Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update default net to nn-1ee1aba5ed4c.nnue #4782

Closed

Conversation

linrock
Copy link
Contributor

@linrock linrock commented Sep 11, 2023

Created by retraining the master net on a dataset composed by:

  • adding Leela data from T60 jul-dec 2020, T77 nov 2021, T80 jun-jul 2023
  • deduplicating and unminimizing parts of the dataset before interleaving

Trained initially with max epoch 800, then increased near the end of training twice. First to 960, then 1200.
After training, post-processing involved:

python3 easy_train.py \
  --experiment-name 2048-retrain-S6-sk28 \
  --training-dataset /data/S6.binpack \
  --early-fen-skipping 28 \
  --start-from-engine-test-net True \
  --max_epoch 1200 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --tui False \
  --seed $RANDOM \
  --gpus 0

In the list of datasets below, periods in the filename represent the sequence of steps applied to arrive at the particular binpack. For example:

test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack

  1. test77 dec2021 data rescored with 16 TB of syzygy tablebases during data conversion
  2. filtered with csv_filter_v6_dd.py - v6 filtering and deduplication in one step
  3. minimized with the original mar2023 implementation of minimize_binpack in the tools branch
  4. unminimized by removing all positions with score == 32002 (VALUE_NONE)

Binpacks were:

DATASETS=(
  leela96-filt-v2.min.unminimized.binpack
  dfrc99-16tb7p-eval-filt-v2.min.unminimized.binpack

  # most of the 0dd1cebea57 v6-dd dataset (without test80-jul2022)
  # https://github.com/official-stockfish/Stockfish/pull/4606
  test60-novdec2021-12tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test78-jantomay2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test78-juntosep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test79-apr2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test79-may2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test80-jun2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test80-aug2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test80-sep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test80-oct2022-16tb7p.filter-v6-dd.min.binpack
  test80-nov2022-16tb7p.filter-v6-dd.min.binpack
  test80-jan2023-3of3-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
  test80-feb2023-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack

  # older Leela data, recently converted
  test60-octnovdec2020-2tb7p.min.unminimized.binpack
  test60-julaugsep2020-2tb7p.min.binpack
  test77-nov2021-2tb7p.min.dd.binpack

  # newer Leela data
  test80-mar2023-2tb7p.min.unminimized.binpack
  test80-apr2023-2tb7p.filter-v6-sk16.min.unminimized.binpack
  test80-may2023-2tb7p.min.dd.binpack
  test80-jun2023-2tb7p.min.binpack
  test80-jul2023-2tb7p.binpack
)
python3 interleave_binpacks.py ${DATASETS[@]} /data/S6.binpack

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch1059 : 2.7 +/- 1.6

Passed STC:
https://tests.stockfishchess.org/tests/view/64fc8d705dab775b5359db42
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 168352 W: 43216 L: 42704 D: 82432
Ptnml(0-2): 599, 19672, 43134, 20160, 611

Passed LTC:
https://tests.stockfishchess.org/tests/view/64fd44a75dab775b5359f065
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 154194 W: 39436 L: 38881 D: 75877
Ptnml(0-2): 78, 16577, 43238, 17120, 84

bench 1672490

Created by retraining the master net on a dataset composed by:
- adding Leela data from T60 jul-dec 2020, T77 nov 2021, T80 jun-jul 2023
- deduplicating and unminimizing parts of the dataset before interleaving

Trained initially with max epoch 800, then increased near the end of training twice.
First to 960, then 1200. After training, post-processing involved:
- greedy permuting L1 weights with official-stockfish#4620
- greedy 2- and 3- cycle permuting with official-stockfish#4640

  python3 easy_train.py \
    --experiment-name 2048-retrain-S6-sk28 \
    --training-dataset /data/S6.binpack \
    --early-fen-skipping 28 \
    --start-from-engine-test-net True \
    --max_epoch 1200 \
    --lr 4.375e-4 \
    --gamma 0.995 \
    --start-lambda 1.0 \
    --end-lambda 0.7 \
    --tui False \
    --seed $RANDOM \
    --gpus 0

In the list of datasets below, periods in the filename represent the sequence of steps
applied to arrive at the particular binpack. For example:

test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
1. test77 dec2021 data rescored with 16 TB of syzygy tablebases during data conversion
2. filtered with csv_filter_v6_dd.py - v6 filtering and deduplication in one step
3. minimized with the original mar2023 implementation of `minimize_binpack` in the tools branch
4. unminimized by removing all positions with score == 32002 (`VALUE_NONE`)

Binpacks were:
- filtered with: https://github.com/linrock/nnue-data
- unminimized with: https://github.com/linrock/Stockfish/tree/tools-unminify
- deduplicated with: https://github.com/linrock/Stockfish/tree/tools-dd

  DATASETS=(
    leela96-filt-v2.min.unminimized.binpack
    dfrc99-16tb7p-eval-filt-v2.min.unminimized.binpack

    # most of the 0dd1cebea57 v6-dd dataset (without test80-jul2022)
    # official-stockfish#4606
    test60-novdec2021-12tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test77-dec2021-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test78-jantomay2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test78-juntosep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test79-apr2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test79-may2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test80-jun2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test80-aug2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test80-sep2022-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test80-oct2022-16tb7p.filter-v6-dd.min.binpack
    test80-nov2022-16tb7p.filter-v6-dd.min.binpack
    test80-jan2023-3of3-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack
    test80-feb2023-16tb7p.filter-v6-dd.min-mar2023.unminimized.binpack

    # older Leela data, recently converted
    test60-octnovdec2020-2tb7p.min.unminimized.binpack
    test60-julaugsep2020-2tb7p.min.binpack
    test77-nov2021-2tb7p.min.dd.binpack

    # newer Leela data
    test80-mar2023-2tb7p.min.unminimized.binpack
    test80-apr2023-2tb7p.filter-v6-sk16.min.unminimized.binpack
    test80-may2023-2tb7p.min.dd.binpack
    test80-jun2023-2tb7p.min.binpack
    test80-jul2023-2tb7p.binpack
  )
  python3 interleave_binpacks.py ${DATASETS[@]} /data/S6.binpack

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch1059 : 2.7 +/- 1.6

Passed STC:
https://tests.stockfishchess.org/tests/view/64fc8d705dab775b5359db42
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 168352 W: 43216 L: 42704 D: 82432
Ptnml(0-2): 599, 19672, 43134, 20160, 611

Passed LTC:
https://tests.stockfishchess.org/tests/view/64fd44a75dab775b5359f065
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 154194 W: 39436 L: 38881 D: 75877
Ptnml(0-2): 78, 16577, 43238, 17120, 84

bench 1672490
@Disservin Disservin added bench-change Changes the bench 🚀 gainer Gains elo to be merged Will be merged shortly labels Sep 11, 2023
@Disservin Disservin closed this in 3d1b067 Sep 11, 2023
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
      leela93-filt-v1.min.binpack
      dfrc99-16tb7p-filt-v2.min.binpack
      test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.min.binpack
      test80-jan2023-16tb7p.v6-sk20.min.binpack
      test78-janfeb2022-16tb7p.min.binpack
      test79-apr2022-16tb7p.min.binpack
      test80-apr2022-16tb7p.min.binpack
      test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
      leela96-filt-v2.min.binpack
      dfrc99-16tb7p-filt-v2.min.binpack
      test78-juntosep2022-16tb7p-filter-v6-dd.min.binpack
      test79-may2022-16tb7p.filter-v6-dd.min.binpack
      test80-jun2022-16tb7p.filter-v6-dd.min.binpack
      test80-sep2022-16tb7p.filter-v6-dd.min.binpack
      test80-nov2022-16tb7p.filter-v6-dd.min.binpack
      test80-jan2023-2tb7p.filter-v6-dd.min.binpack
      test80-mar2023-2tb7p.v6-sk16.min.binpack
      test60-novdec2021-16tb7p.min.binpack
      test77-dec2021-16tb7p.min.binpack
      test78-aprmay2022-16tb7p.min.binpack
      test79-apr2022-16tb7p.min.binpack
      test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782

L1 weights permuted with:
```
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
      leela93-filt-v1.min.binpack
      dfrc99-16tb7p-filt-v2.min.binpack
      test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.min.binpack
      test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
      test78-janfeb2022-16tb7p.min.binpack
      test79-apr2022-16tb7p.min.binpack
      test80-apr2022-16tb7p.min.binpack
      test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
      leela96-filt-v2.min.binpack
      dfrc99-16tb7p-filt-v2.min.binpack
      test78-juntosep2022-16tb7p-filter-v6-dd.min.binpack
      test79-may2022-16tb7p.filter-v6-dd.min.binpack
      test80-jun2022-16tb7p.filter-v6-dd.min.binpack
      test80-sep2022-16tb7p.filter-v6-dd.min.binpack
      test80-nov2022-16tb7p.filter-v6-dd.min.binpack
      test80-jan2023-2tb7p.filter-v6-dd.min.binpack
      test80-mar2023-2tb7p.v6-sk16.min.binpack
      test60-novdec2021-16tb7p.min.binpack
      test77-dec2021-16tb7p.min.binpack
      test78-aprmay2022-16tb7p.min.binpack
      test79-apr2022-16tb7p.min.binpack
      test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782

L1 weights permuted with:
```
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

```
1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
     leela93-filt-v1.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test78-janfeb2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-apr2022-16tb7p.min.binpack
     test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
     leela96-filt-v2.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min.binpack
     test79-may2022-16tb7p.filter-v6-dd.min.binpack
     test80-jun2022-16tb7p.filter-v6-dd.min.binpack
     test80-sep2022-16tb7p.filter-v6-dd.min.binpack
     test80-nov2022-16tb7p.filter-v6-dd.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-mar2023-2tb7p.v6-sk16.min.binpack
     test60-novdec2021-16tb7p.min.binpack
     test77-dec2021-16tb7p.min.binpack
     test78-aprmay2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782
```

L1 weights permuted with:
```bash
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

```
1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
     leela93-filt-v1.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test78-janfeb2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-apr2022-16tb7p.min.binpack
     test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
     leela96-filt-v2.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test79-may2022-16tb7p.filter-v6-dd.min.binpack
     test80-jun2022-16tb7p.filter-v6-dd.min.binpack
     test80-sep2022-16tb7p.filter-v6-dd.min.binpack
     test80-nov2022-16tb7p.filter-v6-dd.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-mar2023-2tb7p.v6-sk16.min.binpack
     test60-novdec2021-16tb7p.min.binpack
     test77-dec2021-16tb7p.min.binpack
     test78-aprmay2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782
```

L1 weights permuted with:
```bash
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch
- permuting L1 weights with official-stockfish/nnue-pytorch#254

The datasets used in stages 1-5 were fully minimized. A strong epoch after
each training stage was chosen for the next. The 6 stages were:

```
1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
     leela93-filt-v1.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test78-janfeb2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-apr2022-16tb7p.min.binpack
     test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
     leela96-filt-v2.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test79-may2022-16tb7p.filter-v6-dd.min.binpack
     test80-jun2022-16tb7p.filter-v6-dd.min.binpack
     test80-sep2022-16tb7p.filter-v6-dd.min.binpack
     test80-nov2022-16tb7p.filter-v6-dd.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-mar2023-2tb7p.v6-sk16.min.binpack
     test60-novdec2021-16tb7p.min.binpack
     test77-dec2021-16tb7p.min.binpack
     test78-aprmay2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782
```

L1 weights permuted with:
```bash
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Sep 21, 2023
Creating this net involved:
- a 6-stage training process from scratch. The datasets used in stages 1-5 were fully minimized.
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

```
1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
     leela93-filt-v1.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test78-janfeb2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-apr2022-16tb7p.min.binpack
     test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
     leela96-filt-v2.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test79-may2022-16tb7p.filter-v6-dd.min.binpack
     test80-jun2022-16tb7p.filter-v6-dd.min.binpack
     test80-sep2022-16tb7p.filter-v6-dd.min.binpack
     test80-nov2022-16tb7p.filter-v6-dd.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-mar2023-2tb7p.v6-sk16.min.binpack
     test60-novdec2021-16tb7p.min.binpack
     test77-dec2021-16tb7p.min.binpack
     test78-aprmay2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782
```

L1 weights permuted with:
```bash
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

bench 1246812
vondele pushed a commit to vondele/Stockfish that referenced this pull request Sep 22, 2023
Creating this net involved:
- a 6-stage training process from scratch. The datasets used in stages 1-5 were fully minimized.
- permuting L1 weights with official-stockfish/nnue-pytorch#254

A strong epoch after each training stage was chosen for the next. The 6 stages were:

```
1. 400 epochs, lambda 1.0, default LR and gamma
   UHOx2-wIsRight-multinet-dfrc-n5000 (135G)
     nodes5000pv2_UHO.binpack
     data_pv-2_diff-100_nodes-5000.binpack
     wrongIsRight_nodes5000pv2.binpack
     multinet_pv-2_diff-100_nodes-5000.binpack
     dfrc_n5000.binpack

2. 800 epochs, end-lambda 0.75, LR 4.375e-4, gamma 0.995, skip 12
   LeelaFarseer-T78juntoaugT79marT80dec.binpack (141G)
     T60T70wIsRightFarseerT60T74T75T76.binpack
     test78-junjulaug2022-16tb7p.no-db.min.binpack
     test79-mar2022-16tb7p.no-db.min.binpack
     test80-dec2022-16tb7p.no-db.min.binpack

3. 800 epochs, end-lambda 0.725, LR 4.375e-4, gamma 0.995, skip 20
   leela93-v1-dfrc99-v2-T78juntosepT80jan-v6dd-T78janfebT79aprT80aprmay.min.binpack
     leela93-filt-v1.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test78-janfeb2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-apr2022-16tb7p.min.binpack
     test80-may2022-16tb7p.min.binpack

4. 800 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 24
   leela96-dfrc99-v2-T78juntosepT79mayT80junsepnovjan-v6dd-T80mar23-v6-T60novdecT77decT78aprmayT79aprT80may23.min.binpack
     leela96-filt-v2.min.binpack
     dfrc99-16tb7p-filt-v2.min.binpack
     test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.binpack
     test79-may2022-16tb7p.filter-v6-dd.min.binpack
     test80-jun2022-16tb7p.filter-v6-dd.min.binpack
     test80-sep2022-16tb7p.filter-v6-dd.min.binpack
     test80-nov2022-16tb7p.filter-v6-dd.min.binpack
     test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack
     test80-mar2023-2tb7p.v6-sk16.min.binpack
     test60-novdec2021-16tb7p.min.binpack
     test77-dec2021-16tb7p.min.binpack
     test78-aprmay2022-16tb7p.min.binpack
     test79-apr2022-16tb7p.min.binpack
     test80-may2023-2tb7p.min.binpack

5. 960 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 960 near the end of the first 800 epochs
   5af11540bbfe dataset: official-stockfish#4635

6. 1000 epochs, end-lambda 0.7, LR 4.375e-4, gamma 0.995, skip 28
   Increased max-epoch to 1000 near the end of the first 800 epochs
   1ee1aba5ed dataset: official-stockfish#4782
```

L1 weights permuted with:
```bash
python3 serialize.py $nnue $nnue_permuted \
  --features=HalfKAv2_hm \
  --ft_optimize \
  --ft_optimize_data=/data/fishpack32.binpack \
  --ft_optimize_count=10000
```

Speed measurements from 100 bench runs at depth 13 with profile-build x86-64-avx2:
```
sf_base =  1329051 +/-   2224 (95%)
sf_test =  1163344 +/-   2992 (95%)
diff    =  -165706 +/-   4913 (95%)
speedup = -12.46807% +/- 0.370% (95%)
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move (vs. L1-2048 nn-1ee1aba5ed4c.nnue)
ep959 : 16.2 +/- 2.3

Failed 10+0.1 STC:
https://tests.stockfishchess.org/tests/view/6501beee2cd016da89abab21
LLR: -2.92 (-2.94,2.94) <0.00,2.00>
Total: 13184 W: 3285 L: 3535 D: 6364
Ptnml(0-2): 85, 1662, 3334, 1440, 71

Failed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/6505cf9a72620bc881ea908e
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 64248 W: 16224 L: 16374 D: 31650
Ptnml(0-2): 26, 6788, 18640, 6650, 20

Passed 60+0.6 th 8 VLTC SMP (STC bounds):
https://tests.stockfishchess.org/tests/view/65084a4618698b74c2e541dc
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 90630 W: 23372 L: 23033 D: 44225
Ptnml(0-2): 13, 8490, 27968, 8833, 11

Passed 60+0.6 th 8 VLTC SMP:
https://tests.stockfishchess.org/tests/view/6501d45d2cd016da89abacdb
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 137804 W: 35764 L: 35276 D: 66764
Ptnml(0-2): 31, 13006, 42326, 13522, 17

closes official-stockfish#4795

bench 1246812
linrock added a commit to linrock/Stockfish that referenced this pull request Dec 28, 2023
Created by retraining the master big net `nn-0000000000a0.nnue` on the same
dataset with the ranger21 optimizer and more WDL skipping at training time.

More WDL skipping is meant to increase lambda accuracy and train on fewer
misevaluated positions where position scores are unlikely to correlate
with game outcomes. Inspired by:
- repeated reports in discord #events-discuss about SF misplaying due to wrong endgame
  evals, possibly due to Leela's endgame weaknesses reflected in training data
- an attempt to reduce the skewed dataset piece count distribution where there are much
  more positions with less than 16 pieces, since the target piece count distribution in
  the trainer is symmetric around 16

The faster convergence seen with ranger21 is meant to:
- prune experiment ideas more quickly since fewer epochs are needed to reach elo maxima
- research faster potential trainings by shortening each run

```yaml
experiment-name: 2560-S7-Re-514G-ranger21-more-wdl-skip
training-dataset: /data/S6-514G.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip

num-epochs: 1200
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py

Implementations based off of Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
- Experiment 336 - ranger21 https://github.com/Sopel97/nnue-pytorch/tree/experiment_336
- Experiment 351 - more WDL skipping

The version of the ranger21 optimizer used is:
https://github.com/lessw2020/Ranger21/blob/b507df6/ranger21/ranger21.py

The dataset is the exact same as in:
official-stockfish#4782

Local elo at 25k nodes per move:
nn-epoch619.nnue : 6.2 +/- 4.2

Passed STC:
https://tests.stockfishchess.org/tests/view/658a029779aa8af82b94fbe6
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 46528 W: 11985 L: 11650 D: 22893
Ptnml(0-2): 154, 5489, 11688, 5734, 199

Passed LTC:
https://tests.stockfishchess.org/tests/view/658a448979aa8af82b95010f
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 265326 W: 66378 L: 65574 D: 133374
Ptnml(0-2): 153, 30175, 71254, 30877, 204

This was additionally tested with the latest DualNNUE and passed SPRTs:

Passed STC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658bcd5c79aa8af82b951846
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 296128 W: 76273 L: 75554 D: 144301
Ptnml(0-2): 1223, 35768, 73617, 35979, 1477

Passed LTC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658c988d79aa8af82b95240f
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 75618 W: 19085 L: 18680 D: 37853
Ptnml(0-2): 45, 8420, 20497, 8779, 68

bench 1049162
Disservin pushed a commit that referenced this pull request Dec 30, 2023
Created by retraining the master big net `nn-0000000000a0.nnue` on the same
dataset with the ranger21 optimizer and more WDL skipping at training time.

More WDL skipping is meant to increase lambda accuracy and train on fewer
misevaluated positions where position scores are unlikely to correlate
with game outcomes. Inspired by:
- repeated reports in discord #events-discuss about SF misplaying due to wrong endgame
  evals, possibly due to Leela's endgame weaknesses reflected in training data
- an attempt to reduce the skewed dataset piece count distribution where there
  are much more positions with less than 16 pieces, since the target piece count
  distribution in the trainer is symmetric around 16

The faster convergence seen with ranger21 is meant to:
- prune experiment ideas more quickly since fewer epochs are needed to reach elo maxima
- research faster potential trainings by shortening each run

```yaml
experiment-name: 2560-S7-Re-514G-ranger21-more-wdl-skip
training-dataset: /data/S6-514G.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip

num-epochs: 1200
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py

Implementations based off of Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
- Experiment 336 - ranger21 https://github.com/Sopel97/nnue-pytorch/tree/experiment_336
- Experiment 351 - more WDL skipping

The version of the ranger21 optimizer used is:
https://github.com/lessw2020/Ranger21/blob/b507df6/ranger21/ranger21.py

The dataset is the exact same as in:
#4782

Local elo at 25k nodes per move:
nn-epoch619.nnue : 6.2 +/- 4.2

Passed STC:
https://tests.stockfishchess.org/tests/view/658a029779aa8af82b94fbe6
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 46528 W: 11985 L: 11650 D: 22893
Ptnml(0-2): 154, 5489, 11688, 5734, 199

Passed LTC:
https://tests.stockfishchess.org/tests/view/658a448979aa8af82b95010f
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 265326 W: 66378 L: 65574 D: 133374
Ptnml(0-2): 153, 30175, 71254, 30877, 204

This was additionally tested with the latest DualNNUE and passed SPRTs:

Passed STC vs. #4919
https://tests.stockfishchess.org/tests/view/658bcd5c79aa8af82b951846
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 296128 W: 76273 L: 75554 D: 144301
Ptnml(0-2): 1223, 35768, 73617, 35979, 1477

Passed LTC vs. #4919
https://tests.stockfishchess.org/tests/view/658c988d79aa8af82b95240f
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 75618 W: 19085 L: 18680 D: 37853
Ptnml(0-2): 45, 8420, 20497, 8779, 68

closes #4942

Bench: 1304666
linrock added a commit to linrock/Stockfish that referenced this pull request Jan 8, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best dataset
- increasing training loss for positions where predicted win rates were higher than estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S5-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win rates were higher than suggested
by the win rate model based on the training data, by multiplying with: ((qf > pt) * 0.1 + 1)

This was a variant of experiments found to be promising from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

Bench: 1219824
linrock added a commit to linrock/Stockfish that referenced this pull request Jan 8, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best dataset
- increasing training loss for positions where predicted win rates were higher than
  estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S5-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win rates were higher than suggested
by the win rate model based on the training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was
a variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

Bench: 1219824
linrock added a commit to linrock/Stockfish that referenced this pull request Jan 8, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best dataset
- increasing training loss for positions where predicted win rates were higher than
  estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win rates were higher than suggested
by the win rate model based on the training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was
a variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

Bench: 1219824
Disservin pushed a commit that referenced this pull request Jan 8, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL
  skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best
  dataset
- increasing training loss for positions where predicted win rates were
  higher than estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # #4782
  - /data/S6-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win
rates were higher than suggested by the win rate model based on the
training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a
variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a
batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

closes #4972

Bench: 1219824
Disservin pushed a commit to Disservin/Stockfish that referenced this pull request Jan 8, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL
  skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best
  dataset
- increasing training loss for positions where predicted win rates were
  higher than estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win
rates were higher than suggested by the win rate model based on the
training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a
variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a
batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

closes official-stockfish#4972

Bench: 1219824
rn5f107s2 pushed a commit to rn5f107s2/Stockfish that referenced this pull request Jan 14, 2024
Created by retraining the master big net `nn-0000000000a0.nnue` on the same
dataset with the ranger21 optimizer and more WDL skipping at training time.

More WDL skipping is meant to increase lambda accuracy and train on fewer
misevaluated positions where position scores are unlikely to correlate
with game outcomes. Inspired by:
- repeated reports in discord #events-discuss about SF misplaying due to wrong endgame
  evals, possibly due to Leela's endgame weaknesses reflected in training data
- an attempt to reduce the skewed dataset piece count distribution where there
  are much more positions with less than 16 pieces, since the target piece count
  distribution in the trainer is symmetric around 16

The faster convergence seen with ranger21 is meant to:
- prune experiment ideas more quickly since fewer epochs are needed to reach elo maxima
- research faster potential trainings by shortening each run

```yaml
experiment-name: 2560-S7-Re-514G-ranger21-more-wdl-skip
training-dataset: /data/S6-514G.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip

num-epochs: 1200
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py

Implementations based off of Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
- Experiment 336 - ranger21 https://github.com/Sopel97/nnue-pytorch/tree/experiment_336
- Experiment 351 - more WDL skipping

The version of the ranger21 optimizer used is:
https://github.com/lessw2020/Ranger21/blob/b507df6/ranger21/ranger21.py

The dataset is the exact same as in:
official-stockfish#4782

Local elo at 25k nodes per move:
nn-epoch619.nnue : 6.2 +/- 4.2

Passed STC:
https://tests.stockfishchess.org/tests/view/658a029779aa8af82b94fbe6
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 46528 W: 11985 L: 11650 D: 22893
Ptnml(0-2): 154, 5489, 11688, 5734, 199

Passed LTC:
https://tests.stockfishchess.org/tests/view/658a448979aa8af82b95010f
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 265326 W: 66378 L: 65574 D: 133374
Ptnml(0-2): 153, 30175, 71254, 30877, 204

This was additionally tested with the latest DualNNUE and passed SPRTs:

Passed STC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658bcd5c79aa8af82b951846
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 296128 W: 76273 L: 75554 D: 144301
Ptnml(0-2): 1223, 35768, 73617, 35979, 1477

Passed LTC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658c988d79aa8af82b95240f
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 75618 W: 19085 L: 18680 D: 37853
Ptnml(0-2): 45, 8420, 20497, 8779, 68

closes official-stockfish#4942

Bench: 1304666
rn5f107s2 pushed a commit to rn5f107s2/Stockfish that referenced this pull request Jan 14, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL
  skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best
  dataset
- increasing training loss for positions where predicted win rates were
  higher than estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win
rates were higher than suggested by the win rate model based on the
training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a
variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a
batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

closes official-stockfish#4972

Bench: 1219824
windfishballad pushed a commit to windfishballad/Stockfish that referenced this pull request Jan 23, 2024
Created by retraining the master big net `nn-0000000000a0.nnue` on the same
dataset with the ranger21 optimizer and more WDL skipping at training time.

More WDL skipping is meant to increase lambda accuracy and train on fewer
misevaluated positions where position scores are unlikely to correlate
with game outcomes. Inspired by:
- repeated reports in discord #events-discuss about SF misplaying due to wrong endgame
  evals, possibly due to Leela's endgame weaknesses reflected in training data
- an attempt to reduce the skewed dataset piece count distribution where there
  are much more positions with less than 16 pieces, since the target piece count
  distribution in the trainer is symmetric around 16

The faster convergence seen with ranger21 is meant to:
- prune experiment ideas more quickly since fewer epochs are needed to reach elo maxima
- research faster potential trainings by shortening each run

```yaml
experiment-name: 2560-S7-Re-514G-ranger21-more-wdl-skip
training-dataset: /data/S6-514G.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip

num-epochs: 1200
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Experiment yaml configs converted to easy_train.sh commands with:
https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py

Implementations based off of Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
- Experiment 336 - ranger21 https://github.com/Sopel97/nnue-pytorch/tree/experiment_336
- Experiment 351 - more WDL skipping

The version of the ranger21 optimizer used is:
https://github.com/lessw2020/Ranger21/blob/b507df6/ranger21/ranger21.py

The dataset is the exact same as in:
official-stockfish#4782

Local elo at 25k nodes per move:
nn-epoch619.nnue : 6.2 +/- 4.2

Passed STC:
https://tests.stockfishchess.org/tests/view/658a029779aa8af82b94fbe6
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 46528 W: 11985 L: 11650 D: 22893
Ptnml(0-2): 154, 5489, 11688, 5734, 199

Passed LTC:
https://tests.stockfishchess.org/tests/view/658a448979aa8af82b95010f
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 265326 W: 66378 L: 65574 D: 133374
Ptnml(0-2): 153, 30175, 71254, 30877, 204

This was additionally tested with the latest DualNNUE and passed SPRTs:

Passed STC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658bcd5c79aa8af82b951846
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 296128 W: 76273 L: 75554 D: 144301
Ptnml(0-2): 1223, 35768, 73617, 35979, 1477

Passed LTC vs. official-stockfish#4919
https://tests.stockfishchess.org/tests/view/658c988d79aa8af82b95240f
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 75618 W: 19085 L: 18680 D: 37853
Ptnml(0-2): 45, 8420, 20497, 8779, 68

closes official-stockfish#4942

Bench: 1304666
windfishballad pushed a commit to windfishballad/Stockfish that referenced this pull request Jan 23, 2024
Created by retraining the previous main net nn-b1e55edbea57.nnue with:
- some of the same options as before: ranger21 optimizer, more WDL
  skipping
- adding T80 aug filter-v6, sep, and oct 2023 data to the previous best
  dataset
- increasing training loss for positions where predicted win rates were
  higher than estimated match results from training data position scores

```yaml
experiment-name: 2560--S8-r21-more-wdl-skip-10p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-10p-more-loss-high-q

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Training loss was increased by 10% for positions where predicted win
rates were higher than suggested by the win rate model based on the
training data, by multiplying with: ((qf > pt) * 0.1 + 1). This was a
variant of experiments from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 302 - increase loss when prediction too high, vondele’s idea
Experiment 309 - increase loss when prediction too high, normalize in a
batch

Passed STC:
https://tests.stockfishchess.org/tests/view/6597a21c79aa8af82b95fd5c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 148320 W: 37960 L: 37475 D: 72885
Ptnml(0-2): 542, 17565, 37383, 18206, 464

Passed LTC:
https://tests.stockfishchess.org/tests/view/659834a679aa8af82b960845
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 55188 W: 13955 L: 13592 D: 27641
Ptnml(0-2): 34, 6162, 14834, 6535, 29

closes official-stockfish#4972

Bench: 1219824
linrock added a commit to linrock/Stockfish that referenced this pull request Feb 16, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

Bench: 1265463
Disservin pushed a commit that referenced this pull request Feb 17, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # #4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: #4942
- increasing loss when Q is too high: #4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes #5056

Bench: 1351997
xu-shawn pushed a commit to xu-shawn/Stockfish that referenced this pull request Feb 17, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes official-stockfish#5056

Bench: 1351997
xu-shawn pushed a commit to xu-shawn/Stockfish that referenced this pull request Feb 19, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes official-stockfish#5056

Bench: 1351997
xu-shawn pushed a commit to xu-shawn/Stockfish that referenced this pull request Feb 19, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes official-stockfish#5056

Bench: 1351997
xu-shawn pushed a commit to xu-shawn/Stockfish that referenced this pull request Feb 19, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes official-stockfish#5056

Bench: 1351997
TierynnB pushed a commit to TierynnB/Stockfish that referenced this pull request Feb 22, 2024
Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
- some of the same options as before: ranger21, more WDL skipping
- the addition of T80 nov+dec 2023 data
- increasing loss by 15% when prediction is too high, up from 10%
- use of torch.compile to speed up training by over 25%

```yaml
experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

training-dataset:
  # official-stockfish#4782
  - /data/S6-514G-1ee1aba5ed.binpack
  - /data/test80-aug2023-2tb7p.v6.min.binpack
  - /data/test80-sep2023-2tb7p.binpack
  - /data/test80-oct2023-2tb7p.binpack
  - /data/test80-nov2023-2tb7p.binpack
  - /data/test80-dec2023-2tb7p.binpack
early-fen-skipping: 28

start-from-engine-test-net: True
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

num-epochs: 1000
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

Epoch 819 trained with the above config led to this PR. Use of torch.compile
decorators in nnue-pytorch model.py was found to speed up training by at least
25% on Ampere gpus when using recent pytorch compiled with cuda 12:
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

See recent main net PRs for more info on
- ranger21 and more WDL skipping: official-stockfish#4942
- increasing loss when Q is too high: official-stockfish#4972

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
LLR: 2.98 (-2.94,2.94) <0.00,2.00>
Total: 78336 W: 20504 L: 20115 D: 37717
Ptnml(0-2): 317, 9225, 19721, 9562, 343

Passed LTC:
https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 41016 W: 10492 L: 10159 D: 20365
Ptnml(0-2): 22, 4533, 11071, 4854, 28

closes official-stockfish#5056

Bench: 1351997
TierynnB added a commit to TierynnB/Stockfish that referenced this pull request Feb 23, 2024
commit 524083a
Merge: 8a0206f 4a5ba40
Author: Tierynn Byrnes <[email protected]>
Date:   Fri Feb 23 08:42:30 2024 +1000

    Merge branch 'TM_Change_2' of https://github.com/TierynnB/Stockfish into TM_Change_2

commit 8a0206f
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:13:26 2024 +1000

    use current time instead of '1' for timeLeft formula.

    make timeLeft a double, timepoint seemed unecessary since it was always casting back to double anyway.

    fixed comments

    Squashed commits

commit 4a5ba40
Merge: ce952bf 676a1d7
Author: Tierynn Byrnes <[email protected]>
Date:   Fri Feb 23 08:01:21 2024 +1000

    Merge branch 'TM_Change_2' of https://github.com/TierynnB/Stockfish into TM_Change_2

commit ce952bf
Author: cj5716 <[email protected]>
Date:   Tue Feb 13 17:46:37 2024 +0800

    Simplify PV node reduction

    Reduce less on PV nodes even with an upperbound TT entry.

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65cb3a861d8e83c78bfd0497
    LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
    Total: 118752 W: 30441 L: 30307 D: 58004
    Ptnml(0-2): 476, 14179, 29921, 14335, 465

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65cd3b951d8e83c78bfd2b0d
    LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
    Total: 155058 W: 38549 L: 38464 D: 78045
    Ptnml(0-2): 85, 17521, 42219, 17632, 72

    closes official-stockfish#5057

    Bench: 1303971

commit 4acf810
Author: Linmiao Xu <[email protected]>
Date:   Tue Feb 6 11:21:15 2024 -0500

    Update default main net to nn-b1a57edbea57.nnue

    Created by retraining the previous main net `nn-baff1edbea57.nnue` with:
    - some of the same options as before: ranger21, more WDL skipping
    - the addition of T80 nov+dec 2023 data
    - increasing loss by 15% when prediction is too high, up from 10%
    - use of torch.compile to speed up training by over 25%

    ```yaml
    experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28

    training-dataset:
      # official-stockfish#4782
      - /data/S6-514G-1ee1aba5ed.binpack
      - /data/test80-aug2023-2tb7p.v6.min.binpack
      - /data/test80-sep2023-2tb7p.binpack
      - /data/test80-oct2023-2tb7p.binpack
      - /data/test80-nov2023-2tb7p.binpack
      - /data/test80-dec2023-2tb7p.binpack
    early-fen-skipping: 28

    start-from-engine-test-net: True
    nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile

    num-epochs: 1000
    lr: 4.375e-4
    gamma: 0.995
    start-lambda: 1.0
    end-lambda: 0.7
    ```

    Epoch 819 trained with the above config led to this PR. Use of torch.compile
    decorators in nnue-pytorch model.py was found to speed up training by at least
    25% on Ampere gpus when using recent pytorch compiled with cuda 12:
    https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

    See recent main net PRs for more info on
    - ranger21 and more WDL skipping: official-stockfish#4942
    - increasing loss when Q is too high: official-stockfish#4972

    Training data can be found at:
    https://robotmoon.com/nnue-training-data/

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52
    LLR: 2.98 (-2.94,2.94) <0.00,2.00>
    Total: 78336 W: 20504 L: 20115 D: 37717
    Ptnml(0-2): 317, 9225, 19721, 9562, 343

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9
    LLR: 2.95 (-2.94,2.94) <0.50,2.50>
    Total: 41016 W: 10492 L: 10159 D: 20365
    Ptnml(0-2): 22, 4533, 11071, 4854, 28

    closes official-stockfish#5056

    Bench: 1351997

commit 40c6cdf
Author: cj5716 <[email protected]>
Date:   Tue Feb 13 17:50:16 2024 +0800

    Simplify TT PV reduction

    This also removes some incorrect fail-high logic.

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65cb3b641d8e83c78bfd04a9
    LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
    Total: 87968 W: 22634 L: 22468 D: 42866
    Ptnml(0-2): 315, 10436, 22323, 10588, 322

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65cccee21d8e83c78bfd222c
    LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
    Total: 70794 W: 17846 L: 17672 D: 35276
    Ptnml(0-2): 44, 7980, 19189, 8126, 58

    closes official-stockfish#5055

    Bench: 1474424

commit 9299d01
Author: Gahtan Nahdi <[email protected]>
Date:   Sat Feb 10 03:51:05 2024 +0700

    Remove penalty for quiet ttMove that fails low

    Passed STC non-reg:
    https://tests.stockfishchess.org/tests/view/65c691a7c865510db0286e6e
    LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
    Total: 234336 W: 60258 L: 60255 D: 113823
    Ptnml(0-2): 966, 28141, 58918, 28210, 933

    Passed LTC non-reg:
    https://tests.stockfishchess.org/tests/view/65c8d0d31d8e83c78bfcd4a6
    LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
    Total: 235206 W: 59134 L: 59132 D: 116940
    Ptnml(0-2): 135, 26908, 63517, 26906, 137

    official-stockfish#5054

    Bench: 1287996

commit 676a1d7
Merge: 7d0cd7b 3c3f88b
Author: Tierynn Byrnes <[email protected]>
Date:   Fri Feb 23 07:58:38 2024 +1000

    Merge branch 'TM_Change_2' of https://github.com/TierynnB/Stockfish into TM_Change_2

commit 7d0cd7b
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:13:26 2024 +1000

    parent 8b67b7e
    author Tierynn Byrnes <[email protected]> 1708290806 +1000
    committer Tierynn Byrnes <[email protected]> 1708638981 +1000

    use current time instead of '1' for timeLeft
    formula.

    make timeLeft a double, timepoint seemed
    unecessary since it was always casting back to double anyway.

commit 3c3f88b
Merge: 76c50a0 61e8083
Author: Tierynn Byrnes <[email protected]>
Date:   Fri Feb 23 07:54:46 2024 +1000

    Merge branch 'TM_Change_2' of https://github.com/TierynnB/Stockfish into TM_Change_2

commit 76c50a0
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:13:26 2024 +1000

    use current time instead of '1' for timeLeft
    formula.

    make timeLeft a double, timepoint seemed
    unecessary since it was always casting back to double anyway.

    fixed comments

commit 61e8083
Merge: 5cf3f49 8afec41
Author: Tierynn Byrnes <[email protected]>
Date:   Thu Feb 22 19:44:21 2024 +1000

    Merge branch 'TM_Change_2' of https://github.com/TierynnB/Stockfish into TM_Change_2

commit 5cf3f49
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:13:26 2024 +1000

    use current time instead of '1' for timeLeft
    formula.

    make timeLeft a double, timepoint seemed
    unecessary since it was always casting back to double anyway.

commit 8afec41
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:50:30 2024 +1000

    fixed comments

commit de4a3c4
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:32:09 2024 +1000

    make timeLeft a double, timepoint seemed unecessary since it was always casting back to double anyway.

commit e1f6b87
Merge: 8b67b7e fc41f64
Author: Lemmy <[email protected]>
Date:   Mon Feb 19 07:14:27 2024 +1000

    Merge branch 'official-stockfish:master' into TM_Change_2

commit 8b67b7e
Author: Tierynn Byrnes <[email protected]>
Date:   Mon Feb 19 07:13:26 2024 +1000

    use current time instead of '1' for timeLeft formula.
linrock added a commit to linrock/Stockfish that referenced this pull request Mar 5, 2024
Created by retraining the previous main net `nn-b1a57edbea57.nnue` with:
- some of the same options as before:
  - ranger21, more WDL skipping, 15% more loss when Q is too high
- removal of the huge 514G pre-interleaved binpack
- removal of SF-generated dfrc data (dfrc99-16tb7p-filt-v2.min.binpack)
- interleaving many binpacks at training time
- training with some bestmove capture positions where SEE < 0
- increased usage of torch.compile to speed up training by up to 40%

```yaml
experiment-name: 2560--S10-dfrc0-to-dec2023-skip-more-wdl-15p-more-loss-high-q-see-ge0-sk28
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
start-from-engine-test-net: True

early-fen-skipping: 28
training-dataset:
  # similar, not the exact same as:
  # official-stockfish#4635
  - /data/S5-5af/leela96.v2.min.binpack
  - /data/S5-5af/test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
  - /data/S5-5af/test77-2021-12-dec-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-06-to-09-juntosep-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-04-apr-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-05-may-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2022-06-jun-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-07-jul-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-08-aug-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-09-sep-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-10-oct-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-11-nov-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
  - /data/S5-5af/test80-2023-02-feb-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2023-03-mar-2tb7p.min.unmin.binpack
  - /data/S5-5af/test80-2023-04-apr-2tb7p.binpack
  - /data/S5-5af/test80-2023-05-may-2tb7p.min.dd.binpack

  # official-stockfish#4782
  - /data/S6-1ee1aba5ed/test80-2023-06-jun-2tb7p.binpack
  - /data/S6-1ee1aba5ed/test80-2023-07-jul-2tb7p.min.binpack

  # official-stockfish#4972
  - /data/S8-baff1edbea57/test80-2023-08-aug-2tb7p.v6.min.binpack
  - /data/S8-baff1edbea57/test80-2023-09-sep-2tb7p.binpack
  - /data/S8-baff1edbea57/test80-2023-10-oct-2tb7p.binpack

  # official-stockfish#5056
  - /data/S9-b1a57edbea57/test80-2023-11-nov-2tb7p.binpack
  - /data/S9-b1a57edbea57/test80-2023-12-dec-2tb7p.binpack

num-epochs: 800
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

This particular net was reached at epoch 759. Use of more torch.compile decorators
in nnue-pytorch model.py than in the previous main net training run sped up training
by up to 40% on Tesla gpus when using recent pytorch compiled with cuda 12:
https://github.com/linrock/nnue-tools/blob/7fb9831/Dockerfile

Skipping positions with bestmove captures where static exchange evaluation is >= 0
is based on the implementation from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 293 - only skip captures with see>=0

Positions with bestmove captures where score == 0 are always skipped for
compatibility with minimized binpacks, since the original minimizer sets
scores to 0 for slight improvements in compression.

The trainer branch used was:
https://github.com/linrock/nnue-pytorch/tree/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more

Binpacks were renamed to be sorted chronologically by default when sorted by name.
The binpack data are otherwise the same as binpacks with similar names in the prior
naming convention.

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65e3ddd1f2ef6c733362ae5c
LLR: 2.92 (-2.94,2.94) <0.00,2.00>
Total: 149792 W: 39153 L: 38661 D: 71978
Ptnml(0-2): 675, 17586, 37905, 18032, 698

Passed LTC:
https://tests.stockfishchess.org/tests/view/65e4d91c416ecd92c162a69b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64416 W: 16517 L: 16135 D: 31764
Ptnml(0-2): 38, 7218, 17313, 7602, 37

Bench: 1536373
linrock added a commit to linrock/Stockfish that referenced this pull request Mar 5, 2024
Created by retraining the previous main net `nn-b1a57edbea57.nnue` with:
- some of the same options as before:
  - ranger21, more WDL skipping, 15% more loss when Q is too high
- removal of the huge 514G pre-interleaved binpack
- removal of SF-generated dfrc data (dfrc99-16tb7p-filt-v2.min.binpack)
- interleaving many binpacks at training time
- training with some bestmove capture positions where SEE < 0
- increased usage of torch.compile to speed up training by up to 40%

```yaml
experiment-name: 2560--S10-dfrc0-to-dec2023-skip-more-wdl-15p-more-loss-high-q-see-ge0-sk28
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
start-from-engine-test-net: True

early-fen-skipping: 28
training-dataset:
  # similar, not the exact same as:
  # official-stockfish#4635
  - /data/S5-5af/leela96.v2.min.binpack
  - /data/S5-5af/test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
  - /data/S5-5af/test77-2021-12-dec-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-06-to-09-juntosep-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-04-apr-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-05-may-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2022-06-jun-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-07-jul-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-08-aug-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-09-sep-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-10-oct-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-11-nov-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
  - /data/S5-5af/test80-2023-02-feb-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2023-03-mar-2tb7p.min.unmin.binpack
  - /data/S5-5af/test80-2023-04-apr-2tb7p.binpack
  - /data/S5-5af/test80-2023-05-may-2tb7p.min.dd.binpack

  # official-stockfish#4782
  - /data/S6-1ee1aba5ed/test80-2023-06-jun-2tb7p.binpack
  - /data/S6-1ee1aba5ed/test80-2023-07-jul-2tb7p.min.binpack

  # official-stockfish#4972
  - /data/S8-baff1edbea57/test80-2023-08-aug-2tb7p.v6.min.binpack
  - /data/S8-baff1edbea57/test80-2023-09-sep-2tb7p.binpack
  - /data/S8-baff1edbea57/test80-2023-10-oct-2tb7p.binpack

  # official-stockfish#5056
  - /data/S9-b1a57edbea57/test80-2023-11-nov-2tb7p.binpack
  - /data/S9-b1a57edbea57/test80-2023-12-dec-2tb7p.binpack

num-epochs: 800
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

This particular net was reached at epoch 759. Use of more torch.compile decorators
in nnue-pytorch model.py than in the previous main net training run sped up training
by up to 40% on Tesla gpus when using recent pytorch compiled with cuda 12:
https://github.com/linrock/nnue-tools/blob/7fb9831/Dockerfile

Skipping positions with bestmove captures where static exchange evaluation is >= 0
is based on the implementation from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 293 - only skip captures with see>=0

Positions with bestmove captures where score == 0 are always skipped for
compatibility with minimized binpacks, since the original minimizer sets
scores to 0 for slight improvements in compression.

The trainer branch used was:
https://github.com/linrock/nnue-pytorch/tree/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more

Binpacks were renamed to be sorted chronologically by default when sorted by name.
The binpack data are otherwise the same as binpacks with similar names in the prior
naming convention.

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65e3ddd1f2ef6c733362ae5c
LLR: 2.92 (-2.94,2.94) <0.00,2.00>
Total: 149792 W: 39153 L: 38661 D: 71978
Ptnml(0-2): 675, 17586, 37905, 18032, 698

Passed LTC:
https://tests.stockfishchess.org/tests/view/65e4d91c416ecd92c162a69b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64416 W: 16517 L: 16135 D: 31764
Ptnml(0-2): 38, 7218, 17313, 7602, 37

Bench: 1373183
Disservin pushed a commit that referenced this pull request Mar 7, 2024
Created by retraining the previous main net `nn-b1a57edbea57.nnue` with:
- some of the same options as before:
  - ranger21, more WDL skipping, 15% more loss when Q is too high
- removal of the huge 514G pre-interleaved binpack
- removal of SF-generated dfrc data (dfrc99-16tb7p-filt-v2.min.binpack)
- interleaving many binpacks at training time
- training with some bestmove capture positions where SEE < 0
- increased usage of torch.compile to speed up training by up to 40%

```yaml
experiment-name: 2560--S10-dfrc0-to-dec2023-skip-more-wdl-15p-more-loss-high-q-see-ge0-sk28
nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
start-from-engine-test-net: True

early-fen-skipping: 28
training-dataset:
  # similar, not the exact same as:
  # #4635
  - /data/S5-5af/leela96.v2.min.binpack
  - /data/S5-5af/test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
  - /data/S5-5af/test77-2021-12-dec-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test78-2022-06-to-09-juntosep-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-04-apr-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test79-2022-05-may-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2022-06-jun-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-07-jul-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-08-aug-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-09-sep-16tb7p.v6-dd.min.unmin.binpack
  - /data/S5-5af/test80-2022-10-oct-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2022-11-nov-16tb7p.v6-dd.min.binpack

  - /data/S5-5af/test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
  - /data/S5-5af/test80-2023-02-feb-16tb7p.v6-dd.min.binpack
  - /data/S5-5af/test80-2023-03-mar-2tb7p.min.unmin.binpack
  - /data/S5-5af/test80-2023-04-apr-2tb7p.binpack
  - /data/S5-5af/test80-2023-05-may-2tb7p.min.dd.binpack

  # #4782
  - /data/S6-1ee1aba5ed/test80-2023-06-jun-2tb7p.binpack
  - /data/S6-1ee1aba5ed/test80-2023-07-jul-2tb7p.min.binpack

  # #4972
  - /data/S8-baff1edbea57/test80-2023-08-aug-2tb7p.v6.min.binpack
  - /data/S8-baff1edbea57/test80-2023-09-sep-2tb7p.binpack
  - /data/S8-baff1edbea57/test80-2023-10-oct-2tb7p.binpack

  # #5056
  - /data/S9-b1a57edbea57/test80-2023-11-nov-2tb7p.binpack
  - /data/S9-b1a57edbea57/test80-2023-12-dec-2tb7p.binpack

num-epochs: 800
lr: 4.375e-4
gamma: 0.995
start-lambda: 1.0
end-lambda: 0.7
```

This particular net was reached at epoch 759. Use of more torch.compile decorators
in nnue-pytorch model.py than in the previous main net training run sped up training
by up to 40% on Tesla gpus when using recent pytorch compiled with cuda 12:
https://github.com/linrock/nnue-tools/blob/7fb9831/Dockerfile

Skipping positions with bestmove captures where static exchange evaluation is >= 0
is based on the implementation from Sopel's NNUE training & experimentation log:
https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
Experiment 293 - only skip captures with see>=0

Positions with bestmove captures where score == 0 are always skipped for
compatibility with minimized binpacks, since the original minimizer sets
scores to 0 for slight improvements in compression.

The trainer branch used was:
https://github.com/linrock/nnue-pytorch/tree/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more

Binpacks were renamed to be sorted chronologically by default when sorted by name.
The binpack data are otherwise the same as binpacks with similar names in the prior
naming convention.

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Passed STC:
https://tests.stockfishchess.org/tests/view/65e3ddd1f2ef6c733362ae5c
LLR: 2.92 (-2.94,2.94) <0.00,2.00>
Total: 149792 W: 39153 L: 38661 D: 71978
Ptnml(0-2): 675, 17586, 37905, 18032, 698

Passed LTC:
https://tests.stockfishchess.org/tests/view/65e4d91c416ecd92c162a69b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64416 W: 16517 L: 16135 D: 31764
Ptnml(0-2): 38, 7218, 17313, 7602, 37

closes #5090

Bench: 1373183
bftjoe added a commit to bftjoe/Stockfish that referenced this pull request Mar 8, 2024
commit 632f1c21cd271e7c4c242fdafa328a55ec63b9cb
Author: Robert Nurnberg @ elitebook <[email protected]>
Date:   Thu Mar 7 22:01:40 2024 +0100

    Fix wrong constant usage in go mate

    Fixes an oversight in official-stockfish/Stockfish#5094

    In theory, master could stop search when run with `go mate 247` and return a TB loss (not a mate score). Also fixes the spelling of opponenWorsening.

    closes official-stockfish/Stockfish#5096

    No functional change

commit 0f01a516d2ddd475bbe3bccab176dbbccb879053
Author: Muzhen Gaming <[email protected]>
Date:   Mon Mar 4 18:48:02 2024 +0800

    VLTC time management tune

    Result of 35k games of SPSA tuning at 180+1.8. Tuning attempt can be
    found here:
    https://tests.stockfishchess.org/tests/view/65e40599f2ef6c733362b03b

    Passed VLTC 180+1.8:
    https://tests.stockfishchess.org/tests/view/65e5a6f5416ecd92c162b5d4
    LLR: 2.94 (-2.94,2.94) <0.00,2.00>
    Total: 31950 W: 8225 L: 7949 D: 15776
    Ptnml(0-2): 3, 3195, 9309, 3459, 9

    Passed VLTC 240+2.4:
    https://tests.stockfishchess.org/tests/view/65e714de0ec64f0526c3d1f1
    LLR: 2.94 (-2.94,2.94) <0.50,2.50>
    Total: 65108 W: 16558 L: 16202 D: 32348
    Ptnml(0-2): 7, 6366, 19449, 6728, 4

    closes official-stockfish/Stockfish#5095

    Bench: 1714391

commit 748791f80dbc29793e473e3e9eda83ffa0afcfaa
Author: Shahin M. Shahin <[email protected]>
Date:   Wed Mar 6 20:56:55 2024 +0300

    Fix `go mate x` in multithreading

    Fixes two issues with master for go mate x:

    - when running go mate x in losing positions, master always goes to the
      maximal depth, arguably against what the UCI protocol demands

    - when running go mate x in winning positions with multiple
      threads, master may return non-mate scores from the search (this issue
      is present in stockfish since at least sf16) The issues are fixed by
      (a) also checking if score is mate -x and by (b) only letting
      mainthread stop the search for go mate x commands, and by not looking
      for a best thread but using mainthread as per the default. Related:
        niklasf/python-chess#1070

    More diagnostics can be found here peregrineshahin#6 (comment)

    closes official-stockfish/Stockfish#5094

    No functional change

    Co-Authored-By: Robert Nürnberg <[email protected]>

commit 6136d094c5f46456964889754ae2d6098834b14f
Author: Michael Chaly <[email protected]>
Date:   Thu Mar 7 11:57:18 2024 +0300

    Introduce double extensions for PV nodes

    Our double/triple extensions were allowed only for non-pv nodes. This
    patch allows them to be done for PV nodes, with some stricter
    conditions.

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65d657ec1d8e83c78bfddab8
    LLR: 2.95 (-2.94,2.94) <0.00,2.00>
    Total: 339424 W: 88097 L: 87318 D: 164009
    Ptnml(0-2): 1573, 39935, 85729, 41090, 1385

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65dd63824b19edc854ebc433
    LLR: 2.94 (-2.94,2.94) <0.50,2.50>
    Total: 459564 W: 115812 L: 114614 D: 229138
    Ptnml(0-2): 248, 51441, 125173, 52705, 215

    closes official-stockfish/Stockfish#5093

    Bench: 1714391

commit 1db969e6200afe4f023469a56aa5edf755d92bbb
Author: rn5f107s2 <[email protected]>
Date:   Thu Feb 15 23:01:02 2024 +0100

    Reduce futility_margin if opponents last move was bad

    This reduces the futiltiy_margin if our opponents last move was bad by
    around ~1/3 when not improving and ~1/2.7 when improving, the idea being
    to retroactively futility prune moves that were played, but turned out
    to be bad.  A bad move is being defined as their staticEval before their
    move being lower as our staticEval now is. If the depth is 2 and we are
    improving the opponent worsening flag is not set, in order to not risk
    having a too low futility_margin, due to the fact that when these
    conditions are met the futility_margin already drops quite low.

    Passed STC:
    https://tests.stockfishchess.org/tests/live_elo/65e3977bf2ef6c733362aae3
    LLR: 2.94 (-2.94,2.94) <0.00,2.00>
    Total: 122432 W: 31884 L: 31436 D: 59112
    Ptnml(0-2): 467, 14404, 31035, 14834, 476

    Passed LTC:
    https://tests.stockfishchess.org/tests/live_elo/65e47f40f2ef6c733362b6d2
    LLR: 2.94 (-2.94,2.94) <0.50,2.50>
    Total: 421692 W: 106572 L: 105452 D: 209668
    Ptnml(0-2): 216, 47217, 114865, 48327, 221

    closes official-stockfish/Stockfish#5092

    Bench: 1565939

commit bd579ab5d1a931a09a62f2ed33b5149ada7bc65f
Author: Linmiao Xu <[email protected]>
Date:   Fri Mar 1 10:34:03 2024 -0800

    Update default main net to nn-1ceb1ade0001.nnue

    Created by retraining the previous main net `nn-b1a57edbea57.nnue` with:
    - some of the same options as before:
      - ranger21, more WDL skipping, 15% more loss when Q is too high
    - removal of the huge 514G pre-interleaved binpack
    - removal of SF-generated dfrc data (dfrc99-16tb7p-filt-v2.min.binpack)
    - interleaving many binpacks at training time
    - training with some bestmove capture positions where SEE < 0
    - increased usage of torch.compile to speed up training by up to 40%

    ```yaml
    experiment-name: 2560--S10-dfrc0-to-dec2023-skip-more-wdl-15p-more-loss-high-q-see-ge0-sk28
    nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more
    start-from-engine-test-net: True

    early-fen-skipping: 28
    training-dataset:
      # similar, not the exact same as:
      # official-stockfish/Stockfish#4635
      - /data/S5-5af/leela96.v2.min.binpack
      - /data/S5-5af/test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
      - /data/S5-5af/test77-2021-12-dec-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test78-2022-06-to-09-juntosep-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test79-2022-04-apr-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test79-2022-05-may-16tb7p.v6-dd.min.binpack

      - /data/S5-5af/test80-2022-06-jun-16tb7p.v6-dd.min.unmin.binpack
      - /data/S5-5af/test80-2022-07-jul-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test80-2022-08-aug-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test80-2022-09-sep-16tb7p.v6-dd.min.unmin.binpack
      - /data/S5-5af/test80-2022-10-oct-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test80-2022-11-nov-16tb7p.v6-dd.min.binpack

      - /data/S5-5af/test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
      - /data/S5-5af/test80-2023-02-feb-16tb7p.v6-dd.min.binpack
      - /data/S5-5af/test80-2023-03-mar-2tb7p.min.unmin.binpack
      - /data/S5-5af/test80-2023-04-apr-2tb7p.binpack
      - /data/S5-5af/test80-2023-05-may-2tb7p.min.dd.binpack

      # official-stockfish/Stockfish#4782
      - /data/S6-1ee1aba5ed/test80-2023-06-jun-2tb7p.binpack
      - /data/S6-1ee1aba5ed/test80-2023-07-jul-2tb7p.min.binpack

      # official-stockfish/Stockfish#4972
      - /data/S8-baff1edbea57/test80-2023-08-aug-2tb7p.v6.min.binpack
      - /data/S8-baff1edbea57/test80-2023-09-sep-2tb7p.binpack
      - /data/S8-baff1edbea57/test80-2023-10-oct-2tb7p.binpack

      # official-stockfish/Stockfish#5056
      - /data/S9-b1a57edbea57/test80-2023-11-nov-2tb7p.binpack
      - /data/S9-b1a57edbea57/test80-2023-12-dec-2tb7p.binpack

    num-epochs: 800
    lr: 4.375e-4
    gamma: 0.995
    start-lambda: 1.0
    end-lambda: 0.7
    ```

    This particular net was reached at epoch 759. Use of more torch.compile decorators
    in nnue-pytorch model.py than in the previous main net training run sped up training
    by up to 40% on Tesla gpus when using recent pytorch compiled with cuda 12:
    https://github.com/linrock/nnue-tools/blob/7fb9831/Dockerfile

    Skipping positions with bestmove captures where static exchange evaluation is >= 0
    is based on the implementation from Sopel's NNUE training & experimentation log:
    https://docs.google.com/document/d/1gTlrr02qSNKiXNZ_SuO4-RjK4MXBiFlLE6jvNqqMkAY
    Experiment 293 - only skip captures with see>=0

    Positions with bestmove captures where score == 0 are always skipped for
    compatibility with minimized binpacks, since the original minimizer sets
    scores to 0 for slight improvements in compression.

    The trainer branch used was:
    https://github.com/linrock/nnue-pytorch/tree/r21-more-wdl-skip-15p-more-loss-high-q-skip-see-ge0-torch-compile-more

    Binpacks were renamed to be sorted chronologically by default when sorted by name.
    The binpack data are otherwise the same as binpacks with similar names in the prior
    naming convention.

    Training data can be found at:
    https://robotmoon.com/nnue-training-data/

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65e3ddd1f2ef6c733362ae5c
    LLR: 2.92 (-2.94,2.94) <0.00,2.00>
    Total: 149792 W: 39153 L: 38661 D: 71978
    Ptnml(0-2): 675, 17586, 37905, 18032, 698

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65e4d91c416ecd92c162a69b
    LLR: 2.94 (-2.94,2.94) <0.50,2.50>
    Total: 64416 W: 16517 L: 16135 D: 31764
    Ptnml(0-2): 38, 7218, 17313, 7602, 37

    closes official-stockfish/Stockfish#5090

    Bench: 1373183

commit a96b0d46093c67707e4e75e7aa5aa057b7c131a2
Author: FauziAkram <[email protected]>
Date:   Mon Mar 4 16:13:36 2024 +0300

    Update elo estimates

    Tests used to change the elo worth of some functions:

    https://tests.stockfishchess.org/tests/view/65c3f69dc865510db0283eef
    https://tests.stockfishchess.org/tests/view/65c3f935c865510db0283f2a
    https://tests.stockfishchess.org/tests/view/65d1489f1d8e83c78bfd7dbf
    https://tests.stockfishchess.org/tests/view/65ce9d361d8e83c78bfd4951
    https://tests.stockfishchess.org/tests/view/65cfcd901d8e83c78bfd6184

    closes official-stockfish/Stockfish#5089

    No functional change

commit a615efb19f5dfb4b205ed3a9dd8525e54e8777cc
Author: FauziAkram <[email protected]>
Date:   Mon Feb 26 18:08:22 2024 +0300

    Simplify Time Management

    Instead of having a formula for using extra time with larger increments.
    Simply set it to 1 when the increment is lower than 0.5s and to 1.1 when
    the increment is higher.

    The values can later on be further improved.

    Passed STC:
    https://tests.stockfishchess.org/tests/view/65d25d3c1d8e83c78bfd9293
    LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
    Total: 27488 W: 7077 L: 6848 D: 13563
    Ptnml(0-2): 96, 3041, 7267, 3218, 122

    Passed LTC:
    https://tests.stockfishchess.org/tests/view/65d2a72c1d8e83c78bfd97fa
    LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
    Total: 137568 W: 34612 L: 34512 D: 68444
    Ptnml(0-2): 60, 14672, 39221, 14770, 61

    Passed VLTC:
    https://tests.stockfishchess.org/tests/view/65d7d7d39b2da0226a5a205b
    LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
    Total: 139650 W: 35229 L: 35134 D: 69287
    Ptnml(0-2): 33, 14227, 41218, 14306, 41

    Passed also the TCEC TC style suggested by vondele:
    https://tests.stockfishchess.org/tests/view/65e4ca73416ecd92c162a57d
    LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
    Total: 134150 W: 34278 L: 34163 D: 65709
    Ptnml(0-2): 561, 15727, 34444, 15722, 621

    closes official-stockfish/Stockfish#5076

    Bench: 1553115
linrock added a commit to linrock/Stockfish that referenced this pull request May 17, 2024
Created by first retraining the spsa-tuned master net `nn-ae6a388e4a1a.nnue` with:
- using v6-dd data without bestmove captures removed
- addition of T80 mar2024 data
- increasing loss by 20% when Q is too high
- torch.compile changes for marginal training speed gains

And then SPSA tuning weights of epoch 899 following methods described in:
official-stockfish#5149

This net was reached at 92k out of 120k steps in this 70+0.7 th 7 SPSA tuning run:
https://tests.stockfishchess.org/tests/view/66413b7df9f4e8fc783c9bbb
Thanks to @Viren6 for suggesting usage of:
- c value 4 for the weights
- c value 128 for the biases

Scripts for automating applying fishtest spsa params to exporting tuned .nnue are in:
https://github.com/linrock/nnue-tools/tree/master/spsa

Before spsa tuning, epoch 899 was nn-f85738aefa84.nnue
https://tests.stockfishchess.org/tests/view/663e5c893a2f9702074bc167

After initially training with max-epoch 800, training was resumed with max-epoch 1000.

```
experiment-name: 3072--S11--more-data-v6-dd-t80-mar2024--see-ge0-20p-more-loss-high-q-sk28-l8
nnue-pytorch-branch: linrock/nnue-pytorch/3072-r21-skip-more-wdl-see-ge0-20p-more-loss-high-q-torch-compile-more

start-from-engine-test-net: False
start-from-model: /data/config/apr2024-3072/nn-ae6a388e4a1a.nnue

early-fen-skipping: 28
training-dataset:
  /data/S11-mar2024/:
    - leela96.v2.min.binpack

    - test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
    - test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack

    - test80-2022-06-jun-16tb7p.v6-dd.min.binpack

    - test80-2022-08-aug-16tb7p.v6-dd.min.binpack
    - test80-2022-09-sep-16tb7p.v6-dd.min.binpack

    - test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
    - test80-2023-02-feb-16tb7p.v6-sk20.min.binpack
    - test80-2023-03-mar-2tb7p.v6-sk16.min.binpack
    - test80-2023-04-apr-2tb7p.v6-sk16.min.binpack
    - test80-2023-05-may-2tb7p.v6.min.binpack

    # official-stockfish#4782
    - test80-2023-06-jun-2tb7p.binpack
    - test80-2023-07-jul-2tb7p.binpack

    # official-stockfish#4972
    - test80-2023-08-aug-2tb7p.v6.min.binpack
    - test80-2023-09-sep-2tb7p.binpack
    - test80-2023-10-oct-2tb7p.binpack

    # S9 new data: official-stockfish#5056
    - test80-2023-11-nov-2tb7p.binpack
    - test80-2023-12-dec-2tb7p.binpack

    # S10 new data: official-stockfish#5149
    - test80-2024-01-jan-2tb7p.binpack
    - test80-2024-02-feb-2tb7p.binpack

    # S11 new data
    - test80-2024-03-mar-2tb7p.binpack

  /data/filt-v6-dd/:
    - test77-dec2021-16tb7p-filter-v6-dd.binpack
    - test78-juntosep2022-16tb7p-filter-v6-dd.binpack
    - test79-apr2022-16tb7p-filter-v6-dd.binpack
    - test79-may2022-16tb7p-filter-v6-dd.binpack
    - test80-jul2022-16tb7p-filter-v6-dd.binpack
    - test80-oct2022-16tb7p-filter-v6-dd.binpack
    - test80-nov2022-16tb7p-filter-v6-dd.binpack

num-epochs: 1000

lr: 4.375e-4
gamma: 0.995
start-lambda: 0.8
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch899.nnue : 4.6 +/- 1.4

Passed STC:
https://tests.stockfishchess.org/tests/view/6645454893ce6da3e93b31ae
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 95232 W: 24598 L: 24194 D: 46440
Ptnml(0-2): 294, 11215, 24180, 11647, 280

Passed LTC:
https://tests.stockfishchess.org/tests/view/6645522d93ce6da3e93b31df
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 320544 W: 81432 L: 80524 D: 158588
Ptnml(0-2): 164, 35659, 87696, 36611, 142

bench 1955748
linrock added a commit to linrock/Stockfish that referenced this pull request May 17, 2024
Created by first retraining the spsa-tuned main net `nn-ae6a388e4a1a.nnue` with:
- using v6-dd data without bestmove captures removed
- addition of T80 mar2024 data
- increasing loss by 20% when Q is too high
- torch.compile changes for marginal training speed gains

And then SPSA tuning weights of epoch 899 following methods described in:
official-stockfish#5149

This net was reached at 92k out of 120k steps in this 70+0.7 th 7 SPSA tuning run:
https://tests.stockfishchess.org/tests/view/66413b7df9f4e8fc783c9bbb
Thanks to @Viren6 for suggesting usage of:
- c value 4 for the weights
- c value 128 for the biases

Scripts for automating applying fishtest spsa params to exporting tuned .nnue are in:
https://github.com/linrock/nnue-tools/tree/master/spsa

Before spsa tuning, epoch 899 was nn-f85738aefa84.nnue
https://tests.stockfishchess.org/tests/view/663e5c893a2f9702074bc167

After initially training with max-epoch 800, training was resumed with max-epoch 1000.

```
experiment-name: 3072--S11--more-data-v6-dd-t80-mar2024--see-ge0-20p-more-loss-high-q-sk28-l8
nnue-pytorch-branch: linrock/nnue-pytorch/3072-r21-skip-more-wdl-see-ge0-20p-more-loss-high-q-torch-compile-more

start-from-engine-test-net: False
start-from-model: /data/config/apr2024-3072/nn-ae6a388e4a1a.nnue

early-fen-skipping: 28
training-dataset:
  /data/S11-mar2024/:
    - leela96.v2.min.binpack

    - test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
    - test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack

    - test80-2022-06-jun-16tb7p.v6-dd.min.binpack

    - test80-2022-08-aug-16tb7p.v6-dd.min.binpack
    - test80-2022-09-sep-16tb7p.v6-dd.min.binpack

    - test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
    - test80-2023-02-feb-16tb7p.v6-sk20.min.binpack
    - test80-2023-03-mar-2tb7p.v6-sk16.min.binpack
    - test80-2023-04-apr-2tb7p.v6-sk16.min.binpack
    - test80-2023-05-may-2tb7p.v6.min.binpack

    # official-stockfish#4782
    - test80-2023-06-jun-2tb7p.binpack
    - test80-2023-07-jul-2tb7p.binpack

    # official-stockfish#4972
    - test80-2023-08-aug-2tb7p.v6.min.binpack
    - test80-2023-09-sep-2tb7p.binpack
    - test80-2023-10-oct-2tb7p.binpack

    # S9 new data: official-stockfish#5056
    - test80-2023-11-nov-2tb7p.binpack
    - test80-2023-12-dec-2tb7p.binpack

    # S10 new data: official-stockfish#5149
    - test80-2024-01-jan-2tb7p.binpack
    - test80-2024-02-feb-2tb7p.binpack

    # S11 new data
    - test80-2024-03-mar-2tb7p.binpack

  /data/filt-v6-dd/:
    - test77-dec2021-16tb7p-filter-v6-dd.binpack
    - test78-juntosep2022-16tb7p-filter-v6-dd.binpack
    - test79-apr2022-16tb7p-filter-v6-dd.binpack
    - test79-may2022-16tb7p-filter-v6-dd.binpack
    - test80-jul2022-16tb7p-filter-v6-dd.binpack
    - test80-oct2022-16tb7p-filter-v6-dd.binpack
    - test80-nov2022-16tb7p-filter-v6-dd.binpack

num-epochs: 1000

lr: 4.375e-4
gamma: 0.995
start-lambda: 0.8
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch899.nnue : 4.6 +/- 1.4

Passed STC:
https://tests.stockfishchess.org/tests/view/6645454893ce6da3e93b31ae
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 95232 W: 24598 L: 24194 D: 46440
Ptnml(0-2): 294, 11215, 24180, 11647, 280

Passed LTC:
https://tests.stockfishchess.org/tests/view/6645522d93ce6da3e93b31df
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 320544 W: 81432 L: 80524 D: 158588
Ptnml(0-2): 164, 35659, 87696, 36611, 142

bench 1955748
linrock added a commit to linrock/Stockfish that referenced this pull request May 17, 2024
Created by first retraining the spsa-tuned main net `nn-ae6a388e4a1a.nnue` with:
- using v6-dd data without bestmove captures removed
- addition of T80 mar2024 data
- increasing loss by 20% when Q is too high
- torch.compile changes for marginal training speed gains

And then SPSA tuning weights of epoch 899 following methods described in:
official-stockfish#5149

This net was reached at 92k out of 120k steps in this 70+0.7 th 7 SPSA tuning run:
https://tests.stockfishchess.org/tests/view/66413b7df9f4e8fc783c9bbb
Thanks to @Viren6 for suggesting usage of:
- c value 4 for the weights
- c value 128 for the biases

Scripts for automating applying fishtest spsa params to exporting tuned .nnue are in:
https://github.com/linrock/nnue-tools/tree/master/spsa

Before spsa tuning, epoch 899 was nn-f85738aefa84.nnue
https://tests.stockfishchess.org/tests/view/663e5c893a2f9702074bc167

After initially training with max-epoch 800, training was resumed with max-epoch 1000.

```
experiment-name: 3072--S11--more-data-v6-dd-t80-mar2024--see-ge0-20p-more-loss-high-q-sk28-l8
nnue-pytorch-branch: linrock/nnue-pytorch/3072-r21-skip-more-wdl-see-ge0-20p-more-loss-high-q-torch-compile-more

start-from-engine-test-net: False
start-from-model: /data/config/apr2024-3072/nn-ae6a388e4a1a.nnue

early-fen-skipping: 28
training-dataset:
  /data/S11-mar2024/:
    - leela96.v2.min.binpack

    - test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
    - test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack

    - test80-2022-06-jun-16tb7p.v6-dd.min.binpack

    - test80-2022-08-aug-16tb7p.v6-dd.min.binpack
    - test80-2022-09-sep-16tb7p.v6-dd.min.binpack

    - test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
    - test80-2023-02-feb-16tb7p.v6-sk20.min.binpack
    - test80-2023-03-mar-2tb7p.v6-sk16.min.binpack
    - test80-2023-04-apr-2tb7p.v6-sk16.min.binpack
    - test80-2023-05-may-2tb7p.v6.min.binpack

    # official-stockfish#4782
    - test80-2023-06-jun-2tb7p.binpack
    - test80-2023-07-jul-2tb7p.binpack

    # official-stockfish#4972
    - test80-2023-08-aug-2tb7p.v6.min.binpack
    - test80-2023-09-sep-2tb7p.binpack
    - test80-2023-10-oct-2tb7p.binpack

    # S9 new data: official-stockfish#5056
    - test80-2023-11-nov-2tb7p.binpack
    - test80-2023-12-dec-2tb7p.binpack

    # S10 new data: official-stockfish#5149
    - test80-2024-01-jan-2tb7p.binpack
    - test80-2024-02-feb-2tb7p.binpack

    # S11 new data
    - test80-2024-03-mar-2tb7p.binpack

  /data/filt-v6-dd/:
    - test77-dec2021-16tb7p-filter-v6-dd.binpack
    - test78-juntosep2022-16tb7p-filter-v6-dd.binpack
    - test79-apr2022-16tb7p-filter-v6-dd.binpack
    - test79-may2022-16tb7p-filter-v6-dd.binpack
    - test80-jul2022-16tb7p-filter-v6-dd.binpack
    - test80-oct2022-16tb7p-filter-v6-dd.binpack
    - test80-nov2022-16tb7p-filter-v6-dd.binpack

num-epochs: 1000

lr: 4.375e-4
gamma: 0.995
start-lambda: 0.8
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch899.nnue : 4.6 +/- 1.4

Passed STC:
https://tests.stockfishchess.org/tests/view/6645454893ce6da3e93b31ae
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 95232 W: 24598 L: 24194 D: 46440
Ptnml(0-2): 294, 11215, 24180, 11647, 280

Passed LTC:
https://tests.stockfishchess.org/tests/view/6645522d93ce6da3e93b31df
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 320544 W: 81432 L: 80524 D: 158588
Ptnml(0-2): 164, 35659, 87696, 36611, 142

bench 1995552
vondele pushed a commit to vondele/Stockfish that referenced this pull request May 18, 2024
Created by first retraining the spsa-tuned main net `nn-ae6a388e4a1a.nnue` with:
- using v6-dd data without bestmove captures removed
- addition of T80 mar2024 data
- increasing loss by 20% when Q is too high
- torch.compile changes for marginal training speed gains

And then SPSA tuning weights of epoch 899 following methods described in:
official-stockfish#5149

This net was reached at 92k out of 120k steps in this 70+0.7 th 7 SPSA tuning run:
https://tests.stockfishchess.org/tests/view/66413b7df9f4e8fc783c9bbb
Thanks to @Viren6 for suggesting usage of:
- c value 4 for the weights
- c value 128 for the biases

Scripts for automating applying fishtest spsa params to exporting tuned .nnue are in:
https://github.com/linrock/nnue-tools/tree/master/spsa

Before spsa tuning, epoch 899 was nn-f85738aefa84.nnue
https://tests.stockfishchess.org/tests/view/663e5c893a2f9702074bc167

After initially training with max-epoch 800, training was resumed with max-epoch 1000.

```
experiment-name: 3072--S11--more-data-v6-dd-t80-mar2024--see-ge0-20p-more-loss-high-q-sk28-l8
nnue-pytorch-branch: linrock/nnue-pytorch/3072-r21-skip-more-wdl-see-ge0-20p-more-loss-high-q-torch-compile-more

start-from-engine-test-net: False
start-from-model: /data/config/apr2024-3072/nn-ae6a388e4a1a.nnue

early-fen-skipping: 28
training-dataset:
  /data/S11-mar2024/:
    - leela96.v2.min.binpack

    - test60-2021-11-12-novdec-12tb7p.v6-dd.min.binpack
    - test78-2022-01-to-05-jantomay-16tb7p.v6-dd.min.binpack

    - test80-2022-06-jun-16tb7p.v6-dd.min.binpack

    - test80-2022-08-aug-16tb7p.v6-dd.min.binpack
    - test80-2022-09-sep-16tb7p.v6-dd.min.binpack

    - test80-2023-01-jan-16tb7p.v6-sk20.min.binpack
    - test80-2023-02-feb-16tb7p.v6-sk20.min.binpack
    - test80-2023-03-mar-2tb7p.v6-sk16.min.binpack
    - test80-2023-04-apr-2tb7p.v6-sk16.min.binpack
    - test80-2023-05-may-2tb7p.v6.min.binpack

    # official-stockfish#4782
    - test80-2023-06-jun-2tb7p.binpack
    - test80-2023-07-jul-2tb7p.binpack

    # official-stockfish#4972
    - test80-2023-08-aug-2tb7p.v6.min.binpack
    - test80-2023-09-sep-2tb7p.binpack
    - test80-2023-10-oct-2tb7p.binpack

    # S9 new data: official-stockfish#5056
    - test80-2023-11-nov-2tb7p.binpack
    - test80-2023-12-dec-2tb7p.binpack

    # S10 new data: official-stockfish#5149
    - test80-2024-01-jan-2tb7p.binpack
    - test80-2024-02-feb-2tb7p.binpack

    # S11 new data
    - test80-2024-03-mar-2tb7p.binpack

  /data/filt-v6-dd/:
    - test77-dec2021-16tb7p-filter-v6-dd.binpack
    - test78-juntosep2022-16tb7p-filter-v6-dd.binpack
    - test79-apr2022-16tb7p-filter-v6-dd.binpack
    - test79-may2022-16tb7p-filter-v6-dd.binpack
    - test80-jul2022-16tb7p-filter-v6-dd.binpack
    - test80-oct2022-16tb7p-filter-v6-dd.binpack
    - test80-nov2022-16tb7p-filter-v6-dd.binpack

num-epochs: 1000

lr: 4.375e-4
gamma: 0.995
start-lambda: 0.8
end-lambda: 0.7
```

Training data can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch899.nnue : 4.6 +/- 1.4

Passed STC:
https://tests.stockfishchess.org/tests/view/6645454893ce6da3e93b31ae
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 95232 W: 24598 L: 24194 D: 46440
Ptnml(0-2): 294, 11215, 24180, 11647, 280

Passed LTC:
https://tests.stockfishchess.org/tests/view/6645522d93ce6da3e93b31df
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 320544 W: 81432 L: 80524 D: 158588
Ptnml(0-2): 164, 35659, 87696, 36611, 142

closes official-stockfish#5254

bench 1995552
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
bench-change Changes the bench 🚀 gainer Gains elo to be merged Will be merged shortly
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants