Joint work gvreuls / vondele
* Download the default NNUE net in AppVeyor
* Download net in travis CI `make net`
* Adjust tests to cover more archs, speedup instrumented testing
* Introduce 'mixed' bench as default, with further options:
classical, NNUE, mixed.
mixed (default) and NNUE require the default net to be present,
which can be obtained with
```
make net
```
Further examples (first is equivalent to `./stockfish bench`):
```
./stockfish bench 16 1 13 default depth mixed
./stockfish bench 16 1 13 default depth classical
./stockfish bench 16 1 13 default depth NNUE
```
The net is now downloaded automatically if needed for `profile-build`
(usual `build` works fine without net present)
PGO gives a nice speedup on fishtest:
passed STC:
LLR: 2.93 (-2.94,2.94) {-0.50,1.50}
Total: 3360 W: 469 L: 343 D: 2548
Ptnml(0-2): 20, 246, 1030, 356, 28
https://tests.stockfishchess.org/tests/view/5f31b5499081672066537569
passed LTC:
LLR: 2.97 (-2.94,2.94) {0.25,1.75}
Total: 8824 W: 609 L: 502 D: 7713
Ptnml(0-2): 8, 430, 3438, 519, 17
https://tests.stockfishchess.org/tests/view/5f31c87b908167206653757c
closes https://github.com/official-stockfish/Stockfish/pull/2931
fixes https://github.com/official-stockfish/Stockfish/issues/2907
requires fishtest updates before commit
Bench: 4290577
This patch 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
* Update our continuous integration machinery
Ubuntu 16.04 can now be used with travis. Updating all the other stuff
when there.
Invoking the lld linker seems to save 5 minutes with clang on linux.
No functional change.
* fix
After a helpful suggestion from AppVeyor support staff, moving the Stockfish
execution from ps to cmd seems to work. Alternative to PR #1624 tested in PR #1637.
No functional change.
And set x86 and x64 platforms for real.
Currently this is broken and the same binary is compiled for all platforms.
This is becuase we use a custom build step. OTH the default
build step seems not compatible with cmake generated *sln file.
No functional change.
