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c079acc26f
Architecture changes:
Duplicated activation after the 1024->15 layer with squared crelu (so 15->15*2). As proposed by vondele.
Trainer changes:
Added bias to L1 factorization, which was previously missing (no measurable improvement but at least neutral in principle)
For retraining linearly reduce lambda parameter from 1.0 at epoch 0 to 0.75 at epoch 800.
reduce max_skipping_rate from 15 to 10 (compared to vondele's outstanding PR)
Note: This network was trained with a ~0.8% error in quantization regarding the newly added activation function.
This will be fixed in the released trainer version. Expect a trainer PR tomorrow.
Note: The inference implementation cuts a corner to merge results from two activation functions.
This could possibly be resolved nicer in the future. AVX2 implementation likely not necessary, but NEON is missing.
First training session invocation:
python3 train.py \
../nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
../nnue-pytorch-training/data/nodes5000pv2_UHO.binpack \
--gpus "$3," \
--threads 4 \
--num-workers 8 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--lambda=1.0 \
--max_epochs=400 \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2
Second training session invocation:
python3 train.py \
../nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
../nnue-pytorch-training/data/T60T70wIsRightFarseerT60T74T75T76.binpack \
--gpus "$3," \
--threads 4 \
--num-workers 8 \
--batch-size 16384 \
--progress_bar_refresh_rate 20 \
--random-fen-skipping 3 \
--features=HalfKAv2_hm^ \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--max_epochs=800 \
--resume-from-model /data/sopel/nnue/nnue-pytorch-training/data/exp367/nn-exp367-run3-epoch399.pt \
--default_root_dir ../nnue-pytorch-training/experiment_$1/run_$2
Passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 27288 W: 7445 L: 7178 D: 12665
Ptnml(0-2): 159, 3002, 7054, 3271, 158
https://tests.stockfishchess.org/tests/view/627e8c001919125939623644
Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 21792 W: 5969 L: 5727 D: 10096
Ptnml(0-2): 25, 2152, 6294, 2406, 19
https://tests.stockfishchess.org/tests/view/627f2a855734b18b2e2ece47
closes https://github.com/official-stockfish/Stockfish/pull/4020
Bench: 6481017
63 lines
1.8 KiB
C++
63 lines
1.8 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#ifndef EVALUATE_H_INCLUDED
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#define EVALUATE_H_INCLUDED
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#include <string>
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#include <optional>
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#include "types.h"
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namespace Stockfish {
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class Position;
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namespace Eval {
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std::string trace(Position& pos);
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Value evaluate(const Position& pos);
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extern bool useNNUE;
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extern std::string currentEvalFileName;
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// The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
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// for the build process (profile-build and fishtest) to work. Do not change the
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// name of the macro, as it is used in the Makefile.
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#define EvalFileDefaultName "nn-3c0aa92af1da.nnue"
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namespace NNUE {
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std::string trace(Position& pos);
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Value evaluate(const Position& pos, bool adjusted = false);
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void init();
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void verify();
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bool load_eval(std::string name, std::istream& stream);
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bool save_eval(std::ostream& stream);
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bool save_eval(const std::optional<std::string>& filename);
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} // namespace NNUE
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} // namespace Eval
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} // namespace Stockfish
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#endif // #ifndef EVALUATE_H_INCLUDED
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