 2-3% speedup.
This patch makes the code more specific to the net architecture,
changing input features of the net will require additional changes
to the incremental update code as discussed in the PR #3193 and #3191.
Passed STC:
https://tests.stockfishchess.org/tests/view/5f9056712c92c7fe3a8c60d0
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 10040 W: 1116 L: 968 D: 7956
Ptnml(0-2): 42, 722, 3365, 828, 63
closes https://github.com/official-stockfish/Stockfish/pull/3193
No functional change.
Idea of this patch can be described as following - in case we have consecutive fail highs and we reach late enough moves at root node probability of remaining quiet moves being able to produce even bigger value than moves that produced previous cutoff (so ones that should be high in move ordering but now they fail to produce beta cutoff because we actually reached high move count) should be quiet small so we can reduce them more.
passed STC
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 53392 W: 5681 L: 5474 D: 42237
Ptnml(0-2): 214, 4104, 17894, 4229, 255
https://tests.stockfishchess.org/tests/view/5f88501adcdad978fe8c527e
passed LTC
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 59136 W: 2773 L: 2564 D: 53799
Ptnml(0-2): 30, 2117, 25078, 2300, 43
https://tests.stockfishchess.org/tests/view/5f884dbfdcdad978fe8c527a
closes https://github.com/official-stockfish/Stockfish/pull/3184
Bench: 4066972
We now include the total pawn count in the scaling factor for the output
of the NNUE evaluation network. This should have the effect of trying to
keep more pawns when SF has the advantage, but exchange them when she
is defending.
Thanks to Alexander Pagel (Lolligerhans) for the idea of using the
value of pawns to ease the comparison with the rest of the material
estimation.
Passed STC:
LLR: 2.93 (-2.94,2.94) {-0.25,1.25}
Total: 15072 W: 1700 L: 1539 D: 11833
Ptnml(0-2): 65, 1202, 4845, 1355, 69
https://tests.stockfishchess.org/tests/view/5f7235a63b22d6afa50699b3
Passed LTC:
LLR: 2.93 (-2.94,2.94) {0.25,1.25}
Total: 25880 W: 1270 L: 1124 D: 23486
Ptnml(0-2): 23, 980, 10788, 1126, 23
https://tests.stockfishchess.org/tests/view/5f723b483b22d6afa5069a99
closes https://github.com/official-stockfish/Stockfish/pull/3164
Bench: 3776081
Idea is that division by fraction of 2 is slightly faster than by other numbers so parameters are adjusted in a way that division in null move pruning depth reduction features dividing by 256 instead of dividing by 213.
Other than this patch is almost non-functional - difference starts to exist by depth 133.
passed STC
https://tests.stockfishchess.org/tests/view/5f70dd943b22d6afa50693c5
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 57048 W: 6616 L: 6392 D: 44040
Ptnml(0-2): 304, 4583, 18531, 4797, 309
passed LTC
https://tests.stockfishchess.org/tests/view/5f7180db3b22d6afa506941f
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 45960 W: 2419 L: 2229 D: 41312
Ptnml(0-2): 43, 1779, 19137, 1987, 34
closes https://github.com/official-stockfish/Stockfish/pull/3159
bench 3789924
Current master uses a constant scale factor of 5/4 = 1.25 for the output
of the NNUE network, for compatibility with search and classical evaluation.
We modify this scale factor to make it dependent on the phase of the game,
going from about 1.5 in the opening to 1.0 for pure pawn endgames.
This helps Stockfish to avoid exchanges of pieces (heavy pieces in particular)
when she has the advantage, keeping more material on the board when attacking.
Passed STC:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 14744 W: 1771 L: 1599 D: 11374
Ptnml(0-2): 87, 1184, 4664, 1344, 93
https://tests.stockfishchess.org/tests/view/5f6fb0a63b22d6afa506904f
Passed LTC:
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 8912 W: 512 L: 393 D: 8007
Ptnml(0-2): 7, 344, 3637, 459, 9
https://tests.stockfishchess.org/tests/view/5f6fcf533b22d6afa5069066
closes https://github.com/official-stockfish/Stockfish/pull/3154
Bench: 3943952
- Clean signature of functions in namespace NNUE
- Add comment for countermove based pruning
- Remove bestMoveCount variable
- Add const qualifier to kpp_board_index array
- Fix spaces in get_best_thread()
- Fix indention in capture LMR code in search.cpp
- Rename TtmemDeleter to LargePageDeleter
Closes https://github.com/official-stockfish/Stockfish/pull/3063
No functional change
Use TT memory functions to allocate memory for the NNUE weights. This
should provide a small speed-up on systems where large pages are not
automatically used, including Windows and some Linux distributions.
Further, since we now have a wrapper for std::aligned_alloc(), we can
simplify the TT memory management a bit:
- We no longer need to store separate pointers to the hash table and
its underlying memory allocation.
- We also get to merge the Linux-specific and default implementations
of aligned_ttmem_alloc().
Finally, we'll enable the VirtualAlloc code path with large page
support also for Win32.
STC: https://tests.stockfishchess.org/tests/view/5f66595823a84a47b9036fba
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 14896 W: 1854 L: 1686 D: 11356
Ptnml(0-2): 65, 1224, 4742, 1312, 105
closes https://github.com/official-stockfish/Stockfish/pull/3081
No functional change.
RootInTB was an incorrectly shared global, probably leading to wrong scoreing
Minor:
setting TB global state from input by all threads (all threads write same values)
setting Limits global state by all threads (idem)
thread counting for finalization
CI can be enabled once races are fixed in the learner, manually goes like:
```
make clean && make -j2 ARCH=x86-64-modern sanitize=thread optimize=no debug=yes build
../tests/instrumented_learn.sh --sanitizer-thread
```
Needs some review.
This PR sets the "comp" variable simply to "clang",
which seems to be more consistent and allows a small simplification.
The PR also moves the section that sets "profile_make" and "profile_use" to after the NDK section,
which ensures that these variables are now set correctly for NDK/clang.
closes https://github.com/official-stockfish/Stockfish/pull/3121
No functional change
NNUE appears to provide a more stable eval than the classic eval,
so the time use dependencies on bestMoveChanges, fallingEval,
etc may need to change to make the best use of available time.
This change doubles the effect of totBestMoveChanges when giving
more time because the choice of best move is unstable.
STC:
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 101928 W: 11995 L: 11698 D: 78235 Elo +0.78
Ptnml(0-2): 592, 8707, 32103, 8936, 626
https://tests.stockfishchess.org/tests/view/5f538a462d02727c56b36cec
LTC:
LLR: 2.94 (-2.94,2.94) {0.25,1.25}
Total: 186392 W: 10383 L: 9877 D: 166132 Elo +0.81
Ptnml(0-2): 207, 8370, 75539, 8870, 210
https://tests.stockfishchess.org/tests/view/5f54a9712d02727c56b36d5a
closes https://github.com/official-stockfish/Stockfish/pull/3119
Bench 4222126
No need to initialize StatScore at rootNode. Current Logic is redundant because at subsequent levels the grandchildren statScore is initialized to zero.
closes https://github.com/official-stockfish/Stockfish/pull/3122
Non functional change.
additionally allow all options to be converted to string.
Without this, restoring of the options (multi_think.cpp:117) can't work.
fixes https://github.com/nodchip/Stockfish/issues/128
Now gensfen/learn pass with debug=yes in CI
Restore the default NNUE setting (enabled) after a bench command.
This also makes the resulting program settings independent of the
number of FENs that are being benched.
Fixes issue #3112.
closes https://github.com/official-stockfish/Stockfish/pull/3113
No functional change.
This fixes#3108 and removes some NNUE code that is currently not used.
At the moment, do_null_move() copies the accumulator from the previous
state into the new state, which is correct. It then clears the "computed_score"
flag because the side to move has changed, and with the other side to move
NNUE will return a completely different evaluation (normally with changed
sign but also with different NNUE-internal tempo bonus).
The problem is that do_null_move() clears the wrong flag. It clears the
computed_score flag of the old state, not of the new state. It turns out
that this almost never affects the search. For example, fixing it does not
change the current bench (but it does change the previous bench). This is
because the search code usually avoids calling evaluate() after a null move.
This PR corrects do_null_move() by removing the computed_score flag altogether.
The flag is not needed because nnue_evaluate() is never called twice on a position.
This PR also removes some unnecessary {}s and inserts a few blank lines
in the modified NNUE files in line with SF coding style.
Resulf ot STC non-regression test:
LLR: 2.95 (-2.94,2.94) {-1.25,0.25}
Total: 26328 W: 3118 L: 3012 D: 20198
Ptnml(0-2): 126, 2208, 8397, 2300, 133
https://tests.stockfishchess.org/tests/view/5f553ccc2d02727c56b36db1
closes https://github.com/official-stockfish/Stockfish/pull/3109
bench: 4109324
1) Only access UCI option if defined
2) disable -Werror for now.
3) disable a few target that don't have _mm_malloc.
4) Add profile-learn target, with small speedup.
5) just test on Linux + gcc (skip macOS, unclear openblas, skip linux+clang, unclear omp/std::filesystem).
makes it a little easier to change the BLAS library used,
doesn't hardcode the mingw headers. Works on Linux with openblas installed.
Should be no change on Windows.
fixes valgrind errors as seen with:
```
setoption name Use NNUE value true
isready
gensfen depth 6 loop 10 use_draw_in_training_data_generation 1 eval_limit 32000 output_file_name training_data/training_data.bin use_raw_nnue_eval 0
quit
```
the latter script now runs without valgrind errors on linux
Official release version of Stockfish 12
Bench: 3624569
-----------------------
It is our pleasure to release Stockfish 12 to users world-wide
Downloads will be freely available at
https://stockfishchess.org/download/
This version 12 of Stockfish plays significantly stronger than
any of its predecessors. In a match against Stockfish 11,
Stockfish 12 will typically win at least ten times more game pairs
than it loses.
This jump in strength, visible in regular progression tests during
development[1], results from the introduction of an efficiently
updatable neural network (NNUE) for the evaluation in Stockfish[2],
and associated tuning of the engine as a whole. The concept of the
NNUE evaluation was first introduced in shogi, and ported to
Stockfish afterward. Stockfish remains a CPU-only engine, since the
NNUE networks can be very efficiently evaluated on CPUs. The
recommended parameters of the NNUE network are embedded in
distributed binaries, and Stockfish will use NNUE by default.
Both the NNUE and the classical evaluations are available, and
can be used to assign values to positions that are later used in
alpha-beta (PVS) search to find the best move. The classical
evaluation computes this value as a function of various chess
concepts, handcrafted by experts, tested and tuned using fishtest.
The NNUE evaluation computes this value with a neural network based
on basic inputs. The network is optimized and trained on the
evaluations of millions of positions.
The Stockfish project builds on a thriving community of enthusiasts
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 chess fans to join the fishtest testing
framework and programmers to contribute on github[3].
Stay safe and enjoy chess!
The Stockfish team
[1] https://github.com/glinscott/fishtest/wiki/Regression-Tests
[2] https://github.com/official-stockfish/Stockfish/commit/84f3e867903f62480c33243dd0ecbffd342796fc
[3] https://stockfishchess.org/get-involved/
std::sort() is not stable so different implementations can produce different results:
use the stable version instead.
Observed for '8/6k1/5r2/8/8/8/1K6/Q7 w - - 0 1' yielding different bench results for gcc and MSVC
and 3-4-5 syzygy TB prior to this patch.
closes https://github.com/official-stockfish/Stockfish/pull/3083
No functional change.
covers the most important cases from the user perspective:
It embeds the default net in the binary, so a download of that binary will result
in a working engine with the default net. The engine will be functional in the default mode
without any additional user action.
It allows non-default nets to be used, which will be looked for in up to
three directories (working directory, location of the binary, and optionally a specific default directory).
This mechanism is also kept for those developers that use MSVC,
the one compiler that doesn't have an easy mechanism for embedding data.
It is possible to disable embedding, and instead specify a specific directory, e.g. linux distros might want to use
CXXFLAGS="-DNNUE_EMBEDDING_OFF -DDEFAULT_NNUE_DIRECTORY=/usr/share/games/stockfish/" make -j ARCH=x86-64 profile-build
passed STC non-regression:
https://tests.stockfishchess.org/tests/view/5f4a581c150f0aef5f8ae03a
LLR: 2.95 (-2.94,2.94) {-1.25,-0.25}
Total: 66928 W: 7202 L: 7147 D: 52579
Ptnml(0-2): 291, 5309, 22211, 5360, 293
closes https://github.com/official-stockfish/Stockfish/pull/3070
fixes https://github.com/official-stockfish/Stockfish/issues/3030
No functional change.
This patch removes the EvalList structure from the Position object and generally simplifies the interface between do_move() and the NNUE code.
The NNUE evaluation function first calculates the "accumulator". The accumulator consists of two halves: one for white's perspective, one for black's perspective.
If the "friendly king" has moved or the accumulator for the parent position is not available, the accumulator for this half has to be calculated from scratch. To do this, the NNUE node needs to know the positions and types of all non-king pieces and the position of the friendly king. This information can easily be obtained from the Position object.
If the "friendly king" has not moved, its half of the accumulator can be calculated by incrementally updating the accumulator for the previous position. For this, the NNUE code needs to know which pieces have been added to which squares and which pieces have been removed from which squares. In principle this information can be derived from the Position object and StateInfo struct (in the same way as undo_move() does this). However, it is probably a bit faster to prepare this information in do_move(), so I have kept the DirtyPiece struct. Since the DirtyPiece struct now stores the squares rather than "PieceSquare" indices, there are now at most three "dirty pieces" (previously two). A promotion move that captures a piece removes the capturing pawn and the captured piece from the board (to SQ_NONE) and moves the promoted piece to the promotion square (from SQ_NONE).
An STC test has confirmed a small speedup:
https://tests.stockfishchess.org/tests/view/5f43f06b5089a564a10d850a
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 87704 W: 9763 L: 9500 D: 68441
Ptnml(0-2): 426, 6950, 28845, 7197, 434
closes https://github.com/official-stockfish/Stockfish/pull/3068
No functional change
to prevent user errors or generating untested code,
check explicitly that the ARCH variable is equivalent to a supported architecture
as listed in `make help`.
To nevertheless compile for an untested target the user can override the internal
variable, passing the undocumented `SUPPORTED_ARCH=true` to make.
closes https://github.com/official-stockfish/Stockfish/pull/3062
No functional change.
Fix the issue where a TT entry with key16==0 would always be reported
as a miss. Instead, we'll use depth8 to detect whether the TT entry is
occupied. In order to do that, we'll change DEPTH_OFFSET to -7
(depth8==0) to distinguish between an unoccupied entry and the
otherwise lowest possible depth, i.e., DEPTH_NONE (depth8==1).
To prevent a performance regression, we'll reorder the TT entry fields
by the access order of TranspositionTable::probe(). Memory in general
works fastest when accessed in sequential order. We'll also match the
store order in TTEntry::save() with the entry field order, and
re-order the 'if-or' expressions in TTEntry::save() from the cheapest
to the most expensive.
Finally, as we now have a proper TT entry occupancy test, we'll fix a
minor corner case with hashfull reporting. To reproduce:
- Use a big hash
- Either:
a. Start 31 very quick searches (this wraparounds generation to 0); or
b. Force generation of the first search to 0.
- go depth infinite
Before the fix, hashfull would incorrectly report nearly full hash
immediately after the search start, since
TranspositionTable::hashfull() used to consider only the entry
generation and not whether the entry was actually occupied.
STC:
LLR: 2.95 (-2.94,2.94) {-0.25,1.25}
Total: 36848 W: 4091 L: 3898 D: 28859
Ptnml(0-2): 158, 2996, 11972, 3091, 207
https://tests.stockfishchess.org/tests/view/5f3f98d5dc02a01a0c2881f7
LTC:
LLR: 2.95 (-2.94,2.94) {0.25,1.25}
Total: 32280 W: 1828 L: 1653 D: 28799
Ptnml(0-2): 34, 1428, 13051, 1583, 44
https://tests.stockfishchess.org/tests/view/5f3fe77a87a5c3c63d8f5332
closes https://github.com/official-stockfish/Stockfish/pull/3048
Bench: 3760677
due to downclocking on current chips (tested up to cascade lake)
supporting avx512 and vnni512, it is better to use avx2 or vnni256
in multithreaded (in particular hyperthreaded) engine use.
In single threaded use, the picture is different.
gcc compilation for vnni256 requires a toolchain for gcc >= 9.
closes https://github.com/official-stockfish/Stockfish/pull/3038
No functional change
In recent Macs, it is possible to use the Clang compiler provided by Apple
to compile Stockfish out of the box, and this is the method used by default
in our Makefile (the Makefile sets the macosx-version-min=10.14 flag to select
the right libc++ library for the Clang compiler with recent c++17 support).
But it is quite possible to compile and run Stockfish on older Macs! Below
we describe a method to install a recent GNU compiler on these Macs, to get
the c++17 support. We have tested the following procedure to install gcc10 on
machines running Mac OS 10.7, Mac OS 10.9 and Mac OS 10.13:
1) install XCode for your machine.
2) install Apple command-line developer tools for XCode, by typing the following
command in a Terminal:
```
sudo xcode-select --install
```
3) go to the Stockfish "src" directory, then try a default build and run Stockfish:
```
make clean
make build
make net
./stockfish
```
4) if step 3 worked, congrats! You have a compiler recent enough on your Mac
to compile Stockfish. If not, continue with step 5 to install GNU gcc10 :-)
5) install the MacPorts package manager (https://www.macports.org/install.php),
for instance using the fast method in the "macOS Package (.pkg) Installer"
section of the page.
6) use the "port" command to install the gcc10 package of MacPorts by typing the
following command:
```
sudo port install gcc10
```
With this step, MacPorts will install the gcc10 compiler under the name "g++-mp-10"
in the /opt/local/bin directory:
```
which g++-mp-10
/opt/local/bin/g++-mp-10 <--- answer
```
7) You can now go back to the "src" directory of Stockfish, and try to build
Stockfish by pointing at the right compiler:
```
make clean
make build COMP=gcc COMPCXX=/opt/local/bin/g++-mp-10
make net
./stockfish
```
8) Enjoy Stockfish on Macintosh!
See this pull request for further discussion:
https://github.com/official-stockfish/Stockfish/pull/3049
No functional change
Changes to deal with compilation (particularly profile-build) on macOS.
(1) The default toolchain has gcc masquerading as clang,
the previous Makefile was not picking up the required changes
to the different profiling tools.
(2) The previous Makefile test for gccisclang occurred before
a potential overwrite of CXX by COMPCXX
(3) llvm-profdata no longer runs as a command on macOS and
instead is invoked by ``xcrun llvm-profdata``
(4) Needs to support use of true gcc using e.g.
``make build ... COMPCXX=g++-10``
(5) enable profile-build in travis for macOS
closes https://github.com/official-stockfish/Stockfish/pull/3043
No functional change
some GUIs do not show the error message when the engine terminates in the no-net case, as it is send to cerr.
Instead send it as an info string, which the GUI will more likely display.
closes https://github.com/official-stockfish/Stockfish/pull/3031
No functional change.
add new ARCH targets
x86-32-sse41-popcnt > x86 32-bit with sse41 and popcnt support
x86-32-sse2 > x86 32-bit with sse2 support
x86-32 > x86 32-bit generic (with mmx and sse support)
retire x86-32-old (use general-32)
closes https://github.com/official-stockfish/Stockfish/pull/3022
No functional change.
The easiest way to use the NDK in conjunction with this Makefile (tested on linux-x86_64):
1. Download the latest NDK (r21d) from Google from https://developer.android.com/ndk/downloads
2. Place and unzip the NDK in $HOME/ndk folder
3. Export the path variable e.g., `export PATH=$PATH:$HOME/ndk/android-ndk-r21d/toolchains/llvm/prebuilt/linux-x86_64/bin`
4. cd to your Stockfish/src dir
5. Issue `make -j ARCH=armv8 COMP=ndk build` (use `ARCH=armv7` or `ARCH=armv7-neon` for older CPUs)
6. Optionally `make -j ARCH=armv8 COMP=ndk strip`
7. That's all. Enjoy!
Improves support from Raspberry Pi (incomplete?) and compiling on arm in general
closes https://github.com/official-stockfish/Stockfish/pull/3015
fixes https://github.com/official-stockfish/Stockfish/issues/2860
fixes https://github.com/official-stockfish/Stockfish/issues/2641
Support is still fragile as we're missing CI on these targets. Nevertheless tested with:
```bash
# build crosses from ubuntu 20.04 on x86 to various arch/OS combos
# tested with suitable packages installed
# (build-essentials, mingw-w64, g++-arm-linux-gnueabihf, NDK (r21d) from google)
# cross to Android
export PATH=$HOME/ndk/android-ndk-r21d/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH
make clean && make -j build ARCH=armv7 COMP=ndk && make -j build ARCH=armv7 COMP=ndk strip
make clean && make -j build ARCH=armv7-neon COMP=ndk && make -j build ARCH=armv7-neon COMP=ndk strip
make clean && make -j build ARCH=armv8 COMP=ndk && make -j build ARCH=armv8 COMP=ndk strip
# cross to Raspberry Pi
make clean && make -j build ARCH=armv7 COMP=gcc COMPILER=arm-linux-gnueabihf-g++
make clean && make -j build ARCH=armv7-neon COMP=gcc COMPILER=arm-linux-gnueabihf-g++
# cross to Windows
make clean && make -j build ARCH=x86-64-modern COMP=mingw
```
No functional change
This patch fixes the byte order when reading 16- and 32-bit values from the network file on a big-endian machine.
Bytes are ordered in read_le() using unsigned arithmetic, which doesn't need tricks to determine the endianness of the machine. Unfortunately the compiler doesn't seem to be able to optimise the ordering operation, but reading in the weights is not a time-critical operation and the extra time it takes should not be noticeable.
Big endian systems are still untested with NNUE.
fixes#3007
closes https://github.com/official-stockfish/Stockfish/pull/3009
No functional change.
Increases the use of NNUE evaluation in positions without captures/pawn moves,
by increasing the NNUEThreshold threshold with rule50_count.
This patch will force Stockfish to use NNUE eval more and more in materially
unbalanced positions, when it seems that the classical eval is struggling to
win and only manages to shuffle. This will ask the (slower) NNUE eval to
double-check the potential fortress branches of the search tree, but only
when necessary.
passed STC:
https://tests.stockfishchess.org/tests/view/5f36f1bf11a9b1a1dbf192d8
LLR: 2.93 (-2.94,2.94) {-0.50,1.50}
Total: 51824 W: 5836 L: 5653 D: 40335
Ptnml(0-2): 264, 4356, 16512, 4493, 287
passed LTC:
https://tests.stockfishchess.org/tests/view/5f37836111a9b1a1dbf1936d
LLR: 2.93 (-2.94,2.94) {0.25,1.75}
Total: 29768 W: 1747 L: 1590 D: 26431
Ptnml(0-2): 33, 1347, 11977, 1484, 43
closes https://github.com/official-stockfish/Stockfish/pull/3011
Bench: 4173967
Do not show the details of the default architecture for a simple "make help"
invocation, as the details are most likely to confuse beginners. Instead we
make it clear which architecture is the default and put an example at the end
of the Makefile as an incentative to use "make help ARCH=blah" to discover
the flags used by the different architectures.
```
make help
make help ARCH=x86-64-ssse3
```
Also clean-up and modernize a bit the Makefile examples while at it.
closes https://github.com/official-stockfish/Stockfish/pull/2996
No functional change
Adds support for Vector Neural Network Instructions (avx512), as available on Intel Cascade Lake
The _mm512_dpbusd_epi32() intrinsic (vpdpbusd instruction) is taylor made for NNUE.
on a cascade lake CPU (AWS C5.24x.large, gcc 10) NNUE eval is at roughly 78% nps of classical
(single core test)
bench 1024 1 24 default depth:
target classical NNUE ratio
vnni 2207232 1725987 78.20
avx512 2216789 1671734 75.41
avx2 2194006 1611263 73.44
modern 2185001 1352469 61.90
closes https://github.com/official-stockfish/Stockfish/pull/2987
No functional change
I mentioned this a while back in discord, but nothing seems to have ever come from it. Anyway, to the best of my knowledge most current training data gen is being done at relatively low fixed depths. With this in mind, the change to not allow LMP in PvNodes should result in a fairly significant increase in strength and reliability of the PV.
fails to build on that target, because of missing Intel Intrinsics.
macOS has posix_memalign() since ~2014 so we can simplify the code and just use that for all Apple platforms.
closes https://github.com/official-stockfish/Stockfish/pull/2985
No functional change.
this workaround is possibly rather a windows & gcc specific problem. See e.g.
https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412#c25
on Linux with gcc 8 this patch brings roughly a 8% speedup.
However, probably needs some testing in the wild.
includes a workaround for an old msys make (3.81) installation (fixes#2984)
No functional change
Change condition from three friendly pieces to two. This now means that we only extend castling on the king side if there are no other friendly pieces aside from king and rook. For the queen side, we only extend if there is only a rook and another friendly piece or if there is only a single rook and no other friendly piece but this is very rare.
STC:
LLR: 3.20 (-2.94,2.94) {-0.50,1.50}
Total: 31144 W: 4086 L: 3903 D: 23155
Ptnml(0-2): 227, 2843, 9278, 2968, 256
https://tests.stockfishchess.org/tests/view/5f31487f9081672066537516
LTC:
LLR: 2.93 (-2.94,2.94) {0.25,1.75}
Total: 57816 W: 3786 L: 3538 D: 50492
Ptnml(0-2): 92, 2991, 22488, 3251, 86
https://tests.stockfishchess.org/tests/view/5f3167c3908167206653753d
closes https://github.com/official-stockfish/Stockfish/pull/2980
Bench: 4244812
Joint work gvreuls / vondele
* Download the default NNUE net in AppVeyor
* Download net in travis CI `make net`
* Adjust tests to cover more archs, speedup instrumented testing
* Introduce 'mixed' bench as default, with further options:
classical, NNUE, mixed.
mixed (default) and NNUE require the default net to be present,
which can be obtained with
```
make net
```
Further examples (first is equivalent to `./stockfish bench`):
```
./stockfish bench 16 1 13 default depth mixed
./stockfish bench 16 1 13 default depth classical
./stockfish bench 16 1 13 default depth NNUE
```
The net is now downloaded automatically if needed for `profile-build`
(usual `build` works fine without net present)
PGO gives a nice speedup on fishtest:
passed STC:
LLR: 2.93 (-2.94,2.94) {-0.50,1.50}
Total: 3360 W: 469 L: 343 D: 2548
Ptnml(0-2): 20, 246, 1030, 356, 28
https://tests.stockfishchess.org/tests/view/5f31b5499081672066537569
passed LTC:
LLR: 2.97 (-2.94,2.94) {0.25,1.75}
Total: 8824 W: 609 L: 502 D: 7713
Ptnml(0-2): 8, 430, 3438, 519, 17
https://tests.stockfishchess.org/tests/view/5f31c87b908167206653757c
closes https://github.com/official-stockfish/Stockfish/pull/2931
fixes https://github.com/official-stockfish/Stockfish/issues/2907
requires fishtest updates before commit
Bench: 4290577
This patch allows old x86 CPUs, from AMD K8 (which the x86-64 baseline
targets) all the way down to the Pentium MMX, to benefit from NNUE with
comparable performance hit versus hand-written eval as on more modern
processors.
NPS of the bench with NNUE enabled on a Pentium III 1.13 GHz (using the
MMX code):
master: 38951
this patch: 80586
NPS of the bench with NNUE enabled using baseline x86-64 arch, which is
how linux distros are likely to package stockfish, on a modern CPU
(using the SSE2 code):
master: 882584
this patch: 1203945
closes https://github.com/official-stockfish/Stockfish/pull/2956
No functional change.
Makefile targets x86-64-sse42, x86-sse3 are removed; x86-64-sse41
is renamed to x86-64-sse41-popcnt (it did enable popcnt).
Makefile variables sse3, sse42, their associated compilation flags
and code in misc.cpp are removed.
closes https://github.com/official-stockfish/Stockfish/pull/2922
No functional change
despite usage of alignas, the generated (avx2/avx512) code with older compilers needs to use
unaligned loads with older gcc (e.g. confirmed crash with gcc 7.3/mingw on abrok).
Better performance thus requires gcc >= 9 on hardware supporting avx2/avx512
closes https://github.com/official-stockfish/Stockfish/pull/2969
No functional change
The idea of this patch is that positions are usually more complex and hard to evaluate even if there are more pawns.
This patch adjusts NNUE threshold usage depending on number of pawns in position, if pawn count is <3 we use the
classical evaluation more often, for pawn count = 3 patch the is non-functional,
with pawn count > 3 NNUE evaluation is used more often.
passed STC
https://tests.stockfishchess.org/tests/view/5f2f02d09081672066536b1f
LLR: 2.96 (-2.94,2.94) {-0.50,1.50}
Total: 36520 W: 5011 L: 4823 D: 26686
Ptnml(0-2): 299, 3482, 10548, 3594, 337
passed LTC
https://tests.stockfishchess.org/tests/view/5f2f4c329081672066536b5c
LLR: 2.98 (-2.94,2.94) {0.25,1.75}
Total: 39272 W: 2630 L: 2433 D: 34209
Ptnml(0-2): 53, 2066, 15218, 2229, 70
closes https://github.com/official-stockfish/Stockfish/pull/2960
bench 4084753
This patch tries to run multiple LTO threads in parallel, speeding up
the build process of optimized builds if the -j make parameter is used.
This mitigates the longer linking times of optimized builds since the
integration of the NNUE code. Roughly 2x build speedup.
I've tried a similar patch some two years ago but it ran into trouble
with old compiler versions then. Since we're on the C++17 standard now
these old compilers should be obsolete.
closes https://github.com/official-stockfish/Stockfish/pull/2943
No functional change.
This patch increases LMRdepth threshold for futility pruning at parent nodes so it can apply more often.
With radical change to evaluation approach it seems that search is really far from optimal state, especially it parts that use static evaluation of position.
passed STC
https://tests.stockfishchess.org/tests/view/5f2da75661e3b6af64881fd0
LLR: 2.93 (-2.94,2.94) {-0.50,1.50}
Total: 8744 W: 1305 L: 1156 D: 6283
Ptnml(0-2): 75, 789, 2500, 928, 80
passed LTC
https://tests.stockfishchess.org/tests/view/5f2dcb2a61e3b6af64881ff3
LLR: 2.98 (-2.94,2.94) {0.25,1.75}
Total: 17728 W: 1256 L: 1117 D: 15355
Ptnml(0-2): 22, 961, 6774, 1070, 37
Bench: 4067325
Allow any pawn in front of a minor piece to replace the pawn protection
requirement for outposts.
+-------+ +-------+
| . . o | | o . . | o Their pawns
| . o x | | o . . | x Our pawns
| o N . | | x o B | N,B New (reachable) outpost
| . . . | | . _ . | _ Reachable square behind a pawn
+-------+ +-------+
N outpost B reaches
outpost
We want outposts to be secured by pawns against major pieces. If
a minor is shielded by any pawn from above, it is rarely at the same
time protected by our pawn attacks from below. However, the pawn shield
in itself offers some degree of protection.
A pawn shield will now suffice to replace the pawn protection for the
outpost (and reachable outpost) bonus.
This effect stacks with the existing "minor behind pawn" bonus.
STC
https://tests.stockfishchess.org/tests/view/5f2bcd14b3ebe5cbfee85b2c
LLR: 2.94 (-2.94,2.94) {-0.50,1.50}
Total: 27248 W: 5353 L: 5119 D: 16776
Ptnml(0-2): 462, 3174, 6185, 3274, 529
LTC
https://tests.stockfishchess.org/tests/view/5f2bfef5b3ebe5cbfee85b5a
LLR: 2.96 (-2.94,2.94) {0.25,1.75}
Total: 99432 W: 12580 L: 12130 D: 74722
Ptnml(0-2): 696, 8903, 30049, 9391, 677
Closes#2935
Bench: 4143673
This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish.
Both the NNUE and the classical evaluations are available, and can be used to
assign a value to a position that is later used in alpha-beta (PVS) search to find the
best move. The classical evaluation computes this value as a function of various chess
concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation
computes this value with a neural network based on basic inputs. The network is optimized
and trained on the evalutions of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
tools to train and develop the NNUE networks.
This patch is the result of contributions of various authors, from various communities,
including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather,
rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler,
dorzechowski, and vondele.
This new evaluation needed various changes to fishtest and the corresponding infrastructure,
for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged.
The first networks have been provided by gekkehenker and sergiovieri, with the latter
net (nn-97f742aaefcd.nnue) being the current default.
The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option,
provided the `EvalFile` option points the the network file (depending on the GUI, with full path).
The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on
the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest:
60000 @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c
ELO: 92.77 +-2.1 (95%) LOS: 100.0%
Total: 60000 W: 24193 L: 8543 D: 27264
Ptnml(0-2): 609, 3850, 9708, 10948, 4885
40000 @ 20+0.2 th 8
https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58
ELO: 89.47 +-2.0 (95%) LOS: 100.0%
Total: 40000 W: 12756 L: 2677 D: 24567
Ptnml(0-2): 74, 1583, 8550, 7776, 2017
At the same time, the impact on the classical evaluation remains minimal, causing no significant
regression:
sprt @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b
LLR: 2.94 (-2.94,2.94) {-6.00,-4.00}
Total: 34936 W: 6502 L: 6825 D: 21609
Ptnml(0-2): 571, 4082, 8434, 3861, 520
sprt @ 60+0.6 th 1
https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d
LLR: 2.93 (-2.94,2.94) {-6.00,-4.00}
Total: 10088 W: 1232 L: 1265 D: 7591
Ptnml(0-2): 49, 914, 3170, 843, 68
The needed networks can be found at https://tests.stockfishchess.org/nns
It is recommended to use the default one as indicated by the `EvalFile` UCI option.
Guidelines for testing new nets can be found at
https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests
Integration has been discussed in various issues:
https://github.com/official-stockfish/Stockfish/issues/2823https://github.com/official-stockfish/Stockfish/issues/2728
The integration branch will be closed after the merge:
https://github.com/official-stockfish/Stockfish/pull/2825https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip
closes https://github.com/official-stockfish/Stockfish/pull/2912
This will be an exciting time for computer chess, looking forward to seeing the evolution of
this approach.
Bench: 4746616
2020-08-06 16:37:45 +02:00
103 changed files with 23085 additions and 3506 deletions
- git log HEAD | grep "\b[Bb]ench[ :]\+[0-9]\{7\}" | head -n 1 | sed "s/[^0-9]*\([0-9]*\).*/\1/g" > git_sig
- export benchref=$(cat git_sig)
- echo "Reference bench:" $benchref
#
# Compiler version string
- $COMPILER -v
#
# Verify bench number against various builds
- export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
- make clean && make -j2 ARCH=x86-64 optimize=no debug=yes build && ../tests/signature.sh $benchref
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-32 optimize=no debug=yes build && ../tests/signature.sh $benchref; fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-32 build && ../tests/signature.sh $benchref; fi
#
# Check perft and reproducible search
- export CXXFLAGS="-Werror"
- make clean && make -j2 ARCH=x86-64 build
- ../tests/perft.sh
- ../tests/reprosearch.sh
#
# Valgrind
#
- export CXXFLAGS="-O1 -fno-inline"
- if [ -x "$(command -v valgrind )" ]; then make clean && make -j2 ARCH=x86-64 debug=yes optimize=no build > /dev/null && ../tests/instrumented.sh --valgrind; fi
- if [ -x "$(command -v valgrind )" ]; then ../tests/instrumented.sh --valgrind-thread; fi
#
# Sanitizer
#
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-64 sanitize=undefined optimize=no debug=yes build > /dev/null && ../tests/instrumented.sh --sanitizer-undefined; fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-64 sanitize=thread optimize=no debug=yes build > /dev/null && ../tests/instrumented.sh --sanitizer-thread; fi
Performs a standard benchmark using various options. The signature of a version
(standard node count) is obtained using all defaults. `bench` is currently
`bench 16 1 13 default depth mixed`.
* #### compiler
Give information about the compiler and environment used for building a binary.
* #### d
Display the current position, with ascii art and fen.
* #### eval
Return the evaluation of the current position.
* #### export_net [filename]
Exports the currently loaded network to a file.
If the currently loaded network is the embedded network and the filename
is not specified then the network is saved to the file matching the name
of the embedded network, as defined in evaluate.h.
If the currently loaded network is not the embedded network (some net set
through the UCI setoption) then the filename parameter is required and the
network is saved into that file.
* #### flip
Flips the side to move.
### Generating Training Data
To generate training data from the classic eval, use the generate_training_data command with the setting "Use NNUE" set to "false". The given example is generation in its simplest form. There are more commands.
```
uci
setoption name PruneAtShallowDepth value false
setoption name Use NNUE value false
setoption name Threads value X
setoption name Hash value Y
setoption name SyzygyPath value path
isready
generate_training_data depth A count B keep_draws 1 eval_limit 32000
```
-`A` is the searched depth per move, or how far the engine looks forward. This value is an integer.
-`B` is the amount of positions generated. This value is also an integer.
Specify how many threads and how much memory you would like to use with the `x` and `y` values. The option SyzygyPath is not necessary, but if you would like to use it, you must first have Syzygy endgame tablebases on your computer, which you can find [here](http://oics.olympuschess.com/tracker/index.php). You will need to have a torrent client to download these tablebases, as that is probably the fastest way to obtain them. The `path` is the path to the folder containing those tablebases. It does not have to be surrounded in quotes.
This will create a file named "training_data.binpack" in the same folder as the binary containing the generated training data. Once generation is done, you can rename the file to something like "1billiondepth12.binpack" to remember the depth and quantity of the positions and move it to a folder named "trainingdata" in the same directory as the binaries.
You will also need validation data that is used for loss calculation and accuracy computation. Validation data is generated in the same way as training data, but generally at most 1 million positions should be used as there's no need for more and it would just slow the learning process down. It may also be better to slightly increase the depth for validation data. After generation you can rename the validation data file to "val.binpack" and drop it in a folder named "validationdata" in the same directory to make it easier.
## Training data formats.
Currently there are 3 training data formats. Two of them are supported directly.
-`.bin` - the original training data format. Uses 40 bytes per entry. Is supported directly by the `generate_training_data` command.
-`.plain` - a human readable training data format. This one is not supported directly by the `generate_training_data` command. It should not be used for data exchange because it's less compact than other formats. It is mostly useful for inspection of the data.
-`.binpack` - a compact binary training data format that exploits positions chains to further reduce size. It uses on average between 2 to 3 bytes per entry when generating data with `generate_training_data`. It is supported directly by `generate_training_data` command. It is currently the default for the `generate_training_data` command. A more in depth description can be found [here](docs/binpack.md)
### Conversion between formats.
There is a builting converted that support all 3 formats described above. Any of them can be converted to any other. For more information and usage guide see [here](docs/convert.md).
## A note on classical evaluation versus NNUE evaluation
Both approaches assign a value to a position that is used in alpha-beta (PVS) search
to find the best move. The classical evaluation computes this value as a function
of various chess concepts, handcrafted by experts, tested and tuned using fishtest.
The NNUE evaluation computes this value with a neural network based on basic
inputs (e.g. piece positions only). The network is optimized and trained
on the evalutions of millions of positions at moderate search depth.
on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
are already enabled and no configuration is needed.
are already enabled, and no configuration is needed.
### Support on Windows
The use of large pages requires "Lock Pages in Memory" privilege. See
[Enable the Lock Pages in Memory Option (Windows)](https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/enable-the-lock-pages-in-memory-option-windows)
on how to enable this privilege. Logout/login may be needed
afterwards. Due to memory fragmentation, it may not always be
possible to allocate large pages even when enabled. A reboot
might alleviate this problem. To determine whether large pages
are in use, see the engine log.
on how to enable this privilege, then run [RAMMap](https://docs.microsoft.com/en-us/sysinternals/downloads/rammap)
to double-check that large pages are used. We suggest that you reboot
your computer after you have enabled large pages, because long Windows
sessions suffer from memory fragmentation, which may prevent Stockfish
from getting large pages: a fresh session is better in this regard.
## Compiling Stockfish yourself from the sources
@@ -225,26 +305,26 @@ targets with corresponding descriptions.
```
cd src
make help
make net
make build ARCH=x86-64-modern
```
When not using the Makefile to compile (for instance with Microsoft MSVC) you
When not using the Makefile to compile (for instance, with Microsoft MSVC) you
need to manually set/unset some switches in the compiler command line; see
file *types.h* for a quick reference.
When reporting an issue or a bug, please tell us which version and
compiler you used to create your executable. These informations can
be found by typing the following commands in a console:
When reporting an issue or a bug, please tell us which Stockfish version
and which compiler you used to create your executable. This information
can be found by typing the following command in a console:
```
./stockfish
compiler
./stockfish compiler
```
## Understanding the code base and participating in the project
Stockfish's improvement over the last couple of years has been a great
community effort. There are a few ways to help contribute to its growth.
Stockfish's improvement over the last decade has been a great community
effort. There are a few ways to help contribute to its growth.
### Donating hardware
@@ -265,8 +345,9 @@ generic rather than being focused on Stockfish's precise implementation.
Nevertheless, a helpful resource.
* The latest source can always be found on [GitHub](https://github.com/official-stockfish/Stockfish).
Discussions about Stockfish take place in the [FishCooking](https://groups.google.com/forum/#!forum/fishcooking)
group and engine testing is done on [Fishtest](https://tests.stockfishchess.org/tests).
Discussions about Stockfish take place these days mainly in the [FishCooking](https://groups.google.com/forum/#!forum/fishcooking)
group and on the [Stockfish Discord channel](https://discord.gg/nv8gDtt).
The engine testing is done on [Fishtest](https://tests.stockfishchess.org/tests).
If you want to help improve Stockfish, please read this [guideline](https://github.com/glinscott/fishtest/wiki/Creating-my-first-test)
first, where the basics of Stockfish development are explained.
@@ -274,16 +355,17 @@ first, where the basics of Stockfish development are explained.
## Terms of use
Stockfish is free, and distributed under the **GNU General Public License version 3**
(GPL v3). Essentially, this means that you are free to do almost exactly
(GPL v3). Essentially, this means you are free to do almost exactly
what you want with the program, including distributing it among your
friends, making it available for download from your website, selling
friends, making it available for download from your website, selling
it (either by itself or as part of some bigger software package), or
using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute Stockfish in
some way, you must always include the full source code, or a pointer
to where the source code can be found. If you make any changes to the
source code, these changes must also be made available under the GPL.
some way, you MUST always include the license and the full source code
(or a pointer to where the source code can be found) to generate the
exact binary you are distributing. If you make any changes to the
source code, these changes must also be made available under the GPL v3.
For full details, read the copy of the GPL v3 found in the file named
Binpack is a binary training data storage format designed to take advantage of position chains differing by a single move. Therefore it is very good at compactly storing data generated from real games (as opposed to random positions for example sourced from an opening book).
It is currently implemented through a single header library in `extra/nnue_data_binpack_format.h`.
Below follows a rough description of the format in a BNF-like notation.
```
[[nodiscard]] std::uint16_t signedToUnsigned(std::int16_t a) {
std::uint16_t r;
std::memcpy(&r, &a, sizeof(std::uint16_t));
if (r & 0x8000) r ^= 0x7FFF; // flip value bits if negative
r = (r << 1) | (r >> 15); // store sign bit at bit 0
`convert` allows conversion of training data between any of `.plain`, `.bin`, and `.binpack`.
As all commands in stockfish `convert` can be invoked either from command line (as `stockfish.exe convert ...`) or in the interactive prompt.
The syntax of this command is as follows:
```
convert from_path to_path [append] [validate]
```
`from_path` is the path to the file to convert from. The type of the data is deduced based on its extension (one of `.plain`, `.bin`, `.binpack`).
`to_path` is the path to an output file. The type of the data is deduced from its extension. If the file does not exist it is created.
`append` and `validate` can come in any order and are optional.
If `append` not specified then the output file will be truncated prior to any writes. If `append` is specified then the converted training data will be appended to the end of the output file.
If `validate` is specified then the conversion will stop on the first illegal move found and a diagnostic will be shown.
`generate_training_data` command allows generation of training data from self-play in a manner that suits training better than traditional games. It introduces random moves to diversify openings, and fixed depth evaluation.
As all commands in stockfish `generate_training_data` can be invoked either from command line (as `stockfish.exe generate_training_data ...`, but this is not recommended because it's not possible to specify UCI options before `generate_training_data` executes) or in the interactive prompt.
It is recommended to set the `PruneAtShallowDepth` UCI option to `false` as it will increase the quality of fixed depth searches.
It is recommended to keep the `EnableTranspositionTable` UCI option at the default `true` value as it will make the generation process faster without noticably harming the uniformity of the data.
`generate_training_data` takes named parameters in the form of `generate_training_data param_1_name param_1_value param_2_name param_2_value ...`.
Currently the following options are available:
`set_recommended_uci_options` - this is a modifier not a parameter, no value follows it. If specified then some UCI options are set to recommended values.
`depth` - sets minimum and maximum depth of evaluation of each position. Default: 3.
`mindepth` - minimum depth of evaluation of each position. If not specified then the same as `depth`.
`maxdepth` - minimum depth of evaluation of each position. If not specified then the same as `depth`.
`nodes` - the number of nodes to use for evaluation of each position. This number is multiplied by the number of PVs of the current search. This does NOT override the `depth` and `depth2` options. If specified then whichever of depth or nodes limit is reached first applies.
`count` - the number of training data entries to generate. 1 entry == 1 position. If both `count` and `max_time_*` are specified the data generation process will end when any of conditions is fullfilled. Default: 8000000000 (8B).
`max_time_seconds`, `max_time_minutes`, `max_time_hours` - specifies the maximum runtime for the data generation. The data generation will NOT be interrupted while a self-play game is in progress. If both `count` and `max_time_*` are specified the data generation process will end when any of conditions is fullfilled. Default: \~250 years.
`output_file_name` - the name of the file to output to. If the extension is not present or doesn't match the selected training data format the right extension will be appened. Default: generated_kifu
`eval_limit` - evaluations with higher absolute value than this will not be written and will terminate a self-play game. Should not exceed 10000 which is VALUE_KNOWN_WIN, but is only hardcapped at mate in 2 (\~30000). Default: 3000
`random_move_min_ply` - the minimal ply at which a random move may be executed instead of a move chosen by search. Default: 1.
`random_move_max_ply` - the maximal ply at which a random move may be executed instead of a move chosen by search. Default: 24.
`random_move_count` - maximum number of random moves in a single self-play game. Default: 5.
`random_move_like_apery` - either 0 or 1. If 1 then random king moves will be followed by a random king move from the opponent whenever possible with 50% probability. Default: 0.
`random_multi_pv` - the number of PVs used for determining the random move. If not specified then a truly random move will be chosen. If specified then a multiPV search will be performed the random move will be one of the moves chosen by the search.
`random_multi_pv_diff` - Makes the multiPV random move selection consider only moves that are at most `random_multi_pv_diff` worse than the next best move. Default: 30000 (all multiPV moves).
`random_multi_pv_depth` - the depth to use for multiPV search for random move. Default: `depth2`.
`random_multi_pv_nodes` - the maximum number of nodes for a multiPV search for random move. Default: `nodes`.
`write_min_ply` - minimum ply for which the training data entry will be emitted. Default: 16.
`write_max_ply` - maximum ply for which the training data entry will be emitted. Default: 400.
`book` - a path to an opening book to use for the starting positions. Currently only .epd format is supported. If not specified then the starting position is always the standard chess starting position.
`save_every` - the number of training data entries per file. If not specified then there will be always one file. If specified there may be more than one file generated (each having at most `save_every` training data entries) and each file will have a unique number attached.
`random_file_name` - if specified then the output filename will be chosen randomly. Overrides `output_file_name`.
`keep_draws` - either 0 or 1. If 1 then training data from drawn games will be emitted too. Default: 1.
`adjudicate_draws_by_score` - either 0 or 1. If 1 then drawn games will be adjudicated when the score remains 0 for at least 8 plies after ply 80. Default: 1.
`adjudicate_draws_by_insufficient_mating_material` - either 0 or 1. If 1 then position with insufficient material will be adjudicated as draws. Default: 1.
`data_format` - format of the training data to use. Either `bin` or `binpack`. Default: `binpack`.
`seed` - seed for the PRNG. Can be either a number or a string. If it's a string then its hash will be used. If not specified then the current time will be used.
`generate_training_data_nonpv` command allows generation of training data from self-play in a manner that suits training better than traditional games. It plays fixed nodes self play games for exploration and records [some of] the evaluated positions. Then rescores them with fixed depth search.
As all commands in stockfish `generate_training_data_nonpv` can be invoked either from command line (as `stockfish.exe generate_training_data_nonpv ...`, but this is not recommended because it's not possible to specify UCI options before `generate_training_data_nonpv` executes) or in the interactive prompt.
It is recommended to set the `PruneAtShallowDepth` UCI option to `false` as it will increase the quality of fixed depth searches.
It is recommended to keep the `EnableTranspositionTable` UCI option at the default `true` value as it will make the generation process faster without noticably harming the uniformity of the data.
`generate_training_data_nonpv` takes named parameters in the form of `generate_training_data_nonpv param_1_name param_1_value param_2_name param_2_value ...`.
Currently the following options are available:
`depth` - the search depth to use for rescoring. Default: 3.
`count` - the number of training data entries to generate. 1 entry == 1 position. Default: 1000000 (1M).
`exploration_min_nodes` - the min number of nodes to use for exploraton during selfplay. Default: 5000.
`exploration_max_nodes` - the max number of nodes to use for exploraton during selfplay. The number of nodes is chosen from a uniform distribution between min and max. Default: 15000.
`exploration_save_rate` - the ratio of positions seen during exploration self play games that are saved for later rescoring. Default: 0.01 (meaning 1 in 100 positions seen during search get saved for rescoring).
`output_file` - the name of the file to output to. If the extension is not present or doesn't match the selected training data format the right extension will be appened. Default: generated_gensfen_nonpv
`eval_limit` - evaluations with higher absolute value than this will not be written and will terminate a self-play game. Should not exceed 10000 which is VALUE_KNOWN_WIN, but is only hardcapped at mate in 2 (\~30000). Default: 4000
`exploration_eval_limit` - same as `eval_limit` but used during exploration with a value from fixed depth search.
`exploration_min_pieces` - the min number of pieces in the self play games to start the fixed depth search. Note that even if there's N pieces on the board the fixed nodes search usually reaches positions with less pieces and they are saved too. Default: 8.
`exploration_max_ply` the max ply for the exploration self play. Default: 200.
`smart_fen_skipping` - this is a flag option. When specified some position that are not good candidates for teaching are removed from the output. This includes positions where the best move is a capture or promotion, and position where a king is in check.
`book` - a path to an opening book to use for the starting positions. Currently only .epd format is supported. If not specified then the starting position is always the standard chess starting position.
`data_format` - format of the training data to use. Either `bin` or `binpack`. Default: `binpack`.
`seed` - seed for the PRNG. Can be either a number or a string. If it's a string then its hash will be used. If not specified then the current time will be used.
`gather_statistics` command allows gathering various statistics from a .bin or a .binpack file. The syntax is `gather_statistics (GROUP)* input_file FILENAME`. There can be many groups specified. Any statistic gatherer that belongs to at least one of the specified groups will be used.
Simplest usage: `stockfish.exe gather_statistics all input_file a.binpack`
Any name that doesn't designate an argument name or is not an argument will be interpreted as a group name.
## Parameters
`input_file` - the path to the .bin or .binpack input file to read
`output_file` - optional path to the output file to write the results too. Results are always written on the console, so if this is specified the results will be written in both places.
`max_count` - the maximum number of positions to process. Default: no limit.
## Groups
`all` - a special group designating all statistics gatherers available.
`position_count` - the total number of positions in the file.
`king`, `king_square_count` - the number of times a king was on each square. Output is layed out as a chessboard, with the 8th rank being the topmost. Separate values for white and black kings.
`move`, `move_from_count` - same as `king_square_count` but for from_sq(move)
`move`, `move_to_count` - same as `king_square_count` but for to_sq(move)
`move`, `move_type` - the number of moves with each type. Includes normal, captures, castling, promotions, enpassant. The groups are not disjoint.
`move`, `moved_piece_type` - the number of times a piece of each type was moved
`piece_count` - the histogram of the number of pieces on the board
`ply_discontinuities` - the number of times the ply jumped by a value different than 1 between two consecutive positions. Usually the number of games.
`material_imbalance` - the histogram of imbalances, with values computed using "simple eval", that is pawn=1, bishop=knight=3, rook=5, queen=9
`results` - the distribution of game results
`endgames_6man` - distribution of endgame configurations for <=6 pieces (including kings)
`transform` command exposes subcommands that perform some specific transformation over data. The call syntax is `transform <subcommand>`. Currently implemented subcommands are listed and described below.
## `nudged_static`
`transform nudged_static` takes named parameters in the form of `transform nudged_static param_1_name param_1_value param_2_name param_2_value ...` and flag parameters which don't require values.
This command goes through positions in the input files and replaces the scores with new ones - generated from static eval - but slightly adjusted based on the scores in the original input file.
Currently the following options are available:
`input_file` - path to the input file. Supports bin and binpack formats. Default: in.binpack.
`output_file` - path to the output file. Supports bin and binpack formats. Default: out.binpack.
`absolute` - states that the adjustment should be bounded by an absolute value. After this token follows the maximum absolute adjustment. Values are always adjusted towards scores in the input file. This is the default mode. Default maximum adjustement: 5.
`relative` - states that the adjustment should be bounded by a value relative in magnitude to the static eval value. After this token follows the maximum relative change - a floating point value greater than 0. For example a value of 0.1 only allows changing the static eval by at most 10% towards the score from the input file.
`interpolate` states that the output score should be a value interpolated between static eval and the score from the input file. After this token follows the interpolation constant `t`. `t` of 0 means that only static eval is used. `t` of 1 means that only score from the input file is used. `t` of 0.5 means that the static eval and input score are averaged. It accepts values outside of range `<0, 1>`, but the usefulness is questionable.
## `rescore`
`transform rescore` takes named parameters in the form of `transform rescore param_1_name param_1_value param_2_name param_2_value ...` and flag parameters which don't require values.
This tool respects the UCI option `Threads` and uses all available threads.
This command takes a path to the input file that is either a .epd file which contains one FEN per line or a .bin or .binpack file and outputs a .bin or .binpack file with these positions rescored with specified depth search.
Currently the following options are available:
`input_file` - path to the input file. Default: in.binpack.
`output_file` - path to the output .bin or .binpack file. The file is opened in append mode. Default: out.binpack.
`depth` - the search depth to use for rescoring. Default: 3.
`keep_moves` - whether to keep moves from the input file if available. Allows to keep compression in .binpack. Default: 1.
`research_count` - number of additional searches of depth N done on the same position before using the eval. Default: 0.
`validate_training_data` allows validation of training data of types `.plain`, `.bin`, and `.binpack`.
As all commands in stockfish `validate_training_data` can be invoked either from command line (as `stockfish.exe validate_training_data ...`) or in the interactive prompt.
The syntax of this command is as follows:
```
validate_training_data in_path
```
`in_path` is the path to the file to validate. The type of the data is deduced based on its extension (one of `.plain`, `.bin`, `.binpack`).
This script converts pgn files into text file to apply `learn convert_bin` command. You need to import [python-chess](https://pypi.org/project/python-chess/) to use this script.
pip install python-chess
# Example of Qhapaq's finetune using `pgn_to_plain`
## Download data
You can download data from [here](http://rebel13.nl/index.html)
## Convert pgn files
**Important : convert text will be superheavy (approx 200 byte / position)**
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