Update nnue architecture to SFNNv16 and net nn-89cb98a217f7.nnue
Failed STC (https://tests.stockfishchess.org/tests/view/6a4f36355529b8472df7ff7e):
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 144768 W: 38003 L: 38051 D: 68714
Ptnml(0-2): 521, 17154, 37050, 17170, 489
Passed LTC (https://tests.stockfishchess.org/tests/view/6a501dad5529b8472df80103):
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 189888 W: 49613 L: 48984 D: 91291
Ptnml(0-2): 122, 20406, 53256, 21041, 119
Passed VLTC (https://tests.stockfishchess.org/tests/view/6a52892c5529b8472df80481):
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 46124 W: 12156 L: 11865 D: 22103
Ptnml(0-2): 12, 4523, 13696, 4824, 7
Passed VVLTC (https://tests.stockfishchess.org/tests/view/6a566a285529b8472df80b84):
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 44698 W: 11805 L: 11507 D: 21386
Ptnml(0-2): 9, 3819, 14395, 4117, 9
This PR adds pawn pair features, a new set of features that is indexed by pairs of pawns occupying the same or adjacent files (hence "3-wide"). Because these are a superset of pawn–pawn interactions in threat inputs (including the recently added pawn-pusher inputs), those are removed. HalfKA features remain unchanged.
Pawn-pair features were invented by Jonathan for Pawnocchio, based on his observation that in a net trained on *all* pairs of pawns, the most important pawn pairs were those differing by at most one file.
sscg13 made the necessary changes to the trainer (https://github.com/official-stockfish/nnue-pytorch/pull/502) and initial changes to inference. The slowdown was originally far too large (some 10%); following major optimizations from Jonathan and a bit from me, the slowdown is small enough for a comfortable LTC pass; on my machine I get a 3.5% slowdown overall. I expect there are further "easy" speedups in the new code, so this gap should narrow a bit.
Nettest PR: https://github.com/vondele/nettest/pull/359
closes https://github.com/official-stockfish/Stockfish/pull/6982
Bench: 2718396
Co-Authored-By: anematode <timothy.herchen@gmail.com>
Co-Authored-By: Jonathan Hallström <lmj.hallstrom@gmail.com>