Testing the New Stockfish Net of 45 Mb vs 20 Mb, Stockfish 190521 vs Stockfish 13 (TC = 1m+1s)

Testing the New Stockfish Net of 45 Mb vs 20 Mb, Stockfish 190521 vs Stockfish 13 (TC = 1m+1s)

Hardware Threadripper 2950x, RTX 2080 TI, SSD 2 TB Evo 970 Plus.
Perfect Book 2021 to 6 Moves.

7 Man Table bases.
1 GB Hash
1 Threads For Stockfish.
TC = 1m+1s

Very interesting, in the hope that's no hoax.

Admin wrote:

Very interesting, in the hope that's no hoax.

If this is a hoax. It is an official Stockfish hoax by Stockfish.

Author: Tomasz Sobczyk
Date: Tue May 18 18:06:23 2021 +0200
Timestamp: 1621353983

New NNUE architecture and net

Introduces a new NNUE network architecture and associated network parameters,
as obtained by a new pytorch trainer.

The network is already very strong at short TC, without regression at longer TC,
and has potential for further improvements.

https://tests.stockfishchess.org/tests/view/60a159c65085663412d0921d
TC: 10s+0.1s, 1 thread
ELO: 21.74 +-3.4 (95%) LOS: 100.0%
Total: 10000 W: 1559 L: 934 D: 7507 Elo +21.74
Ptnml(0-2): 38, 701, 2972, 1176, 113

https://tests.stockfishchess.org/tests/view/60a187005085663412d0925b
TC: 60s+0.6s, 1 thread
ELO: 5.85 +-1.7 (95%) LOS: 100.0%
Total: 20000 W: 1381 L: 1044 D: 17575 Elo +5.85
Ptnml(0-2): 27, 885, 7864, 1172, 52

https://tests.stockfishchess.org/tests/view/60a2beede229097940a03806
TC: 20s+0.2s, 8 threads
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 34272 W: 1610 L: 1452 D: 31210 Elo +1.60
Ptnml(0-2): 30, 1285, 14350, 1439, 32

https://tests.stockfishchess.org/tests/view/60a2d687e229097940a03c72
TC: 60s+0.6s, 8 threads
LLR: 2.94 (-2.94,2.94) <-2.50,0.50>
Total: 45544 W: 1262 L: 1214 D: 43068 Elo +0.37
Ptnml(0-2): 12, 1129, 20442, 1177, 12

The network has been trained (by vondele) using the https://github.com/glinscott/nnue-pytorch/ trainer (started by glinscott),
specifically the branch https://github.com/Sopel97/nnue-pytorch/tree/experiment_56.
The data used are in 64 billion positions (193GB total) generated and scored with the current master net
d8: https://drive.google.com/file/d/1hOOYSDKgOOp38ZmD0N4DV82TOLHzjUiF/view?usp=sharing
d9: https://drive.google.com/file/d/1VlhnHL8f-20AXhGkILujnNXHwy9T-MQw/view?usp=sharing
d10: https://drive.google.com/file/d/1ZC5upzBYMmMj1gMYCkt6rCxQG0GnO3Kk/view?usp=sharing
fishtest_d9: https://drive.google.com/file/d/1GQHt0oNgKaHazwJFTRbXhlCN3FbUedFq/view?usp=sharing

This network also contains a few architectural changes with respect to the current master:

Size changed from 256x2-32-32-1 to 512x2-16-32-1
~15-20% slower
~2x larger
adds a special path for 16 valued ClippedReLU
fixes affine transform code for 16 inputs/outputs, buy using InputDimensions instead of PaddedInputDimensions
this is safe now because the inputs are processed in groups of 4 in the current affine transform code
The feature set changed from HalfKP to HalfKAv2
Includes information about the kings like HalfKA
Packs king features better, resulting in 8% size reduction compared to HalfKA
The board is flipped for the black's perspective, instead of rotated like in the current master
PSQT values for each feature
the feature transformer now outputs a part that is fowarded directly to the output and allows learning piece values more directly than the previous network architecture. The effect is visible for high imbalance positions, where the current master network outputs evaluations skewed towards zero.
8 PSQT values per feature, chosen based on (popcount(pos.pieces()) - 1) / 4
initialized to classical material values on the start of the training
8 subnetworks (512x2->16->32->1), chosen based on (popcount(pos.pieces()) - 1) / 4
only one subnetwork is evaluated for any position, no or marginal speed loss

A diagram of the network is available: https://user-images.githubusercontent.com/8037982/118656988-553a1700-b7eb-11eb-82ef-56a11cbebbf2.png
A more complete description: https://github.com/glinscott/nnue-pytorch/blob/master/docs/nnue.md

closes https://github.com/official-stockfish/Stockfish/pull/3474

Bench: 3806488
see source

Downloaded it, will put it into cute within a couple of hours, 1000 games tomorrow morning.

Started live match, sf45mb vs sf13, tc=40/120, 100 games, gambit positions.

http://rebel13.nl/pgn4web-3.05/live-test.html

It will be interesting to see how the results compare. Gambit book vs Standard book.

I linked your testing on my channel for my subs to check out your results.