mirror of
https://github.com/sockspls/badfish
synced 2025-04-29 16:23:09 +00:00
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>
This commit is contained in:
Linmiao Xu
Disservin
parent
a5a76a6370
commit
584d9efedc
12 changed files with 293 additions and 194 deletions
32
src/Makefile
32
src/Makefile
|
|
@ -806,7 +806,7 @@ help:
|
|||
@echo "help > Display architecture details"
|
||||
@echo "profile-build > standard build with profile-guided optimization"
|
||||
@echo "build > skip profile-guided optimization"
|
||||
@echo "net > Download the default nnue net"
|
||||
@echo "net > Download the default nnue nets"
|
||||
@echo "strip > Strip executable"
|
||||
@echo "install > Install executable"
|
||||
@echo "clean > Clean up"
|
||||
|
|
@ -922,16 +922,7 @@ profileclean:
|
|||
@rm -f stockfish.res
|
||||
@rm -f ./-lstdc++.res
|
||||
|
||||
# set up shell variables for the net stuff
|
||||
netvariables:
|
||||
$(eval nnuenet := $(shell grep EvalFileDefaultName evaluate.h | grep define | sed 's/.*\(nn-[a-z0-9]\{12\}.nnue\).*/\1/'))
|
||||
$(eval nnuedownloadurl1 := https://tests.stockfishchess.org/api/nn/$(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))
|
||||
$(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))
|
||||
|
||||
# evaluation network (nnue)
|
||||
net: netvariables
|
||||
define fetch_network
|
||||
@echo "Default net: $(nnuenet)"
|
||||
@if [ "x$(curl_or_wget)" = "x" ]; then \
|
||||
echo "Neither curl nor wget is installed. Install one of these tools unless the net has been downloaded manually"; \
|
||||
|
|
@ -966,7 +957,24 @@ net: netvariables
|
|||
if [ "$(nnuenet)" = "nn-"`$(shasum_command) $(nnuenet) | cut -c1-12`".nnue" ]; then \
|
||||
echo "Network validated"; break; \
|
||||
fi; \
|
||||
fi; \
|
||||
fi;
|
||||
endef
|
||||
|
||||
# set up shell variables for the net stuff
|
||||
define netvariables
|
||||
$(eval nnuenet := $(shell grep $(1) evaluate.h | grep define | sed 's/.*\(nn-[a-z0-9]\{12\}.nnue\).*/\1/'))
|
||||
$(eval nnuedownloadurl1 := https://tests.stockfishchess.org/api/nn/$(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))
|
||||
$(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))
|
||||
endef
|
||||
|
||||
# evaluation network (nnue)
|
||||
net:
|
||||
$(call netvariables, EvalFileDefaultNameBig)
|
||||
$(call fetch_network)
|
||||
$(call netvariables, EvalFileDefaultNameSmall)
|
||||
$(call fetch_network)
|
||||
|
||||
format:
|
||||
$(CLANG-FORMAT) -i $(SRCS) $(HEADERS) -style=file
|
||||
|
|
|
|||
153
src/evaluate.cpp
153
src/evaluate.cpp
|
|
@ -23,6 +23,7 @@
|
|||
#include <cmath>
|
||||
#include <cstdlib>
|
||||
#include <fstream>
|
||||
#include <initializer_list>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <sstream>
|
||||
|
|
@ -31,6 +32,7 @@
|
|||
#include "incbin/incbin.h"
|
||||
#include "misc.h"
|
||||
#include "nnue/evaluate_nnue.h"
|
||||
#include "nnue/nnue_architecture.h"
|
||||
#include "position.h"
|
||||
#include "thread.h"
|
||||
#include "types.h"
|
||||
|
|
@ -44,11 +46,15 @@
|
|||
// const unsigned int gEmbeddedNNUESize; // the size of the embedded file
|
||||
// Note that this does not work in Microsoft Visual Studio.
|
||||
#if !defined(_MSC_VER) && !defined(NNUE_EMBEDDING_OFF)
|
||||
INCBIN(EmbeddedNNUE, EvalFileDefaultName);
|
||||
INCBIN(EmbeddedNNUEBig, EvalFileDefaultNameBig);
|
||||
INCBIN(EmbeddedNNUESmall, EvalFileDefaultNameSmall);
|
||||
#else
|
||||
const unsigned char gEmbeddedNNUEData[1] = {0x0};
|
||||
const unsigned char* const gEmbeddedNNUEEnd = &gEmbeddedNNUEData[1];
|
||||
const unsigned int gEmbeddedNNUESize = 1;
|
||||
const unsigned char gEmbeddedNNUEBigData[1] = {0x0};
|
||||
const unsigned char* const gEmbeddedNNUEBigEnd = &gEmbeddedNNUEBigData[1];
|
||||
const unsigned int gEmbeddedNNUEBigSize = 1;
|
||||
const unsigned char gEmbeddedNNUESmallData[1] = {0x0};
|
||||
const unsigned char* const gEmbeddedNNUESmallEnd = &gEmbeddedNNUESmallData[1];
|
||||
const unsigned int gEmbeddedNNUESmallSize = 1;
|
||||
#endif
|
||||
|
||||
|
||||
|
|
@ -56,7 +62,9 @@ namespace Stockfish {
|
|||
|
||||
namespace Eval {
|
||||
|
||||
std::string currentEvalFileName = "None";
|
||||
std::string currentEvalFileName[2] = {"None", "None"};
|
||||
const std::string EvFiles[2] = {"EvalFile", "EvalFileSmall"};
|
||||
const std::string EvFileNames[2] = {EvalFileDefaultNameBig, EvalFileDefaultNameSmall};
|
||||
|
||||
// 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"
|
||||
|
|
@ -67,84 +75,96 @@ std::string currentEvalFileName = "None";
|
|||
// variable to have the engine search in a special directory in their distro.
|
||||
void NNUE::init() {
|
||||
|
||||
std::string eval_file = std::string(Options["EvalFile"]);
|
||||
if (eval_file.empty())
|
||||
eval_file = EvalFileDefaultName;
|
||||
for (NetSize netSize : {Big, Small})
|
||||
{
|
||||
// change after fishtest supports EvalFileSmall
|
||||
std::string eval_file =
|
||||
std::string(netSize == Small ? EvalFileDefaultNameSmall : Options[EvFiles[netSize]]);
|
||||
if (eval_file.empty())
|
||||
eval_file = EvFileNames[netSize];
|
||||
|
||||
#if defined(DEFAULT_NNUE_DIRECTORY)
|
||||
std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory,
|
||||
stringify(DEFAULT_NNUE_DIRECTORY)};
|
||||
std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory,
|
||||
stringify(DEFAULT_NNUE_DIRECTORY)};
|
||||
#else
|
||||
std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory};
|
||||
std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory};
|
||||
#endif
|
||||
|
||||
for (const std::string& directory : dirs)
|
||||
if (currentEvalFileName != eval_file)
|
||||
for (const std::string& directory : dirs)
|
||||
{
|
||||
if (directory != "<internal>")
|
||||
if (currentEvalFileName[netSize] != eval_file)
|
||||
{
|
||||
std::ifstream stream(directory + eval_file, std::ios::binary);
|
||||
if (NNUE::load_eval(eval_file, stream))
|
||||
currentEvalFileName = eval_file;
|
||||
}
|
||||
if (directory != "<internal>")
|
||||
{
|
||||
std::ifstream stream(directory + eval_file, std::ios::binary);
|
||||
if (NNUE::load_eval(eval_file, stream, netSize))
|
||||
currentEvalFileName[netSize] = eval_file;
|
||||
}
|
||||
|
||||
if (directory == "<internal>" && eval_file == EvalFileDefaultName)
|
||||
{
|
||||
// C++ way to prepare a buffer for a memory stream
|
||||
class MemoryBuffer: public std::basic_streambuf<char> {
|
||||
public:
|
||||
MemoryBuffer(char* p, size_t n) {
|
||||
setg(p, p, p + n);
|
||||
setp(p, p + n);
|
||||
}
|
||||
};
|
||||
if (directory == "<internal>" && eval_file == EvFileNames[netSize])
|
||||
{
|
||||
// C++ way to prepare a buffer for a memory stream
|
||||
class MemoryBuffer: public std::basic_streambuf<char> {
|
||||
public:
|
||||
MemoryBuffer(char* p, size_t n) {
|
||||
setg(p, p, p + n);
|
||||
setp(p, p + n);
|
||||
}
|
||||
};
|
||||
|
||||
MemoryBuffer buffer(
|
||||
const_cast<char*>(reinterpret_cast<const char*>(gEmbeddedNNUEData)),
|
||||
size_t(gEmbeddedNNUESize));
|
||||
(void) gEmbeddedNNUEEnd; // Silence warning on unused variable
|
||||
MemoryBuffer buffer(
|
||||
const_cast<char*>(reinterpret_cast<const char*>(
|
||||
netSize == Small ? gEmbeddedNNUESmallData : gEmbeddedNNUEBigData)),
|
||||
size_t(netSize == Small ? gEmbeddedNNUESmallSize : gEmbeddedNNUEBigSize));
|
||||
(void) gEmbeddedNNUEBigEnd; // Silence warning on unused variable
|
||||
(void) gEmbeddedNNUESmallEnd;
|
||||
|
||||
std::istream stream(&buffer);
|
||||
if (NNUE::load_eval(eval_file, stream))
|
||||
currentEvalFileName = eval_file;
|
||||
std::istream stream(&buffer);
|
||||
if (NNUE::load_eval(eval_file, stream, netSize))
|
||||
currentEvalFileName[netSize] = eval_file;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Verifies that the last net used was loaded successfully
|
||||
void NNUE::verify() {
|
||||
|
||||
std::string eval_file = std::string(Options["EvalFile"]);
|
||||
if (eval_file.empty())
|
||||
eval_file = EvalFileDefaultName;
|
||||
|
||||
if (currentEvalFileName != eval_file)
|
||||
for (NetSize netSize : {Big, Small})
|
||||
{
|
||||
// change after fishtest supports EvalFileSmall
|
||||
std::string eval_file =
|
||||
std::string(netSize == Small ? EvalFileDefaultNameSmall : Options[EvFiles[netSize]]);
|
||||
if (eval_file.empty())
|
||||
eval_file = EvFileNames[netSize];
|
||||
|
||||
std::string msg1 =
|
||||
"Network evaluation parameters compatible with the engine must be available.";
|
||||
std::string msg2 = "The network file " + eval_file + " was not loaded successfully.";
|
||||
std::string msg3 = "The UCI option EvalFile might need to specify the full path, "
|
||||
"including the directory name, to the network file.";
|
||||
std::string msg4 = "The default net can be downloaded from: "
|
||||
"https://tests.stockfishchess.org/api/nn/"
|
||||
+ std::string(EvalFileDefaultName);
|
||||
std::string msg5 = "The engine will be terminated now.";
|
||||
if (currentEvalFileName[netSize] != eval_file)
|
||||
{
|
||||
std::string msg1 =
|
||||
"Network evaluation parameters compatible with the engine must be available.";
|
||||
std::string msg2 = "The network file " + eval_file + " was not loaded successfully.";
|
||||
std::string msg3 = "The UCI option EvalFile might need to specify the full path, "
|
||||
"including the directory name, to the network file.";
|
||||
std::string msg4 = "The default net can be downloaded from: "
|
||||
"https://tests.stockfishchess.org/api/nn/"
|
||||
+ std::string(EvFileNames[netSize]);
|
||||
std::string msg5 = "The engine will be terminated now.";
|
||||
|
||||
sync_cout << "info string ERROR: " << msg1 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg2 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg3 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg4 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg5 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg1 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg2 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg3 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg4 << sync_endl;
|
||||
sync_cout << "info string ERROR: " << msg5 << sync_endl;
|
||||
|
||||
exit(EXIT_FAILURE);
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
|
||||
sync_cout << "info string NNUE evaluation using " << eval_file << sync_endl;
|
||||
}
|
||||
|
||||
sync_cout << "info string NNUE evaluation using " << eval_file << sync_endl;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Returns a static, purely materialistic evaluation of the position from
|
||||
// the point of view of the given color. It can be divided by PawnValue to get
|
||||
// an approximation of the material advantage on the board in terms of pawns.
|
||||
|
|
@ -163,18 +183,19 @@ Value Eval::evaluate(const Position& pos) {
|
|||
int v;
|
||||
Color stm = pos.side_to_move();
|
||||
int shuffling = pos.rule50_count();
|
||||
int simpleEval = simple_eval(pos, stm) + (int(pos.key() & 7) - 3);
|
||||
|
||||
bool lazy = std::abs(simpleEval) >= RookValue + KnightValue + 16 * shuffling * shuffling
|
||||
+ std::abs(pos.this_thread()->bestValue)
|
||||
+ std::abs(pos.this_thread()->rootSimpleEval);
|
||||
int simpleEval = simple_eval(pos, stm);
|
||||
|
||||
bool lazy = std::abs(simpleEval) > 2300;
|
||||
if (lazy)
|
||||
v = simpleEval;
|
||||
else
|
||||
{
|
||||
int nnueComplexity;
|
||||
Value nnue = NNUE::evaluate(pos, true, &nnueComplexity);
|
||||
bool smallNet = std::abs(simpleEval) > 1100;
|
||||
|
||||
int nnueComplexity;
|
||||
|
||||
Value nnue = smallNet ? NNUE::evaluate<NNUE::Small>(pos, true, &nnueComplexity)
|
||||
: NNUE::evaluate<NNUE::Big>(pos, true, &nnueComplexity);
|
||||
|
||||
int optimism = pos.this_thread()->optimism[stm];
|
||||
|
||||
|
|
@ -217,7 +238,7 @@ std::string Eval::trace(Position& pos) {
|
|||
ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
|
||||
|
||||
Value v;
|
||||
v = NNUE::evaluate(pos, false);
|
||||
v = NNUE::evaluate<NNUE::Big>(pos, false);
|
||||
v = pos.side_to_move() == WHITE ? v : -v;
|
||||
ss << "NNUE evaluation " << 0.01 * UCI::to_cp(v) << " (white side)\n";
|
||||
|
||||
|
|
|
|||
|
|
@ -34,12 +34,13 @@ std::string trace(Position& pos);
|
|||
int simple_eval(const Position& pos, Color c);
|
||||
Value evaluate(const Position& pos);
|
||||
|
||||
extern std::string currentEvalFileName;
|
||||
extern std::string currentEvalFileName[2];
|
||||
|
||||
// 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-b1e55edbea57.nnue"
|
||||
#define EvalFileDefaultNameBig "nn-b1e55edbea57.nnue"
|
||||
#define EvalFileDefaultNameSmall "nn-c01dc0ffeede.nnue"
|
||||
|
||||
namespace NNUE {
|
||||
|
||||
|
|
|
|||
|
|
@ -40,14 +40,18 @@
|
|||
namespace Stockfish::Eval::NNUE {
|
||||
|
||||
// Input feature converter
|
||||
LargePagePtr<FeatureTransformer> featureTransformer;
|
||||
LargePagePtr<FeatureTransformer<TransformedFeatureDimensionsBig, &StateInfo::accumulatorBig>>
|
||||
featureTransformerBig;
|
||||
LargePagePtr<FeatureTransformer<TransformedFeatureDimensionsSmall, &StateInfo::accumulatorSmall>>
|
||||
featureTransformerSmall;
|
||||
|
||||
// Evaluation function
|
||||
AlignedPtr<Network> network[LayerStacks];
|
||||
AlignedPtr<Network<TransformedFeatureDimensionsBig, L2Big, L3Big>> networkBig[LayerStacks];
|
||||
AlignedPtr<Network<TransformedFeatureDimensionsSmall, L2Small, L3Small>> networkSmall[LayerStacks];
|
||||
|
||||
// Evaluation function file name
|
||||
std::string fileName;
|
||||
std::string netDescription;
|
||||
// Evaluation function file names
|
||||
std::string fileName[2];
|
||||
std::string netDescription[2];
|
||||
|
||||
namespace Detail {
|
||||
|
||||
|
|
@ -91,11 +95,20 @@ bool write_parameters(std::ostream& stream, const T& reference) {
|
|||
|
||||
|
||||
// Initialize the evaluation function parameters
|
||||
static void initialize() {
|
||||
static void initialize(NetSize netSize) {
|
||||
|
||||
Detail::initialize(featureTransformer);
|
||||
for (std::size_t i = 0; i < LayerStacks; ++i)
|
||||
Detail::initialize(network[i]);
|
||||
if (netSize == Small)
|
||||
{
|
||||
Detail::initialize(featureTransformerSmall);
|
||||
for (std::size_t i = 0; i < LayerStacks; ++i)
|
||||
Detail::initialize(networkSmall[i]);
|
||||
}
|
||||
else
|
||||
{
|
||||
Detail::initialize(featureTransformerBig);
|
||||
for (std::size_t i = 0; i < LayerStacks; ++i)
|
||||
Detail::initialize(networkBig[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// Read network header
|
||||
|
|
@ -122,39 +135,57 @@ static bool write_header(std::ostream& stream, std::uint32_t hashValue, const st
|
|||
}
|
||||
|
||||
// Read network parameters
|
||||
static bool read_parameters(std::istream& stream) {
|
||||
static bool read_parameters(std::istream& stream, NetSize netSize) {
|
||||
|
||||
std::uint32_t hashValue;
|
||||
if (!read_header(stream, &hashValue, &netDescription))
|
||||
if (!read_header(stream, &hashValue, &netDescription[netSize]))
|
||||
return false;
|
||||
if (hashValue != HashValue)
|
||||
if (hashValue != HashValue[netSize])
|
||||
return false;
|
||||
if (!Detail::read_parameters(stream, *featureTransformer))
|
||||
if (netSize == Big && !Detail::read_parameters(stream, *featureTransformerBig))
|
||||
return false;
|
||||
if (netSize == Small && !Detail::read_parameters(stream, *featureTransformerSmall))
|
||||
return false;
|
||||
for (std::size_t i = 0; i < LayerStacks; ++i)
|
||||
if (!Detail::read_parameters(stream, *(network[i])))
|
||||
{
|
||||
if (netSize == Big && !Detail::read_parameters(stream, *(networkBig[i])))
|
||||
return false;
|
||||
if (netSize == Small && !Detail::read_parameters(stream, *(networkSmall[i])))
|
||||
return false;
|
||||
}
|
||||
return stream && stream.peek() == std::ios::traits_type::eof();
|
||||
}
|
||||
|
||||
// Write network parameters
|
||||
static bool write_parameters(std::ostream& stream) {
|
||||
static bool write_parameters(std::ostream& stream, NetSize netSize) {
|
||||
|
||||
if (!write_header(stream, HashValue, netDescription))
|
||||
if (!write_header(stream, HashValue[netSize], netDescription[netSize]))
|
||||
return false;
|
||||
if (!Detail::write_parameters(stream, *featureTransformer))
|
||||
if (netSize == Big && !Detail::write_parameters(stream, *featureTransformerBig))
|
||||
return false;
|
||||
if (netSize == Small && !Detail::write_parameters(stream, *featureTransformerSmall))
|
||||
return false;
|
||||
for (std::size_t i = 0; i < LayerStacks; ++i)
|
||||
if (!Detail::write_parameters(stream, *(network[i])))
|
||||
{
|
||||
if (netSize == Big && !Detail::write_parameters(stream, *(networkBig[i])))
|
||||
return false;
|
||||
if (netSize == Small && !Detail::write_parameters(stream, *(networkSmall[i])))
|
||||
return false;
|
||||
}
|
||||
return bool(stream);
|
||||
}
|
||||
|
||||
void hint_common_parent_position(const Position& pos) {
|
||||
featureTransformer->hint_common_access(pos);
|
||||
|
||||
int simpleEval = simple_eval(pos, pos.side_to_move());
|
||||
if (abs(simpleEval) > 1100)
|
||||
featureTransformerSmall->hint_common_access(pos);
|
||||
else
|
||||
featureTransformerBig->hint_common_access(pos);
|
||||
}
|
||||
|
||||
// Evaluation function. Perform differential calculation.
|
||||
template<NetSize Net_Size>
|
||||
Value evaluate(const Position& pos, bool adjusted, int* complexity) {
|
||||
|
||||
// We manually align the arrays on the stack because with gcc < 9.3
|
||||
|
|
@ -165,19 +196,28 @@ Value evaluate(const Position& pos, bool adjusted, int* complexity) {
|
|||
|
||||
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
|
||||
TransformedFeatureType
|
||||
transformedFeaturesUnaligned[FeatureTransformer::BufferSize
|
||||
+ alignment / sizeof(TransformedFeatureType)];
|
||||
transformedFeaturesUnaligned[FeatureTransformer < Small ? TransformedFeatureDimensionsSmall
|
||||
: TransformedFeatureDimensionsBig,
|
||||
nullptr
|
||||
> ::BufferSize + alignment / sizeof(TransformedFeatureType)];
|
||||
|
||||
auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
|
||||
#else
|
||||
alignas(alignment) TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
|
||||
|
||||
alignas(alignment) TransformedFeatureType
|
||||
transformedFeatures[FeatureTransformer < Net_Size == Small ? TransformedFeatureDimensionsSmall
|
||||
: TransformedFeatureDimensionsBig,
|
||||
nullptr > ::BufferSize];
|
||||
#endif
|
||||
|
||||
ASSERT_ALIGNED(transformedFeatures, alignment);
|
||||
|
||||
const int bucket = (pos.count<ALL_PIECES>() - 1) / 4;
|
||||
const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
|
||||
const auto positional = network[bucket]->propagate(transformedFeatures);
|
||||
const auto psqt = Net_Size == Small
|
||||
? featureTransformerSmall->transform(pos, transformedFeatures, bucket)
|
||||
: featureTransformerBig->transform(pos, transformedFeatures, bucket);
|
||||
const auto positional = Net_Size == Small ? networkSmall[bucket]->propagate(transformedFeatures)
|
||||
: networkBig[bucket]->propagate(transformedFeatures);
|
||||
|
||||
if (complexity)
|
||||
*complexity = std::abs(psqt - positional) / OutputScale;
|
||||
|
|
@ -190,6 +230,9 @@ Value evaluate(const Position& pos, bool adjusted, int* complexity) {
|
|||
return static_cast<Value>((psqt + positional) / OutputScale);
|
||||
}
|
||||
|
||||
template Value evaluate<Big>(const Position& pos, bool adjusted, int* complexity);
|
||||
template Value evaluate<Small>(const Position& pos, bool adjusted, int* complexity);
|
||||
|
||||
struct NnueEvalTrace {
|
||||
static_assert(LayerStacks == PSQTBuckets);
|
||||
|
||||
|
|
@ -205,13 +248,14 @@ static NnueEvalTrace trace_evaluate(const Position& pos) {
|
|||
constexpr uint64_t alignment = CacheLineSize;
|
||||
|
||||
#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
|
||||
TransformedFeatureType
|
||||
transformedFeaturesUnaligned[FeatureTransformer::BufferSize
|
||||
+ alignment / sizeof(TransformedFeatureType)];
|
||||
TransformedFeatureType transformedFeaturesUnaligned
|
||||
[FeatureTransformer<TransformedFeatureDimensionsBig, nullptr>::BufferSize
|
||||
+ alignment / sizeof(TransformedFeatureType)];
|
||||
|
||||
auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
|
||||
#else
|
||||
alignas(alignment) TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
|
||||
alignas(alignment) TransformedFeatureType
|
||||
transformedFeatures[FeatureTransformer<TransformedFeatureDimensionsBig, nullptr>::BufferSize];
|
||||
#endif
|
||||
|
||||
ASSERT_ALIGNED(transformedFeatures, alignment);
|
||||
|
|
@ -220,8 +264,8 @@ static NnueEvalTrace trace_evaluate(const Position& pos) {
|
|||
t.correctBucket = (pos.count<ALL_PIECES>() - 1) / 4;
|
||||
for (IndexType bucket = 0; bucket < LayerStacks; ++bucket)
|
||||
{
|
||||
const auto materialist = featureTransformer->transform(pos, transformedFeatures, bucket);
|
||||
const auto positional = network[bucket]->propagate(transformedFeatures);
|
||||
const auto materialist = featureTransformerBig->transform(pos, transformedFeatures, bucket);
|
||||
const auto positional = networkBig[bucket]->propagate(transformedFeatures);
|
||||
|
||||
t.psqt[bucket] = static_cast<Value>(materialist / OutputScale);
|
||||
t.positional[bucket] = static_cast<Value>(positional / OutputScale);
|
||||
|
|
@ -310,7 +354,7 @@ std::string trace(Position& pos) {
|
|||
|
||||
// We estimate the value of each piece by doing a differential evaluation from
|
||||
// the current base eval, simulating the removal of the piece from its square.
|
||||
Value base = evaluate(pos);
|
||||
Value base = evaluate<NNUE::Big>(pos);
|
||||
base = pos.side_to_move() == WHITE ? base : -base;
|
||||
|
||||
for (File f = FILE_A; f <= FILE_H; ++f)
|
||||
|
|
@ -325,16 +369,16 @@ std::string trace(Position& pos) {
|
|||
auto st = pos.state();
|
||||
|
||||
pos.remove_piece(sq);
|
||||
st->accumulator.computed[WHITE] = false;
|
||||
st->accumulator.computed[BLACK] = false;
|
||||
st->accumulatorBig.computed[WHITE] = false;
|
||||
st->accumulatorBig.computed[BLACK] = false;
|
||||
|
||||
Value eval = evaluate(pos);
|
||||
Value eval = evaluate<NNUE::Big>(pos);
|
||||
eval = pos.side_to_move() == WHITE ? eval : -eval;
|
||||
v = base - eval;
|
||||
|
||||
pos.put_piece(pc, sq);
|
||||
st->accumulator.computed[WHITE] = false;
|
||||
st->accumulator.computed[BLACK] = false;
|
||||
st->accumulatorBig.computed[WHITE] = false;
|
||||
st->accumulatorBig.computed[BLACK] = false;
|
||||
}
|
||||
|
||||
writeSquare(f, r, pc, v);
|
||||
|
|
@ -379,24 +423,24 @@ std::string trace(Position& pos) {
|
|||
|
||||
|
||||
// Load eval, from a file stream or a memory stream
|
||||
bool load_eval(std::string name, std::istream& stream) {
|
||||
bool load_eval(const std::string name, std::istream& stream, NetSize netSize) {
|
||||
|
||||
initialize();
|
||||
fileName = name;
|
||||
return read_parameters(stream);
|
||||
initialize(netSize);
|
||||
fileName[netSize] = name;
|
||||
return read_parameters(stream, netSize);
|
||||
}
|
||||
|
||||
// Save eval, to a file stream or a memory stream
|
||||
bool save_eval(std::ostream& stream) {
|
||||
bool save_eval(std::ostream& stream, NetSize netSize) {
|
||||
|
||||
if (fileName.empty())
|
||||
if (fileName[netSize].empty())
|
||||
return false;
|
||||
|
||||
return write_parameters(stream);
|
||||
return write_parameters(stream, netSize);
|
||||
}
|
||||
|
||||
// Save eval, to a file given by its name
|
||||
bool save_eval(const std::optional<std::string>& filename) {
|
||||
bool save_eval(const std::optional<std::string>& filename, NetSize netSize) {
|
||||
|
||||
std::string actualFilename;
|
||||
std::string msg;
|
||||
|
|
@ -405,7 +449,8 @@ bool save_eval(const std::optional<std::string>& filename) {
|
|||
actualFilename = filename.value();
|
||||
else
|
||||
{
|
||||
if (currentEvalFileName != EvalFileDefaultName)
|
||||
if (currentEvalFileName[netSize]
|
||||
!= (netSize == Small ? EvalFileDefaultNameSmall : EvalFileDefaultNameBig))
|
||||
{
|
||||
msg = "Failed to export a net. "
|
||||
"A non-embedded net can only be saved if the filename is specified";
|
||||
|
|
@ -413,11 +458,11 @@ bool save_eval(const std::optional<std::string>& filename) {
|
|||
sync_cout << msg << sync_endl;
|
||||
return false;
|
||||
}
|
||||
actualFilename = EvalFileDefaultName;
|
||||
actualFilename = (netSize == Small ? EvalFileDefaultNameSmall : EvalFileDefaultNameBig);
|
||||
}
|
||||
|
||||
std::ofstream stream(actualFilename, std::ios_base::binary);
|
||||
bool saved = save_eval(stream);
|
||||
bool saved = save_eval(stream, netSize);
|
||||
|
||||
msg = saved ? "Network saved successfully to " + actualFilename : "Failed to export a net";
|
||||
|
||||
|
|
|
|||
|
|
@ -39,9 +39,11 @@ class Position;
|
|||
namespace Stockfish::Eval::NNUE {
|
||||
|
||||
// Hash value of evaluation function structure
|
||||
constexpr std::uint32_t HashValue =
|
||||
FeatureTransformer::get_hash_value() ^ Network::get_hash_value();
|
||||
|
||||
constexpr std::uint32_t HashValue[2] = {
|
||||
FeatureTransformer<TransformedFeatureDimensionsBig, nullptr>::get_hash_value()
|
||||
^ Network<TransformedFeatureDimensionsBig, L2Big, L3Big>::get_hash_value(),
|
||||
FeatureTransformer<TransformedFeatureDimensionsSmall, nullptr>::get_hash_value()
|
||||
^ Network<TransformedFeatureDimensionsSmall, L2Small, L3Small>::get_hash_value()};
|
||||
|
||||
// Deleter for automating release of memory area
|
||||
template<typename T>
|
||||
|
|
@ -67,12 +69,13 @@ 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);
|
||||
template<NetSize Net_Size>
|
||||
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);
|
||||
bool load_eval(const std::string name, std::istream& stream, NetSize netSize);
|
||||
bool save_eval(std::ostream& stream, NetSize netSize);
|
||||
bool save_eval(const std::optional<std::string>& filename, NetSize netSize);
|
||||
|
||||
} // namespace Stockfish::Eval::NNUE
|
||||
|
||||
|
|
|
|||
|
|
@ -29,8 +29,9 @@
|
|||
namespace Stockfish::Eval::NNUE {
|
||||
|
||||
// Class that holds the result of affine transformation of input features
|
||||
template<IndexType Size>
|
||||
struct alignas(CacheLineSize) Accumulator {
|
||||
std::int16_t accumulation[2][TransformedFeatureDimensions];
|
||||
std::int16_t accumulation[2][Size];
|
||||
std::int32_t psqtAccumulation[2][PSQTBuckets];
|
||||
bool computed[2];
|
||||
};
|
||||
|
|
|
|||
|
|
@ -37,14 +37,28 @@ namespace Stockfish::Eval::NNUE {
|
|||
// Input features used in evaluation function
|
||||
using FeatureSet = Features::HalfKAv2_hm;
|
||||
|
||||
// Number of input feature dimensions after conversion
|
||||
constexpr IndexType TransformedFeatureDimensions = 2560;
|
||||
constexpr IndexType PSQTBuckets = 8;
|
||||
constexpr IndexType LayerStacks = 8;
|
||||
enum NetSize {
|
||||
Big,
|
||||
Small
|
||||
};
|
||||
|
||||
// Number of input feature dimensions after conversion
|
||||
constexpr IndexType TransformedFeatureDimensionsBig = 2560;
|
||||
constexpr int L2Big = 15;
|
||||
constexpr int L3Big = 32;
|
||||
|
||||
constexpr IndexType TransformedFeatureDimensionsSmall = 128;
|
||||
constexpr int L2Small = 15;
|
||||
constexpr int L3Small = 32;
|
||||
|
||||
constexpr IndexType PSQTBuckets = 8;
|
||||
constexpr IndexType LayerStacks = 8;
|
||||
|
||||
template<IndexType L1, int L2, int L3>
|
||||
struct Network {
|
||||
static constexpr int FC_0_OUTPUTS = 15;
|
||||
static constexpr int FC_1_OUTPUTS = 32;
|
||||
static constexpr IndexType TransformedFeatureDimensions = L1;
|
||||
static constexpr int FC_0_OUTPUTS = L2;
|
||||
static constexpr int FC_1_OUTPUTS = L3;
|
||||
|
||||
Layers::AffineTransformSparseInput<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
|
||||
Layers::SqrClippedReLU<FC_0_OUTPUTS + 1> ac_sqr_0;
|
||||
|
|
@ -84,13 +98,13 @@ struct Network {
|
|||
|
||||
std::int32_t propagate(const TransformedFeatureType* transformedFeatures) {
|
||||
struct alignas(CacheLineSize) Buffer {
|
||||
alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
|
||||
alignas(CacheLineSize) decltype(ac_sqr_0)::OutputType
|
||||
alignas(CacheLineSize) typename decltype(fc_0)::OutputBuffer fc_0_out;
|
||||
alignas(CacheLineSize) typename decltype(ac_sqr_0)::OutputType
|
||||
ac_sqr_0_out[ceil_to_multiple<IndexType>(FC_0_OUTPUTS * 2, 32)];
|
||||
alignas(CacheLineSize) decltype(ac_0)::OutputBuffer ac_0_out;
|
||||
alignas(CacheLineSize) decltype(fc_1)::OutputBuffer fc_1_out;
|
||||
alignas(CacheLineSize) decltype(ac_1)::OutputBuffer ac_1_out;
|
||||
alignas(CacheLineSize) decltype(fc_2)::OutputBuffer fc_2_out;
|
||||
alignas(CacheLineSize) typename decltype(ac_0)::OutputBuffer ac_0_out;
|
||||
alignas(CacheLineSize) typename decltype(fc_1)::OutputBuffer fc_1_out;
|
||||
alignas(CacheLineSize) typename decltype(ac_1)::OutputBuffer ac_1_out;
|
||||
alignas(CacheLineSize) typename decltype(fc_2)::OutputBuffer fc_2_out;
|
||||
|
||||
Buffer() { std::memset(this, 0, sizeof(*this)); }
|
||||
};
|
||||
|
|
@ -108,7 +122,7 @@ struct Network {
|
|||
ac_sqr_0.propagate(buffer.fc_0_out, buffer.ac_sqr_0_out);
|
||||
ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
|
||||
std::memcpy(buffer.ac_sqr_0_out + FC_0_OUTPUTS, buffer.ac_0_out,
|
||||
FC_0_OUTPUTS * sizeof(decltype(ac_0)::OutputType));
|
||||
FC_0_OUTPUTS * sizeof(typename decltype(ac_0)::OutputType));
|
||||
fc_1.propagate(buffer.ac_sqr_0_out, buffer.fc_1_out);
|
||||
ac_1.propagate(buffer.fc_1_out, buffer.ac_1_out);
|
||||
fc_2.propagate(buffer.ac_1_out, buffer.fc_2_out);
|
||||
|
|
|
|||
|
|
@ -186,11 +186,6 @@ static constexpr int BestRegisterCount() {
|
|||
|
||||
return 1;
|
||||
}
|
||||
|
||||
static constexpr int NumRegs =
|
||||
BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
|
||||
static constexpr int NumPsqtRegs =
|
||||
BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
|
||||
#if defined(__GNUC__)
|
||||
#pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
|
@ -198,6 +193,8 @@ static constexpr int NumPsqtRegs =
|
|||
|
||||
|
||||
// Input feature converter
|
||||
template<IndexType TransformedFeatureDimensions,
|
||||
Accumulator<TransformedFeatureDimensions> StateInfo::*accPtr>
|
||||
class FeatureTransformer {
|
||||
|
||||
private:
|
||||
|
|
@ -205,6 +202,11 @@ class FeatureTransformer {
|
|||
static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
|
||||
|
||||
#ifdef VECTOR
|
||||
static constexpr int NumRegs =
|
||||
BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
|
||||
static constexpr int NumPsqtRegs =
|
||||
BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
|
||||
|
||||
static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
|
||||
static constexpr IndexType PsqtTileHeight = NumPsqtRegs * sizeof(psqt_vec_t) / 4;
|
||||
static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions");
|
||||
|
|
@ -253,8 +255,8 @@ class FeatureTransformer {
|
|||
update_accumulator<BLACK>(pos);
|
||||
|
||||
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
|
||||
const auto& accumulation = pos.state()->accumulator.accumulation;
|
||||
const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation;
|
||||
const auto& accumulation = (pos.state()->*accPtr).accumulation;
|
||||
const auto& psqtAccumulation = (pos.state()->*accPtr).psqtAccumulation;
|
||||
|
||||
const auto psqt =
|
||||
(psqtAccumulation[perspectives[0]][bucket] - psqtAccumulation[perspectives[1]][bucket])
|
||||
|
|
@ -323,7 +325,7 @@ class FeatureTransformer {
|
|||
// 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])
|
||||
while (st->previous && !(st->*accPtr).computed[Perspective])
|
||||
{
|
||||
// This governs when a full feature refresh is needed and how many
|
||||
// updates are better than just one full refresh.
|
||||
|
|
@ -381,7 +383,7 @@ class FeatureTransformer {
|
|||
|
||||
for (; i >= 0; --i)
|
||||
{
|
||||
states_to_update[i]->accumulator.computed[Perspective] = true;
|
||||
(states_to_update[i]->*accPtr).computed[Perspective] = true;
|
||||
|
||||
const StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
|
||||
|
||||
|
|
@ -402,9 +404,9 @@ class FeatureTransformer {
|
|||
assert(states_to_update[0]);
|
||||
|
||||
auto accIn =
|
||||
reinterpret_cast<const vec_t*>(&st->accumulator.accumulation[Perspective][0]);
|
||||
reinterpret_cast<const vec_t*>(&(st->*accPtr).accumulation[Perspective][0]);
|
||||
auto accOut = reinterpret_cast<vec_t*>(
|
||||
&states_to_update[0]->accumulator.accumulation[Perspective][0]);
|
||||
&(states_to_update[0]->*accPtr).accumulation[Perspective][0]);
|
||||
|
||||
const IndexType offsetR0 = HalfDimensions * removed[0][0];
|
||||
auto columnR0 = reinterpret_cast<const vec_t*>(&weights[offsetR0]);
|
||||
|
|
@ -428,10 +430,10 @@ class FeatureTransformer {
|
|||
vec_add_16(columnR0[k], columnR1[k]));
|
||||
}
|
||||
|
||||
auto accPsqtIn = reinterpret_cast<const psqt_vec_t*>(
|
||||
&st->accumulator.psqtAccumulation[Perspective][0]);
|
||||
auto accPsqtIn =
|
||||
reinterpret_cast<const psqt_vec_t*>(&(st->*accPtr).psqtAccumulation[Perspective][0]);
|
||||
auto accPsqtOut = reinterpret_cast<psqt_vec_t*>(
|
||||
&states_to_update[0]->accumulator.psqtAccumulation[Perspective][0]);
|
||||
&(states_to_update[0]->*accPtr).psqtAccumulation[Perspective][0]);
|
||||
|
||||
const IndexType offsetPsqtR0 = PSQTBuckets * removed[0][0];
|
||||
auto columnPsqtR0 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtR0]);
|
||||
|
|
@ -463,7 +465,7 @@ class FeatureTransformer {
|
|||
{
|
||||
// Load accumulator
|
||||
auto accTileIn = reinterpret_cast<const vec_t*>(
|
||||
&st->accumulator.accumulation[Perspective][j * TileHeight]);
|
||||
&(st->*accPtr).accumulation[Perspective][j * TileHeight]);
|
||||
for (IndexType k = 0; k < NumRegs; ++k)
|
||||
acc[k] = vec_load(&accTileIn[k]);
|
||||
|
||||
|
|
@ -489,7 +491,7 @@ class FeatureTransformer {
|
|||
|
||||
// Store accumulator
|
||||
auto accTileOut = reinterpret_cast<vec_t*>(
|
||||
&states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
|
||||
&(states_to_update[i]->*accPtr).accumulation[Perspective][j * TileHeight]);
|
||||
for (IndexType k = 0; k < NumRegs; ++k)
|
||||
vec_store(&accTileOut[k], acc[k]);
|
||||
}
|
||||
|
|
@ -499,7 +501,7 @@ class FeatureTransformer {
|
|||
{
|
||||
// Load accumulator
|
||||
auto accTilePsqtIn = reinterpret_cast<const psqt_vec_t*>(
|
||||
&st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
|
||||
&(st->*accPtr).psqtAccumulation[Perspective][j * PsqtTileHeight]);
|
||||
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
|
||||
psqt[k] = vec_load_psqt(&accTilePsqtIn[k]);
|
||||
|
||||
|
|
@ -525,8 +527,8 @@ class FeatureTransformer {
|
|||
|
||||
// Store accumulator
|
||||
auto accTilePsqtOut = reinterpret_cast<psqt_vec_t*>(
|
||||
&states_to_update[i]
|
||||
->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
|
||||
&(states_to_update[i]->*accPtr)
|
||||
.psqtAccumulation[Perspective][j * PsqtTileHeight]);
|
||||
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
|
||||
vec_store_psqt(&accTilePsqtOut[k], psqt[k]);
|
||||
}
|
||||
|
|
@ -535,13 +537,12 @@ class FeatureTransformer {
|
|||
#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));
|
||||
std::memcpy((states_to_update[i]->*accPtr).accumulation[Perspective],
|
||||
(st->*accPtr).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];
|
||||
(states_to_update[i]->*accPtr).psqtAccumulation[Perspective][k] =
|
||||
(st->*accPtr).psqtAccumulation[Perspective][k];
|
||||
|
||||
st = states_to_update[i];
|
||||
|
||||
|
|
@ -551,10 +552,10 @@ class FeatureTransformer {
|
|||
const IndexType offset = HalfDimensions * index;
|
||||
|
||||
for (IndexType j = 0; j < HalfDimensions; ++j)
|
||||
st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
|
||||
(st->*accPtr).accumulation[Perspective][j] -= weights[offset + j];
|
||||
|
||||
for (std::size_t k = 0; k < PSQTBuckets; ++k)
|
||||
st->accumulator.psqtAccumulation[Perspective][k] -=
|
||||
(st->*accPtr).psqtAccumulation[Perspective][k] -=
|
||||
psqtWeights[index * PSQTBuckets + k];
|
||||
}
|
||||
|
||||
|
|
@ -564,10 +565,10 @@ class FeatureTransformer {
|
|||
const IndexType offset = HalfDimensions * index;
|
||||
|
||||
for (IndexType j = 0; j < HalfDimensions; ++j)
|
||||
st->accumulator.accumulation[Perspective][j] += weights[offset + j];
|
||||
(st->*accPtr).accumulation[Perspective][j] += weights[offset + j];
|
||||
|
||||
for (std::size_t k = 0; k < PSQTBuckets; ++k)
|
||||
st->accumulator.psqtAccumulation[Perspective][k] +=
|
||||
(st->*accPtr).psqtAccumulation[Perspective][k] +=
|
||||
psqtWeights[index * PSQTBuckets + k];
|
||||
}
|
||||
}
|
||||
|
|
@ -586,7 +587,7 @@ class FeatureTransformer {
|
|||
// 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;
|
||||
auto& accumulator = pos.state()->*accPtr;
|
||||
accumulator.computed[Perspective] = true;
|
||||
FeatureSet::IndexList active;
|
||||
FeatureSet::append_active_indices<Perspective>(pos, active);
|
||||
|
|
@ -663,12 +664,12 @@ class FeatureTransformer {
|
|||
// 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])
|
||||
if ((pos.state()->*accPtr).computed[Perspective])
|
||||
return;
|
||||
|
||||
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
|
||||
|
||||
if (oldest_st->accumulator.computed[Perspective])
|
||||
if ((oldest_st->*accPtr).computed[Perspective])
|
||||
{
|
||||
// Only update current position accumulator to minimize work.
|
||||
StateInfo* states_to_update[2] = {pos.state(), nullptr};
|
||||
|
|
@ -685,7 +686,7 @@ class FeatureTransformer {
|
|||
|
||||
auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
|
||||
|
||||
if (oldest_st->accumulator.computed[Perspective])
|
||||
if ((oldest_st->*accPtr).computed[Perspective])
|
||||
{
|
||||
if (next == nullptr)
|
||||
return;
|
||||
|
|
|
|||
|
|
@ -684,10 +684,10 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
|
|||
++st->pliesFromNull;
|
||||
|
||||
// Used by NNUE
|
||||
st->accumulator.computed[WHITE] = false;
|
||||
st->accumulator.computed[BLACK] = false;
|
||||
auto& dp = st->dirtyPiece;
|
||||
dp.dirty_num = 1;
|
||||
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
|
||||
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] = false;
|
||||
auto& dp = st->dirtyPiece;
|
||||
dp.dirty_num = 1;
|
||||
|
||||
Color us = sideToMove;
|
||||
Color them = ~us;
|
||||
|
|
@ -964,15 +964,15 @@ void Position::do_null_move(StateInfo& newSt) {
|
|||
assert(!checkers());
|
||||
assert(&newSt != st);
|
||||
|
||||
std::memcpy(&newSt, st, offsetof(StateInfo, accumulator));
|
||||
std::memcpy(&newSt, st, offsetof(StateInfo, accumulatorBig));
|
||||
|
||||
newSt.previous = st;
|
||||
st = &newSt;
|
||||
|
||||
st->dirtyPiece.dirty_num = 0;
|
||||
st->dirtyPiece.piece[0] = NO_PIECE; // Avoid checks in UpdateAccumulator()
|
||||
st->accumulator.computed[WHITE] = false;
|
||||
st->accumulator.computed[BLACK] = false;
|
||||
st->dirtyPiece.dirty_num = 0;
|
||||
st->dirtyPiece.piece[0] = NO_PIECE; // Avoid checks in UpdateAccumulator()
|
||||
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
|
||||
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] = false;
|
||||
|
||||
if (st->epSquare != SQ_NONE)
|
||||
{
|
||||
|
|
|
|||
|
|
@ -27,6 +27,7 @@
|
|||
|
||||
#include "bitboard.h"
|
||||
#include "nnue/nnue_accumulator.h"
|
||||
#include "nnue/nnue_architecture.h"
|
||||
#include "types.h"
|
||||
|
||||
namespace Stockfish {
|
||||
|
|
@ -57,8 +58,9 @@ struct StateInfo {
|
|||
int repetition;
|
||||
|
||||
// Used by NNUE
|
||||
Eval::NNUE::Accumulator accumulator;
|
||||
DirtyPiece dirtyPiece;
|
||||
Eval::NNUE::Accumulator<Eval::NNUE::TransformedFeatureDimensionsBig> accumulatorBig;
|
||||
Eval::NNUE::Accumulator<Eval::NNUE::TransformedFeatureDimensionsSmall> accumulatorSmall;
|
||||
DirtyPiece dirtyPiece;
|
||||
};
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@
|
|||
#include "misc.h"
|
||||
#include "movegen.h"
|
||||
#include "nnue/evaluate_nnue.h"
|
||||
#include "nnue/nnue_architecture.h"
|
||||
#include "position.h"
|
||||
#include "search.h"
|
||||
#include "thread.h"
|
||||
|
|
@ -320,7 +321,7 @@ void UCI::loop(int argc, char* argv[]) {
|
|||
std::string f;
|
||||
if (is >> std::skipws >> f)
|
||||
filename = f;
|
||||
Eval::NNUE::save_eval(filename);
|
||||
Eval::NNUE::save_eval(filename, Eval::NNUE::Big);
|
||||
}
|
||||
else if (token == "--help" || token == "help" || token == "--license" || token == "license")
|
||||
sync_cout
|
||||
|
|
|
|||
|
|
@ -82,7 +82,9 @@ void init(OptionsMap& o) {
|
|||
o["SyzygyProbeDepth"] << Option(1, 1, 100);
|
||||
o["Syzygy50MoveRule"] << Option(true);
|
||||
o["SyzygyProbeLimit"] << Option(7, 0, 7);
|
||||
o["EvalFile"] << Option(EvalFileDefaultName, on_eval_file);
|
||||
o["EvalFile"] << Option(EvalFileDefaultNameBig, on_eval_file);
|
||||
// Enable this after fishtest workers support EvalFileSmall
|
||||
// o["EvalFileSmall"] << Option(EvalFileDefaultNameSmall, on_eval_file);
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue