Merge branch 'master' into clusterMergeMaster14

closes https://github.com/official-stockfish/Stockfish/pull/3980
This commit is contained in:
Joost VandeVondele
2022-04-15 09:27:16 +02:00
59 changed files with 1344 additions and 1232 deletions
+75 -19
View File
@@ -5,6 +5,7 @@ on:
- master
- tools
- github_ci
- github_ci_armv7
pull_request:
branches:
- master
@@ -20,6 +21,12 @@ jobs:
strategy:
matrix:
config:
# set the variable for the required tests:
# run_expensive_tests: true
# run_32bit_tests: true
# run_64bit_tests: true
# run_armv8_tests: true
# run_armv7_tests: true
- {
name: "Ubuntu 20.04 GCC",
os: ubuntu-20.04,
@@ -35,18 +42,31 @@ jobs:
os: ubuntu-20.04,
compiler: clang++,
comp: clang,
run_expensive_tests: false,
run_32bit_tests: true,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "Ubuntu 20.04 NDK armv8",
os: ubuntu-20.04,
compiler: aarch64-linux-android21-clang++,
comp: ndk,
run_armv8_tests: true,
shell: 'bash {0}'
}
- {
name: "Ubuntu 20.04 NDK armv7",
os: ubuntu-20.04,
compiler: armv7a-linux-androideabi21-clang++,
comp: ndk,
run_armv7_tests: true,
shell: 'bash {0}'
}
- {
name: "MacOS 10.15 Apple Clang",
os: macos-10.15,
compiler: clang++,
comp: clang,
run_expensive_tests: false,
run_32bit_tests: false,
run_64bit_tests: true,
shell: 'bash {0}'
}
@@ -55,33 +75,37 @@ jobs:
os: macos-10.15,
compiler: g++-10,
comp: gcc,
run_expensive_tests: false,
run_32bit_tests: false,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "Windows 2019 Mingw-w64 GCC x86_64",
os: windows-2019,
name: "Windows 2022 Mingw-w64 GCC x86_64",
os: windows-2022,
compiler: g++,
comp: gcc,
run_expensive_tests: false,
run_32bit_tests: false,
comp: mingw,
run_64bit_tests: true,
msys_sys: 'mingw64',
msys_env: 'x86_64',
msys_env: 'x86_64-gcc',
shell: 'msys2 {0}'
}
- {
name: "Windows 2019 Mingw-w64 GCC i686",
os: windows-2019,
name: "Windows 2022 Mingw-w64 GCC i686",
os: windows-2022,
compiler: g++,
comp: gcc,
run_expensive_tests: false,
comp: mingw,
run_32bit_tests: true,
run_64bit_tests: false,
msys_sys: 'mingw32',
msys_env: 'i686',
msys_env: 'i686-gcc',
shell: 'msys2 {0}'
}
- {
name: "Windows 2022 Mingw-w64 Clang x86_64",
os: windows-2022,
compiler: clang++,
comp: clang,
run_64bit_tests: true,
msys_sys: 'clang64',
msys_env: 'clang-x86_64-clang',
shell: 'msys2 {0}'
}
@@ -98,14 +122,14 @@ jobs:
if: runner.os == 'Linux'
run: |
sudo apt update
sudo apt install expect valgrind g++-multilib
sudo apt install expect valgrind g++-multilib qemu-user
- name: Setup msys and install required packages
if: runner.os == 'Windows'
uses: msys2/setup-msys2@v2
with:
msystem: ${{matrix.config.msys_sys}}
install: mingw-w64-${{matrix.config.msys_env}}-gcc make git expect
install: mingw-w64-${{matrix.config.msys_env}} make git expect
- name: Download the used network from the fishtest framework
run: |
@@ -118,6 +142,7 @@ jobs:
- name: Check compiler
run: |
export PATH=$PATH:$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin
$COMPILER -v
- name: Test help target
@@ -239,6 +264,37 @@ jobs:
make clean
make -j2 ARCH=x86-64-vnni256 build
# armv8 tests
- name: Test armv8 build
if: ${{ matrix.config.run_armv8_tests }}
run: |
export PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv8 build
../tests/signature.sh $benchref
# armv7 tests
- name: Test armv7 build
if: ${{ matrix.config.run_armv7_tests }}
run: |
export PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv7 build
../tests/signature.sh $benchref
- name: Test armv7-neon build
if: ${{ matrix.config.run_armv7_tests }}
run: |
export PATH=$ANDROID_NDK_HOME/toolchains/llvm/prebuilt/linux-x86_64/bin:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv7-neon build
../tests/signature.sh $benchref
# Other tests
- name: Check perft and search reproducibility
+5
View File
@@ -31,6 +31,7 @@ Arjun Temurnikar
Artem Solopiy (EntityFX)
Auguste Pop
Balint Pfliegel
Ben Chaney (Chaneybenjamini)
Ben Koshy (BKSpurgeon)
Bill Henry (VoyagerOne)
Bojun Guo (noobpwnftw, Nooby)
@@ -132,6 +133,7 @@ Michael Whiteley (protonspring)
Michel Van den Bergh (vdbergh)
Miguel Lahoz (miguel-l)
Mikael Bäckman (mbootsector)
Mike Babigian (Farseer)
Mira
Miroslav Fontán (Hexik)
Moez Jellouli (MJZ1977)
@@ -153,6 +155,7 @@ Pascal Romaret
Pasquale Pigazzini (ppigazzini)
Patrick Jansen (mibere)
pellanda
Peter Schneider (pschneider1968)
Peter Zsifkovits (CoffeeOne)
Praveen Kumar Tummala (praveentml)
Rahul Dsilva (silversolver1)
@@ -165,6 +168,7 @@ Rodrigo Exterckötter Tjäder
Ron Britvich (Britvich)
Ronald de Man (syzygy1, syzygy)
rqs
Rui Coelho (ruicoelhopedro)
Ryan Schmitt
Ryan Takker
Sami Kiminki (skiminki)
@@ -194,6 +198,7 @@ tttak
Unai Corzo (unaiic)
Uri Blass (uriblass)
Vince Negri (cuddlestmonkey)
xefoci7612
zz4032
+42 -32
View File
@@ -10,24 +10,28 @@ Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in ord
to be used comfortably. Read the documentation for your GUI of choice for information
about how to use Stockfish with it.
The Stockfish engine features two evaluation functions for chess, the classical
evaluation based on handcrafted terms, and the NNUE evaluation based on efficiently
updatable neural networks. The classical evaluation runs efficiently on almost all
CPU architectures, while the NNUE evaluation benefits from the vector
intrinsics available on most CPUs (sse2, avx2, neon, or similar).
The Stockfish engine features two evaluation functions for chess. The efficiently
updatable neural network (NNUE) based evaluation is the default and by far the strongest.
The classical evaluation based on handcrafted terms remains available. The strongest
network is integrated in the binary and downloaded automatically during the build process.
The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2,
avx2, neon, or similar).
## Files
This distribution of Stockfish consists of the following files:
* [Readme.md](https://github.com/official-stockfish/Stockfish/blob/master/README.md), the file you are currently reading.
* [Readme.md](https://github.com/official-stockfish/Stockfish/blob/master/README.md),
the file you are currently reading.
* [Copying.txt](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt), a text file containing the GNU General Public License version 3.
* [Copying.txt](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt),
a text file containing the GNU General Public License version 3.
* [AUTHORS](https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS), a text file with the list of authors for the project
* [AUTHORS](https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS),
a text file with the list of authors for the project
* [src](https://github.com/official-stockfish/Stockfish/tree/master/src), a subdirectory containing the full source code, including a Makefile
* [src](https://github.com/official-stockfish/Stockfish/tree/master/src),
a subdirectory containing the full source code, including a Makefile
that can be used to compile Stockfish on Unix-like systems.
* a file with the .nnue extension, storing the neural network for the NNUE
@@ -37,7 +41,7 @@ This distribution of Stockfish consists of the following files:
The Universal Chess Interface (UCI) is a standard protocol used to communicate with
a chess engine, and is the recommended way to do so for typical graphical user interfaces
(GUI) or chess tools. Stockfish implements the majority of it options as described
(GUI) or chess tools. Stockfish implements the majority of its options as described
in [the UCI protocol](https://www.shredderchess.com/download/div/uci.zip).
Developers can see the default values for UCI options available in Stockfish by typing
@@ -68,9 +72,9 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
* #### EvalFile
The name of the file of the NNUE evaluation parameters. Depending on the GUI the
filename might have to include the full path to the folder/directory that contains the file.
Other locations, such as the directory that contains the binary and the working directory,
are also searched.
filename might have to include the full path to the folder/directory that contains
the file. Other locations, such as the directory that contains the binary and the
working directory, are also searched.
* #### UCI_AnalyseMode
An option handled by your GUI.
@@ -103,7 +107,7 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
Example: `C:\tablebases\wdl345;C:\tablebases\wdl6;D:\tablebases\dtz345;D:\tablebases\dtz6`
It is recommended to store .rtbw files on an SSD. There is no loss in storing
the .rtbz files on a regular HD. It is recommended to verify all md5 checksums
the .rtbz files on a regular HDD. It is recommended to verify all md5 checksums
of the downloaded tablebase files (`md5sum -c checksum.md5`) as corruption will
lead to engine crashes.
@@ -138,8 +142,9 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
For developers the following non-standard commands might be of interest, mainly useful for debugging:
* #### bench *ttSize threads limit fenFile limitType evalType*
Performs a standard benchmark using various options. The signature of a version (standard node
count) is obtained using all defaults. `bench` is currently `bench 16 1 13 default depth mixed`.
Performs a standard benchmark using various options. The signature of a version
(standard node count) is obtained using all defaults. `bench` is currently
`bench 16 1 13 default depth mixed`.
* #### compiler
Give information about the compiler and environment used for building a binary.
@@ -175,22 +180,27 @@ on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
tools to train and develop the NNUE networks. On CPUs supporting modern vector instructions
(avx2 and similar), the NNUE evaluation results in much stronger playing strength, even
if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps
is typical).
[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first
version of the needed tools to train and develop the NNUE networks. Today, more
advanced training tools are available in
[the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/),
while data generation tools are available in
[a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation
results in much stronger playing strength, even if the nodes per second computed by
the engine is somewhat lower (roughly 80% of nps is typical).
Notes:
1) the NNUE evaluation depends on the Stockfish binary and the network parameter
file (see the EvalFile UCI option). Not every parameter file is compatible with a given
Stockfish binary, but the default value of the EvalFile UCI option is the name of a network
that is guaranteed to be compatible with that binary.
1) the NNUE evaluation depends on the Stockfish binary and the network parameter file
(see the EvalFile UCI option). Not every parameter file is compatible with a given
Stockfish binary, but the default value of the EvalFile UCI option is the name of a
network that is guaranteed to be compatible with that binary.
2) to use the NNUE evaluation, the additional data file with neural network parameters
needs to be available. Normally, this file is already embedded in the binary or it
can be downloaded. The filename for the default (recommended) net can be found as the default
needs to be available. Normally, this file is already embedded in the binary or it can
be downloaded. The filename for the default (recommended) net can be found as the default
value of the `EvalFile` UCI option, with the format `nn-[SHA256 first 12 digits].nnue`
(for instance, `nn-c157e0a5755b.nnue`). This file can be downloaded from
```
@@ -345,10 +355,10 @@ 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.
some way, you MUST always include the license and the full source code
(or a pointer to where the source code can be found) to generate the
exact binary you are distributing. If you make any changes to the
source code, these changes must also be made available under the GPL v3.
For full details, read the copy of the GPL v3 found in the file named
[*Copying.txt*](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt).
+93 -68
View File
@@ -1,118 +1,127 @@
Contributors to Fishtest with >10,000 CPU hours, as of Jun 29, 2021.
Contributors to Fishtest with >10,000 CPU hours, as of 2022-02-05.
Thank you!
Username CPU Hours Games played
-----------------------------------------------------
noobpwnftw 27649494 1834734733
mlang 1426107 89454622
dew 1380910 82831648
------------------------------------------------------------------
noobpwnftw 30730952 2158431735
mlang 2729669 187335452
technologov 1696847 74478658
dew 1635640 97483012
grandphish2 1062754 64955639
tvijlbrief 795993 51894442
okrout 773704 63465204
TueRens 766198 47770388
mibere 703840 46867607
grandphish2 692707 41737913
tvijlbrief 669642 42371594
JojoM 597778 35297180
TueRens 519226 31823562
cw 458421 30307421
fastgm 439667 25950040
gvreuls 436599 28177460
JojoM 703005 42689868
pemo 634102 29868807
linrock 626939 17408017
gvreuls 517442 33605006
cw 503905 33850487
fastgm 482847 29004732
crunchy 427035 27344275
CSU_Dynasty 374765 25106278
Fisherman 326901 21822979
ctoks 325477 21767943
velislav 295343 18844324
linrock 292789 10624427
bcross 278584 19488961
okrout 262818 13803272
pemo 245982 11376085
CSU_Dynasty 415864 28116776
ctoks 403102 26737127
oz 357710 26490208
bcross 331095 23165889
Fisherman 327231 21829379
velislav 321708 20729264
leszek 303654 19063973
Dantist 251015 15843226
mgrabiak 231973 15162494
glinscott 217799 13780820
leszek 212346 12959025
robal 213960 13665726
nordlandia 211692 13484886
drabel 200914 13755384
bking_US 198894 11876016
drabel 196463 13450602
robal 195473 12375650
mgrabiak 187226 12016564
Dantist 183202 10990484
mhoram 180229 11610075
Thanar 179852 12365359
vdv 175274 9889046
vdv 175544 9904472
spams 157128 10319326
marrco 150295 9402141
marrco 150300 9402229
sqrt2 147963 9724586
mhoram 141278 8901241
vdbergh 137429 8955089
CoffeeOne 137100 5024116
vdbergh 137041 8926915
malala 136182 8002293
xoto 133702 9156676
davar 122092 7960001
xoto 133759 9159372
rpngn 131285 8657757
davar 122661 7996937
dsmith 122059 7570238
amicic 119659 7937885
Data 113305 8220352
BrunoBanani 112960 7436849
CypressChess 108321 7759588
MaZePallas 102823 6633619
sterni1971 100532 5880772
sunu 100167 7040199
DesolatedDodo 99038 6414626
ElbertoOne 99028 7023771
skiminki 98123 6478402
brabos 92118 6186135
oz 92100 6486640
cuistot 90358 5351004
psk 89957 5984901
amicic 89156 5392305
sunu 88851 6028873
racerschmacer 85712 6119648
Vizvezdenec 83761 5344740
sschnee 83003 4840890
0x3C33 82614 5271253
BRAVONE 81239 5054681
racerschmacer 80899 5759262
cuistot 80300 4606144
nssy 76497 5259388
teddybaer 75125 5407666
Pking_cda 73776 5293873
zeryl 73335 4774257
jromang 72192 5057715
solarlight 70517 5028306
dv8silencer 70287 3883992
Bobo1239 68515 4652287
manap 66273 4121774
skiminki 65088 4023328
tinker 64333 4268790
sschnee 60767 3500800
qurashee 57344 3168264
yurikvelo 63371 4335060
qurashee 61208 3429862
robnjr 57262 4053117
Wolfgang 57014 3561352
Freja 56938 3733019
ttruscott 56010 3680085
rkl 55132 4164467
renouve 53811 3501516
finfish 51360 3370515
eva42 51272 3599691
Calis007 51182 3131552
eastorwest 51058 3451555
rap 49985 3219146
pb00067 49727 3298270
Spprtr 48260 3141959
bigpen0r 47667 3336927
ronaldjerum 47654 3240695
bigpen0r 47653 3335327
eastorwest 47585 3221629
MaxKlaxxMiner 47584 2972142
biffhero 46564 3111352
megaman7de 45992 2952006
Fifis 45843 3088497
VoyagerOne 45476 3452465
yurikvelo 44834 3034550
speedycpu 43842 3003273
jbwiebe 43305 2805433
Spprtr 42279 2680153
DesolatedDodo 42007 2447516
Antihistamine 41788 2761312
mhunt 41735 2691355
homyur 39893 2850481
gri 39871 2515779
Fifis 38776 2529121
oryx 38724 2966648
SC 37290 2731014
oryx 38867 2976992
SC 37299 2731694
Garf 37213 2986270
csnodgrass 36207 2688994
jmdana 36157 2210661
strelock 34716 2074055
rpngn 33951 2057395
Garf 33922 2751802
EthanOConnor 33370 2090311
slakovv 32915 2021889
armo9494 32129 2551682
tolkki963 32114 1932256
manapbk 30987 1810399
DMBK 30675 2383552
Prcuvu 30377 2170122
anst 30301 2190091
jkiiski 30136 1904470
gopeto 29886 1979118
hyperbolic.tom 29840 2017394
chuckstablers 29659 2093438
Pyafue 29650 1902349
Wolfgang 29260 1658936
zeryl 28156 1579911
OuaisBla 27636 1578800
DMBK 27051 1999456
chriswk 26902 1868317
achambord 26582 1767323
Patrick_G 26276 1801617
@@ -121,11 +130,13 @@ SFTUser 25182 1675689
nabildanial 24942 1519409
Sharaf_DG 24765 1786697
ncfish1 24411 1520927
rodneyc 24227 1409514
rodneyc 24275 1410450
agg177 23890 1395014
belzedar94 23707 1593860
JanErik 23408 1703875
Isidor 23388 1680691
Norabor 23164 1591830
Norabor 23339 1602636
Ente 23093 1642458
cisco2015 22897 1762669
Zirie 22542 1472937
team-oh 22272 1636708
@@ -138,68 +149,82 @@ nesoneg 21494 1463031
sphinx 21211 1384728
jjoshua2 21001 1423089
horst.prack 20878 1465656
Ente 20865 1477066
user213718 20783 1379584
0xB00B1ES 20590 1208666
j3corre 20405 941444
Adrian.Schmidt123 20316 1281436
wei 19973 1745989
MaxKlaxxMiner 19850 1009176
Roady 19848 1335928
rstoesser 19569 1293588
gopeto 19491 1174952
eudhan 19274 1283717
vulcan 18871 1729392
jundery 18445 1115855
megaman7de 18377 1067540
iisiraider 18247 1101015
ville 17883 1384026
chris 17698 1487385
purplefishies 17595 1092533
dju 17353 978595
kdave 17183 1242754
DragonLord 17014 1162790
thirdlife 16996 447356
spcc 16932 1130940
fishtester 16644 1123000
Ulysses 16490 1184400
IgorLeMasson 16064 1147232
ako027ako 15671 1173203
chuckstablers 15289 891576
Nikolay.IT 15154 1068349
Andrew Grant 15114 895539
OssumOpossum 14857 1007129
Karby 14808 867120
AndreasKrug 14608 1152093
enedene 14476 905279
jsys14 14340 844792
bpfliegel 14298 884523
mpx86 14019 759568
jpulman 13982 870599
crocogoat 13803 1117422
joster 13794 950160
Nesa92 13786 1114691
mbeier 13650 1044928
Hjax 13535 915487
jsys14 13459 785000
Dark_wizzie 13422 1007152
Rudolphous 13244 883140
MarcusTullius 13221 843169
Machariel 13010 863104
mabichito 12903 749391
thijsk 12886 722107
AdrianSA 12860 804972
infinigon 12807 937332
Flopzee 12698 894821
fatmurphy 12547 853210
Rudolphous 12520 832340
scuzzi 12511 845761
SapphireBrand 12416 969604
modolief 12386 896470
Machariel 12335 810784
Farseer 12249 694108
pgontarz 12151 848794
stocky 11954 699440
mschmidt 11941 803401
dbernier 11609 818636
Maxim 11543 836024
pirt 11516 894513
infinity 11470 727027
aga 11409 695071
torbjo 11395 729145
Thomas A. Anderson 11372 732094
savage84 11358 670860
markkulix 11331 739098
FormazChar 11308 847735
d64 11263 789184
MooTheCow 11237 720174
snicolet 11106 869170
ali-al-zhrani 11086 767926
AndreasKrug 10875 887457
pirt 10806 836519
ali-al-zhrani 11098 768494
whelanh 11067 235676
basepi 10637 744851
michaelrpg 10508 739039
Cubox 10621 826448
michaelrpg 10509 739239
OIVAS7572 10420 995586
dzjp 10343 732529
aga 10302 622975
Garruk 10332 703905
ols 10259 570669
lbraesch 10252 647825
FormazChar 10059 757283
Jackfish 10098 682338
-88
View File
@@ -1,88 +0,0 @@
version: 1.0.{build}
clone_depth: 50
branches:
only:
- master
# Operating system (build VM template)
os: Visual Studio 2019
# Build platform, i.e. x86, x64, AnyCPU. This setting is optional.
platform:
- x86
- x64
# build Configuration, i.e. Debug, Release, etc.
configuration:
- Debug
- Release
matrix:
# The build fail immediately once one of the job fails
fast_finish: true
# Scripts that are called at very beginning, before repo cloning
init:
- cmake --version
- msbuild /version
before_build:
- ps: |
# Get sources
$src = get-childitem -Path *.cpp -Recurse | select -ExpandProperty FullName
$src = $src -join ' '
$src = $src.Replace("\", "/")
# Build CMakeLists.txt
$t = 'cmake_minimum_required(VERSION 3.17)',
'project(Stockfish)',
'set(CMAKE_CXX_STANDARD 17)',
'set(CMAKE_CXX_STANDARD_REQUIRED ON)',
'set (CMAKE_CXX_EXTENSIONS OFF)',
'set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_SOURCE_DIR}/src)',
'set(source_files', $src, ')',
'add_executable(stockfish ${source_files})'
# Write CMakeLists.txt withouth BOM
$MyPath = (Get-Item -Path "." -Verbose).FullName + '\CMakeLists.txt'
$Utf8NoBomEncoding = New-Object System.Text.UTF8Encoding $False
[System.IO.File]::WriteAllLines($MyPath, $t, $Utf8NoBomEncoding)
# Obtain bench reference from git log
$b = git log HEAD | sls "\b[Bb]ench[ :]+[0-9]{7}" | select -first 1
$bench = $b -match '\D+(\d+)' | % { $matches[1] }
Write-Host "Reference bench:" $bench
$g = "Visual Studio 16 2019"
If (${env:PLATFORM} -eq 'x64') { $a = "x64" }
If (${env:PLATFORM} -eq 'x86') { $a = "Win32" }
cmake -G "${g}" -A ${a} .
Write-Host "Generated files for: " $g $a
build_script:
- cmake --build . --config %CONFIGURATION% -- /verbosity:minimal
- ps: |
# Download default NNUE net from fishtest
$nnuenet = Get-Content -Path src\evaluate.h | Select-String -CaseSensitive -Pattern "EvalFileDefaultName" | Select-String -CaseSensitive -Pattern "nn-[a-z0-9]{12}.nnue"
$dummy = $nnuenet -match "(?<nnuenet>nn-[a-z0-9]{12}.nnue)"
$nnuenet = $Matches.nnuenet
Write-Host "Default net:" $nnuenet
$nnuedownloadurl = "https://tests.stockfishchess.org/api/nn/$nnuenet"
$nnuefilepath = "src\${env:CONFIGURATION}\$nnuenet"
if (Test-Path -Path $nnuefilepath) {
Write-Host "Already available."
} else {
Write-Host "Downloading $nnuedownloadurl to $nnuefilepath"
Invoke-WebRequest -Uri $nnuedownloadurl -OutFile $nnuefilepath
}
before_test:
- cd src/%CONFIGURATION%
- stockfish bench 2> out.txt >NUL
- ps: |
# Verify bench number
$s = (gc "./out.txt" | out-string)
$r = ($s -match 'Nodes searched \D+(\d+)' | % { $matches[1] })
Write-Host "Engine bench:" $r
Write-Host "Reference bench:" $bench
If ($r -ne $bench) { exit 1 }
+98 -48
View File
@@ -1,5 +1,5 @@
# Stockfish, a UCI chess playing engine derived from Glaurung 2.1
# Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
# Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
#
# Stockfish is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
@@ -19,11 +19,29 @@
### Section 1. General Configuration
### ==========================================================================
### Establish the operating system name
KERNEL = $(shell uname -s)
ifeq ($(KERNEL),Linux)
OS = $(shell uname -o)
endif
### Target Windows OS
ifeq ($(OS),Windows_NT)
ifneq ($(COMP),ndk)
target_windows = yes
endif
else ifeq ($(COMP),mingw)
target_windows = yes
ifeq ($(WINE_PATH),)
WINE_PATH = $(shell which wine)
endif
endif
### Executable name
ifeq ($(COMP),mingw)
EXE = stockfish.exe
ifeq ($(target_windows),yes)
EXE = stockfish.exe
else
EXE = stockfish
EXE = stockfish
endif
### Installation dir definitions
@@ -32,9 +50,9 @@ BINDIR = $(PREFIX)/bin
### Built-in benchmark for pgo-builds
ifeq ($(SDE_PATH),)
PGOBENCH = ./$(EXE) bench
PGOBENCH = $(WINE_PATH) ./$(EXE) bench
else
PGOBENCH = $(SDE_PATH) -- ./$(EXE) bench
PGOBENCH = $(SDE_PATH) -- $(WINE_PATH) ./$(EXE) bench
endif
### Source and object files
@@ -47,12 +65,6 @@ OBJS = $(notdir $(SRCS:.cpp=.o))
VPATH = syzygy:nnue:nnue/features
### Establish the operating system name
KERNEL = $(shell uname -s)
ifeq ($(KERNEL),Linux)
OS = $(shell uname -o)
endif
### ==========================================================================
### Section 2. High-level Configuration
### ==========================================================================
@@ -78,6 +90,7 @@ endif
# ssse3 = yes/no --- -mssse3 --- Use Intel Supplemental Streaming SIMD Extensions 3
# sse41 = yes/no --- -msse4.1 --- Use Intel Streaming SIMD Extensions 4.1
# avx2 = yes/no --- -mavx2 --- Use Intel Advanced Vector Extensions 2
# avxvnni = yes/no --- -mavxvnni --- Use Intel Vector Neural Network Instructions AVX
# avx512 = yes/no --- -mavx512bw --- Use Intel Advanced Vector Extensions 512
# vnni256 = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 256
# vnni512 = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 512
@@ -101,8 +114,8 @@ endif
# explicitly check for the list of supported architectures (as listed with make help),
# the user can override with `make ARCH=x86-32-vnni256 SUPPORTED_ARCH=true`
ifeq ($(ARCH), $(filter $(ARCH), \
x86-64-vnni512 x86-64-vnni256 x86-64-avx512 x86-64-bmi2 x86-64-avx2 \
x86-64-sse41-popcnt x86-64-modern x86-64-ssse3 x86-64-sse3-popcnt \
x86-64-vnni512 x86-64-vnni256 x86-64-avx512 x86-64-avxvnni x86-64-bmi2 \
x86-64-avx2 x86-64-sse41-popcnt x86-64-modern x86-64-ssse3 x86-64-sse3-popcnt \
x86-64 x86-32-sse41-popcnt x86-32-sse2 x86-32 ppc-64 ppc-32 e2k \
armv7 armv7-neon armv8 apple-silicon general-64 general-32))
SUPPORTED_ARCH=true
@@ -123,11 +136,13 @@ sse2 = no
ssse3 = no
sse41 = no
avx2 = no
avxvnni = no
avx512 = no
vnni256 = no
vnni512 = no
neon = no
mpi = no
arm_version = 0
STRIP = strip
### 2.2 Architecture specific
@@ -139,7 +154,7 @@ ifeq ($(findstring x86,$(ARCH)),x86)
ifeq ($(findstring x86-32,$(ARCH)),x86-32)
arch = i386
bits = 32
sse = yes
sse = no
mmx = yes
else
arch = x86_64
@@ -194,6 +209,17 @@ ifeq ($(findstring -avx2,$(ARCH)),-avx2)
avx2 = yes
endif
ifeq ($(findstring -avxvnni,$(ARCH)),-avxvnni)
popcnt = yes
sse = yes
sse2 = yes
ssse3 = yes
sse41 = yes
avx2 = yes
avxvnni = yes
pext = yes
endif
ifeq ($(findstring -bmi2,$(ARCH)),-bmi2)
popcnt = yes
sse = yes
@@ -264,6 +290,7 @@ ifeq ($(ARCH),armv7)
arch = armv7
prefetch = yes
bits = 32
arm_version = 7
endif
ifeq ($(ARCH),armv7-neon)
@@ -272,6 +299,7 @@ ifeq ($(ARCH),armv7-neon)
popcnt = yes
neon = yes
bits = 32
arm_version = 7
endif
ifeq ($(ARCH),armv8)
@@ -279,6 +307,7 @@ ifeq ($(ARCH),armv8)
prefetch = yes
popcnt = yes
neon = yes
arm_version = 8
endif
ifeq ($(ARCH),apple-silicon)
@@ -286,6 +315,7 @@ ifeq ($(ARCH),apple-silicon)
prefetch = yes
popcnt = yes
neon = yes
arm_version = 8
endif
ifeq ($(ARCH),ppc-32)
@@ -349,29 +379,27 @@ ifeq ($(COMP),gcc)
endif
endif
ifeq ($(target_windows),yes)
LDFLAGS += -static
endif
ifeq ($(COMP),mingw)
comp=mingw
ifeq ($(KERNEL),Linux)
ifeq ($(bits),64)
ifeq ($(shell which x86_64-w64-mingw32-c++-posix),)
ifeq ($(shell which x86_64-w64-mingw32-c++-posix 2> /dev/null),)
CXX=x86_64-w64-mingw32-c++
else
CXX=x86_64-w64-mingw32-c++-posix
endif
else
ifeq ($(shell which i686-w64-mingw32-c++-posix),)
ifeq ($(shell which i686-w64-mingw32-c++-posix 2> /dev/null),)
CXX=i686-w64-mingw32-c++
else
CXX=i686-w64-mingw32-c++-posix
endif
endif
else
CXX=g++
endif
CXXFLAGS += -pedantic -Wextra -Wshadow
LDFLAGS += -static
endif
ifeq ($(COMP),icc)
@@ -383,17 +411,19 @@ endif
ifeq ($(COMP),clang)
comp=clang
CXX=clang++
ifeq ($(target_windows),yes)
CXX=x86_64-w64-mingw32-clang++
endif
CXXFLAGS += -pedantic -Wextra -Wshadow
ifneq ($(KERNEL),Darwin)
ifneq ($(KERNEL),OpenBSD)
ifneq ($(KERNEL),FreeBSD)
ifeq ($(filter $(KERNEL),Darwin OpenBSD FreeBSD),)
ifeq ($(target_windows),)
ifneq ($(RTLIB),compiler-rt)
LDFLAGS += -latomic
endif
endif
endif
endif
ifeq ($(arch),$(filter $(arch),armv7 armv8))
ifeq ($(OS),Android)
@@ -425,11 +455,19 @@ ifeq ($(COMP),ndk)
ifeq ($(arch),armv7)
CXX=armv7a-linux-androideabi16-clang++
CXXFLAGS += -mthumb -march=armv7-a -mfloat-abi=softfp -mfpu=neon
ifneq ($(shell which arm-linux-androideabi-strip 2>/dev/null),)
STRIP=arm-linux-androideabi-strip
else
STRIP=llvm-strip
endif
endif
ifeq ($(arch),armv8)
CXX=aarch64-linux-android21-clang++
ifneq ($(shell which aarch64-linux-android-strip 2>/dev/null),)
STRIP=aarch64-linux-android-strip
else
STRIP=llvm-strip
endif
endif
LDFLAGS += -static-libstdc++ -pie -lm -latomic
endif
@@ -443,6 +481,9 @@ else ifeq ($(comp),clang)
else
profile_make = gcc-profile-make
profile_use = gcc-profile-use
ifeq ($(KERNEL),Darwin)
EXTRAPROFILEFLAGS = -fvisibility=hidden
endif
endif
### Travis CI script uses COMPILER to overwrite CXX
@@ -503,10 +544,16 @@ ifeq ($(optimize),yes)
endif
endif
ifeq ($(comp),$(filter $(comp),gcc clang icc))
ifeq ($(KERNEL),Darwin)
ifeq ($(comp),$(filter $(comp),clang icc))
CXXFLAGS += -mdynamic-no-pic
endif
ifeq ($(comp),gcc)
ifneq ($(arch),arm64)
CXXFLAGS += -mdynamic-no-pic
endif
endif
endif
ifeq ($(comp),clang)
@@ -546,6 +593,13 @@ ifeq ($(avx2),yes)
endif
endif
ifeq ($(avxvnni),yes)
CXXFLAGS += -DUSE_VNNI -DUSE_AVXVNNI
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
CXXFLAGS += -mavxvnni
endif
endif
ifeq ($(avx512),yes)
CXXFLAGS += -DUSE_AVX512
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
@@ -596,7 +650,7 @@ ifeq ($(mmx),yes)
endif
ifeq ($(neon),yes)
CXXFLAGS += -DUSE_NEON
CXXFLAGS += -DUSE_NEON=$(arm_version)
ifeq ($(KERNEL),Linux)
ifneq ($(COMP),ndk)
ifneq ($(arch),armv8)
@@ -621,9 +675,7 @@ ifeq ($(optimize),yes)
ifeq ($(debug), no)
ifeq ($(comp),clang)
CXXFLAGS += -flto
ifneq ($(findstring MINGW,$(KERNEL)),)
CXXFLAGS += -fuse-ld=lld
else ifneq ($(findstring MSYS,$(KERNEL)),)
ifeq ($(target_windows),yes)
CXXFLAGS += -fuse-ld=lld
endif
LDFLAGS += $(CXXFLAGS)
@@ -634,25 +686,17 @@ ifeq ($(debug), no)
ifeq ($(gccisclang),)
CXXFLAGS += -flto
LDFLAGS += $(CXXFLAGS) -flto=jobserver
ifneq ($(findstring MINGW,$(KERNEL)),)
LDFLAGS += -save-temps
else ifneq ($(findstring MSYS,$(KERNEL)),)
LDFLAGS += -save-temps
endif
else
CXXFLAGS += -flto
LDFLAGS += $(CXXFLAGS)
endif
# To use LTO and static linking on windows, the tool chain requires a recent gcc:
# gcc version 10.1 in msys2 or TDM-GCC version 9.2 are known to work, older might not.
# So, only enable it for a cross from Linux by default.
# To use LTO and static linking on Windows,
# the tool chain requires gcc version 10.1 or later.
else ifeq ($(comp),mingw)
ifeq ($(KERNEL),Linux)
ifneq ($(arch),i386)
CXXFLAGS += -flto
LDFLAGS += $(CXXFLAGS) -flto=jobserver
endif
LDFLAGS += $(CXXFLAGS) -save-temps
endif
endif
endif
@@ -700,6 +744,7 @@ help:
@echo "x86-64-vnni512 > x86 64-bit with vnni support 512bit wide"
@echo "x86-64-vnni256 > x86 64-bit with vnni support 256bit wide"
@echo "x86-64-avx512 > x86 64-bit with avx512 support"
@echo "x86-64-avxvnni > x86 64-bit with avxvnni support"
@echo "x86-64-bmi2 > x86 64-bit with bmi2 support"
@echo "x86-64-avx2 > x86 64-bit with avx2 support"
@echo "x86-64-sse41-popcnt > x86 64-bit with sse41 and popcnt support"
@@ -762,7 +807,7 @@ profile-build: net config-sanity objclean profileclean
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) $(profile_make)
@echo ""
@echo "Step 2/4. Running benchmark for pgo-build ..."
$(PGOBENCH) > /dev/null
$(PGOBENCH) 2>&1 | tail -n 4
@echo ""
@echo "Step 3/4. Building optimized executable ..."
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) objclean
@@ -777,7 +822,7 @@ strip:
install:
-mkdir -p -m 755 $(BINDIR)
-cp $(EXE) $(BINDIR)
-strip $(BINDIR)/$(EXE)
$(STRIP) $(BINDIR)/$(EXE)
# clean all
clean: objclean profileclean
@@ -809,15 +854,16 @@ net:
# clean binaries and objects
objclean:
@rm -f $(EXE) *.o ./syzygy/*.o ./nnue/*.o ./nnue/features/*.o
@rm -f stockfish stockfish.exe *.o ./syzygy/*.o ./nnue/*.o ./nnue/features/*.o
# clean auxiliary profiling files
profileclean:
@rm -rf profdir
@rm -f bench.txt *.gcda *.gcno ./syzygy/*.gcda ./nnue/*.gcda ./nnue/features/*.gcda *.s
@rm -f stockfish.profdata *.profraw
@rm -f stockfish.exe.lto_wrapper_args
@rm -f stockfish.exe.ltrans.out
@rm -f stockfish.*args*
@rm -f stockfish.*lt*
@rm -f stockfish.res
@rm -f ./-lstdc++.res
default:
@@ -848,11 +894,13 @@ config-sanity: net
@echo "ssse3: '$(ssse3)'"
@echo "sse41: '$(sse41)'"
@echo "avx2: '$(avx2)'"
@echo "avxvnni: '$(avxvnni)'"
@echo "avx512: '$(avx512)'"
@echo "vnni256: '$(vnni256)'"
@echo "vnni512: '$(vnni512)'"
@echo "neon: '$(neon)'"
@echo "mpi: '$(mpi)'"
@echo "arm_version: '$(arm_version)'"
@echo ""
@echo "Flags:"
@echo "CXX: $(CXX)"
@@ -904,12 +952,14 @@ gcc-profile-make:
@mkdir -p profdir
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) \
EXTRACXXFLAGS='-fprofile-generate=profdir' \
EXTRACXXFLAGS+=$(EXTRAPROFILEFLAGS) \
EXTRALDFLAGS='-lgcov' \
all
gcc-profile-use:
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) \
EXTRACXXFLAGS='-fprofile-use=profdir -fno-peel-loops -fno-tracer' \
EXTRACXXFLAGS+=$(EXTRAPROFILEFLAGS) \
EXTRALDFLAGS='-lgcov' \
all
+2 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -87,6 +87,7 @@ const vector<string> Defaults = {
// Chess 960
"setoption name UCI_Chess960 value true",
"bbqnnrkr/pppppppp/8/8/8/8/PPPPPPPP/BBQNNRKR w HFhf - 0 1 moves g2g3 d7d5 d2d4 c8h3 c1g5 e8d6 g5e7 f7f6",
"nqbnrkrb/pppppppp/8/8/8/8/PPPPPPPP/NQBNRKRB w KQkq - 0 1",
"setoption name UCI_Chess960 value false"
};
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+52 -34
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -109,6 +109,7 @@ namespace Eval {
MemoryBuffer buffer(const_cast<char*>(reinterpret_cast<const char*>(gEmbeddedNNUEData)),
size_t(gEmbeddedNNUESize));
(void) gEmbeddedNNUEEnd; // Silence warning on unused variable
istream stream(&buffer);
if (load_eval(eval_file, stream))
@@ -196,8 +197,8 @@ using namespace Trace;
namespace {
// Threshold for lazy and space evaluation
constexpr Value LazyThreshold1 = Value(3130);
constexpr Value LazyThreshold2 = Value(2204);
constexpr Value LazyThreshold1 = Value(3631);
constexpr Value LazyThreshold2 = Value(2084);
constexpr Value SpaceThreshold = Value(11551);
// KingAttackWeights[PieceType] contains king attack weights by piece type
@@ -232,58 +233,58 @@ namespace {
// BishopPawns[distance from edge] contains a file-dependent penalty for pawns on
// squares of the same color as our bishop.
constexpr Score BishopPawns[int(FILE_NB) / 2] = {
S(3, 8), S(3, 9), S(2, 8), S(3, 8)
S(3, 8), S(3, 9), S(2, 7), S(3, 7)
};
// KingProtector[knight/bishop] contains penalty for each distance unit to own king
constexpr Score KingProtector[] = { S(8, 9), S(6, 9) };
constexpr Score KingProtector[] = { S(9, 9), S(7, 9) };
// Outpost[knight/bishop] contains bonuses for each knight or bishop occupying a
// pawn protected square on rank 4 to 6 which is also safe from a pawn attack.
constexpr Score Outpost[] = { S(57, 38), S(31, 24) };
constexpr Score Outpost[] = { S(54, 34), S(31, 25) };
// PassedRank[Rank] contains a bonus according to the rank of a passed pawn
constexpr Score PassedRank[RANK_NB] = {
S(0, 0), S(7, 27), S(16, 32), S(17, 40), S(64, 71), S(170, 174), S(278, 262)
S(0, 0), S(2, 38), S(15, 36), S(22, 50), S(64, 81), S(166, 184), S(284, 269)
};
constexpr Score RookOnClosedFile = S(10, 5);
constexpr Score RookOnOpenFile[] = { S(19, 6), S(47, 26) };
constexpr Score RookOnOpenFile[] = { S(18, 8), S(49, 26) };
// ThreatByMinor/ByRook[attacked PieceType] contains bonuses according to
// which piece type attacks which one. Attacks on lesser pieces which are
// pawn-defended are not considered.
constexpr Score ThreatByMinor[PIECE_TYPE_NB] = {
S(0, 0), S(5, 32), S(55, 41), S(77, 56), S(89, 119), S(79, 162)
S(0, 0), S(6, 37), S(64, 50), S(82, 57), S(103, 130), S(81, 163)
};
constexpr Score ThreatByRook[PIECE_TYPE_NB] = {
S(0, 0), S(3, 44), S(37, 68), S(42, 60), S(0, 39), S(58, 43)
S(0, 0), S(3, 44), S(36, 71), S(44, 59), S(0, 39), S(60, 39)
};
constexpr Value CorneredBishop = Value(50);
// Assorted bonuses and penalties
constexpr Score UncontestedOutpost = S( 1, 10);
constexpr Score UncontestedOutpost = S( 0, 10);
constexpr Score BishopOnKingRing = S( 24, 0);
constexpr Score BishopXRayPawns = S( 4, 5);
constexpr Score FlankAttacks = S( 8, 0);
constexpr Score Hanging = S( 69, 36);
constexpr Score Hanging = S( 72, 40);
constexpr Score KnightOnQueen = S( 16, 11);
constexpr Score LongDiagonalBishop = S( 45, 0);
constexpr Score MinorBehindPawn = S( 18, 3);
constexpr Score PassedFile = S( 11, 8);
constexpr Score PawnlessFlank = S( 17, 95);
constexpr Score ReachableOutpost = S( 31, 22);
constexpr Score RestrictedPiece = S( 7, 7);
constexpr Score PassedFile = S( 13, 8);
constexpr Score PawnlessFlank = S( 19, 97);
constexpr Score ReachableOutpost = S( 33, 19);
constexpr Score RestrictedPiece = S( 6, 7);
constexpr Score RookOnKingRing = S( 16, 0);
constexpr Score SliderOnQueen = S( 60, 18);
constexpr Score ThreatByKing = S( 24, 89);
constexpr Score SliderOnQueen = S( 62, 21);
constexpr Score ThreatByKing = S( 24, 87);
constexpr Score ThreatByPawnPush = S( 48, 39);
constexpr Score ThreatBySafePawn = S(173, 94);
constexpr Score ThreatBySafePawn = S(167, 99);
constexpr Score TrappedRook = S( 55, 13);
constexpr Score WeakQueenProtection = S( 14, 0);
constexpr Score WeakQueen = S( 56, 15);
constexpr Score WeakQueen = S( 57, 19);
#undef S
@@ -992,7 +993,9 @@ namespace {
// Early exit if score is high
auto lazy_skip = [&](Value lazyThreshold) {
return abs(mg_value(score) + eg_value(score)) > lazyThreshold + pos.non_pawn_material() / 32;
return abs(mg_value(score) + eg_value(score)) > lazyThreshold
+ std::abs(pos.this_thread()->bestValue) * 5 / 4
+ pos.non_pawn_material() / 32;
};
if (lazy_skip(LazyThreshold1))
@@ -1071,8 +1074,8 @@ make_v:
&& pos.piece_on(SQ_G7) == B_PAWN)
correction += CorneredBishop;
return pos.side_to_move() == WHITE ? Value(5 * correction)
: -Value(5 * correction);
return pos.side_to_move() == WHITE ? Value(3 * correction)
: -Value(3 * correction);
}
} // namespace Eval
@@ -1084,27 +1087,37 @@ make_v:
Value Eval::evaluate(const Position& pos) {
Value v;
bool useClassical = false;
// Deciding between classical and NNUE eval: for high PSQ imbalance we use classical,
// Deciding between classical and NNUE eval (~10 Elo): for high PSQ imbalance we use classical,
// but we switch to NNUE during long shuffling or with high material on the board.
if ( !useNNUE
|| abs(eg_value(pos.psq_score())) * 5 > (850 + pos.non_pawn_material() / 64) * (5 + pos.rule50_count()))
v = Evaluation<NO_TRACE>(pos).value(); // classical
else
|| ((pos.this_thread()->depth > 9 || pos.count<ALL_PIECES>() > 7) &&
abs(eg_value(pos.psq_score())) * 5 > (856 + pos.non_pawn_material() / 64) * (5 + pos.rule50_count())))
{
int scale = 883
+ 32 * pos.count<PAWN>()
+ 32 * pos.non_pawn_material() / 1024;
v = Evaluation<NO_TRACE>(pos).value(); // classical
useClassical = abs(v) >= 297;
}
v = NNUE::evaluate(pos, true) * scale / 1024; // NNUE
// If result of a classical evaluation is much lower than threshold fall back to NNUE
if (useNNUE && !useClassical)
{
Value nnue = NNUE::evaluate(pos, true); // NNUE
int scale = 1036 + 20 * pos.non_pawn_material() / 1024;
Color stm = pos.side_to_move();
Value optimism = pos.this_thread()->optimism[stm];
Value psq = (stm == WHITE ? 1 : -1) * eg_value(pos.psq_score());
int complexity = 35 * abs(nnue - psq) / 256;
optimism = optimism * (44 + complexity) / 31;
v = (nnue + optimism) * scale / 1024 - optimism;
if (pos.is_chess960())
v += fix_FRC(pos);
}
// Damp down the evaluation linearly when shuffling
v = v * (100 - pos.rule50_count()) / 100;
v = v * (207 - pos.rule50_count()) / 207;
// Guarantee evaluation does not hit the tablebase range
v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
@@ -1129,7 +1142,12 @@ std::string Eval::trace(Position& pos) {
std::memset(scores, 0, sizeof(scores));
pos.this_thread()->trend = SCORE_ZERO; // Reset any dynamic contempt
// Reset any global variable used in eval
pos.this_thread()->depth = 0;
pos.this_thread()->trend = SCORE_ZERO;
pos.this_thread()->bestValue = VALUE_ZERO;
pos.this_thread()->optimism[WHITE] = VALUE_ZERO;
pos.this_thread()->optimism[BLACK] = VALUE_ZERO;
v = Evaluation<TRACE>(pos).value();
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -39,7 +39,7 @@ namespace Eval {
// The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
// for the build process (profile-build and fishtest) to work. Do not change the
// name of the macro, as it is used in the Makefile.
#define EvalFileDefaultName "nn-13406b1dcbe0.nnue"
#define EvalFileDefaultName "nn-6877cd24400e.nnue"
namespace NNUE {
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+30 -11
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -36,6 +36,8 @@ typedef bool(*fun1_t)(LOGICAL_PROCESSOR_RELATIONSHIP,
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX, PDWORD);
typedef bool(*fun2_t)(USHORT, PGROUP_AFFINITY);
typedef bool(*fun3_t)(HANDLE, CONST GROUP_AFFINITY*, PGROUP_AFFINITY);
typedef bool(*fun4_t)(USHORT, PGROUP_AFFINITY, USHORT, PUSHORT);
typedef WORD(*fun5_t)();
}
#endif
@@ -67,7 +69,7 @@ namespace {
/// Version number. If Version is left empty, then compile date in the format
/// DD-MM-YY and show in engine_info.
const string Version = "14.1";
const string Version = "";
/// Our fancy logging facility. The trick here is to replace cin.rdbuf() and
/// cout.rdbuf() with two Tie objects that tie cin and cout to a file stream. We
@@ -495,11 +497,11 @@ void bindThisThread(size_t) {}
#else
/// best_group() retrieves logical processor information using Windows specific
/// API and returns the best group id for the thread with index idx. Original
/// best_node() retrieves logical processor information using Windows specific
/// API and returns the best node id for the thread with index idx. Original
/// code from Texel by Peter Österlund.
int best_group(size_t idx) {
int best_node(size_t idx) {
int threads = 0;
int nodes = 0;
@@ -513,7 +515,8 @@ int best_group(size_t idx) {
if (!fun1)
return -1;
// First call to get returnLength. We expect it to fail due to null buffer
// First call to GetLogicalProcessorInformationEx() to get returnLength.
// We expect the call to fail due to null buffer.
if (fun1(RelationAll, nullptr, &returnLength))
return -1;
@@ -521,7 +524,7 @@ int best_group(size_t idx) {
SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX *buffer, *ptr;
ptr = buffer = (SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX*)malloc(returnLength);
// Second call, now we expect to succeed
// Second call to GetLogicalProcessorInformationEx(), now we expect to succeed
if (!fun1(RelationAll, buffer, &returnLength))
{
free(buffer);
@@ -571,22 +574,38 @@ int best_group(size_t idx) {
void bindThisThread(size_t idx) {
// Use only local variables to be thread-safe
int group = best_group(idx);
int node = best_node(idx);
if (group == -1)
if (node == -1)
return;
// Early exit if the needed API are not available at runtime
HMODULE k32 = GetModuleHandle("Kernel32.dll");
auto fun2 = (fun2_t)(void(*)())GetProcAddress(k32, "GetNumaNodeProcessorMaskEx");
auto fun3 = (fun3_t)(void(*)())GetProcAddress(k32, "SetThreadGroupAffinity");
auto fun4 = (fun4_t)(void(*)())GetProcAddress(k32, "GetNumaNodeProcessorMask2");
auto fun5 = (fun5_t)(void(*)())GetProcAddress(k32, "GetMaximumProcessorGroupCount");
if (!fun2 || !fun3)
return;
if (!fun4 || !fun5)
{
GROUP_AFFINITY affinity;
if (fun2(group, &affinity))
fun3(GetCurrentThread(), &affinity, nullptr);
if (fun2(node, &affinity)) // GetNumaNodeProcessorMaskEx
fun3(GetCurrentThread(), &affinity, nullptr); // SetThreadGroupAffinity
}
else
{
// If a numa node has more than one processor group, we assume they are
// sized equal and we spread threads evenly across the groups.
USHORT elements, returnedElements;
elements = fun5(); // GetMaximumProcessorGroupCount
GROUP_AFFINITY *affinity = (GROUP_AFFINITY*)malloc(elements * sizeof(GROUP_AFFINITY));
if (fun4(node, affinity, elements, &returnedElements)) // GetNumaNodeProcessorMask2
fun3(GetCurrentThread(), &affinity[idx % returnedElements], nullptr); // SetThreadGroupAffinity
free(affinity);
}
}
#endif
+34 -6
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -90,9 +90,6 @@ static inline const bool IsLittleEndian = (Le.c[0] == 4);
class RunningAverage {
public:
// Constructor
RunningAverage() {}
// Reset the running average to rational value p / q
void set(int64_t p, int64_t q)
{ average = p * PERIOD * RESOLUTION / q; }
@@ -102,8 +99,11 @@ class RunningAverage {
{ average = RESOLUTION * v + (PERIOD - 1) * average / PERIOD; }
// Test if average is strictly greater than rational a / b
bool is_greater(int64_t a, int64_t b)
{ return b * average > a * PERIOD * RESOLUTION ; }
bool is_greater(int64_t a, int64_t b) const
{ return b * average > a * (PERIOD * RESOLUTION); }
int64_t value() const
{ return average / (PERIOD * RESOLUTION); }
private :
static constexpr int64_t PERIOD = 4096;
@@ -138,6 +138,34 @@ private:
std::size_t size_ = 0;
};
/// sigmoid(t, x0, y0, C, P, Q) implements a sigmoid-like function using only integers,
/// with the following properties:
///
/// - sigmoid is centered in (x0, y0)
/// - sigmoid has amplitude [-P/Q , P/Q] instead of [-1 , +1]
/// - limit is (y0 - P/Q) when t tends to -infinity
/// - limit is (y0 + P/Q) when t tends to +infinity
/// - the slope can be adjusted using C > 0, smaller C giving a steeper sigmoid
/// - the slope of the sigmoid when t = x0 is P/(Q*C)
/// - sigmoid is increasing with t when P > 0 and Q > 0
/// - to get a decreasing sigmoid, change sign of P
/// - mean value of the sigmoid is y0
///
/// Use <https://www.desmos.com/calculator/jhh83sqq92> to draw the sigmoid
inline int64_t sigmoid(int64_t t, int64_t x0,
int64_t y0,
int64_t C,
int64_t P,
int64_t Q)
{
assert(C > 0);
assert(Q != 0);
return y0 + P * (t-x0) / (Q * (std::abs(t-x0) + C)) ;
}
/// xorshift64star Pseudo-Random Number Generator
/// This class is based on original code written and dedicated
/// to the public domain by Sebastiano Vigna (2014).
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+54 -17
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -18,6 +18,7 @@
#include <cassert>
#include "bitboard.h"
#include "movepick.h"
namespace Stockfish {
@@ -56,11 +57,14 @@ namespace {
/// ordering is at the current node.
/// MovePicker constructor for the main search
MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHistory* mh, const LowPlyHistory* lp,
const CapturePieceToHistory* cph, const PieceToHistory** ch, Move cm, const Move* killers, int pl)
: pos(p), mainHistory(mh), lowPlyHistory(lp), captureHistory(cph), continuationHistory(ch),
ttMove(ttm), refutations{{killers[0], 0}, {killers[1], 0}, {cm, 0}}, depth(d), ply(pl) {
MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHistory* mh,
const CapturePieceToHistory* cph,
const PieceToHistory** ch,
Move cm,
const Move* killers)
: pos(p), mainHistory(mh), captureHistory(cph), continuationHistory(ch),
ttMove(ttm), refutations{{killers[0], 0}, {killers[1], 0}, {cm, 0}}, depth(d)
{
assert(d > 0);
stage = (pos.checkers() ? EVASION_TT : MAIN_TT) +
@@ -69,9 +73,11 @@ MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHist
/// MovePicker constructor for quiescence search
MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHistory* mh,
const CapturePieceToHistory* cph, const PieceToHistory** ch, Square rs)
: pos(p), mainHistory(mh), captureHistory(cph), continuationHistory(ch), ttMove(ttm), recaptureSquare(rs), depth(d) {
const CapturePieceToHistory* cph,
const PieceToHistory** ch,
Square rs)
: pos(p), mainHistory(mh), captureHistory(cph), continuationHistory(ch), ttMove(ttm), recaptureSquare(rs), depth(d)
{
assert(d <= 0);
stage = (pos.checkers() ? EVASION_TT : QSEARCH_TT) +
@@ -82,9 +88,9 @@ MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHist
/// MovePicker constructor for ProbCut: we generate captures with SEE greater
/// than or equal to the given threshold.
MovePicker::MovePicker(const Position& p, Move ttm, Value th, const CapturePieceToHistory* cph)
: pos(p), captureHistory(cph), ttMove(ttm), threshold(th) {
MovePicker::MovePicker(const Position& p, Move ttm, Value th, Depth d, const CapturePieceToHistory* cph)
: pos(p), captureHistory(cph), ttMove(ttm), threshold(th), depth(d)
{
assert(!pos.checkers());
stage = PROBCUT_TT + !(ttm && pos.capture(ttm)
@@ -100,9 +106,34 @@ void MovePicker::score() {
static_assert(Type == CAPTURES || Type == QUIETS || Type == EVASIONS, "Wrong type");
Bitboard threatened, threatenedByPawn, threatenedByMinor, threatenedByRook;
if constexpr (Type == QUIETS)
{
Color us = pos.side_to_move();
// squares threatened by pawns
threatenedByPawn = pos.attacks_by<PAWN>(~us);
// squares threatened by minors or pawns
threatenedByMinor = pos.attacks_by<KNIGHT>(~us) | pos.attacks_by<BISHOP>(~us) | threatenedByPawn;
// squares threatened by rooks, minors or pawns
threatenedByRook = pos.attacks_by<ROOK>(~us) | threatenedByMinor;
// pieces threatened by pieces of lesser material value
threatened = (pos.pieces(us, QUEEN) & threatenedByRook)
| (pos.pieces(us, ROOK) & threatenedByMinor)
| (pos.pieces(us, KNIGHT, BISHOP) & threatenedByPawn);
}
else
{
// Silence unused variable warnings
(void) threatened;
(void) threatenedByPawn;
(void) threatenedByMinor;
(void) threatenedByRook;
}
for (auto& m : *this)
if constexpr (Type == CAPTURES)
m.value = int(PieceValue[MG][pos.piece_on(to_sq(m))]) * 6
m.value = 6 * int(PieceValue[MG][pos.piece_on(to_sq(m))])
+ (*captureHistory)[pos.moved_piece(m)][to_sq(m)][type_of(pos.piece_on(to_sq(m)))];
else if constexpr (Type == QUIETS)
@@ -111,7 +142,12 @@ void MovePicker::score() {
+ (*continuationHistory[1])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[3])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[5])[pos.moved_piece(m)][to_sq(m)]
+ (ply < MAX_LPH ? 6 * (*lowPlyHistory)[ply][from_to(m)] : 0);
+ (threatened & from_sq(m) ?
(type_of(pos.moved_piece(m)) == QUEEN && !(to_sq(m) & threatenedByRook) ? 50000
: type_of(pos.moved_piece(m)) == ROOK && !(to_sq(m) & threatenedByMinor) ? 25000
: !(to_sq(m) & threatenedByPawn) ? 15000
: 0)
: 0);
else // Type == EVASIONS
{
@@ -165,11 +201,12 @@ top:
endMoves = generate<CAPTURES>(pos, cur);
score<CAPTURES>();
partial_insertion_sort(cur, endMoves, -3000 * depth);
++stage;
goto top;
case GOOD_CAPTURE:
if (select<Best>([&](){
if (select<Next>([&](){
return pos.see_ge(*cur, Value(-69 * cur->value / 1024)) ?
// Move losing capture to endBadCaptures to be tried later
true : (*endBadCaptures++ = *cur, false); }))
@@ -237,10 +274,10 @@ top:
return select<Best>([](){ return true; });
case PROBCUT:
return select<Best>([&](){ return pos.see_ge(*cur, threshold); });
return select<Next>([&](){ return pos.see_ge(*cur, threshold); });
case QCAPTURE:
if (select<Best>([&](){ return depth > DEPTH_QS_RECAPTURES
if (select<Next>([&](){ return depth > DEPTH_QS_RECAPTURES
|| to_sq(*cur) == recaptureSquare; }))
return *(cur - 1);
+7 -17
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -88,12 +88,6 @@ enum StatsType { NoCaptures, Captures };
/// the move's from and to squares, see www.chessprogramming.org/Butterfly_Boards
typedef Stats<int16_t, 14365, COLOR_NB, int(SQUARE_NB) * int(SQUARE_NB)> ButterflyHistory;
/// At higher depths LowPlyHistory records successful quiet moves near the root
/// and quiet moves which are/were in the PV (ttPv). LowPlyHistory is populated during
/// iterative deepening and at each new search the data is shifted down by 2 plies
constexpr int MAX_LPH = 4;
typedef Stats<int16_t, 10692, MAX_LPH, int(SQUARE_NB) * int(SQUARE_NB)> LowPlyHistory;
/// CounterMoveHistory stores counter moves indexed by [piece][to] of the previous
/// move, see www.chessprogramming.org/Countermove_Heuristic
typedef Stats<Move, NOT_USED, PIECE_NB, SQUARE_NB> CounterMoveHistory;
@@ -123,18 +117,16 @@ class MovePicker {
public:
MovePicker(const MovePicker&) = delete;
MovePicker& operator=(const MovePicker&) = delete;
MovePicker(const Position&, Move, Value, const CapturePieceToHistory*);
MovePicker(const Position&, Move, Depth, const ButterflyHistory*,
const CapturePieceToHistory*,
const PieceToHistory**,
Move,
const Move*);
MovePicker(const Position&, Move, Depth, const ButterflyHistory*,
const CapturePieceToHistory*,
const PieceToHistory**,
Square);
MovePicker(const Position&, Move, Depth, const ButterflyHistory*,
const LowPlyHistory*,
const CapturePieceToHistory*,
const PieceToHistory**,
Move,
const Move*,
int);
MovePicker(const Position&, Move, Value, Depth, const CapturePieceToHistory*);
Move next_move(bool skipQuiets = false);
private:
@@ -145,7 +137,6 @@ private:
const Position& pos;
const ButterflyHistory* mainHistory;
const LowPlyHistory* lowPlyHistory;
const CapturePieceToHistory* captureHistory;
const PieceToHistory** continuationHistory;
Move ttMove;
@@ -154,7 +145,6 @@ private:
Square recaptureSquare;
Value threshold;
Depth depth;
int ply;
ExtMove moves[MAX_MOVES];
};
+11 -23
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -109,7 +109,7 @@ namespace Stockfish::Eval::NNUE {
{
write_little_endian<std::uint32_t>(stream, Version);
write_little_endian<std::uint32_t>(stream, hashValue);
write_little_endian<std::uint32_t>(stream, desc.size());
write_little_endian<std::uint32_t>(stream, (std::uint32_t)desc.size());
stream.write(&desc[0], desc.size());
return !stream.fail();
}
@@ -143,31 +143,26 @@ namespace Stockfish::Eval::NNUE {
// overaligning stack variables with alignas() doesn't work correctly.
constexpr uint64_t alignment = CacheLineSize;
int delta = 7;
int delta = 10 - pos.non_pawn_material() / 1515;
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
TransformedFeatureType transformedFeaturesUnaligned[
FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
char bufferUnaligned[Network::BufferSize + alignment];
auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
auto* buffer = align_ptr_up<alignment>(&bufferUnaligned[0]);
#else
alignas(alignment)
TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
alignas(alignment) char buffer[Network::BufferSize];
#endif
ASSERT_ALIGNED(transformedFeatures, alignment);
ASSERT_ALIGNED(buffer, alignment);
const std::size_t bucket = (pos.count<ALL_PIECES>() - 1) / 4;
const int bucket = (pos.count<ALL_PIECES>() - 1) / 4;
const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures, buffer)[0];
const auto positional = network[bucket]->propagate(transformedFeatures);
// Give more value to positional evaluation when material is balanced
if ( adjusted
&& abs(pos.non_pawn_material(WHITE) - pos.non_pawn_material(BLACK)) <= RookValueMg - BishopValueMg)
// Give more value to positional evaluation when adjusted flag is set
if (adjusted)
return static_cast<Value>(((128 - delta) * psqt + (128 + delta) * positional) / 128 / OutputScale);
else
return static_cast<Value>((psqt + positional) / OutputScale);
@@ -191,27 +186,20 @@ namespace Stockfish::Eval::NNUE {
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
TransformedFeatureType transformedFeaturesUnaligned[
FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
char bufferUnaligned[Network::BufferSize + alignment];
auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
auto* buffer = align_ptr_up<alignment>(&bufferUnaligned[0]);
#else
alignas(alignment)
TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
alignas(alignment) char buffer[Network::BufferSize];
#endif
ASSERT_ALIGNED(transformedFeatures, alignment);
ASSERT_ALIGNED(buffer, alignment);
NnueEvalTrace t{};
t.correctBucket = (pos.count<ALL_PIECES>() - 1) / 4;
for (std::size_t bucket = 0; bucket < LayerStacks; ++bucket) {
const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto output = network[bucket]->propagate(transformedFeatures, buffer);
int materialist = psqt;
int positional = output[0];
for (IndexType bucket = 0; bucket < LayerStacks; ++bucket) {
const auto materialist = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);
t.psqt[bucket] = static_cast<Value>( materialist / OutputScale );
t.positional[bucket] = static_cast<Value>( positional / OutputScale );
@@ -234,7 +222,7 @@ namespace Stockfish::Eval::NNUE {
{
buffer[1] = '0' + cp / 10000; cp %= 10000;
buffer[2] = '0' + cp / 1000; cp %= 1000;
buffer[3] = '0' + cp / 100; cp %= 100;
buffer[3] = '0' + cp / 100;
buffer[4] = ' ';
}
else if (cp >= 1000)
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+77 -92
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -63,20 +63,17 @@ namespace Stockfish::Eval::NNUE::Layers {
{
# if defined(USE_SSE2)
// At least a multiple of 16, with SSE2.
static_assert(PaddedInputDimensions % 16 == 0);
constexpr IndexType NumChunks = PaddedInputDimensions / 16;
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
const __m128i Zeros = _mm_setzero_si128();
const auto inputVector = reinterpret_cast<const __m128i*>(input);
# elif defined(USE_MMX)
static_assert(InputDimensions % 8 == 0);
constexpr IndexType NumChunks = InputDimensions / 8;
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 8;
const __m64 Zeros = _mm_setzero_si64();
const auto inputVector = reinterpret_cast<const __m64*>(input);
# elif defined(USE_NEON)
static_assert(PaddedInputDimensions % 16 == 0);
constexpr IndexType NumChunks = PaddedInputDimensions / 16;
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
# endif
@@ -151,24 +148,27 @@ namespace Stockfish::Eval::NNUE::Layers {
}
#endif
template <typename PreviousLayer, IndexType OutDims, typename Enabled = void>
template <IndexType InDims, IndexType OutDims, typename Enabled = void>
class AffineTransform;
// A specialization for large inputs.
template <typename PreviousLayer, IndexType OutDims>
class AffineTransform<PreviousLayer, OutDims, std::enable_if_t<(PreviousLayer::OutputDimensions >= 2*64-1)>> {
template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= 2*64)>> {
public:
// Input/output type
using InputType = typename PreviousLayer::OutputType;
using InputType = std::uint8_t;
using OutputType = std::int32_t;
static_assert(std::is_same<InputType, std::uint8_t>::value, "");
// Number of input/output dimensions
static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions;
static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = OutDims;
static constexpr IndexType PaddedInputDimensions =
ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
static constexpr IndexType PaddedOutputDimensions =
ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions >= 128, "Something went wrong. This specialization should not have been chosen.");
@@ -181,6 +181,9 @@ namespace Stockfish::Eval::NNUE::Layers {
#elif defined (USE_SSSE3)
static constexpr const IndexType InputSimdWidth = 16;
static constexpr const IndexType MaxNumOutputRegs = 8;
#elif defined (USE_NEON)
static constexpr const IndexType InputSimdWidth = 8;
static constexpr const IndexType MaxNumOutputRegs = 8;
#else
// The fallback implementation will not have permuted weights.
// We define these to avoid a lot of ifdefs later.
@@ -200,20 +203,12 @@ namespace Stockfish::Eval::NNUE::Layers {
static_assert(OutputDimensions % NumOutputRegs == 0);
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
// Size of the forward propagation buffer used from the input layer to this layer
static constexpr std::size_t BufferSize =
PreviousLayer::BufferSize + SelfBufferSize;
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
std::uint32_t hashValue = 0xCC03DAE4u;
hashValue += OutputDimensions;
hashValue ^= PreviousLayer::get_hash_value() >> 1;
hashValue ^= PreviousLayer::get_hash_value() << 31;
hashValue ^= prevHash >> 1;
hashValue ^= prevHash << 31;
return hashValue;
}
@@ -240,11 +235,10 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
if (!previousLayer.read_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
@@ -252,11 +246,10 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
if (!previousLayer.write_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();
@@ -264,58 +257,66 @@ namespace Stockfish::Eval::NNUE::Layers {
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
const auto input = previousLayer.propagate(
transformedFeatures, buffer + SelfBufferSize);
OutputType* output = reinterpret_cast<OutputType*>(buffer);
const InputType* input, OutputType* output) const {
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_setzero _mm512_setzero_si512
#define vec_set_32 _mm512_set1_epi32
#define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
using acc_vec_t = __m512i;
using bias_vec_t = __m128i;
using weight_vec_t = __m512i;
using in_vec_t = __m512i;
#define vec_zero _mm512_setzero_si512()
#define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
#define vec_hadd Simd::m512_hadd
#define vec_haddx4 Simd::m512_haddx4
#elif defined (USE_AVX2)
using vec_t = __m256i;
#define vec_setzero _mm256_setzero_si256
#define vec_set_32 _mm256_set1_epi32
#define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
using acc_vec_t = __m256i;
using bias_vec_t = __m128i;
using weight_vec_t = __m256i;
using in_vec_t = __m256i;
#define vec_zero _mm256_setzero_si256()
#define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
#define vec_hadd Simd::m256_hadd
#define vec_haddx4 Simd::m256_haddx4
#elif defined (USE_SSSE3)
using vec_t = __m128i;
#define vec_setzero _mm_setzero_si128
#define vec_set_32 _mm_set1_epi32
#define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
using acc_vec_t = __m128i;
using bias_vec_t = __m128i;
using weight_vec_t = __m128i;
using in_vec_t = __m128i;
#define vec_zero _mm_setzero_si128()
#define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
#define vec_hadd Simd::m128_hadd
#define vec_haddx4 Simd::m128_haddx4
#elif defined (USE_NEON)
using acc_vec_t = int32x4_t;
using bias_vec_t = int32x4_t;
using weight_vec_t = int8x8_t;
using in_vec_t = int8x8_t;
#define vec_zero {0}
#define vec_add_dpbusd_32x2 Simd::neon_m128_add_dpbusd_epi32x2
#define vec_hadd Simd::neon_m128_hadd
#define vec_haddx4 Simd::neon_m128_haddx4
#endif
#if defined (USE_SSSE3)
const vec_t* invec = reinterpret_cast<const vec_t*>(input);
#if defined (USE_SSSE3) || defined (USE_NEON)
const in_vec_t* invec = reinterpret_cast<const in_vec_t*>(input);
// Perform accumulation to registers for each big block
for (IndexType bigBlock = 0; bigBlock < NumBigBlocks; ++bigBlock)
{
vec_t acc[NumOutputRegs] = { vec_setzero() };
acc_vec_t acc[NumOutputRegs] = { vec_zero };
// Each big block has NumOutputRegs small blocks in each "row", one per register.
// We process two small blocks at a time to save on one addition without VNNI.
for (IndexType smallBlock = 0; smallBlock < NumSmallBlocksPerOutput; smallBlock += 2)
{
const vec_t* weightvec =
reinterpret_cast<const vec_t*>(
const weight_vec_t* weightvec =
reinterpret_cast<const weight_vec_t*>(
weights
+ bigBlock * BigBlockSize
+ smallBlock * SmallBlockSize * NumOutputRegs);
const vec_t in0 = invec[smallBlock + 0];
const vec_t in1 = invec[smallBlock + 1];
const in_vec_t in0 = invec[smallBlock + 0];
const in_vec_t in1 = invec[smallBlock + 1];
for (IndexType k = 0; k < NumOutputRegs; ++k)
vec_add_dpbusd_32x2(acc[k], in0, weightvec[k], in1, weightvec[k + NumOutputRegs]);
@@ -324,8 +325,8 @@ namespace Stockfish::Eval::NNUE::Layers {
// Horizontally add all accumulators.
if constexpr (NumOutputRegs % 4 == 0)
{
__m128i* outputvec = reinterpret_cast<__m128i*>(output);
const __m128i* biasvec = reinterpret_cast<const __m128i*>(biases);
bias_vec_t* outputvec = reinterpret_cast<bias_vec_t*>(output);
const bias_vec_t* biasvec = reinterpret_cast<const bias_vec_t*>(biases);
for (IndexType k = 0; k < NumOutputRegs; k += 4)
{
@@ -343,9 +344,7 @@ namespace Stockfish::Eval::NNUE::Layers {
}
}
# undef vec_setzero
# undef vec_set_32
# undef vec_add_dpbusd_32
# undef vec_zero
# undef vec_add_dpbusd_32x2
# undef vec_hadd
# undef vec_haddx4
@@ -365,26 +364,28 @@ namespace Stockfish::Eval::NNUE::Layers {
using BiasType = OutputType;
using WeightType = std::int8_t;
PreviousLayer previousLayer;
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
};
template <typename PreviousLayer, IndexType OutDims>
class AffineTransform<PreviousLayer, OutDims, std::enable_if_t<(PreviousLayer::OutputDimensions < 2*64-1)>> {
template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < 2*64)>> {
public:
// Input/output type
using InputType = typename PreviousLayer::OutputType;
// Input/output type
using InputType = std::uint8_t;
using OutputType = std::int32_t;
static_assert(std::is_same<InputType, std::uint8_t>::value, "");
// Number of input/output dimensions
static constexpr IndexType InputDimensions =
PreviousLayer::OutputDimensions;
static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = OutDims;
static constexpr IndexType PaddedInputDimensions =
ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
static constexpr IndexType PaddedOutputDimensions =
ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions < 128, "Something went wrong. This specialization should not have been chosen.");
@@ -393,20 +394,12 @@ namespace Stockfish::Eval::NNUE::Layers {
static constexpr const IndexType InputSimdWidth = SimdWidth;
#endif
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
// Size of the forward propagation buffer used from the input layer to this layer
static constexpr std::size_t BufferSize =
PreviousLayer::BufferSize + SelfBufferSize;
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
std::uint32_t hashValue = 0xCC03DAE4u;
hashValue += OutputDimensions;
hashValue ^= PreviousLayer::get_hash_value() >> 1;
hashValue ^= PreviousLayer::get_hash_value() << 31;
hashValue ^= prevHash >> 1;
hashValue ^= prevHash << 31;
return hashValue;
}
@@ -429,10 +422,9 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
if (!previousLayer.read_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
@@ -440,21 +432,17 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
if (!previousLayer.write_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();
}
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
const auto input = previousLayer.propagate(
transformedFeatures, buffer + SelfBufferSize);
const auto output = reinterpret_cast<OutputType*>(buffer);
const InputType* input, OutputType* output) const {
#if defined (USE_AVX2)
using vec_t = __m256i;
@@ -479,12 +467,11 @@ namespace Stockfish::Eval::NNUE::Layers {
#if defined (USE_SSSE3)
const auto inputVector = reinterpret_cast<const vec_t*>(input);
static_assert(InputDimensions % 8 == 0);
static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
if constexpr (OutputDimensions % OutputSimdWidth == 0)
{
constexpr IndexType NumChunks = InputDimensions / 4;
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
const auto input32 = reinterpret_cast<const std::int32_t*>(input);
@@ -543,8 +530,6 @@ namespace Stockfish::Eval::NNUE::Layers {
using BiasType = OutputType;
using WeightType = std::int8_t;
PreviousLayer previousLayer;
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
};
+12 -33
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -26,51 +26,41 @@
namespace Stockfish::Eval::NNUE::Layers {
// Clipped ReLU
template <typename PreviousLayer>
template <IndexType InDims>
class ClippedReLU {
public:
// Input/output type
using InputType = typename PreviousLayer::OutputType;
using InputType = std::int32_t;
using OutputType = std::uint8_t;
static_assert(std::is_same<InputType, std::int32_t>::value, "");
// Number of input/output dimensions
static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions;
static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = InputDimensions;
static constexpr IndexType PaddedOutputDimensions =
ceil_to_multiple<IndexType>(OutputDimensions, 32);
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
// Size of the forward propagation buffer used from the input layer to this layer
static constexpr std::size_t BufferSize =
PreviousLayer::BufferSize + SelfBufferSize;
using OutputBuffer = OutputType[PaddedOutputDimensions];
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
std::uint32_t hashValue = 0x538D24C7u;
hashValue += PreviousLayer::get_hash_value();
hashValue += prevHash;
return hashValue;
}
// Read network parameters
bool read_parameters(std::istream& stream) {
return previousLayer.read_parameters(stream);
bool read_parameters(std::istream&) {
return true;
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
return previousLayer.write_parameters(stream);
bool write_parameters(std::ostream&) const {
return true;
}
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
const auto input = previousLayer.propagate(
transformedFeatures, buffer + SelfBufferSize);
const auto output = reinterpret_cast<OutputType*>(buffer);
const InputType* input, OutputType* output) const {
#if defined(USE_AVX2)
if constexpr (InputDimensions % SimdWidth == 0) {
@@ -181,19 +171,8 @@ namespace Stockfish::Eval::NNUE::Layers {
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
}
// Affine transform layers expect that there is at least
// ceil_to_multiple(OutputDimensions, 32) initialized values.
// We cannot do this in the affine transform because it requires
// preallocating space here.
for (IndexType i = OutputDimensions; i < PaddedOutputDimensions; ++i) {
output[i] = 0;
}
return output;
}
private:
PreviousLayer previousLayer;
};
} // namespace Stockfish::Eval::NNUE::Layers
-73
View File
@@ -1,73 +0,0 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
// NNUE evaluation function layer InputSlice definition
#ifndef NNUE_LAYERS_INPUT_SLICE_H_INCLUDED
#define NNUE_LAYERS_INPUT_SLICE_H_INCLUDED
#include "../nnue_common.h"
namespace Stockfish::Eval::NNUE::Layers {
// Input layer
template <IndexType OutDims, IndexType Offset = 0>
class InputSlice {
public:
// Need to maintain alignment
static_assert(Offset % MaxSimdWidth == 0, "");
// Output type
using OutputType = TransformedFeatureType;
// Output dimensionality
static constexpr IndexType OutputDimensions = OutDims;
// Size of forward propagation buffer used from the input layer to this layer
static constexpr std::size_t BufferSize = 0;
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
std::uint32_t hashValue = 0xEC42E90Du;
hashValue ^= OutputDimensions ^ (Offset << 10);
return hashValue;
}
// Read network parameters
bool read_parameters(std::istream& /*stream*/) {
return true;
}
// Write network parameters
bool write_parameters(std::ostream& /*stream*/) const {
return true;
}
// Forward propagation
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures,
char* /*buffer*/) const {
return transformedFeatures + Offset;
}
private:
};
} // namespace Stockfish::Eval::NNUE::Layers
#endif // #ifndef NNUE_LAYERS_INPUT_SLICE_H_INCLUDED
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+92 -19
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -21,39 +21,112 @@
#ifndef NNUE_ARCHITECTURE_H_INCLUDED
#define NNUE_ARCHITECTURE_H_INCLUDED
#include <memory>
#include "nnue_common.h"
#include "features/half_ka_v2_hm.h"
#include "layers/input_slice.h"
#include "layers/affine_transform.h"
#include "layers/clipped_relu.h"
#include "../misc.h"
namespace Stockfish::Eval::NNUE {
// Input features used in evaluation function
using FeatureSet = Features::HalfKAv2_hm;
// Input features used in evaluation function
using FeatureSet = Features::HalfKAv2_hm;
// Number of input feature dimensions after conversion
constexpr IndexType TransformedFeatureDimensions = 1024;
constexpr IndexType PSQTBuckets = 8;
constexpr IndexType LayerStacks = 8;
// Number of input feature dimensions after conversion
constexpr IndexType TransformedFeatureDimensions = 1024;
constexpr IndexType PSQTBuckets = 8;
constexpr IndexType LayerStacks = 8;
namespace Layers {
struct Network
{
static constexpr int FC_0_OUTPUTS = 15;
static constexpr int FC_1_OUTPUTS = 32;
// Define network structure
using InputLayer = InputSlice<TransformedFeatureDimensions * 2>;
using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 8>>;
using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
using OutputLayer = AffineTransform<HiddenLayer2, 1>;
Layers::AffineTransform<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
Layers::AffineTransform<FC_0_OUTPUTS, FC_1_OUTPUTS> fc_1;
Layers::ClippedReLU<FC_1_OUTPUTS> ac_1;
Layers::AffineTransform<FC_1_OUTPUTS, 1> fc_2;
} // namespace Layers
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
// input slice hash
std::uint32_t hashValue = 0xEC42E90Du;
hashValue ^= TransformedFeatureDimensions * 2;
using Network = Layers::OutputLayer;
hashValue = decltype(fc_0)::get_hash_value(hashValue);
hashValue = decltype(ac_0)::get_hash_value(hashValue);
hashValue = decltype(fc_1)::get_hash_value(hashValue);
hashValue = decltype(ac_1)::get_hash_value(hashValue);
hashValue = decltype(fc_2)::get_hash_value(hashValue);
static_assert(TransformedFeatureDimensions % MaxSimdWidth == 0, "");
static_assert(Network::OutputDimensions == 1, "");
static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
return hashValue;
}
// Read network parameters
bool read_parameters(std::istream& stream) {
if (!fc_0.read_parameters(stream)) return false;
if (!ac_0.read_parameters(stream)) return false;
if (!fc_1.read_parameters(stream)) return false;
if (!ac_1.read_parameters(stream)) return false;
if (!fc_2.read_parameters(stream)) return false;
return true;
}
// Read network parameters
bool write_parameters(std::ostream& stream) const {
if (!fc_0.write_parameters(stream)) return false;
if (!ac_0.write_parameters(stream)) return false;
if (!fc_1.write_parameters(stream)) return false;
if (!ac_1.write_parameters(stream)) return false;
if (!fc_2.write_parameters(stream)) return false;
return true;
}
std::int32_t propagate(const TransformedFeatureType* transformedFeatures)
{
struct alignas(CacheLineSize) Buffer
{
alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
alignas(CacheLineSize) decltype(ac_0)::OutputBuffer ac_0_out;
alignas(CacheLineSize) decltype(fc_1)::OutputBuffer fc_1_out;
alignas(CacheLineSize) decltype(ac_1)::OutputBuffer ac_1_out;
alignas(CacheLineSize) decltype(fc_2)::OutputBuffer fc_2_out;
Buffer()
{
std::memset(this, 0, sizeof(*this));
}
};
#if defined(__clang__) && (__APPLE__)
// workaround for a bug reported with xcode 12
static thread_local auto tlsBuffer = std::make_unique<Buffer>();
// Access TLS only once, cache result.
Buffer& buffer = *tlsBuffer;
#else
alignas(CacheLineSize) static thread_local Buffer buffer;
#endif
fc_0.propagate(transformedFeatures, buffer.fc_0_out);
ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
fc_1.propagate(buffer.ac_0_out, buffer.fc_1_out);
ac_1.propagate(buffer.fc_1_out, buffer.ac_1_out);
fc_2.propagate(buffer.ac_1_out, buffer.fc_2_out);
// buffer.fc_0_out[FC_0_OUTPUTS] is such that 1.0 is equal to 127*(1<<WeightScaleBits) in quantized form
// but we want 1.0 to be equal to 600*OutputScale
std::int32_t fwdOut = int(buffer.fc_0_out[FC_0_OUTPUTS]) * (600*OutputScale) / (127*(1<<WeightScaleBits));
std::int32_t outputValue = buffer.fc_2_out[0] + fwdOut;
return outputValue;
}
};
} // namespace Stockfish::Eval::NNUE
+4 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -109,7 +109,7 @@ namespace Stockfish::Eval::NNUE {
// write_little_endian() is our utility to write an integer (signed or unsigned, any size)
// to a stream in little-endian order. We swap the byte order before the write if
// necessary to always write in little endian order, independantly of the byte
// necessary to always write in little endian order, independently of the byte
// ordering of the compiling machine.
template <typename IntType>
inline void write_little_endian(std::ostream& stream, IntType value) {
@@ -127,11 +127,11 @@ namespace Stockfish::Eval::NNUE {
{
for (; i + 1 < sizeof(IntType); ++i)
{
u[i] = v;
u[i] = (std::uint8_t)v;
v >>= 8;
}
}
u[i] = v;
u[i] = (std::uint8_t)v;
stream.write(reinterpret_cast<char*>(u), sizeof(IntType));
}
+102 -128
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -47,12 +47,22 @@ namespace Stockfish::Eval::NNUE {
#define vec_store(a,b) _mm512_store_si512(a,b)
#define vec_add_16(a,b) _mm512_add_epi16(a,b)
#define vec_sub_16(a,b) _mm512_sub_epi16(a,b)
#define vec_mul_16(a,b) _mm512_mullo_epi16(a,b)
#define vec_zero() _mm512_setzero_epi32()
#define vec_set_16(a) _mm512_set1_epi16(a)
#define vec_max_16(a,b) _mm512_max_epi16(a,b)
#define vec_min_16(a,b) _mm512_min_epi16(a,b)
inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
vec_t compacted = _mm512_packs_epi16(_mm512_srli_epi16(a,7),_mm512_srli_epi16(b,7));
return _mm512_permutexvar_epi64(_mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7), compacted);
}
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a,b) _mm256_store_si256(a,b)
#define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
#define vec_zero_psqt() _mm256_setzero_si256()
#define NumRegistersSIMD 32
#define MaxChunkSize 64
#elif USE_AVX2
typedef __m256i vec_t;
@@ -61,12 +71,22 @@ namespace Stockfish::Eval::NNUE {
#define vec_store(a,b) _mm256_store_si256(a,b)
#define vec_add_16(a,b) _mm256_add_epi16(a,b)
#define vec_sub_16(a,b) _mm256_sub_epi16(a,b)
#define vec_mul_16(a,b) _mm256_mullo_epi16(a,b)
#define vec_zero() _mm256_setzero_si256()
#define vec_set_16(a) _mm256_set1_epi16(a)
#define vec_max_16(a,b) _mm256_max_epi16(a,b)
#define vec_min_16(a,b) _mm256_min_epi16(a,b)
inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
vec_t compacted = _mm256_packs_epi16(_mm256_srli_epi16(a,7), _mm256_srli_epi16(b,7));
return _mm256_permute4x64_epi64(compacted, 0b11011000);
}
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a,b) _mm256_store_si256(a,b)
#define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
#define vec_zero_psqt() _mm256_setzero_si256()
#define NumRegistersSIMD 16
#define MaxChunkSize 32
#elif USE_SSE2
typedef __m128i vec_t;
@@ -75,12 +95,19 @@ namespace Stockfish::Eval::NNUE {
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_epi16(a,b)
#define vec_sub_16(a,b) _mm_sub_epi16(a,b)
#define vec_mul_16(a,b) _mm_mullo_epi16(a,b)
#define vec_zero() _mm_setzero_si128()
#define vec_set_16(a) _mm_set1_epi16(a)
#define vec_max_16(a,b) _mm_max_epi16(a,b)
#define vec_min_16(a,b) _mm_min_epi16(a,b)
#define vec_msb_pack_16(a,b) _mm_packs_epi16(_mm_srli_epi16(a,7),_mm_srli_epi16(b,7))
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a,b) *(a)=(b)
#define vec_add_psqt_32(a,b) _mm_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm_sub_epi32(a,b)
#define vec_zero_psqt() _mm_setzero_si128()
#define NumRegistersSIMD (Is64Bit ? 16 : 8)
#define MaxChunkSize 16
#elif USE_MMX
typedef __m64 vec_t;
@@ -89,12 +116,26 @@ namespace Stockfish::Eval::NNUE {
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_pi16(a,b)
#define vec_sub_16(a,b) _mm_sub_pi16(a,b)
#define vec_mul_16(a,b) _mm_mullo_pi16(a,b)
#define vec_zero() _mm_setzero_si64()
#define vec_set_16(a) _mm_set1_pi16(a)
inline vec_t vec_max_16(vec_t a,vec_t b){
vec_t comparison = _mm_cmpgt_pi16(a,b);
return _mm_or_si64(_mm_and_si64(comparison, a), _mm_andnot_si64(comparison, b));
}
inline vec_t vec_min_16(vec_t a,vec_t b){
vec_t comparison = _mm_cmpgt_pi16(a,b);
return _mm_or_si64(_mm_and_si64(comparison, b), _mm_andnot_si64(comparison, a));
}
#define vec_msb_pack_16(a,b) _mm_packs_pi16(_mm_srli_pi16(a,7),_mm_srli_pi16(b,7))
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a,b) *(a)=(b)
#define vec_add_psqt_32(a,b) _mm_add_pi32(a,b)
#define vec_sub_psqt_32(a,b) _mm_sub_pi32(a,b)
#define vec_zero_psqt() _mm_setzero_si64()
#define vec_cleanup() _mm_empty()
#define NumRegistersSIMD 8
#define MaxChunkSize 8
#elif USE_NEON
typedef int16x8_t vec_t;
@@ -103,12 +144,24 @@ namespace Stockfish::Eval::NNUE {
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) vaddq_s16(a,b)
#define vec_sub_16(a,b) vsubq_s16(a,b)
#define vec_mul_16(a,b) vmulq_s16(a,b)
#define vec_zero() vec_t{0}
#define vec_set_16(a) vdupq_n_s16(a)
#define vec_max_16(a,b) vmaxq_s16(a,b)
#define vec_min_16(a,b) vminq_s16(a,b)
inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
const int8x8_t shifta = vshrn_n_s16(a, 7);
const int8x8_t shiftb = vshrn_n_s16(b, 7);
const int8x16_t compacted = vcombine_s8(shifta,shiftb);
return *reinterpret_cast<const vec_t*> (&compacted);
}
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a,b) *(a)=(b)
#define vec_add_psqt_32(a,b) vaddq_s32(a,b)
#define vec_sub_psqt_32(a,b) vsubq_s32(a,b)
#define vec_zero_psqt() psqt_vec_t{0}
#define NumRegistersSIMD 16
#define MaxChunkSize 16
#else
#undef VECTOR
@@ -123,8 +176,10 @@ namespace Stockfish::Eval::NNUE {
// We use __m* types as template arguments, which causes GCC to emit warnings
// about losing some attribute information. This is irrelevant to us as we
// only take their size, so the following pragma are harmless.
#if defined(__GNUC__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
template <typename SIMDRegisterType,
typename LaneType,
@@ -156,9 +211,9 @@ namespace Stockfish::Eval::NNUE {
static constexpr int NumRegs = BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
static constexpr int NumPsqtRegs = BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
#if defined(__GNUC__)
#pragma GCC diagnostic pop
#endif
#endif
@@ -183,7 +238,7 @@ namespace Stockfish::Eval::NNUE {
// Number of input/output dimensions
static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
static constexpr IndexType OutputDimensions = HalfDimensions * 2;
static constexpr IndexType OutputDimensions = HalfDimensions;
// Size of forward propagation buffer
static constexpr std::size_t BufferSize =
@@ -191,7 +246,7 @@ namespace Stockfish::Eval::NNUE {
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
return FeatureSet::HashValue ^ OutputDimensions;
return FeatureSet::HashValue ^ (OutputDimensions * 2);
}
// Read network parameters
@@ -229,136 +284,55 @@ namespace Stockfish::Eval::NNUE {
) / 2;
#if defined(USE_AVX512)
constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2);
static_assert(HalfDimensions % (SimdWidth * 2) == 0);
const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
const __m512i Zero = _mm512_setzero_si512();
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
auto out = reinterpret_cast<__m512i*>(&output[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
__m512i sum0 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
(accumulation[perspectives[p]])[j * 2 + 0]);
__m512i sum1 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
(accumulation[perspectives[p]])[j * 2 + 1]);
const IndexType offset = (HalfDimensions / 2) * p;
_mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control,
_mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero)));
#if defined(VECTOR)
constexpr IndexType OutputChunkSize = MaxChunkSize;
static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
vec_t Zero = vec_zero();
vec_t One = vec_set_16(127);
const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
const vec_t* in1 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][HalfDimensions / 2]));
vec_t* out = reinterpret_cast< vec_t*>(output + offset);
for (IndexType j = 0; j < NumOutputChunks; j += 1)
{
const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero);
const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero);
const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero);
const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero);
const vec_t pa = vec_mul_16(sum0a, sum1a);
const vec_t pb = vec_mul_16(sum0b, sum1b);
out[j] = vec_msb_pack_16(pa, pb);
}
#else
for (IndexType j = 0; j < HalfDimensions / 2; ++j) {
BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
BiasType sum1 = accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
sum0 = std::max<int>(0, std::min<int>(127, sum0));
sum1 = std::max<int>(0, std::min<int>(127, sum1));
output[offset + j] = static_cast<OutputType>(sum0 * sum1 / 128);
}
#endif
}
#if defined(vec_cleanup)
vec_cleanup();
#endif
return psqt;
#elif defined(USE_AVX2)
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
constexpr int Control = 0b11011000;
const __m256i Zero = _mm256_setzero_si256();
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
auto out = reinterpret_cast<__m256i*>(&output[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
__m256i sum0 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
(accumulation[perspectives[p]])[j * 2 + 0]);
__m256i sum1 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
(accumulation[perspectives[p]])[j * 2 + 1]);
_mm256_store_si256(&out[j], _mm256_permute4x64_epi64(
_mm256_max_epi8(_mm256_packs_epi16(sum0, sum1), Zero), Control));
}
}
return psqt;
#elif defined(USE_SSE2)
#ifdef USE_SSE41
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m128i Zero = _mm_setzero_si128();
#else
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m128i k0x80s = _mm_set1_epi8(-128);
#endif
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
auto out = reinterpret_cast<__m128i*>(&output[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
__m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>
(accumulation[perspectives[p]])[j * 2 + 0]);
__m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>
(accumulation[perspectives[p]])[j * 2 + 1]);
const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
#ifdef USE_SSE41
_mm_store_si128(&out[j], _mm_max_epi8(packedbytes, Zero));
#else
_mm_store_si128(&out[j], _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s));
#endif
}
}
return psqt;
#elif defined(USE_MMX)
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
auto out = reinterpret_cast<__m64*>(&output[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
__m64 sum0 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 0]);
__m64 sum1 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 1]);
const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
}
}
_mm_empty();
return psqt;
#elif defined(USE_NEON)
constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2);
const int8x8_t Zero = {0};
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
for (IndexType j = 0; j < NumChunks; ++j)
{
int16x8_t sum = reinterpret_cast<const int16x8_t*>(accumulation[perspectives[p]])[j];
out[j] = vmax_s8(vqmovn_s16(sum), Zero);
}
}
return psqt;
#else
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = HalfDimensions * p;
for (IndexType j = 0; j < HalfDimensions; ++j)
{
BiasType sum = accumulation[perspectives[p]][j];
output[offset + j] = static_cast<OutputType>(std::max<int>(0, std::min<int>(127, sum)));
}
}
return psqt;
#endif
} // end of function transform()
+1 -1
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@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+18 -7
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -120,12 +120,12 @@ public:
Bitboard attackers_to(Square s) const;
Bitboard attackers_to(Square s, Bitboard occupied) const;
Bitboard slider_blockers(Bitboard sliders, Square s, Bitboard& pinners) const;
template<PieceType Pt> Bitboard attacks_by(Color c) const;
// Properties of moves
bool legal(Move m) const;
bool pseudo_legal(const Move m) const;
bool capture(Move m) const;
bool capture_or_promotion(Move m) const;
bool gives_check(Move m) const;
Piece moved_piece(Move m) const;
Piece captured_piece() const;
@@ -285,6 +285,22 @@ inline Bitboard Position::attackers_to(Square s) const {
return attackers_to(s, pieces());
}
template<PieceType Pt>
inline Bitboard Position::attacks_by(Color c) const {
if constexpr (Pt == PAWN)
return c == WHITE ? pawn_attacks_bb<WHITE>(pieces(WHITE, PAWN))
: pawn_attacks_bb<BLACK>(pieces(BLACK, PAWN));
else
{
Bitboard threats = 0;
Bitboard attackers = pieces(c, Pt);
while (attackers)
threats |= attacks_bb<Pt>(pop_lsb(attackers), pieces());
return threats;
}
}
inline Bitboard Position::checkers() const {
return st->checkersBB;
}
@@ -352,11 +368,6 @@ inline bool Position::is_chess960() const {
return chess960;
}
inline bool Position::capture_or_promotion(Move m) const {
assert(is_ok(m));
return type_of(m) != NORMAL ? type_of(m) != CASTLING : !empty(to_sq(m));
}
inline bool Position::capture(Move m) const {
assert(is_ok(m));
// Castling is encoded as "king captures rook"
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+234 -247
View File
File diff suppressed because it is too large Load Diff
+2 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -74,6 +74,7 @@ struct RootMove {
Value score = -VALUE_INFINITE;
Value previousScore = -VALUE_INFINITE;
Value averageScore = -VALUE_INFINITE;
int selDepth = 0;
int tbRank = 0;
Value tbScore;
+50 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -46,6 +46,13 @@
#define USE_INLINE_ASM
#endif
// Use either the AVX512 or AVX-VNNI version of the VNNI instructions.
#if defined(USE_AVXVNNI)
#define VNNI_PREFIX "%{vex%} "
#else
#define VNNI_PREFIX ""
#endif
namespace Stockfish::Simd {
#if defined (USE_AVX512)
@@ -208,7 +215,7 @@ namespace Stockfish::Simd {
# if defined (USE_VNNI)
# if defined (USE_INLINE_ASM)
asm(
"vpdpbusd %[b], %[a], %[acc]\n\t"
VNNI_PREFIX "vpdpbusd %[b], %[a], %[acc]\n\t"
: [acc]"+v"(acc)
: [a]"v"(a), [b]"vm"(b)
);
@@ -240,8 +247,8 @@ namespace Stockfish::Simd {
# if defined (USE_VNNI)
# if defined (USE_INLINE_ASM)
asm(
"vpdpbusd %[b0], %[a0], %[acc]\n\t"
"vpdpbusd %[b1], %[a1], %[acc]\n\t"
VNNI_PREFIX "vpdpbusd %[b0], %[a0], %[acc]\n\t"
VNNI_PREFIX "vpdpbusd %[b1], %[a1], %[acc]\n\t"
: [acc]"+v"(acc)
: [a0]"v"(a0), [b0]"vm"(b0), [a1]"v"(a1), [b1]"vm"(b1)
);
@@ -336,6 +343,45 @@ namespace Stockfish::Simd {
#endif
#if defined (USE_NEON)
[[maybe_unused]] static int neon_m128_reduce_add_epi32(int32x4_t s) {
# if USE_NEON >= 8
return vaddvq_s32(s);
# else
return s[0] + s[1] + s[2] + s[3];
# endif
}
[[maybe_unused]] static int neon_m128_hadd(int32x4_t sum, int bias) {
return neon_m128_reduce_add_epi32(sum) + bias;
}
[[maybe_unused]] static int32x4_t neon_m128_haddx4(
int32x4_t sum0, int32x4_t sum1, int32x4_t sum2, int32x4_t sum3,
int32x4_t bias) {
int32x4_t hsums {
neon_m128_reduce_add_epi32(sum0),
neon_m128_reduce_add_epi32(sum1),
neon_m128_reduce_add_epi32(sum2),
neon_m128_reduce_add_epi32(sum3)
};
return vaddq_s32(hsums, bias);
}
[[maybe_unused]] static void neon_m128_add_dpbusd_epi32x2(
int32x4_t& acc,
int8x8_t a0, int8x8_t b0,
int8x8_t a1, int8x8_t b1) {
int16x8_t product = vmull_s8(a0, b0);
product = vmlal_s8(product, a1, b1);
acc = vpadalq_s16(acc, product);
}
#endif
}
#endif // STOCKFISH_SIMD_H_INCLUDED
+11 -11
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -770,7 +770,7 @@ Ret do_probe_table(const Position& pos, T* entry, WDLScore wdl, ProbeState* resu
goto encode_remaining; // With pawns we have finished special treatments
}
// In positions withouth pawns, we further flip the squares to ensure leading
// In positions without pawns, we further flip the squares to ensure leading
// piece is below RANK_5.
if (rank_of(squares[0]) > RANK_4)
for (int i = 0; i < size; ++i)
@@ -813,7 +813,7 @@ Ret do_probe_table(const Position& pos, T* entry, WDLScore wdl, ProbeState* resu
// Rs "together" in 62 * 61 / 2 ways (we divide by 2 because rooks can be
// swapped and still get the same position.)
//
// In case we have at least 3 unique pieces (inlcuded kings) we encode them
// In case we have at least 3 unique pieces (included kings) we encode them
// together.
if (entry->hasUniquePieces) {
@@ -828,7 +828,7 @@ Ret do_probe_table(const Position& pos, T* entry, WDLScore wdl, ProbeState* resu
+ (squares[1] - adjust1)) * 62
+ squares[2] - adjust2;
// First piece is on a1-h8 diagonal, second below: map this occurence to
// First piece is on a1-h8 diagonal, second below: map this occurrence to
// 6 to differentiate from the above case, rank_of() maps a1-d4 diagonal
// to 0...3 and finally MapB1H1H7[] maps the b1-h1-h7 triangle to 0..27.
else if (off_A1H8(squares[1]))
@@ -858,7 +858,7 @@ encode_remaining:
idx *= d->groupIdx[0];
Square* groupSq = squares + d->groupLen[0];
// Encode remainig pawns then pieces according to square, in ascending order
// Encode remaining pawns then pieces according to square, in ascending order
bool remainingPawns = entry->hasPawns && entry->pawnCount[1];
while (d->groupLen[++next])
@@ -886,7 +886,7 @@ encode_remaining:
// Group together pieces that will be encoded together. The general rule is that
// a group contains pieces of same type and color. The exception is the leading
// group that, in case of positions withouth pawns, can be formed by 3 different
// group that, in case of positions without pawns, can be formed by 3 different
// pieces (default) or by the king pair when there is not a unique piece apart
// from the kings. When there are pawns, pawns are always first in pieces[].
//
@@ -918,7 +918,7 @@ void set_groups(T& e, PairsData* d, int order[], File f) {
//
// This ensures unique encoding for the whole position. The order of the
// groups is a per-table parameter and could not follow the canonical leading
// pawns/pieces -> remainig pawns -> remaining pieces. In particular the
// pawns/pieces -> remaining pawns -> remaining pieces. In particular the
// first group is at order[0] position and the remaining pawns, when present,
// are at order[1] position.
bool pp = e.hasPawns && e.pawnCount[1]; // Pawns on both sides
@@ -938,7 +938,7 @@ void set_groups(T& e, PairsData* d, int order[], File f) {
d->groupIdx[1] = idx;
idx *= Binomial[d->groupLen[1]][48 - d->groupLen[0]];
}
else // Remainig pieces
else // Remaining pieces
{
d->groupIdx[next] = idx;
idx *= Binomial[d->groupLen[next]][freeSquares];
@@ -948,7 +948,7 @@ void set_groups(T& e, PairsData* d, int order[], File f) {
d->groupIdx[n] = idx;
}
// In Recursive Pairing each symbol represents a pair of childern symbols. So
// In Recursive Pairing each symbol represents a pair of children symbols. So
// read d->btree[] symbols data and expand each one in his left and right child
// symbol until reaching the leafs that represent the symbol value.
uint8_t set_symlen(PairsData* d, Sym s, std::vector<bool>& visited) {
@@ -1318,7 +1318,7 @@ void Tablebases::init(const std::string& paths) {
for (auto p : bothOnDiagonal)
MapKK[p.first][p.second] = code++;
// Binomial[] stores the Binomial Coefficents using Pascal rule. There
// Binomial[] stores the Binomial Coefficients using Pascal rule. There
// are Binomial[k][n] ways to choose k elements from a set of n elements.
Binomial[0][0] = 1;
@@ -1338,7 +1338,7 @@ void Tablebases::init(const std::string& paths) {
for (int leadPawnsCnt = 1; leadPawnsCnt <= 5; ++leadPawnsCnt)
for (File f = FILE_A; f <= FILE_D; ++f)
{
// Restart the index at every file because TB table is splitted
// Restart the index at every file because TB table is split
// by file, so we can reuse the same index for different files.
int idx = 0;
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -38,7 +38,7 @@ enum WDLScore {
// Possible states after a probing operation
enum ProbeState {
FAIL = 0, // Probe failed (missing file table)
OK = 1, // Probe succesful
OK = 1, // Probe successful
CHANGE_STM = -1, // DTZ should check the other side
ZEROING_BEST_MOVE = 2 // Best move zeroes DTZ (capture or pawn move)
};
+3 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -59,7 +59,6 @@ void Thread::clear() {
counterMoves.fill(MOVE_NONE);
mainHistory.fill(0);
lowPlyHistory.fill(0);
captureHistory.fill(0);
for (bool inCheck : { false, true })
@@ -67,7 +66,7 @@ void Thread::clear() {
{
for (auto& to : continuationHistory[inCheck][c])
for (auto& h : to)
h->fill(0);
h->fill(-71);
continuationHistory[inCheck][c][NO_PIECE][0]->fill(Search::CounterMovePruneThreshold - 1);
}
}
@@ -165,6 +164,7 @@ void ThreadPool::clear() {
main()->callsCnt = 0;
main()->bestPreviousScore = VALUE_INFINITE;
main()->bestPreviousAverageScore = VALUE_INFINITE;
main()->previousTimeReduction = 1.0;
}
+6 -7
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -61,21 +61,19 @@ public:
Pawns::Table pawnsTable;
Material::Table materialTable;
size_t pvIdx, pvLast;
RunningAverage doubleExtensionAverage[COLOR_NB];
uint64_t nodesLastExplosive;
uint64_t nodesLastNormal;
RunningAverage complexityAverage;
std::atomic<uint64_t> nodes, tbHits, TTsaves, bestMoveChanges;
int selDepth, nmpMinPly;
Color nmpColor;
ExplosionState state;
Value bestValue, optimism[COLOR_NB];
Position rootPos;
StateInfo rootState;
Search::RootMoves rootMoves;
Depth rootDepth, completedDepth;
Depth rootDepth, completedDepth, depth;
Value rootDelta;
CounterMoveHistory counterMoves;
ButterflyHistory mainHistory;
LowPlyHistory lowPlyHistory;
CapturePieceToHistory captureHistory;
ContinuationHistory continuationHistory[2][2];
Score trend;
@@ -99,6 +97,7 @@ struct MainThread : public Thread {
double previousTimeReduction;
Value bestPreviousScore;
Value bestPreviousAverageScore;
Value iterValue[4];
int callsCnt;
bool stopOnPonderhit;
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+3 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -40,9 +40,9 @@ void TTEntry::save(Key k, Value v, bool pv, Bound b, Depth d, Move m, Value ev)
move16 = (uint16_t)m;
// Overwrite less valuable entries (cheapest checks first)
if (b == BOUND_EXACT
if ( b == BOUND_EXACT
|| (uint16_t)k != key16
|| d - DEPTH_OFFSET > depth8 - 4)
|| d - DEPTH_OFFSET + 2 * pv > depth8 - 4)
{
assert(d > DEPTH_OFFSET);
assert(d < 256 + DEPTH_OFFSET);
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -84,7 +84,7 @@ class Tune {
static Tune& instance() { static Tune t; return t; } // Singleton
// Use polymorphism to accomodate Entry of different types in the same vector
// Use polymorphism to accommodate Entry of different types in the same vector
struct EntryBase {
virtual ~EntryBase() = default;
virtual void init_option() = 0;
+1 -10
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -173,11 +173,6 @@ enum Bound {
BOUND_EXACT = BOUND_UPPER | BOUND_LOWER
};
enum ExplosionState {
EXPLOSION_NONE,
MUST_CALM_DOWN
};
enum Value : int {
VALUE_ZERO = 0,
VALUE_DRAW = 0,
@@ -470,10 +465,6 @@ constexpr Move make_move(Square from, Square to) {
return Move((from << 6) + to);
}
constexpr Move reverse_move(Move m) {
return make_move(to_sq(m), from_sq(m));
}
template<MoveType T>
constexpr Move make(Square from, Square to, PieceType pt = KNIGHT) {
return Move(T + ((pt - KNIGHT) << 12) + (from << 6) + to);
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -43,7 +43,7 @@ cat << EOF > repeat.exp
expect eof
EOF
# to increase the likelyhood of finding a non-reproducible case,
# to increase the likelihood of finding a non-reproducible case,
# the allowed number of nodes are varied systematically
for i in `seq 1 20`
do