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Small cleanups (2)
- fix a small compile error under MSVC - improve sigmoid comment and assert - fix formatting in README.md closes https://github.com/official-stockfish/Stockfish/pull/3960 No functional change
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mstembera
Stéphane Nicolet
parent
004ea2c25e
commit
f3a2296e59
3 changed files with 44 additions and 36 deletions
71
README.md
71
README.md
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@ -10,23 +10,28 @@ Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in ord
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to be used comfortably. Read the documentation for your GUI of choice for information
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about how to use Stockfish with it.
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The Stockfish engine features two evaluation functions for chess.
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The efficiently updatable neural network (NNUE) based evaluation is the default and by far the strongest.
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The classical evaluation based on handcrafted terms remains available.
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The strongest network is integrated in the binary and downloaded automatically during the build process.
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The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).
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The Stockfish engine features two evaluation functions for chess. The efficiently
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updatable neural network (NNUE) based evaluation is the default and by far the strongest.
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The classical evaluation based on handcrafted terms remains available. The strongest
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network is integrated in the binary and downloaded automatically during the build process.
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The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2,
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avx2, neon, or similar).
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## Files
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This distribution of Stockfish consists of the following files:
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* [Readme.md](https://github.com/official-stockfish/Stockfish/blob/master/README.md), the file you are currently reading.
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* [Readme.md](https://github.com/official-stockfish/Stockfish/blob/master/README.md),
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the file you are currently reading.
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* [Copying.txt](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt), a text file containing the GNU General Public License version 3.
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* [Copying.txt](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt),
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a text file containing the GNU General Public License version 3.
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* [AUTHORS](https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS), a text file with the list of authors for the project
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* [AUTHORS](https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS),
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a text file with the list of authors for the project
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* [src](https://github.com/official-stockfish/Stockfish/tree/master/src), a subdirectory containing the full source code, including a Makefile
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* [src](https://github.com/official-stockfish/Stockfish/tree/master/src),
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a subdirectory containing the full source code, including a Makefile
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that can be used to compile Stockfish on Unix-like systems.
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* a file with the .nnue extension, storing the neural network for the NNUE
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@ -67,9 +72,9 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
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* #### EvalFile
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The name of the file of the NNUE evaluation parameters. Depending on the GUI the
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filename might have to include the full path to the folder/directory that contains the file.
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Other locations, such as the directory that contains the binary and the working directory,
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are also searched.
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filename might have to include the full path to the folder/directory that contains
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the file. Other locations, such as the directory that contains the binary and the
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working directory, are also searched.
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* #### UCI_AnalyseMode
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An option handled by your GUI.
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@ -137,8 +142,9 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
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For developers the following non-standard commands might be of interest, mainly useful for debugging:
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* #### bench *ttSize threads limit fenFile limitType evalType*
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Performs a standard benchmark using various options. The signature of a version (standard node
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count) is obtained using all defaults. `bench` is currently `bench 16 1 13 default depth mixed`.
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Performs a standard benchmark using various options. The signature of a version
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(standard node count) is obtained using all defaults. `bench` is currently
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`bench 16 1 13 default depth mixed`.
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* #### compiler
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Give information about the compiler and environment used for building a binary.
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@ -174,26 +180,27 @@ on the evaluations of millions of positions at moderate search depth.
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The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
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It can be evaluated efficiently on CPUs, and exploits the fact that only parts
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of the neural network need to be updated after a typical chess move.
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[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first version of
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the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available
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in [the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/), while data generation tools
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are available in [a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
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[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first
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version of the needed tools to train and develop the NNUE networks. Today, more
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advanced training tools are available in
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[the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/),
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while data generation tools are available in
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[a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
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On CPUs supporting modern vector instructions
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(avx2 and similar), the NNUE evaluation results in much stronger playing strength, even
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if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps
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is typical).
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On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation
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results in much stronger playing strength, even if the nodes per second computed by
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the engine is somewhat lower (roughly 80% of nps is typical).
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Notes:
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1) the NNUE evaluation depends on the Stockfish binary and the network parameter
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file (see the EvalFile UCI option). Not every parameter file is compatible with a given
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Stockfish binary, but the default value of the EvalFile UCI option is the name of a network
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that is guaranteed to be compatible with that binary.
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1) the NNUE evaluation depends on the Stockfish binary and the network parameter file
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(see the EvalFile UCI option). Not every parameter file is compatible with a given
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Stockfish binary, but the default value of the EvalFile UCI option is the name of a
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network that is guaranteed to be compatible with that binary.
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2) to use the NNUE evaluation, the additional data file with neural network parameters
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needs to be available. Normally, this file is already embedded in the binary or it
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can be downloaded. The filename for the default (recommended) net can be found as the default
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needs to be available. Normally, this file is already embedded in the binary or it can
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be downloaded. The filename for the default (recommended) net can be found as the default
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value of the `EvalFile` UCI option, with the format `nn-[SHA256 first 12 digits].nnue`
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(for instance, `nn-c157e0a5755b.nnue`). This file can be downloaded from
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```
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@ -321,10 +328,10 @@ it (either by itself or as part of some bigger software package), or
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using it as the starting point for a software project of your own.
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The only real limitation is that whenever you distribute Stockfish in
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some way, you MUST always include the license, the full source code, or a pointer
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to where the source code can be found, to generate the exact binary
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you are distributing. If you make any changes to the source code,
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these changes must also be made available under the GPL v3.
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some way, you MUST always include the license and the full source code
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(or a pointer to where the source code can be found) to generate the
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exact binary you are distributing. If you make any changes to the
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source code, these changes must also be made available under the GPL v3.
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For full details, read the copy of the GPL v3 found in the file named
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[*Copying.txt*](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt).
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@ -152,7 +152,7 @@ private:
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/// - the slope can be adjusted using C > 0, smaller C giving a steeper sigmoid
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/// - the slope of the sigmoid when t = x0 is P/(Q*C)
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/// - sigmoid is increasing with t when P > 0 and Q > 0
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/// - to get a decreasing sigmoid, call with -t, or change sign of P
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/// - to get a decreasing sigmoid, change sign of P
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/// - mean value of the sigmoid is y0
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///
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/// Use <https://www.desmos.com/calculator/jhh83sqq92> to draw the sigmoid
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@ -163,7 +163,8 @@ inline int64_t sigmoid(int64_t t, int64_t x0,
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int64_t P,
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int64_t Q)
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{
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assert(C > 0 && Q != 0);
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assert(C > 0);
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assert(Q != 0);
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return y0 + P * (t-x0) / (Q * (std::abs(t-x0) + C)) ;
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}
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@ -107,8 +107,8 @@ void MovePicker::score() {
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for (auto& m : *this)
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if constexpr (Type == CAPTURES)
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m.value = 6 * PieceValue[MG][pos.piece_on(to_sq(m))]
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+ (*captureHistory)[pos.moved_piece(m)][to_sq(m)][type_of(pos.piece_on(to_sq(m)))];
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m.value = 6 * int(PieceValue[MG][pos.piece_on(to_sq(m))])
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+ (*captureHistory)[pos.moved_piece(m)][to_sq(m)][type_of(pos.piece_on(to_sq(m)))];
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else if constexpr (Type == QUIETS)
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m.value = (*mainHistory)[pos.side_to_move()][from_to(m)]
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