Some notes:
- Both tests were conducted on top of #5848.
- Based on tuning suggestions, the extension for capturing the
previously moved piece was removed/simplified. (Developers can attempt
to reintroduce it post-merge if needed.)
- Initially, bonusScale = std::max(bonusScale, -2); was included but
later removed in the second test upon Viz's request, however, it was
nearly non-functional anyway.
Passed VVLTC under STC bounds:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 31508 W: 8153 L: 7895 D: 15460
Ptnml(0-2): 1, 2747, 10005, 2995, 6
https://tests.stockfishchess.org/tests/view/679fdc7a0774dfd78deb1350
Passed VVLTC under LTC bounds:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55026 W: 14370 L: 14046 D: 26610
Ptnml(0-2): 7, 4957, 17262, 5279, 8
https://tests.stockfishchess.org/tests/view/679fec920774dfd78deb19b8
closes https://github.com/official-stockfish/Stockfish/pull/5856
Bench: 2757788
Now exits during startup.
```
./stockfish
Stockfish dev-20250202-243c7c6a by the Stockfish developers (see AUTHORS file)
x1,5,0,10,0.5,0.0020
Option: "x1" was already added!
```
i.e. prevents and helps debug this case
```cpp
int x1 = 5;
TUNE(x1);
TUNE(x1);
```
closes https://github.com/official-stockfish/Stockfish/pull/5847
No functional change
This condition can never be true, it's superfluous.
It never triggers even with a bench 16 1 20 run.
To met the condition it would imply that the previous recapture was done
by a higher rated piece than a Queen.
This is only the case when the King recaptures and that's already
handled in line 1161: (return (attackers & ~pieces(stm)) ? res ^ 1).
closes https://github.com/official-stockfish/Stockfish/pull/5839
No functional change
Continuation of work done by @FauziAkram and @Viren6 They had a series
of patches that decrease pruning for ttPv nodes - and it passed as a
gainer at lower time controls while revert passed as a gainer at higher
time controls.
So it's a logical continuation of this work that increases pruning for
ttPv nodes in hopes of scaling to longer TCs.
Fixed games STC:
https://tests.stockfishchess.org/tests/view/679ee3910774dfd78deb0efd
Elo: -4.98 ± 2.1 (95%) LOS: 0.0%
Total: 28584 W: 7229 L: 7639 D: 13716
Ptnml(0-2): 143, 3579, 7219, 3247, 104
nElo: -9.54 ± 4.0 (95%) PairsRatio: 0.90
Passed VVLTC with STC bounds:
https://tests.stockfishchess.org/tests/view/679d21f70774dfd78deaf553
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 323282 W: 83729 L: 83105 D: 156448
Ptnml(0-2): 37, 29842, 101269, 30446, 47
Passed VVLTC with LTC bounds:
https://tests.stockfishchess.org/tests/view/679e7a970774dfd78deb0cd3
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 113712 W: 29485 L: 29051 D: 55176
Ptnml(0-2): 13, 10376, 35640, 10818, 9
closes https://github.com/official-stockfish/Stockfish/pull/5844
Bench: 2964045
Remove depth condition in propagation rule for ttPv state from a node to
it childs.
Because this change marks more nodes as ttPv, we have a time sensitive
ttPv reduction rule and the STC snd LTC seems to show bad scaling. So i
have also submitted a VLTC non-regression to check the scaling at higher
time control.
The results gives a little indication that we have perhaps good scaling
with more ttPv nodes so that could be further explored.
Passed non-regression STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 82528 W: 21627 L: 21453 D: 39448
Ptnml(0-2): 317, 9809, 20891, 9877, 370
https://tests.stockfishchess.org/tests/view/678e608cd63764e34db49ad7
Passed non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 310440 W: 78879 L: 78956 D: 152605
Ptnml(0-2): 255, 34915, 84938, 34876, 236
https://tests.stockfishchess.org/tests/view/678fab89ac8f8f5496155f3c
Passed non-regression VLTC for scaling verification:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 59496 W: 15158 L: 14983 D: 29355
Ptnml(0-2): 15, 6039, 17470, 6204, 20
https://tests.stockfishchess.org/tests/view/6794bd1f4f7de645171fb33b
closes https://github.com/official-stockfish/Stockfish/pull/5819
Bench: 1829507
From cppreference: "It is undefined behavior to read from the member of the
union that wasn't most recently written. Many compilers implement, as a
non-standard language extension, the ability to read inactive members of a
union."
closes https://github.com/official-stockfish/Stockfish/pull/5811
no functional change
* Remove from comments, hardcoded exact values for parameters that are subject to tuning.
* Remove the Elo worth, as they are now completely outdated, making them irrelevant and potentially misleading.
* Consolidated scaling-related comments into a single section for clarity. Used asterisks (*) to highlight parameters significantly affected by scaling, given their separation in the code.
closes https://github.com/official-stockfish/Stockfish/pull/5810
No functional change
In the move generation the moves are generated in the order pawns, knight, bishops, rooks, queens and king.
This follows increasing type_of(pos.moved_piece(m)) term, so in master a capturing was sorted after a capturing rook if the same piece was captured in evasion.
Because we use a stable sorting method (stable means the order of elements with the same value are not changed) and generate the moves in the above order we do'nt need the removed term.
Passed STC:
LLR: 2.98 (-2.94,2.94) <-1.75,0.25>
Total: 170560 W: 44222 L: 44148 D: 82190
Ptnml(0-2): 569, 18792, 46488, 18858, 573
https://tests.stockfishchess.org/tests/view/678530ee460e2910c51de21d
closes https://github.com/official-stockfish/Stockfish/pull/5784
No functional change
The +1 was a quick fix to avoid the division by zero, a more correct approach is to use 1ms as the minimum reported timestamp to avoid a division by zero.
Later timestamps no longer include an additional 1ms.
closes https://github.com/official-stockfish/Stockfish/pull/5778
No functional change
https://tests.stockfishchess.org/tests/view/6782decb6ddf09c0b4b6e1b0
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 105920 W: 27571 L: 27181 D: 51168
Ptnml(0-2): 284, 10808, 30403, 11164, 301
- If we only need to know if attackers exist we can skip some
calculations.
- Also calculating slider/magic attackers first is better because the
double lookup is slow due to memory latency.
- I also included a couple of very minor cleanups in search that
probably don't warrant their own PR but I can open separately if
that's better.
closes https://github.com/official-stockfish/Stockfish/pull/5762
No functional change
Since it's introduction at probcut step the nnue network has changed
substantially and now it no longer seems useful.
Passed non-regression test at STC
https://tests.stockfishchess.org/tests/view/675fe27986d5ee47d9542d86
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 118656 W: 30732 L: 30609 D: 57315
Ptnml(0-2): 319, 12767, 33049, 12858, 335
N.B.: It may be useful to reintroduce it here at probcut if we know that
a node that was cut away previously now has to be explored. Exploring
new ground will deliver no tt-hits so in this case the hint for common
parent position might be useful.
No functional change
* Limit use of `StatsEntry` wrapper to arithmetic types
* Generalize `Stats` to `MultiArray` by discarding the template parameter `D`
* Allow `MultiArray::fill` to take any type assignable to element type
* Remove now-unused operator overloads on `StatsEntry`
closes https://github.com/official-stockfish/Stockfish/pull/5750
No functional change
Since we don't have color dependent parameters in NNUE eval, it also has
no sense IMO to have color dependent parameters in correction histories.
Ideally a fixed depth search on a single thread should be determistic,
so delivering the same result (move) if we just flip colors on the
board.
Patch replaces 2 parameters (122 and 185) with just one value 154 (= the avg of the two).
Passed STC-non regression
https://tests.stockfishchess.org/tests/view/6740a63286d5ee47d953f656
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 122336 W: 31499 L: 31372 D: 59465
Ptnml(0-2): 336, 14535, 31301, 14658, 338
Passed LTC-non regression
https://tests.stockfishchess.org/tests/view/67419bae86d5ee47d953f7b6
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 101400 W: 25870 L: 25731 D: 49799
Ptnml(0-2): 78, 11109, 28166, 11290, 57
closes https://github.com/official-stockfish/Stockfish/pull/5698
Bench: 1215483
Simplify statscores for captures, setting them to 0
A recent tweak of Vizvezdenec finds substantial elo gain from giving
captures a separate statscore, which is used mainly for reductions. The
idea is that the old combination of quiet histories was inappropriate
and that a value based on the capture history is more suitable. This
simplification sets the statscore for captures to 0, suggesting that the
elo gain came from rectifying the quiet history/capture mismatch.
Passed STC (against a slightly older version of Viz's patch)
https://tests.stockfishchess.org/tests/view/673ac6e286d5ee47d953f0ec
LR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 57312 W: 14872 L: 14672 D: 27768
Ptnml(0-2): 152, 6761, 14649, 6923, 171
Passed LTC (against Viz's newest patch)
https://tests.stockfishchess.org/tests/view/673cd00686d5ee47d953f2db
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 88236 W: 22510 L: 22358 D: 43368
Ptnml(0-2): 70, 9530, 24745, 9724, 49
closes https://github.com/official-stockfish/Stockfish/pull/5691
Bench: 959947
Current condition is convoluted and hard to understand because of
several negations. Also added 2 comments to make the concept behind the
condition better understandable.
closes https://github.com/official-stockfish/Stockfish/pull/5685
No functional change
Since no correction histories are ever used inside Movepick, and many
existing histories are closely integrated into search, it might be more
logical to separate them into their own header. PR based on #5650
closes https://github.com/official-stockfish/Stockfish/pull/5652
No functional change
After some simplifications bonuses and maluses are the same for quiet
and non-quiet moves so it makes no sense to use quietMoveBonus/Malus,
instead use just bonus/malus.
closes https://github.com/official-stockfish/Stockfish/pull/5649
No functional change
The Makefile is notoriously slow on windows, because of new processes
being spawned I believe. This pr improves it a little bit for the help
and config-sanity targets, with the latter also improving `make -j
build` because it depends on that. On the same machine ubuntu (wsl) is
more than 3 times faster, if there are other improvements we can make
I'd be happy to hear about them. Ultimately
https://github.com/official-stockfish/Stockfish/pull/5543 also aims to
improve this I believe, but it will take some additional time before
that lands.
```
make config-sanity:
patch: 6.199s
master: 12.738s
make help:
patch: 3.1s
master: 11.49s
make -j build:
patch: 36s
master: 43.25s
make -j build:
master ubuntu: 10s
```
closes https://github.com/official-stockfish/Stockfish/pull/5642
No functional change
That allows 'make -j profile-build' work on ppc64 architectures, setting the use of
the appropriate SIMD extension, Altivec or VSX.
For VSX, gcc allows to map SSE2 intrinsics and get benefit of the existing SIMD code.
On PowerMac G5, using altivec provides a performance improvement of 30%.
On Talos 2, using vsx provides a performance improvement of 120%.
closes https://github.com/official-stockfish/Stockfish/pull/5624
No functional change
`speedtest [threads] [hash_MiB] [time_s]`. `threads` default to system concurrency. `hash_MiB` defaults to `threads*128`. `time_s` defaults to 150.
Intended to be used with default parameters, as a stable hardware benchmark.
Example:
```
C:\dev\stockfish-master\src>stockfish.exe speedtest
Stockfish dev-20240928-nogit by the Stockfish developers (see AUTHORS file)
info string Using 16 threads
Warmup position 3/3
Position 258/258
===========================
Version : Stockfish dev-20240928-nogit
Compiled by : g++ (GNUC) 13.2.0 on MinGW64
Compilation architecture : x86-64-vnni256
Compilation settings : 64bit VNNI BMI2 AVX2 SSE41 SSSE3 SSE2 POPCNT
Compiler __VERSION__ macro : 13.2.0
Large pages : yes
User invocation : speedtest
Filled invocation : speedtest 16 2048 150
Available processors : 0-15
Thread count : 16
Thread binding : none
TT size [MiB] : 2048
Hash max, avg [per mille] :
single search : 40, 21
single game : 631, 428
Total nodes searched : 2099917842
Total search time [s] : 153.937
Nodes/second : 13641410
```
-------------------------------
Small unrelated tweaks:
- Network verification output is now handled as a callback.
- TT hashfull queries allow specifying maximum entry age.
closes https://github.com/official-stockfish/Stockfish/pull/5354
No functional change
Idea of this patch comes from the fact that current history heuristics
are mostly populated by low depth entries since our stat bonus reaches
maximum value at depth 5-6 and number of low depth nodes is much bigger
than number of high depth nodes. But it doesn't make a whole lost of
sense to use this low-depth centered histories to sort moves at root.
Current patch introduces special history table that is used exclusively
at root, it remembers which quiet moves were good and which quiet moves
were not good there and uses this information for move ordering.
Passed STC:
https://tests.stockfishchess.org/tests/view/66dda74adc53972b68218cc9
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 127680 W: 33579 L: 33126 D: 60975
Ptnml(0-2): 422, 15098, 32391, 15463, 466
Passed LTC:
https://tests.stockfishchess.org/tests/view/66dead2adc53972b68218d34
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 381978 W: 96958 L: 95923 D: 189097
Ptnml(0-2): 277, 42165, 105089, 43162, 296
closes https://github.com/official-stockfish/Stockfish/pull/5595
Bench: 1611283
Since an unknown amount of time the instrumented CI has been a bit
flawed, explained here
https://github.com/official-stockfish/Stockfish/issues/5185. It also
experiences random timeout issues where restarting the workflow fixes it
or very long run times (more than other workflows) and is not very
portable.
The intention of this commit is to port the instrumented.sh to python
which also works on other operating systems. It should also be
relatively easy for beginners to add new tests to assert stockfish's
output and to run it.
From the source directory the following command can be run.
`python3 ../tests/instrumented.py --none ./stockfish`
A test runner will go over the test suites and run the test cases.
All instrumented tests should have been ported over.
The required python version for this is should be 3.7 (untested) + the
requests package, testing.py includes some infrastructure code which
setups the testing.
fixes https://github.com/official-stockfish/Stockfish/issues/5185
closes https://github.com/official-stockfish/Stockfish/pull/5583
No functional change
The recent commit introduced a bug in the net downloading script that
the file is not downloaded correctly and the content is redirected to
stdout.
closes https://github.com/official-stockfish/Stockfish/pull/5585
No functional change
- A small quality of file change is to change the type of engine path
from a string to an optional string, skips the binary directory
lookup, which is commonly disabled by people who create wasm builds or
include stockfish as a library.
closes https://github.com/official-stockfish/Stockfish/pull/5575
No functional change
Fixes https://github.com/official-stockfish/Stockfish/issues/5564
This patch extracts the net downloading script in Makefile into an
external script file. Also the script is moderately rewritten for
improved readability and speed.
* Use wget preferentially over curl, as curl is known to have slight
overhead.
* Use command instead of hash to check if command exists. Reportedly,
hash always returns zero in some POSIX shells even when the command
fails.
* Command existence checks (wget/curl, sha256sum) are performed only
once at the beginning.
* Each of common patterns is encapsulated in a function
(get_nnue_filename, validate_network).
* Print out error/warning messages to stderr.
closes https://github.com/official-stockfish/Stockfish/pull/5563
No functional change
Co-authored-by: Disservin <disservin.social@gmail.com>
Official release version of Stockfish 17
Bench: 1484730
---
Stockfish 17
Today we have the pleasure to announce a new major release of Stockfish. As
always, you can freely download it at https://stockfishchess.org/download and
use it in the GUI of your choice.
Don’t forget to join our Discord server[1] to get in touch with the community
of developers and users of the project!
*Quality of chess play*
In tests against Stockfish 16, this release brings an Elo gain of up to 46
points[2] and wins up to 4.5 times more game pairs[3] than it loses. In
practice, high-quality moves are now found in less time, with a user upgrading
from Stockfish 14 being able to analyze games at least 6 times[4] faster with
Stockfish 17 while maintaining roughly the same quality.
During this development period, Stockfish won its 9th consecutive first place
in the main league of the Top Chess Engine Championship (TCEC)[5], and the 24th
consecutive first place in the main events (bullet, blitz, and rapid) of the
Computer Chess Championship (CCC)[6].
*Update highlights*
*Improved engine lines*
This release introduces principal variations (PVs) that are more informative
for mate and decisive table base (TB) scores. In both cases, the PV will
contain all moves up to checkmate. For mate scores, the PV shown is the best
variation known to the engine at that point, while for table base wins, it
follows, based on the TB, a sequence of moves that preserves the game outcome
to checkmate.
*NUMA performance optimization*
For high-end computers with multiple CPUs (typically a dual-socket architecture
with 100+ cores), this release automatically improves performance with a
`NumaPolicy` setting that optimizes non-uniform memory access (NUMA). Although
typical consumer hardware will not benefit, speedups of up to 2.8x[7] have been
measured.
*Shoutouts*
*ChessDB*
During the past 1.5 years, hundreds of cores have been continuously running
Stockfish to grow a database of analyzed positions. This chess cloud
database[8] now contains well over 45 billion positions, providing excellent
coverage of all openings and commonly played lines. This database is already
integrated into GUIs such as En Croissant[9] and Nibbler[10], which access it
through the public API.
*Leela Chess Zero*
Generally considered to be the strongest GPU engine, it continues to provide
open data which is essential for training our NNUE networks. They released
version 0.31.1[11] of their engine a few weeks ago, check it out!
*Website redesign*
Our website has undergone a redesign in recent months, most notably in our home
page[12], now featuring a darker color scheme and a more modern aesthetic,
while still maintaining its core identity. We hope you'll like it as much as we
do!
*Thank you*
The Stockfish project builds on a thriving community of enthusiasts (thanks
everybody!) 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 express our gratitude for the 11k stars[13] that light up our
GitHub project! Thank you for your support and encouragement – your recognition
means a lot to us.
We invite our chess fans to join the Fishtest testing framework[14] to
contribute compute resources needed for development. Programmers can contribute
to the project either directly to Stockfish[15] (C++), to Fishtest[16] (HTML,
CSS, JavaScript, and Python), to our trainer nnue-pytorch[17] (C++ and Python),
or to our website[18] (HTML, CSS/SCSS, and JavaScript).
The Stockfish team
[1] https://discord.gg/GWDRS3kU6R
[2] https://tests.stockfishchess.org/tests/view/66d738ba9de3e7f9b33d159a
[3] https://tests.stockfishchess.org/tests/view/66d738f39de3e7f9b33d15a0
[4] https://github.com/official-stockfish/Stockfish/wiki/Useful-data#equivalent-time-odds-and-normalized-game-pair-elo
[5] https://en.wikipedia.org/wiki/Stockfish_(chess)#Top_Chess_Engine_Championship
[6] https://en.wikipedia.org/wiki/Stockfish_(chess)#Chess.com_Computer_Chess_Championship
[7] https://github.com/official-stockfish/Stockfish/pull/5285
[8] https://chessdb.cn/queryc_en/
[9] https://encroissant.org/
[10] https://github.com/rooklift/nibbler
[11] https://github.com/LeelaChessZero/lc0/releases/tag/v0.31.1
[12] https://stockfishchess.org/
[13] https://github.com/official-stockfish/Stockfish/stargazers
[14] https://github.com/official-stockfish/fishtest/wiki/Running-the-worker
[15] https://github.com/official-stockfish/Stockfish
[16] https://github.com/official-stockfish/fishtest
[17] https://github.com/official-stockfish/nnue-pytorch
[18] https://github.com/official-stockfish/stockfish-web
Created from 2 distinct spsa tunes of the latest main net (nn-31337bea577c.nnue)
and applying the params to the prior main net (nn-e8bac1c07a5a.nnue). This
effectively reverts the modifications to output weights and biases in
https://github.com/official-stockfish/Stockfish/pull/5509
SPSA:
A: 6000, alpha: 0.602, gamma: 0.101
1st - 437 feature transformer biases where values are < 25
54k / 120k games at 180+1.8
https://tests.stockfishchess.org/tests/view/66af98ac4ff211be9d4edad0
nn-808259761cca.nnue
2nd - 208 L2 weights where values are zero
112k / 120k games at 180+1.8
https://tests.stockfishchess.org/tests/view/66b0c3074ff211be9d4edbe5
nn-a56cb8c3d477.nnue
When creating the above 2 nets (nn-808259761cca.nnue, nn-a56cb8c3d477.nnue),
spsa params were unintentionally applied to nn-e8bac1c07a5a.nnue rather
than nn-31337bea577c.nnue due to an issue in a script that creates nets
by applying spsa results to base nets.
Since they both passed STC and were neutral or slightly positive at LTC,
they were combined to see if the elo from each set of params was additive.
The 2 nets can be merged on top of nn-e8bac1c07a5a.nnue with:
https://github.com/linrock/nnue-tools/blob/90942d3/spsa/combine_nnue.py
```
python3 combine_nnue.py \
nn-e8bac1c07a5a.nnue \
nn-808259761cca.nnue \
nn-a56cb8c3d477.nnue
```
Merging yields nn-87caa003fc6a.nnue which was renamed to nn-1111cefa1111.nnue
with an updated nnue-namer around 10x faster than before by:
- using a prefix trie for efficient prefix matches
- modifying 4 non-functional bytes near the end of the file instead of 2
https://github.com/linrock/nnue-namer
Thanks to @MinetaS for pointing out in #nnue-dev what the non-functional bytes are:
L3 is 32, 4 bytes for biases, 32 bytes for weights. (fc_2)
So -38 and -37 are technically -2 and -1 of fc_1 (type AffineTransform<30, 32>)
And since InputDimension is padded to 32 there are total 32 of 2 adjacent bytes padding.
So yes, it's non-functional whatever values are there.
It's possible to tweak bytes at -38 - 32 * N and -37 - 32 * N given N = 0 ... 31
The net renamed with the new method passed non-regression STC vs. the original net:
https://tests.stockfishchess.org/tests/view/66c0f0a821503a509c13b332
To print the spsa params with nnue-pytorch:
```
import features
from serialize import NNUEReader
feature_set = features.get_feature_set_from_name("HalfKAv2_hm")
with open("nn-31337bea577c.nnue", "rb") as f:
model = NNUEReader(f, feature_set).model
c_end = 16
for i,ft_bias in enumerate(model.input.bias.data[:3072]):
value = int(ft_bias * 254)
if abs(value) < 25:
print(f"ftB[{i}],{value},-1024,1024,{c_end},0.0020")
c_end = 6
for i in range(8):
for j in range(32):
for k in range(30):
value = int(model.layer_stacks.l2.weight.data[32 * i + j, k] * 64)
if value == 0:
print(f"twoW[{i}][{j}][{k}],{value},-127,127,{c_end},0.0020")
```
New params found with the same method as:
https://github.com/official-stockfish/Stockfish/pull/5459
Passed STC:
https://tests.stockfishchess.org/tests/view/66b4d4464ff211be9d4edf6e
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 136416 W: 35753 L: 35283 D: 65380
Ptnml(0-2): 510, 16159, 34416, 16597, 526
Passed LTC:
https://tests.stockfishchess.org/tests/view/66b76e814ff211be9d4ee1cc
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 159336 W: 40753 L: 40178 D: 78405
Ptnml(0-2): 126, 17497, 43864, 18038, 143
closes https://github.com/official-stockfish/Stockfish/pull/5534
bench 1613043
This patch moves the DotProd code into the propagation function which
has sequential access optimization. To prove the speedup, the comparison
is done without the sparse layer. With the sparse layer the effect is
marginal (GCC 0.3%, LLVM/Clang 0.1%).
For both tests, binary is compiled with GCC 14.1. Each test had 50 runs.
Sparse layer included:
```
speedup = +0.0030
P(speedup > 0) = 1.0000
```
Sparse layer excluded:
```
speedup = +0.0561
P(speedup > 0) = 1.0000
```
closes https://github.com/official-stockfish/Stockfish/pull/5520
No functional change
Since simplification of quiet checks in qsearch this depth isn't used by
any function at all apart movepicker, which also doesn't use passed
qsearch depth in any way, so can be removed. No functional change.
closes https://github.com/official-stockfish/Stockfish/pull/5514
No functional change
Created by updating output weights (256) and biases (8)
of the previous main net with values found with spsa around
101k / 120k games at 140+1.4.
264 spsa params: output weights and biases in nn-e8bac1c07a5a.nnue
A: 6000, alpha: 0.602, gamma: 0.101
weights: [-127, 127], c_end = 6
biases: [-8192, 8192], c_end = 64
Among the 264 params, 189 weights and all 8 biases were changed.
Changes in the weights:
- mean: -0.111 +/- 3.57
- range: [-8, 8]
Found with the same method as:
https://github.com/official-stockfish/Stockfish/pull/5459
Due to the original name (nn-ea8c9128c325.nnue) being too similar
to the previous main net (nn-e8bac1c07a5a.nnue) and creating confusion,
it was renamed by making non-functional changes to the .nnue file
the same way as past nets with:
https://github.com/linrock/nnue-namer
To verify that bench is the same and view the modified non-functional bytes:
```
echo -e "setoption name EvalFile value nn-ea8c9128c325.nnue\nbench" | ./stockfish
echo -e "setoption name EvalFile value nn-31337bea577c.nnue\nbench" | ./stockfish
cmp -l nn-ea8c9128c325.nnue nn-31337bea577c.nnue
diff <(xxd nn-ea8c9128c325.nnue) <(xxd nn-31337bea577c.nnue)
```
Passed STC:
https://tests.stockfishchess.org/tests/view/669564154ff211be9d4ec080
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 57280 W: 15139 L: 14789 D: 27352
Ptnml(0-2): 209, 6685, 14522, 6995, 229
Passed LTC:
https://tests.stockfishchess.org/tests/view/669694204ff211be9d4ec1b4
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 63030 W: 16093 L: 15720 D: 31217
Ptnml(0-2): 47, 6766, 17516, 7139, 47
closes https://github.com/official-stockfish/Stockfish/pull/5509
bench 1371485
even if beta is below TB range, once we return probcutBeta with beta + 390 we
can return wrong TB value, and guard against ttData.value being `VALUE_NONE`
closes https://github.com/official-stockfish/Stockfish/pull/5499
bench: 1440277
These values represent the lowest Elo rating in the skill level calculation,
and the highest one, but it's not clear from the code where these values come
from other than the comment. This should improve code readability and
maintainability. It makes the purpose of the values clear and allows for easy
modification if the Elo range for skill level calculation changes in the
future. Moved the Skill struct definition from search.cpp to search.h header
file to define the Search::Skill struct, making it accessible from other files.
closes https://github.com/official-stockfish/Stockfish/pull/5508
No functional change
in the case of MultiPV, the first move of the Nth multiPV could actually turn a
winning position in a losing one, so don't attempt to correct it. Instead,
always perform the first move without correction.
Fixes#5505
Closes https://github.com/official-stockfish/Stockfish/pull/5506
No functional change
now checks correctness of PV lines with TB score.
uses 3-4-5 man table bases, downloaded from lichess,
which are cached with the appropriate action.
closes https://github.com/official-stockfish/Stockfish/pull/5500
No functional change
This patch removes lmrDepth limit for quiet moves history based pruning.
Previously removal of this type of depth limits was considered bad because it
was performing bad for matetrack - but with this pruning heuristic this
shouldn't be that bad because it's "naturally" depth limited by history
threshold and should be completely disabled at depth >= 15 or so. Also this
heuristic in previous years was known to scale non-linearly - bigger lmrDepth
thresholds were better at longer time controls and removing it completely
probably should scale pretty well.
Passed STC:
https://tests.stockfishchess.org/tests/view/6692b89b4ff211be9d4eab21
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 114464 W: 29675 L: 29545 D: 55244
Ptnml(0-2): 372, 12516, 31329, 12640, 375
Passed LTC:
https://tests.stockfishchess.org/tests/view/6692c4554ff211be9d4eab3d
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 67746 W: 17182 L: 17014 D: 33550
Ptnml(0-2): 28, 6993, 19652, 7183, 17
closes https://github.com/official-stockfish/Stockfish/pull/5485
Bench: 1250388
now uses the following format:
`info string Found 510 WDL and 510 DTZ tablebase files (up to 6-man).`
this clarifies exactly what has been found, as the difference matters,
e.g. for the PV extension of TB scores.
closes https://github.com/official-stockfish/Stockfish/pull/5471
No functional change
- Capitalize comments
- Reformat multi-lines comments to equalize the widths of the lines
- Try to keep the width of comments around 85 characters
- Remove periods at the end of single-line comments
closes https://github.com/official-stockfish/Stockfish/pull/5469
No functional change
In probcut move loop, everything is enclosed within a large if statement. I've
changed it to guard clauses to stay consistent with other move loops.
closes https://github.com/official-stockfish/Stockfish/pull/5463
No functional change
Created by modifying L2 weights from the previous main net (nn-74f1d263ae9a.nnue)
with params found by spsa around 9k / 120k games at 120+1.2.
370 spsa params - L2 weights in nn-74f1d263ae9a.nnue where |val| >= 50
A: 6000, alpha: 0.602, gamma: 0.101
weights: [-127, 127], c_end = 6
To print the spsa params with nnue-pytorch:
```
import features
from serialize import NNUEReader
feature_set = features.get_feature_set_from_name("HalfKAv2_hm")
with open("nn-74f1d263ae9a.nnue", "rb") as f:
model = NNUEReader(f, feature_set).model
c_end = 6
for i in range(8):
for j in range(32):
for k in range(30):
value = int(model.layer_stacks.l2.weight[32 * i + j, k] * 64)
if abs(value) >= 50:
print(f"twoW[{i}][{j}][{k}],{value},-127,127,{c_end},0.0020")
```
Among the 370 params, 229 weights were changed.
avg change: 0.0961 ± 1.67
range: [-4, 3]
The number of weights changed, grouped by layer stack index,
shows more weights were modified in the lower piece count buckets:
[54, 52, 29, 23, 22, 18, 14, 17]
Found with the same method described in:
https://github.com/official-stockfish/Stockfish/pull/5459
Passed STC:
https://tests.stockfishchess.org/tests/view/668aec9a58083e5fd88239e7
LLR: 3.00 (-2.94,2.94) <0.00,2.00>
Total: 52384 W: 13569 L: 13226 D: 25589
Ptnml(0-2): 127, 6141, 13335, 6440, 149
Passed LTC:
https://tests.stockfishchess.org/tests/view/668af50658083e5fd8823a0b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 46974 W: 12006 L: 11668 D: 23300
Ptnml(0-2): 25, 4992, 13121, 5318, 31
closes https://github.com/official-stockfish/Stockfish/pull/5466
bench 1300471
Always use the posix function posix_memalign() as aligned memory
allocator on Apple computers. This should allow to compile Stockfish
out of the box on all versions of Mac OS X.
Patch tested on the following systems (apart from the CI) :
• Mac OS 10.9.6 (arch x86-64-sse41-popcnt) with gcc-10
• Mac OS 10.13.6 (arch x86-64-bmi2) with gcc-10, gcc-14 and clang-11
• Mac OS 14.1.1 (arch apple-silicon) with clang-15
closes https://github.com/official-stockfish/Stockfish/pull/5462
No functional change
Created by setting output weights (256) and biases (8) of the previous main net
nn-ddcfb9224cdb.nnue to values found around 12k / 120k spsa games at 120+1.2
This used modified fishtest dev workers to construct .nnue files from
spsa params, then load them with EvalFile when running tests:
https://github.com/linrock/fishtest/tree/spsa-file-modified-nnue/worker
Inspired by researching loading spsa params from files:
https://github.com/official-stockfish/fishtest/pull/1926
Scripts for modifying nnue files and preparing params:
https://github.com/linrock/nnue-pytorch/tree/no-gpu-modify-nnue
spsa params:
weights: [-127, 127], c_end = 6
biases: [-8192, 8192], c_end = 64
Example of reading output weights and biases from the previous main net using
nnue-pytorch and printing spsa params in a format compatible with fishtest:
```
import features
from serialize import NNUEReader
feature_set = features.get_feature_set_from_name("HalfKAv2_hm")
with open("nn-ddcfb9224cdb.nnue", "rb") as f:
model = NNUEReader(f, feature_set).model
c_end_weights = 6
c_end_biases = 64
for i in range(8):
for j in range(32):
value = round(int(model.layer_stacks.output.weight[i, j] * 600 * 16) / 127)
print(f"oW[{i}][{j}],{value},-127,127,{c_end_weights},0.0020")
for i in range(8):
value = int(model.layer_stacks.output.bias[i] * 600 * 16)
print(f"oB[{i}],{value},-8192,8192,{c_end_biases},0.0020")
```
For more info on spsa tuning params in nets:
https://github.com/official-stockfish/Stockfish/pull/5149https://github.com/official-stockfish/Stockfish/pull/5254
Passed STC:
https://tests.stockfishchess.org/tests/view/66894d64e59d990b103f8a37
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 32000 W: 8443 L: 8137 D: 15420
Ptnml(0-2): 80, 3627, 8309, 3875, 109
Passed LTC:
https://tests.stockfishchess.org/tests/view/6689668ce59d990b103f8b8b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 172176 W: 43822 L: 43225 D: 85129
Ptnml(0-2): 97, 18821, 47633, 19462, 75
closes https://github.com/official-stockfish/Stockfish/pull/5459
bench 1120091
Currently (after #5407), SF has the property that any PV line with a decisive
TB score contains the corresponding TB position, with a score that correctly
identifies the depth at which TB are entered. The PV line that follows might
not preserve the game outcome, but can easily be verified and extended based on
TB information. This patch provides this functionality, simply extending the PV
lines on output, this doesn't affect search.
Indeed, if DTZ tables are available, search based PV lines that correspond to
decisive TB scores are verified to preserve game outcome, truncating the line
as needed. Subsequently, such PV lines are extended with a game outcome
preserving line until mate, as a possible continuation. These lines are not
optimal mating lines, but are similar to what a user could produce on a website
like https://syzygy-tables.info/ clicking always the top ranked move, i.e.
minimizing or maximizing DTZ (with a simple tie-breaker for moves that have
identical DTZ), and are thus an just an illustration of how to game can be won.
A similar approach is already in established in
https://github.com/joergoster/Stockfish/tree/matefish2
This also contributes to addressing #5175 where SF can give an incorrect TB
win/loss for positions in TB with a movecounter that doesn't reflect optimal
play. While the full solution requires either TB generated differently, or a
search when ranking rootmoves, current SF will eventually find a draw in these
cases, in practice quite quickly, e.g.
`1kq5/q2r4/5K2/8/8/8/8/7Q w - - 96 1`
`8/8/6k1/3B4/3K4/4N3/8/8 w - - 54 106`
Gives the same results as master on an extended set of test positions from
https://github.com/mcostalba/Stockfish/commit/9173d29c414ddb8f4bec74e4db3ccbe664c66bf9
with the exception of the above mentioned fen where this commit improves.
With https://github.com/vondele/matetrack using 6men TB, all generated PVs verify:
```
Using ../Stockfish/src/stockfish.syzygyExtend on matetrack.epd with --nodes 1000000 --syzygyPath /chess/syzygy/3-4-5-6/WDL:/chess/syzygy/3-4-5-6/DTZ
Engine ID: Stockfish dev-20240704-ff227954
Total FENs: 6555
Found mates: 3299
Best mates: 2582
Found TB wins: 568
```
As repeated DTZ probing could be slow a procedure (100ms+ on HDD, a few ms on
SSD), the extension is only done as long as the time taken is less than half
the `Move Overhead` parameter. For tournaments where these lines might be of
interest to the user, a suitable `Move Overhead` might be needed (e.g. TCEC has
1000ms already).
closes https://github.com/official-stockfish/Stockfish/pull/5414
No functional change
To avoid output that depends on timing, output currmove and similar only from depth > 30
onward. Current choice of 3s makes the output of the same search depending on
the system load, and doesn't always start at move 1. Depth 30 is nowadays
reached in a few seconds on most systems.
closes https://github.com/official-stockfish/Stockfish/pull/5436
No functional change
do not upload some unneeded intermediate directories,
disable running authenticated git commands with the checkout action.
Thanks to Yaron A for the report.
closes https://github.com/official-stockfish/Stockfish/pull/5435
No functional change
Follow up from #5404 ... current location leads to troubles with Aquarium GUI
Fixes#5430
Now prints the information on threads and available processors at the beginning
of search, where info about the networks is already printed (and is known to
work)
closes https://github.com/official-stockfish/Stockfish/pull/5433
No functional change.
This patch introduces history updates to probcut. Standard depth - 3 bonus and
maluses are given to the capture that caused fail high and previously searched
captures, respectively. Similar to #5243, a negative history fill is applied to
compensate for an increase in capture history average, thus improving the
scaling of this patch.
Passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 84832 W: 21941 L: 21556 D: 41335
Ptnml(0-2): 226, 9927, 21688, 10386, 189
https://tests.stockfishchess.org/tests/view/6682fab9389b9ee542b1d029
Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 104298 W: 26469 L: 26011 D: 51818
Ptnml(0-2): 43, 11458, 28677, 11940, 31
https://tests.stockfishchess.org/tests/view/6682ff06389b9ee542b1d0a0
closes https://github.com/official-stockfish/Stockfish/pull/5428
bench 1281351
this action plays games under fast-chess with a `debug=yes` compiled binary.
It checks for triggered asserts in the code, or generally for engine disconnects.
closes https://github.com/official-stockfish/Stockfish/pull/5403
No functional change
try to avoid missing good moves for opponent or engine, by updating bestMove
also when value == bestValue (i.e. value == alpha) under certain conditions.
In particular require this is at higher depth in the tree, leaving the logic
near the root unchanged, and only apply randomly. Avoid doing this near mate
scores, leaving mate PVs intact.
Passed SMP STC 6+0.06 th7 :
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 42040 W: 10930 L: 10624 D: 20486
Ptnml(0-2): 28, 4682, 11289, 4998, 23
https://tests.stockfishchess.org/tests/view/66608b00c340c8eed7757d1d
Passed SMP LTC 24+0.24 th7 :
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 73692 W: 18978 L: 18600 D: 36114
Ptnml(0-2): 9, 7421, 21614, 7787, 15
https://tests.stockfishchess.org/tests/view/666095e8c340c8eed7757d49
closes https://github.com/official-stockfish/Stockfish/pull/5367
Bench 1205168
In case a stop is received during multithreaded searches, the PV of the best
thread might be printed without the correct upperbound/lowerbound indicators.
This was due to the pvIdx variable being incremented after receiving the stop.
passed STC:
https://tests.stockfishchess.org/tests/view/666985da602682471b064d08
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 196576 W: 51039 L: 50996 D: 94541
Ptnml(0-2): 760, 22545, 51603, 22652, 728
closes https://github.com/official-stockfish/Stockfish/pull/5391
Bench: 1160467
currently extensions can cause depth to exceed MAX_PLY.
This triggers the assert near line 542 in search when running a binary compiled with `debug=yes` on a testcase like:
```
position fen 7K/P1p1p1p1/2P1P1Pk/6pP/3p2P1/1P6/3P4/8 w - - 0 1
go nodes 1000000
```
passed STC
https://tests.stockfishchess.org/tests/view/6668a56a602682471b064c8d
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 143936 W: 37338 L: 37238 D: 69360
Ptnml(0-2): 514, 16335, 38149, 16477, 493
closes https://github.com/official-stockfish/Stockfish/pull/5383
Bench: 1160467
Move the engine options into the engine class, also avoid duplicated
initializations after startup. UCIEngine needs to register an add_listener to
listen to all option changes and print these. Also avoid a double
initialization of the TT, which was the case with the old state.
closes https://github.com/official-stockfish/Stockfish/pull/5356
No functional change
1. Fix GetProcessGroupAffinity still not getting properly aligned memory
sometimes.
2. Fix a very theoretically possible heap corruption if
GetActiveProcessorGroupCount changes between calls.
3. Fully determine affinity on Windows 11 and Windows Server 2022. It
should only ever be indeterminate in case of an error.
4. Separate isDeterminate for old and new API, as they are &'d together
we still can end up with a subset of processors even if one API is
indeterminate.
5. likely_used_old_api() that is based on actual affinity that's been
detected
6. IMPORTANT: Gather affinities at startup, so that we only later use
the affinites set at startup. Not only does this prevent us from our
own calls interfering with detection but it also means subsequent
setoption NumaPolicy calls should behave as expected.
7. Fix ERROR_INSUFFICIENT_BUFFER from GetThreadSelectedCpuSetMasks being
treated like an error.
Should resolve
https://github.com/vondele/Stockfish/commit/02ff76630b358e5f958793cc93df0009d2da65a5#commitcomment-142790025
closes https://github.com/official-stockfish/Stockfish/pull/5372
Bench: 1231853
Can be used in case a GUI (e.g. ChessBase 17 see #5307) sets affinity to a
single processor group, but the user would like to use the full capabilities of
the hardware. Improves affinity handling on Windows in case of multiple
available APIs and existing affinities.
closes https://github.com/official-stockfish/Stockfish/pull/5353
No functional change
Passed STC:
https://tests.stockfishchess.org/tests/view/665ce3f8fd45fb0f907c537f
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 282784 W: 73032 L: 73082 D: 136670
Ptnml(0-2): 680, 31845, 76364, 31851, 652
Recently when I overhauled these comments, Disservin asked why these
were so much lower: they're a relic from when we had a third QS stage at
-5. Now we don't, so fix these to the obvious place.
I was fairly sure it was nonfunctional but ran the nonreg to be double
sure.
closes https://github.com/official-stockfish/Stockfish/pull/5343
Bench: 1057383
Previously, we had two type aliases, LargePagePtr and AlignedPtr, which
required manually initializing the aligned memory for the pointer.
The new helpers:
- make_unique_aligned
- make_unique_large_page
are now available for allocating aligned memory (with large pages). They
behave similarly to std::make_unique, ensuring objects allocated with
these functions follow RAII.
The old approach had issues with initializing non-trivial types or
arrays of objects. The evaluation function of the network is now a
unique pointer to an array instead of an array of unique pointers.
Memory related functions have been moved into memory.h
Passed High Hash Pressure Test Non-Regression STC:
https://tests.stockfishchess.org/tests/view/665b2b36586058766677cfd2
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 476992 W: 122426 L: 122677 D: 231889
Ptnml(0-2): 1145, 51027, 134419, 50744, 1161
Failed Normal Non-Regression STC:
https://tests.stockfishchess.org/tests/view/665b2997586058766677cfc8
LLR: -2.94 (-2.94,2.94) <-1.75,0.25>
Total: 877312 W: 225233 L: 226395 D: 425684
Ptnml(0-2): 2110, 94642, 246239, 93630, 2035
Probably a fluke since there shouldn't be a real slowndown and it has also
passed the high hash pressure test.
closes https://github.com/official-stockfish/Stockfish/pull/5332
No functional change
This reverts commit 783dfc2eb2.
could lead to a division by zero for:
ttValue = (ttValue * tte->depth() + beta) / (tte->depth() + 1)
as other threads can overwrite the tte with a QS depth of -1.
closes https://github.com/official-stockfish/Stockfish/pull/5338
Bench: 1280020
This is reintroduction of the recently simplified logic - if positive tt cutoff
occurs return not a tt value but smth between it and beta. Difference is that
instead of static linear combination there we use basically the same formula as
we do in the main search - with the only difference being using tt depth
instead of depth, which makes a lot of sense.
Passed STC:
https://tests.stockfishchess.org/tests/view/665b3a34f4a1fd0c208ea870
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 54944 W: 14239 L: 13896 D: 26809
Ptnml(0-2): 151, 6407, 14008, 6760, 146
Passed LTC:
https://tests.stockfishchess.org/tests/view/665b520011645bd3d3fac341
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 90540 W: 23070 L: 22640 D: 44830
Ptnml(0-2): 39, 9903, 24965, 10315, 48
closes https://github.com/official-stockfish/Stockfish/pull/5336
bench 1381237
The idea is, that if we have the information that the singular search failed low and therefore produced an upperbound score, we can use the score from singularsearch as approximate upperbound as to what bestValue our non ttMoves will produce. If this value is well below alpha, we assume that all non-ttMoves will score below alpha and therfore can skip more moves.
This patch also sets up variables for future patches wanting to use teh singular search result outside of singular extensions, in singularBound and singularValue, meaning further patches using this search result to affect various pruning techniques can be tried.
Passed STC:
https://tests.stockfishchess.org/tests/view/6658d13e6b0e318cefa90120
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 85632 W: 22112 L: 21725 D: 41795
Ptnml(0-2): 243, 10010, 21947, 10349, 267
Passed LTC:
https://tests.stockfishchess.org/tests/view/6658dd356b0e318cefa9016a
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 243978 W: 62014 L: 61272 D: 120692
Ptnml(0-2): 128, 26598, 67791, 27348, 124
closes https://github.com/official-stockfish/Stockfish/pull/5325
bench 1397172
Specialize and privatize NumaConfig::get_process_affinity.
Only enable NUMA capability for 64-bit Windows.
Following #5307 and some more testing it was determined that the way affinity
was being determined on Windows was incorrect, based on incorrect assumptions
about GetNumaProcessorNodeEx.
This patch fixes the issue by attempting to retrieve the actual process'
processor affinity using Windows API. However one issue persists that is not
addressable due to limitations of Windows, and will have to be considered a
limitation. If affinities were set using SetThreadAffinityMask instead of
SetThreadSelectedCpuSetMasks and GetProcessGroupAffinity returns more than 1
group it is NOT POSSIBLE to determine the affinity programmatically on Windows.
In such case the implementation assumes no affinites are set and will consider
all processors available for execution.
closes https://github.com/official-stockfish/Stockfish/pull/5312
No functional change
This PR updates the internal WDL model, using data from 2.5M games played by SF-dev (3c62ad7).
Note that the normalizing constant has increased from 329 to 368.
Changes to the fitting procedure:
* the value for --materialMin was increased from 10 to 17: including data with less material leads to less accuracy for larger material count values
* the data was filtered to only include single thread LTC games at 60+0.6
* the data was filtered to only include games from master against patches that are (approximatively) within 5 nElo of master
For more information and plots of the model see PR#5309
closes https://github.com/official-stockfish/Stockfish/pull/5309
No functional change
As stockfish nets and search evolve, the existing time control appears
to give too little time at STC, roughly correct at LTC, and too little
at VLTC+.
This change adds an adjustment to the optExtra calculation. This
adjustment is easy to retune and refine, so it should be easier to keep
up-to-date than the more complex calculations used for optConstant and
optScale.
Passed STC 10+0.1:
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 169568 W: 43803 L: 43295 D: 82470
Ptnml(0-2): 485, 19679, 44055, 19973, 592
https://tests.stockfishchess.org/tests/view/66531865a86388d5e27da9fa
Yellow LTC 60+0.6:
LLR: -2.94 (-2.94,2.94) <0.50,2.50>
Total: 209970 W: 53087 L: 52914 D: 103969
Ptnml(0-2): 91, 19652, 65314, 19849, 79
https://tests.stockfishchess.org/tests/view/6653e38ba86388d5e27daaa0
Passed VLTC 180+1.8 :
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 85618 W: 21735 L: 21342 D: 42541
Ptnml(0-2): 15, 8267, 25848, 8668, 11
https://tests.stockfishchess.org/tests/view/6655131da86388d5e27db95f
closes https://github.com/official-stockfish/Stockfish/pull/5297
Bench: 1212167
Allow for NUMA memory replication for NNUE weights. Bind threads to ensure execution on a specific NUMA node.
This patch introduces NUMA memory replication, currently only utilized for the NNUE weights. Along with it comes all machinery required to identify NUMA nodes and bind threads to specific processors/nodes. It also comes with small changes to Thread and ThreadPool to allow easier execution of custom functions on the designated thread. Old thread binding (WinProcGroup) machinery is removed because it's incompatible with this patch. Small changes to unrelated parts of the code were made to ensure correctness, like some classes being made unmovable, raw pointers replaced with unique_ptr. etc.
Windows 7 and Windows 10 is partially supported. Windows 11 is fully supported. Linux is fully supported, with explicit exclusion of Android. No additional dependencies.
-----------------
A new UCI option `NumaPolicy` is introduced. It can take the following values:
```
system - gathers NUMA node information from the system (lscpu or windows api), for each threads binds it to a single NUMA node
none - assumes there is 1 NUMA node, never binds threads
auto - this is the default value, depends on the number of set threads and NUMA nodes, will only enable binding on multinode systems and when the number of threads reaches a threshold (dependent on node size and count)
[[custom]] -
// ':'-separated numa nodes
// ','-separated cpu indices
// supports "first-last" range syntax for cpu indices,
for example '0-15,32-47:16-31,48-63'
```
Setting `NumaPolicy` forces recreation of the threads in the ThreadPool, which in turn forces the recreation of the TT.
The threads are distributed among NUMA nodes in a round-robin fashion based on fill percentage (i.e. it will strive to fill all NUMA nodes evenly). Threads are bound to NUMA nodes, not specific processors, because that's our only requirement and the OS can schedule them better.
Special care is made that maximum memory usage on systems that do not require memory replication stays as previously, that is, unnecessary copies are avoided.
On linux the process' processor affinity is respected. This means that if you for example use taskset to restrict Stockfish to a single NUMA node then the `system` and `auto` settings will only see a single NUMA node (more precisely, the processors included in the current affinity mask) and act accordingly.
-----------------
We can't ensure that a memory allocation takes place on a given NUMA node without using libnuma on linux, or using appropriate custom allocators on windows (https://learn.microsoft.com/en-us/windows/win32/memory/allocating-memory-from-a-numa-node), so to avoid complications the current implementation relies on first-touch policy. Due to this we also rely on the memory allocator to give us a new chunk of untouched memory from the system. This appears to work reliably on linux, but results may vary.
MacOS is not supported, because AFAIK it's not affected, and implementation would be problematic anyway.
Windows is supported since Windows 7 (https://learn.microsoft.com/en-us/windows/win32/api/processtopologyapi/nf-processtopologyapi-setthreadgroupaffinity). Until Windows 11/Server 2022 NUMA nodes are split such that they cannot span processor groups. This is because before Windows 11/Server 2022 it's not possible to set thread affinity spanning processor groups. The splitting is done manually in some cases (required after Windows 10 Build 20348). Since Windows 11/Server 2022 we can set affinites spanning processor group so this splitting is not done, so the behaviour is pretty much like on linux.
Linux is supported, **without** libnuma requirement. `lscpu` is expected.
-----------------
Passed 60+1 @ 256t 16000MB hash: https://tests.stockfishchess.org/tests/view/6654e443a86388d5e27db0d8
```
LLR: 2.95 (-2.94,2.94) <0.00,10.00>
Total: 278 W: 110 L: 29 D: 139
Ptnml(0-2): 0, 1, 56, 82, 0
```
Passed SMP STC: https://tests.stockfishchess.org/tests/view/6654fc74a86388d5e27db1cd
```
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 67152 W: 17354 L: 17177 D: 32621
Ptnml(0-2): 64, 7428, 18408, 7619, 57
```
Passed STC: https://tests.stockfishchess.org/tests/view/6654fb27a86388d5e27db15c
```
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 131648 W: 34155 L: 34045 D: 63448
Ptnml(0-2): 426, 13878, 37096, 14008, 416
```
fixes#5253
closes https://github.com/official-stockfish/Stockfish/pull/5285
No functional change
This speedup was first inspired by a comment by @AndyGrant on my recent
PR "If mullo_epi16 would preserve the signedness, then this could be
used to remove 50% of the max operations during the halfkp-pairwise
mat-mul relu deal."
That got me thinking, because although mullo_epi16 did not preserve the
signedness, mulhi_epi16 did, and so we could shift left and then use
mulhi_epi16, instead of shifting right after the mullo.
However, due to some issues with shifting into the sign bit, the FT
weights and biases had to be multiplied by 2 for the optimisation to
work.
Speedup on "Arch=x86-64-bmi2 COMP=clang", courtesy of @Torom
Result of 50 runs
base (...es/stockfish) = 962946 +/- 1202
test (...ise-max-less) = 979696 +/- 1084
diff = +16750 +/- 1794
speedup = +0.0174
P(speedup > 0) = 1.0000
CPU: 4 x Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
Hyperthreading: on
Also a speedup on "COMP=gcc", courtesy of Torom once again
Result of 50 runs
base (...tockfish_gcc) = 966033 +/- 1574
test (...max-less_gcc) = 983319 +/- 1513
diff = +17286 +/- 2515
speedup = +0.0179
P(speedup > 0) = 1.0000
CPU: 4 x Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
Hyperthreading: on
Passed STC:
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 67712 W: 17715 L: 17358 D: 32639
Ptnml(0-2): 225, 7472, 18140, 7759, 260
https://tests.stockfishchess.org/tests/view/664c1d75830eb9f886616906
closes https://github.com/official-stockfish/Stockfish/pull/5282
No functional change
Also "fix" movepicker to allow depths between CHECKS and NO_CHECKS,
which makes them easier to tweak (not that they get tweaked hardly ever)
(This was more beneficial when there was a third stage to DEPTH_QS, but
it's still an improvement now)
closes https://github.com/official-stockfish/Stockfish/pull/5205
No functional change
Removes some max calls
Some speedup stats, courtesy of @AndyGrant (albeit measured in an alternate implementation)
Dev 749240 nps
Base 748495 nps
Gain 0.100%
289936 games
STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 203040 W: 52213 L: 52179 D: 98648
Ptnml(0-2): 480, 20722, 59139, 20642, 537
https://tests.stockfishchess.org/tests/view/664805fe6dcff0d1d6b05f2ccloses#5261
No functional change
Created by first retraining the spsa-tuned main net `nn-ae6a388e4a1a.nnue` with:
- using v6-dd data without bestmove captures removed
- addition of T80 mar2024 data
- increasing loss by 20% when Q is too high
- torch.compile changes for marginal training speed gains
And then SPSA tuning weights of epoch 899 following methods described in:
https://github.com/official-stockfish/Stockfish/pull/5149
This net was reached at 92k out of 120k steps in this 70+0.7 th 7 SPSA tuning run:
https://tests.stockfishchess.org/tests/view/66413b7df9f4e8fc783c9bbb
Thanks to @Viren6 for suggesting usage of:
- c value 4 for the weights
- c value 128 for the biases
Scripts for automating applying fishtest spsa params to exporting tuned .nnue are in:
https://github.com/linrock/nnue-tools/tree/master/spsa
Before spsa tuning, epoch 899 was nn-f85738aefa84.nnue
https://tests.stockfishchess.org/tests/view/663e5c893a2f9702074bc167
After initially training with max-epoch 800, training was resumed with max-epoch 1000.
```
experiment-name: 3072--S11--more-data-v6-dd-t80-mar2024--see-ge0-20p-more-loss-high-q-sk28-l8
nnue-pytorch-branch: linrock/nnue-pytorch/3072-r21-skip-more-wdl-see-ge0-20p-more-loss-high-q-torch-compile-more
start-from-engine-test-net: False
start-from-model: /data/config/apr2024-3072/nn-ae6a388e4a1a.nnue
early-fen-skipping: 28
training-dataset:
/data/S11-mar2024/:
- leela96.v2.min.binpack
- test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
- test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
- test80-2022-06-jun-16tb7p.v6-dd.min.binpack
- test80-2022-08-aug-16tb7p.v6-dd.min.binpack
- test80-2022-09-sep-16tb7p.v6-dd.min.binpack
- test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
- test80-2023-02-feb-16tb7p.v6-sk20.min.binpack
- test80-2023-03-mar-2tb7p.v6-sk16.min.binpack
- test80-2023-04-apr-2tb7p.v6-sk16.min.binpack
- test80-2023-05-may-2tb7p.v6.min.binpack
# https://github.com/official-stockfish/Stockfish/pull/4782
- test80-2023-06-jun-2tb7p.binpack
- test80-2023-07-jul-2tb7p.binpack
# https://github.com/official-stockfish/Stockfish/pull/4972
- test80-2023-08-aug-2tb7p.v6.min.binpack
- test80-2023-09-sep-2tb7p.binpack
- test80-2023-10-oct-2tb7p.binpack
# S9 new data: https://github.com/official-stockfish/Stockfish/pull/5056
- test80-2023-11-nov-2tb7p.binpack
- test80-2023-12-dec-2tb7p.binpack
# S10 new data: https://github.com/official-stockfish/Stockfish/pull/5149
- test80-2024-01-jan-2tb7p.binpack
- test80-2024-02-feb-2tb7p.binpack
# S11 new data
- test80-2024-03-mar-2tb7p.binpack
/data/filt-v6-dd/:
- test77-dec2021-16tb7p-filter-v6-dd.binpack
- test78-juntosep2022-16tb7p-filter-v6-dd.binpack
- test79-apr2022-16tb7p-filter-v6-dd.binpack
- test79-may2022-16tb7p-filter-v6-dd.binpack
- test80-jul2022-16tb7p-filter-v6-dd.binpack
- test80-oct2022-16tb7p-filter-v6-dd.binpack
- test80-nov2022-16tb7p-filter-v6-dd.binpack
num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 0.8
end-lambda: 0.7
```
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch899.nnue : 4.6 +/- 1.4
Passed STC:
https://tests.stockfishchess.org/tests/view/6645454893ce6da3e93b31ae
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 95232 W: 24598 L: 24194 D: 46440
Ptnml(0-2): 294, 11215, 24180, 11647, 280
Passed LTC:
https://tests.stockfishchess.org/tests/view/6645522d93ce6da3e93b31df
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 320544 W: 81432 L: 80524 D: 158588
Ptnml(0-2): 164, 35659, 87696, 36611, 142
closes https://github.com/official-stockfish/Stockfish/pull/5254
bench 1995552
Basically the same idea as it is for continuation/main history, but it
has some tweaks.
1) it has * 2 multiplier for bonus instead of full/half bonus - for
whatever reason this seems to work better;
2) attempts with this type of big bonuses scaled somewhat poorly (or
were unlucky at longer time controls), but after measuring the fact
that average value of pawn history in LMR after adding this bonuses
increased by substantial number (for multiplier 1,5 it increased by
smth like 400~ from 8192 cap) attempts were made to make default pawn
history negative to compensate it - and version with multiplier 2 and
initial fill value -900 passed.
Passed STC:
https://tests.stockfishchess.org/tests/view/66424815f9f4e8fc783cba59
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 115008 W: 30001 L: 29564 D: 55443
Ptnml(0-2): 432, 13629, 28903, 14150, 390
Passed LTC:
https://tests.stockfishchess.org/tests/view/6642f5437134c82f3f7a3ffa
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 56448 W: 14432 L: 14067 D: 27949
Ptnml(0-2): 36, 6268, 15254, 6627, 39
Bench: 1857237
Stockfish appears to take too much time on the first move of a game and
then not enough on moves 2,3,4... Probably caused by most of the factors
that increase time usually applying on the first move.
Attempts to give more time to the subsequent moves have not worked so
far, but this change to simply reduce first move time by 5% worked.
STC 10+0.1 :
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 78496 W: 20516 L: 20135 D: 37845
Ptnml(0-2): 340, 8859, 20456, 9266, 327
https://tests.stockfishchess.org/tests/view/663d47bf507ebe1c0e9200ba
LTC 60+0.6 :
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 94872 W: 24179 L: 23751 D: 46942
Ptnml(0-2): 61, 9743, 27405, 10161, 66
https://tests.stockfishchess.org/tests/view/663e779cbb28828150dd9089
closes https://github.com/official-stockfish/Stockfish/pull/5235
Bench: 1876282
1. The current time management system utilizes limits.inc and
limits.time, which can represent either milliseconds or node count,
depending on whether the nodestime option is active. There have been
several modifications which brought Elo gain for typical uses (i.e.
real-time matches), however some of these changes overlooked such
distinction. This patch adjusts constants and multiplication/division to
more accurately simulate real TC conditions when nodestime is used.
2. The advance_nodes_time function has a bug that can extend the time
limit when availableNodes reaches exact zero. This patch fixes the bug
by initializing the variable to -1 and make sure it does not go below
zero.
3. elapsed_time function is newly introduced to print PV in the UCI
output based on real time. This makes PV output more consistent with the
behavior of trivial use cases.
closes https://github.com/official-stockfish/Stockfish/pull/5186
No functional changes
If there is an upper bound stored in the transposition table, but we still have a ttMove, the upperbound indicates that the last time the ttMove was tried, it failed low. This fail low indicates that the ttMove may not be good, so this patch introduces a depth reduction of one for cutnodes with such ttMoves.
Passed STC:
https://tests.stockfishchess.org/tests/view/663be4d1ca93dad645f7f45f
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 139424 W: 35900 L: 35433 D: 68091
Ptnml(0-2): 425, 16357, 35743, 16700, 487
Passed LTC:
https://tests.stockfishchess.org/tests/view/663bec95ca93dad645f7f5c8
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 129690 W: 32902 L: 32390 D: 64398
Ptnml(0-2): 63, 14304, 35610, 14794, 74
closes https://github.com/official-stockfish/Stockfish/pull/5227
bench 2257437
Make it formula more in line with what we use in search - current formula is more or less the one we used years ago for search but since then it was remade, this patch remakes qsearch formula to almost exactly the same as we use in search - with sum of conthist 0, 1 and pawn structure history.
Passed STC:
https://tests.stockfishchess.org/tests/view/6639c8421343f0cb16716206
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 84992 W: 22414 L: 22019 D: 40559
Ptnml(0-2): 358, 9992, 21440, 10309, 397
Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 119136 W: 30407 L: 29916 D: 58813
Ptnml(0-2): 46, 13192, 32622, 13641, 67
closes https://github.com/official-stockfish/Stockfish/pull/5224
Bench: 2138659
The idea came to me by checking for trends from the megafauzi tunes, since the values of the divisor for this specific formula were as follows:
stc: 15990
mtc: 16117
ltc: 14805
vltc: 12719
new vltc passed by Muzhen: 12076
This shows a clear trend related to time control, the higher it is, the lower the optimum value for the divisor seems to be.
So I tried a simple formula, using educated guesses based on some calculations, tests show it works pretty fine, and it can still be further tuned at VLTC in the future to scale even better.
Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 431360 W: 110791 L: 109898 D: 210671
Ptnml(0-2): 1182, 50846, 110698, 51805, 1149
https://tests.stockfishchess.org/tests/view/663770409819650825aa269f
Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 114114 W: 29109 L: 28625 D: 56380
Ptnml(0-2): 105, 12628, 31101, 13124, 99
https://tests.stockfishchess.org/tests/view/66378c099819650825aa73f6https://github.com/official-stockfish/Stockfish/pull/5223
bench: 2273551
This adds the functions `update_refutations` and `update_quiet_histories` to better distinguish the two. `update_quiet_stats` now just calls both of these functions.
The functional side of this patch is two-fold:
1. Stop refutations being updated when we carry out multicut
2. Update pawn history every time we update other quiet histories
Yellow STC:
LLR: -2.95 (-2.94,2.94) <0.00,2.00>
Total: 238976 W: 61506 L: 61415 D: 116055
Ptnml(0-2): 846, 28628, 60456, 28705, 853
https://tests.stockfishchess.org/tests/view/66321b5ed01fb9ac9bcdca83
However, it passed in <-1.75, 0.25> bounds:
$ python3 sprt.py --wins 61506 --losses 61415 --draws 116055 --elo0 -1.75 --elo1 0.25
ELO: 0.132 +- 0.998 [-0.865, 1.13]
LLR: 4.15 [-1.75, 0.25] (-2.94, 2.94)
H1 Accepted
Passed LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 399126 W: 100730 L: 100896 D: 197500
Ptnml(0-2): 116, 44328, 110843, 44158, 118
https://tests.stockfishchess.org/tests/view/66357b0473559a8aa857ba6fcloses#5215
Bench 2370967
Saves a (currently) 800 KB allocation and deallocation when running
`eval`, not particularly significant and zero impact on play but not
necessary either.
closes https://github.com/official-stockfish/Stockfish/pull/5201
No functional change
Adds size in memory as well as layer sizes as in
info string NNUE evaluation using nn-ae6a388e4a1a.nnue (132MiB, (22528, 3072, 15, 32, 1))
info string NNUE evaluation using nn-baff1ede1f90.nnue (6MiB, (22528, 128, 15, 32, 1))
For example, the size in MiB is useful to keep the fishtest memory sizes up-to-date,
the L1-L3 sizes give a useful hint about the architecture used.
closes https://github.com/official-stockfish/Stockfish/pull/5193
No functional change
For each thread persist an accumulator cache for the network, where each
cache contains multiple entries for each of the possible king squares.
When the accumulator needs to be refreshed, the cached entry is used to more
efficiently update the accumulator, instead of rebuilding it from scratch.
This idea, was first described by Luecx (author of Koivisto) and
is commonly referred to as "Finny Tables".
When the accumulator needs to be refreshed, instead of filling it with
biases and adding every piece from scratch, we...
1. Take the `AccumulatorRefreshEntry` associated with the new king bucket
2. Calculate the features to activate and deactivate (from differences
between bitboards in the entry and bitboards of the actual position)
3. Apply the updates on the refresh entry
4. Copy the content of the refresh entry accumulator to the accumulator
we were refreshing
5. Copy the bitboards from the position to the refresh entry, to match
the newly updated accumulator
Results at STC:
https://tests.stockfishchess.org/tests/view/662301573fe04ce4cefc1386
(first version)
https://tests.stockfishchess.org/tests/view/6627fa063fe04ce4cefc6560
(final)
Non-Regression between first and final:
https://tests.stockfishchess.org/tests/view/662801e33fe04ce4cefc660a
STC SMP:
https://tests.stockfishchess.org/tests/view/662808133fe04ce4cefc667c
closes https://github.com/official-stockfish/Stockfish/pull/5183
No functional change
Parameters Tune, adding also another tunable parameter (npmDiv) to be
variable for different nets (bignet, smallnet, psqtOnly smallnet). P.s:
The changed values are only the parameters where there is agreement
among the different time controls, so in other words, the tunings are
telling us that changing these specific values to this specific
direction is good in all time controls, so there shouldn't be a high
risk of regressing at longer time controls.
Passed STC:
LLR: 2.97 (-2.94,2.94) <0.00,2.00>
Total: 39552 W: 10329 L: 9999 D: 19224
Ptnml(0-2): 156, 4592, 9989, 4844, 195
https://tests.stockfishchess.org/tests/view/661be9a0bd68065432a088c0
Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 56394 W: 14439 L: 14078 D: 27877
Ptnml(0-2): 30, 6152, 15480, 6497, 38
https://tests.stockfishchess.org/tests/view/661c746296961e72eb565406
closes https://github.com/official-stockfish/Stockfish/pull/5187
Bench: 1836777
Previously it was possible to also get the node counter after running a bench with perft, i.e.
`./stockfish bench 1 1 5 current perft`, caused by a small regression from the uci refactoring.
```
Nodes searched: 4865609
===========================
Total time (ms) : 18
Nodes searched : 4865609
Nodes/second : 270311611
````
closes https://github.com/official-stockfish/Stockfish/pull/5188
No functional change
We change the definition of "age" from "age of this position" to "age of this TT entry".
In this way, despite being on the same position, when we save into TT, we always prefer the new entry as compared to the old one.
Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 152256 W: 39597 L: 39110 D: 73549
Ptnml(0-2): 556, 17562, 39398, 18063, 549
https://tests.stockfishchess.org/tests/view/6620faee3fe04ce4cefbf215
Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 51564 W: 13242 L: 12895 D: 25427
Ptnml(0-2): 24, 5464, 14463, 5803, 28
https://tests.stockfishchess.org/tests/view/66231ab53fe04ce4cefc153ecloses#5184
Bench 1479416
It makes more sense to not (potentially) change the developers alsr entropy setting to make the test run through. This should be an active choice even if the test then might fail locally for them.
closes https://github.com/official-stockfish/Stockfish/pull/5182
No functional change
the recent refactoring has shown some limitations of our testing, hence we add a couple of more tests including:
* expected mate score
* expected mated score
* expected in TB win score
* expected in TB loss score
* expected info line output
* expected info line output (wdl)
closes https://github.com/official-stockfish/Stockfish/pull/5181
No functional change
Fix another case of 9032c6cbe7
* TB values can have a distance of 0, mainly when we are in a tb position but haven't found mate.
* Add a missing whitespace to UCIEngine::on_update_no_moves()
Closes https://github.com/official-stockfish/Stockfish/pull/5172
No functional change
Also fixes searchmoves.
Drop the need of a Position object in uci.cpp.
A side note, it is still required for the static functions,
but these should be moved to a different namespace/class
later on, since sf kinda relies on them.
closes https://github.com/official-stockfish/Stockfish/pull/5169
No functional change
The assignment (ss + 1)->excludedMove = Move::none() can be simplified away because when that line is reached, (ss + 1)->excludedMove is always already none. The only moment stack[x]->excludedMove is modified, is during singular search, but it is reset to none right after the singular search is finished.
closes https://github.com/official-stockfish/Stockfish/pull/5153
No functional change
The same functionality is available by using COMPCXX and having another variable which does the same is just confusing.
There was only one mention on Stockfish Wiki about this which has been changed to COMPCXX.
closes https://github.com/official-stockfish/Stockfish/pull/5154
No functional change
Part 2 of the Split UCI into UCIEngine and Engine refactor.
This creates function callbacks for search to use when an update should occur.
The benching in uci.cpp for example does this to extract the total nodes
searched.
No functional change
This is another refactor which aims to decouple uci from stockfish. A new engine
class manages all engine related logic and uci is a "small" wrapper around it.
In the future we should also try to remove the need for the Position object in
the uci and replace the options with an actual options struct instead of using a
map. Also convert the std::string's in the Info structs a string_view.
closes#5147
No functional change
In the last couple of months we sometimes saw duplicated prereleases uploaded to GitHub, possibly due to some racy behavior when concurrent jobs create a prerelease. This now creates an empty prerelease at the beginning of the CI and the binaries are later just attached to this one.
closes https://github.com/official-stockfish/Stockfish/pull/5144
No functional change
This PR proposes to change the parameter dependence of Stockfish's
internal WDL model from full move counter to material count. In addition
it ensures that an evaluation of 100 centipawns always corresponds to a
50% win probability at fishtest LTC, whereas for master this holds only
at move number 32. See also
https://github.com/official-stockfish/Stockfish/pull/4920 and the
discussion therein.
The new model was fitted based on about 340M positions extracted from
5.6M fishtest LTC games from the last three weeks, involving SF versions
from e67cc979fd (SF 16.1) to current
master.
The involved commands are for
[WDL_model](https://github.com/official-stockfish/WDL_model) are:
```
./updateWDL.sh --firstrev e67cc979fd
python scoreWDL.py updateWDL.json --plot save --pgnName update_material.png --momType "material" --momTarget 58 --materialMin 10 --modelFitting optimizeProbability
```
The anchor `58` for the material count value was chosen to be as close
as possible to the observed average material count of fishtest LTC games
at move 32 (`43`), while not changing the value of
`NormalizeToPawnValue` compared to the move-based WDL model by more than
1.
The patch only affects the displayed cp and wdl values.
closes https://github.com/official-stockfish/Stockfish/pull/5121
No functional change
Before, one always had to keep track of the bonus one assigns to a history to stop
the stats from overflowing. This is a quality of life improvement. Since this would often go unnoticed during benching.
Passed non-regression bounds:
https://tests.stockfishchess.org/tests/view/65ef2af40ec64f0526c44cbc
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 179232 W: 46513 L: 46450 D: 86269
Ptnml(0-2): 716, 20323, 47452, 20432, 693
closes https://github.com/official-stockfish/Stockfish/pull/5116
No functional change
Reported by @Torom over discord.
> dev build fails on Raspberry Pi 5 with clang
```
clang++ -o stockfish benchmark.o bitboard.o evaluate.o main.o misc.o movegen.o movepick.o position.o search.o thread.o timeman.o tt.o uci.o ucioption.o tune.o tbprobe.o nnue_misc.o half_ka_v2_hm.o network.o -fprofile-instr-generate -latomic -lpthread -Wall -Wcast-qual -fno-exceptions -std=c++17 -fprofile-instr-generate -pedantic -Wextra -Wshadow -Wmissing-prototypes -Wconditional-uninitialized -DUSE_PTHREADS -DNDEBUG -O3 -funroll-loops -DIS_64BIT -DUSE_POPCNT -DUSE_NEON=8 -march=armv8.2-a+dotprod -DUSE_NEON_DOTPROD -DGIT_SHA=627974c9 -DGIT_DATE=20240312 -DARCH=armv8-dotprod -flto=full
/tmp/lto-llvm-e9300e.o: in function `_GLOBAL__sub_I_network.cpp':
ld-temp.o:(.text.startup+0x704c): relocation truncated to fit: R_AARCH64_LDST64_ABS_LO12_NC against symbol `gEmbeddedNNUEBigEnd' defined in .rodata section in /tmp/lto-llvm-e9300e.o
/usr/bin/ld: ld-temp.o:(.text.startup+0x704c): warning: one possible cause of this error is that the symbol is being referenced in the indicated code as if it had a larger alignment than was declared where it was defined
ld-temp.o:(.text.startup+0x7068): relocation truncated to fit: R_AARCH64_LDST64_ABS_LO12_NC against symbol `gEmbeddedNNUESmallEnd' defined in .rodata section in /tmp/lto-llvm-e9300e.o
/usr/bin/ld: ld-temp.o:(.text.startup+0x7068): warning: one possible cause of this error is that the symbol is being referenced in the indicated code as if it had a larger alignment than was declared where it was defined
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make[2]: *** [Makefile:1051: stockfish] Error 1
make[2]: Leaving directory '/home/torsten/chess/Stockfish_master/src'
make[1]: *** [Makefile:1058: clang-profile-make] Error 2
make[1]: Leaving directory '/home/torsten/chess/Stockfish_master/src'
make: *** [Makefile:886: profile-build] Error 2
```
closes https://github.com/official-stockfish/Stockfish/pull/5106
No functional change
- fix naming convention for `workingDirectory`
- use type alias for `EvalFiles` everywhere
- move `ponderMode` into `LimitsType`
- move limits parsing into standalone static function
closes https://github.com/official-stockfish/Stockfish/pull/5098
No functional change
Fixes two issues with master for go mate x:
- when running go mate x in losing positions, master always goes to the
maximal depth, arguably against what the UCI protocol demands
- when running go mate x in winning positions with multiple
threads, master may return non-mate scores from the search (this issue
is present in stockfish since at least sf16) The issues are fixed by
(a) also checking if score is mate -x and by (b) only letting
mainthread stop the search for go mate x commands, and by not looking
for a best thread but using mainthread as per the default. Related:
niklasf/python-chess#1070
More diagnostics can be found here peregrineshahin#6 (comment)
closes https://github.com/official-stockfish/Stockfish/pull/5094
No functional change
Co-Authored-By: Robert Nürnberg <28635489+robertnurnberg@users.noreply.github.com>
This reduces the futiltiy_margin if our opponents last move was bad by
around ~1/3 when not improving and ~1/2.7 when improving, the idea being
to retroactively futility prune moves that were played, but turned out
to be bad. A bad move is being defined as their staticEval before their
move being lower as our staticEval now is. If the depth is 2 and we are
improving the opponent worsening flag is not set, in order to not risk
having a too low futility_margin, due to the fact that when these
conditions are met the futility_margin already drops quite low.
Passed STC:
https://tests.stockfishchess.org/tests/live_elo/65e3977bf2ef6c733362aae3
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 122432 W: 31884 L: 31436 D: 59112
Ptnml(0-2): 467, 14404, 31035, 14834, 476
Passed LTC:
https://tests.stockfishchess.org/tests/live_elo/65e47f40f2ef6c733362b6d2
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 421692 W: 106572 L: 105452 D: 209668
Ptnml(0-2): 216, 47217, 114865, 48327, 221
closes https://github.com/official-stockfish/Stockfish/pull/5092
Bench: 1565939
Created by retraining the previous main net `nn-b1a57edbea57.nnue` with:
- some of the same options as before:
- ranger21, more WDL skipping, 15% more loss when Q is too high
- removal of the huge 514G pre-interleaved binpack
- removal of SF-generated dfrc data (dfrc99-16tb7p-filt-v2.min.binpack)
- interleaving many binpacks at training time
- training with some bestmove capture positions where SEE < 0
- increased usage of torch.compile to speed up training by up to 40%
```yaml
experiment-name: 2560--S10-dfrc0-to-dec2023-skip-more-wdl-15p-more-loss-high-q-see-ge0-sk28
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
start-from-engine-test-net: True
early-fen-skipping: 28
training-dataset:
# similar, not the exact same as:
# https://github.com/official-stockfish/Stockfish/pull/4635
- /data/S5-5af/leela96.v2.min.binpack
- /data/S5-5af/test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
- /data/S5-5af/test77-2021-12-dec-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test78-2022-06-to-09-juntosep-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test79-2022-04-apr-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test79-2022-05-may-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2022-06-jun-16tb7p.v6-dd.min.unmin.binpack
- /data/S5-5af/test80-2022-07-jul-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2022-08-aug-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2022-09-sep-16tb7p.v6-dd.min.unmin.binpack
- /data/S5-5af/test80-2022-10-oct-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2022-11-nov-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
- /data/S5-5af/test80-2023-02-feb-16tb7p.v6-dd.min.binpack
- /data/S5-5af/test80-2023-03-mar-2tb7p.min.unmin.binpack
- /data/S5-5af/test80-2023-04-apr-2tb7p.binpack
- /data/S5-5af/test80-2023-05-may-2tb7p.min.dd.binpack
# https://github.com/official-stockfish/Stockfish/pull/4782
- /data/S6-1ee1aba5ed/test80-2023-06-jun-2tb7p.binpack
- /data/S6-1ee1aba5ed/test80-2023-07-jul-2tb7p.min.binpack
# https://github.com/official-stockfish/Stockfish/pull/4972
- /data/S8-baff1edbea57/test80-2023-08-aug-2tb7p.v6.min.binpack
- /data/S8-baff1edbea57/test80-2023-09-sep-2tb7p.binpack
- /data/S8-baff1edbea57/test80-2023-10-oct-2tb7p.binpack
# https://github.com/official-stockfish/Stockfish/pull/5056
- /data/S9-b1a57edbea57/test80-2023-11-nov-2tb7p.binpack
- /data/S9-b1a57edbea57/test80-2023-12-dec-2tb7p.binpack
num-epochs: 800
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```
This particular net was reached at epoch 759. Use of more torch.compile decorators
in nnue-pytorch model.py than in the previous main net training run sped up training
by up to 40% on Tesla gpus when using recent pytorch compiled with cuda 12:
https://github.com/linrock/nnue-tools/blob/7fb9831/Dockerfile
Skipping positions with bestmove captures where static exchange evaluation is >= 0
is based on the implementation from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 293 - only skip captures with see>=0
Positions with bestmove captures where score == 0 are always skipped for
compatibility with minimized binpacks, since the original minimizer sets
scores to 0 for slight improvements in compression.
The trainer branch used was:
https://github.com/linrock/nnue-pytorch/tree/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
Binpacks were renamed to be sorted chronologically by default when sorted by name.
The binpack data are otherwise the same as binpacks with similar names in the prior
naming convention.
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Passed STC:
https://tests.stockfishchess.org/tests/view/65e3ddd1f2ef6c733362ae5c
LLR: 2.92 (-2.94,2.94) <0.00,2.00>
Total: 149792 W: 39153 L: 38661 D: 71978
Ptnml(0-2): 675, 17586, 37905, 18032, 698
Passed LTC:
https://tests.stockfishchess.org/tests/view/65e4d91c416ecd92c162a69b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64416 W: 16517 L: 16135 D: 31764
Ptnml(0-2): 38, 7218, 17313, 7602, 37
closes https://github.com/official-stockfish/Stockfish/pull/5090
Bench: 1373183
Based on 130M positions from 2.1M games.
```
Look recursively in directory pgns for games from SPRT tests using books
matching "UHO_4060_v..epd|UHO_Lichess_4852_v1.epd" for SF revisions
between 8e75548f2a (from 2024-02-17
17:11:46 +0100) and HEAD (from 2024-02-17 17:13:07 +0100). Based on
127920843 positions from 2109240 games, NormalizeToPawnValue should
change from 345 to 356.
```
The patch only affects the UCI-reported cp and wdl values.
closes https://github.com/official-stockfish/Stockfish/pull/5070
No functional change
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%
```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28
training-dataset:
# https://github.com/official-stockfish/Stockfish/pull/4782
- /data/S6-514G-1ee1aba5ed.binpack
- /data/test80-aug2023-2tb7p.v6.min.binpack
- /data/test80-sep2023-2tb7p.binpack
- /data/test80-oct2023-2tb7p.binpack
- /data/test80-nov2023-2tb7p.binpack
- /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28
start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile
num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```
Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch
See recent main net PRs for more info on
- ranger21 and more WDL skipping: https://github.com/official-stockfish/Stockfish/pull/4942
- increasing loss when Q is too high: https://github.com/official-stockfish/Stockfish/pull/4972
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343
Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28
closes https://github.com/official-stockfish/Stockfish/pull/5056
Bench: 1351997
This introduces a form of node counting which can
be used to further tweak the usage of our search
time.
The current approach stops the search when almost
all nodes are searched on a single move.
The idea originally came from Koivisto, but the
implemention is a bit different, Koivisto scales
the optimal time by the nodes effort and then
determines if the search should be stopped.
We just scale down the `totalTime` and stop the
search if we exceed it and the effort is large
enough.
Passed STC:
https://tests.stockfishchess.org/tests/view/65c8e0661d8e83c78bfcd5ec
LLR: 2.97 (-2.94,2.94) <0.00,2.00>
Total: 88672 W: 22907 L: 22512 D: 43253
Ptnml(0-2): 310, 10163, 23041, 10466, 356
Passed LTC:
https://tests.stockfishchess.org/tests/view/65ca632b1d8e83c78bfcf554
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 170856 W: 42910 L: 42320 D: 85626
Ptnml(0-2): 104, 18337, 47960, 18919, 108
closes https://github.com/official-stockfish/Stockfish/pull/5053
Bench: 1198939
This patch does similar thing to how it's done for
qsearch - in case of fail high adjust result to
lower value. Difference is that it is done only
for non-pv nodes and it's depth dependent - so
lower depth entries will have bigger adjustment
and higher depth entries will have smaller
adjustment.
Passed STC:
https://tests.stockfishchess.org/tests/view/65c3c0cbc865510db0283b21
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 112032 W: 29142 L: 28705 D: 54185
Ptnml(0-2): 479, 13152, 28326, 13571, 488
Passed LTC:
https://tests.stockfishchess.org/tests/view/65c52e62c865510db02855d5
LLR: 2.96 (-2.94,2.94) <0.50,2.50>
Total: 132480 W: 33457 L: 32936 D: 66087
Ptnml(0-2): 67, 14697, 36222, 15156, 98
closes https://github.com/official-stockfish/Stockfish/pull/5047
Bench: 1168241
In both search and qsearch, there are instances
where we do unadjustedStaticEval = ss->staticEval
= eval/bestValue = tte->eval(), but immediately
after re-assign ss-static and eval/bestValue to
some new value, which makes the initial assignment
redundant.
closes https://github.com/official-stockfish/Stockfish/pull/5045
No functional change
- Update codeql to v3
- Switch from dev-drprasad to native github cli
- Update softprops/action-gh-release to node 20 commit
`thollander/actions-comment-pull-request` needs to
be bumped to node20 too, but the author hasnt done
so atm
closes https://github.com/official-stockfish/Stockfish/pull/5044
No functional change
Move divisor from capture scoring to good capture
check and sligthly increase it.
This has several effects:
- its a speedup because for quience and probcut
search the division now never happens. For main
search its delayed and can be avoided if a good
capture triggers a cutoff
- through the higher resolution of scores we have
a more granular sorting
STC: https://tests.stockfishchess.org/tests/view/65bf2a93c865510db027dc27
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 470016 W: 122150 L: 121173 D: 226693
Ptnml(0-2): 2133, 55705, 118374, 56644, 2152
LTC: https://tests.stockfishchess.org/tests/view/65c1d16dc865510db0281339
LLR: 2.96 (-2.94,2.94) <0.50,2.50>
Total: 98988 W: 25121 L: 24667 D: 49200
Ptnml(0-2): 77, 10998, 26884, 11464, 71
closes https://github.com/official-stockfish/Stockfish/pull/5036
Bench: 1233867
This refactors the CI workflows to group some
logic and makes sure that all (pre)release
binaries are actually tested.
The screenshot below shows the execution logic of
the reworked ci,
https://github.com/Disservin/Stockfish/actions/runs/7773581379.
You can also hover over the cards to see the
execution flow.
The `matrix.json` and `arm_matrix.json` define the
binaries which will be uploaded to GitHub.
Afterwards a matrix is created and each job
compiles a profile guided build for that arch and
uploads that as an artifact to GitHub. The
Binaries/ARM_Binaries workflow's are called when
the previous step has been completed, and uploads
all artifacts to the (pre)release.
This also fixes some indentations and renames the
workflows, see
https://github.com/official-stockfish/Stockfish/actions,
where every workflow is called `Stockfish` vs
https://github.com/Disservin/Stockfish/actions. It
also increases the parallel compilation used for
make from `-j2 to -j4`.
It now also prevents the prerelease action from
running on forks.
A test release can be viewed here
https://github.com/Disservin/Stockfish/releases.
closes https://github.com/official-stockfish/Stockfish/pull/5035
No functional change
This replaces the PvNode condition and tte Pv call previously with using
the precomputed ttPv, and also removes the multiplier of 2. This new
depth condition occurs with approximately equal frequency (47%) to the
old depth condition (measured when the other conditions in the if are
true), so non-linear scaling behaviour isn't expected.
Passed Non-Reg STC:
https://tests.stockfishchess.org/tests/view/65b0e132c865510db026da27
LLR: 2.97 (-2.94,2.94) <-1.75,0.25>
Total: 243232 W: 62432 L: 62437 D: 118363
Ptnml(0-2): 910, 28937, 61900, 28986, 883
Passed Non-Reg LTC:
https://tests.stockfishchess.org/tests/view/65b2053bc865510db026eea1
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 190596 W: 47666 L: 47618 D: 95312
Ptnml(0-2): 115, 21710, 51596, 21766, 111
closes https://github.com/official-stockfish/Stockfish/pull/5015
Bench: 1492957
This splits the logic of search and perft. Before, threads were started,
which then constructed a search object, which then started perft and
returned immediately. All of this is unnecessary, instead uci should
start perft right away.
closes https://github.com/official-stockfish/Stockfish/pull/5008
No functional change
Update the internal WDL model. After the dual net merge, the internal
evaluations have drifted upwards a bit. With this PR
`NormalizeToPawnValue` changes from `328` to `345`.
The new model was fitted based on about 200M positions extracted from
3.4M fishtest LTC games from the last two weeks, involving SF versions
from 6deb88728f to current master.
Apart from the WDL model parameter update, this PR implements the
following changes:
WDL Model:
- an incorrect 8-move shift in master's WDL model has been fixed
- the polynomials `p_a` and `p_b` are fitted over the move range [8, 120]
- the coefficients for `p_a` and `p_b` are optimized by maximizing the
probability of predicting the observed outcome (credits to @vondele)
SF code:
- for wdl values, move will be clamped to `max(8, min(120, move))`
- no longer clamp the internal eval to [-4000,4000]
- compute `NormalizeToPawnValue` with `round`, not `trunc`
The PR only affects displayed `cp` and `wdl` values.
closes https://github.com/official-stockfish/Stockfish/pull/5002
No functional change
The idea of this is to unroll the futility_margin calculation to allow
for the improving flag to have a greater effect on the futility margin.
The current factor is 1.5 instead of the previous 1 resulting in a
deduction of an extra margin/2 from futilit_margin if improving. The
chosen value was not tuned, meaning that there is room for tweaking it.
This patch is partially inspired by @Vizvezdenec, who, although quite
different in execution, tested another idea where the futility_margin is
lowered further when improving [1].
[1]: (first take) https://tests.stockfishchess.org/tests/view/65a56b1879aa8af82b97164b
Passed STC:
https://tests.stockfishchess.org/tests/live_elo/65a8bfc179aa8af82b974e3c
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 161152 W: 41321 L: 40816 D: 79015
Ptnml(0-2): 559, 19030, 40921, 19479, 587
Passed rebased LTC:
https://tests.stockfishchess.org/tests/live_elo/65a8b9ef79aa8af82b974dc0
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 96024 W: 24172 L: 23728 D: 48124
Ptnml(0-2): 56, 10598, 26275, 11012, 71
closes https://github.com/official-stockfish/Stockfish/pull/5000
Bench: 1281703
This addresses the issue where Stockfish may output non-proven checkmate
scores if the search is prematurely halted, either due to a time control
or node limit, before it explores other possibilities where the
checkmate score could have been delayed or refuted.
The fix also replaces staving off from proven mated scores in a
multithread environment making use of the threads instead of a negative
effect with multithreads (1t was better in proving mated in scores than
more threads).
Issue reported on mate tracker repo by and this PR is co-authored with
@robertnurnberg Special thanks to @AndyGrant for outlining that a fix is
eventually possible.
Passed Adj off SMP STC:
https://tests.stockfishchess.org/tests/view/65a125d779aa8af82b96c3eb
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 303256 W: 75823 L: 75892 D: 151541
Ptnml(0-2): 406, 35269, 80395, 35104, 454
Passed Adj off SMP LTC:
https://tests.stockfishchess.org/tests/view/65a37add79aa8af82b96f0f7
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 56056 W: 13951 L: 13770 D: 28335
Ptnml(0-2): 11, 5910, 16002, 6097, 8
Passed all tests in matetrack without any better mate for opponent found in 1t and multithreads.
Fixed bugs in https://github.com/official-stockfish/Stockfish/pull/4976
closes https://github.com/official-stockfish/Stockfish/pull/4990
Bench: 1308279
Co-Authored-By: Robert Nürnberg <28635489+robertnurnberg@users.noreply.github.com>
Also remove dead code, `rootSimpleEval` is no longer used since the introduction of dual net.
`iterBestValue` is also no longer used in evaluate and can be reduced to a local variable.
closes https://github.com/official-stockfish/Stockfish/pull/4979
No functional change
This aims to remove some of the annoying global structure which Stockfish has.
Overall there is no major elo regression to be expected.
Non regression SMP STC (paused, early version):
https://tests.stockfishchess.org/tests/view/65983d7979aa8af82b9608f1
LLR: 0.23 (-2.94,2.94) <-1.75,0.25>
Total: 76232 W: 19035 L: 19096 D: 38101
Ptnml(0-2): 92, 8735, 20515, 8690, 84
Non regression STC (early version):
https://tests.stockfishchess.org/tests/view/6595b3a479aa8af82b95da7f
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 185344 W: 47027 L: 46972 D: 91345
Ptnml(0-2): 571, 21285, 48943, 21264, 609
Non regression SMP STC:
https://tests.stockfishchess.org/tests/view/65a0715c79aa8af82b96b7e4
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 142936 W: 35761 L: 35662 D: 71513
Ptnml(0-2): 209, 16400, 38135, 16531, 193
These global structures/variables add hidden dependencies and allow data
to be mutable from where it shouldn't it be (i.e. options). They also
prevent Stockfish from internal selfplay, which would be a nice thing to
be able to do, i.e. instantiate two Stockfish instances and let them
play against each other. It will also allow us to make Stockfish a
library, which can be easier used on other platforms.
For consistency with the old search code, `thisThread` has been kept,
even though it is not strictly necessary anymore. This the first major
refactor of this kind (in recent time), and future changes are required,
to achieve the previously described goals. This includes cleaning up the
dependencies, transforming the network to be self contained and coming
up with a plan to deal with proper tablebase memory management (see
comments for more information on this).
The removal of these global structures has been discussed in parts with
Vondele and Sopel.
closes https://github.com/official-stockfish/Stockfish/pull/4968
No functional change
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL
skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best
dataset
- increasing training loss for positions where predicted win rates were
higher than estimated match results from training data position scores
```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28
training-dataset:
# https://github.com/official-stockfish/Stockfish/pull/4782
- /data/S6-1ee1aba5ed.binpack
- /data/test80-aug2023-2tb7p.v6.min.binpack
- /data/test80-sep2023-2tb7p.binpack
- /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28
start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q
num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Training loss was increased by 10% for positions where predicted win
rates were higher than suggested by the win rate model based on the
training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a
variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a
batch
Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464
Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29
closes https://github.com/official-stockfish/Stockfish/pull/4972
Bench: 1219824
Created by training an L1-128 net from scratch with a wider range of
evals in the training data and wld-fen-skipping disabled during
training. The differences in this training data compared to the first
dual nnue PR are:
- removal of all positions with 3 pieces
- when piece count >= 16, keep positions with simple eval above 750
- when piece count < 16, remove positions with simple eval above 3000
The asymmetric data filtering was meant to flatten the training data
piece count distribution, which was previously heavily skewed towards
positions with low piece counts.
Additionally, the simple eval range where the smallnet is used was
widened to cover more positions previously evaluated by the big net and
simple eval.
```yaml
experiment-name: 128--S1-hse-S7-v4-S3-v1-no-wld-skip
training-dataset:
- /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack
- /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack
- /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack
- /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-v4.binpack
- /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-v4.binpack
- /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-v4.binpack
- /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-v4.binpack
- /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-v4.binpack
- /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-v4.binpack
wld-fen-skipping: False
start-from-engine-test-net: False
nnue-pytorch-branch: linrock/nnue-pytorch/L1-128
engine-test-branch: linrock/Stockfish/L1-128-nolazy
engine-base-branch: linrock/Stockfish/L1-128
num-epochs: 500
start-lambda: 1.0
end-lambda: 1.0
```
Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py
Binpacks interleaved at training time with:
https://github.com/official-stockfish/nnue-pytorch/pull/259
FT weights permuted with 10k positions from fishpack32.binpack with:
https://github.com/official-stockfish/nnue-pytorch/pull/254
Data filtered for high simple eval positions (v4) with:
https://github.com/linrock/Stockfish/blob/b9c8440/src/tools/transform.cpp#L640-L675
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move of
L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data:
nn-epoch319.nnue : -241.7 +/- 3.2
Passed STC vs. 36db936:
https://tests.stockfishchess.org/tests/view/6576b3484d789acf40aabbfe
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 21920 W: 5680 L: 5381 D: 10859
Ptnml(0-2): 82, 2488, 5520, 2789, 81
Passed LTC vs. DualNNUE #4915:
https://tests.stockfishchess.org/tests/view/65775c034d789acf40aac7e3
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 147606 W: 36619 L: 36063 D: 74924
Ptnml(0-2): 98, 16591, 39891, 17103, 120
closes https://github.com/official-stockfish/Stockfish/pull/4919
Bench: 1438336
Add a `.git-blame-ignore-revs` file which can be used to skip specified
commits when blaming, this is useful to ignore formatting commits, like
clang-format #4790.
Github blame automatically supports this file format, as well as other
third party tools. Git itself needs to be told about the file name to
work, the following command will add it to the current git repo. `git
config blame.ignoreRevsFile .git-blame-ignore-revs`, alternatively one
has to specify it with every blame. `git blame --ignore-revs-file
.git-blame-ignore-revs search.cpp`
Supported since git 2.23.
closes https://github.com/official-stockfish/Stockfish/pull/4969
No functional change
Only Direction type is using two of the enable overload macros.
Aside from this, only two of the overloads are even being used.
Therefore, we can just define the needed overloads and remove the macros.
closes https://github.com/official-stockfish/Stockfish/pull/4966
No functional change.
Remove a redundant int cast in the calculation of fwdOut. The variable
OutputType is already defined as std::int32_t, which is an integer type, making
the cast unnecessary.
closes https://github.com/official-stockfish/Stockfish/pull/4961
No functional change
The primary rationale behind this lies in the fact that enums were not
originally designed to be employed in the manner we currently utilize them.
The Value enum was used like a type alias throughout the code and was often
misused. Furthermore, changing the underlying size of the enum to int16_t broke
everything, mostly because of the operator overloads for the Value enum, were
causing data to be truncated. Since Value is now a type alias, the operator
overloads are no longer required.
Passed Non-Regression STC:
https://tests.stockfishchess.org/tests/view/6593b8bb79aa8af82b95b401
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 235296 W: 59919 L: 59917 D: 115460
Ptnml(0-2): 743, 27085, 62054, 26959, 807
closes https://github.com/official-stockfish/Stockfish/pull/4960
No functional change
As some have noticed, a security alert has been complaining about a for loop in
our TB code for quite some now. Though it was never a real issue, so not of high
importance.
A few lines earlier the symlen vector is resized
`d->symlen.resize(number<uint16_t, LittleEndian>(data));` while this code seems
odd at first, it resizes the array to at most (2 << 16) - 1 elements, basically
making the infinite loop issue impossible to occur.
closes https://github.com/official-stockfish/Stockfish/pull/4953
No functional change
Idea from Caissa (https://github.com/Witek902/Caissa) chess engine.
With given pawn structure collect data with how often search result and by how
much it was better / worse than static evalution of position and use it to
adjust static evaluation of positions with given pawn structure. Details:
1. excludes positions with fail highs and moves producing it being a capture;
2. update value is function of not only difference between best value and static
evaluation but also is multiplied by linear function of depth;
3. maximum update value is maximum value of correction history divided by 2;
4. correction history itself is divided by 32 when applied so maximum value of
static evaluation adjustment is 32 internal units.
Passed STC:
https://tests.stockfishchess.org/tests/view/658fc7b679aa8af82b955cac
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 128672 W: 32757 L: 32299 D: 63616
Ptnml(0-2): 441, 15241, 32543, 15641, 470
Passed LTC:
https://tests.stockfishchess.org/tests/view/65903f6979aa8af82b9566f1
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 97422 W: 24626 L: 24178 D: 48618
Ptnml(0-2): 41, 10837, 26527, 11245, 61
closes https://github.com/official-stockfish/Stockfish/pull/4950
Bench: 1157852
For developing an Android GUI it can be helpful to use the Emulator on Windows.
Therefor an android_x86-64 library of Stockfish is needed. It would be nice to
compile it "out-of-the-box".
This change is originally suggested by Craftyawesome
closes https://github.com/official-stockfish/Stockfish/pull/4927
No functional change
This fixes futility pruning return values after recent tweaks, `eval` is
guaranteed to be less than the mate-in range but it can be as low value such
that the average between eval and beta can still fall in the mated-in range when
beta is as low in mated range. i.e. (eval + beta) / 2 being at mated-range which
can break mates.
Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/658f3eed79aa8af82b955139
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 117408 W: 29891 L: 29761 D: 57756
Ptnml(0-2): 386, 13355, 31120, 13429, 414
Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/658f8b7a79aa8af82b9557bd
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 60240 W: 14962 L: 14786 D: 30492
Ptnml(0-2): 22, 6257, 17390, 6425, 26
changes signature at higher depth e.g. `128 1 15`
closes https://github.com/official-stockfish/Stockfish/pull/4944
Bench: 1304666
Instead of returning strict fail soft fail high return value between value from
search and beta (somewhat by analogy to futility pruning and probcut).
This seems to be somewhat depth sensitive heuristic which performed much worse
at LTC while performing much better at STC if it is more aggressive, passed
version is the least aggressive one.
Passed STC:
https://tests.stockfishchess.org/tests/view/657b06414d789acf40ab1475
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 212352 W: 53900 L: 53315 D: 105137
Ptnml(0-2): 809, 25236, 53520, 25783, 828
Passed LTC:
https://tests.stockfishchess.org/tests/view/657ce36f393ac02e79120a7c
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 319362 W: 79541 L: 78630 D: 161191
Ptnml(0-2): 202, 35839, 86709, 36708, 223
closes https://github.com/official-stockfish/Stockfish/pull/4928
Bench: 974739
Sometimes if we count the reported PV length, it turns out to be longer than the
selective depth reported. This fixes this behavior by applying the selective
depth to qsearch since we do report PVs from it as well.
Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/656cf5b66980e15f69c7499d
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 223648 W: 56372 L: 56356 D: 110920
Ptnml(0-2): 710, 25580, 59231, 25590, 713
closes https://github.com/official-stockfish/Stockfish/pull/4903
No functional change
We use following line to clamp the search depth in some range:
Depth d = std::clamp(newDepth - r, 1, newDepth + 1);
Through negative extension its possible that the maximum value becomes smaller than the minimum value but then the behavior is undefined (see https://en.cppreference.com/w/cpp/algorithm/clamp). So replace this line with a safe implementation.
Remark:
We have in recent master already one line where up to 3 negative extensions are possible which could trigger this undefined behavior but this can only be happen for completed depth > 24 so its not discovered by our default bench. Recent negative extension tests by @fauzi shows then this undefined behavior with wrong bench numbers.
closes https://github.com/official-stockfish/Stockfish/pull/4877
No functional change
- updates the SDE action to v2.2
- removes the linux x86-32 builds, which were almost unused,
and the build process under SDE started failing recently,
possibly related to glibc update (The futex facility returned an unexpected error code.)
closes https://github.com/official-stockfish/Stockfish/pull/4875
No functional change
Huge credit goes also to candirufish,
as the idea was first tried by him, and then tuned by me at multiple phases.
Tweaking the futility pruning formula to be a bit more selective about when pruning is applied.
Adjust the value added to the static eval based on the bestValue relative to ss->staticEval. If bestValue is significantly lower, we add a larger value.
Passed STC:
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 37120 W: 9590 L: 9266 D: 18264
Ptnml(0-2): 130, 4301, 9385, 4603, 141
https://tests.stockfishchess.org/tests/view/6544cf90136acbc573523247
Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 49632 W: 12381 L: 12033 D: 25218
Ptnml(0-2): 30, 5429, 13549, 5779, 29
https://tests.stockfishchess.org/tests/view/65453bc1136acbc573523a3c
closes https://github.com/official-stockfish/Stockfish/pull/4861
bench: 1107118
Corrects some incorrect or outdated comments.
Credit is shared with yaneurao (see 38e830a#commitcomment-131131500) and locutus2
closes#4852
No functional change.
Original idea by Seer chess engine https://github.com/connormcmonigle/seer-nnue,
coding done by @Disservin, code refactoring done by @locutus2 to match the style
of other histories.
This patch introduces pawn structure based history, which assings moves values
based on last digits of pawn structure hash and piece type of moved piece and
landing square of the move. Idea is that good places for pieces are quite often
determined by pawn structure of position. Used in 3 different places
- sorting of quiet moves, sorting of quiet check evasions and in history based
pruning in search.
Passed STC:
https://tests.stockfishchess.org/tests/view/65391d08cc309ae83955dbaf
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 155488 W: 39408 L: 38913 D: 77167
Ptnml(0-2): 500, 18427, 39408, 18896, 513
Passed LTC:
https://tests.stockfishchess.org/tests/view/653a36a2cc309ae83955f181
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 70110 W: 17548 L: 17155 D: 35407
Ptnml(0-2): 33, 7859, 18889, 8230, 44
closes https://github.com/official-stockfish/Stockfish/pull/4849
Bench: 1257882
Co-Authored-By: Disservin <disservin.social@gmail.com>
Co-Authored-By: Stefan Geschwentner <locutus2@users.noreply.github.com>
- remove the blank line between the declaration of the function and it's
comment, leads to better IDE support when hovering over a function to see it's
description
- remove the unnecessary duplication of the function name in the functions
description
- slightly refactored code for lsb, msb in bitboard.h There are still a few
things we can be improved later on, move the description of a function where
it was declared (instead of implemented) and add descriptions to functions
which are behind macros ifdefs
closes https://github.com/official-stockfish/Stockfish/pull/4840
No functional change
Performance improvement for the shell commands in the Makefile.
By using expanded variables, the shell commands are only
evaluated once, instead of every time they are used.
closes https://github.com/official-stockfish/Stockfish/pull/4838
No functional change
This introduces clang-format to enforce a consistent code style for Stockfish.
Having a documented and consistent style across the code will make contributing easier
for new developers, and will make larger changes to the codebase easier to make.
To facilitate formatting, this PR includes a Makefile target (`make format`) to format the code,
this requires clang-format (version 17 currently) to be installed locally.
Installing clang-format is straightforward on most OS and distros
(e.g. with https://apt.llvm.org/, brew install clang-format, etc), as this is part of quite commonly
used suite of tools and compilers (llvm / clang).
Additionally, a CI action is present that will verify if the code requires formatting,
and comment on the PR as needed. Initially, correct formatting is not required, it will be
done by maintainers as part of the merge or in later commits, but obviously this is encouraged.
fixes https://github.com/official-stockfish/Stockfish/issues/3608
closes https://github.com/official-stockfish/Stockfish/pull/4790
Co-Authored-By: Joost VandeVondele <Joost.VandeVondele@gmail.com>
This patch is a simplification and a fix to dealing with null moves scores that returns proven mates or TB scores by preventing 'null move pruning' if the nullvalue is in that range.
Current solution downgrades nullValues on the non-PV node but the value can be used in a transposed PV-node to the same position afterwards (Triangulation), the later is prone to propagate a wrong score (96.05) to root that will not be refuted unless we search further.
Score of (96.05) can be obtained be two methods,
maxim static-eval returned on Pv update (mostly qSearch)
this downgrade (clamp) in NMP
and theoretically can happen with or without TBs but the second scenario is more dangerous than the first.
This fixes the reproducible case in very common scenarios with TBs as shown in the debugging at discord.
Fixes: #4699
Passed STC:
https://tests.stockfishchess.org/tests/view/64c1eca8dc56e1650abba6f9
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 670048 W: 171132 L: 171600 D: 327316
Ptnml(0-2): 2134, 75687, 179820, 75279, 2104
Passed LTC:
https://tests.stockfishchess.org/tests/view/64c5e130dc56e1650abc0438
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 92868 W: 23642 L: 23499 D: 45727
Ptnml(0-2): 52, 9509, 27171, 9648, 54
closes https://github.com/official-stockfish/Stockfish/pull/4715
Bench: 1327410
After removing classic evaluation VALUE_KNOWN_WIN is not anymore returned explicit evaluation. So remove and replace it with VALUE_TB_WIN_IN_MAX_PLY.
Measurement on my big bench (bench 16 1 16 pos1000.fen) verifies that at least with current net the calculated evaluation lies always in the open interval (-VALUE_KNOWN_WIN, VALUE_KNOWN_WIN).
So i consider this a non-functional change. But to be safe i tested this also at LTC as requested by Stephane Nicolet.
STC:
https://tests.stockfishchess.org/tests/view/64f9db40eaf01be8259a6ed5
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 455296 W: 115981 L: 116217 D: 223098
Ptnml(0-2): 1415, 50835, 123420, 50527, 1451
LTC:
https://tests.stockfishchess.org/tests/view/650bfd867ca0d3f7bbf25feb
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 35826 W: 9170 L: 8973 D: 17683
Ptnml(0-2): 12, 3523, 10645, 3722, 11
closes https://github.com/official-stockfish/Stockfish/pull/4792
Bench: 1603079
in the case of avx512 and vnni512 archs.
Up to 17% speedup, depending on the compiler, e.g.
```
AMD pro 7840u (zen4 phoenix apu 4nm)
bash bench_parallel.sh ./stockfish_avx512_gcc13 ./stockfish_avx512_pr_gcc13 20 10
sf_base = 1077737 +/- 8446 (95%)
sf_test = 1264268 +/- 8543 (95%)
diff = 186531 +/- 4280 (95%)
speedup = 17.308% +/- 0.397% (95%)
```
Prior to this patch, it appears gcc spills registers.
closes https://github.com/official-stockfish/Stockfish/pull/4796
No functional change
The commit adds a CI workflow that uses the included-what-you-use (IWYU)
tool to check for missing or superfluous includes in .cpp files and
their corresponding .h files. This means that some .h files (especially
in the nnue folder) are not checked yet.
The CI setup looks like this:
- We build IWYU from source to include some yet unreleased fixes.
This IWYU version targets LLVM 17. Thus, we get the latest release
candidate of LLVM 17 from LLVM's nightly packages.
- The Makefile now has an analyze target that just build the object
files (without linking)
- The CI uses the analyze target with the IWYU tool as compiler to
analyze the compiled .cpp file and its corresponding .h file.
- If IWYU suggests a change the build fails (-Xiwyu --error).
- To avoid false positives we use LLVM's libc++ as standard library
- We have a custom mappings file that adds some mappings that are
missing in IWYU's default mappings
We also had to add one IWYU pragma to prevent a false positive in
movegen.h.
https://github.com/official-stockfish/Stockfish/pull/4783
No functional change
deal with the general case
About a 8.6% speedup (for general arch)
Results for 200 tests for each version:
Base Test Diff
Mean 141741 153998 -12257
StDev 2990 3042 3742
p-value: 0.999
speedup: 0.086
closes https://github.com/official-stockfish/Stockfish/pull/4786
No functional change
The commit removes all uses of ICC's __INTEL_COMPILER macro and other
references to ICC. It also adds ICX info to the compiler command and
fixes two typos in Makefile's help output.
closes https://github.com/official-stockfish/Stockfish/pull/4769
No functional change
Created by retraining the master net on a dataset composed by:
- adding Leela data from T60 jul-dec 2020, T77 nov 2021, T80 jun-jul 2023
- deduplicating and unminimizing parts of the dataset before interleaving
Trained initially with max epoch 800, then increased near the end of training
twice. First to 960, then 1200. After training, post-processing involved:
- greedy permuting L1 weights with https://github.com/official-stockfish/Stockfish/pull/4620
- greedy 2- and 3- cycle permuting with https://github.com/official-stockfish/Stockfish/pull/4640
python3 easy_train.py \
--experiment-name 2048-retrain-S6-sk28 \
--training-dataset /data/S6.binpack \
--early-fen-skipping 28 \
--start-from-engine-test-net True \
--max_epoch 1200 \
--lr 4.375e-4 \
--gamma 0.995 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--tui False \
--seed $RANDOM \
--gpus 0
In the list of datasets below, periods in the filename represent the sequence of
steps applied to arrive at the particular binpack. For example:
test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
1. test77 dec2021 data rescored with 16 TB of syzygy tablebases during data conversion
2. filtered with csv_filter_v6_dd.py - v6 filtering and deduplication in one step
3. minimized with the original mar2023 implementation of `minimize_binpack` in
the tools branch
4. unminimized by removing all positions with score == 32002 (`VALUE_NONE`)
Binpacks were:
- filtered with: https://github.com/linrock/nnue-data
- unminimized with: https://github.com/linrock/Stockfish/tree/tools-unminify
- deduplicated with: https://github.com/linrock/Stockfish/tree/tools-dd
DATASETS=(
leela96-filt-v2.min.unminimized.binpack
dfrc99-16tb7p-eval-filt-v2.min.unminimized.binpack
# most of the 0dd1cebea57 v6-dd dataset (without test80-jul2022)
# https://github.com/official-stockfish/Stockfish/pull/4606
test60-novdec2021-12tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test78-jantomay2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test78-juntosep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test79-apr2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test79-may2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test80-jun2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test80-aug2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test80-sep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test80-oct2022-16tb7p.filter-v6-dd.min.binpack
test80-nov2022-16tb7p.filter-v6-dd.min.binpack
test80-jan2023-3of3-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
test80-feb2023-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
# older Leela data, recently converted
test60-octnovdec2020-2tb7p.min.unminimized.binpack
test60-julaugsep2020-2tb7p.min.binpack
test77-nov2021-2tb7p.min.dd.binpack
# newer Leela data
test80-mar2023-2tb7p.min.unminimized.binpack
test80-apr2023-2tb7p.filter-v6-sk16.min.unminimized.binpack
test80-may2023-2tb7p.min.dd.binpack
test80-jun2023-2tb7p.min.binpack
test80-jul2023-2tb7p.binpack
)
python3 interleave_binpacks.py ${DATASETS[@]} /data/S6.binpack
Training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch1059 : 2.7 +/- 1.6
Passed STC:
https://tests.stockfishchess.org/tests/view/64fc8d705dab775b5359db42
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 168352 W: 43216 L: 42704 D: 82432
Ptnml(0-2): 599, 19672, 43134, 20160, 611
Passed LTC:
https://tests.stockfishchess.org/tests/view/64fd44a75dab775b5359f065
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 154194 W: 39436 L: 38881 D: 75877
Ptnml(0-2): 78, 16577, 43238, 17120, 84
closes https://github.com/official-stockfish/Stockfish/pull/4782
Bench: 1603079
Add more static checks regarding the SIMD width match.
STC: https://tests.stockfishchess.org/tests/view/64f5c568a9bc5a78c669e70e
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 125216 W: 31756 L: 31636 D: 61824
Ptnml(0-2): 327, 13993, 33848, 14113, 327
Fixes a bug introduced in 2f2f45f, where with AVX-512 the weights and input to
the last layer were being read out of bounds. Now AVX-512 is only used for the
layers it can be used for. Additional static assertions have been added to
prevent more errors like this in the future.
closes https://github.com/official-stockfish/Stockfish/pull/4773
No functional change
This is a cleanup PR that prepares the automatic checking of missing or
superfluous #include directives via the include-what-you-use (IWYU) tool
on the CI. Unfortunately, IWYU proposes additional includes for
"namespace std" although we don't need them.
To avoid the problem, the commit removes all "using namespace std"
statements from the code and directly uses the std:: prefix instead.
Alternatively, we could add specific usings (e.g. "using std::string")
foreach used type. Also, a mix of both approaches would be possible.
I decided for the prefix approach because most of the files were already
using the std:: prefixes despite the "using namespace std".
closes https://github.com/official-stockfish/Stockfish/pull/4772
No functional change
This patch implements the pure materialistic evaluation called simple_eval()
to gain a speed-up during Stockfish search.
We use the so-called lazy evaluation trick: replace the accurate but slow
NNUE network evaluation by the super-fast simple_eval() if the position
seems to be already won (high material advantage). To guard against some
of the most obvious blunders introduced by this idea, this patch uses the
following features which will raise the lazy evaluation threshold in some
situations:
- avoid lazy evals on shuffling branches in the search tree
- avoid lazy evals if the position at root already has a material imbalance
- avoid lazy evals if the search value at root is already winning/losing.
Moreover, we add a small random noise to the simple_eval() term. This idea
(stochastic mobility in the minimax tree) was worth about 200 Elo in the pure
simple_eval() player on Lichess.
Overall, the current implementation in this patch evaluates about 2% of the
leaves in the search tree lazily.
--------------------------------------------
STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 60352 W: 15585 L: 15234 D: 29533
Ptnml(0-2): 216, 6906, 15578, 7263, 213
https://tests.stockfishchess.org/tests/view/64f1d9bcbd9967ffae366209
LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 35106 W: 8990 L: 8678 D: 17438
Ptnml(0-2): 14, 3668, 9887, 3960, 24
https://tests.stockfishchess.org/tests/view/64f25204f5b0c54e3f04c0e7
verification run at VLTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 74362 W: 19088 L: 18716 D: 36558
Ptnml(0-2): 6, 7226, 22348, 7592, 9
https://tests.stockfishchess.org/tests/view/64f2ecdbf5b0c54e3f04d3ae
All three tests above were run with adjudication off, we also verified that
there was no regression on matetracker (thanks Disservin!).
----------------------------------------------
closes https://github.com/official-stockfish/Stockfish/pull/4771
Bench: 1393714
To enhance code clarity and prevent potential confusion with the
'r' variable assigned to reduction later in the code, this pull
request renames it to 'reductionScale' when we use the same name
in the reduction() function.
Using distinct variable names for separate functions improves code
readability and maintainability.
closes https://github.com/official-stockfish/Stockfish/pull/4765
No functional change
The UCI protocol is rather technical and has little value in our README. Instead
it should be explained in our wiki. "Contributing" is moved above "Compiling
Stockfish" to make it more prominent.
Also move the CONTRIBUTING.md into the root directory and include it in the
distributed artifacts/releases.
closes https://github.com/official-stockfish/Stockfish/pull/4766
No functional change
This patch decays a little the evaluation (up to a few percent) for
positions which have a large complexity measure (material imbalance,
positional compensations, etc).
This may have nice consequences on the playing style, as it modifies
the search differently for attack and defense, both effects being
desirable:
- to see the effect on positions when Stockfish is defending, let us
suppose for instance that the side to move is Stockfish and the nnue
evaluation on the principal variation is -100 : this patch will decay
positions with an evaluation of -103 (say) to the same level, provided
they have huge material imbalance or huge positional compensation.
In other words, chaotic positions with an evaluation of -103 are now
comparable in our search tree to stable positions with an evaluation
of -100, and chaotic positions with an evaluation of -102 are now
preferred to stable positions with an evaluation of -100.
- the effect on positions when Stockfish is attacking is the opposite.
Let us suppose for instance that the side to move is Stockfish and the
nnue evaluation on the principal variation is +100 : this patch will
decay the evaluation to +97 if the positions on the principal variation
have huge material imbalance or huge positional compensation. In other
words, stable positions with an evaluation of +97 are now comparable
in our search tree to chaotic positions with an evaluation of +100,
and stable positions with an evaluation of +98 are now preferred to
chaotic positions with an evaluation of +100.
So the effect of this small change of evaluation on the playing style
is that Stockfish should now play a little bit more turbulent when
defending, and choose slightly simpler lines when attacking.
passed STC:
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 268448 W: 68713 L: 68055 D: 131680
Ptnml(0-2): 856, 31514, 68943, 31938, 973
https://tests.stockfishchess.org/tests/view/64e252bb99700912526653ed
passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 141060 W: 36066 L: 35537 D: 69457
Ptnml(0-2): 71, 15179, 39522, 15666, 92
https://tests.stockfishchess.org/tests/view/64e4447a9009777747553725
closes https://github.com/official-stockfish/Stockfish/pull/4762
Bench: 1426295
Increase reduction on retrying a move we just retreated that falls in a repetition:
if current move can be the same move from previous previous turn then we retreated
that move on the previous turn, this patch increases reduction if retrying that move
results in a repetition.
How to continue from there? Maybe we some variants of this idea could bring Elo too
(only testing the destination square, or triangulations, etc.)
Passed STC:
https://tests.stockfishchess.org/tests/view/64e1aede883cbb7cbd9ad976
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 424000 W: 108675 L: 107809 D: 207516
Ptnml(0-2): 1296, 47350, 113896, 48108, 1350
Passed LTC:
https://tests.stockfishchess.org/tests/view/64e32d629970091252666872
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 89682 W: 22976 L: 22569 D: 44137
Ptnml(0-2): 39, 8843, 26675, 9240, 44
closes https://github.com/official-stockfish/Stockfish/pull/4757
bench: 1574347
Squared numbers are never negative, so barring any wraparound there
is no need to clamp to 0. From reading the code, there's no obvious
way to get wraparound, so the entire operation can be simplified
away. Updated original truncated code comments to be sensible.
Verified by running ./stockfish bench 128 1 24 and by the following test:
STC: https://tests.stockfishchess.org/tests/view/64da4db95b17f7c21c0eabe7
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 60224 W: 15425 L: 15236 D: 29563
Ptnml(0-2): 195, 6576, 16382, 6763, 196
closes https://github.com/official-stockfish/Stockfish/pull/4751
No functional change
Current master fails to compile for ARMv8 on Raspi cause gcc (version 10.2.1)
does not like to cast between signed and unsigned vector types. This patch
fixes it by using unsigned vector pointer for ARM to avoid implicite cast.
closes https://github.com/official-stockfish/Stockfish/pull/4752
No functional change
a) Add further tests to CI to cover most features. This uncovered a potential race
in case setoption was sent between two searches. As the UCI protocol requires
this sent to be went the engine is not searching, setoption now ensures that
this is the case.
b) Remove some unused code
closes https://github.com/official-stockfish/Stockfish/pull/4730
No functional change
If an incorrect network file is present at the start of the compilation stage, the
Makefile script now correctly removes it before trying to download a clean version.
closes https://github.com/official-stockfish/Stockfish/pull/4726
No functional change
Based on vondele's deletepsqt branch:
https://github.com/vondele/Stockfish/commit/369f5b051
This huge simplification uses a weighted material differences instead of
the positional piece square tables (psqt) in the semi-classical complexity
calculation. Tuned weights using spsa at 45+0.45 with:
int pawnMult = 100;
int knightMult = 325;
int bishopMult = 350;
int rookMult = 500;
int queenMult = 900;
TUNE(SetRange(0, 200), pawnMult);
TUNE(SetRange(0, 650), knightMult);
TUNE(SetRange(0, 700), bishopMult);
TUNE(SetRange(200, 800), rookMult);
TUNE(SetRange(600, 1200), queenMult);
The values obtained via this tuning session were for a model where
the psqt replacement formula was always from the point of view of White,
even if the side to move was Black. We re-used the same values for an
implementation with a psqt replacement from the point of view of the side
to move, testing the result both on our standard book on positions with
a strong White bias, and an alternate book with positions with a strong
Black bias.
We note that with the patch the last use of the venerable "Score" type
disappears in Stockfish codebase (the Score type was used in classical
evaluation to get a tampered eval interpolating values smoothly from the
early midgame stage to the endgame stage). We leave it to another commit
to clean all occurrences of Score in the code and the comments.
-------
Passed non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 142542 W: 36264 L: 36168 D: 70110
Ptnml(0-2): 76, 15578, 39856, 15696, 65
https://tests.stockfishchess.org/tests/view/64c8cb495b17f7c21c0cf9f8
Passed non-regression LTC (with a book with Black bias):
https://tests.stockfishchess.org/tests/view/64c8f9295b17f7c21c0cfdaf
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 494814 W: 125565 L: 125827 D: 243422
Ptnml(0-2): 244, 53926, 139346, 53630, 261
------
closes https://github.com/official-stockfish/Stockfish/pull/4713
Bench: 1655985
Also make two get_weight_index() static methods constexpr, for
consistency with the other static get_hash_value() method right above.
Tested for speed by user Torom (thanks).
closes https://github.com/official-stockfish/Stockfish/pull/4708
No functional change
This patch changes the frequency with which the time is checked, changing
frequency from every 1024 counted nodes to every 512 counted nodes. The
master value was tuned for the old classical eval, the patch takes the
roughly 2x slowdown in nps with SFNNUEv7 into account. This could reduce
a bit the losses on time on fishtest, but they are probably unrelated.
passed STC:
https://tests.stockfishchess.org/tests/view/64bb8ae5dc56e1650abb1b11
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 76576 W: 19677 L: 19501 D: 37398
Ptnml(0-2): 274, 8592, 20396, 8736, 290
closes https://github.com/official-stockfish/Stockfish/pull/4704
No functional change
allows for building x86-64-avx2 and x86-64-bmi2 binaries for mac
install coreutils
show hardware capabilities as seen by the compilers
move some tests from sse41 to avx2 as platforms support it
closes https://github.com/official-stockfish/Stockfish/pull/4692
No functional change
Explicitly describe the architecture as deprecated,
it remains available as its current alias x86-64-sse41-popcnt
CPUs that support just this instruction set are now years old,
any few years old Intel or AMD CPU supports x86-64-avx2. However,
naming things 'modern' doesn't age well, so instead use explicit names.
Adjust CI accordingly. Wiki, fishtest, downloader done as well.
closes https://github.com/official-stockfish/Stockfish/pull/4691
No functional change.
use a fixed compiler on Linux and Windows (right now gcc 11).
build avxvnni on Windows (Linux needs updated core utils)
build x86-32 on Linux (Windows needs other mingw)
fix a Makefile issue where a failed PGOBENCH would not stop the build
reuse the WINE_PATH for SDE as we do for QEMU
use WINE_PATH variable also for the signature
verify the bench for each of the binaries
do not build x86-64-avx2 on macos
closes https://github.com/official-stockfish/Stockfish/pull/4682
No functional change
use intel's Software Development Emulator (SDE) in the actions that build the binaries.
This allows for building on Windows and Linux binaries for
- x86-64-avx512
- x86-64-vnni256
- x86-64-vnni512
(x86-64-avxvnni needs more recent gcc in the actions)
also build x86-64-avx2 on macos.
closes https://github.com/official-stockfish/Stockfish/pull/4679
No functional change
since the introduction of NNUE (first released with Stockfish 12), we
have maintained the classical evaluation as part of SF in frozen form.
The idea that this code could lead to further inputs to the NN or
search did not materialize. Now, after five releases, this PR removes
the classical evaluation from SF. Even though this evaluation is
probably the best of its class, it has become unimportant for the
engine's strength, and there is little need to maintain this
code (roughly 25% of SF) going forward, or to expend resources on
trying to improve its integration in the NNUE eval.
Indeed, it had still a very limited use in the current SF, namely
for the evaluation of positions that are nearly decided based on
material difference, where the speed of the classical evaluation
outweights its inaccuracies. This impact on strength is small,
roughly 2Elo, and probably decreasing in importance as the TC grows.
Potentially, removal of this code could lead to the development of
techniques to have faster, but less accurate NN evaluation,
for certain positions.
STC
https://tests.stockfishchess.org/tests/view/64a320173ee09aa549c52157
Elo: -2.35 ± 1.1 (95%) LOS: 0.0%
Total: 100000 W: 24916 L: 25592 D: 49492
Ptnml(0-2): 287, 12123, 25841, 11477, 272
nElo: -4.62 ± 2.2 (95%) PairsRatio: 0.95
LTC
https://tests.stockfishchess.org/tests/view/64a320293ee09aa549c5215b
Elo: -1.74 ± 1.0 (95%) LOS: 0.0%
Total: 100000 W: 25010 L: 25512 D: 49478
Ptnml(0-2): 44, 11069, 28270, 10579, 38
nElo: -3.72 ± 2.2 (95%) PairsRatio: 0.96
VLTC SMP
https://tests.stockfishchess.org/tests/view/64a3207c3ee09aa549c52168
Elo: -1.70 ± 0.9 (95%) LOS: 0.0%
Total: 100000 W: 25673 L: 26162 D: 48165
Ptnml(0-2): 8, 9455, 31569, 8954, 14
nElo: -3.95 ± 2.2 (95%) PairsRatio: 0.95
closes https://github.com/official-stockfish/Stockfish/pull/4674
Bench: 1444646
- loop through the commits starting from the latest one
- read the bench value from the last match, if any, of the template
in the commit body text
closes https://github.com/official-stockfish/Stockfish/pull/4627
No functional change
Current logic can apply Null move pruning
on a dead-lost position returning an unproven loss
(i.e. in TB loss score or mated in losing score) on nonPv nodes.
on a default bench, this can be observed by adding this debugging line:
```
if (nullValue >= beta)
{
// Do not return unproven mate or TB scores
nullValue = std::min(nullValue, VALUE_TB_WIN_IN_MAX_PLY-1);
dbg_hit_on(nullValue <= VALUE_TB_LOSS_IN_MAX_PLY); // Hit #0: Total 73983 Hits 1 Hit Rate (%) 0.00135166
if (thisThread->nmpMinPly || depth < 14)
return nullValue;
```
This fixes this very rare issue (happens at ~0.00135166% of the time) by
eliminating the need to try Null Move Pruning with dead-lost positions
and leaving it to be determined by a normal searching flow.
The previous try to fix was not as safe enough because it was capping
the returned value to (out of TB range) thus reviving the dead-lost position
based on an artificial clamp (i.e. the in TB score/mate score can be lost on that nonPv node):
https://tests.stockfishchess.org/tests/view/649756d5dc7002ce609cd794
Final fix:
Passed STC:
https://tests.stockfishchess.org/tests/view/649a5446dc7002ce609d1049
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 577280 W: 153613 L: 153965 D: 269702
Ptnml(0-2): 1320, 60594, 165190, 60190, 1346
Passed LTC:
https://tests.stockfishchess.org/tests/view/649cd048dc7002ce609d4801
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 246432 W: 66769 L: 66778 D: 112885
Ptnml(0-2): 83, 22105, 78847, 22100, 81
closes https://github.com/official-stockfish/Stockfish/pull/4649
Bench: 2425978
using github actions, create a prerelease for the latest commit to master.
As such a development version will be available on github, in addition to the latest release.
closes https://github.com/official-stockfish/Stockfish/pull/4622
No functional change
Implemented LEB128 (de)compression for the feature transformer.
Reduces embedded network size from 70 MiB to 39 Mib.
The new nn-78bacfcee510.nnue corresponds to the master net compressed.
closes https://github.com/official-stockfish/Stockfish/pull/4617
No functional change
Use block sparse input for the first fully connected layer on architectures with at least SSSE3.
Depending on the CPU architecture, this yields a speedup of up to 10%, e.g.
```
Result of 100 runs of 'bench 16 1 13 default depth NNUE'
base (...ockfish-base) = 959345 +/- 7477
test (...ckfish-patch) = 1054340 +/- 9640
diff = +94995 +/- 3999
speedup = +0.0990
P(speedup > 0) = 1.0000
CPU: 8 x AMD Ryzen 7 5700U with Radeon Graphics
Hyperthreading: on
```
Passed STC:
https://tests.stockfishchess.org/tests/view/6485aa0965ffe077ca12409c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 8864 W: 2479 L: 2223 D: 4162
Ptnml(0-2): 13, 829, 2504, 1061, 25
This commit includes a net with reordered weights, to increase the likelihood of block sparse inputs,
but otherwise equivalent to the previous master net (nn-ea57bea57e32.nnue).
Activation data collected with https://github.com/AndrovT/Stockfish/tree/log-activations, running bench 16 1 13 varied_1000.epd depth NNUE on this data. Net parameters permuted with https://gist.github.com/AndrovT/9e3fbaebb7082734dc84d27e02094cb3.
closes https://github.com/official-stockfish/Stockfish/pull/4612
No functional change
Created by retraining an earlier epoch (ep659) of the experiment that led to the first SFNNv6 net:
- First retrained on the nn-0dd1cebea573 dataset
- Then retrained with skip 20 on a smaller dataset containing unfiltered Leela data
- And then retrained again with skip 27 on the nn-0dd1cebea573 dataset
The equivalent 7-step training sequence from scratch that led here was:
1. max-epoch 400, lambda 1.0, constant LR 9.75e-4, T79T77-filter-v6-dd.min.binpack
ep379 chosen for retraining in step2
2. max-epoch 800, end-lambda 0.75, T60T70wIsRightFarseerT60T74T75T76.binpack
ep679 chosen for retraining in step3
3. max-epoch 800, end-lambda 0.75, skip 28, nn-e1fb1ade4432 dataset
ep799 chosen for retraining in step4
4. max-epoch 800, end-lambda 0.7, skip 28, nn-e1fb1ade4432 dataset
ep759 became nn-8d69132723e2.nnue (first SFNNv6 net)
ep659 chosen for retraining in step5
5. max-epoch 800, end-lambda 0.7, skip 28, nn-0dd1cebea573 dataset
ep759 chosen for retraining in step6
6. max-epoch 800, end-lambda 0.7, skip 20, leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack
ep639 chosen for retraining in step7
7. max-epoch 800, end-lambda 0.7, skip 27, nn-0dd1cebea573 dataset
ep619 became nn-ea57bea57e32.nnue
For the last retraining (step7):
python3 easy_train.py
--experiment-name L1-1536-Re6-masterShuffled-ep639-sk27-Re5-leela-dfrc-v2-T77toT80small-Re4-masterShuffled-ep659-Re3-sameAs-Re2-leela96-dfrc99-16t-v2-T60novdecT80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-Re1-LeelaFarseer-new-T77T79 \
--training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
--early-fen-skipping 27 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--max_epoch 800 \
--start-from-engine-test-net False \
--start-from-model /data/L1-1536-Re5-leela-dfrc-v2-T77toT80small-epoch639.nnue \
--lr 4.375e-4 \
--gamma 0.995 \
--tui False \
--seed $RANDOM \
--gpus "0,"
For preparing the step6 leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack dataset:
python3 interleave_binpacks.py \
leela96-filt-v2.binpack \
dfrc99-16tb7p-eval-filt-v2.binpack \
test77-dec2021-16tb7p.no-db.min-mar2023.binpack \
test78-janfeb2022-16tb7p.no-db.min-mar2023.binpack \
test79-apr2022-16tb7p-filter-v6-dd.binpack \
test80-apr2022-16tb7p.no-db.min-mar2023.binpack \
/data/leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack
The unfiltered Leela data used for the step6 dataset can be found at:
https://robotmoon.com/nnue-training-data
Local elo at 25k nodes per move:
nn-epoch619.nnue : 2.3 +/- 1.9
Passed STC:
https://tests.stockfishchess.org/tests/view/6480d43c6e6ce8d9fc6d7cc8
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 40992 W: 11017 L: 10706 D: 19269
Ptnml(0-2): 113, 4400, 11170, 4689, 124
Passed LTC:
https://tests.stockfishchess.org/tests/view/648119ac6e6ce8d9fc6d8208
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 129174 W: 35059 L: 34579 D: 59536
Ptnml(0-2): 66, 12548, 38868, 13050, 55
closes https://github.com/official-stockfish/Stockfish/pull/4611
bench: 2370027
Created by retraining an earlier epoch of the experiment leading to the first SFNNv6 net
on a more-randomized version of the nn-e1fb1ade4432.nnue dataset mixed with unfiltered
T80 apr2023 data. Trained using early-fen-skipping 28 and max-epoch 960.
The trainer settings and epochs used in the 5-step training sequence leading here were:
1. train from scratch for 400 epochs, lambda 1.0, constant LR 9.75e-4, T79T77-filter-v6-dd.min.binpack
2. retrain ep379, max-epoch 800, end-lambda 0.75, T60T70wIsRightFarseerT60T74T75T76.binpack
3. retrain ep679, max-epoch 800, end-lambda 0.75, skip 28, nn-e1fb1ade4432 dataset
4. retrain ep799, max-epoch 800, end-lambda 0.7, skip 28, nn-e1fb1ade4432 dataset
5. retrain ep439, max-epoch 960, end-lambda 0.7, skip 28, shuffled nn-e1fb1ade4432 + T80 apr2023
This net was epoch 559 of the final (step 5) retraining:
```bash
python3 easy_train.py \
--experiment-name L1-1536-Re4-leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr-shuffled-sk28 \
--training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
--early-fen-skipping 28 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--max_epoch 960 \
--start-from-engine-test-net False \
--start-from-model /data/L1-1536-Re3-nn-epoch439.nnue \
--engine-test-branch linrock/Stockfish/L1-1536 \
--lr 4.375e-4 \
--gamma 0.995 \
--tui False \
--seed $RANDOM \
--gpus "0,"
```
During data preparation, most binpacks were unminimized by removing positions with
score 32002 (`VALUE_NONE`). This makes the tradeoff of increasing dataset filesize
on disk to increase the randomness of positions in interleaved datasets.
The code used for unminimizing is at:
https://github.com/linrock/Stockfish/tree/tools-unminify
For preparing the dataset used in this experiment:
```bash
python3 interleave_binpacks.py \
leela96-filt-v2.binpack \
dfrc99-16tb7p-eval-filt-v2.binpack \
filt-v6-dd-min/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd-min/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd-min/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd-min/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd-min/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd-min/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd-min/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
test80-apr2023-2tb7p.binpack \
/data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack
```
T80 apr2023 data was converted using lc0-rescorer with ~2tb of tablebases and can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move vs. nn-e1fb1ade4432.nnue (L1 size 1024):
nn-epoch559.nnue : 25.7 +/- 1.6
Passed STC:
https://tests.stockfishchess.org/tests/view/647cd3b87cf638f0f53f9cbb
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 59200 W: 16000 L: 15660 D: 27540
Ptnml(0-2): 159, 6488, 15996, 6768, 189
Passed LTC:
https://tests.stockfishchess.org/tests/view/647d58de726f6b400e4085d8
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 58800 W: 16002 L: 15657 D: 27141
Ptnml(0-2): 44, 5607, 17748, 5962, 39
closes https://github.com/official-stockfish/Stockfish/pull/4606
bench 2141197
Created by training a new net from scratch with L1 size increased from 1024 to 1536.
Thanks to Vizvezdenec for the idea of exploring larger net sizes after recent
training data improvements.
A new net was first trained with lambda 1.0 and constant LR 8.75e-4. Then a strong net
from a later epoch in the training run was chosen for retraining with start-lambda 1.0
and initial LR 4.375e-4 decaying with gamma 0.995. Retraining was performed a total of
3 times, for this 4-step process:
1. 400 epochs, lambda 1.0 on filtered T77+T79 v6 deduplicated data
2. 800 epochs, end-lambda 0.75 on T60T70wIsRightFarseerT60T74T75T76.binpack
3. 800 epochs, end-lambda 0.75 and early-fen-skipping 28 on the master dataset
4. 800 epochs, end-lambda 0.7 and early-fen-skipping 28 on the master dataset
In the training sequence that reached the new nn-8d69132723e2.nnue net,
the epochs used for the 3x retraining runs were:
1. epoch 379 trained on T77T79-filter-v6-dd.min.binpack
2. epoch 679 trained on T60T70wIsRightFarseerT60T74T75T76.binpack
3. epoch 799 trained on the master dataset
For training from scratch:
python3 easy_train.py \
--experiment-name new-L1-1536-T77T79-filter-v6dd \
--training-dataset /data/T77T79-filter-v6-dd.min.binpack \
--max_epoch 400 \
--lambda 1.0 \
--start-from-engine-test-net False \
--engine-test-branch linrock/Stockfish/L1-1536 \
--nnue-pytorch-branch linrock/Stockfish/misc-fixes-L1-1536 \
--tui False \
--gpus "0," \
--seed $RANDOM
Retraining commands were similar to each other. For the 3rd retraining run:
python3 easy_train.py \
--experiment-name L1-1536-T77T79-v6dd-Re1-LeelaFarseer-Re2-masterDataset-Re3-sameData \
--training-dataset /data/leela96-dfrc99-v2-T60novdecT80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack \
--early-fen-skipping 28 \
--max_epoch 800 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--lr 4.375e-4 \
--gamma 0.995 \
--start-from-engine-test-net False \
--start-from-model /data/L1-1536-T77T79-v6dd-Re1-LeelaFarseer-Re2-masterDataset-nn-epoch799.nnue \
--engine-test-branch linrock/Stockfish/L1-1536 \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
--tui False \
--gpus "0," \
--seed $RANDOM
The T77+T79 data used is a subset of the master dataset available at:
https://robotmoon.com/nnue-training-data/
T60T70wIsRightFarseerT60T74T75T76.binpack is available at:
https://drive.google.com/drive/folders/1S9-ZiQa_3ApmjBtl2e8SyHxj4zG4V8gG
Local elo at 25k nodes per move vs. nn-e1fb1ade4432.nnue (L1 size 1024):
nn-epoch759.nnue : 26.9 +/- 1.6
Failed STC
https://tests.stockfishchess.org/tests/view/64742485d29264e4cfa75f97
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 13728 W: 3588 L: 3829 D: 6311
Ptnml(0-2): 71, 1661, 3610, 1482, 40
Failing LTC
https://tests.stockfishchess.org/tests/view/64752d7c4a36543c4c9f3618
LLR: -1.91 (-2.94,2.94) <0.50,2.50>
Total: 35424 W: 9522 L: 9603 D: 16299
Ptnml(0-2): 24, 3579, 10585, 3502, 22
Passed VLTC 180+1.8
https://tests.stockfishchess.org/tests/view/64752df04a36543c4c9f3638
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 47616 W: 13174 L: 12863 D: 21579
Ptnml(0-2): 13, 4261, 14952, 4566, 16
Passed VLTC SMP 60+0.6 th 8
https://tests.stockfishchess.org/tests/view/647446ced29264e4cfa761e5
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 19942 W: 5694 L: 5451 D: 8797
Ptnml(0-2): 6, 1504, 6707, 1749, 5
closes https://github.com/official-stockfish/Stockfish/pull/4593
bench 2222567
This change removes one of the constants in the calculation of optimism. It also changes the 2 constants used with the scale value so that they are independent, instead of applying a constant to the scale and then adjusting it again when it is applied to the optimism. This might make the tuning of these constants cleaner and more reliable in the future.
STC 10+0.1 (accidentally run as an Elo gainer:
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 154080 W: 41119 L: 40651 D: 72310
Ptnml(0-2): 375, 16840, 42190, 17212, 423
https://tests.stockfishchess.org/tests/live_elo/64653eabf3b1a4e86c317f77
LTC 60+0.6:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 217434 W: 58382 L: 58363 D: 100689
Ptnml(0-2): 66, 21075, 66419, 21088, 69
https://tests.stockfishchess.org/tests/live_elo/6465d077f3b1a4e86c318d6c
closes https://github.com/official-stockfish/Stockfish/pull/4576
bench: 3190961
Created by retraining nn-dabb1ed23026.nnue with a dataset composed of:
* The previous best dataset (nn-1ceb1a57d117.nnue dataset)
* Adding de-duplicated T80 data from feb2023 and the last 10 days of jan2023, filtered with v6-dd
Initially trained with the same options as the recent master net (nn-1ceb1a57d117.nnue).
Around epoch 890, training was manually stopped and max epoch increased to 1000.
```
python3 easy_train.py \
--experiment-name leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd \
--training-dataset /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
--start-from-engine-test-net True \
--early-fen-skipping 30 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--max_epoch 900 \
--lr 4.375e-4 \
--gamma 0.995 \
--tui False \
--gpus "0," \
--seed $RANDOM
```
The same v6-dd filtering and binpack minimizer was used for preparing the recent nn-1ceb1a57d117.nnue dataset.
```
python3 interleave_binpacks.py \
leela96-filt-v2.binpack \
dfrc99-filt-v2.binpack \
T60-nov2021-12tb7p-eval-filt-v2.binpack \
T60-dec2021-12tb7p-eval-filt-v2.binpack \
filt-v6/test80-aug2022-16tb7p-filter-v6.min-mar2023.binpack \
filt-v6/test80-sep2022-16tb7p-filter-v6.min-mar2023.binpack \
filt-v6-dd/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-jan2022-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
/data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack
```
Links for downloading the training data components can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch919.nnue : 2.6 +/- 2.8
Passed STC vs. nn-dabb1ed23026.nnue
https://tests.stockfishchess.org/tests/view/644420df94ff3db5625f2af5
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 125960 W: 33898 L: 33464 D: 58598
Ptnml(0-2): 351, 13920, 34021, 14320, 368
Passed LTC vs. nn-1ceb1a57d117.nnue
https://tests.stockfishchess.org/tests/view/64469f128d30316529b3dc46
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 24544 W: 6817 L: 6542 D: 11185
Ptnml(0-2): 8, 2252, 7488, 2505, 19
closes https://github.com/official-stockfish/Stockfish/pull/4546
bench 3714847
* Extending v6 filtering to data from T77 dec2021, T79 may2022, and T80 nov2022
* Reducing the number of duplicate positions, prioritizing position scores seen later in time
* Using a binpack minimizer to reduce the overall data size
Trained the same way as the previous master net, aside from the dataset changes:
```
python3 easy_train.py \
--experiment-name leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd \
--training-dataset /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
--start-from-engine-test-net True \
--early-fen-skipping 30 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--max_epoch 900 \
--lr 4.375e-4 \
--gamma 0.995 \
--tui False \
--gpus "0," \
--seed $RANDOM
```
The new v6-dd filtering reduces duplicate positions by iterating over hourly data files within leela test runs, starting with the most recent, then keeping positions the first time they're seen and ignoring positions that are seen again. This ordering was done with the assumption that position scores seen later in time are generally more accurate than scores seen earlier in the test run. Positions are de-duplicated based on piece orientations, the first token in fen strings.
The binpack minimizer was run with default settings after first merging monthly data into single binpacks.
```
python3 interleave_binpacks.py \
leela96-filt-v2.binpack \
dfrc99-filt-v2.binpack \
T60-nov2021-12tb7p-eval-filt-v2.binpack \
T60-dec2021-12tb7p-eval-filt-v2.binpack \
filt-v6/test80-aug2022-16tb7p-filter-v6.min-mar2023.binpack \
filt-v6/test80-sep2022-16tb7p-filter-v6.min-mar2023.binpack \
filt-v6-dd/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
/data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd.binpack
```
The code for v6-dd filtering is available along with training data preparation scripts at:
https://github.com/linrock/nnue-data
Links for downloading the training data components:
https://robotmoon.com/nnue-training-data/
The binpack minimizer is from: #4447
Local elo at 25k nodes per move:
nn-epoch859.nnue : 1.2 +/- 2.6
Passed STC:
https://tests.stockfishchess.org/tests/view/643aad7db08900ff1bc5a832
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 565040 W: 150225 L: 149162 D: 265653
Ptnml(0-2): 1875, 62137, 153229, 63608, 1671
Passed LTC:
https://tests.stockfishchess.org/tests/view/643ecf2fa43cf30e719d2042
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 1014840 W: 274645 L: 272456 D: 467739
Ptnml(0-2): 515, 98565, 306970, 100956, 414
closes https://github.com/official-stockfish/Stockfish/pull/4545
bench 3476305
This idea is a result of my second condition combination tuning for reductions:
https://tests.stockfishchess.org/tests/view/643ed5573806eca398f06d61
There were used two parameters per combination: one for the 'sign' of the first and the second condition in a combination. Values >= 50 indicate using a condition directly and values <= -50 means use the negation of a condition.
Each condition pair (X,Y) had two occurances dependent of the order of the two conditions:
- if X < Y the parameters used for more reduction
- if X > Y the parameters used for less reduction
- if X = Y then only one condition is present and A[X][X][0]/A[X][X][1] stands for using more/less reduction for only this condition.
The parameter pair A[7][2][0] (value = -94.70) and A[7][2][1] (value = 93.60) was one of the strongest signals with values near 100/-100.
Here condition nr. 7 was '(ss+1)->cutoffCnt > 3' and condition nr. 2 'move == ttMove'. For condition nr. 7 the negation is used because A[7][2][0] is negative.
This translates finally to less reduction (because 7 > 2) for tt moves if child cutoffs <= 3.
STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 65728 W: 17704 L: 17358 D: 30666
Ptnml(0-2): 184, 7092, 18008, 7354, 226
https://tests.stockfishchess.org/tests/view/643ff767ef2529086a7ed042
LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 139200 W: 37776 L: 37282 D: 64142
Ptnml(0-2): 58, 13241, 42509, 13733, 59
https://tests.stockfishchess.org/tests/view/6440bfa9ef2529086a7edbc7
closes https://github.com/official-stockfish/Stockfish/pull/4538
Bench: 3548023
This patch is a simplification of my recent elo gainer.
Logically the Elo gainer didn't make much sense and this patch simplifies it into smth more logical.
Instead of assigning negative bonuses to all non-first moves that enter PV nodes
we assign positive bonuses in full depth search after LMR only for moves that
will result in a fail high - thus not assigning positive bonuses
for moves that will go to pv search - so doing "almost" the same as we do in master now for them.
Logic differs for some other moves, though, but this removes some lines of code.
Passed STC:
https://tests.stockfishchess.org/tests/view/642cf5cf77ff3301150dc5ec
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 409320 W: 109124 L: 109308 D: 190888
Ptnml(0-2): 1149, 45385, 111751, 45251, 1124
Passed LTC:
https://tests.stockfishchess.org/tests/view/642fe75d20eb941419bde200
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 260336 W: 70280 L: 70303 D: 119753
Ptnml(0-2): 99, 25236, 79528, 25199, 106
closes https://github.com/official-stockfish/Stockfish/pull/4522
Bench: 4286815
Since bestValue becomes value and beta - alpha is always non-negative,
extraReduction is always false, hence it has no effect.
This patch includes small changes to improve readability.
closes https://github.com/official-stockfish/Stockfish/pull/4505
No functional change
The current implementation generates warnings on MSVC. However, we have
no real use cases for double-typed UCI option values now. Also parameter
tuning only accepts following three types:
int, Value, Score
closes https://github.com/official-stockfish/Stockfish/pull/4505
No functional change
Replace the deprecated Intel compiler icc with its newer icx variant.
This newer compiler is based on clang, and yields good performance.
As before, currently only linux is supported.
closes https://github.com/official-stockfish/Stockfish/pull/4478
No functional change
Made advanced Windows API calls (those from Advapi32.dll) dynamically
linked to avoid link errors when compiling using
Intel icx compiler for Windows.
https://github.com/official-stockfish/Stockfish/pull/4467
No functional change
this makes it easier to compile under MSVC, even though we recommend gcc/clang for production compiles at the moment.
In Win32 API, by default, most null-terminated character strings arguments are of wchar_t (UTF16, formerly UCS16-LE) type, i.e. 2 bytes (at least) per character. So, src/misc.cpp should have proper type. Respectively, for src/syzygy/tbprobe.cpp, in Widows, file paths should be std::wstring rather than std::string. However, this requires a very big number of changes, since the config files are also keeping the 8-bit-per-character std::string strings. Therefore, just one change of using 8-byte-per-character CreateFileA make it compile under MSVC.
closes https://github.com/official-stockfish/Stockfish/pull/4438
No functional change
Created by retraining the master net with these modifications:
* New filtering methods for existing data from T80 sep+oct2022, T79 apr2022, T78 jun+jul+aug+sep2022, T77 dec2021
* Adding new filtered data from T80 aug2022 and T78 apr+may2022
* Increasing early-fen-skipping from 28 to 30
```
python3 easy_train.py \
--experiment-name leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3-sk30 \
--training-dataset /data/leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
--start-from-engine-test-net True \
--early-fen-skipping 30 \
--max_epoch 900 \
--start-lambda 1.0 \
--end-lambda 0.7 \
--lr 4.375e-4 \
--gamma 0.995 \
--tui False \
--gpus "0," \
--seed $RANDOM
```
The v3 filtering used for data from T77dec 2021 differs from v2 filtering in that:
* To improve binpack compression, positions after ply 28 were skipped during training by setting position scores to VALUE_NONE (32002) instead of removing them entirely
* All early-game positions with ply <= 28 were removed to maximize binpack compression
* Only bestmove captures at d6pv2 search were skipped, not 2nd bestmove captures
* Binpack compression was repaired for the remaining positions by effectively replacing bestmoves with "played moves" to maintain contiguous sequences of positions in the training game data
After improving binpack compression, The T77 dec2021 data size was reduced from 95G to 19G.
The v6 filtering used for data from T80augsepoctT79aprT78aprtosep 2022 differs from v2 in that:
* All positions with only one legal move were removed
* Tighter score differences at d6pv2 search were used to remove more positions with only one good move than before
* d6pv2 search was not used to remove positions where the best 2 moves were captures
```
python3 interleave_binpacks.py \
nn-547-dataset/leela96-eval-filt-v2.binpack \
nn-547-dataset/dfrc99-eval-filt-v2.binpack \
nn-547-dataset/test80-nov2022-12tb7p-eval-filt-v2-d6.binpack \
nn-547-dataset/T79-may2022-12tb7p-eval-filt-v2.binpack \
nn-547-dataset/T60-nov2021-12tb7p-eval-filt-v2.binpack \
nn-547-dataset/T60-dec2021-12tb7p-eval-filt-v2.binpack \
filt-v6/test80-aug2022-16tb7p-filter-v6.binpack \
filt-v6/test80-sep2022-16tb7p-filter-v6.binpack \
filt-v6/test80-oct2022-16tb7p-filter-v6.binpack \
filt-v6/test79-apr2022-16tb7p-filter-v6.binpack \
filt-v6/test78-aprmay2022-16tb7p-filter-v6.binpack \
filt-v6/test78-junjulaug2022-16tb7p-filter-v6.binpack \
filt-v6/test78-sep2022-16tb7p-filter-v6.binpack \
filt-v3/test77-dec2021-16tb7p-filt-v3.binpack \
/data/leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3.binpack
```
The code for the new data filtering methods is available at:
https://github.com/linrock/Stockfish/tree/nnue-data-v3/nnue-data
The code for giving hexword names to .nnue files is at:
https://github.com/linrock/nnue-namer
Links for downloading the training data components can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch779.nnue : 0.6 +/- 3.1
Passed STC:
https://tests.stockfishchess.org/tests/view/64212412db43ab2ba6f8efb0
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 82256 W: 22185 L: 21809 D: 38262
Ptnml(0-2): 286, 9065, 22067, 9407, 303
Passed LTC:
https://tests.stockfishchess.org/tests/view/64223726db43ab2ba6f91d6c
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 30840 W: 8437 L: 8149 D: 14254
Ptnml(0-2): 14, 2891, 9323, 3177, 15
closes https://github.com/official-stockfish/Stockfish/pull/4465
bench 5101970
This patch simplifies initialization of statScore to "always set it up to 0" instead of setting it up to 0 two plies deeper.
Reason for why it was done in previous way partially was because of LMR usage of previous statScore which was simplified long time ago so it makes sense to make in more simple there.
Passed STC:
https://tests.stockfishchess.org/tests/view/641a86d1db43ab2ba6f7b31d
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 115648 W: 30895 L: 30764 D: 53989
Ptnml(0-2): 368, 12741, 31473, 12876, 366
Passed LTC:
https://tests.stockfishchess.org/tests/view/641b1c31db43ab2ba6f7d17a
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 175576 W: 47122 L: 47062 D: 81392
Ptnml(0-2): 91, 17077, 53390, 17141, 89
closes https://github.com/official-stockfish/Stockfish/pull/4460
bench 5081969
Patch analyzes field after SEE exchanges concluded with a recapture by
the opponent:
if opponent Queen/Rook/King results under attack after the exchanges, we
consider the move sharp and don't prune it.
Important note:
By accident I forgot to adjust 'occupied' when the king takes part in
the exchanges.
As result of this a move is considered sharp too, when opponent king
apparently can evade check by recapturing.
Surprisingly this seems contribute to patch's strength.
STC:
https://tests.stockfishchess.org/tests/view/640b16132644b62c33947397
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 116400 W: 31239 L: 30817 D: 54344
Ptnml(0-2): 350, 12742, 31618, 13116, 374
LTC:
https://tests.stockfishchess.org/tests/view/640c88092644b62c3394c1c5
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 177600 W: 47988 L: 47421 D: 82191
Ptnml(0-2): 62, 16905, 54317, 17436, 80
closes https://github.com/official-stockfish/Stockfish/pull/4453
bench: 5012145
Since st is a member of position we don't need to pass it separately as
parameter.
While being there also remove some line in pos_is_ok, where
a copy of StateInfo was made by using default copy constructor and
then verified it's correctedness by doing a memcmp.
There is no point in doing that.
Passed non-regression test
https://tests.stockfishchess.org/tests/view/64098d562644b62c33942b35
LLR: 3.24 (-2.94,2.94) <-1.75,0.25>
Total: 548960 W: 145834 L: 146134 D: 256992
Ptnml(0-2): 1617, 57652, 156261, 57314, 1636
closes https://github.com/official-stockfish/Stockfish/pull/4444
No functional change
Keep incbin.h with the same mode as the other source files.
A mode diff might show up when working with patch files or sending the source code between devices.
This patch should fix such behaviour.
closes https://github.com/official-stockfish/Stockfish/pull/4442
No functional change
in a some of cases movepicker returned some moves more than once which lead
to them being searched more than once. This bug was possible because of how
we use queen promotions - they are generated as a captures but are not
included in position function which checks if move is a capture. Thus if
any refutation (killer or countermove) was a queen promotion it was
searched twice - once as a capture and one as a refutation.
This patch affects various things, namely stats assignments for queen promotions
and other moves if best move is queen promotion,
also some heuristics in search and qsearch.
With this patch every queen promotion is now considered a capture.
After this patch number of found duplicated moves is 0 during normal 13 depth bench run.
Passed STC:
https://tests.stockfishchess.org/tests/view/63f77e01e74a12625bcd87d7
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 80920 W: 21455 L: 21289 D: 38176
Ptnml(0-2): 198, 8839, 22241, 8963, 219
Passed LTC:
https://tests.stockfishchess.org/tests/view/63f7e020e74a12625bcd9a76
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 89712 W: 23674 L: 23533 D: 42505
Ptnml(0-2): 24, 8737, 27202, 8860, 33
closes https://github.com/official-stockfish/Stockfish/pull/4405
bench 4681731
Call the recently added hint function for NNUE accumulator update after a failed probcut search.
In this case we already searched at least some captures and tt move which, however, is not sufficient for a cutoff.
So it seems we have a greater chance that the full search will also have no cutoff and hence all moves have to be searched.
STC: https://tests.stockfishchess.org/tests/view/63fa74a4e74a12625bce1823
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 70096 W: 18770 L: 18423 D: 32903
Ptnml(0-2): 191, 7342, 19654, 7651, 210
To be sure that we have no heavy interaction retest on top of #4410.
Rebased STC: https://tests.stockfishchess.org/tests/view/63fb2f62e74a12625bce3b03
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 137688 W: 36790 L: 36349 D: 64549
Ptnml(0-2): 397, 14373, 38919, 14702, 453
closes https://github.com/official-stockfish/Stockfish/pull/4411
No functional change
Credits to Stefan Geschwentner (locutus2) showing that the hint
is useful on PvNodes. In contrast to his test,
this version avoids to use the hint when in check.
I believe checking positions aren't good candidates for the hint
because:
- evasion moves are rather few, so a checking pos. has much less childs
than a normal position
- if the king has to move the NNUE eval can't use incremental updates,
so the child nodes have to do a full refresh anyway.
Passed STC:
https://tests.stockfishchess.org/tests/view/63f9c5b1e74a12625bcdf585
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 124472 W: 33268 L: 32846 D: 58358
Ptnml(0-2): 350, 12986, 35170, 13352, 378
closes https://github.com/official-stockfish/Stockfish/pull/4410
no functional change
Params found with the nevergrad TBPSA optimizer via nevergrad4sf modified to:
* use SPRT LLR with fishtest STC elo gainer bounds [0, 2] as the objective function
* increase the game batch size after each new optimal point is found
The params were the optimal point after TBPSA iteration 7 and 160 nevergrad evaluations with:
* initial batch size of 96 games per evaluation
* batch size increase of 64 games after each iteration
* a budget of 512 evaluations
* TC: fixed 1.5 million nodes per move, no time limit
nevergrad4sf enables optimizing stockfish params with TBPSA:
https://github.com/vondele/nevergrad4sf
Using pentanomial game results with smaller game batch sizes was inspired by:
Use of SPRT LLR calculated from pentanomial game results as the objective function was an experiment at maximizing the information from game batches to reduce the computational cost for TBPSA to converge on good parameters.
For the exact code used to find the params:
https://github.com/linrock/tuning-fork
Passed STC:
https://tests.stockfishchess.org/tests/view/63f4ef5ee74a12625bcd114a
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 66552 W: 17736 L: 17390 D: 31426
Ptnml(0-2): 164, 7229, 18166, 7531, 186
Passed LTC:
https://tests.stockfishchess.org/tests/view/63f56028e74a12625bcd2550
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 71264 W: 19150 L: 18787 D: 33327
Ptnml(0-2): 23, 6728, 21771, 7083, 27
closes https://github.com/official-stockfish/Stockfish/pull/4401
bench 3687580
This patch introduces `hint_common_parent_position()` to signal that potentially several child nodes will require an NNUE eval. By populating explicitly the accumulator, these subsequent evaluations can be performed more efficiently.
This was based on the observation that calculating the evaluation in an excluded move position yielded a significant Elo gain, even though the evaluation itself was already available (work by pb00067).
Sopel wrote the code to perform just the accumulator update. This PR is based on cleaned up code that
passed STC:
https://tests.stockfishchess.org/tests/view/63f62f9be74a12625bcd4aa0
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 110368 W: 29607 L: 29167 D: 51594
Ptnml(0-2): 41, 10551, 33572, 10967, 53
and in an the earlier (equivalent) version
passed STC:
https://tests.stockfishchess.org/tests/view/63f3c3fee74a12625bcce2a6
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 47552 W: 12786 L: 12467 D: 22299
Ptnml(0-2): 120, 5107, 12997, 5438, 114
passed LTC:
https://tests.stockfishchess.org/tests/view/63f45cc2e74a12625bccfa63
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 110368 W: 29607 L: 29167 D: 51594
Ptnml(0-2): 41, 10551, 33572, 10967, 53
closes https://github.com/official-stockfish/Stockfish/pull/4402
Bench: 3726250
The sdot instruction computes (and accumulates) a signed dot product,
which is quite handy for Stockfish's NNUE code. The instruction is
optional for Armv8.2 and Armv8.3, and mandatory for Armv8.4 and above.
The commit adds a new 'arm-dotprod' architecture with enabled dot
product support. It also enables dot product support for the existing
'apple-silicon' architecture, which is at least Armv8.5.
The following local speed test was performed on an Apple M1 with
ARCH=apple-silicon. I had to remove CPU pinning from the benchmark
script. However, the results were still consistent: Checking both
binaries against themselves reported a speedup of +0.0000 and +0.0005,
respectively.
```
Result of 100 runs
==================
base (...ish.037ef3e1) = 1917997 +/- 7152
test (...fish.dotprod) = 2159682 +/- 9066
diff = +241684 +/- 2923
speedup = +0.1260
P(speedup > 0) = 1.0000
CPU: 10 x arm
Hyperthreading: off
```
Fixes#4193
closes https://github.com/official-stockfish/Stockfish/pull/4400
No functional change
Created by retraining the master net on a dataset composed of:
* Most of the previous best dataset filtered to remove positions likely having only one good move
* Adding training data from Leela T77 dec2021 rescored with 16tb of 7-piece tablebases
Trained with end lambda 0.7 and max epoch 900. Positions with ply <= 28 were removed from most of the previous best dataset before training began. A new nnue-pytorch trainer param for skipping early plies was used to skip plies <= 24 in the unfiltered and additional Leela T77 parts of the dataset.
```
python easy_train.py \
--experiment-name leela96-dfrc99-T80octnovT79aprmayT60novdec-eval-filt-v2-T78augsep-12tb-T77dec-16tb-lambda7-sk24 \
--training-dataset /data/leela96-dfrc99-T80octnovT79aprmayT60novdec-eval-filt-v2-T78augsep-12tb-T77dec-16tb.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/easy-train-early-fen-skipping \
--early-fen-skipping 24 \
--gpus "0," \
--start-from-engine-test-net True \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 900
```
The depth6 multipv2 search filtering method is the same as the one used for filtering recent best datasets, with a lower eval difference threshold to remove slightly more positions than before. These parts of the dataset were filtered:
* 96% of T60T70wIsRightFarseerT60T74T75T76.binpack
* 99% of dfrc_n5000.binpack
* T80 oct + nov 2022 data, no positions with castling flags, rescored with ~600gb 7p tablebases
* T79 apr + may 2022 data, rescored with 12tb 7p tablebases
* T60 nov + dec 2021 data, rescored with 12tb 7p tablebases
These parts of the dataset were not filtered. Positions with ply <= 24 were skipped during training:
* T78 aug + sep 2022 data, rescored with 12tb 7p tablebases
* 84% of T77 dec 2021 data, rescored with 16tb 7p tablebases
The code and exact evaluation thresholds used for data filtering can be found at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff-t2/src/filter
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch859.nnue : 3.5 +/ 1.2
Passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
https://tests.stockfishchess.org/tests/view/63dfeefc73223e7f52ad769f
Total: 219744 W: 58572 L: 58002 D: 103170
Ptnml(0-2): 609, 24446, 59284, 24832, 701
Passed LTC:
https://tests.stockfishchess.org/tests/view/63e268fc73223e7f52ade7b6
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 91256 W: 24528 L: 24121 D: 42607
Ptnml(0-2): 48, 8863, 27390, 9288, 39
closes https://github.com/official-stockfish/Stockfish/pull/4387
bench 3841998
This patch is a simplification / code normalisation in qsearch.
Adds steps in comments the same way we have in search;
Makes a separate "pruning" stage instead of heuristics randomly being spread over qsearch code;
Reorders pruning heuristics from least taxing ones to more taxing ones;
Removes repeated check for best value not being mated, instead uses 1 check - thus removes some lines of code.
Moves prefetch and move setup after pruning - makes no sense to do them if move will actually get pruned.
Passed non-regression test:
https://tests.stockfishchess.org/tests/view/63dd2c5ff9a50a69252c1413
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 113504 W: 29898 L: 29770 D: 53836
Ptnml(0-2): 287, 11861, 32327, 11991, 286
https://github.com/official-stockfish/Stockfish/pull/4382
Non-functional change.
PR consists of 2 improvements on nodes with excludeMove:
1. Remove xoring the posKey with make_key(excludedMove)
Since we never call tte->save anymore with excludedMove,
the unique left purpose of the xoring was to avoid a TT hit.
Nevertheless on a normal bench run this produced ~25 false positives
(key collisions)
To avoid that we now forbid early TT cutoff's with excludeMove
Maybe these accesses to TT with xored key caused useless misses
in the CPU caches (L1, L2 ...)
Now doing the probe with the same key as the enclosing search does,
should hit the CPU cache.
2. Don't probe Tablebases with excludedMove.
This can't be tested on fishtest, but it's obvious that
tablebases don't deliver any information about suboptimal moves.
Side note:
Very surprisingly it looks like we cannot use static eval's from
TT since they slightly differ over time due to changing optimism.
Attempts to use static eval's from TT did loose about 13 ELO.
This is something about to investigate.
LTC: https://tests.stockfishchess.org/tests/view/63dc0f8de9d4cdfbe672d0c6
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 44736 W: 12046 L: 11733 D: 20957
Ptnml(0-2): 12, 4212, 13617, 4505, 22
An analogue of this passed STC & LTC
see PR #4374 (thanks Dubslow for reviewing!)
closes https://github.com/official-stockfish/Stockfish/pull/4380
Bench: 4758694
This patch adds more debugging slots up to 32 per type and provide tools
to calculate standard deviation and Pearson's correlation coefficient.
However, due to slot being 0 at default, dbg_hit_on(c, b) has to be removed.
Initial idea from snicolet/Stockfish@d8ab604
closes https://github.com/official-stockfish/Stockfish/pull/4354
No functional change
Current master prunes all moves with negative SEE values in qsearch.
This patch sets constant negative threshold thus allowing some moves with negative SEE values to be searched.
Value of threshold is completely arbitrary and can be tweaked - also it as function of depth can be tried.
Original idea by author of Alexandria engine.
Passed STC
https://tests.stockfishchess.org/tests/view/63d79a59a67dd929a5564976
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 34864 W: 9392 L: 9086 D: 16386
Ptnml(0-2): 113, 3742, 9429, 4022, 126
Passed LTC
https://tests.stockfishchess.org/tests/view/63d8074aa67dd929a5565bc2
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 91616 W: 24532 L: 24126 D: 42958
Ptnml(0-2): 32, 8840, 27662, 9238, 36
closes https://github.com/official-stockfish/Stockfish/pull/4376
Bench: 4010877
update the WLD model with about 400M positions extracted from recent LTC games after the net updates.
This ensures that the 50% win rate is again at 1.0 eval.
closes https://github.com/official-stockfish/Stockfish/pull/4373
No functional change.
Beyond the simplification, this could be considered a bugfix from a certain point of view.
However, the effect is very subtle and essentially impossible for users to notice.
5372f81cc8 added about 2 Elo at LTC, but only for second and later `go` commands; now, with
this patch, the first `go` command will also benefit from that gain. Games under time
controls are unaffected (as per the tests).
STC: https://tests.stockfishchess.org/tests/view/63c3d291330c0d3d051d48a8
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 473792 W: 124858 L: 125104 D: 223830
Ptnml(0-2): 1338, 49653, 135063, 49601, 1241
LTC: https://tests.stockfishchess.org/tests/view/63c8cd56a83c702aac083bc9
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 290728 W: 76926 L: 76978 D: 136824
Ptnml(0-2): 106, 27987, 89221, 27953, 97
closes https://github.com/official-stockfish/Stockfish/pull/4361
bench 4208265
This patch results in search values for a TB win/loss to be reported in a way that does not change with normalization, i.e. will be consistent over time.
A value of 200.00 pawns is now reported upon entering a TB won position. Values smaller than 200.00 relate to the distance in plies from the root to the probed position position,
with 1 cp being 1 ply distance.
closes https://github.com/official-stockfish/Stockfish/pull/4353
No functional change
Created by retraining the master net with Leela T78 data from Aug+Sep 2022 added to the previous best dataset. Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
```
python easy_train.py \
--experiment-name leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p-sk28-lambda7 \
--training-dataset /data/leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--start-from-engine-test-net True \
--gpus "0," \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 800
```
Around epoch 750, training was manually paused and max epoch increased to 950 before resuming. The additional Leela training data from T78 was prepared in the same way as the previous best dataset.
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
While the local elo ratings during this experiment were much lower than in recent master nets, several later epochs had a consistent elo above zero, and this was hypothesized to represent potential strength at slower time controls.
Local elo at 25k nodes per move
leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p-sk28-lambda7
nn-epoch819.nnue : 0.4 +/- 1.1 (nn-bc24c101ada0.nnue)
nn-epoch799.nnue : 0.3 +/- 1.2
nn-epoch759.nnue : 0.3 +/- 1.1
nn-epoch839.nnue : 0.2 +/- 1.4
Passed STC
https://tests.stockfishchess.org/tests/view/63cabf6f0eefe8694a0c6013
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 41608 W: 11161 L: 10848 D: 19599
Ptnml(0-2): 116, 4496, 11281, 4781, 130
Passed LTC
https://tests.stockfishchess.org/tests/view/63cb1856344bb01c191af263
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 76760 W: 20517 L: 20137 D: 36106
Ptnml(0-2): 34, 7435, 23070, 7799, 42
closes https://github.com/official-stockfish/Stockfish/pull/4351
bench 3941848
Bit-shifting is a single instruction, and should be faster than an array lookup
on supported architectures. Besides (ever so slightly) speeding up the
conversion of a square into a bitboard, we may see minor general performance
improvements due to preserving more of the CPU's existing cache.
passed STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 47280 W: 12469 L: 12271 D: 22540
Ptnml(0-2): 128, 4893, 13402, 5087, 130
https://tests.stockfishchess.org/tests/view/63c5cfe618c20f4929c5fe46
Small speedup locally:
```
Result of 20 runs
==================
base (./stockfish.master ) = 1752135 +/- 10943
test (./stockfish.patch ) = 1763939 +/- 10818
diff = +11804 +/- 4731
speedup = +0.0067
P(speedup > 0) = 1.0000
CPU: 16 x AMD Ryzen 9 3950X 16-Core Processor
```
Closes https://github.com/official-stockfish/Stockfish/pull/4343
Bench: 4106793
The accumulator should be an earlyclobber because it is written before
all input operands are read. Otherwise, the asm code computes a wrong
result if the accumulator shares a register with one of the other input
operands (which happens if we pass in the same expression for the
accumulator and the operand).
Closes https://github.com/official-stockfish/Stockfish/pull/4339
No functional change
Created by retraining the master net on a dataset composed of:
* The Leela-dfrc_n5000.binpack dataset filtered with depth6 multipv2 search to remove positions with only one good move, in addition to removing positions where either of the two best moves are captures
* The same Leela T80 oct+nov 2022 training data used in recent best datasets
* Additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022
Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
```
python easy_train.py \
--experiment-name leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7 \
--training-dataset /data/leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--start-from-engine-test-net True \
--gpus "0," \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 800
```
Around epoch 780, training was manually paused and max epoch increased to 920 before resuming.
During depth6 multipv2 data filtering, positions were considered to have only one good move if the score of the best move was significantly better than the 2nd best move in a way that changes the outcome of the game:
* the best move leads to a significant advantage while the 2nd best move equalizes or loses
* the best move is about equal while the 2nd best move loses
The modified stockfish branch and exact score thresholds used for filtering are at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff/src/filter
About 95% of the Leela portion and 96% of the DFRC portion of the Leela-dfrc_n5000.binpack dataset was filtered. Unfiltered parts of the dataset were left out.
The additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022 was WDL-rescored with about 12TB of syzygy 7-piece tablebases where the material difference is less than around 6 pawns. Best moves were exported to .plain data files during data conversion with the lc0 rescorer.
The exact training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move
experiment_leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7
run_0/nn-epoch899.nnue : 3.8 +/- 1.6
Passed STC
https://tests.stockfishchess.org/tests/view/63bed1f540aa064159b9c89b
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 103344 W: 27392 L: 26991 D: 48961
Ptnml(0-2): 333, 11223, 28099, 11744, 273
Passed LTC
https://tests.stockfishchess.org/tests/view/63c010415705810de2deb3ec
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 21712 W: 5891 L: 5619 D: 10202
Ptnml(0-2): 12, 2022, 6511, 2304, 7
closes https://github.com/official-stockfish/Stockfish/pull/4338
bench 4106793
Removed sprintf() which generated a warning, because of security reasons.
Replace NULL with nullptr
Replace typedef with using
Do not inherit from std::vector. Use composition instead.
optimize mutex-unlocking
closes https://github.com/official-stockfish/Stockfish/pull/4327
No functional change
If a global function has no previous declaration, either the declaration
is missing in the corresponding header file or the function should be
declared static. Static functions are local to the translation unit,
which allows the compiler to apply some optimizations earlier (when
compiling the translation unit rather than during link-time
optimization).
The commit enables the warning for gcc, clang, and mingw. It also fixes
the reported warnings by declaring the functions static or by adding a
header file (benchmark.h).
closes https://github.com/official-stockfish/Stockfish/pull/4325
No functional change
This is a later epoch (epoch 859) from the same experiment run that trained yesterday's master net nn-60fa44e376d9.nnue (epoch 779). The experiment was manually paused around epoch 790 and unpaused with max epoch increased to 900 mainly to get more local elo data without letting the GPU idle.
nn-60fa44e376d9.nnue is from #4314
nn-335a9b2d8a80.nnue is from #4295
Local elo vs. nn-335a9b2d8a80.nnue at 25k nodes per move:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
run_0/nn-epoch779.nnue (nn-60fa44e376d9.nnue) : 5.0 +/- 1.2
run_0/nn-epoch859.nnue (nn-a3dc078bafc7.nnue) : 5.6 +/- 1.6
Passed STC vs. nn-335a9b2d8a80.nnue
https://tests.stockfishchess.org/tests/view/63ae10495bd1e5f27f13d94f
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 37536 W: 10088 L: 9781 D: 17667
Ptnml(0-2): 110, 4006, 10223, 4325, 104
An LTC test vs. nn-335a9b2d8a80.nnue was paused due to nn-60fa44e376d9.nnue passing LTC first:
https://tests.stockfishchess.org/tests/view/63ae5d34331d5fca5113703b
Passed LTC vs. nn-60fa44e376d9.nnue
https://tests.stockfishchess.org/tests/view/63af1e41465d2b022dbce4e7
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 148704 W: 39672 L: 39155 D: 69877
Ptnml(0-2): 59, 14443, 44843, 14936, 71
closes https://github.com/official-stockfish/Stockfish/pull/4319
bench 3984365
Created by retraining the master net on the previous best dataset with additional filtering. No new data was added.
More of the Leela-dfrc_n5000.binpack part of the dataset was pre-filtered with depth6 multipv2 search to remove bestmove captures. About 93% of the previous Leela/SF data and 99% of the SF dfrc data was filtered. Unfiltered parts of the dataset were left out. The new Leela T80 oct+nov data is the same as before. All early game positions with ply count <= 28 were skipped during training by modifying the training data loader in nnue-pytorch.
Trained in a similar way as recent master nets, with a different nnue-pytorch branch for early ply skipping:
python3 easy_train.py \
--experiment-name=leela93-dfrc99-filt-only-T80-oct-nov-skip28 \
--training-dataset=/data/leela93-dfrc99-filt-only-T80-oct-nov.binpack \
--start-from-engine-test-net True \
--nnue-pytorch-branch=linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--gpus="0," \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--tui=False \
--seed=$RANDOM \
--max_epoch=800 \
--network-testing-threads 20 \
--num-workers 6
For the exact training data used: https://robotmoon.com/nnue-training-data/
Details about the previous best dataset: #4295
Local testing at a fixed 25k nodes:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
Local Elo: run_0/nn-epoch779.nnue : 5.1 +/- 1.5
Passed STC
https://tests.stockfishchess.org/tests/view/63adb3acae97a464904fd4e8
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 36504 W: 9847 L: 9538 D: 17119
Ptnml(0-2): 108, 3981, 9784, 4252, 127
Passed LTC
https://tests.stockfishchess.org/tests/view/63ae0ae25bd1e5f27f13d884
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 36592 W: 10017 L: 9717 D: 16858
Ptnml(0-2): 17, 3461, 11037, 3767, 14
closes https://github.com/official-stockfish/Stockfish/pull/4314
bench 4015511
In both modified methods, the variable 'result' is checked to detect
whether the probe operation failed. However, the variable is not
initialized on all paths, so the check might test an uninitialized
value.
A test position (with TB) is given by:
position fen 3K1k2/R7/8/8/8/8/8/R6Q w - - 0 1 moves a1b1 f8g8 b1a1 g8f8 a1b1 f8g8 b1a1
This is now fixed by always initializing the variable.
closes https://github.com/official-stockfish/Stockfish/pull/4309
No functional change
Created by retraining the master net with a combination of:
the previous best dataset (Leela-dfrc_n5000.binpack), with about half the dataset filtered using depth6 multipv2 search to throw away positions where either of the 2 best moves are captures
Leela T80 Oct and Nov training data rescored with best moves, adding ~9.5 billion positions
Trained effectively the same way as the previous master net:
python3 easy_train.py \
--experiment-name=leela-dfrc-filtered-T80-oct-nov \
--training-dataset=/data/leela-dfrc-filtered-T80-oct-nov.binpack \
--start-from-engine-test-net True \
--gpus="0," \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--tui=False \
--seed=$RANDOM \
--max_epoch=800 \
--auto-exit-timeout-on-training-finished=900 \
--network-testing-threads 20 \
--num-workers 6
Local testing at a fixed 25k nodes:
experiments/experiment_leela-dfrc-filtered-T80-oct-nov/training/run_0/nn-epoch779.nnue
localElo: run_0/nn-epoch779.nnue : 4.7 +/- 3.1
The new Leela T80 part of the dataset was prepared by downloading test80 training data from all of Oct 2022 and Nov 2022, rescoring with syzygy 6-piece tablebases and ~600 GB of 7-piece tablebases, saving best moves to exported .plain files, removing all positions with castling flags, then converting to binpacks and using interleave_binpacks.py to merge them together. Scripts used in this data conversion process are available at:
https://github.com/linrock/lc0-data-converter
Filtering binpack data using depth6 multipv2 search was done by modifying transform.cpp in the tools branch:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-no-rescore
Links for downloading the training data (total size: 338 GB) are available at:
https://robotmoon.com/nnue-training-data/
Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 30544 W: 8244 L: 7947 D: 14353
Ptnml(0-2): 93, 3243, 8302, 3542, 92
https://tests.stockfishchess.org/tests/view/63a0d377264a0cf18f86f82b
Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 32464 W: 8866 L: 8573 D: 15025
Ptnml(0-2): 19, 3054, 9794, 3345, 20
https://tests.stockfishchess.org/tests/view/63a10bc9fb452d3c44b1e016
closes https://github.com/official-stockfish/Stockfish/pull/4295
Bench 3554904
Instead of allowing .depend for specific build-related targets, filter
non-build-related targets (i.e. help, clean) so that other targets can
normally execute .depend target.
closes https://github.com/official-stockfish/Stockfish/pull/4293
No functional change
If ttMove is doubly extended, we allow a depth growth of the remaining moves.
The idea is to get a more realistic score comparison, because of the depth
difference. We take some care to avoid this extension for high depths,
in order to avoid the cost, since the search result is supposed
to be more accurate in this case.
This pull request includes some small cleanups.
STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 60256 W: 16189 L: 15848 D: 28219
Ptnml(0-2): 182, 6546, 16330, 6889, 181
https://tests.stockfishchess.org/tests/view/639109a1792a529ae8f27777
LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 106232 W: 28487 L: 28053 D: 49692
Ptnml(0-2): 46, 10224, 32145, 10652, 49
https://tests.stockfishchess.org/tests/view/63914cba792a529ae8f282ee
closes https://github.com/official-stockfish/Stockfish/pull/4271
Bench: 3622368
Add a constraint so that the dependency build only occurs when users
actually run build tasks.
This fixes a bug on some systems where gcc/g++ is not available.
closes https://github.com/official-stockfish/Stockfish/pull/4255
No functional change
fixes the lowerbound/upperbound output by avoiding
scores outside the alpha,beta bracket. Since SF search
uses fail-soft we can't simply take the returned value
as score.
closes https://github.com/official-stockfish/Stockfish/pull/4259
No functional change
Official release version of Stockfish 15.1
Bench: 3467381
---
Today, we have the pleasure to announce Stockfish 15.1.
As usual, downloads will be freely available at stockfishchess.org/download
*Elo gain and competition results*
With this release, version 5 of the NNUE neural net architecture has
been introduced, and the training data has been extended to include
Fischer random chess (FRC) positions. As a result, Elo gains are largest
for FRC, reaching up to 50 Elo for doubly randomized FRC[1] (DFRC).
More importantly, also for standard chess this release progressed and
will win two times more game pairs than it loses[2] against
Stockfish 15. Stockfish continues to win in a dominating way[3] all
chess engine tournaments, including the TCEC Superfinal, Cup, FRC, DFRC,
and Swiss as well as the CCC Bullet, Blitz, and Rapid events.
*New evaluation*
This release also introduces a new convention for the evaluation that
is reported by search. An evaluation of +1 is now no longer tied to the
value of one pawn, but to the likelihood of winning the game. With
a +1 evaluation, Stockfish has now a 50% chance of winning the game
against an equally strong opponent. This convention scales down
evaluations a bit compared to Stockfish 15 and allows for consistent
evaluations in the future.
*ChessBase settlement*
In this release period, the Stockfish team has successfully enforced
its GPL license against ChessBase. This has been an intense process that
included filing a lawsuit[4], a court hearing[5], and finally
negotiating a settlement[6] that established that ChessBase infringed on
the license by not distributing the Stockfish derivatives Fat Fritz 2
and Houdini 6 as free software, and that ensures ChessBase will respect
the Free Software principles in the future. This settlement has been
covered by major chess sites (see e.g. lichess.org[7] and chess.com[8]),
and we are proud that it has been hailed as a ‘historic violation
settlement[9]’ by the Software Freedom Conservancy.
*Thank you*
The Stockfish project builds on a thriving community of enthusiasts
(thanks everybody!) that contribute their expertise, time, and resources
to build a free and open-source chess engine that is robust, widely
available, and very strong. We invite our chess fans to join the
fishtest testing framework and programmers to contribute to the
project[10].
The Stockfish team
[1] https://tests.stockfishchess.org/tests/view/638a6170d2b9c924c4c62cb4
[2] https://tests.stockfishchess.org/tests/view/638a4dd7d2b9c924c4c6297b
[3] https://en.wikipedia.org/wiki/Stockfish_(chess)#Competition_results
[4] https://stockfishchess.org/blog/2021/our-lawsuit-against-chessbase/
[5] https://stockfishchess.org/blog/2022/public-court-hearing-soon/
[6] https://stockfishchess.org/blog/2022/chessbase-stockfish-agreement/
[7] https://lichess.org/blog/Y3u1mRAAACIApBVn/settlement-reached-in-stockfish-v-chessbase
[8] https://www.chess.com/news/view/chessbase-stockfish-reach-settlement
[9] https://sfconservancy.org/news/2022/nov/28/sfc-named-trusted-party-in-gpl-case/
[10] https://stockfishchess.org/get-involved/
If multiple threads have the same best move,
pick the thread with the largest contribution to the confidence vote.
This thread will later be used to display PV, so this patch is
about user-friendliness and/or least surprises, it non-functional for playing strenght.
closes https://github.com/official-stockfish/Stockfish/pull/4246
No functional change
fixes the lowerbound/upperbound output by taking the alpha,beta bracket
into account also if a bestThread is selected that is different from the master thread.
Instead of keeping track which bounds where used in the specific search,
in this version we simply store the quality (exact, upperbound,
lowerbound) of the score along with the actual score as information on
rootMove.
closes https://github.com/official-stockfish/Stockfish/pull/4239
No functional change
This updates the WDL model based on the LTC statistics (2M games).
Relatively small change, note that this also adjusts the NormalizeToPawnValue (now 361),
to keep win prob at 50% for 100cp.
closes https://github.com/official-stockfish/Stockfish/pull/4236
No functional change.
Github Actions allows us to use up to 20 workers.
This way we can launch multiple different checks
at the same time and optimize the overall time
the CI takes a bit.
closes https://github.com/official-stockfish/Stockfish/pull/4223
No functional change
For development versions of Stockfish, the version will now look like
dev-20221107-dca9a0533
indicating a development version, the date of the last commit,
and the git SHA of that commit. If git is not available,
the fallback is the date of compilation. Releases will continue to be
versioned as before.
Additionally, this PR extends the CI to create binary artifacts,
i.e. pushes to master will automatically build Stockfish and upload
the binaries to github.
closes https://github.com/official-stockfish/Stockfish/pull/4220
No functional change
Normalizes the internal value as reported by evaluate or search
to the UCI centipawn result used in output. This value is derived from
the win_rate_model() such that Stockfish outputs an advantage of
"100 centipawns" for a position if the engine has a 50% probability to win
from this position in selfplay at fishtest LTC time control.
The reason to introduce this normalization is that our evaluation is, since NNUE,
no longer related to the classical parameter PawnValueEg (=208). This leads to
the current evaluation changing quite a bit from release to release, for example,
the eval needed to have 50% win probability at fishtest LTC (in cp and internal Value):
June 2020 : 113cp (237)
June 2021 : 115cp (240)
April 2022 : 134cp (279)
July 2022 : 167cp (348)
With this patch, a 100cp advantage will have a fixed interpretation,
i.e. a 50% win chance. To keep this value steady, it will be needed to update the win_rate_model()
from time to time, based on fishtest data. This analysis can be performed with
a set of scripts currently available at https://github.com/vondele/WLD_model
fixes https://github.com/official-stockfish/Stockfish/issues/4155
closes https://github.com/official-stockfish/Stockfish/pull/4216
No functional change
Joint work by Ofek Shochat and Stéphane Nicolet.
passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 93288 W: 24996 L: 24601 D: 43691
Ptnml(0-2): 371, 10263, 24989, 10642, 379
https://tests.stockfishchess.org/tests/view/63448f4f4bc7650f07541987
passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 84168 W: 22771 L: 22377 D: 39020
Ptnml(0-2): 47, 8181, 25234, 8575, 47
https://tests.stockfishchess.org/tests/view/6345186d4bc7650f07542fbd
================
It seems there are two effects with this patch:
effect A :
If Stockfish is winning at root, we have optimism > 0 for all leaves in
the search tree where Stockfish is to move. There, if (psq - nnue) > 0
(ie if the advantage is more materialistic than positional), then the
product D = optimism * (psq - nnue) will be positive, nnueComplexity will
increase, and the eval will increase from SF point of view.
So the effect A is that if Stockfish is winning at root, she will slightly
favor in the search tree (in other words, search more) the positions where
she can convert her advantage via materialist means.
effect B :
If Stockfish is losing at root, we have optimism > 0 for all leaves in
the search tree where the opponent is to move. There, if (psq - nnue) < 0
(ie if the opponent advantage is more positional than materialistic), then
the product D = optimism * (psq-nnue) will be negative, nnueComplexity will
decrease, and the eval will decrease from the opponent point of view.
So the effect B is that Stockfish will slightly favor in the search tree
(search more) the branches where she can defend by slowly reducing the
opponent positional advantage.
=================
closes https://github.com/official-stockfish/Stockfish/pull/4195
bench: 4673898
relatively soon servers with 512 threads will be available 'quite commonly',
anticipate even more threads, and increase our current maximum from 512 to 1024.
closes https://github.com/official-stockfish/Stockfish/pull/4152
No functional change.
If the elapsed time is close to the available time, the time management thread can signal that the next iterations should be searched at the same depth (Threads.increaseDepth = false). While the rootDepth increases, the adjustedDepth is kept constant with the searchAgainCounter.
In exceptional cases, when threading is used and the master thread, which controls the time management, signals to not increaseDepth, but by itself takes a long time to finish the iteration, the helper threads can search repeatedly at the same depth. This search finishes more and more quickly, leading to helper threads that report a rootDepth of MAX_DEPTH (245). The latter is not optimal as it is confusing for the user, stops search on these threads, and leads to an incorrect bias in the thread voting scheme. Probably with only a small impact on strength.
This behavior was observed almost two years ago,
see https://github.com/official-stockfish/Stockfish/issues/2717
This patch fixes#2717 by ensuring the effective depth increases at once every four iterations,
even in increaseDepth is false.
Depth 245 searches (for non-trivial positions) were indeed absent with this patch,
but frequent with master in the tests below:
https://discord.com/channels/435943710472011776/813919248455827515/994872720800088095
Total pgns: 2173
Base: 2867
Patch: 0
it passed non-regression testing in various setups:
SMP STC:
https://tests.stockfishchess.org/tests/view/62bfecc96178ffe6394ba036
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 37288 W: 10171 L: 10029 D: 17088
Ptnml(0-2): 75, 3777, 10793, 3929, 70
SMP LTC:
https://tests.stockfishchess.org/tests/view/62c08f6f49b62510394be066
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 190568 W: 52125 L: 52186 D: 86257
Ptnml(0-2): 70, 17854, 59504, 17779, 77
LTC:
https://tests.stockfishchess.org/tests/view/62c08b6049b62510394bdfb6
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 48120 W: 13204 L: 13083 D: 21833
Ptnml(0-2): 54, 4458, 14919, 4571, 58
Special thanks to miguel-I, Disservin, ruicoelhopedro and others for analysing the problem,
the data, and coming up with the key insight, needed to fix this longstanding issue.
closes https://github.com/official-stockfish/Stockfish/pull/4104
Bench: 5182295
First things first...
this PR is being made from court. Today, Tord and Stéphane, with broad support
of the developer community are defending their complaint, filed in Munich, against ChessBase.
With their products Houdini 6 and Fat Fritz 2, both Stockfish derivatives,
ChessBase violated repeatedly the Stockfish GPLv3 license. Tord and Stéphane have terminated
their license with ChessBase permanently. Today we have the opportunity to present
our evidence to the judge and enforce that termination. To read up, have a look at our blog post
https://stockfishchess.org/blog/2022/public-court-hearing-soon/ and
https://stockfishchess.org/blog/2021/our-lawsuit-against-chessbase/
This PR introduces a net trained with an enhanced data set and a modified loss function in the trainer.
A slight adjustment for the scaling was needed to get a pass on standard chess.
passed STC:
https://tests.stockfishchess.org/tests/view/62c0527a49b62510394bd610
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 135008 W: 36614 L: 36152 D: 62242
Ptnml(0-2): 640, 15184, 35407, 15620, 653
passed LTC:
https://tests.stockfishchess.org/tests/view/62c17e459e7d9997a12d458e
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 28864 W: 8007 L: 7749 D: 13108
Ptnml(0-2): 47, 2810, 8466, 3056, 53
Local testing at a fixed 25k nodes resulted in
Test run1026/easy_train_data/experiments/experiment_2/training/run_0/nn-epoch799.nnue
localElo: 4.2 +- 1.6
The real strength of the net is in FRC and DFRC chess where it gains significantly.
Tested at STC with slightly different scaling:
FRC:
https://tests.stockfishchess.org/tests/view/62c13a4002ba5d0a774d20d4
Elo: 29.78 +-3.4 (95%) LOS: 100.0%
Total: 10000 W: 2007 L: 1152 D: 6841
Ptnml(0-2): 31, 686, 2804, 1355, 124
nElo: 59.24 +-6.9 (95%) PairsRatio: 2.06
DFRC:
https://tests.stockfishchess.org/tests/view/62c13a5702ba5d0a774d20d9
Elo: 55.25 +-3.9 (95%) LOS: 100.0%
Total: 10000 W: 2984 L: 1407 D: 5609
Ptnml(0-2): 51, 636, 2266, 1779, 268
nElo: 96.95 +-7.2 (95%) PairsRatio: 2.98
Tested at LTC with identical scaling:
FRC:
https://tests.stockfishchess.org/tests/view/62c26a3c9e7d9997a12d6caf
Elo: 16.20 +-2.5 (95%) LOS: 100.0%
Total: 10000 W: 1192 L: 726 D: 8082
Ptnml(0-2): 10, 403, 3727, 831, 29
nElo: 44.12 +-6.7 (95%) PairsRatio: 2.08
DFRC:
https://tests.stockfishchess.org/tests/view/62c26a539e7d9997a12d6cb2
Elo: 40.94 +-3.0 (95%) LOS: 100.0%
Total: 10000 W: 2215 L: 1042 D: 6743
Ptnml(0-2): 10, 410, 3053, 1451, 76
nElo: 92.77 +-6.9 (95%) PairsRatio: 3.64
This is due to the mixing in a significant fraction of DFRC training data in the final training round. The net is
trained using the easy_train.py script in the following way:
```
python easy_train.py \
--training-dataset=../Leela-dfrc_n5000.binpack \
--experiment-name=2 \
--nnue-pytorch-branch=vondele/nnue-pytorch/lossScan4 \
--additional-training-arg=--param-index=2 \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--start-from-engine-test-net True \
--tui=False \
--seed=$RANDOM \
--max_epoch=800 \
--auto-exit-timeout-on-training-finished=900 \
--network-testing-threads 8 \
--num-workers 12
```
where the data set used (Leela-dfrc_n5000.binpack) is a combination of our previous best data set (mix of Leela and some SF data) and DFRC data, interleaved to form:
The data is available in https://drive.google.com/drive/folders/1S9-ZiQa_3ApmjBtl2e8SyHxj4zG4V8gG?usp=sharing
Leela mix: https://drive.google.com/file/d/1JUkMhHSfgIYCjfDNKZUMYZt6L5I7Ra6G/view?usp=sharing
DFRC: https://drive.google.com/file/d/17vDaff9LAsVo_1OfsgWAIYqJtqR8aHlm/view?usp=sharing
The training branch used is
https://github.com/vondele/nnue-pytorch/commits/lossScan4
A PR to the main trainer repo will be made later. This contains a revised loss function, now computing the loss from the score based on the win rate model, which is a more accurate representation than what we had before. Scaling constants are tweaked there as well.
closes https://github.com/official-stockfish/Stockfish/pull/4100
Bench: 5186781
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
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
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
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
# Use only 'java' to analyze code written in Java, Kotlin, or both
# Use only 'javascript' to analyze code written in JavaScript, TypeScript or both
# Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
steps:
- name:Checkout repository
uses:actions/checkout@v4
with:
persist-credentials:false
# Initializes the CodeQL tools for scanning.
- name:Initialize CodeQL
uses:github/codeql-action/init@v3
with:
languages:${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
- 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
# test help target
- make help
# Verify bench number against various builds
- export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
- make clean && make -j2 ARCH=x86-64-modern optimize=no debug=yes build && ../tests/signature.sh $benchref
- export CXXFLAGS="-Werror"
- make clean && make -j2 ARCH=x86-64-modern build && ../tests/signature.sh $benchref
- make clean && make -j2 ARCH=x86-64-ssse3 build && ../tests/signature.sh $benchref
- make clean && make -j2 ARCH=x86-64-sse3-popcnt build && ../tests/signature.sh $benchref
- make clean && make -j2 ARCH=x86-64 build && ../tests/signature.sh $benchref
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=general-64 build && ../tests/signature.sh $benchref; fi
- if [[ "$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-sse41-popcnt build && ../tests/signature.sh $benchref; fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-32-sse2 build && ../tests/signature.sh $benchref; fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=x86-32 build && ../tests/signature.sh $benchref; fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then make clean && make -j2 ARCH=general-32 build && ../tests/signature.sh $benchref; fi
# workaround: exclude a custom version of llvm+clang, which doesn't find llvm-profdata on ubuntu
- if [[ "$TRAVIS_OS_NAME" != "linux" || "$COMP" == "gcc" ]]; then make clean && make -j2 ARCH=x86-64-modern profile-build && ../tests/signature.sh $benchref; fi
# compile only for some more advanced architectures (might not run in travis)
- make clean && make -j2 ARCH=x86-64-avx2 build
- make clean && make -j2 ARCH=x86-64-bmi2 build
- make clean && make -j2 ARCH=x86-64-avx512 build
- make clean && make -j2 ARCH=x86-64-vnni512 build
- make clean && make -j2 ARCH=x86-64-vnni256 build
#
# Check perft and reproducible search
- make clean && make -j2 ARCH=x86-64-modern build
- ../tests/perft.sh
- ../tests/reprosearch.sh
#
# Valgrind
#
- export CXXFLAGS="-O1 -fno-inline"
- if [ -x "$(command -v valgrind )" ]; then make clean && make -j2 ARCH=x86-64-modern 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-modern 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-modern sanitize=thread optimize=no debug=yes build > /dev/null && ../tests/instrumented.sh --sanitizer-thread; fi
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, 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
Stockfish has support for 32 or 64-bit CPUs, certain hardware
instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Stockfish
directly from the source code with the included Makefile in the folder
`src`. In general it is recommended to run `make help` to see a list of make
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
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:
```
./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.
__See [Contributing Guide](CONTRIBUTING.md).__
### Donating hardware
Improving Stockfish requires a massive amount of testing. You can donate
your hardware resources by installing the [Fishtest Worker](https://github.com/glinscott/fishtest/wiki/Running-the-worker:-overview)
and view the current tests on [Fishtest](https://tests.stockfishchess.org/tests).
Improving Stockfish requires a massive amount of testing. You can donate your
hardware resources by installing the [Fishtest Worker][worker-link] and viewing
the current tests on [Fishtest][fishtest-link].
### Improving the code
If you want to help improve the code, there are several valuable resources:
* [In this wiki,](https://www.chessprogramming.org) many techniques used in
In the [chessprogramming wiki][programming-link], many techniques used in
Stockfish are explained with a lot of background information.
The [section on Stockfish][programmingsf-link] describes many features
and techniques used by Stockfish. However, it is generic rather than
focused on Stockfish's precise implementation.
* [The section on Stockfish](https://www.chessprogramming.org/Stockfish)
describes many features and techniques used by Stockfish. However, it is
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 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)
The engine testing is done on [Fishtest][fishtest-link].
If you want to help improve Stockfish, please read this [guideline][guideline-link]
first, where the basics of Stockfish development are explained.
Discussions about Stockfish take place these days mainly in the Stockfish
[Discord server][discord-link]. This is also the best place to ask questions
about the codebase and how to improve it.
## Compiling Stockfish
Stockfish has support for 32 or 64-bit CPUs, certain hardware instructions,
big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Stockfish directly from the
source code with the included Makefile in the folder `src`. In general, it is
recommended to run `make help` to see a list of make targets with corresponding
descriptions. An example suitable for most Intel and AMD chips:
```
cd src
make -j profile-build
```
Detailed compilation instructions for all platforms can be found in our
[documentation][wiki-compile-link]. Our wiki also has information about
the [UCI commands][wiki-uci-link] supported by Stockfish.
## Terms of use
Stockfish is free, and distributed under the**GNU General Public License version 3**
(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
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.
Stockfish is free and distributed under the
[**GNU General Public License version 3**][license-link] (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 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, 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.
The only real limitation is that whenever you distribute Stockfish in 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 GPL v3.
For full details, read the copy of the GPL v3 found in the file named
*Copying.txt*.
## Acknowledgements
Stockfish uses neural networks trained on [data provided by the Leela Chess Zero
project][lc0-data-link], which is made available under the [Open Database License][odbl-link] (ODbL).
# if defined(TARGET_OS_IPHONE) && !defined(INCBIN_SILENCE_BITCODE_WARNING)
# warning "incbin is incompatible with bitcode. Using the library will break upload to App Store if you have bitcode enabled. Add `#define INCBIN_SILENCE_BITCODE_WARNING` before including this header to silence this warning."
# endif
/* The directives are different for Apple branded compilers */
#define TUNE_CONDITIONS() int UNIQUE(c, __LINE__) = (Conditions.init(__COUNTER__), 0); \
TUNE(Conditions,set_conditions)
}// namespace Stockfish
#endif // #ifndef TUNE_H_INCLUDED
#endif // #ifndef TUNE_H_INCLUDED
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