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
since master only tries nullmoves on cutNodes already with 99.0224% of the
cases running bench, We can try null moves at 100% of cutNodes and achieve such
simplification, by making passing false already equivalent to passing !cutNode
This is a more correct form of PR #5482
Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/66941c044ff211be9d4ebf5f
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 153216 W: 39856 L: 39764 D: 73596
Ptnml(0-2): 590, 18174, 38979, 18284, 581
Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/6694e5cd4ff211be9d4ebfdf
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 67842 W: 17178 L: 17004 D: 33660
Ptnml(0-2): 52, 7437, 18759, 7631, 42
closes https://github.com/official-stockfish/Stockfish/pull/5490
bench: 1345400
Co-Authored-By: FauziAkram <11150271+fauziakram@users.noreply.github.com>
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
