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Author SHA1 Message Date
mstembera
f77bac3dca Remove stale Cache::clear() method
closes https://github.com/official-stockfish/Stockfish/pull/5666

No functional change
2024-11-13 20:11:03 +01:00
cj5716
8ee9905d8b Remove PSQT-only mode
Passed STC:
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 94208 W: 24270 L: 24112 D: 45826
Ptnml(0-2): 286, 11186, 24009, 11330, 293
https://tests.stockfishchess.org/tests/view/6635ddd773559a8aa8582826

Passed LTC:
LLR: 2.95 (-2.94,2.94) <-1.75,0.25>
Total: 114960 W: 29107 L: 28982 D: 56871
Ptnml(0-2): 37, 12683, 31924, 12790, 46
https://tests.stockfishchess.org/tests/view/663604a973559a8aa85881ed

closes #5214

Bench 1653939
2024-05-05 12:36:20 +02:00
mstembera
be142337d8 Accumulator cache bugfix and cleanup
STC:
https://tests.stockfishchess.org/tests/view/663068913a05f1bf7a511dc2
LLR: 2.98 (-2.94,2.94) <-1.75,0.25>
Total: 70304 W: 18211 L: 18026 D: 34067
Ptnml(0-2): 232, 7966, 18582, 8129, 243

1) Fixes a bug introduced in
   https://github.com/official-stockfish/Stockfish/pull/5194. Only one
   psqtOnly flag was used for two perspectives which was causing
   wrong entries to be cleared and marked.
2) The finny caches should be cleared like histories and not at the
   start of every search.

closes https://github.com/official-stockfish/Stockfish/pull/5203

No functional change
2024-05-01 14:17:32 +02:00
mstembera
a129c0695b Combine remove and add in update_accumulator_refresh_cache()
Combine remove and add in update_accumulator_refresh_cache().
Move remove before add to match other parts of the code.

STC:
https://tests.stockfishchess.org/tests/view/662d96dc6115ff6764c7f4ca
LLR: 2.95 (-2.94,2.94) <0.00,2.00>
Total: 364032 W: 94421 L: 93624 D: 175987
Ptnml(0-2): 1261, 41983, 94811, 42620, 1341

closes https://github.com/official-stockfish/Stockfish/pull/5194

Bench: 1836777
2024-04-28 21:35:48 +02:00
mstembera
940a3a7383 Cache small net w/ psqtOnly support
Caching the small net in the same way as the big net allows them to
share the same code path and completely removes
update_accumulator_refresh().

STC:
https://tests.stockfishchess.org/tests/view/662bfb5ed46f72253dcfed85
LLR: 2.94 (-2.94,2.94) <-1.75,0.25>
Total: 151712 W: 39252 L: 39158 D: 73302
Ptnml(0-2): 565, 17474, 39683, 17570, 564

closes https://github.com/official-stockfish/Stockfish/pull/5194

Bench: 1836777
2024-04-28 21:30:19 +02:00
Disservin
3502c8ae42 Fix missing initialization of AccumulatorCaches in Eval::trace
Add a constructor to `AccumulatorCaches` instead of just calling
`clear(networks)` to prevent similar issues from appearing in the
future.

fixes https://github.com/official-stockfish/Stockfish/issues/5190

closes https://github.com/official-stockfish/Stockfish/pull/5191

No functional change
2024-04-28 21:26:36 +02:00
gab8192
49ef4c935a Implement accumulator refresh table
For each thread persist an accumulator cache for the network, where each
cache contains multiple entries for each of the possible king squares.
When the accumulator needs to be refreshed, the cached entry is used to more
efficiently update the accumulator, instead of rebuilding it from scratch.
This idea, was first described by Luecx (author of Koivisto) and
is commonly referred to as "Finny Tables".

When the accumulator needs to be refreshed, instead of filling it with
biases and adding every piece from scratch, we...

1. Take the `AccumulatorRefreshEntry` associated with the new king bucket
2. Calculate the features to activate and deactivate (from differences
   between bitboards in the entry and bitboards of the actual position)
3. Apply the updates on the refresh entry
4. Copy the content of the refresh entry accumulator to the accumulator
   we were refreshing
5. Copy the bitboards from the position to the refresh entry, to match
   the newly updated accumulator

Results at STC:
https://tests.stockfishchess.org/tests/view/662301573fe04ce4cefc1386
(first version)
https://tests.stockfishchess.org/tests/view/6627fa063fe04ce4cefc6560
(final)

Non-Regression between first and final:
https://tests.stockfishchess.org/tests/view/662801e33fe04ce4cefc660a

STC SMP:
https://tests.stockfishchess.org/tests/view/662808133fe04ce4cefc667c

closes https://github.com/official-stockfish/Stockfish/pull/5183

No functional change
2024-04-24 18:38:20 +02:00
mstembera
7831131591 Only evaluate the PSQT part of the small net for large evals.
Thanks to Viren6 for suggesting to set complexity to 0.

STC https://tests.stockfishchess.org/tests/view/65d7d6709b2da0226a5a203f
LLR: 2.92 (-2.94,2.94) <0.00,2.00>
Total: 328384 W: 85316 L: 84554 D: 158514
Ptnml(0-2): 1414, 39076, 82486, 39766, 1450

LTC https://tests.stockfishchess.org/tests/view/65dce6d290f639b028a54d2e
LLR: 2.95 (-2.94,2.94) <0.50,2.50>
Total: 165162 W: 41918 L: 41330 D: 81914
Ptnml(0-2): 102, 18332, 45124, 18922, 101

closes https://github.com/official-stockfish/Stockfish/pull/5083

bench: 1504003
2024-03-03 15:29:58 +01:00
Linmiao Xu
584d9efedc Dual NNUE with L1-128 smallnet
Credit goes to @mstembera for:
- writing the code enabling dual NNUE:
  https://github.com/official-stockfish/Stockfish/pull/4898
- the idea of trying L1-128 trained exclusively on high simple eval
  positions

The L1-128 smallnet is:
- epoch 399 of a single-stage training from scratch
- trained only on positions from filtered data with high material
  difference
  - defined by abs(simple_eval) > 1000

```yaml
experiment-name: 128--S1-only-hse-v2

training-dataset:
  - /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack
  - /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack
  - /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack

  - /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-1k.binpack
  - /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack

  - /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-1k.binpack

  - /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack

  - /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack

  # T80 2022
  - /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-1k.binpack

  # T80 2023
  - /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-1k.binpack
  - /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-1k.binpack

start-from-engine-test-net: False

nnue-pytorch-branch: linrock/nnue-pytorch/L1-128
engine-test-branch: linrock/Stockfish/L1-128-nolazy
engine-base-branch: linrock/Stockfish/L1-128

num-epochs: 500
lambda: 1.0
```

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

Binpacks interleaved at training time with:
https://github.com/official-stockfish/nnue-pytorch/pull/259

Data filtered for high simple eval positions with:
https://github.com/linrock/nnue-data/blob/32d6a68/filter_high_simple_eval_plain.py
https://github.com/linrock/Stockfish/blob/61dbfe/src/tools/transform.cpp#L626-L655

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

Local elo at 25k nodes per move of
L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data:
nn-epoch399.nnue : -318.1 +/- 2.1

Passed STC:
https://tests.stockfishchess.org/tests/view/6574cb9d95ea6ba1fcd49e3b
LLR: 2.93 (-2.94,2.94) <0.00,2.00>
Total: 62432 W: 15875 L: 15521 D: 31036
Ptnml(0-2): 177, 7331, 15872, 7633, 203

Passed LTC:
https://tests.stockfishchess.org/tests/view/6575da2d4d789acf40aaac6e
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 64830 W: 16118 L: 15738 D: 32974
Ptnml(0-2): 43, 7129, 17697, 7497, 49

closes https://github.com/official-stockfish/Stockfish/pulls

Bench: 1330050

Co-Authored-By: mstembera <5421953+mstembera@users.noreply.github.com>
2024-01-07 21:15:52 +01:00
Disservin
444f03ee95 Update copyright year
closes https://github.com/official-stockfish/Stockfish/pull/4954

No functional change
2024-01-04 15:47:10 +01:00
Disservin
2d0237db3f add clang-format
This introduces clang-format to enforce a consistent code style for Stockfish.

Having a documented and consistent style across the code will make contributing easier
for new developers, and will make larger changes to the codebase easier to make.

To facilitate formatting, this PR includes a Makefile target (`make format`) to format the code,
this requires clang-format (version 17 currently) to be installed locally.

Installing clang-format is straightforward on most OS and distros
(e.g. with https://apt.llvm.org/, brew install clang-format, etc), as this is part of quite commonly
used suite of tools and compilers (llvm / clang).

Additionally, a CI action is present that will verify if the code requires formatting,
and comment on the PR as needed. Initially, correct formatting is not required, it will be
done by maintainers as part of the merge or in later commits, but obviously this is encouraged.

fixes https://github.com/official-stockfish/Stockfish/issues/3608
closes https://github.com/official-stockfish/Stockfish/pull/4790

Co-Authored-By: Joost VandeVondele <Joost.VandeVondele@gmail.com>
2023-10-22 16:06:27 +02:00
Disservin
3c0e86a91e Cleanup includes
Reorder a few includes, include "position.h" where it was previously missing
and apply include-what-you-use suggestions. Also make the order of the includes
consistent, in the following way:

1. Related header (for .cpp files)
2. A blank line
3. C/C++ headers
4. A blank line
5. All other header files

closes https://github.com/official-stockfish/Stockfish/pull/4763
fixes https://github.com/official-stockfish/Stockfish/issues/4707

No functional change
2023-09-03 08:24:51 +02:00
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
Brad Knox
ad926d34c0 Update copyright years
Happy New Year!

closes https://github.com/official-stockfish/Stockfish/pull/3881

No functional change
2022-01-06 15:45:45 +01:00
Tomasz Sobczyk
900f249f59 Reduce the number of accumulator states
Reduce from 3 to 2. Make the intent of the states clearer.

STC: https://tests.stockfishchess.org/tests/view/60c50111457376eb8bcaad03
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 61888 W: 5007 L: 4944 D: 51937
Ptnml(0-2): 164, 3947, 22649, 4030, 154

LTC: https://tests.stockfishchess.org/tests/view/60c52b1c457376eb8bcaad2c
LLR: 2.94 (-2.94,2.94) <-2.50,0.50>
Total: 20248 W: 688 L: 618 D: 18942
Ptnml(0-2): 7, 551, 8946, 605, 15

closes https://github.com/official-stockfish/Stockfish/pull/3548

No functional change.
2021-06-14 11:22:08 +02:00
Tomasz Sobczyk
e8d64af123 New NNUE architecture and net
Introduces a new NNUE network architecture and associated network parameters,
as obtained by a new pytorch trainer.

The network is already very strong at short TC, without regression at longer TC,
and has potential for further improvements.

https://tests.stockfishchess.org/tests/view/60a159c65085663412d0921d
TC: 10s+0.1s, 1 thread
ELO: 21.74 +-3.4 (95%) LOS: 100.0%
Total: 10000 W: 1559 L: 934 D: 7507
Ptnml(0-2): 38, 701, 2972, 1176, 113

https://tests.stockfishchess.org/tests/view/60a187005085663412d0925b
TC: 60s+0.6s, 1 thread
ELO: 5.85 +-1.7 (95%) LOS: 100.0%
Total: 20000 W: 1381 L: 1044 D: 17575
Ptnml(0-2): 27, 885, 7864, 1172, 52

https://tests.stockfishchess.org/tests/view/60a2beede229097940a03806
TC: 20s+0.2s, 8 threads
LLR: 2.93 (-2.94,2.94) <0.50,3.50>
Total: 34272 W: 1610 L: 1452 D: 31210
Ptnml(0-2): 30, 1285, 14350, 1439, 32

https://tests.stockfishchess.org/tests/view/60a2d687e229097940a03c72
TC: 60s+0.6s, 8 threads
LLR: 2.94 (-2.94,2.94) <-2.50,0.50>
Total: 45544 W: 1262 L: 1214 D: 43068
Ptnml(0-2): 12, 1129, 20442, 1177, 12

The network has been trained (by vondele) using the https://github.com/glinscott/nnue-pytorch/ trainer (started by glinscott),
specifically the branch https://github.com/Sopel97/nnue-pytorch/tree/experiment_56.
The data used are in 64 billion positions (193GB total) generated and scored with the current master net
d8: https://drive.google.com/file/d/1hOOYSDKgOOp38ZmD0N4DV82TOLHzjUiF/view?usp=sharing
d9: https://drive.google.com/file/d/1VlhnHL8f-20AXhGkILujnNXHwy9T-MQw/view?usp=sharing
d10: https://drive.google.com/file/d/1ZC5upzBYMmMj1gMYCkt6rCxQG0GnO3Kk/view?usp=sharing
fishtest_d9: https://drive.google.com/file/d/1GQHt0oNgKaHazwJFTRbXhlCN3FbUedFq/view?usp=sharing

This network also contains a few architectural changes with respect to the current master:

    Size changed from 256x2-32-32-1 to 512x2-16-32-1
        ~15-20% slower
        ~2x larger
        adds a special path for 16 valued ClippedReLU
        fixes affine transform code for 16 inputs/outputs, buy using InputDimensions instead of PaddedInputDimensions
            this is safe now because the inputs are processed in groups of 4 in the current affine transform code
    The feature set changed from HalfKP to HalfKAv2
        Includes information about the kings like HalfKA
        Packs king features better, resulting in 8% size reduction compared to HalfKA
    The board is flipped for the black's perspective, instead of rotated like in the current master
    PSQT values for each feature
        the feature transformer now outputs a part that is fowarded directly to the output and allows learning piece values more directly than the previous network architecture. The effect is visible for high imbalance positions, where the current master network outputs evaluations skewed towards zero.
        8 PSQT values per feature, chosen based on (popcount(pos.pieces()) - 1) / 4
        initialized to classical material values on the start of the training
    8 subnetworks (512x2->16->32->1), chosen based on (popcount(pos.pieces()) - 1) / 4
        only one subnetwork is evaluated for any position, no or marginal speed loss

A diagram of the network is available: https://user-images.githubusercontent.com/8037982/118656988-553a1700-b7eb-11eb-82ef-56a11cbebbf2.png
A more complete description: https://github.com/glinscott/nnue-pytorch/blob/master/docs/nnue.md

closes https://github.com/official-stockfish/Stockfish/pull/3474

Bench: 3806488
2021-05-18 18:06:23 +02:00
Tomasz Sobczyk
b748b46714 Cleanup and simplify NNUE code.
A lot of optimizations happend since the NNUE was introduced
and since then some parts of the code were left unused. This
got to the point where asserts were have to be made just to
let people know that modifying something will not have any
effects or may even break everything due to the assumptions
being made. Removing these parts removes those inexisting
"false dependencies". Additionally:

 * append_changed_indices now takes the king pos and stateinfo
   explicitly, no more misleading pos parameter
 * IndexList is removed in favor of a generic ValueList.
   Feature transformer just instantiates the type it needs.
 * The update cost and refresh requirement is deferred to the
   feature set once again, but now doesn't go through the whole
   FeatureSet machinery and just calls HalfKP directly.
 * accumulator no longer has a singular dimension.
 * The PS constants and the PieceSquareIndex array are made local
   to the HalfKP feature set because they are specific to it and
   DO differ for other feature sets.
 * A few names are changed to more descriptive

Passed STC non-regression:
https://tests.stockfishchess.org/tests/view/608421dd95e7f1852abd2790
LLR: 2.95 (-2.94,2.94) <-2.50,0.50>
Total: 180008 W: 16186 L: 16258 D: 147564
Ptnml(0-2): 587, 12593, 63725, 12503, 596

closes https://github.com/official-stockfish/Stockfish/pull/3441

No functional change
2021-04-25 13:16:30 +02:00
Tomasz Sobczyk
fbbd4adc3c Unify naming convention of the NNUE code
matches the rest of the stockfish code base

closes https://github.com/official-stockfish/Stockfish/pull/3437

No functional change
2021-04-24 12:49:29 +02:00
Dieter Dobbelaere
7ffae17f85 Add Stockfish namespace.
fixes #3350 and is a small cleanup that might make it easier to use SF
in separate projects, like a NNUE trainer or similar.

closes https://github.com/official-stockfish/Stockfish/pull/3370

No functional change.
2021-03-07 14:26:54 +01:00
Joost VandeVondele
c4d67d77c9 Update copyright years
No functional change
2021-01-08 17:04:23 +01:00
syzygy1
2046d5da30 More incremental accumulator updates
This patch was inspired by c065abd which updates the accumulator,
if possible, based on the accumulator of two plies back if
the accumulator of the preceding ply is not available.

With this patch we look back even further in the position history
in an attempt to reduce the number of complete recomputations.
When we find a usable accumulator for the position N plies back,
we also update the accumulator of the position N-1 plies back
because that accumulator is most likely to be helpful later
when evaluating positions in sibling branches.
By not updating all intermediate accumulators immediately,
we avoid doing too much work that is not certain to be useful.
Overall, roughly 2-3% speedup.

This patch makes the code more specific to the net architecture,
changing input features of the net will require additional changes
to the incremental update code as discussed in the PR #3193 and #3191.

Passed STC:
https://tests.stockfishchess.org/tests/view/5f9056712c92c7fe3a8c60d0
LLR: 2.94 (-2.94,2.94) {-0.25,1.25}
Total: 10040 W: 1116 L: 968 D: 7956
Ptnml(0-2): 42, 722, 3365, 828, 63

closes https://github.com/official-stockfish/Stockfish/pull/3193

No functional change.
2020-10-22 20:50:16 +02:00
syzygy1
fc27d158c0 Bug fix in do_null_move() and NNUE simplification.
This fixes #3108 and removes some NNUE code that is currently not used.

At the moment, do_null_move() copies the accumulator from the previous
state into the new state, which is correct. It then clears the "computed_score"
flag because the side to move has changed, and with the other side to move
NNUE will return a completely different evaluation (normally with changed
sign but also with different NNUE-internal tempo bonus).

The problem is that do_null_move() clears the wrong flag. It clears the
computed_score flag of the old state, not of the new state. It turns out
that this almost never affects the search. For example, fixing it does not
change the current bench (but it does change the previous bench). This is
because the search code usually avoids calling evaluate() after a null move.

This PR corrects do_null_move() by removing the computed_score flag altogether.
The flag is not needed because nnue_evaluate() is never called twice on a position.

This PR also removes some unnecessary {}s and inserts a few blank lines
in the modified NNUE files in line with SF coding style.

Resulf ot STC non-regression test:
LLR: 2.95 (-2.94,2.94) {-1.25,0.25}
Total: 26328 W: 3118 L: 3012 D: 20198
Ptnml(0-2): 126, 2208, 8397, 2300, 133
https://tests.stockfishchess.org/tests/view/5f553ccc2d02727c56b36db1

closes https://github.com/official-stockfish/Stockfish/pull/3109

bench: 4109324
2020-09-08 22:53:17 +02:00
mstembera
f948cd008d Cleanup and optimize SSE/AVX code
AVX512 +4% faster
AVX2 +1% faster
SSSE3 +5% faster

passed non-regression STC:
STC https://tests.stockfishchess.org/tests/view/5f31249f90816720665374f6
LLR: 2.96 (-2.94,2.94) {-1.50,0.50}
Total: 17576 W: 2344 L: 2245 D: 12987
Ptnml(0-2): 127, 1570, 5292, 1675, 124

closes https://github.com/official-stockfish/Stockfish/pull/2962

No functional change
2020-08-10 14:38:17 +02:00
nodchip
84f3e86790 Add NNUE evaluation
This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish.

Both the NNUE and the classical evaluations are available, and can be used to
assign a value to a position that is later 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. The network is optimized
and trained on the evalutions 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) provides additional
tools to train and develop the NNUE networks.

This patch is the result of contributions of various authors, from various communities,
including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather,
rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler,
dorzechowski, and vondele.

This new evaluation needed various changes to fishtest and the corresponding infrastructure,
for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged.

The first networks have been provided by gekkehenker and sergiovieri, with the latter
net (nn-97f742aaefcd.nnue) being the current default.

The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option,
provided the `EvalFile` option points the the network file (depending on the GUI, with full path).

The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on
the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest:

60000 @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c
ELO: 92.77 +-2.1 (95%) LOS: 100.0%
Total: 60000 W: 24193 L: 8543 D: 27264
Ptnml(0-2): 609, 3850, 9708, 10948, 4885

40000 @ 20+0.2 th 8
https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58
ELO: 89.47 +-2.0 (95%) LOS: 100.0%
Total: 40000 W: 12756 L: 2677 D: 24567
Ptnml(0-2): 74, 1583, 8550, 7776, 2017

At the same time, the impact on the classical evaluation remains minimal, causing no significant
regression:

sprt @ 10+0.1 th 1
https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b
LLR: 2.94 (-2.94,2.94) {-6.00,-4.00}
Total: 34936 W: 6502 L: 6825 D: 21609
Ptnml(0-2): 571, 4082, 8434, 3861, 520

sprt @ 60+0.6 th 1
https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d
LLR: 2.93 (-2.94,2.94) {-6.00,-4.00}
Total: 10088 W: 1232 L: 1265 D: 7591
Ptnml(0-2): 49, 914, 3170, 843, 68

The needed networks can be found at https://tests.stockfishchess.org/nns
It is recommended to use the default one as indicated by the `EvalFile` UCI option.

Guidelines for testing new nets can be found at
https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests

Integration has been discussed in various issues:
https://github.com/official-stockfish/Stockfish/issues/2823
https://github.com/official-stockfish/Stockfish/issues/2728

The integration branch will be closed after the merge:
https://github.com/official-stockfish/Stockfish/pull/2825
https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip

closes https://github.com/official-stockfish/Stockfish/pull/2912

This will be an exciting time for computer chess, looking forward to seeing the evolution of
this approach.

Bench: 4746616
2020-08-06 16:37:45 +02:00