Ethereal

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Ethereal night sky [1]

Ethereal,
an UCI compliant open source chess engine written by Andrew Grant in C, licensed under the GNU GPL and first officially released in June 2016 [2]. Ethereal is greatly influenced from Crafty, Stockfish, TSCP, MadChess, and Fruit [3]. On October 09, 2020, Andrew Grant initially announced his withdrawal from Ethereal's development with the releases of Ethereal V12.75 [4] and Ethereal 12.75 SF-NNUE, the latter a NNUE implementation based on Stockfish NNUE, to deliberately demonstrate how everybody may considerably improve the playing strength of their engines without much effort [5]. However, Andrew Grant voted for another direction, and announced a commercial release of Ethereal 13.00 (NNUE) , the free standard version still available on Github [6].

Ethereal 13 (NNUE)

Ethereal 13 (NNUE), the commercial Ethereal version for AVX2 systems was released on June 04, 2021. The program comes with two NNUEs for evaluation, one for standard chess, and the secondary network trained exclusively for Chess960. The NNUEs are not derived from, trained on, nor duplicated from the works of the Stockfish team [6]. Andrew Grant on his NNUE approach [7]:

Ethereal is using the HalfKP paradigm, with a 40960x256 -> 512x32x32x1 Network. This is the textbook approach, but with some changes. Firstly, not all weights are quantized to int8 / int16 for the input layer. Instead, the network goes like this: int16_t => int16_t => (int32_t -> float_t) => float_t => float_t. This approach allows us to never have to pack the data downwards, saving many operations, and also lets us take a slightly more expensive approach to the later layers in exchange for massively increased precision. If I eventually add support for AVX (not avx2) machines, it will be a significant gain as AVX does not have 256-bit vector support for integer types in a meaningful way.
During training the Network actually has 43850 input parameters, using a few factorization of the board to aid in training without having tens of billions of positions. In practice, each Net was trained somewhere between 2 and 4 billion positions total, evaluated by Ethereal / Ethereal NNUE. The networks are trained using a modified form of the Adam optimizer, which allows better performance for datasets with extremely sparse input fields. For example, with a Batch Size of 16384, only about 50% of the 43,850 parameters are used on average.
Data generation for a given network takes about 3 weeks, completed on a 104 core machine. From there, processing that data down into a list of FENs and then into the format used by Ethereal's NNTrainer takes another 12 hours or so. Finally, training the actual Network can take a few days, with many stops and starts to drop the learning rate and find a global optima.
The trainer itself is a fully original work, written in C and making use of all 104 threads. It includes some AVX2 and even AVX512 code for use in updating the network parameters. This toolkit was used in training the Halogen networks as well. It is fairly flexible and trying things like HalfKA, changing layer sizes, adding layers, changing activation functions, or adding more factorizers is only a few minutes of effort in the code. It rivals speeds of GPU based trainers, by leveraging massive SMP and efficient implementations.

Features

[8]

Board Representation

Search

Evaluation

Misc

Publications

Forum Posts

2016 ...

Re: How to speed up my engine by Andrew Grant, CCC, May 03, 2016

2017

2018

2019

2020 ...

Re: Final Release of Ethereal, V12.75 by Andrew Grant, CCC, October 09, 2020
Re: Final Release of Ethereal, V12.75 by Andrew Grant, CCC, November 12, 2020

2021

Re: Commercial Release of Ethereal 13.00 (NNUE) for AVX2 Systems by Andrew Grant, CCC, June 04, 2021

2022

External Links

Chess Engine

Misc

References

  1. A total eclipse of the Moon is an impressive spectacle. But it also provides another viewing opportunity: a dark, moonlight-free starry sky. At Cerro Paranal in the Chilean Atacama Desert, one of the most remote places in the world, the distance from sources of light pollution makes the night sky all the more remarkable during a total lunar eclipse. This panoramic photo, taken by ESO Photo Ambassador Yuri Beletsky, shows the view of the starry sky from the site of ESO’s Very Large Telescope (VLT) at Cerro Paranal during the total lunar eclipse of December 21, 2010. The reddish disc of the Moon is seen on the right of the image, while the Milky Way arches across the heavens in all its beauty. Another faint glow of light is also visible, surrounding the brilliant planet Venus in the bottom left corner of the picture. This phenomenon, known as zodiacal light, is produced by sunlight reflecting off dust in the plane of the planets. It is so faint that it’s normally obscured by moonlight or light pollution. Image by ESO, Yuri Beletsky, CC BY 4.0, Wikimedia Commons, Beauty from Wikipedia
  2. Ethereal - Official Release by Andrew Grant, CCRL Discussion Board, June 25, 2016
  3. Ethereal/README.md at master · AndyGrant/Ethereal · GitHub
  4. Final Release of Ethereal, V12.75 by Andrew Grant, CCC, October 09, 2020
  5. Re: Final Release of Ethereal, V12.75 by Andrew Grant, CCC, October 09, 2020
  6. 6.0 6.1 Commercial Release of Ethereal 13.00 (NNUE) for AVX2 Systems by Andrew Grant, CCC, June 04, 2021
  7. Re: Commercial Release of Ethereal 13.00 (NNUE) for AVX2 Systems by Andrew Grant, CCC, June 04, 2021
  8. Features based on GitHub - AndyGrant/Ethereal: Ethereal UCI Chess Engine
  9. Ethereal's 8.60 Lazy SMP inspired by Demolito by Lucas Braesch, see Release of Ethereal 8.60 SMP by Andrew Grant, CCC, December 10, 2017
  10. Evaluation & Tuning in Chess Engines by Andrew Grant, CCC, August 24, 2020
  11. Ethereal Tuning - Data Dump by Andrew Grant, CCC, October 10, 2020
  12. Evaluation & Tuning in Chess Engines by Andrew Grant, CCC, August 24, 2020
  13. Ethereal Tuning - Data Dump by Andrew Grant, CCC, October 10, 2020

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