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Merge commit 'db147fe2586527a854516016699949af53dc5b17' into cluster

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This is the last commit before there are more conflicts.
This commit is contained in:
Steinar H. Gunderson
2025-12-24 15:14:23 +01:00
parent b7045f1bf6 db147fe258
commit 649004ed47
13 changed files with 336 additions and 485 deletions
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AUTHORS
+1
View File
@@ -46,6 +46,7 @@ Bryan Cross (crossbr)
candirufish
Chess13234
Chris Cain (ceebo)
Ciekce
clefrks
Clemens L. (rn5f107s2)
Cody Ho (aesrentai)
src/engine.cpp
+7 -3
View File
@@ -141,14 +141,18 @@ void Engine::verify_networks() const {
}
void Engine::load_networks() {
networks.big.load(binaryDirectory, options["EvalFile"]);
networks.small.load(binaryDirectory, options["EvalFileSmall"]);
load_big_network(options["EvalFile"]);
load_small_network(options["EvalFileSmall"]);
}
void Engine::load_big_network(const std::string& file) { networks.big.load(binaryDirectory, file); }
void Engine::load_big_network(const std::string& file) {
networks.big.load(binaryDirectory, file);
threads.clear();
}
void Engine::load_small_network(const std::string& file) {
networks.small.load(binaryDirectory, file);
threads.clear();
}
void Engine::save_network(const std::pair<std::optional<std::string>, std::string> files[2]) {
src/evaluate.cpp
+11 -15
View File
@@ -56,36 +56,32 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
int simpleEval = simple_eval(pos, pos.side_to_move());
bool smallNet = std::abs(simpleEval) > SmallNetThreshold;
bool psqtOnly = std::abs(simpleEval) > PsqtOnlyThreshold;
int nnueComplexity;
int v;
Value nnue = smallNet ? networks.small.evaluate(pos, nullptr, true, &nnueComplexity, psqtOnly)
: networks.big.evaluate(pos, &caches.big, true, &nnueComplexity, false);
Value nnue = smallNet ? networks.small.evaluate(pos, &caches.small, true, &nnueComplexity)
: networks.big.evaluate(pos, &caches.big, true, &nnueComplexity);
const auto adjustEval = [&](int optDiv, int nnueDiv, int npmDiv, int pawnCountConstant,
int pawnCountMul, int npmConstant, int evalDiv,
int shufflingConstant, int shufflingDiv) {
const auto adjustEval = [&](int nnueDiv, int pawnCountConstant, int pawnCountMul,
int npmConstant, int evalDiv, int shufflingConstant) {
// Blend optimism and eval with nnue complexity and material imbalance
optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / optDiv;
optimism += optimism * (nnueComplexity + std::abs(simpleEval - nnue)) / 584;
nnue -= nnue * (nnueComplexity * 5 / 3) / nnueDiv;
int npm = pos.non_pawn_material() / npmDiv;
int npm = pos.non_pawn_material() / 64;
v = (nnue * (npm + pawnCountConstant + pawnCountMul * pos.count<PAWN>())
+ optimism * (npmConstant + npm))
/ evalDiv;
// Damp down the evaluation linearly when shuffling
int shuffling = pos.rule50_count();
v = v * (shufflingConstant - shuffling) / shufflingDiv;
v = v * (shufflingConstant - shuffling) / 207;
};
if (!smallNet)
adjustEval(524, 32395, 66, 942, 11, 139, 1058, 178, 204);
else if (psqtOnly)
adjustEval(517, 32857, 65, 908, 7, 155, 1006, 224, 238);
adjustEval(32395, 942, 11, 139, 1058, 178);
else
adjustEval(515, 32793, 63, 944, 9, 140, 1067, 206, 206);
adjustEval(32793, 944, 9, 140, 1067, 206);
// 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);
@@ -99,11 +95,11 @@ Value Eval::evaluate(const Eval::NNUE::Networks& networks,
// Trace scores are from white's point of view
std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {
auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
if (pos.checkers())
return "Final evaluation: none (in check)";
auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
std::stringstream ss;
ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';
src/evaluate.h
+1 -1
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@@ -29,7 +29,7 @@ class Position;
namespace Eval {
constexpr inline int SmallNetThreshold = 1274, PsqtOnlyThreshold = 2389;
constexpr inline int SmallNetThreshold = 1274;
// 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
src/nnue/network.cpp
+8 -11
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@@ -190,8 +190,7 @@ template<typename Arch, typename Transformer>
Value Network<Arch, Transformer>::evaluate(const Position& pos,
AccumulatorCaches::Cache<FTDimensions>* cache,
bool adjusted,
int* complexity,
bool psqtOnly) const {
int* complexity) const {
// We manually align the arrays on the stack because with gcc < 9.3
// overaligning stack variables with alignas() doesn't work correctly.
@@ -212,12 +211,11 @@ Value Network<Arch, Transformer>::evaluate(const Position&
ASSERT_ALIGNED(transformedFeatures, alignment);
const int bucket = (pos.count<ALL_PIECES>() - 1) / 4;
const auto psqt =
featureTransformer->transform(pos, cache, transformedFeatures, bucket, psqtOnly);
const auto positional = !psqtOnly ? (network[bucket]->propagate(transformedFeatures)) : 0;
const auto psqt = featureTransformer->transform(pos, cache, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);
if (complexity)
*complexity = !psqtOnly ? std::abs(psqt - positional) / OutputScale : 0;
*complexity = std::abs(psqt - positional) / OutputScale;
// Give more value to positional evaluation when adjusted flag is set
if (adjusted)
@@ -263,10 +261,9 @@ void Network<Arch, Transformer>::verify(std::string evalfilePath) const {
template<typename Arch, typename Transformer>
void Network<Arch, Transformer>::hint_common_access(const Position& pos,
AccumulatorCaches::Cache<FTDimensions>* cache,
bool psqtOnl) const {
featureTransformer->hint_common_access(pos, cache, psqtOnl);
void Network<Arch, Transformer>::hint_common_access(
const Position& pos, AccumulatorCaches::Cache<FTDimensions>* cache) const {
featureTransformer->hint_common_access(pos, cache);
}
template<typename Arch, typename Transformer>
@@ -295,7 +292,7 @@ Network<Arch, Transformer>::trace_evaluate(const Position&
for (IndexType bucket = 0; bucket < LayerStacks; ++bucket)
{
const auto materialist =
featureTransformer->transform(pos, cache, transformedFeatures, bucket, false);
featureTransformer->transform(pos, cache, transformedFeatures, bucket);
const auto positional = network[bucket]->propagate(transformedFeatures);
t.psqt[bucket] = static_cast<Value>(materialist / OutputScale);
src/nnue/network.h
+2 -4
View File
@@ -56,13 +56,11 @@ class Network {
Value evaluate(const Position& pos,
AccumulatorCaches::Cache<FTDimensions>* cache,
bool adjusted = false,
int* complexity = nullptr,
bool psqtOnly = false) const;
int* complexity = nullptr) const;
void hint_common_access(const Position& pos,
AccumulatorCaches::Cache<FTDimensions>* cache,
bool psqtOnl) const;
AccumulatorCaches::Cache<FTDimensions>* cache) const;
void verify(std::string evalfilePath) const;
NnueEvalTrace trace_evaluate(const Position& pos,
src/nnue/nnue_accumulator.h
+13 -17
View File
@@ -38,7 +38,6 @@ struct alignas(CacheLineSize) Accumulator {
std::int16_t accumulation[COLOR_NB][Size];
std::int32_t psqtAccumulation[COLOR_NB][PSQTBuckets];
bool computed[COLOR_NB];
bool computedPSQT[COLOR_NB];
};
@@ -59,28 +58,25 @@ struct AccumulatorCaches {
struct alignas(CacheLineSize) Cache {
struct alignas(CacheLineSize) Entry {
BiasType accumulation[COLOR_NB][Size];
PSQTWeightType psqtAccumulation[COLOR_NB][PSQTBuckets];
Bitboard byColorBB[COLOR_NB][COLOR_NB];
Bitboard byTypeBB[COLOR_NB][PIECE_TYPE_NB];
BiasType accumulation[Size];
PSQTWeightType psqtAccumulation[PSQTBuckets];
Bitboard byColorBB[COLOR_NB];
Bitboard byTypeBB[PIECE_TYPE_NB];
// To initialize a refresh entry, we set all its bitboards empty,
// so we put the biases in the accumulation, without any weights on top
void clear(const BiasType* biases) {
std::memset(byColorBB, 0, sizeof(byColorBB));
std::memset(byTypeBB, 0, sizeof(byTypeBB));
std::memcpy(accumulation[WHITE], biases, Size * sizeof(BiasType));
std::memcpy(accumulation[BLACK], biases, Size * sizeof(BiasType));
std::memset(psqtAccumulation, 0, sizeof(psqtAccumulation));
std::memcpy(accumulation, biases, sizeof(accumulation));
std::memset((uint8_t*) this + offsetof(Entry, psqtAccumulation), 0,
sizeof(Entry) - offsetof(Entry, psqtAccumulation));
}
};
template<typename Network>
void clear(const Network& network) {
for (auto& entry : entries)
for (auto& entries1D : entries)
for (auto& entry : entries1D)
entry.clear(network.featureTransformer->biases);
}
@@ -89,19 +85,19 @@ struct AccumulatorCaches {
entry.clear(biases);
}
Entry& operator[](Square sq) { return entries[sq]; }
std::array<Entry, COLOR_NB>& operator[](Square sq) { return entries[sq]; }
std::array<Entry, SQUARE_NB> entries;
std::array<std::array<Entry, COLOR_NB>, SQUARE_NB> entries;
};
template<typename Networks>
void clear(const Networks& networks) {
big.clear(networks.big);
small.clear(networks.small);
}
// When adding a new cache for a network, i.e. the smallnet
// the appropriate condition must be added to FeatureTransformer::update_accumulator_refresh.
Cache<TransformedFeatureDimensionsBig> big;
Cache<TransformedFeatureDimensionsSmall> small;
};
} // namespace Stockfish::Eval::NNUE
src/nnue/nnue_feature_transformer.h
+114 -262
View File
@@ -307,10 +307,9 @@ class FeatureTransformer {
std::int32_t transform(const Position& pos,
AccumulatorCaches::Cache<HalfDimensions>* cache,
OutputType* output,
int bucket,
bool psqtOnly) const {
update_accumulator<WHITE>(pos, cache, psqtOnly);
update_accumulator<BLACK>(pos, cache, psqtOnly);
int bucket) const {
update_accumulator<WHITE>(pos, cache);
update_accumulator<BLACK>(pos, cache);
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
const auto& psqtAccumulation = (pos.state()->*accPtr).psqtAccumulation;
@@ -318,9 +317,6 @@ class FeatureTransformer {
(psqtAccumulation[perspectives[0]][bucket] - psqtAccumulation[perspectives[1]][bucket])
/ 2;
if (psqtOnly)
return psqt;
const auto& accumulation = (pos.state()->*accPtr).accumulation;
for (IndexType p = 0; p < 2; ++p)
@@ -373,23 +369,20 @@ class FeatureTransformer {
} // end of function transform()
void hint_common_access(const Position& pos,
AccumulatorCaches::Cache<HalfDimensions>* cache,
bool psqtOnly) const {
hint_common_access_for_perspective<WHITE>(pos, cache, psqtOnly);
hint_common_access_for_perspective<BLACK>(pos, cache, psqtOnly);
AccumulatorCaches::Cache<HalfDimensions>* cache) const {
hint_common_access_for_perspective<WHITE>(pos, cache);
hint_common_access_for_perspective<BLACK>(pos, cache);
}
private:
template<Color Perspective>
[[nodiscard]] std::pair<StateInfo*, StateInfo*>
try_find_computed_accumulator(const Position& pos, bool psqtOnly) const {
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->*accPtr).computedPSQT[Perspective]
|| (!psqtOnly && !(st->*accPtr).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.
@@ -402,19 +395,24 @@ class FeatureTransformer {
return {st, next};
}
// NOTE: The parameter states_to_update is an array of position states, ending with nullptr.
// NOTE: The parameter states_to_update is an array of position states.
// 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.
// by repeatedly applying ->previous from states_to_update[i+1].
// computed_st must be reachable by repeatedly applying ->previous on
// states_to_update[0], if not nullptr.
// states_to_update[0].
template<Color Perspective, size_t N>
void update_accumulator_incremental(const Position& pos,
StateInfo* computed_st,
StateInfo* states_to_update[N],
bool psqtOnly) const {
StateInfo* states_to_update[N]) const {
static_assert(N > 0);
assert(states_to_update[N - 1] == nullptr);
assert([&]() {
for (size_t i = 0; i < N; ++i)
{
if (states_to_update[i] == nullptr)
return false;
}
return true;
}());
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
@@ -423,11 +421,7 @@ class FeatureTransformer {
psqt_vec_t psqt[NumPsqtRegs];
#endif
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);
@@ -435,28 +429,17 @@ class FeatureTransformer {
// 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];
FeatureSet::IndexList removed[N], added[N];
for (int i = N - 1; i >= 0; --i)
{
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]->*accPtr).computed[Perspective] = !psqtOnly;
(states_to_update[i]->*accPtr).computedPSQT[Perspective] = true;
(states_to_update[i]->*accPtr).computed[Perspective] = true;
const 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]);
}
for (StateInfo* st2 = states_to_update[i]; st2 != end_state; st2 = st2->previous)
FeatureSet::append_changed_indices<Perspective>(ksq, st2->dirtyPiece, removed[i],
added[i]);
}
StateInfo* st = computed_st;
@@ -464,13 +447,10 @@ class FeatureTransformer {
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
#ifdef VECTOR
if (states_to_update[1] == nullptr && (removed[0].size() == 1 || removed[0].size() == 2)
&& added[0].size() == 1)
if (N == 1 && (removed[0].size() == 1 || removed[0].size() == 2) && added[0].size() == 1)
{
assert(states_to_update[0]);
if (!psqtOnly)
{
auto accIn =
reinterpret_cast<const vec_t*>(&(st->*accPtr).accumulation[Perspective][0]);
auto accOut = reinterpret_cast<vec_t*>(
@@ -497,7 +477,6 @@ class FeatureTransformer {
accOut[k] = vec_sub_16(vec_add_16(accIn[k], columnA[k]),
vec_add_16(columnR0[k], columnR1[k]));
}
}
auto accPsqtIn =
reinterpret_cast<const psqt_vec_t*>(&(st->*accPtr).psqtAccumulation[Perspective][0]);
@@ -530,7 +509,6 @@ class FeatureTransformer {
}
else
{
if (!psqtOnly)
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
// Load accumulator
@@ -539,7 +517,7 @@ class FeatureTransformer {
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTileIn[k]);
for (IndexType i = 0; states_to_update[i]; ++i)
for (IndexType i = 0; i < N; ++i)
{
// Difference calculation for the deactivated features
for (const auto index : removed[i])
@@ -560,9 +538,8 @@ class FeatureTransformer {
}
// Store accumulator
auto accTileOut =
reinterpret_cast<vec_t*>(&(states_to_update[i]->*accPtr)
.accumulation[Perspective][j * TileHeight]);
auto accTileOut = reinterpret_cast<vec_t*>(
&(states_to_update[i]->*accPtr).accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_store(&accTileOut[k], acc[k]);
}
@@ -576,7 +553,7 @@ class FeatureTransformer {
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_load_psqt(&accTilePsqtIn[k]);
for (IndexType i = 0; states_to_update[i]; ++i)
for (IndexType i = 0; i < N; ++i)
{
// Difference calculation for the deactivated features
for (const auto index : removed[i])
@@ -606,12 +583,10 @@ class FeatureTransformer {
}
}
#else
for (IndexType i = 0; states_to_update[i]; ++i)
for (IndexType i = 0; i < N; ++i)
{
if (!psqtOnly)
std::memcpy((states_to_update[i]->*accPtr).accumulation[Perspective],
(st->*accPtr).accumulation[Perspective],
HalfDimensions * sizeof(BiasType));
(st->*accPtr).accumulation[Perspective], HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(states_to_update[i]->*accPtr).psqtAccumulation[Perspective][k] =
@@ -621,13 +596,10 @@ class FeatureTransformer {
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
if (!psqtOnly)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
(st->*accPtr).accumulation[Perspective][j] -= weights[offset + j];
}
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(st->*accPtr).psqtAccumulation[Perspective][k] -=
@@ -636,13 +608,10 @@ class FeatureTransformer {
// Difference calculation for the activated features
for (const auto index : added[i])
{
if (!psqtOnly)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
(st->*accPtr).accumulation[Perspective][j] += weights[offset + j];
}
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(st->*accPtr).psqtAccumulation[Perspective][k] +=
@@ -658,21 +627,15 @@ class FeatureTransformer {
assert(cache != nullptr);
Square ksq = pos.square<KING>(Perspective);
auto& entry = (*cache)[ksq];
auto& accumulator = pos.state()->*accPtr;
accumulator.computed[Perspective] = true;
accumulator.computedPSQT[Perspective] = true;
auto& entry = (*cache)[ksq][Perspective];
FeatureSet::IndexList removed, added;
for (Color c : {WHITE, BLACK})
{
for (PieceType pt = PAWN; pt <= KING; ++pt)
{
const Piece piece = make_piece(c, pt);
const Bitboard oldBB =
entry.byColorBB[Perspective][c] & entry.byTypeBB[Perspective][pt];
const Bitboard oldBB = entry.byColorBB[c] & entry.byTypeBB[pt];
const Bitboard newBB = pos.pieces(c, pt);
Bitboard toRemove = oldBB & ~newBB;
Bitboard toAdd = newBB & ~oldBB;
@@ -690,27 +653,33 @@ class FeatureTransformer {
}
}
auto& accumulator = pos.state()->*accPtr;
accumulator.computed[Perspective] = true;
#ifdef VECTOR
vec_t acc[NumRegs];
psqt_vec_t psqt[NumPsqtRegs];
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto entryTile =
reinterpret_cast<vec_t*>(&entry.accumulation[Perspective][j * TileHeight]);
auto entryTile = reinterpret_cast<vec_t*>(&entry.accumulation[j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = entryTile[k];
for (int i = 0; i < int(added.size()); ++i)
int i = 0;
for (; i < int(std::min(removed.size(), added.size())); ++i)
{
IndexType index = added[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
IndexType indexR = removed[i];
const IndexType offsetR = HalfDimensions * indexR + j * TileHeight;
auto columnR = reinterpret_cast<const vec_t*>(&weights[offsetR]);
IndexType indexA = added[i];
const IndexType offsetA = HalfDimensions * indexA + j * TileHeight;
auto columnA = reinterpret_cast<const vec_t*>(&weights[offsetA]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
acc[k] = vec_add_16(vec_sub_16(acc[k], columnR[k]), columnA[k]);
}
for (int i = 0; i < int(removed.size()); ++i)
for (; i < int(removed.size()); ++i)
{
IndexType index = removed[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
@@ -719,6 +688,15 @@ class FeatureTransformer {
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_sub_16(acc[k], column[k]);
}
for (; i < int(added.size()); ++i)
{
IndexType index = added[i];
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]);
}
for (IndexType k = 0; k < NumRegs; k++)
vec_store(&entryTile[k], acc[k]);
@@ -726,20 +704,11 @@ class FeatureTransformer {
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
auto entryTilePsqt = reinterpret_cast<psqt_vec_t*>(
&entry.psqtAccumulation[Perspective][j * PsqtTileHeight]);
auto entryTilePsqt =
reinterpret_cast<psqt_vec_t*>(&entry.psqtAccumulation[j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = entryTilePsqt[k];
for (int i = 0; i < int(added.size()); ++i)
{
IndexType 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]);
}
for (int i = 0; i < int(removed.size()); ++i)
{
IndexType index = removed[i];
@@ -749,140 +718,9 @@ class FeatureTransformer {
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
}
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&entryTilePsqt[k], psqt[k]);
}
#else
for (const auto index : added)
for (int i = 0; i < int(added.size()); ++i)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
entry.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
entry.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
for (const auto index : removed)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
entry.accumulation[Perspective][j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
entry.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
}
#endif
// The accumulator of the refresh entry has been updated.
// Now copy its content to the actual accumulator we were refreshing
std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation[Perspective],
sizeof(int32_t) * PSQTBuckets);
std::memcpy(accumulator.accumulation[Perspective], entry.accumulation[Perspective],
sizeof(BiasType) * HalfDimensions);
for (Color c : {WHITE, BLACK})
entry.byColorBB[Perspective][c] = pos.pieces(c);
for (PieceType pt = PAWN; pt <= KING; ++pt)
entry.byTypeBB[Perspective][pt] = pos.pieces(pt);
}
template<Color Perspective>
void
update_accumulator_refresh(const Position& pos,
[[maybe_unused]] AccumulatorCaches::Cache<HalfDimensions>* cache,
bool psqtOnly) const {
// When we are refreshing the accumulator of the big net,
// redirect to the version of refresh that uses the refresh table.
// Using the cache for the small net is not beneficial.
if constexpr (HalfDimensions == Eval::NNUE::TransformedFeatureDimensionsBig)
{
update_accumulator_refresh_cache<Perspective>(pos, cache);
return;
}
#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()->*accPtr;
accumulator.computed[Perspective] = !psqtOnly;
accumulator.computedPSQT[Perspective] = true;
FeatureSet::IndexList active;
FeatureSet::append_active_indices<Perspective>(pos, active);
#ifdef VECTOR
if (!psqtOnly)
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];
int i = 0;
for (; i < int(active.size()) - 1; i += 2)
{
IndexType index0 = active[i];
IndexType index1 = active[i + 1];
const IndexType offset0 = HalfDimensions * index0 + j * TileHeight;
const IndexType offset1 = HalfDimensions * index1 + j * TileHeight;
auto column0 = reinterpret_cast<const vec_t*>(&weights[offset0]);
auto column1 = reinterpret_cast<const vec_t*>(&weights[offset1]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], vec_add_16(column0[k], column1[k]));
}
for (; i < int(active.size()); ++i)
{
IndexType index = active[i];
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();
int i = 0;
for (; i < int(active.size()) - 1; i += 2)
{
IndexType index0 = active[i];
IndexType index1 = active[i + 1];
const IndexType offset0 = PSQTBuckets * index0 + j * PsqtTileHeight;
const IndexType offset1 = PSQTBuckets * index1 + j * PsqtTileHeight;
auto columnPsqt0 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset0]);
auto columnPsqt1 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset1]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] =
vec_add_psqt_32(psqt[k], vec_add_psqt_32(columnPsqt0[k], columnPsqt1[k]));
}
for (; i < int(active.size()); ++i)
{
IndexType index = active[i];
IndexType index = added[i];
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
@@ -890,40 +728,52 @@ class FeatureTransformer {
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]);
vec_store_psqt(&entryTilePsqt[k], psqt[k]);
}
#else
if (!psqtOnly)
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)
{
if (!psqtOnly)
for (const auto index : removed)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
accumulator.accumulation[Perspective][j] += weights[offset + j];
}
entry.accumulation[j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
accumulator.psqtAccumulation[Perspective][k] +=
psqtWeights[index * PSQTBuckets + k];
entry.psqtAccumulation[k] -= psqtWeights[index * PSQTBuckets + k];
}
for (const auto index : added)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
entry.accumulation[j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
entry.psqtAccumulation[k] += psqtWeights[index * PSQTBuckets + k];
}
#endif
// The accumulator of the refresh entry has been updated.
// Now copy its content to the actual accumulator we were refreshing
std::memcpy(accumulator.accumulation[Perspective], entry.accumulation,
sizeof(BiasType) * HalfDimensions);
std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation,
sizeof(int32_t) * PSQTBuckets);
for (Color c : {WHITE, BLACK})
entry.byColorBB[c] = pos.pieces(c);
for (PieceType pt = PAWN; pt <= KING; ++pt)
entry.byTypeBB[pt] = pos.pieces(pt);
}
template<Color Perspective>
void hint_common_access_for_perspective(const Position& pos,
AccumulatorCaches::Cache<HalfDimensions>* cache,
bool psqtOnly) const {
AccumulatorCaches::Cache<HalfDimensions>* cache) const {
// Works like update_accumulator, but performs less work.
// Updates ONLY the accumulator for pos.
@@ -931,33 +781,28 @@ 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()->*accPtr).computed[Perspective]
|| (psqtOnly && (pos.state()->*accPtr).computedPSQT[Perspective]))
if ((pos.state()->*accPtr).computed[Perspective])
return;
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos, psqtOnly);
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
if ((oldest_st->*accPtr).computed[Perspective]
|| (psqtOnly && (oldest_st->*accPtr).computedPSQT[Perspective]))
if ((oldest_st->*accPtr).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,
psqtOnly);
StateInfo* states_to_update[1] = {pos.state()};
update_accumulator_incremental<Perspective, 1>(pos, oldest_st, states_to_update);
}
else
update_accumulator_refresh<Perspective>(pos, cache, psqtOnly);
update_accumulator_refresh_cache<Perspective>(pos, cache);
}
template<Color Perspective>
void update_accumulator(const Position& pos,
AccumulatorCaches::Cache<HalfDimensions>* cache,
bool psqtOnly) const {
AccumulatorCaches::Cache<HalfDimensions>* cache) const {
auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos, psqtOnly);
auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
if ((oldest_st->*accPtr).computed[Perspective]
|| (psqtOnly && (oldest_st->*accPtr).computedPSQT[Perspective]))
if ((oldest_st->*accPtr).computed[Perspective])
{
if (next == nullptr)
return;
@@ -967,14 +812,21 @@ class FeatureTransformer {
// 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};
if (next == pos.state())
{
StateInfo* states_to_update[1] = {next};
update_accumulator_incremental<Perspective, 3>(pos, oldest_st, states_to_update,
psqtOnly);
update_accumulator_incremental<Perspective, 1>(pos, oldest_st, states_to_update);
}
else
update_accumulator_refresh<Perspective>(pos, cache, psqtOnly);
{
StateInfo* states_to_update[2] = {next, pos.state()};
update_accumulator_incremental<Perspective, 2>(pos, oldest_st, states_to_update);
}
}
else
update_accumulator_refresh_cache<Perspective>(pos, cache);
}
template<IndexType Size>
src/nnue/nnue_misc.cpp
+4 -8
View File
@@ -48,9 +48,9 @@ void hint_common_parent_position(const Position& pos,
int simpleEvalAbs = std::abs(simple_eval(pos, pos.side_to_move()));
if (simpleEvalAbs > Eval::SmallNetThreshold)
networks.small.hint_common_access(pos, nullptr, simpleEvalAbs > Eval::PsqtOnlyThreshold);
networks.small.hint_common_access(pos, &caches.small);
else
networks.big.hint_common_access(pos, &caches.big, false);
networks.big.hint_common_access(pos, &caches.big);
}
namespace {
@@ -148,18 +148,14 @@ trace(Position& pos, const Eval::NNUE::Networks& networks, Eval::NNUE::Accumulat
auto st = pos.state();
pos.remove_piece(sq);
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
st->accumulatorBig.computedPSQT[WHITE] = st->accumulatorBig.computedPSQT[BLACK] =
false;
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] = false;
Value eval = networks.big.evaluate(pos, &caches.big);
eval = pos.side_to_move() == WHITE ? eval : -eval;
v = base - eval;
pos.put_piece(pc, sq);
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
st->accumulatorBig.computedPSQT[WHITE] = st->accumulatorBig.computedPSQT[BLACK] =
false;
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] = false;
}
writeSquare(f, r, pc, v);
src/position.cpp
+2 -8
View File
@@ -681,10 +681,7 @@ void Position::do_move(Move m, StateInfo& newSt, bool givesCheck) {
// Used by NNUE
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
st->accumulatorBig.computedPSQT[WHITE] = st->accumulatorBig.computedPSQT[BLACK] =
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] =
st->accumulatorSmall.computedPSQT[WHITE] = st->accumulatorSmall.computedPSQT[BLACK] =
false;
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] = false;
auto& dp = st->dirtyPiece;
dp.dirty_num = 1;
@@ -971,10 +968,7 @@ void Position::do_null_move(StateInfo& newSt, TranspositionTable& tt) {
st->dirtyPiece.dirty_num = 0;
st->dirtyPiece.piece[0] = NO_PIECE; // Avoid checks in UpdateAccumulator()
st->accumulatorBig.computed[WHITE] = st->accumulatorBig.computed[BLACK] =
st->accumulatorBig.computedPSQT[WHITE] = st->accumulatorBig.computedPSQT[BLACK] =
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] =
st->accumulatorSmall.computedPSQT[WHITE] = st->accumulatorSmall.computedPSQT[BLACK] =
false;
st->accumulatorSmall.computed[WHITE] = st->accumulatorSmall.computed[BLACK] = false;
if (st->epSquare != SQ_NONE)
{
src/search.cpp
+100 -85
View File
@@ -60,9 +60,9 @@ static constexpr double EvalLevel[10] = {0.981, 0.956, 0.895, 0.949, 0.913,
// Futility margin
Value futility_margin(Depth d, bool noTtCutNode, bool improving, bool oppWorsening) {
Value futilityMult = 118 - 45 * noTtCutNode;
Value improvingDeduction = 52 * improving * futilityMult / 32;
Value worseningDeduction = (316 + 48 * improving) * oppWorsening * futilityMult / 1024;
Value futilityMult = 126 - 46 * noTtCutNode;
Value improvingDeduction = 58 * improving * futilityMult / 32;
Value worseningDeduction = (323 + 52 * improving) * oppWorsening * futilityMult / 1024;
return futilityMult * d - improvingDeduction - worseningDeduction;
}
@@ -74,15 +74,15 @@ constexpr int futility_move_count(bool improving, Depth depth) {
// Add correctionHistory value to raw staticEval and guarantee evaluation does not hit the tablebase range
Value to_corrected_static_eval(Value v, const Worker& w, const Position& pos) {
auto cv = w.correctionHistory[pos.side_to_move()][pawn_structure_index<Correction>(pos)];
v += cv * std::abs(cv) / 9260;
v += cv * std::abs(cv) / 7350;
return std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
}
// History and stats update bonus, based on depth
int stat_bonus(Depth d) { return std::clamp(214 * d - 318, 16, 1304); }
int stat_bonus(Depth d) { return std::clamp(208 * d - 297, 16, 1406); }
// History and stats update malus, based on depth
int stat_malus(Depth d) { return (d < 4 ? 572 * d - 284 : 1355); }
int stat_malus(Depth d) { return (d < 4 ? 520 * d - 312 : 1479); }
// Add a small random component to draw evaluations to avoid 3-fold blindness
Value value_draw(size_t nodes) { return VALUE_DRAW - 1 + Value(nodes & 0x2); }
@@ -115,6 +115,9 @@ Value value_to_tt(Value v, int ply);
Value value_from_tt(Value v, int ply, int r50c);
void update_pv(Move* pv, Move move, const Move* childPv);
void update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus);
void update_refutations(const Position& pos, Stack* ss, Search::Worker& workerThread, Move move);
void update_quiet_histories(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus);
void update_quiet_stats(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus);
void update_all_stats(const Position& pos,
@@ -148,9 +151,6 @@ Search::Worker::Worker(SharedState& sharedState,
void Search::Worker::start_searching() {
// Initialize accumulator refresh entries
refreshTable.clear(networks);
// Non-main threads go directly to iterative_deepening()
if (!is_mainthread())
{
@@ -352,12 +352,12 @@ void Search::Worker::iterative_deepening() {
// Reset aspiration window starting size
Value avg = rootMoves[pvIdx].averageScore;
delta = 10 + avg * avg / 11480;
delta = 10 + avg * avg / 9530;
alpha = std::max(avg - delta, -VALUE_INFINITE);
beta = std::min(avg + delta, VALUE_INFINITE);
// Adjust optimism based on root move's averageScore (~4 Elo)
optimism[us] = 122 * avg / (std::abs(avg) + 92);
optimism[us] = 119 * avg / (std::abs(avg) + 88);
optimism[~us] = -optimism[us];
// Start with a small aspiration window and, in the case of a fail
@@ -550,10 +550,12 @@ void Search::Worker::clear() {
for (StatsType c : {NoCaptures, Captures})
for (auto& to : continuationHistory[inCheck][c])
for (auto& h : to)
h->fill(-65);
h->fill(-60);
for (size_t i = 1; i < reductions.size(); ++i)
reductions[i] = int((20.14 + std::log(size_t(options["Threads"])) / 2) * std::log(i));
reductions[i] = int((18.93 + std::log(size_t(options["Threads"])) / 2) * std::log(i));
refreshTable.clear(networks);
}
@@ -642,7 +644,6 @@ Value Search::Worker::search(
bestMove = Move::none();
(ss + 2)->killers[0] = (ss + 2)->killers[1] = Move::none();
(ss + 2)->cutoffCnt = 0;
ss->multipleExtensions = (ss - 1)->multipleExtensions;
Square prevSq = ((ss - 1)->currentMove).is_ok() ? ((ss - 1)->currentMove).to_sq() : SQ_NONE;
ss->statScore = 0;
@@ -785,7 +786,7 @@ Value Search::Worker::search(
// Use static evaluation difference to improve quiet move ordering (~9 Elo)
if (((ss - 1)->currentMove).is_ok() && !(ss - 1)->inCheck && !priorCapture)
{
int bonus = std::clamp(-14 * int((ss - 1)->staticEval + ss->staticEval), -1644, 1384);
int bonus = std::clamp(-13 * int((ss - 1)->staticEval + ss->staticEval), -1796, 1526);
bonus = bonus > 0 ? 2 * bonus : bonus / 2;
thisThread->mainHistory[~us][((ss - 1)->currentMove).from_to()] << bonus;
if (type_of(pos.piece_on(prevSq)) != PAWN && ((ss - 1)->currentMove).type_of() != PROMOTION)
@@ -808,7 +809,7 @@ Value Search::Worker::search(
// If eval is really low check with qsearch if it can exceed alpha, if it can't,
// return a fail low.
// Adjust razor margin according to cutoffCnt. (~1 Elo)
if (eval < alpha - 471 - (275 - 148 * ((ss + 1)->cutoffCnt > 3)) * depth * depth)
if (eval < alpha - 433 - (302 - 141 * ((ss + 1)->cutoffCnt > 3)) * depth * depth)
{
value = qsearch<NonPV>(pos, ss, alpha - 1, alpha);
if (value < alpha)
@@ -817,23 +818,23 @@ Value Search::Worker::search(
// Step 8. Futility pruning: child node (~40 Elo)
// The depth condition is important for mate finding.
if (!ss->ttPv && depth < 12
if (!ss->ttPv && depth < 11
&& eval - futility_margin(depth, cutNode && !ss->ttHit, improving, opponentWorsening)
- (ss - 1)->statScore / 286
- (ss - 1)->statScore / 254
>= beta
&& eval >= beta && eval < VALUE_TB_WIN_IN_MAX_PLY && (!ttMove || ttCapture))
return beta > VALUE_TB_LOSS_IN_MAX_PLY ? (eval + beta) / 2 : eval;
// Step 9. Null move search with verification search (~35 Elo)
if (!PvNode && (ss - 1)->currentMove != Move::null() && (ss - 1)->statScore < 18001
&& eval >= beta && ss->staticEval >= beta - 21 * depth + 312 && !excludedMove
if (!PvNode && (ss - 1)->currentMove != Move::null() && (ss - 1)->statScore < 16993
&& eval >= beta && ss->staticEval >= beta - 19 * depth + 326 && !excludedMove
&& pos.non_pawn_material(us) && ss->ply >= thisThread->nmpMinPly
&& beta > VALUE_TB_LOSS_IN_MAX_PLY)
{
assert(eval - beta >= 0);
// Null move dynamic reduction based on depth and eval
Depth R = std::min(int(eval - beta) / 152, 6) + depth / 3 + 4;
Depth R = std::min(int(eval - beta) / 134, 6) + depth / 3 + 4;
ss->currentMove = Move::null();
ss->continuationHistory = &thisThread->continuationHistory[0][0][NO_PIECE][0];
@@ -875,13 +876,13 @@ Value Search::Worker::search(
return qsearch<PV>(pos, ss, alpha, beta);
// For cutNodes without a ttMove, we decrease depth by 2 if depth is high enough.
if (cutNode && depth >= 8 && !ttMove)
depth -= 2;
if (cutNode && depth >= 8 && (!ttMove || tte->bound() == BOUND_UPPER))
depth -= 1 + !ttMove;
// Step 11. ProbCut (~10 Elo)
// If we have a good enough capture (or queen promotion) and a reduced search returns a value
// much above beta, we can (almost) safely prune the previous move.
probCutBeta = beta + 169 - 63 * improving;
probCutBeta = beta + 159 - 66 * improving;
if (
!PvNode && depth > 3
&& std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY
@@ -938,7 +939,7 @@ Value Search::Worker::search(
moves_loop: // When in check, search starts here
// Step 12. A small Probcut idea, when we are in check (~4 Elo)
probCutBeta = beta + 452;
probCutBeta = beta + 420;
if (ss->inCheck && !PvNode && ttCapture && (tte->bound() & BOUND_LOWER)
&& tte->depth() >= depth - 4 && ttValue >= probCutBeta
&& std::abs(ttValue) < VALUE_TB_WIN_IN_MAX_PLY && std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY)
@@ -1016,20 +1017,22 @@ moves_loop: // When in check, search starts here
if (capture || givesCheck)
{
Piece capturedPiece = pos.piece_on(move.to_sq());
int captHist =
thisThread->captureHistory[movedPiece][move.to_sq()][type_of(capturedPiece)];
// Futility pruning for captures (~2 Elo)
if (!givesCheck && lmrDepth < 7 && !ss->inCheck)
{
Piece capturedPiece = pos.piece_on(move.to_sq());
Value futilityValue =
ss->staticEval + 285 + 277 * lmrDepth + PieceValue[capturedPiece]
+ thisThread->captureHistory[movedPiece][move.to_sq()][type_of(capturedPiece)]
/ 7;
Value futilityValue = ss->staticEval + 295 + 280 * lmrDepth
+ PieceValue[capturedPiece] + captHist / 7;
if (futilityValue <= alpha)
continue;
}
// SEE based pruning for captures and checks (~11 Elo)
if (!pos.see_ge(move, -203 * depth))
int seeHist = std::clamp(captHist / 32, -197 * depth, 196 * depth);
if (!pos.see_ge(move, -186 * depth - seeHist))
continue;
}
else
@@ -1037,22 +1040,22 @@ moves_loop: // When in check, search starts here
int history =
(*contHist[0])[movedPiece][move.to_sq()]
+ (*contHist[1])[movedPiece][move.to_sq()]
+ (*contHist[3])[movedPiece][move.to_sq()]
+ (*contHist[3])[movedPiece][move.to_sq()] / 2
+ thisThread->pawnHistory[pawn_structure_index(pos)][movedPiece][move.to_sq()];
// Continuation history based pruning (~2 Elo)
if (lmrDepth < 6 && history < -4173 * depth)
if (lmrDepth < 6 && history < -4081 * depth)
continue;
history += 2 * thisThread->mainHistory[us][move.from_to()];
lmrDepth += history / 5285;
lmrDepth += history / 4768;
Value futilityValue =
ss->staticEval + (bestValue < ss->staticEval - 54 ? 128 : 57) + 131 * lmrDepth;
ss->staticEval + (bestValue < ss->staticEval - 52 ? 134 : 54) + 142 * lmrDepth;
// Futility pruning: parent node (~13 Elo)
if (!ss->inCheck && lmrDepth < 14 && futilityValue <= alpha)
if (!ss->inCheck && lmrDepth < 13 && futilityValue <= alpha)
{
if (bestValue <= futilityValue && std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY
&& futilityValue < VALUE_TB_WIN_IN_MAX_PLY)
@@ -1063,7 +1066,7 @@ moves_loop: // When in check, search starts here
lmrDepth = std::max(lmrDepth, 0);
// Prune moves with negative SEE (~4 Elo)
if (!pos.see_ge(move, -27 * lmrDepth * lmrDepth))
if (!pos.see_ge(move, -28 * lmrDepth * lmrDepth))
continue;
}
}
@@ -1083,11 +1086,11 @@ moves_loop: // When in check, search starts here
// so changing them requires tests at these types of time controls.
// Recursive singular search is avoided.
if (!rootNode && move == ttMove && !excludedMove
&& depth >= 4 - (thisThread->completedDepth > 33) + ss->ttPv
&& depth >= 4 - (thisThread->completedDepth > 32) + ss->ttPv
&& std::abs(ttValue) < VALUE_TB_WIN_IN_MAX_PLY && (tte->bound() & BOUND_LOWER)
&& tte->depth() >= depth - 3)
{
Value singularBeta = ttValue - (65 + 59 * (ss->ttPv && !PvNode)) * depth / 63;
Value singularBeta = ttValue - (65 + 52 * (ss->ttPv && !PvNode)) * depth / 63;
Depth singularDepth = newDepth / 2;
ss->excludedMove = move;
@@ -1097,17 +1100,16 @@ moves_loop: // When in check, search starts here
if (value < singularBeta)
{
extension = 1;
int doubleMargin = 251 * PvNode - 241 * !ttCapture;
int tripleMargin =
135 + 234 * PvNode - 248 * !ttCapture + 124 * (ss->ttPv || !ttCapture);
int quadMargin = 447 + 354 * PvNode - 300 * !ttCapture + 206 * ss->ttPv;
// We make sure to limit the extensions in some way to avoid a search explosion
if (!PvNode && ss->multipleExtensions <= 16)
{
extension = 2 + (value < singularBeta - 11 && !ttCapture);
depth += depth < 14;
}
if (PvNode && !ttCapture && ss->multipleExtensions <= 5
&& value < singularBeta - 38)
extension = 2;
extension = 1 + (value < singularBeta - doubleMargin)
+ (value < singularBeta - tripleMargin)
+ (value < singularBeta - quadMargin);
depth += ((!PvNode) && (depth < 14));
}
// Multi-cut pruning
@@ -1116,7 +1118,12 @@ moves_loop: // When in check, search starts here
// we assume this expected cut-node is not singular (multiple moves fail high),
// and we can prune the whole subtree by returning a softbound.
else if (singularBeta >= beta)
{
if (!ttCapture)
update_quiet_histories(pos, ss, *this, ttMove, -stat_malus(depth));
return singularBeta;
}
// Negative extensions
// If other moves failed high over (ttValue - margin) without the ttMove on a reduced search,
@@ -1141,13 +1148,12 @@ moves_loop: // When in check, search starts here
else if (PvNode && move == ttMove && move.to_sq() == prevSq
&& thisThread->captureHistory[movedPiece][move.to_sq()]
[type_of(pos.piece_on(move.to_sq()))]
> 3807)
> 4016)
extension = 1;
}
// Add extension to new depth
newDepth += extension;
ss->multipleExtensions = (ss - 1)->multipleExtensions + (extension >= 2);
// Speculative prefetch as early as possible
prefetch(tt.first_entry(pos.key_after(move)));
@@ -1167,6 +1173,9 @@ moves_loop: // When in check, search starts here
if (ss->ttPv)
r -= 1 + (ttValue > alpha) + (tte->depth() >= depth);
else if (cutNode && move != ttMove && move != ss->killers[0])
r++;
// Increase reduction for cut nodes (~4 Elo)
if (cutNode)
r += 2 - (tte->depth() >= depth && ss->ttPv);
@@ -1191,10 +1200,10 @@ moves_loop: // When in check, search starts here
ss->statScore = 2 * thisThread->mainHistory[us][move.from_to()]
+ (*contHist[0])[movedPiece][move.to_sq()]
+ (*contHist[1])[movedPiece][move.to_sq()]
+ (*contHist[3])[movedPiece][move.to_sq()] - 5024;
+ (*contHist[3])[movedPiece][move.to_sq()] - 5078;
// Decrease/increase reduction for moves with a good/bad history (~8 Elo)
r -= ss->statScore / 13182;
r -= ss->statScore / (17662 - std::min(depth, 16) * 105);
// Step 17. Late moves reduction / extension (LMR, ~117 Elo)
if (depth >= 2 && moveCount > 1 + rootNode)
@@ -1213,7 +1222,7 @@ moves_loop: // When in check, search starts here
{
// Adjust full-depth search based on LMR results - if the result
// was good enough search deeper, if it was bad enough search shallower.
const bool doDeeperSearch = value > (bestValue + 42 + 2 * newDepth); // (~1 Elo)
const bool doDeeperSearch = value > (bestValue + 40 + 2 * newDepth); // (~1 Elo)
const bool doShallowerSearch = value < bestValue + newDepth; // (~2 Elo)
newDepth += doDeeperSearch - doShallowerSearch;
@@ -1331,7 +1340,7 @@ moves_loop: // When in check, search starts here
else
{
// Reduce other moves if we have found at least one score improvement (~2 Elo)
if (depth > 2 && depth < 12 && beta < 13546 && value > -13478)
if (depth > 2 && depth < 13 && beta < 15868 && value > -14630)
depth -= 2;
assert(depth > 0);
@@ -1374,9 +1383,9 @@ moves_loop: // When in check, search starts here
// Bonus for prior countermove that caused the fail low
else if (!priorCapture && prevSq != SQ_NONE)
{
int bonus = (depth > 5) + (PvNode || cutNode) + ((ss - 1)->statScore < -14761)
+ ((ss - 1)->moveCount > 11)
+ (!ss->inCheck && bestValue <= ss->staticEval - 142);
int bonus = (depth > 5) + (PvNode || cutNode) + ((ss - 1)->statScore < -14455)
+ ((ss - 1)->moveCount > 10) + (!ss->inCheck && bestValue <= ss->staticEval - 130)
+ (!(ss - 1)->inCheck && bestValue <= -(ss - 1)->staticEval - 77);
update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
stat_bonus(depth) * bonus);
thisThread->mainHistory[~us][((ss - 1)->currentMove).from_to()]
@@ -1537,7 +1546,7 @@ Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta,
if (bestValue > alpha)
alpha = bestValue;
futilityBase = ss->staticEval + 250;
futilityBase = ss->staticEval + 270;
}
const PieceToHistory* contHist[] = {(ss - 1)->continuationHistory,
@@ -1612,12 +1621,16 @@ Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta,
break;
// Continuation history based pruning (~3 Elo)
if (!capture && (*contHist[0])[pos.moved_piece(move)][move.to_sq()] < 0
&& (*contHist[1])[pos.moved_piece(move)][move.to_sq()] < 0)
if (!capture
&& (*contHist[0])[pos.moved_piece(move)][move.to_sq()]
+ (*contHist[1])[pos.moved_piece(move)][move.to_sq()]
+ thisThread->pawnHistory[pawn_structure_index(pos)][pos.moved_piece(move)]
[move.to_sq()]
<= 4000)
continue;
// Do not search moves with bad enough SEE values (~5 Elo)
if (!pos.see_ge(move, -79))
if (!pos.see_ge(move, -69))
continue;
}
@@ -1685,7 +1698,7 @@ Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta,
Depth Search::Worker::reduction(bool i, Depth d, int mn, int delta) {
int reductionScale = reductions[d] * reductions[mn];
return (reductionScale + 1150 - delta * 832 / rootDelta) / 1024 + (!i && reductionScale > 1025);
return (reductionScale + 1318 - delta * 760 / rootDelta) / 1024 + (!i && reductionScale > 1066);
}
TimePoint Search::Worker::elapsed() const {
@@ -1769,7 +1782,6 @@ void update_all_stats(const Position& pos,
int captureCount,
Depth depth) {
Color us = pos.side_to_move();
CapturePieceToHistory& captureHistory = workerThread.captureHistory;
Piece moved_piece = pos.moved_piece(bestMove);
PieceType captured;
@@ -1779,26 +1791,14 @@ void update_all_stats(const Position& pos,
if (!pos.capture_stage(bestMove))
{
int bestMoveBonus = bestValue > beta + 185 ? quietMoveBonus // larger bonus
int bestMoveBonus = bestValue > beta + 165 ? quietMoveBonus // larger bonus
: stat_bonus(depth); // smaller bonus
// Increase stats for the best move in case it was a quiet move
update_quiet_stats(pos, ss, workerThread, bestMove, bestMoveBonus);
int pIndex = pawn_structure_index(pos);
workerThread.pawnHistory[pIndex][moved_piece][bestMove.to_sq()] << quietMoveBonus;
// Decrease stats for all non-best quiet moves
for (int i = 0; i < quietCount; ++i)
{
workerThread
.pawnHistory[pIndex][pos.moved_piece(quietsSearched[i])][quietsSearched[i].to_sq()]
<< -quietMoveMalus;
workerThread.mainHistory[us][quietsSearched[i].from_to()] << -quietMoveMalus;
update_continuation_histories(ss, pos.moved_piece(quietsSearched[i]),
quietsSearched[i].to_sq(), -quietMoveMalus);
}
update_quiet_histories(pos, ss, workerThread, quietsSearched[i], -quietMoveMalus);
}
else
{
@@ -1839,10 +1839,8 @@ void update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus) {
}
}
// Updates move sorting heuristics
void update_quiet_stats(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus) {
void update_refutations(const Position& pos, Stack* ss, Search::Worker& workerThread, Move move) {
// Update killers
if (ss->killers[0] != move)
@@ -1851,10 +1849,6 @@ void update_quiet_stats(
ss->killers[0] = move;
}
Color us = pos.side_to_move();
workerThread.mainHistory[us][move.from_to()] << bonus;
update_continuation_histories(ss, pos.moved_piece(move), move.to_sq(), bonus);
// Update countermove history
if (((ss - 1)->currentMove).is_ok())
{
@@ -1862,6 +1856,27 @@ void update_quiet_stats(
workerThread.counterMoves[pos.piece_on(prevSq)][prevSq] = move;
}
}
void update_quiet_histories(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus) {
Color us = pos.side_to_move();
workerThread.mainHistory[us][move.from_to()] << bonus;
update_continuation_histories(ss, pos.moved_piece(move), move.to_sq(), bonus);
int pIndex = pawn_structure_index(pos);
workerThread.pawnHistory[pIndex][pos.moved_piece(move)][move.to_sq()] << bonus;
}
// Updates move sorting heuristics
void update_quiet_stats(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus) {
update_refutations(pos, ss, workerThread, move);
update_quiet_histories(pos, ss, workerThread, move, bonus);
}
}
// When playing with strength handicap, choose the best move among a set of RootMoves
src/search.h
-1
View File
@@ -76,7 +76,6 @@ struct Stack {
bool inCheck;
bool ttPv;
bool ttHit;
int multipleExtensions;
int cutoffCnt;
};
src/thread.cpp
+3
View File
@@ -166,6 +166,9 @@ void ThreadPool::clear() {
for (Thread* th : threads)
th->worker->clear();
if (threads.size() == 0)
return;
main_manager()->callsCnt = 0;
main_manager()->bestPreviousScore = VALUE_INFINITE;
main_manager()->bestPreviousAverageScore = VALUE_INFINITE;