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https://github.com/HChaZZY/Stockfish.git
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Accumulator cache bugfix and cleanup
STC: https://tests.stockfishchess.org/tests/view/663068913a05f1bf7a511dc2 LLR: 2.98 (-2.94,2.94) <-1.75,0.25> Total: 70304 W: 18211 L: 18026 D: 34067 Ptnml(0-2): 232, 7966, 18582, 8129, 243 1) Fixes a bug introduced in https://github.com/official-stockfish/Stockfish/pull/5194. Only one psqtOnly flag was used for two perspectives which was causing wrong entries to be cleared and marked. 2) The finny caches should be cleared like histories and not at the start of every search. closes https://github.com/official-stockfish/Stockfish/pull/5203 No functional change
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@@ -59,31 +59,27 @@ struct AccumulatorCaches {
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struct alignas(CacheLineSize) Cache {
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struct alignas(CacheLineSize) Entry {
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BiasType accumulation[COLOR_NB][Size];
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PSQTWeightType psqtAccumulation[COLOR_NB][PSQTBuckets];
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Bitboard byColorBB[COLOR_NB][COLOR_NB];
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Bitboard byTypeBB[COLOR_NB][PIECE_TYPE_NB];
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BiasType accumulation[Size];
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PSQTWeightType psqtAccumulation[PSQTBuckets];
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Bitboard byColorBB[COLOR_NB];
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Bitboard byTypeBB[PIECE_TYPE_NB];
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bool psqtOnly;
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// To initialize a refresh entry, we set all its bitboards empty,
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// so we put the biases in the accumulation, without any weights on top
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void clear(const BiasType* biases) {
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std::memset(byColorBB, 0, sizeof(byColorBB));
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std::memset(byTypeBB, 0, sizeof(byTypeBB));
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psqtOnly = false;
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std::memcpy(accumulation[WHITE], biases, Size * sizeof(BiasType));
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std::memcpy(accumulation[BLACK], biases, Size * sizeof(BiasType));
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std::memset(psqtAccumulation, 0, sizeof(psqtAccumulation));
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std::memcpy(accumulation, biases, sizeof(accumulation));
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std::memset((uint8_t*) this + offsetof(Entry, psqtAccumulation), 0,
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sizeof(Entry) - offsetof(Entry, psqtAccumulation));
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}
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};
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template<typename Network>
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void clear(const Network& network) {
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for (auto& entry : entries)
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entry.clear(network.featureTransformer->biases);
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for (auto& entries1D : entries)
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for (auto& entry : entries1D)
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entry.clear(network.featureTransformer->biases);
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}
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void clear(const BiasType* biases) {
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@@ -91,9 +87,9 @@ struct AccumulatorCaches {
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entry.clear(biases);
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}
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Entry& operator[](Square sq) { return entries[sq]; }
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std::array<Entry, COLOR_NB>& operator[](Square sq) { return entries[sq]; }
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std::array<Entry, SQUARE_NB> entries;
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std::array<std::array<Entry, COLOR_NB>, SQUARE_NB> entries;
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};
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template<typename Networks>
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@@ -652,7 +652,7 @@ class FeatureTransformer {
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assert(cache != nullptr);
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Square ksq = pos.square<KING>(Perspective);
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auto& entry = (*cache)[ksq];
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auto& entry = (*cache)[ksq][Perspective];
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FeatureSet::IndexList removed, added;
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if (entry.psqtOnly && !psqtOnly)
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@@ -666,9 +666,8 @@ class FeatureTransformer {
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{
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for (PieceType pt = PAWN; pt <= KING; ++pt)
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{
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const Piece piece = make_piece(c, pt);
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const Bitboard oldBB =
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entry.byColorBB[Perspective][c] & entry.byTypeBB[Perspective][pt];
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const Piece piece = make_piece(c, pt);
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const Bitboard oldBB = entry.byColorBB[c] & entry.byTypeBB[pt];
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const Bitboard newBB = pos.pieces(c, pt);
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Bitboard toRemove = oldBB & ~newBB;
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Bitboard toAdd = newBB & ~oldBB;
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@@ -698,8 +697,7 @@ class FeatureTransformer {
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if (!psqtOnly)
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for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
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{
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auto entryTile =
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reinterpret_cast<vec_t*>(&entry.accumulation[Perspective][j * TileHeight]);
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auto entryTile = reinterpret_cast<vec_t*>(&entry.accumulation[j * TileHeight]);
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for (IndexType k = 0; k < NumRegs; ++k)
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acc[k] = entryTile[k];
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@@ -741,8 +739,8 @@ class FeatureTransformer {
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for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
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{
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auto entryTilePsqt = reinterpret_cast<psqt_vec_t*>(
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&entry.psqtAccumulation[Perspective][j * PsqtTileHeight]);
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auto entryTilePsqt =
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reinterpret_cast<psqt_vec_t*>(&entry.psqtAccumulation[j * PsqtTileHeight]);
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for (std::size_t k = 0; k < NumPsqtRegs; ++k)
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psqt[k] = entryTilePsqt[k];
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@@ -777,11 +775,11 @@ class FeatureTransformer {
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{
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const IndexType offset = HalfDimensions * index;
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for (IndexType j = 0; j < HalfDimensions; ++j)
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entry.accumulation[Perspective][j] -= weights[offset + j];
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entry.accumulation[j] -= weights[offset + j];
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}
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for (std::size_t k = 0; k < PSQTBuckets; ++k)
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entry.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
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entry.psqtAccumulation[k] -= psqtWeights[index * PSQTBuckets + k];
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}
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for (const auto index : added)
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{
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@@ -789,11 +787,11 @@ class FeatureTransformer {
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{
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const IndexType offset = HalfDimensions * index;
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for (IndexType j = 0; j < HalfDimensions; ++j)
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entry.accumulation[Perspective][j] += weights[offset + j];
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entry.accumulation[j] += weights[offset + j];
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}
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for (std::size_t k = 0; k < PSQTBuckets; ++k)
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entry.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
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entry.psqtAccumulation[k] += psqtWeights[index * PSQTBuckets + k];
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}
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#endif
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@@ -802,17 +800,17 @@ class FeatureTransformer {
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// Now copy its content to the actual accumulator we were refreshing
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if (!psqtOnly)
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std::memcpy(accumulator.accumulation[Perspective], entry.accumulation[Perspective],
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std::memcpy(accumulator.accumulation[Perspective], entry.accumulation,
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sizeof(BiasType) * HalfDimensions);
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std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation[Perspective],
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std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation,
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sizeof(int32_t) * PSQTBuckets);
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for (Color c : {WHITE, BLACK})
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entry.byColorBB[Perspective][c] = pos.pieces(c);
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entry.byColorBB[c] = pos.pieces(c);
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for (PieceType pt = PAWN; pt <= KING; ++pt)
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entry.byTypeBB[Perspective][pt] = pos.pieces(pt);
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entry.byTypeBB[pt] = pos.pieces(pt);
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entry.psqtOnly = psqtOnly;
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}
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@@ -147,9 +147,6 @@ Search::Worker::Worker(SharedState& sharedState,
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void Search::Worker::start_searching() {
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// Initialize accumulator refresh entries
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refreshTable.clear(networks);
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// Non-main threads go directly to iterative_deepening()
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if (!is_mainthread())
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{
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@@ -506,6 +503,8 @@ void Search::Worker::clear() {
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for (size_t i = 1; i < reductions.size(); ++i)
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reductions[i] = int((20.14 + std::log(size_t(options["Threads"])) / 2) * std::log(i));
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refreshTable.clear(networks);
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}
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