Stockfish/README.md

Stockfish NNUE

Overview

Stockfish NNUE is a port of a shogi neural network named NNUE (efficiently updateable neural network backwards) to Stockfish 11. To learn more about the Stockfish chess engine, look here for an overview and here for the official repository.

Building

To compile:

make -jN ARCH=... build

To compile with Profile Guided Optimizations. Requires that the computer that is used for compilation supports the selected ARCH.

make -jN ARCH=... profile-build

N is the number of threads to use for compilation.

ARCH is one of: 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, x86-32-sse41-popcnt, x86-32-sse2, x86-32, ppc-64, ppc-32, armv7, armv7-neon, armv8, apple-silicon, general-64, general-32.

ARCH needs to be chosen based based on the instruction set of the CPU that will run stockfish. x86-64-modern will produce a binary that works on most common processors, but other options may increase performance for specific hardware.

Additional options:

• blas=[yes/no] - whether to use an external BLAS library. Default is no. Using an external BLAS library may have a significantly improve learning performance and by default expects openBLAS to be installed.

  • Ponder

    Let Stockfish ponder its next move while the opponent is thinking.

  • MultiPV

    Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.

  • Use NNUE

    Toggle between the NNUE and classical evaluation functions. If set to "true", the network parameters must be available to load from file (see also EvalFile), if they are not embedded in the binary.

  • 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.

  • UCI_AnalyseMode

    An option handled by your GUI.

  • UCI_Chess960

    An option handled by your GUI. If true, Stockfish will play Chess960.

  • UCI_ShowWDL

    If enabled, show approximate WDL statistics as part of the engine output. These WDL numbers model expected game outcomes for a given evaluation and game ply for engine self-play at fishtest LTC conditions (60+0.6s per game).

  • UCI_LimitStrength

    Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.

  • UCI_Elo

    If enabled by UCI_LimitStrength, aim for an engine strength of the given Elo. This Elo rating has been calibrated at a time control of 60s+0.6s and anchored to CCRL 40/4.

  • Skill Level

    Lower the Skill Level in order to make Stockfish play weaker (see also UCI_LimitStrength). Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a weaker move will be played.

  • SyzygyPath

    Path to the folders/directories storing the Syzygy tablebase files. Multiple directories are to be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":".

    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 of the downloaded tablebase files (md5sum -c checksum.md5) as corruption will lead to engine crashes.

  • SyzygyProbeDepth

    Minimum remaining search depth for which a position is probed. Set this option to a higher value to probe less agressively if you experience too much slowdown (in terms of nps) due to TB probing.

  • Syzygy50MoveRule

    Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.

  • SyzygyProbeLimit

    Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).

  • Contempt

    A positive value for contempt favors middle game positions and avoids draws, effective for the classical evaluation only.

  • Analysis Contempt

    By default, contempt is set to prefer the side to move. Set this option to "White" or "Black" to analyse with contempt for that side, or "Off" to disable contempt.

  • Move Overhead

    Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.

  • Slow Mover

    Lower values will make Stockfish take less time in games, higher values will make it think longer.

  • nodestime

    Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.

  • Clear Hash

    Clear the hash table.

  • Debug Log File

    Write all communication to and from the engine into a text file.

A note on classical and NNUE evaluation

Both approaches assign a value to a position that is 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 (e.g. piece positions only). The network is optimized and trained 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 provides additional tools to train and develop the NNUE networks.

On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation results in stronger playing strength, even if the nodes per second computed by the engine is somewhat lower (roughly 60% of nps is typical).

Note that the NNUE evaluation depends on the Stockfish binary and the network parameter file (see EvalFile). Not every parameter file is compatible with a given Stockfish binary. The default value of the EvalFile UCI option is the name of a network that is guaranteed to be compatible with that binary.

What to expect from Syzygybases?

If the engine is searching a position that is not in the tablebases (e.g. a position with 8 pieces), it will access the tablebases during the search. If the engine reports a very large score (typically 153.xx), this means that it has found a winning line into a tablebase position.

If the engine is given a position to search that is in the tablebases, it will use the tablebases at the beginning of the search to preselect all good moves, i.e. all moves that preserve the win or preserve the draw while taking into account the 50-move rule. It will then perform a search only on those moves. The engine will not move immediately, unless there is only a single good move. The engine likely will not report a mate score even if the position is known to be won.

It is therefore clear that this behaviour is not identical to what one might be used to with Nalimov tablebases. There are technical reasons for this difference, the main technical reason being that Nalimov tablebases use the DTM metric (distance-to-mate), while Syzygybases use a variation of the DTZ metric (distance-to-zero, zero meaning any move that resets the 50-move counter). This special metric is one of the reasons that Syzygybases are more compact than Nalimov tablebases, while still storing all information needed for optimal play and in addition being able to take into account the 50-move rule.

Large Pages

Stockfish supports large pages on Linux and Windows. Large pages make the hash access more efficient, improving the engine speed, especially on large hash sizes. Typical increases are 5..10% in terms of nps, but speed increases up to 30% have been measured. The support is automatic. Stockfish attempts to use large pages when available and will fall back to regular memory allocation when this is not the case.

Support on Linux

Large page support on Linux is obtained by the Linux kernel transparent huge pages functionality. Typically, transparent huge pages are already enabled and no configuration is needed.

Support on Windows

The use of large pages requires "Lock Pages in Memory" privilege. See Enable the Lock Pages in Memory Option (Windows) on how to enable this privilege. Logout/login may be needed afterwards. Due to memory fragmentation, it may not always be possible to allocate large pages even when enabled. A reboot might alleviate this problem. To determine whether large pages are in use, see the engine log.

Compiling Stockfish yourself from the sources

Stockfish has support for 32 or 64-bit CPUs, certain hardware instructions, big-endian machines such as Power PC, and other platforms.

On Unix-like systems, it should be easy to compile Stockfish directly from the source code with the included Makefile in the folder src. In general it is recommended to run make help to see a list of make targets with corresponding descriptions.

uci
setoption name Use NNUE value false
setoption name Threads value x
setoption name Hash value y
setoption name SyzygyPath value path
isready
gensfen depth a loop b use_draw_in_training_data_generation 1 eval_limit 32000

Specify how many threads and how much memory you would like to use with the x and y values. The option SyzygyPath is not necessary, but if you would like to use it, you must first have Syzygy endgame tablebases on your computer, which you can find here. You will need to have a torrent client to download these tablebases, as that is probably the fastest way to obtain them. The path is the path to the folder containing those tablebases. It does not have to be surrounded in quotes.

This will save a file named "generated_kifu.bin" in the same folder as the binary. Once generation is done, rename the file to something like "1billiondepth12.bin" to remember the depth and quantity of the positions and move it to a folder named "trainingdata" in the same directory as the binaries.

Generation Parameters

• Depth is the searched depth per move, or how far the engine looks forward. This value is an integer.
• Loop is the amount of positions generated. This value is also an integer

Generating Validation Data

The process is the same as the generation of training data, except for the fact that you need to set loop to 1 million, because you don't need a lot of validation data. The depth should be the same as before or slightly higher than the depth of the training data. After generation rename the validation data file to val.bin and drop it in a folder named "validationdata" in the same directory to make it easier.

Training a Completely New Network

Use the "learn" binary. Create an empty folder named "evalsave" in the same directory as the binaries.

uci
setoption name SkipLoadingEval value true
setoption name Training value true
setoption name Use NNUE value true
setoption name Threads value x
isready
learn targetdir trainingdata loop 100 batchsize 1000000 use_draw_in_training 1 use_draw_in_validation 1 eta 1 lambda 1 eval_limit 32000 nn_batch_size 1000 newbob_decay 0.5 eval_save_interval 250000000 loss_output_interval 1000000 mirror_percentage 50 validation_set_file_name validationdata\val.bin

Nets get saved in the "evalsave" folder.

Training Parameters

• eta is the learning rate
• lambda is the amount of weight it puts to eval of learning data vs win/draw/loss results. 1 puts all weight on eval, lambda 0 puts all weight on WDL results.

Reinforcement Learning

If you would like to do some reinforcement learning on your original network, you must first generate training data using the learn binaries with the setting Use NNUE set to pure. Make sure that your previously trained network is in the eval folder. Use the commands specified above. Make sure SkipLoadingEval is set to false so that the data generated is using the neural net's eval by typing the command setoption name SkipLoadingEval value false before typing the isready command. You should aim to generate less positions than the first run, around 1/10 of the number of positions generated in the first run. The depth should be higher as well. You should also do the same for validation data, with the depth being higher than the last run.

After you have generated the training data, you must move it into your training data folder and delete the older data so that the binary does not accidentally train on the same data again. Do the same for the validation data and name it to val-1.bin to make it less confusing. Make sure the evalsave folder is empty. Then, using the same binary, type in the training commands shown above. Do NOT set SkipLoadingEval to true, it must be false or you will get a completely new network, instead of a network trained with reinforcement learning. You should also set eval_save_interval to a number that is lower than the amount of positions in your training data, perhaps also 1/10 of the original value. The validation file should be set to the new validation data, not the old data.

After training is finished, your new net should be located in the "final" folder under the "evalsave" directory. You should test this new network against the older network to see if there are any improvements.

Using Your Trained Net

If you want to use your generated net, copy the net located in the "final" folder under the "evalsave" directory and move it into a new folder named "eval" under the directory with the binaries. You can then use the halfkp_256x2 binaries pertaining to your CPU with a standard chess GUI, such as Cutechess. Refer to the releases page to find out which binary is best for your CPU.

If the engine does not load any net file, or shows "Error! *** not found or wrong format", please try to specify the net with the full file path with the "EvalFile" option by typing the command setoption name EvalFile value path where path is the full file path. The "Use NNUE" option must be set to true with the command setoption name Use NNUE value true.

Resources

• Stockfish NNUE Wiki
• Training instructions from the creator of the Elmo shogi engine
• Original Talkchess thread discussing Stockfish NNUE
• Guide to Stockfish NNUE
• Unofficial Stockfish Discord

A more updated list can be found in the #sf-nnue-resources channel in the Discord.