Chess engine: Wasp 5.50 NNUE second compiles
Rating CEDR=3157 (22 place)
Wasp it is a complete rewrite of previous chess engine called Zarkov.
Wasp Release Notes - April 12, 2022
Most Recent Executable files:
Wasp550-windows-avx.exe : Windows 64-bit, for Intel Haswell or newer CPU
(uses popcnt, ctz, clz, 256-bit MM registers, fmadd)
Wasp550-windows.exe : Windows 64-bit, for Intel Nehalem or newer CPU
(uses popcnt, bsf, bsr, 128-bit MM registers)
Wasp550-windows-ancient.exe : Windows 64-bit, for older CPU's
(uses software popcnt, ctz, clz)
Wasp550-linux-avx : Linux 64-bit, for Intel Haswell or newer CPU
Wasp550-linux-sse : Linux 64-bit, for Intel Nehalem or newer CPU
Wasp550-linux-ancient : Linux 64-bit, for older CPU's
Wasp550-RPi3 : Raspberry Pi 3
Wasp550-RPiZ : Raspberry Pi Z
Benchmark data (Ryzen 3950x at 3.9 Ghz):
command: Wasp550-windows-avx.exe -bench
total time= 31.81 seconds, nodes= 39329256, nps= 1236240
UCI Options:
Name Default Description
Value
----------------------------------------------------------------------------
ConfigFilePath none Path to a file containing UCI commands (optional)
Hash 64 Main Hash table size in Mbytes.
Threads 1 Number of search threads.
Clear_Hash NA Clear the main Hash table.
NN_file embedded Path to neural network weights file
Use_NN_eval true Enable or disable using NN eval. If false then
the hand-crafted eval (hce) is used.
OwnBook false Enable or disable opening book.
OwnBook_File Path to OwnBook file (polyglot .bin format).
OwnBook_Depth 24 Book probe depth in half-moves.
OwnBook2_File 2nd OwnBook file, probed if no move in 1st book.
OwnBook2_Depth 0 2nd book probe depth in half-moves.
OwnBook_Variety 25 Adjusts how Wasp chooses book moves based on the
"weight" value for each move. Increasing
OwnBook_Variety causes moves with lower weight to
be played more frequently while reducing the
value gives more priority to higher weighted
moves.
SyzygyPath Path to Syzygy tablebases.
SyzygyProbeDepth6 1 Minimum probe depth for 6-piece TB's. If Syzygy
TB's are on a hard drive, this parameter should
probably be increased to 4 or 5.
Log false Write UCI output to the file "wasp.log".
Ponder true Enable or disable thinking on opponent's time.
MultiPV 1 Wasp will do searches to find this number of best
moves (and associated principle variations).
UCI_Chess960 false Set to true if playing Chess960.
MoveOverhead 50 This number of milliseconds is subtracted from
from the normal target search time to account for
I/O overhead or lag.
UCI_LimitStrength false If set to true, Wasp will limit playing strength
by reducing the nodes searched per second.
UCI_Elo XXXX The default is the maximum Elo estimated from the
nodes/second for a short search done at program
startup. If a value below this is given and the
UCI_LimitStrength parameter is set to true, Wasp
will reduce playing strength by searching at a
slower rate.
Contempt 0 When nearly all the pieces and pawns are on the
board, Wasp will add this value to it's own score.
As material is traded, this penalty/bonus is pulled
toward 0. A negative value encourages Wasp to play
for a draw while a positive value encourages it
to avoid a draw.
Selectivity 100 Increase to search more deeply, decrease for
a wider search.
Optional Command Line options
-cfg config_file # Wasp will read UCI commands from a configuration file
-log # Wasp will log UCI I/O to the file "wasp.log"
-hash hashsize # Wasp will use hashsize Mbytes for main hashtable
-threads N # Wasp will search using N threads
I estimate Wasp 5.50 to be about 50 Elo stronger than Wasp 5.20.
The Neural Network used for position evaluation is now embedded int the Wasp binary file, so an nn.bin file is no longer required.
The Neural Network structure is as follows:
- 1824 Inputs (2x2x7x64 for piece/square combinations
plus 32 for material difference).
- Only one hidden layer with 640 nodes, using "leaky ReLU" activation.
- Three outputs, using sigmoid activation. If either side has any
non-pawn pieces the evaluation is interpolated from the first
two outputs. For king and pawn endgames the third output is used.
The NN was trained on a dataset of about 130M positions taken from
games between Wasp and various engines of roughly similar strength
(including previous versions of Wasp). The target value used for
training is based 50% on game result and 50% on Wasp's score from a
10K node search. The net was trained using a total of about 150 billion
samples which took about 56 hours using 24 threads. I continue to add
positions to the training dataset and hope that this will give some
improvement for future versions.
A few small changes were made to the search. These include modifications
to the criteria used for static null move pruning, constants used for updating the move history counters, and late-move reduction criteria.
Wasp 5.50 NNUE - download
Our social media:
Comments
Post a Comment