This is the output of the second hidden layer.
These ints are again divided by 64 and clipped/clamped to 32 8-bit ints from 0 to 127. The resulting 32-element vector of 8-bit ints is multiplied by a 32x32 matrix of 8-bit weights to get a 32-element vector of 32-bit ints, to which another vector of 32-bit biases is added. This is the output of the first hidden layer. The sum vector is divided by 64 and clipped/clamped to a 32-element vector of 8-bit ints from 0 to 127. This 512-element vector of 8-bit ints is then multiplied by a 32x512 matrix of 8-bit weights to get a 32-element vector of 32-bit ints, to which a vector of 32-bit biases is added. In this step the 16-bit elements are clipped/clamped to a value from 0 to 127. The "transform" step of the NNUE evaluation forms a 512-element vector of 8-bit ints where the first half is formed from the 256-element vector of the side to move and the second half is formed from the 256-element vector of the other side.
#Fishtest stockfish plus#
The accumulator has a "white king" half and a "black king" half, where each half is a 256-element vector of 16-bit ints, which is equal to the sum of the weights of the "active" (pt, sq, ksq) features plus a 256-element vector of 16-bit biases. The remaining three layers with 2x256x32, 32x32 and 32x1 weights are computational less expensive, hidden layers apply a ReLu activation, best calculated using appropriate SIMD instructions performing fast 8-bit/ 16-bit integer vector arithmetic, like MMX, SSE2 or AVX2 on x86/ x86-64, or, if available, AVX-512.Įxplanation by Ronald de Man, who did the Stockfish NNUE port to CFish : Where only a tiny fraction of its neurons need to be considered in case of none king moves.
#Fishtest stockfish update#
The efficiency of NNUE is due to incremental update of the input layer outputs in make and unmake move, However, and that seems also a relict from Shogi with its 180 degrees rotational 9x9 board symmetry, instead of vertical flipping (xor 56), rotation is applied (xor 63). The input weights are arranged in such a way, that color flipped king-piece configurations in both halves share the same index. The so called HalfKP structure consists of two halves covering input layer and first hidden layer, each half of the input layer associated to one of the two kings, cross coupled with the side to move or not to move halves of the first hidden layer.įor each either black or white king placement, the 10 none king pieces on their particular squares are the boolean inputs, along with a relict from Shogi piece drop (BONA_PIECE_ZERO),Ħ4 x (64 x 10 + 1) = 41,024 inputs for each half, which are multiplied by a 16-bit integer weight vector for 256 outputs per half, in total, 256 x 41,024 = 10,502,144 weights.Īs emphasized by Ronald de Man in a CCC forum discussion , The input layer is heavily overparametrized, feeding in the board representation for all king placements per side The neural network consists of four layers. On September 02, 2020, Stockfish 12 was released with a huge jump in playing strength due to the introduction of NNUE and further tuning.
#Fishtest stockfish code#
The training code still remained in Nodchip's repository for a while then replaced by PyTorch NNUE training. In August that playing code merged to the master branch and become an official part of the engine. In July 2020, the playing code of NNUE was put into the official Stockfish repository as a branch for further development and examination. In August 2020, Fishtest revealed Stockfish NNUE was stronger than the classical one at least 80 Elo. Despite the approximately halved search speed, Stockfish NNUE became stronger than its original. The computer chess community bursts out enthusiastically due to its rapidly raising playing strength with different networks trained using a mixture of supervised and reinforcement learning methods.
In summer 2020, with more people involved in testing and training, Īfter support and announcements by Henk Drost in May 2020 Īnd subsequent enhancements, Stockfish NNUE was established and recognized. In 2019, Nodchip incorporated NNUE into Stockfish 10 - as a proof of concept, and with the intention to give something back to the Stockfish community. YaneuraOu's author Motohiro Isozaki made an unbelievable prediction that NNUE can help to increase Stockfish strength by around 100 points, almost one year before revealing. Were previously successfully applied in Shogi evaluation functions embedded in a Stockfish based search, such as YaneuraOu ,Īnd Kristallweizen.
A Stockfish branch by Hisayori Noda aka Nodchip, which uses Efficiently Updatable Neural Networks - stylized as ƎUИИ or reversed as NNUE - to replace its standard evaluation.
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