This patch removes the UCI option for setting Contempt in classical evaluation.
It is exactly equivalent to using Contempt=0 for the UCI contempt value and keeping
the dynamic part in the algo (renaming this dynamic part `trend` to better describe
what it does). We have tried quite hard to implement a working Contempt feature for
NNUE but nothing really worked, so it is probably time to give up.
Interested chess fans wishing to keep playing with the UCI option for Contempt and
use it with the classical eval are urged to download the version tagged "SF_Classical"
of Stockfish (dated 31 July 2020), as it was the last version where our search
algorithm was tuned for the classical eval and is probably our strongest classical
player ever: https://github.com/official-stockfish/Stockfish/tags
This patch increase the weight of pawns and pieces from 28 to 32
in the scaling formula we apply to the output of the NNUE pure eval.
Increasing this gradient for pawns and pieces means that Stockfish
will try a little harder to keep material when she has the advantage,
and try a little bit harder to escape into an endgame when she is
under pressure.
c:\nnue>python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --lambda=1.0 --max_epochs=440 --seed %random%%random% --default_root_dir exp/run_10 --resume-from-model ./pt/nn-3b20abec10c1.pt
`
all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing
Each set was concatenated together - making one large Wrong_NNUE 2 binpack and one large Training so the were approximately equal in size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training_Data binpack .
Net nn-3b20abec10c1.nnue was chosen as the --resume-from-model with the idea that through learning, the manually hex edited values will be learned and will not need to be manually adjusted going forward. They would also be fine tuned by the learning process.
Tomasz Sobczyk [Thu, 17 Jun 2021 10:36:06 +0000 (12:36 +0200)]
Change trace with NNUE eval support
This patch adds some more output to the `eval` command. It adds a board display
with estimated piece values (method is remove-piece, evaluate, put-piece), and
splits the NNUE evaluation with (psqt,layers) for each bucket for the NNUE net.
Example:
```
./stockfish
position fen 3Qb1k1/1r2ppb1/pN1n2q1/Pp1Pp1Pr/4P2p/4BP2/4B1R1/1R5K b - - 11 40
eval
proukornew [Fri, 18 Jun 2021 21:52:46 +0000 (00:52 +0300)]
Fix for Cygwin's environment build-profile (fixed)
The Cygwin environment has two g++ compilers, each with a different problem
for compiling Stockfish at the moment:
(a) g++.exe : full posix build compiler, linked to cygwin dll.
=> This one has a problem embedding the net.
(b) x86_64-w64-mingw32-g++.exe : native Windows build compiler.
=> This one manages to embed the net, but has a problem related to libgcov
when we use the profile-build target of Stockfish.
This patch solves the problem for compiler (b), so that our recommended command line
if you want to build an optimized version of Stockfish on Cygwin becomes something
like the following (you can change the ARCH value to whatever you want, but note
the COMP and CXX variables pointing at the right compiler):
```
make -j profile-build ARCH=x86-64-modern COMP=mingw CXX=x86_64-w64-mingw32-c++.exe
```
c:\nnue>python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --lambda=1.0 --max_epochs=440 --seed %random%%random% --default_root_dir exp/run_8 --resume-from-model ./pt/nn-6ad41a9207d0.pt
`
all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing
Each set was concatenated together - make one large Wrong_NNUE 2 binpack and one large Training_Data of approximate size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training _Data binpack .
nn-6ad41a9207d0.pt was derived from a net vondele ran which passed STC quickly,
but faltered in LTC. https://tests.stockfishchess.org/tests/view/60cba666457376eb8bcab443
Optimization of vondele's nn-33c9d39e5eb6.nnue using SPSA
https://tests.stockfishchess.org/tests/view/60ca68be457376eb8bcab28b
Setting: ck values are default based on how large the parameters are
The new values for this net are the raw values at the end of the tuning (80k games)
The significant changes are in buckets 1 and 2 (5-12 pieces) so the main difference is in playing endgames if we compare it to nn-33c9. There is also change in bucket 7 (29-32 pieces) but not as substantial as the changes in buckets 1 and 2. If we interpret the changes based on an experiment a few months ago, this new net plays more optimistically during endgames and less optimistically during openings.
This net was created by @pleomati, who manually edited with an hex editor
10 values randomly chosen in the LCSFNet10 net (nn-6ad41a9207d0.nnue) to
create this one. The LCSFNet10 net was trained by Joost VandeVondele from
a dataset combining Stockfish games and Leela games (16x10^9 positions from
SF self-play at depth 9, and 6.3x10^9 positions from Leela games, so overall
72% of Stockfish positions and 28% of Leela positions).
Revert "Fix for Cygwin's environment build-profile"
This reverts commit "Fix for Cygwin's environment build-profile", as it was
giving errors for "make clean" on some Windows environments. See comments in
https://github.com/official-stockfish/Stockfish/commit/68bf362ea2385a641be9f5ed9ce2acdf55a1ecf1
Possibly somebody can propose a solution that would fix Cygwin builds and
not break on other system too, stay tuned! :-)
proukornew [Thu, 13 May 2021 21:49:28 +0000 (00:49 +0300)]
Fix for Cygwin's environment build-profile
The Cygwin environment has two g++ compilers, each with a different problem
for compiling Stockfish at the moment:
(a) g++.exe : full posix build compiler, linked to cygwin dll.
=> This one has a problem embedding the net.
(b) x86_64-w64-mingw32-g++.exe : native Windows build compiler.
=> This one manages to embed the net, but has a problem related to libgcov
when we use the profile-build target of Stockfish.
This patch solves the problem for compiler (b), so that our recommended command line
if you want to build an optimized version of Stockfish on Cygwin becomes something
like the following (you can change the ARCH value to whatever you want, but note
the COMP and CXX variables pointing at the right compiler):
```
make -j profile-build ARCH=x86-64-modern COMP=mingw CXX=x86_64-w64-mingw32-c++.exe
```
As the previous net, this net is trained on Leela games as provided by borg.
See also https://lczero.org/blog/2021/06/the-importance-of-open-data/
The particular data set, which is a mix of T60 and T74 data, is now available as a single binpack:
https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing
J. Oster [Mon, 14 Jun 2021 15:28:30 +0000 (17:28 +0200)]
Fix a rare case of wrong TB ranking
of a root move leading to a 3-fold repetition.
With this small fix a draw ranking and thus a draw score is being applied.
This works for both, ranking by dtz or wdl tables.
This net is the result of training on data used by the Leela project. More precisely,
we shuffled T60 and T74 data kindly provided by borg (for different Tnn, the data is
a result of Leela selfplay with differently sized Leela nets).
The data is available at vondele's google drive:
https://drive.google.com/drive/folders/1mftuzYdl9o6tBaceR3d_VBQIrgKJsFpl.
The Leela data comes in small chunks of .binpack files. To shuffle them, we simply
used a small python script to randomly rename the files, and then concatenated them
using `cat`. As validation data we picked a file of T60 data. We will further investigate
T74 data.
The training for the NNUE architecture used 200 epochs with the Python trainer from
the Stockfish project. Unlike the previous run we tried with this data, this run does
not have adjusted scaling — not because we didn't want to, but because we forgot.
However, this training randomly skips 40% more positions than previous run. The loss
was very spiky and decreased slower than it does usually.
Training loss: https://github.com/official-stockfish/images/blob/main/training-loss-8e47cf062333.png
Validation loss: https://github.com/official-stockfish/images/blob/main/validation-loss-8e47cf062333.png
This is the exact training command:
python train.py --smart-fen-skipping --random-fen-skipping 14 --batch-size 16384 --threads 4 --num-workers 4 --gpus 1 trainingdata\training_data.binpack validationdata\val.binpack
Tomasz Sobczyk [Thu, 10 Jun 2021 15:43:42 +0000 (17:43 +0200)]
Register count for feature transformer
Compute optimal register count for feature transformer accumulation dynamically.
This also introduces a change where AVX512 would only use 8 registers instead of 16
(now possible due to a 2x increase in feature transformer size).
Vizvezdenec [Sat, 29 May 2021 03:39:14 +0000 (06:39 +0300)]
Do less LMR extensions
This patch restricts LMR extensions (of non-transposition table moves) from being
used when the transposition table move was extended by two plies via singular
extension. This may serve to limit search explosions in certain positions.
This makes a lot of sense because the precondition for the tt-move to have been
singular extended by two plies is that the result of the alternate search (with
excluded the tt-move) has been a hard fail low: it is natural to later search less
for non tt-moves in this situation.
The current state of depth/extensions/reductions management is getting quite tricky
in our search algo, see https://github.com/official-stockfish/Stockfish/pull/3546#issuecomment-860174549
for some discussion. Suggestions welcome!
Tomasz Sobczyk [Wed, 9 Jun 2021 09:21:55 +0000 (11:21 +0200)]
Read NNUE net faster
Load feature transformer weights in bulk on little-endian machines.
This is in particular useful to test new nets with c-chess-cli,
see https://github.com/lucasart/c-chess-cli/issues/44
Double extensions can lead to search explosions, for specific positions.
Currently, however, these double extensions are worth about 10Elo and cannot
be removed. This patch instead limits the number of double extensions given
to a maximum of 3.
This fixes https://github.com/official-stockfish/Stockfish/issues/3532
where the following testcase was shown to be problematic:
```
uci
setoption name Hash value 4
setoption name Contempt value 0
ucinewgame
position fen 8/Pk6/8/1p6/8/P1K5/8/6B1 w - - 37 130
go depth 20
```
It would be nice if a future patch could use some of the macros at
the top of the file to unify the code between the distincts SIMD
instruction sets (of course, unifying the Relu will be the challenge).
Trained with pytorch using the master branch and recommended settings,
the data used is the previous 64B binpack enhanced with a 2B binpack
generated using an opening book of positions for with the static eval
is significantly different from d9 search.
book : https://drive.google.com/file/d/1rHcKY5rv34kwku6g89OhnE8Bkfq3UWau/view?usp=sharing
book generation: https://github.com/vondele/Stockfish/commit/3ce43ab0c4ce09c1fc5bca5ca27a248e67fddd24
binpack : https://drive.google.com/file/d/1rHcKY5rv34kwku6g89OhnE8Bkfq3UWau/view?usp=sharing
J. Oster [Mon, 31 May 2021 15:46:40 +0000 (17:46 +0200)]
Pre-initialize ss->ply
We pre-initialize ss->ply over the whole stack. There is no need
to re-assign the same value(s) over and over again while searching.
Probably a tiny speedup on longer searches.
1. it simplifies away the so-called "lazy" path in the NNUE evaluation internals,
where we trusted the psqt head alone to avoid the costly "positional" head in
some cases;
2. it raises a little bit the NNUEThreshold1 in evaluate.cpp (from 682 to 800),
which increases the limit where we switched from NNUE eval to Classical eval.
Both effects increase the number of positional evaluations done by our new net
architecture, but the results of our tests below seem to indicate that the loss
of speed will be compensated by the gain of eval quality.
Tomasz Sobczyk [Tue, 25 May 2021 11:09:40 +0000 (13:09 +0200)]
Expose the lazy threshold for the feature transformer PSQT as a parameter.
Definition of the lazy threshold moved to evaluate.cpp where all others are.
Lazy threshold only used for real searches, not used for the "eval" call.
This preserves the purity of NNUE evaluation, which is useful to verify
consistency between the engine and the NNUE trainer.
Sometimes change the (materialist, positional) balance
Our new nets output two values for the side to move in the last layer.
We can interpret the first value as a material evaluation of the
position, and the second one as the dynamic, positional value of the
location of pieces.
This patch changes the balance for the (materialist, positional) parts
of the score from (128, 128) to (121, 135) when the piece material is
equal between the two players, but keeps the standard (128, 128) balance
when one player is at least an exchange up.
The Tempo variable was introduced 10 years ago in our search because the
classical evaluation function was antisymmetrical in White and Black by design
to gain speed:
Eval(White to play) = -Eval(Black to play)
Nowadays our neural networks know which side is to play in a position when
they evaluate a position and are trained on real games, so the neural network
encodes the advantage of moving as an output of search. This patch shows that
the Tempo variable is not necessary anymore.
We also tried a match (20000 games) at STC using purely classical, result was neutral:
https://tests.stockfishchess.org/tests/view/60a4eebcce8ea25a3ef03db5
Note: there are two locations left in search.cpp where we assume antisymmetry
of evaluation (in relation with a speed optimization for null moves in lines
770 and 1439), but as the values are just used for heuristic pruning this
approximation should not hurt too much because the order of magnitude is still
true most of the time.
Vizvezdenec [Wed, 19 May 2021 17:57:04 +0000 (20:57 +0300)]
Simplify usage of LMR for captures
This patch simplifies a lot of "enablers" for LMR when move is a capture or promotion.
After it we will have only 2 conditions - if node is a cutNode
or if it's an allNode that was not in PV,
so all captures or promotions wouldn't go thru LMR at any PVnodes.
Vizvezdenec [Tue, 18 May 2021 07:02:20 +0000 (10:02 +0300)]
Do more continuation history based pruning
This patch increases lmrDepth threshold for continuation history based pruning in search.
This part of code for a long time was known to be really TC sensitive - decreasing
this threshold easily passed lower time controls but failed badly at LTC,
on the other hand it increase was part of a tuning that resulted
in being negative at STC but was +12 elo at 180+1.8.
After recent simplification of special conditions that sometimes
increase it from 4 to 5 it was logical to overall test at longer
time controls if 5 is better than 4 with deeper searches.
The network has been trained (by vondele) using the https://github.com/glinscott/nnue-pytorch/ trainer (started by glinscott),
specifically the branch https://github.com/Sopel97/nnue-pytorch/tree/experiment_56.
The data used are in 64 billion positions (193GB total) generated and scored with the current master net
d8: https://drive.google.com/file/d/1hOOYSDKgOOp38ZmD0N4DV82TOLHzjUiF/view?usp=sharing
d9: https://drive.google.com/file/d/1VlhnHL8f-20AXhGkILujnNXHwy9T-MQw/view?usp=sharing
d10: https://drive.google.com/file/d/1ZC5upzBYMmMj1gMYCkt6rCxQG0GnO3Kk/view?usp=sharing
fishtest_d9: https://drive.google.com/file/d/1GQHt0oNgKaHazwJFTRbXhlCN3FbUedFq/view?usp=sharing
This network also contains a few architectural changes with respect to the current master:
Size changed from 256x2-32-32-1 to 512x2-16-32-1
~15-20% slower
~2x larger
adds a special path for 16 valued ClippedReLU
fixes affine transform code for 16 inputs/outputs, buy using InputDimensions instead of PaddedInputDimensions
this is safe now because the inputs are processed in groups of 4 in the current affine transform code
The feature set changed from HalfKP to HalfKAv2
Includes information about the kings like HalfKA
Packs king features better, resulting in 8% size reduction compared to HalfKA
The board is flipped for the black's perspective, instead of rotated like in the current master
PSQT values for each feature
the feature transformer now outputs a part that is fowarded directly to the output and allows learning piece values more directly than the previous network architecture. The effect is visible for high imbalance positions, where the current master network outputs evaluations skewed towards zero.
8 PSQT values per feature, chosen based on (popcount(pos.pieces()) - 1) / 4
initialized to classical material values on the start of the training
8 subnetworks (512x2->16->32->1), chosen based on (popcount(pos.pieces()) - 1) / 4
only one subnetwork is evaluated for any position, no or marginal speed loss
A diagram of the network is available: https://user-images.githubusercontent.com/8037982/118656988-553a1700-b7eb-11eb-82ef-56a11cbebbf2.png
A more complete description: https://github.com/glinscott/nnue-pytorch/blob/master/docs/nnue.md
- Comment for Countemove pruning -> Continuation history
- Fix comment in input_slice.h
- Shorter lines in Makefile
- Comment for scale factor
- Fix comment for pinners in see_ge()
- Change Thread.id() signature to size_t
- Trailing space in reprosearch.sh
- Add Douglas Matos Gomes to the AUTHORS file
- Introduce comment for undo_null_move()
- Use Stockfish coding style for export_net()
- Change date in AUTHORS file
Vizvezdenec [Fri, 14 May 2021 21:55:45 +0000 (00:55 +0300)]
Simplification for countermoves based pruning
Simplify away two extra conditions in countermoves based pruning.
These conditions (both of them) were introduced quite a long time ago
via speculative LTCs and seem to no longer bring any benefit.
E2K: added support for MCST Elbrus 2000 CPU architecture
e2k (Elbrus 2000) - this is a VLIW/EPIC architecture,
the like Intel Itanium (IA-64) architecture.
The architecture has half native / half software support
for most Intel/AMD SIMD (e.g. MMX/SSE/SSE2/SSE3/SSSE3/SSE4.1/SSE4.2/AES/AVX/AVX2 & 3DNow!/SSE4a/XOP/FMA4) via intrinsics.
Tomasz Sobczyk [Fri, 7 May 2021 10:24:12 +0000 (12:24 +0200)]
Exporting the currently loaded network file
This PR adds an ability to export any currently loaded network.
The export_net command now takes an optional filename parameter.
If the loaded net is not the embedded net the filename parameter is required.
Two changes were required to support this:
* the "architecture" string, which is really just a some kind of description in the net, is now saved into netDescription on load and correctly saved on export.
* the AffineTransform scrambles weights for some architectures and sparsifies them, such that retrieving the index is hard. This is solved by having a temporary scrambled<->unscrambled index lookup table when loading the network, and the actual index is saved for each individual weight that makes it to canSaturate16. This increases the size of the canSaturate16 entries by 6 bytes.
Vizvezdenec [Wed, 5 May 2021 16:03:20 +0000 (19:03 +0300)]
Cleanup of likelyFailLow logic
This patch broadens and simplifies definition of PvNode that is likely to fail low.
New definition can be described as following "If node was already researched
at depth >= current depth and failed low there" which is more logical than the
previous version and takes less space + allows to not recompute it every time during move loop.
Introduce variable tempo for nnue depending on logarithm of estimated
strength, where strength is the product of time and number of threads.
The original idea here was that NNUE is best with a slightly different
tempo value to classical, since its style of play is slightly different.
It turns out that the best tempo for NNUE varies with strength of play,
so a formula is used which gives about 19 for STC and 24 for LTC under
current fishtest settings.
This patch simplifies QUIET_CHECKS pawn move generator by merging discovery check
move generator with direct check move generator. It also simplifies emptySquares
instantiation. In addition, I added a comment in generate_moves() to clarify Check
branches.
Tomasz Sobczyk [Sat, 24 Apr 2021 13:08:11 +0000 (15:08 +0200)]
Cleanup and simplify NNUE code.
A lot of optimizations happend since the NNUE was introduced
and since then some parts of the code were left unused. This
got to the point where asserts were have to be made just to
let people know that modifying something will not have any
effects or may even break everything due to the assumptions
being made. Removing these parts removes those inexisting
"false dependencies". Additionally:
* append_changed_indices now takes the king pos and stateinfo
explicitly, no more misleading pos parameter
* IndexList is removed in favor of a generic ValueList.
Feature transformer just instantiates the type it needs.
* The update cost and refresh requirement is deferred to the
feature set once again, but now doesn't go through the whole
FeatureSet machinery and just calls HalfKP directly.
* accumulator no longer has a singular dimension.
* The PS constants and the PieceSquareIndex array are made local
to the HalfKP feature set because they are specific to it and
DO differ for other feature sets.
* A few names are changed to more descriptive
NNUE evaluation is incapable of recognizing trivially drawn bishop endgames
(the wrong-colored rook pawn), which are in fact ubiquitous and stock standard
in chess analysis. Switching off NNUE evaluation in KBPs vs KPs endgames is
a measure that stops Stockfish from trading down to a drawn version of these
endings when we presumably have advantage. The patch is able to edge over master
in endgame positions.
Patch tested for Elo gain with the "endgame.epd" book, and verified for
non-regression with our usual book (see the pull request for details).
Verified for correctness of `EVASIONS` by running perft:
```
./stockfish b3nch 16 1 6 default perft (replace 3 by e in b3nch)
Nodes searched : 71608931810
```
Also tested for correctness on Chess960 with a similar code shown here:
https://github.com/official-stockfish/Stockfish/pull/3418#issuecomment-816630295
This patch removes the recently introduced distanceFromPv logic, and replaces
it with following logic: if reduction of moves with low movecount is really
negative, we search them deeper than the first move.
This patch increases the weight of pawns in the scale factor applied
to the output of the NNUE evaluation. This has the effect that Stockfish
will try a little bit harder to keep more pawns in position where the
engine has the advantage, and exchange more pawns in bad positions.
bmc4 [Tue, 30 Mar 2021 03:42:45 +0000 (00:42 -0300)]
Simplify King Evasion
Simplify away the removal of some illegal `KING`-evasion moves during move
generation. Verified for correctness by running perft on the following positions:
Tomasz Sobczyk [Sat, 27 Feb 2021 10:52:18 +0000 (11:52 +0100)]
Allow using Intel SDE for PGO builds.
The software development emulator (SDE) allows to run binaries compiled
for architectures not supported by the actual CPU. This is useful to
do PGO builds for newer architectures. The SDE can currently be obtained from
https://software.intel.com/content/www/us/en/develop/articles/intel-software-development-emulator.html
This patch introduces a new optional makefile argument SDE_PATH.
If not empty it should contain the path to the sde executable
Guy Vreuls [Sat, 13 Mar 2021 16:40:07 +0000 (17:40 +0100)]
Use reference instead of pointer for pop_lsb() signature
This patch changes the pop_lsb() signature from Square pop_lsb(Bitboard*) to
Square pop_lsb(Bitboard&). This is more idomatic for C++ style signatures.
Vizvezdenec [Mon, 15 Mar 2021 18:05:01 +0000 (19:05 +0100)]
Add a specific FRC correction from classical to NNUE
our net currently is not trained on FRC games, and so doesn't know about the important pattern of a bishop that is cornered in FRC.
This patch introduces a term we have in the classical evaluation for this case, and adds it to the NNUE eval.
Since fishtest doesn't support FRC right now, the patch was tested locally at STC conditions,
starting from the book of FRC starting positions.
Score of master vs patch: 993 - 2226 - 6781 [0.438] 10000
Which corresponds to approximately 40 Elo
The patch passes non-regression testing for traditional chess (where it adds one branch).
bmc4 [Mon, 15 Mar 2021 19:06:42 +0000 (16:06 -0300)]
Change definition of between_bb()
We remark that in current master, most of our use cases for between_bb() can be
optimized if the second parameter of the function is added to the segment. So we
change the definition of between_bb(s1, s2) such that it excludes s1 but includes s2.
We also use a precomputed array for between_bb() for another small speed gain
(see https://tests.stockfishchess.org/tests/view/604d09f72433018de7a389fb).
Vizvezdenec [Thu, 11 Mar 2021 11:51:20 +0000 (14:51 +0300)]
Remove advanced_pawn_push()
Continuation of work by @topologist: we now do futility pruning and movecount
pruning in qsearch() for pawn pushes up to the 7th rank. So the condition to
avoid the pruning is if the move is a promotion or not. This allows to get rid
of the advanced_pawn_push() function in position.h alltogether.
bmc4 [Tue, 16 Mar 2021 19:51:31 +0000 (20:51 +0100)]
Introduce least_significant_square_bb()
Introducing least_significant_square_bb(). It is a function that returns a value equal
to square_bb(lsb(bb)), but it uses fewer instruction. It should speed up more on older
processors like armv7-a Clang.
bmc4 [Fri, 5 Mar 2021 11:57:43 +0000 (08:57 -0300)]
Use Bitboard over Square in movegen
It uses pos.checkers() on target when movegen is the type of EVASION.
It simplify the code. And it's also expected a slightly speed up,
because Bitboard is more direct when doing bitwise.
projects / stockfish / log