Created by retraining the master net on a dataset composed of:
* Most of the previous best dataset filtered to remove positions likely having only one good move
* Adding training data from Leela T77 dec2021 rescored with 16tb of 7-piece tablebases
Trained with end lambda 0.7 and max epoch 900. Positions with ply <= 28 were removed from most of the previous best dataset before training began. A new nnue-pytorch trainer param for skipping early plies was used to skip plies <= 24 in the unfiltered and additional Leela T77 parts of the dataset.
```
python easy_train.py \
--experiment-name leela96-dfrc99-T80octnovT79aprmayT60novdec-eval-filt-v2-T78augsep-12tb-T77dec-16tb-lambda7-sk24 \
--training-dataset /data/leela96-dfrc99-T80octnovT79aprmayT60novdec-eval-filt-v2-T78augsep-12tb-T77dec-16tb.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/easy-train-early-fen-skipping \
--early-fen-skipping 24 \
--gpus "0," \
--start-from-engine-test-net True \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 900
```
The depth6 multipv2 search filtering method is the same as the one used for filtering recent best datasets, with a lower eval difference threshold to remove slightly more positions than before. These parts of the dataset were filtered:
* 96% of T60T70wIsRightFarseerT60T74T75T76.binpack
* 99% of dfrc_n5000.binpack
* T80 oct + nov 2022 data, no positions with castling flags, rescored with ~600gb 7p tablebases
* T79 apr + may 2022 data, rescored with 12tb 7p tablebases
* T60 nov + dec 2021 data, rescored with 12tb 7p tablebases
These parts of the dataset were not filtered. Positions with ply <= 24 were skipped during training:
* T78 aug + sep 2022 data, rescored with 12tb 7p tablebases
* 84% of T77 dec 2021 data, rescored with 16tb 7p tablebases
The code and exact evaluation thresholds used for data filtering can be found at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff-t2/src/filter
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch859.nnue : 3.5 +/ 1.2
Passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
https://tests.stockfishchess.org/tests/view/63dfeefc73223e7f52ad769f
Total: 219744 W: 58572 L: 58002 D: 103170
Ptnml(0-2): 609, 24446, 59284, 24832, 701
Passed LTC:
https://tests.stockfishchess.org/tests/view/63e268fc73223e7f52ade7b6
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 91256 W: 24528 L: 24121 D: 42607
Ptnml(0-2): 48, 8863, 27390, 9288, 39
closes https://github.com/official-stockfish/Stockfish/pull/4387
bench 3841998
This patch is a simplification / code normalisation in qsearch.
Adds steps in comments the same way we have in search;
Makes a separate "pruning" stage instead of heuristics randomly being spread over qsearch code;
Reorders pruning heuristics from least taxing ones to more taxing ones;
Removes repeated check for best value not being mated, instead uses 1 check - thus removes some lines of code.
Moves prefetch and move setup after pruning - makes no sense to do them if move will actually get pruned.
Passed non-regression test:
https://tests.stockfishchess.org/tests/view/63dd2c5ff9a50a69252c1413
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 113504 W: 29898 L: 29770 D: 53836
Ptnml(0-2): 287, 11861, 32327, 11991, 286
https://github.com/official-stockfish/Stockfish/pull/4382
Non-functional change.
PR consists of 2 improvements on nodes with excludeMove:
1. Remove xoring the posKey with make_key(excludedMove)
Since we never call tte->save anymore with excludedMove,
the unique left purpose of the xoring was to avoid a TT hit.
Nevertheless on a normal bench run this produced ~25 false positives
(key collisions)
To avoid that we now forbid early TT cutoff's with excludeMove
Maybe these accesses to TT with xored key caused useless misses
in the CPU caches (L1, L2 ...)
Now doing the probe with the same key as the enclosing search does,
should hit the CPU cache.
2. Don't probe Tablebases with excludedMove.
This can't be tested on fishtest, but it's obvious that
tablebases don't deliver any information about suboptimal moves.
Side note:
Very surprisingly it looks like we cannot use static eval's from
TT since they slightly differ over time due to changing optimism.
Attempts to use static eval's from TT did loose about 13 ELO.
This is something about to investigate.
LTC: https://tests.stockfishchess.org/tests/view/63dc0f8de9d4cdfbe672d0c6
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 44736 W: 12046 L: 11733 D: 20957
Ptnml(0-2): 12, 4212, 13617, 4505, 22
An analogue of this passed STC & LTC
see PR #4374 (thanks Dubslow for reviewing!)
closes https://github.com/official-stockfish/Stockfish/pull/4380
Bench: 4758694
This patch adds more debugging slots up to 32 per type and provide tools
to calculate standard deviation and Pearson's correlation coefficient.
However, due to slot being 0 at default, dbg_hit_on(c, b) has to be removed.
Initial idea from snicolet/Stockfish@d8ab604
closes https://github.com/official-stockfish/Stockfish/pull/4354
No functional change
Current master prunes all moves with negative SEE values in qsearch.
This patch sets constant negative threshold thus allowing some moves with negative SEE values to be searched.
Value of threshold is completely arbitrary and can be tweaked - also it as function of depth can be tried.
Original idea by author of Alexandria engine.
Passed STC
https://tests.stockfishchess.org/tests/view/63d79a59a67dd929a5564976
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 34864 W: 9392 L: 9086 D: 16386
Ptnml(0-2): 113, 3742, 9429, 4022, 126
Passed LTC
https://tests.stockfishchess.org/tests/view/63d8074aa67dd929a5565bc2
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 91616 W: 24532 L: 24126 D: 42958
Ptnml(0-2): 32, 8840, 27662, 9238, 36
closes https://github.com/official-stockfish/Stockfish/pull/4376
Bench: 4010877
update the WLD model with about 400M positions extracted from recent LTC games after the net updates.
This ensures that the 50% win rate is again at 1.0 eval.
closes https://github.com/official-stockfish/Stockfish/pull/4373
No functional change.
Beyond the simplification, this could be considered a bugfix from a certain point of view.
However, the effect is very subtle and essentially impossible for users to notice.
5372f81cc8 added about 2 Elo at LTC, but only for second and later `go` commands; now, with
this patch, the first `go` command will also benefit from that gain. Games under time
controls are unaffected (as per the tests).
STC: https://tests.stockfishchess.org/tests/view/63c3d291330c0d3d051d48a8
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 473792 W: 124858 L: 125104 D: 223830
Ptnml(0-2): 1338, 49653, 135063, 49601, 1241
LTC: https://tests.stockfishchess.org/tests/view/63c8cd56a83c702aac083bc9
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 290728 W: 76926 L: 76978 D: 136824
Ptnml(0-2): 106, 27987, 89221, 27953, 97
closes https://github.com/official-stockfish/Stockfish/pull/4361
bench 4208265
This patch results in search values for a TB win/loss to be reported in a way that does not change with normalization, i.e. will be consistent over time.
A value of 200.00 pawns is now reported upon entering a TB won position. Values smaller than 200.00 relate to the distance in plies from the root to the probed position position,
with 1 cp being 1 ply distance.
closes https://github.com/official-stockfish/Stockfish/pull/4353
No functional change
Created by retraining the master net with Leela T78 data from Aug+Sep 2022 added to the previous best dataset. Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
```
python easy_train.py \
--experiment-name leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p-sk28-lambda7 \
--training-dataset /data/leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--start-from-engine-test-net True \
--gpus "0," \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 800
```
Around epoch 750, training was manually paused and max epoch increased to 950 before resuming. The additional Leela training data from T78 was prepared in the same way as the previous best dataset.
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
While the local elo ratings during this experiment were much lower than in recent master nets, several later epochs had a consistent elo above zero, and this was hypothesized to represent potential strength at slower time controls.
Local elo at 25k nodes per move
leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p-sk28-lambda7
nn-epoch819.nnue : 0.4 +/- 1.1 (nn-bc24c101ada0.nnue)
nn-epoch799.nnue : 0.3 +/- 1.2
nn-epoch759.nnue : 0.3 +/- 1.1
nn-epoch839.nnue : 0.2 +/- 1.4
Passed STC
https://tests.stockfishchess.org/tests/view/63cabf6f0eefe8694a0c6013
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 41608 W: 11161 L: 10848 D: 19599
Ptnml(0-2): 116, 4496, 11281, 4781, 130
Passed LTC
https://tests.stockfishchess.org/tests/view/63cb1856344bb01c191af263
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 76760 W: 20517 L: 20137 D: 36106
Ptnml(0-2): 34, 7435, 23070, 7799, 42
closes https://github.com/official-stockfish/Stockfish/pull/4351
bench 3941848
Bit-shifting is a single instruction, and should be faster than an array lookup
on supported architectures. Besides (ever so slightly) speeding up the
conversion of a square into a bitboard, we may see minor general performance
improvements due to preserving more of the CPU's existing cache.
passed STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 47280 W: 12469 L: 12271 D: 22540
Ptnml(0-2): 128, 4893, 13402, 5087, 130
https://tests.stockfishchess.org/tests/view/63c5cfe618c20f4929c5fe46
Small speedup locally:
```
Result of 20 runs
==================
base (./stockfish.master ) = 1752135 +/- 10943
test (./stockfish.patch ) = 1763939 +/- 10818
diff = +11804 +/- 4731
speedup = +0.0067
P(speedup > 0) = 1.0000
CPU: 16 x AMD Ryzen 9 3950X 16-Core Processor
```
Closes https://github.com/official-stockfish/Stockfish/pull/4343
Bench: 4106793
The accumulator should be an earlyclobber because it is written before
all input operands are read. Otherwise, the asm code computes a wrong
result if the accumulator shares a register with one of the other input
operands (which happens if we pass in the same expression for the
accumulator and the operand).
Closes https://github.com/official-stockfish/Stockfish/pull/4339
No functional change
Created by retraining the master net on a dataset composed of:
* The Leela-dfrc_n5000.binpack dataset filtered with depth6 multipv2 search to remove positions with only one good move, in addition to removing positions where either of the two best moves are captures
* The same Leela T80 oct+nov 2022 training data used in recent best datasets
* Additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022
Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
```
python easy_train.py \
--experiment-name leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7 \
--training-dataset /data/leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p.binpack \
--nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--start-from-engine-test-net True \
--gpus "0," \
--start-lambda 1.0 \
--end-lambda 0.7 \
--gamma 0.995 \
--lr 4.375e-4 \
--tui False \
--seed $RANDOM \
--max_epoch 800
```
Around epoch 780, training was manually paused and max epoch increased to 920 before resuming.
During depth6 multipv2 data filtering, positions were considered to have only one good move if the score of the best move was significantly better than the 2nd best move in a way that changes the outcome of the game:
* the best move leads to a significant advantage while the 2nd best move equalizes or loses
* the best move is about equal while the 2nd best move loses
The modified stockfish branch and exact score thresholds used for filtering are at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff/src/filter
About 95% of the Leela portion and 96% of the DFRC portion of the Leela-dfrc_n5000.binpack dataset was filtered. Unfiltered parts of the dataset were left out.
The additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022 was WDL-rescored with about 12TB of syzygy 7-piece tablebases where the material difference is less than around 6 pawns. Best moves were exported to .plain data files during data conversion with the lc0 rescorer.
The exact training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move
experiment_leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7
run_0/nn-epoch899.nnue : 3.8 +/- 1.6
Passed STC
https://tests.stockfishchess.org/tests/view/63bed1f540aa064159b9c89b
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 103344 W: 27392 L: 26991 D: 48961
Ptnml(0-2): 333, 11223, 28099, 11744, 273
Passed LTC
https://tests.stockfishchess.org/tests/view/63c010415705810de2deb3ec
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 21712 W: 5891 L: 5619 D: 10202
Ptnml(0-2): 12, 2022, 6511, 2304, 7
closes https://github.com/official-stockfish/Stockfish/pull/4338
bench 4106793
Removed sprintf() which generated a warning, because of security reasons.
Replace NULL with nullptr
Replace typedef with using
Do not inherit from std::vector. Use composition instead.
optimize mutex-unlocking
closes https://github.com/official-stockfish/Stockfish/pull/4327
No functional change
If a global function has no previous declaration, either the declaration
is missing in the corresponding header file or the function should be
declared static. Static functions are local to the translation unit,
which allows the compiler to apply some optimizations earlier (when
compiling the translation unit rather than during link-time
optimization).
The commit enables the warning for gcc, clang, and mingw. It also fixes
the reported warnings by declaring the functions static or by adding a
header file (benchmark.h).
closes https://github.com/official-stockfish/Stockfish/pull/4325
No functional change
This is a later epoch (epoch 859) from the same experiment run that trained yesterday's master net nn-60fa44e376d9.nnue (epoch 779). The experiment was manually paused around epoch 790 and unpaused with max epoch increased to 900 mainly to get more local elo data without letting the GPU idle.
nn-60fa44e376d9.nnue is from #4314
nn-335a9b2d8a80.nnue is from #4295
Local elo vs. nn-335a9b2d8a80.nnue at 25k nodes per move:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
run_0/nn-epoch779.nnue (nn-60fa44e376d9.nnue) : 5.0 +/- 1.2
run_0/nn-epoch859.nnue (nn-a3dc078bafc7.nnue) : 5.6 +/- 1.6
Passed STC vs. nn-335a9b2d8a80.nnue
https://tests.stockfishchess.org/tests/view/63ae10495bd1e5f27f13d94f
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 37536 W: 10088 L: 9781 D: 17667
Ptnml(0-2): 110, 4006, 10223, 4325, 104
An LTC test vs. nn-335a9b2d8a80.nnue was paused due to nn-60fa44e376d9.nnue passing LTC first:
https://tests.stockfishchess.org/tests/view/63ae5d34331d5fca5113703b
Passed LTC vs. nn-60fa44e376d9.nnue
https://tests.stockfishchess.org/tests/view/63af1e41465d2b022dbce4e7
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 148704 W: 39672 L: 39155 D: 69877
Ptnml(0-2): 59, 14443, 44843, 14936, 71
closes https://github.com/official-stockfish/Stockfish/pull/4319
bench 3984365
Created by retraining the master net on the previous best dataset with additional filtering. No new data was added.
More of the Leela-dfrc_n5000.binpack part of the dataset was pre-filtered with depth6 multipv2 search to remove bestmove captures. About 93% of the previous Leela/SF data and 99% of the SF dfrc data was filtered. Unfiltered parts of the dataset were left out. The new Leela T80 oct+nov data is the same as before. All early game positions with ply count <= 28 were skipped during training by modifying the training data loader in nnue-pytorch.
Trained in a similar way as recent master nets, with a different nnue-pytorch branch for early ply skipping:
python3 easy_train.py \
--experiment-name=leela93-dfrc99-filt-only-T80-oct-nov-skip28 \
--training-dataset=/data/leela93-dfrc99-filt-only-T80-oct-nov.binpack \
--start-from-engine-test-net True \
--nnue-pytorch-branch=linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \
--gpus="0," \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--tui=False \
--seed=$RANDOM \
--max_epoch=800 \
--network-testing-threads 20 \
--num-workers 6
For the exact training data used: https://robotmoon.com/nnue-training-data/
Details about the previous best dataset: #4295
Local testing at a fixed 25k nodes:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
Local Elo: run_0/nn-epoch779.nnue : 5.1 +/- 1.5
Passed STC
https://tests.stockfishchess.org/tests/view/63adb3acae97a464904fd4e8
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 36504 W: 9847 L: 9538 D: 17119
Ptnml(0-2): 108, 3981, 9784, 4252, 127
Passed LTC
https://tests.stockfishchess.org/tests/view/63ae0ae25bd1e5f27f13d884
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 36592 W: 10017 L: 9717 D: 16858
Ptnml(0-2): 17, 3461, 11037, 3767, 14
closes https://github.com/official-stockfish/Stockfish/pull/4314
bench 4015511
In both modified methods, the variable 'result' is checked to detect
whether the probe operation failed. However, the variable is not
initialized on all paths, so the check might test an uninitialized
value.
A test position (with TB) is given by:
position fen 3K1k2/R7/8/8/8/8/8/R6Q w - - 0 1 moves a1b1 f8g8 b1a1 g8f8 a1b1 f8g8 b1a1
This is now fixed by always initializing the variable.
closes https://github.com/official-stockfish/Stockfish/pull/4309
No functional change
Created by retraining the master net with a combination of:
the previous best dataset (Leela-dfrc_n5000.binpack), with about half the dataset filtered using depth6 multipv2 search to throw away positions where either of the 2 best moves are captures
Leela T80 Oct and Nov training data rescored with best moves, adding ~9.5 billion positions
Trained effectively the same way as the previous master net:
python3 easy_train.py \
--experiment-name=leela-dfrc-filtered-T80-oct-nov \
--training-dataset=/data/leela-dfrc-filtered-T80-oct-nov.binpack \
--start-from-engine-test-net True \
--gpus="0," \
--start-lambda=1.0 \
--end-lambda=0.75 \
--gamma=0.995 \
--lr=4.375e-4 \
--tui=False \
--seed=$RANDOM \
--max_epoch=800 \
--auto-exit-timeout-on-training-finished=900 \
--network-testing-threads 20 \
--num-workers 6
Local testing at a fixed 25k nodes:
experiments/experiment_leela-dfrc-filtered-T80-oct-nov/training/run_0/nn-epoch779.nnue
localElo: run_0/nn-epoch779.nnue : 4.7 +/- 3.1
The new Leela T80 part of the dataset was prepared by downloading test80 training data from all of Oct 2022 and Nov 2022, rescoring with syzygy 6-piece tablebases and ~600 GB of 7-piece tablebases, saving best moves to exported .plain files, removing all positions with castling flags, then converting to binpacks and using interleave_binpacks.py to merge them together. Scripts used in this data conversion process are available at:
https://github.com/linrock/lc0-data-converter
Filtering binpack data using depth6 multipv2 search was done by modifying transform.cpp in the tools branch:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-no-rescore
Links for downloading the training data (total size: 338 GB) are available at:
https://robotmoon.com/nnue-training-data/
Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 30544 W: 8244 L: 7947 D: 14353
Ptnml(0-2): 93, 3243, 8302, 3542, 92
https://tests.stockfishchess.org/tests/view/63a0d377264a0cf18f86f82b
Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 32464 W: 8866 L: 8573 D: 15025
Ptnml(0-2): 19, 3054, 9794, 3345, 20
https://tests.stockfishchess.org/tests/view/63a10bc9fb452d3c44b1e016
closes https://github.com/official-stockfish/Stockfish/pull/4295
Bench 3554904
Instead of allowing .depend for specific build-related targets, filter
non-build-related targets (i.e. help, clean) so that other targets can
normally execute .depend target.
closes https://github.com/official-stockfish/Stockfish/pull/4293
No functional change
