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Author SHA1 Message Date
Joost VandeVondele 68e1e9b381 Stockfish 16
Official release version of Stockfish 16

Bench: 2593605

---

Stockfish 16

A new major release of Stockfish is now available at

https://stockfishchess.org/download/

*Quality of chess play*

Stockfish continues to demonstrate its ability to discover superior moves
with remarkable speed. In self-play against Stockfish 15, this new
release gains up to 50 Elo[1] and wins up to 12 times more game pairs[2]
than it loses. In major chess engine tournaments, Stockfish reliably tops
the rankings[3] winning the TCEC season 24 Superfinal, Swiss, Fischer
Random, and Double Random Chess tournaments and the CCC 19 Bullet,
20 Blitz, and 20 Rapid competitions. Leela Chess Zero[4] was the
challenger in most finals, putting top-engine chess now firmly in the
hands of teams embracing free and open-source software.

*Progress made*

This updated version of Stockfish introduces several enhancements,
including an upgraded neural net architecture (SFNNv6)[5], improved
implementation, and refined parameterization. The ongoing utilization
of Leela’s data combined with a novel inference approach exploiting
sparsity[6], and network compression[7] ensure a speedy evaluation and
modest binary sizes while allowing for more weights and higher accuracy.
The search has undergone more optimization, leading to improved
performance, particularly in longer analyses[8]. Additionally,
the Fishtest framework has been improved and is now able to run the
tests needed to validate new ideas with 10000s of CPU cores.

*Usability improvements*

Stockfish now comes with documentation, found in the wiki folder when
downloading it or on GitHub[9]. Additionally, Stockfish now includes
a clear and consistent forced tablebase win score, displaying a value
of 200 minus the number of plies required to reach a tablebase win[10].
Furthermore, the UCI_Elo option, to reduce its strength, has been
calibrated[11]. It is worth noting that the evaluation system remains
consistent with Stockfish 15.1[12], maintaining the choice that 100cp
means a 50% chance of winning the game against an equal opponent[13].
Finally, binaries of our latest development version are now provided
continuously as pre-releases on GitHub making it easier for
enthusiasts to download the latest and strongest version of
the program[14], we thank Roman Korba for having provided a similar
service for a long time.

*Thank you*

The success of the Stockfish project relies on the vibrant community
of passionate enthusiasts (we appreciate each and every one of you!)
who generously contribute their knowledge, time, and resources.
Together, this dedicated community works towards the common goal of
developing a powerful, freely accessible, and open-source chess engine.
We invite all chess enthusiasts to join the Fishtest testing framework
and contribute to the project[15].

The Stockfish team

[1] https://tests.stockfishchess.org/tests/view/649409f0dc7002ce609c99cc
[2] https://tests.stockfishchess.org/tests/view/649409d7dc7002ce609c99c6
[3] https://en.wikipedia.org/wiki/Stockfish_(chess)#Competition_results
[4] https://lczero.org/
[5] https://github.com/official-stockfish/Stockfish/commit/c1fff71
[6] https://github.com/official-stockfish/Stockfish/commit/38e6166
[7] https://github.com/official-stockfish/Stockfish/commit/a46087e
[8] https://github.com/official-stockfish/Stockfish/commit/472e726
[9] https://github.com/official-stockfish/Stockfish/wiki/
[10] https://github.com/official-stockfish/Stockfish/commit/def2966
[11] https://github.com/official-stockfish/Stockfish/commit/a08b8d4
[12] https://github.com/official-stockfish/Stockfish/commit/52e84e4
[13] https://github.com/official-stockfish/Stockfish/wiki/Stockfish-FAQ#interpretation-of-the-stockfish-evaluation
[14] https://github.com/official-stockfish/Stockfish/releases?q=prerelease%3Atrue
[15] https://stockfishchess.org/get-involved/
2023-06-29 08:00:10 +02:00
Linmiao Xu a49b3ba7ed Update default net to nn-5af11540bbfe.nnue
Created by retraining the sparsified master net (nn-cd2ff4716c34.nnue) on
a 100% minified dataset including Leela transformers data from T80 may2023.

Weights permuted with the exact methods and code in:
https://github.com/official-stockfish/Stockfish/pull/4620

LEB128 compression done with the new serialize.py param in:
https://github.com/glinscott/nnue-pytorch/pull/251

Initially trained with max epoch 800. Around epoch 780, training was paused
and max epoch raised to 960.

python3 easy_train.py \
  --experiment-name L1-1536-sparse-master-retrain \
  --training-dataset /data/leela96-dfrc99-v2-T60novdecT77decT78jantosepT79aprmayT80juntonovjan-v6dd-T80febtomay2023.min.binpack \
  --early-fen-skipping 27 \
  --start-from-engine-test-net True \
  --max_epoch 960 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --tui False \
  --seed $RANDOM \
  --gpus 0

For preparing the training dataset (interleaved size 328G):

python3 interleave_binpacks.py \
  leela96-filt-v2.min.binpack \
  dfrc99-16tb7p-eval-filt-v2.min.binpack \
  filt-v6-dd-min/test60-novdec2021-12tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test77-dec2021-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test78-jantomay2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test78-juntosep2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test79-apr2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test79-may2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-jun2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-jul2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-aug2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-sep2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-oct2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-nov2022-16tb7p-filter-v6-dd.min.binpack \
  filt-v6-dd-min/test80-jan2023-16tb7p-filter-v6-dd.min.binpack \
  test80-2023/test80-feb2023-16tb7p-no-db.min.binpack \
  test80-2023/test80-mar2023-2tb7p-no-db.min.binpack \
  test80-2023/test80-apr2023-2tb7p-no-db.min.binpack \
  test80-2023/test80-may2023-2tb7p-no-db.min.binpack \
  /data/leela96-dfrc99-v2-T60novdecT77decT78jantosepT79aprmayT80juntonovjan-v6dd-T80febtomay2023.min.binpack

Minified binpacks and Leela T80 training data from 2023 available at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch879.nnue : 3.9 +/- 5.7

Passed STC:
https://tests.stockfishchess.org/tests/view/64928c1bdc7002ce609c7690
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 72000 W: 19242 L: 18889 D: 33869
Ptnml(0-2): 182, 7787, 19716, 8126, 189

Passed LTC:
https://tests.stockfishchess.org/tests/view/64930a37dc7002ce609c82e3
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 54552 W: 14978 L: 14647 D: 24927
Ptnml(0-2): 23, 5123, 16650, 5460, 20

closes https://github.com/official-stockfish/Stockfish/pull/4635

bench 2593605
2023-06-22 10:33:19 +02:00
peregrineshahin 48f7c74f15 Fix Potential in TB cutoffs for NMP.
Removes the second dependency on beta and caps the return value to VALUE_TB_WIN_IN_MAX_PLY - 1

Earlier tests:

STC:
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 193632 W: 51372 L: 51326 D: 90934
Ptnml(0-2): 447, 20111, 55687, 20091, 480
https://tests.stockfishchess.org/tests/view/6486ee4465ffe077ca125bc1

LTC:
LLR: 2.97 (-2.94,2.94) <-1.75,0.25>
Total: 331758 W: 89538 L: 89624 D: 152596
Ptnml(0-2): 114, 30121, 105516, 29993, 135
https://tests.stockfishchess.org/tests/view/6489401af42a44347ed7be42

updated constant:
LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 100260 W: 27143 L: 27017 D: 46100
Ptnml(0-2): 34, 8842, 32248, 8976, 30
https://tests.stockfishchess.org/tests/view/6492fcafdc7002ce609c818c

closes: https://github.com/official-stockfish/Stockfish/pull/4632
fixes: https://github.com/official-stockfish/Stockfish/issues/4598

bench: 2370027
2023-06-22 10:26:17 +02:00
Joost VandeVondele 52e84e4b46 Update winrate model with June data
Retained 748191776 scored positions for analysis

const int NormalizeToPawnValue = 328;
Corresponding spread = 60;
Corresponding normalized spread = 0.18337766691628035;
Draw rate at 0.0 eval at move 32 = 0.9914715947898592;

closes https://github.com/official-stockfish/Stockfish/pull/4636

No functional change
2023-06-22 10:17:44 +02:00
Joost VandeVondele 02728736ed Update top CPU contributors
closes https://github.com/official-stockfish/Stockfish/pull/4629

No functional change
2023-06-22 10:15:51 +02:00
disservin aec8fb3fde Fix failing CI of pull requests
adds a guard to prevent pull requests from trying to delete the previous pre-release

closing https://github.com/official-stockfish/Stockfish/pull/4631

No functional change.
2023-06-20 18:50:12 +02:00
Joerg Oster 6eaa1c3ecd Fix indentation in qsearch.
https://github.com/official-stockfish/Stockfish/pull/4630

No functional change.
2023-06-20 10:47:07 +02:00
disservin a68a1c1154 create prereleases upon push to master
using github actions, create a prerelease for the latest commit to master.
As such a development version will be available on github, in addition to the latest release.

closes https://github.com/official-stockfish/Stockfish/pull/4622

No functional change
2023-06-20 08:55:54 +02:00
maxim a46087ee30 Compressed network parameters
Implemented LEB128 (de)compression for the feature transformer.
Reduces embedded network size from 70 MiB to 39 Mib.

The new nn-78bacfcee510.nnue corresponds to the master net compressed.

closes https://github.com/official-stockfish/Stockfish/pull/4617

No functional change
2023-06-19 21:37:23 +02:00
cj5716 0187546275 Small cleanup
This non-functional change keeps formatting consistent.

closes https://github.com/official-stockfish/Stockfish/pull/4623

Bench 2370027
2023-06-16 19:14:58 +02:00
AndrovT 32d3284df5 Permute master net weights to increase sparsity
Activation data collection using https://github.com/AndrovT/Stockfish/commit/ac468039ab544b03ad9a22c859a4217729c10a77 run as

bench 16 1 13 varied_1000.epd depth NNUE log.bin

on FENs from https://gist.github.com/AndrovT/7eae6918eb50764227e2bafe7938953c.

Permutation found using https://gist.github.com/AndrovT/359c831b7223c637e9156b01eb96949e.
Uses a greedy algorithm that goes sequentially through the output positions and
chooses a neuron for that position such that the number of nonzero quartets is the smallest.

Net weights permuted using https://gist.github.com/AndrovT/9e3fbaebb7082734dc84d27e02094cb3.

Benchmark:

Result of 100 runs of 'bench 16 1 13 default depth NNUE'
========================================================
base (...kfish-master) =     885869  +/- 7395
test (./stockfish    ) =     895885  +/- 7368
diff                   =     +10016  +/- 2984

speedup        = +0.0113
P(speedup > 0) =  1.0000

Passed STC:
https://tests.stockfishchess.org/tests/view/648866c4713491385c804728
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 126784 W: 34003 L: 33586 D: 59195
Ptnml(0-2): 283, 13001, 36437, 13358, 313

closes https://github.com/official-stockfish/Stockfish/pull/4620

No functional change.
2023-06-14 18:36:39 +02:00
peregrineshahin 14bfec2a98 Consistent bench extraction with fishtest.
Consistent with recent fishtest commit https://github.com/glinscott/fishtest/commit/c0d174396f7fb1c0b3243aaa6cc73769079f3ff9

closes https://github.com/official-stockfish/Stockfish/pull/4619

No functional change
2023-06-14 18:34:57 +02:00
Viren6 887bbd8b3d Remove setting of static to none if in check in qsearch
Small simplification

Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/6487924d713491385c8034ae
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 59616 W: 15885 L: 15703 D: 28028
Ptnml(0-2): 144, 6130, 17086, 6296, 152

closes https://github.com/official-stockfish/Stockfish/pull/4618

No functional change.
2023-06-14 18:33:56 +02:00
Andreas Matthies 7922e07af8 Fix for MSVC compilation.
MSVC needs two more explicit casts to compile new affine_transform_sparse_input.
See https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm256_castsi256_ps
and https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html#text=_mm_castsi128_ps

closes https://github.com/official-stockfish/Stockfish/pull/4616

No functional change
2023-06-13 08:45:25 +02:00
Stéphane Nicolet 92c949e12e Clean-up code indentation in qsearch
closes https://github.com/official-stockfish/Stockfish/pull/4615

No functional change
2023-06-13 08:42:55 +02:00
Michael Chaly ece90bca9c Improve comments
Fix comments for IIR, also document non-linear scaling in extensions.
Also add explicitly the bench, to fix an issue after the last commit.

closes https://github.com/official-stockfish/Stockfish/pull/4614

bench 2370027
2023-06-12 21:17:31 +02:00
AndrovT 38e61663d8 Use block sparse input for the first layer.
Use block sparse input for the first fully connected layer on architectures with at least SSSE3.

Depending on the CPU architecture, this yields a speedup of up to 10%, e.g.

```
Result of 100 runs of 'bench 16 1 13 default depth NNUE'

base (...ockfish-base) =     959345  +/- 7477
test (...ckfish-patch) =    1054340  +/- 9640
diff                   =     +94995  +/- 3999

speedup        = +0.0990
P(speedup > 0) =  1.0000

CPU: 8 x AMD Ryzen 7 5700U with Radeon Graphics
Hyperthreading: on
```

Passed STC:
https://tests.stockfishchess.org/tests/view/6485aa0965ffe077ca12409c
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 8864 W: 2479 L: 2223 D: 4162
Ptnml(0-2): 13, 829, 2504, 1061, 25

This commit includes a net with reordered weights, to increase the likelihood of block sparse inputs,
but otherwise equivalent to the previous master net (nn-ea57bea57e32.nnue).

Activation data collected with https://github.com/AndrovT/Stockfish/tree/log-activations, running bench 16 1 13 varied_1000.epd depth NNUE on this data. Net parameters permuted with https://gist.github.com/AndrovT/9e3fbaebb7082734dc84d27e02094cb3.

closes https://github.com/official-stockfish/Stockfish/pull/4612

No functional change
2023-06-12 20:41:27 +02:00
Joost VandeVondele b7ee7290b5 Add network export to CI
verify the network written by export_net matches the original

closes https://github.com/official-stockfish/Stockfish/pull/4613

No functional change
2023-06-12 20:35:44 +02:00
Linmiao Xu 932f5a2d65 Update default net to nn-ea57bea57e32.nnue
Created by retraining an earlier epoch (ep659) of the experiment that led to the first SFNNv6 net:
- First retrained on the nn-0dd1cebea573 dataset
- Then retrained with skip 20 on a smaller dataset containing unfiltered Leela data
- And then retrained again with skip 27 on the nn-0dd1cebea573 dataset

The equivalent 7-step training sequence from scratch that led here was:

1. max-epoch 400, lambda 1.0, constant LR 9.75e-4, T79T77-filter-v6-dd.min.binpack
   ep379 chosen for retraining in step2

2. max-epoch 800, end-lambda 0.75, T60T70wIsRightFarseerT60T74T75T76.binpack
   ep679 chosen for retraining in step3

3. max-epoch 800, end-lambda 0.75, skip 28, nn-e1fb1ade4432 dataset
   ep799 chosen for retraining in step4

4. max-epoch 800, end-lambda 0.7, skip 28, nn-e1fb1ade4432 dataset
   ep759 became nn-8d69132723e2.nnue (first SFNNv6 net)
   ep659 chosen for retraining in step5

5. max-epoch 800, end-lambda 0.7, skip 28, nn-0dd1cebea573 dataset
   ep759 chosen for retraining in step6

6. max-epoch 800, end-lambda 0.7, skip 20, leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack
   ep639 chosen for retraining in step7

7. max-epoch 800, end-lambda 0.7, skip 27, nn-0dd1cebea573 dataset
   ep619 became nn-ea57bea57e32.nnue

For the last retraining (step7):

python3 easy_train.py
  --experiment-name L1-1536-Re6-masterShuffled-ep639-sk27-Re5-leela-dfrc-v2-T77toT80small-Re4-masterShuffled-ep659-Re3-sameAs-Re2-leela96-dfrc99-16t-v2-T60novdecT80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-Re1-LeelaFarseer-new-T77T79 \
  --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
  --early-fen-skipping 27 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --max_epoch 800 \
  --start-from-engine-test-net False \
  --start-from-model /data/L1-1536-Re5-leela-dfrc-v2-T77toT80small-epoch639.nnue \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --tui False \
  --seed $RANDOM \
  --gpus "0,"

For preparing the step6 leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack dataset:

python3 interleave_binpacks.py \
  leela96-filt-v2.binpack \
  dfrc99-16tb7p-eval-filt-v2.binpack \
  test77-dec2021-16tb7p.no-db.min-mar2023.binpack \
  test78-janfeb2022-16tb7p.no-db.min-mar2023.binpack \
  test79-apr2022-16tb7p-filter-v6-dd.binpack \
  test80-apr2022-16tb7p.no-db.min-mar2023.binpack \
  /data/leela-dfrc-v2-T77decT78janfebT79aprT80apr.binpack

The unfiltered Leela data used for the step6 dataset can be found at:
https://robotmoon.com/nnue-training-data

Local elo at 25k nodes per move:
nn-epoch619.nnue : 2.3 +/- 1.9

Passed STC:
https://tests.stockfishchess.org/tests/view/6480d43c6e6ce8d9fc6d7cc8
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 40992 W: 11017 L: 10706 D: 19269
Ptnml(0-2): 113, 4400, 11170, 4689, 124

Passed LTC:
https://tests.stockfishchess.org/tests/view/648119ac6e6ce8d9fc6d8208
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 129174 W: 35059 L: 34579 D: 59536
Ptnml(0-2): 66, 12548, 38868, 13050, 55

closes https://github.com/official-stockfish/Stockfish/pull/4611

bench: 2370027
2023-06-11 15:23:52 +02:00
Guenther Demetz e1dd005583 Reintroduce SEE verification against discovered attacks
Reintroduces https://github.com/official-stockfish/Stockfish/pull/4453
along with https://github.com/official-stockfish/Stockfish/pull/4469

Leaving out
https://github.com/official-stockfish/Stockfish/pull/4533
https://github.com/official-stockfish/Stockfish/pull/4572

Passed STC:
https://tests.stockfishchess.org/tests/view/647d8c37726f6b400e408a0a
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 143168 W: 38346 L: 37892 D: 66930
Ptnml(0-2): 352, 15672, 39164, 15962, 434

Passed LTC:
https://tests.stockfishchess.org/tests/view/647ee8c528c4431bcb58e432
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 71538 W: 19560 L: 19190 D: 32788
Ptnml(0-2): 49, 6905, 21499, 7259, 57

closes https://github.com/official-stockfish/Stockfish/pull/4609

bench: 2595430
2023-06-11 15:13:57 +02:00
cj5716 a9a6915e08 Simplify multiplier for improvement
This simplifies a `* 99 / 1300` term into just `/ 13`.

Passed non-regression STC:
LLR: 2.92 (-2.94,2.94) <-1.75,0.25>
Total: 58816 W: 15727 L: 15540 D: 27549
Ptnml(0-2): 149, 6370, 16203, 6517, 169
https://tests.stockfishchess.org/tests/view/647d25e4726f6b400e408165

Passed non-regression LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 154386 W: 41749 L: 41669 D: 70968
Ptnml(0-2): 94, 14992, 46956, 15042, 109
https://tests.stockfishchess.org/tests/view/647d9b3c726f6b400e408b2a

closes https://github.com/official-stockfish/Stockfish/pull/4608

Bench:  2511327
2023-06-11 15:05:54 +02:00
Linmiao Xu 54ad986768 Remove optimism multiplier in nnue eval calculation
The same formula had passed SPRT against an earlier version of master.

Passed non-regression STC vs. d99942f:
https://tests.stockfishchess.org/tests/view/6478e76654dd118e1d98f72e
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 118720 W: 31402 L: 31277 D: 56041
Ptnml(0-2): 301, 13148, 32344, 13259, 308

Passed non-regression LTC vs. d99942f:
https://tests.stockfishchess.org/tests/view/647a22c154dd118e1d991146
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 74286 W: 20019 L: 19863 D: 34404
Ptnml(0-2): 31, 7189, 22540, 7359, 24

The earlier patch had conflicted with a faster SPRT passer, so this
was tested again after rebasing on latest master.

Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/647d6e46726f6b400e408790
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 166176 W: 44309 L: 44234 D: 77633
Ptnml(0-2): 461, 18252, 45557, 18387, 431

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/647eb00ba268d1bc11255e7b
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 28170 W: 7713 L: 7513 D: 12944
Ptnml(0-2): 14, 2609, 8635, 2817, 10

closes https://github.com/official-stockfish/Stockfish/pull/4607

bench 2503095
2023-06-06 21:21:56 +02:00
Linmiao Xu 373359b44d Update default net to nn-0dd1cebea573.nnue
Created by retraining an earlier epoch of the experiment leading to the first SFNNv6 net
on a more-randomized version of the nn-e1fb1ade4432.nnue dataset mixed with unfiltered
T80 apr2023 data. Trained using early-fen-skipping 28 and max-epoch 960.

The trainer settings and epochs used in the 5-step training sequence leading here were:
1. train from scratch for 400 epochs, lambda 1.0, constant LR 9.75e-4, T79T77-filter-v6-dd.min.binpack
2. retrain ep379, max-epoch 800, end-lambda 0.75, T60T70wIsRightFarseerT60T74T75T76.binpack
3. retrain ep679, max-epoch 800, end-lambda 0.75, skip 28, nn-e1fb1ade4432 dataset
4. retrain ep799, max-epoch 800, end-lambda 0.7, skip 28, nn-e1fb1ade4432 dataset
5. retrain ep439, max-epoch 960, end-lambda 0.7, skip 28, shuffled nn-e1fb1ade4432 + T80 apr2023

This net was epoch 559 of the final (step 5) retraining:

```bash
python3 easy_train.py \
  --experiment-name L1-1536-Re4-leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr-shuffled-sk28 \
  --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
  --early-fen-skipping 28 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --max_epoch 960 \
  --start-from-engine-test-net False \
  --start-from-model /data/L1-1536-Re3-nn-epoch439.nnue \
  --engine-test-branch linrock/Stockfish/L1-1536 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --tui False \
  --seed $RANDOM \
  --gpus "0,"
```

During data preparation, most binpacks were unminimized by removing positions with
score 32002 (`VALUE_NONE`). This makes the tradeoff of increasing dataset filesize
on disk to increase the randomness of positions in interleaved datasets.
The code used for unminimizing is at:
https://github.com/linrock/Stockfish/tree/tools-unminify

For preparing the dataset used in this experiment:

```bash
python3 interleave_binpacks.py \
  leela96-filt-v2.binpack \
  dfrc99-16tb7p-eval-filt-v2.binpack \
  filt-v6-dd-min/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd-min/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd-min/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd-min/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd-min/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd-min/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd-min/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.binpack \
  filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
  test80-apr2023-2tb7p.binpack \
  /data/leela96-dfrc99-T60novdec-v2-T80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd-T80apr.binpack
```

T80 apr2023 data was converted using lc0-rescorer with ~2tb of tablebases and can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move vs. nn-e1fb1ade4432.nnue (L1 size 1024):
nn-epoch559.nnue : 25.7 +/- 1.6

Passed STC:
https://tests.stockfishchess.org/tests/view/647cd3b87cf638f0f53f9cbb
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 59200 W: 16000 L: 15660 D: 27540
Ptnml(0-2): 159, 6488, 15996, 6768, 189

Passed LTC:
https://tests.stockfishchess.org/tests/view/647d58de726f6b400e4085d8
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 58800 W: 16002 L: 15657 D: 27141
Ptnml(0-2): 44, 5607, 17748, 5962, 39

closes https://github.com/official-stockfish/Stockfish/pull/4606

bench 2141197
2023-06-06 21:17:36 +02:00
disservin 295f57829e Add binaries to releases with github actions
when a release is made with a tag matching sf_* the binaries will also be uploaded to the release as assets.

closes https://github.com/official-stockfish/Stockfish/pull/4596

No functional change.
2023-06-06 21:12:24 +02:00
peregrineshahin b60738e01b Fix no previous moves on root.
guards against no previous move existing if qSearch is called on the root node (i.e. when razoring).

Passed Non-regression STC:
https://tests.stockfishchess.org/tests/view/647d242d726f6b400e408143
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 53120 W: 14167 L: 13976 D: 24977
Ptnml(0-2): 109, 5597, 14981, 5740, 133

closes https://github.com/official-stockfish/Stockfish/pull/4604

Bench: 2551691
2023-06-06 21:07:43 +02:00
Michael Chaly 8dea070538 Move internal iterative reduction before probcut
This patch moves IIR before probcut which allows probcut
to be produced at lower depths. Comments in IIR are also slightly updated.

Passed STC:
https://tests.stockfishchess.org/tests/view/6472d604d29264e4cfa749fd
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 387616 W: 103295 L: 102498 D: 181823
Ptnml(0-2): 976, 42322, 106381, 43187, 942

Passed LTC:
https://tests.stockfishchess.org/tests/view/6475eb8c4a36543c4c9f42e8
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 202836 W: 54901 L: 54281 D: 93654
Ptnml(0-2): 85, 19609, 61422, 20205, 97

closes https://github.com/official-stockfish/Stockfish/pull/4597

bench 2551691
2023-06-04 23:12:23 +02:00
Linmiao Xu ced0311890 Remove static eval threshold for extensions when giving check
Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/647685d54a36543c4c9f4f2a
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 114688 W: 30701 L: 30571 D: 53416
Ptnml(0-2): 336, 12708, 31136, 12818, 346

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/64774b02b81f005b572de770
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 107310 W: 28920 L: 28796 D: 49594
Ptnml(0-2): 33, 10427, 32621, 10531, 43

closes https://github.com/official-stockfish/Stockfish/pull/4599

bench 2597974
2023-06-04 23:05:28 +02:00
Guenther Demetz 5930c0defb Simplify away SEE verification
After 4 simplificatons over PR#4453 the idea does not yield significant
improvement anymore. Maybe also
https://tests.stockfishchess.org/tests/view/640c88092644b62c3394c1c5 was
a fluke.

Passed non-regression bounds:

STC:
https://tests.stockfishchess.org/tests/view/64705389c079b6583146d873
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 131936 W: 35040 L: 34930 D: 61966
Ptnml(0-2): 336, 14559, 36035, 14735, 303

LTC:
https://tests.stockfishchess.org/tests/view/6471a2ade549d9cf2fb213cd
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 407700 W: 109999 L: 110164 D: 187537
Ptnml(0-2): 279, 39913, 123689, 39632, 337

closes https://github.com/official-stockfish/Stockfish/pull/4595

bench: 2675974
2023-06-04 23:01:14 +02:00
Linmiao Xu 6cf8d938c5 Simplify blending nnue complexity with optimism
Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/6478a26d54dd118e1d98f21c
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 241248 W: 64058 L: 64063 D: 113127
Ptnml(0-2): 644, 26679, 65960, 26720, 621

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/647b464854dd118e1d9928b2
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 24336 W: 6658 L: 6451 D: 11227
Ptnml(0-2): 8, 2316, 7312, 2525, 7

closes https://github.com/official-stockfish/Stockfish/pull/4602

bench 2425813
2023-06-04 22:56:44 +02:00
Muzhen Gaming 06186b786e Search tuning at very long time control with new net
The most significant change would be the singularBeta formula.
It was first tested by cj5716 (see https://tests.stockfishchess.org/tests/view/647317c9d29264e4cfa74ec7),
and I took much inspiration from that idea.

LTC (fixed games): https://tests.stockfishchess.org/tests/view/6479d8da54dd118e1d990b12
Elo: 0.61 ± 1.2 (95%) LOS: 83.4%
Total: 60000 W: 16278 L: 16172 D: 27550
Ptnml(0-2): 16, 5845, 18179, 5937, 23
nElo: 1.38 ± 2.8 (95%) PairsRatio: 1.02

VLTC 180+1.8: https://tests.stockfishchess.org/tests/view/6479da1454dd118e1d990b2b
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 33224 W: 9261 L: 8984 D: 14979
Ptnml(0-2): 5, 2809, 10710, 3080, 8

SMP VLTC 8-thread: https://tests.stockfishchess.org/tests/view/647b0fe354dd118e1d992425
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 61398 W: 17386 L: 17081 D: 26931
Ptnml(0-2): 7, 4571, 21232, 4888, 1

closes https://github.com/official-stockfish/Stockfish/pull/4603

Bench: 2805878
2023-06-04 22:54:13 +02:00
Michael Chaly d99942f254 Small simplification for probcut in check
Remove depth condition from there as not longer needed.

Passed STC:
https://tests.stockfishchess.org/tests/view/647367cad29264e4cfa753e6
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 254336 W: 67830 L: 67847 D: 118659
Ptnml(0-2): 580, 28181, 69697, 28096, 614

Passed LTC:
https://tests.stockfishchess.org/tests/view/647576184a36543c4c9f3af7
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 80706 W: 22048 L: 21898 D: 36760
Ptnml(0-2): 28, 7721, 24712, 7857, 35

closes https://github.com/official-stockfish/Stockfish/pull/4594

bench 2381945
2023-05-31 09:06:30 +02:00
Linmiao Xu 07bd8adcbc Simplify nnue eval complexity calculation
Remove a multiplier when blending nnue complexity with semi-classical complexity.

Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/6473a71dd29264e4cfa75839
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 124768 W: 33180 L: 33060 D: 58528
Ptnml(0-2): 314, 13797, 34030, 13941, 302

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/6474af3dd29264e4cfa768f4
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 108180 W: 29008 L: 28884 D: 50288
Ptnml(0-2): 29, 10420, 33075, 10530, 36

closes https://github.com/official-stockfish/Stockfish/pull/4592

bench 2316827
2023-05-31 08:54:38 +02:00
Linmiao Xu c1fff71650 Update NNUE architecture to SFNNv6 with larger L1 size of 1536
Created by training a new net from scratch with L1 size increased from 1024 to 1536.
Thanks to Vizvezdenec for the idea of exploring larger net sizes after recent
training data improvements.

A new net was first trained with lambda 1.0 and constant LR 8.75e-4. Then a strong net
from a later epoch in the training run was chosen for retraining with start-lambda 1.0
and initial LR 4.375e-4 decaying with gamma 0.995. Retraining was performed a total of
3 times, for this 4-step process:

1. 400 epochs, lambda 1.0 on filtered T77+T79 v6 deduplicated data
2. 800 epochs, end-lambda 0.75 on T60T70wIsRightFarseerT60T74T75T76.binpack
3. 800 epochs, end-lambda 0.75 and early-fen-skipping 28 on the master dataset
4. 800 epochs, end-lambda 0.7 and early-fen-skipping 28 on the master dataset

In the training sequence that reached the new nn-8d69132723e2.nnue net,
the epochs used for the 3x retraining runs were:

1. epoch 379 trained on T77T79-filter-v6-dd.min.binpack
2. epoch 679 trained on T60T70wIsRightFarseerT60T74T75T76.binpack
3. epoch 799 trained on the master dataset

For training from scratch:

python3 easy_train.py \
  --experiment-name new-L1-1536-T77T79-filter-v6dd \
  --training-dataset /data/T77T79-filter-v6-dd.min.binpack \
  --max_epoch 400 \
  --lambda 1.0 \
  --start-from-engine-test-net False \
  --engine-test-branch linrock/Stockfish/L1-1536 \
  --nnue-pytorch-branch linrock/Stockfish/misc-fixes-L1-1536 \
  --tui False \
  --gpus "0," \
  --seed $RANDOM

Retraining commands were similar to each other. For the 3rd retraining run:

python3 easy_train.py \
  --experiment-name L1-1536-T77T79-v6dd-Re1-LeelaFarseer-Re2-masterDataset-Re3-sameData \
  --training-dataset /data/leela96-dfrc99-v2-T60novdecT80juntonovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack \
  --early-fen-skipping 28 \
  --max_epoch 800 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --start-from-engine-test-net False \
  --start-from-model /data/L1-1536-T77T79-v6dd-Re1-LeelaFarseer-Re2-masterDataset-nn-epoch799.nnue \
  --engine-test-branch linrock/Stockfish/L1-1536 \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes-L1-1536 \
  --tui False \
  --gpus "0," \
  --seed $RANDOM

The T77+T79 data used is a subset of the master dataset available at:
https://robotmoon.com/nnue-training-data/

T60T70wIsRightFarseerT60T74T75T76.binpack is available at:
https://drive.google.com/drive/folders/1S9-ZiQa_3ApmjBtl2e8SyHxj4zG4V8gG

Local elo at 25k nodes per move vs. nn-e1fb1ade4432.nnue (L1 size 1024):
nn-epoch759.nnue : 26.9 +/- 1.6

Failed STC
https://tests.stockfishchess.org/tests/view/64742485d29264e4cfa75f97
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 13728 W: 3588 L: 3829 D: 6311
Ptnml(0-2): 71, 1661, 3610, 1482, 40

Failing LTC
https://tests.stockfishchess.org/tests/view/64752d7c4a36543c4c9f3618
LLR: -1.91 (-2.94,2.94) <0.50,2.50>
Total: 35424 W: 9522 L: 9603 D: 16299
Ptnml(0-2): 24, 3579, 10585, 3502, 22

Passed VLTC 180+1.8
https://tests.stockfishchess.org/tests/view/64752df04a36543c4c9f3638
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 47616 W: 13174 L: 12863 D: 21579
Ptnml(0-2): 13, 4261, 14952, 4566, 16

Passed VLTC SMP 60+0.6 th 8
https://tests.stockfishchess.org/tests/view/647446ced29264e4cfa761e5
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 19942 W: 5694 L: 5451 D: 8797
Ptnml(0-2): 6, 1504, 6707, 1749, 5

closes https://github.com/official-stockfish/Stockfish/pull/4593

bench 2222567
2023-05-31 08:51:22 +02:00
windfishballad 7e9b131efb Removed quadratic term in optimism
Remove term which is quadratic in optimism in the eval.
Simplifies and should also remove the bias towards side to move making the eval better for analysis.

STC: https://tests.stockfishchess.org/tests/view/6470a9d8c29e0d4352b0bca5
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 154432 W: 41127 L: 41040 D: 72265
Ptnml(0-2): 380, 17094, 42190, 17163, 389

LTC: https://tests.stockfishchess.org/tests/view/6471e9b3e549d9cf2fb219ef
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 127926 W: 34474 L: 34369 D: 59083
Ptnml(0-2): 43, 12505, 38776, 12582, 57

closes https://github.com/official-stockfish/Stockfish/pull/4590

Bench: 2541211
2023-05-28 20:05:50 +02:00
Joost VandeVondele 7f0b19dedf Update CPU contributors list
update CPU contributors list, the previous update was a couple of months ago,
and unfortunately, was not quite accurate for the number of games played.

This version is based clean calculation from the DB and
an updated script that tracks things (see https://github.com/glinscott/fishtest/pull/1702).

closes https://github.com/official-stockfish/Stockfish/pull/4589

No functional change
2023-05-28 20:03:33 +02:00
cj5716 c701745cf2 Remove ss->ttHit condition where ttValue != VALUE_NONE
Simplification is done at 3 separate places in the code.
Thanks to peregrineshahin for helping me find 2 of such places.
(See original PR #4584)

Passed non-regression test
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 120256 W: 32204 L: 32085 D: 55967
Ptnml(0-2): 292, 12473, 34483, 12584, 296
https://tests.stockfishchess.org/tests/view/646f045968661bfd984325e3

closes https://github.com/official-stockfish/Stockfish/pull/4587

No functional change
2023-05-25 20:49:11 +02:00
FauziAkram cedd73f4aa Simplify Futility pruning for captures
Passed STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 412928 W: 109433 L: 109620 D: 193875
Ptnml(0-2): 1071, 45929, 112650, 45744, 1070
https://tests.stockfishchess.org/tests/view/6468eac40db5177f2b76ef4d

Passed LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 190200 W: 51465 L: 51420 D: 87315
Ptnml(0-2): 58, 18585, 57788, 18592, 77
https://tests.stockfishchess.org/tests/view/646b66520db5177f2b772a84

closes https://github.com/official-stockfish/Stockfish/pull/4583

bench: 2486604
2023-05-25 20:42:43 +02:00
FauziAkram a989aa1825 Simplify Prune moves with negative SEE
Passed STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 57760 W: 15472 L: 15286 D: 27002
Ptnml(0-2): 123, 6025, 16430, 6147, 155
https://tests.stockfishchess.org/tests/view/6468eb6b0db5177f2b76ef62

Passed LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 93966 W: 25274 L: 25141 D: 43551
Ptnml(0-2): 33, 8498, 29792, 8623, 37
https://tests.stockfishchess.org/tests/view/6469570b0db5177f2b76f81b

closes: https://github.com/official-stockfish/Stockfish/pull/4579

Bench: 2304063
2023-05-25 20:40:09 +02:00
Muzhen Gaming b64c97825e Simplify delta calculation in aspiration window
Simplification STC: https://tests.stockfishchess.org/tests/view/6468cb200db5177f2b76ecbb
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 220416 W: 58503 L: 58487 D: 103426
Ptnml(0-2): 596, 24384, 60188, 24488, 552

Simplification LTC: https://tests.stockfishchess.org/tests/view/646a15840db5177f2b770704
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 177756 W: 47882 L: 47825 D: 82049
Ptnml(0-2): 55, 17430, 53858, 17473, 62

closes https://github.com/official-stockfish/Stockfish/pull/4581

Bench: 2304063
2023-05-25 20:37:53 +02:00
candirufish d7e72d801f More Depth Reduction
Reduce more for depth > 3 and depth < 12

LTC: https://tests.stockfishchess.org/tests/view/646c5abbd1f14fd69a6f2fab
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 197280 W: 53405 L: 52797 D: 91078
Ptnml(0-2): 62, 19025, 59886, 19577, 90

STC: https://tests.stockfishchess.org/tests/view/646bee71d1f14fd69a6f259d
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 100832 W: 26861 L: 26466 D: 47505
Ptnml(0-2): 240, 10985, 27622, 11278, 291

https://github.com/official-stockfish/Stockfish/pull/4585

bench: 2276617
2023-05-25 20:35:13 +02:00
Michael Chaly df0fb8471e Small simplification in low depth pruning
Uncap low depth pruning lmr depth.
It's anyway capped for most cases apart from futility pruning for captures - removes one std::min call.

Passed STC:
https://tests.stockfishchess.org/tests/view/645e8fa6d55cccb2e64225a1
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 184064 W: 49039 L: 48982 D: 86043
Ptnml(0-2): 462, 20353, 50349, 20402, 466

Passed LTC:
https://tests.stockfishchess.org/tests/view/645f4d48d55cccb2e6423335
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 83886 W: 22613 L: 22465 D: 38808
Ptnml(0-2): 31, 8090, 25546, 8252, 24

closes https://github.com/official-stockfish/Stockfish/pull/4566

bench 3201883
2023-05-20 10:19:03 +02:00
pb00067 7cd650f435 Simplify SEE verfication logic
Passed STC
https://tests.stockfishchess.org/tests/view/6461d51887f6567dd4df27d0
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 177056 W: 47181 L: 47118 D: 82757
Ptnml(0-2): 456, 19381, 48792, 19442, 457

Passed LTC
https://tests.stockfishchess.org/tests/view/64631a9287f6567dd4df4502
2.94 (-2.94,2.94) <-1.75,0.25>
Total: 104346 W: 28062 L: 27935 D: 48349
Ptnml(0-2): 25, 10190, 31631, 10287, 40

closes https://github.com/official-stockfish/Stockfish/pull/4578

bench: 2903251
2023-05-20 10:16:04 +02:00
xoto10 4b085c4777 Simplify optimism calculation
This change removes one of the constants in the calculation of optimism. It also changes the 2 constants used with the scale value so that they are independent, instead of applying a constant to the scale and then adjusting it again when it is applied to the optimism. This might make the tuning of these constants cleaner and more reliable in the future.

STC 10+0.1 (accidentally run as an Elo gainer:
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 154080 W: 41119 L: 40651 D: 72310
Ptnml(0-2): 375, 16840, 42190, 17212, 423
https://tests.stockfishchess.org/tests/live_elo/64653eabf3b1a4e86c317f77

LTC 60+0.6:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 217434 W: 58382 L: 58363 D: 100689
Ptnml(0-2): 66, 21075, 66419, 21088, 69
https://tests.stockfishchess.org/tests/live_elo/6465d077f3b1a4e86c318d6c

closes https://github.com/official-stockfish/Stockfish/pull/4576

bench: 3190961
2023-05-20 10:05:19 +02:00
Stefan Geschwentner 4f24ee0868 Small simplification in history pruning.
Remove the constant term of the history threshold which lowers the chance that pruning occurs.
As compensation allow pruning at a slightly higher depth.

Passed STC:
https://tests.stockfishchess.org/tests/view/64634c9a87f6567dd4df4901
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 101536 W: 27156 L: 27012 D: 47368
Ptnml(0-2): 266, 11165, 27772, 11289, 276

Passed LTC:
https://tests.stockfishchess.org/tests/view/6463d68b17982fde89d2bc2b
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 32154 W: 8741 L: 8543 D: 14870
Ptnml(0-2): 8, 3093, 9687, 3271, 18

Passed LTC: retest on top of VLTC tuning PR 4571 because this changes the history depth factor (use this new factor here)
https://tests.stockfishchess.org/tests/view/6467300e165c4b29ec0afd3f
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 99270 W: 26840 L: 26707 D: 45723
Ptnml(0-2): 36, 9753, 29928, 9878, 40

closes https://github.com/official-stockfish/Stockfish/pull/4578

Bench: 2984341
2023-05-20 09:58:51 +02:00
Muzhen Gaming f030a1c592 Search tuning at very long time control
Many search parameter changes, tuned
(https://tests.stockfishchess.org/tests/view/645e4c67d55cccb2e64220ff)
at ~300k games @ VLTC (120+1.2).

Failed STC:
https://tests.stockfishchess.org/tests/view/6465fcd77968ca827c1410c2
LLR: -2.95 (-2.94,2.94) <0.00,2.00>
Total: 33824 W: 8863 L: 9067 D: 15894
Ptnml(0-2): 89, 3833, 9266, 3641, 83

Neutral LTC:
https://tests.stockfishchess.org/tests/view/646385ce87f6567dd4df4e37
Elo: -0.48 +-1.2 (95%) LOS: 22.2%
Total: 60000 W: 16235 L: 16318 D: 27447
Ptnml(0-2): 27, 5831, 18366, 5750, 26
nElo: -1.08 +-2.8 (95%) PairsRatio: 0.99

Passed VLTC 180+1.8:
https://tests.stockfishchess.org/tests/view/646385f787f6567dd4df4e3e
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 126448 W: 34704 L: 34258 D: 57486
Ptnml(0-2): 9, 10970, 40825, 11406, 14

Passed VLTC SMP 60+0.6 8thread:
https://tests.stockfishchess.org/tests/view/646628de884ce93b65df2ac9
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 59456 W: 16791 L: 16487 D: 26178
Ptnml(0-2): 5, 4473, 20467, 4779, 4

closes https://github.com/official-stockfish/Stockfish/pull/4574

Bench: 3347573
2023-05-20 09:21:46 +02:00
Joost VandeVondele 5f7b26aaa0 Update WLD model
using data of May, recalibrate the WLD model.

closes https://github.com/official-stockfish/Stockfish/pull/4577

No functional change
2023-05-20 09:16:46 +02:00
Michael Chaly 65e2150501 Refine deeper post-lmr searches
This patch improves logic conditions for performing deeper searches after passed LMR.

Instead of exceeding alpha by some margin now it requires to exceed the
current best value - which may be lower than alpha (but never bigger since we
update alpha with bestvalue if it exceeds alpha).

Passed STC:
https://tests.stockfishchess.org/tests/view/6455f78008858de8313775b6
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 209344 W: 55993 L: 55448 D: 97903
Ptnml(0-2): 507, 22798, 57526, 23325, 516

Passed LTC:
https://tests.stockfishchess.org/tests/view/64572d46eb75932ccfebff97
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 66288 W: 17867 L: 17514 D: 30907
Ptnml(0-2): 21, 6240, 20269, 6593, 21

closes https://github.com/official-stockfish/Stockfish/pull/4559

bench 3808503
2023-05-07 22:36:57 +02:00
Michael Chaly 464ebdf127 Small cleanup
In search remove one condition check and reorder conditions. Removes some code.

Passed non-regression test:
https://tests.stockfishchess.org/tests/view/64548fa06206ee34ebf853ad
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 282976 W: 75327 L: 75374 D: 132275
Ptnml(0-2): 604, 29673, 80995, 29598, 618

closes https://github.com/official-stockfish/Stockfish/pull/4557

No functional change
2023-05-07 22:32:21 +02:00
peregrineshahin 28442195c7 Clean up after "Simplify away complexity in evaluation"
closes https://github.com/official-stockfish/Stockfish/pull/4555

No functional change.
2023-05-07 22:31:03 +02:00
Michael Chaly 2429e16289 Reduce more if current node has a lot of refuted moves.
This patch refines idea of cutoff count - in master we reduce more if current node has at least 4 moves that are refuted by search, this patch increases this count by 1 if refutation happened without having a tt move.

Passed STC:
https://tests.stockfishchess.org/tests/view/645363c36206ee34ebf8191d
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 67616 W: 18220 L: 17874 D: 31522
Ptnml(0-2): 142, 7346, 18504, 7656, 160

Passed LTC:
https://tests.stockfishchess.org/tests/view/6453a0ea6206ee34ebf82796
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 195228 W: 52741 L: 52140 D: 90347
Ptnml(0-2): 53, 18718, 59482, 19297, 64

closes https://github.com/official-stockfish/Stockfish/pull/4556

bench 3448916
2023-05-05 07:12:47 +02:00
candirufish 72d542f000 Adjust reductions
Decrease further on cutNodes with tte->depth() >= depth + 3 condition.

LTC: https://tests.stockfishchess.org/tests/view/644dc84bccf5e93df5e50c13
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 155346 W: 42184 L: 41660 D: 71502
Ptnml(0-2): 59, 14765, 47504, 15283, 62

STC: https://tests.stockfishchess.org/tests/view/644d05de68e01d8194cd9bbb
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 121888 W: 32868 L: 32444 D: 56576
Ptnml(0-2): 332, 13273, 33343, 13631, 365

closes https://github.com/official-stockfish/Stockfish/pull/4552

bench: 3739675
2023-05-03 20:41:33 +02:00
Linmiao Xu 21d6b69f7c Update 7 eval and optimism params
Params found using spsa at 30+0.3 with this tuning config:

```
// evaluate.cpp
int nnueOptScaleBase = 1001;
int nnueComplexityMult = 406;
int nnueComplexityOptOffset = 424;
int evalOptComplexityOffset = 272;
int evalOptScaleOffset = 748;
TUNE(SetRange(801, 1201), nnueOptScaleBase);
TUNE(SetRange(306, 506), nnueComplexityMult);
TUNE(SetRange(324, 524), nnueComplexityOptOffset);
TUNE(SetRange(172, 372), evalOptComplexityOffset);
TUNE(SetRange(648, 848), evalOptScaleOffset);

// search.cpp
int searchOptBase = 120;
int searchOptDenom = 161;
TUNE(SetRange(20, 220), searchOptBase);
TUNE(SetRange(111, 211), searchOptDenom);
```

Passed STC:
https://tests.stockfishchess.org/tests/view/644dda8accf5e93df5e50cbe
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 136800 W: 36682 L: 36237 D: 63881
Ptnml(0-2): 353, 14910, 37492, 15229, 416

Passed LTC:
https://tests.stockfishchess.org/tests/view/644eaedb3f31c3bbe4a3d345
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64548 W: 17624 L: 17272 D: 29652
Ptnml(0-2): 33, 6112, 19631, 6466, 32

closes https://github.com/official-stockfish/Stockfish/pull/4550

bench 3670343
2023-05-03 20:37:57 +02:00
Linmiao Xu 41f50b2c83 Update default net to nn-e1fb1ade4432.nnue
Created by retraining nn-dabb1ed23026.nnue with a dataset composed of:

* The previous best dataset (nn-1ceb1a57d117.nnue dataset)
* Adding de-duplicated T80 data from feb2023 and the last 10 days of jan2023, filtered with v6-dd

Initially trained with the same options as the recent master net (nn-1ceb1a57d117.nnue).
Around epoch 890, training was manually stopped and max epoch increased to 1000.

```
python3 easy_train.py \
  --experiment-name leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd \
  --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
  --start-from-engine-test-net True \
  --early-fen-skipping 30 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --max_epoch 900 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --tui False \
  --gpus "0," \
  --seed $RANDOM
```

The same v6-dd filtering and binpack minimizer was used for preparing the recent nn-1ceb1a57d117.nnue dataset.

```
python3 interleave_binpacks.py \
  leela96-filt-v2.binpack \
  dfrc99-filt-v2.binpack \
  T60-nov2021-12tb7p-eval-filt-v2.binpack \
  T60-dec2021-12tb7p-eval-filt-v2.binpack \
  filt-v6/test80-aug2022-16tb7p-filter-v6.min-mar2023.binpack \
  filt-v6/test80-sep2022-16tb7p-filter-v6.min-mar2023.binpack \
  filt-v6-dd/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-jan2022-3of3-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
  /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovjanfebT79aprmayT78jantosepT77dec-v6dd.binpack
```

Links for downloading the training data components can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch919.nnue : 2.6 +/- 2.8

Passed STC vs. nn-dabb1ed23026.nnue
https://tests.stockfishchess.org/tests/view/644420df94ff3db5625f2af5
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 125960 W: 33898 L: 33464 D: 58598
Ptnml(0-2): 351, 13920, 34021, 14320, 368

Passed LTC vs. nn-1ceb1a57d117.nnue
https://tests.stockfishchess.org/tests/view/64469f128d30316529b3dc46
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 24544 W: 6817 L: 6542 D: 11185
Ptnml(0-2): 8, 2252, 7488, 2505, 19

closes https://github.com/official-stockfish/Stockfish/pull/4546

bench 3714847
2023-04-25 08:19:00 +02:00
Linmiao Xu c3ce220408 Created by retraining the master net with these changes to the dataset:
* Extending v6 filtering to data from T77 dec2021, T79 may2022, and T80 nov2022
* Reducing the number of duplicate positions, prioritizing position scores seen later in time
* Using a binpack minimizer to reduce the overall data size

Trained the same way as the previous master net, aside from the dataset changes:

```
python3 easy_train.py \
  --experiment-name leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd \
  --training-dataset /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
  --start-from-engine-test-net True \
  --early-fen-skipping 30 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --max_epoch 900 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --tui False \
  --gpus "0," \
  --seed $RANDOM
```

The new v6-dd filtering reduces duplicate positions by iterating over hourly data files within leela test runs, starting with the most recent, then keeping positions the first time they're seen and ignoring positions that are seen again. This ordering was done with the assumption that position scores seen later in time are generally more accurate than scores seen earlier in the test run. Positions are de-duplicated based on piece orientations, the first token in fen strings.

The binpack minimizer was run with default settings after first merging monthly data into single binpacks.

```
python3 interleave_binpacks.py \
  leela96-filt-v2.binpack \
  dfrc99-filt-v2.binpack \
  T60-nov2021-12tb7p-eval-filt-v2.binpack \
  T60-dec2021-12tb7p-eval-filt-v2.binpack \
  filt-v6/test80-aug2022-16tb7p-filter-v6.min-mar2023.binpack \
  filt-v6/test80-sep2022-16tb7p-filter-v6.min-mar2023.binpack \
  filt-v6-dd/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test80-jul2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-oct2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test80-nov2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test79-apr2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test79-may2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.binpack \
  filt-v6-dd/test78-juntosep2022-16tb7p-filter-v6-dd.binpack \
  filt-v6-dd/test77-dec2021-16tb7p-filter-v6-dd.binpack \
  /data/leela96-dfrc99-T60novdec-v2-T80augsep-v6-T80junjuloctnovT79aprmayT78jantosepT77dec-v6dd.binpack
```

The code for v6-dd filtering is available along with training data preparation scripts at:
https://github.com/linrock/nnue-data

Links for downloading the training data components:
https://robotmoon.com/nnue-training-data/

The binpack minimizer is from: #4447

Local elo at 25k nodes per move:
nn-epoch859.nnue : 1.2 +/- 2.6

Passed STC:
https://tests.stockfishchess.org/tests/view/643aad7db08900ff1bc5a832
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 565040 W: 150225 L: 149162 D: 265653
Ptnml(0-2): 1875, 62137, 153229, 63608, 1671

Passed LTC:
https://tests.stockfishchess.org/tests/view/643ecf2fa43cf30e719d2042
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 1014840 W: 274645 L: 272456 D: 467739
Ptnml(0-2): 515, 98565, 306970, 100956, 414

closes https://github.com/official-stockfish/Stockfish/pull/4545

bench 3476305
2023-04-25 08:17:22 +02:00
Boštjan Mejak b22a1b10bb Update AUTHORS
Improved some comments in the AUTHORS file, sort contributors

closes https://github.com/official-stockfish/Stockfish/pull/4520

No functional change
2023-04-25 08:08:48 +02:00
Stefan Geschwentner ba06c480a7 Less reduction for tt move.
This idea is a result of my second condition combination tuning for reductions:
https://tests.stockfishchess.org/tests/view/643ed5573806eca398f06d61

There were used two parameters per combination: one for the 'sign' of the first and the second condition in a combination. Values >= 50 indicate using a condition directly and values <= -50 means use the negation of a condition.

Each condition pair (X,Y) had two occurances dependent of the order of the two conditions:
- if X < Y the parameters used for more reduction
- if X > Y the parameters used for less reduction
- if X = Y then only one condition is present and A[X][X][0]/A[X][X][1] stands for using more/less reduction for only this condition.

The parameter pair A[7][2][0] (value = -94.70) and A[7][2][1] (value = 93.60) was one of the strongest signals with values near 100/-100.
Here condition nr. 7 was '(ss+1)->cutoffCnt > 3' and condition nr. 2 'move == ttMove'. For condition nr. 7 the negation is used because A[7][2][0] is negative.

This translates finally to less reduction (because 7 > 2) for tt moves if child cutoffs <= 3.

STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 65728 W: 17704 L: 17358 D: 30666
Ptnml(0-2): 184, 7092, 18008, 7354, 226
https://tests.stockfishchess.org/tests/view/643ff767ef2529086a7ed042

LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 139200 W: 37776 L: 37282 D: 64142
Ptnml(0-2): 58, 13241, 42509, 13733, 59
https://tests.stockfishchess.org/tests/view/6440bfa9ef2529086a7edbc7

closes https://github.com/official-stockfish/Stockfish/pull/4538

Bench: 3548023
2023-04-22 11:04:09 +02:00
Muzhen Gaming d64d4ac426 Simplify away depth condition for aspiration window adjust
Simplification STC:
https://tests.stockfishchess.org/tests/view/64351654596a20f264276ded
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 374664 W: 98942 L: 99089 D: 176633
Ptnml(0-2): 1049, 41767, 101878, 41558, 1080

Simplification LTC:
https://tests.stockfishchess.org/tests/view/6439499f605991a801b4f684
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 457880 W: 123021 L: 123233 D: 211626
Ptnml(0-2): 166, 44739, 139335, 44541, 159

closes https://github.com/official-stockfish/Stockfish/pull/4534

Bench: 3879281
2023-04-22 10:59:33 +02:00
Guenther Demetz 7b9b793fd5 Simplification of SEE verification logic
Use same logic for all handled pieces.
Don't prune the move if opponent King, Queen, Rook gets a discovered
attack while or after the exchanges.

remove an obsolete comment in position.cpp

Passed STC non regression:
https://tests.stockfishchess.org/tests/view/6437907594daa91835c290d0
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 107432 W: 28359 L: 28221 D: 50852
Ptnml(0-2): 298, 11724, 29524, 11882, 288

Passed LTC non-regression:
https://tests.stockfishchess.org/tests/view/6438ed2ebd1a5470263c51e8
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 236288 W: 63656 L: 63656 D: 108976
Ptnml(0-2): 99, 22960, 72011, 22990, 84

closes https://github.com/official-stockfish/Stockfish/pull/4533

bench: 3741125
2023-04-22 10:47:51 +02:00
Muzhen Gaming c90dd38903 Simplify away complexity in evaluation
Simplification STC: https://tests.stockfishchess.org/tests/view/64394bc0605991a801b4f6f0
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 72360 W: 19313 L: 19138 D: 33909
Ptnml(0-2): 206, 7883, 19800, 8112, 179

Simplification LTC: https://tests.stockfishchess.org/tests/view/6439e788c233ce943b6bdac1
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 224992 W: 60665 L: 60654 D: 103673
Ptnml(0-2): 96, 21875, 68526, 21920, 79

closes https://github.com/official-stockfish/Stockfish/pull/4530

Bench: 3709369
2023-04-22 10:43:29 +02:00
Torom f9d9c69bc3 Set the length of GIT_SHA to 8 characters
Previously, the length of git commit hashes could vary depending on the git environment.

closes https://github.com/official-stockfish/Stockfish/pull/4527

No functional change
2023-04-22 10:38:25 +02:00
MinetaS 96b6c0b36f Remove some conditions at PV improvement reduction
Non-regression STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 63664 W: 17007 L: 16823 D: 29834
Ptnml(0-2): 163, 6998, 17336, 7162, 173
https://tests.stockfishchess.org/tests/view/6430b124028b029b01ac99f2

Non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 90016 W: 24399 L: 24258 D: 41359
Ptnml(0-2): 52, 8672, 27405, 8841, 38
https://tests.stockfishchess.org/tests/view/64310e74028b029b01ad3131

closes https://github.com/official-stockfish/Stockfish/pull/4526

Bench: 3661938
2023-04-22 10:37:14 +02:00
Michael Chaly acb0d204d5 Simplify stats assignment for Pv nodes
This patch is a simplification of my recent elo gainer.
Logically the Elo gainer didn't make much sense and this patch simplifies it into smth more logical.
Instead of assigning negative bonuses to all non-first moves that enter PV nodes
we assign positive bonuses in full depth search after LMR only for moves that
will result in a fail high - thus not assigning positive bonuses
for moves that will go to pv search - so doing "almost" the same as we do in master now for them.
Logic differs for some other moves, though, but this removes some lines of code.

Passed STC:
https://tests.stockfishchess.org/tests/view/642cf5cf77ff3301150dc5ec
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 409320 W: 109124 L: 109308 D: 190888
Ptnml(0-2): 1149, 45385, 111751, 45251, 1124

Passed LTC:
https://tests.stockfishchess.org/tests/view/642fe75d20eb941419bde200
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 260336 W: 70280 L: 70303 D: 119753
Ptnml(0-2): 99, 25236, 79528, 25199, 106

closes https://github.com/official-stockfish/Stockfish/pull/4522

Bench:  4286815
2023-04-12 20:45:34 +02:00
Stefan Geschwentner 9829bceda9 Remove good killer reduction rule.
STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 246544 W: 65646 L: 65657 D: 115241
Ptnml(0-2): 706, 27350, 67138, 27405, 673
https://tests.stockfishchess.org/tests/view/642e253277ff3301150e9aa2

LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 165136 W: 44878 L: 44809 D: 75449
Ptnml(0-2): 64, 15991, 50378, 16082, 53
https://tests.stockfishchess.org/tests/view/6430db07028b029b01acd87f

closes https://github.com/official-stockfish/Stockfish/pull/4519

Bench: 3746080
2023-04-12 20:43:08 +02:00
Dubslow f66c36277f Remove nmpColor
no benefit seen, neither in game play nor for zugzwang test positions

STC: https://tests.stockfishchess.org/tests/view/642e293977ff3301150e9b55
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 161848 W: 43332 L: 43254 D: 75262
Ptnml(0-2): 418, 16987, 46058, 17021, 440

LTC: https://tests.stockfishchess.org/tests/view/642fea9420eb941419bde296
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 120208 W: 32529 L: 32418 D: 55261
Ptnml(0-2): 35, 11424, 37080, 11525, 40

closes https://github.com/official-stockfish/Stockfish/pull/4511

bench 3979409
2023-04-12 20:40:53 +02:00
peregrineshahin 4ad2713e19 Fix capturing underpromotions issue
Fix underpromotion captures are generated amongst quiets although dealt with as a capture_stage in search, this makes not skipping them when move count pruning kicks-in consistent with updating their histories amongst captures.

Passed STC:
https://tests.stockfishchess.org/tests/view/6415579f65775d3b539e7537
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 118896 W: 31678 L: 31553 D: 55665
Ptnml(0-2): 356, 12911, 32793, 13028, 360

Passed LTC:
https://tests.stockfishchess.org/tests/view/641633b965775d3b539e9e95
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 126800 W: 34255 L: 34148 D: 58397
Ptnml(0-2): 57, 12216, 38763, 12291, 73

see also discussion in https://github.com/official-stockfish/Stockfish/pull/4436
closes https://github.com/official-stockfish/Stockfish/pull/4452

bench: 3979409
2023-04-12 20:38:43 +02:00
Linmiao Xu 7bd23d4d04 Simplify away nnue scale pawn count multiplier
Removes 2x multipliers in nnue scale calculation along with the pawn count term that was recently reintroduced.

Passed non-regression STC:
https://tests.stockfishchess.org/tests/view/64305bc720eb941419bdf72e
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 38008 W: 10234 L: 10021 D: 17753
Ptnml(0-2): 96, 4151, 10323, 4312, 122

Passed non-regression LTC:
https://tests.stockfishchess.org/tests/view/6430b76a028b029b01ac9bfd
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 91232 W: 24686 L: 24547 D: 41999
Ptnml(0-2): 30, 8721, 27986, 8838, 41

closes https://github.com/official-stockfish/Stockfish/pull/4510

bench 4017320
2023-04-10 11:03:52 +02:00
Maxim Masiutin 1a64afb1c6 Do no initialize TM in all cases
Avoid doing full TM initialization if it won't be used, avoids division by zero.

closes https://github.com/official-stockfish/Stockfish/pull/4484

No functional change
2023-04-10 10:56:42 +02:00
mstembera 7a9f67747f Reduce Position::pieces() overloads
Reduce the number of overloads for pieces() by using a more general template implementation.
Secondly simplify some code in search.cpp using the new general functionality.

TC https://tests.stockfishchess.org/tests/view/642ce27877ff3301150dc193
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 269640 W: 71775 L: 71809 D: 126056
Ptnml(0-2): 687, 27294, 78885, 27274, 680

closes https://github.com/official-stockfish/Stockfish/pull/4501

No functional change.
2023-04-10 10:51:45 +02:00
Maxim Masiutin 2f2f45f9f4 Made two specializations for affine transform easier to understand.
Added AVX-512 for the specialization for small inputs

closes https://github.com/official-stockfish/Stockfish/pull/4502

No functional change
2023-04-10 09:29:52 +02:00
MinetaS a5643b89fd Remove extraReduction
Since bestValue becomes value and beta - alpha is always non-negative,
extraReduction is always false, hence it has no effect.

This patch includes small changes to improve readability.

closes https://github.com/official-stockfish/Stockfish/pull/4505

No functional change
2023-04-10 09:28:37 +02:00
MinetaS 6e63dd63a4 Use int conversion for Option class
The current implementation generates warnings on MSVC. However, we have
no real use cases for double-typed UCI option values now. Also parameter
tuning only accepts following three types:

  int, Value, Score

closes https://github.com/official-stockfish/Stockfish/pull/4505

No functional change
2023-04-10 09:27:35 +02:00
Maxim Masiutin 5d258e168f Fix linking / character types of windows API calls
ensures large pages can be allocated again.

closes https://github.com/official-stockfish/Stockfish/pull/4509

No functional change
2023-04-10 09:22:15 +02:00
Joost VandeVondele b36d39de3d Fix rootComplexity calculation
The calculation of rootComplexity can't call eval when in check.
Doing so triggers an assert if compiled in debug mode when
the rootpos is evaluated using classical eval.

Fixes https://github.com/official-stockfish/Stockfish/issues/4512

Passed STC:
https://tests.stockfishchess.org/tests/view/6432697431feee5c6d306876
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 41096 W: 11017 L: 10815 D: 19264
Ptnml(0-2): 113, 4172, 11780, 4366, 117

Running LTC:
https://tests.stockfishchess.org/tests/view/6432974d31feee5c6d306fc0
LLR: 1.76 (-2.94,2.94) <-1.75,0.25>
Total: 73200 W: 19792 L: 19728 D: 33680
Ptnml(0-2): 24, 6659, 23182, 6699, 36

closes https://github.com/official-stockfish/Stockfish/pull/4515

No functional change
2023-04-09 15:19:49 +02:00
FauziAkram 59f2085469 Depth Tweak and tuning
tunes reduction related parameters, and introduces more reduction on found good moves.

credit for this patch goes also to candirufish Yoshie2000 dubslow peregrineshahin Vizvezdenec

Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 38424 W: 10346 L: 10040 D: 18038
Ptnml(0-2): 103, 4111, 10473, 4427, 98
https://tests.stockfishchess.org/tests/view/642ca74277ff3301150db511

Passed LTC:
LLR: 2.97 (-2.94,2.94) <0.50,2.50>
Total: 136968 W: 37151 L: 36660 D: 63157
Ptnml(0-2): 43, 13052, 41808, 13533, 48
https://tests.stockfishchess.org/tests/view/642d632377ff3301150dddbe

closes https://github.com/official-stockfish/Stockfish/pull/4499

bench: 3672914
2023-04-07 09:56:35 +02:00
Michael Chaly 510aca1ef6 Assign negative stat bonuses for quiet moves at Pv nodes
This patch assigns negative stat bonuses for quiet moves
at pv nodes which are searched at depth greater than
this node assumes, so are extended.

Passed STC:
https://tests.stockfishchess.org/tests/view/6426198bdb43ab2ba6f9cfa2
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 548944 W: 147287 L: 146254 D: 255403
Ptnml(0-2): 1662, 59772, 150671, 60605, 1762

Passed LTC:
https://tests.stockfishchess.org/tests/view/642be4f177ff3301150d892d
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 93352 W: 25400 L: 24994 D: 42958
Ptnml(0-2): 44, 8817, 28547, 9225, 43

closes https://github.com/official-stockfish/Stockfish/pull/4495

bench 5044536
2023-04-05 08:23:54 +02:00
Muzhen Gaming a2737d8bb5 Simplify away piece count condition for useClassical
Simplify away the piece count condition for useClassical. In compensation, the psq requirement is increased by 15%.

Also updated the Elo estimate for useClassical, based on recent testing.

Simplification STC:
https://tests.stockfishchess.org/tests/view/642acbb577ff3301150d3ef5
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 51984 W: 13906 L: 13707 D: 24371
Ptnml(0-2): 150, 5638, 14227, 5817, 160

Simplification LTC:
https://tests.stockfishchess.org/tests/view/642b9c5777ff3301150d778a
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 119696 W: 32412 L: 32300 D: 54984
Ptnml(0-2): 53, 11529, 36567, 11651, 48

closes https://github.com/official-stockfish/Stockfish/pull/4494

Bench: 5089321
2023-04-05 08:18:53 +02:00
FauziAkram 9a42bbdf31 Parameters Tweak
Passed STC
LLR: 3.22 (-2.94,2.94) <0.00,2.00>
Total: 664048 W: 177526 L: 176301 D: 310221
Ptnml(0-2): 2002, 72968, 180891, 74129, 2034
https://tests.stockfishchess.org/tests/view/64219901db43ab2ba6f901fa

Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 77576 W: 21125 L: 20750 D: 35701
Ptnml(0-2): 24, 7350, 23669, 7717, 28
https://tests.stockfishchess.org/tests/view/642abe3377ff3301150d3a16

closes https://github.com/official-stockfish/Stockfish/pull/4493

bench: 4522076
2023-04-05 08:15:34 +02:00
Dubslow 77e2b915e1 Simplifiy TM's root complexity
Also requires moving optimism initialization, this is a very early `evaluate()` call.

STC: https://tests.stockfishchess.org/tests/view/6428c39677ff3301150ca0d7
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 51256 W: 13805 L: 13612 D: 23839
Ptnml(0-2): 145, 5283, 14592, 5450, 158

LTC: https://tests.stockfishchess.org/tests/view/64296ff377ff3301150cc519
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 106968 W: 28951 L: 28830 D: 49187
Ptnml(0-2): 47, 9746, 33789, 9843, 59

closes https://github.com/official-stockfish/Stockfish/pull/4492

no functional change
2023-04-05 08:13:34 +02:00
Miguel Lahoz 1fee996999 Remove unneeded bitboard from MP
Recent simplification has removed the need for an extra bitboard in MP struct.
Use a local variable instead.

STC: Passed Non-regression test
https://tests.stockfishchess.org/tests/view/64294ae677ff3301150cba16
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 64872 W: 17383 L: 17203 D: 30286
Ptnml(0-2): 179, 6675, 18546, 6859, 177

closes https://github.com/official-stockfish/Stockfish/pull/4490

No functional change.
2023-04-05 08:10:34 +02:00
FauziAkram 6a6e32dfc8 Decrease Depth more for positions not in TT.
If the position is not in TT, decrease depth by 2
or by 4 if the TT entry for the current position was hit
and the stored depth is greater than or equal to the current depth.

Many thanks to Vizvezdenec as the main idea was his.

Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 70664 W: 18995 L: 18639 D: 33030
Ptnml(0-2): 228, 7712, 19090, 8080, 222
https://tests.stockfishchess.org/tests/view/64258a8bdb43ab2ba6f9b682

Passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 85040 W: 23227 L: 22836 D: 38977
Ptnml(0-2): 26, 8115, 25867, 8466, 46
https://tests.stockfishchess.org/tests/view/64262057db43ab2ba6f9d0e7

closes https://github.com/official-stockfish/Stockfish/pull/4482

bench: 4380438
2023-04-01 16:28:52 +02:00
Maxim Masiutin bc50378ff1 Replace deprecated icc with icx
Replace the deprecated Intel compiler icc with its newer icx variant.
This newer compiler is based on clang, and yields good performance.
As before, currently only linux is supported.

closes https://github.com/official-stockfish/Stockfish/pull/4478

No functional change
2023-04-01 16:16:48 +02:00
MinetaS 38a80c0b47 Simplify away complexityAverage
Instead of tracking the average of complexity values, calculate
complexity of root position at the beginning of the search and use it as
a scaling factor in time management.

Passed non-regression STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 58752 W: 15738 L: 15551 D: 27463
Ptnml(0-2): 164, 6194, 16478, 6371, 169
https://tests.stockfishchess.org/tests/view/6423010edb43ab2ba6f9424a

Passed non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 92872 W: 24865 L: 24729 D: 43278
Ptnml(0-2): 38, 8652, 28929, 8770, 47
https://tests.stockfishchess.org/tests/view/6423c1f0db43ab2ba6f9644f

closes https://github.com/official-stockfish/Stockfish/pull/4472

No functional change
2023-04-01 16:14:30 +02:00
Joost VandeVondele 66bf45b99e Stringify the git info passed
avoid escaping the string in the Makefile.

Alternative to https://github.com/official-stockfish/Stockfish/pull/4476

closes https://github.com/official-stockfish/Stockfish/pull/4481

No functional change.
2023-04-01 15:58:05 +02:00
Muzhen Gaming c3c46feebb Remove reduction for moving threatened piece
Simplify away "Decrease reduction if we move a threatened piece".

Running a dbg_hit_on() shows that this line is only called ~0.12% of the time.

Simplification STC: https://tests.stockfishchess.org/tests/view/641ec2dcdb43ab2ba6f88103
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 146128 W: 39168 L: 39070 D: 67890
Ptnml(0-2): 466, 16117, 39830, 16155, 496

Simplification LTC: https://tests.stockfishchess.org/tests/view/64200689db43ab2ba6f8bca8
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 248016 W: 66703 L: 66714 D: 114599
Ptnml(0-2): 105, 24202, 75406, 24189, 106

closes https://github.com/official-stockfish/Stockfish/pull/4471

Bench: 4961236
2023-04-01 15:53:46 +02:00
Maxim Masiutin e8742bdab3 Made advanced Windows API calls dynamically linked
Made advanced Windows API calls (those from Advapi32.dll) dynamically
linked to avoid link errors when compiling using
Intel icx compiler for Windows.

https://github.com/official-stockfish/Stockfish/pull/4467

No functional change
2023-04-01 15:48:47 +02:00
Maxim Masiutin 7a6fa34f5f Improve compatibility
this makes it easier to compile under MSVC, even though we recommend gcc/clang for production compiles at the moment.

In Win32 API, by default, most null-terminated character strings arguments are of wchar_t (UTF16, formerly UCS16-LE) type, i.e. 2 bytes (at least) per character. So, src/misc.cpp should have proper type. Respectively, for src/syzygy/tbprobe.cpp, in Widows, file paths should be std::wstring rather than std::string. However, this requires a very big number of changes, since the config files are also keeping the 8-bit-per-character std::string strings. Therefore, just one change of using 8-byte-per-character CreateFileA make it compile under MSVC.

closes https://github.com/official-stockfish/Stockfish/pull/4438

No functional change
2023-04-01 15:36:08 +02:00
peregrineshahin 3f01e3f41f Allow PvNode in futility pruning for captures.
Passed non-regression STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 148128 W: 39428 L: 39333 D: 69367
Ptnml(0-2): 492, 16326, 40315, 16457, 474
https://tests.stockfishchess.org/tests/view/641c2dbfdb43ab2ba6f804e8

Passed non-regression LTC:
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 376256 W: 100906 L: 101039 D: 174311
Ptnml(0-2): 186, 36697, 114494, 36566, 185
https://tests.stockfishchess.org/tests/view/641d33b2db43ab2ba6f83338

closes https://github.com/official-stockfish/Stockfish/pull/4470

bench: 4935616
2023-03-29 21:46:23 +02:00
Miguel Lahoz a9c26357de Clean up repetitive declarations for see_ge
The occupied bitboard is only used in one place and is otherwise thrown away.
To simplify use, see_ge function can instead be overloaded.
Repetitive declarations for occupied bitboard can be removed.

Passed non-regression test
https://tests.stockfishchess.org/tests/view/6421c286db43ab2ba6f908eb
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 48912 W: 13196 L: 13001 D: 22715
Ptnml(0-2): 146, 5003, 13967, 5190, 150

closes https://github.com/official-stockfish/Stockfish/pull/4469

No functional change.
2023-03-29 21:43:07 +02:00
Linmiao Xu 37160c4b16 Update default net to nn-dabb1ed23026.nnue
Created by retraining the master net with these modifications:

* New filtering methods for existing data from T80 sep+oct2022, T79 apr2022, T78 jun+jul+aug+sep2022, T77 dec2021
* Adding new filtered data from T80 aug2022 and T78 apr+may2022
* Increasing early-fen-skipping from 28 to 30

```
python3 easy_train.py \
  --experiment-name leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3-sk30 \
  --training-dataset /data/leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/misc-fixes \
  --start-from-engine-test-net True \
  --early-fen-skipping 30 \
  --max_epoch 900 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --lr 4.375e-4 \
  --gamma 0.995 \
  --tui False \
  --gpus "0," \
  --seed $RANDOM
```

The v3 filtering used for data from T77dec 2021 differs from v2 filtering in that:

* To improve binpack compression, positions after ply 28 were skipped during training by setting position scores to VALUE_NONE (32002) instead of removing them entirely
* All early-game positions with ply <= 28 were removed to maximize binpack compression
* Only bestmove captures at d6pv2 search were skipped, not 2nd bestmove captures
* Binpack compression was repaired for the remaining positions by effectively replacing bestmoves with "played moves" to maintain contiguous sequences of positions in the training game data

After improving binpack compression, The T77 dec2021 data size was reduced from 95G to 19G.

The v6 filtering used for data from T80augsepoctT79aprT78aprtosep 2022 differs from v2 in that:

* All positions with only one legal move were removed
* Tighter score differences at d6pv2 search were used to remove more positions with only one good move than before
* d6pv2 search was not used to remove positions where the best 2 moves were captures

```
python3 interleave_binpacks.py \
  nn-547-dataset/leela96-eval-filt-v2.binpack \
  nn-547-dataset/dfrc99-eval-filt-v2.binpack \
  nn-547-dataset/test80-nov2022-12tb7p-eval-filt-v2-d6.binpack \
  nn-547-dataset/T79-may2022-12tb7p-eval-filt-v2.binpack \
  nn-547-dataset/T60-nov2021-12tb7p-eval-filt-v2.binpack \
  nn-547-dataset/T60-dec2021-12tb7p-eval-filt-v2.binpack \
  filt-v6/test80-aug2022-16tb7p-filter-v6.binpack \
  filt-v6/test80-sep2022-16tb7p-filter-v6.binpack \
  filt-v6/test80-oct2022-16tb7p-filter-v6.binpack \
  filt-v6/test79-apr2022-16tb7p-filter-v6.binpack \
  filt-v6/test78-aprmay2022-16tb7p-filter-v6.binpack \
  filt-v6/test78-junjulaug2022-16tb7p-filter-v6.binpack \
  filt-v6/test78-sep2022-16tb7p-filter-v6.binpack \
  filt-v3/test77-dec2021-16tb7p-filt-v3.binpack \
  /data/leela96-dfrc99-T80novT79mayT60novdec-v2-T80augsepoctT79aprT78aprtosep-v6-T77dec-v3.binpack
```

The code for the new data filtering methods is available at:
https://github.com/linrock/Stockfish/tree/nnue-data-v3/nnue-data

The code for giving hexword names to .nnue files is at:
https://github.com/linrock/nnue-namer

Links for downloading the training data components can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch779.nnue : 0.6 +/- 3.1

Passed STC:
https://tests.stockfishchess.org/tests/view/64212412db43ab2ba6f8efb0
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 82256 W: 22185 L: 21809 D: 38262
Ptnml(0-2): 286, 9065, 22067, 9407, 303

Passed LTC:
https://tests.stockfishchess.org/tests/view/64223726db43ab2ba6f91d6c
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 30840 W: 8437 L: 8149 D: 14254
Ptnml(0-2): 14, 2891, 9323, 3177, 15

closes https://github.com/official-stockfish/Stockfish/pull/4465

bench 5101970
2023-03-29 21:37:52 +02:00
Sebastian Buchwald 43108a6198 Reuse existing functions to read/write array of network parameters
closes https://github.com/official-stockfish/Stockfish/pull/4463

No functional change
2023-03-29 21:36:27 +02:00
MinetaS 587bc647d7 Remove non_pawn_material in NNUE::evaluate
After "Use NNUE complexity in search, retune related parameters" commit,
the effect of non-pawn material adjustment has been nearly diminished.
This patch removes pos.non_pawn_material as a simplification, which
passed non-regression tests with both STC and LTC.

Passed non-regression STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 75152 W: 20030 L: 19856 D: 35266
Ptnml(0-2): 215, 8281, 20459, 8357, 264
https://tests.stockfishchess.org/tests/view/641ab471db43ab2ba6f7bc58

Passed non-regression LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 193864 W: 51870 L: 51829 D: 90165
Ptnml(0-2): 86, 18968, 58794, 18987, 97
https://tests.stockfishchess.org/tests/view/641b4fe6db43ab2ba6f7db96

closes https://github.com/official-stockfish/Stockfish/pull/4461

Bench: 5020718
2023-03-25 09:25:49 +01:00
Michael Chaly 1b5738e0c9 Simplify statScore initialization
This patch simplifies initialization of statScore to "always set it up to 0" instead of setting it up to 0 two plies deeper.
Reason for why it was done in previous way partially was because of LMR usage of previous statScore which was simplified long time ago so it makes sense to make in more simple there.

Passed STC:
https://tests.stockfishchess.org/tests/view/641a86d1db43ab2ba6f7b31d
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 115648 W: 30895 L: 30764 D: 53989
Ptnml(0-2): 368, 12741, 31473, 12876, 366

Passed LTC:
https://tests.stockfishchess.org/tests/view/641b1c31db43ab2ba6f7d17a
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 175576 W: 47122 L: 47062 D: 81392
Ptnml(0-2): 91, 17077, 53390, 17141, 89

closes https://github.com/official-stockfish/Stockfish/pull/4460

bench 5081969
2023-03-25 09:22:25 +01:00
FauziAkram b973e40e45 Update Elo estimates for terms in search
Setting the Elo value of some functions which were not set before.
All tests run at 10+0.1 (STC), 25000 games (Same as #4294).
Book used: UHO_XXL_+0.90_+1.19.epd

Values are rounded to the nearest non-negative integer.

Test links:
https://tests.stockfishchess.org/tests/view/6419ab5b65775d3b539f46c6
https://tests.stockfishchess.org/tests/view/6419adb465775d3b539f4730
https://tests.stockfishchess.org/tests/view/6419ae9c65775d3b539f4756
https://tests.stockfishchess.org/tests/view/6419b03f65775d3b539f47a8
https://tests.stockfishchess.org/tests/view/6419b35d65775d3b539f4860
https://tests.stockfishchess.org/tests/view/6419b6b965775d3b539f48e6
https://tests.stockfishchess.org/tests/view/6419cade65775d3b539f4cd5
https://tests.stockfishchess.org/tests/view/6419cbb565775d3b539f4d01
https://tests.stockfishchess.org/tests/view/6419cc6965775d3b539f4d1e

closes https://github.com/official-stockfish/Stockfish/pull/4459

No functional change
2023-03-25 09:20:58 +01:00
pb00067 24b37e4586 Verified SEE pruning for capturing and checking moves.
Patch analyzes field after SEE exchanges concluded with a recapture by
the opponent:
if opponent Queen/Rook/King results under attack after the exchanges, we
consider the move sharp and don't prune it.

Important note:
By accident I forgot to adjust 'occupied' when the king takes part in
the exchanges.
As result of this a move is considered sharp too, when opponent king
apparently can evade check by recapturing.
Surprisingly this seems contribute to patch's strength.

STC:
https://tests.stockfishchess.org/tests/view/640b16132644b62c33947397
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 116400 W: 31239 L: 30817 D: 54344
Ptnml(0-2): 350, 12742, 31618, 13116, 374

LTC:
https://tests.stockfishchess.org/tests/view/640c88092644b62c3394c1c5
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 177600 W: 47988 L: 47421 D: 82191
Ptnml(0-2): 62, 16905, 54317, 17436, 80

closes https://github.com/official-stockfish/Stockfish/pull/4453

bench: 5012145
2023-03-25 09:17:44 +01:00
pb00067 02e4697055 Remove 'si' StateInfo variable/parameter.
Since st is a member of position we don't need to pass it separately as
parameter.

While being there also remove some line in pos_is_ok, where
a copy of StateInfo was made by using default copy constructor and
then verified it's correctedness by doing a memcmp.
There is no point in doing that.

Passed non-regression test
https://tests.stockfishchess.org/tests/view/64098d562644b62c33942b35
LLR: 3.24 (-2.94,2.94) <-1.75,0.25>
Total: 548960 W: 145834 L: 146134 D: 256992
Ptnml(0-2): 1617, 57652, 156261, 57314, 1636

closes https://github.com/official-stockfish/Stockfish/pull/4444

No functional change
2023-03-19 11:29:35 +01:00
disservin af4b62a593 NNUE namespace cleanup
This patch moves the nnue namespace in the appropiate header that correspondes with the definition.
It also makes navigation a bit easier.

closes https://github.com/official-stockfish/Stockfish/pull/4445

No functional change
2023-03-19 11:27:15 +01:00
peregrineshahin 515b66f188 Fix null move issue
Fix altering for stats landing on B1 Square after a null move and fix considering counter-moves on A1 for root node.

fixes https://github.com/official-stockfish/Stockfish/issues/4333 by preventing calls to from_sq and to_sq functions over null-moves and none-moves.

closes https://github.com/official-stockfish/Stockfish/pull/4448

bench: 4980082
2023-03-19 11:25:48 +01:00
pb00067 f0556dcbe3 Small cleanups
remove some unneeded assignments, typos, incorrect comments, add authors entry.

closes https://github.com/official-stockfish/Stockfish/pull/4417

no functional change
2023-03-14 08:38:02 +01:00
mstembera 7077fbdd14 Remove redundant condition from capture_stage()
Change a non functional promotion check to an assert.

closes https://github.com/official-stockfish/Stockfish/pull/4436

No functional change
2023-03-14 08:29:19 +01:00
Sebastian Buchwald d1e17989b5 Fix Makefile for clang 16
The clang 16 release will remove the -fexperimental-new-pass-manager
flag (see https://github.com/llvm/llvm-project/commit/69b2b7282e92a1b576b7bd26f3b16716a5027e8e).
Thus, the commit adapts the Makefile to use this flag only for older
clang versions.

closes https://github.com/official-stockfish/Stockfish/pull/4437

No functional change
2023-03-14 08:25:14 +01:00
Alfredo Menezes 55896a1384 Change mode of incbin.h
Keep incbin.h with the same mode as the other source files.

A mode diff might show up when working with patch files or sending the source code between devices.
This patch should fix such behaviour.

closes https://github.com/official-stockfish/Stockfish/pull/4442

No functional change
2023-03-14 08:23:50 +01:00
Michael Chaly 78532af9dc Do more singular extensions
This patch continues trend of last VLTC tuning - as measured by dubslow
most of it gains was in lowering marging in calculation of singularBeta.
This patch is a manual adjustment on top of it - it lowers multiplier
of depth in calculation of singularBeta even further, from 2/3 to 1,5/2,5.

Was negative at STC:
https://tests.stockfishchess.org/tests/view/64089c632644b62c3393fc12
Elo: -2.49 +-1.7 (95%) LOS: 0.2%
Total: 40000 W: 10601 L: 10888 D: 18511
Ptnml(0-2): 123, 4580, 10875, 4305, 117
nElo: -5.03 +-3.4 (95%) PairsRatio: 0.94

Passed 180+1.8 SPRT:
https://tests.stockfishchess.org/tests/view/640096dae74a12625bcf3b33
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 160952 W: 43753 L: 43342 D: 73857
Ptnml(0-2): 25, 13984, 52039, 14411, 17

Passed 60+0.6 8 threads SPRT:
https://tests.stockfishchess.org/tests/view/640dca8e65775d3b539cb7f6
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 38824 W: 10825 L: 10554 D: 17445
Ptnml(0-2): 0, 2939, 13268, 3200, 5

closes https://github.com/official-stockfish/Stockfish/pull/4443

bench 4776866
2023-03-14 08:22:40 +01:00
Dubslow a48573e15f More negative extensions on nonsingular nonpv nodes.
Following up the previous gainer also in this nonsingular node
section of code. Credit shared with @FauziAkram for realizing
this nonsingular node stuff had some potential, and @XInTheDark
for reminding us that !PvNodes better handle extensions/reductions
than Pv.

Passed STC: https://tests.stockfishchess.org/tests/view/640a7bb32644b62c339457c3
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 136776 W: 36598 L: 36149 D: 64029
Ptnml(0-2): 439, 14834, 37384, 15301, 430

Passed LTC: https://tests.stockfishchess.org/tests/view/640c43a02644b62c3394b23c
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 79536 W: 21363 L: 20984 D: 37189
Ptnml(0-2): 28, 7525, 24285, 7900, 30

closes https://github.com/official-stockfish/Stockfish/pull/4441

Bench: 4444953
2023-03-14 00:41:42 +01:00
Michael Chaly 39da50ed23 Do more negative extensions
This patch does negatively extend transposition table move if singular search failed and tt value is not bigger than alpha.
Logic is close to what we had before recent simplification of negative extensions but uses or condition instead of and condition.

Passed STC:
https://tests.stockfishchess.org/tests/view/6404c8102644b62c33934607
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 119040 W: 31841 L: 31416 D: 55783
Ptnml(0-2): 356, 13070, 32292, 13397, 405

Passed LTC:
https://tests.stockfishchess.org/tests/view/6405abda2644b62c33937119
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 47216 W: 12816 L: 12496 D: 21904
Ptnml(0-2): 12, 4500, 14286, 4776, 34

closes https://github.com/official-stockfish/Stockfish/pull/4430

bench 4747020
2023-03-08 07:16:09 +01:00
Joost VandeVondele 6ce225bb4c Fix TB after capture_stage fix
https://github.com/official-stockfish/Stockfish/commit/5c75c1c2fbb7bb4f0bf7c44fb855c415b788cbf7
introduced a capture_stage() function, but TB usage needs a pure capture() function.

closes https://github.com/official-stockfish/Stockfish/pull/4428

No functional change.
2023-03-08 07:14:49 +01:00
Maxim Masiutin 70dfa141d5 Clarify the description of the x86-64-vnni256 and x86-64-avxvnni architectures
Now it is clearly explained that "x86-64-vnni256" requires full
support of AVX512-VNNI, but only 256-bit operands are used.

closes https://github.com/official-stockfish/Stockfish/pull/4427

No functional change
2023-03-08 07:14:07 +01:00
Joost VandeVondele cdec775a15 Add CITATION.cff file
Make the stockfish software more easily citable, for example in academic papers.

fixes https://github.com/official-stockfish/Stockfish/issues/4419
closes https://github.com/official-stockfish/Stockfish/pull/4422

No functional change
2023-03-05 16:16:16 +01:00
dav1312 3a634f5282 Update README.md
Update and simplify the readme, removing duplicated and outdated stuff and pointing to the new wiki

closes https://github.com/official-stockfish/Stockfish/pull/4421

No functional change
2023-03-05 16:15:12 +01:00
disservin 5c589142ae Add wiki to artifacts
snapshot the wiki https://github.com/official-stockfish/stockfish/wiki as part of the artifacts generated.
This will allow future release to include the wiki pages as a form of documentation

closes https://github.com/official-stockfish/Stockfish/pull/4420

No functional change
2023-03-05 16:14:07 +01:00
Michael Chaly 5c75c1c2fb Fix duplicated moves generation in movepicker
in a some of cases movepicker returned some moves more than once which lead
to them being searched more than once. This bug was possible because of how
we use queen promotions - they are generated as a captures but are not
included in position function which checks if move is a capture. Thus if
any refutation (killer or countermove) was a queen promotion it was
searched twice - once as a capture and one as a refutation.

This patch affects various things, namely stats assignments for queen promotions
and other moves if best move is queen promotion,
also some heuristics in search and qsearch.

With this patch every queen promotion is now considered a capture.

After this patch number of found duplicated moves is 0 during normal 13 depth bench run.

Passed STC:
https://tests.stockfishchess.org/tests/view/63f77e01e74a12625bcd87d7
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 80920 W: 21455 L: 21289 D: 38176
Ptnml(0-2): 198, 8839, 22241, 8963, 219

Passed LTC:
https://tests.stockfishchess.org/tests/view/63f7e020e74a12625bcd9a76
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 89712 W: 23674 L: 23533 D: 42505
Ptnml(0-2): 24, 8737, 27202, 8860, 33

closes https://github.com/official-stockfish/Stockfish/pull/4405

bench 4681731
2023-03-05 16:06:03 +01:00
Linmiao Xu 876906965b Update default net to nn-52471d67216a.nnue
Created by retraining the master net with modifications to the previous best dataset:

* Improving T80 oct+nov 2022 endgame lambda accuracy by rescoring with 12-16tb of syzygy 7p tablebases
* Filtering T78 jun+jul+aug 2022 with d6pv2 search to remove positions with bestmove captures or one good move
* Adding T80 sep 2022 data, rescored with 16tb of 7p tablebases, unfiltered

Trained with max-epoch 900, end-lambda 0.7, and early-fen-skipping 28.

```
python3 easy_train.py \
  --experiment-name leela96-dfrc99-T80octnovT79aprmayT78junjulaugT60novdec-filt-v2-T78sep12tb7p-T77decT80sep16tb7p-lambda7-sk28 \
  --training-dataset /data/leela96-dfrc99-T80octnovT79aprmayT78junjulaugT60novdec-filt-v2-T78sep12tb7p-T77decT80sep16tb7p.binpack \
  --nnue-pytorch-branch linrock/nnue-pytorch/easy-train-early-fen-skipping \
  --early-fen-skipping 28 \
  --start-from-engine-test-net True \
  --gpus "0," \
  --max_epoch 900 \
  --start-lambda 1.0 \
  --end-lambda 0.7 \
  --gamma 0.995 \
  --lr 4.375e-4 \
  --tui False \
  --seed $RANDOM
```

Training data was rescored and d6pv2 filtered in the same way as recent best datasets.
For preparing the merged training dataset:

```
python3 interleave_binpacks.py \
  leela96-eval-filt-v2.binpack \
  dfrc99-eval-filt-v2.binpack \
  test80-oct2022-16tb7p-eval-filt-v2-d6.binpack \
  test80-nov2022-12tb7p-eval-filt-v2-d6.binpack \
  T79-apr2022-12tb7p-eval-filt-v2.binpack \
  T79-may2022-12tb7p-eval-filt-v2.binpack \
  test78-junjulaug2022-16tb7p-eval-filt-v2-d6.binpack \
  T60-nov2021-12tb7p-eval-filt-v2.binpack \
  T60-dec2021-12tb7p-eval-filt-v2.binpack \
  T78-sep2022-12tb7p.binpack \
  test77-dec2021-16gb7p.binpack \
  test80-sep2022-16tb7p.binpack \
  /data/leela96-dfrc99-T80octnovT79aprmayT78junjulaugT60novdec-filt-v2-T78sep12tb7p-T77decT80sep16tb7p.binpack
```

Links for downloading the training data components can be found at:
https://robotmoon.com/nnue-training-data/

Local elo at 25k nodes per move:
nn-epoch839.nnue : 0.6 +/- 1.4

Passed STC:
https://tests.stockfishchess.org/tests/view/63f9ab4be74a12625bcdf02e
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 84656 W: 22681 L: 22302 D: 39673
Ptnml(0-2): 271, 9343, 22734, 9696, 284

Passed LTC:
https://tests.stockfishchess.org/tests/view/63fa3833e74a12625bce0c0e
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 184664 W: 49933 L: 49344 D: 85387
Ptnml(0-2): 111, 17977, 55561, 18578, 105

closes https://github.com/official-stockfish/Stockfish/pull/4416

bench: 4814343
2023-02-27 22:07:52 +01:00
Dubslow 6adbc6fa05 Late counter bonus: boost underestimated moves
The idea here is very intuitive: since we've just proven that the move is good, then if it previously had poor stats, boost those stats more than otherwise.

Passed STC: https://tests.stockfishchess.org/tests/view/63fb504ce74a12625bce4154
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 21128 W: 5763 L: 5481 D: 9884
Ptnml(0-2): 52, 2212, 5759, 2484, 57

Passed LTC: https://tests.stockfishchess.org/tests/view/63fb7825e74a12625bce491b
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 91904 W: 24764 L: 24359 D: 42781
Ptnml(0-2): 45, 8735, 27984, 9146, 42

closes https://github.com/official-stockfish/Stockfish/pull/4415

bench 4318808
2023-02-27 22:03:50 +01:00
Sebastian Buchwald 564456a6a8 Unify type alias declarations
The commit unifies the declaration of type aliases by replacing all
typedefs with corresponding using statements.

closing https://github.com/official-stockfish/Stockfish/pull/4412

No functional change
2023-02-27 08:29:47 +01:00
Stefan Geschwentner ff5a6f8df1 NNUE accumulator update in probcut.
Call the recently added hint function for NNUE accumulator update after a failed probcut search.
In this case we already searched at least some captures and tt move which, however, is not sufficient for a cutoff.
So it seems we have a greater chance that the full search will also have no cutoff and hence all moves have to be searched.

STC: https://tests.stockfishchess.org/tests/view/63fa74a4e74a12625bce1823
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 70096 W: 18770 L: 18423 D: 32903
Ptnml(0-2): 191, 7342, 19654, 7651, 210

To be sure that we have no heavy interaction retest on top of #4410.

Rebased STC: https://tests.stockfishchess.org/tests/view/63fb2f62e74a12625bce3b03
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 137688 W: 36790 L: 36349 D: 64549
Ptnml(0-2): 397, 14373, 38919, 14702, 453

closes https://github.com/official-stockfish/Stockfish/pull/4411

No functional change
2023-02-27 08:28:45 +01:00
pb00067 728b963614 Use common_parent_position hint also at PVNodes TT hits.
Credits to Stefan Geschwentner (locutus2) showing that the hint
is useful on PvNodes. In contrast to his test,
this version avoids to use the hint when in check.
I believe checking positions aren't good candidates for the hint
because:
- evasion moves are rather few, so a checking pos. has much less childs
than a normal position
- if the king has to move the NNUE eval can't use incremental updates,
  so the child nodes have to do a full refresh anyway.

Passed STC:
https://tests.stockfishchess.org/tests/view/63f9c5b1e74a12625bcdf585
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 124472 W: 33268 L: 32846 D: 58358
Ptnml(0-2): 350, 12986, 35170, 13352, 378

closes https://github.com/official-stockfish/Stockfish/pull/4410

no functional change
2023-02-27 08:27:26 +01:00
Alfredo Menezes 98dafda6c8 Simplify condition in step 15
Remove 'ttValue <= alpha' check for negative extension in singular search.
Also apply some small code style changes.

STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 127888 W: 33766 L: 33651 D: 60471
Ptnml(0-2): 303, 14082, 35089, 14137, 333
https://tests.stockfishchess.org/tests/view/63f79528e74a12625bcd8c05

LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 89048 W: 23924 L: 23782 D: 41342
Ptnml(0-2): 27, 8635, 27065, 8763, 34
https://tests.stockfishchess.org/tests/view/63f82177e74a12625bcda6f4

LTC (retest):
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 196360 W: 52514 L: 52475 D: 91371
Ptnml(0-2): 103, 19066, 59780, 19151, 80
https://tests.stockfishchess.org/tests/view/63f934bfe74a12625bcdd929

closes https://github.com/official-stockfish/Stockfish/pull/4407

Bench: 5310866
2023-02-27 08:26:29 +01:00
Michael Chaly 472e726bff Search tuning at very long time control
This patch is a result of tuning session of approximately 100k games at 120+1.2.
Biggest changes are in extensions, stat bonus and depth reduction for nodes without a tt move.

Failed STC:
https://tests.stockfishchess.org/tests/view/63f72c72e74a12625bcd7938
LLR: -2.94 (-2.94,2.94) <0.00,2.00>
Total: 13872 W: 3535 L: 3769 D: 6568
Ptnml(0-2): 56, 1621, 3800, 1419, 40

Close to neutral at LTC:
https://tests.stockfishchess.org/tests/view/63f738f5e74a12625bcd7b8a
Elo: 0.80 +-1.2 (95%) LOS: 90.0%
Total: 60000 W: 16213 L: 16074 D: 27713
Ptnml(0-2): 24, 5718, 18379, 5853, 26
nElo: 1.82 +-2.8 (95%) PairsRatio: 1.02

Passed 180+1.8 VLTC:
https://tests.stockfishchess.org/tests/view/63f868f3e74a12625bcdb33e
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 15864 W: 4449 L: 4202 D: 7213
Ptnml(0-2): 1, 1301, 5083, 1544, 3

Passed 60+0.6 8 threads SMP VLTC:
https://tests.stockfishchess.org/tests/view/63f8a5d6e74a12625bcdbdb3
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 6288 W: 1821 L: 1604 D: 2863
Ptnml(0-2): 0, 402, 2123, 619, 0

closes https://github.com/official-stockfish/Stockfish/pull/4406

bench 4705194
2023-02-24 20:14:44 +01:00
Sebastian Buchwald 29b5ad5dea Fix typo in method name
closes https://github.com/official-stockfish/Stockfish/pull/4404

No functional change
2023-02-24 20:12:53 +01:00
Linmiao Xu 69639d764b Reintroduce nnue pawn scaling with lower lazy thresholds
Params found with the nevergrad TBPSA optimizer via nevergrad4sf modified to:

* use SPRT LLR with fishtest STC elo gainer bounds [0, 2] as the objective function
* increase the game batch size after each new optimal point is found

The params were the optimal point after TBPSA iteration 7 and 160 nevergrad evaluations with:

* initial batch size of 96 games per evaluation
* batch size increase of 64 games after each iteration
* a budget of 512 evaluations
* TC: fixed 1.5 million nodes per move, no time limit

nevergrad4sf enables optimizing stockfish params with TBPSA:
https://github.com/vondele/nevergrad4sf

Using pentanomial game results with smaller game batch sizes was inspired by:

Use of SPRT LLR calculated from pentanomial game results as the objective function was an experiment at maximizing the information from game batches to reduce the computational cost for TBPSA to converge on good parameters.

For the exact code used to find the params:
https://github.com/linrock/tuning-fork

Passed STC:
https://tests.stockfishchess.org/tests/view/63f4ef5ee74a12625bcd114a
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 66552 W: 17736 L: 17390 D: 31426
Ptnml(0-2): 164, 7229, 18166, 7531, 186

Passed LTC:
https://tests.stockfishchess.org/tests/view/63f56028e74a12625bcd2550
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 71264 W: 19150 L: 18787 D: 33327
Ptnml(0-2): 23, 6728, 21771, 7083, 27

closes https://github.com/official-stockfish/Stockfish/pull/4401

bench 3687580
2023-02-23 13:27:57 +01:00
Joost VandeVondele 08385527dd Introduce a function to compute NNUE accumulator
This patch introduces `hint_common_parent_position()` to signal that potentially several child nodes will require an NNUE eval. By populating explicitly the accumulator, these subsequent evaluations can be performed more efficiently.

This was based on the observation that calculating the evaluation in an excluded move position yielded a significant Elo gain, even though the evaluation itself was already available (work by pb00067).

Sopel wrote the code to perform just the accumulator update. This PR is based on cleaned up code that

passed STC:
https://tests.stockfishchess.org/tests/view/63f62f9be74a12625bcd4aa0
 LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 110368 W: 29607 L: 29167 D: 51594
Ptnml(0-2): 41, 10551, 33572, 10967, 53

and in an the earlier (equivalent) version

passed STC:
https://tests.stockfishchess.org/tests/view/63f3c3fee74a12625bcce2a6
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 47552 W: 12786 L: 12467 D: 22299
Ptnml(0-2): 120, 5107, 12997, 5438, 114

passed LTC:
https://tests.stockfishchess.org/tests/view/63f45cc2e74a12625bccfa63
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 110368 W: 29607 L: 29167 D: 51594
Ptnml(0-2): 41, 10551, 33572, 10967, 53

closes https://github.com/official-stockfish/Stockfish/pull/4402

Bench: 3726250
2023-02-23 13:25:35 +01:00
Sebastian Buchwald 77dfcbedce Remove unused macros
closes https://github.com/official-stockfish/Stockfish/pull/4397

No functional change
2023-02-23 13:24:37 +01:00
Sebastian Buchwald b4ad3a3c4b Add support for ARM dot product instructions
The sdot instruction computes (and accumulates) a signed dot product,
which is quite handy for Stockfish's NNUE code. The instruction is
optional for Armv8.2 and Armv8.3, and mandatory for Armv8.4 and above.

The commit adds a new 'arm-dotprod' architecture with enabled dot
product support. It also enables dot product support for the existing
'apple-silicon' architecture, which is at least Armv8.5.

The following local speed test was performed on an Apple M1 with
ARCH=apple-silicon. I had to remove CPU pinning from the benchmark
script. However, the results were still consistent: Checking both
binaries against themselves reported a speedup of +0.0000 and +0.0005,
respectively.

```
Result of 100 runs
==================
base (...ish.037ef3e1) =    1917997  +/- 7152
test (...fish.dotprod) =    2159682  +/- 9066
diff                   =    +241684  +/- 2923

speedup        = +0.1260
P(speedup > 0) =  1.0000

CPU: 10 x arm
Hyperthreading: off
```

Fixes #4193

closes https://github.com/official-stockfish/Stockfish/pull/4400

No functional change
2023-02-23 13:22:03 +01:00
Dubslow 037ef3e18d Remove one reduction call
even though bench is unchanged to depth 28,
due to adjusting depth in singular extensions this might be functional.

STC: https://tests.stockfishchess.org/tests/view/63ec21affe833123fef34153
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 195712 W: 51625 L: 51581 D: 92506
Ptnml(0-2): 504, 20527, 55779, 20513, 533

LTC: https://tests.stockfishchess.org/tests/view/63ed3487fe833123fef375ed
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 32176 W: 8631 L: 8442 D: 15103
Ptnml(0-2): 5, 2794, 10309, 2967, 13

closes https://github.com/official-stockfish/Stockfish/pull/4395

Bench 4283297
2023-02-18 14:01:08 +01:00
Dubslow 085cace457 Simplify late countermove bonus condition
STC: https://tests.stockfishchess.org/tests/view/63d53ac6a67dd929a555e1e2
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 216096 W: 56862 L: 56839 D: 102395
Ptnml(0-2): 648, 24033, 58650, 24082, 635

LTC: https://tests.stockfishchess.org/tests/view/63d7f9a6a67dd929a5565991
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 808512 W: 214060 L: 214610 D: 379842
Ptnml(0-2): 301, 79448, 245293, 78928, 286

closes https://github.com/official-stockfish/Stockfish/pull/4392

Bench: 4283297
2023-02-18 13:34:40 +01:00
mstembera 29c1e072b6 Simplify nnueComplexity calculation.
further simplification after https://github.com/official-stockfish/Stockfish/pull/4377

STC https://tests.stockfishchess.org/tests/view/63e02a3773223e7f52ad8190
LLR: 2.97 (-2.94,2.94) <-1.75,0.25>
Total: 359072 W: 94605 L: 94733 D: 169734
Ptnml(0-2): 994, 39874, 97958, 39686, 1024

LTC https://tests.stockfishchess.org/tests/view/63e3fd12b5f425d71f77002a
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 248424 W: 66020 L: 66030 D: 116374
Ptnml(0-2): 113, 24653, 74689, 24645, 112

closes https://github.com/official-stockfish/Stockfish/pull/4390

bench: 4098325
2023-02-18 13:30:48 +01:00
MinetaS 2c36d1e7e7 Fix overflow in add_dpbusd_epi32x2
This patch fixes 16bit overflow in *_add_dpbusd_epi32x2 functions,
that can be triggered in rare cases depending on the NNUE weights.

While the code leads to some slowdown on affected architectures
(most notably avx2), the fix is simpler than some of the other
options discussed in
https://github.com/official-stockfish/Stockfish/pull/4394

Code suggested by Sopel97.

Result of "bench 4096 1 30 default depth nnue":

| Architecture        | master    | patch (gcc) | patch (clang) |
|---------------------|-----------|-------------|---------------|
| x86-64-vnni512      | 762122798 | 762122798   | 762122798     |
| x86-64-avx512       | 769723503 | 762122798   | 762122798     |
| x86-64-bmi2         | 769723503 | 762122798   | 762122798     |
| x86-64-ssse3        | 769723503 | 762122798   | 762122798     |
| x86-64              | 762122798 | 762122798   | 762122798     |

Following architectures will experience ~4% slowdown due to an
additional instruction in the middle of hot path:

* x86-64-avx512
* x86-64-bmi2
* x86-64-avx2
* x86-64-sse41-popcnt (x86-64-modern)
* x86-64-ssse3
* x86-32-sse41-popcnt

This patch clearly loses Elo against master with both STC and LTC.

Failed non-regression STC (256bit fix only):
LLR: -2.95 (-2.94,2.94) <-1.75,0.25>
Total: 33528 W: 8769 L: 9049 D: 15710
Ptnml(0-2): 96, 3616, 9600, 3376, 76
https://tests.stockfishchess.org/tests/view/63e6a5b44299542b1e26a485

60+0.6 @ 30000 games:
Elo: -1.67 +-1.7 (95%) LOS: 2.8%
Total: 30000 W: 7848 L: 7992 D: 14160
Ptnml(0-2): 12, 2847, 9436, 2683, 22
nElo: -3.84 +-3.9 (95%) PairsRatio: 0.95
https://tests.stockfishchess.org/tests/view/63e7ac716d0e1db55f35a660

However, a test against nn-a3dc078bafc7.nnue, which is the latest "safe"
network not causing the bug, passed with regular bounds.

Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 160456 W: 42658 L: 42175 D: 75623
Ptnml(0-2): 487, 17638, 43469, 18173, 461
https://tests.stockfishchess.org/tests/view/63e89836d62a5d02b0fa82c8

closes https://github.com/official-stockfish/Stockfish/pull/4391
closes https://github.com/official-stockfish/Stockfish/pull/4394

No functional change
2023-02-18 13:23:18 +01:00
disservin 852330ee50 update cuckoo link
use webarchive to link to the cycle detection paper by Kervinck.

closes https://github.com/official-stockfish/Stockfish/pull/4389

No functional change
2023-02-18 13:14:24 +01:00
borg323 e5f6d71b96 Fix build on arm windows
avoids the use of _mm_malloc on arm windows.

fixes #4379
closes https://github.com/official-stockfish/Stockfish/pull/4388

No functional change
2023-02-18 13:14:05 +01:00
Linmiao Xu 05dea2ca46 Update default net to nn-1337b1adec5b.nnue
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
2023-02-09 07:50:27 +01:00
Dubslow e25bcaed3c Update complexityAverage in all branches of static eval
STC: https://tests.stockfishchess.org/tests/view/63dda49573223e7f52ad0f8c
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 117416 W: 31173 L: 31049 D: 55194
Ptnml(0-2): 290, 12246, 33533, 12328, 311

LTC: https://tests.stockfishchess.org/tests/view/63dfa90873223e7f52ad69b8
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 44416 W: 11924 L: 11744 D: 20748
Ptnml(0-2): 5, 4036, 13968, 4172, 27

closes https://github.com/official-stockfish/Stockfish/pull/4385

bench 4758694
2023-02-09 07:46:41 +01:00
Michael Chaly 8f843633db Cleanup and reorder in qsearch
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.
2023-02-09 07:45:05 +01:00
Disservin d5817a5896 remove unnecessary variable
pinned already has to be true for the bitwise &

closes https://github.com/official-stockfish/Stockfish/pull/4381

No functional change
2023-02-09 07:42:52 +01:00
pb00067 8d3457a996 Improve excluded move logic
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
2023-02-03 20:18:50 +01:00
Muzhen Gaming d2f79ff0e0 Remove reduced LMR capture bonus
In LMR, simplify away the reduced capture bonus (i.e. if (capture) bonus /= 6).

Non-regression STC: https://tests.stockfishchess.org/tests/view/63da1da9bbadd17b3787dced
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 28152 W: 7521 L: 7296 D: 13335
Ptnml(0-2): 76, 3069, 7568, 3280, 83

Non-regression LTC: https://tests.stockfishchess.org/tests/view/63da6ad4bbadd17b3787e98c
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 52472 W: 14120 L: 13941 D: 24411
Ptnml(0-2): 16, 5071, 15887, 5242, 20

closes https://github.com/official-stockfish/Stockfish/pull/4378

Bench: 4034016
2023-02-03 20:15:09 +01:00
Michael Chaly 1cdc0f78bd Simplify usage of optimism in complexity
This patch removes one condition in optimism usage in complexity, now negative optimism also impacts it.

Passed STC:
https://tests.stockfishchess.org/tests/view/63d34f43721fe2bff692fb12
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 204920 W: 54343 L: 54309 D: 96268
Ptnml(0-2): 598, 22648, 55897, 22756, 561

Passed LTC:
https://tests.stockfishchess.org/tests/view/63d612a2a67dd929a556075c
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 200712 W: 53207 L: 53172 D: 94333
Ptnml(0-2): 58, 19664, 60901, 19651, 82

closes https://github.com/official-stockfish/Stockfish/pull/4377

bench 4204964
2023-02-03 20:10:08 +01:00
MinetaS 5a30b087c3 Expand statistics tools for engine development
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
2023-02-03 20:07:56 +01:00
Michael Chaly da8513f0ea Do less SEE pruning in qsearch
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
2023-02-02 18:00:14 +01:00
Joost VandeVondele 3589bd008a Update WLD model
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.
2023-02-02 17:58:05 +01:00
MinetaS 7fc0f589d6 Add -Wconditional-uninitialized when using Clang
Add -Wconditional-uninitialized as it is not controlled by -Wall.

closes https://github.com/official-stockfish/Stockfish/pull/4371

No functional change
2023-02-02 17:49:23 +01:00
Muzhen Gaming 0827e00f10 Decrease reduction for killer moves with good history
If move is a main killer and we have a good history, decrease reduction.

STC: https://tests.stockfishchess.org/tests/view/63d38b37721fe2bff693069a
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 46688 W: 12542 L: 12222 D: 21924
Ptnml(0-2): 126, 5013, 12769, 5287, 149

LTC: https://tests.stockfishchess.org/tests/view/63d471e2bde6e5f3cb4be5d3
LLR: 2.93 (-2.94,2.94) <0.50,2.50>
Total: 130976 W: 35033 L: 34555 D: 61388
Ptnml(0-2): 38, 12551, 39833, 13027, 39

closes https://github.com/official-stockfish/Stockfish/pull/4369

Bench: 4069938
2023-02-02 17:45:57 +01:00
MinetaS e4e61cd9cc Remove maxNextDepth
This patch allows full PV search to have double extensions as well when
extension == 1 && doDeeperSearch && doEvenDeeperSearch && !doShallowerSearch
is true, which is extremely rare to occur.

Passed non-regression STC (master 3d2381d):
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 370824 W: 97835 L: 97974 D: 175015
Ptnml(0-2): 1073, 38814, 105731, 38767, 1027
https://tests.stockfishchess.org/tests/view/63c89416a83c702aac08314c

Passed non-regression LTC (master 3d2381d):
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 306048 W: 81173 L: 81237 D: 143638
Ptnml(0-2): 117, 27977, 96901, 27911, 118
https://tests.stockfishchess.org/tests/view/63cc4e84344bb01c191b2658

Bench: 4208265
2023-01-28 16:54:30 +01:00
Dubslow d4d1cec296 Remove previousDepth in favor of completedDepth + 2
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
2023-01-28 16:50:46 +01:00
Sebastian Buchwald 2f67409506 Remove redundant const qualifiers
The const qualifiers are already implied by the constexpr qualifiers.

closes https://github.com/official-stockfish/Stockfish/pull/4359

No functional change
2023-01-28 16:49:27 +01:00
Sebastian Buchwald 2167942b6e Simplify functions to read/write network parameters
closes https://github.com/official-stockfish/Stockfish/pull/4358

No functional change
2023-01-28 16:47:52 +01:00
disservin def296670d Fixed UCI TB win values
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
2023-01-28 16:37:29 +01:00
Michael Chaly d3860f8d5e Rebalance usage of history heuristics in pruning
This patch has multiple effects:

* history heuristics sum in futility pruning now can't exceed some negative value so futility pruning for moves with negative histories should become slightly less aggressive;

* history heuristics are now used in SEE pruning for quiet moves;

Passed STC:
https://tests.stockfishchess.org/tests/view/63cde339c93e8828d0f02e3a
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 88424 W: 23681 L: 23303 D: 41440
Ptnml(0-2): 258, 9559, 24219, 9899, 277

Passed LTC:
https://tests.stockfishchess.org/tests/view/63ce9009c93e8828d0f04e4f
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 79536 W: 21223 L: 20843 D: 37470
Ptnml(0-2): 22, 7599, 24146, 7979, 22

closes https://github.com/official-stockfish/Stockfish/pull/4355

Bench: 4208265
2023-01-25 07:51:19 +01:00
Dubslow 3dd0a7a7cd stat_bonus: replace quadratic with nearly identical line
passed stc: https://tests.stockfishchess.org/tests/view/63ca58c90eefe8694a0c4eac
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 287960 W: 76146 L: 76201 D: 135613
Ptnml(0-2): 947, 31890, 78307, 31943, 893

passed ltc: https://tests.stockfishchess.org/tests/view/63cc8a51344bb01c191b30f0
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 73784 W: 19559 L: 19402 D: 34823
Ptnml(0-2): 33, 7171, 22327, 7328, 33

closes https://github.com/official-stockfish/Stockfish/pull/4352

bench 3990490
2023-01-25 07:48:39 +01:00
Linmiao Xu 596a528c6a Update default net to nn-bc24c101ada0.nnue
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
2023-01-23 07:01:32 +01:00
Jonathan a2038c1a01 apply if constexpr to additional instances
as a form of documentation, and a hint to the compiler.

closes https://github.com/official-stockfish/Stockfish/pull/4345

No functional change
2023-01-22 13:15:46 +01:00
Stephen Touset 734315ff30 Remove precomputed SquareBB
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
2023-01-22 10:55:32 +01:00
Joost VandeVondele a08b8d4e97 Update UCI_Elo parameterization
The old parameterization (https://github.com/official-stockfish/Stockfish/pull/2225/files) has now become quite inaccurate.
This updates the formula based on updated results with master. The formula is based on a fit of the Elo results for games
played between master at various skill levels, and various versions of the Stash engine, which have been ranked at CCRL.

```
   # PLAYER             :  RATING  ERROR  POINTS  PLAYED   (%)
   1 master-skill-19    :  3191.1   40.4   940.0    1707    55
   2 master-skill-18    :  3170.3   39.3  1343.0    2519    53
   3 master-skill-17    :  3141.3   37.8  2282.0    4422    52
   4 master-skill-16    :  3111.2   37.1  2773.0    5423    51
   5 master-skill-15    :  3069.5   37.2  2728.5    5386    51
   6 master-skill-14    :  3024.8   36.1  2702.0    5339    51
   7 master-skill-13    :  2972.9   35.4  2645.5    5263    50
   8 master-skill-12    :  2923.1   35.0  2653.5    5165    51
   9 master-skill-11    :  2855.5   33.6  2524.0    5081    50
  10 master-skill-10    :  2788.3   32.0  2724.5    5511    49
  11 stash-bot-v25.0    :  2744.0   31.5  1952.5    3840    51
  12 master-skill-9     :  2702.8   30.5  2670.0    5018    53
  13 master-skill-8     :  2596.2   28.5  2669.5    4975    54
  14 stash-bot-v21.0    :  2561.2   30.0  1338.0    3366    40
  15 master-skill-7     :  2499.5   28.5  1934.0    4178    46
  16 stash-bot-v20.0    :  2452.6   27.7  1606.5    3378    48
  17 stash-bot-v19.0    :  2425.3   26.7  1787.0    3365    53
  18 master-skill-6     :  2363.2   26.4  2510.5    4379    57
  19 stash-bot-v17.0    :  2280.7   25.4  2209.0    4378    50
  20 master-skill-5     :  2203.7   25.3  2859.5    5422    53
  21 stash-bot-v15.3    :  2200.0   25.4  1757.0    4383    40
  22 stash-bot-v14      :  2145.9   25.5  2890.0    5167    56
  23 stash-bot-v13      :  2042.7   25.8  2263.5    4363    52
  24 stash-bot-v12      :  1963.4   25.8  1769.5    4210    42
  25 master-skill-4     :  1922.9   25.9  2690.0    5399    50
  26 stash-bot-v11      :  1873.0   26.3  2203.5    4335    51
  27 stash-bot-v10      :  1783.8   27.8  2568.5    4301    60
  28 master-skill-3     :  1742.3   27.8  1909.5    4439    43
  29 master-skill-2     :  1608.4   29.4  2064.5    4389    47
  30 stash-bot-v9       :  1582.6   30.2  2130.0    4230    50
  31 master-skill-1     :  1467.6   31.3  2015.5    4244    47
  32 stash-bot-v8       :  1452.8   31.5  1953.5    3780    52
  33 master-skill-0     :  1320.1   32.9   651.5    2083    31
```

Skill 0 .. 19, now covers CCRL Blitz Elo from 1320 to 3190, approximately.
Indeed, the Elo of stash in this analysis is only to within +- 100 Elo of CCRL,
probably because it depends quite a bit on the opponent pool.

To obtain a skill level for a given Elo number, the above data is fit as a 3rd
degree polynomial Skill(Elo). A quick test confirms the correspondence to the above table:

```
Score of master-elo-2721 vs stash-bot-v21.0: 51 - 16 - 19  [0.703] 86
Elo difference: 150.1 +/- 70.2, LOS: 100.0 %, DrawRatio: 22.1 %
```

closes https://github.com/official-stockfish/Stockfish/pull/4341

No functional change.
2023-01-22 10:54:15 +01:00
Sebastian Buchwald da5bcec481 Fix asm modifiers in add_dpbusd_epi32x2 implementations
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
2023-01-22 10:51:02 +01:00
Linmiao Xu 3d2381d76d Update default net to nn-1e7ca356472e.nnue
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
2023-01-14 08:12:11 +01:00
Sebastian Buchwald 4f4e652eca Avoid unnecessary string copies
closes https://github.com/official-stockfish/Stockfish/pull/4326

also fixes typo, closes https://github.com/official-stockfish/Stockfish/pull/4332

No functional change
2023-01-09 20:32:58 +01:00
Sebastian Buchwald e9e7a7b83f Replace some std::string occurrences with std::string_view
std::string_view is more lightweight than std::string. Furthermore,
std::string_view variables can be declared constexpr.

closes https://github.com/official-stockfish/Stockfish/pull/4328

No functional change
2023-01-09 20:28:24 +01:00
Stefano Di Martino 5a88c5bb9b Modernize code base a little bit
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
2023-01-09 20:25:13 +01:00
Sebastian Buchwald 31acd6bab7 Warn if a global function has no previous declaration
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
2023-01-09 20:18:39 +01:00
Jake Senne fcee83810a Only close file if already open
Ensures that the tablebase file is only closed if already open.

Fixes #4268
Closes https://github.com/official-stockfish/Stockfish/pull/4321

No functional change
2023-01-09 20:16:17 +01:00
candirufish 4101893a28 On step 18 increase reduction by 2 if not ttmove and cutnode
stc: https://tests.stockfishchess.org/tests/view/63babc9fcd3db0c8d399f723
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 43104 W: 11711 L: 11389 D: 20004
Ptnml(0-2): 211, 4518, 11793, 4798, 232

ltc: https://tests.stockfishchess.org/tests/view/63bb1857cd3db0c8d39a0661
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 127104 W: 33810 L: 33339 D: 59955
Ptnml(0-2): 39, 12155, 38702, 12608, 48

closes https://github.com/official-stockfish/Stockfish/pull/4334

Bench: 4035725
2023-01-09 20:15:08 +01:00
Dubslow ea0f34120f Late countermove bonus: remove "extraBonus &&"
passed stc: https://tests.stockfishchess.org/tests/view/63a71e409c0589b83751dc25
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 432480 W: 113846 L: 114055 D: 204579
Ptnml(0-2): 1164, 48205, 117701, 48016, 1154

passed ltc: https://tests.stockfishchess.org/tests/view/63aba66639af998100ce1aa9
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 245344 W: 65309 L: 65317 D: 114718
Ptnml(0-2): 117, 24257, 73903, 24307, 88

closes https://github.com/official-stockfish/Stockfish/pull/4322

bench 4379218
2023-01-09 20:12:01 +01:00
mstembera 9fe9ff0082 Fix stack initialization
This fixes a bug where on line 278 the Stack::staticEvals are
initialized to 0. However VALUE_NONE is defined to be 32002 so
this is a bug in master. It is probably due to the calculation
of improvement, where staticEval prior to rootPos can be accessed.

https://tests.stockfishchess.org/tests/view/63ab91cf39af998100ce1666
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 53736 W: 14285 L: 13955 D: 25496
Ptnml(0-2): 121, 5921, 14500, 6159, 167

https://tests.stockfishchess.org/tests/view/63b2af5ee28ed36c814bed52
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 33776 W: 9130 L: 8934 D: 15712
Ptnml(0-2): 14, 3240, 10185, 3434, 15

closes https://github.com/official-stockfish/Stockfish/pull/4320

Bench: 4068510
2023-01-04 09:37:02 +01:00
FauziAkram fc5b59b88b Parameter Tweaks
This patch is a parameter tweak that passed both STC and LTC tests.

STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 80944 W: 21557 L: 21189 D: 38198
Ptnml(0-2): 192, 8883, 22028, 9103, 266
https://tests.stockfishchess.org/tests/view/63b07fe2d421d8f75795a03b

LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 30440 W: 8296 L: 8007 D: 14137
Ptnml(0-2): 6, 2893, 9143, 3162, 16
https://tests.stockfishchess.org/tests/view/63b167d02ab1290f961644db

closes https://github.com/official-stockfish/Stockfish/pull/4318

Bench: 4182223
2023-01-02 19:14:05 +01:00
Linmiao Xu a6fa683418 Update default net to nn-a3dc078bafc7.nnue
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
2023-01-02 19:10:14 +01:00
Sebastian Buchwald b60f9cc451 Update copyright years
Happy New Year!

closes https://github.com/official-stockfish/Stockfish/pull/4315

No functional change
2023-01-02 19:07:38 +01:00
Linmiao Xu be9bc420af Update default net to nn-60fa44e376d9.nnue
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
2023-01-01 12:28:51 +01:00
Sebastian Buchwald 258c13ba8c Remove redundant extern modifier for function declarations
Functions have external linkage by default, so there's no need to
declare them extern.

closes https://github.com/official-stockfish/Stockfish/pull/4308

No functional change
2023-01-01 12:26:36 +01:00
Sebastian Buchwald f09b391ceb Fix comparison with uninitialized variable
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
2023-01-01 12:24:42 +01:00
Muzhen Gaming 64656f8583 Add double bonus for prior countermove fail low
Add a double extra bonus for particularly bad fail low cases. Original idea by Yoshie2000.

STC: https://tests.stockfishchess.org/tests/view/63a2f0d86b5bf07ac7fad543
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 146488 W: 38992 L: 38532 D: 68964
Ptnml(0-2): 385, 16036, 39965, 16450, 408

LTC: https://tests.stockfishchess.org/tests/view/63a3eaeb6b5bf07ac7fafdec
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 103992 W: 27853 L: 27423 D: 48716
Ptnml(0-2): 41, 10029, 31435, 10441, 50

closes https://github.com/official-stockfish/Stockfish/pull/4302

Bench: 3801857
2022-12-24 16:01:50 +01:00
FauziAkram b2bd8699ec Update Elo estimates for terms in search
based on 25k games per term, using the UHO_XXL_+0.90_+1.19.epd book, at STC.

More detailed information in the PR.

closes https://github.com/official-stockfish/Stockfish/pull/4294

No functional change
2022-12-24 15:58:51 +01:00
Linmiao Xu c620886181 Update default net to nn-335a9b2d8a80.nnue
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
2022-12-21 07:14:58 +01:00
MinetaS 20b0226462 Fix a dependency bug
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
2022-12-20 08:14:19 +01:00
Alfredo Menezes c2d507005c Sometimes do a reduced search if LMR is skipped
If the node doesn't go through LMR and r is too big,
reduce search depth by one ply.

STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 664888 W: 176375 L: 175169 D: 313344
Ptnml(0-2): 1965, 73754, 179851, 74858, 2016
https://tests.stockfishchess.org/tests/view/6399414c93ed41c57ede8fb8

LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 150784 W: 40553 L: 40031 D: 70200
Ptnml(0-2): 76, 14668, 45387, 15180, 81
https://tests.stockfishchess.org/tests/view/639dee6e11c576d919dc2b38

closes https://github.com/official-stockfish/Stockfish/pull/4290

Bench: 3727508
2022-12-19 20:46:04 +01:00
Joost VandeVondele 61ea1534ff No error if net available but wget/curl missing
do not error out on missing wget/curl if these tools are not needed later on,
i.e. if the net is available already.

closes https://github.com/official-stockfish/Stockfish/pull/4291
closes https://github.com/official-stockfish/Stockfish/pull/4253

No functional change
2022-12-19 18:17:50 +01:00
mstembera 3a32d3e00c Don't reset increaseDepth back to true after it has been set to false
Resetting increaseDepth back to true each time on the very next iteration was not intended so this is a bug fix and a simplification.
See more discussion here #2482 (comment) Thanks to xoto10

STC: https://tests.stockfishchess.org/tests/view/6398c74693ed41c57ede7bfd
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 51128 W: 13543 L: 13220 D: 24365
Ptnml(0-2): 165, 5363, 14174, 5708, 154

LTC: https://tests.stockfishchess.org/tests/view/6399bcd393ed41c57edea750
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 290864 W: 77282 L: 77334 D: 136248
Ptnml(0-2): 107, 28127, 89029, 28049, 120

closes https://github.com/official-stockfish/Stockfish/pull/4288

bench: 3611278
2022-12-19 18:15:09 +01:00
Michael Chaly 39af98c807 Reintroduce doEvenDeeperSearch
This patch is basically the same as a reverted patch
but now has some guarding against search being stuck - the same
way as we do with double extensions. This should help with
search explosions - albeit slowly but they eventually should be resolved.

passed STC:
https://tests.stockfishchess.org/tests/view/639733d0b4e52c95053f3485
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 514048 W: 136423 L: 135435 D: 242190
Ptnml(0-2): 1425, 56945, 139420, 57685, 1549

passed LTC:
https://tests.stockfishchess.org/tests/view/639ab79b93ed41c57eded5c3
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 113800 W: 30642 L: 30190 D: 52968
Ptnml(0-2): 53, 11092, 34178, 11504, 73

closes https://github.com/official-stockfish/Stockfish/pull/4287

bench 3611278
2022-12-19 18:11:12 +01:00
PikaCat 726e90ccfa Badge link fix
Fix the badge link issue mentioned in https://github.com/badges/shields/issues/8671

closes https://github.com/official-stockfish/Stockfish/pull/4285

No functional change
2022-12-19 18:09:52 +01:00
NguyenPham 3659a9fda0 Fixed the help of Makefile
make profile-build more prominent, adjust comments

closes https://github.com/official-stockfish/Stockfish/pull/4284

No functional change
2022-12-19 18:08:12 +01:00
VoyagerOne 7cf93f8b71 Simplify Capture Scoring
The parameters are now in one place for easier tuning.
New formula is very similar to current.

STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 48176 W: 12819 L: 12616 D: 22741
Ptnml(0-2): 139, 5316, 13001, 5467, 165

LTC:
LLR: 2.97 (-2.94,2.94) <-1.75,0.25>
Total: 176752 W: 47364 L: 47304 D: 82084
Ptnml(0-2): 83, 17302, 53536, 17382, 73
https://tests.stockfishchess.org/tests/view/638ec7d068532fcbf79dfa15

closes https://github.com/official-stockfish/Stockfish/pull/4281

Bench: 3410998
2022-12-12 08:20:48 +01:00
ppigazzini 5fe1fa0210 GitHub Actions: install NDK once and clean up yaml
Use Ubuntu 22.04 as runner for NDK to avoid a qemu bug with `profile-build`

closes https://github.com/official-stockfish/Stockfish/pull/4280

No functional change
2022-12-12 08:17:52 +01:00
mstembera 310928e985 Avoid truncated PV in the threaded case
strongly prefer to pick as bestThread those threads with a longer PV,
among those threads that all found the same bestmove.

extended discussion in #4244
closes https://github.com/official-stockfish/Stockfish/pull/4278

No functional change
2022-12-12 08:15:19 +01:00
Joost VandeVondele 955edf1d1d Revert "doEvenDeeperSearch + tuning"
This reverts commit 98965c139d.

The increase of depth could lead to search explosions,
most visible with TB.

fixes https://github.com/official-stockfish/Stockfish/issues/4276
closes https://github.com/official-stockfish/Stockfish/pull/4256

Bench: 3872306
2022-12-12 08:14:26 +01:00
disservin 8f817ef082 Fix lower/upper bounds output
Commit cb0c7a9848 doesnt reset the lower/upper bounds back to false.

fixes #4273
closes https://github.com/official-stockfish/Stockfish/pull/4274

No functional change
2022-12-09 22:58:49 +01:00
Joost VandeVondele 3a30b478d2 CI workflows, install git on windows
ensures the SF dev version is reported correctly

closes https://github.com/official-stockfish/Stockfish/pull/4272

No functional change
2022-12-09 22:58:22 +01:00
Joost VandeVondele aedf0251e6 CI workflows, install git on windows
ensures the SF dev version is reported correctly

No functional change
2022-12-09 17:56:55 +01:00
Douglas Matos Gomes 44ecadee10 Simplify redundant condition.
closes https://github.com/official-stockfish/Stockfish/pull/4270

No functional change
2022-12-09 17:01:16 +01:00
Alfredo Menezes 9d3fd011f1 Extend all moves at low depth if ttMove is doubly extended
If ttMove is doubly extended, we allow a depth growth of the remaining moves.
The idea is to get a more realistic score comparison, because of the depth
difference. We take some care to avoid this extension for high depths,
in order to avoid the cost, since the search result is supposed
to be more accurate in this case.

This pull request includes some small cleanups.

STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 60256 W: 16189 L: 15848 D: 28219
Ptnml(0-2): 182, 6546, 16330, 6889, 181
https://tests.stockfishchess.org/tests/view/639109a1792a529ae8f27777

LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 106232 W: 28487 L: 28053 D: 49692
Ptnml(0-2): 46, 10224, 32145, 10652, 49
https://tests.stockfishchess.org/tests/view/63914cba792a529ae8f282ee

closes https://github.com/official-stockfish/Stockfish/pull/4271

Bench: 3622368
2022-12-09 16:59:55 +01:00
ppigazzini aa603cfeeb GitHub Action: upload ARM artifacts
And some clean up in other files.

closes https://github.com/official-stockfish/Stockfish/pull/4269

No functional change
2022-12-09 16:54:43 +01:00
MinetaS 74fb936dbd Invoke .depend only on build targets
Add a constraint so that the dependency build only occurs when users
actually run build tasks.

This fixes a bug on some systems where gcc/g++ is not available.

closes https://github.com/official-stockfish/Stockfish/pull/4255

No functional change
2022-12-08 20:48:20 +01:00
Guenther Demetz cb0c7a9848 Correctly output lowerbound/upperbound scores
fixes the lowerbound/upperbound output by avoiding
scores outside the alpha,beta bracket. Since SF search
uses fail-soft we can't simply take the returned value
as score.

closes https://github.com/official-stockfish/Stockfish/pull/4259

No functional change
2022-12-08 20:43:21 +01:00
FauziAkram 98965c139d doEvenDeeperSearch + tuning
Credit for the main idea of doEvenDeeperSearch goes to Vizvezdenec,
tuning by FauziAkram: Expansion of existing logic of doDeeperSearch -
if value from LMR is really really good do full depth search not
1 ply deeper but rather 2 instead.

Passed STC:
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 330048 W: 87672 L: 86942 D: 155434
Ptnml(0-2): 1012, 36739, 88912, 37229, 1132
https://tests.stockfishchess.org/tests/view/638a1cadd2b9c924c4c621d2

Passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 216696 W: 57891 L: 57240 D: 101565
Ptnml(0-2): 72, 21221, 65152, 21790, 113
https://tests.stockfishchess.org/tests/view/638c7d52a971f1f096c68fe2

closes https://github.com/official-stockfish/Stockfish/pull/4256

Bench: 3461830
2022-12-08 20:41:45 +01:00
ppigazzini 9fc203a3d0 Set the right PATH for ARM compiler and build tests in CI
Fix for the GitHub upgrade:
https://github.com/actions/runner-images/issues/5879
that broke our ARM workflows because it changed the value of
the ANDROID_NDK_HOME variable referenced in our PATH.

closes https://github.com/official-stockfish/Stockfish/pull/4267

No functional change
2022-12-08 20:36:52 +01:00
Joost VandeVondele e8caa6640d Restore development version
No functional change
2022-12-08 20:33:32 +01:00
Joost VandeVondele 758f9c9350 Stockfish 15.1
Official release version of Stockfish 15.1

Bench: 3467381

---

Today, we have the pleasure to announce Stockfish 15.1.

As usual, downloads will be freely available at stockfishchess.org/download

*Elo gain and competition results*

With this release, version 5 of the NNUE neural net architecture has
been introduced, and the training data has been extended to include
Fischer random chess (FRC) positions. As a result, Elo gains are largest
for FRC, reaching up to 50 Elo for doubly randomized FRC[1] (DFRC).
More importantly, also for standard chess this release progressed and
will win two times more game pairs than it loses[2] against
Stockfish 15. Stockfish continues to win in a dominating way[3] all
chess engine tournaments, including the TCEC Superfinal, Cup, FRC, DFRC,
and Swiss as well as the CCC Bullet, Blitz, and Rapid events.

*New evaluation*

This release also introduces a new convention for the evaluation that
is reported by search. An evaluation of +1 is now no longer tied to the
value of one pawn, but to the likelihood of winning the game. With
a +1 evaluation, Stockfish has now a 50% chance of winning the game
against an equally strong opponent. This convention scales down
evaluations a bit compared to Stockfish 15 and allows for consistent
evaluations in the future.

*ChessBase settlement*

In this release period, the Stockfish team has successfully enforced
its GPL license against ChessBase. This has been an intense process that
included filing a lawsuit[4], a court hearing[5], and finally
negotiating a settlement[6] that established that ChessBase infringed on
the license by not distributing the Stockfish derivatives Fat Fritz 2
and Houdini 6 as free software, and that ensures ChessBase will respect
the Free Software principles in the future. This settlement has been
covered by major chess sites (see e.g. lichess.org[7] and chess.com[8]),
and we are proud that it has been hailed as a ‘historic violation
settlement[9]’ by the Software Freedom Conservancy.

*Thank you*

The Stockfish project builds on a thriving community of enthusiasts
(thanks everybody!) that contribute their expertise, time, and resources
to build a free and open-source chess engine that is robust, widely
available, and very strong. We invite our chess fans to join the
fishtest testing framework and programmers to contribute to the
project[10].

The Stockfish team

[1] https://tests.stockfishchess.org/tests/view/638a6170d2b9c924c4c62cb4
[2] https://tests.stockfishchess.org/tests/view/638a4dd7d2b9c924c4c6297b
[3] https://en.wikipedia.org/wiki/Stockfish_(chess)#Competition_results
[4] https://stockfishchess.org/blog/2021/our-lawsuit-against-chessbase/
[5] https://stockfishchess.org/blog/2022/public-court-hearing-soon/
[6] https://stockfishchess.org/blog/2022/chessbase-stockfish-agreement/
[7] https://lichess.org/blog/Y3u1mRAAACIApBVn/settlement-reached-in-stockfish-v-chessbase
[8] https://www.chess.com/news/view/chessbase-stockfish-reach-settlement
[9] https://sfconservancy.org/news/2022/nov/28/sfc-named-trusted-party-in-gpl-case/
[10] https://stockfishchess.org/get-involved/
2022-12-04 14:17:15 +01:00
Joost VandeVondele d60f5de967 Fix bestThread selection
If multiple threads have the same best move,
pick the thread with the largest contribution to the confidence vote.
This thread will later be used to display PV, so this patch is
about user-friendliness and/or least surprises, it non-functional for playing strenght.

closes https://github.com/official-stockfish/Stockfish/pull/4246

No functional change
2022-12-02 20:06:59 +01:00
VoyagerOne c7118fb46d Simply do full sort on captures.
STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 42712 W: 11413 L: 11203 D: 20096
Ptnml(0-2): 145, 4661, 11544, 4851, 155
https://tests.stockfishchess.org/tests/view/6384df57d2b9c924c4c53900

LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 239072 W: 64065 L: 64067 D: 110940
Ptnml(0-2): 106, 23735, 71859, 23727, 109
https://tests.stockfishchess.org/tests/view/63851120d2b9c924c4c541ee

closes https://github.com/official-stockfish/Stockfish/pull/4249

Bench: 3467381
2022-12-02 20:05:50 +01:00
VoyagerOne 6a6faac04d Remove PvNode Parameter for cutoff LMR
STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 198520 W: 52673 L: 52632 D: 93215
Ptnml(0-2): 645, 22241, 53499, 22178, 697
https://tests.stockfishchess.org/tests/view/63746e8f9849fa7a36a6698f

LTC:
LLR: 2.97 (-2.94,2.94) <-1.75,0.25>
Total: 253568 W: 67487 L: 67501 D: 118580
Ptnml(0-2): 109, 25222, 76141, 25198, 114
https://tests.stockfishchess.org/tests/view/63839859d2b9c924c4c4feb7

closes https://github.com/official-stockfish/Stockfish/pull/4248

Bench: 3733322
2022-12-02 20:03:49 +01:00
Guenther Demetz f5a31b7e57 Correctly output lowerbound/upperbound in threaded searches
fixes the lowerbound/upperbound output by taking the alpha,beta bracket
into account also if a bestThread is selected that is different from the master thread.

Instead of keeping track which bounds where used in the specific search,
in this version we simply store the quality (exact, upperbound,
lowerbound) of the score along with the actual score as information on
rootMove.

closes https://github.com/official-stockfish/Stockfish/pull/4239

No functional change
2022-11-23 21:45:06 +01:00
peregrineshahin 1370127fcd Simplify both quiet check evasions' conditions
passed Non-regression STC:
https://tests.stockfishchess.org/tests/view/6370b647f1b748d4819e0b64
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 162904 W: 43249 L: 43171 D: 76484
Ptnml(0-2): 491, 17089, 46220, 17155, 497

closes https://github.com/official-stockfish/Stockfish/pull/4228

No functional change
2022-11-23 21:36:22 +01:00
VoyagerOne 85ae65db1d Skip full depth search in LMR depending on depth
dynamically adjust newDepth, and skip full depth search if newDepth doesn't exceed the previous search depth.
This affects the used newDepth for future searches, and influences the stat bonus for the move.

Passed STC:
https://tests.stockfishchess.org/tests/view/63795500aa34433735bc1cfe
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 112776 W: 30082 L: 29663 D: 53031
Ptnml(0-2): 352, 12453, 30423, 12744, 416

Passed LTC:
https://tests.stockfishchess.org/tests/view/6379ea39aa34433735bc2f9b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 83576 W: 22559 L: 22169 D: 38848
Ptnml(0-2): 38, 8011, 25303, 8395, 41

closes https://github.com/official-stockfish/Stockfish/pull/4240

Bench: 4390318
2022-11-23 21:25:14 +01:00
Joost VandeVondele d8f3209fb4 Update Top CPU Contributors
list as of 2022-11-19. Thanks!

closes https://github.com/official-stockfish/Stockfish/pull/4234

No functional change
2022-11-20 10:00:42 +01:00
Joost VandeVondele 3411631162 Update WDL model for current SF
This updates the WDL model based on the LTC statistics  (2M games).

Relatively small change, note that this also adjusts the NormalizeToPawnValue (now 361),
to keep win prob at 50% for 100cp.

closes https://github.com/official-stockfish/Stockfish/pull/4236

No functional change.
2022-11-20 09:59:35 +01:00
Joost VandeVondele d756d97a66 Fix a missing conversion
This conversion to cp was overlooked.

closes https://github.com/official-stockfish/Stockfish/pull/4235

No functional change
2022-11-20 09:58:07 +01:00
VoyagerOne 41c6a74d37 Simplification away Cutoff Reset
STC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 150184 W: 39913 L: 39819 D: 70452
Ptnml(0-2): 493, 16796, 40474, 16782, 547
https://tests.stockfishchess.org/tests/view/63723e9e54d69a2f33911d3c

LTC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 58880 W: 15890 L: 15717 D: 27273
Ptnml(0-2): 35, 5765, 17659, 5954, 27
https://tests.stockfishchess.org/tests/view/6373baf49849fa7a36a65427

closes https://github.com/official-stockfish/Stockfish/pull/4231

Bench: 4035152
2022-11-19 09:29:04 +01:00
Michael Chaly 219fa2f0a7 Do shallower search in case of lmr being not successful enough
In case of a move passing LMR but it results being not too far from
the current best search result produce a full depth search with reduced depth.

Original idea by lonfom169 .

Passed STC:
https://tests.stockfishchess.org/tests/view/6373409b54d69a2f33913fbd
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 169504 W: 45351 L: 44848 D: 79305
Ptnml(0-2): 598, 18853, 45353, 19344, 604

Passed LTC:
https://tests.stockfishchess.org/tests/view/6374c58528e3405283eb8d2d
LLR: 2.96 (-2.94,2.94) <0.50,2.50>
Total: 51144 W: 13802 L: 13471 D: 23871
Ptnml(0-2): 19, 4928, 15362, 5229, 34

closes https://github.com/official-stockfish/Stockfish/pull/4230

bench 4277005
2022-11-19 09:23:26 +01:00
disservin 6c1df553fa speedup CI
Github Actions allows us to use up to 20 workers.
This way we can launch multiple different checks
at the same time and optimize the overall time
the CI takes a bit.

closes https://github.com/official-stockfish/Stockfish/pull/4223

No functional change
2022-11-07 21:42:04 +01:00
disservin a413900791 Remove trend
Simplify trend away.

passed Non-regression STC:
https://tests.stockfishchess.org/tests/view/63642a63a90afcecbd1cb887
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 130000 W: 34683 L: 34567 D: 60750
Ptnml(0-2): 455, 14424, 35135, 14522, 464

passed Non-regression LTC:
https://tests.stockfishchess.org/tests/view/636566fda90afcecbd1cded9
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 81592 W: 21938 L: 21787 D: 37867
Ptnml(0-2): 42, 8035, 24490, 8188, 41

closes https://github.com/official-stockfish/Stockfish/pull/4222

Bench: 4239512
2022-11-07 08:00:05 +01:00
disservin e048d11825 Change versioning and save binaries as CI artifacts
For development versions of Stockfish, the version will now look like
dev-20221107-dca9a0533
indicating a development version, the date of the last commit,
and the git SHA of that commit. If git is not available,
the fallback is the date of compilation. Releases will continue to be
versioned as before.

Additionally, this PR extends the CI to create binary artifacts,
i.e. pushes to master will automatically build Stockfish and upload
the binaries to github.

closes https://github.com/official-stockfish/Stockfish/pull/4220

No functional change
2022-11-07 07:56:58 +01:00
Joost VandeVondele ad2aa8c06f Normalize evaluation
Normalizes the internal value as reported by evaluate or search
to the UCI centipawn result used in output. This value is derived from
the win_rate_model() such that Stockfish outputs an advantage of
"100 centipawns" for a position if the engine has a 50% probability to win
from this position in selfplay at fishtest LTC time control.

The reason to introduce this normalization is that our evaluation is, since NNUE,
no longer related to the classical parameter PawnValueEg (=208). This leads to
the current evaluation changing quite a bit from release to release, for example,
the eval needed to have 50% win probability at fishtest LTC (in cp and internal Value):

June 2020  :   113cp (237)
June 2021  :   115cp (240)
April 2022 :   134cp (279)
July 2022  :   167cp (348)

With this patch, a 100cp advantage will have a fixed interpretation,
i.e. a 50% win chance. To keep this value steady, it will be needed to update the win_rate_model()
from time to time, based on fishtest data. This analysis can be performed with
a set of scripts currently available at https://github.com/vondele/WLD_model

fixes https://github.com/official-stockfish/Stockfish/issues/4155
closes https://github.com/official-stockfish/Stockfish/pull/4216

No functional change
2022-11-05 09:15:53 +01:00
Joost VandeVondele 61a2cb84a6 Mark variable as potentially unused
fixes CI when compiled with -Werror

closes https://github.com/official-stockfish/Stockfish/pull/4221

No functional change
2022-11-05 09:15:14 +01:00
kurt22i d09653df0d Adjust reduction less at medium depths
This patch dampens the reduction increase/decrease from statScore at mid-range depths.
Inspired by patterns noticed in this tune: https://tests.stockfishchess.org/tests/view/635188930e5f47a8d0ffe8f5

Passed STC:
https://tests.stockfishchess.org/tests/view/63599dfd6b27ef94d9ec04af
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 87464 W: 23519 L: 23134 D: 40811
Ptnml(0-2): 319, 9599, 23524, 9958, 332

Passed LTC:
https://tests.stockfishchess.org/tests/view/635a73046b27ef94d9ec2313
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 154792 W: 41746 L: 41214 D: 71832
Ptnml(0-2): 79, 15181, 46349, 15703, 84

closes https://github.com/official-stockfish/Stockfish/pull/4213

Bench 4271738
2022-10-30 16:19:09 +01:00
Joost VandeVondele f154ed7a2d Update MacOS CI
move to 12 following actions runner update deprecation
(see https://github.com/actions/runner-images/issues/5583)

closes https://github.com/official-stockfish/Stockfish/pull/4212

No functional change
2022-10-30 16:17:49 +01:00
Clausable 8333b2a94c Fix README typos, update AUTHORS
closes https://github.com/official-stockfish/Stockfish/pull/4208

No functional change
2022-10-27 08:15:46 +02:00
dav1312 a5500edc55 Add issue template
Add an issue template using GitHub's form schema
https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema

closes https://github.com/official-stockfish/Stockfish/pull/4210

No functional change.
2022-10-26 20:28:12 +02:00
Michael Chaly 4ec8945eaf Use TT moves more often in qsearch
During the recapture phase of quiescence search (where we limit the generated moves to recaptures on the last seen capture square),
the move picker will now emit the tt move, even if the tt move is not a recapture.

Passed STC :
https://tests.stockfishchess.org/tests/view/6350df2928d3a71cb1eef838
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 90280 W: 24001 L: 23845 D: 42434
Ptnml(0-2): 273, 9779, 24941, 9813, 334

Passed LTC :
https://tests.stockfishchess.org/tests/view/6351308b28d3a71cb1ef06ce
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 104504 W: 27937 L: 27807 D: 48760
Ptnml(0-2): 54, 10378, 31260, 10504, 56

closes https://github.com/official-stockfish/Stockfish/pull/4206

Bench: 4540268
2022-10-23 20:22:04 +02:00
Clement 5604b255e6 Add RISC-V 64-bit support
adds a riscv64 target architecture to the Makefile to support RISC-V 64-bit.
Compiled and tested on VisionFive 2 board.

closes https://github.com/official-stockfish/Stockfish/pull/4205

No functional change.
2022-10-23 20:18:08 +02:00
disservin 804394b939 enable bit manipulation instruction set 1
bmi1 enables the use of _blsr_u64 for pop_lsb, and is availabe when avx2 is.

verified a small speedup (0.2 - 0.6%)

closes https://github.com/official-stockfish/Stockfish/pull/4202

No functional change
2022-10-23 20:08:18 +02:00
MinetaS 234d2156fd Apply -flto-partition=one / -flto=full
This patch fixes a potential bug derived from an incompatibility between LTO and top-level assembly code (INCBIN).

Passed non-regression STC (master e90341f):
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 119352 W: 31986 L: 31862 D: 55504
Ptnml(0-2): 439, 12624, 33400, 12800, 413
https://tests.stockfishchess.org/tests/view/634aacf84bc7650f0755188b

closes https://github.com/official-stockfish/Stockfish/pull/4201

No functional change
2022-10-23 19:58:47 +02:00
Rodrigo Roim 79c5f3a692 Fix tablebase probe for dtz >1000 w/o 50 move rule
For qn4N1/6R1/3K4/8/B2k4/8/8/8 w - - 0 1, white loses with DTZ 1034.
See https://syzygy-tables.info/?fen=qn4N1/6R1/3K4/8/B2k4/8/8/8_w_-_-_0_1

Prior to this fix, due to a too small hard-coded value, Stockfish interpreted this as winning.
The new value picked (1<<18) is large enough to deal with the largest DTZ values that can be stored in the current syzygy format.

closes https://github.com/official-stockfish/Stockfish/pull/4187

No functional change.
2022-10-16 12:58:48 +02:00
xoto10 9be2977da7 Adjust timeman constants
Adjust timeman constants to use more time in early part of game.

STC @ 10+0.1 th 1 :
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 93984 W: 25177 L: 24787 D: 44020
Ptnml(0-2): 350, 10096, 25729, 10448, 369
https://tests.stockfishchess.org/tests/live_elo/6339077135f43d649ff6162a

LTC @ 60+0.6 th 1 :
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 329368 W: 88953 L: 88093 D: 152322
Ptnml(0-2): 170, 31457, 100594, 32269, 194
https://tests.stockfishchess.org/tests/live_elo/6339baed35f43d649ff63142

Sudden death 10+0 :
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 20400 W: 5908 L: 5588 D: 8904
Ptnml(0-2): 177, 2252, 5128, 2360, 283
https://tests.stockfishchess.org/tests/live_elo/6347c9384bc7650f07549ba7

Sudden death 10+0, no adjudication :
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 17920 W: 4755 L: 4442 D: 8723
Ptnml(0-2): 137, 1985, 4466, 2172, 200
https://tests.stockfishchess.org/tests/live_elo/634806e84bc7650f0754a639

closes https://github.com/official-stockfish/Stockfish/pull/4188

No functional change
2022-10-16 11:51:41 +02:00
Stéphane Nicolet d6b6360ff5 Tweak the formula for NNUE complexity
Joint work by Ofek Shochat and Stéphane Nicolet.

passed STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 93288 W: 24996 L: 24601 D: 43691
Ptnml(0-2): 371, 10263, 24989, 10642, 379
https://tests.stockfishchess.org/tests/view/63448f4f4bc7650f07541987

passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 84168 W: 22771 L: 22377 D: 39020
Ptnml(0-2): 47, 8181, 25234, 8575, 47
https://tests.stockfishchess.org/tests/view/6345186d4bc7650f07542fbd

================

It seems there are two effects with this patch:

effect A :

If Stockfish is winning at root, we have optimism > 0 for all leaves in
the search tree where Stockfish is to move. There, if (psq - nnue) > 0
(ie if the advantage is more materialistic than positional), then the
product D = optimism * (psq - nnue) will be positive, nnueComplexity will
increase, and the eval will increase from SF point of view.

So the effect A is that if Stockfish is winning at root, she will slightly
favor in the search tree (in other words, search more) the positions where
she can convert her advantage via materialist means.

effect B :

If Stockfish is losing at root, we have optimism > 0 for all leaves in
the search tree where the opponent is to move. There, if (psq - nnue) < 0
(ie if the opponent advantage is more positional than materialistic), then
the product D = optimism * (psq-nnue) will be negative, nnueComplexity will
decrease, and the eval will decrease from the opponent point of view.

So the effect B is that Stockfish will slightly favor in the search tree
(search more) the branches where she can defend by slowly reducing the
opponent positional advantage.

=================

closes https://github.com/official-stockfish/Stockfish/pull/4195

bench: 4673898
2022-10-16 11:49:07 +02:00
Dubslow f97a86e213 Remove depth condition from razoring
The eval condition depends on depth anyways, so this patch is nearly (not quite) non-functional

passed STC:
https://tests.stockfishchess.org/tests/view/63428169fb7ccb2ea9be2629
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 185992 W: 49612 L: 49558 D: 86822
Ptnml(0-2): 618, 19956, 51842, 19914, 666

passed LTC:
https://tests.stockfishchess.org/tests/view/634418b14bc7650f07540760
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 126816 W: 34147 L: 34043 D: 58626
Ptnml(0-2): 74, 11941, 39281, 12031, 81

closes https://github.com/official-stockfish/Stockfish/pull/4196

bench 4148700
2022-10-16 11:45:16 +02:00
mstembera 93f71ecfe1 Optimize make_index() using templates and lookup tables.
https://tests.stockfishchess.org/tests/view/634517e54bc7650f07542f99
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 642672 W: 171819 L: 170658 D: 300195
Ptnml(0-2): 2278, 68077, 179416, 69336, 2229

this also introduces `-flto-partition=one` as suggested by MinetaS (Syine Mineta)
to avoid linking errors due to LTO on 32 bit mingw. This change was tested in isolation as well

https://tests.stockfishchess.org/tests/view/634aacf84bc7650f0755188b
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 119352 W: 31986 L: 31862 D: 55504
Ptnml(0-2): 439, 12624, 33400, 12800, 413

closes https://github.com/official-stockfish/Stockfish/pull/4199

No functional change
2022-10-16 11:42:19 +02:00
Stefan Geschwentner e90341f9c9 Tweak history initialization
Simplify initialization of continuation history by using everywhere the same starting value.

STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 90952 W: 24312 L: 24153 D: 42487
Ptnml(0-2): 356, 10168, 24290, 10285, 377
https://tests.stockfishchess.org/tests/view/633948f235f43d649ff61fd0

LTC:
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 162416 W: 43540 L: 43466 D: 75410
Ptnml(0-2): 77, 16289, 48417, 16333, 92
https://tests.stockfishchess.org/tests/view/6339ee8a35f43d649ff63986

closes  https://github.com/official-stockfish/Stockfish/pull/4186

Bench: 4156027
2022-10-08 18:09:02 +02:00
Giacomo Lorenzetti d5271af0ee Remove old line in "Futility pruning for captures"
The line is no longer needed after https://github.com/official-stockfish/Stockfish/commit/910cf8b21839eb9f1991934a5436eea112021723.
This patch incidentally applies "Futility Pruning for Captures" also in case of en-passant, changing the bench signature.

Passed STC:
https://tests.stockfishchess.org/tests/view/6332c1f1208c26088697b731
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 68760 W: 18440 L: 18256 D: 32064
Ptnml(0-2): 267, 7530, 18595, 7728, 260

Passed LTC:
https://tests.stockfishchess.org/tests/view/633312e9208c26088697c59b
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 455552 W: 121910 L: 122123 D: 211519
Ptnml(0-2): 253, 45439, 136600, 45236, 248

closes https://github.com/official-stockfish/Stockfish/pull/4185

Bench: 4374521
2022-10-08 18:07:30 +02:00
Joost VandeVondele da937e219e Revert "Mix alpha and statScore for reduction"
This reverts commit 8bab09749d.

In this form the patch reduces mate finding effectiveness, as the large alpha value has negative influence on the reductions.

see also https://github.com/official-stockfish/Stockfish/pull/4183

Bench: 4114228
2022-10-05 22:59:05 +02:00
FauziAkram 8bab09749d Mix alpha and statScore for reduction
Idea by @xoto10, and tuning by @FauziAkram.

Passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 57832 W: 15540 L: 15199 D: 27093
Ptnml(0-2): 207, 6343, 15477, 6680, 209
https://tests.stockfishchess.org/tests/view/6338db6f35f43d649ff60fdc

passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 50968 W: 13770 L: 13440 D: 23758
Ptnml(0-2): 25, 4905, 15306, 5211, 37
https://tests.stockfishchess.org/tests/view/6339777035f43d649ff62686

Links to the tuning sessions:
https://tests.stockfishchess.org/tests/view/63345725a004bed9a2e47b28
https://tests.stockfishchess.org/tests/view/63345728a004bed9a2e47b2a

closes https://github.com/official-stockfish/Stockfish/pull/4183

Bench: 4426602
2022-10-04 01:07:27 +02:00
disservin f436bf77ad Use less reduction for escaping moves
This patch reuses the threatenedPieces variable (which is calculated in movepicker)
to reduce less in the search tree the moves which escape a capture.

passed STC:
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 314352 W: 84042 L: 83328 D: 146982
Ptnml(0-2): 1105, 35084, 84207, 35552, 1228
https://tests.stockfishchess.org/tests/view/63355f37a004bed9a2e4a17f

passed LTC:
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 90752 W: 24556 L: 24147 D: 42049
Ptnml(0-2): 59, 8855, 27123, 9296, 43
https://tests.stockfishchess.org/tests/view/63383a7735f43d649ff5fa8b

closes https://github.com/official-stockfish/Stockfish/pull/4181

bench: 4114228
2022-10-03 11:50:31 +02:00
peregrineshahin 232bf19be4 Simplify both position calls in useClassical
Simplify the use of classical evaluation when using default settings to only be dependent on piece count and decisive PSQ

passed STC:
https://tests.stockfishchess.org/tests/view/632d32a7006ef9eb96d86ce9
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 108048 W: 28904 L: 28763 D: 50381
Ptnml(0-2): 383, 12060, 29006, 12183, 392

passed LTC:
https://tests.stockfishchess.org/tests/view/632d705a006ef9eb96d87649
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 76600 W: 20671 L: 20516 D: 35413
Ptnml(0-2): 34, 7533, 23023, 7664, 46

Inspired by sorais, credit to him.

closes https://github.com/official-stockfish/Stockfish/pull/4177

bench  4173163
2022-09-27 07:54:36 +02:00
Brad Knox 4339a756ac Update README.md
Adding some svg icons and additional information, insert links as references

closes https://github.com/official-stockfish/Stockfish/pull/4176

No functional change
2022-09-27 07:52:38 +02:00
Torsten Hellwig 70e51a5bc8 Always output hashfull
This removes the restriction that no hashfull information is printed within the first second of a search.
On modern systems, a non-zero value is returned within 6 ms with default settings.

passed STC:
https://tests.stockfishchess.org/tests/view/63277b08b9c0caa5f4a798e4
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 290096 W: 77505 L: 77561 D: 135030
Ptnml(0-2): 1008, 30713, 81592, 30797, 938

closes https://github.com/official-stockfish/Stockfish/pull/4174

No functional change
2022-09-27 07:48:05 +02:00
mstembera 29295ecfd3 Simplify EVASIONS scoring
remove some multipliers & adjust, doesn't change the move ordering

STC https://tests.stockfishchess.org/tests/view/6325c1c9b9c0caa5f4a759ae
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 192760 W: 51528 L: 51482 D: 89750
Ptnml(0-2): 642, 20490, 54148, 20380, 720

Credit to locutus2

closes https://github.com/official-stockfish/Stockfish/pull/4171

No functional change
2022-09-27 07:44:46 +02:00
mstembera dc0c441b7c Prioritize checks in movepicker
give a little bonus for moving pieces to squares where they give check

STC: https://tests.stockfishchess.org/tests/view/631da742162491686d2e40b5
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 80072 W: 21753 L: 21368 D: 36951
Ptnml(0-2): 421, 8876, 21075, 9225, 439

LTC: https://tests.stockfishchess.org/tests/view/631dd9e6b85daa436625de1d
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 263480 W: 70916 L: 70158 D: 122406
Ptnml(0-2): 322, 26156, 78029, 26908, 325

similar ideas have been tested by Viz and Guenther

closes https://github.com/official-stockfish/Stockfish/pull/4165

bench: 4326572
2022-09-17 09:30:52 +02:00
atumanian 154e7afed0 Simplify trend and optimism.
This patch simplifies the formulas used to compute the trend and optimism values before each search iteration.
As a side effect, this removes the parameters which make the relationship between the displayed evaluation value
and the expected game result asymmetric.

I've also provided links to the results of isotonic regression analysis of the relationship between the evaluation and game result (statistical data and a graph) for both tests, which demonstrate that the new version has a more symmetric relationship:

STC: [Data and graph](https://github.com/official-stockfish/Stockfish/discussions/4150#discussioncomment-3548954)
LTC: [Data and graph](https://github.com/official-stockfish/Stockfish/discussions/4150#discussioncomment-3626311)
See also https://github.com/official-stockfish/Stockfish/issues/4142

passed STC:
https://tests.stockfishchess.org/tests/view/6313f44b8202a039920e27e6
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 108016 W: 28903 L: 28760 D: 50353
Ptnml(0-2): 461, 12075, 28850, 12104, 518

passed LTC:
https://tests.stockfishchess.org/tests/view/631de45db85daa436625dfe6
LLR: 3.01 (-2.94,2.94) <-1.75,0.25>
Total: 34792 W: 9412 L: 9209 D: 16171
Ptnml(0-2): 24, 3374, 10397, 3577, 24

Furthermore, this does not measurably impact Elo strength against weaker engines,
as demonstrated in a match of master and patch vs SF13:

This patch vs SF 13:
https://tests.stockfishchess.org/tests/view/631fa34ae1612778c344c6eb
Elo: 141.66 +-1.2 (95%) LOS: 100.0%
Total: 100000 W: 48182 L: 9528 D: 42290
Ptnml(0-2): 96, 1426, 13277, 30130, 5071
nElo: 284.13 +-3.3 (95%) PairsRatio: 23.13

Master vs SF 13:
https://tests.stockfishchess.org/tests/view/631fa3ece1612778c344c6ff
Elo: 143.26 +-1.2 (95%) LOS: 100.0%
Total: 100000 W: 48525 L: 9479 D: 41996
Ptnml(0-2): 94, 1537, 13098, 29771, 5500
nElo: 281.70 +-3.3 (95%) PairsRatio: 21.63

closes: https://github.com/official-stockfish/Stockfish/pull/4163

Bench: 4425574
2022-09-17 09:13:07 +02:00
Joost VandeVondele 5a871e174f Explicitly annotate a few variables
as [[maybe_unused]], avoiding the (void)foo trick.

closes https://github.com/official-stockfish/Stockfish/pull/4162

No functional change
2022-09-17 09:05:35 +02:00
mstembera 82bb21dc7a Optimize AVX2 path in NNUE evaluation
always selecting AffineTransform specialization for small inputs.

A related patch was tested as

Initially tested as a simplification
STC https://tests.stockfishchess.org/tests/view/6317c3f437f41b13973d6dff
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 58072 W: 15619 L: 15425 D: 27028
Ptnml(0-2): 241, 6191, 15992, 6357, 255

Elo gain speedup test
STC https://tests.stockfishchess.org/tests/view/63181c1b37f41b13973d79dc
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 184496 W: 49922 L: 49401 D: 85173
Ptnml(0-2): 851, 19397, 51208, 19964, 828

and this patch gained in testing

speedup        = +0.0071
P(speedup > 0) =  1.0000
on CPU: 16 x AMD Ryzen 9 3950X

closes https://github.com/official-stockfish/Stockfish/pull/4158

No functional change
2022-09-11 14:19:57 +02:00
Michael Chaly 1591e5ac3b Do less singular extensions for former PVnode
Patch is a reintroduction of logic what was simplified a while ago
in a slightly different form. Do bigger extension offset in
case of non-pv node having a pv.

passed STC
https://tests.stockfishchess.org/tests/view/631977c048f27688a06e66d5
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 23296 W: 6404 L: 6108 D: 10784
Ptnml(0-2): 88, 2539, 6118, 2795, 108

passed LTC
https://tests.stockfishchess.org/tests/view/631989cb48f27688a06e696c
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 235592 W: 63890 L: 63188 D: 108514
Ptnml(0-2): 275, 23392, 69804, 24006, 319

closes https://github.com/official-stockfish/Stockfish/pull/4159

Bench: 3993611
2022-09-11 14:15:54 +02:00
Dubslow 9fa258ee1d Razor also on PV nodes
Simplification introduced by xoto10

blue LTC vs new master:
https://tests.stockfishchess.org/tests/view/631ad4ef9cfa5e9b648d1b4e
LLR: 2.93 (-2.94,2.94) <-1.75,0.25>
Total: 59184 W: 16002 L: 15828 D: 27354
Ptnml(0-2): 65, 5777, 17747, 5925, 78

blue STC vs old master:
https://tests.stockfishchess.org/tests/view/6306b87b902a848543334c25
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 213944 W: 57184 L: 57159 D: 99601
Ptnml(0-2): 877, 23448, 58331, 23405, 911

blue LTC vs old master:
https://tests.stockfishchess.org/tests/view/63070e6b902a8485433357e7
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 192080 W: 52050 L: 52006 D: 88024
Ptnml(0-2): 232, 18981, 57611, 18943, 273

closes https://github.com/official-stockfish/Stockfish/pull/4147

bench 4208975
2022-09-11 14:13:24 +02:00
Michael Chaly eaf2c8207f Further LTC tuning of search parameters
Tuning done by bigpenor with some hand adjustments on top by Viz.

Had a good performance at fixed games 180+1.8:
https://tests.stockfishchess.org/tests/view/631836b437f41b13973d7da1
Elo: 1.35 +-1.2 (95%) LOS: 98.6%
Total: 60000 W: 16422 L: 16189 D: 27389
Ptnml(0-2): 39, 5335, 18992, 5622, 12
nElo: 3.13 +-2.8 (95%) PairsRatio: 1.05

Passed 60+0.6 8 threads SPRT:
https://tests.stockfishchess.org/tests/view/631ba0ff74bc4fe483a99db3
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 29712 W: 8301 L: 8039 D: 13372
Ptnml(0-2): 12, 2318, 9925, 2598, 3

closes https://github.com/official-stockfish/Stockfish/pull/4160

bench 3938073
2022-09-11 14:05:39 +02:00
FauziAkram 5eeb96d0e7 VLTC tuning
Tuning some parameters that scale well with longer time control:

Failed STC:
https://tests.stockfishchess.org/tests/view/6313424d8202a039920e130a
LLR: -2.94 (-2.94,2.94) <-1.75,0.25>
Total: 42680 W: 11231 L: 11540 D: 19909
Ptnml(0-2): 191, 5008, 11232, 4737, 172

Passed LTC:
https://tests.stockfishchess.org/tests/view/6311e2cd874169ca52ae7933
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 53448 W: 14782 L: 14437 D: 24229
Ptnml(0-2): 101, 5214, 15740, 5577, 92

Passed VLTC:
https://tests.stockfishchess.org/tests/view/6312530cfa99a92e3002c927
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 123336 W: 33465 L: 33007 D: 56864
Ptnml(0-2): 38, 11466, 38204, 11920, 40

closes https://github.com/official-stockfish/Stockfish/pull/4154

Bench: 5609606
2022-09-07 07:38:04 +02:00
Joost VandeVondele a4d18d23a9 Provide network download fallback
in case the base infrastructure for providing the networks

https://tests.stockfishchess.org/nns

is down, use an alternate github repo for downloading networks during the build.

fixes #4149
fixes #4140

closes https://github.com/official-stockfish/Stockfish/pull/4151

No functional change
2022-09-07 07:32:53 +02:00
Joost VandeVondele dddf8fc2b4 Increase the maximum number of threads to 1024
relatively soon servers with 512 threads will be available 'quite commonly',
anticipate even more threads, and increase our current maximum from 512 to 1024.

closes https://github.com/official-stockfish/Stockfish/pull/4152

No functional change.
2022-09-07 07:31:48 +02:00
dav1312 97860cb575 Disable ARM CI tests
Temporarily disable ARM CI tests until a mitigation is implemented

closes https://github.com/official-stockfish/Stockfish/pull/4148

No functional change.
2022-08-29 19:15:14 +02:00
mstembera 02ef1f4496 Make key_after() more consistent with key()
STC: https://tests.stockfishchess.org/tests/view/62f8547123d42b50a8dac674
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 176640 W: 47699 L: 47189 D: 81752
Ptnml(0-2): 776, 18599, 49129, 18971, 845

A bug fix plus non functional speed optimization. Position::key_after(Move m) is now
consistent with Position::key() thus prefetching correct TT entries which speeds things up.
Related PR #3759

closes https://github.com/official-stockfish/Stockfish/pull/4130

No functional change
2022-08-17 19:56:15 +02:00
Joost VandeVondele 15ac117ac4 Simplify the use of classical eval
no benefit of the fallback term (exercised rarely).
Cleanup the associated code.

passed STC
https://tests.stockfishchess.org/tests/view/62f62c2b6f0a08af9f776367
LLR: 2.96 (-2.94,2.94) <-1.75,0.25>
Total: 67832 W: 18334 L: 18148 D: 31350
Ptnml(0-2): 369, 7171, 18609, 7439, 328

passed LTC
https://tests.stockfishchess.org/tests/view/62f68beb6f0a08af9f77710e
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 104664 W: 28363 L: 28233 D: 48068
Ptnml(0-2): 169, 10162, 31511, 10350, 140

closes https://github.com/official-stockfish/Stockfish/pull/4132

Bench: 6079565
2022-08-15 18:01:37 +02:00
Michael Chaly 5f290352cd Simplify away smp adjustment in TT use
Passed STC
https://tests.stockfishchess.org/tests/view/62f7d81f23d42b50a8dab568
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 98160 W: 26307 L: 26165 D: 45688
Ptnml(0-2): 201, 10282, 27960, 10448, 189

Passed LTC
https://tests.stockfishchess.org/tests/view/62f8d1a623d42b50a8dad4fb
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 81544 W: 22346 L: 22200 D: 36998
Ptnml(0-2): 44, 7542, 25446, 7704, 36

closes https://github.com/official-stockfish/Stockfish/pull/4131

No functional change (single threaded).
2022-08-15 17:54:56 +02:00
mckx00 3370f69881 Make LMR code easier to follow
Remove flags doFullDepthSearch and didLMR, and reorder instruction.

Small measured speedup.

Closes https://github.com/official-stockfish/Stockfish/pull/4129

No functional change.
2022-08-15 17:51:51 +02:00
mstembera 4568f6369b Report longest PV lines for multithreaded search
In case several threads find the same bestmove,
report the longest PV line found.

closes https://github.com/official-stockfish/Stockfish/pull/4126

No functional change.
2022-08-15 17:46:27 +02:00
Joost VandeVondele 1054a483ca Remove an unneeded randomization of evals.
most of the effect comes from the randomization of 3-folds.

passed STC:
https://tests.stockfishchess.org/tests/view/62e697e97e84186e5d19af6f
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 572976 W: 153168 L: 153539 D: 266269
Ptnml(0-2): 2505, 64783, 152364, 64250, 2586

passed LTC:
https://tests.stockfishchess.org/tests/view/62ee5977523c86dcd6957154
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 704808 W: 191212 L: 191680 D: 321916
Ptnml(0-2): 1340, 70579, 208972, 70235, 1278

closes https://github.com/official-stockfish/Stockfish/pull/4128

Bench: 5868987
2022-08-12 14:30:33 +02:00
Stefan Geschwentner 0a01dd044f Cleanup code
This PR includes following cleanups:
- Remove the unused depth variable in the thread class.
- cleanup ValueList (added from mstembera)

closes https://github.com/official-stockfish/Stockfish/pull/4127

No functional change.
2022-08-12 14:29:40 +02:00
Joost VandeVondele e639c45577 Update WDL model for current SF
This updates the WDL model based on the LTC statistics for the two weeks (3M games).

for old results see:

https://github.com/official-stockfish/Stockfish/pull/3981
https://github.com/official-stockfish/Stockfish/pull/3582
https://github.com/official-stockfish/Stockfish/pull/2778

closes https://github.com/official-stockfish/Stockfish/pull/4115

No functional change.
2022-08-06 13:57:30 +02:00
Joost VandeVondele 7cc929f437 Update CPU contributors list
Thanks for your contributions!

closes https://github.com/official-stockfish/Stockfish/pull/4116

No functional change
2022-08-06 13:53:55 +02:00
lonfom169 b8f4903fbb Reintroduce singularQuietLMR
STC:
LLR: 2.96 (-2.94,2.94) <0.00,2.00>
Total: 88912 W: 23972 L: 23580 D: 41360
Ptnml(0-2): 365, 9820, 23712, 10176, 383
https://tests.stockfishchess.org/tests/view/62e9537a400addce2c13399b

LTC:
LLR: 2.97 (-2.94,2.94) <0.50,2.50>
Total: 85672 W: 23607 L: 23192 D: 38873
Ptnml(0-2): 219, 8316, 25365, 8703, 233
https://tests.stockfishchess.org/tests/view/62e9a174400addce2c1346e4

closes https://github.com/official-stockfish/Stockfish/pull/4122

Bench: 5921315
2022-08-06 13:52:36 +02:00
Stefan Geschwentner 675f6a038b Tweak history updates
In general the history update bonus is slightly decreased by 11% which gives a slower saturation speed.
In addition only for main history the divisor is halfed (used history values are doubled to maintain same maximum)
which have an effect in the opposite direction on saturation speed.

STC:
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 157088 W: 42673 L: 42168 D: 72247
Ptnml(0-2): 857, 17346, 41642, 17833, 866
https://tests.stockfishchess.org/tests/view/62e5517ab383a712b13867c5

LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 325592 W: 88705 L: 87753 D: 149134
Ptnml(0-2): 594, 32288, 96076, 33248, 590
https://tests.stockfishchess.org/tests/view/62e5e4f4b383a712b1387d53

closes https://github.com/official-stockfish/Stockfish/pull/4119

Bench: 5518728
2022-08-06 13:50:01 +02:00
Michael Chaly 582c88ee94 Do more TT cutoffs in case of exact bound
The idea is that these TT entries are considered move valuable in TT replacement scheme - they are always overwriting other entries. So it makes sence for them to produce more aggressive cutoffs.

passed STC
https://tests.stockfishchess.org/tests/view/62e4d407b383a712b1385410
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 96632 W: 26045 L: 25659 D: 44928
Ptnml(0-2): 434, 10635, 25770, 11065, 412

passed LTC
https://tests.stockfishchess.org/tests/view/62e523e2b383a712b1386193
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 77960 W: 21363 L: 20989 D: 35608
Ptnml(0-2): 190, 7591, 23009, 8035, 155

closes https://github.com/official-stockfish/Stockfish/pull/4114

bench 5820568
2022-07-31 11:03:28 +02:00
Dubslow 18389e269d remove useClassical depth condition
passed STC:
https://tests.stockfishchess.org/tests/view/62e0c3e98e4fa6ae472695ed
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 293568 W: 78934 L: 79151 D: 135483
Ptnml(0-2): 1344, 31488, 81366, 31213, 1373

passed LTC:
https://tests.stockfishchess.org/tests/view/62e190aa8e4fa6ae4726b5b5
LLR: 2.98 (-2.94,2.94) <-2.25,0.25>
Total: 187392 W: 50971 L: 51028 D: 85393
Ptnml(0-2): 384, 17801, 57369, 17772, 370

other attempts to otherwise tune this parameter failed, bounds 6,7,10,11 failed STC, 8 passed STC but failed LTC

closes https://github.com/official-stockfish/Stockfish/pull/4112

bench 5796377
2022-07-31 11:00:31 +02:00
Dubslow c4a644922d Simplify reduction condition for cutNodes
LMR: for cutNodes, dont exclude killer moves. This was a prelude to reducing
allNodes, altho that's failed so far.

STC https://tests.stockfishchess.org/tests/view/62d64ad147ae1768b34a27c3
LLR: 2.95 (-2.94,2.94) <-2.25,0.25>
Total: 37064 W: 10044 L: 9889 D: 17131
Ptnml(0-2): 162, 4115, 9828, 4260, 167

LTC https://tests.stockfishchess.org/tests/view/62d66cc047ae1768b34a2b14
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 39832 W: 10796 L: 10659 D: 18377
Ptnml(0-2): 69, 3969, 11706, 4100, 72

closes https://github.com/official-stockfish/Stockfish/pull/4109

bench: 5697891
2022-07-24 09:18:38 +02:00
Joost VandeVondele 4b4b7d1209 Update default net to nn-ad9b42354671.nnue
using trainer branch https://github.com/glinscott/nnue-pytorch/pull/208 with a slightly
tweaked loss function (power 2.5 instead of 2.6), otherwise same training as in
the previous net update https://github.com/official-stockfish/Stockfish/pull/4100

passed STC:
LLR: 2.97 (-2.94,2.94) <0.00,2.50>
Total: 367536 W: 99465 L: 98573 D: 169498
Ptnml(0-2): 1820, 40994, 97117, 42148, 1689
https://tests.stockfishchess.org/tests/view/62cc43fe50dcbecf5fc1c5b8

passed LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 25032 W: 6802 L: 6553 D: 11677
Ptnml(0-2): 40, 2424, 7341, 2669, 42
https://tests.stockfishchess.org/tests/view/62ce5f421dacb46e4d5fd277

closes https://github.com/official-stockfish/Stockfish/pull/4107

Bench: 5905619
2022-07-13 18:01:20 +02:00
Michael Chaly 95d24b77df Simplify away some unneeded code in time management
The lower bound of the clamp is never used since complexity can't be negative and thus is unneeded.

closes https://github.com/official-stockfish/Stockfish/pull/4105

No functional change
2022-07-13 18:00:39 +02:00
Joost VandeVondele 2e02dd7936 Limit the researching at same depth.
If the elapsed time is close to the available time, the time management thread can signal that the next iterations should be searched at the same depth (Threads.increaseDepth = false). While the rootDepth increases, the adjustedDepth is kept constant with the searchAgainCounter.

In exceptional cases, when threading is used and the master thread, which controls the time management, signals to not increaseDepth, but by itself takes a long time to finish the iteration, the helper threads can search repeatedly at the same depth. This search finishes more and more quickly, leading to helper threads that report a rootDepth of MAX_DEPTH (245). The latter is not optimal as it is confusing for the user, stops search on these threads, and leads to an incorrect bias in the thread voting scheme. Probably with only a small impact on strength.

This behavior was observed almost two years ago,
see https://github.com/official-stockfish/Stockfish/issues/2717

This patch fixes #2717 by ensuring the effective depth increases at once every four iterations,
even in increaseDepth is false.

Depth 245 searches (for non-trivial positions) were indeed absent with this patch,
but frequent with master in the tests below:
https://discord.com/channels/435943710472011776/813919248455827515/994872720800088095
Total pgns: 2173
Base: 2867
Patch: 0

it passed non-regression testing in various setups:

SMP STC:
https://tests.stockfishchess.org/tests/view/62bfecc96178ffe6394ba036
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 37288 W: 10171 L: 10029 D: 17088
Ptnml(0-2): 75, 3777, 10793, 3929, 70

SMP LTC:
https://tests.stockfishchess.org/tests/view/62c08f6f49b62510394be066
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 190568 W: 52125 L: 52186 D: 86257
Ptnml(0-2): 70, 17854, 59504, 17779, 77

LTC:
https://tests.stockfishchess.org/tests/view/62c08b6049b62510394bdfb6
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 48120 W: 13204 L: 13083 D: 21833
Ptnml(0-2): 54, 4458, 14919, 4571, 58

Special thanks to miguel-I,  Disservin, ruicoelhopedro and others for analysing the problem,
the data, and coming up with the key insight, needed to fix this longstanding issue.

closes https://github.com/official-stockfish/Stockfish/pull/4104

Bench: 5182295
2022-07-09 10:58:04 +02:00
Dubslow aa18b68033 Time mgmt fix division.
oversight changed the corresponding float division to integer division in a previous tune https://github.com/official-stockfish/Stockfish/commit/442c40b43de8ede1e424efa674c8d45322e3b43c it is stronger to keep the original float division.

green LTC: https://tests.stockfishchess.org/tests/view/62bf34bc0340fb1e0cc934e7
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 38952 W: 10738 L: 10467 D: 17747
Ptnml(0-2): 46, 3576, 11968, 3833, 53

yellow STC: https://tests.stockfishchess.org/tests/view/62bff6506178ffe6394ba1d1
LLR: -2.95 (-2.94,2.94) <0.00,2.50>
Total: 226960 W: 61265 L: 61062 D: 104633
Ptnml(0-2): 938, 24398, 62582, 24647, 915

further slightly tweaked tests confirm this Elo gain.

closes https://github.com/official-stockfish/Stockfish/pull/4097

No functional change
2022-07-09 10:53:17 +02:00
Michael Chaly c2aaaa65f9 Simplify away FRC correction term
Since new net is trained partially using FRC data this part of adjustment that penalises bishops that are locked in the corner is no longer needed - net should "know" this things itself much better.

STC on FRC book :
https://tests.stockfishchess.org/tests/view/62c3031b9e7d9997a12d852f
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 22048 W: 3003 L: 2845 D: 16200
Ptnml(0-2): 96, 1778, 7149, 1874, 127

LTC on FRC book :
https://tests.stockfishchess.org/tests/view/62c32e939e7d9997a12d8c5e
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 36784 W: 3138 L: 3037 D: 30609
Ptnml(0-2): 36, 1842, 14537, 1939, 38

STC on DFRC book :
https://tests.stockfishchess.org/tests/view/62c32efb9e7d9997a12d8c6f
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 20424 W: 3903 L: 3721 D: 12800
Ptnml(0-2): 172, 1984, 5724, 2154, 178

LTC on DFRC book :
https://tests.stockfishchess.org/tests/view/62c358c79e7d9997a12d9319
LLR: 2.93 (-2.94,2.94) <-2.25,0.25>
Total: 53784 W: 7581 L: 7480 D: 38723
Ptnml(0-2): 87, 3887, 18856, 3962, 100

closes https://github.com/official-stockfish/Stockfish/pull/4101

bench 5182295
2022-07-05 13:55:50 +02:00
Joost VandeVondele 85f8ee6199 Update default net to nn-3c0054ea9860.nnu
First things first...

this PR is being made from court. Today, Tord and Stéphane, with broad support
of the developer community are defending their complaint, filed in Munich, against ChessBase.
With their products Houdini 6 and Fat Fritz 2, both Stockfish derivatives,
ChessBase violated repeatedly the Stockfish GPLv3 license. Tord and Stéphane have terminated
their license with ChessBase permanently. Today we have the opportunity to present
our evidence to the judge and enforce that termination. To read up, have a look at our blog post
https://stockfishchess.org/blog/2022/public-court-hearing-soon/ and
https://stockfishchess.org/blog/2021/our-lawsuit-against-chessbase/

This PR introduces a net trained with an enhanced data set and a modified loss function in the trainer.
A slight adjustment for the scaling was needed to get a pass on standard chess.

passed STC:
https://tests.stockfishchess.org/tests/view/62c0527a49b62510394bd610
LLR: 2.94 (-2.94,2.94) <0.00,2.50>
Total: 135008 W: 36614 L: 36152 D: 62242
Ptnml(0-2): 640, 15184, 35407, 15620, 653

passed LTC:
https://tests.stockfishchess.org/tests/view/62c17e459e7d9997a12d458e
LLR: 2.94 (-2.94,2.94) <0.50,3.00>
Total: 28864 W: 8007 L: 7749 D: 13108
Ptnml(0-2): 47, 2810, 8466, 3056, 53

Local testing at a fixed 25k nodes resulted in
Test run1026/easy_train_data/experiments/experiment_2/training/run_0/nn-epoch799.nnue
localElo: 4.2  +-      1.6

The real strength of the net is in FRC and DFRC chess where it gains significantly.

Tested at STC with slightly different scaling:
FRC:
https://tests.stockfishchess.org/tests/view/62c13a4002ba5d0a774d20d4
Elo: 29.78 +-3.4 (95%) LOS: 100.0%
Total: 10000 W: 2007 L: 1152 D: 6841
Ptnml(0-2): 31, 686, 2804, 1355, 124
nElo: 59.24 +-6.9 (95%) PairsRatio: 2.06

DFRC:
https://tests.stockfishchess.org/tests/view/62c13a5702ba5d0a774d20d9
Elo: 55.25 +-3.9 (95%) LOS: 100.0%
Total: 10000 W: 2984 L: 1407 D: 5609
Ptnml(0-2): 51, 636, 2266, 1779, 268
nElo: 96.95 +-7.2 (95%) PairsRatio: 2.98

Tested at LTC with identical scaling:
FRC:
https://tests.stockfishchess.org/tests/view/62c26a3c9e7d9997a12d6caf
Elo: 16.20 +-2.5 (95%) LOS: 100.0%
Total: 10000 W: 1192 L: 726 D: 8082
Ptnml(0-2): 10, 403, 3727, 831, 29
nElo: 44.12 +-6.7 (95%) PairsRatio: 2.08

DFRC:
https://tests.stockfishchess.org/tests/view/62c26a539e7d9997a12d6cb2
Elo: 40.94 +-3.0 (95%) LOS: 100.0%
Total: 10000 W: 2215 L: 1042 D: 6743
Ptnml(0-2): 10, 410, 3053, 1451, 76
nElo: 92.77 +-6.9 (95%) PairsRatio: 3.64

This is due to the mixing in a significant fraction of DFRC training data in the final training round. The net is
trained using the easy_train.py script in the following way:

```
python easy_train.py \
     --training-dataset=../Leela-dfrc_n5000.binpack \
     --experiment-name=2 \
     --nnue-pytorch-branch=vondele/nnue-pytorch/lossScan4 \
     --additional-training-arg=--param-index=2 \
     --start-lambda=1.0 \
     --end-lambda=0.75 \
     --gamma=0.995 \
     --lr=4.375e-4 \
     --start-from-engine-test-net True \
     --tui=False \
     --seed=$RANDOM \
     --max_epoch=800 \
     --auto-exit-timeout-on-training-finished=900 \
     --network-testing-threads 8  \
     --num-workers 12
```

where the data set used (Leela-dfrc_n5000.binpack) is a combination of our previous best data set (mix of Leela and some SF data) and DFRC data, interleaved to form:
The data is available in https://drive.google.com/drive/folders/1S9-ZiQa_3ApmjBtl2e8SyHxj4zG4V8gG?usp=sharing
Leela mix: https://drive.google.com/file/d/1JUkMhHSfgIYCjfDNKZUMYZt6L5I7Ra6G/view?usp=sharing
DFRC: https://drive.google.com/file/d/17vDaff9LAsVo_1OfsgWAIYqJtqR8aHlm/view?usp=sharing

The training branch used is
https://github.com/vondele/nnue-pytorch/commits/lossScan4
A PR to the main trainer repo will be made later. This contains a revised loss function, now computing the loss from the score based on the win rate model, which is a more accurate representation than what we had before. Scaling constants are tweaked there as well.

closes https://github.com/official-stockfish/Stockfish/pull/4100

Bench: 5186781
2022-07-04 15:42:34 +02:00
Dubslow 442c40b43d Use NNUE complexity in search, retune related parameters
This builds on ideas of xoto10 and mstembera to use more output from NNUE in the search algorithm.

passed STC:
https://tests.stockfishchess.org/tests/view/62ae454fe7ee5525ef88a957
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 89208 W: 24127 L: 23753 D: 41328
Ptnml(0-2): 400, 9886, 23642, 10292, 384

passed LTC:
https://tests.stockfishchess.org/tests/view/62acc6ddd89eb6cf1e0750a1
LLR: 2.93 (-2.94,2.94) <0.50,3.00>
Total: 56352 W: 15430 L: 15115 D: 25807
Ptnml(0-2): 44, 5501, 16782, 5794, 55

closes https://github.com/official-stockfish/Stockfish/pull/4088

bench 5332964
2022-06-20 08:30:57 +02:00
Dubslow 5304b561ab LMR: remove deeper
...apparently it wasn't doing much anymore. inspired by rufish's recent attempts to improve this.

passed STC:
https://tests.stockfishchess.org/tests/view/62abca2cd89eb6cf1e072c04
LLR: 2.95 (-2.94,2.94) <-2.25,0.25>
Total: 85576 W: 22766 L: 22683 D: 40127
Ptnml(0-2): 362, 9607, 22741, 9742, 336

passed LTC:
https://tests.stockfishchess.org/tests/view/62ac90ffd89eb6cf1e07488f
LLR: 2.93 (-2.94,2.94) <-2.25,0.25>
Total: 48248 W: 13018 L: 12896 D: 22334
Ptnml(0-2): 32, 4773, 14400, 4879, 40

closes https://github.com/official-stockfish/Stockfish/pull/4088

bench 5578988
2022-06-20 08:25:50 +02:00
bmc4 4d6a11a04c Don't change ttPv at probCut
STC:
LLR: 2.96 (-2.94,2.94) <-2.25,0.25>
Total: 35672 W: 9618 L: 9462 D: 16592
Ptnml(0-2): 151, 3890, 9601, 4040, 154
https://tests.stockfishchess.org/tests/view/62ab03f750949cfc241b1965

LTC:
LLR: 2.93 (-2.94,2.94) <-2.25,0.25>
Total: 54160 W: 14626 L: 14511 D: 25023
Ptnml(0-2): 42, 5414, 16056, 5523, 45
https://tests.stockfishchess.org/tests/view/62ab5e6fd89eb6cf1e071b87

closes https://github.com/official-stockfish/Stockfish/pull/4088

bench: 5798229
2022-06-20 08:24:07 +02:00
bmc4 6edc29d720 Simplify away condition in ttSave in probCut
Remove condition for tte->save in probCut so it always saves on probCut cutoff.

STC:
LLR: 2.95 (-2.94,2.94) <-2.25,0.25>
Total: 47848 W: 12921 L: 12782 D: 22145
Ptnml(0-2): 207, 5340, 12715, 5431, 231
https://tests.stockfishchess.org/tests/view/62a1f7c87bd8e641e44436f7

LTC:
LLR: 2.97 (-2.94,2.94) <-2.25,0.25>
Total: 132736 W: 35895 L: 35881 D: 60960
Ptnml(0-2): 109, 13384, 39360, 13414, 101
https://tests.stockfishchess.org/tests/view/62a2421a7bd8e641e444434f

closes https://github.com/official-stockfish/Stockfish/pull/4069

bench: 5845802
2022-06-16 07:12:01 +02:00
mstembera 2d5dcf3d18 Minor simplifications and cleanup in search
STC: https://tests.stockfishchess.org/tests/view/629d6775593a4a9b6482c1ec
LLR: 2.93 (-2.94,2.94) <-2.25,0.25>
Total: 77416 W: 20683 L: 20589 D: 36144
Ptnml(0-2): 317, 8690, 20620, 8744, 337

LTC: https://tests.stockfishchess.org/tests/view/629db4be593a4a9b6482ceef
LLR: 2.95 (-2.94,2.94) <-2.25,0.25>
Total: 106544 W: 28752 L: 28705 D: 49087
Ptnml(0-2): 97, 10692, 31641, 10751, 91

closes https://github.com/official-stockfish/Stockfish/pull/4059

Bench: 5913510
2022-06-16 07:06:43 +02:00
ppigazzini d54b85b4bd Restore NDKv21 for GitHub Actions
GitHub updated the versions of NDK installed on the Actions runners
breaking the ARM tests.
Restore the NDKv21 using the GitHub suggested mitigation, see:
https://github.com/actions/virtual-environments/issues/5595

closes https://github.com/official-stockfish/Stockfish/pull/4077

No functional change
2022-06-16 07:03:52 +02:00
candirufish 00297cfef0 Use qsearch on step 11 if depth is equal to or below 0
larger reduction of depth if no TT entry is found, and go in qsearch as needed.

stc:
https://tests.stockfishchess.org/tests/view/629dfacd593a4a9b6482db72
LLR: 2.93 (-2.94,2.94) <0.00,2.50>
Total: 31920 W: 8591 L: 8322 D: 15007
Ptnml(0-2): 127, 3551, 8376, 3738, 168

ltc:
https://tests.stockfishchess.org/tests/view/629e304e593a4a9b6482e451
LLR: 2.95 (-2.94,2.94) <0.50,3.00>
Total: 17488 W: 4842 L: 4614 D: 8032
Ptnml(0-2): 13, 1670, 5151, 1896, 14

closes https://github.com/official-stockfish/Stockfish/pull/4056

Bench: 5870283
2022-06-07 08:34:14 +02:00
Boštjan Mejak 809849fa27 Wording of help output and comments.
Improved the output text that is diplayed when executing the 'help' command.
Also, some comments were fixed along the way.

closes https://github.com/official-stockfish/Stockfish/pull/4048
closes https://github.com/official-stockfish/Stockfish/pull/4044

No functional change
2022-06-07 08:30:07 +02:00
Dubslow 90cf8e7d2b Remove LMR condition for complex pos
Inspired by Kia's similar test: https://tests.stockfishchess.org/tests/view/6292898c1e7cd5f29966fbe0

Passed STC:
https://tests.stockfishchess.org/tests/view/62941588b0d5a7d1b780ed4b
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 266872 W: 70850 L: 71033 D: 124989
Ptnml(0-2): 1180, 30114, 70941, 30111, 1090

Passed LTC:
https://tests.stockfishchess.org/tests/view/62964a754628d33daa24f062
LLR: 2.95 (-2.94,2.94) <-2.25,0.25>
Total: 70160 W: 18756 L: 18662 D: 32742
Ptnml(0-2): 42, 6976, 20950, 7070, 42

closes https://github.com/official-stockfish/Stockfish/pull/4047

Bench 6237567
2022-06-02 07:49:31 +02:00
xoto10 7f1333ccf8 Blend nnue complexity with classical.
Following mstembera's test of the complexity value derived from nnue values,
this change blends that idea with the old complexity calculation.

STC 10+0.1:
LLR: 2.95 (-2.94,2.94) <0.00,2.50>
Total: 42320 W: 11436 L: 11148 D: 19736
Ptnml(0-2): 209, 4585, 11263, 4915, 188
https://tests.stockfishchess.org/tests/live_elo/6295c9239c8c2fcb2bad7fd9

LTC 60+0.6:
LLR: 2.98 (-2.94,2.94) <0.50,3.00>
Total: 34600 W: 9393 L: 9125 D: 16082
Ptnml(0-2): 32, 3323, 10319, 3597, 29
https://tests.stockfishchess.org/tests/view/6295fd5d9c8c2fcb2bad88cf

closes https://github.com/official-stockfish/Stockfish/pull/4046

Bench 6078140
2022-06-02 07:47:23 +02:00
candirufish 653bd0817c cutnode and movecount lmr extension simplification
Passed STC
https://tests.stockfishchess.org/tests/view/6294133cb0d5a7d1b780ece3
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 41072 W: 11052 L: 10908 D: 19112
Ptnml(0-2): 153, 4324, 11461, 4422, 176

Passed LTC
ltc: https://tests.stockfishchess.org/tests/view/62947ae6b0d5a7d1b780fe86
LLR: 2.94 (-2.94,2.94) <-2.25,0.25>
Total: 102736 W: 27509 L: 27459 D: 47768
Ptnml(0-2): 98, 9734, 31669, 9754, 113

closes https://github.com/official-stockfish/Stockfish/pull/4045

Bench: 6410652
2022-06-02 07:44:22 +02:00
Michael Chaly 8fadbcf1b2 Add info about elo gained from some heuristics
Add info about qsearch and impact of main and continuation histories.

Based on these tests:
https://tests.stockfishchess.org/tests/view/62946ffcb0d5a7d1b780fc7e
https://tests.stockfishchess.org/tests/view/628facb71e7cd5f299669534
https://tests.stockfishchess.org/tests/view/628eade11e7cd5f299666f2e

closes https://github.com/official-stockfish/Stockfish/pull/4041

No functional change.
2022-06-02 07:43:14 +02:00
103 changed files with 3358 additions and 19281 deletions
+65
View File
@@ -0,0 +1,65 @@
name: Report issue
description: Create a report to help us fix issues with the engine
body:
- type: textarea
attributes:
label: Describe the issue
description: A clear and concise description of what you're experiencing.
validations:
required: true
- type: textarea
attributes:
label: Expected behavior
description: A clear and concise description of what you expected to happen.
validations:
required: true
- type: textarea
attributes:
label: Steps to reproduce
description: |
Steps to reproduce the behavior.
You can also use this section to paste the command line output.
placeholder: |
```
position startpos moves g2g4 e7e5 f2f3
go mate 1
info string NNUE evaluation using nn-6877cd24400e.nnue enabled
info depth 1 seldepth 1 multipv 1 score mate 1 nodes 33 nps 11000 tbhits 0 time 3 pv d8h4
bestmove d8h4
```
validations:
required: true
- type: textarea
attributes:
label: Anything else?
description: |
Anything that will give us more context about the issue you are encountering.
You can also use this section to propose ideas on how to solve the issue.
validations:
required: false
- type: dropdown
attributes:
label: Operating system
options:
- All
- Windows
- Linux
- MacOS
- Android
- Other or N/A
validations:
required: true
- type: input
attributes:
label: Stockfish version
description: |
This can be found by running the engine.
You can also use the commit ID.
placeholder: Stockfish 15 / e6e324e
validations:
required: true
+8
View File
@@ -0,0 +1,8 @@
blank_issues_enabled: false
contact_links:
- name: Discord server
url: https://discord.gg/GWDRS3kU6R
about: Feel free to ask for support or have a chat with us in our Discord server!
- name: Discussions, Q&A, ideas, show us something...
url: https://github.com/official-stockfish/Stockfish/discussions/new
about: Do you have an idea for Stockfish? Do you want to show something that you made? Please open a discussion about it!
+31 -240
View File
@@ -1,260 +1,51 @@
name: Stockfish
on:
push:
tags:
- '*'
branches:
- master
- tools
- github_ci
- github_ci_armv7
pull_request:
branches:
- master
- tools
jobs:
Stockfish:
name: ${{ matrix.config.name }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
strategy:
matrix:
config:
# set the variable for the required tests:
# run_expensive_tests: true
# run_32bit_tests: true
# run_64bit_tests: true
- {
name: "Ubuntu 20.04 GCC",
os: ubuntu-20.04,
compiler: g++,
comp: gcc,
run_expensive_tests: true,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "Ubuntu 20.04 Clang",
os: ubuntu-20.04,
compiler: clang++,
comp: clang,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "MacOS 10.15 Apple Clang",
os: macos-10.15,
compiler: clang++,
comp: clang,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "MacOS 10.15 GCC 10",
os: macos-10.15,
compiler: g++-10,
comp: gcc,
run_64bit_tests: true,
shell: 'bash {0}'
}
- {
name: "Windows 2022 Mingw-w64 GCC x86_64",
os: windows-2022,
compiler: g++,
comp: mingw,
run_64bit_tests: true,
msys_sys: 'mingw64',
msys_env: 'x86_64-gcc',
shell: 'msys2 {0}'
}
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
Prerelease:
if: github.ref == 'refs/heads/master'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Download required linux packages
if: runner.os == 'Linux'
# returns null if no pre-release exists
- name: Get Commit SHA of Latest Pre-release
run: |
sudo apt update
sudo apt install expect valgrind g++-multilib
# Install required packages
sudo apt-get update
sudo apt-get install -y curl jq
- name: Setup msys and install required packages
if: runner.os == 'Windows'
uses: msys2/setup-msys2@v2
echo "COMMIT_SHA=$(jq -r 'map(select(.prerelease)) | first | .tag_name' <<< $(curl -s https://api.github.com/repos/${{ github.repository_owner }}/Stockfish/releases))" >> $GITHUB_ENV
# delete old previous pre-release and tag
- uses: dev-drprasad/delete-tag-and-release@v0.2.1
if: env.COMMIT_SHA != 'null'
with:
msystem: ${{matrix.config.msys_sys}}
install: mingw-w64-${{matrix.config.msys_env}} make git expect
tag_name: ${{ env.COMMIT_SHA }}
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Download the used network from the fishtest framework
run: |
make net
- name: Extract the bench number from the commit history
run: |
git log HEAD | grep "\b[Bb]ench[ :]\+[0-9]\{7\}" | head -n 1 | sed "s/[^0-9]*\([0-9]*\).*/\1/g" > git_sig
[ -s git_sig ] && echo "benchref=$(cat git_sig)" >> $GITHUB_ENV && echo "Reference bench:" $(cat git_sig) || echo "No bench found"
- name: Check compiler
run: |
$COMPILER -v
- name: Test help target
run: |
make help
# x86-32 tests
- name: Test debug x86-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
export CXXFLAGS="-D_GLIBCXX_DEBUG"
make clean
make -j2 ARCH=x86-32 optimize=no debug=yes build
../tests/signature.sh $benchref
- name: Test x86-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32 build
../tests/signature.sh $benchref
- name: Test x86-32-sse41-popcnt build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse41-popcnt build
../tests/signature.sh $benchref
- name: Test x86-32-sse2 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse2 build
../tests/signature.sh $benchref
- name: Test general-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=general-32 build
../tests/signature.sh $benchref
# x86-64 tests
- name: Test debug x86-64-modern build
if: ${{ matrix.config.run_64bit_tests }}
run: |
export CXXFLAGS="-D_GLIBCXX_DEBUG"
make clean
make -j2 ARCH=x86-64-modern optimize=no debug=yes build
../tests/signature.sh $benchref
- name: Test x86-64-modern build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
../tests/signature.sh $benchref
- name: Test x86-64-ssse3 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-ssse3 build
../tests/signature.sh $benchref
- name: Test x86-64-sse3-popcnt build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-sse3-popcnt build
../tests/signature.sh $benchref
- name: Test x86-64 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64 build
../tests/signature.sh $benchref
- name: Test general-64 build
if: matrix.config.run_64bit_tests
run: |
make clean
make -j2 ARCH=general-64 build
../tests/signature.sh $benchref
# x86-64 with newer extensions tests
- name: Compile x86-64-avx2 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-avx2 build
- name: Compile x86-64-bmi2 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-bmi2 build
- name: Compile x86-64-avx512 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-avx512 build
- name: Compile x86-64-vnni512 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-vnni512 build
- name: Compile x86-64-vnni256 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-vnni256 build
# Other tests
- name: Check perft and search reproducibility
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
../tests/perft.sh
../tests/reprosearch.sh
# Sanitizers
- name: Run under valgrind
if: ${{ matrix.config.run_expensive_tests }}
run: |
export CXXFLAGS="-O1 -fno-inline"
make clean
make -j2 ARCH=x86-64-modern debug=yes optimize=no build > /dev/null
../tests/instrumented.sh --valgrind
../tests/instrumented.sh --valgrind-thread
- name: Run with UB sanitizer
if: ${{ matrix.config.run_expensive_tests }}
run: |
export CXXFLAGS="-O1 -fno-inline"
make clean
make -j2 ARCH=x86-64-modern sanitize=undefined optimize=no debug=yes build > /dev/null
../tests/instrumented.sh --sanitizer-undefined
- name: Run with thread sanitizer
if: ${{ matrix.config.run_expensive_tests }}
run: |
export CXXFLAGS="-O1 -fno-inline"
make clean
make -j2 ARCH=x86-64-modern sanitize=thread optimize=no debug=yes build > /dev/null
../tests/instrumented.sh --sanitizer-thread
Sanitizers:
uses: ./.github/workflows/stockfish_sanitizers.yml
Tests:
uses: ./.github/workflows/stockfish_test.yml
Compiles:
uses: ./.github/workflows/stockfish_compile_test.yml
Binaries:
if: github.ref == 'refs/heads/master' || (startsWith(github.ref_name, 'sf_') && github.ref_type == 'tag')
uses: ./.github/workflows/stockfish_binaries.yml
ARM_Binaries:
if: github.ref == 'refs/heads/master' || (startsWith(github.ref_name, 'sf_') && github.ref_type == 'tag')
uses: ./.github/workflows/stockfish_arm_binaries.yml
@@ -0,0 +1,158 @@
name: Stockfish
on:
workflow_call:
jobs:
Stockfish:
name: ${{ matrix.config.name }} ${{ matrix.binaries }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
EMU: ${{ matrix.config.emu }}
EXT: ${{ matrix.config.ext }}
OS: ${{ matrix.config.os }}
BINARY: ${{ matrix.binaries }}
strategy:
matrix:
config:
- name: Android NDK aarch64
os: ubuntu-22.04
compiler: aarch64-linux-android21-clang++
emu: qemu-aarch64
comp: ndk
shell: bash {0}
- name: Android NDK arm
os: ubuntu-22.04
compiler: armv7a-linux-androideabi21-clang++
emu: qemu-arm
comp: ndk
shell: bash {0}
binaries:
- armv8
- armv7
- armv7-neon
exclude:
- binaries: armv8
config: {compiler: armv7a-linux-androideabi21-clang++}
- binaries: armv7
config: {compiler: aarch64-linux-android21-clang++}
- binaries: armv7-neon
config: {compiler: aarch64-linux-android21-clang++}
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Download required linux packages
if: runner.os == 'Linux'
run: |
sudo apt update
sudo apt install qemu-user
- name: Install NDK
if: runner.os == 'Linux'
run: |
if [ $COMP == ndk ]; then
NDKV="21.4.7075529"
ANDROID_ROOT=/usr/local/lib/android
ANDROID_SDK_ROOT=$ANDROID_ROOT/sdk
SDKMANAGER=$ANDROID_SDK_ROOT/cmdline-tools/latest/bin/sdkmanager
echo "y" | $SDKMANAGER "ndk;$NDKV"
ANDROID_NDK_ROOT=$ANDROID_SDK_ROOT/ndk/$NDKV
ANDROID_NDK_BIN=$ANDROID_NDK_ROOT/toolchains/llvm/prebuilt/linux-x86_64/bin
echo "ANDROID_NDK_BIN=$ANDROID_NDK_BIN" >> $GITHUB_ENV
fi
- name: Download the used network from the fishtest framework
run: make net
- name: Check compiler
run: |
if [ $COMP == ndk ]; then
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
fi
$COMPILER -v
- name: Test help target
run: make help
- name: Check git
run: git --version
# Compile profile guided builds
- name: Compile ${{ matrix.binaries }} build
run: |
if [ $COMP == ndk ]; then
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
fi
make clean
make -j2 profile-build ARCH=$BINARY COMP=$COMP WINE_PATH=$EMU
make strip ARCH=$BINARY COMP=$COMP
mv ./stockfish$EXT ../stockfish-android-$BINARY$EXT
- name: Remove non src files
run: rm -f *.o .depend *.nnue
- name: Download wiki
run: |
git clone https://github.com/official-stockfish/Stockfish.wiki.git ../wiki
cd ../wiki
rm -rf .git
- name: Create tar archive.
run: |
cd ..
mkdir stockfish
cp -r wiki stockfish/
cp -r src stockfish/
cp stockfish-android-$BINARY$EXT stockfish/
cp "Top CPU Contributors.txt" stockfish/
cp Copying.txt stockfish/
cp AUTHORS stockfish/
cp CITATION.cff stockfish/
cp README.md stockfish/
tar -cvf stockfish-android-$BINARY.tar stockfish
- name: Upload binaries
uses: actions/upload-artifact@v3
with:
name: stockfish-android-${{ matrix.binaries }}
path: stockfish-android-${{ matrix.binaries }}.tar
- name: Release
if: startsWith(github.ref_name, 'sf_') && github.ref_type == 'tag'
uses: softprops/action-gh-release@v1
with:
files: stockfish-android-${{ matrix.binaries }}.tar
- name: Get last commit sha
id: last_commit
run: echo "COMMIT_SHA=$(git rev-parse HEAD | cut -c 1-8)" >> $GITHUB_ENV
- name: Get commit date
id: commit_date
run: echo "COMMIT_DATE=$(git show -s --date=format:'%Y%m%d' --format=%cd HEAD)" >> $GITHUB_ENV
# Make sure that an old ci which still runs on master doesn't recreate a prerelease
- name: Check Pullable Commits
id: check_commits
run: |
git fetch
CHANGES=$(git rev-list HEAD..origin/master --count)
echo "CHANGES=$CHANGES" >> $GITHUB_ENV
- name: Prerelease
if: github.ref_name == 'master' && env.CHANGES == '0'
continue-on-error: true
uses: softprops/action-gh-release@v1
with:
name: Stockfish dev-${{ env.COMMIT_DATE }}-${{ env.COMMIT_SHA }}
tag_name: stockfish-dev-${{ env.COMMIT_DATE }}-${{ env.COMMIT_SHA }}
prerelease: true
files: stockfish-android-${{ matrix.binaries }}.tar
+168
View File
@@ -0,0 +1,168 @@
name: Stockfish
on:
workflow_call:
jobs:
Stockfish:
name: ${{ matrix.config.name }} ${{ matrix.binaries }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
EXT: ${{ matrix.config.ext }}
NAME: ${{ matrix.config.simple_name }}
BINARY: ${{ matrix.binaries }}
strategy:
matrix:
config:
- name: Ubuntu 20.04 GCC
os: ubuntu-20.04
simple_name: ubuntu
compiler: g++
comp: gcc
shell: bash {0}
archive_ext: tar
- name: MacOS 12 Apple Clang
os: macos-12
simple_name: macos
compiler: clang++
comp: clang
shell: bash {0}
archive_ext: tar
- name: Windows 2022 Mingw-w64 GCC x86_64
os: windows-2022
simple_name: windows
compiler: g++
comp: mingw
msys_sys: mingw64
msys_env: x86_64-gcc
shell: msys2 {0}
ext: .exe
archive_ext: zip
binaries:
- x86-64
- x86-64-modern
- x86-64-avx2
exclude:
- binaries: x86-64-avx2
config: {os: macos-12}
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Download required linux packages
if: runner.os == 'Linux'
run: sudo apt update
- name: Setup msys and install required packages
if: runner.os == 'Windows'
uses: msys2/setup-msys2@v2
with:
msystem: ${{ matrix.config.msys_sys }}
install: mingw-w64-${{ matrix.config.msys_env }} make git zip
- name: Download the used network from the fishtest framework
run: make net
- name: Check compiler
run: $COMPILER -v
- name: Test help target
run: make help
- name: Check git
run: git --version
# Compile profile guided builds
- name: Compile ${{ matrix.binaries }} build
run: |
make -j2 profile-build ARCH=$BINARY COMP=$COMP
make strip ARCH=$BINARY COMP=$COMP
mv ./stockfish$EXT ../stockfish-$NAME-$BINARY$EXT
- name: Remove non src files
run: git clean -fx
- name: Download wiki
run: |
git clone https://github.com/official-stockfish/Stockfish.wiki.git ../wiki
rm -rf ../wiki/.git
- name: Create directory.
run: |
cd ..
mkdir stockfish
cp -r wiki stockfish/
cp -r src stockfish/
cp stockfish-$NAME-$BINARY$EXT stockfish/
cp "Top CPU Contributors.txt" stockfish/
cp Copying.txt stockfish/
cp AUTHORS stockfish/
cp CITATION.cff stockfish/
cp README.md stockfish/
- name: Create tar
if: runner.os != 'Windows'
run: |
cd ..
tar -cvf stockfish-$NAME-$BINARY.tar stockfish
- name: Create zip
if: runner.os == 'Windows'
run: |
cd ..
zip -r stockfish-$NAME-$BINARY.zip stockfish
- name: Upload binaries
if: runner.os != 'Windows'
uses: actions/upload-artifact@v3
with:
name: stockfish-${{ matrix.config.os }}-${{ matrix.binaries }}
path: stockfish-${{ matrix.config.simple_name }}-${{ matrix.binaries }}.tar
# Artifacts automatically get zipped
# to avoid double zipping, we use the unzipped directory
- name: Upload binaries
if: runner.os == 'Windows'
uses: actions/upload-artifact@v3
with:
name: stockfish-${{ matrix.config.os }}-${{ matrix.binaries }}
path: stockfish
- name: Release
if: startsWith(github.ref_name, 'sf_') && github.ref_type == 'tag'
uses: softprops/action-gh-release@v1
with:
files: stockfish-${{ matrix.config.simple_name }}-${{ matrix.binaries }}.${{ matrix.config.archive_ext }}
- name: Get last commit sha
id: last_commit
run: echo "COMMIT_SHA=$(git rev-parse HEAD | cut -c 1-8)" >> $GITHUB_ENV
- name: Get commit date
id: commit_date
run: echo "COMMIT_DATE=$(git show -s --date=format:'%Y%m%d' --format=%cd HEAD)" >> $GITHUB_ENV
# Make sure that an old ci which still runs on master doesn't recreate a prerelease
- name: Check Pullable Commits
id: check_commits
run: |
git fetch
CHANGES=$(git rev-list HEAD..origin/master --count)
echo "CHANGES=$CHANGES" >> $GITHUB_ENV
- name: Prerelease
if: github.ref_name == 'master' && env.CHANGES == '0'
continue-on-error: true
uses: softprops/action-gh-release@v1
with:
name: Stockfish dev-${{ env.COMMIT_DATE }}-${{ env.COMMIT_SHA }}
tag_name: stockfish-dev-${{ env.COMMIT_DATE }}-${{ env.COMMIT_SHA }}
prerelease: true
files: stockfish-${{ matrix.config.simple_name }}-${{ matrix.binaries }}.${{ matrix.config.archive_ext }}
@@ -0,0 +1,102 @@
name: Stockfish
on:
workflow_call:
jobs:
Stockfish:
name: ${{ matrix.config.name }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
strategy:
matrix:
config:
- name: Ubuntu 20.04 GCC
os: ubuntu-20.04
compiler: g++
comp: gcc
shell: bash {0}
- name: Ubuntu 20.04 Clang
os: ubuntu-20.04
compiler: clang++
comp: clang
shell: bash {0}
- name: MacOS 12 Apple Clang
os: macos-12
compiler: clang++
comp: clang
shell: bash {0}
- name: MacOS 12 GCC 11
os: macos-12
compiler: g++-11
comp: gcc
shell: bash {0}
- name: Windows 2022 Mingw-w64 GCC x86_64
os: windows-2022
compiler: g++
comp: mingw
msys_sys: mingw64
msys_env: x86_64-gcc
shell: msys2 {0}
- name: Windows 2022 Mingw-w64 Clang x86_64
os: windows-2022
compiler: clang++
comp: clang
msys_sys: clang64
msys_env: clang-x86_64-clang
shell: msys2 {0}
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Setup msys and install required packages
if: runner.os == 'Windows'
uses: msys2/setup-msys2@v2
with:
msystem: ${{matrix.config.msys_sys}}
install: mingw-w64-${{matrix.config.msys_env}} make git
- name: Download the used network from the fishtest framework
run: make net
- name: Check compiler
run: $COMPILER -v
- name: Test help target
run: make help
- name: Check git
run: git --version
# x86-64 with newer extensions tests
- name: Compile x86-64-avx2 build
run: |
make clean
make -j2 ARCH=x86-64-avx2 build
- name: Compile x86-64-bmi2 build
run: |
make clean
make -j2 ARCH=x86-64-bmi2 build
- name: Compile x86-64-avx512 build
run: |
make clean
make -j2 ARCH=x86-64-avx512 build
- name: Compile x86-64-vnni512 build
run: |
make clean
make -j2 ARCH=x86-64-vnni512 build
- name: Compile x86-64-vnni256 build
run: |
make clean
make -j2 ARCH=x86-64-vnni256 build
@@ -0,0 +1,66 @@
name: Stockfish
on:
workflow_call:
jobs:
Stockfish:
name: ${{ matrix.sanitizers.name }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
CXXFLAGS: "-Werror"
strategy:
matrix:
config:
- name: Ubuntu 20.04 GCC
os: ubuntu-20.04
compiler: g++
comp: gcc
shell: bash {0}
sanitizers:
- name: Run with thread sanitizer
make_option: sanitize=thread
instrumented_option: sanitizer-thread
- name: Run with UB sanitizer
make_option: sanitize=undefined
instrumented_option: sanitizer-undefined
- name: Run under valgrind
make_option: ""
instrumented_option: valgrind
- name: Run under valgrind-thread
make_option: ""
instrumented_option: valgrind-thread
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Download required linux packages
run: |
sudo apt update
sudo apt install expect valgrind g++-multilib
- name: Download the used network from the fishtest framework
run: make net
- name: Check compiler
run: $COMPILER -v
- name: Test help target
run: make help
- name: Check git
run: git --version
# Sanitizers
- name: ${{ matrix.sanitizers.name }}
run: |
export CXXFLAGS="-O1 -fno-inline"
make clean
make -j2 ARCH=x86-64-modern ${{ matrix.sanitizers.make_option }} debug=yes optimize=no build > /dev/null
../tests/instrumented.sh --${{ matrix.sanitizers.instrumented_option }}
+256
View File
@@ -0,0 +1,256 @@
name: Stockfish
on:
workflow_call:
jobs:
Stockfish:
name: ${{ matrix.config.name }}
runs-on: ${{ matrix.config.os }}
env:
COMPILER: ${{ matrix.config.compiler }}
COMP: ${{ matrix.config.comp }}
CXXFLAGS: "-Werror"
strategy:
matrix:
config:
- name: Ubuntu 20.04 GCC
os: ubuntu-20.04
compiler: g++
comp: gcc
run_32bit_tests: true
run_64bit_tests: true
shell: bash {0}
- name: Ubuntu 20.04 Clang
os: ubuntu-20.04
compiler: clang++
comp: clang
run_32bit_tests: true
run_64bit_tests: true
shell: bash {0}
- name: Android NDK aarch64
os: ubuntu-22.04
compiler: aarch64-linux-android21-clang++
comp: ndk
run_armv8_tests: true
shell: bash {0}
- name: Android NDK arm
os: ubuntu-22.04
compiler: armv7a-linux-androideabi21-clang++
comp: ndk
run_armv7_tests: true
shell: bash {0}
- name: MacOS 12 Apple Clang
os: macos-12
compiler: clang++
comp: clang
run_64bit_tests: true
shell: bash {0}
- name: MacOS 12 GCC 11
os: macos-12
compiler: g++-11
comp: gcc
run_64bit_tests: true
shell: bash {0}
- name: Windows 2022 Mingw-w64 GCC x86_64
os: windows-2022
compiler: g++
comp: mingw
run_64bit_tests: true
msys_sys: mingw64
msys_env: x86_64-gcc
shell: msys2 {0}
- name: Windows 2022 Mingw-w64 GCC i686
os: windows-2022
compiler: g++
comp: mingw
run_32bit_tests: true
msys_sys: mingw32
msys_env: i686-gcc
shell: msys2 {0}
- name: Windows 2022 Mingw-w64 Clang x86_64
os: windows-2022
compiler: clang++
comp: clang
run_64bit_tests: true
msys_sys: clang64
msys_env: clang-x86_64-clang
shell: msys2 {0}
defaults:
run:
working-directory: src
shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Download required linux packages
if: runner.os == 'Linux'
run: |
sudo apt update
sudo apt install expect valgrind g++-multilib qemu-user
- name: Install NDK
if: runner.os == 'Linux'
run: |
if [ $COMP == ndk ]; then
NDKV="21.4.7075529"
ANDROID_ROOT=/usr/local/lib/android
ANDROID_SDK_ROOT=$ANDROID_ROOT/sdk
SDKMANAGER=$ANDROID_SDK_ROOT/cmdline-tools/latest/bin/sdkmanager
echo "y" | $SDKMANAGER "ndk;$NDKV"
ANDROID_NDK_ROOT=$ANDROID_SDK_ROOT/ndk/$NDKV
ANDROID_NDK_BIN=$ANDROID_NDK_ROOT/toolchains/llvm/prebuilt/linux-x86_64/bin
echo "ANDROID_NDK_BIN=$ANDROID_NDK_BIN" >> $GITHUB_ENV
fi
- name: Setup msys and install required packages
if: runner.os == 'Windows'
uses: msys2/setup-msys2@v2
with:
msystem: ${{ matrix.config.msys_sys }}
install: mingw-w64-${{ matrix.config.msys_env }} make git expect
- name: Download the used network from the fishtest framework
run: make net
- name: Extract the bench number from the commit history
run: |
git log HEAD | grep -o "\b[Bb]ench[ :]\+[1-9][0-9]\{5,9\}\b" | head -n 1 | sed "s/[^0-9]//g" > git_sig
[ -s git_sig ] && echo "benchref=$(cat git_sig)" >> $GITHUB_ENV && echo "Reference bench:" $(cat git_sig) || echo "No bench found"
- name: Check compiler
run: |
if [ $COMP == ndk ]; then
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
fi
$COMPILER -v
- name: Test help target
run: make help
- name: Check git
run: git --version
# x86-32 tests
- name: Test debug x86-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
make clean
make -j2 ARCH=x86-32 optimize=no debug=yes build
../tests/signature.sh $benchref
- name: Test x86-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32 build
../tests/signature.sh $benchref
- name: Test x86-32-sse41-popcnt build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse41-popcnt build
../tests/signature.sh $benchref
- name: Test x86-32-sse2 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse2 build
../tests/signature.sh $benchref
- name: Test general-32 build
if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=general-32 build
../tests/signature.sh $benchref
# x86-64 tests
- name: Test debug x86-64-modern build
if: ${{ matrix.config.run_64bit_tests }}
run: |
export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
make clean
make -j2 ARCH=x86-64-modern optimize=no debug=yes build
../tests/signature.sh $benchref
- name: Test x86-64-modern build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
../tests/signature.sh $benchref
- name: Test x86-64-ssse3 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-ssse3 build
../tests/signature.sh $benchref
- name: Test x86-64-sse3-popcnt build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-sse3-popcnt build
../tests/signature.sh $benchref
- name: Test x86-64 build
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64 build
../tests/signature.sh $benchref
- name: Test general-64 build
if: matrix.config.run_64bit_tests
run: |
make clean
make -j2 ARCH=general-64 build
../tests/signature.sh $benchref
# armv8 tests
- name: Test armv8 build
if: ${{ matrix.config.run_armv8_tests }}
run: |
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv8 build
../tests/signature.sh $benchref
# armv7 tests
- name: Test armv7 build
if: ${{ matrix.config.run_armv7_tests }}
run: |
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv7 build
../tests/signature.sh $benchref
- name: Test armv7-neon build
if: ${{ matrix.config.run_armv7_tests }}
run: |
export PATH=${{ env.ANDROID_NDK_BIN }}:$PATH
export LDFLAGS="-static -Wno-unused-command-line-argument"
make clean
make -j2 ARCH=armv7-neon build
../tests/signature.sh $benchref
# Other tests
- name: Check perft and search reproducibility
if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
../tests/perft.sh
../tests/reprosearch.sh
+37 -22
View File
@@ -1,17 +1,15 @@
# List of authors for Stockfish
# Founders of the Stockfish project and fishtest infrastructure
# Founders of the Stockfish project and Fishtest infrastructure
Tord Romstad (romstad)
Marco Costalba (mcostalba)
Joona Kiiski (zamar)
Gary Linscott (glinscott)
# Authors and inventors of NNUE, training, NNUE port
# Authors and inventors of NNUE, training, and NNUE port
Yu Nasu (ynasu87)
Motohiro Isozaki (yaneurao)
Hisayori Noda (nodchip)
# all other authors of the code in alphabetical order
# All other authors of Stockfish code (in alphabetical order)
Aditya (absimaldata)
Adrian Petrescu (apetresc)
Ajith Chandy Jose (ajithcj)
@@ -21,6 +19,7 @@ Alexander Kure
Alexander Pagel (Lolligerhans)
Alfredo Menezes (lonfom169)
Ali AlZhrani (Cooffe)
Andreas Matthies (Matthies)
Andrei Vetrov (proukornew)
Andrew Grant (AndyGrant)
Andrey Neporada (nepal)
@@ -35,18 +34,23 @@ Ben Chaney (Chaneybenjamini)
Ben Koshy (BKSpurgeon)
Bill Henry (VoyagerOne)
Bojun Guo (noobpwnftw, Nooby)
borg323
Boštjan Mejak (PedanticHacker)
braich
Brian Sheppard (SapphireBrand, briansheppard-toast)
Bruno de Melo Costa (BM123499)
Bruno Pellanda (pellanda)
Bryan Cross (crossbr)
candirufish
Chess13234
Chris Cain (ceebo)
clefrks
Dale Weiler (graphitemaster)
Dan Schmidt (dfannius)
Daniel Axtens (daxtens)
Daniel Dugovic (ddugovic)
Dan Schmidt (dfannius)
Dariusz Orzechowski (dorzechowski)
David (dav1312)
David Zar
Daylen Yang (daylen)
Deshawn Mohan-Smith (GoldenRare)
@@ -61,7 +65,6 @@ Eelco de Groot (KingDefender)
Elvin Liu (solarlight2)
erbsenzaehler
Ernesto Gatti
Linmiao Xu (linrock)
Fabian Beuke (madnight)
Fabian Fichter (ianfab)
Fanael Linithien (Fanael)
@@ -74,32 +77,35 @@ George Sobala (gsobala)
gguliash
Giacomo Lorenzetti (G-Lorenz)
Gian-Carlo Pascutto (gcp)
Goh CJ (cj5716)
Gontran Lemaire (gonlem)
Goodkov Vasiliy Aleksandrovich (goodkov)
Gregor Cramer
GuardianRM
Günther Demetz (pb00067, pb00068)
Guy Vreuls (gvreuls)
Günther Demetz (pb00067, pb00068)
Henri Wiechers
Hiraoka Takuya (HiraokaTakuya)
homoSapiensSapiens
Hongzhi Cheng
Ivan Ivec (IIvec)
Jacques B. (Timshel)
Jake Senne (w1wwwwww)
Jan Ondruš (hxim)
Jared Kish (Kurtbusch)
Jared Kish (Kurtbusch, kurt22i)
Jarrod Torriero (DU-jdto)
Jean Gauthier (OuaisBla)
Jean-Francois Romang (jromang)
Jean Gauthier (OuaisBla)
Jekaa
Jerry Donald Watson (jerrydonaldwatson)
jjoshua2
Jonathan Calovski (Mysseno)
Jonathan Buladas Dumale (SFisGOD)
Jonathan Calovski (Mysseno)
Jonathan McDermid (jonathanmcdermid)
Joost VandeVondele (vondele)
Jörg Oster (joergoster)
Joseph Ellis (jhellis3)
Joseph R. Prostko
Jörg Oster (joergoster)
Julian Willemer (NightlyKing)
jundery
Justin Blanchard (UncombedCoconut)
@@ -113,6 +119,7 @@ Krystian Kuzniarek (kuzkry)
Leonardo Ljubičić (ICCF World Champion)
Leonid Pechenik (lp--)
Liam Keegan (lkeegan)
Linmiao Xu (linrock)
Linus Arver (listx)
loco-loco
Lub van den Berg (ElbertoOne)
@@ -127,6 +134,7 @@ Matt Ginsberg (mattginsberg)
Matthew Lai (matthewlai)
Matthew Sullivan (Matt14916)
Max A. (Disservin)
Maxim Masiutin (maximmasiutin)
Maxim Molchanov (Maxim)
Michael An (man)
Michael Byrne (MichaelB7)
@@ -141,35 +149,39 @@ Mira
Miroslav Fontán (Hexik)
Moez Jellouli (MJZ1977)
Mohammed Li (tthsqe12)
Muzhen J (XInTheDark)
Nathan Rugg (nmrugg)
Nick Pelling (nickpelling)
Nguyen Pham (nguyenpham)
Nicklas Persson (NicklasPersson)
Nick Pelling (nickpelling)
Niklas Fiekas (niklasf)
Nikolay Kostov (NikolayIT)
Nguyen Pham (nguyenpham)
Norman Schmidt (FireFather)
notruck
Ofek Shochat (OfekShochat, ghostway)
Ondrej Mosnáček (WOnder93)
Ondřej Mišina (AndrovT)
Oskar Werkelin Ahlin
Pablo Vazquez
Panthee
Pascal Romaret
Pasquale Pigazzini (ppigazzini)
Patrick Jansen (mibere)
pellanda
Peter Schneider (pschneider1968)
Peter Zsifkovits (CoffeeOne)
PikaCat
Praveen Kumar Tummala (praveentml)
Prokop Randáček (ProkopRandacek)
Rahul Dsilva (silversolver1)
Ralph Stößer (Ralph Stoesser)
Raminder Singh
renouve
Reuven Peleg
Richard Lloyd
Reuven Peleg (R-Peleg)
Richard Lloyd (Richard-Lloyd)
Rodrigo Exterckötter Tjäder
Ron Britvich (Britvich)
Rodrigo Roim (roim)
Ronald de Man (syzygy1, syzygy)
Ron Britvich (Britvich)
rqs
Rui Coelho (ruicoelhopedro)
Ryan Schmitt
@@ -180,20 +192,22 @@ Sergei Antonov (saproj)
Sergei Ivanov (svivanov72)
Sergio Vieri (sergiovieri)
sf-x
Shahin M. Shahin (peregrine)
Shane Booth (shane31)
Shawn Varghese (xXH4CKST3RXx)
Siad Daboul (Topologist)
Stefan Geschwentner (locutus2)
Stefano Cardanobile (Stefano80)
Stefano Di Martino (StefanoD)
Steinar Gunderson (sesse)
Stéphane Nicolet (snicolet)
Prokop Randáček (ProkopRandacek)
Syine Mineta (MinetaS)
Thanar2
thaspel
theo77186
Tomasz Sobczyk (Sopel97)
Tom Truscott
Tom Vijlbrief (tomtor)
Tomasz Sobczyk (Sopel97)
Torsten Franz (torfranz, tfranzer)
Torsten Hellwig (Torom)
Tracey Emery (basepr1me)
@@ -201,11 +215,12 @@ tttak
Unai Corzo (unaiic)
Uri Blass (uriblass)
Vince Negri (cuddlestmonkey)
Viren
windfishballad
xefoci7612
zz4032
# Additionally, we acknowledge the authors and maintainers of fishtest,
# an amazing and essential framework for the development of Stockfish!
# an amazing and essential framework for Stockfish development!
#
# https://github.com/glinscott/fishtest/blob/master/AUTHORS
+23
View File
@@ -0,0 +1,23 @@
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Stockfish
message: >-
Please cite this software using the metadata from this
file.
type: software
authors:
- name: The Stockfish developers (see AUTHORS file)
repository-code: 'https://github.com/official-stockfish/Stockfish'
url: 'https://stockfishchess.org/'
repository-artifact: 'https://stockfishchess.org/download/'
abstract: Stockfish is a free and strong UCI chess engine.
keywords:
- chess
- artificial intelligence (AI)
- tree search
- alpha-beta search
- neural networks (NN)
- efficiently updatable neural networks (NNUE)
license: GPL-3.0
+122 -329
View File
@@ -1,371 +1,164 @@
<div align="center">
[![Stockfish][stockfish128-logo]][website-link]
<h3>Stockfish</h3>
A free and strong UCI chess engine.
<br>
<strong>[Explore Stockfish docs »][wiki-link]</strong>
<br>
<br>
[Report bug][issue-link]
·
[Open a discussion][discussions-link]
·
[Discord][discord-link]
·
[Blog][website-blog-link]
[![Build][build-badge]][build-link]
[![License][license-badge]][license-link]
<br>
[![Release][release-badge]][release-link]
[![Commits][commits-badge]][commits-link]
<br>
[![Website][website-badge]][website-link]
[![Fishtest][fishtest-badge]][fishtest-link]
[![Discord][discord-badge]][discord-link]
</div>
## Overview
[![Build Status](https://github.com/official-stockfish/Stockfish/actions/workflows/stockfish.yml/badge.svg)](https://github.com/official-stockfish/Stockfish/actions)
[Stockfish][website-link] is a **free and strong UCI chess engine** derived from
Glaurung 2.1 that analyzes chess positions and computes the optimal moves.
[Stockfish](https://stockfishchess.org) is a free, powerful UCI chess engine
derived from Glaurung 2.1. Stockfish is not a complete chess program and requires a
UCI-compatible graphical user interface (GUI) (e.g. XBoard with PolyGlot, Scid,
Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in order
to be used comfortably. Read the documentation for your GUI of choice for information
about how to use Stockfish with it.
Stockfish **does not include a graphical user interface** (GUI) that is required
to display a chessboard and to make it easy to input moves. These GUIs are
developed independently from Stockfish and are available online. **Read the
documentation for your GUI** of choice for information about how to use
Stockfish with it.
The Stockfish engine features two evaluation functions for chess. The efficiently
updatable neural network (NNUE) based evaluation is the default and by far the strongest.
The classical evaluation based on handcrafted terms remains available. The strongest
network is integrated in the binary and downloaded automatically during the build process.
The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2,
avx2, neon, or similar).
See also the Stockfish [documentation][wiki-usage-link] for further usage help.
## Files
This distribution of Stockfish consists of the following files:
* [README.md](https://github.com/official-stockfish/Stockfish/blob/master/README.md),
the file you are currently reading.
* [README.md][readme-link], the file you are currently reading.
* [Copying.txt](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt),
a text file containing the GNU General Public License version 3.
* [Copying.txt][license-link], a text file containing the GNU General Public
License version 3.
* [AUTHORS](https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS),
a text file with the list of authors for the project
* [AUTHORS][authors-link], a text file with the list of authors for the project.
* [src](https://github.com/official-stockfish/Stockfish/tree/master/src),
a subdirectory containing the full source code, including a Makefile
that can be used to compile Stockfish on Unix-like systems.
* [src][src-link], a subdirectory containing the full source code, including a
Makefile that can be used to compile Stockfish on Unix-like systems.
* a file with the .nnue extension, storing the neural network for the NNUE
evaluation. Binary distributions will have this file embedded.
## The UCI protocol and available options
## The UCI protocol
The Universal Chess Interface (UCI) is a standard protocol used to communicate with
a chess engine, and is the recommended way to do so for typical graphical user interfaces
(GUI) or chess tools. Stockfish implements the majority of its options as described
in [the UCI protocol](https://www.shredderchess.com/download/div/uci.zip).
The [Universal Chess Interface][uci-link] (UCI) is a standard text-based protocol
used to communicate with a chess engine and is the recommended way to do so for
typical graphical user interfaces (GUI) or chess tools. Stockfish implements the
majority of its options.
Developers can see the default values for UCI options available in Stockfish by typing
`./stockfish uci` in a terminal, but the majority of users will typically see them and
change them via a chess GUI. This is a list of available UCI options in Stockfish:
Developers can see the default values for the UCI options available in Stockfish
by typing `./stockfish uci` in a terminal, but most users should typically use a
chess GUI to interact with Stockfish.
* #### Threads
The number of CPU threads used for searching a position. For best performance, set
this equal to the number of CPU cores available.
For more information on UCI or debug commands, see our [documentation][wiki-commands-link].
* #### Hash
The size of the hash table in MB. It is recommended to set Hash after setting Threads.
## Compiling Stockfish
* #### Clear Hash
Clear the hash table.
Stockfish has support for 32 or 64-bit CPUs, certain hardware instructions,
big-endian machines such as Power PC, and other platforms.
* #### Ponder
Let Stockfish ponder its next move while the opponent is thinking.
* #### MultiPV
Output the N best lines (principal variations, PVs) when searching.
Leave at 1 for best performance.
* #### Use NNUE
Toggle between the NNUE and classical evaluation functions. If set to "true",
the network parameters must be available to load from file (see also EvalFile),
if they are not embedded in the binary.
* #### EvalFile
The name of the file of the NNUE evaluation parameters. Depending on the GUI the
filename might have to include the full path to the folder/directory that contains
the file. Other locations, such as the directory that contains the binary and the
working directory, are also searched.
* #### UCI_AnalyseMode
An option handled by your GUI.
* #### UCI_Chess960
An option handled by your GUI. If true, Stockfish will play Chess960.
* #### UCI_ShowWDL
If enabled, show approximate WDL statistics as part of the engine output.
These WDL numbers model expected game outcomes for a given evaluation and
game ply for engine self-play at fishtest LTC conditions (60+0.6s per game).
* #### UCI_LimitStrength
Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.
* #### UCI_Elo
If enabled by UCI_LimitStrength, aim for an engine strength of the given Elo.
This Elo rating has been calibrated at a time control of 60s+0.6s and anchored to CCRL 40/4.
* #### Skill Level
Lower the Skill Level in order to make Stockfish play weaker (see also UCI_LimitStrength).
Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a
weaker move will be played.
* #### SyzygyPath
Path to the folders/directories storing the Syzygy tablebase files. Multiple
directories are to be separated by ";" on Windows and by ":" on Unix-based
operating systems. Do not use spaces around the ";" or ":".
Example: `C:\tablebases\wdl345;C:\tablebases\wdl6;D:\tablebases\dtz345;D:\tablebases\dtz6`
It is recommended to store .rtbw files on an SSD. There is no loss in storing
the .rtbz files on a regular HDD. It is recommended to verify all md5 checksums
of the downloaded tablebase files (`md5sum -c checksum.md5`) as corruption will
lead to engine crashes.
* #### SyzygyProbeDepth
Minimum remaining search depth for which a position is probed. Set this option
to a higher value to probe less aggressively if you experience too much slowdown
(in terms of nps) due to tablebase probing.
* #### Syzygy50MoveRule
Disable to let fifty-move rule draws detected by Syzygy tablebase probes count
as wins or losses. This is useful for ICCF correspondence games.
* #### SyzygyProbeLimit
Limit Syzygy tablebase probing to positions with at most this many pieces left
(including kings and pawns).
* #### Move Overhead
Assume a time delay of x ms due to network and GUI overheads. This is useful to
avoid losses on time in those cases.
* #### Slow Mover
Lower values will make Stockfish take less time in games, higher values will
make it think longer.
* #### nodestime
Tells the engine to use nodes searched instead of wall time to account for
elapsed time. Useful for engine testing.
* #### Debug Log File
Write all communication to and from the engine into a text file.
For developers the following non-standard commands might be of interest, mainly useful for debugging:
* #### bench *ttSize threads limit fenFile limitType evalType*
Performs a standard benchmark using various options. The signature of a version
(standard node count) is obtained using all defaults. `bench` is currently
`bench 16 1 13 default depth mixed`.
* #### compiler
Give information about the compiler and environment used for building a binary.
* #### d
Display the current position, with ascii art and fen.
* #### eval
Return the evaluation of the current position.
* #### export_net [filename]
Exports the currently loaded network to a file.
If the currently loaded network is the embedded network and the filename
is not specified then the network is saved to the file matching the name
of the embedded network, as defined in evaluate.h.
If the currently loaded network is not the embedded network (some net set
through the UCI setoption) then the filename parameter is required and the
network is saved into that file.
* #### flip
Flips the side to move.
### Generating Training Data
To generate training data from the classic eval, use the generate_training_data command with the setting "Use NNUE" set to "false". The given example is generation in its simplest form. There are more commands.
On Unix-like systems, it should be easy to compile Stockfish directly from the
source code with the included Makefile in the folder `src`. In general, it is
recommended to run `make help` to see a list of make targets with corresponding
descriptions.
```
uci
setoption name PruneAtShallowDepth value false
setoption name Use NNUE value false
setoption name Threads value X
setoption name Hash value Y
setoption name SyzygyPath value path
isready
generate_training_data depth A count B keep_draws 1 eval_limit 32000
cd src
make -j build ARCH=x86-64-modern
```
- `A` is the searched depth per move, or how far the engine looks forward. This value is an integer.
- `B` is the amount of positions generated. This value is also an integer.
Detailed compilation instructions for all platforms can be found in our
[documentation][wiki-compile-link].
Specify how many threads and how much memory you would like to use with the `x` and `y` values. The option SyzygyPath is not necessary, but if you would like to use it, you must first have Syzygy endgame tablebases on your computer, which you can find [here](http://oics.olympuschess.com/tracker/index.php). You will need to have a torrent client to download these tablebases, as that is probably the fastest way to obtain them. The `path` is the path to the folder containing those tablebases. It does not have to be surrounded in quotes.
This will create a file named "training_data.binpack" in the same folder as the binary containing the generated training data. Once generation is done, you can rename the file to something like "1billiondepth12.binpack" to remember the depth and quantity of the positions and move it to a folder named "trainingdata" in the same directory as the binaries.
You will also need validation data that is used for loss calculation and accuracy computation. Validation data is generated in the same way as training data, but generally at most 1 million positions should be used as there's no need for more and it would just slow the learning process down. It may also be better to slightly increase the depth for validation data. After generation you can rename the validation data file to "val.binpack" and drop it in a folder named "validationdata" in the same directory to make it easier.
## Training data formats.
Currently there are 3 training data formats. Two of them are supported directly.
- `.bin` - the original training data format. Uses 40 bytes per entry. Is supported directly by the `generate_training_data` command.
- `.plain` - a human readable training data format. This one is not supported directly by the `generate_training_data` command. It should not be used for data exchange because it's less compact than other formats. It is mostly useful for inspection of the data.
- `.binpack` - a compact binary training data format that exploits positions chains to further reduce size. It uses on average between 2 to 3 bytes per entry when generating data with `generate_training_data`. It is supported directly by `generate_training_data` command. It is currently the default for the `generate_training_data` command. A more in depth description can be found [here](docs/binpack.md)
### Conversion between formats.
There is a builting converted that support all 3 formats described above. Any of them can be converted to any other. For more information and usage guide see [here](docs/convert.md).
## A note on classical evaluation versus NNUE evaluation
Both approaches assign a value to a position that is used in alpha-beta (PVS) search
to find the best move. The classical evaluation computes this value as a function
of various chess concepts, handcrafted by experts, tested and tuned using fishtest.
The NNUE evaluation computes this value with a neural network based on basic
inputs (e.g. piece positions only). The network is optimized and trained
on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
It can be evaluated efficiently on CPUs, and exploits the fact that only parts
of the neural network need to be updated after a typical chess move.
[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first
version of the needed tools to train and develop the NNUE networks. Today, more
advanced training tools are available in
[the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/),
while data generation tools are available in
[a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation
results in much stronger playing strength, even if the nodes per second computed by
the engine is somewhat lower (roughly 80% of nps is typical).
Notes:
1) the NNUE evaluation depends on the Stockfish binary and the network parameter file
(see the EvalFile UCI option). Not every parameter file is compatible with a given
Stockfish binary, but the default value of the EvalFile UCI option is the name of a
network that is guaranteed to be compatible with that binary.
2) to use the NNUE evaluation, the additional data file with neural network parameters
needs to be available. Normally, this file is already embedded in the binary or it can
be downloaded. The filename for the default (recommended) net can be found as the default
value of the `EvalFile` UCI option, with the format `nn-[SHA256 first 12 digits].nnue`
(for instance, `nn-c157e0a5755b.nnue`). This file can be downloaded from
```
https://tests.stockfishchess.org/api/nn/[filename]
```
replacing `[filename]` as needed.
## What to expect from the Syzygy tablebases?
If the engine is searching a position that is not in the tablebases (e.g.
a position with 8 pieces), it will access the tablebases during the search.
If the engine reports a very large score (typically 153.xx), this means
it has found a winning line into a tablebase position.
If the engine is given a position to search that is in the tablebases, it
will use the tablebases at the beginning of the search to preselect all
good moves, i.e. all moves that preserve the win or preserve the draw while
taking into account the 50-move rule.
It will then perform a search only on those moves. **The engine will not move
immediately**, unless there is only a single good move. **The engine likely
will not report a mate score, even if the position is known to be won.**
It is therefore clear that this behaviour is not identical to what one might
be used to with Nalimov tablebases. There are technical reasons for this
difference, the main technical reason being that Nalimov tablebases use the
DTM metric (distance-to-mate), while the Syzygy tablebases use a variation of the
DTZ metric (distance-to-zero, zero meaning any move that resets the 50-move
counter). This special metric is one of the reasons that the Syzygy tablebases are
more compact than Nalimov tablebases, while still storing all information
needed for optimal play and in addition being able to take into account
the 50-move rule.
## Large Pages
Stockfish supports large pages on Linux and Windows. Large pages make
the hash access more efficient, improving the engine speed, especially
on large hash sizes. Typical increases are 5..10% in terms of nodes per
second, but speed increases up to 30% have been measured. The support is
automatic. Stockfish attempts to use large pages when available and
will fall back to regular memory allocation when this is not the case.
### Support on Linux
Large page support on Linux is obtained by the Linux kernel
transparent huge pages functionality. Typically, transparent huge pages
are already enabled, and no configuration is needed.
### Support on Windows
The use of large pages requires "Lock Pages in Memory" privilege. See
[Enable the Lock Pages in Memory Option (Windows)](https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/enable-the-lock-pages-in-memory-option-windows)
on how to enable this privilege, then run [RAMMap](https://docs.microsoft.com/en-us/sysinternals/downloads/rammap)
to double-check that large pages are used. We suggest that you reboot
your computer after you have enabled large pages, because long Windows
sessions suffer from memory fragmentation, which may prevent Stockfish
from getting large pages: a fresh session is better in this regard.
## Compiling Stockfish yourself from the sources
Stockfish has support for 32 or 64-bit CPUs, certain hardware
instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Stockfish
directly from the source code with the included Makefile in the folder
`src`. In general it is recommended to run `make help` to see a list of make
targets with corresponding descriptions.
```
cd src
make help
make net
make build ARCH=x86-64-modern
```
When not using the Makefile to compile (for instance, with Microsoft MSVC) you
need to manually set/unset some switches in the compiler command line; see
file *types.h* for a quick reference.
When reporting an issue or a bug, please tell us which Stockfish version
and which compiler you used to create your executable. This information
can be found by typing the following command in a console:
```
./stockfish compiler
```
## Understanding the code base and participating in the project
Stockfish's improvement over the last decade has been a great community
effort. There are a few ways to help contribute to its growth.
## Contributing
### Donating hardware
Improving Stockfish requires a massive amount of testing. You can donate
your hardware resources by installing the [Fishtest Worker](https://github.com/glinscott/fishtest/wiki/Running-the-worker:-overview)
and view the current tests on [Fishtest](https://tests.stockfishchess.org/tests).
Improving Stockfish requires a massive amount of testing. You can donate your
hardware resources by installing the [Fishtest Worker][worker-link] and viewing
the current tests on [Fishtest][fishtest-link].
### Improving the code
If you want to help improve the code, there are several valuable resources:
* [In this wiki,](https://www.chessprogramming.org) many techniques used in
In the [chessprogramming wiki][programming-link], many techniques used in
Stockfish are explained with a lot of background information.
The [section on Stockfish][programmingsf-link] describes many features
and techniques used by Stockfish. However, it is generic rather than
focused on Stockfish's precise implementation.
* [The section on Stockfish](https://www.chessprogramming.org/Stockfish)
describes many features and techniques used by Stockfish. However, it is
generic rather than being focused on Stockfish's precise implementation.
Nevertheless, a helpful resource.
* The latest source can always be found on [GitHub](https://github.com/official-stockfish/Stockfish).
Discussions about Stockfish take place these days mainly in the [FishCooking](https://groups.google.com/forum/#!forum/fishcooking)
group and on the [Stockfish Discord channel](https://discord.gg/nv8gDtt).
The engine testing is done on [Fishtest](https://tests.stockfishchess.org/tests).
If you want to help improve Stockfish, please read this [guideline](https://github.com/glinscott/fishtest/wiki/Creating-my-first-test)
The engine testing is done on [Fishtest][fishtest-link].
If you want to help improve Stockfish, please read this [guideline][guideline-link]
first, where the basics of Stockfish development are explained.
Discussions about Stockfish take place these days mainly in the Stockfish
[Discord server][discord-link]. This is also the best place to ask questions
about the codebase and how to improve it.
## Terms of use
Stockfish is free, and distributed under the **GNU General Public License version 3**
(GPL v3). Essentially, this means you are free to do almost exactly
what you want with the program, including distributing it among your
friends, making it available for download from your website, selling
it (either by itself or as part of some bigger software package), or
using it as the starting point for a software project of your own.
Stockfish is free and distributed under the
[**GNU General Public License version 3**][license-link] (GPL v3). Essentially,
this means you are free to do almost exactly what you want with the program,
including distributing it among your friends, making it available for download
from your website, selling it (either by itself or as part of some bigger
software package), or using it as the starting point for a software project of
your own.
The only real limitation is that whenever you distribute Stockfish in
some way, you MUST always include the license and the full source code
(or a pointer to where the source code can be found) to generate the
exact binary you are distributing. If you make any changes to the
source code, these changes must also be made available under the GPL v3.
The only real limitation is that whenever you distribute Stockfish in some way,
you MUST always include the license and the full source code (or a pointer to
where the source code can be found) to generate the exact binary you are
distributing. If you make any changes to the source code, these changes must
also be made available under GPL v3.
For full details, read the copy of the GPL v3 found in the file named
[*Copying.txt*](https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt).
[authors-link]: https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS
[build-link]: https://github.com/official-stockfish/Stockfish/actions/workflows/stockfish.yml
[commits-link]: https://github.com/official-stockfish/Stockfish/commits/master
[discord-link]: https://discord.gg/GWDRS3kU6R
[issue-link]: https://github.com/official-stockfish/Stockfish/issues/new?assignees=&labels=&template=BUG-REPORT.yml
[discussions-link]: https://github.com/official-stockfish/Stockfish/discussions/new
[fishtest-link]: https://tests.stockfishchess.org/tests
[guideline-link]: https://github.com/glinscott/fishtest/wiki/Creating-my-first-test
[license-link]: https://github.com/official-stockfish/Stockfish/blob/master/Copying.txt
[programming-link]: https://www.chessprogramming.org/Main_Page
[programmingsf-link]: https://www.chessprogramming.org/Stockfish
[readme-link]: https://github.com/official-stockfish/Stockfish/blob/master/README.md
[release-link]: https://github.com/official-stockfish/Stockfish/releases/latest
[src-link]: https://github.com/official-stockfish/Stockfish/tree/master/src
[stockfish128-logo]: https://stockfishchess.org/images/logo/icon_128x128.png
[uci-link]: https://backscattering.de/chess/uci/
[website-link]: https://stockfishchess.org
[website-blog-link]: https://stockfishchess.org/blog/
[wiki-link]: https://github.com/official-stockfish/Stockfish/wiki
[wiki-usage-link]: https://github.com/official-stockfish/Stockfish/wiki/Download-and-usage
[wiki-compile-link]: https://github.com/official-stockfish/Stockfish/wiki/Compiling-from-source
[wiki-commands-link]: https://github.com/official-stockfish/Stockfish/wiki/Commands
[worker-link]: https://github.com/glinscott/fishtest/wiki/Running-the-worker
[build-badge]: https://img.shields.io/github/actions/workflow/status/official-stockfish/Stockfish/stockfish.yml?branch=master&style=for-the-badge&label=stockfish&logo=github
[commits-badge]: https://img.shields.io/github/commits-since/official-stockfish/Stockfish/latest?style=for-the-badge
[discord-badge]: https://img.shields.io/discord/435943710472011776?style=for-the-badge&label=discord&logo=Discord
[fishtest-badge]: https://img.shields.io/website?style=for-the-badge&down_color=red&down_message=Offline&label=Fishtest&up_color=success&up_message=Online&url=https%3A%2F%2Ftests.stockfishchess.org%2Ftests%2Ffinished
[license-badge]: https://img.shields.io/github/license/official-stockfish/Stockfish?style=for-the-badge&label=license&color=success
[release-badge]: https://img.shields.io/github/v/release/official-stockfish/Stockfish?style=for-the-badge&label=official%20release
[website-badge]: https://img.shields.io/website?style=for-the-badge&down_color=red&down_message=Offline&label=website&up_color=success&up_message=Online&url=https%3A%2F%2Fstockfishchess.org
+134 -102
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@@ -1,235 +1,267 @@
Contributors to Fishtest with >10,000 CPU hours, as of 2022-04-14.
Contributors to Fishtest with >10,000 CPU hours, as of 2023-06-20.
Thank you!
Username CPU Hours Games played
------------------------------------------------------------------
noobpwnftw 31714850 2267266129
mlang 2954099 198421098
technologov 2324150 102449398
dew 1670874 99276012
grandphish2 1134273 68070459
okrout 901194 77738874
TueRens 821388 50207666
tvijlbrief 795993 51894442
pemo 744463 32486677
JojoM 724378 43660674
noobpwnftw 37457426 2850540907
technologov 14135647 742892808
linrock 4423514 303254809
mlang 3026000 200065824
dew 1689162 100033738
okrout 1578136 148855886
pemo 1508508 48814305
grandphish2 1461406 91540343
TueRens 1194790 70400852
JojoM 947612 61773190
tvijlbrief 796125 51897690
sebastronomy 742434 38218524
mibere 703840 46867607
linrock 626939 17408017
gvreuls 534079 34352532
cw 507221 34006775
fastgm 489749 29344518
gvreuls 651026 42988582
oz 543438 39314736
cw 517858 34869755
fastgm 503862 30260818
leszek 467278 33514883
CSU_Dynasty 464940 31177118
ctoks 434416 28506889
crunchy 427035 27344275
CSU_Dynasty 424643 28525220
ctoks 415771 27364603
oz 369200 27017658
bcross 342642 23671289
maximmasiutin 424795 26577722
bcross 415722 29060963
olafm 395922 32268020
rpngn 348378 24560289
velislav 342567 22138992
Fisherman 327231 21829379
velislav 325670 20911076
leszek 321295 19874113
Dantist 274747 16910258
mgrabiak 237604 15418700
robal 217959 13840386
glinscott 217799 13780820
nordlandia 211692 13484886
drabel 201967 13798360
mgrabiak 300612 20608380
Dantist 296386 18031762
nordlandia 246201 16189678
robal 241300 15656382
marrco 234581 17714473
ncfish1 227517 15233777
glinscott 208125 13277240
drabel 204167 13930674
mhoram 202894 12601997
bking_US 198894 11876016
mhoram 194862 12261809
Thanar 179852 12365359
vdv 175544 9904472
spams 157128 10319326
rpngn 154081 9652139
marrco 150300 9402229
sqrt2 147963 9724586
vdbergh 137430 8955097
DesolatedDodo 146350 9536172
Calis007 143165 9478764
vdbergh 138650 9064413
CoffeeOne 137100 5024116
armo9494 136191 9460264
malala 136182 8002293
xoto 133759 9159372
davar 125240 8117121
davar 129023 8376525
DMBK 122960 8980062
dsmith 122059 7570238
amicic 119659 7937885
amicic 119661 7938029
Data 113305 8220352
BrunoBanani 112960 7436849
CypressChess 108321 7759588
DesolatedDodo 106811 6776980
CypressChess 108331 7759788
skiminki 107583 7218170
jcAEie 105675 8238962
MaZePallas 102823 6633619
sterni1971 100532 5880772
sunu 100167 7040199
zeryl 99331 6221261
thirdlife 99124 2242380
ElbertoOne 99028 7023771
skiminki 98123 6478402
cuistot 98853 6069816
bigpen0r 94809 6529203
brabos 92118 6186135
cuistot 90358 5351004
Wolfgang 91939 6105872
psk 89957 5984901
racerschmacer 85712 6119648
sschnee 88235 5268000
racerschmacer 85805 6122790
Fifis 85722 5709729
Dubslow 84986 6042456
Vizvezdenec 83761 5344740
zeryl 83680 5250995
sschnee 83003 4840890
0x3C33 82614 5271253
BRAVONE 81239 5054681
nssy 76497 5259388
jromang 76106 5236025
teddybaer 75125 5407666
jromang 74796 5175825
tolkki963 74762 5149662
megaman7de 74351 4940352
Wencey 74181 4711488
Pking_cda 73776 5293873
Calis007 72477 4088576
yurikvelo 73150 5004382
markkulix 72607 5304642
Bobo1239 70579 4794999
solarlight 70517 5028306
dv8silencer 70287 3883992
Bobo1239 68515 4652287
manap 66273 4121774
yurikvelo 65716 4457300
tinker 64333 4268790
Wolfgang 62644 3817410
qurashee 61208 3429862
Mineta 59357 4418202
Spprtr 58723 3911011
AGI 58147 4325994
robnjr 57262 4053117
Freja 56938 3733019
MaxKlaxxMiner 56879 3423958
MarcusTullius 56746 3762951
ttruscott 56010 3680085
rkl 55132 4164467
renouve 53811 3501516
megaman7de 52434 3243016
MaxKlaxxMiner 51977 3153032
javran 53785 4627608
finfish 51360 3370515
eva42 51272 3599691
eastorwest 51058 3451555
eastorwest 51117 3454811
rap 49985 3219146
pb00067 49727 3298270
Spprtr 48920 3161711
bigpen0r 47667 3336927
pb00067 49733 3298934
OuaisBla 48626 3445134
ronaldjerum 47654 3240695
biffhero 46564 3111352
Fifis 45843 3088497
VoyagerOne 45476 3452465
jmdana 44893 3065205
maposora 44597 4039578
oryx 44570 3454238
speedycpu 43842 3003273
jbwiebe 43305 2805433
GPUex 42378 3133332
Antihistamine 41788 2761312
mhunt 41735 2691355
homyur 39893 2850481
gri 39871 2515779
armo9494 39064 2832326
oryx 38867 2976992
Garf 37741 2999686
SC 37299 2731694
Garf 37213 2986270
tolkki963 37059 2154330
csnodgrass 36207 2688994
jmdana 36157 2210661
strelock 34716 2074055
DMBK 34010 2482916
szupaw 34102 2880346
EthanOConnor 33370 2090311
slakovv 32915 2021889
gopeto 30993 2028106
Gelma 31771 1551204
gopeto 31671 2060990
kdave 31157 2198362
manapbk 30987 1810399
Prcuvu 30377 2170122
anst 30301 2190091
jkiiski 30136 1904470
spcc 29925 1901692
hyperbolic.tom 29840 2017394
chuckstablers 29659 2093438
Pyafue 29650 1902349
ncfish1 29105 1704011
belzedar94 27935 1789106
OuaisBla 27636 1578800
belzedar94 28846 1811530
chriswk 26902 1868317
xwziegtm 26897 2124586
achambord 26582 1767323
Patrick_G 26276 1801617
yorkman 26193 1992080
Ulysses 25288 1689730
SFTUser 25182 1675689
nabildanial 24942 1519409
Sharaf_DG 24765 1786697
rodneyc 24275 1410450
Maxim 24705 1502062
rodneyc 24376 1416402
agg177 23890 1395014
Goatminola 23763 1956036
Ente 23639 1671638
Jopo12321 23467 1483172
JanErik 23408 1703875
Isidor 23388 1680691
Norabor 23339 1602636
Ente 23270 1651432
cisco2015 22897 1762669
MarcusTullius 22688 1274821
Norabor 23371 1603244
cisco2015 22920 1763301
jsys14 22824 1591906
Zirie 22542 1472937
team-oh 22272 1636708
Roady 22220 1465606
MazeOfGalious 21978 1629593
sg4032 21947 1643265
sg4032 21947 1643353
ianh2105 21725 1632562
xor12 21628 1680365
dex 21612 1467203
nesoneg 21494 1463031
Roady 21323 1433822
user213718 21454 1404128
sphinx 21211 1384728
user213718 21196 1397710
spcc 21065 1311338
AndreasKrug 21097 1634811
jjoshua2 21001 1423089
Zake9298 20938 1565848
horst.prack 20878 1465656
0xB00B1ES 20590 1208666
j3corre 20405 941444
kdave 20364 1389254
Adrian.Schmidt123 20316 1281436
Ulysses 20217 1351500
markkulix 19976 1115258
wei 19973 1745989
notchris 19958 1800128
Serpensin 19840 1697528
Gaster319 19712 1677310
fishtester 19617 1257388
rstoesser 19569 1293588
eudhan 19274 1283717
fishtester 18995 1238686
votoanthuan 19108 1609992
vulcan 18871 1729392
Karpovbot 18766 1053178
qoo_charly_cai 18543 1284937
jundery 18445 1115855
iisiraider 18247 1101015
ville 17883 1384026
chris 17698 1487385
purplefishies 17595 1092533
dju 17353 978595
Wencey 17125 805964
dju 17414 981289
iisiraider 17275 1049015
DragonLord 17014 1162790
thirdlife 16996 447356
redstone59 16842 1461780
Alb11747 16787 1213926
IgorLeMasson 16064 1147232
Karby 15982 979610
scuzzi 15757 968735
ako027ako 15671 1173203
AndreasKrug 15550 1194497
Nikolay.IT 15154 1068349
Andrew Grant 15114 895539
scuzzi 14928 953313
Naven94 15054 834762
OssumOpossum 14857 1007129
Karby 14808 867120
jsys14 14652 855642
ZacHFX 14783 1021842
enedene 14476 905279
bpfliegel 14298 884523
bpfliegel 14233 882523
mpx86 14019 759568
jpulman 13982 870599
Skiff84 13826 721996
crocogoat 13803 1117422
joster 13794 950160
Nesa92 13786 1114691
joster 13710 946160
mbeier 13650 1044928
Hjax 13535 915487
Nullvalue 13468 1140498
Dark_wizzie 13422 1007152
Jopo12321 13367 678852
Rudolphous 13244 883140
pirt 13100 1009897
Machariel 13010 863104
infinigon 12991 943216
mabichito 12903 749391
thijsk 12886 722107
AdrianSA 12860 804972
infinigon 12807 937332
Flopzee 12698 894821
korposzczur 12606 838168
fatmurphy 12547 853210
SapphireBrand 12416 969604
Oakwen 12399 844109
deflectooor 12386 579392
modolief 12386 896470
Farseer 12249 694108
Jackfish 12213 805008
pgontarz 12151 848794
pirt 12008 923149
dbernier 12103 860824
getraideBFF 12072 1024966
stocky 11954 699440
mschmidt 11941 803401
dbernier 11609 818636
Maxim 11543 836024
MooTheCow 11870 773598
FormazChar 11766 885707
whelanh 11557 245188
3cho 11494 1031076
infinity 11470 727027
aga 11409 695071
aga 11412 695127
torbjo 11395 729145
Thomas A. Anderson 11372 732094
savage84 11358 670860
FormazChar 11349 850327
d64 11263 789184
MooTheCow 11237 720174
ali-al-zhrani 11245 779246
snicolet 11106 869170
ali-al-zhrani 11098 768494
whelanh 11067 235676
Jackfish 10978 720078
deflectooor 10886 520116
dapper 11032 771402
ols 10947 624903
Karmatron 10828 677458
basepi 10637 744851
Cubox 10621 826448
michaelrpg 10509 739239
OIVAS7572 10420 995586
jojo2357 10419 929708
WoodMan777 10380 873720
Garruk 10365 706465
dzjp 10343 732529
Garruk 10334 704065
ols 10259 570669
lbraesch 10252 647825
qoo_charly_cai 10212 620407
Naven94 10069 503192
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# Binpack
Binpack is a binary training data storage format designed to take advantage of position chains differing by a single move. Therefore it is very good at compactly storing data generated from real games (as opposed to random positions for example sourced from an opening book).
It is currently implemented through a single header library in `extra/nnue_data_binpack_format.h`.
Below follows a rough description of the format in a BNF-like notation.
```
[[nodiscard]] std::uint16_t signedToUnsigned(std::int16_t a) {
std::uint16_t r;
std::memcpy(&r, &a, sizeof(std::uint16_t));
if (r & 0x8000) r ^= 0x7FFF; // flip value bits if negative
r = (r << 1) | (r >> 15); // store sign bit at bit 0
return r;
}
file := <block>*
block := BINP<chain>*
chain := <stem><movetext>
stem := <pos><move><score><ply_and_result><rule50> (32 bytes)
pos := https://github.com/Sopel97/nnue_data_compress/blob/master/src/chess/Position.h#L1166 (24 bytes)
move := https://github.com/Sopel97/nnue_data_compress/blob/master/src/chess/Chess.h#L1044 (2 bytes)
score := signedToUnsigned(score) (2 bytes, big endian)
ply_and_result := ply bitwise_or (signedToUnsigned(result) << 14) (2 bytes, big endian)
rule50 := rule_50_counter (2 bytes, big endian)
// this is a small defect from old version,
I didn't want to break backwards compatibility. Effectively means that there's
one byte left for something else in the future because rule50 always fits in one byte.
movetext := <count><move_and_score>*
count := number of plies in the movetext (2 bytes, big endian). Can be 0.
move_and_score := <encoded_move><encoded_score> (~2 bytes)
encoded_move := oof this one is complicated to explain.
https://github.com/Sopel97/nnue_data_compress/blob/master/src/compress_file.cpp#L827.
https://github.com/Sopel97/chess_pos_db/blob/master/docs/bcgn/variable_length.md
encoded_score := https://en.wikipedia.org/wiki/Variable-width_encoding
with block size of 4 bits + 1 bit for extension bit.
Encoded value is signedToUnsigned(-prev_score - current_score)
(scores are always seen from the perspective of side to move in <pos>, that's why the '-' before prev_score)
```
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# Convert
`convert` allows conversion of training data between any of `.plain`, `.bin`, and `.binpack`.
As all commands in stockfish `convert` can be invoked either from command line (as `stockfish.exe convert ...`) or in the interactive prompt.
The syntax of this command is as follows:
```
convert from_path to_path [append] [validate]
```
`from_path` is the path to the file to convert from. The type of the data is deduced based on its extension (one of `.plain`, `.bin`, `.binpack`).
`to_path` is the path to an output file. The type of the data is deduced from its extension. If the file does not exist it is created.
`append` and `validate` can come in any order and are optional.
If `append` not specified then the output file will be truncated prior to any writes. If `append` is specified then the converted training data will be appended to the end of the output file.
If `validate` is specified then the conversion will stop on the first illegal move found and a diagnostic will be shown.
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# generate_training_data
`generate_training_data` command allows generation of training data from self-play in a manner that suits training better than traditional games. It introduces random moves to diversify openings, and fixed depth evaluation.
As all commands in stockfish `generate_training_data` can be invoked either from command line (as `stockfish.exe generate_training_data ...`, but this is not recommended because it's not possible to specify UCI options before `generate_training_data` executes) or in the interactive prompt.
It is recommended to set the `PruneAtShallowDepth` UCI option to `false` as it will increase the quality of fixed depth searches.
It is recommended to keep the `EnableTranspositionTable` UCI option at the default `true` value as it will make the generation process faster without noticably harming the uniformity of the data.
`generate_training_data` takes named parameters in the form of `generate_training_data param_1_name param_1_value param_2_name param_2_value ...`.
Currently the following options are available:
`set_recommended_uci_options` - this is a modifier not a parameter, no value follows it. If specified then some UCI options are set to recommended values.
`depth` - sets minimum and maximum depth of evaluation of each position. Default: 3.
`mindepth` - minimum depth of evaluation of each position. If not specified then the same as `depth`.
`maxdepth` - minimum depth of evaluation of each position. If not specified then the same as `depth`.
`nodes` - the number of nodes to use for evaluation of each position. This number is multiplied by the number of PVs of the current search. This does NOT override the `depth` and `depth2` options. If specified then whichever of depth or nodes limit is reached first applies.
`count` - the number of training data entries to generate. 1 entry == 1 position. If both `count` and `max_time_*` are specified the data generation process will end when any of conditions is fullfilled. Default: 8000000000 (8B).
`max_time_seconds`, `max_time_minutes`, `max_time_hours` - specifies the maximum runtime for the data generation. The data generation will NOT be interrupted while a self-play game is in progress. If both `count` and `max_time_*` are specified the data generation process will end when any of conditions is fullfilled. Default: \~250 years.
`output_file_name` - the name of the file to output to. If the extension is not present or doesn't match the selected training data format the right extension will be appened. Default: generated_kifu
`eval_limit` - evaluations with higher absolute value than this will not be written and will terminate a self-play game. Should not exceed 10000 which is VALUE_KNOWN_WIN, but is only hardcapped at mate in 2 (\~30000). Default: 3000
`random_move_min_ply` - the minimal ply at which a random move may be executed instead of a move chosen by search. Default: 1.
`random_move_max_ply` - the maximal ply at which a random move may be executed instead of a move chosen by search. Default: 24.
`random_move_count` - maximum number of random moves in a single self-play game. Default: 5.
`random_move_like_apery` - either 0 or 1. If 1 then random king moves will be followed by a random king move from the opponent whenever possible with 50% probability. Default: 0.
`random_multi_pv` - the number of PVs used for determining the random move. If not specified then a truly random move will be chosen. If specified then a multiPV search will be performed the random move will be one of the moves chosen by the search.
`random_multi_pv_diff` - Makes the multiPV random move selection consider only moves that are at most `random_multi_pv_diff` worse than the next best move. Default: 30000 (all multiPV moves).
`random_multi_pv_depth` - the depth to use for multiPV search for random move. Default: `depth2`.
`random_multi_pv_nodes` - the maximum number of nodes for a multiPV search for random move. Default: `nodes`.
`write_min_ply` - minimum ply for which the training data entry will be emitted. Default: 16.
`write_max_ply` - maximum ply for which the training data entry will be emitted. Default: 400.
`book` - a path to an opening book to use for the starting positions. Currently only .epd format is supported. If not specified then the starting position is always the standard chess starting position.
`save_every` - the number of training data entries per file. If not specified then there will be always one file. If specified there may be more than one file generated (each having at most `save_every` training data entries) and each file will have a unique number attached.
`random_file_name` - if specified then the output filename will be chosen randomly. Overrides `output_file_name`.
`keep_draws` - either 0 or 1. If 1 then training data from drawn games will be emitted too. Default: 1.
`adjudicate_draws_by_score` - either 0 or 1. If 1 then drawn games will be adjudicated when the score remains 0 for at least 8 plies after ply 80. Default: 1.
`adjudicate_draws_by_insufficient_mating_material` - either 0 or 1. If 1 then position with insufficient material will be adjudicated as draws. Default: 1.
`data_format` - format of the training data to use. Either `bin` or `binpack`. Default: `binpack`.
`seed` - seed for the PRNG. Can be either a number or a string. If it's a string then its hash will be used. If not specified then the current time will be used.
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# generate_training_data_nonpv
`generate_training_data_nonpv` command allows generation of training data from self-play in a manner that suits training better than traditional games. It plays fixed nodes self play games for exploration and records [some of] the evaluated positions. Then rescores them with fixed depth search.
As all commands in stockfish `generate_training_data_nonpv` can be invoked either from command line (as `stockfish.exe generate_training_data_nonpv ...`, but this is not recommended because it's not possible to specify UCI options before `generate_training_data_nonpv` executes) or in the interactive prompt.
It is recommended to set the `PruneAtShallowDepth` UCI option to `false` as it will increase the quality of fixed depth searches.
It is recommended to keep the `EnableTranspositionTable` UCI option at the default `true` value as it will make the generation process faster without noticably harming the uniformity of the data.
`generate_training_data_nonpv` takes named parameters in the form of `generate_training_data_nonpv param_1_name param_1_value param_2_name param_2_value ...`.
Currently the following options are available:
`depth` - the search depth to use for rescoring. Default: 3.
`count` - the number of training data entries to generate. 1 entry == 1 position. Default: 1000000 (1M).
`exploration_min_nodes` - the min number of nodes to use for exploraton during selfplay. Default: 5000.
`exploration_max_nodes` - the max number of nodes to use for exploraton during selfplay. The number of nodes is chosen from a uniform distribution between min and max. Default: 15000.
`exploration_save_rate` - the ratio of positions seen during exploration self play games that are saved for later rescoring. Default: 0.01 (meaning 1 in 100 positions seen during search get saved for rescoring).
`output_file` - the name of the file to output to. If the extension is not present or doesn't match the selected training data format the right extension will be appened. Default: generated_gensfen_nonpv
`eval_limit` - evaluations with higher absolute value than this will not be written and will terminate a self-play game. Should not exceed 10000 which is VALUE_KNOWN_WIN, but is only hardcapped at mate in 2 (\~30000). Default: 4000
`exploration_eval_limit` - same as `eval_limit` but used during exploration with a value from fixed depth search.
`exploration_min_pieces` - the min number of pieces in the self play games to start the fixed depth search. Note that even if there's N pieces on the board the fixed nodes search usually reaches positions with less pieces and they are saved too. Default: 8.
`exploration_max_ply` the max ply for the exploration self play. Default: 200.
`smart_fen_skipping` - this is a flag option. When specified some position that are not good candidates for teaching are removed from the output. This includes positions where the best move is a capture or promotion, and position where a king is in check.
`book` - a path to an opening book to use for the starting positions. Currently only .epd format is supported. If not specified then the starting position is always the standard chess starting position.
`data_format` - format of the training data to use. Either `bin` or `binpack`. Default: `binpack`.
`seed` - seed for the PRNG. Can be either a number or a string. If it's a string then its hash will be used. If not specified then the current time will be used.
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# Stats
`gather_statistics` command allows gathering various statistics from a .bin or a .binpack file. The syntax is `gather_statistics (GROUP)* input_file FILENAME`. There can be many groups specified. Any statistic gatherer that belongs to at least one of the specified groups will be used.
Simplest usage: `stockfish.exe gather_statistics all input_file a.binpack`
Any name that doesn't designate an argument name or is not an argument will be interpreted as a group name.
## Parameters
`input_file` - the path to the .bin or .binpack input file to read
`output_file` - optional path to the output file to write the results too. Results are always written on the console, so if this is specified the results will be written in both places.
`max_count` - the maximum number of positions to process. Default: no limit.
## Groups
`all` - a special group designating all statistics gatherers available.
`position_count` - the total number of positions in the file.
`king`, `king_square_count` - the number of times a king was on each square. Output is layed out as a chessboard, with the 8th rank being the topmost. Separate values for white and black kings.
`move`, `move_from_count` - same as `king_square_count` but for from_sq(move)
`move`, `move_to_count` - same as `king_square_count` but for to_sq(move)
`move`, `move_type` - the number of moves with each type. Includes normal, captures, castling, promotions, enpassant. The groups are not disjoint.
`move`, `moved_piece_type` - the number of times a piece of each type was moved
`piece_count` - the histogram of the number of pieces on the board
`ply_discontinuities` - the number of times the ply jumped by a value different than 1 between two consecutive positions. Usually the number of games.
`material_imbalance` - the histogram of imbalances, with values computed using "simple eval", that is pawn=1, bishop=knight=3, rook=5, queen=9
`results` - the distribution of game results
`endgames_6man` - distribution of endgame configurations for <=6 pieces (including kings)
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# Transform
`transform` command exposes subcommands that perform some specific transformation over data. The call syntax is `transform <subcommand>`. Currently implemented subcommands are listed and described below.
## `nudged_static`
`transform nudged_static` takes named parameters in the form of `transform nudged_static param_1_name param_1_value param_2_name param_2_value ...` and flag parameters which don't require values.
This command goes through positions in the input files and replaces the scores with new ones - generated from static eval - but slightly adjusted based on the scores in the original input file.
Currently the following options are available:
`input_file` - path to the input file. Supports bin and binpack formats. Default: in.binpack.
`output_file` - path to the output file. Supports bin and binpack formats. Default: out.binpack.
`absolute` - states that the adjustment should be bounded by an absolute value. After this token follows the maximum absolute adjustment. Values are always adjusted towards scores in the input file. This is the default mode. Default maximum adjustement: 5.
`relative` - states that the adjustment should be bounded by a value relative in magnitude to the static eval value. After this token follows the maximum relative change - a floating point value greater than 0. For example a value of 0.1 only allows changing the static eval by at most 10% towards the score from the input file.
`interpolate` states that the output score should be a value interpolated between static eval and the score from the input file. After this token follows the interpolation constant `t`. `t` of 0 means that only static eval is used. `t` of 1 means that only score from the input file is used. `t` of 0.5 means that the static eval and input score are averaged. It accepts values outside of range `<0, 1>`, but the usefulness is questionable.
## `rescore`
`transform rescore` takes named parameters in the form of `transform rescore param_1_name param_1_value param_2_name param_2_value ...` and flag parameters which don't require values.
This tool respects the UCI option `Threads` and uses all available threads.
This command takes a path to the input file that is either a .epd file which contains one FEN per line or a .bin or .binpack file and outputs a .bin or .binpack file with these positions rescored with specified depth search.
Currently the following options are available:
`input_file` - path to the input file. Default: in.binpack.
`output_file` - path to the output .bin or .binpack file. The file is opened in append mode. Default: out.binpack.
`depth` - the search depth to use for rescoring. Default: 3.
`keep_moves` - whether to keep moves from the input file if available. Allows to keep compression in .binpack. Default: 1.
`research_count` - number of additional searches of depth N done on the same position before using the eval. Default: 0.
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# validate_training_data
`validate_training_data` allows validation of training data of types `.plain`, `.bin`, and `.binpack`.
As all commands in stockfish `validate_training_data` can be invoked either from command line (as `stockfish.exe validate_training_data ...`) or in the interactive prompt.
The syntax of this command is as follows:
```
validate_training_data in_path
```
`in_path` is the path to the file to validate. The type of the data is deduced based on its extension (one of `.plain`, `.bin`, `.binpack`).
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# `pgn_to_plain`
This script converts pgn files into text file to apply `learn convert_bin` command. You need to import [python-chess](https://pypi.org/project/python-chess/) to use this script.
pip install python-chess
# Example of Qhapaq's finetune using `pgn_to_plain`
## Download data
You can download data from [here](http://rebel13.nl/index.html)
## Convert pgn files
**Important : convert text will be superheavy (approx 200 byte / position)**
python pgn_to_plain.py --pgn "pgn/*.pgn" --start_ply 1 --output converted_pgn.txt
`--pgn` option supports wildcard. When you use pgn files with elo >= 3300, You will get 1.7 GB text file.
## Convert into training data
### Example build command
make nnue-learn ARCH=x86-64
See `src/Makefile` for detail.
### Convert
./stockfish
learn convert_bin converted_pgn.txt output_file_name pgn_bin.bin
learn shuffle pgn_bin.bin
You also need to prepare validation data for training like following.
python pgn_to_plain.py --pgn "pgn/ccrl-40-15-3400.pgn" --start_ply 1 --output ccrl-40-15-3400.txt
./stockfish
learn convert_bin ccrl-40-15-3400.txt ccrl-40-15-3400_plain.bin
### Learn
./stockfish
setoption name Threads value 8
learn shuffled_sfen.bin newbob_decay 0.5 validation_set_file_name ccrl-40-15-3400_plain.bin nn_batch_size 50000 batchsize 1000000 eval_save_interval 8000000 eta 0.05 lambda 0.0 eval_limit 3000 mirror_percentage 0 use_draw_in_training 1
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import sys
ENTRY_SIZE = 40
NUM_ENTRIES_IN_CHUNK = 1024*1024
def copy(infile, outfile, count, times):
if times > 1:
outfile.write(infile.read(count*ENTRY_SIZE)*times)
else:
offset = 0
while offset < count:
to_read = NUM_ENTRIES_IN_CHUNK if offset + NUM_ENTRIES_IN_CHUNK <= count else count - offset
outfile.write(infile.read(to_read*ENTRY_SIZE))
offset += NUM_ENTRIES_IN_CHUNK
def work():
filename = sys.argv[1]
offset = int(sys.argv[2])
count = int(sys.argv[3])
times = int(sys.argv[4]) if len(sys.argv) >= 5 else 1
with open(filename, 'rb') as infile:
infile.seek(offset * ENTRY_SIZE)
filename_parts = filename.split('.')
out_path = '.'.join(filename_parts[:-1]) + '_' + str(offset) + '_' + str(count) + '_' + str(times) + '.' + filename_parts[-1]
with open(out_path, 'wb') as outfile:
copy(infile, outfile, count, times)
def show_help():
print('Usage: python extract_bin.py filename offset count [times]')
print('filename - the path to the .bin file to process')
print('offset - the number of sfens to skip')
print('count - the number of sfens to extract')
print('times - the number of times to repeat the extracted sfens. Default = 1')
print('The result is saved in a new file named `filename.stem`_`offset`_`count`_`times`.bin')
if len(sys.argv) < 4:
show_help()
else:
work()
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import struct
import sys
import os
import random
from pathlib import Path
def copy_next_chunk(in_file, out_file):
chunk_header = in_file.read(8)
assert chunk_header[0:4] == b"BINP"
size = struct.unpack("<I", chunk_header[4:])[0]
out_file.write(chunk_header)
data = in_file.read(size)
out_file.write(data)
return size + 8
def main():
if len(sys.argv) < 4:
print("Usage: python interleave_binpacks.py infile1 ... infileN outfile")
print(" The output binpack, will contain all data from the input files.")
print(" Data is read sequentially from the input, randomly alternating between files.")
return
# open last arg as output file name
out_filename = sys.argv[-1]
print("outfile: ", out_filename)
if Path(out_filename).exists():
print(
"Output path {} already exists. Please specify a path to a file that does not exist.".format(
out_filename
)
)
return
out_file = open(out_filename, "wb")
# open other args as input file names, and get their sizes
in_filenames = []
for i in range(1, len(sys.argv) - 1):
in_filenames.append(sys.argv[i])
print("infiles: ", in_filenames)
in_files = []
in_files_remaining = []
for in_filename in in_filenames:
in_file = open(in_filename, "rb")
in_files.append(in_file)
file_size = os.path.getsize(in_filename)
in_files_remaining.append(file_size)
# randomly pick a file, with a probability related to their sizes.
# copy from the front and keep track of remaining sizes
total_remaining = sum(in_files_remaining)
print("Merging {} bytes ".format(total_remaining))
total_size = 0
report_every = 100
prev_mib = -report_every
while total_remaining > 0:
where = random.randrange(total_remaining)
i = 0
while where >= in_files_remaining[i]:
where -= in_files_remaining[i]
i += 1
size = copy_next_chunk(in_files[i], out_file)
in_files_remaining[i] -= size
total_remaining -= size
total_size += size
mib = total_size // 1024 // 1024
if mib // 100 != prev_mib // 100:
print("Copied {} MiB".format(mib))
prev_mib = mib
out_file.close()
for in_file in in_files:
in_file.close()
print("Merged {} bytes".format(total_size))
main()
-110
View File
@@ -1,110 +0,0 @@
import chess.pgn
import argparse
import glob
import re
from typing import List
# todo close in c++ tools using pgn-extract
# https://www.cs.kent.ac.uk/people/staff/djb/pgn-extract/help.html#-w
commentRe = re.compile("([+-]*M*[0-9.]*)/([0-9]*)")
mateRe = re.compile("([+-])M([0-9]*)")
flip_black = False
def parse_result(result_str:str, board:chess.Board) -> int:
if result_str == "1/2-1/2":
return 0
if result_str == "0-1":
if board.turn == chess.WHITE:
return -1
else:
return 1
elif result_str == "1-0":
if board.turn == chess.WHITE:
return 1
else:
return -1
else:
print("illegal result", result_str)
raise ValueError
def game_sanity_check(game: chess.pgn.Game) -> bool:
if not game.headers["Result"] in ["1/2-1/2", "0-1", "1-0"]:
print("invalid result", game.headers["Result"])
return False
return True
def parse_comment_for_score(comment_str: str, board: chess.Board) -> int:
global commentRe
global mateRe
global flip_black
try:
m = commentRe.search(comment_str)
if m:
score = m.group(1)
# depth = int(m.group(2))
m = mateRe.search(score)
if m:
if m.group(1) == "+":
score = 32000 - int(m.group(2))
else:
score = -32000 + int(m.group(2))
else:
score = int(float(score) * 208) # pawn to SF PawnValueEg
if flip_black and board.turn == chess.BLACK:
score = -score
else:
score = 0
except:
score = 0
return score
def parse_game(game: chess.pgn.Game, writer, start_play: int=1)->None:
board: chess.Board = game.board()
if not game_sanity_check(game):
return
result: str = game.headers["Result"]
ply = 0
for node in game.mainline():
move = node.move
if ply >= start_play:
comment: str = node.comment
writer.write("fen " + board.fen() + "\n")
writer.write("move " + str(move) + "\n")
writer.write("score " + str(parse_comment_for_score(comment, board)) + "\n")
writer.write("ply " + str(ply)+"\n")
writer.write("result " + str(parse_result(result, board)) +"\n")
writer.write("e\n")
ply += 1
board.push(move)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--pgn", type=str, required=True)
parser.add_argument("--start_ply", type=int, default=1)
parser.add_argument("--output", type=str, default="plain.txt")
parser.add_argument("--flip_black_score", action='store_true', dest='flip_black_score', help="Flip black score. Default: False")
args = parser.parse_args()
global flip_black
flip_black = args.flip_black_score
pgn_files: List[str] = glob.glob(args.pgn)
pgn_files = sorted(pgn_files, key=lambda x:float(re.findall("-(\d+).pgn",x)[0] if re.findall("-(\d+).pgn",x) else 0.0))
f = open(args.output, 'w')
for pgn_file in pgn_files:
print("parse", pgn_file)
pgn_loader = open(pgn_file)
while True:
game = chess.pgn.read_game(pgn_loader)
if game is None:
break
parse_game(game, f, args.start_ply)
f.close()
if __name__=="__main__":
main()
-89
View File
@@ -1,89 +0,0 @@
import struct
import sys
import os
import random
from pathlib import Path
def index_binpack(file):
print('Indexing...')
index = []
offset = 0
report_every = 100
prev_mib = -report_every
while file.peek():
chunk_header = file.read(8)
assert chunk_header[0:4] == b'BINP'
size = struct.unpack('<I', chunk_header[4:])[0]
file.seek(size, os.SEEK_CUR)
index.append((offset, size + 8))
offset += size + 8
mib = offset // 1024 // 1024
if mib // 100 != prev_mib // 100:
print('Indexed {} MiB'.format(mib))
prev_mib = mib
return index
def copy_binpack_indexed(in_file, index, out_files):
print('Copying...')
total_size = 0
report_every = 100
prev_mib = -report_every
nextfile = 0
for offset, size in index:
in_file.seek(offset, os.SEEK_SET)
data = in_file.read(size)
assert len(data) == size
out_files[nextfile].write(data)
nextfile = (nextfile + 1) % len(out_files)
total_size += size
mib = total_size // 1024 // 1024
if mib // 100 != prev_mib // 100:
print('Copied {} MiB'.format(mib))
prev_mib = mib
def main():
if len(sys.argv) < 3:
print('Usage: python shuffle_binpack.py infile outfile [split_count]')
return
in_filename = sys.argv[1]
if len(sys.argv) > 3:
# split the infile in split_count pieces, creating new outfile names based on the provided name
basefile = sys.argv[2]
split_count = int(sys.argv[3])
base=os.path.splitext(basefile)[0]
ext=os.path.splitext(basefile)[1]
out_filenames = []
for i in range(split_count):
out_filenames.append(base+"_{}".format(i)+ext)
else:
out_filenames = [sys.argv[2]]
for out_filename in out_filenames:
if (Path(out_filename).exists()):
print('Output path {} already exists. Please specify a path to a file that does not exist.'.format(out_filename))
return
print(out_filenames)
in_file = open(in_filename, 'rb')
index = index_binpack(in_file)
print('Shuffling...')
random.shuffle(index)
out_files = []
for out_filename in out_filenames:
out_files.append(open(out_filename, 'wb'))
copy_binpack_indexed(in_file, index, out_files)
in_file.close()
for out_file in out_files:
out_file.close()
main()
+177 -127
View File
@@ -1,5 +1,5 @@
# Stockfish, a UCI chess playing engine derived from Glaurung 2.1
# Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
# Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
#
# Stockfish is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
@@ -44,12 +44,6 @@ else
EXE = stockfish
endif
### Establish the operating system name
KERNEL = $(shell uname -s)
ifeq ($(KERNEL),Linux)
OS = $(shell uname -o)
endif
### Installation dir definitions
PREFIX = /usr/local
BINDIR = $(PREFIX)/bin
@@ -57,61 +51,51 @@ BINDIR = $(PREFIX)/bin
### Built-in benchmark for pgo-builds
ifeq ($(SDE_PATH),)
PGOBENCH = $(WINE_PATH) ./$(EXE) bench
PGOGENSFEN = $(WINE_PATH) ./$(EXE) gensfen depth 3 loop 1000 sfen_format bin output_file_name $(PGO_TRAINING_DATA_FILE)
else
PGOBENCH = $(SDE_PATH) -- $(WINE_PATH) ./$(EXE) bench
PGOGENSFEN = $(SDE_PATH) -- $(WINE_PATH) ./$(EXE) gensfen depth 3 loop 1000 sfen_format bin output_file_name $(PGO_TRAINING_DATA_FILE)
endif
### Source and object files
SRCS = benchmark.cpp bitbase.cpp bitboard.cpp endgame.cpp evaluate.cpp main.cpp \
material.cpp misc.cpp movegen.cpp movepick.cpp pawns.cpp position.cpp psqt.cpp \
search.cpp thread.cpp timeman.cpp tt.cpp uci.cpp ucioption.cpp tune.cpp syzygy/tbprobe.cpp \
nnue/evaluate_nnue.cpp \
nnue/features/half_ka_v2_hm.cpp \
tools/validate_training_data.cpp \
tools/sfen_packer.cpp \
tools/training_data_generator.cpp \
tools/training_data_generator_nonpv.cpp \
tools/opening_book.cpp \
tools/convert.cpp \
tools/transform.cpp \
tools/stats.cpp
nnue/evaluate_nnue.cpp nnue/features/half_ka_v2_hm.cpp
OBJS = $(notdir $(SRCS:.cpp=.o))
VPATH = syzygy:nnue:nnue/features:eval:extra:tools
VPATH = syzygy:nnue:nnue/features
### ==========================================================================
### Section 2. High-level Configuration
### ==========================================================================
#
# flag --- Comp switch --- Description
# flag --- Comp switch --- Description
# ----------------------------------------------------------------------------
#
# debug = yes/no --- -DNDEBUG --- Enable/Disable debug mode
# debug = yes/no --- -DNDEBUG --- Enable/Disable debug mode
# sanitize = none/<sanitizer> ... (-fsanitize )
# --- ( undefined ) --- enable undefined behavior checks
# --- ( thread ) --- enable threading error checks
# --- ( address ) --- enable memory access checks
# --- ...etc... --- see compiler documentation for supported sanitizers
# optimize = yes/no --- (-O3/-fast etc.) --- Enable/Disable optimizations
# arch = (name) --- (-arch) --- Target architecture
# bits = 64/32 --- -DIS_64BIT --- 64-/32-bit operating system
# prefetch = yes/no --- -DUSE_PREFETCH --- Use prefetch asm-instruction
# popcnt = yes/no --- -DUSE_POPCNT --- Use popcnt asm-instruction
# pext = yes/no --- -DUSE_PEXT --- Use pext x86_64 asm-instruction
# sse = yes/no --- -msse --- Use Intel Streaming SIMD Extensions
# mmx = yes/no --- -mmmx --- Use Intel MMX instructions
# sse2 = yes/no --- -msse2 --- Use Intel Streaming SIMD Extensions 2
# ssse3 = yes/no --- -mssse3 --- Use Intel Supplemental Streaming SIMD Extensions 3
# sse41 = yes/no --- -msse4.1 --- Use Intel Streaming SIMD Extensions 4.1
# avx2 = yes/no --- -mavx2 --- Use Intel Advanced Vector Extensions 2
# avxvnni = yes/no --- -mavxvnni --- Use Intel Vector Neural Network Instructions AVX
# avx512 = yes/no --- -mavx512bw --- Use Intel Advanced Vector Extensions 512
# vnni256 = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 256
# vnni512 = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 512
# neon = yes/no --- -DUSE_NEON --- Use ARM SIMD architecture
# --- ( undefined ) --- enable undefined behavior checks
# --- ( thread ) --- enable threading error checks
# --- ( address ) --- enable memory access checks
# --- ...etc... --- see compiler documentation for supported sanitizers
# optimize = yes/no --- (-O3/-fast etc.) --- Enable/Disable optimizations
# arch = (name) --- (-arch) --- Target architecture
# bits = 64/32 --- -DIS_64BIT --- 64-/32-bit operating system
# prefetch = yes/no --- -DUSE_PREFETCH --- Use prefetch asm-instruction
# popcnt = yes/no --- -DUSE_POPCNT --- Use popcnt asm-instruction
# pext = yes/no --- -DUSE_PEXT --- Use pext x86_64 asm-instruction
# sse = yes/no --- -msse --- Use Intel Streaming SIMD Extensions
# mmx = yes/no --- -mmmx --- Use Intel MMX instructions
# sse2 = yes/no --- -msse2 --- Use Intel Streaming SIMD Extensions 2
# ssse3 = yes/no --- -mssse3 --- Use Intel Supplemental Streaming SIMD Extensions 3
# sse41 = yes/no --- -msse4.1 --- Use Intel Streaming SIMD Extensions 4.1
# avx2 = yes/no --- -mavx2 --- Use Intel Advanced Vector Extensions 2
# avxvnni = yes/no --- -mavxvnni --- Use Intel Vector Neural Network Instructions AVX
# avx512 = yes/no --- -mavx512bw --- Use Intel Advanced Vector Extensions 512
# vnni256 = yes/no --- -mavx256vnni --- Use Intel Vector Neural Network Instructions 512 with 256bit operands
# vnni512 = yes/no --- -mavx512vnni --- Use Intel Vector Neural Network Instructions 512
# neon = yes/no --- -DUSE_NEON --- Use ARM SIMD architecture
# dotprod = yes/no --- -DUSE_NEON_DOTPROD --- Use ARM advanced SIMD Int8 dot product instructions
#
# Note that Makefile is space sensitive, so when adding new architectures
# or modifying existing flags, you have to make sure there are no extra spaces
@@ -133,7 +117,7 @@ ifeq ($(ARCH), $(filter $(ARCH), \
x86-64-vnni512 x86-64-vnni256 x86-64-avx512 x86-64-avxvnni x86-64-bmi2 \
x86-64-avx2 x86-64-sse41-popcnt x86-64-modern x86-64-ssse3 x86-64-sse3-popcnt \
x86-64 x86-32-sse41-popcnt x86-32-sse2 x86-32 ppc-64 ppc-32 e2k \
armv7 armv7-neon armv8 apple-silicon general-64 general-32))
armv7 armv7-neon armv8 armv8-dotprod apple-silicon general-64 general-32 riscv64))
SUPPORTED_ARCH=true
else
SUPPORTED_ARCH=false
@@ -157,6 +141,7 @@ avx512 = no
vnni256 = no
vnni512 = no
neon = no
dotprod = no
arm_version = 0
STRIP = strip
@@ -325,11 +310,21 @@ ifeq ($(ARCH),armv8)
arm_version = 8
endif
ifeq ($(ARCH),armv8-dotprod)
arch = armv8
prefetch = yes
popcnt = yes
neon = yes
dotprod = yes
arm_version = 8
endif
ifeq ($(ARCH),apple-silicon)
arch = arm64
prefetch = yes
popcnt = yes
neon = yes
dotprod = yes
arm_version = 8
endif
@@ -355,7 +350,11 @@ ifeq ($(findstring e2k,$(ARCH)),e2k)
popcnt = yes
endif
ifeq ($(ARCH),riscv64)
arch = riscv64
endif
endif
### ==========================================================================
### Section 3. Low-level Configuration
@@ -368,9 +367,8 @@ ifeq ($(MAKELEVEL),0)
export ENV_LDFLAGS := $(LDFLAGS)
endif
ADDITIONAL_INCLUDE_DIRECTORIES = -I.
CXXFLAGS = $(ENV_CXXFLAGS) -Wall -Wcast-qual -fno-exceptions -std=c++17 $(ADDITIONAL_INCLUDE_DIRECTORIES) $(EXTRACXXFLAGS)
DEPENDFLAGS = $(ENV_DEPENDFLAGS) -std=c++17 $(ADDITIONAL_INCLUDE_DIRECTORIES)
CXXFLAGS = $(ENV_CXXFLAGS) -Wall -Wcast-qual -fno-exceptions -std=c++17 $(EXTRACXXFLAGS)
DEPENDFLAGS = $(ENV_DEPENDFLAGS) -std=c++17
LDFLAGS = $(ENV_LDFLAGS) $(EXTRALDFLAGS)
ifeq ($(COMP),)
@@ -380,13 +378,16 @@ endif
ifeq ($(COMP),gcc)
comp=gcc
CXX=g++
CXXFLAGS += -pedantic -Wextra -Wshadow -lstdc++fs
CXXFLAGS += -pedantic -Wextra -Wshadow -Wmissing-declarations
ifeq ($(arch),$(filter $(arch),armv7 armv8))
ifeq ($(arch),$(filter $(arch),armv7 armv8 riscv64))
ifeq ($(OS),Android)
CXXFLAGS += -m$(bits)
LDFLAGS += -m$(bits)
endif
ifeq ($(ARCH),riscv64)
CXXFLAGS += -latomic
endif
else
CXXFLAGS += -m$(bits)
LDFLAGS += -m$(bits)
@@ -421,13 +422,14 @@ ifeq ($(COMP),mingw)
CXX=i686-w64-mingw32-c++-posix
endif
endif
CXXFLAGS += -pedantic -Wextra -Wshadow
CXXFLAGS += -pedantic -Wextra -Wshadow -Wmissing-declarations
endif
ifeq ($(COMP),icc)
comp=icc
CXX=icpc
CXXFLAGS += -diag-disable 1476,10120 -Wcheck -Wabi -Wdeprecated -strict-ansi
ifeq ($(COMP),icx)
comp=icx
CXX=icpx
CXXFLAGS += --intel -pedantic -Wextra -Wshadow -Wmissing-prototypes \
-Wconditional-uninitialized -Wabi -Wdeprecated
endif
ifeq ($(COMP),clang)
@@ -437,7 +439,8 @@ ifeq ($(COMP),clang)
CXX=x86_64-w64-mingw32-clang++
endif
CXXFLAGS += -pedantic -Wextra -Wshadow
CXXFLAGS += -pedantic -Wextra -Wshadow -Wmissing-prototypes \
-Wconditional-uninitialized
ifeq ($(filter $(KERNEL),Darwin OpenBSD FreeBSD),)
ifeq ($(target_windows),)
@@ -447,11 +450,14 @@ ifeq ($(COMP),clang)
endif
endif
ifeq ($(arch),$(filter $(arch),armv7 armv8))
ifeq ($(arch),$(filter $(arch),armv7 armv8 riscv64))
ifeq ($(OS),Android)
CXXFLAGS += -m$(bits)
LDFLAGS += -m$(bits)
endif
ifeq ($(ARCH),riscv64)
CXXFLAGS += -latomic
endif
else
CXXFLAGS += -m$(bits)
LDFLAGS += -m$(bits)
@@ -459,8 +465,8 @@ ifeq ($(COMP),clang)
endif
ifeq ($(KERNEL),Darwin)
CXXFLAGS += -mmacosx-version-min=10.15
LDFLAGS += -mmacosx-version-min=10.15
CXXFLAGS += -mmacosx-version-min=10.14
LDFLAGS += -mmacosx-version-min=10.14
ifneq ($(arch),any)
CXXFLAGS += -arch $(arch)
LDFLAGS += -arch $(arch)
@@ -494,9 +500,9 @@ ifeq ($(COMP),ndk)
LDFLAGS += -static-libstdc++ -pie -lm -latomic
endif
ifeq ($(comp),icc)
profile_make = icc-profile-make
profile_use = icc-profile-use
ifeq ($(comp),icx)
profile_make = icx-profile-make
profile_use = icx-profile-use
else ifeq ($(comp),clang)
profile_make = clang-profile-make
profile_use = clang-profile-use
@@ -520,7 +526,7 @@ endif
### Sometimes gcc is really clang
ifeq ($(COMP),gcc)
gccversion = $(shell $(CXX) --version)
gccversion = $(shell $(CXX) --version 2>/dev/null)
gccisclang = $(findstring clang,$(gccversion))
ifneq ($(gccisclang),)
profile_make = clang-profile-make
@@ -536,13 +542,13 @@ ifneq ($(comp),mingw)
# Haiku has pthreads in its libroot, so only link it in on other platforms
ifneq ($(KERNEL),Haiku)
ifneq ($(COMP),ndk)
LDFLAGS += -lpthread
LDFLAGS += -lpthread
endif
endif
endif
endif
endif
### 3.2.2 Debugging
### 3.2.1 Debugging
ifeq ($(debug),no)
CXXFLAGS += -DNDEBUG
else
@@ -567,7 +573,7 @@ ifeq ($(optimize),yes)
endif
ifeq ($(KERNEL),Darwin)
ifeq ($(comp),$(filter $(comp),clang icc))
ifeq ($(comp),$(filter $(comp),clang icx))
CXXFLAGS += -mdynamic-no-pic
endif
@@ -579,7 +585,10 @@ ifeq ($(optimize),yes)
endif
ifeq ($(comp),clang)
CXXFLAGS += -fexperimental-new-pass-manager
clangmajorversion = $(shell $(CXX) -dumpversion 2>/dev/null | cut -f1 -d.)
ifeq ($(shell expr $(clangmajorversion) \< 16),1)
CXXFLAGS += -fexperimental-new-pass-manager
endif
endif
endif
@@ -600,8 +609,6 @@ endif
ifeq ($(popcnt),yes)
ifeq ($(arch),$(filter $(arch),ppc64 armv7 armv8 arm64))
CXXFLAGS += -DUSE_POPCNT
else ifeq ($(comp),icc)
CXXFLAGS += -msse3 -DUSE_POPCNT
else
CXXFLAGS += -msse3 -mpopcnt -DUSE_POPCNT
endif
@@ -610,63 +617,63 @@ endif
### 3.6 SIMD architectures
ifeq ($(avx2),yes)
CXXFLAGS += -DUSE_AVX2
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
CXXFLAGS += -mavx2
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mavx2 -mbmi
endif
endif
ifeq ($(avxvnni),yes)
CXXFLAGS += -DUSE_VNNI -DUSE_AVXVNNI
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mavxvnni
endif
endif
ifeq ($(avx512),yes)
CXXFLAGS += -DUSE_AVX512
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mavx512f -mavx512bw
endif
endif
ifeq ($(vnni256),yes)
CXXFLAGS += -DUSE_VNNI
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mavx512f -mavx512bw -mavx512vnni -mavx512dq -mavx512vl -mprefer-vector-width=256
endif
endif
ifeq ($(vnni512),yes)
CXXFLAGS += -DUSE_VNNI
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
CXXFLAGS += -mavx512vnni -mavx512dq -mavx512vl
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mavx512f -mavx512bw -mavx512vnni -mavx512dq -mavx512vl -mprefer-vector-width=512
endif
endif
ifeq ($(sse41),yes)
CXXFLAGS += -DUSE_SSE41
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -msse4.1
endif
endif
ifeq ($(ssse3),yes)
CXXFLAGS += -DUSE_SSSE3
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mssse3
endif
endif
ifeq ($(sse2),yes)
CXXFLAGS += -DUSE_SSE2
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -msse2
endif
endif
ifeq ($(mmx),yes)
CXXFLAGS += -DUSE_MMX
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mmmx
endif
endif
@@ -679,24 +686,43 @@ ifeq ($(neon),yes)
CXXFLAGS += -mfpu=neon
endif
endif
endif
endif
ifeq ($(dotprod),yes)
CXXFLAGS += -march=armv8.2-a+dotprod -DUSE_NEON_DOTPROD
endif
### 3.7 pext
ifeq ($(pext),yes)
CXXFLAGS += -DUSE_PEXT
ifeq ($(comp),$(filter $(comp),gcc clang mingw))
ifeq ($(comp),$(filter $(comp),gcc clang mingw icx))
CXXFLAGS += -mbmi2
endif
endif
### 3.7.1 Try to include git commit sha for versioning
GIT_SHA = $(shell git rev-parse HEAD 2>/dev/null | cut -c 1-8)
ifneq ($(GIT_SHA), )
CXXFLAGS += -DGIT_SHA=$(GIT_SHA)
endif
### 3.7.2 Try to include git commit date for versioning
GIT_DATE = $(shell git show -s --date=format:'%Y%m%d' --format=%cd HEAD 2>/dev/null)
ifneq ($(GIT_DATE), )
CXXFLAGS += -DGIT_DATE=$(GIT_DATE)
endif
### 3.8 Link Time Optimization
### This is a mix of compile and link time options because the lto link phase
### needs access to the optimization flags.
ifeq ($(optimize),yes)
ifeq ($(debug), no)
ifeq ($(comp),clang)
CXXFLAGS += -flto
ifeq ($(comp),$(filter $(comp),clang icx))
CXXFLAGS += -flto=full
ifeq ($(comp),icx)
CXXFLAGS += -fwhole-program-vtables
endif
ifeq ($(target_windows),yes)
CXXFLAGS += -fuse-ld=lld
endif
@@ -706,21 +732,19 @@ ifeq ($(debug), no)
# GCC on some systems.
else ifeq ($(comp),gcc)
ifeq ($(gccisclang),)
CXXFLAGS += -flto
CXXFLAGS += -flto -flto-partition=one
LDFLAGS += $(CXXFLAGS) -flto=jobserver
else
CXXFLAGS += -flto
CXXFLAGS += -flto=full
LDFLAGS += $(CXXFLAGS)
endif
# To use LTO and static linking on Windows,
# the tool chain requires gcc version 10.1 or later.
else ifeq ($(comp),mingw)
ifneq ($(arch),i386)
CXXFLAGS += -flto
CXXFLAGS += -flto -flto-partition=one
LDFLAGS += $(CXXFLAGS) -save-temps
endif
endif
endif
endif
@@ -745,19 +769,19 @@ help:
@echo "Supported targets:"
@echo ""
@echo "help > Display architecture details"
@echo "build > Standard build"
@echo "profile-build > standard build with profile-guided optimization"
@echo "build > skip profile-guided optimization"
@echo "net > Download the default nnue net"
@echo "profile-build > Faster build (with profile-guided optimization)"
@echo "strip > Strip executable"
@echo "install > Install executable"
@echo "clean > Clean up"
@echo ""
@echo "Supported archs:"
@echo ""
@echo "x86-64-vnni512 > x86 64-bit with vnni support 512bit wide"
@echo "x86-64-vnni256 > x86 64-bit with vnni support 256bit wide"
@echo "x86-64-vnni512 > x86 64-bit with vnni 512bit support"
@echo "x86-64-vnni256 > x86 64-bit with vnni 512bit support, limit operands to 256bit wide"
@echo "x86-64-avx512 > x86 64-bit with avx512 support"
@echo "x86-64-avxvnni > x86 64-bit with avxvnni support"
@echo "x86-64-avxvnni > x86 64-bit with vnni 256bit support"
@echo "x86-64-bmi2 > x86 64-bit with bmi2 support"
@echo "x86-64-avx2 > x86 64-bit with avx2 support"
@echo "x86-64-sse41-popcnt > x86 64-bit with sse41 and popcnt support"
@@ -773,27 +797,30 @@ help:
@echo "armv7 > ARMv7 32-bit"
@echo "armv7-neon > ARMv7 32-bit with popcnt and neon"
@echo "armv8 > ARMv8 64-bit with popcnt and neon"
@echo "armv8-dotprod > ARMv8 64-bit with popcnt, neon and dot product support"
@echo "e2k > Elbrus 2000"
@echo "apple-silicon > Apple silicon ARM64"
@echo "general-64 > unspecified 64-bit"
@echo "general-32 > unspecified 32-bit"
@echo "riscv64 > RISC-V 64-bit"
@echo ""
@echo "Supported compilers:"
@echo ""
@echo "gcc > Gnu compiler (default)"
@echo "mingw > Gnu compiler with MinGW under Windows"
@echo "clang > LLVM Clang compiler"
@echo "icc > Intel compiler"
@echo "icx > Intel oneAPI DPC++/C++ Compiler"
@echo "ndk > Google NDK to cross-compile for Android"
@echo ""
@echo "Simple examples. If you don't know what to do, you likely want to run: "
@echo "Simple examples. If you don't know what to do, you likely want to run one of: "
@echo ""
@echo "make -j build ARCH=x86-64 (A portable, slow compile for 64-bit systems)"
@echo "make -j build ARCH=x86-32 (A portable, slow compile for 32-bit systems)"
@echo "make -j profile-build ARCH=x86-64-avx2 # typically a fast compile for common systems "
@echo "make -j profile-build ARCH=x86-64-modern # A more portable compile for 64-bit systems "
@echo "make -j profile-build ARCH=x86-64 # A portable compile for 64-bit systems "
@echo ""
@echo "Advanced examples, for experienced users looking for performance: "
@echo "Advanced examples, for experienced users: "
@echo ""
@echo "make help ARCH=x86-64-bmi2"
@echo "make -j profile-build ARCH=x86-64-bmi2"
@echo "make -j profile-build ARCH=x86-64-bmi2 COMP=gcc COMPCXX=g++-9.0"
@echo "make -j build ARCH=x86-64-ssse3 COMP=clang"
@echo ""
@@ -808,8 +835,10 @@ endif
.PHONY: help build profile-build strip install clean net objclean profileclean \
config-sanity icc-profile-use icc-profile-make gcc-profile-use gcc-profile-make \
clang-profile-use clang-profile-make
config-sanity \
icx-profile-use icx-profile-make \
gcc-profile-use gcc-profile-make \
clang-profile-use clang-profile-make FORCE
build: net config-sanity
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) all
@@ -821,7 +850,6 @@ profile-build: net config-sanity objclean profileclean
@echo ""
@echo "Step 2/4. Running benchmark for pgo-build ..."
$(PGOBENCH) 2>&1 | tail -n 4
$(PGOGENSFEN) 2>&1 | tail -n 4
@echo ""
@echo "Step 3/4. Building optimized executable ..."
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) objclean
@@ -846,34 +874,46 @@ clean: objclean profileclean
net:
$(eval nnuenet := $(shell grep EvalFileDefaultName evaluate.h | grep define | sed 's/.*\(nn-[a-z0-9]\{12\}.nnue\).*/\1/'))
@echo "Default net: $(nnuenet)"
$(eval nnuedownloadurl := https://tests.stockfishchess.org/api/nn/$(nnuenet))
$(eval nnuedownloadurl1 := https://tests.stockfishchess.org/api/nn/$(nnuenet))
$(eval nnuedownloadurl2 := https://github.com/official-stockfish/networks/raw/master/$(nnuenet))
$(eval curl_or_wget := $(shell if hash curl 2>/dev/null; then echo "curl -skL"; elif hash wget 2>/dev/null; then echo "wget -qO-"; fi))
@if test -f "$(nnuenet)"; then \
echo "Already available."; \
else \
if [ "x$(curl_or_wget)" = "x" ]; then \
echo "Automatic download failed: neither curl nor wget is installed. Install one of these tools or download the net manually"; exit 1; \
else \
echo "Downloading $(nnuedownloadurl)"; $(curl_or_wget) $(nnuedownloadurl) > $(nnuenet);\
fi; \
fi;
@if [ "x$(curl_or_wget)" = "x" ]; then \
echo "Neither curl nor wget is installed. Install one of these tools unless the net has been downloaded manually"; \
fi
$(eval shasum_command := $(shell if hash shasum 2>/dev/null; then echo "shasum -a 256 "; elif hash sha256sum 2>/dev/null; then echo "sha256sum "; fi))
@if [ "x$(shasum_command)" != "x" ]; then \
if [ "$(nnuenet)" != "nn-"`$(shasum_command) $(nnuenet) | cut -c1-12`".nnue" ]; then \
echo "Failed download or $(nnuenet) corrupted, please delete!"; exit 1; \
fi \
else \
echo "shasum / sha256sum not found, skipping net validation"; \
@if [ "x$(shasum_command)" = "x" ]; then \
echo "shasum / sha256sum not found, skipping net validation"; \
fi
@for nnuedownloadurl in "$(nnuedownloadurl1)" "$(nnuedownloadurl2)"; do \
if test -f "$(nnuenet)"; then \
echo "$(nnuenet) available."; \
else \
if [ "x$(curl_or_wget)" != "x" ]; then \
echo "Downloading $${nnuedownloadurl}"; $(curl_or_wget) $${nnuedownloadurl} > $(nnuenet);\
else \
echo "No net found and download not possible"; exit 1;\
fi; \
fi; \
if [ "x$(shasum_command)" != "x" ]; then \
if [ "$(nnuenet)" != "nn-"`$(shasum_command) $(nnuenet) | cut -c1-12`".nnue" ]; then \
echo "Removing failed download"; rm -f $(nnuenet); \
else \
echo "Network validated"; break; \
fi; \
fi; \
done
@if ! test -f "$(nnuenet)"; then \
echo "Failed to download $(nnuenet)."; \
fi
# clean binaries and objects
objclean:
@rm -f $(EXE) *.o ./syzygy/*.o ./nnue/*.o ./nnue/features/*.o ./tools/*.o ./extra/*.o ./eval/*.o
@rm -f stockfish stockfish.exe *.o ./syzygy/*.o ./nnue/*.o ./nnue/features/*.o
# clean auxiliary profiling files
profileclean:
@rm -rf profdir
@rm -f bench.txt *.gcda *.gcno ./syzygy/*.gcda ./nnue/*.gcda ./nnue/features/*.gcda *.s ./tools/*.gcda ./extra/*.gcda ./eval/*.gcda
@rm -f bench.txt *.gcda *.gcno ./syzygy/*.gcda ./nnue/*.gcda ./nnue/features/*.gcda *.s
@rm -f stockfish.profdata *.profraw
@rm -f stockfish.*args*
@rm -f stockfish.*lt*
@@ -913,7 +953,9 @@ config-sanity: net
@echo "vnni256: '$(vnni256)'"
@echo "vnni512: '$(vnni512)'"
@echo "neon: '$(neon)'"
@echo "dotprod: '$(dotprod)'"
@echo "arm_version: '$(arm_version)'"
@echo "target_windows: '$(target_windows)'"
@echo ""
@echo "Flags:"
@echo "CXX: $(CXX)"
@@ -927,7 +969,7 @@ config-sanity: net
@test "$(SUPPORTED_ARCH)" = "true"
@test "$(arch)" = "any" || test "$(arch)" = "x86_64" || test "$(arch)" = "i386" || \
test "$(arch)" = "ppc64" || test "$(arch)" = "ppc" || test "$(arch)" = "e2k" || \
test "$(arch)" = "armv7" || test "$(arch)" = "armv8" || test "$(arch)" = "arm64"
test "$(arch)" = "armv7" || test "$(arch)" = "armv8" || test "$(arch)" = "arm64" || test "$(arch)" = "riscv64"
@test "$(bits)" = "32" || test "$(bits)" = "64"
@test "$(prefetch)" = "yes" || test "$(prefetch)" = "no"
@test "$(popcnt)" = "yes" || test "$(popcnt)" = "no"
@@ -942,12 +984,16 @@ config-sanity: net
@test "$(vnni256)" = "yes" || test "$(vnni256)" = "no"
@test "$(vnni512)" = "yes" || test "$(vnni512)" = "no"
@test "$(neon)" = "yes" || test "$(neon)" = "no"
@test "$(comp)" = "gcc" || test "$(comp)" = "icc" || test "$(comp)" = "mingw" || test "$(comp)" = "clang" \
@test "$(comp)" = "gcc" || test "$(comp)" = "icx" || test "$(comp)" = "mingw" || test "$(comp)" = "clang" \
|| test "$(comp)" = "armv7a-linux-androideabi16-clang" || test "$(comp)" = "aarch64-linux-android21-clang"
$(EXE): $(OBJS)
+$(CXX) -o $@ $(OBJS) $(LDFLAGS)
# Force recompilation to ensure version info is up-to-date
misc.o: FORCE
FORCE:
clang-profile-make:
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) \
EXTRACXXFLAGS='-fprofile-instr-generate ' \
@@ -976,18 +1022,22 @@ gcc-profile-use:
EXTRALDFLAGS='-lgcov' \
all
icc-profile-make:
@mkdir -p profdir
icx-profile-make:
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) \
EXTRACXXFLAGS='-prof-gen=srcpos -prof_dir ./profdir' \
EXTRACXXFLAGS='-fprofile-instr-generate ' \
EXTRALDFLAGS=' -fprofile-instr-generate' \
all
icc-profile-use:
icx-profile-use:
$(XCRUN) llvm-profdata merge -output=stockfish.profdata *.profraw
$(MAKE) ARCH=$(ARCH) COMP=$(COMP) \
EXTRACXXFLAGS='-prof_use -prof_dir ./profdir' \
EXTRACXXFLAGS='-fprofile-instr-use=stockfish.profdata' \
EXTRALDFLAGS='-fprofile-use ' \
all
.depend: $(SRCS)
-@$(CXX) $(DEPENDFLAGS) -MM $(SRCS) > $@ 2> /dev/null
ifeq (, $(filter $(MAKECMDGOALS), help strip install clean net objclean profileclean config-sanity))
-include .depend
endif
+3 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -16,6 +16,8 @@
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "benchmark.h"
#include <fstream>
#include <iostream>
#include <istream>
+34
View File
@@ -0,0 +1,34 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef BENCHMARK_H_INCLUDED
#define BENCHMARK_H_INCLUDED
#include <iosfwd>
#include <string>
#include <vector>
namespace Stockfish {
class Position;
std::vector<std::string> setup_bench(const Position&, std::istream&);
} // namespace Stockfish
#endif // #ifndef BENCHMARK_H_INCLUDED
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -5
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -27,7 +27,6 @@ namespace Stockfish {
uint8_t PopCnt16[1 << 16];
uint8_t SquareDistance[SQUARE_NB][SQUARE_NB];
Bitboard SquareBB[SQUARE_NB];
Bitboard LineBB[SQUARE_NB][SQUARE_NB];
Bitboard BetweenBB[SQUARE_NB][SQUARE_NB];
Bitboard PseudoAttacks[PIECE_TYPE_NB][SQUARE_NB];
@@ -82,9 +81,6 @@ void Bitboards::init() {
for (unsigned i = 0; i < (1 << 16); ++i)
PopCnt16[i] = uint8_t(std::bitset<16>(i).count());
for (Square s = SQ_A1; s <= SQ_H8; ++s)
SquareBB[s] = (1ULL << s);
for (Square s1 = SQ_A1; s1 <= SQ_H8; ++s1)
for (Square s2 = SQ_A1; s2 <= SQ_H8; ++s2)
SquareDistance[s1][s2] = std::max(distance<File>(s1, s2), distance<Rank>(s1, s2));
+2 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -74,7 +74,6 @@ constexpr Bitboard KingFlank[FILE_NB] = {
extern uint8_t PopCnt16[1 << 16];
extern uint8_t SquareDistance[SQUARE_NB][SQUARE_NB];
extern Bitboard SquareBB[SQUARE_NB];
extern Bitboard BetweenBB[SQUARE_NB][SQUARE_NB];
extern Bitboard LineBB[SQUARE_NB][SQUARE_NB];
extern Bitboard PseudoAttacks[PIECE_TYPE_NB][SQUARE_NB];
@@ -108,7 +107,7 @@ extern Magic BishopMagics[SQUARE_NB];
inline Bitboard square_bb(Square s) {
assert(is_ok(s));
return SquareBB[s];
return (1ULL << s);
}
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+46 -112
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -27,8 +27,6 @@
#include <streambuf>
#include <vector>
#include "nnue/evaluate_nnue.h"
#include "bitboard.h"
#include "evaluate.h"
#include "material.h"
@@ -38,6 +36,7 @@
#include "timeman.h"
#include "uci.h"
#include "incbin/incbin.h"
#include "nnue/evaluate_nnue.h"
// Macro to embed the default efficiently updatable neural network (NNUE) file
// data in the engine binary (using incbin.h, by Dale Weiler).
@@ -54,28 +53,15 @@
const unsigned int gEmbeddedNNUESize = 1;
#endif
using namespace std;
namespace Stockfish {
namespace Eval {
namespace NNUE {
string currentEvalFileName = "None";
UseNNUEMode useNNUE;
static UseNNUEMode nnue_mode_from_option(const UCI::Option& mode)
{
if (mode == "false")
return UseNNUEMode::False;
else if (mode == "true")
return UseNNUEMode::True;
else if (mode == "pure")
return UseNNUEMode::Pure;
return UseNNUEMode::False;
}
}
bool useNNUE;
string currentEvalFileName = "None";
/// NNUE::init() tries to load a NNUE network at startup time, or when the engine
/// receives a UCI command "setoption name EvalFile value nn-[a-z0-9]{12}.nnue"
@@ -87,8 +73,8 @@ namespace Eval {
void NNUE::init() {
useNNUE = nnue_mode_from_option(Options["Use NNUE"]);
if (useNNUE == UseNNUEMode::False)
useNNUE = Options["Use NNUE"];
if (!useNNUE)
return;
string eval_file = string(Options["EvalFile"]);
@@ -96,20 +82,18 @@ namespace Eval {
eval_file = EvalFileDefaultName;
#if defined(DEFAULT_NNUE_DIRECTORY)
#define stringify2(x) #x
#define stringify(x) stringify2(x)
vector<string> dirs = { "<internal>" , "" , CommandLine::binaryDirectory , stringify(DEFAULT_NNUE_DIRECTORY) };
#else
vector<string> dirs = { "<internal>" , "" , CommandLine::binaryDirectory };
#endif
for (string directory : dirs)
for (const string& directory : dirs)
if (currentEvalFileName != eval_file)
{
if (directory != "<internal>")
{
ifstream stream(directory + eval_file, ios::binary);
if (load_eval(eval_file, stream))
if (NNUE::load_eval(eval_file, stream))
currentEvalFileName = eval_file;
}
@@ -125,7 +109,7 @@ namespace Eval {
(void) gEmbeddedNNUEEnd; // Silence warning on unused variable
istream stream(&buffer);
if (load_eval(eval_file, stream))
if (NNUE::load_eval(eval_file, stream))
currentEvalFileName = eval_file;
}
}
@@ -138,7 +122,7 @@ namespace Eval {
if (eval_file.empty())
eval_file = EvalFileDefaultName;
if (useNNUE != UseNNUEMode::False && currentEvalFileName != eval_file)
if (useNNUE && currentEvalFileName != eval_file)
{
string msg1 = "If the UCI option \"Use NNUE\" is set to true, network evaluation parameters compatible with the engine must be available.";
@@ -156,7 +140,7 @@ namespace Eval {
exit(EXIT_FAILURE);
}
if (useNNUE != UseNNUEMode::False)
if (useNNUE)
sync_cout << "info string NNUE evaluation using " << eval_file << " enabled" << sync_endl;
else
sync_cout << "info string classical evaluation enabled" << sync_endl;
@@ -173,24 +157,24 @@ namespace Trace {
Score scores[TERM_NB][COLOR_NB];
double to_cp(Value v) { return double(v) / PawnValueEg; }
static double to_cp(Value v) { return double(v) / UCI::NormalizeToPawnValue; }
void add(int idx, Color c, Score s) {
static void add(int idx, Color c, Score s) {
scores[idx][c] = s;
}
void add(int idx, Score w, Score b = SCORE_ZERO) {
static void add(int idx, Score w, Score b = SCORE_ZERO) {
scores[idx][WHITE] = w;
scores[idx][BLACK] = b;
}
std::ostream& operator<<(std::ostream& os, Score s) {
static std::ostream& operator<<(std::ostream& os, Score s) {
os << std::setw(5) << to_cp(mg_value(s)) << " "
<< std::setw(5) << to_cp(eg_value(s));
return os;
}
std::ostream& operator<<(std::ostream& os, Term t) {
static std::ostream& operator<<(std::ostream& os, Term t) {
if (t == MATERIAL || t == IMBALANCE || t == WINNABLE || t == TOTAL)
os << " ---- ----" << " | " << " ---- ----";
@@ -207,8 +191,8 @@ using namespace Trace;
namespace {
// Threshold for lazy and space evaluation
constexpr Value LazyThreshold1 = Value(3631);
constexpr Value LazyThreshold2 = Value(2084);
constexpr Value LazyThreshold1 = Value(3622);
constexpr Value LazyThreshold2 = Value(1962);
constexpr Value SpaceThreshold = Value(11551);
// KingAttackWeights[PieceType] contains king attack weights by piece type
@@ -402,10 +386,10 @@ namespace {
template<Tracing T> template<Color Us, PieceType Pt>
Score Evaluation<T>::pieces() {
constexpr Color Them = ~Us;
constexpr Direction Down = -pawn_push(Us);
constexpr Bitboard OutpostRanks = (Us == WHITE ? Rank4BB | Rank5BB | Rank6BB
: Rank5BB | Rank4BB | Rank3BB);
constexpr Color Them = ~Us;
[[maybe_unused]] constexpr Direction Down = -pawn_push(Us);
[[maybe_unused]] constexpr Bitboard OutpostRanks = (Us == WHITE ? Rank4BB | Rank5BB | Rank6BB
: Rank5BB | Rank4BB | Rank3BB);
Bitboard b1 = pos.pieces(Us, Pt);
Bitboard b, bb;
Score score = SCORE_ZERO;
@@ -444,7 +428,7 @@ namespace {
int mob = popcount(b & mobilityArea[Us]);
mobility[Us] += MobilityBonus[Pt - 2][mob];
if (Pt == BISHOP || Pt == KNIGHT)
if constexpr (Pt == BISHOP || Pt == KNIGHT)
{
// Bonus if the piece is on an outpost square or can reach one
// Bonus for knights (UncontestedOutpost) if few relevant targets
@@ -995,7 +979,7 @@ namespace {
// Initialize score by reading the incrementally updated scores included in
// the position object (material + piece square tables) and the material
// imbalance. Score is computed internally from the white point of view.
Score score = pos.psq_score() + me->imbalance() + pos.this_thread()->trend;
Score score = pos.psq_score() + me->imbalance();
// Probe the pawn hash table
pe = Pawns::probe(pos);
@@ -1056,38 +1040,6 @@ make_v:
return v;
}
/// Fisher Random Chess: correction for cornered bishops, to fix chess960 play with NNUE
Value fix_FRC(const Position& pos) {
constexpr Bitboard Corners = 1ULL << SQ_A1 | 1ULL << SQ_H1 | 1ULL << SQ_A8 | 1ULL << SQ_H8;
if (!(pos.pieces(BISHOP) & Corners))
return VALUE_ZERO;
int correction = 0;
if ( pos.piece_on(SQ_A1) == W_BISHOP
&& pos.piece_on(SQ_B2) == W_PAWN)
correction -= CorneredBishop;
if ( pos.piece_on(SQ_H1) == W_BISHOP
&& pos.piece_on(SQ_G2) == W_PAWN)
correction -= CorneredBishop;
if ( pos.piece_on(SQ_A8) == B_BISHOP
&& pos.piece_on(SQ_B7) == B_PAWN)
correction += CorneredBishop;
if ( pos.piece_on(SQ_H8) == B_BISHOP
&& pos.piece_on(SQ_G7) == B_PAWN)
correction += CorneredBishop;
return pos.side_to_move() == WHITE ? Value(3 * correction)
: -Value(3 * correction);
}
} // namespace Eval
@@ -1096,51 +1048,35 @@ make_v:
Value Eval::evaluate(const Position& pos) {
pos.this_thread()->on_eval();
assert(!pos.checkers());
Value v;
Value psq = pos.psq_eg_stm();
if (NNUE::useNNUE == NNUE::UseNNUEMode::Pure) {
v = NNUE::evaluate(pos);
// We use the much less accurate but faster Classical eval when the NNUE
// option is set to false. Otherwise we use the NNUE eval unless the
// PSQ advantage is decisive. (~4 Elo at STC, 1 Elo at LTC)
bool useClassical = !useNNUE || abs(psq) > 2048;
// Guarantee evaluation does not hit the tablebase range
v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
return v;
}
// Deciding between classical and NNUE eval (~10 Elo): for high PSQ imbalance we use classical,
// but we switch to NNUE during long shuffling or with high material on the board.
bool useClassical = (pos.this_thread()->depth > 9 || pos.count<ALL_PIECES>() > 7) &&
abs(eg_value(pos.psq_score())) * 5 > (856 + pos.non_pawn_material() / 64) * (10 + pos.rule50_count());
// Deciding between classical and NNUE eval (~10 Elo): for high PSQ imbalance we use classical,
// but we switch to NNUE during long shuffling or with high material on the board.
if (NNUE::useNNUE == NNUE::UseNNUEMode::False || useClassical)
if (useClassical)
v = Evaluation<NO_TRACE>(pos).value();
else
{
v = Evaluation<NO_TRACE>(pos).value(); // classical
useClassical = abs(v) >= 297;
}
int nnueComplexity;
int npm = pos.non_pawn_material() / 64;
// If result of a classical evaluation is much lower than threshold fall back to NNUE
if (NNUE::useNNUE != NNUE::UseNNUEMode::False && !useClassical)
{
Value nnue = NNUE::evaluate(pos, true); // NNUE
int scale = 1080 + 110 * pos.non_pawn_material() / 5120;
Color stm = pos.side_to_move();
Value optimism = pos.this_thread()->optimism[stm];
Value psq = (stm == WHITE ? 1 : -1) * eg_value(pos.psq_score());
int complexity = (278 * abs(nnue - psq)) / 256;
Color stm = pos.side_to_move();
Value optimism = pos.this_thread()->optimism[stm];
optimism = optimism * (251 + complexity) / 256;
v = (nnue * scale + optimism * (scale - 852)) / 1024;
Value nnue = NNUE::evaluate(pos, true, &nnueComplexity);
if (pos.is_chess960())
v += fix_FRC(pos);
// Blend optimism with nnue complexity and (semi)classical complexity
optimism += optimism * (nnueComplexity + abs(psq - nnue)) / 512;
v = (nnue * (945 + npm) + optimism * (150 + npm)) / 1024;
}
// Damp down the evaluation linearly when shuffling
v = v * (195 - pos.rule50_count()) / 211;
v = v * (200 - pos.rule50_count()) / 214;
// Guarantee evaluation does not hit the tablebase range
v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
@@ -1166,8 +1102,6 @@ std::string Eval::trace(Position& pos) {
std::memset(scores, 0, sizeof(scores));
// Reset any global variable used in eval
pos.this_thread()->depth = 0;
pos.this_thread()->trend = SCORE_ZERO;
pos.this_thread()->bestValue = VALUE_ZERO;
pos.this_thread()->optimism[WHITE] = VALUE_ZERO;
pos.this_thread()->optimism[BLACK] = VALUE_ZERO;
@@ -1197,14 +1131,14 @@ std::string Eval::trace(Position& pos) {
<< "| Total | " << Term(TOTAL)
<< "+------------+-------------+-------------+-------------+\n";
if (NNUE::useNNUE != NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
ss << '\n' << NNUE::trace(pos) << '\n';
ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
v = pos.side_to_move() == WHITE ? v : -v;
ss << "\nClassical evaluation " << to_cp(v) << " (white side)\n";
if (NNUE::useNNUE != NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
{
v = NNUE::evaluate(pos, false);
v = pos.side_to_move() == WHITE ? v : -v;
@@ -1214,7 +1148,7 @@ std::string Eval::trace(Position& pos) {
v = evaluate(pos);
v = pos.side_to_move() == WHITE ? v : -v;
ss << "Final evaluation " << to_cp(v) << " (white side)";
if (NNUE::useNNUE != NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
ss << " [with scaled NNUE, hybrid, ...]";
ss << "\n";
+5 -18
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -33,32 +33,19 @@ namespace Eval {
std::string trace(Position& pos);
Value evaluate(const Position& pos);
extern bool useNNUE;
extern std::string currentEvalFileName;
// The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
// for the build process (profile-build and fishtest) to work. Do not change the
// name of the macro, as it is used in the Makefile.
#define EvalFileDefaultName "nn-3c0aa92af1da.nnue"
#define EvalFileDefaultName "nn-5af11540bbfe.nnue"
namespace NNUE {
enum struct UseNNUEMode
{
False,
True,
Pure
};
extern UseNNUEMode useNNUE;
extern std::string currentEvalFileName;
std::string trace(Position& pos);
Value evaluate(const Position& pos, bool adjusted = false);
void init();
void verify();
bool load_eval(std::string name, std::istream& stream);
bool save_eval(std::ostream& stream);
bool save_eval(const std::optional<std::string>& filename);
} // namespace NNUE
} // namespace Eval
File diff suppressed because it is too large Load Diff
Executable → Regular
View File
+1 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -18,8 +18,6 @@
#include <iostream>
#include "nnue/evaluate_nnue.h"
#include "bitboard.h"
#include "endgame.h"
#include "position.h"
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+3 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -28,7 +28,7 @@ namespace Stockfish::Material {
/// Material::Entry contains various information about a material configuration.
/// It contains a material imbalance evaluation, a function pointer to a special
/// endgame evaluation function (which in most cases is NULL, meaning that the
/// endgame evaluation function (which in most cases is nullptr, meaning that the
/// standard evaluation function will be used), and scale factors.
///
/// The scale factors are used to scale the evaluation score up or down. For
@@ -62,7 +62,7 @@ struct Entry {
uint8_t factor[COLOR_NB];
};
typedef HashTable<Entry, 8192> Table;
using Table = HashTable<Entry, 8192>;
Entry* probe(const Position& pos);
+166 -79
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -32,21 +32,26 @@
// the calls at compile time), try to load them at runtime. To do this we need
// first to define the corresponding function pointers.
extern "C" {
typedef bool(*fun1_t)(LOGICAL_PROCESSOR_RELATIONSHIP,
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX, PDWORD);
typedef bool(*fun2_t)(USHORT, PGROUP_AFFINITY);
typedef bool(*fun3_t)(HANDLE, CONST GROUP_AFFINITY*, PGROUP_AFFINITY);
typedef bool(*fun4_t)(USHORT, PGROUP_AFFINITY, USHORT, PUSHORT);
typedef WORD(*fun5_t)();
using fun1_t = bool(*)(LOGICAL_PROCESSOR_RELATIONSHIP,
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX, PDWORD);
using fun2_t = bool(*)(USHORT, PGROUP_AFFINITY);
using fun3_t = bool(*)(HANDLE, CONST GROUP_AFFINITY*, PGROUP_AFFINITY);
using fun4_t = bool(*)(USHORT, PGROUP_AFFINITY, USHORT, PUSHORT);
using fun5_t = WORD(*)();
using fun6_t = bool(*)(HANDLE, DWORD, PHANDLE);
using fun7_t = bool(*)(LPCSTR, LPCSTR, PLUID);
using fun8_t = bool(*)(HANDLE, BOOL, PTOKEN_PRIVILEGES, DWORD, PTOKEN_PRIVILEGES, PDWORD);
}
#endif
#include <cmath>
#include <cstdlib>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <sstream>
#include <string_view>
#include <vector>
#include <cstdlib>
#if defined(__linux__) && !defined(__ANDROID__)
#include <stdlib.h>
@@ -65,13 +70,10 @@ using namespace std;
namespace Stockfish {
SynchronizedRegionLogger sync_region_cout(std::cout);
namespace {
/// Version number. If Version is left empty, then compile date in the format
/// DD-MM-YY and show in engine_info.
const string Version = "";
/// Version number or dev.
constexpr string_view version = "16";
/// Our fancy logging facility. The trick here is to replace cin.rdbuf() and
/// cout.rdbuf() with two Tie objects that tie cin and cout to a file stream. We
@@ -140,23 +142,41 @@ public:
} // namespace
/// engine_info() returns the full name of the current Stockfish version. This
/// will be either "Stockfish <Tag> DD-MM-YY" (where DD-MM-YY is the date when
/// the program was compiled) or "Stockfish <Version>", depending on whether
/// Version is empty.
/// engine_info() returns the full name of the current Stockfish version.
/// For local dev compiles we try to append the commit sha and commit date
/// from git if that fails only the local compilation date is set and "nogit" is specified:
/// Stockfish dev-YYYYMMDD-SHA
/// or
/// Stockfish dev-YYYYMMDD-nogit
///
/// For releases (non dev builds) we only include the version number:
/// Stockfish version
string engine_info(bool to_uci) {
stringstream ss;
ss << "Stockfish " << version << setfill('0');
const string months("Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec");
string month, day, year;
stringstream ss, date(__DATE__); // From compiler, format is "Sep 21 2008"
ss << "Stockfish " << Version << setfill('0');
if (Version.empty())
if constexpr (version == "dev")
{
ss << "-";
#ifdef GIT_DATE
ss << stringify(GIT_DATE);
#else
constexpr string_view months("Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec");
string month, day, year;
stringstream date(__DATE__); // From compiler, format is "Sep 21 2008"
date >> month >> day >> year;
ss << setw(2) << day << setw(2) << (1 + months.find(month) / 4) << year.substr(2);
ss << year << setw(2) << setfill('0') << (1 + months.find(month) / 4) << setw(2) << setfill('0') << day;
#endif
ss << "-";
#ifdef GIT_SHA
ss << stringify(GIT_SHA);
#else
ss << "nogit";
#endif
}
ss << (to_uci ? "\nid author ": " by ")
@@ -170,8 +190,6 @@ string engine_info(bool to_uci) {
std::string compiler_info() {
#define stringify2(x) #x
#define stringify(x) stringify2(x)
#define make_version_string(major, minor, patch) stringify(major) "." stringify(minor) "." stringify(patch)
/// Predefined macros hell:
@@ -283,21 +301,94 @@ std::string compiler_info() {
/// Debug functions used mainly to collect run-time statistics
static std::atomic<int64_t> hits[2], means[2];
constexpr int MaxDebugSlots = 32;
void dbg_hit_on(bool b) { ++hits[0]; if (b) ++hits[1]; }
void dbg_hit_on(bool c, bool b) { if (c) dbg_hit_on(b); }
void dbg_mean_of(int v) { ++means[0]; means[1] += v; }
namespace {
template<size_t N>
struct DebugInfo {
std::atomic<int64_t> data[N] = { 0 };
constexpr inline std::atomic<int64_t>& operator[](int index) { return data[index]; }
};
DebugInfo<2> hit[MaxDebugSlots];
DebugInfo<2> mean[MaxDebugSlots];
DebugInfo<3> stdev[MaxDebugSlots];
DebugInfo<6> correl[MaxDebugSlots];
} // namespace
void dbg_hit_on(bool cond, int slot) {
++hit[slot][0];
if (cond)
++hit[slot][1];
}
void dbg_mean_of(int64_t value, int slot) {
++mean[slot][0];
mean[slot][1] += value;
}
void dbg_stdev_of(int64_t value, int slot) {
++stdev[slot][0];
stdev[slot][1] += value;
stdev[slot][2] += value * value;
}
void dbg_correl_of(int64_t value1, int64_t value2, int slot) {
++correl[slot][0];
correl[slot][1] += value1;
correl[slot][2] += value1 * value1;
correl[slot][3] += value2;
correl[slot][4] += value2 * value2;
correl[slot][5] += value1 * value2;
}
void dbg_print() {
if (hits[0])
cerr << "Total " << hits[0] << " Hits " << hits[1]
<< " hit rate (%) " << 100 * hits[1] / hits[0] << endl;
int64_t n;
auto E = [&n](int64_t x) { return double(x) / n; };
auto sqr = [](double x) { return x * x; };
if (means[0])
cerr << "Total " << means[0] << " Mean "
<< (double)means[1] / means[0] << endl;
for (int i = 0; i < MaxDebugSlots; ++i)
if ((n = hit[i][0]))
std::cerr << "Hit #" << i
<< ": Total " << n << " Hits " << hit[i][1]
<< " Hit Rate (%) " << 100.0 * E(hit[i][1])
<< std::endl;
for (int i = 0; i < MaxDebugSlots; ++i)
if ((n = mean[i][0]))
{
std::cerr << "Mean #" << i
<< ": Total " << n << " Mean " << E(mean[i][1])
<< std::endl;
}
for (int i = 0; i < MaxDebugSlots; ++i)
if ((n = stdev[i][0]))
{
double r = sqrtl(E(stdev[i][2]) - sqr(E(stdev[i][1])));
std::cerr << "Stdev #" << i
<< ": Total " << n << " Stdev " << r
<< std::endl;
}
for (int i = 0; i < MaxDebugSlots; ++i)
if ((n = correl[i][0]))
{
double r = (E(correl[i][5]) - E(correl[i][1]) * E(correl[i][3]))
/ ( sqrtl(E(correl[i][2]) - sqr(E(correl[i][1])))
* sqrtl(E(correl[i][4]) - sqr(E(correl[i][3]))));
std::cerr << "Correl. #" << i
<< ": Total " << n << " Coefficient " << r
<< std::endl;
}
}
@@ -358,8 +449,10 @@ void* std_aligned_alloc(size_t alignment, size_t size) {
#if defined(POSIXALIGNEDALLOC)
void *mem;
return posix_memalign(&mem, alignment, size) ? nullptr : mem;
#elif defined(_WIN32)
#elif defined(_WIN32) && !defined(_M_ARM) && !defined(_M_ARM64)
return _mm_malloc(size, alignment);
#elif defined(_WIN32)
return _aligned_malloc(size, alignment);
#else
return std::aligned_alloc(alignment, size);
#endif
@@ -369,8 +462,10 @@ void std_aligned_free(void* ptr) {
#if defined(POSIXALIGNEDALLOC)
free(ptr);
#elif defined(_WIN32)
#elif defined(_WIN32) && !defined(_M_ARM) && !defined(_M_ARM64)
_mm_free(ptr);
#elif defined(_WIN32)
_aligned_free(ptr);
#else
free(ptr);
#endif
@@ -380,10 +475,9 @@ void std_aligned_free(void* ptr) {
#if defined(_WIN32)
static void* aligned_large_pages_alloc_windows(size_t allocSize) {
static void* aligned_large_pages_alloc_windows([[maybe_unused]] size_t allocSize) {
#if !defined(_WIN64)
(void)allocSize; // suppress unused-parameter compiler warning
return nullptr;
#else
@@ -395,11 +489,30 @@ static void* aligned_large_pages_alloc_windows(size_t allocSize) {
if (!largePageSize)
return nullptr;
// We need SeLockMemoryPrivilege, so try to enable it for the process
if (!OpenProcessToken(GetCurrentProcess(), TOKEN_ADJUST_PRIVILEGES | TOKEN_QUERY, &hProcessToken))
// Dynamically link OpenProcessToken, LookupPrivilegeValue and AdjustTokenPrivileges
HMODULE hAdvapi32 = GetModuleHandle(TEXT("advapi32.dll"));
if (!hAdvapi32)
hAdvapi32 = LoadLibrary(TEXT("advapi32.dll"));
auto fun6 = (fun6_t)(void(*)())GetProcAddress(hAdvapi32, "OpenProcessToken");
if (!fun6)
return nullptr;
auto fun7 = (fun7_t)(void(*)())GetProcAddress(hAdvapi32, "LookupPrivilegeValueA");
if (!fun7)
return nullptr;
auto fun8 = (fun8_t)(void(*)())GetProcAddress(hAdvapi32, "AdjustTokenPrivileges");
if (!fun8)
return nullptr;
if (LookupPrivilegeValue(NULL, SE_LOCK_MEMORY_NAME, &luid))
// We need SeLockMemoryPrivilege, so try to enable it for the process
if (!fun6( // OpenProcessToken()
GetCurrentProcess(), TOKEN_ADJUST_PRIVILEGES | TOKEN_QUERY, &hProcessToken))
return nullptr;
if (fun7( // LookupPrivilegeValue(nullptr, SE_LOCK_MEMORY_NAME, &luid)
nullptr, "SeLockMemoryPrivilege", &luid))
{
TOKEN_PRIVILEGES tp { };
TOKEN_PRIVILEGES prevTp { };
@@ -411,17 +524,18 @@ static void* aligned_large_pages_alloc_windows(size_t allocSize) {
// Try to enable SeLockMemoryPrivilege. Note that even if AdjustTokenPrivileges() succeeds,
// we still need to query GetLastError() to ensure that the privileges were actually obtained.
if (AdjustTokenPrivileges(
if (fun8( // AdjustTokenPrivileges()
hProcessToken, FALSE, &tp, sizeof(TOKEN_PRIVILEGES), &prevTp, &prevTpLen) &&
GetLastError() == ERROR_SUCCESS)
{
// Round up size to full pages and allocate
allocSize = (allocSize + largePageSize - 1) & ~size_t(largePageSize - 1);
mem = VirtualAlloc(
NULL, allocSize, MEM_RESERVE | MEM_COMMIT | MEM_LARGE_PAGES, PAGE_READWRITE);
nullptr, allocSize, MEM_RESERVE | MEM_COMMIT | MEM_LARGE_PAGES, PAGE_READWRITE);
// Privilege no longer needed, restore previous state
AdjustTokenPrivileges(hProcessToken, FALSE, &prevTp, 0, NULL, NULL);
fun8( // AdjustTokenPrivileges ()
hProcessToken, FALSE, &prevTp, 0, nullptr, nullptr);
}
}
@@ -439,7 +553,7 @@ void* aligned_large_pages_alloc(size_t allocSize) {
// Fall back to regular, page aligned, allocation if necessary
if (!mem)
mem = VirtualAlloc(NULL, allocSize, MEM_RESERVE | MEM_COMMIT, PAGE_READWRITE);
mem = VirtualAlloc(nullptr, allocSize, MEM_RESERVE | MEM_COMMIT, PAGE_READWRITE);
return mem;
}
@@ -503,7 +617,7 @@ void bindThisThread(size_t) {}
/// API and returns the best node id for the thread with index idx. Original
/// code from Texel by Peter Österlund.
int best_node(size_t idx) {
static int best_node(size_t idx) {
int threads = 0;
int nodes = 0;
@@ -512,7 +626,7 @@ int best_node(size_t idx) {
DWORD byteOffset = 0;
// Early exit if the needed API is not available at runtime
HMODULE k32 = GetModuleHandle("Kernel32.dll");
HMODULE k32 = GetModuleHandle(TEXT("Kernel32.dll"));
auto fun1 = (fun1_t)(void(*)())GetProcAddress(k32, "GetLogicalProcessorInformationEx");
if (!fun1)
return -1;
@@ -582,7 +696,7 @@ void bindThisThread(size_t idx) {
return;
// Early exit if the needed API are not available at runtime
HMODULE k32 = GetModuleHandle("Kernel32.dll");
HMODULE k32 = GetModuleHandle(TEXT("Kernel32.dll"));
auto fun2 = (fun2_t)(void(*)())GetProcAddress(k32, "GetNumaNodeProcessorMaskEx");
auto fun3 = (fun3_t)(void(*)())GetProcAddress(k32, "SetThreadGroupAffinity");
auto fun4 = (fun4_t)(void(*)())GetProcAddress(k32, "GetNumaNodeProcessorMask2");
@@ -628,8 +742,7 @@ string argv0; // path+name of the executable binary, as given by argv
string binaryDirectory; // path of the executable directory
string workingDirectory; // path of the working directory
void init(int argc, char* argv[]) {
(void)argc;
void init([[maybe_unused]] int argc, char* argv[]) {
string pathSeparator;
// extract the path+name of the executable binary
@@ -671,30 +784,4 @@ void init(int argc, char* argv[]) {
} // namespace CommandLine
// Returns a string that represents the current time. (Used when learning evaluation functions)
std::string now_string()
{
// Using std::ctime(), localtime() gives a warning that MSVC is not secure.
// This shouldn't happen in the C++ standard, but...
#if defined(_MSC_VER)
// C4996 : 'ctime' : This function or variable may be unsafe.Consider using ctime_s instead.
#pragma warning(disable : 4996)
#endif
auto now = std::chrono::system_clock::now();
auto tp = std::chrono::system_clock::to_time_t(now);
auto result = string(std::ctime(&tp));
// remove line endings if they are included at the end
while (*result.rbegin() == '\n' || (*result.rbegin() == '\r'))
result.pop_back();
return result;
}
void sleep(int ms)
{
std::this_thread::sleep_for(std::chrono::milliseconds(ms));
}
} // namespace Stockfish
+10 -542
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -19,24 +19,18 @@
#ifndef MISC_H_INCLUDED
#define MISC_H_INCLUDED
#include <algorithm>
#include <cassert>
#include <chrono>
#include <functional>
#include <mutex>
#include <ostream>
#include <string>
#include <vector>
#include <iostream>
#include <cstdint>
#include <cmath>
#include <cctype>
#include <sstream>
#include <deque>
#include "types.h"
#define stringify2(x) #x
#define stringify(x) stringify2(x)
namespace Stockfish {
std::string engine_info(bool to_uci = false);
@@ -48,26 +42,13 @@ void std_aligned_free(void* ptr);
void* aligned_large_pages_alloc(size_t size); // memory aligned by page size, min alignment: 4096 bytes
void aligned_large_pages_free(void* mem); // nop if mem == nullptr
void dbg_hit_on(bool b);
void dbg_hit_on(bool c, bool b);
void dbg_mean_of(int v);
void dbg_hit_on(bool cond, int slot = 0);
void dbg_mean_of(int64_t value, int slot = 0);
void dbg_stdev_of(int64_t value, int slot = 0);
void dbg_correl_of(int64_t value1, int64_t value2, int slot = 0);
void dbg_print();
/// Debug macro to write to std::err if NDEBUG flag is set, and do nothing otherwise
#if defined(NDEBUG)
#define debug 1 && std::cerr
#else
#define debug 0 && std::cerr
#endif
inline void hit_any_key() {
#ifndef NDEBUG
debug << "Hit any key to continue..." << std::endl << std::flush;
system("read"); // on Windows, should be system("pause");
#endif
}
typedef std::chrono::milliseconds::rep TimePoint; // A value in milliseconds
using TimePoint = std::chrono::milliseconds::rep; // A value in milliseconds
static_assert(sizeof(TimePoint) == sizeof(int64_t), "TimePoint should be 64 bits");
inline TimePoint now() {
return std::chrono::duration_cast<std::chrono::milliseconds>
@@ -108,299 +89,20 @@ static inline const union { uint32_t i; char c[4]; } Le = { 0x01020304 };
static inline const bool IsLittleEndian = (Le.c[0] == 4);
// RunningAverage : a class to calculate a running average of a series of values.
// For efficiency, all computations are done with integers.
class RunningAverage {
public:
// Reset the running average to rational value p / q
void set(int64_t p, int64_t q)
{ average = p * PERIOD * RESOLUTION / q; }
// Update average with value v
void update(int64_t v)
{ average = RESOLUTION * v + (PERIOD - 1) * average / PERIOD; }
// Test if average is strictly greater than rational a / b
bool is_greater(int64_t a, int64_t b) const
{ return b * average > a * (PERIOD * RESOLUTION); }
int64_t value() const
{ return average / (PERIOD * RESOLUTION); }
private :
static constexpr int64_t PERIOD = 4096;
static constexpr int64_t RESOLUTION = 1024;
int64_t average;
};
template <typename T, std::size_t MaxSize>
class ValueList {
public:
std::size_t size() const { return size_; }
void resize(std::size_t newSize) { size_ = newSize; }
void push_back(const T& value) { values_[size_++] = value; }
T& operator[](std::size_t index) { return values_[index]; }
T* begin() { return values_; }
T* end() { return values_ + size_; }
const T& operator[](std::size_t index) const { return values_[index]; }
const T* begin() const { return values_; }
const T* end() const { return values_ + size_; }
void swap(ValueList& other) {
const std::size_t maxSize = std::max(size_, other.size_);
for (std::size_t i = 0; i < maxSize; ++i) {
std::swap(values_[i], other.values_[i]);
}
std::swap(size_, other.size_);
}
private:
T values_[MaxSize];
std::size_t size_ = 0;
};
// This logger allows printing many parts in a region atomically
// but doesn't block the threads trying to append to other regions.
// Instead if some region tries to pring while other region holds
// the lock the messages are queued to be printed as soon as the
// current region releases the lock.
struct SynchronizedRegionLogger
{
using RegionId = std::uint64_t;
struct Region
{
friend struct SynchronizedRegionLogger;
Region() :
logger(nullptr), region_id(0), is_held(false)
{
}
Region(const Region&) = delete;
Region& operator=(const Region&) = delete;
Region(Region&& other) :
logger(other.logger), region_id(other.region_id), is_held(other.is_held)
{
other.logger = nullptr;
other.is_held = false;
}
Region& operator=(Region&& other) {
if (is_held && logger != nullptr)
{
logger->release_region(region_id);
}
logger = other.logger;
region_id = other.region_id;
is_held = other.is_held;
other.is_held = false;
return *this;
}
~Region() { unlock(); }
void unlock() {
if (is_held) {
is_held = false;
if (logger != nullptr)
logger->release_region(region_id);
}
}
Region& operator << (std::ostream&(*pManip)(std::ostream&)) {
if (logger != nullptr)
logger->write(region_id, pManip);
return *this;
}
template <typename T>
Region& operator << (const T& value) {
if (logger != nullptr)
logger->write(region_id, value);
return *this;
}
private:
SynchronizedRegionLogger* logger;
RegionId region_id;
bool is_held;
Region(SynchronizedRegionLogger& log, RegionId id) :
logger(&log), region_id(id), is_held(true)
{
}
};
private:
struct RegionBookkeeping
{
RegionBookkeeping(RegionId rid) : id(rid), is_held(true) {}
std::vector<std::string> pending_parts;
RegionId id;
bool is_held;
};
RegionId init_next_region()
{
static RegionId next_id = 0;
std::lock_guard lock(mutex);
const auto id = next_id++;
regions.emplace_back(id);
return id;
}
void write(RegionId id, std::ostream&(*pManip)(std::ostream&)) {
std::lock_guard lock(mutex);
if (regions.empty())
return;
if (id == regions.front().id) {
// We can just directly print to the output because
// we are at the front of the region queue.
out << *pManip;
} else {
// We have to schedule the print until previous regions are
// processed
auto* region = find_region_nolock(id);
if (region == nullptr)
return;
std::stringstream ss;
ss << *pManip;
region->pending_parts.emplace_back(std::move(ss).str());
}
}
template <typename T>
void write(RegionId id, const T& value) {
std::lock_guard lock(mutex);
if (regions.empty())
return;
if (id == regions.front().id) {
// We can just directly print to the output because
// we are at the front of the region queue.
out << value;
} else {
// We have to schedule the print until previous regions are
// processed
auto* region = find_region_nolock(id);
if (region == nullptr)
return;
std::stringstream ss;
ss << value;
region->pending_parts.emplace_back(std::move(ss).str());
}
}
std::ostream& out;
std::deque<RegionBookkeeping> regions;
std::mutex mutex;
RegionBookkeeping* find_region_nolock(RegionId id) {
// Linear search because the amount of concurrent regions should be small.
auto it = std::find_if(
regions.begin(),
regions.end(),
[id](const RegionBookkeeping& r) { return r.id == id; });
if (it == regions.end())
return nullptr;
else
return &*it;
}
void release_region(RegionId id) {
std::lock_guard lock(mutex);
auto* region = find_region_nolock(id);
if (region == nullptr)
return;
region->is_held = false;
process_backlog_nolock();
}
void process_backlog_nolock()
{
while(!regions.empty()) {
auto& region = regions.front();
for(auto& part : region.pending_parts) {
out << part;
}
// If the region is still held then we don't
// want to start printing stuff from the next region.
if (region.is_held)
break;
regions.pop_front();
}
}
public:
SynchronizedRegionLogger(std::ostream& s) :
out(s)
{
}
[[nodiscard]] Region new_region() {
const auto id = init_next_region();
return Region(*this, id);
}
};
extern SynchronizedRegionLogger sync_region_cout;
/// sigmoid(t, x0, y0, C, P, Q) implements a sigmoid-like function using only integers,
/// with the following properties:
///
/// - sigmoid is centered in (x0, y0)
/// - sigmoid has amplitude [-P/Q , P/Q] instead of [-1 , +1]
/// - limit is (y0 - P/Q) when t tends to -infinity
/// - limit is (y0 + P/Q) when t tends to +infinity
/// - the slope can be adjusted using C > 0, smaller C giving a steeper sigmoid
/// - the slope of the sigmoid when t = x0 is P/(Q*C)
/// - sigmoid is increasing with t when P > 0 and Q > 0
/// - to get a decreasing sigmoid, change sign of P
/// - mean value of the sigmoid is y0
///
/// Use <https://www.desmos.com/calculator/jhh83sqq92> to draw the sigmoid
inline int64_t sigmoid(int64_t t, int64_t x0,
int64_t y0,
int64_t C,
int64_t P,
int64_t Q)
{
assert(C > 0);
assert(Q != 0);
return y0 + P * (t-x0) / (Q * (std::abs(t-x0) + C)) ;
}
/// xorshift64star Pseudo-Random Number Generator
/// This class is based on original code written and dedicated
@@ -417,19 +119,6 @@ inline int64_t sigmoid(int64_t t, int64_t x0,
/// For further analysis see
/// <http://vigna.di.unimi.it/ftp/papers/xorshift.pdf>
static uint64_t string_hash(const std::string& str)
{
uint64_t h = 525201411107845655ull;
for (auto c : str) {
h ^= static_cast<uint64_t>(c);
h *= 0x5bd1e9955bd1e995ull;
h ^= h >> 47;
}
return h;
}
class PRNG {
uint64_t s;
@@ -441,9 +130,7 @@ class PRNG {
}
public:
PRNG() { set_seed_from_time(); }
PRNG(uint64_t seed) : s(seed) { assert(seed); }
PRNG(const std::string& seed) { set_seed(seed); }
template<typename T> T rand() { return T(rand64()); }
@@ -451,57 +138,11 @@ public:
/// Output values only have 1/8th of their bits set on average.
template<typename T> T sparse_rand()
{ return T(rand64() & rand64() & rand64()); }
// Returns a random number from 0 to n-1. (Not uniform distribution, but this is enough in reality)
uint64_t rand(uint64_t n) { return rand<uint64_t>() % n; }
// Return the random seed used internally.
uint64_t get_seed() const { return s; }
void set_seed(uint64_t seed) { s = seed; }
uint64_t next_random_seed()
{
uint64_t seed = 0;
for(int i = 0; i < 64; ++i)
{
const auto off = rand64() % 64;
seed |= (rand64() & (uint64_t(1) << off)) >> off;
seed <<= 1;
}
return seed;
}
void set_seed_from_time()
{
set_seed(std::chrono::system_clock::now().time_since_epoch().count());
}
void set_seed(const std::string& str)
{
if (str.empty())
{
set_seed_from_time();
}
else if (std::all_of(str.begin(), str.end(), [](char c) { return std::isdigit(c);} )) {
set_seed(std::stoull(str));
}
else
{
set_seed(string_hash(str));
}
}
};
// Display a random seed. (For debugging)
inline std::ostream& operator<<(std::ostream& os, PRNG& prng)
{
os << "PRNG::seed = " << std::hex << prng.get_seed() << std::dec;
return os;
}
inline uint64_t mul_hi64(uint64_t a, uint64_t b) {
#if defined(__GNUC__) && defined(IS_64BIT)
__extension__ typedef unsigned __int128 uint128;
__extension__ using uint128 = unsigned __int128;
return ((uint128)a * (uint128)b) >> 64;
#else
uint64_t aL = (uint32_t)a, aH = a >> 32;
@@ -513,74 +154,6 @@ inline uint64_t mul_hi64(uint64_t a, uint64_t b) {
#endif
}
// This bitset can be accessed concurrently, provided
// the concurrent accesses are performed on distinct
// instances of underlying type. That means the cuncurrent
// accesses need to be spaced by at least
// bits_per_bucket bits.
// But at least best_concurrent_access_stride bits
// is recommended to prevent false sharing.
template <uint64_t N>
struct LargeBitset
{
private:
constexpr static uint64_t cache_line_size = 64;
public:
using UnderlyingType = uint64_t;
constexpr static uint64_t num_bits = N;
constexpr static uint64_t bits_per_bucket = 8 * sizeof(uint64_t);
constexpr static uint64_t num_buckets = (num_bits + bits_per_bucket - 1) / bits_per_bucket;
constexpr static uint64_t best_concurrent_access_stride = 8 * cache_line_size;
LargeBitset()
{
std::fill(std::begin(bits), std::end(bits), 0);
}
void set(uint64_t idx)
{
const uint64_t bucket = idx / bits_per_bucket;
const uint64_t bit = uint64_t(1) << (idx % bits_per_bucket);
bits[bucket] |= bit;
}
bool test(uint64_t idx) const
{
const uint64_t bucket = idx / bits_per_bucket;
const uint64_t bit = uint64_t(1) << (idx % bits_per_bucket);
return bits[bucket] & bit;
}
uint64_t count() const
{
uint64_t c = 0;
uint64_t i = 0;
for (; i < num_buckets - 3; i += 4)
{
uint64_t c0 = popcount(bits[i+0]);
uint64_t c1 = popcount(bits[i+1]);
uint64_t c2 = popcount(bits[i+2]);
uint64_t c3 = popcount(bits[i+3]);
c0 += c1;
c2 += c3;
c += c0 + c2;
}
for (; i < num_buckets; ++i)
{
c += popcount(bits[i]);
}
return c;
}
private:
alignas(cache_line_size) UnderlyingType bits[num_buckets];
};
/// Under Windows it is not possible for a process to run on more than one
/// logical processor group. This usually means to be limited to use max 64
/// cores. To overcome this, some special platform specific API should be
@@ -591,111 +164,6 @@ namespace WinProcGroup {
void bindThisThread(size_t idx);
}
// Returns a string that represents the current time. (Used for log output when learning evaluation function)
std::string now_string();
void sleep(int ms);
namespace Algo {
// Fisher-Yates
template <typename Rng, typename T>
void shuffle(std::vector<T>& buf, Rng&& prng)
{
const auto size = buf.size();
for (uint64_t i = 0; i < size; ++i)
std::swap(buf[i], buf[prng.rand(size - i) + i]);
}
// split the string
inline std::vector<std::string> split(const std::string& input, char delimiter) {
std::istringstream stream(input);
std::string field;
std::vector<std::string> fields;
while (std::getline(stream, field, delimiter)) {
fields.push_back(field);
}
return fields;
}
}
// --------------------
// Path
// --------------------
// Something like Path class in C#. File name manipulation.
// Match with the C# method name.
struct Path
{
// Combine the path name and file name and return it.
// If the folder name is not an empty string, append it if there is no'/' or'\\' at the end.
static std::string combine(const std::string& folder, const std::string& filename)
{
if (folder.length() >= 1 && *folder.rbegin() != '/' && *folder.rbegin() != '\\')
return folder + "/" + filename;
return folder + filename;
}
// Get the file name part (excluding the folder name) from the full path expression.
static std::string get_file_name(const std::string& path)
{
// I don't know which "\" or "/" is used.
auto path_index1 = path.find_last_of("\\") + 1;
auto path_index2 = path.find_last_of("/") + 1;
auto path_index = std::max(path_index1, path_index2);
return path.substr(path_index);
}
};
// It is ignored when new even though alignas is specified & because it is ignored when the STL container allocates memory,
// A custom allocator used for that.
template <typename T>
class AlignedAllocator {
public:
using value_type = T;
AlignedAllocator() {}
AlignedAllocator(const AlignedAllocator&) {}
AlignedAllocator(AlignedAllocator&&) {}
template <typename U> AlignedAllocator(const AlignedAllocator<U>&) {}
T* allocate(std::size_t n) { return (T*)std_aligned_alloc(alignof(T), n * sizeof(T)); }
void deallocate(T* p, std::size_t ) { std_aligned_free(p); }
};
template <typename T>
class CacheLineAlignedAllocator {
public:
using value_type = T;
constexpr static uint64_t cache_line_size = 64;
CacheLineAlignedAllocator() {}
CacheLineAlignedAllocator(const CacheLineAlignedAllocator&) {}
CacheLineAlignedAllocator(CacheLineAlignedAllocator&&) {}
template <typename U> CacheLineAlignedAllocator(const CacheLineAlignedAllocator<U>&) {}
T* allocate(std::size_t n) { return (T*)std_aligned_alloc(cache_line_size, n * sizeof(T)); }
void deallocate(T* p, std::size_t) { std_aligned_free(p); }
};
// --------------------
// Dependency Wrapper
// --------------------
namespace Dependency
{
// In the Linux environment, if you getline() the text file is'\r\n'
// Since'\r' remains at the end, write a wrapper to remove this'\r'.
// So when calling getline() on fstream,
// just write getline() instead of std::getline() and use this function.
extern bool getline(std::ifstream& fs, std::string& s);
}
namespace CommandLine {
void init(int argc, char* argv[]);
+22 -14
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -25,13 +25,21 @@ namespace Stockfish {
namespace {
template<GenType Type, Direction D>
ExtMove* make_promotions(ExtMove* moveList, Square to) {
template<GenType Type, Direction D, bool Enemy>
ExtMove* make_promotions(ExtMove* moveList, [[maybe_unused]] Square to) {
if (Type == CAPTURES || Type == EVASIONS || Type == NON_EVASIONS)
if constexpr (Type == CAPTURES || Type == EVASIONS || Type == NON_EVASIONS)
{
*moveList++ = make<PROMOTION>(to - D, to, QUEEN);
if constexpr (Enemy && Type == CAPTURES)
{
*moveList++ = make<PROMOTION>(to - D, to, ROOK);
*moveList++ = make<PROMOTION>(to - D, to, BISHOP);
*moveList++ = make<PROMOTION>(to - D, to, KNIGHT);
}
}
if (Type == QUIETS || Type == EVASIONS || Type == NON_EVASIONS)
if constexpr ((Type == QUIETS && !Enemy) || Type == EVASIONS || Type == NON_EVASIONS)
{
*moveList++ = make<PROMOTION>(to - D, to, ROOK);
*moveList++ = make<PROMOTION>(to - D, to, BISHOP);
@@ -60,18 +68,18 @@ namespace {
Bitboard pawnsNotOn7 = pos.pieces(Us, PAWN) & ~TRank7BB;
// Single and double pawn pushes, no promotions
if (Type != CAPTURES)
if constexpr (Type != CAPTURES)
{
Bitboard b1 = shift<Up>(pawnsNotOn7) & emptySquares;
Bitboard b2 = shift<Up>(b1 & TRank3BB) & emptySquares;
if (Type == EVASIONS) // Consider only blocking squares
if constexpr (Type == EVASIONS) // Consider only blocking squares
{
b1 &= target;
b2 &= target;
}
if (Type == QUIET_CHECKS)
if constexpr (Type == QUIET_CHECKS)
{
// To make a quiet check, you either make a direct check by pushing a pawn
// or push a blocker pawn that is not on the same file as the enemy king.
@@ -102,21 +110,21 @@ namespace {
Bitboard b2 = shift<UpLeft >(pawnsOn7) & enemies;
Bitboard b3 = shift<Up >(pawnsOn7) & emptySquares;
if (Type == EVASIONS)
if constexpr (Type == EVASIONS)
b3 &= target;
while (b1)
moveList = make_promotions<Type, UpRight>(moveList, pop_lsb(b1));
moveList = make_promotions<Type, UpRight, true>(moveList, pop_lsb(b1));
while (b2)
moveList = make_promotions<Type, UpLeft >(moveList, pop_lsb(b2));
moveList = make_promotions<Type, UpLeft, true>(moveList, pop_lsb(b2));
while (b3)
moveList = make_promotions<Type, Up >(moveList, pop_lsb(b3));
moveList = make_promotions<Type, Up, false>(moveList, pop_lsb(b3));
}
// Standard and en passant captures
if (Type == CAPTURES || Type == EVASIONS || Type == NON_EVASIONS)
if constexpr (Type == CAPTURES || Type == EVASIONS || Type == NON_EVASIONS)
{
Bitboard b1 = shift<UpRight>(pawnsNotOn7) & enemies;
Bitboard b2 = shift<UpLeft >(pawnsNotOn7) & enemies;
@@ -264,7 +272,7 @@ ExtMove* generate<LEGAL>(const Position& pos, ExtMove* moveList) {
moveList = pos.checkers() ? generate<EVASIONS >(pos, moveList)
: generate<NON_EVASIONS>(pos, moveList);
while (cur != moveList)
if ( ((pinned && pinned & from_sq(*cur)) || from_sq(*cur) == ksq || type_of(*cur) == EN_PASSANT)
if ( ((pinned & from_sq(*cur)) || from_sq(*cur) == ksq || type_of(*cur) == EN_PASSANT)
&& !pos.legal(*cur))
*cur = (--moveList)->move;
else
+1 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -68,9 +68,6 @@ struct MoveList {
return std::find(begin(), end(), move) != end();
}
// returns the i th element
const ExtMove at(size_t i) const { assert(0 <= i && i < size()); return begin()[i]; }
private:
ExtMove moveList[MAX_MOVES], *last;
};
+26 -37
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -82,82 +82,71 @@ MovePicker::MovePicker(const Position& p, Move ttm, Depth d, const ButterflyHist
stage = (pos.checkers() ? EVASION_TT : QSEARCH_TT) +
!( ttm
&& (pos.checkers() || depth > DEPTH_QS_RECAPTURES || to_sq(ttm) == recaptureSquare)
&& pos.pseudo_legal(ttm));
}
/// MovePicker constructor for ProbCut: we generate captures with SEE greater
/// than or equal to the given threshold.
MovePicker::MovePicker(const Position& p, Move ttm, Value th, Depth d, const CapturePieceToHistory* cph)
: pos(p), captureHistory(cph), ttMove(ttm), threshold(th), depth(d)
MovePicker::MovePicker(const Position& p, Move ttm, Value th, const CapturePieceToHistory* cph)
: pos(p), captureHistory(cph), ttMove(ttm), threshold(th)
{
assert(!pos.checkers());
stage = PROBCUT_TT + !(ttm && pos.capture(ttm)
stage = PROBCUT_TT + !(ttm && pos.capture_stage(ttm)
&& pos.pseudo_legal(ttm)
&& pos.see_ge(ttm, threshold));
}
/// MovePicker::score() assigns a numerical value to each move in a list, used
/// for sorting. Captures are ordered by Most Valuable Victim (MVV), preferring
/// captures with a good history. Quiets moves are ordered using the histories.
/// captures with a good history. Quiets moves are ordered using the history tables.
template<GenType Type>
void MovePicker::score() {
static_assert(Type == CAPTURES || Type == QUIETS || Type == EVASIONS, "Wrong type");
Bitboard threatened, threatenedByPawn, threatenedByMinor, threatenedByRook;
[[maybe_unused]] Bitboard threatenedByPawn, threatenedByMinor, threatenedByRook, threatenedPieces;
if constexpr (Type == QUIETS)
{
Color us = pos.side_to_move();
// squares threatened by pawns
threatenedByPawn = pos.attacks_by<PAWN>(~us);
// squares threatened by minors or pawns
threatenedByMinor = pos.attacks_by<KNIGHT>(~us) | pos.attacks_by<BISHOP>(~us) | threatenedByPawn;
// squares threatened by rooks, minors or pawns
threatenedByRook = pos.attacks_by<ROOK>(~us) | threatenedByMinor;
// pieces threatened by pieces of lesser material value
threatened = (pos.pieces(us, QUEEN) & threatenedByRook)
| (pos.pieces(us, ROOK) & threatenedByMinor)
| (pos.pieces(us, KNIGHT, BISHOP) & threatenedByPawn);
}
else
{
// Silence unused variable warnings
(void) threatened;
(void) threatenedByPawn;
(void) threatenedByMinor;
(void) threatenedByRook;
// Pieces threatened by pieces of lesser material value
threatenedPieces = (pos.pieces(us, QUEEN) & threatenedByRook)
| (pos.pieces(us, ROOK) & threatenedByMinor)
| (pos.pieces(us, KNIGHT, BISHOP) & threatenedByPawn);
}
for (auto& m : *this)
if constexpr (Type == CAPTURES)
m.value = 6 * int(PieceValue[MG][pos.piece_on(to_sq(m))])
+ (*captureHistory)[pos.moved_piece(m)][to_sq(m)][type_of(pos.piece_on(to_sq(m)))];
m.value = (7 * int(PieceValue[MG][pos.piece_on(to_sq(m))])
+ (*captureHistory)[pos.moved_piece(m)][to_sq(m)][type_of(pos.piece_on(to_sq(m)))]) / 16;
else if constexpr (Type == QUIETS)
m.value = (*mainHistory)[pos.side_to_move()][from_to(m)]
m.value = 2 * (*mainHistory)[pos.side_to_move()][from_to(m)]
+ 2 * (*continuationHistory[0])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[1])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[3])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[5])[pos.moved_piece(m)][to_sq(m)]
+ (threatened & from_sq(m) ?
+ (threatenedPieces & from_sq(m) ?
(type_of(pos.moved_piece(m)) == QUEEN && !(to_sq(m) & threatenedByRook) ? 50000
: type_of(pos.moved_piece(m)) == ROOK && !(to_sq(m) & threatenedByMinor) ? 25000
: !(to_sq(m) & threatenedByPawn) ? 15000
: 0)
: 0);
: 0)
+ bool(pos.check_squares(type_of(pos.moved_piece(m))) & to_sq(m)) * 16384;
else // Type == EVASIONS
{
if (pos.capture(m))
if (pos.capture_stage(m))
m.value = PieceValue[MG][pos.piece_on(to_sq(m))]
- Value(type_of(pos.moved_piece(m)));
- Value(type_of(pos.moved_piece(m)))
+ (1 << 28);
else
m.value = (*mainHistory)[pos.side_to_move()][from_to(m)]
+ 2 * (*continuationHistory[0])[pos.moved_piece(m)][to_sq(m)]
- (1 << 28);
m.value = (*mainHistory)[pos.side_to_move()][from_to(m)]
+ (*continuationHistory[0])[pos.moved_piece(m)][to_sq(m)];
}
}
@@ -168,7 +157,7 @@ Move MovePicker::select(Pred filter) {
while (cur < endMoves)
{
if (T == Best)
if constexpr (T == Best)
std::swap(*cur, *std::max_element(cur, endMoves));
if (*cur != ttMove && filter())
@@ -201,13 +190,13 @@ top:
endMoves = generate<CAPTURES>(pos, cur);
score<CAPTURES>();
partial_insertion_sort(cur, endMoves, -3000 * depth);
partial_insertion_sort(cur, endMoves, std::numeric_limits<int>::min());
++stage;
goto top;
case GOOD_CAPTURE:
if (select<Next>([&](){
return pos.see_ge(*cur, Value(-69 * cur->value / 1024)) ?
return pos.see_ge(*cur, Value(-cur->value)) ?
// Move losing capture to endBadCaptures to be tried later
true : (*endBadCaptures++ = *cur, false); }))
return *(cur - 1);
@@ -226,7 +215,7 @@ top:
case REFUTATION:
if (select<Next>([&](){ return *cur != MOVE_NONE
&& !pos.capture(*cur)
&& !pos.capture_stage(*cur)
&& pos.pseudo_legal(*cur); }))
return *(cur - 1);
++stage;
+11 -9
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -62,14 +62,14 @@ public:
template <typename T, int D, int Size, int... Sizes>
struct Stats : public std::array<Stats<T, D, Sizes...>, Size>
{
typedef Stats<T, D, Size, Sizes...> stats;
using stats = Stats<T, D, Size, Sizes...>;
void fill(const T& v) {
// For standard-layout 'this' points to first struct member
assert(std::is_standard_layout<stats>::value);
typedef StatsEntry<T, D> entry;
using entry = StatsEntry<T, D>;
entry* p = reinterpret_cast<entry*>(this);
std::fill(p, p + sizeof(*this) / sizeof(entry), v);
}
@@ -86,22 +86,24 @@ enum StatsType { NoCaptures, Captures };
/// unsuccessful during the current search, and is used for reduction and move
/// ordering decisions. It uses 2 tables (one for each color) indexed by
/// the move's from and to squares, see www.chessprogramming.org/Butterfly_Boards
typedef Stats<int16_t, 14365, COLOR_NB, int(SQUARE_NB) * int(SQUARE_NB)> ButterflyHistory;
/// (~11 elo)
using ButterflyHistory = Stats<int16_t, 7183, COLOR_NB, int(SQUARE_NB) * int(SQUARE_NB)>;
/// CounterMoveHistory stores counter moves indexed by [piece][to] of the previous
/// move, see www.chessprogramming.org/Countermove_Heuristic
typedef Stats<Move, NOT_USED, PIECE_NB, SQUARE_NB> CounterMoveHistory;
using CounterMoveHistory = Stats<Move, NOT_USED, PIECE_NB, SQUARE_NB>;
/// CapturePieceToHistory is addressed by a move's [piece][to][captured piece type]
typedef Stats<int16_t, 10692, PIECE_NB, SQUARE_NB, PIECE_TYPE_NB> CapturePieceToHistory;
using CapturePieceToHistory = Stats<int16_t, 10692, PIECE_NB, SQUARE_NB, PIECE_TYPE_NB>;
/// PieceToHistory is like ButterflyHistory but is addressed by a move's [piece][to]
typedef Stats<int16_t, 29952, PIECE_NB, SQUARE_NB> PieceToHistory;
using PieceToHistory = Stats<int16_t, 29952, PIECE_NB, SQUARE_NB>;
/// ContinuationHistory is the combined history of a given pair of moves, usually
/// the current one given a previous one. The nested history table is based on
/// PieceToHistory instead of ButterflyBoards.
typedef Stats<PieceToHistory, NOT_USED, PIECE_NB, SQUARE_NB> ContinuationHistory;
/// (~63 elo)
using ContinuationHistory = Stats<PieceToHistory, NOT_USED, PIECE_NB, SQUARE_NB>;
/// MovePicker class is used to pick one pseudo-legal move at a time from the
@@ -126,7 +128,7 @@ public:
const CapturePieceToHistory*,
const PieceToHistory**,
Square);
MovePicker(const Position&, Move, Value, Depth, const CapturePieceToHistory*);
MovePicker(const Position&, Move, Value, const CapturePieceToHistory*);
Move next_move(bool skipQuiets = false);
private:
+34 -32
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -18,15 +18,15 @@
// Code for calculating NNUE evaluation function
#include <fstream>
#include <iomanip>
#include <iostream>
#include <set>
#include <sstream>
#include <iomanip>
#include <fstream>
#include <string_view>
#include "../evaluate.h"
#include "../position.h"
#include "../misc.h"
#include "../uci.h"
#include "../types.h"
@@ -83,7 +83,7 @@ namespace Stockfish::Eval::NNUE {
} // namespace Detail
// Initialize the evaluation function parameters
void initialize() {
static void initialize() {
Detail::initialize(featureTransformer);
for (std::size_t i = 0; i < LayerStacks; ++i)
@@ -91,7 +91,7 @@ namespace Stockfish::Eval::NNUE {
}
// Read network header
bool read_header(std::istream& stream, std::uint32_t* hashValue, std::string* desc)
static bool read_header(std::istream& stream, std::uint32_t* hashValue, std::string* desc)
{
std::uint32_t version, size;
@@ -105,7 +105,7 @@ namespace Stockfish::Eval::NNUE {
}
// Write network header
bool write_header(std::ostream& stream, std::uint32_t hashValue, const std::string& desc)
static bool write_header(std::ostream& stream, std::uint32_t hashValue, const std::string& desc)
{
write_little_endian<std::uint32_t>(stream, Version);
write_little_endian<std::uint32_t>(stream, hashValue);
@@ -115,7 +115,7 @@ namespace Stockfish::Eval::NNUE {
}
// Read network parameters
bool read_parameters(std::istream& stream) {
static bool read_parameters(std::istream& stream) {
std::uint32_t hashValue;
if (!read_header(stream, &hashValue, &netDescription)) return false;
@@ -127,7 +127,7 @@ namespace Stockfish::Eval::NNUE {
}
// Write network parameters
bool write_parameters(std::ostream& stream) {
static bool write_parameters(std::ostream& stream) {
if (!write_header(stream, HashValue, netDescription)) return false;
if (!Detail::write_parameters(stream, *featureTransformer)) return false;
@@ -136,14 +136,19 @@ namespace Stockfish::Eval::NNUE {
return (bool)stream;
}
void hint_common_parent_position(const Position& pos) {
if (Eval::useNNUE)
featureTransformer->hint_common_access(pos);
}
// Evaluation function. Perform differential calculation.
Value evaluate(const Position& pos, bool adjusted) {
Value evaluate(const Position& pos, bool adjusted, int* complexity) {
// We manually align the arrays on the stack because with gcc < 9.3
// overaligning stack variables with alignas() doesn't work correctly.
constexpr uint64_t alignment = CacheLineSize;
int delta = 10 - pos.non_pawn_material() / 1515;
constexpr int delta = 24;
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
TransformedFeatureType transformedFeaturesUnaligned[
@@ -161,9 +166,12 @@ namespace Stockfish::Eval::NNUE {
const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);
if (complexity)
*complexity = abs(psqt - positional) / OutputScale;
// Give more value to positional evaluation when adjusted flag is set
if (adjusted)
return static_cast<Value>(((128 - delta) * psqt + (128 + delta) * positional) / 128 / OutputScale);
return static_cast<Value>(((1024 - delta) * psqt + (1024 + delta) * positional) / (1024 * OutputScale));
else
return static_cast<Value>((psqt + positional) / OutputScale);
}
@@ -208,7 +216,7 @@ namespace Stockfish::Eval::NNUE {
return t;
}
static const std::string PieceToChar(" PNBRQK pnbrqk");
constexpr std::string_view PieceToChar(" PNBRQK pnbrqk");
// format_cp_compact() converts a Value into (centi)pawns and writes it in a buffer.
@@ -217,7 +225,7 @@ namespace Stockfish::Eval::NNUE {
buffer[0] = (v < 0 ? '-' : v > 0 ? '+' : ' ');
int cp = std::abs(100 * v / PawnValueEg);
int cp = std::abs(100 * v / UCI::NormalizeToPawnValue);
if (cp >= 10000)
{
buffer[1] = '0' + cp / 10000; cp %= 10000;
@@ -242,14 +250,15 @@ namespace Stockfish::Eval::NNUE {
}
// format_cp_aligned_dot() converts a Value into (centi)pawns and writes it in a buffer,
// always keeping two decimals. The buffer must have capacity for at least 7 chars.
static void format_cp_aligned_dot(Value v, char* buffer) {
// format_cp_aligned_dot() converts a Value into (centi)pawns, always keeping two decimals.
static void format_cp_aligned_dot(Value v, std::stringstream &stream) {
const double cp = 1.0 * std::abs(int(v)) / UCI::NormalizeToPawnValue;
buffer[0] = (v < 0 ? '-' : v > 0 ? '+' : ' ');
double cp = 1.0 * std::abs(int(v)) / PawnValueEg;
sprintf(&buffer[1], "%6.2f", cp);
stream << (v < 0 ? '-' : v > 0 ? '+' : ' ')
<< std::setiosflags(std::ios::fixed)
<< std::setw(6)
<< std::setprecision(2)
<< cp;
}
@@ -329,17 +338,10 @@ namespace Stockfish::Eval::NNUE {
for (std::size_t bucket = 0; bucket < LayerStacks; ++bucket)
{
char buffer[3][8];
std::memset(buffer, '\0', sizeof(buffer));
format_cp_aligned_dot(t.psqt[bucket], buffer[0]);
format_cp_aligned_dot(t.positional[bucket], buffer[1]);
format_cp_aligned_dot(t.psqt[bucket] + t.positional[bucket], buffer[2]);
ss << "| " << bucket << " "
<< " | " << buffer[0] << " "
<< " | " << buffer[1] << " "
<< " | " << buffer[2] << " "
ss << "| " << bucket << " ";
ss << " | "; format_cp_aligned_dot(t.psqt[bucket], ss); ss << " "
<< " | "; format_cp_aligned_dot(t.positional[bucket], ss); ss << " "
<< " | "; format_cp_aligned_dot(t.psqt[bucket] + t.positional[bucket], ss); ss << " "
<< " |";
if (bucket == t.correctBucket)
ss << " <-- this bucket is used";
+10 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -31,6 +31,7 @@ namespace Stockfish::Eval::NNUE {
constexpr std::uint32_t HashValue =
FeatureTransformer::get_hash_value() ^ Network::get_hash_value();
// Deleter for automating release of memory area
template <typename T>
struct AlignedDeleter {
@@ -54,6 +55,14 @@ namespace Stockfish::Eval::NNUE {
template <typename T>
using LargePagePtr = std::unique_ptr<T, LargePageDeleter<T>>;
std::string trace(Position& pos);
Value evaluate(const Position& pos, bool adjusted = false, int* complexity = nullptr);
void hint_common_parent_position(const Position& pos);
bool load_eval(std::string name, std::istream& stream);
bool save_eval(std::ostream& stream);
bool save_eval(const std::optional<std::string>& filename);
} // namespace Stockfish::Eval::NNUE
#endif // #ifndef NNUE_EVALUATE_NNUE_H_INCLUDED
+17 -16
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -24,50 +24,51 @@
namespace Stockfish::Eval::NNUE::Features {
// Orient a square according to perspective (rotates by 180 for black)
inline Square HalfKAv2_hm::orient(Color perspective, Square s, Square ksq) {
return Square(int(s) ^ (bool(perspective) * SQ_A8) ^ ((file_of(ksq) < FILE_E) * SQ_H1));
}
// Index of a feature for a given king position and another piece on some square
inline IndexType HalfKAv2_hm::make_index(Color perspective, Square s, Piece pc, Square ksq) {
Square o_ksq = orient(perspective, ksq, ksq);
return IndexType(orient(perspective, s, ksq) + PieceSquareIndex[perspective][pc] + PS_NB * KingBuckets[o_ksq]);
template<Color Perspective>
inline IndexType HalfKAv2_hm::make_index(Square s, Piece pc, Square ksq) {
return IndexType((int(s) ^ OrientTBL[Perspective][ksq]) + PieceSquareIndex[Perspective][pc] + KingBuckets[Perspective][ksq]);
}
// Get a list of indices for active features
template<Color Perspective>
void HalfKAv2_hm::append_active_indices(
const Position& pos,
Color perspective,
IndexList& active
) {
Square ksq = pos.square<KING>(perspective);
Square ksq = pos.square<KING>(Perspective);
Bitboard bb = pos.pieces();
while (bb)
{
Square s = pop_lsb(bb);
active.push_back(make_index(perspective, s, pos.piece_on(s), ksq));
active.push_back(make_index<Perspective>(s, pos.piece_on(s), ksq));
}
}
// Explicit template instantiations
template void HalfKAv2_hm::append_active_indices<WHITE>(const Position& pos, IndexList& active);
template void HalfKAv2_hm::append_active_indices<BLACK>(const Position& pos, IndexList& active);
// append_changed_indices() : get a list of indices for recently changed features
template<Color Perspective>
void HalfKAv2_hm::append_changed_indices(
Square ksq,
const DirtyPiece& dp,
Color perspective,
IndexList& removed,
IndexList& added
) {
for (int i = 0; i < dp.dirty_num; ++i) {
if (dp.from[i] != SQ_NONE)
removed.push_back(make_index(perspective, dp.from[i], dp.piece[i], ksq));
removed.push_back(make_index<Perspective>(dp.from[i], dp.piece[i], ksq));
if (dp.to[i] != SQ_NONE)
added.push_back(make_index(perspective, dp.to[i], dp.piece[i], ksq));
added.push_back(make_index<Perspective>(dp.to[i], dp.piece[i], ksq));
}
}
// Explicit template instantiations
template void HalfKAv2_hm::append_changed_indices<WHITE>(Square ksq, const DirtyPiece& dp, IndexList& removed, IndexList& added);
template void HalfKAv2_hm::append_changed_indices<BLACK>(Square ksq, const DirtyPiece& dp, IndexList& removed, IndexList& added);
int HalfKAv2_hm::update_cost(const StateInfo* st) {
return st->dirtyPiece.dirty_num;
}
+46 -18
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -49,8 +49,8 @@ namespace Stockfish::Eval::NNUE::Features {
PS_B_ROOK = 7 * SQUARE_NB,
PS_W_QUEEN = 8 * SQUARE_NB,
PS_B_QUEEN = 9 * SQUARE_NB,
PS_KING = 10 * SQUARE_NB,
PS_NB = 11 * SQUARE_NB
PS_KING = 10 * SQUARE_NB,
PS_NB = 11 * SQUARE_NB
};
static constexpr IndexType PieceSquareIndex[COLOR_NB][PIECE_NB] = {
@@ -62,11 +62,9 @@ namespace Stockfish::Eval::NNUE::Features {
PS_NONE, PS_W_PAWN, PS_W_KNIGHT, PS_W_BISHOP, PS_W_ROOK, PS_W_QUEEN, PS_KING, PS_NONE }
};
// Orient a square according to perspective (rotates by 180 for black)
static Square orient(Color perspective, Square s, Square ksq);
// Index of a feature for a given king position and another piece on some square
static IndexType make_index(Color perspective, Square s, Piece pc, Square ksq);
template<Color Perspective>
static IndexType make_index(Square s, Piece pc, Square ksq);
public:
// Feature name
@@ -79,15 +77,45 @@ namespace Stockfish::Eval::NNUE::Features {
static constexpr IndexType Dimensions =
static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_NB) / 2;
static constexpr int KingBuckets[64] = {
-1, -1, -1, -1, 31, 30, 29, 28,
-1, -1, -1, -1, 27, 26, 25, 24,
-1, -1, -1, -1, 23, 22, 21, 20,
-1, -1, -1, -1, 19, 18, 17, 16,
-1, -1, -1, -1, 15, 14, 13, 12,
-1, -1, -1, -1, 11, 10, 9, 8,
-1, -1, -1, -1, 7, 6, 5, 4,
-1, -1, -1, -1, 3, 2, 1, 0
#define B(v) (v * PS_NB)
static constexpr int KingBuckets[COLOR_NB][SQUARE_NB] = {
{ B(28), B(29), B(30), B(31), B(31), B(30), B(29), B(28),
B(24), B(25), B(26), B(27), B(27), B(26), B(25), B(24),
B(20), B(21), B(22), B(23), B(23), B(22), B(21), B(20),
B(16), B(17), B(18), B(19), B(19), B(18), B(17), B(16),
B(12), B(13), B(14), B(15), B(15), B(14), B(13), B(12),
B( 8), B( 9), B(10), B(11), B(11), B(10), B( 9), B( 8),
B( 4), B( 5), B( 6), B( 7), B( 7), B( 6), B( 5), B( 4),
B( 0), B( 1), B( 2), B( 3), B( 3), B( 2), B( 1), B( 0) },
{ B( 0), B( 1), B( 2), B( 3), B( 3), B( 2), B( 1), B( 0),
B( 4), B( 5), B( 6), B( 7), B( 7), B( 6), B( 5), B( 4),
B( 8), B( 9), B(10), B(11), B(11), B(10), B( 9), B( 8),
B(12), B(13), B(14), B(15), B(15), B(14), B(13), B(12),
B(16), B(17), B(18), B(19), B(19), B(18), B(17), B(16),
B(20), B(21), B(22), B(23), B(23), B(22), B(21), B(20),
B(24), B(25), B(26), B(27), B(27), B(26), B(25), B(24),
B(28), B(29), B(30), B(31), B(31), B(30), B(29), B(28) }
};
#undef B
// Orient a square according to perspective (rotates by 180 for black)
static constexpr int OrientTBL[COLOR_NB][SQUARE_NB] = {
{ SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1,
SQ_H1, SQ_H1, SQ_H1, SQ_H1, SQ_A1, SQ_A1, SQ_A1, SQ_A1 },
{ SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8,
SQ_H8, SQ_H8, SQ_H8, SQ_H8, SQ_A8, SQ_A8, SQ_A8, SQ_A8 }
};
// Maximum number of simultaneously active features.
@@ -95,16 +123,16 @@ namespace Stockfish::Eval::NNUE::Features {
using IndexList = ValueList<IndexType, MaxActiveDimensions>;
// Get a list of indices for active features
template<Color Perspective>
static void append_active_indices(
const Position& pos,
Color perspective,
IndexList& active);
// Get a list of indices for recently changed features
template<Color Perspective>
static void append_changed_indices(
Square ksq,
const DirtyPiece& dp,
Color perspective,
IndexList& removed,
IndexList& added
);
+70 -46
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -25,13 +25,13 @@
#include <algorithm>
#include <type_traits>
#include "../nnue_common.h"
#include "../../simd.h"
#include "simd.h"
/*
This file contains the definition for a fully connected layer (aka affine transform).
Two approaches are employed, depending on the sizes of the transform.
Approach 1:
Approach 1 (a specialization for large inputs):
- used when the PaddedInputDimensions >= 128
- uses AVX512 if possible
- processes inputs in batches of 2*InputSimdWidth
@@ -42,9 +42,8 @@
depends on the architecture (the amount of registers)
- accumulate + hadd is used
Approach 2:
Approach 2 (a specialization for small inputs):
- used when the PaddedInputDimensions < 128
- does not use AVX512
- expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32.
- that's why AVX512 is hard to implement
- expected use-case is small layers
@@ -72,6 +71,10 @@ namespace Stockfish::Eval::NNUE::Layers {
const __m64 Zeros = _mm_setzero_si64();
const auto inputVector = reinterpret_cast<const __m64*>(input);
# elif defined(USE_NEON_DOTPROD)
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
# elif defined(USE_NEON)
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
@@ -123,6 +126,14 @@ namespace Stockfish::Eval::NNUE::Layers {
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
output[i] = _mm_cvtsi64_si32(sum);
# elif defined(USE_NEON_DOTPROD)
int32x4_t sum = {biases[i]};
const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
for (IndexType j = 0; j < NumChunks; ++j) {
sum = vdotq_s32(sum, inputVector[j], row[j]);
}
output[i] = vaddvq_s32(sum);
# elif defined(USE_NEON)
int32x4_t sum = {biases[i]};
const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
@@ -151,9 +162,15 @@ namespace Stockfish::Eval::NNUE::Layers {
template <IndexType InDims, IndexType OutDims, typename Enabled = void>
class AffineTransform;
// A specialization for large inputs.
#if defined (USE_AVX512)
constexpr IndexType LargeInputSize = 2 * 64;
#else
constexpr IndexType LargeInputSize = std::numeric_limits<IndexType>::max();
#endif
// A specialization for large inputs
template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= 2*64)>> {
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= LargeInputSize)>> {
public:
// Input/output type
using InputType = std::uint8_t;
@@ -170,36 +187,39 @@ namespace Stockfish::Eval::NNUE::Layers {
using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions >= 128, "Something went wrong. This specialization should not have been chosen.");
static_assert(PaddedInputDimensions >= LargeInputSize, "Something went wrong. This specialization (for large inputs) should not have been chosen.");
#if defined (USE_AVX512)
static constexpr const IndexType InputSimdWidth = 64;
static constexpr const IndexType MaxNumOutputRegs = 16;
static constexpr IndexType InputSimdWidth = 64;
static constexpr IndexType MaxNumOutputRegs = 16;
#elif defined (USE_AVX2)
static constexpr const IndexType InputSimdWidth = 32;
static constexpr const IndexType MaxNumOutputRegs = 8;
static constexpr IndexType InputSimdWidth = 32;
static constexpr IndexType MaxNumOutputRegs = 8;
#elif defined (USE_SSSE3)
static constexpr const IndexType InputSimdWidth = 16;
static constexpr const IndexType MaxNumOutputRegs = 8;
static constexpr IndexType InputSimdWidth = 16;
static constexpr IndexType MaxNumOutputRegs = 8;
#elif defined (USE_NEON_DOTPROD)
static constexpr IndexType InputSimdWidth = 16;
static constexpr IndexType MaxNumOutputRegs = 8;
#elif defined (USE_NEON)
static constexpr const IndexType InputSimdWidth = 8;
static constexpr const IndexType MaxNumOutputRegs = 8;
static constexpr IndexType InputSimdWidth = 8;
static constexpr IndexType MaxNumOutputRegs = 8;
#else
// The fallback implementation will not have permuted weights.
// We define these to avoid a lot of ifdefs later.
static constexpr const IndexType InputSimdWidth = 1;
static constexpr const IndexType MaxNumOutputRegs = 1;
static constexpr IndexType InputSimdWidth = 1;
static constexpr IndexType MaxNumOutputRegs = 1;
#endif
// A big block is a region in the weight matrix of the size [PaddedInputDimensions, NumOutputRegs].
// A small block is a region of size [InputSimdWidth, 1]
static constexpr const IndexType NumOutputRegs = std::min(MaxNumOutputRegs, OutputDimensions);
static constexpr const IndexType SmallBlockSize = InputSimdWidth;
static constexpr const IndexType BigBlockSize = NumOutputRegs * PaddedInputDimensions;
static constexpr const IndexType NumSmallBlocksInBigBlock = BigBlockSize / SmallBlockSize;
static constexpr const IndexType NumSmallBlocksPerOutput = PaddedInputDimensions / SmallBlockSize;
static constexpr const IndexType NumBigBlocks = OutputDimensions / NumOutputRegs;
static constexpr IndexType NumOutputRegs = std::min(MaxNumOutputRegs, OutputDimensions);
static constexpr IndexType SmallBlockSize = InputSimdWidth;
static constexpr IndexType BigBlockSize = NumOutputRegs * PaddedInputDimensions;
static constexpr IndexType NumSmallBlocksInBigBlock = BigBlockSize / SmallBlockSize;
static constexpr IndexType NumSmallBlocksPerOutput = PaddedInputDimensions / SmallBlockSize;
static constexpr IndexType NumBigBlocks = OutputDimensions / NumOutputRegs;
static_assert(OutputDimensions % NumOutputRegs == 0);
@@ -235,8 +255,7 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
read_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
@@ -246,8 +265,7 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
write_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
@@ -286,6 +304,15 @@ namespace Stockfish::Eval::NNUE::Layers {
#define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
#define vec_hadd Simd::m128_hadd
#define vec_haddx4 Simd::m128_haddx4
#elif defined (USE_NEON_DOTPROD)
using acc_vec_t = int32x4_t;
using bias_vec_t = int32x4_t;
using weight_vec_t = int8x16_t;
using in_vec_t = int8x16_t;
#define vec_zero {0}
#define vec_add_dpbusd_32x2 Simd::dotprod_m128_add_dpbusd_epi32x2
#define vec_hadd Simd::neon_m128_hadd
#define vec_haddx4 Simd::neon_m128_haddx4
#elif defined (USE_NEON)
using acc_vec_t = int32x4_t;
using bias_vec_t = int32x4_t;
@@ -368,8 +395,9 @@ namespace Stockfish::Eval::NNUE::Layers {
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
};
// A specialization for small inputs
template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < 2*64)>> {
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < LargeInputSize)>> {
public:
// Input/output type
// Input/output type
@@ -387,12 +415,7 @@ namespace Stockfish::Eval::NNUE::Layers {
using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions < 128, "Something went wrong. This specialization should not have been chosen.");
#if defined (USE_SSSE3)
static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
static constexpr const IndexType InputSimdWidth = SimdWidth;
#endif
static_assert(PaddedInputDimensions < LargeInputSize, "Something went wrong. This specialization (for small inputs) should not have been chosen.");
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
@@ -422,8 +445,7 @@ namespace Stockfish::Eval::NNUE::Layers {
// Read network parameters
bool read_parameters(std::istream& stream) {
for (IndexType i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
read_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
@@ -432,8 +454,7 @@ namespace Stockfish::Eval::NNUE::Layers {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
for (IndexType i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
write_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
@@ -444,29 +465,34 @@ namespace Stockfish::Eval::NNUE::Layers {
const OutputType* propagate(
const InputType* input, OutputType* output) const {
#if defined (USE_AVX2)
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_setzero _mm512_setzero_si512
#define vec_set_32 _mm512_set1_epi32
#define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
#define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
#define vec_hadd Simd::m512_hadd
#elif defined (USE_AVX2)
using vec_t = __m256i;
#define vec_setzero _mm256_setzero_si256
#define vec_set_32 _mm256_set1_epi32
#define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
#define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
#define vec_add_dpbusd_32x4 Simd::m256_add_dpbusd_epi32x4
#define vec_hadd Simd::m256_hadd
#define vec_haddx4 Simd::m256_haddx4
#elif defined (USE_SSSE3)
using vec_t = __m128i;
#define vec_setzero _mm_setzero_si128
#define vec_set_32 _mm_set1_epi32
#define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
#define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
#define vec_add_dpbusd_32x4 Simd::m128_add_dpbusd_epi32x4
#define vec_hadd Simd::m128_hadd
#define vec_haddx4 Simd::m128_haddx4
#endif
#if defined (USE_SSSE3)
const auto inputVector = reinterpret_cast<const vec_t*>(input);
static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
if constexpr (OutputDimensions % OutputSimdWidth == 0)
@@ -512,9 +538,7 @@ namespace Stockfish::Eval::NNUE::Layers {
# undef vec_set_32
# undef vec_add_dpbusd_32
# undef vec_add_dpbusd_32x2
# undef vec_add_dpbusd_32x4
# undef vec_hadd
# undef vec_haddx4
#else
// Use old implementation for the other architectures.
affine_transform_non_ssse3<
@@ -0,0 +1,286 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
// Definition of layer AffineTransformSparseInput of NNUE evaluation function
#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
#define NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
#include <iostream>
#include <algorithm>
#include <array>
#include <type_traits>
#include "../nnue_common.h"
#include "affine_transform.h"
#include "simd.h"
/*
This file contains the definition for a fully connected layer (aka affine transform) with block sparse input.
*/
namespace Stockfish::Eval::NNUE::Layers {
#if defined(__GNUC__) // GCC, Clang, ICC
static inline IndexType lsb_(std::uint32_t b) {
assert(b);
return IndexType(__builtin_ctzl(b));
}
#elif defined(_MSC_VER) // MSVC
static inline IndexType lsb_(std::uint32_t b) {
assert(b);
unsigned long idx;
_BitScanForward(&idx, b);
return (IndexType) idx;
}
#else // Compiler is neither GCC nor MSVC compatible
#error "Compiler not supported."
#endif
#if defined(USE_SSSE3)
alignas(CacheLineSize) static inline const std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = [](){
std::array<std::array<std::uint16_t, 8>, 256> v{};
for (int i = 0; i < 256; ++i)
{
int j = i;
int k = 0;
while(j)
{
const IndexType lsbIndex = lsb_(std::uint32_t(j));
j &= j - 1;
v[i][k] = lsbIndex;
++k;
}
}
return v;
}();
alignas(CacheLineSize) static inline const std::array<unsigned, 256> lookup_count = [](){
std::array<unsigned, 256> v;
for (int i = 0; i < 256; ++i)
{
int j = i;
int k = 0;
while(j)
{
j &= j - 1;
++k;
}
v[i] = k;
}
return v;
}();
// Find indices of nonzero numbers in an int32_t array
template<const IndexType InputDimensions>
void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
#elif defined (USE_AVX2)
using vec_t = __m256i;
#define vec_nnz(a) _mm256_movemask_ps(_mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
#elif defined (USE_SSSE3)
using vec_t = __m128i;
#define vec_nnz(a) _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
#endif
constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t);
// Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8)
constexpr IndexType ChunkSize = std::max<IndexType>(InputSimdWidth, 8);
constexpr IndexType NumChunks = InputDimensions / ChunkSize;
constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth;
constexpr IndexType OutputsPerChunk = ChunkSize / 8;
const auto inputVector = reinterpret_cast<const vec_t*>(input);
IndexType count = 0;
__m128i base = _mm_set1_epi16(0);
__m128i increment = _mm_set1_epi16(8);
for (IndexType i = 0; i < NumChunks; ++i)
{
// bitmask of nonzero values in this chunk
unsigned nnz = 0;
for (IndexType j = 0; j < InputsPerChunk; ++j)
{
const vec_t inputChunk = inputVector[i * InputsPerChunk + j];
nnz |= (unsigned)vec_nnz(inputChunk) << (j * InputSimdWidth);
}
for (IndexType j = 0; j < OutputsPerChunk; ++j)
{
const auto lookup = (nnz >> (j * 8)) & 0xFF;
const auto offsets = _mm_loadu_si128(reinterpret_cast<const __m128i*>(&lookup_indices[lookup]));
_mm_storeu_si128(reinterpret_cast<__m128i*>(out + count), _mm_add_epi16(base, offsets));
count += lookup_count[lookup];
base = _mm_add_epi16(base, increment);
}
}
count_out = count;
}
# undef vec_nnz
#endif
// Sparse input implementation
template <IndexType InDims, IndexType OutDims>
class AffineTransformSparseInput {
public:
// Input/output type
// Input/output type
using InputType = std::uint8_t;
using OutputType = std::int32_t;
// Number of input/output dimensions
static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = OutDims;
static_assert(OutputDimensions % 16 == 0, "Only implemented for OutputDimensions divisible by 16.");
static constexpr IndexType PaddedInputDimensions =
ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
static constexpr IndexType PaddedOutputDimensions =
ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
#if defined (USE_SSSE3)
static constexpr IndexType ChunkSize = 4;
#else
static constexpr IndexType ChunkSize = 1;
#endif
using OutputBuffer = OutputType[PaddedOutputDimensions];
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
std::uint32_t hashValue = 0xCC03DAE4u;
hashValue += OutputDimensions;
hashValue ^= prevHash >> 1;
hashValue ^= prevHash << 31;
return hashValue;
}
static IndexType get_weight_index_scrambled(IndexType i)
{
return
(i / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize +
i / PaddedInputDimensions * ChunkSize +
i % ChunkSize;
}
static IndexType get_weight_index(IndexType i)
{
#if defined (USE_SSSE3)
return get_weight_index_scrambled(i);
#else
return i;
#endif
}
// Read network parameters
bool read_parameters(std::istream& stream) {
read_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
write_little_endian<BiasType>(stream, biases, OutputDimensions);
for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();
}
// Forward propagation
const OutputType* propagate(
const InputType* input, OutputType* output) const {
#if defined (USE_SSSE3)
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_setzero _mm512_setzero_si512
#define vec_set_32 _mm512_set1_epi32
#define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
#elif defined (USE_AVX2)
using vec_t = __m256i;
#define vec_setzero _mm256_setzero_si256
#define vec_set_32 _mm256_set1_epi32
#define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
#elif defined (USE_SSSE3)
using vec_t = __m128i;
#define vec_setzero _mm_setzero_si128
#define vec_set_32 _mm_set1_epi32
#define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
#endif
static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
std::uint16_t nnz[NumChunks];
IndexType count;
const auto input32 = reinterpret_cast<const std::int32_t*>(input);
// Find indices of nonzero 32bit blocks
find_nnz<NumChunks>(input32, nnz, count);
const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
vec_t acc[NumRegs];
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = biasvec[k];
for (IndexType j = 0; j < count; ++j)
{
const auto i = nnz[j];
const vec_t in = vec_set_32(input32[i]);
const auto col = reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * ChunkSize]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_add_dpbusd_32(acc[k], in, col[k]);
}
vec_t* outptr = reinterpret_cast<vec_t*>(output);
for (IndexType k = 0; k < NumRegs; ++k)
outptr[k] = acc[k];
# undef vec_setzero
# undef vec_set_32
# undef vec_add_dpbusd_32
#else
// Use dense implementation for the other architectures.
affine_transform_non_ssse3<
InputDimensions,
PaddedInputDimensions,
OutputDimensions>(output, weights, biases, input);
#endif
return output;
}
private:
using BiasType = OutputType;
using WeightType = std::int8_t;
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
};
} // namespace Stockfish::Eval::NNUE::Layers
#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
View File
+34 -18
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -153,7 +153,7 @@ namespace Stockfish::Simd {
asm(
"vpdpbusd %[b0], %[a0], %[acc]\n\t"
"vpdpbusd %[b1], %[a1], %[acc]\n\t"
: [acc]"+v"(acc)
: [acc]"+&v"(acc)
: [a0]"v"(a0), [b0]"vm"(b0), [a1]"v"(a1), [b1]"vm"(b1)
);
# else
@@ -165,18 +165,19 @@ namespace Stockfish::Simd {
__m512i tmp0 = _mm512_maddubs_epi16(a0, b0);
__m512i tmp1 = _mm512_maddubs_epi16(a1, b1);
asm(
"vpaddsw %[tmp0], %[tmp1], %[tmp0]\n\t"
"vpmaddwd %[tmp0], %[ones], %[tmp0]\n\t"
"vpmaddwd %[tmp1], %[ones], %[tmp1]\n\t"
"vpaddd %[tmp0], %[tmp1], %[tmp0]\n\t"
"vpaddd %[acc], %[tmp0], %[acc]\n\t"
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
: [tmp1]"v"(tmp1), [ones]"v"(_mm512_set1_epi16(1))
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0), [tmp1]"+&v"(tmp1)
: [ones]"v"(_mm512_set1_epi16(1))
);
# else
__m512i product0 = _mm512_maddubs_epi16(a0, b0);
__m512i product1 = _mm512_maddubs_epi16(a1, b1);
product0 = _mm512_adds_epi16(product0, product1);
product0 = _mm512_madd_epi16(product0, _mm512_set1_epi16(1));
acc = _mm512_add_epi32(acc, product0);
product1 = _mm512_madd_epi16(product1, _mm512_set1_epi16(1));
acc = _mm512_add_epi32(acc, _mm512_add_epi32(product0, product1));
# endif
# endif
}
@@ -249,7 +250,7 @@ namespace Stockfish::Simd {
asm(
VNNI_PREFIX "vpdpbusd %[b0], %[a0], %[acc]\n\t"
VNNI_PREFIX "vpdpbusd %[b1], %[a1], %[acc]\n\t"
: [acc]"+v"(acc)
: [acc]"+&v"(acc)
: [a0]"v"(a0), [b0]"vm"(b0), [a1]"v"(a1), [b1]"vm"(b1)
);
# else
@@ -261,18 +262,19 @@ namespace Stockfish::Simd {
__m256i tmp0 = _mm256_maddubs_epi16(a0, b0);
__m256i tmp1 = _mm256_maddubs_epi16(a1, b1);
asm(
"vpaddsw %[tmp0], %[tmp1], %[tmp0]\n\t"
"vpmaddwd %[tmp0], %[ones], %[tmp0]\n\t"
"vpmaddwd %[tmp1], %[ones], %[tmp1]\n\t"
"vpaddd %[tmp0], %[tmp1], %[tmp0]\n\t"
"vpaddd %[acc], %[tmp0], %[acc]\n\t"
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
: [tmp1]"v"(tmp1), [ones]"v"(_mm256_set1_epi16(1))
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0), [tmp1]"+&v"(tmp1)
: [ones]"v"(_mm256_set1_epi16(1))
);
# else
__m256i product0 = _mm256_maddubs_epi16(a0, b0);
__m256i product1 = _mm256_maddubs_epi16(a1, b1);
product0 = _mm256_adds_epi16(product0, product1);
product0 = _mm256_madd_epi16(product0, _mm256_set1_epi16(1));
acc = _mm256_add_epi32(acc, product0);
product1 = _mm256_madd_epi16(product1, _mm256_set1_epi16(1));
acc = _mm256_add_epi32(acc, _mm256_add_epi32(product0, product1));
# endif
# endif
}
@@ -326,23 +328,37 @@ namespace Stockfish::Simd {
__m128i tmp0 = _mm_maddubs_epi16(a0, b0);
__m128i tmp1 = _mm_maddubs_epi16(a1, b1);
asm(
"paddsw %[tmp1], %[tmp0]\n\t"
"pmaddwd %[ones], %[tmp0]\n\t"
"pmaddwd %[ones], %[tmp1]\n\t"
"paddd %[tmp1], %[tmp0]\n\t"
"paddd %[tmp0], %[acc]\n\t"
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
: [tmp1]"v"(tmp1), [ones]"v"(_mm_set1_epi16(1))
: [acc]"+v"(acc), [tmp0]"+&v"(tmp0), [tmp1]"+&v"(tmp1)
: [ones]"v"(_mm_set1_epi16(1))
);
# else
__m128i product0 = _mm_maddubs_epi16(a0, b0);
__m128i product1 = _mm_maddubs_epi16(a1, b1);
product0 = _mm_adds_epi16(product0, product1);
product0 = _mm_madd_epi16(product0, _mm_set1_epi16(1));
acc = _mm_add_epi32(acc, product0);
product1 = _mm_madd_epi16(product1, _mm_set1_epi16(1));
acc = _mm_add_epi32(acc, _mm_add_epi32(product0, product1));
# endif
}
#endif
#if defined (USE_NEON_DOTPROD)
[[maybe_unused]] static void dotprod_m128_add_dpbusd_epi32x2(
int32x4_t& acc,
int8x16_t a0, int8x16_t b0,
int8x16_t a1, int8x16_t b1) {
acc = vdotq_s32(acc, a0, b0);
acc = vdotq_s32(acc, a1, b1);
}
#endif
#if defined (USE_NEON)
[[maybe_unused]] static int neon_m128_reduce_add_epi32(int32x4_t s) {
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -106,7 +106,7 @@ namespace Stockfish::Eval::NNUE::Layers {
for (IndexType i = Start; i < InputDimensions; ++i) {
output[i] = static_cast<OutputType>(
// realy should be /127 but we need to make it fast
// really should be /127 but we need to make it fast
// needs to be accounted for in the trainer
std::max(0ll, std::min(127ll, (((long long)input[i] * input[i]) >> (2 * WeightScaleBits)) / 128)));
}
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+15 -16
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -27,6 +27,7 @@
#include "features/half_ka_v2_hm.h"
#include "layers/affine_transform_sparse_input.h"
#include "layers/affine_transform.h"
#include "layers/clipped_relu.h"
#include "layers/sqr_clipped_relu.h"
@@ -39,7 +40,7 @@ namespace Stockfish::Eval::NNUE {
using FeatureSet = Features::HalfKAv2_hm;
// Number of input feature dimensions after conversion
constexpr IndexType TransformedFeatureDimensions = 1024;
constexpr IndexType TransformedFeatureDimensions = 1536;
constexpr IndexType PSQTBuckets = 8;
constexpr IndexType LayerStacks = 8;
@@ -48,7 +49,7 @@ struct Network
static constexpr int FC_0_OUTPUTS = 15;
static constexpr int FC_1_OUTPUTS = 32;
Layers::AffineTransform<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
Layers::AffineTransformSparseInput<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
Layers::SqrClippedReLU<FC_0_OUTPUTS + 1> ac_sqr_0;
Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
Layers::AffineTransform<FC_0_OUTPUTS * 2, FC_1_OUTPUTS> fc_1;
@@ -72,22 +73,20 @@ struct Network
// Read network parameters
bool read_parameters(std::istream& stream) {
if (!fc_0.read_parameters(stream)) return false;
if (!ac_0.read_parameters(stream)) return false;
if (!fc_1.read_parameters(stream)) return false;
if (!ac_1.read_parameters(stream)) return false;
if (!fc_2.read_parameters(stream)) return false;
return true;
return fc_0.read_parameters(stream)
&& ac_0.read_parameters(stream)
&& fc_1.read_parameters(stream)
&& ac_1.read_parameters(stream)
&& fc_2.read_parameters(stream);
}
// Read network parameters
// Write network parameters
bool write_parameters(std::ostream& stream) const {
if (!fc_0.write_parameters(stream)) return false;
if (!ac_0.write_parameters(stream)) return false;
if (!fc_1.write_parameters(stream)) return false;
if (!ac_1.write_parameters(stream)) return false;
if (!fc_2.write_parameters(stream)) return false;
return true;
return fc_0.write_parameters(stream)
&& ac_0.write_parameters(stream)
&& fc_1.write_parameters(stream)
&& ac_1.write_parameters(stream)
&& fc_2.write_parameters(stream);
}
std::int32_t propagate(const TransformedFeatureType* transformedFeatures)
+78 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -21,8 +21,6 @@
#ifndef NNUE_COMMON_H_INCLUDED
#define NNUE_COMMON_H_INCLUDED
#include "../types.h"
#include <cstring>
#include <iostream>
@@ -59,6 +57,9 @@ namespace Stockfish::Eval::NNUE {
// Size of cache line (in bytes)
constexpr std::size_t CacheLineSize = 64;
constexpr const char Leb128MagicString[] = "COMPRESSED_LEB128";
constexpr const std::size_t Leb128MagicStringSize = sizeof(Leb128MagicString) - 1;
// SIMD width (in bytes)
#if defined(USE_AVX2)
constexpr std::size_t SimdWidth = 32;
@@ -161,6 +162,80 @@ namespace Stockfish::Eval::NNUE {
write_little_endian<IntType>(stream, values[i]);
}
template <typename IntType>
inline void read_leb_128(std::istream& stream, IntType* out, std::size_t count) {
static_assert(std::is_signed_v<IntType>, "Not implemented for unsigned types");
char leb128MagicString[Leb128MagicStringSize];
stream.read(leb128MagicString, Leb128MagicStringSize);
assert(strncmp(Leb128MagicString, leb128MagicString, Leb128MagicStringSize) == 0);
const std::uint32_t BUF_SIZE = 4096;
std::uint8_t buf[BUF_SIZE];
auto bytes_left = read_little_endian<std::uint32_t>(stream);
std::uint32_t buf_pos = BUF_SIZE;
for (std::size_t i = 0; i < count; ++i) {
IntType result = 0;
size_t shift = 0;
do {
if (buf_pos == BUF_SIZE) {
stream.read(reinterpret_cast<char*>(buf), std::min(bytes_left, BUF_SIZE));
buf_pos = 0;
}
std::uint8_t byte = buf[buf_pos++];
--bytes_left;
result |= (byte & 0x7f) << shift;
shift += 7;
if ((byte & 0x80) == 0) {
out[i] = sizeof(IntType) * 8 <= shift || (byte & 0x40) == 0 ? result : result | ~((1 << shift) - 1);
break;
}
} while (shift < sizeof(IntType) * 8);
}
assert(bytes_left == 0);
}
template <typename IntType>
inline void write_leb_128(std::ostream& stream, const IntType* values, std::size_t count) {
static_assert(std::is_signed_v<IntType>, "Not implemented for unsigned types");
stream.write(Leb128MagicString, Leb128MagicStringSize);
std::uint32_t byte_count = 0;
for (std::size_t i = 0; i < count; ++i) {
IntType value = values[i];
std::uint8_t byte;
do {
byte = value & 0x7f;
value >>= 7;
++byte_count;
} while ((byte & 0x40) == 0 ? value != 0 : value != -1);
}
write_little_endian(stream, byte_count);
const std::uint32_t BUF_SIZE = 4096;
std::uint8_t buf[BUF_SIZE];
std::uint32_t buf_pos = 0;
auto flush = [&]() {
if (buf_pos > 0) {
stream.write(reinterpret_cast<char*>(buf), buf_pos);
buf_pos = 0;
}
};
auto write = [&](std::uint8_t byte) {
buf[buf_pos++] = byte;
if (buf_pos == BUF_SIZE) flush();
};
for (std::size_t i = 0; i < count; ++i) {
IntType value = values[i];
while (true) {
std::uint8_t byte = value & 0x7f;
value >>= 7;
if ((byte & 0x40) == 0 ? value == 0 : value == -1) {
write(byte);
break;
}
write(byte | 0x80);
}
}
flush();
}
} // namespace Stockfish::Eval::NNUE
#endif // #ifndef NNUE_COMMON_H_INCLUDED
+285 -202
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -24,10 +24,8 @@
#include "nnue_common.h"
#include "nnue_architecture.h"
#include "../misc.h"
#include "../position.h"
#include <cstring> // std::memset()
#include <utility> // std::pair
namespace Stockfish::Eval::NNUE {
@@ -44,8 +42,8 @@ namespace Stockfish::Eval::NNUE {
"Per feature PSQT values cannot be processed at granularity lower than 8 at a time.");
#ifdef USE_AVX512
typedef __m512i vec_t;
typedef __m256i psqt_vec_t;
using vec_t = __m512i;
using psqt_vec_t = __m256i;
#define vec_load(a) _mm512_load_si512(a)
#define vec_store(a,b) _mm512_store_si512(a,b)
#define vec_add_16(a,b) _mm512_add_epi16(a,b)
@@ -68,8 +66,8 @@ namespace Stockfish::Eval::NNUE {
#define MaxChunkSize 64
#elif USE_AVX2
typedef __m256i vec_t;
typedef __m256i psqt_vec_t;
using vec_t = __m256i;
using psqt_vec_t = __m256i;
#define vec_load(a) _mm256_load_si256(a)
#define vec_store(a,b) _mm256_store_si256(a,b)
#define vec_add_16(a,b) _mm256_add_epi16(a,b)
@@ -92,8 +90,8 @@ namespace Stockfish::Eval::NNUE {
#define MaxChunkSize 32
#elif USE_SSE2
typedef __m128i vec_t;
typedef __m128i psqt_vec_t;
using vec_t = __m128i;
using psqt_vec_t = __m128i;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_epi16(a,b)
@@ -113,8 +111,8 @@ namespace Stockfish::Eval::NNUE {
#define MaxChunkSize 16
#elif USE_MMX
typedef __m64 vec_t;
typedef __m64 psqt_vec_t;
using vec_t = __m64;
using psqt_vec_t = __m64;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_pi16(a,b)
@@ -141,8 +139,8 @@ namespace Stockfish::Eval::NNUE {
#define MaxChunkSize 8
#elif USE_NEON
typedef int16x8_t vec_t;
typedef int32x4_t psqt_vec_t;
using vec_t = int16x8_t;
using psqt_vec_t = int32x4_t;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) vaddq_s16(a,b)
@@ -255,9 +253,9 @@ namespace Stockfish::Eval::NNUE {
// Read network parameters
bool read_parameters(std::istream& stream) {
read_little_endian<BiasType >(stream, biases , HalfDimensions );
read_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
read_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
read_leb_128<BiasType >(stream, biases , HalfDimensions );
read_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
return !stream.fail();
}
@@ -265,17 +263,17 @@ namespace Stockfish::Eval::NNUE {
// Write network parameters
bool write_parameters(std::ostream& stream) const {
write_little_endian<BiasType >(stream, biases , HalfDimensions );
write_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
write_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
write_leb_128<BiasType >(stream, biases , HalfDimensions );
write_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
return !stream.fail();
}
// Convert input features
std::int32_t transform(const Position& pos, OutputType* output, int bucket) const {
update_accumulator(pos, WHITE);
update_accumulator(pos, BLACK);
update_accumulator<WHITE>(pos);
update_accumulator<BLACK>(pos);
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
const auto& accumulation = pos.state()->accumulator.accumulation;
@@ -335,18 +333,41 @@ namespace Stockfish::Eval::NNUE {
#endif
return psqt;
} // end of function transform()
} // end of function transform()
void hint_common_access(const Position& pos) const {
hint_common_access_for_perspective<WHITE>(pos);
hint_common_access_for_perspective<BLACK>(pos);
}
private:
void update_accumulator(const Position& pos, const Color perspective) const {
template<Color Perspective>
[[nodiscard]] std::pair<StateInfo*, StateInfo*> try_find_computed_accumulator(const Position& pos) const {
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
StateInfo *st = pos.state(), *next = nullptr;
int gain = FeatureSet::refresh_cost(pos);
while (st->previous && !st->accumulator.computed[Perspective])
{
// This governs when a full feature refresh is needed and how many
// updates are better than just one full refresh.
if ( FeatureSet::requires_refresh(st, Perspective)
|| (gain -= FeatureSet::update_cost(st) + 1) < 0)
break;
next = st;
st = st->previous;
}
return { st, next };
}
// The size must be enough to contain the largest possible update.
// That might depend on the feature set and generally relies on the
// feature set's update cost calculation to be correct and never
// allow updates with more added/removed features than MaxActiveDimensions.
// NOTE: The parameter states_to_update is an array of position states, ending with nullptr.
// All states must be sequential, that is states_to_update[i] must either be reachable
// by repeatedly applying ->previous from states_to_update[i+1] or states_to_update[i] == nullptr.
// computed_st must be reachable by repeatedly applying ->previous on states_to_update[0], if not nullptr.
template<Color Perspective, size_t N>
void update_accumulator_incremental(const Position& pos, StateInfo* computed_st, StateInfo* states_to_update[N]) const {
static_assert(N > 0);
assert(states_to_update[N-1] == nullptr);
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
@@ -355,232 +376,294 @@ namespace Stockfish::Eval::NNUE {
psqt_vec_t psqt[NumPsqtRegs];
#endif
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
StateInfo *st = pos.state(), *next = nullptr;
int gain = FeatureSet::refresh_cost(pos);
while (st->previous && !st->accumulator.computed[perspective])
if (states_to_update[0] == nullptr)
return;
// Update incrementally going back through states_to_update.
// Gather all features to be updated.
const Square ksq = pos.square<KING>(Perspective);
// The size must be enough to contain the largest possible update.
// That might depend on the feature set and generally relies on the
// feature set's update cost calculation to be correct and never
// allow updates with more added/removed features than MaxActiveDimensions.
FeatureSet::IndexList removed[N-1], added[N-1];
{
// This governs when a full feature refresh is needed and how many
// updates are better than just one full refresh.
if ( FeatureSet::requires_refresh(st, perspective)
|| (gain -= FeatureSet::update_cost(st) + 1) < 0)
break;
next = st;
st = st->previous;
int i = N-2; // last potential state to update. Skip last element because it must be nullptr.
while (states_to_update[i] == nullptr)
--i;
StateInfo *st2 = states_to_update[i];
for (; i >= 0; --i)
{
states_to_update[i]->accumulator.computed[Perspective] = true;
StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
for (; st2 != end_state; st2 = st2->previous)
FeatureSet::append_changed_indices<Perspective>(
ksq, st2->dirtyPiece, removed[i], added[i]);
}
}
if (st->accumulator.computed[perspective])
StateInfo* st = computed_st;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
#ifdef VECTOR
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
if (next == nullptr)
return;
// Load accumulator
auto accTile = reinterpret_cast<vec_t*>(
&st->accumulator.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTile[k]);
// Update incrementally in two steps. First, we update the "next"
// accumulator. Then, we update the current accumulator (pos.state()).
// Gather all features to be updated.
const Square ksq = pos.square<KING>(perspective);
FeatureSet::IndexList removed[2], added[2];
FeatureSet::append_changed_indices(
ksq, next->dirtyPiece, perspective, removed[0], added[0]);
for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous)
FeatureSet::append_changed_indices(
ksq, st2->dirtyPiece, perspective, removed[1], added[1]);
// Mark the accumulators as computed.
next->accumulator.computed[perspective] = true;
pos.state()->accumulator.computed[perspective] = true;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
StateInfo *states_to_update[3] =
{ next, next == pos.state() ? nullptr : pos.state(), nullptr };
#ifdef VECTOR
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
// Load accumulator
auto accTile = reinterpret_cast<vec_t*>(
&st->accumulator.accumulation[perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTile[k]);
for (IndexType i = 0; states_to_update[i]; ++i)
{
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_sub_16(acc[k], column[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
// Store accumulator
accTile = reinterpret_cast<vec_t*>(
&states_to_update[i]->accumulator.accumulation[perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_store(&accTile[k], acc[k]);
}
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
// Load accumulator
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&st->accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_load_psqt(&accTilePsqt[k]);
for (IndexType i = 0; states_to_update[i]; ++i)
{
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
// Store accumulator
accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&states_to_update[i]->accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
}
#else
for (IndexType i = 0; states_to_update[i]; ++i)
{
std::memcpy(states_to_update[i]->accumulator.accumulation[perspective],
st->accumulator.accumulation[perspective],
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
states_to_update[i]->accumulator.psqtAccumulation[perspective][k] = st->accumulator.psqtAccumulation[perspective][k];
st = states_to_update[i];
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
st->accumulator.accumulation[perspective][j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
st->accumulator.psqtAccumulation[perspective][k] -= psqtWeights[index * PSQTBuckets + k];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_sub_16(acc[k], column[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
st->accumulator.accumulation[perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
st->accumulator.psqtAccumulation[perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
}
#endif
}
else
{
// Refresh the accumulator
auto& accumulator = pos.state()->accumulator;
accumulator.computed[perspective] = true;
FeatureSet::IndexList active;
FeatureSet::append_active_indices(pos, perspective, active);
#ifdef VECTOR
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto biasesTile = reinterpret_cast<const vec_t*>(
&biases[j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = biasesTile[k];
for (const auto index : active)
{
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
for (unsigned k = 0; k < NumRegs; ++k)
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
auto accTile = reinterpret_cast<vec_t*>(
&accumulator.accumulation[perspective][j * TileHeight]);
for (unsigned k = 0; k < NumRegs; k++)
// Store accumulator
accTile = reinterpret_cast<vec_t*>(
&states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_store(&accTile[k], acc[k]);
}
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
// Load accumulator
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_load_psqt(&accTilePsqt[k]);
for (IndexType i = 0; states_to_update[i]; ++i)
{
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_zero_psqt();
for (const auto index : active)
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
// Store accumulator
accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&states_to_update[i]->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
}
#else
std::memcpy(accumulator.accumulation[perspective], biases,
#else
for (IndexType i = 0; states_to_update[i]; ++i)
{
std::memcpy(states_to_update[i]->accumulator.accumulation[Perspective],
st->accumulator.accumulation[Perspective],
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
accumulator.psqtAccumulation[perspective][k] = 0;
states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k];
for (const auto index : active)
st = states_to_update[i];
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
accumulator.accumulation[perspective][j] += weights[offset + j];
st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
accumulator.psqtAccumulation[perspective][k] += psqtWeights[index * PSQTBuckets + k];
st->accumulator.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
st->accumulator.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
#endif
}
#endif
#if defined(USE_MMX)
_mm_empty();
#endif
}
template<Color Perspective>
void update_accumulator_refresh(const Position& pos) const {
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
// is defined in the VECTOR code below, once in each branch
vec_t acc[NumRegs];
psqt_vec_t psqt[NumPsqtRegs];
#endif
// Refresh the accumulator
// Could be extracted to a separate function because it's done in 2 places,
// but it's unclear if compilers would correctly handle register allocation.
auto& accumulator = pos.state()->accumulator;
accumulator.computed[Perspective] = true;
FeatureSet::IndexList active;
FeatureSet::append_active_indices<Perspective>(pos, active);
#ifdef VECTOR
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto biasesTile = reinterpret_cast<const vec_t*>(
&biases[j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = biasesTile[k];
for (const auto index : active)
{
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
auto accTile = reinterpret_cast<vec_t*>(
&accumulator.accumulation[Perspective][j * TileHeight]);
for (unsigned k = 0; k < NumRegs; k++)
vec_store(&accTile[k], acc[k]);
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_zero_psqt();
for (const auto index : active)
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
&accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
#else
std::memcpy(accumulator.accumulation[Perspective], biases,
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
accumulator.psqtAccumulation[Perspective][k] = 0;
for (const auto index : active)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
accumulator.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
#endif
#if defined(USE_MMX)
_mm_empty();
#endif
}
template<Color Perspective>
void hint_common_access_for_perspective(const Position& pos) const {
// Works like update_accumulator, but performs less work.
// Updates ONLY the accumulator for pos.
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
// Fast early exit.
if (pos.state()->accumulator.computed[Perspective])
return;
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
if (oldest_st->accumulator.computed[Perspective])
{
// Only update current position accumulator to minimize work.
StateInfo* states_to_update[2] = { pos.state(), nullptr };
update_accumulator_incremental<Perspective, 2>(pos, oldest_st, states_to_update);
}
else
{
update_accumulator_refresh<Perspective>(pos);
}
}
template<Color Perspective>
void update_accumulator(const Position& pos) const {
auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
if (oldest_st->accumulator.computed[Perspective])
{
if (next == nullptr)
return;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
// Currently we update 2 accumulators.
// 1. for the current position
// 2. the next accumulator after the computed one
// The heuristic may change in the future.
StateInfo *states_to_update[3] =
{ next, next == pos.state() ? nullptr : pos.state(), nullptr };
update_accumulator_incremental<Perspective, 3>(pos, oldest_st, states_to_update);
}
else
{
update_accumulator_refresh<Perspective>(pos);
}
}
alignas(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -61,7 +61,7 @@ struct Entry {
int blockedCount;
};
typedef HashTable<Entry, 131072> Table;
using Table = HashTable<Entry, 131072>;
Entry* probe(const Position& pos);
+57 -94
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -22,8 +22,7 @@
#include <cstring> // For std::memset, std::memcmp
#include <iomanip>
#include <sstream>
#include "nnue/evaluate_nnue.h"
#include <string_view>
#include "bitboard.h"
#include "misc.h"
@@ -34,9 +33,6 @@
#include "uci.h"
#include "syzygy/tbprobe.h"
#include "tools/packed_sfen.h"
#include "tools/sfen_packer.h"
using std::string;
namespace Stockfish {
@@ -51,7 +47,7 @@ namespace Zobrist {
namespace {
const string PieceToChar(" PNBRQK pnbrqk");
constexpr std::string_view PieceToChar(" PNBRQK pnbrqk");
constexpr Piece Pieces[] = { W_PAWN, W_KNIGHT, W_BISHOP, W_ROOK, W_QUEEN, W_KING,
B_PAWN, B_KNIGHT, B_BISHOP, B_ROOK, B_QUEEN, B_KING };
@@ -101,7 +97,7 @@ std::ostream& operator<<(std::ostream& os, const Position& pos) {
// Marcel van Kervinck's cuckoo algorithm for fast detection of "upcoming repetition"
// situations. Description of the algorithm in the following paper:
// https://marcelk.net/2013-04-06/paper/upcoming-rep-v2.pdf
// http://web.archive.org/web/20201107002606/https://marcelk.net/2013-04-06/paper/upcoming-rep-v2.pdf
// First and second hash functions for indexing the cuckoo tables
inline int H1(Key h) { return h & 0x1fff; }
@@ -134,7 +130,7 @@ void Position::init() {
// Prepare the cuckoo tables
std::memset(cuckoo, 0, sizeof(cuckoo));
std::memset(cuckooMove, 0, sizeof(cuckooMove));
int count = 0;
[[maybe_unused]] int count = 0;
for (Piece pc : Pieces)
for (Square s1 = SQ_A1; s1 <= SQ_H8; ++s1)
for (Square s2 = Square(s1 + 1); s2 <= SQ_H8; ++s2)
@@ -286,7 +282,7 @@ Position& Position::set(const string& fenStr, bool isChess960, StateInfo* si, Th
chess960 = isChess960;
thisThread = th;
set_state(st);
set_state();
assert(pos_is_ok());
@@ -317,60 +313,59 @@ void Position::set_castling_right(Color c, Square rfrom) {
/// Position::set_check_info() sets king attacks to detect if a move gives check
void Position::set_check_info(StateInfo* si) const {
void Position::set_check_info() const {
si->blockersForKing[WHITE] = slider_blockers(pieces(BLACK), square<KING>(WHITE), si->pinners[BLACK]);
si->blockersForKing[BLACK] = slider_blockers(pieces(WHITE), square<KING>(BLACK), si->pinners[WHITE]);
st->blockersForKing[WHITE] = slider_blockers(pieces(BLACK), square<KING>(WHITE), st->pinners[BLACK]);
st->blockersForKing[BLACK] = slider_blockers(pieces(WHITE), square<KING>(BLACK), st->pinners[WHITE]);
Square ksq = square<KING>(~sideToMove);
si->checkSquares[PAWN] = pawn_attacks_bb(~sideToMove, ksq);
si->checkSquares[KNIGHT] = attacks_bb<KNIGHT>(ksq);
si->checkSquares[BISHOP] = attacks_bb<BISHOP>(ksq, pieces());
si->checkSquares[ROOK] = attacks_bb<ROOK>(ksq, pieces());
si->checkSquares[QUEEN] = si->checkSquares[BISHOP] | si->checkSquares[ROOK];
si->checkSquares[KING] = 0;
st->checkSquares[PAWN] = pawn_attacks_bb(~sideToMove, ksq);
st->checkSquares[KNIGHT] = attacks_bb<KNIGHT>(ksq);
st->checkSquares[BISHOP] = attacks_bb<BISHOP>(ksq, pieces());
st->checkSquares[ROOK] = attacks_bb<ROOK>(ksq, pieces());
st->checkSquares[QUEEN] = st->checkSquares[BISHOP] | st->checkSquares[ROOK];
st->checkSquares[KING] = 0;
}
/// Position::set_state() computes the hash keys of the position, and other
/// data that once computed is updated incrementally as moves are made.
/// The function is only used when a new position is set up, and to verify
/// the correctness of the StateInfo data when running in debug mode.
/// The function is only used when a new position is set up
void Position::set_state(StateInfo* si) const {
void Position::set_state() const {
si->key = si->materialKey = 0;
si->pawnKey = Zobrist::noPawns;
si->nonPawnMaterial[WHITE] = si->nonPawnMaterial[BLACK] = VALUE_ZERO;
si->checkersBB = attackers_to(square<KING>(sideToMove)) & pieces(~sideToMove);
st->key = st->materialKey = 0;
st->pawnKey = Zobrist::noPawns;
st->nonPawnMaterial[WHITE] = st->nonPawnMaterial[BLACK] = VALUE_ZERO;
st->checkersBB = attackers_to(square<KING>(sideToMove)) & pieces(~sideToMove);
set_check_info(si);
set_check_info();
for (Bitboard b = pieces(); b; )
{
Square s = pop_lsb(b);
Piece pc = piece_on(s);
si->key ^= Zobrist::psq[pc][s];
st->key ^= Zobrist::psq[pc][s];
if (type_of(pc) == PAWN)
si->pawnKey ^= Zobrist::psq[pc][s];
st->pawnKey ^= Zobrist::psq[pc][s];
else if (type_of(pc) != KING)
si->nonPawnMaterial[color_of(pc)] += PieceValue[MG][pc];
st->nonPawnMaterial[color_of(pc)] += PieceValue[MG][pc];
}
if (si->epSquare != SQ_NONE)
si->key ^= Zobrist::enpassant[file_of(si->epSquare)];
if (st->epSquare != SQ_NONE)
st->key ^= Zobrist::enpassant[file_of(st->epSquare)];
if (sideToMove == BLACK)
si->key ^= Zobrist::side;
st->key ^= Zobrist::side;
si->key ^= Zobrist::castling[si->castlingRights];
st->key ^= Zobrist::castling[st->castlingRights];
for (Piece pc : Pieces)
for (int cnt = 0; cnt < pieceCount[pc]; ++cnt)
si->materialKey ^= Zobrist::psq[pc][cnt];
st->materialKey ^= Zobrist::psq[pc][cnt];
}
@@ -573,8 +568,7 @@ bool Position::pseudo_legal(const Move m) const {
: MoveList<NON_EVASIONS>(*this).contains(m);
// Is not a promotion, so promotion piece must be empty
if (promotion_type(m) - KNIGHT != NO_PIECE_TYPE)
return false;
assert(promotion_type(m) - KNIGHT == NO_PIECE_TYPE);
// If the 'from' square is not occupied by a piece belonging to the side to
// move, the move is obviously not legal.
@@ -759,7 +753,7 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
else
st->nonPawnMaterial[them] -= PieceValue[MG][captured];
if (Eval::NNUE::useNNUE != Eval::NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
{
dp.dirty_num = 2; // 1 piece moved, 1 piece captured
dp.piece[1] = captured;
@@ -770,9 +764,6 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
// Update board and piece lists
remove_piece(capsq);
if (type_of(m) == EN_PASSANT)
board[capsq] = NO_PIECE;
// Update material hash key and prefetch access to materialTable
k ^= Zobrist::psq[captured][capsq];
st->materialKey ^= Zobrist::psq[captured][pieceCount[captured]];
@@ -803,7 +794,7 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
// Move the piece. The tricky Chess960 castling is handled earlier
if (type_of(m) != CASTLING)
{
if (Eval::NNUE::useNNUE != Eval::NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
{
dp.piece[0] = pc;
dp.from[0] = from;
@@ -834,7 +825,7 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
remove_piece(to);
put_piece(promotion, to);
if (Eval::NNUE::useNNUE != Eval::NNUE::UseNNUEMode::False)
if (Eval::useNNUE)
{
// Promoting pawn to SQ_NONE, promoted piece from SQ_NONE
dp.to[0] = SQ_NONE;
@@ -873,7 +864,7 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
sideToMove = ~sideToMove;
// Update king attacks used for fast check detection
set_check_info(st);
set_check_info();
// Calculate the repetition info. It is the ply distance from the previous
// occurrence of the same position, negative in the 3-fold case, or zero
@@ -972,7 +963,7 @@ void Position::do_castling(Color us, Square from, Square& to, Square& rfrom, Squ
rto = relative_square(us, kingSide ? SQ_F1 : SQ_D1);
to = relative_square(us, kingSide ? SQ_G1 : SQ_C1);
if (Do && Eval::NNUE::useNNUE != Eval::NNUE::UseNNUEMode::False)
if (Do && Eval::useNNUE)
{
auto& dp = st->dirtyPiece;
dp.piece[0] = make_piece(us, KING);
@@ -1006,7 +997,6 @@ void Position::do_null_move(StateInfo& newSt) {
newSt.previous = st;
st = &newSt;
// Used by NNUE
st->dirtyPiece.dirty_num = 0;
st->dirtyPiece.piece[0] = NO_PIECE; // Avoid checks in UpdateAccumulator()
st->accumulator.computed[WHITE] = false;
@@ -1026,7 +1016,7 @@ void Position::do_null_move(StateInfo& newSt) {
sideToMove = ~sideToMove;
set_check_info(st);
set_check_info();
st->repetition = 0;
@@ -1060,7 +1050,10 @@ Key Position::key_after(Move m) const {
if (captured)
k ^= Zobrist::psq[captured][to];
return k ^ Zobrist::psq[pc][to] ^ Zobrist::psq[pc][from];
k ^= Zobrist::psq[pc][to] ^ Zobrist::psq[pc][from];
return (captured || type_of(pc) == PAWN)
? k : adjust_key50<true>(k);
}
@@ -1068,7 +1061,7 @@ Key Position::key_after(Move m) const {
/// SEE value of move is greater or equal to the given threshold. We'll use an
/// algorithm similar to alpha-beta pruning with a null window.
bool Position::see_ge(Move m, Value threshold) const {
bool Position::see_ge(Move m, Bitboard& occupied, Value threshold) const {
assert(is_ok(m));
@@ -1087,7 +1080,7 @@ bool Position::see_ge(Move m, Value threshold) const {
return true;
assert(color_of(piece_on(from)) == sideToMove);
Bitboard occupied = pieces() ^ from ^ to;
occupied = pieces() ^ from ^ to; // xoring to is important for pinned piece logic
Color stm = sideToMove;
Bitboard attackers = attackers_to(to, occupied);
Bitboard stmAttackers, bb;
@@ -1105,10 +1098,12 @@ bool Position::see_ge(Move m, Value threshold) const {
// Don't allow pinned pieces to attack as long as there are
// pinners on their original square.
if (pinners(~stm) & occupied)
{
stmAttackers &= ~blockers_for_king(stm);
if (!stmAttackers)
break;
if (!stmAttackers)
break;
}
res ^= 1;
@@ -1116,45 +1111,44 @@ bool Position::see_ge(Move m, Value threshold) const {
// the bitboard 'attackers' any X-ray attackers behind it.
if ((bb = stmAttackers & pieces(PAWN)))
{
occupied ^= least_significant_square_bb(bb);
if ((swap = PawnValueMg - swap) < res)
break;
occupied ^= least_significant_square_bb(bb);
attackers |= attacks_bb<BISHOP>(to, occupied) & pieces(BISHOP, QUEEN);
}
else if ((bb = stmAttackers & pieces(KNIGHT)))
{
occupied ^= least_significant_square_bb(bb);
if ((swap = KnightValueMg - swap) < res)
break;
occupied ^= least_significant_square_bb(bb);
}
else if ((bb = stmAttackers & pieces(BISHOP)))
{
occupied ^= least_significant_square_bb(bb);
if ((swap = BishopValueMg - swap) < res)
break;
occupied ^= least_significant_square_bb(bb);
attackers |= attacks_bb<BISHOP>(to, occupied) & pieces(BISHOP, QUEEN);
}
else if ((bb = stmAttackers & pieces(ROOK)))
{
occupied ^= least_significant_square_bb(bb);
if ((swap = RookValueMg - swap) < res)
break;
occupied ^= least_significant_square_bb(bb);
attackers |= attacks_bb<ROOK>(to, occupied) & pieces(ROOK, QUEEN);
}
else if ((bb = stmAttackers & pieces(QUEEN)))
{
occupied ^= least_significant_square_bb(bb);
if ((swap = QueenValueMg - swap) < res)
break;
occupied ^= least_significant_square_bb(bb);
attackers |= (attacks_bb<BISHOP>(to, occupied) & pieces(BISHOP, QUEEN))
| (attacks_bb<ROOK >(to, occupied) & pieces(ROOK , QUEEN));
}
@@ -1168,6 +1162,11 @@ bool Position::see_ge(Move m, Value threshold) const {
return bool(res);
}
bool Position::see_ge(Move m, Value threshold) const {
Bitboard occupied;
return see_ge(m, occupied, threshold);
}
/// Position::is_draw() tests whether the position is drawn by 50-move rule
/// or by repetition. It does not detect stalemates.
@@ -1183,22 +1182,6 @@ bool Position::is_draw(int ply) const {
}
/// Position::is_fifty_move_draw() returns true if a game can be claimed
/// by a fifty-move draw rule.
bool Position::is_fifty_move_draw() const {
return (st->rule50 > 99 && (!checkers() || MoveList<LEGAL>(*this).size()));
}
/// Position::is_three_fold_repetition() returns true if there is 3-fold repetition.
bool Position::is_three_fold_repetition() const {
return st->repetition < 0;
}
// Position::has_repeated() tests whether there has been at least one repetition
// of positions since the last capture or pawn move.
@@ -1340,12 +1323,6 @@ bool Position::pos_is_ok() const {
if (p1 != p2 && (pieces(p1) & pieces(p2)))
assert(0 && "pos_is_ok: Bitboards");
StateInfo si = *st;
ASSERT_ALIGNED(&si, Eval::NNUE::CacheLineSize);
set_state(&si);
if (std::memcmp(&si, st, sizeof(StateInfo)))
assert(0 && "pos_is_ok: State");
for (Piece pc : Pieces)
if ( pieceCount[pc] != popcount(pieces(color_of(pc), type_of(pc)))
@@ -1367,18 +1344,4 @@ bool Position::pos_is_ok() const {
return true;
}
// Add a function that directly unpacks for speed. It's pretty tough.
// Write it by combining packer::unpack() and Position::set().
// If there is a problem with the passed phase and there is an error, non-zero is returned.
int Position::set_from_packed_sfen(const Tools::PackedSfen& sfen , StateInfo* si, Thread* th, bool frc)
{
return Tools::set_from_packed_sfen(*this, sfen, si, th, frc);
}
// Get the packed sfen. Returns to the buffer specified in the argument.
void Position::sfen_pack(Tools::PackedSfen& sfen, bool resetCastlingRights)
{
sfen = Tools::sfen_pack(*this, resetCastlingRights);
}
} // namespace Stockfish
+40 -55
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -31,9 +31,6 @@
#include "nnue/nnue_accumulator.h"
#include "tools/packed_sfen.h"
#include "tools/sfen_packer.h"
namespace Stockfish {
/// StateInfo struct stores information needed to restore a Position object to
@@ -71,7 +68,7 @@ struct StateInfo {
/// start position to the position just before the search starts). Needed by
/// 'draw by repetition' detection. Use a std::deque because pointers to
/// elements are not invalidated upon list resizing.
typedef std::unique_ptr<std::deque<StateInfo>> StateListPtr;
using StateListPtr = std::unique_ptr<std::deque<StateInfo>>;
/// Position class stores information regarding the board representation as
@@ -95,10 +92,9 @@ public:
// Position representation
Bitboard pieces(PieceType pt) const;
Bitboard pieces(PieceType pt1, PieceType pt2) const;
template<typename ...PieceTypes> Bitboard pieces(PieceType pt, PieceTypes... pts) const;
Bitboard pieces(Color c) const;
Bitboard pieces(Color c, PieceType pt) const;
Bitboard pieces(Color c, PieceType pt1, PieceType pt2) const;
template<typename ...PieceTypes> Bitboard pieces(Color c, PieceTypes... pts) const;
Piece piece_on(Square s) const;
Square ep_square() const;
bool empty(Square s) const;
@@ -129,7 +125,7 @@ public:
bool legal(Move m) const;
bool pseudo_legal(const Move m) const;
bool capture(Move m) const;
bool capture_or_promotion(Move m) const;
bool capture_stage(Move m) const;
bool gives_check(Move m) const;
Piece moved_piece(Move m) const;
Piece captured_piece() const;
@@ -148,6 +144,7 @@ public:
// Static Exchange Evaluation
bool see_ge(Move m, Value threshold = VALUE_ZERO) const;
bool see_ge(Move m, Bitboard& occupied, Value threshold = VALUE_ZERO) const;
// Accessing hash keys
Key key() const;
@@ -161,12 +158,11 @@ public:
bool is_chess960() const;
Thread* this_thread() const;
bool is_draw(int ply) const;
bool is_fifty_move_draw() const;
bool is_three_fold_repetition() const;
bool has_game_cycle(int ply) const;
bool has_repeated() const;
int rule50_count() const;
Score psq_score() const;
Value psq_eg_stm() const;
Value non_pawn_material(Color c) const;
Value non_pawn_material() const;
@@ -177,41 +173,21 @@ public:
// Used by NNUE
StateInfo* state() const;
// --sfenization helper
friend int Tools::set_from_packed_sfen(Position& pos, const Tools::PackedSfen& sfen, StateInfo* si, Thread* th, bool frc);
// Get the packed sfen. Returns to the buffer specified in the argument.
// Do not include gamePly in pack.
void sfen_pack(Tools::PackedSfen& sfen, bool resetCastlingRights);
// It is slow to go through sfen, so I made a function to set packed sfen directly.
// Equivalent to pos.set(sfen_unpack(data),si,th);.
// If there is a problem with the passed phase and there is an error, non-zero is returned.
// PackedSfen does not include gamePly so it cannot be restored. If you want to set it, specify it with an argument.
int set_from_packed_sfen(const Tools::PackedSfen& sfen, StateInfo* si, Thread* th, bool frc);
void clear() { std::memset(this, 0, sizeof(Position)); }
// Give the board, hand piece, and turn, and return the sfen.
//static std::string sfen_from_rawdata(Piece board[81], Hand hands[2], Color turn, int gamePly);
// Returns the position of the ball on the c side.
Square king_square(Color c) const { return lsb(pieces(c, KING)); }
void put_piece(Piece pc, Square s);
void remove_piece(Square s);
private:
// Initialization helpers (used while setting up a position)
void set_castling_right(Color c, Square rfrom);
void set_state(StateInfo* si) const;
void set_check_info(StateInfo* si) const;
void set_state() const;
void set_check_info() const;
// Other helpers
void move_piece(Square from, Square to);
template<bool Do>
void do_castling(Color us, Square from, Square& to, Square& rfrom, Square& rto);
template<bool AfterMove>
Key adjust_key50(Key k) const;
// Data members
Piece board[SQUARE_NB];
@@ -229,7 +205,7 @@ private:
bool chess960;
};
extern std::ostream& operator<<(std::ostream& os, const Position& pos);
std::ostream& operator<<(std::ostream& os, const Position& pos);
inline Color Position::side_to_move() const {
return sideToMove;
@@ -252,20 +228,18 @@ inline Bitboard Position::pieces(PieceType pt = ALL_PIECES) const {
return byTypeBB[pt];
}
inline Bitboard Position::pieces(PieceType pt1, PieceType pt2) const {
return pieces(pt1) | pieces(pt2);
template<typename ...PieceTypes>
inline Bitboard Position::pieces(PieceType pt, PieceTypes... pts) const {
return pieces(pt) | pieces(pts...);
}
inline Bitboard Position::pieces(Color c) const {
return byColorBB[c];
}
inline Bitboard Position::pieces(Color c, PieceType pt) const {
return pieces(c) & pieces(pt);
}
inline Bitboard Position::pieces(Color c, PieceType pt1, PieceType pt2) const {
return pieces(c) & (pieces(pt1) | pieces(pt2));
template<typename ...PieceTypes>
inline Bitboard Position::pieces(Color c, PieceTypes... pts) const {
return pieces(c) & pieces(pts...);
}
template<PieceType Pt> inline int Position::count(Color c) const {
@@ -354,8 +328,14 @@ inline int Position::pawns_on_same_color_squares(Color c, Square s) const {
}
inline Key Position::key() const {
return st->rule50 < 14 ? st->key
: st->key ^ make_key((st->rule50 - 14) / 8);
return adjust_key50<false>(st->key);
}
template<bool AfterMove>
inline Key Position::adjust_key50(Key k) const
{
return st->rule50 < 14 - AfterMove
? k : k ^ make_key((st->rule50 - (14 - AfterMove)) / 8);
}
inline Key Position::pawn_key() const {
@@ -370,6 +350,10 @@ inline Score Position::psq_score() const {
return psq;
}
inline Value Position::psq_eg_stm() const {
return (sideToMove == WHITE ? 1 : -1) * eg_value(psq);
}
inline Value Position::non_pawn_material(Color c) const {
return st->nonPawnMaterial[c];
}
@@ -396,15 +380,18 @@ inline bool Position::is_chess960() const {
return chess960;
}
inline bool Position::capture_or_promotion(Move m) const {
assert(is_ok(m));
return type_of(m) != NORMAL ? type_of(m) != CASTLING : !empty(to_sq(m));
}
inline bool Position::capture(Move m) const {
assert(is_ok(m));
// Castling is encoded as "king captures rook"
return (!empty(to_sq(m)) && type_of(m) != CASTLING) || type_of(m) == EN_PASSANT;
return (!empty(to_sq(m)) && type_of(m) != CASTLING)
|| type_of(m) == EN_PASSANT;
}
// returns true if a move is generated from the capture stage
// having also queen promotions covered, i.e. consistency with the capture stage move generation
// is needed to avoid the generation of duplicate moves.
inline bool Position::capture_stage(Move m) const {
assert(is_ok(m));
return capture(m) || promotion_type(m) == QUEEN;
}
inline Piece Position::captured_piece() const {
@@ -458,8 +445,6 @@ inline StateInfo* Position::state() const {
return st;
}
static const char* const StartFEN = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1";
} // namespace Stockfish
#endif // #ifndef POSITION_H_INCLUDED
+1 -1
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@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+2 -2
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -30,7 +30,7 @@ namespace Stockfish::PSQT
extern Score psq[PIECE_NB][SQUARE_NB];
// Fill psqt array from a set of internally linked parameters
extern void init();
void init();
} // namespace Stockfish::PSQT
+448 -1649
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File diff suppressed because it is too large Load Diff
+6 -40
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -24,7 +24,6 @@
#include "misc.h"
#include "movepick.h"
#include "types.h"
#include "uci.h"
namespace Stockfish {
@@ -32,10 +31,6 @@ class Position;
namespace Search {
/// Threshold used for countermoves based pruning
constexpr int CounterMovePruneThreshold = 0;
extern bool prune_at_shallow_depth;
/// Stack struct keeps track of the information we need to remember from nodes
/// shallower and deeper in the tree during the search. Each search thread has
@@ -76,13 +71,16 @@ struct RootMove {
Value score = -VALUE_INFINITE;
Value previousScore = -VALUE_INFINITE;
Value averageScore = -VALUE_INFINITE;
Value uciScore = -VALUE_INFINITE;
bool scoreLowerbound = false;
bool scoreUpperbound = false;
int selDepth = 0;
int tbRank = 0;
Value tbScore;
std::vector<Move> pv;
};
typedef std::vector<RootMove> RootMoves;
using RootMoves = std::vector<RootMove>;
/// LimitsType struct stores information sent by GUI about available time to
@@ -94,7 +92,6 @@ struct LimitsType {
time[WHITE] = time[BLACK] = inc[WHITE] = inc[BLACK] = npmsec = movetime = TimePoint(0);
movestogo = depth = mate = perft = infinite = 0;
nodes = 0;
silent = false;
}
bool use_time_management() const {
@@ -105,9 +102,6 @@ struct LimitsType {
TimePoint time[COLOR_NB], inc[COLOR_NB], npmsec, movetime, startTime;
int movestogo, depth, mate, perft, infinite;
int64_t nodes;
// Silent mode that does not output to the screen (for continuous self-play in process)
// Do not output PV at this time.
bool silent;
};
extern LimitsType Limits;
@@ -115,35 +109,7 @@ extern LimitsType Limits;
void init();
void clear();
// A pair of reader and evaluation value. Returned by Tools::search(),Tools::qsearch().
using ValueAndPV = std::pair<Value, std::vector<Move>>;
ValueAndPV qsearch(Position& pos);
ValueAndPV search(Position& pos, int depth_, size_t multiPV = 1, uint64_t nodesLimit = 0);
namespace MCTS {
struct MctsContinuation {
std::uint64_t numVisits;
Value value;
float actionValue;
std::vector<Move> pv;
};
ValueAndPV search_mcts(
Position& pos,
std::uint64_t nodes,
Depth leafDepth,
float explorationFactor);
std::vector<MctsContinuation> search_mcts_multipv(
Position& pos,
std::uint64_t numPlayouts,
Depth leafDepth,
float explorationFactor);
}
}
} // namespace Search
} // namespace Stockfish
+21 -22
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -24,9 +24,10 @@
#include <fstream>
#include <iostream>
#include <list>
#include <sstream>
#include <type_traits>
#include <mutex>
#include <sstream>
#include <string_view>
#include <type_traits>
#include "../bitboard.h"
#include "../movegen.h"
@@ -52,13 +53,14 @@
using namespace Stockfish::Tablebases;
int Stockfish::Tablebases::MaxCardinality = 0;
int Stockfish::Tablebases::MaxCardinality;
namespace Stockfish {
namespace {
constexpr int TBPIECES = 7; // Max number of supported pieces
constexpr int MAX_DTZ = 1 << 18; // Max DTZ supported, large enough to deal with the syzygy TB limit.
enum { BigEndian, LittleEndian };
enum TBType { WDL, DTZ }; // Used as template parameter
@@ -69,7 +71,7 @@ enum TBFlag { STM = 1, Mapped = 2, WinPlies = 4, LossPlies = 8, Wide = 16, Singl
inline WDLScore operator-(WDLScore d) { return WDLScore(-int(d)); }
inline Square operator^(Square s, int i) { return Square(int(s) ^ i); }
const std::string PieceToChar = " PNBRQK pnbrqk";
constexpr std::string_view PieceToChar = " PNBRQK pnbrqk";
int MapPawns[SQUARE_NB];
int MapB1H1H7[SQUARE_NB];
@@ -140,7 +142,7 @@ struct SparseEntry {
static_assert(sizeof(SparseEntry) == 6, "SparseEntry must be 6 bytes");
typedef uint16_t Sym; // Huffman symbol
using Sym = uint16_t; // Huffman symbol
struct LR {
enum Side { Left, Right };
@@ -198,13 +200,10 @@ public:
}
}
// Memory map the file and check it. File should be already open and will be
// closed after mapping.
// Memory map the file and check it.
uint8_t* map(void** baseAddress, uint64_t* mapping, TBType type) {
assert(is_open());
close(); // Need to re-open to get native file descriptor
if (is_open())
close(); // Need to re-open to get native file descriptor
#ifndef _WIN32
struct stat statbuf;
@@ -235,7 +234,7 @@ public:
}
#else
// Note FILE_FLAG_RANDOM_ACCESS is only a hint to Windows and as such may get ignored.
HANDLE fd = CreateFile(fname.c_str(), GENERIC_READ, FILE_SHARE_READ, nullptr,
HANDLE fd = CreateFileA(fname.c_str(), GENERIC_READ, FILE_SHARE_READ, nullptr,
OPEN_EXISTING, FILE_FLAG_RANDOM_ACCESS, nullptr);
if (fd == INVALID_HANDLE_VALUE)
@@ -328,7 +327,7 @@ struct PairsData {
// first access, when the corresponding file is memory mapped.
template<TBType Type>
struct TBTable {
typedef typename std::conditional<Type == WDL, WDLScore, int>::type Ret;
using Ret = typename std::conditional<Type == WDL, WDLScore, int>::type;
static constexpr int Sides = Type == WDL ? 2 : 1;
@@ -1513,7 +1512,7 @@ int Tablebases::probe_dtz(Position& pos, ProbeState* result) {
// A return value false indicates that not all probes were successful.
bool Tablebases::root_probe(Position& pos, Search::RootMoves& rootMoves) {
ProbeState result;
ProbeState result = OK;
StateInfo st;
// Obtain 50-move counter for the root position
@@ -1522,7 +1521,7 @@ bool Tablebases::root_probe(Position& pos, Search::RootMoves& rootMoves) {
// Check whether a position was repeated since the last zeroing move.
bool rep = pos.has_repeated();
int dtz, bound = Options["Syzygy50MoveRule"] ? 900 : 1;
int dtz, bound = Options["Syzygy50MoveRule"] ? (MAX_DTZ - 100) : 1;
// Probe and rank each move
for (auto& m : rootMoves)
@@ -1565,8 +1564,8 @@ bool Tablebases::root_probe(Position& pos, Search::RootMoves& rootMoves) {
// Better moves are ranked higher. Certain wins are ranked equally.
// Losing moves are ranked equally unless a 50-move draw is in sight.
int r = dtz > 0 ? (dtz + cnt50 <= 99 && !rep ? 1000 : 1000 - (dtz + cnt50))
: dtz < 0 ? (-dtz * 2 + cnt50 < 100 ? -1000 : -1000 + (-dtz + cnt50))
int r = dtz > 0 ? (dtz + cnt50 <= 99 && !rep ? MAX_DTZ : MAX_DTZ - (dtz + cnt50))
: dtz < 0 ? (-dtz * 2 + cnt50 < 100 ? -MAX_DTZ : -MAX_DTZ + (-dtz + cnt50))
: 0;
m.tbRank = r;
@@ -1574,9 +1573,9 @@ bool Tablebases::root_probe(Position& pos, Search::RootMoves& rootMoves) {
// 1 cp to cursed wins and let it grow to 49 cp as the positions gets
// closer to a real win.
m.tbScore = r >= bound ? VALUE_MATE - MAX_PLY - 1
: r > 0 ? Value((std::max( 3, r - 800) * int(PawnValueEg)) / 200)
: r > 0 ? Value((std::max( 3, r - (MAX_DTZ - 200)) * int(PawnValueEg)) / 200)
: r == 0 ? VALUE_DRAW
: r > -bound ? Value((std::min(-3, r + 800) * int(PawnValueEg)) / 200)
: r > -bound ? Value((std::min(-3, r + (MAX_DTZ - 200)) * int(PawnValueEg)) / 200)
: -VALUE_MATE + MAX_PLY + 1;
}
@@ -1590,9 +1589,9 @@ bool Tablebases::root_probe(Position& pos, Search::RootMoves& rootMoves) {
// A return value false indicates that not all probes were successful.
bool Tablebases::root_probe_wdl(Position& pos, Search::RootMoves& rootMoves) {
static const int WDL_to_rank[] = { -1000, -899, 0, 899, 1000 };
static const int WDL_to_rank[] = { -MAX_DTZ, -MAX_DTZ + 101, 0, MAX_DTZ - 101, MAX_DTZ };
ProbeState result;
ProbeState result = OK;
StateInfo st;
WDLScore wdl;
+1 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -31,8 +31,6 @@ enum WDLScore {
WDLDraw = 0, // Draw
WDLCursedWin = 1, // Win, but draw under 50-move rule
WDLWin = 2, // Win
WDLScoreNone = -1000
};
// Possible states after a probing operation
+37 -72
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -34,10 +34,9 @@ ThreadPool Threads; // Global object
/// Thread constructor launches the thread and waits until it goes to sleep
/// in idle_loop(). Note that 'searching' and 'exit' should be already set.
Thread::Thread(size_t n) : idx(n), stdThread(&Thread::idle_loop, this), maxNodes(0) {
Thread::Thread(size_t n) : idx(n), stdThread(&Thread::idle_loop, this) {
wait_for_search_finished();
wait_for_worker_finished();
}
@@ -61,33 +60,21 @@ void Thread::clear() {
counterMoves.fill(MOVE_NONE);
mainHistory.fill(0);
captureHistory.fill(0);
previousDepth = 0;
for (bool inCheck : { false, true })
for (StatsType c : { NoCaptures, Captures })
{
for (auto& to : continuationHistory[inCheck][c])
for (auto& h : to)
h->fill(-71);
continuationHistory[inCheck][c][NO_PIECE][0]->fill(Search::CounterMovePruneThreshold - 1);
}
for (auto& h : to)
h->fill(-71);
}
/// Thread::start_searching() wakes up the thread that will start the search
void Thread::start_searching() {
std::lock_guard<std::mutex> lk(mutex);
searching = true;
cv.notify_one(); // Wake up the thread in idle_loop()
}
void Thread::execute_with_worker(std::function<void(Thread&)> t)
{
std::lock_guard<std::mutex> lk(mutex);
worker = std::move(t);
mutex.lock();
searching = true;
mutex.unlock(); // Unlock before notifying saves a few CPU-cycles
cv.notify_one(); // Wake up the thread in idle_loop()
}
@@ -102,12 +89,6 @@ void Thread::wait_for_search_finished() {
}
void Thread::wait_for_worker_finished() {
std::unique_lock<std::mutex> lk(mutex);
cv.wait(lk, [&]{ return !searching; });
}
/// Thread::idle_loop() is where the thread is parked, blocked on the
/// condition variable, when it has no work to do.
@@ -125,25 +106,15 @@ void Thread::idle_loop() {
{
std::unique_lock<std::mutex> lk(mutex);
searching = false;
worker = nullptr;
cv.notify_one(); // Wake up anyone waiting for search finished
cv.wait(lk, [&]{ return searching; });
if (exit)
return;
auto wrk = std::move(worker);
lk.unlock();
if (wrk)
{
wrk(*this);
}
else
{
search();
}
search();
}
}
@@ -153,20 +124,20 @@ void Thread::idle_loop() {
void ThreadPool::set(size_t requested) {
if (size() > 0) // destroy any existing thread(s)
if (threads.size() > 0) // destroy any existing thread(s)
{
main()->wait_for_search_finished();
while (size() > 0)
delete back(), pop_back();
while (threads.size() > 0)
delete threads.back(), threads.pop_back();
}
if (requested > 0) // create new thread(s)
{
push_back(new MainThread(0));
threads.push_back(new MainThread(0));
while (size() < requested)
push_back(new Thread(size()));
while (threads.size() < requested)
threads.push_back(new Thread(threads.size()));
clear();
// Reallocate the hash with the new threadpool size
@@ -182,7 +153,7 @@ void ThreadPool::set(size_t requested) {
void ThreadPool::clear() {
for (Thread* th : *this)
for (Thread* th : threads)
th->clear();
main()->callsCnt = 0;
@@ -191,13 +162,6 @@ void ThreadPool::clear() {
main()->previousTimeReduction = 1.0;
}
void ThreadPool::execute_with_workers(const std::function<void(Thread&)>& worker)
{
for(Thread* th : *this)
{
th->execute_with_worker(worker);
}
}
/// ThreadPool::start_thinking() wakes up main thread waiting in idle_loop() and
/// returns immediately. Main thread will wake up other threads and start the search.
@@ -218,8 +182,11 @@ void ThreadPool::start_thinking(Position& pos, StateListPtr& states,
|| std::count(limits.searchmoves.begin(), limits.searchmoves.end(), m))
rootMoves.emplace_back(m);
if (!rootMoves.empty())
Tablebases::rank_root_moves(pos, rootMoves);
// After ownership transfer 'states' becomes empty, so if we stop the search
// and call 'go' again without setting a new position states.get() == NULL.
// and call 'go' again without setting a new position states.get() == nullptr.
assert(states.get() || setupStates.get());
if (states.get())
@@ -230,7 +197,7 @@ void ThreadPool::start_thinking(Position& pos, StateListPtr& states,
// be deduced from a fen string, so set() clears them and they are set from
// setupStates->back() later. The rootState is per thread, earlier states are shared
// since they are read-only.
for (Thread* th : *this)
for (Thread* th : threads)
{
th->nodes = th->tbHits = th->nmpMinPly = th->bestMoveChanges = 0;
th->rootDepth = th->completedDepth = 0;
@@ -244,20 +211,23 @@ void ThreadPool::start_thinking(Position& pos, StateListPtr& states,
Thread* ThreadPool::get_best_thread() const {
Thread* bestThread = front();
Thread* bestThread = threads.front();
std::map<Move, int64_t> votes;
Value minScore = VALUE_NONE;
// Find minimum score of all threads
for (Thread* th: *this)
for (Thread* th: threads)
minScore = std::min(minScore, th->rootMoves[0].score);
// Vote according to score and depth, and select the best thread
for (Thread* th : *this)
{
votes[th->rootMoves[0].pv[0]] +=
(th->rootMoves[0].score - minScore + 14) * int(th->completedDepth);
auto thread_value = [minScore](Thread* th) {
return (th->rootMoves[0].score - minScore + 14) * int(th->completedDepth);
};
for (Thread* th : threads)
votes[th->rootMoves[0].pv[0]] += thread_value(th);
for (Thread* th : threads)
if (abs(bestThread->rootMoves[0].score) >= VALUE_TB_WIN_IN_MAX_PLY)
{
// Make sure we pick the shortest mate / TB conversion or stave off mate the longest
@@ -266,9 +236,11 @@ Thread* ThreadPool::get_best_thread() const {
}
else if ( th->rootMoves[0].score >= VALUE_TB_WIN_IN_MAX_PLY
|| ( th->rootMoves[0].score > VALUE_TB_LOSS_IN_MAX_PLY
&& votes[th->rootMoves[0].pv[0]] > votes[bestThread->rootMoves[0].pv[0]]))
&& ( votes[th->rootMoves[0].pv[0]] > votes[bestThread->rootMoves[0].pv[0]]
|| ( votes[th->rootMoves[0].pv[0]] == votes[bestThread->rootMoves[0].pv[0]]
&& thread_value(th) * int(th->rootMoves[0].pv.size() > 2)
> thread_value(bestThread) * int(bestThread->rootMoves[0].pv.size() > 2)))))
bestThread = th;
}
return bestThread;
}
@@ -278,8 +250,8 @@ Thread* ThreadPool::get_best_thread() const {
void ThreadPool::start_searching() {
for (Thread* th : *this)
if (th != front())
for (Thread* th : threads)
if (th != threads.front())
th->start_searching();
}
@@ -288,16 +260,9 @@ void ThreadPool::start_searching() {
void ThreadPool::wait_for_search_finished() const {
for (Thread* th : *this)
if (th != front())
for (Thread* th : threads)
if (th != threads.front())
th->wait_for_search_finished();
}
void ThreadPool::wait_for_workers_finished() const {
for (Thread* th : *this)
th->wait_for_worker_finished();
}
} // namespace Stockfish
+13 -98
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -24,7 +24,6 @@
#include <mutex>
#include <thread>
#include <vector>
#include <functional>
#include "material.h"
#include "movepick.h"
@@ -40,76 +39,40 @@ namespace Stockfish {
/// pointer to an entry its life time is unlimited and we don't have
/// to care about someone changing the entry under our feet.
namespace Detail {
template <typename T>
struct TypeIdentity {
using Type = T;
};
}
class Thread {
std::mutex mutex;
std::condition_variable cv;
size_t idx;
bool exit = false, searching = true; // Set before starting std::thread
std::function<void(Thread&)> worker;
std::function<void(Position&)> on_eval_callback;
NativeThread stdThread;
public:
explicit Thread(size_t);
virtual ~Thread();
virtual void search();
// The function object to be executed is taken by value to remove
// the need for separate lvalue and rvalue overloads.
// The worker thread needs to have ownership of the task
// to be executed because otherwise there's no way to manage its lifetime.
virtual void execute_with_worker(std::function<void(Thread&)> t);
void clear();
void idle_loop();
void start_searching();
void wait_for_search_finished();
size_t id() const { return idx; }
void wait_for_worker_finished();
template <typename FuncT>
void set_eval_callback(FuncT&& f) { on_eval_callback = std::forward<FuncT>(f); }
void clear_eval_callback() { on_eval_callback = nullptr; }
void on_eval() { if (on_eval_callback) on_eval_callback(rootPos); }
Pawns::Table pawnsTable;
Material::Table materialTable;
size_t pvIdx, pvLast;
RunningAverage complexityAverage;
std::atomic<uint64_t> nodes, tbHits, bestMoveChanges;
int selDepth, nmpMinPly;
Color nmpColor;
Value bestValue, optimism[COLOR_NB];
uint64_t maxNodes;
Position rootPos;
StateInfo rootState;
Search::RootMoves rootMoves;
Depth rootDepth, completedDepth, depth, previousDepth;
Depth rootDepth, completedDepth;
Value rootDelta;
CounterMoveHistory counterMoves;
ButterflyHistory mainHistory;
CapturePieceToHistory captureHistory;
ContinuationHistory continuationHistory[2][2];
Score trend;
int failedHighCnt;
bool rootInTB;
int Cardinality;
bool UseRule50;
Depth ProbeDepth;
};
@@ -136,84 +99,36 @@ struct MainThread : public Thread {
/// parking and, most importantly, launching a thread. All the access to threads
/// is done through this class.
struct ThreadPool : public std::vector<Thread*> {
// Each thread gets its own copy of the `worker` function object.
// This means that each worker thread will have exclusive access
// to the state of the `worker` function object.
void execute_with_workers(const std::function<void(Thread&)>& worker);
template <typename IndexT, typename FuncT>
void for_each_index_with_workers(
IndexT begin,
typename Detail::TypeIdentity<IndexT>::Type end,
FuncT func)
{
// This value must outlive the function call.
// It's fairly safe if we make it static
// because for_each_index_with_workers
// is not reentrant nor thread safe.
static std::atomic<IndexT> i_atomic;
i_atomic.store(begin);
execute_with_workers(
[end, func](Thread& th) mutable {
for(;;) {
const auto i = i_atomic.fetch_add(1);
if (i >= end)
break;
func(th, i);
}
});
}
template <typename IndexT, typename FuncT>
void for_each_index_chunk_with_workers(
IndexT begin,
typename Detail::TypeIdentity<IndexT>::Type end,
FuncT func)
{
// This value must outlive the function call.
// It's fairly safe if we make it static
// because for_each_index_with_workers
// is not reentrant nor thread safe.
const IndexT size = end - begin;
const IndexT chunk_size = (size + this->size()) / this->size();
execute_with_workers(
[chunk_size, end, func](Thread& th) mutable {
const IndexT thread_id = th.id();
const IndexT offset = chunk_size * thread_id;
if (offset >= end)
return;
const IndexT count = offset + chunk_size > end ? end - offset : chunk_size;
func(th, offset, count);
});
}
struct ThreadPool {
void start_thinking(Position&, StateListPtr&, const Search::LimitsType&, bool = false);
void clear();
void set(size_t);
MainThread* main() const { return static_cast<MainThread*>(front()); }
MainThread* main() const { return static_cast<MainThread*>(threads.front()); }
uint64_t nodes_searched() const { return accumulate(&Thread::nodes); }
uint64_t tb_hits() const { return accumulate(&Thread::tbHits); }
Thread* get_best_thread() const;
void start_searching();
void wait_for_search_finished() const;
void wait_for_workers_finished() const;
std::atomic_bool stop, increaseDepth;
auto cbegin() const noexcept { return threads.cbegin(); }
auto begin() noexcept { return threads.begin(); }
auto end() noexcept { return threads.end(); }
auto cend() const noexcept { return threads.cend(); }
auto size() const noexcept { return threads.size(); }
auto empty() const noexcept { return threads.empty(); }
private:
StateListPtr setupStates;
std::vector<Thread*> threads;
uint64_t accumulate(std::atomic<uint64_t> Thread::* member) const {
uint64_t sum = 0;
for (Thread* th : *this)
for (Thread* th : threads)
sum += (th->*member).load(std::memory_order_relaxed);
return sum;
}
+4 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -41,7 +41,7 @@ void* start_routine(void* ptr)
P* p = reinterpret_cast<P*>(ptr);
(p->first->*(p->second))(); // Call member function pointer
delete p;
return NULL;
return nullptr;
}
class NativeThread {
@@ -56,7 +56,7 @@ public:
pthread_attr_setstacksize(attr, TH_STACK_SIZE);
pthread_create(&thread, attr, start_routine<T>, new P(obj, fun));
}
void join() { pthread_join(thread, NULL); }
void join() { pthread_join(thread, nullptr); }
};
} // namespace Stockfish
@@ -65,7 +65,7 @@ public:
namespace Stockfish {
typedef std::thread NativeThread;
using NativeThread = std::thread;
} // namespace Stockfish
+8 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -36,6 +36,12 @@ TimeManagement Time; // Our global time management object
void TimeManagement::init(Search::LimitsType& limits, Color us, int ply) {
// if we have no time, no need to initialize TM, except for the start time,
// which is used by movetime.
startTime = limits.startTime;
if (limits.time[us] == 0)
return;
TimePoint moveOverhead = TimePoint(Options["Move Overhead"]);
TimePoint slowMover = TimePoint(Options["Slow Mover"]);
TimePoint npmsec = TimePoint(Options["nodestime"]);
@@ -59,8 +65,6 @@ void TimeManagement::init(Search::LimitsType& limits, Color us, int ply) {
limits.npmsec = npmsec;
}
startTime = limits.startTime;
// Maximum move horizon of 50 moves
int mtg = limits.movestogo ? std::min(limits.movestogo, 50) : 50;
@@ -80,7 +84,7 @@ void TimeManagement::init(Search::LimitsType& limits, Color us, int ply) {
// game time for the current move, so also cap to 20% of available game time.
if (limits.movestogo == 0)
{
optScale = std::min(0.0084 + std::pow(ply + 3.0, 0.5) * 0.0042,
optScale = std::min(0.0120 + std::pow(ply + 3.0, 0.45) * 0.0039,
0.2 * limits.time[us] / double(timeLeft))
* optExtra;
maxScale = std::min(7.0, 4.0 + ply / 12.0);
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
-815
View File
@@ -1,815 +0,0 @@
#include "convert.h"
#include "uci.h"
#include "misc.h"
#include "thread.h"
#include "position.h"
#include "tt.h"
#include "extra/nnue_data_binpack_format.h"
#include "nnue/evaluate_nnue.h"
#include "syzygy/tbprobe.h"
#include <sstream>
#include <fstream>
#include <unordered_set>
#include <iomanip>
#include <list>
#include <cmath> // std::exp(),std::pow(),std::log()
#include <cstring> // memcpy()
#include <memory>
#include <limits>
#include <optional>
#include <chrono>
#include <random>
#include <regex>
#include <filesystem>
using namespace std;
namespace sys = std::filesystem;
namespace Stockfish::Tools
{
bool fen_is_ok(Position& pos, std::string input_fen) {
std::string pos_fen = pos.fen();
std::istringstream ss_input(input_fen);
std::istringstream ss_pos(pos_fen);
// example : "2r4r/4kpp1/nb1np3/p2p3p/B2P1BP1/PP6/4NPKP/2R1R3 w - h6 0 24"
// --> "2r4r/4kpp1/nb1np3/p2p3p/B2P1BP1/PP6/4NPKP/2R1R3"
std::string str_input, str_pos;
ss_input >> str_input;
ss_pos >> str_pos;
// Only compare "Piece placement field" between input_fen and pos.fen().
return str_input == str_pos;
}
void convert_bin(
const vector<string>& filenames,
const string& output_file_name,
const int ply_minimum,
const int ply_maximum,
const int interpolate_eval,
const int src_score_min_value,
const int src_score_max_value,
const int dest_score_min_value,
const int dest_score_max_value,
const bool check_invalid_fen,
const bool check_illegal_move)
{
std::cout << "check_invalid_fen=" << check_invalid_fen << std::endl;
std::cout << "check_illegal_move=" << check_illegal_move << std::endl;
std::fstream fs;
uint64_t data_size = 0;
uint64_t filtered_size = 0;
uint64_t filtered_size_fen = 0;
uint64_t filtered_size_move = 0;
uint64_t filtered_size_ply = 0;
auto th = Threads.main();
auto& tpos = th->rootPos;
// convert plain rag to packed sfenvalue for Yaneura king
fs.open(output_file_name, ios::app | ios::binary);
StateListPtr states;
for (auto filename : filenames) {
std::cout << "convert " << filename << " ... ";
std::string line;
ifstream ifs;
ifs.open(filename);
PackedSfenValue p;
data_size = 0;
filtered_size = 0;
filtered_size_fen = 0;
filtered_size_move = 0;
filtered_size_ply = 0;
p.gamePly = 1; // Not included in apery format. Should be initialized
bool ignore_flag_fen = false;
bool ignore_flag_move = false;
bool ignore_flag_ply = false;
while (std::getline(ifs, line)) {
std::stringstream ss(line);
std::string token;
std::string value;
ss >> token;
if (token == "fen") {
states = StateListPtr(new std::deque<StateInfo>(1)); // Drop old and create a new one
std::string input_fen = line.substr(4);
tpos.set(input_fen, false, &states->back(), Threads.main());
if (check_invalid_fen && !fen_is_ok(tpos, input_fen)) {
ignore_flag_fen = true;
filtered_size_fen++;
}
else {
tpos.sfen_pack(p.sfen, false);
}
}
else if (token == "move") {
ss >> value;
Move move = UCI::to_move(tpos, value);
if (check_illegal_move && move == MOVE_NONE) {
ignore_flag_move = true;
filtered_size_move++;
}
else {
p.move = move;
}
}
else if (token == "score") {
double score;
ss >> score;
// Training Formula ?Issue #71 ?nodchip/Stockfish https://github.com/nodchip/Stockfish/issues/71
// Normalize to [0.0, 1.0].
score = (score - src_score_min_value) / (src_score_max_value - src_score_min_value);
// Scale to [dest_score_min_value, dest_score_max_value].
score = score * (dest_score_max_value - dest_score_min_value) + dest_score_min_value;
p.score = std::clamp((int32_t)std::round(score), -(int32_t)VALUE_MATE, (int32_t)VALUE_MATE);
}
else if (token == "ply") {
int temp;
ss >> temp;
if (temp < ply_minimum || temp > ply_maximum) {
ignore_flag_ply = true;
filtered_size_ply++;
}
p.gamePly = uint16_t(temp); // No cast here?
if (interpolate_eval != 0) {
p.score = min(3000, interpolate_eval * temp);
}
}
else if (token == "result") {
int temp;
ss >> temp;
p.game_result = int8_t(temp); // Do you need a cast here?
if (interpolate_eval) {
p.score = p.score * p.game_result;
}
}
else if (token == "e") {
if (!(ignore_flag_fen || ignore_flag_move || ignore_flag_ply)) {
fs.write((char*)&p, sizeof(PackedSfenValue));
data_size += 1;
// debug
// std::cout<<tpos<<std::endl;
// std::cout<<p.score<<","<<int(p.gamePly)<<","<<int(p.game_result)<<std::endl;
}
else {
filtered_size++;
}
ignore_flag_fen = false;
ignore_flag_move = false;
ignore_flag_ply = false;
}
}
std::cout << "done " << data_size << " parsed " << filtered_size << " is filtered"
<< " (invalid fen:" << filtered_size_fen << ", illegal move:" << filtered_size_move << ", invalid ply:" << filtered_size_ply << ")" << std::endl;
ifs.close();
}
std::cout << "all done" << std::endl;
fs.close();
}
static inline void ltrim(std::string& s) {
s.erase(s.begin(), std::find_if(s.begin(), s.end(), [](int ch) {
return !std::isspace(ch);
}));
}
static inline void rtrim(std::string& s) {
s.erase(std::find_if(s.rbegin(), s.rend(), [](int ch) {
return !std::isspace(ch);
}).base(), s.end());
}
static inline void trim(std::string& s) {
ltrim(s);
rtrim(s);
}
int parse_game_result_from_pgn_extract(std::string result) {
// White Win
if (result == "\"1-0\"") {
return 1;
}
// Black Win
else if (result == "\"0-1\"") {
return -1;
}
// Draw
else {
return 0;
}
}
// 0.25 --> 0.25 * PawnValueEg
// #-4 --> -mate_in(4)
// #3 --> mate_in(3)
// -M4 --> -mate_in(4)
// +M3 --> mate_in(3)
Value parse_score_from_pgn_extract(std::string eval, bool& success) {
success = true;
if (eval.substr(0, 1) == "#") {
if (eval.substr(1, 1) == "-") {
return -mate_in(stoi(eval.substr(2, eval.length() - 2)));
}
else {
return mate_in(stoi(eval.substr(1, eval.length() - 1)));
}
}
else if (eval.substr(0, 2) == "-M") {
//std::cout << "eval=" << eval << std::endl;
return -mate_in(stoi(eval.substr(2, eval.length() - 2)));
}
else if (eval.substr(0, 2) == "+M") {
//std::cout << "eval=" << eval << std::endl;
return mate_in(stoi(eval.substr(2, eval.length() - 2)));
}
else {
char* endptr;
double value = strtod(eval.c_str(), &endptr);
if (*endptr != '\0') {
success = false;
return VALUE_ZERO;
}
else {
return Value(value * static_cast<double>(PawnValueEg));
}
}
}
// for Debug
//#define DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT
bool is_like_fen(std::string fen) {
int count_space = std::count(fen.cbegin(), fen.cend(), ' ');
int count_slash = std::count(fen.cbegin(), fen.cend(), '/');
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
//std::cout << "count_space=" << count_space << std::endl;
//std::cout << "count_slash=" << count_slash << std::endl;
#endif
return count_space == 5 && count_slash == 7;
}
void convert_bin_from_pgn_extract(
const vector<string>& filenames,
const string& output_file_name,
const bool pgn_eval_side_to_move,
const bool convert_no_eval_fens_as_score_zero)
{
std::cout << "pgn_eval_side_to_move=" << pgn_eval_side_to_move << std::endl;
std::cout << "convert_no_eval_fens_as_score_zero=" << convert_no_eval_fens_as_score_zero << std::endl;
auto th = Threads.main();
auto& pos = th->rootPos;
std::fstream ofs;
ofs.open(output_file_name, ios::out | ios::binary);
int game_count = 0;
int fen_count = 0;
for (auto filename : filenames) {
std::cout << now_string() << " convert " << filename << std::endl;
ifstream ifs;
ifs.open(filename);
int game_result = 0;
std::string line;
while (std::getline(ifs, line)) {
if (line.empty()) {
continue;
}
else if (line.substr(0, 1) == "[") {
std::regex pattern_result(R"(\[Result (.+?)\])");
std::smatch match;
// example: [Result "1-0"]
if (std::regex_search(line, match, pattern_result)) {
game_result = parse_game_result_from_pgn_extract(match.str(1));
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
std::cout << "game_result=" << game_result << std::endl;
#endif
game_count++;
if (game_count % 10000 == 0) {
std::cout << now_string() << " game_count=" << game_count << ", fen_count=" << fen_count << std::endl;
}
}
continue;
}
else {
int gamePly = 1;
auto itr = line.cbegin();
while (true) {
gamePly++;
PackedSfenValue psv;
memset((char*)&psv, 0, sizeof(PackedSfenValue));
// fen
{
bool fen_found = false;
while (!fen_found) {
std::regex pattern_bracket(R"(\{(.+?)\})");
std::smatch match;
if (!std::regex_search(itr, line.cend(), match, pattern_bracket)) {
break;
}
itr += match.position(0) + match.length(0) - 1;
std::string str_fen = match.str(1);
trim(str_fen);
if (is_like_fen(str_fen)) {
fen_found = true;
StateInfo si;
pos.set(str_fen, false, &si, th);
pos.sfen_pack(psv.sfen, false);
}
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
std::cout << "str_fen=" << str_fen << std::endl;
std::cout << "fen_found=" << fen_found << std::endl;
#endif
}
if (!fen_found) {
break;
}
}
// move
{
std::regex pattern_move(R"(\}(.+?)\{)");
std::smatch match;
if (!std::regex_search(itr, line.cend(), match, pattern_move)) {
break;
}
itr += match.position(0) + match.length(0) - 1;
std::string str_move = match.str(1);
trim(str_move);
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
std::cout << "str_move=" << str_move << std::endl;
#endif
psv.move = UCI::to_move(pos, str_move);
}
// eval
bool eval_found = false;
{
std::regex pattern_bracket(R"(\{(.+?)\})");
std::smatch match;
if (!std::regex_search(itr, line.cend(), match, pattern_bracket)) {
break;
}
std::string str_eval_clk = match.str(1);
trim(str_eval_clk);
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
std::cout << "str_eval_clk=" << str_eval_clk << std::endl;
#endif
// example: { [%eval 0.25] [%clk 0:10:00] }
// example: { [%eval #-4] [%clk 0:10:00] }
// example: { [%eval #3] [%clk 0:10:00] }
// example: { +0.71/22 1.2s }
// example: { -M4/7 0.003s }
// example: { M3/245 0.017s }
// example: { +M1/245 0.010s, White mates }
// example: { 0.60 }
// example: { book }
// example: { rnbqkb1r/pp3ppp/2p1pn2/3p4/2PP4/2N2N2/PP2PPPP/R1BQKB1R w KQkq - 0 5 }
// Considering the absence of eval
if (!is_like_fen(str_eval_clk)) {
itr += match.position(0) + match.length(0) - 1;
if (str_eval_clk != "book") {
std::regex pattern_eval1(R"(\[\%eval (.+?)\])");
std::regex pattern_eval2(R"((.+?)\/)");
std::string str_eval;
if (std::regex_search(str_eval_clk, match, pattern_eval1) ||
std::regex_search(str_eval_clk, match, pattern_eval2)) {
str_eval = match.str(1);
trim(str_eval);
}
else {
str_eval = str_eval_clk;
}
bool success = false;
Value value = parse_score_from_pgn_extract(str_eval, success);
if (success) {
eval_found = true;
psv.score = std::clamp(value, -VALUE_MATE, VALUE_MATE);
}
#if defined(DEBUG_CONVERT_BIN_FROM_PGN_EXTRACT)
std::cout << "str_eval=" << str_eval << std::endl;
std::cout << "success=" << success << ", psv.score=" << psv.score << std::endl;
#endif
}
}
}
// write
if (eval_found || convert_no_eval_fens_as_score_zero) {
if (!eval_found && convert_no_eval_fens_as_score_zero) {
psv.score = 0;
}
psv.gamePly = gamePly;
psv.game_result = game_result;
if (pos.side_to_move() == BLACK) {
if (!pgn_eval_side_to_move) {
psv.score *= -1;
}
psv.game_result *= -1;
}
ofs.write((char*)&psv, sizeof(PackedSfenValue));
fen_count++;
}
}
game_result = 0;
}
}
}
std::cout << now_string() << " game_count=" << game_count << ", fen_count=" << fen_count << std::endl;
std::cout << now_string() << " all done" << std::endl;
ofs.close();
}
void convert_plain(
const vector<string>& filenames,
const string& output_file_name)
{
Position tpos;
std::ofstream ofs;
ofs.open(output_file_name, ios::app);
auto th = Threads.main();
for (auto filename : filenames) {
std::cout << "convert " << filename << " ... ";
// Just convert packedsfenvalue to text
std::fstream fs;
fs.open(filename, ios::in | ios::binary);
PackedSfenValue p;
while (true)
{
if (fs.read((char*)&p, sizeof(PackedSfenValue))) {
StateInfo si;
tpos.set_from_packed_sfen(p.sfen, &si, th, false);
// write as plain text
ofs << "fen " << tpos.fen() << std::endl;
ofs << "move " << UCI::move(Move(p.move), false) << std::endl;
ofs << "score " << p.score << std::endl;
ofs << "ply " << int(p.gamePly) << std::endl;
ofs << "result " << int(p.game_result) << std::endl;
ofs << "e" << std::endl;
}
else {
break;
}
}
fs.close();
std::cout << "done" << std::endl;
}
ofs.close();
std::cout << "all done" << std::endl;
}
static inline const std::string plain_extension = ".plain";
static inline const std::string bin_extension = ".bin";
static inline const std::string binpack_extension = ".binpack";
static bool file_exists(const std::string& name)
{
std::ifstream f(name);
return f.good();
}
static bool ends_with(const std::string& lhs, const std::string& end)
{
if (end.size() > lhs.size()) return false;
return std::equal(end.rbegin(), end.rend(), lhs.rbegin());
}
static bool is_convert_of_type(
const std::string& input_path,
const std::string& output_path,
const std::string& expected_input_extension,
const std::string& expected_output_extension)
{
return ends_with(input_path, expected_input_extension)
&& ends_with(output_path, expected_output_extension);
}
using ConvertFunctionType = void(std::string inputPath, std::string outputPath, std::ios_base::openmode om, bool validate);
static ConvertFunctionType* get_convert_function(const std::string& input_path, const std::string& output_path)
{
if (is_convert_of_type(input_path, output_path, plain_extension, bin_extension))
return binpack::convertPlainToBin;
if (is_convert_of_type(input_path, output_path, plain_extension, binpack_extension))
return binpack::convertPlainToBinpack;
if (is_convert_of_type(input_path, output_path, bin_extension, plain_extension))
return binpack::convertBinToPlain;
if (is_convert_of_type(input_path, output_path, bin_extension, binpack_extension))
return binpack::convertBinToBinpack;
if (is_convert_of_type(input_path, output_path, binpack_extension, plain_extension))
return binpack::convertBinpackToPlain;
if (is_convert_of_type(input_path, output_path, binpack_extension, bin_extension))
return binpack::convertBinpackToBin;
return nullptr;
}
static void convert(const std::string& input_path, const std::string& output_path, std::ios_base::openmode om, bool validate)
{
if(!file_exists(input_path))
{
std::cerr << "Input file does not exist.\n";
return;
}
auto func = get_convert_function(input_path, output_path);
if (func != nullptr)
{
func(input_path, output_path, om, validate);
}
else
{
std::cerr << "Conversion between files of these types is not supported.\n";
}
}
static void convert(const std::vector<std::string>& args)
{
if (args.size() < 2 || args.size() > 4)
{
std::cerr << "Invalid arguments.\n";
std::cerr << "Usage: convert from_path to_path [append] [validate]\n";
return;
}
const bool append = std::find(args.begin() + 2, args.end(), "append") != args.end();
const bool validate = std::find(args.begin() + 2, args.end(), "validate") != args.end();
const std::ios_base::openmode openmode =
append
? std::ios_base::app
: std::ios_base::trunc;
convert(args[0], args[1], openmode, validate);
}
void convert(istringstream& is)
{
std::vector<std::string> args;
while (true)
{
std::string token = "";
is >> token;
if (token == "")
break;
args.push_back(token);
}
convert(args);
}
static void append_files_from_dir(
std::vector<std::string>& filenames,
const std::string& base_dir,
const std::string& target_dir)
{
string kif_base_dir = Path::combine(base_dir, target_dir);
sys::path p(kif_base_dir); // Origin of enumeration
std::for_each(sys::directory_iterator(p), sys::directory_iterator(),
[&](const sys::path& path) {
if (sys::is_regular_file(path))
filenames.push_back(Path::combine(target_dir, path.filename().generic_string()));
});
}
static void rebase_files(
std::vector<std::string>& filenames,
const std::string& base_dir)
{
for (auto& file : filenames)
{
file = Path::combine(base_dir, file);
}
}
void convert_bin_from_pgn_extract(std::istringstream& is)
{
std::vector<std::string> filenames;
string base_dir;
string target_dir;
bool pgn_eval_side_to_move = false;
bool convert_no_eval_fens_as_score_zero = false;
string output_file_name = "shuffled_sfen.bin";
while (true)
{
string option;
is >> option;
if (option == "")
break;
if (option == "targetdir") is >> target_dir;
else if (option == "targetfile")
{
std::string filename;
is >> filename;
filenames.push_back(filename);
}
else if (option == "basedir") is >> base_dir;
else if (option == "pgn_eval_side_to_move") is >> pgn_eval_side_to_move;
else if (option == "convert_no_eval_fens_as_score_zero") is >> convert_no_eval_fens_as_score_zero;
else if (option == "output_file_name") is >> output_file_name;
else
{
cout << "Unknown option: " << option << ". Ignoring.\n";
}
}
if (!target_dir.empty())
{
append_files_from_dir(filenames, base_dir, target_dir);
}
rebase_files(filenames, base_dir);
Eval::NNUE::init();
cout << "convert_bin_from_pgn-extract.." << endl;
convert_bin_from_pgn_extract(
filenames,
output_file_name,
pgn_eval_side_to_move,
convert_no_eval_fens_as_score_zero);
}
void convert_bin(std::istringstream& is)
{
std::vector<std::string> filenames;
string base_dir;
string target_dir;
int ply_minimum = 0;
int ply_maximum = 114514;
bool interpolate_eval = 0;
bool check_invalid_fen = false;
bool check_illegal_move = false;
bool pgn_eval_side_to_move = false;
bool convert_no_eval_fens_as_score_zero = false;
double src_score_min_value = 0.0;
double src_score_max_value = 1.0;
double dest_score_min_value = 0.0;
double dest_score_max_value = 1.0;
string output_file_name = "shuffled_sfen.bin";
while (true)
{
string option;
is >> option;
if (option == "")
break;
if (option == "targetdir") is >> target_dir;
else if (option == "targetfile")
{
std::string filename;
is >> filename;
filenames.push_back(filename);
}
else if (option == "basedir") is >> base_dir;
else if (option == "ply_minimum") is >> ply_minimum;
else if (option == "ply_maximum") is >> ply_maximum;
else if (option == "interpolate_eval") is >> interpolate_eval;
else if (option == "check_invalid_fen") is >> check_invalid_fen;
else if (option == "check_illegal_move") is >> check_illegal_move;
else if (option == "pgn_eval_side_to_move") is >> pgn_eval_side_to_move;
else if (option == "convert_no_eval_fens_as_score_zero") is >> convert_no_eval_fens_as_score_zero;
else if (option == "src_score_min_value") is >> src_score_min_value;
else if (option == "src_score_max_value") is >> src_score_max_value;
else if (option == "dest_score_min_value") is >> dest_score_min_value;
else if (option == "dest_score_max_value") is >> dest_score_max_value;
else if (option == "output_file_name") is >> output_file_name;
else
{
cout << "Unknown option: " << option << ". Ignoring.\n";
}
}
if (!target_dir.empty())
{
append_files_from_dir(filenames, base_dir, target_dir);
}
rebase_files(filenames, base_dir);
Eval::NNUE::init();
cout << "convert_bin.." << endl;
convert_bin(
filenames,
output_file_name,
ply_minimum,
ply_maximum,
interpolate_eval,
src_score_min_value,
src_score_max_value,
dest_score_min_value,
dest_score_max_value,
check_invalid_fen,
check_illegal_move
);
}
void convert_plain(std::istringstream& is)
{
std::vector<std::string> filenames;
string base_dir;
string target_dir;
string output_file_name = "shuffled_sfen.bin";
while (true)
{
string option;
is >> option;
if (option == "")
break;
if (option == "targetdir") is >> target_dir;
else if (option == "targetfile")
{
std::string filename;
is >> filename;
filenames.push_back(filename);
}
else if (option == "basedir") is >> base_dir;
else if (option == "output_file_name") is >> output_file_name;
else
{
cout << "Unknown option: " << option << ". Ignoring.\n";
}
}
if (!target_dir.empty())
{
append_files_from_dir(filenames, base_dir, target_dir);
}
rebase_files(filenames, base_dir);
Eval::NNUE::init();
cout << "convert_plain.." << endl;
convert_plain(filenames, output_file_name);
}
}
-18
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#ifndef _CONVERT_H_
#define _CONVERT_H_
#include <vector>
#include <string>
#include <sstream>
namespace Stockfish::Tools {
void convert(std::istringstream& is);
void convert_bin_from_pgn_extract(std::istringstream& is);
void convert_bin(std::istringstream& is);
void convert_plain(std::istringstream& is);
}
#endif
-43
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#include "opening_book.h"
#include <fstream>
namespace Stockfish::Tools {
EpdOpeningBook::EpdOpeningBook(const std::string& file, PRNG& prng) :
OpeningBook(file)
{
std::ifstream in(file);
if (!in)
{
return;
}
std::string line;
while (std::getline(in, line))
{
if (line.empty())
continue;
fens.emplace_back(line);
}
Algo::shuffle(fens, prng);
}
static bool ends_with(const std::string& lhs, const std::string& end)
{
if (end.size() > lhs.size()) return false;
return std::equal(end.rbegin(), end.rend(), lhs.rbegin());
}
std::unique_ptr<OpeningBook> open_opening_book(const std::string& filename, PRNG& prng)
{
if (ends_with(filename, ".epd"))
return std::make_unique<EpdOpeningBook>(filename, prng);
return nullptr;
}
}
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#ifndef LEARN_OPENING_BOOK_H
#define LEARN_OPENING_BOOK_H
#include "misc.h"
#include "position.h"
#include "thread.h"
#include <vector>
#include <random>
#include <optional>
#include <string>
#include <cstdint>
#include <memory>
#include <mutex>
namespace Stockfish::Tools {
struct OpeningBook {
const std::string& next_fen()
{
assert(fens.size() > 0);
std::unique_lock lock(mutex);
auto& fen = fens[current_index++];
if (current_index >= fens.size())
current_index = 0;
return fen;
}
std::size_t size() const { return fens.size(); }
const std::string& get_filename() const { return filename; }
protected:
OpeningBook(const std::string& file) :
filename(file),
current_index(0)
{
}
std::mutex mutex;
std::string filename;
std::vector<std::string> fens;
std::size_t current_index;
};
struct EpdOpeningBook : OpeningBook {
EpdOpeningBook(const std::string& file, PRNG& prng);
};
std::unique_ptr<OpeningBook> open_opening_book(const std::string& filename, PRNG& prng);
}
#endif
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#ifndef _PACKED_SFEN_H_
#define _PACKED_SFEN_H_
#include <vector>
#include <cstdint>
namespace Stockfish::Tools {
// packed sfen
struct PackedSfen { std::uint8_t data[32]; };
// Structure in which PackedSfen and evaluation value are integrated
// If you write different contents for each option, it will be a problem when reusing the teacher game
// For the time being, write all the following members regardless of the options.
struct PackedSfenValue
{
// phase
PackedSfen sfen;
// Evaluation value returned from Tools::search()
std::int16_t score;
// PV first move
// Used when finding the match rate with the teacher
std::uint16_t move;
// Trouble of the phase from the initial phase.
std::uint16_t gamePly;
// 1 if the player on this side ultimately wins the game. -1 if you are losing.
// 0 if a draw is reached.
// The draw is in the teacher position generation command gensfen,
// Only write if LEARN_GENSFEN_DRAW_RESULT is enabled.
std::int8_t game_result;
// When exchanging the file that wrote the teacher aspect with other people
//Because this structure size is not fixed, pad it so that it is 40 bytes in any environment.
std::uint8_t padding;
// 32 + 2 + 2 + 2 + 1 + 1 = 40bytes
};
// Phase array: PSVector stands for packed sfen vector.
using PSVector = std::vector<PackedSfenValue>;
}
#endif
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#include "sfen_packer.h"
#include "packed_sfen.h"
#include "misc.h"
#include "position.h"
#include <sstream>
#include <fstream>
#include <cstring> // std::memset()
using namespace std;
namespace Stockfish::Tools {
// Class that handles bitstream
// useful when doing aspect encoding
struct BitStream
{
// Set the memory to store the data in advance.
// Assume that memory is cleared to 0.
void set_data(std::uint8_t* data_) { data = data_; reset(); }
// Get the pointer passed in set_data().
uint8_t* get_data() const { return data; }
// Get the cursor.
int get_cursor() const { return bit_cursor; }
// reset the cursor
void reset() { bit_cursor = 0; }
// Write 1bit to the stream.
// If b is non-zero, write out 1. If 0, write 0.
void write_one_bit(int b)
{
if (b)
data[bit_cursor / 8] |= 1 << (bit_cursor & 7);
++bit_cursor;
}
// Get 1 bit from the stream.
int read_one_bit()
{
int b = (data[bit_cursor / 8] >> (bit_cursor & 7)) & 1;
++bit_cursor;
return b;
}
// write n bits of data
// Data shall be written out from the lower order of d.
void write_n_bit(int d, int n)
{
for (int i = 0; i <n; ++i)
write_one_bit(d & (1 << i));
}
// read n bits of data
// Reverse conversion of write_n_bit().
int read_n_bit(int n)
{
int result = 0;
for (int i = 0; i < n; ++i)
result |= read_one_bit() ? (1 << i) : 0;
return result;
}
private:
// Next bit position to read/write.
int bit_cursor;
// data entity
std::uint8_t* data;
};
// Class for compressing/decompressing sfen
// sfen can be packed to 256bit (32bytes) by Huffman coding.
// This is proven by mini. The above is Huffman coding.
//
// Internal format = 1-bit turn + 7-bit king position *2 + piece on board (Huffman coding) + hand piece (Huffman coding)
// Side to move (White = 0, Black = 1) (1bit)
// White King Position (6 bits)
// Black King Position (6 bits)
// Huffman Encoding of the board
// Castling availability (1 bit x 4)
// En passant square (1 or 1 + 6 bits)
// Rule 50 (6 bits)
// Game play (8 bits)
//
// TODO(someone): Rename SFEN to FEN.
//
struct SfenPacker
{
void pack(const Position& pos, bool resetCastlingRights);
// sfen packed by pack() (256bit = 32bytes)
// Or sfen to decode with unpack()
uint8_t *data; // uint8_t[32];
BitStream stream;
// Output the board pieces to stream.
void write_board_piece_to_stream(Piece pc);
// Read one board piece from stream
Piece read_board_piece_from_stream();
};
// Huffman coding
// * is simplified from mini encoding to make conversion easier.
//
// Huffman Encoding
//
// Empty xxxxxxx0
// Pawn xxxxx001 + 1 bit (Color)
// Knight xxxxx011 + 1 bit (Color)
// Bishop xxxxx101 + 1 bit (Color)
// Rook xxxxx111 + 1 bit (Color)
// Queen xxxx1001 + 1 bit (Color)
//
// Worst case:
// - 32 empty squares 32 bits
// - 30 pieces 150 bits
// - 2 kings 12 bits
// - castling rights 4 bits
// - ep square 7 bits
// - rule50 7 bits
// - game ply 16 bits
// - TOTAL 228 bits < 256 bits
struct HuffmanedPiece
{
int code; // how it will be coded
int bits; // How many bits do you have
};
constexpr HuffmanedPiece huffman_table[] =
{
{0b0000,1}, // NO_PIECE
{0b0001,4}, // PAWN
{0b0011,4}, // KNIGHT
{0b0101,4}, // BISHOP
{0b0111,4}, // ROOK
{0b1001,4}, // QUEEN
};
// Pack sfen and store in data[32].
void SfenPacker::pack(const Position& pos, bool resetCastlingRights)
{
memset(data, 0, 32 /* 256bit */);
stream.set_data(data);
// turn
// Side to move.
stream.write_one_bit((int)(pos.side_to_move()));
// 7-bit positions for leading and trailing balls
// White king and black king, 6 bits for each.
for(auto c: Colors)
stream.write_n_bit(pos.king_square(c), 6);
// Write the pieces on the board other than the kings.
for (Rank r = RANK_8; r >= RANK_1; --r)
{
for (File f = FILE_A; f <= FILE_H; ++f)
{
Piece pc = pos.piece_on(make_square(f, r));
if (type_of(pc) == KING)
continue;
write_board_piece_to_stream(pc);
}
}
if (resetCastlingRights)
{
stream.write_n_bit(0, 4);
}
else
{
stream.write_one_bit(pos.can_castle(WHITE_OO));
stream.write_one_bit(pos.can_castle(WHITE_OOO));
stream.write_one_bit(pos.can_castle(BLACK_OO));
stream.write_one_bit(pos.can_castle(BLACK_OOO));
}
if (pos.ep_square() == SQ_NONE) {
stream.write_one_bit(0);
}
else {
stream.write_one_bit(1);
stream.write_n_bit(static_cast<int>(pos.ep_square()), 6);
}
stream.write_n_bit(pos.state()->rule50, 6);
const int fm = 1 + (pos.game_ply()-(pos.side_to_move() == BLACK)) / 2;
stream.write_n_bit(fm, 8);
// Write high bits of half move. This is a fix for the
// limited range of half move counter.
// This is backwards compatibile.
stream.write_n_bit(fm >> 8, 8);
// Write the highest bit of rule50 at the end. This is a backwards
// compatibile fix for rule50 having only 6 bits stored.
// This bit is just ignored by the old parsers.
stream.write_n_bit(pos.state()->rule50 >> 6, 1);
assert(stream.get_cursor() <= 256);
}
// Output the board pieces to stream.
void SfenPacker::write_board_piece_to_stream(Piece pc)
{
// piece type
PieceType pr = type_of(pc);
auto c = huffman_table[pr];
stream.write_n_bit(c.code, c.bits);
if (pc == NO_PIECE)
return;
// first and second flag
stream.write_one_bit(color_of(pc));
}
// Read one board piece from stream
Piece SfenPacker::read_board_piece_from_stream()
{
PieceType pr = NO_PIECE_TYPE;
int code = 0, bits = 0;
while (true)
{
code |= stream.read_one_bit() << bits;
++bits;
assert(bits <= 6);
for (pr = NO_PIECE_TYPE; pr <KING; ++pr)
if (huffman_table[pr].code == code
&& huffman_table[pr].bits == bits)
goto Found;
}
Found:;
if (pr == NO_PIECE_TYPE)
return NO_PIECE;
// first and second flag
Color c = (Color)stream.read_one_bit();
return make_piece(c, pr);
}
int set_from_packed_sfen(Position& pos, const PackedSfen& sfen, StateInfo* si, Thread* th, bool frc)
{
SfenPacker packer;
auto& stream = packer.stream;
// TODO: separate streams for writing and reading. Here we actually have to
// const_cast which is not safe in the long run.
stream.set_data(const_cast<uint8_t*>(reinterpret_cast<const uint8_t*>(&sfen)));
pos.clear();
std::memset(si, 0, sizeof(StateInfo));
si->accumulator.computed[WHITE] = false;
si->accumulator.computed[BLACK] = false;
pos.st = si;
// Active color
pos.sideToMove = (Color)stream.read_one_bit();
// First the position of the ball
for (auto c : Colors)
pos.board[stream.read_n_bit(6)] = make_piece(c, KING);
// Piece placement
for (Rank r = RANK_8; r >= RANK_1; --r)
{
for (File f = FILE_A; f <= FILE_H; ++f)
{
auto sq = make_square(f, r);
// it seems there are already balls
Piece pc;
if (type_of(pos.board[sq]) != KING)
{
assert(pos.board[sq] == NO_PIECE);
pc = packer.read_board_piece_from_stream();
}
else
{
pc = pos.board[sq];
// put_piece() will catch ASSERT unless you remove it all.
pos.board[sq] = NO_PIECE;
}
// There may be no pieces, so skip in that case.
if (pc == NO_PIECE)
continue;
pos.put_piece(Piece(pc), sq);
if (stream.get_cursor()> 256)
return 1;
}
}
// Castling availability.
pos.st->castlingRights = 0;
if (stream.read_one_bit()) {
Square rsq;
for (rsq = relative_square(WHITE, SQ_H1); pos.piece_on(rsq) != W_ROOK; --rsq) {}
pos.set_castling_right(WHITE, rsq);
}
if (stream.read_one_bit()) {
Square rsq;
for (rsq = relative_square(WHITE, SQ_A1); pos.piece_on(rsq) != W_ROOK; ++rsq) {}
pos.set_castling_right(WHITE, rsq);
}
if (stream.read_one_bit()) {
Square rsq;
for (rsq = relative_square(BLACK, SQ_H1); pos.piece_on(rsq) != B_ROOK; --rsq) {}
pos.set_castling_right(BLACK, rsq);
}
if (stream.read_one_bit()) {
Square rsq;
for (rsq = relative_square(BLACK, SQ_A1); pos.piece_on(rsq) != B_ROOK; ++rsq) {}
pos.set_castling_right(BLACK, rsq);
}
// En passant square. Ignore if no pawn capture is possible
if (stream.read_one_bit()) {
Square ep_square = static_cast<Square>(stream.read_n_bit(6));
pos.st->epSquare = ep_square;
if (!(pos.attackers_to(pos.st->epSquare) & pos.pieces(pos.sideToMove, PAWN))
|| !(pos.pieces(~pos.sideToMove, PAWN) & (pos.st->epSquare + pawn_push(~pos.sideToMove))))
pos.st->epSquare = SQ_NONE;
}
else {
pos.st->epSquare = SQ_NONE;
}
// Halfmove clock
pos.st->rule50 = stream.read_n_bit(6);
// Fullmove number
pos.gamePly = stream.read_n_bit(8);
// Read the highest bit of rule50. This was added as a fix for rule50
// counter having only 6 bits stored.
// In older entries this will just be a zero bit.
pos.gamePly |= stream.read_n_bit(8) << 8;
// Read the highest bit of rule50. This was added as a fix for rule50
// counter having only 6 bits stored.
// In older entries this will just be a zero bit.
pos.st->rule50 |= stream.read_n_bit(1) << 6;
// Convert from fullmove starting from 1 to gamePly starting from 0,
// handle also common incorrect FEN with fullmove = 0.
pos.gamePly = std::max(2 * (pos.gamePly - 1), 0) + (pos.sideToMove == BLACK);
assert(stream.get_cursor() <= 256);
pos.chess960 = frc;
pos.thisThread = th;
pos.set_state(pos.st);
assert(pos.pos_is_ok());
return 0;
}
PackedSfen sfen_pack(Position& pos, bool resetCastlingRights)
{
PackedSfen sfen;
SfenPacker sp;
sp.data = (uint8_t*)&sfen;
sp.pack(pos, resetCastlingRights);
return sfen;
}
}
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#ifndef _SFEN_PACKER_H_
#define _SFEN_PACKER_H_
#include "types.h"
#include "packed_sfen.h"
#include <cstdint>
namespace Stockfish {
class Position;
struct StateInfo;
class Thread;
}
namespace Stockfish::Tools {
int set_from_packed_sfen(Position& pos, const PackedSfen& sfen, StateInfo* si, Thread* th, bool frc);
PackedSfen sfen_pack(Position& pos, bool resetCastlingRights);
}
#endif
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#include "sfen_stream.h"
#include "packed_sfen.h"
#include "misc.h"
#include <string>
#include <vector>
#include <deque>
#include <memory>
#include <mutex>
#include <list>
#include <atomic>
#include <optional>
#include <iostream>
#include <cstdint>
#include <thread>
#include <functional>
namespace Stockfish::Tools{
enum struct SfenReaderMode
{
Sequential,
Cyclic
};
// Sfen reader
struct SfenReader
{
// Number of phases buffered by each thread 0.1M phases. 4M phase at 40HT
static constexpr size_t DEFAULT_THREAD_BUFFER_SIZE = 10 * 1000;
// Buffer for reading files (If this is made larger,
// the shuffle becomes larger and the phases may vary.
// If it is too large, the memory consumption will increase.
// SFEN_READ_SIZE is a multiple of THREAD_BUFFER_SIZE.
static constexpr const size_t DEFAULT_SFEN_READ_SIZE = 1000 * 1000 * 10;
// Do not use std::random_device().
// Because it always the same integers on MinGW.
SfenReader(
const std::vector<std::string>& filenames_,
bool do_shuffle,
SfenReaderMode mode_,
int thread_num,
const std::string& seed,
size_t read_size = DEFAULT_SFEN_READ_SIZE,
size_t buffer_size = DEFAULT_THREAD_BUFFER_SIZE
) :
filenames(filenames_.begin(), filenames_.end()),
mode(mode_),
// Due to the implementation of waiting for buffer empty a bit
// the read size must be at least twice the buffer size.
sfen_read_size(std::max(read_size, buffer_size * 2)),
thread_buffer_size(buffer_size),
prng(seed)
{
packed_sfens.resize(thread_num);
total_read = 0;
end_of_files = false;
shuffle = do_shuffle;
stop_flag = false;
num_buffers_in_pool.store(0);
file_worker_thread = std::thread([&] {
this->file_read_worker();
});
}
~SfenReader()
{
stop_flag = true;
if (file_worker_thread.joinable())
file_worker_thread.join();
}
// Load the phase for calculation such as mse.
PSVector read_some(uint64_t count, uint64_t count_tries, std::function<bool(const PackedSfenValue&)> do_take)
{
PSVector psv;
psv.reserve(count);
for (uint64_t i = 0; i < count_tries; ++i)
{
PackedSfenValue ps;
if (!read_to_thread_buffer(0, ps))
{
std::cout << "ERROR (sfen_reader): Reading failed." << std::endl;
return psv;
}
if (do_take(ps))
{
psv.push_back(ps);
if (psv.size() >= count)
break;
}
}
return psv;
}
// [ASYNC] Thread returns one aspect. Otherwise returns false.
bool read_to_thread_buffer(size_t thread_id, PackedSfenValue& ps)
{
// If there are any positions left in the thread buffer
// then retrieve one and return it.
auto& thread_ps = packed_sfens[thread_id];
// Fill the read buffer if there is no remaining buffer,
// but if it doesn't even exist, finish.
// If the buffer is empty, fill it.
if ((thread_ps == nullptr || thread_ps->empty())
&& !read_to_thread_buffer_impl(thread_id))
return false;
// read_to_thread_buffer_impl() returned true,
// Since the filling of the thread buffer with the
// phase has been completed successfully
// thread_ps->rbegin() is alive.
ps = thread_ps->back();
thread_ps->pop_back();
// If you've run out of buffers, call delete yourself to free this buffer.
if (thread_ps->empty())
{
thread_ps.reset();
}
return true;
}
// [ASYNC] Read some aspects into thread buffer.
bool read_to_thread_buffer_impl(size_t thread_id)
{
while (true)
{
{
std::unique_lock<std::mutex> lk(mutex);
// If you can fill from the file buffer, that's fine.
if (packed_sfens_pool.size() != 0)
{
// It seems that filling is possible, so fill and finish.
packed_sfens[thread_id] = std::move(packed_sfens_pool.front());
packed_sfens_pool.pop_front();
num_buffers_in_pool.fetch_sub(1);
total_read += thread_buffer_size;
return true;
}
}
// The file to read is already gone. No more use.
if (end_of_files)
return false;
// Waiting for file worker to fill packed_sfens_pool.
// The mutex isn't locked, so it should fill up soon.
// Poor man's condition variable.
sleep(1);
}
}
void file_read_worker()
{
std::string currentFilename;
uint64_t numEntriesReadFromCurrentFile = 0;
auto open_next_file = [&]() {
// no more
for(;;)
{
sfen_input_stream.reset();
if (filenames.empty())
return false;
// Get the next file name.
currentFilename = filenames.front();
filenames.pop_front();
numEntriesReadFromCurrentFile = 0;
sfen_input_stream = open_sfen_input_file(currentFilename);
auto out = sync_region_cout.new_region();
if (sfen_input_stream == nullptr)
{
out << "INFO (sfen_reader): File does not exist: " << currentFilename << '\n';
}
else
{
out << "INFO (sfen_reader): Opened file for reading: " << currentFilename << '\n';
// in case the file is empty or was deleted.
if (sfen_input_stream->eof())
{
out << " - File empty, nothing to read.\n";
}
else
{
return true;
}
}
}
};
if (sfen_input_stream == nullptr && !open_next_file())
{
auto out = sync_region_cout.new_region();
out << "INFO (sfen_reader): End of files." << std::endl;
end_of_files = true;
return;
}
// We want to set the `end_of_files` only after we read everything AND copy to the buffer pool.
bool local_end_of_files = false;
while (!local_end_of_files)
{
// Wait for the buffer to run out.
// This size() is read only, so you don't need to lock it.
while (!stop_flag && num_buffers_in_pool.load() >= sfen_read_size / thread_buffer_size)
sleep(100);
if (stop_flag)
return;
PSVector sfens;
sfens.reserve(sfen_read_size);
// Read from the file into the file buffer.
while (sfens.size() < sfen_read_size)
{
std::optional<PackedSfenValue> p = sfen_input_stream->next();
if (p.has_value())
{
sfens.push_back(*p);
++numEntriesReadFromCurrentFile;
}
else
{
if (mode == SfenReaderMode::Cyclic
&& numEntriesReadFromCurrentFile > 0)
{
// The file contained data so we add it again to the end of the queue.
filenames.emplace_back(currentFilename);
}
if(!open_next_file())
{
// There was no next file. Abort.
auto out = sync_region_cout.new_region();
out << "INFO (sfen_reader): End of files." << std::endl;
local_end_of_files = true;
break;
}
}
}
// Shuffle the read phase data.
if (shuffle)
{
Algo::shuffle(sfens, prng);
}
std::vector<std::unique_ptr<PSVector>> buffers;
for (size_t offset = 0; offset < sfens.size(); offset += thread_buffer_size)
{
const size_t count =
offset + thread_buffer_size > sfens.size()
? sfens.size() - offset
: thread_buffer_size;
// Delete this pointer on the receiving side.
auto buf = std::make_unique<PSVector>();
buf->resize(count);
memcpy(
buf->data(),
&sfens[offset],
sizeof(PackedSfenValue) * count);
buffers.emplace_back(std::move(buf));
}
{
std::unique_lock<std::mutex> lk(mutex);
// The mutex lock is required because the%
// contents of packed_sfens_pool are changed.
for (auto& buf : buffers)
{
num_buffers_in_pool.fetch_add(1);
packed_sfens_pool.emplace_back(std::move(buf));
}
}
}
end_of_files = true;
}
protected:
// worker thread reading file in background
std::thread file_worker_thread;
// sfen files
std::deque<std::string> filenames;
std::atomic<bool> stop_flag;
// number of phases read (file to memory buffer)
std::atomic<uint64_t> total_read;
// Do not shuffle when reading the phase.
bool shuffle;
SfenReaderMode mode;
size_t sfen_read_size;
size_t thread_buffer_size;
// Random number to shuffle when reading the phase
PRNG prng;
// Did you read the files and reached the end?
std::atomic<bool> end_of_files;
// handle of sfen file
std::unique_ptr<BasicSfenInputStream> sfen_input_stream;
// sfen for each thread
// (When the thread is used up, the thread should call delete to release it.)
std::vector<std::unique_ptr<PSVector>> packed_sfens;
// Mutex when accessing packed_sfens_pool
std::mutex mutex;
// pool of sfen. The worker thread read from the file is added here.
// Each worker thread fills its own packed_sfens[thread_id] from here.
// * Lock and access the mutex.
std::list<std::unique_ptr<PSVector>> packed_sfens_pool;
std::atomic<size_t> num_buffers_in_pool;
};
}
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#ifndef _SFEN_STREAM_H_
#define _SFEN_STREAM_H_
#include "packed_sfen.h"
#include "extra/nnue_data_binpack_format.h"
#include <optional>
#include <fstream>
#include <string>
#include <memory>
namespace Stockfish::Tools {
enum struct SfenOutputType
{
Bin,
Binpack
};
static bool ends_with(const std::string& lhs, const std::string& end)
{
if (end.size() > lhs.size()) return false;
return std::equal(end.rbegin(), end.rend(), lhs.rbegin());
}
static bool has_extension(const std::string& filename, const std::string& extension)
{
return ends_with(filename, "." + extension);
}
static std::string filename_with_extension(const std::string& filename, const std::string& ext)
{
if (ends_with(filename, ext))
{
return filename;
}
else
{
return filename + "." + ext;
}
}
struct BasicSfenInputStream
{
virtual std::optional<PackedSfenValue> next() = 0;
virtual bool eof() const = 0;
virtual ~BasicSfenInputStream() {}
};
struct BinSfenInputStream : BasicSfenInputStream
{
static constexpr auto openmode = std::ios::in | std::ios::binary;
static inline const std::string extension = "bin";
BinSfenInputStream(std::string filename) :
m_stream(filename, openmode),
m_eof(!m_stream)
{
}
std::optional<PackedSfenValue> next() override
{
PackedSfenValue e;
if(m_stream.read(reinterpret_cast<char*>(&e), sizeof(PackedSfenValue)))
{
return e;
}
else
{
m_eof = true;
return std::nullopt;
}
}
bool eof() const override
{
return m_eof;
}
~BinSfenInputStream() override {}
private:
std::fstream m_stream;
bool m_eof;
};
struct BinpackSfenInputStream : BasicSfenInputStream
{
static constexpr auto openmode = std::ios::in | std::ios::binary;
static inline const std::string extension = "binpack";
BinpackSfenInputStream(std::string filename) :
m_stream(filename, openmode),
m_eof(!m_stream.hasNext())
{
}
std::optional<PackedSfenValue> next() override
{
static_assert(sizeof(binpack::nodchip::PackedSfenValue) == sizeof(PackedSfenValue));
if (!m_stream.hasNext())
{
m_eof = true;
return std::nullopt;
}
auto training_data_entry = m_stream.next();
auto v = binpack::trainingDataEntryToPackedSfenValue(training_data_entry);
PackedSfenValue psv;
// same layout, different types. One is from generic library.
std::memcpy(&psv, &v, sizeof(PackedSfenValue));
return psv;
}
bool eof() const override
{
return m_eof;
}
~BinpackSfenInputStream() override {}
private:
binpack::CompressedTrainingDataEntryReader m_stream;
bool m_eof;
};
struct BasicSfenOutputStream
{
virtual void write(const PSVector& sfens) = 0;
virtual ~BasicSfenOutputStream() {}
};
struct BinSfenOutputStream : BasicSfenOutputStream
{
static constexpr auto openmode = std::ios::out | std::ios::binary | std::ios::app;
static inline const std::string extension = "bin";
BinSfenOutputStream(std::string filename) :
m_stream(filename_with_extension(filename, extension), openmode)
{
}
void write(const PSVector& sfens) override
{
m_stream.write(reinterpret_cast<const char*>(sfens.data()), sizeof(PackedSfenValue) * sfens.size());
}
~BinSfenOutputStream() override {}
private:
std::fstream m_stream;
};
struct BinpackSfenOutputStream : BasicSfenOutputStream
{
static constexpr auto openmode = std::ios::out | std::ios::binary | std::ios::app;
static inline const std::string extension = "binpack";
BinpackSfenOutputStream(std::string filename) :
m_stream(filename_with_extension(filename, extension), openmode)
{
}
void write(const PSVector& sfens) override
{
static_assert(sizeof(binpack::nodchip::PackedSfenValue) == sizeof(PackedSfenValue));
for(auto& sfen : sfens)
{
// The library uses a type that's different but layout-compatibile.
binpack::nodchip::PackedSfenValue e;
std::memcpy(&e, &sfen, sizeof(binpack::nodchip::PackedSfenValue));
m_stream.addTrainingDataEntry(binpack::packedSfenValueToTrainingDataEntry(e));
}
}
~BinpackSfenOutputStream() override {}
private:
binpack::CompressedTrainingDataEntryWriter m_stream;
};
inline std::unique_ptr<BasicSfenInputStream> open_sfen_input_file(const std::string& filename)
{
if (has_extension(filename, BinSfenInputStream::extension))
return std::make_unique<BinSfenInputStream>(filename);
else if (has_extension(filename, BinpackSfenInputStream::extension))
return std::make_unique<BinpackSfenInputStream>(filename);
return nullptr;
}
inline std::unique_ptr<BasicSfenOutputStream> create_new_sfen_output(const std::string& filename, SfenOutputType sfen_output_type)
{
switch(sfen_output_type)
{
case SfenOutputType::Bin:
return std::make_unique<BinSfenOutputStream>(filename);
case SfenOutputType::Binpack:
return std::make_unique<BinpackSfenOutputStream>(filename);
}
assert(false);
return nullptr;
}
inline std::unique_ptr<BasicSfenOutputStream> create_new_sfen_output(const std::string& filename)
{
if (has_extension(filename, BinSfenOutputStream::extension))
return std::make_unique<BinSfenOutputStream>(filename);
else if (has_extension(filename, BinpackSfenOutputStream::extension))
return std::make_unique<BinpackSfenOutputStream>(filename);
return nullptr;
}
}
#endif
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#include "packed_sfen.h"
#include "sfen_stream.h"
#include "misc.h"
#include "extra/nnue_data_binpack_format.h"
#include "syzygy/tbprobe.h"
#include <cstring>
#include <fstream>
#include <limits>
#include <list>
#include <memory>
#include <optional>
#include <shared_mutex>
#include <thread>
#include <atomic>
namespace Stockfish::Tools {
// Helper class for exporting Sfen
struct SfenWriter
{
// Amount of sfens required to flush the buffer.
static constexpr size_t SFEN_WRITE_SIZE = 5000;
// File name to write and number of threads to create
SfenWriter(std::string filename_, int thread_num, uint64_t save_count, SfenOutputType sfen_output_type)
{
sfen_buffers_pool.reserve((size_t)thread_num * 10);
sfen_buffers.resize(thread_num);
auto out = sync_region_cout.new_region();
out << "INFO (sfen_writer): Creating new data file at " << filename_ << std::endl;
sfen_format = sfen_output_type;
output_file_stream = create_new_sfen_output(filename_, sfen_format);
filename = filename_;
save_every = save_count;
finished = false;
file_worker_thread = std::thread([&] { this->file_write_worker(); });
}
~SfenWriter()
{
flush();
finished = true;
file_worker_thread.join();
output_file_stream.reset();
#if !defined(NDEBUG)
{
// All buffers should be empty since file_worker_thread
// should have written everything before exiting.
for (const auto& p : sfen_buffers) { assert(p == nullptr); (void)p ; }
assert(sfen_buffers_pool.empty());
}
#endif
}
void write(size_t thread_id, const PackedSfenValue& psv)
{
// We have a buffer for each thread and add it there.
// If the buffer overflows, write it to a file.
// This buffer is prepared for each thread.
auto& buf = sfen_buffers[thread_id];
// Secure since there is no buf at the first time
// and immediately after writing the thread buffer.
if (!buf)
{
buf = std::make_unique<PSVector>();
buf->reserve(SFEN_WRITE_SIZE);
}
// Buffer is exclusive to this thread.
// There is no need for a critical section.
buf->push_back(psv);
if (buf->size() >= SFEN_WRITE_SIZE)
{
// If you load it in sfen_buffers_pool, the worker will do the rest.
// Critical section since sfen_buffers_pool is shared among threads.
std::unique_lock<std::mutex> lk(mutex);
sfen_buffers_pool.emplace_back(std::move(buf));
}
}
void flush()
{
for (size_t i = 0; i < sfen_buffers.size(); ++i)
{
flush(i);
}
}
// Move what remains in the buffer for your thread to a buffer for writing to a file.
void flush(size_t thread_id)
{
std::unique_lock<std::mutex> lk(mutex);
auto& buf = sfen_buffers[thread_id];
// There is a case that buf==nullptr, so that check is necessary.
if (buf && buf->size() != 0)
{
sfen_buffers_pool.emplace_back(std::move(buf));
}
}
// Dedicated thread to write to file
void file_write_worker()
{
while (!finished || sfen_buffers_pool.size())
{
std::vector<std::unique_ptr<PSVector>> buffers;
{
std::unique_lock<std::mutex> lk(mutex);
// Atomically swap take the filled buffers and
// create a new buffer pool for threads to fill.
buffers = std::move(sfen_buffers_pool);
sfen_buffers_pool = std::vector<std::unique_ptr<PSVector>>();
}
if (!buffers.size())
{
// Poor man's condition variable.
sleep(100);
}
else
{
for (auto& buf : buffers)
{
output_file_stream->write(*buf);
sfen_write_count += buf->size();
// Add the processed number here, and if it exceeds save_every,
// change the file name and reset this counter.
sfen_write_count_current_file += buf->size();
if (sfen_write_count_current_file >= save_every)
{
sfen_write_count_current_file = 0;
// Sequential number attached to the file
int n = (int)(sfen_write_count / save_every);
// Rename the file and open it again.
// Add ios::app in consideration of overwriting.
// (Depending on the operation, it may not be necessary.)
std::string new_filename = filename + "_" + std::to_string(n);
output_file_stream = create_new_sfen_output(new_filename, sfen_format);
auto out = sync_region_cout.new_region();
out << "INFO (sfen_writer): Creating new data file at " << new_filename << std::endl;
}
}
}
}
}
private:
std::unique_ptr<BasicSfenOutputStream> output_file_stream;
// A new net is saved after every save_every sfens are processed.
uint64_t save_every = std::numeric_limits<uint64_t>::max();
// File name passed in the constructor
std::string filename;
// Thread to write to the file
std::thread file_worker_thread;
// Flag that all threads have finished
std::atomic<bool> finished;
SfenOutputType sfen_format;
// buffer before writing to file
// sfen_buffers is the buffer for each thread
// sfen_buffers_pool is a buffer for writing.
// After loading the phase in the former buffer by SFEN_WRITE_SIZE,
// transfer it to the latter.
std::vector<std::unique_ptr<PSVector>> sfen_buffers;
std::vector<std::unique_ptr<PSVector>> sfen_buffers_pool;
// Mutex required to access sfen_buffers_pool
std::mutex mutex;
// Number of sfens written in total, and the
// number of sfens written in the current file.
uint64_t sfen_write_count = 0;
uint64_t sfen_write_count_current_file = 0;
};
}
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#ifndef _STATS_H_
#define _STATS_H_
#include <sstream>
namespace Stockfish::Tools::Stats {
void gather_statistics(std::istringstream& is);
}
#endif
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#include "training_data_generator.h"
#include "sfen_writer.h"
#include "packed_sfen.h"
#include "opening_book.h"
#include "misc.h"
#include "position.h"
#include "thread.h"
#include "tt.h"
#include "uci.h"
#include "extra/nnue_data_binpack_format.h"
#include "nnue/evaluate_nnue.h"
#include "syzygy/tbprobe.h"
#include <atomic>
#include <chrono>
#include <climits>
#include <cmath>
#include <cstring>
#include <fstream>
#include <iomanip>
#include <limits>
#include <list>
#include <memory>
#include <optional>
#include <random>
#include <shared_mutex>
#include <sstream>
#include <unordered_set>
using namespace std;
namespace Stockfish::Tools
{
// Class to generate sfen with multiple threads
struct TrainingDataGenerator
{
struct Params
{
// Min and max depths for search during gensfen
int search_depth_min = 3;
int search_depth_max = -1;
// Number of the nodes to be searched.
// 0 represents no limits.
uint64_t nodes = 0;
// Upper limit of evaluation value of generated situation
int eval_limit = 3000;
// minimum ply with random move
// maximum ply with random move
// Number of random moves in one station
int random_move_minply = 1;
int random_move_maxply = 24;
int random_move_count = 5;
// Move kings with a probability of 1/N when randomly moving like Apery software.
// When you move the king again, there is a 1/N chance that it will randomly moved
// once in the opponent's turn.
// Apery has N=2. Specifying 0 here disables this function.
int random_move_like_apery = 0;
// For when using multi pv instead of random move.
// random_multi_pv is the number of candidates for MultiPV.
// When adopting the move of the candidate move, the difference
// between the evaluation value of the move of the 1st place
// and the evaluation value of the move of the Nth place is.
// Must be in the range random_multi_pv_diff.
// random_multi_pv_depth is the search depth for MultiPV.
int random_multi_pv = 0;
int random_multi_pv_diff = 32000;
int random_multi_pv_depth = -1;
uint64_t random_multi_pv_nodes = 0;
// The minimum and maximum ply (number of steps from
// the initial phase) of the sfens to write out.
int write_minply = 16;
int write_maxply = 400;
uint64_t save_every = std::numeric_limits<uint64_t>::max();
std::string output_file_name = "training_data";
SfenOutputType sfen_format = SfenOutputType::Binpack;
std::string seed;
bool write_out_draw_game_in_training_data_generation = true;
bool detect_draw_by_consecutive_low_score = true;
bool detect_draw_by_insufficient_mating_material = true;
uint64_t num_threads;
std::string book;
void enforce_constraints()
{
search_depth_max = std::max(search_depth_min, search_depth_max);
// Limit the maximum to a one-stop score. (Otherwise you might not end the loop)
eval_limit = std::min(eval_limit, (int)mate_in(2));
save_every = std::max(save_every, REPORT_STATS_EVERY);
num_threads = Options["Threads"];
if (random_multi_pv_depth == -1)
random_multi_pv_depth = search_depth_max;
if (random_multi_pv_nodes == 0)
random_multi_pv_nodes = nodes;
}
};
// Hash to limit the export of identical sfens
static constexpr uint64_t GENSFEN_HASH_SIZE = 64 * 1024 * 1024;
// It must be 2**N because it will be used as the mask to calculate hash_index.
static_assert((GENSFEN_HASH_SIZE& (GENSFEN_HASH_SIZE - 1)) == 0);
static constexpr uint64_t REPORT_DOT_EVERY = 5000;
static constexpr uint64_t REPORT_STATS_EVERY = 200000;
static_assert(REPORT_STATS_EVERY % REPORT_DOT_EVERY == 0);
TrainingDataGenerator(
const Params& prm
) :
params(prm),
sfen_writer(prm.output_file_name, prm.num_threads, prm.save_every, prm.sfen_format)
{
hash.resize(GENSFEN_HASH_SIZE);
prngs.reserve(prm.num_threads);
auto seed = prm.seed;
for (uint64_t i = 0; i < prm.num_threads; ++i)
{
prngs.emplace_back(seed);
seed = prngs.back().next_random_seed();
}
if (!prm.book.empty())
{
opening_book = open_opening_book(prm.book, prngs[0]);
if (opening_book == nullptr)
{
std::cout << "WARNING: Failed to open opening book " << prm.book << ". Falling back to startpos.\n";
}
}
// Output seed to veryfy by the user if it's not identical by chance.
std::cout << prngs[0] << std::endl;
}
void generate(uint64_t limit, uint64_t limit_seconds);
private:
Params params;
std::vector<PRNG> prngs;
std::mutex stats_mutex;
TimePoint last_stats_report_time;
// sfen exporter
SfenWriter sfen_writer;
SynchronizedRegionLogger::Region out;
vector<Key> hash; // 64MB*sizeof(HASH_KEY) = 512MB
std::unique_ptr<OpeningBook> opening_book;
static void set_gensfen_search_limits();
void generate_worker(
Thread& th,
std::atomic<uint64_t>& counter,
uint64_t limit,
uint64_t limit_seconds);
bool was_seen_before(const Position& pos);
optional<int8_t> get_current_game_result(
Position& pos,
const vector<int>& move_hist_scores) const;
vector<uint8_t> generate_random_move_flags(PRNG& prng);
optional<Move> choose_random_move(
PRNG& prng,
Position& pos,
std::vector<uint8_t>& random_move_flag,
int ply,
int& random_move_c);
bool commit_psv(
Thread& th,
PSVector& sfens,
int8_t lastTurnIsWin,
std::atomic<uint64_t>& counter,
uint64_t limit,
Color result_color);
void initial_report();
void report(uint64_t done, uint64_t new_done);
void maybe_report(uint64_t done);
};
void TrainingDataGenerator::set_gensfen_search_limits()
{
// About Search::Limits
// Be careful because this member variable is global and affects other threads.
auto& limits = Search::Limits;
// Make the search equivalent to the "go infinite" command. (Because it is troublesome if time management is done)
limits.infinite = true;
// Since PV is an obstacle when displayed, erase it.
limits.silent = true;
// If you use this, it will be compared with the accumulated nodes of each thread. Therefore, do not use it.
limits.nodes = 0;
// depth is also processed by the one passed as an argument of Tools::search().
limits.depth = 0;
}
void TrainingDataGenerator::generate(uint64_t limit, uint64_t limit_seconds)
{
last_stats_report_time = 0;
initial_report();
set_gensfen_search_limits();
std::atomic<uint64_t> counter{0};
Threads.execute_with_workers([&counter, limit, limit_seconds, this](Thread& th) {
generate_worker(th, counter, limit, limit_seconds);
});
Threads.wait_for_workers_finished();
sfen_writer.flush();
report(counter.load(), counter.load() % REPORT_STATS_EVERY + 1);
std::cout << std::endl;
}
void TrainingDataGenerator::generate_worker(
Thread& th,
std::atomic<uint64_t>& counter,
uint64_t limit,
uint64_t limit_seconds)
{
// For the time being, it will be treated as a draw
// at the maximum number of steps to write.
// Maximum StateInfo + Search PV to advance to leaf buffer
std::vector<StateInfo, AlignedAllocator<StateInfo>> states(
params.write_maxply + MAX_PLY /* == search_depth_min + α */);
StateInfo si;
auto& prng = prngs[th.id()];
// end flag
bool quit = false;
const auto start_time = now();
const bool frc = Options["UCI_Chess960"];
// repeat until the specified number of times
while (!quit)
{
if (static_cast<uint64_t>(now() - start_time) / 1000 >= limit_seconds)
{
break;
}
// It is necessary to set a dependent thread for Position.
// When parallelizing, Threads (since this is a vector<Thread*>,
// Do the same for up to Threads[0]...Threads[thread_num-1].
auto& pos = th.rootPos;
if (opening_book != nullptr)
{
auto& fen = opening_book->next_fen();
pos.set(fen, frc, &si, &th);
}
else
{
pos.set(StartFEN, frc, &si, &th);
}
int resign_counter = 0;
bool should_resign = prng.rand(10) > 1;
// Vector for holding the sfens in the current simulated game.
PSVector packed_sfens;
packed_sfens.reserve(params.write_maxply + MAX_PLY);
// Precomputed flags. Used internally by choose_random_move.
vector<uint8_t> random_move_flag = generate_random_move_flags(prng);
// A counter that keeps track of the number of random moves
// When random_move_minply == -1, random moves are
// performed continuously, so use it at this time.
// Used internally by choose_random_move.
int actual_random_move_count = 0;
// Save history of move scores for adjudication
vector<int> move_hist_scores;
auto flush_psv = [&](int8_t result) {
quit = commit_psv(th, packed_sfens, result, counter, limit, pos.side_to_move());
};
for (int ply = 0; ; ++ply)
{
// Current search depth
const int depth = params.search_depth_min + (int)prng.rand(params.search_depth_max - params.search_depth_min + 1);
// Starting search calls init_for_search
auto [search_value, search_pv] = Search::search(pos, depth, 1, params.nodes);
// This has to be performed after search because it needs to know
// rootMoves which are filled in init_for_search.
const auto result = get_current_game_result(pos, move_hist_scores);
if (result.has_value())
{
flush_psv(result.value());
break;
}
// Always adjudivate by eval limit.
// Also because of this we don't have to check for TB/MATE scores
if (abs(search_value) >= params.eval_limit)
{
resign_counter++;
if ((should_resign && resign_counter >= 4) || abs(search_value) >= VALUE_KNOWN_WIN) {
flush_psv((search_value >= params.eval_limit) ? 1 : -1);
break;
}
}
else
{
resign_counter = 0;
}
// In case there is no PV and the game was not ended here
// there is nothing we can do, we can't continue the game,
// we don't know the result, so discard this game.
if (search_pv.empty())
{
break;
}
// Save the move score for adjudication.
move_hist_scores.push_back(search_value);
// Discard stuff before write_minply is reached
// because it can harm training due to overfitting.
// Initial positions would be too common.
if (ply >= params.write_minply && !was_seen_before(pos))
{
auto& psv = packed_sfens.emplace_back();
// Here we only write the position data.
// Result is added after the whole game is done.
pos.sfen_pack(psv.sfen, pos.is_chess960());
psv.score = search_value;
psv.move = search_pv[0];
psv.gamePly = ply;
}
// Update the next move according to best search result or random move.
auto random_move = choose_random_move(prng, pos, random_move_flag, ply, actual_random_move_count);
const Move next_move = random_move.has_value() ? *random_move : search_pv[0];
// We don't have the whole game yet, but it ended,
// so the writing process ends and the next game starts.
// This shouldn't really happen.
if (!is_ok(next_move))
{
break;
}
// Do move.
pos.do_move(next_move, states[ply]);
}
}
}
bool TrainingDataGenerator::was_seen_before(const Position& pos)
{
// Look into the position hashtable to see if the same
// position was seen before.
// This is a good heuristic to exlude already seen
// positions without many false positives.
auto key = pos.key();
auto hash_index = (size_t)(key & (GENSFEN_HASH_SIZE - 1));
auto old_key = hash[hash_index];
if (key == old_key)
{
return true;
}
else
{
// Replace with the current key.
hash[hash_index] = key;
return false;
}
}
optional<int8_t> TrainingDataGenerator::get_current_game_result(
Position& pos,
const vector<int>& move_hist_scores) const
{
// Variables for draw adjudication.
// Todo: Make this as an option.
// start the adjudication when ply reaches this value
constexpr int adj_draw_ply = 80;
// 4 move scores for each side have to be checked
constexpr int adj_draw_cnt = 8;
// move score in CP
constexpr int adj_draw_score = 0;
// For the time being, it will be treated as a
// draw at the maximum number of steps to write.
const int ply = move_hist_scores.size();
// has it reached the max length or is a draw by fifty-move rule
// or by 3-fold repetition
if (ply >= params.write_maxply
|| pos.is_fifty_move_draw()
|| pos.is_three_fold_repetition())
{
return 0;
}
if(pos.this_thread()->rootMoves.empty())
{
// If there is no legal move
return pos.checkers()
? -1 /* mate */
: 0 /* stalemate */;
}
// Adjudicate game to a draw if the last 4 scores of each engine is 0.
if (params.detect_draw_by_consecutive_low_score)
{
if (ply >= adj_draw_ply)
{
int num_cons_plies_within_draw_score = 0;
bool is_adj_draw = false;
for (auto it = move_hist_scores.rbegin();
it != move_hist_scores.rend(); ++it)
{
if (abs(*it) <= adj_draw_score)
{
num_cons_plies_within_draw_score++;
}
else
{
// Draw scores must happen on consecutive plies
break;
}
if (num_cons_plies_within_draw_score >= adj_draw_cnt)
{
is_adj_draw = true;
break;
}
}
if (is_adj_draw)
{
return 0;
}
}
}
// Draw by insufficient mating material
if (params.detect_draw_by_insufficient_mating_material)
{
if (pos.count<ALL_PIECES>() <= 4)
{
int num_pieces = pos.count<ALL_PIECES>();
// (1) KvK
if (num_pieces == 2)
{
return 0;
}
// (2) KvK + 1 minor piece
if (num_pieces == 3)
{
int minor_pc = pos.count<BISHOP>(WHITE) + pos.count<KNIGHT>(WHITE) +
pos.count<BISHOP>(BLACK) + pos.count<KNIGHT>(BLACK);
if (minor_pc == 1)
{
return 0;
}
}
// (3) KBvKB, bishops of the same color
else if (num_pieces == 4)
{
if (pos.count<BISHOP>(WHITE) == 1 && pos.count<BISHOP>(BLACK) == 1)
{
// Color of bishops is black.
if ((pos.pieces(WHITE, BISHOP) & DarkSquares)
&& (pos.pieces(BLACK, BISHOP) & DarkSquares))
{
return 0;
}
// Color of bishops is white.
if ((pos.pieces(WHITE, BISHOP) & ~DarkSquares)
&& (pos.pieces(BLACK, BISHOP) & ~DarkSquares))
{
return 0;
}
}
}
}
}
return nullopt;
}
vector<uint8_t> TrainingDataGenerator::generate_random_move_flags(PRNG& prng)
{
vector<uint8_t> random_move_flag;
// Depending on random move selection parameters setup
// the array of flags that indicates whether a random move
// be taken at a given ply.
// Make an array like a[0] = 0 ,a[1] = 1, ...
// Fisher-Yates shuffle and take out the first N items.
// Actually, I only want N pieces, so I only need
// to shuffle the first N pieces with Fisher-Yates.
vector<int> a;
a.reserve((size_t)params.random_move_maxply);
// random_move_minply ,random_move_maxply is specified by 1 origin,
// Note that we are handling 0 origin here.
for (int i = std::max(params.random_move_minply - 1, 0); i < params.random_move_maxply; ++i)
{
a.push_back(i);
}
// In case of Apery random move, insert() may be called random_move_count times.
// Reserve only the size considering it.
random_move_flag.resize((size_t)params.random_move_maxply + params.random_move_count);
// A random move that exceeds the size() of a[] cannot be applied, so limit it.
for (int i = 0; i < std::min(params.random_move_count, (int)a.size()); ++i)
{
swap(a[i], a[prng.rand((uint64_t)a.size() - i) + i]);
random_move_flag[a[i]] = true;
}
return random_move_flag;
}
optional<Move> TrainingDataGenerator::choose_random_move(
PRNG& prng,
Position& pos,
std::vector<uint8_t>& random_move_flag,
int ply,
int& random_move_c)
{
optional<Move> random_move;
// Randomly choose one from legal move
if (
// 1. Random move of random_move_count times from random_move_minply to random_move_maxply
(params.random_move_minply != -1 && ply < (int)random_move_flag.size() && random_move_flag[ply]) ||
// 2. A mode to perform random move of random_move_count times after leaving the startpos
(params.random_move_minply == -1 && random_move_c < params.random_move_count))
{
++random_move_c;
// It's not a mate, so there should be one legal move...
if (params.random_multi_pv == 0)
{
// Normal random move
MoveList<LEGAL> list(pos);
// I don't really know the goodness and badness of making this the Apery method.
if (params.random_move_like_apery == 0
|| prng.rand(params.random_move_like_apery) != 0)
{
// Normally one move from legal move
random_move = list.at((size_t)prng.rand((uint64_t)list.size()));
}
else
{
// if you can move the king, move the king
Move moves[8]; // Near 8
Move* p = &moves[0];
for (auto& m : list)
{
if (type_of(pos.moved_piece(m)) == KING)
{
*(p++) = m;
}
}
size_t n = p - &moves[0];
if (n != 0)
{
// move to move the king
random_move = moves[prng.rand(n)];
// In Apery method, at this time there is a 1/2 chance
// that the opponent will also move randomly
if (prng.rand(2) == 0)
{
// Is it a simple hack to add a "1" next to random_move_flag[ply]?
random_move_flag.insert(random_move_flag.begin() + ply + 1, 1, true);
}
}
else
{
// Normally one move from legal move
random_move = list.at((size_t)prng.rand((uint64_t)list.size()));
}
}
}
else
{
Search::search(pos, params.random_multi_pv_depth, params.random_multi_pv, params.random_multi_pv_nodes);
// Select one from the top N hands of root Moves
auto& rm = pos.this_thread()->rootMoves;
uint64_t s = min((uint64_t)rm.size(), (uint64_t)params.random_multi_pv);
for (uint64_t i = 1; i < s; ++i)
{
// The difference from the evaluation value of rm[0] must
// be within the range of random_multi_pv_diff.
// It can be assumed that rm[x].score is arranged in descending order.
if (rm[0].score > rm[i].score + params.random_multi_pv_diff)
{
s = i;
break;
}
}
random_move = rm[prng.rand(s)].pv[0];
}
}
return random_move;
}
// Write out the phases loaded in sfens to a file.
// result: win/loss in the next phase after the final phase in sfens
// 1 when winning. -1 when losing. Pass 0 for a draw.
// Return value: true if the specified number of
// sfens has already been reached and the process ends.
bool TrainingDataGenerator::commit_psv(
Thread& th,
PSVector& sfens,
int8_t result,
std::atomic<uint64_t>& counter,
uint64_t limit,
Color result_color)
{
if (!params.write_out_draw_game_in_training_data_generation && result == 0)
{
// We didn't write anything so why quit.
return false;
}
auto side_to_move_from_sfen = [](auto& sfen){
return (Color)(sfen.sfen.data[0] & 1);
};
// From the final stage (one step before) to the first stage, give information on the outcome of the game for each stage.
// The phases stored in sfens are assumed to be continuous (in order).
for (auto it = sfens.rbegin(); it != sfens.rend(); ++it)
{
// The side to move is packed as the lowest bit of the first byte
const Color side_to_move = side_to_move_from_sfen(*it);
it->game_result = side_to_move == result_color ? result : -result;
}
const bool frc = th.rootPos.is_chess960();
// Write sfens in move order to make potential compression easier
for (auto& sfen : sfens)
{
// Skip positions with castling bestmove in FRC so that we don't
// need to support it in the trainer.
if (frc && type_of((Move)sfen.move) == CASTLING)
{
continue;
}
// Return true if there is already enough data generated.
const auto iter = counter.fetch_add(1);
if (iter >= limit)
return true;
// because `iter` was done, now we do one more
maybe_report(iter + 1);
// Write out one sfen.
sfen_writer.write(th.id(), sfen);
}
return false;
}
void TrainingDataGenerator::initial_report()
{
out = sync_region_cout.new_region();
const auto now_time = now();
out
<< '\n'
<< 0 << " sfens, "
<< "at " << now_string() << endl;
last_stats_report_time = now_time;
out = sync_region_cout.new_region();
}
void TrainingDataGenerator::report(uint64_t done, uint64_t new_done)
{
const auto now_time = now();
const TimePoint elapsed = now_time - last_stats_report_time + 1;
out
<< '\n'
<< done << " sfens, "
<< new_done * 1000 / elapsed << " sfens/second, "
<< "at " << now_string() << endl;
last_stats_report_time = now_time;
out = sync_region_cout.new_region();
}
void TrainingDataGenerator::maybe_report(uint64_t done)
{
if (done % REPORT_DOT_EVERY == 0)
{
std::lock_guard lock(stats_mutex);
if (last_stats_report_time == 0)
{
last_stats_report_time = now();
out = sync_region_cout.new_region();
}
if (done != 0)
{
out << '.';
if (done % REPORT_STATS_EVERY == 0)
{
report(done, REPORT_STATS_EVERY);
}
}
}
}
// Command to generate a game record
void generate_training_data(istringstream& is)
{
// Number of generated game records default = 8 billion phases (Ponanza specification)
uint64_t loop_max = 8000000000UL;
uint64_t time_max = 8000000000UL;
TrainingDataGenerator::Params params;
// Add a random number to the end of the file name.
bool random_file_name = false;
std::string sfen_format = "binpack";
string token;
while (true)
{
token = "";
is >> token;
if (token == "")
break;
if (token == "depth")
{
is >> params.search_depth_min;
params.search_depth_max = params.search_depth_min;
}
else if (token == "min_depth")
is >> params.search_depth_min;
else if (token == "max_depth")
is >> params.search_depth_max;
else if (token == "nodes")
is >> params.nodes;
else if (token == "count")
is >> loop_max;
else if (token == "max_time_seconds")
is >> time_max;
else if (token == "max_time_minutes")
{
is >> time_max;
time_max *= 60;
}
else if (token == "max_time_hours")
{
is >> time_max;
time_max *= 3600;
}
else if (token == "output_file_name")
is >> params.output_file_name;
else if (token == "eval_limit")
is >> params.eval_limit;
else if (token == "random_move_min_ply")
is >> params.random_move_minply;
else if (token == "random_move_max_ply")
is >> params.random_move_maxply;
else if (token == "random_move_count")
is >> params.random_move_count;
else if (token == "random_move_like_apery")
is >> params.random_move_like_apery;
else if (token == "random_multi_pv")
is >> params.random_multi_pv;
else if (token == "random_multi_pv_diff")
is >> params.random_multi_pv_diff;
else if (token == "random_multi_pv_depth")
is >> params.random_multi_pv_depth;
else if (token == "random_multi_pv_nodes")
is >> params.random_multi_pv_nodes;
else if (token == "write_min_ply")
is >> params.write_minply;
else if (token == "write_max_ply")
is >> params.write_maxply;
else if (token == "save_every")
is >> params.save_every;
else if (token == "book")
is >> params.book;
else if (token == "random_file_name")
is >> random_file_name;
else if (token == "keep_draws")
is >> params.write_out_draw_game_in_training_data_generation;
else if (token == "adjudicate_draws_by_score")
is >> params.detect_draw_by_consecutive_low_score;
else if (token == "adjudicate_draws_by_insufficient_material")
is >> params.detect_draw_by_insufficient_mating_material;
else if (token == "data_format")
is >> sfen_format;
else if (token == "seed")
is >> params.seed;
else if (token == "set_recommended_uci_options")
{
UCI::setoption("Skill Level", "20");
UCI::setoption("UCI_LimitStrength", "false");
UCI::setoption("PruneAtShallowDepth", "false");
UCI::setoption("EnableTranspositionTable", "true");
}
else
{
cout << "ERROR: Unknown option " << token << ". Exiting...\n";
return;
}
}
if (!sfen_format.empty())
{
if (sfen_format == "bin")
params.sfen_format = SfenOutputType::Bin;
else if (sfen_format == "binpack")
params.sfen_format = SfenOutputType::Binpack;
else
cout << "WARNING: Unknown sfen format `" << sfen_format << "`. Using bin\n";
}
if (random_file_name)
{
// Give a random number to output_file_name at this point.
// Do not use std::random_device(). Because it always the same integers on MinGW.
PRNG r(params.seed);
// Just in case, reassign the random numbers.
for (int i = 0; i < 10; ++i)
r.rand(1);
auto to_hex = [](uint64_t u) {
std::stringstream ss;
ss << std::hex << u;
return ss.str();
};
// I don't want to wear 64bit numbers by accident, so I'next_move going to make a 64bit number 2 just in case.
params.output_file_name += "_" + to_hex(r.rand<uint64_t>()) + to_hex(r.rand<uint64_t>());
}
params.enforce_constraints();
std::cout << "INFO: Executing generate_training_data command\n";
std::cout << "INFO: Parameters:\n";
std::cout
<< " - search_depth_min = " << params.search_depth_min << endl
<< " - search_depth_max = " << params.search_depth_max << endl
<< " - nodes = " << params.nodes << endl
<< " - count = " << loop_max << endl
<< " - max_time_seconds = " << time_max << endl
<< " - eval_limit = " << params.eval_limit << endl
<< " - num threads (UCI) = " << params.num_threads << endl
<< " - random_move_min_ply = " << params.random_move_minply << endl
<< " - random_move_max_ply = " << params.random_move_maxply << endl
<< " - random_move_count = " << params.random_move_count << endl
<< " - random_move_like_apery = " << params.random_move_like_apery << endl
<< " - random_multi_pv = " << params.random_multi_pv << endl
<< " - random_multi_pv_diff = " << params.random_multi_pv_diff << endl
<< " - random_multi_pv_depth = " << params.random_multi_pv_depth << endl
<< " - random_multi_pv_nodes = " << params.random_multi_pv_nodes << endl
<< " - write_min_ply = " << params.write_minply << endl
<< " - write_max_ply = " << params.write_maxply << endl
<< " - book = " << params.book << endl
<< " - output_file_name = " << params.output_file_name << endl
<< " - save_every = " << params.save_every << endl
<< " - random_file_name = " << random_file_name << endl
<< " - write_drawn_games = " << params.write_out_draw_game_in_training_data_generation << endl
<< " - draw by low score = " << params.detect_draw_by_consecutive_low_score << endl
<< " - draw by insuff. mat. = " << params.detect_draw_by_insufficient_mating_material << endl;
// Show if the training data generator uses NNUE.
Eval::NNUE::verify();
Threads.main()->ponder = false;
TrainingDataGenerator gensfen(params);
gensfen.generate(loop_max, time_max);
std::cout << "INFO: generate_training_data finished." << endl;
}
}
-14
View File
@@ -1,14 +0,0 @@
#ifndef _GENSFEN_H_
#define _GENSFEN_H_
#include "position.h"
#include <sstream>
namespace Stockfish::Tools {
// Automatic generation of teacher position
void generate_training_data(std::istringstream& is);
}
#endif
-498
View File
@@ -1,498 +0,0 @@
#include "training_data_generator_nonpv.h"
#include "sfen_writer.h"
#include "packed_sfen.h"
#include "opening_book.h"
#include "misc.h"
#include "position.h"
#include "thread.h"
#include "tt.h"
#include "uci.h"
#include "extra/nnue_data_binpack_format.h"
#include "nnue/evaluate_nnue.h"
#include "syzygy/tbprobe.h"
#include <atomic>
#include <chrono>
#include <climits>
#include <cmath>
#include <cstring>
#include <fstream>
#include <iomanip>
#include <limits>
#include <list>
#include <memory>
#include <optional>
#include <random>
#include <shared_mutex>
#include <sstream>
#include <unordered_set>
using namespace std;
namespace Stockfish::Tools
{
// Class to generate sfen with multiple threads
struct TrainingDataGeneratorNonPv
{
struct Params
{
// The depth for search on the fens gathered during exploration
int search_depth = 3;
// the min/max number of nodes to use for exploration per ply
int exploration_min_nodes = 5000;
int exploration_max_nodes = 15000;
// The pct of positions explored that are saved for rescoring
float exploration_save_rate = 0.01;
// Upper limit of evaluation value of generated situation
int eval_limit = 4000;
// the upper limit on evaluation during exploration selfplay
int exploration_eval_limit = 4000;
int exploration_max_ply = 200;
int exploration_min_pieces = 8;
std::string output_file_name = "training_data_nonpv";
SfenOutputType sfen_format = SfenOutputType::Binpack;
std::string seed;
int num_threads;
std::string book;
bool smart_fen_skipping = false;
void enforce_constraints()
{
// Limit the maximum to a one-stop score. (Otherwise you might not end the loop)
eval_limit = std::min(eval_limit, (int)mate_in(2));
exploration_eval_limit = std::min(eval_limit, (int)mate_in(2));
exploration_min_nodes = std::max(100, exploration_min_nodes);
exploration_max_nodes = std::max(exploration_min_nodes, exploration_max_nodes);
num_threads = Options["Threads"];
}
};
static constexpr uint64_t REPORT_DOT_EVERY = 5000;
static constexpr uint64_t REPORT_STATS_EVERY = 200000;
static_assert(REPORT_STATS_EVERY % REPORT_DOT_EVERY == 0);
TrainingDataGeneratorNonPv(
const Params& prm
) :
params(prm),
prng(prm.seed),
sfen_writer(prm.output_file_name, prm.num_threads, std::numeric_limits<uint64_t>::max(), prm.sfen_format)
{
if (!prm.book.empty())
{
opening_book = open_opening_book(prm.book, prng);
if (opening_book == nullptr)
{
std::cout << "WARNING: Failed to open opening book " << prm.book << ". Falling back to startpos.\n";
}
}
// Output seed to veryfy by the user if it's not identical by chance.
std::cout << prng << std::endl;
}
void generate(uint64_t limit);
private:
Params params;
PRNG prng;
std::mutex stats_mutex;
TimePoint last_stats_report_time;
// sfen exporter
SfenWriter sfen_writer;
SynchronizedRegionLogger::Region out;
std::unique_ptr<OpeningBook> opening_book;
static void set_gensfen_search_limits();
void generate_worker(
Thread& th,
std::atomic<uint64_t>& counter,
uint64_t limit);
bool commit_psv(
Thread& th,
PSVector& sfens,
std::atomic<uint64_t>& counter,
uint64_t limit);
PSVector do_exploration(
Thread& th,
int count);
void report(uint64_t done, uint64_t new_done);
void maybe_report(uint64_t done);
};
void TrainingDataGeneratorNonPv::set_gensfen_search_limits()
{
// About Search::Limits
// Be careful because this member variable is global and affects other threads.
auto& limits = Search::Limits;
// Make the search equivalent to the "go infinite" command. (Because it is troublesome if time management is done)
limits.infinite = true;
// Since PV is an obstacle when displayed, erase it.
limits.silent = true;
// If you use this, it will be compared with the accumulated nodes of each thread. Therefore, do not use it.
limits.nodes = 0;
// depth is also processed by the one passed as an argument of Tools::search().
limits.depth = 0;
}
void TrainingDataGeneratorNonPv::generate(uint64_t limit)
{
last_stats_report_time = 0;
set_gensfen_search_limits();
std::atomic<uint64_t> counter{0};
Threads.execute_with_workers([&counter, limit, this](Thread& th) {
generate_worker(th, counter, limit);
});
Threads.wait_for_workers_finished();
sfen_writer.flush();
if (limit % REPORT_STATS_EVERY != 0)
{
report(limit, limit % REPORT_STATS_EVERY);
}
std::cout << std::endl;
}
PSVector TrainingDataGeneratorNonPv::do_exploration(
Thread& th,
int count)
{
constexpr int max_depth = 30;
PSVector psv;
std::vector<StateInfo, AlignedAllocator<StateInfo>> states(
max_depth + MAX_PLY /* == search_depth_min + α */);
th.set_eval_callback([this, &psv](Position& pos) {
if ((double)prng.rand<uint64_t>() / std::numeric_limits<uint64_t>::max() < params.exploration_save_rate)
{
psv.emplace_back();
pos.sfen_pack(psv.back().sfen, pos.is_chess960());
}
});
auto& pos = th.rootPos;
StateInfo si;
const bool frc = Options["UCI_Chess960"];
for (int i = 0; i < count; ++i)
{
if (opening_book != nullptr)
{
auto& fen = opening_book->next_fen();
pos.set(fen, frc, &si, &th);
}
else
{
pos.set(StartFEN, frc, &si, &th);
}
for(int ply = 0; ply < params.exploration_max_ply; ++ply)
{
auto nodes = prng.rand(params.exploration_max_nodes - params.exploration_min_nodes + 1) + params.exploration_min_nodes;
auto [search_value, search_pv] = Search::search(pos, max_depth, 1, nodes);
if (search_pv.empty())
{
break;
}
if (std::abs(search_value) > params.exploration_eval_limit)
{
break;
}
pos.do_move(search_pv[0], states[ply]);
if (popcount(pos.pieces()) < params.exploration_min_pieces)
{
break;
}
}
}
th.clear_eval_callback();
return psv;
}
void TrainingDataGeneratorNonPv::generate_worker(
Thread& th,
std::atomic<uint64_t>& counter,
uint64_t limit)
{
constexpr int exploration_batch_size = 1;
StateInfo si;
PSVector psv;
// end flag
bool quit = false;
const bool frc = Options["UCI_Chess960"];
// repeat until the specified number of times
while (!quit)
{
// It is necessary to set a dependent thread for Position.
// When parallelizing, Threads (since this is a vector<Thread*>,
// Do the same for up to Threads[0]...Threads[thread_num-1].
auto& pos = th.rootPos;
auto packed_sfens = do_exploration(th, exploration_batch_size);
psv.clear();
for (auto& ps : packed_sfens)
{
pos.set_from_packed_sfen(ps.sfen, &si, &th, frc);
pos.state()->rule50 = 0;
if (params.smart_fen_skipping && pos.checkers())
{
continue;
}
auto [search_value, search_pv] = Search::search(pos, params.search_depth, 1);
if (search_pv.empty())
{
continue;
}
if (std::abs(search_value) > params.eval_limit)
{
continue;
}
if (params.smart_fen_skipping && pos.capture_or_promotion(search_pv[0]))
{
continue;
}
auto& new_ps = psv.emplace_back();
pos.sfen_pack(new_ps.sfen, pos.is_chess960());
new_ps.score = search_value;
new_ps.move = search_pv[0];
new_ps.gamePly = 1;
new_ps.game_result = 0;
new_ps.padding = 0;
}
quit = commit_psv(th, psv, counter, limit);
}
}
// Write out the phases loaded in sfens to a file.
// result: win/loss in the next phase after the final phase in sfens
// 1 when winning. -1 when losing. Pass 0 for a draw.
// Return value: true if the specified number of
// sfens has already been reached and the process ends.
bool TrainingDataGeneratorNonPv::commit_psv(
Thread& th,
PSVector& sfens,
std::atomic<uint64_t>& counter,
uint64_t limit)
{
const bool frc = th.rootPos.is_chess960();
// Write sfens in move order to make potential compression easier
for (auto& sfen : sfens)
{
// Skip positions with castling bestmove in FRC so that we don't
// need to support it in the trainer.
if (frc && type_of((Move)sfen.move) == CASTLING)
{
continue;
}
// Return true if there is already enough data generated.
const auto iter = counter.fetch_add(1);
if (iter >= limit)
return true;
// because `iter` was done, now we do one more
maybe_report(iter + 1);
// Write out one sfen.
sfen_writer.write(th.id(), sfen);
}
return false;
}
void TrainingDataGeneratorNonPv::report(uint64_t done, uint64_t new_done)
{
const auto now_time = now();
const TimePoint elapsed = now_time - last_stats_report_time + 1;
out
<< endl
<< done << " sfens, "
<< new_done * 1000 / elapsed << " sfens/second, "
<< "at " << now_string() << sync_endl;
last_stats_report_time = now_time;
out = sync_region_cout.new_region();
}
void TrainingDataGeneratorNonPv::maybe_report(uint64_t done)
{
if (done % REPORT_DOT_EVERY == 0)
{
std::lock_guard lock(stats_mutex);
if (last_stats_report_time == 0)
{
last_stats_report_time = now();
out = sync_region_cout.new_region();
}
if (done != 0)
{
out << '.';
if (done % REPORT_STATS_EVERY == 0)
{
report(done, REPORT_STATS_EVERY);
}
}
}
}
// Command to generate a game record
void generate_training_data_nonpv(istringstream& is)
{
// Number of generated game records default = 8 billion phases (Ponanza specification)
TrainingDataGeneratorNonPv::Params params;
uint64_t count = 1'000'000;
// Add a random number to the end of the file name.
std::string sfen_format = "binpack";
string token;
while (true)
{
token = "";
is >> token;
if (token == "")
break;
if (token == "depth")
is >> params.search_depth;
else if (token == "count")
is >> count;
else if (token == "output_file")
is >> params.output_file_name;
else if (token == "exploration_eval_limit")
is >> params.exploration_eval_limit;
else if (token == "eval_limit")
is >> params.eval_limit;
else if (token == "exploration_min_nodes")
is >> params.exploration_min_nodes;
else if (token == "exploration_max_nodes")
is >> params.exploration_max_nodes;
else if (token == "exploration_min_pieces")
is >> params.exploration_min_pieces;
else if (token == "exploration_save_rate")
is >> params.exploration_save_rate;
else if (token == "book")
is >> params.book;
else if (token == "data_format")
is >> sfen_format;
else if (token == "seed")
is >> params.seed;
else if (token == "smart_fen_skipping")
params.smart_fen_skipping = true;
else if (token == "set_recommended_uci_options")
{
UCI::setoption("Skill Level", "20");
UCI::setoption("UCI_LimitStrength", "false");
UCI::setoption("PruneAtShallowDepth", "false");
UCI::setoption("EnableTranspositionTable", "true");
}
else
{
cout << "ERROR: Unknown option " << token << ". Exiting...\n";
return;
}
}
if (!sfen_format.empty())
{
if (sfen_format == "bin")
params.sfen_format = SfenOutputType::Bin;
else if (sfen_format == "binpack")
params.sfen_format = SfenOutputType::Binpack;
else
cout << "WARNING: Unknown sfen format `" << sfen_format << "`. Using bin\n";
}
params.enforce_constraints();
std::cout << "INFO: Executing generate_training_data_nonpv command\n";
std::cout << "INFO: Parameters:\n";
std::cout
<< " - search_depth = " << params.search_depth << endl
<< " - output_file = " << params.output_file_name << endl
<< " - exploration_eval_limit = " << params.exploration_eval_limit << endl
<< " - eval_limit = " << params.eval_limit << endl
<< " - exploration_min_nodes = " << params.exploration_min_nodes << endl
<< " - exploration_max_nodes = " << params.exploration_max_nodes << endl
<< " - exploration_min_pieces = " << params.exploration_min_pieces << endl
<< " - exploration_save_rate = " << params.exploration_save_rate << endl
<< " - book = " << params.book << endl
<< " - data_format = " << sfen_format << endl
<< " - seed = " << params.seed << endl
<< " - count = " << count << endl;
// Show if the training data generator uses NNUE.
Eval::NNUE::verify();
Threads.main()->ponder = false;
TrainingDataGeneratorNonPv gensfen(params);
gensfen.generate(count);
std::cout << "INFO: generate_training_data_nonpv finished." << endl;
}
}
-12
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@@ -1,12 +0,0 @@
#ifndef _GENSFEN_NONPV_H_
#define _GENSFEN_NONPV_H_
#include <sstream>
namespace Stockfish::Tools {
// Automatic generation of teacher position
void generate_training_data_nonpv(std::istringstream& is);
}
#endif
File diff suppressed because it is too large Load Diff
-12
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@@ -1,12 +0,0 @@
#ifndef _TRANSFORM_H_
#define _TRANSFORM_H_
#include <sstream>
namespace Stockfish::Tools {
void transform(std::istringstream& is);
}
#endif
-122
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@@ -1,122 +0,0 @@
#include "validate_training_data.h"
#include "uci.h"
#include "misc.h"
#include "thread.h"
#include "position.h"
#include "tt.h"
#include "extra/nnue_data_binpack_format.h"
#include "nnue/evaluate_nnue.h"
#include "syzygy/tbprobe.h"
#include <sstream>
#include <fstream>
#include <unordered_set>
#include <iomanip>
#include <list>
#include <cmath> // std::exp(),std::pow(),std::log()
#include <cstring> // memcpy()
#include <memory>
#include <limits>
#include <optional>
#include <chrono>
#include <random>
#include <regex>
#include <filesystem>
using namespace std;
namespace sys = std::filesystem;
namespace Stockfish::Tools
{
static inline const std::string plain_extension = ".plain";
static inline const std::string bin_extension = ".bin";
static inline const std::string binpack_extension = ".binpack";
static bool file_exists(const std::string& name)
{
std::ifstream f(name);
return f.good();
}
static bool ends_with(const std::string& lhs, const std::string& end)
{
if (end.size() > lhs.size()) return false;
return std::equal(end.rbegin(), end.rend(), lhs.rbegin());
}
static bool is_validation_of_type(
const std::string& input_path,
const std::string& expected_input_extension)
{
return ends_with(input_path, expected_input_extension);
}
using ValidateFunctionType = void(std::string inputPath);
static ValidateFunctionType* get_validate_function(const std::string& input_path)
{
if (is_validation_of_type(input_path, plain_extension))
return binpack::validatePlain;
if (is_validation_of_type(input_path, bin_extension))
return binpack::validateBin;
if (is_validation_of_type(input_path, binpack_extension))
return binpack::validateBinpack;
return nullptr;
}
static void validate_training_data(const std::string& input_path)
{
if(!file_exists(input_path))
{
std::cerr << "Input file does not exist.\n";
return;
}
auto func = get_validate_function(input_path);
if (func != nullptr)
{
func(input_path);
}
else
{
std::cerr << "Validation of files of this type is not supported.\n";
}
}
static void validate_training_data(const std::vector<std::string>& args)
{
if (args.size() != 1)
{
std::cerr << "Invalid arguments.\n";
std::cerr << "Usage: validate in_path\n";
return;
}
validate_training_data(args[0]);
}
void validate_training_data(istringstream& is)
{
std::vector<std::string> args;
while (true)
{
std::string token = "";
is >> token;
if (token == "")
break;
args.push_back(token);
}
validate_training_data(args);
}
}
-12
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@@ -1,12 +0,0 @@
#ifndef _VALIDATE_TRAINING_DATA_H_
#define _VALIDATE_TRAINING_DATA_H_
#include <vector>
#include <string>
#include <sstream>
namespace Stockfish::Tools {
void validate_training_data(std::istringstream& is);
}
#endif
+3 -13
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -30,16 +30,11 @@ namespace Stockfish {
TranspositionTable TT; // Our global transposition table
bool TranspositionTable::enable_transposition_table = true;
/// TTEntry::save() populates the TTEntry with a new node's data, possibly
/// overwriting an old position. Update is not atomic and can be racy.
void TTEntry::save(Key k, Value v, bool pv, Bound b, Depth d, Move m, Value ev) {
if (!TranspositionTable::enable_transposition_table) {
return;
}
// Preserve any existing move for the same position
if (m || (uint16_t)k != key16)
move16 = (uint16_t)m;
@@ -92,7 +87,7 @@ void TranspositionTable::clear() {
std::vector<std::thread> threads;
for (size_t idx = 0; idx < Options["Threads"]; ++idx)
for (size_t idx = 0; idx < size_t(Options["Threads"]); ++idx)
{
threads.emplace_back([this, idx]() {
@@ -103,7 +98,7 @@ void TranspositionTable::clear() {
// Each thread will zero its part of the hash table
const size_t stride = size_t(clusterCount / Options["Threads"]),
start = size_t(stride * idx),
len = idx != Options["Threads"] - 1 ?
len = idx != size_t(Options["Threads"]) - 1 ?
stride : clusterCount - start;
std::memset(&table[start], 0, len * sizeof(Cluster));
@@ -124,11 +119,6 @@ void TranspositionTable::clear() {
TTEntry* TranspositionTable::probe(const Key key, bool& found) const {
if (!enable_transposition_table) {
found = false;
return first_entry(0);
}
TTEntry* const tte = first_entry(key);
const uint16_t key16 = (uint16_t)key; // Use the low 16 bits as key inside the cluster
+1 -3
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -92,8 +92,6 @@ public:
return &table[mul_hi64(key, clusterCount)].entry[0];
}
static bool enable_transposition_table;
private:
friend struct TTEntry;
+1 -1
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
+4 -4
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -26,8 +26,8 @@
namespace Stockfish {
typedef std::pair<int, int> Range; // Option's min-max values
typedef Range (RangeFun) (int);
using Range = std::pair<int, int>; // Option's min-max values
using RangeFun = Range (int);
// Default Range function, to calculate Option's min-max values
inline Range default_range(int v) {
@@ -75,7 +75,7 @@ struct SetRange {
class Tune {
typedef void (PostUpdate) (); // Post-update function
using PostUpdate = void (); // Post-update function
Tune() { read_results(); }
Tune(const Tune&) = delete;
+13 -15
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -103,8 +103,8 @@ constexpr bool Is64Bit = true;
constexpr bool Is64Bit = false;
#endif
typedef uint64_t Key;
typedef uint64_t Bitboard;
using Key = uint64_t;
using Bitboard = uint64_t;
constexpr int MAX_MOVES = 256;
constexpr int MAX_PLY = 246;
@@ -137,8 +137,6 @@ enum Color {
WHITE, BLACK, COLOR_NB = 2
};
constexpr Color Colors[2] = { WHITE, BLACK };
enum CastlingRights {
NO_CASTLING,
WHITE_OO,
@@ -188,6 +186,9 @@ enum Value : int {
VALUE_MATE_IN_MAX_PLY = VALUE_MATE - MAX_PLY,
VALUE_MATED_IN_MAX_PLY = -VALUE_MATE_IN_MAX_PLY,
// In the code, we make the assumption that these values
// are such that non_pawn_material() can be used to uniquely
// identify the material on the board.
PawnValueMg = 126, PawnValueEg = 208,
KnightValueMg = 781, KnightValueEg = 854,
BishopValueMg = 825, BishopValueEg = 915,
@@ -217,7 +218,7 @@ constexpr Value PieceValue[PHASE_NB][PIECE_NB] = {
VALUE_ZERO, PawnValueEg, KnightValueEg, BishopValueEg, RookValueEg, QueenValueEg, VALUE_ZERO, VALUE_ZERO }
};
typedef int Depth;
using Depth = int;
enum : int {
DEPTH_QS_CHECKS = 0,
@@ -415,6 +416,10 @@ inline Color color_of(Piece pc) {
return Color(pc >> 3);
}
constexpr bool is_ok(Move m) {
return m != MOVE_NONE && m != MOVE_NULL;
}
constexpr bool is_ok(Square s) {
return s >= SQ_A1 && s <= SQ_H8;
}
@@ -444,18 +449,15 @@ constexpr Direction pawn_push(Color c) {
}
constexpr Square from_sq(Move m) {
assert(is_ok(m));
return Square((m >> 6) & 0x3F);
}
constexpr Square to_sq(Move m) {
assert(is_ok(m));
return Square(m & 0x3F);
}
// Return relative square when turning the board 180 degrees
constexpr Square rotate180(Square sq) {
return (Square)(sq ^ 0x3F);
}
constexpr int from_to(Move m) {
return m & 0xFFF;
}
@@ -477,10 +479,6 @@ constexpr Move make(Square from, Square to, PieceType pt = KNIGHT) {
return Move(T + ((pt - KNIGHT) << 12) + (from << 6) + to);
}
constexpr bool is_ok(Move m) {
return from_sq(m) != to_sq(m); // Catch MOVE_NULL and MOVE_NONE
}
/// Based on a congruential pseudo random number generator
constexpr Key make_key(uint64_t seed) {
return seed * 6364136223846793005ULL + 1442695040888963407ULL;
+95 -181
View File
@@ -1,6 +1,6 @@
/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
@@ -22,36 +22,32 @@
#include <sstream>
#include <string>
#include "nnue/evaluate_nnue.h"
#include "benchmark.h"
#include "evaluate.h"
#include "movegen.h"
#include "position.h"
#include "search.h"
#include "syzygy/tbprobe.h"
#include "thread.h"
#include "timeman.h"
#include "tt.h"
#include "uci.h"
#include "tools/validate_training_data.h"
#include "tools/training_data_generator.h"
#include "tools/training_data_generator_nonpv.h"
#include "tools/convert.h"
#include "tools/transform.h"
#include "tools/stats.h"
#include "syzygy/tbprobe.h"
#include "nnue/evaluate_nnue.h"
using namespace std;
namespace Stockfish {
extern vector<string> setup_bench(const Position&, istream&);
namespace {
// position() is called when engine receives the "position" UCI command.
// The function sets up the position described in the given FEN string ("fen")
// or the starting position ("startpos") and then makes the moves given in the
// following move list ("moves").
// FEN string for the initial position in standard chess
const char* StartFEN = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1";
// position() is called when the engine receives the "position" UCI command.
// It sets up the position that is described in the given FEN string ("fen") or
// the initial position ("startpos") and then makes the moves given in the following
// move list ("moves").
void position(Position& pos, istringstream& is, StateListPtr& states) {
@@ -63,7 +59,7 @@ namespace {
if (token == "startpos")
{
fen = StartFEN;
is >> token; // Consume "moves" token if any
is >> token; // Consume the "moves" token, if any
}
else if (token == "fen")
while (is >> token && token != "moves")
@@ -71,10 +67,10 @@ namespace {
else
return;
states = StateListPtr(new std::deque<StateInfo>(1)); // Drop old and create a new one
states = StateListPtr(new std::deque<StateInfo>(1)); // Drop the old state and create a new one
pos.set(fen, Options["UCI_Chess960"], &states->back(), Threads.main());
// Parse move list (if any)
// Parse the move list, if any
while (is >> token && (m = UCI::to_move(pos, token)) != MOVE_NONE)
{
states->emplace_back();
@@ -82,8 +78,8 @@ namespace {
}
}
// trace_eval() prints the evaluation for the current position, consistent with the UCI
// options set so far.
// trace_eval() prints the evaluation of the current position, consistent with
// the UCI options set so far.
void trace_eval(Position& pos) {
@@ -97,30 +93,33 @@ namespace {
}
// setoption() is called when engine receives the "setoption" UCI command. The
// function updates the UCI option ("name") to the given value ("value").
// setoption() is called when the engine receives the "setoption" UCI command.
// The function updates the UCI option ("name") to the given value ("value").
void setoption_from_stream(istringstream& is) {
void setoption(istringstream& is) {
string token, name, value;
is >> token; // Consume "name" token
is >> token; // Consume the "name" token
// Read option name (can contain spaces)
// Read the option name (can contain spaces)
while (is >> token && token != "value")
name += (name.empty() ? "" : " ") + token;
// Read option value (can contain spaces)
// Read the option value (can contain spaces)
while (is >> token)
value += (value.empty() ? "" : " ") + token;
UCI::setoption(name, value);
if (Options.count(name))
Options[name] = value;
else
sync_cout << "No such option: " << name << sync_endl;
}
// go() is called when engine receives the "go" UCI command. The function sets
// the thinking time and other parameters from the input string, then starts
// the search.
// go() is called when the engine receives the "go" UCI command. The function
// sets the thinking time and other parameters from the input string, then starts
// with a search.
void go(Position& pos, istringstream& is, StateListPtr& states) {
@@ -128,7 +127,7 @@ namespace {
string token;
bool ponderMode = false;
limits.startTime = now(); // As early as possible!
limits.startTime = now(); // The search starts as early as possible
while (is >> token)
if (token == "searchmoves") // Needs to be the last command on the line
@@ -152,9 +151,9 @@ namespace {
}
// bench() is called when engine receives the "bench" command. Firstly
// a list of UCI commands is setup according to bench parameters, then
// it is run one by one printing a summary at the end.
// bench() is called when the engine receives the "bench" command.
// Firstly, a list of UCI commands is set up according to the bench
// parameters, then it is run one by one, printing a summary at the end.
void bench(Position& pos, istream& args, StateListPtr& states) {
@@ -162,7 +161,7 @@ namespace {
uint64_t num, nodes = 0, cnt = 1;
vector<string> list = setup_bench(pos, args);
num = count_if(list.begin(), list.end(), [](string s) { return s.find("go ") == 0 || s.find("eval") == 0; });
num = count_if(list.begin(), list.end(), [](const string& s) { return s.find("go ") == 0 || s.find("eval") == 0; });
TimePoint elapsed = now();
@@ -183,14 +182,14 @@ namespace {
else
trace_eval(pos);
}
else if (token == "setoption") setoption_from_stream(is);
else if (token == "setoption") setoption(is);
else if (token == "position") position(pos, is, states);
else if (token == "ucinewgame") { Search::clear(); elapsed = now(); } // Search::clear() may take some while
else if (token == "ucinewgame") { Search::clear(); elapsed = now(); } // Search::clear() may take a while
}
elapsed = now() - elapsed + 1; // Ensure positivity to avoid a 'divide by zero'
dbg_print(); // Just before exiting
dbg_print();
cerr << "\n==========================="
<< "\nTotal time (ms) : " << elapsed
@@ -198,110 +197,40 @@ namespace {
<< "\nNodes/second : " << 1000 * nodes / elapsed << endl;
}
} // namespace
namespace UCI {
void setoption(const std::string& name, const std::string& value)
{
if (Options.count(name))
Options[name] = value;
else
sync_cout << "No such option: " << name << sync_endl;
}
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
// The win rate model returns the probability of winning (in per mille units) given an
// eval and a game ply. It fits the LTC fishtest statistics rather accurately.
int win_rate_model(Value v, int ply) {
// The model captures only up to 240 plies, so limit input (and rescale)
// The model only captures up to 240 plies, so limit the input and then rescale
double m = std::min(240, ply) / 64.0;
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
double as[] = {-1.17202460e-01, 5.94729104e-01, 1.12065546e+01, 1.22606222e+02};
double bs[] = {-1.79066759, 11.30759193, -17.43677612, 36.47147479};
// The coefficients of a third-order polynomial fit is based on the fishtest data
// for two parameters that need to transform eval to the argument of a logistic
// function.
constexpr double as[] = { 0.38036525, -2.82015070, 23.17882135, 307.36768407};
constexpr double bs[] = { -2.29434733, 13.27689788, -14.26828904, 63.45318330 };
// Enforce that NormalizeToPawnValue corresponds to a 50% win rate at ply 64
static_assert(UCI::NormalizeToPawnValue == int(as[0] + as[1] + as[2] + as[3]));
double a = (((as[0] * m + as[1]) * m + as[2]) * m) + as[3];
double b = (((bs[0] * m + bs[1]) * m + bs[2]) * m) + bs[3];
// Transform eval to centipawns with limited range
double x = std::clamp(double(100 * v) / PawnValueEg, -1000.0, 1000.0);
// Transform the eval to centipawns with limited range
double x = std::clamp(double(v), -4000.0, 4000.0);
// Return win rate in per mille (rounded to nearest)
// Return the win rate in per mille units rounded to the nearest value
return int(0.5 + 1000 / (1 + std::exp((a - x) / b)));
}
} // namespace
// --------------------
// Call qsearch(),search() directly for testing
// --------------------
void qsearch_cmd(Position& pos)
{
cout << "qsearch : ";
auto pv = Search::qsearch(pos);
cout << "Value = " << pv.first << " , " << UCI::value(pv.first) << " , PV = ";
for (auto m : pv.second)
cout << UCI::move(m, false) << " ";
cout << endl;
}
void search_cmd(Position& pos, istringstream& is)
{
string token;
int depth = 1;
int multi_pv = (int)Options["MultiPV"];
while (is >> token)
{
if (token == "depth")
is >> depth;
if (token == "multipv")
is >> multi_pv;
}
cout << "search depth = " << depth << " , multi_pv = " << multi_pv << " : ";
auto pv = Search::search(pos, depth, multi_pv);
cout << "Value = " << pv.first << " , " << UCI::value(pv.first) << " , PV = ";
for (auto m : pv.second)
cout << UCI::move(m, false) << " ";
cout << endl;
}
void search_mcts_cmd(Position& pos, istringstream& is)
{
string token;
int nodes = 1000;
int leafDepth = 3;
float explorationFactor = 0.25f;
while (is >> token)
{
if (token == "nodes")
is >> nodes;
if (token == "leaf_depth")
is >> leafDepth;
if (token == "exploration_factor")
is >> explorationFactor;
}
cout << "search nodes = " << nodes << " , leaf_depth = " << leafDepth << " :\n";
auto continuations = Search::MCTS::search_mcts_multipv(pos, nodes, leafDepth, explorationFactor);
for (auto&& [numVisits, value, actionValue, pv] : continuations)
{
cout << "NumVisits = " << numVisits << " , Value = " << UCI::value(value) << " , ActionValue = " << actionValue << " , PV = ";
for (auto m : pv)
cout << UCI::move(m, false) << " ";
cout << endl;
}
cout << endl;
}
/// UCI::loop() waits for a command from stdin, parses it and calls the appropriate
/// function. Also intercepts EOF from stdin to ensure gracefully exiting if the
/// GUI dies unexpectedly. When called with some command line arguments, e.g. to
/// run 'bench', once the command is executed the function returns immediately.
/// In addition to the UCI ones, also some additional debug commands are supported.
/// UCI::loop() waits for a command from the stdin, parses it and then calls the appropriate
/// function. It also intercepts an end-of-file (EOF) indication from the stdin to ensure a
/// graceful exit if the GUI dies unexpectedly. When called with some command-line arguments,
/// like running 'bench', the function returns immediately after the command is executed.
/// In addition to the UCI ones, some additional debug commands are also supported.
void UCI::loop(int argc, char* argv[]) {
@@ -315,38 +244,38 @@ void UCI::loop(int argc, char* argv[]) {
cmd += std::string(argv[i]) + " ";
do {
if (argc == 1 && !getline(cin, cmd)) // Block here waiting for input or EOF
if (argc == 1 && !getline(cin, cmd)) // Wait for an input or an end-of-file (EOF) indication
cmd = "quit";
istringstream is(cmd);
token.clear(); // Avoid a stale if getline() returns empty or blank line
token.clear(); // Avoid a stale if getline() returns nothing or a blank line
is >> skipws >> token;
if ( token == "quit"
|| token == "stop")
Threads.stop = true;
// The GUI sends 'ponderhit' to tell us the user has played the expected move.
// So 'ponderhit' will be sent if we were told to ponder on the same move the
// user has played. We should continue searching but switch from pondering to
// normal search.
// The GUI sends 'ponderhit' to tell that the user has played the expected move.
// So, 'ponderhit' is sent if pondering was done on the same move that the user
// has played. The search should continue, but should also switch from pondering
// to the normal search.
else if (token == "ponderhit")
Threads.main()->ponder = false; // Switch to normal search
Threads.main()->ponder = false; // Switch to the normal search
else if (token == "uci")
sync_cout << "id name " << engine_info(true)
<< "\n" << Options
<< "\nuciok" << sync_endl;
else if (token == "setoption") setoption_from_stream(is);
else if (token == "setoption") setoption(is);
else if (token == "go") go(pos, is, states);
else if (token == "position") position(pos, is, states);
else if (token == "ucinewgame") Search::clear();
else if (token == "isready") sync_cout << "readyok" << sync_endl;
// Additional custom non-UCI commands, mainly for debugging.
// Do not use these commands during a search!
// Add custom non-UCI commands, mainly for debugging purposes.
// These commands must not be used during a search!
else if (token == "flip") pos.flip();
else if (token == "bench") bench(pos, is, states);
else if (token == "d") sync_cout << pos << sync_endl;
@@ -360,45 +289,25 @@ void UCI::loop(int argc, char* argv[]) {
filename = f;
Eval::NNUE::save_eval(filename);
}
else if (token == "generate_training_data") Tools::generate_training_data(is);
else if (token == "generate_training_data_nonpv") Tools::generate_training_data_nonpv(is);
else if (token == "convert") Tools::convert(is);
else if (token == "validate_training_data") Tools::validate_training_data(is);
else if (token == "convert_bin") Tools::convert_bin(is);
else if (token == "convert_plain") Tools::convert_plain(is);
else if (token == "convert_bin_from_pgn_extract") Tools::convert_bin_from_pgn_extract(is);
else if (token == "transform") Tools::transform(is);
else if (token == "gather_statistics") Tools::Stats::gather_statistics(is);
// Command to call qsearch(),search() directly for testing
else if (token == "qsearch") qsearch_cmd(pos);
else if (token == "search_mcts") search_mcts_cmd(pos, is);
else if (token == "search") search_cmd(pos, is);
else if (token == "tasktest")
{
Threads.execute_with_workers([](auto& th) {
std::cout << th.id() << '\n';
});
}
else if (token == "--help" || token == "help" || token == "--license" || token == "license")
sync_cout << "\nStockfish is a powerful chess engine and free software licensed under the GNU GPLv3."
"\nStockfish is normally used with a separate graphical user interface (GUI)."
"\nStockfish implements the universal chess interface (UCI) to exchange information."
"\nFor further information see https://github.com/official-stockfish/Stockfish#readme"
"\nor the corresponding README.md and Copying.txt files distributed with this program.\n" << sync_endl;
sync_cout << "\nStockfish is a powerful chess engine for playing and analyzing."
"\nIt is released as free software licensed under the GNU GPLv3 License."
"\nStockfish is normally used with a graphical user interface (GUI) and implements"
"\nthe Universal Chess Interface (UCI) protocol to communicate with a GUI, an API, etc."
"\nFor any further information, visit https://github.com/official-stockfish/Stockfish#readme"
"\nor read the corresponding README.md and Copying.txt files distributed along with this program.\n" << sync_endl;
else if (!token.empty() && token[0] != '#')
sync_cout << "Unknown command: '" << cmd << "'. Type help for more information." << sync_endl;
} while (token != "quit" && argc == 1); // Command line args are one-shot
} while (token != "quit" && argc == 1); // The command-line arguments are one-shot
}
/// UCI::value() converts a Value to a string suitable for use with the UCI
/// protocol specification:
/// UCI::value() converts a Value to a string by adhering to the UCI protocol specification:
///
/// cp <x> The score from the engine's point of view in centipawns.
/// mate <y> Mate in y moves, not plies. If the engine is getting mated
/// use negative values for y.
/// mate <y> Mate in 'y' moves (not plies). If the engine is getting mated,
/// uses negative values for 'y'.
string UCI::value(Value v) {
@@ -406,8 +315,13 @@ string UCI::value(Value v) {
stringstream ss;
if (abs(v) < VALUE_MATE_IN_MAX_PLY)
ss << "cp " << v * 100 / PawnValueEg;
if (abs(v) < VALUE_TB_WIN_IN_MAX_PLY)
ss << "cp " << v * 100 / NormalizeToPawnValue;
else if (abs(v) < VALUE_MATE_IN_MAX_PLY)
{
const int ply = VALUE_MATE_IN_MAX_PLY - 1 - std::abs(v); // recompute ss->ply
ss << "cp " << (v > 0 ? 20000 - ply : -20000 + ply);
}
else
ss << "mate " << (v > 0 ? VALUE_MATE - v + 1 : -VALUE_MATE - v) / 2;
@@ -415,8 +329,8 @@ string UCI::value(Value v) {
}
/// UCI::wdl() report WDL statistics given an evaluation and a game ply, based on
/// data gathered for fishtest LTC games.
/// UCI::wdl() reports the win-draw-loss (WDL) statistics given an evaluation
/// and a game ply based on the data gathered for fishtest LTC games.
string UCI::wdl(Value v, int ply) {
@@ -439,21 +353,21 @@ std::string UCI::square(Square s) {
/// UCI::move() converts a Move to a string in coordinate notation (g1f3, a7a8q).
/// The only special case is castling, where we print in the e1g1 notation in
/// normal chess mode, and in e1h1 notation in chess960 mode. Internally all
/// castling moves are always encoded as 'king captures rook'.
/// The only special case is castling where the e1g1 notation is printed in
/// standard chess mode and in e1h1 notation it is printed in Chess960 mode.
/// Internally, all castling moves are always encoded as 'king captures rook'.
string UCI::move(Move m, bool chess960) {
Square from = from_sq(m);
Square to = to_sq(m);
if (m == MOVE_NONE)
return "(none)";
if (m == MOVE_NULL)
return "0000";
Square from = from_sq(m);
Square to = to_sq(m);
if (type_of(m) == CASTLING && !chess960)
to = make_square(to > from ? FILE_G : FILE_C, rank_of(from));
@@ -471,8 +385,8 @@ string UCI::move(Move m, bool chess960) {
Move UCI::to_move(const Position& pos, string& str) {
if (str.length() == 5) // Junior could send promotion piece in uppercase
str[4] = char(tolower(str[4]));
if (str.length() == 5)
str[4] = char(tolower(str[4])); // The promotion piece character must be lowercased
for (const auto& m : MoveList<LEGAL>(pos))
if (str == UCI::move(m, pos.is_chess960()))

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