Update default main net to nn-b1a57edbea57.nnue #5056
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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
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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
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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
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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
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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
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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
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Feb 23, 2024
commit 524083a Merge: 8a0206f 4a5ba40 Author: Tierynn Byrnes <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <125858804+cj5716@users.noreply.github.com> 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 <linmiao.xu@gmail.com> 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 <125858804+cj5716@users.noreply.github.com> 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 <155860115+gahtan-syarif@users.noreply.github.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> Date: Mon Feb 19 07:13:26 2024 +1000 parent 8b67b7e author Tierynn Byrnes <t.byrnes47@gmail.com> 1708290806 +1000 committer Tierynn Byrnes <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> 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 <t.byrnes47@gmail.com> Date: Mon Feb 19 07:50:30 2024 +1000 fixed comments commit de4a3c4 Author: Tierynn Byrnes <t.byrnes47@gmail.com> 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 <10430540+TierynnB@users.noreply.github.com> Date: Mon Feb 19 07:14:27 2024 +1000 Merge branch 'official-stockfish:master' into TM_Change_2 commit 8b67b7e Author: Tierynn Byrnes <t.byrnes47@gmail.com> Date: Mon Feb 19 07:13:26 2024 +1000 use current time instead of '1' for timeLeft formula.
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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
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Mar 8, 2024
commit 632f1c21cd271e7c4c242fdafa328a55ec63b9cb
Author: Robert Nurnberg @ elitebook <robert.nurnberg@gmx.de>
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 <61100393+XInTheDark@users.noreply.github.com>
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 <41402573+peregrineshahin@users.noreply.github.com>
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 <28635489+robertnurnberg@users.noreply.github.com>
commit 6136d094c5f46456964889754ae2d6098834b14f
Author: Michael Chaly <Vizvezdenec@gmail.com>
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 <clemens.lerchl@gmail.com>
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 <linmiao.xu@gmail.com>
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 <fauzi.dabat@hotmail.com>
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 <fauzi.dabat@hotmail.com>
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
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Created by retraining the previous main net
nn-baff1edbea57.nnuewith: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
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