This is a rather involved merge, since the cluster changes interact
fairly badly with the refactoring we're merging:
The new code wants the search to know nothing at all about the UCI protocol,
it just calls a callback with some worker information (including a Position),
which then transforms that information into a InfoFull which is then sent
through another callback, upon which UCIEngine converts it to UCI and writes
it to stdout.
On the other hand, the MPI information wants to exchange PVs between workers
at this point, and it wants to use the UCI string as the primary medium of
exchange. Position is a lot of work to serialize, so we choose a middle road;
we capture the InfoFull by switching out the callback, serializes it, does the
MPI exchange on that, unserializes it and then continues with the print callback
(if there is new information).
Created by modifying L2 weights from the previous main net (nn-74f1d263ae9a.nnue)
with params found by spsa around 9k / 120k games at 120+1.2.
370 spsa params - L2 weights in nn-74f1d263ae9a.nnue where |val| >= 50
A: 6000, alpha: 0.602, gamma: 0.101
weights: [-127, 127], c_end = 6
To print the spsa params with nnue-pytorch:
```
import features
from serialize import NNUEReader
feature_set = features.get_feature_set_from_name("HalfKAv2_hm")
with open("nn-74f1d263ae9a.nnue", "rb") as f:
model = NNUEReader(f, feature_set).model
c_end = 6
for i in range(8):
for j in range(32):
for k in range(30):
value = int(model.layer_stacks.l2.weight[32 * i + j, k] * 64)
if abs(value) >= 50:
print(f"twoW[{i}][{j}][{k}],{value},-127,127,{c_end},0.0020")
```
Among the 370 params, 229 weights were changed.
avg change: 0.0961 ± 1.67
range: [-4, 3]
The number of weights changed, grouped by layer stack index,
shows more weights were modified in the lower piece count buckets:
[54, 52, 29, 23, 22, 18, 14, 17]
Found with the same method described in:
https://github.com/official-stockfish/Stockfish/pull/5459
Passed STC:
https://tests.stockfishchess.org/tests/view/668aec9a58083e5fd88239e7
LLR: 3.00 (-2.94,2.94) <0.00,2.00>
Total: 52384 W: 13569 L: 13226 D: 25589
Ptnml(0-2): 127, 6141, 13335, 6440, 149
Passed LTC:
https://tests.stockfishchess.org/tests/view/668af50658083e5fd8823a0b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 46974 W: 12006 L: 11668 D: 23300
Ptnml(0-2): 25, 4992, 13121, 5318, 31
closes https://github.com/official-stockfish/Stockfish/pull/5466
bench 1300471
Always use the posix function posix_memalign() as aligned memory
allocator on Apple computers. This should allow to compile Stockfish
out of the box on all versions of Mac OS X.
Patch tested on the following systems (apart from the CI) :
• Mac OS 10.9.6 (arch x86-64-sse41-popcnt) with gcc-10
• Mac OS 10.13.6 (arch x86-64-bmi2) with gcc-10, gcc-14 and clang-11
• Mac OS 14.1.1 (arch apple-silicon) with clang-15
closes https://github.com/official-stockfish/Stockfish/pull/5462
No functional change
Created by setting output weights (256) and biases (8) of the previous main net
nn-ddcfb9224cdb.nnue to values found around 12k / 120k spsa games at 120+1.2
This used modified fishtest dev workers to construct .nnue files from
spsa params, then load them with EvalFile when running tests:
https://github.com/linrock/fishtest/tree/spsa-file-modified-nnue/worker
Inspired by researching loading spsa params from files:
https://github.com/official-stockfish/fishtest/pull/1926
Scripts for modifying nnue files and preparing params:
https://github.com/linrock/nnue-pytorch/tree/no-gpu-modify-nnue
spsa params:
weights: [-127, 127], c_end = 6
biases: [-8192, 8192], c_end = 64
Example of reading output weights and biases from the previous main net using
nnue-pytorch and printing spsa params in a format compatible with fishtest:
```
import features
from serialize import NNUEReader
feature_set = features.get_feature_set_from_name("HalfKAv2_hm")
with open("nn-ddcfb9224cdb.nnue", "rb") as f:
model = NNUEReader(f, feature_set).model
c_end_weights = 6
c_end_biases = 64
for i in range(8):
for j in range(32):
value = round(int(model.layer_stacks.output.weight[i, j] * 600 * 16) / 127)
print(f"oW[{i}][{j}],{value},-127,127,{c_end_weights},0.0020")
for i in range(8):
value = int(model.layer_stacks.output.bias[i] * 600 * 16)
print(f"oB[{i}],{value},-8192,8192,{c_end_biases},0.0020")
```
For more info on spsa tuning params in nets:
https://github.com/official-stockfish/Stockfish/pull/5149https://github.com/official-stockfish/Stockfish/pull/5254
Passed STC:
https://tests.stockfishchess.org/tests/view/66894d64e59d990b103f8a37
LLR: 2.94 (-2.94,2.94) <0.00,2.00>
Total: 32000 W: 8443 L: 8137 D: 15420
Ptnml(0-2): 80, 3627, 8309, 3875, 109
Passed LTC:
https://tests.stockfishchess.org/tests/view/6689668ce59d990b103f8b8b
LLR: 2.94 (-2.94,2.94) <0.50,2.50>
Total: 172176 W: 43822 L: 43225 D: 85129
Ptnml(0-2): 97, 18821, 47633, 19462, 75
closes https://github.com/official-stockfish/Stockfish/pull/5459
bench 1120091
