This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish.
Both the NNUE and the classical evaluations are available, and can be used to
assign a value to a position that is later used in alpha-beta (PVS) search to find the
best move. The classical evaluation computes this value as a function of various chess
concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation
computes this value with a neural network based on basic inputs. The network is optimized
and trained on the evalutions of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
tools to train and develop the NNUE networks.
This patch is the result of contributions of various authors, from various communities,
including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather,
rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler,
dorzechowski, and vondele.
This new evaluation needed various changes to fishtest and the corresponding infrastructure,
for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged.
The first networks have been provided by gekkehenker and sergiovieri, with the latter
net (nn-97f742aaefcd.nnue) being the current default.
The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option,
provided the `EvalFile` option points the the network file (depending on the GUI, with full path).
The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on
the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest:
60000 @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c
ELO: 92.77 +-2.1 (95%) LOS: 100.0%
Total: 60000 W: 24193 L: 8543 D: 27264
Ptnml(0-2): 609, 3850, 9708, 10948, 4885
40000 @ 20+0.2 th 8
https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58
ELO: 89.47 +-2.0 (95%) LOS: 100.0%
Total: 40000 W: 12756 L: 2677 D: 24567
Ptnml(0-2): 74, 1583, 8550, 7776, 2017
At the same time, the impact on the classical evaluation remains minimal, causing no significant
regression:
sprt @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b
LLR: 2.94 (-2.94,2.94) {-6.00,-4.00}
Total: 34936 W: 6502 L: 6825 D: 21609
Ptnml(0-2): 571, 4082, 8434, 3861, 520
sprt @ 60+0.6 th 1
https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d
LLR: 2.93 (-2.94,2.94) {-6.00,-4.00}
Total: 10088 W: 1232 L: 1265 D: 7591
Ptnml(0-2): 49, 914, 3170, 843, 68
The needed networks can be found at https://tests.stockfishchess.org/nns
It is recommended to use the default one as indicated by the `EvalFile` UCI option.
Guidelines for testing new nets can be found at
https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests
Integration has been discussed in various issues:
https://github.com/official-stockfish/Stockfish/issues/2823https://github.com/official-stockfish/Stockfish/issues/2728
The integration branch will be closed after the merge:
https://github.com/official-stockfish/Stockfish/pull/2825https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip
closes https://github.com/official-stockfish/Stockfish/pull/2912
This will be an exciting time for computer chess, looking forward to seeing the evolution of
this approach.
Bench: 4746616
Only 'nnue' target and only gcc/mingw.
(does not clean profile data generated by other compilers)
To use:
make profile-nnue ARCH=arch
(see 'make help' for list of supported archs)
This allows building modern compiles with SSE41 enabled,
which gives a nice speedup on my Bulldozer CPU.
For example:
make nnue ARCH=x86-64-modern sse41=yes -j
* Supports popcnt (thanks @daylen)
* bits = 64 is now the default
Tested with g++ (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04) 7.5.0 on ThunderX CN8890,
yields about 9% speedup.
Also tested with clang version 6.0.0-1ubuntu2 (tags/RELEASE_600/final).
closes https://github.com/official-stockfish/Stockfish/pull/2770
No functional change.
fixes https://github.com/official-stockfish/Stockfish/issues/2660
The problem was caused by .depend being created with a rule for tbprobe.o not for syzygy/tbprobe.o.
This patch keeps an explicit list of sources (SRCS), generates OBJS,
and compiles all object files to the src/ directory, consistent with .depend.
VPATH is used to search the syzygy directory as needed.
joint work with @gvreuls
closes https://github.com/official-stockfish/Stockfish/pull/2664
No functional change
The purpose of the code is to allow developers to easily and flexibly
setup SF for a tuning session. Mainly you have just to remove 'const'
qualifiers from the variables you want to tune and flag them for
tuning, so if you have:
int myKing = 10;
Score myBonus = S(5, 15);
Value myValue[][2] = { { V(100), V(20) }, { V(7), V(78) } };
and at the end of the update you may want to call
a post update function:
void my_post_update();
If instead of default Option's min-max values,
you prefer your custom ones, returned by:
std::pair<int, int> my_range(int value)
Or you jus want to set the range directly, you can
simply add below:
TUNE(SetRange(my_range), myKing, SetRange(-200, 200), myBonus, myValue, my_post_update);
And all the magic happens :-)
At startup all the parameters are printed in a
format suitable to be copy-pasted in fishtest.
In case the post update function is slow and you have many
parameters to tune, you can add:
UPDATE_ON_LAST();
And the values update, including post update function call, will
be done only once, after the engine receives the last UCI option.
The last option is the one defined and created as the last one, so
this assumes that the GUI sends the options in the same order in
which have been defined.
closes https://github.com/official-stockfish/Stockfish/pull/2654
No functional change.
- Cleanups by Alain
- Group king attacks and king defenses
- Signature of futility_move_count()
- Use is_discovery_check_on_king()
- Simplify backward definition
- Use static asserts in move generator
- Factor a statement in move generator
No functional change
Follow-up to previous commit. Update the documentation for the user when using `make`,
to show the preferred bmi2 compile in the advanced examples section.
Note: I made a mistake in the previous commit comment, the documentation is shown when
using `make` or `make help`, not `make --help`.
No functional change
The only change done to the Makefile to get a somewhat faster binary as
discussed in #2291 is to add -msse4 to the compile options of the bmi2 build.
Since all processors supporting bmi2 also support sse4 this can be done easily.
It is a useful step to avoid sending around custom and poorly tested builds.
The speedup isn't enough to pass [0,4] but it is roughly 1.15Elo and a LOS of 90%:
LLR: -2.95 (-2.94,2.94) [0.00,4.00]
Total: 93009 W: 20519 L: 20316 D: 52174
Also rewrite the documentation for the user when using `make --help`, so that
the order of architectures for x86-64 has the more performant build one on top.
Closes https://github.com/official-stockfish/Stockfish/pull/2300
No functional change
PowerPC has had popcount instructions for a long time, at least as far
back as POWER5 (released 2004). Enable them via a gcc builtin.
Using a gcc builtin has the added bonus that if compiled for a processor
that lacks a hardware instruction, gcc will include a software popcount
implementation that does not use the instruction. It might be slower
than the table lookups (or it might be faster) but it will certainly work.
So this isn't going to break anything.
On my POWER8 VM, this leads to a ~4.27% speedup.
Fir prefetch, the gcc builtin generates a 'dcbt' instruction, which is
supported at least as far back as the G5 (2002) and POWER4 (2001).
This leads to a ~5% speedup on my POWER8 VM.
No functional change
nodchip