Add a HalfKA architecture (a product of K - king, and A - any piece) along with all required infrastructure. HalfKA doesn't discriminate kings compared to HalfKP. Keep old architecture as the default one.

src/Makefile

@@ -50,9 +50,12 @@ SRCS = benchmark.cpp bitbase.cpp bitboard.cpp endgame.cpp evaluate.cpp main.cpp
 	nnue/evaluate_nnue.cpp \
 	nnue/evaluate_nnue_learner.cpp \
 	nnue/features/half_kp.cpp \
+	nnue/features/half_ka.cpp \
 	nnue/features/half_relative_kp.cpp \
+	nnue/features/half_relative_ka.cpp \
 	nnue/features/k.cpp \
 	nnue/features/p.cpp \
+	nnue/features/a.cpp \
 	nnue/features/castling_right.cpp \
 	nnue/features/enpassant.cpp \
 	nnue/nnue_test_command.cpp \

src/nnue/architectures/halfka_256x2-32-32.h

@@ -0,0 +1,54 @@
+/*
+    Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+    Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+
+    Stockfish is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    Stockfish is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+// Definition of input features and network structure used in NNUE evaluation function
+
+#ifndef NNUE_HALFKA_256X2_32_32_H_INCLUDED
+#define NNUE_HALFKA_256X2_32_32_H_INCLUDED
+
+#include "nnue/features/feature_set.h"
+#include "nnue/features/half_ka.h"
+
+#include "nnue/layers/input_slice.h"
+#include "nnue/layers/affine_transform.h"
+#include "nnue/layers/clipped_relu.h"
+
+namespace Eval::NNUE {
+
+    // Input features used in evaluation function
+    using RawFeatures = Features::FeatureSet<
+        Features::HalfKA<Features::Side::kFriend>>;
+
+    // Number of input feature dimensions after conversion
+    constexpr IndexType kTransformedFeatureDimensions = 256;
+
+    namespace Layers {
+
+        // Define network structure
+        using InputLayer = InputSlice<kTransformedFeatureDimensions * 2>;
+        using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 32>>;
+        using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
+        using OutputLayer = AffineTransform<HiddenLayer2, 1>;
+
+    }  // namespace Layers
+
+    using Network = Layers::OutputLayer;
+
+}  // namespace Eval::NNUE
+
+#endif // #ifndef NNUE_HALFA_256X2_32_32_H_INCLUDED

src/nnue/evaluate_nnue_learner.cpp

@@ -7,6 +7,7 @@
 
 #include "trainer/features/factorizer_feature_set.h"
 #include "trainer/features/factorizer_half_kp.h"
+#include "trainer/features/factorizer_half_ka.h"
 #include "trainer/trainer_feature_transformer.h"
 #include "trainer/trainer_input_slice.h"
 #include "trainer/trainer_affine_transform.h"

src/nnue/features/a.cpp

@@ -0,0 +1,50 @@
+#include "a.h"
+#include "index_list.h"
+
+// Definition of input feature A of NNUE evaluation function
+namespace Eval::NNUE::Features {
+
+    // Orient a square according to perspective (flip rank for black)
+    inline Square orient(Color perspective, Square s) {
+        return Square(int(s) ^ (bool(perspective) * SQ_A8));
+    }
+
+    // Find the index of the feature quantity from the king position and PieceSquare
+    inline IndexType A::make_index(
+        Color perspective, Square s, Piece pc) {
+        return IndexType(orient(perspective, s) + kpp_board_index[pc][perspective]);
+    }
+
+    // Get a list of indices with a value of 1 among the features
+    void A::append_active_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* active) {
+
+        Bitboard bb = pos.pieces();
+        while (bb) {
+            Square s = pop_lsb(&bb);
+            active->push_back(make_index(perspective, s, pos.piece_on(s)));
+        }
+    }
+
+    // Get a list of indices whose values ​​have changed from the previous one in the feature quantity
+    void A::append_changed_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* removed,
+        IndexList* added) {
+
+        const auto& dp = pos.state()->dirtyPiece;
+        for (int i = 0; i < dp.dirty_num; ++i) {
+            Piece pc = dp.piece[i];
+
+            if (dp.from[i] != SQ_NONE)
+              removed->push_back(make_index(perspective, dp.from[i], pc));
+
+            if (dp.to[i] != SQ_NONE)
+              added->push_back(make_index(perspective, dp.to[i], pc));
+        }
+    }
+
+}  // namespace Eval::NNUE::Features

src/nnue/features/a.h

@@ -0,0 +1,54 @@
+#ifndef _NNUE_FEATURES_A_H_
+#define _NNUE_FEATURES_A_H_
+
+#include "features_common.h"
+
+#include "evaluate.h"
+
+// Definition of input feature A of NNUE evaluation function
+// A is a union of P features and K features, so technically the
+// same effect can be achieved by including both P and K features
+// but it would result in slower index appending because
+// P would conditionally exclude K features and vice versa,
+// where A doesn't have any conditionals.
+namespace Eval::NNUE::Features {
+
+    // Feature P: PieceSquare of pieces other than balls
+    class A {
+    public:
+        // feature quantity name
+        static constexpr const char* kName = "A";
+
+        // Hash value embedded in the evaluation function file
+        static constexpr std::uint32_t kHashValue = 0x7A4C414Cu;
+
+        // number of feature dimensions
+        static constexpr IndexType kDimensions = PS_END2;
+
+        // The maximum value of the number of indexes whose value is 1 at the same time among the feature values
+        static constexpr IndexType kMaxActiveDimensions = 32;
+
+        // Timing of full calculation instead of difference calculation
+        static constexpr TriggerEvent kRefreshTrigger = TriggerEvent::kNone;
+
+        // Get a list of indices with a value of 1 among the features
+        static void append_active_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* active);
+
+        // Get a list of indices whose values ​​have changed from the previous one in the feature quantity
+        static void append_changed_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* removed,
+            IndexList* added);
+
+    private:
+        // Index of a feature for a given piece on some square
+        static IndexType make_index(Color perspective, Square s, Piece pc);
+    };
+
+}  // namespace Eval::NNUE::Features
+
+#endif // #ifndef _NNUE_FEATURES_UNION_P_K_H_

src/nnue/features/half_ka.cpp

@@ -0,0 +1,89 @@
+/*
+    Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+    Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+
+    Stockfish is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    Stockfish is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+//Definition of input features HalfKA of NNUE evaluation function
+
+#include "half_ka.h"
+#include "index_list.h"
+
+namespace Eval::NNUE::Features {
+
+    // Orient a square according to perspective (flip rank for black)
+    inline Square orient(Color perspective, Square s) {
+        return Square(int(s) ^ (bool(perspective) * SQ_A8));
+    }
+
+    // Find the index of the feature quantity from the king position and PieceSquare
+    template <Side AssociatedKing>
+    inline IndexType HalfKA<AssociatedKing>::make_index(
+        Color perspective,
+        Square s,
+        Piece pc,
+        Square ksq) {
+
+        return IndexType(orient(perspective, s) + kpp_board_index[pc][perspective] + PS_END2 * ksq);
+    }
+
+    // Get a list of indices for active features
+    template <Side AssociatedKing>
+    void HalfKA<AssociatedKing>::append_active_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* active) {
+
+        Square ksq = orient(
+            perspective,
+            pos.square<KING>(
+                AssociatedKing == Side::kFriend ? perspective : ~perspective));
+
+        Bitboard bb = pos.pieces();
+        while (bb) {
+            Square s = pop_lsb(&bb);
+            active->push_back(make_index(perspective, s, pos.piece_on(s), ksq));
+        }
+    }
+
+    // Get a list of indices for recently changed features
+    template <Side AssociatedKing>
+    void HalfKA<AssociatedKing>::append_changed_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* removed,
+        IndexList* added) {
+
+        Square ksq = orient(
+            perspective,
+            pos.square<KING>(
+                AssociatedKing == Side::kFriend ? perspective : ~perspective));
+
+        const auto& dp = pos.state()->dirtyPiece;
+        for (int i = 0; i < dp.dirty_num; ++i) {
+            Piece pc = dp.piece[i];
+
+            if (dp.from[i] != SQ_NONE)
+                removed->push_back(make_index(perspective, dp.from[i], pc, ksq));
+
+            if (dp.to[i] != SQ_NONE)
+                added->push_back(make_index(perspective, dp.to[i], pc, ksq));
+        }
+    }
+
+    template class HalfKA<Side::kFriend>;
+    template class HalfKA<Side::kEnemy>;
+
+}  // namespace Eval::NNUE::Features

src/nnue/features/half_ka.h

@@ -0,0 +1,75 @@
+/*
+    Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+    Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+
+    Stockfish is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    Stockfish is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+#ifndef NNUE_FEATURES_HALF_KA_H_INCLUDED
+#define NNUE_FEATURES_HALF_KA_H_INCLUDED
+
+#include "features_common.h"
+
+#include "evaluate.h"
+
+//Definition of input features HalfKPK of NNUE evaluation function
+namespace Eval::NNUE::Features {
+
+    // Feature HalfKPK: Combination of the position of own king
+    // and the position of pieces other than kings
+    template <Side AssociatedKing>
+    class HalfKA {
+
+    public:
+        // Feature name
+        static constexpr const char* kName = (AssociatedKing == Side::kFriend) ?
+            "HalfKA(Friend)" : "HalfKA(Enemy)";
+
+        // Hash value embedded in the evaluation file
+        static constexpr std::uint32_t kHashValue =
+            0x5F134CB9u ^ (AssociatedKing == Side::kFriend);
+
+        // Number of feature dimensions
+        static constexpr IndexType kDimensions =
+            static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_END2);
+
+        // Maximum number of simultaneously active features
+        static constexpr IndexType kMaxActiveDimensions = 32;
+
+        // Trigger for full calculation instead of difference calculation
+        static constexpr TriggerEvent kRefreshTrigger =
+            (AssociatedKing == Side::kFriend) ?
+            TriggerEvent::kFriendKingMoved : TriggerEvent::kEnemyKingMoved;
+
+        // Get a list of indices for active features
+        static void append_active_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* active);
+
+        // Get a list of indices for recently changed features
+        static void append_changed_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* removed,
+            IndexList* added);
+
+    private:
+        // Index of a feature for a given king position and another piece on some square
+        static IndexType make_index(Color perspective, Square s, Piece pc, Square sq_k);
+    };
+
+}  // namespace Eval::NNUE::Features
+
+#endif // #ifndef NNUE_FEATURES_HALF_KA_H_INCLUDED

src/nnue/features/half_relative_ka.cpp

@@ -0,0 +1,86 @@
+#include "half_relative_ka.h"
+#include "index_list.h"
+
+//Definition of input features HalfRelativeKA of NNUE evaluation function
+namespace Eval::NNUE::Features {
+
+    // Orient a square according to perspective (flip rank for black)
+    inline Square orient(Color perspective, Square s) {
+        return Square(int(s) ^ (bool(perspective) * SQ_A8));
+    }
+
+    // Find the index of the feature quantity from the ball position and PieceSquare
+    template <Side AssociatedKing>
+    inline IndexType HalfRelativeKA<AssociatedKing>::make_index(
+        Color perspective,
+        Square s,
+        Piece pc,
+        Square sq_k) {
+
+        const IndexType p = IndexType(orient(perspective, s) + kpp_board_index[pc][perspective]);
+        return make_index(sq_k, p);
+    }
+
+    // Find the index of the feature quantity from the ball position and PieceSquare
+    template <Side AssociatedKing>
+    inline IndexType HalfRelativeKA<AssociatedKing>::make_index(
+        Square sq_k,
+        IndexType p) {
+
+        constexpr IndexType W = kBoardWidth;
+        constexpr IndexType H = kBoardHeight;
+        const IndexType piece_index = (p - PS_W_PAWN) / SQUARE_NB;
+        const Square sq_p = static_cast<Square>((p - PS_W_PAWN) % SQUARE_NB);
+        const IndexType relative_file = file_of(sq_p) - file_of(sq_k) + (W / 2);
+        const IndexType relative_rank = rank_of(sq_p) - rank_of(sq_k) + (H / 2);
+        return H * W * piece_index + H * relative_file + relative_rank;
+    }
+
+    // Get a list of indices with a value of 1 among the features
+    template <Side AssociatedKing>
+    void HalfRelativeKA<AssociatedKing>::append_active_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* active) {
+
+        Square ksq = orient(
+            perspective,
+            pos.square<KING>(
+                AssociatedKing == Side::kFriend ? perspective : ~perspective));
+
+        Bitboard bb = pos.pieces();
+        while (bb) {
+            Square s = pop_lsb(&bb);
+            active->push_back(make_index(perspective, s, pos.piece_on(s), ksq));
+        }
+    }
+
+    // Get a list of indices whose values ​​have changed from the previous one in the feature quantity
+    template <Side AssociatedKing>
+    void HalfRelativeKA<AssociatedKing>::append_changed_indices(
+        const Position& pos,
+        Color perspective,
+        IndexList* removed,
+        IndexList* added) {
+
+        Square ksq = orient(
+            perspective,
+            pos.square<KING>(
+                AssociatedKing == Side::kFriend ? perspective : ~perspective));
+
+        const auto& dp = pos.state()->dirtyPiece;
+        for (int i = 0; i < dp.dirty_num; ++i) {
+            Piece pc = dp.piece[i];
+
+            if (dp.from[i] != SQ_NONE)
+                removed->push_back(make_index(perspective, dp.from[i], pc, ksq));
+
+            if (dp.to[i] != SQ_NONE)
+                added->push_back(make_index(perspective, dp.to[i], pc, ksq));
+        }
+    }
+
+    template class HalfRelativeKA<Side::kFriend>;
+    template class HalfRelativeKA<Side::kEnemy>;
+
+}  // namespace Eval::NNUE::Features

src/nnue/features/half_relative_ka.h

@@ -0,0 +1,68 @@
+#ifndef _NNUE_FEATURES_HALF_RELATIVE_KA_H_
+#define _NNUE_FEATURES_HALF_RELATIVE_KA_H_
+
+#include "features_common.h"
+
+#include "evaluate.h"
+
+// Definition of input features HalfRelativeKA of NNUE evaluation function
+// K - King
+// A - Any piece
+// KA - product of K and A
+namespace Eval::NNUE::Features {
+
+    // Feature HalfRelativeKA: Relative position of each piece other than the ball based on own ball or enemy ball
+    template <Side AssociatedKing>
+    class HalfRelativeKA {
+    public:
+        // feature quantity name
+        static constexpr const char* kName = (AssociatedKing == Side::kFriend) ?
+            "HalfRelativeKA(Friend)" : "HalfRelativeKA(Enemy)";
+
+        // Hash value embedded in the evaluation function file
+        static constexpr std::uint32_t kHashValue =
+            0xA123051Fu ^ (AssociatedKing == Side::kFriend);
+
+        static constexpr IndexType kNumPieceKinds = 6 * 2;
+
+        // width of the virtual board with the ball in the center
+        static constexpr IndexType kBoardWidth = FILE_NB * 2 - 1;
+
+        // height of a virtual board with balls in the center
+        static constexpr IndexType kBoardHeight = RANK_NB * 2 - 1;
+
+        // number of feature dimensions
+        static constexpr IndexType kDimensions =
+            kNumPieceKinds * kBoardHeight * kBoardWidth;
+
+        // The maximum value of the number of indexes whose value is 1 at the same time among the feature values
+        static constexpr IndexType kMaxActiveDimensions = 32;
+
+        // Timing of full calculation instead of difference calculation
+        static constexpr TriggerEvent kRefreshTrigger =
+            (AssociatedKing == Side::kFriend) ?
+            TriggerEvent::kFriendKingMoved : TriggerEvent::kEnemyKingMoved;
+
+        // Get a list of indices with a value of 1 among the features
+        static void append_active_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* active);
+
+        // Get a list of indices whose values ​​have changed from the previous one in the feature quantity
+        static void append_changed_indices(
+            const Position& pos,
+            Color perspective,
+            IndexList* removed,
+            IndexList* added);
+
+        // Find the index of the feature quantity from the ball position and PieceSquare
+        static IndexType make_index(Square s, IndexType p);
+
+        // Find the index of the feature quantity from the ball position and PieceSquare
+        static IndexType make_index(Color perspective, Square s, Piece pc, Square sq_k);
+    };
+
+}  // namespace Eval::NNUE::Features
+
+#endif // #ifndef _NNUE_FEATURES_HALF_RELATIVE_KA_H_

src/nnue/trainer/features/factorizer_half_ka.h

@@ -0,0 +1,93 @@
+#ifndef _NNUE_TRAINER_FEATURES_FACTORIZER_HALF_KA_H_
+#define _NNUE_TRAINER_FEATURES_FACTORIZER_HALF_KA_H_
+
+#include "factorizer.h"
+
+#include "nnue/features/half_ka.h"
+#include "nnue/features/a.h"
+#include "nnue/features/half_relative_ka.h"
+
+// Specialization of NNUE evaluation function feature conversion class template for HalfKA
+namespace Eval::NNUE::Features {
+
+    // Class template that converts input features into learning features
+    // Specialization for HalfKA
+    template <Side AssociatedKing>
+    class Factorizer<HalfKA<AssociatedKing>> {
+    private:
+        using FeatureType = HalfKA<AssociatedKing>;
+
+        // The maximum value of the number of indexes whose value is 1 at the same time among the feature values
+        static constexpr IndexType kMaxActiveDimensions =
+            FeatureType::kMaxActiveDimensions;
+
+        // Type of learning feature
+        enum TrainingFeatureType {
+            kFeaturesHalfKA,
+            kFeaturesA,
+            kFeaturesHalfRelativeKA,
+            kNumTrainingFeatureTypes,
+        };
+
+        // Learning feature information
+        static constexpr FeatureProperties kProperties[] = {
+            // kFeaturesHalfKPK
+            {true, FeatureType::kDimensions},
+            // kFeaturesPK
+            {true, Factorizer<A>::get_dimensions()},
+            // kFeaturesHalfRelativeKPK
+            {true, Factorizer<HalfRelativeKA<AssociatedKing>>::get_dimensions()},
+        };
+
+        static_assert(get_array_length(kProperties) == kNumTrainingFeatureTypes, "");
+
+    public:
+        static constexpr std::string get_name() {
+            return std::string("Factorizer<") + FeatureType::kName + ">";
+        }
+
+        static constexpr std::string get_factorizers_string() {
+            return "  - " + get_name();
+        }
+
+        // Get the dimensionality of the learning feature
+        static constexpr IndexType get_dimensions() {
+            return get_active_dimensions(kProperties);
+        }
+
+        // Get index of learning feature and scale of learning rate
+        static void append_training_features(
+            IndexType base_index, std::vector<TrainingFeature>* training_features) {
+
+            // kFeaturesHalfKPK
+            IndexType index_offset = append_base_feature<FeatureType>(
+                kProperties[kFeaturesHalfKA], base_index, training_features);
+
+            const auto sq_k = static_cast<Square>(base_index / PS_END2);
+            const auto a = static_cast<IndexType>(base_index % PS_END2);
+
+            // kFeaturesPK
+            index_offset += inherit_features_if_required<A>(
+                index_offset, kProperties[kFeaturesA], a, training_features);
+
+            // kFeaturesHalfRelativeKPK
+            if (a >= PS_W_PAWN) {
+                index_offset += inherit_features_if_required<HalfRelativeKA<AssociatedKing>>(
+                    index_offset, kProperties[kFeaturesHalfRelativeKA],
+                    HalfRelativeKA<AssociatedKing>::make_index(sq_k, a),
+                    training_features);
+            }
+            else {
+                index_offset += skip_features(kProperties[kFeaturesHalfRelativeKA]);
+            }
+
+            assert(index_offset == get_dimensions());
+        }
+    };
+
+    template <Side AssociatedKing>
+    constexpr FeatureProperties Factorizer<HalfKA<AssociatedKing>>::kProperties[];
+
+}  // namespace Eval::NNUE::Features
+
+#endif // #ifndef _NNUE_TRAINER_FEATURES_FACTORIZER_HALF_KA_H_