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Overview

Pikafish is a free, powerful UCI xiangqi engine derived from Stockfish. Pikafish is not a complete xiangqi program and requires a UCI-compatible graphical user interface (GUI) (e.g. VinXiangQi or BHGUI) in order to be used comfortably. Read the documentation for your GUI of choice for information about how to use Pikafish with it.

The Pikafish engine features the efficiently updatable neural network (NNUE) based evaluation for xiangqi, which is by far the strongest. The strongest network can be downloaded from our official website. The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).

Terms of use

Pikafish is free, and distributed under the GNU General Public License version 3 (GPL v3). Essentially, this means you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your website, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.

The only real limitation is that whenever you distribute Pikafish in some way, you MUST always include the license and the full source code (or a pointer to where the source code can be found) to generate the exact binary you are distributing. If you make any changes to the source code, these changes must also be made available under the GPL v3.

For full details, read the copy of the GPL v3 found in the file named Copying.txt.

The weight file (pikafish.nnue) released with the pikafish and the weight file further derived from the weight are:

1. Only for legal use, any consequences caused by any use beyond the legal scope shall be borne by the user.
2. Only licensed for personal, non-commercial use only, any commercial use requires a separate commercial license from the Pikafish team.

Open Source chess engines have accelerated the development of computer xiangqi in immeasurable ways. If not for the early adopters of the Open Source methods, computer xiangqi would not be what it is today. Powerful programs like Stockfish and Pikafish simply would not exist in their current forms. All of this is possible because the authors have empowered users by granting them rights to the code, only asking that you carry on propagating the licenses attached to their code. This is a small ask, for such a great gift, and yet we live in a time where that gift is not appreciated by some, and worse taken advantage of.

Goal of the project

The current goal of the project is to make Pikafish suppress all commercial engines and become the top 1. Terminating the domination of those commercial engines like AlphaCat (done), BugChess, and Cyclone in xiangqi. Making the strongest xiangqi engine free and open source to everyone.

I believe if Stockfish can do that for chess, Pikafish can do that for xiangqi.

Files

This distribution of Pikafish consists of the following files:

• README.md, the file you are currently reading.

• Copying.txt, a text file containing the GNU General Public License version 3.

• NNUE-License.txt, a text file containing the License for NNUE weights.

• AUTHORS, a text file with the list of authors for the official Pikafish project.

• src, a subdirectory containing the full source code, including a Makefile that can be used to compile Pikafish on Unix-like systems.

The UCI protocol and available options

The Universal Chess Interface (UCI) is a standard protocol used to communicate with a chess engine, and is the recommended way to do so for typical graphical user interfaces (GUI) or chess tools. Pikafish implements the majority of its options as described in the UCI protocol.

Developers can see the default values for UCI options available in Pikafish by typing ./pikafish uci in a terminal, but the majority of users will typically see them and change them via a chess GUI. This is a list of available UCI options in Pikafish:

• Threads

  The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.

• Hash

  The size of the hash table in MB. It is recommended to set Hash after setting Threads.

• Clear Hash

  Clear the hash table.

• Ponder

  Let Pikafish 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.

• 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

  If enabled, use original score to determine engine score outputs. If disabled (by default) , use WDL statistics to determine engine score outputs.

• 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 Pikafish 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.

• 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 Pikafish 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.

• Debug Log File

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

For developers the following non-standard commands might be of interest, mainly useful for debugging:

• bench ttSize threads limit fenFile limitType

  Performs a standard benchmark using various options. The signature of a version (standard node count) is obtained using all defaults. bench is currently bench 16 1 13 default depth.

• compiler

  Give information about the compiler and environment used for building a binary.

• d

  Display the current position, with ascii art and fen.

• eval

  Return the evaluation of the current position.

• export_net [filename]

  Exports the currently loaded network to a file. If the currently loaded network is the embedded network and the filename is not specified then the network is saved to the file matching the name of the embedded network, as defined in evaluate.h. The filename parameter is required and the network is saved into that file.

• flip

  Flips the side to move.

A note on NNUE evaluation

This approach assigns a value to a position that is used in alpha-beta (PVS) search to find the best move. 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 provided the first version of the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available in the nnue-pytorch repository, while data generation tools are available in a dedicated branch.

On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation results in much stronger playing strength, even if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps is typical).

Notes:

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

2. to use the NNUE evaluation, the additional data file with neural network parameters needs to be available. The filename for the default (recommended) net can be found as the default value of the EvalFile UCI option, with the format pikafish.nnue. This file can be downloaded from http://test.pikafish.org.

Large Pages

Pikafish 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 nodes per second, but speed increases up to 30% have been measured. The support is automatic. Pikafish 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, then run RAMMap to double-check that large pages are used. We suggest that you reboot your computer after you have enabled large pages, because long Windows sessions suffer from memory fragmentation, which may prevent Stockfish from getting large pages: a fresh session is better in this regard.

Compiling Pikafish yourself from the sources

Pikafish 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 Pikafish 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.

    cd src
    make help
    make build ARCH=x86-64-modern

When not using the Makefile to compile (for instance, with Microsoft MSVC) you need to manually set/unset some switches in the compiler command line; see file types.h for a quick reference.

When reporting an issue or a bug, please tell us which Pikafish version and which compiler you used to create your executable. This information can be found by typing the following command in a console:

    ./pikafish compiler

Understanding the code base and participating in the project

Pikafish's improvement over the last decade has been a great community effort. There are a few ways to help contribute to its growth.

Donating hardware

Improving Pikafish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and view the current tests on Fishtest.

Improving the code

If you want to help improve the code, there are several valuable resources:

• In this wiki, many techniques used in Pikafish are explained with a lot of background information.

• The section on Stockfish describes many features and techniques used by Pikafish. However, it is generic rather than being focused on Stockfish's precise implementation. Nevertheless, a helpful resource.

• The latest source can always be found on GitHub. Discussions about Pikafish take place these days mainly in the Pikafish 分布式训练 / Vin象棋连线 QQ group. The engine testing is done on Fishtest. If you want to help improve Pikafish, please read this guideline first, where the basics of Pikafish development are explained.