Merge remote-tracking branch 'upstream/master' into tools_merge_20210513

AUTHORS

@@ -27,6 +27,7 @@ Andy Duplain
 Antoine Champion (antoinechampion)
 Aram Tumanian (atumanian)
 Arjun Temurnikar
+Artem Solopiy (EntityFX)
 Auguste Pop
 Balint Pfliegel
 Ben Koshy (BKSpurgeon)

README.md

@@ -33,9 +33,14 @@ This distribution of Stockfish consists of the following files:
   * a file with the .nnue extension, storing the neural network for the NNUE
     evaluation. Binary distributions will have this file embedded.
 
-## UCI options
+## The UCI protocol and available options
 
-Currently, Stockfish has the following UCI options:
+The Universal Chess Interface (UCI) is a standard protocol used to communicate with a chess engine,
+and is the recommended way to do so for typical graphical user interfaces (GUI) or chess tools.
+
+Stockfish implements most commands as described in [the UCI protocol](https://www.shredderchess.com/download/div/uci.zip)
+
+For users, the following UCI options, which can typically be set via a GUI, are available in Stockfish:
 
   * #### Threads
     The number of CPU threads used for searching a position. For best performance, set
@@ -136,6 +141,33 @@ Currently, Stockfish has the following UCI options:
   * #### Debug Log File
     Write all communication to and from the engine into a text file.
 
+For developers the following non-standard commands might be of interest, mainly useful for debugging:
+
+  * #### bench ttSize threads limit fenFile limitType evalType
+    Performs a standard benchmark using various options. The signature or standard node
+    count is obtained using all defaults. `bench` is currently `bench 16 1 13 default depth mixed`.
+
+  * #### compiler
+    Give information about the compiler and environment used for building a binary.
+
+  * #### d
+    Display the current position, with ascii art and fen.
+
+  * #### eval
+    Return the evaluation of the current position.
+
+  * #### export_net [filename]
+    Exports the currently loaded network to a file.
+    If the currently loaded network is the embedded network and the filename
+    is not specified then the network is saved to the file matching the name
+    of the embedded network, as defined in evaluate.h.
+    If the currently loaded network is not the embedded network (some net set
+    through the UCI setoption) then the filename parameter is required and the
+    network is saved into that file.
+
+  * #### flip
+    Flips the side to move.
+
 ### Generating Training Data
 
 To generate training data from the classic eval, use the generate_training_data command with the setting "Use NNUE" set to "false". The given example is generation in its simplest form. There are more commands.

src/Makefile

@@ -107,6 +107,7 @@ ifeq ($(ARCH), $(filter $(ARCH), \
                  x86-64-vnni512 x86-64-vnni256 x86-64-avx512 x86-64-bmi2 x86-64-avx2 \
                  x86-64-sse41-popcnt x86-64-modern x86-64-ssse3 x86-64-sse3-popcnt \
                  x86-64 x86-32-sse41-popcnt x86-32-sse2 x86-32 ppc-64 ppc-32 \
+                 e2k \
                  armv7 armv7-neon armv8 apple-silicon general-64 general-32))
    SUPPORTED_ARCH=true
 else
@@ -301,6 +302,17 @@ ifeq ($(ARCH),ppc-64)
 	prefetch = yes
 endif
 
+ifeq ($(findstring e2k,$(ARCH)),e2k)
+	arch = e2k
+	mmx = yes
+	bits = 64
+	sse = yes
+	sse2 = yes
+	ssse3 = yes
+	sse41 = yes
+	popcnt = yes
+endif
+
 endif
 
 ### ==========================================================================
@@ -524,7 +536,6 @@ ifeq ($(popcnt),yes)
 	endif
 endif
 
-
 ifeq ($(avx2),yes)
 	CXXFLAGS += -DUSE_AVX2
 	ifeq ($(comp),$(filter $(comp),gcc clang mingw))
@@ -692,6 +703,7 @@ help:
 	@echo "armv7                   > ARMv7 32-bit"
 	@echo "armv7-neon              > ARMv7 32-bit with popcnt and neon"
 	@echo "armv8                   > ARMv8 64-bit with popcnt and neon"
+	@echo "e2k                     > Elbrus 2000"
 	@echo "apple-silicon           > Apple silicon ARM64"
 	@echo "general-64              > unspecified 64-bit"
 	@echo "general-32              > unspecified 32-bit"
@@ -841,6 +853,7 @@ config-sanity: net
 	@test "$(SUPPORTED_ARCH)" = "true"
 	@test "$(arch)" = "any" || test "$(arch)" = "x86_64" || test "$(arch)" = "i386" || \
 	 test "$(arch)" = "ppc64" || test "$(arch)" = "ppc" || \
+	 test "$(arch)" = "e2k" || \
 	 test "$(arch)" = "armv7" || test "$(arch)" = "armv8" || test "$(arch)" = "arm64"
 	@test "$(bits)" = "32" || test "$(bits)" = "64"
 	@test "$(prefetch)" = "yes" || test "$(prefetch)" = "no"

src/evaluate.cpp

@@ -35,6 +35,7 @@
 #include "misc.h"
 #include "pawns.h"
 #include "thread.h"
+#include "timeman.h"
 #include "uci.h"
 #include "incbin/incbin.h"
 
@@ -126,8 +127,28 @@ namespace Eval {
           }
     }
 
-    /// NNUE::verify() verifies that the last net used was loaded successfully
+  void NNUE::export_net(const std::optional<std::string>& filename) {
-    void verify() {
+    std::string actualFilename;
+    if (filename.has_value()) {
+      actualFilename = filename.value();
+    } else {
+      if (eval_file_loaded != EvalFileDefaultName) {
+        sync_cout << "Failed to export a net. A non-embedded net can only be saved if the filename is specified." << sync_endl;
+        return;
+      }
+      actualFilename = EvalFileDefaultName;
+    }
+
+    ofstream stream(actualFilename, std::ios_base::binary);
+    if (save_eval(stream)) {
+        sync_cout << "Network saved successfully to " << actualFilename << "." << sync_endl;
+    } else {
+        sync_cout << "Failed to export a net." << sync_endl;
+    }
+  }
+
+  /// NNUE::verify() verifies that the last net used was loaded successfully
+  void NNUE::verify() {
 
       string eval_file = string(Options["EvalFile"]);
 
@@ -1120,7 +1141,7 @@ Value Eval::evaluate(const Position& pos) {
                     + material / 32
                     - 4 * pos.rule50_count();
 
-         Value nnue = NNUE::evaluate(pos) * scale / 1024 + Tempo;
+         Value nnue = NNUE::evaluate(pos) * scale / 1024 + Time.tempoNNUE;
 
          if (pos.is_chess960())
              nnue += fix_FRC(pos);

src/evaluate.h

@@ -20,6 +20,7 @@
 #define EVALUATE_H_INCLUDED
 
 #include <string>
+#include <optional>
 
 #include "types.h"
 
@@ -50,7 +51,9 @@ namespace Eval {
 
     Value evaluate(const Position& pos);
     bool load_eval(std::string name, std::istream& stream);
+    bool save_eval(std::ostream& stream);
     void init();
+    void export_net(const std::optional<std::string>& filename);
     void verify();
   }
 

src/misc.cpp

@@ -51,7 +51,7 @@ typedef bool(*fun3_t)(HANDLE, CONST GROUP_AFFINITY*, PGROUP_AFFINITY);
 #include <sys/mman.h>
 #endif
 
-#if defined(__APPLE__) || defined(__ANDROID__) || defined(__OpenBSD__) || (defined(__GLIBCXX__) && !defined(_GLIBCXX_HAVE_ALIGNED_ALLOC) && !defined(_WIN32))
+#if defined(__APPLE__) || defined(__ANDROID__) || defined(__OpenBSD__) || (defined(__GLIBCXX__) && !defined(_GLIBCXX_HAVE_ALIGNED_ALLOC) && !defined(_WIN32)) || defined(__e2k__)
 #define POSIXALIGNEDALLOC
 #include <stdlib.h>
 #endif
@@ -194,6 +194,18 @@ std::string compiler_info() {
      compiler += "(version ";
      compiler += stringify(_MSC_FULL_VER) "." stringify(_MSC_BUILD);
      compiler += ")";
+  #elif defined(__e2k__) && defined(__LCC__)
+    #define dot_ver2(n) \
+      compiler += (char)'.'; \
+      compiler += (char)('0' + (n) / 10); \
+      compiler += (char)('0' + (n) % 10);
+
+     compiler += "MCST LCC ";
+     compiler += "(version ";
+     compiler += std::to_string(__LCC__ / 100);
+     dot_ver2(__LCC__ % 100)
+     dot_ver2(__LCC_MINOR__)
+     compiler += ")";
   #elif __GNUC__
      compiler += "g++ (GNUC) ";
      compiler += make_version_string(__GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__);

src/misc.h

@@ -86,6 +86,49 @@ T* align_ptr_up(T* ptr)
   return reinterpret_cast<T*>(reinterpret_cast<char*>((ptrint + (Alignment - 1)) / Alignment * Alignment));
 }
 
+template <typename T>
+class ValueListInserter {
+public:
+  ValueListInserter(T* v, std::size_t& s) :
+    values(v),
+    size(&s)
+  {
+  }
+
+  void push_back(const T& value) { values[(*size)++] = value; }
+private:
+  T* values;
+  std::size_t* size;
+};
+
+template <typename T, std::size_t MaxSize>
+class ValueList {
+
+public:
+  std::size_t size() const { return size_; }
+  void resize(std::size_t newSize) { size_ = newSize; }
+  void push_back(const T& value) { values_[size_++] = value; }
+  T& operator[](std::size_t index) { return values_[index]; }
+  T* begin() { return values_; }
+  T* end() { return values_ + size_; }
+  const T& operator[](std::size_t index) const { return values_[index]; }
+  const T* begin() const { return values_; }
+  const T* end() const { return values_ + size_; }
+  operator ValueListInserter<T>() { return ValueListInserter(values_, size_); }
+
+  void swap(ValueList& other) {
+    const std::size_t maxSize = std::max(size_, other.size_);
+    for (std::size_t i = 0; i < maxSize; ++i) {
+      std::swap(values_[i], other.values_[i]);
+    }
+    std::swap(size_, other.size_);
+  }
+
+private:
+  T values_[MaxSize];
+  std::size_t size_ = 0;
+};
+
 // This logger allows printing many parts in a region atomically
 // but doesn't block the threads trying to append to other regions.
 // Instead if some region tries to pring while other region holds

src/movegen.cpp

@@ -58,19 +58,16 @@ namespace {
     constexpr Direction UpLeft   = (Us == WHITE ? NORTH_WEST : SOUTH_EAST);
 
     const Square ksq = pos.square<KING>(Them);
-    Bitboard emptySquares;
+    const Bitboard emptySquares = Type == QUIETS || Type == QUIET_CHECKS ? target : ~pos.pieces();
+    const Bitboard enemies      = Type == EVASIONS ? pos.checkers()
+                                : Type == CAPTURES ? target : pos.pieces(Them);
 
     Bitboard pawnsOn7    = pos.pieces(Us, PAWN) &  TRank7BB;
     Bitboard pawnsNotOn7 = pos.pieces(Us, PAWN) & ~TRank7BB;
 
-    Bitboard enemies = (Type == EVASIONS ? pos.checkers():
-                        Type == CAPTURES ? target : pos.pieces(Them));
-
     // Single and double pawn pushes, no promotions
     if (Type != CAPTURES)
     {
-        emptySquares = (Type == QUIETS || Type == QUIET_CHECKS ? target : ~pos.pieces());
-
         Bitboard b1 = shift<Up>(pawnsNotOn7)   & emptySquares;
         Bitboard b2 = shift<Up>(b1 & TRank3BB) & emptySquares;
 
@@ -82,22 +79,12 @@ namespace {
 
         if (Type == QUIET_CHECKS)
         {
-            b1 &= pawn_attacks_bb(Them, ksq);
+            // To make a quiet check, you either make a direct check by pushing a pawn
-            b2 &= pawn_attacks_bb(Them, ksq);
+            // or push a blocker pawn that is not on the same file as the enemy king.
-
+            // Discovered check promotion has been already generated amongst the captures.
-            // Add pawn pushes which give discovered check. This is possible only
+            Bitboard dcCandidatePawns = pos.blockers_for_king(Them) & ~file_bb(ksq);
-            // if the pawn is not on the same file as the enemy king, because we
+            b1 &= pawn_attacks_bb(Them, ksq) | shift<   Up>(dcCandidatePawns);
-            // don't generate captures. Note that a possible discovered check
+            b2 &= pawn_attacks_bb(Them, ksq) | shift<Up+Up>(dcCandidatePawns);
-            // promotion has been already generated amongst the captures.
-            Bitboard dcCandidateQuiets = pos.blockers_for_king(Them) & pawnsNotOn7;
-            if (dcCandidateQuiets)
-            {
-                Bitboard dc1 = shift<Up>(dcCandidateQuiets) & emptySquares & ~file_bb(ksq);
-                Bitboard dc2 = shift<Up>(dc1 & TRank3BB) & emptySquares;
-
-                b1 |= dc1;
-                b2 |= dc2;
-            }
         }
 
         while (b1)
@@ -116,16 +103,13 @@ namespace {
     // Promotions and underpromotions
     if (pawnsOn7)
     {
-        if (Type == CAPTURES)
-            emptySquares = ~pos.pieces();
-
-        if (Type == EVASIONS)
-            emptySquares &= target;
-
         Bitboard b1 = shift<UpRight>(pawnsOn7) & enemies;
         Bitboard b2 = shift<UpLeft >(pawnsOn7) & enemies;
         Bitboard b3 = shift<Up     >(pawnsOn7) & emptySquares;
 
+        if (Type == EVASIONS)
+            b3 &= target;
+
         while (b1)
             moveList = make_promotions<Type, UpRight>(moveList, pop_lsb(b1), ksq);
 
@@ -175,19 +159,20 @@ namespace {
   }
 
 
-  template<PieceType Pt, bool Checks>
+  template<Color Us, PieceType Pt, bool Checks>
-  ExtMove* generate_moves(const Position& pos, ExtMove* moveList, Bitboard piecesToMove, Bitboard target) {
+  ExtMove* generate_moves(const Position& pos, ExtMove* moveList, Bitboard target) {
 
     static_assert(Pt != KING && Pt != PAWN, "Unsupported piece type in generate_moves()");
 
-    Bitboard bb = piecesToMove & pos.pieces(Pt);
+    Bitboard bb = pos.pieces(Us, Pt);
 
     while (bb)
     {
         Square from = pop_lsb(bb);
-
         Bitboard b = attacks_bb<Pt>(from, pos.pieces()) & target;
-        if constexpr (Checks)
+
+        // To check, you either move freely a blocker or make a direct check.
+        if (Checks && (Pt == QUEEN || !(pos.blockers_for_king(~Us) & from)))
             b &= pos.check_squares(Pt);
 
         while (b)
@@ -204,42 +189,34 @@ namespace {
     static_assert(Type != LEGAL, "Unsupported type in generate_all()");
 
     constexpr bool Checks = Type == QUIET_CHECKS; // Reduce template instantiations
-    Bitboard target, piecesToMove = pos.pieces(Us);
+    const Square ksq = pos.square<KING>(Us);
+    Bitboard target;
 
-    if(Type == QUIET_CHECKS)
+    if (Type == EVASIONS && more_than_one(pos.checkers()))
-        piecesToMove &= ~pos.blockers_for_king(~Us);
+        goto kingMoves; // Double check, only a king move can save the day
 
-    switch (Type)
+    target = Type == EVASIONS     ?  between_bb(ksq, lsb(pos.checkers()))
-    {
+           : Type == NON_EVASIONS ? ~pos.pieces( Us)
-        case CAPTURES:
+           : Type == CAPTURES     ?  pos.pieces(~Us)
-            target =  pos.pieces(~Us);
+                                  : ~pos.pieces(   ); // QUIETS || QUIET_CHECKS
-            break;
-        case QUIETS:
-        case QUIET_CHECKS:
-            target = ~pos.pieces();
-            break;
-        case EVASIONS:
-            target = between_bb(pos.square<KING>(Us), lsb(pos.checkers()));
-            break;
-        case NON_EVASIONS:
-            target = ~pos.pieces(Us);
-            break;
-    }
 
     moveList = generate_pawn_moves<Us, Type>(pos, moveList, target);
-    moveList = generate_moves<KNIGHT, Checks>(pos, moveList, piecesToMove, target);
+    moveList = generate_moves<Us, KNIGHT, Checks>(pos, moveList, target);
-    moveList = generate_moves<BISHOP, Checks>(pos, moveList, piecesToMove, target);
+    moveList = generate_moves<Us, BISHOP, Checks>(pos, moveList, target);
-    moveList = generate_moves<  ROOK, Checks>(pos, moveList, piecesToMove, target);
+    moveList = generate_moves<Us,   ROOK, Checks>(pos, moveList, target);
-    moveList = generate_moves< QUEEN, Checks>(pos, moveList, piecesToMove, target);
+    moveList = generate_moves<Us,  QUEEN, Checks>(pos, moveList, target);
 
-    if (Type != QUIET_CHECKS && Type != EVASIONS)
+kingMoves:
+    if (!Checks || pos.blockers_for_king(~Us) & ksq)
     {
-        Square ksq = pos.square<KING>(Us);
+        Bitboard b = attacks_bb<KING>(ksq) & (Type == EVASIONS ? ~pos.pieces(Us) : target);
-        Bitboard b = attacks_bb<KING>(ksq) & target;
+        if (Checks)
+            b &= ~attacks_bb<QUEEN>(pos.square<KING>(~Us));
+
         while (b)
             *moveList++ = make_move(ksq, pop_lsb(b));
 
-        if ((Type != CAPTURES) && pos.can_castle(Us & ANY_CASTLING))
+        if ((Type == QUIETS || Type == NON_EVASIONS) && pos.can_castle(Us & ANY_CASTLING))
             for (CastlingRights cr : { Us & KING_SIDE, Us & QUEEN_SIDE } )
                 if (!pos.castling_impeded(cr) && pos.can_castle(cr))
                     *moveList++ = make<CASTLING>(ksq, pos.castling_rook_square(cr));
@@ -253,6 +230,8 @@ namespace {
 
 /// <CAPTURES>     Generates all pseudo-legal captures plus queen and checking knight promotions
 /// <QUIETS>       Generates all pseudo-legal non-captures and underpromotions (except checking knight)
+/// <EVASIONS>     Generates all pseudo-legal check evasions when the side to move is in check
+/// <QUIET_CHECKS> Generates all pseudo-legal non-captures giving check, except castling
 /// <NON_EVASIONS> Generates all pseudo-legal captures and non-captures
 ///
 /// Returns a pointer to the end of the move list.
@@ -260,8 +239,8 @@ namespace {
 template<GenType Type>
 ExtMove* generate(const Position& pos, ExtMove* moveList) {
 
-  static_assert(Type == CAPTURES || Type == QUIETS || Type == NON_EVASIONS, "Unsupported type in generate()");
+  static_assert(Type != LEGAL, "Unsupported type in generate()");
-  assert(!pos.checkers());
+  assert((Type == EVASIONS) == (bool)pos.checkers());
 
   Color us = pos.side_to_move();
 
@@ -272,62 +251,11 @@ ExtMove* generate(const Position& pos, ExtMove* moveList) {
 // Explicit template instantiations
 template ExtMove* generate<CAPTURES>(const Position&, ExtMove*);
 template ExtMove* generate<QUIETS>(const Position&, ExtMove*);
+template ExtMove* generate<EVASIONS>(const Position&, ExtMove*);
+template ExtMove* generate<QUIET_CHECKS>(const Position&, ExtMove*);
 template ExtMove* generate<NON_EVASIONS>(const Position&, ExtMove*);
 
 
-/// generate<QUIET_CHECKS> generates all pseudo-legal non-captures giving check,
-/// except castling. Returns a pointer to the end of the move list.
-template<>
-ExtMove* generate<QUIET_CHECKS>(const Position& pos, ExtMove* moveList) {
-
-  assert(!pos.checkers());
-
-  Color us = pos.side_to_move();
-  Bitboard dc = pos.blockers_for_king(~us) & pos.pieces(us) & ~pos.pieces(PAWN);
-
-  while (dc)
-  {
-     Square from = pop_lsb(dc);
-     PieceType pt = type_of(pos.piece_on(from));
-
-     Bitboard b = attacks_bb(pt, from, pos.pieces()) & ~pos.pieces();
-
-     if (pt == KING)
-         b &= ~attacks_bb<QUEEN>(pos.square<KING>(~us));
-
-     while (b)
-         *moveList++ = make_move(from, pop_lsb(b));
-  }
-
-  return us == WHITE ? generate_all<WHITE, QUIET_CHECKS>(pos, moveList)
-                     : generate_all<BLACK, QUIET_CHECKS>(pos, moveList);
-}
-
-
-/// generate<EVASIONS> generates all pseudo-legal check evasions when the side
-/// to move is in check. Returns a pointer to the end of the move list.
-template<>
-ExtMove* generate<EVASIONS>(const Position& pos, ExtMove* moveList) {
-
-  assert(pos.checkers());
-
-  Color us = pos.side_to_move();
-  Square ksq = pos.square<KING>(us);
-
-  // Generate evasions for king
-  Bitboard b = attacks_bb<KING>(ksq) & ~pos.pieces(us);
-  while (b)
-      *moveList++ = make_move(ksq, pop_lsb(b));
-
-  if (more_than_one(pos.checkers()))
-      return moveList; // Double check, only a king move can save the day
-
-  // Generate blocking interpositions or captures of the checking piece
-  return us == WHITE ? generate_all<WHITE, EVASIONS>(pos, moveList)
-                     : generate_all<BLACK, EVASIONS>(pos, moveList);
-}
-
-
 /// generate<LEGAL> generates all the legal moves in the given position
 
 template<>

src/nnue/architectures/halfkp_256x2-32-32.h

@@ -1,54 +0,0 @@
-/*
-  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2021 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_HALFKP_256X2_32_32_H_INCLUDED
-#define NNUE_HALFKP_256X2_32_32_H_INCLUDED
-
-#include "../features/feature_set.h"
-#include "../features/half_kp.h"
-
-#include "../layers/input_slice.h"
-#include "../layers/affine_transform.h"
-#include "../layers/clipped_relu.h"
-
-namespace Stockfish::Eval::NNUE {
-
-// Input features used in evaluation function
-using RawFeatures = Features::FeatureSet<
-    Features::HalfKP<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 Stockfish::Eval::NNUE
-
-#endif // #ifndef NNUE_HALFKP_256X2_32_32_H_INCLUDED

src/nnue/evaluate_nnue.cpp

@@ -32,26 +32,27 @@
 namespace Stockfish::Eval::NNUE {
 
   // Input feature converter
-  LargePagePtr<FeatureTransformer> feature_transformer;
+  LargePagePtr<FeatureTransformer> featureTransformer;
 
   // Evaluation function
   AlignedPtr<Network> network;
 
   // Evaluation function file name
   std::string fileName;
+  std::string netDescription;
 
   namespace Detail {
 
   // Initialize the evaluation function parameters
   template <typename T>
-  void Initialize(AlignedPtr<T>& pointer) {
+  void initialize(AlignedPtr<T>& pointer) {
 
     pointer.reset(reinterpret_cast<T*>(std_aligned_alloc(alignof(T), sizeof(T))));
     std::memset(pointer.get(), 0, sizeof(T));
   }
 
   template <typename T>
-  void Initialize(LargePagePtr<T>& pointer) {
+  void initialize(LargePagePtr<T>& pointer) {
 
     static_assert(alignof(T) <= 4096, "aligned_large_pages_alloc() may fail for such a big alignment requirement of T");
     pointer.reset(reinterpret_cast<T*>(aligned_large_pages_alloc(sizeof(T))));
@@ -60,85 +61,120 @@ namespace Stockfish::Eval::NNUE {
 
   // Read evaluation function parameters
   template <typename T>
-  bool ReadParameters(std::istream& stream, T& reference) {
+  bool read_parameters(std::istream& stream, T& reference) {
 
     std::uint32_t header;
     header = read_little_endian<std::uint32_t>(stream);
-    if (!stream || header != T::GetHashValue()) return false;
+    if (!stream || header != T::get_hash_value()) return false;
-    return reference.ReadParameters(stream);
+    return reference.read_parameters(stream);
+  }
+
+  // Write evaluation function parameters
+  template <typename T>
+  bool write_parameters(std::ostream& stream, const T& reference) {
+
+    write_little_endian<std::uint32_t>(stream, T::get_hash_value());
+    return reference.write_parameters(stream);
   }
 
   }  // namespace Detail
 
   // Initialize the evaluation function parameters
-  void Initialize() {
+  void initialize() {
 
-    Detail::Initialize(feature_transformer);
+    Detail::initialize(featureTransformer);
-    Detail::Initialize(network);
+    Detail::initialize(network);
   }
 
   // Read network header
-  bool ReadHeader(std::istream& stream, std::uint32_t* hash_value, std::string* architecture)
+  bool read_header(std::istream& stream, std::uint32_t* hashValue, std::string* desc)
   {
     std::uint32_t version, size;
 
     version     = read_little_endian<std::uint32_t>(stream);
-    *hash_value = read_little_endian<std::uint32_t>(stream);
+    *hashValue = read_little_endian<std::uint32_t>(stream);
     size        = read_little_endian<std::uint32_t>(stream);
-    if (!stream || version != kVersion) return false;
+    if (!stream || version != Version) return false;
-    architecture->resize(size);
+    desc->resize(size);
-    stream.read(&(*architecture)[0], size);
+    stream.read(&(*desc)[0], size);
+    return !stream.fail();
+  }
+
+  // Write network header
+  bool write_header(std::ostream& stream, std::uint32_t hashValue, const std::string& desc)
+  {
+    write_little_endian<std::uint32_t>(stream, Version);
+    write_little_endian<std::uint32_t>(stream, hashValue);
+    write_little_endian<std::uint32_t>(stream, desc.size());
+    stream.write(&desc[0], desc.size());
     return !stream.fail();
   }
 
   // Read network parameters
-  bool ReadParameters(std::istream& stream) {
+  bool read_parameters(std::istream& stream) {
 
-    std::uint32_t hash_value;
+    std::uint32_t hashValue;
-    std::string architecture;
+    if (!read_header(stream, &hashValue, &netDescription)) return false;
-    if (!ReadHeader(stream, &hash_value, &architecture)) return false;
+    if (hashValue != HashValue) return false;
-    if (hash_value != kHashValue) return false;
+    if (!Detail::read_parameters(stream, *featureTransformer)) return false;
-    if (!Detail::ReadParameters(stream, *feature_transformer)) return false;
+    if (!Detail::read_parameters(stream, *network)) return false;
-    if (!Detail::ReadParameters(stream, *network)) return false;
     return stream && stream.peek() == std::ios::traits_type::eof();
   }
 
+  // Write network parameters
+  bool write_parameters(std::ostream& stream) {
+
+    if (!write_header(stream, HashValue, netDescription)) return false;
+    if (!Detail::write_parameters(stream, *featureTransformer)) return false;
+    if (!Detail::write_parameters(stream, *network)) return false;
+    return (bool)stream;
+  }
+
   // Evaluation function. Perform differential calculation.
   Value evaluate(const Position& pos) {
 
     // We manually align the arrays on the stack because with gcc < 9.3
     // overaligning stack variables with alignas() doesn't work correctly.
 
-    constexpr uint64_t alignment = kCacheLineSize;
+    constexpr uint64_t alignment = CacheLineSize;
 
 #if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
-    TransformedFeatureType transformed_features_unaligned[
+    TransformedFeatureType transformedFeaturesUnaligned[
-      FeatureTransformer::kBufferSize + alignment / sizeof(TransformedFeatureType)];
+      FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
-    char buffer_unaligned[Network::kBufferSize + alignment];
+    char bufferUnaligned[Network::BufferSize + alignment];
 
-    auto* transformed_features = align_ptr_up<alignment>(&transformed_features_unaligned[0]);
+    auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
-    auto* buffer = align_ptr_up<alignment>(&buffer_unaligned[0]);
+    auto* buffer = align_ptr_up<alignment>(&bufferUnaligned[0]);
 #else
     alignas(alignment)
-      TransformedFeatureType transformed_features[FeatureTransformer::kBufferSize];
+      TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
-    alignas(alignment) char buffer[Network::kBufferSize];
+    alignas(alignment) char buffer[Network::BufferSize];
 #endif
 
-    ASSERT_ALIGNED(transformed_features, alignment);
+    ASSERT_ALIGNED(transformedFeatures, alignment);
     ASSERT_ALIGNED(buffer, alignment);
 
-    feature_transformer->Transform(pos, transformed_features);
+    featureTransformer->transform(pos, transformedFeatures);
-    const auto output = network->Propagate(transformed_features, buffer);
+    const auto output = network->propagate(transformedFeatures, buffer);
 
-    return static_cast<Value>(output[0] / FV_SCALE);
+    return static_cast<Value>(output[0] / OutputScale);
   }
 
   // Load eval, from a file stream or a memory stream
   bool load_eval(std::string name, std::istream& stream) {
 
-    Initialize();
+    initialize();
     fileName = name;
-    return ReadParameters(stream);
+    return read_parameters(stream);
+  }
+
+  // Save eval, to a file stream or a memory stream
+  bool save_eval(std::ostream& stream) {
+
+    if (fileName.empty())
+      return false;
+
+    return write_parameters(stream);
   }
 
 } // namespace Stockfish::Eval::NNUE

src/nnue/evaluate_nnue.h

@@ -28,8 +28,8 @@
 namespace Stockfish::Eval::NNUE {
 
   // Hash value of evaluation function structure
-  constexpr std::uint32_t kHashValue =
+  constexpr std::uint32_t HashValue =
-      FeatureTransformer::GetHashValue() ^ Network::GetHashValue();
+      FeatureTransformer::get_hash_value() ^ Network::get_hash_value();
 
   // Deleter for automating release of memory area
   template <typename T>

src/nnue/features/feature_set.h

@@ -1,69 +0,0 @@
-/*
-  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2021 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/>.
-*/
-
-// A class template that represents the input feature set of the NNUE evaluation function
-
-#ifndef NNUE_FEATURE_SET_H_INCLUDED
-#define NNUE_FEATURE_SET_H_INCLUDED
-
-#include "features_common.h"
-#include <array>
-
-namespace Stockfish::Eval::NNUE::Features {
-
-  // Class template that represents a list of values
-  template <typename T, T... Values>
-  struct CompileTimeList;
-
-  template <typename T, T First, T... Remaining>
-  struct CompileTimeList<T, First, Remaining...> {
-    static constexpr bool Contains(T value) {
-      return value == First || CompileTimeList<T, Remaining...>::Contains(value);
-    }
-    static constexpr std::array<T, sizeof...(Remaining) + 1>
-        kValues = {{First, Remaining...}};
-  };
-
-  // Base class of feature set
-  template <typename Derived>
-  class FeatureSetBase {
-
-  };
-
-  // Class template that represents the feature set
-  template <typename FeatureType>
-  class FeatureSet<FeatureType> : public FeatureSetBase<FeatureSet<FeatureType>> {
-
-   public:
-    // Hash value embedded in the evaluation file
-    static constexpr std::uint32_t kHashValue = FeatureType::kHashValue;
-    // Number of feature dimensions
-    static constexpr IndexType kDimensions = FeatureType::kDimensions;
-    // Maximum number of simultaneously active features
-    static constexpr IndexType kMaxActiveDimensions =
-        FeatureType::kMaxActiveDimensions;
-    // Trigger for full calculation instead of difference calculation
-    using SortedTriggerSet =
-        CompileTimeList<TriggerEvent, FeatureType::kRefreshTrigger>;
-    static constexpr auto kRefreshTriggers = SortedTriggerSet::kValues;
-
-  };
-
-}  // namespace Stockfish::Eval::NNUE::Features
-
-#endif // #ifndef NNUE_FEATURE_SET_H_INCLUDED

src/nnue/features/features_common.h

@@ -1,45 +0,0 @@
-/*
-  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2021 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/>.
-*/
-
-//Common header of input features of NNUE evaluation function
-
-#ifndef NNUE_FEATURES_COMMON_H_INCLUDED
-#define NNUE_FEATURES_COMMON_H_INCLUDED
-
-#include "../../evaluate.h"
-#include "../nnue_common.h"
-
-namespace Stockfish::Eval::NNUE::Features {
-
-  class IndexList;
-
-  template <typename... FeatureTypes>
-  class FeatureSet;
-
-  // Trigger to perform full calculations instead of difference only
-  enum class TriggerEvent {
-    kFriendKingMoved // calculate full evaluation when own king moves
-  };
-
-  enum class Side {
-    kFriend // side to move
-  };
-
-}  // namespace Stockfish::Eval::NNUE::Features
-
-#endif // #ifndef NNUE_FEATURES_COMMON_H_INCLUDED

src/nnue/features/half_kp.cpp

@@ -19,69 +19,68 @@
 //Definition of input features HalfKP of NNUE evaluation function
 
 #include "half_kp.h"
-#include "index_list.h"
+
+#include "../../position.h"
 
 namespace Stockfish::Eval::NNUE::Features {
 
   // Orient a square according to perspective (rotates by 180 for black)
-  inline Square orient(Color perspective, Square s) {
+  inline Square HalfKP::orient(Color perspective, Square s) {
     return Square(int(s) ^ (bool(perspective) * 63));
   }
 
   // Index of a feature for a given king position and another piece on some square
-  inline IndexType make_index(Color perspective, Square s, Piece pc, Square ksq) {
+  inline IndexType HalfKP::make_index(Color perspective, Square s, Piece pc, Square ksq) {
-    return IndexType(orient(perspective, s) + kpp_board_index[perspective][pc] + PS_END * ksq);
+    return IndexType(orient(perspective, s) + PieceSquareIndex[perspective][pc] + PS_NB * ksq);
   }
 
   // Get a list of indices for active features
-  template <Side AssociatedKing>
+  void HalfKP::append_active_indices(
-  void HalfKP<AssociatedKing>::AppendActiveIndices(
+    const Position& pos,
-      const Position& pos, Color perspective, IndexList* active) {
+    Color perspective,
-
+    ValueListInserter<IndexType> active
+  ) {
     Square ksq = orient(perspective, pos.square<KING>(perspective));
     Bitboard bb = pos.pieces() & ~pos.pieces(KING);
     while (bb)
     {
       Square s = pop_lsb(bb);
-      active->push_back(make_index(perspective, s, pos.piece_on(s), ksq));
+      active.push_back(make_index(perspective, s, pos.piece_on(s), ksq));
     }
   }
 
 
-  // AppendChangedIndices() : get a list of indices for recently changed features
+  // append_changed_indices() : get a list of indices for recently changed features
 
-  // IMPORTANT: The `pos` in this function is pretty much useless as it
+  void HalfKP::append_changed_indices(
-  // is not always the position the features are updated to. The feature
+    Square ksq,
-  // transformer code right now can update multiple accumulators per move,
+    StateInfo* st,
-  // but since Stockfish only keeps the full state of the current leaf
+    Color perspective,
-  // search position it is not possible to always pass here the position for
+    ValueListInserter<IndexType> removed,
-  // which the accumulator is being updated. Therefore the only thing that
+    ValueListInserter<IndexType> added
-  // can be reliably extracted from `pos` is the king square for the king
+  ) {
-  // of the `perspective` color (note: not even the other king's square will
+    const auto& dp = st->dirtyPiece;
-  // match reality in all cases, this is also the reason why `dp` is passed
+    Square oriented_ksq = orient(perspective, ksq);
-  // as a parameter and not extracted from pos.state()). This is of particular
-  // problem for future nets with other feature sets, where updating the active
-  // feature might require more information from the intermediate positions. In
-  // this case the only easy solution is to remove the multiple updates from
-  // the feature transformer update code and only update the accumulator for
-  // the current leaf position (the position after the move).
-
-  template <Side AssociatedKing>
-  void HalfKP<AssociatedKing>::AppendChangedIndices(
-      const Position& pos, const DirtyPiece& dp, Color perspective,
-      IndexList* removed, IndexList* added) {
-
-    Square ksq = orient(perspective, pos.square<KING>(perspective));
     for (int i = 0; i < dp.dirty_num; ++i) {
       Piece pc = dp.piece[i];
       if (type_of(pc) == KING) continue;
       if (dp.from[i] != SQ_NONE)
-        removed->push_back(make_index(perspective, dp.from[i], pc, ksq));
+        removed.push_back(make_index(perspective, dp.from[i], pc, oriented_ksq));
       if (dp.to[i] != SQ_NONE)
-        added->push_back(make_index(perspective, dp.to[i], pc, ksq));
+        added.push_back(make_index(perspective, dp.to[i], pc, oriented_ksq));
     }
   }
 
-  template class HalfKP<Side::kFriend>;
+  int HalfKP::update_cost(StateInfo* st) {
+    return st->dirtyPiece.dirty_num;
+  }
+
+  int HalfKP::refresh_cost(const Position& pos) {
+    return pos.count<ALL_PIECES>() - 2;
+  }
+
+  bool HalfKP::requires_refresh(StateInfo* st, Color perspective) {
+    return st->dirtyPiece.piece[0] == make_piece(perspective, KING);
+  }
 
 }  // namespace Stockfish::Eval::NNUE::Features

src/nnue/features/half_kp.h

@@ -21,37 +21,88 @@
 #ifndef NNUE_FEATURES_HALF_KP_H_INCLUDED
 #define NNUE_FEATURES_HALF_KP_H_INCLUDED
 
+#include "../nnue_common.h"
+
 #include "../../evaluate.h"
-#include "features_common.h"
+#include "../../misc.h"
+
+namespace Stockfish {
+  struct StateInfo;
+}
 
 namespace Stockfish::Eval::NNUE::Features {
 
   // Feature HalfKP: Combination of the position of own king
   // and the position of pieces other than kings
-  template <Side AssociatedKing>
   class HalfKP {
 
+    // unique number for each piece type on each square
+    enum {
+      PS_NONE     =  0,
+      PS_W_PAWN   =  1,
+      PS_B_PAWN   =  1 * SQUARE_NB + 1,
+      PS_W_KNIGHT =  2 * SQUARE_NB + 1,
+      PS_B_KNIGHT =  3 * SQUARE_NB + 1,
+      PS_W_BISHOP =  4 * SQUARE_NB + 1,
+      PS_B_BISHOP =  5 * SQUARE_NB + 1,
+      PS_W_ROOK   =  6 * SQUARE_NB + 1,
+      PS_B_ROOK   =  7 * SQUARE_NB + 1,
+      PS_W_QUEEN  =  8 * SQUARE_NB + 1,
+      PS_B_QUEEN  =  9 * SQUARE_NB + 1,
+      PS_NB = 10 * SQUARE_NB + 1
+    };
+
+    static constexpr IndexType PieceSquareIndex[COLOR_NB][PIECE_NB] = {
+      // convention: W - us, B - them
+      // viewed from other side, W and B are reversed
+      { PS_NONE, PS_W_PAWN, PS_W_KNIGHT, PS_W_BISHOP, PS_W_ROOK, PS_W_QUEEN, PS_NONE, PS_NONE,
+        PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_NONE, PS_NONE },
+      { PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_NONE, PS_NONE,
+        PS_NONE, PS_W_PAWN, PS_W_KNIGHT, PS_W_BISHOP, PS_W_ROOK, PS_W_QUEEN, PS_NONE, PS_NONE }
+    };
+
+    // Orient a square according to perspective (rotates by 180 for black)
+    static Square orient(Color perspective, Square s);
+
+    // 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 ksq);
+
    public:
     // Feature name
-    static constexpr const char* kName = "HalfKP(Friend)";
+    static constexpr const char* Name = "HalfKP(Friend)";
+
     // Hash value embedded in the evaluation file
-    static constexpr std::uint32_t kHashValue =
+    static constexpr std::uint32_t HashValue = 0x5D69D5B8u;
-        0x5D69D5B9u ^ (AssociatedKing == Side::kFriend);
+
     // Number of feature dimensions
-    static constexpr IndexType kDimensions =
+    static constexpr IndexType Dimensions =
-        static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_END);
+        static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_NB);
-    // Maximum number of simultaneously active features
+
-    static constexpr IndexType kMaxActiveDimensions = 30; // Kings don't count
+    // Maximum number of simultaneously active features. 30 because kins are not included.
-    // Trigger for full calculation instead of difference calculation
+    static constexpr IndexType MaxActiveDimensions = 30;
-    static constexpr TriggerEvent kRefreshTrigger = TriggerEvent::kFriendKingMoved;
 
     // Get a list of indices for active features
-    static void AppendActiveIndices(const Position& pos, Color perspective,
+    static void append_active_indices(
-                                    IndexList* active);
+      const Position& pos,
+      Color perspective,
+      ValueListInserter<IndexType> active);
 
     // Get a list of indices for recently changed features
-    static void AppendChangedIndices(const Position& pos, const DirtyPiece& dp, Color perspective,
+    static void append_changed_indices(
-                                     IndexList* removed, IndexList* added);
+      Square ksq,
+      StateInfo* st,
+      Color perspective,
+      ValueListInserter<IndexType> removed,
+      ValueListInserter<IndexType> added);
+
+    // Returns the cost of updating one perspective, the most costly one.
+    // Assumes no refresh needed.
+    static int update_cost(StateInfo* st);
+    static int refresh_cost(const Position& pos);
+
+    // Returns whether the change stored in this StateInfo means that
+    // a full accumulator refresh is required.
+    static bool requires_refresh(StateInfo* st, Color perspective);
   };
 
 }  // namespace Stockfish::Eval::NNUE::Features

src/nnue/features/index_list.h

@@ -1,64 +0,0 @@
-/*
-  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
-  Copyright (C) 2004-2021 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 index list of input features
-
-#ifndef NNUE_FEATURES_INDEX_LIST_H_INCLUDED
-#define NNUE_FEATURES_INDEX_LIST_H_INCLUDED
-
-#include "../../position.h"
-#include "../nnue_architecture.h"
-
-namespace Stockfish::Eval::NNUE::Features {
-
-  // Class template used for feature index list
-  template <typename T, std::size_t MaxSize>
-  class ValueList {
-
-   public:
-    std::size_t size() const { return size_; }
-    void resize(std::size_t size) { size_ = size; }
-    void push_back(const T& value) { values_[size_++] = value; }
-    T& operator[](std::size_t index) { return values_[index]; }
-    T* begin() { return values_; }
-    T* end() { return values_ + size_; }
-    const T& operator[](std::size_t index) const { return values_[index]; }
-    const T* begin() const { return values_; }
-    const T* end() const { return values_ + size_; }
-
-    void swap(ValueList& other) {
-      const std::size_t max_size = std::max(size_, other.size_);
-      for (std::size_t i = 0; i < max_size; ++i) {
-        std::swap(values_[i], other.values_[i]);
-      }
-      std::swap(size_, other.size_);
-    }
-
-   private:
-    T values_[MaxSize];
-    std::size_t size_ = 0;
-  };
-
-  //Type of feature index list
-  class IndexList
-      : public ValueList<IndexType, RawFeatures::kMaxActiveDimensions> {
-  };
-
-}  // namespace Stockfish::Eval::NNUE::Features
-
-#endif // NNUE_FEATURES_INDEX_LIST_H_INCLUDED

src/nnue/layers/affine_transform.h

@@ -27,7 +27,7 @@
 namespace Stockfish::Eval::NNUE::Layers {
 
   // Affine transformation layer
-  template <typename PreviousLayer, IndexType OutputDimensions>
+  template <typename PreviousLayer, IndexType OutDims>
   class AffineTransform {
    public:
     // Input/output type
@@ -36,64 +36,68 @@ namespace Stockfish::Eval::NNUE::Layers {
     static_assert(std::is_same<InputType, std::uint8_t>::value, "");
 
     // Number of input/output dimensions
-    static constexpr IndexType kInputDimensions =
+    static constexpr IndexType InputDimensions =
-        PreviousLayer::kOutputDimensions;
+        PreviousLayer::OutputDimensions;
-    static constexpr IndexType kOutputDimensions = OutputDimensions;
+    static constexpr IndexType OutputDimensions = OutDims;
-    static constexpr IndexType kPaddedInputDimensions =
+    static constexpr IndexType PaddedInputDimensions =
-        CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
+        ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
 #if defined (USE_AVX512)
-    static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 2;
+    static constexpr const IndexType OutputSimdWidth = SimdWidth / 2;
 #elif defined (USE_SSSE3)
-    static constexpr const IndexType kOutputSimdWidth = kSimdWidth / 4;
+    static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
 #endif
 
     // Size of forward propagation buffer used in this layer
-    static constexpr std::size_t kSelfBufferSize =
+    static constexpr std::size_t SelfBufferSize =
-        CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
+        ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
 
     // Size of the forward propagation buffer used from the input layer to this layer
-    static constexpr std::size_t kBufferSize =
+    static constexpr std::size_t BufferSize =
-        PreviousLayer::kBufferSize + kSelfBufferSize;
+        PreviousLayer::BufferSize + SelfBufferSize;
 
     // Hash value embedded in the evaluation file
-    static constexpr std::uint32_t GetHashValue() {
+    static constexpr std::uint32_t get_hash_value() {
-      std::uint32_t hash_value = 0xCC03DAE4u;
+      std::uint32_t hashValue = 0xCC03DAE4u;
-      hash_value += kOutputDimensions;
+      hashValue += OutputDimensions;
-      hash_value ^= PreviousLayer::GetHashValue() >> 1;
+      hashValue ^= PreviousLayer::get_hash_value() >> 1;
-      hash_value ^= PreviousLayer::GetHashValue() << 31;
+      hashValue ^= PreviousLayer::get_hash_value() << 31;
-      return hash_value;
+      return hashValue;
     }
 
-   // Read network parameters
+    // Read network parameters
-    bool ReadParameters(std::istream& stream) {
+    bool read_parameters(std::istream& stream) {
-      if (!previous_layer_.ReadParameters(stream)) return false;
+      if (!previousLayer.read_parameters(stream)) return false;
-      for (std::size_t i = 0; i < kOutputDimensions; ++i)
+      for (std::size_t i = 0; i < OutputDimensions; ++i)
-        biases_[i] = read_little_endian<BiasType>(stream);
+        biases[i] = read_little_endian<BiasType>(stream);
-      for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
 #if !defined (USE_SSSE3)
-        weights_[i] = read_little_endian<WeightType>(stream);
+      for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+        weights[i] = read_little_endian<WeightType>(stream);
 #else
-        weights_[
+      std::unique_ptr<uint32_t[]> indexMap = std::make_unique<uint32_t[]>(OutputDimensions * PaddedInputDimensions);
-          (i / 4) % (kPaddedInputDimensions / 4) * kOutputDimensions * 4 +
+      for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
-          i / kPaddedInputDimensions * 4 +
+        const uint32_t scrambledIdx =
-          i % 4
+          (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
-        ] = read_little_endian<WeightType>(stream);
+          i / PaddedInputDimensions * 4 +
+          i % 4;
+        weights[scrambledIdx] = read_little_endian<WeightType>(stream);
+        indexMap[scrambledIdx] = i;
+      }
 
       // Determine if eights of weight and input products can be summed using 16bits
       // without saturation. We assume worst case combinations of 0 and 127 for all inputs.
-      if (kOutputDimensions > 1 && !stream.fail())
+      if (OutputDimensions > 1 && !stream.fail())
       {
           canSaturate16.count = 0;
 #if !defined(USE_VNNI)
-          for (IndexType i = 0; i < kPaddedInputDimensions; i += 16)
+          for (IndexType i = 0; i < PaddedInputDimensions; i += 16)
-              for (IndexType j = 0; j < kOutputDimensions; ++j)
+              for (IndexType j = 0; j < OutputDimensions; ++j)
                   for (int x = 0; x < 2; ++x)
                   {
-                      WeightType* w = &weights_[i * kOutputDimensions + j * 4 + x * 2];
+                      WeightType* w = &weights[i * OutputDimensions + j * 4 + x * 2];
                       int sum[2] = {0, 0};
                       for (int k = 0; k < 8; ++k)
                       {
-                          IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
+                          IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
                           sum[w[idx] < 0] += w[idx];
                       }
                       for (int sign : { -1, 1 })
@@ -102,14 +106,15 @@ namespace Stockfish::Eval::NNUE::Layers {
                               int maxK = 0, maxW = 0;
                               for (int k = 0; k < 8; ++k)
                               {
-                                  IndexType idx = k / 2 * kOutputDimensions * 4 + k % 2;
+                                  IndexType idx = k / 2 * OutputDimensions * 4 + k % 2;
                                   if (maxW < sign * w[idx])
                                       maxK = k, maxW = sign * w[idx];
                               }
 
-                              IndexType idx = maxK / 2 * kOutputDimensions * 4 + maxK % 2;
+                              IndexType idx = maxK / 2 * OutputDimensions * 4 + maxK % 2;
                               sum[sign == -1] -= w[idx];
-                              canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx]);
+                              const uint32_t scrambledIdx = idx + i * OutputDimensions + j * 4 + x * 2;
+                              canSaturate16.add(j, i + maxK / 2 * 4 + maxK % 2 + x * 2, w[idx], indexMap[scrambledIdx]);
                               w[idx] = 0;
                           }
                   }
@@ -125,15 +130,43 @@ namespace Stockfish::Eval::NNUE::Layers {
       return !stream.fail();
     }
 
+    // Write network parameters
+    bool write_parameters(std::ostream& stream) const {
+      if (!previousLayer.write_parameters(stream)) return false;
+      for (std::size_t i = 0; i < OutputDimensions; ++i)
+          write_little_endian<BiasType>(stream, biases[i]);
+#if !defined (USE_SSSE3)
+      for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+          write_little_endian<WeightType>(stream, weights[i]);
+#else
+      std::unique_ptr<WeightType[]> unscrambledWeights = std::make_unique<WeightType[]>(OutputDimensions * PaddedInputDimensions);
+      for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
+          unscrambledWeights[i] =
+              weights[
+                (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+                i / PaddedInputDimensions * 4 +
+                i % 4
+              ];
+      }
+      for (int i = 0; i < canSaturate16.count; ++i)
+          unscrambledWeights[canSaturate16.ids[i].wIdx] = canSaturate16.ids[i].w;
+
+      for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+          write_little_endian<WeightType>(stream, unscrambledWeights[i]);
+#endif
+
+      return !stream.fail();
+    }
+
     // Forward propagation
-    const OutputType* Propagate(
+    const OutputType* propagate(
-        const TransformedFeatureType* transformed_features, char* buffer) const {
+        const TransformedFeatureType* transformedFeatures, char* buffer) const {
-      const auto input = previous_layer_.Propagate(
+      const auto input = previousLayer.propagate(
-          transformed_features, buffer + kSelfBufferSize);
+          transformedFeatures, buffer + SelfBufferSize);
 
 #if defined (USE_AVX512)
 
-      [[maybe_unused]] const __m512i kOnes512 = _mm512_set1_epi16(1);
+      [[maybe_unused]] const __m512i Ones512 = _mm512_set1_epi16(1);
 
       [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
         return _mm512_reduce_add_epi32(sum) + bias;
@@ -144,7 +177,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         acc = _mm512_dpbusd_epi32(acc, a, b);
 #else
         __m512i product0 = _mm512_maddubs_epi16(a, b);
-        product0 = _mm512_madd_epi16(product0, kOnes512);
+        product0 = _mm512_madd_epi16(product0, Ones512);
         acc = _mm512_add_epi32(acc, product0);
 #endif
       };
@@ -164,7 +197,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         product0 = _mm512_add_epi16(product0, product1);
         product2 = _mm512_add_epi16(product2, product3);
         product0 = _mm512_add_epi16(product0, product2);
-        product0 = _mm512_madd_epi16(product0, kOnes512);
+        product0 = _mm512_madd_epi16(product0, Ones512);
         acc = _mm512_add_epi32(acc, product0);
 #endif
       };
@@ -172,7 +205,7 @@ namespace Stockfish::Eval::NNUE::Layers {
 #endif
 #if defined (USE_AVX2)
 
-      [[maybe_unused]] const __m256i kOnes256 = _mm256_set1_epi16(1);
+      [[maybe_unused]] const __m256i Ones256 = _mm256_set1_epi16(1);
 
       [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
         __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
@@ -186,7 +219,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         acc = _mm256_dpbusd_epi32(acc, a, b);
 #else
         __m256i product0 = _mm256_maddubs_epi16(a, b);
-        product0 = _mm256_madd_epi16(product0, kOnes256);
+        product0 = _mm256_madd_epi16(product0, Ones256);
         acc = _mm256_add_epi32(acc, product0);
 #endif
       };
@@ -206,7 +239,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         product0 = _mm256_add_epi16(product0, product1);
         product2 = _mm256_add_epi16(product2, product3);
         product0 = _mm256_add_epi16(product0, product2);
-        product0 = _mm256_madd_epi16(product0, kOnes256);
+        product0 = _mm256_madd_epi16(product0, Ones256);
         acc = _mm256_add_epi32(acc, product0);
 #endif
       };
@@ -214,7 +247,7 @@ namespace Stockfish::Eval::NNUE::Layers {
 #endif
 #if defined (USE_SSSE3)
 
-      [[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1);
+      [[maybe_unused]] const __m128i Ones128 = _mm_set1_epi16(1);
 
       [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
         sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
@@ -224,7 +257,7 @@ namespace Stockfish::Eval::NNUE::Layers {
 
       [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
         __m128i product0 = _mm_maddubs_epi16(a, b);
-        product0 = _mm_madd_epi16(product0, kOnes128);
+        product0 = _mm_madd_epi16(product0, Ones128);
         acc = _mm_add_epi32(acc, product0);
       };
 
@@ -237,7 +270,7 @@ namespace Stockfish::Eval::NNUE::Layers {
         product0 = _mm_add_epi16(product0, product1);
         product2 = _mm_add_epi16(product2, product3);
         product0 = _mm_add_epi16(product0, product2);
-        product0 = _mm_madd_epi16(product0, kOnes128);
+        product0 = _mm_madd_epi16(product0, Ones128);
         acc = _mm_add_epi32(acc, product0);
       };
 
@@ -269,71 +302,71 @@ namespace Stockfish::Eval::NNUE::Layers {
 #if defined (USE_SSSE3)
 
       const auto output = reinterpret_cast<OutputType*>(buffer);
-      const auto input_vector = reinterpret_cast<const vec_t*>(input);
+      const auto inputVector = reinterpret_cast<const vec_t*>(input);
 
-      static_assert(kOutputDimensions % kOutputSimdWidth == 0 || kOutputDimensions == 1);
+      static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
 
-      // kOutputDimensions is either 1 or a multiple of kSimdWidth
+      // OutputDimensions is either 1 or a multiple of SimdWidth
       // because then it is also an input dimension.
-      if constexpr (kOutputDimensions % kOutputSimdWidth == 0)
+      if constexpr (OutputDimensions % OutputSimdWidth == 0)
       {
-          constexpr IndexType kNumChunks = kPaddedInputDimensions / 4;
+          constexpr IndexType NumChunks = PaddedInputDimensions / 4;
 
           const auto input32 = reinterpret_cast<const std::int32_t*>(input);
           vec_t* outptr = reinterpret_cast<vec_t*>(output);
-          std::memcpy(output, biases_, kOutputDimensions * sizeof(OutputType));
+          std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
 
-          for (int i = 0; i < (int)kNumChunks - 3; i += 4)
+          for (int i = 0; i < (int)NumChunks - 3; i += 4)
           {
               const vec_t in0 = vec_set_32(input32[i + 0]);
               const vec_t in1 = vec_set_32(input32[i + 1]);
               const vec_t in2 = vec_set_32(input32[i + 2]);
               const vec_t in3 = vec_set_32(input32[i + 3]);
-              const auto col0 = reinterpret_cast<const vec_t*>(&weights_[(i + 0) * kOutputDimensions * 4]);
+              const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
-              const auto col1 = reinterpret_cast<const vec_t*>(&weights_[(i + 1) * kOutputDimensions * 4]);
+              const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
-              const auto col2 = reinterpret_cast<const vec_t*>(&weights_[(i + 2) * kOutputDimensions * 4]);
+              const auto col2 = reinterpret_cast<const vec_t*>(&weights[(i + 2) * OutputDimensions * 4]);
-              const auto col3 = reinterpret_cast<const vec_t*>(&weights_[(i + 3) * kOutputDimensions * 4]);
+              const auto col3 = reinterpret_cast<const vec_t*>(&weights[(i + 3) * OutputDimensions * 4]);
-              for (int j = 0; j * kOutputSimdWidth < kOutputDimensions; ++j)
+              for (int j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
                   vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
           }
           for (int i = 0; i < canSaturate16.count; ++i)
               output[canSaturate16.ids[i].out] += input[canSaturate16.ids[i].in] * canSaturate16.ids[i].w;
       }
-      else if constexpr (kOutputDimensions == 1)
+      else if constexpr (OutputDimensions == 1)
       {
 #if defined (USE_AVX512)
-          if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) != 0)
+          if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
           {
-              constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+              constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-              const auto input_vector256 = reinterpret_cast<const __m256i*>(input);
+              const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
 
               __m256i sum0 = _mm256_setzero_si256();
-              const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+              const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
 
-              for (int j = 0; j < (int)kNumChunks; ++j)
+              for (int j = 0; j < (int)NumChunks; ++j)
               {
-                  const __m256i in = input_vector256[j];
+                  const __m256i in = inputVector256[j];
                   m256_add_dpbusd_epi32(sum0, in, row0[j]);
               }
-              output[0] = m256_hadd(sum0, biases_[0]);
+              output[0] = m256_hadd(sum0, biases[0]);
           }
           else
 #endif
           {
 #if defined (USE_AVX512)
-              constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
+              constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
 #else
-              constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+              constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
 #endif
               vec_t sum0 = vec_setzero();
-              const auto row0 = reinterpret_cast<const vec_t*>(&weights_[0]);
+              const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
 
-              for (int j = 0; j < (int)kNumChunks; ++j)
+              for (int j = 0; j < (int)NumChunks; ++j)
               {
-                  const vec_t in = input_vector[j];
+                  const vec_t in = inputVector[j];
                   vec_add_dpbusd_32(sum0, in, row0[j]);
               }
-              output[0] = vec_hadd(sum0, biases_[0]);
+              output[0] = vec_hadd(sum0, biases[0]);
           }
       }
 
@@ -344,80 +377,80 @@ namespace Stockfish::Eval::NNUE::Layers {
       auto output = reinterpret_cast<OutputType*>(buffer);
 
 #if defined(USE_SSE2)
-      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-      const __m128i kZeros = _mm_setzero_si128();
+      const __m128i Zeros = _mm_setzero_si128();
-      const auto input_vector = reinterpret_cast<const __m128i*>(input);
+      const auto inputVector = reinterpret_cast<const __m128i*>(input);
 
 #elif defined(USE_MMX)
-      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-      const __m64 kZeros = _mm_setzero_si64();
+      const __m64 Zeros = _mm_setzero_si64();
-      const auto input_vector = reinterpret_cast<const __m64*>(input);
+      const auto inputVector = reinterpret_cast<const __m64*>(input);
 
 #elif defined(USE_NEON)
-      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-      const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
+      const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
 #endif
 
-      for (IndexType i = 0; i < kOutputDimensions; ++i) {
+      for (IndexType i = 0; i < OutputDimensions; ++i) {
-        const IndexType offset = i * kPaddedInputDimensions;
+        const IndexType offset = i * PaddedInputDimensions;
 
 #if defined(USE_SSE2)
-        __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
+        __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
-        __m128i sum_hi = kZeros;
+        __m128i sumHi = Zeros;
-        const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
+        const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m128i row_j = _mm_load_si128(&row[j]);
-          __m128i input_j = _mm_load_si128(&input_vector[j]);
+          __m128i input_j = _mm_load_si128(&inputVector[j]);
-          __m128i extended_row_lo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+          __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
-          __m128i extended_row_hi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
+          __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
-          __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
+          __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
-          __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
+          __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
-          __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
+          __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
-          __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
+          __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
-          sum_lo = _mm_add_epi32(sum_lo, product_lo);
+          sumLo = _mm_add_epi32(sumLo, productLo);
-          sum_hi = _mm_add_epi32(sum_hi, product_hi);
+          sumHi = _mm_add_epi32(sumHi, productHi);
         }
-        __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
+        __m128i sum = _mm_add_epi32(sumLo, sumHi);
-        __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+        __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
-        sum = _mm_add_epi32(sum, sum_high_64);
+        sum = _mm_add_epi32(sum, sumHigh_64);
         __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
         sum = _mm_add_epi32(sum, sum_second_32);
         output[i] = _mm_cvtsi128_si32(sum);
 
 #elif defined(USE_MMX)
-        __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
+        __m64 sumLo = _mm_cvtsi32_si64(biases[i]);
-        __m64 sum_hi = kZeros;
+        __m64 sumHi = Zeros;
-        const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
+        const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m64 row_j = row[j];
-          __m64 input_j = input_vector[j];
+          __m64 input_j = inputVector[j];
-          __m64 extended_row_lo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
+          __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
-          __m64 extended_row_hi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
+          __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
-          __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
+          __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
-          __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
+          __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
-          __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
+          __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
-          __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
+          __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
-          sum_lo = _mm_add_pi32(sum_lo, product_lo);
+          sumLo = _mm_add_pi32(sumLo, productLo);
-          sum_hi = _mm_add_pi32(sum_hi, product_hi);
+          sumHi = _mm_add_pi32(sumHi, productHi);
         }
-        __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
+        __m64 sum = _mm_add_pi32(sumLo, sumHi);
         sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
         output[i] = _mm_cvtsi64_si32(sum);
 
 #elif defined(USE_NEON)
-        int32x4_t sum = {biases_[i]};
+        int32x4_t sum = {biases[i]};
-        const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
+        const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
-          int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
+          int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
-          product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
+          product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
           sum = vpadalq_s16(sum, product);
         }
         output[i] = sum[0] + sum[1] + sum[2] + sum[3];
 
 #else
-        OutputType sum = biases_[i];
+        OutputType sum = biases[i];
-        for (IndexType j = 0; j < kInputDimensions; ++j) {
+        for (IndexType j = 0; j < InputDimensions; ++j) {
-          sum += weights_[offset + j] * input[j];
+          sum += weights[offset + j] * input[j];
         }
         output[i] = sum;
 #endif
@@ -436,20 +469,22 @@ namespace Stockfish::Eval::NNUE::Layers {
     using BiasType = OutputType;
     using WeightType = std::int8_t;
 
-    PreviousLayer previous_layer_;
+    PreviousLayer previousLayer;
 
-    alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
+    alignas(CacheLineSize) BiasType biases[OutputDimensions];
-    alignas(kCacheLineSize) WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+    alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
 #if defined (USE_SSSE3)
     struct CanSaturate {
         int count;
         struct Entry {
+            uint32_t wIdx;
             uint16_t out;
             uint16_t in;
             int8_t w;
-        } ids[kPaddedInputDimensions * kOutputDimensions * 3 / 4];
+        } ids[PaddedInputDimensions * OutputDimensions * 3 / 4];
 
-        void add(int i, int j, int8_t w) {
+        void add(int i, int j, int8_t w, uint32_t wIdx) {
+            ids[count].wIdx = wIdx;
             ids[count].out = i;
             ids[count].in = j;
             ids[count].w = w;

src/nnue/layers/clipped_relu.h

@@ -35,130 +35,135 @@ namespace Stockfish::Eval::NNUE::Layers {
     static_assert(std::is_same<InputType, std::int32_t>::value, "");
 
     // Number of input/output dimensions
-    static constexpr IndexType kInputDimensions =
+    static constexpr IndexType InputDimensions =
-        PreviousLayer::kOutputDimensions;
+        PreviousLayer::OutputDimensions;
-    static constexpr IndexType kOutputDimensions = kInputDimensions;
+    static constexpr IndexType OutputDimensions = InputDimensions;
 
     // Size of forward propagation buffer used in this layer
-    static constexpr std::size_t kSelfBufferSize =
+    static constexpr std::size_t SelfBufferSize =
-        CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
+        ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
 
     // Size of the forward propagation buffer used from the input layer to this layer
-    static constexpr std::size_t kBufferSize =
+    static constexpr std::size_t BufferSize =
-        PreviousLayer::kBufferSize + kSelfBufferSize;
+        PreviousLayer::BufferSize + SelfBufferSize;
 
     // Hash value embedded in the evaluation file
-    static constexpr std::uint32_t GetHashValue() {
+    static constexpr std::uint32_t get_hash_value() {
-      std::uint32_t hash_value = 0x538D24C7u;
+      std::uint32_t hashValue = 0x538D24C7u;
-      hash_value += PreviousLayer::GetHashValue();
+      hashValue += PreviousLayer::get_hash_value();
-      return hash_value;
+      return hashValue;
     }
 
     // Read network parameters
-    bool ReadParameters(std::istream& stream) {
+    bool read_parameters(std::istream& stream) {
-      return previous_layer_.ReadParameters(stream);
+      return previousLayer.read_parameters(stream);
+    }
+
+    // Write network parameters
+    bool write_parameters(std::ostream& stream) const {
+      return previousLayer.write_parameters(stream);
     }
 
     // Forward propagation
-    const OutputType* Propagate(
+    const OutputType* propagate(
-        const TransformedFeatureType* transformed_features, char* buffer) const {
+        const TransformedFeatureType* transformedFeatures, char* buffer) const {
-      const auto input = previous_layer_.Propagate(
+      const auto input = previousLayer.propagate(
-          transformed_features, buffer + kSelfBufferSize);
+          transformedFeatures, buffer + SelfBufferSize);
       const auto output = reinterpret_cast<OutputType*>(buffer);
 
   #if defined(USE_AVX2)
-      constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = InputDimensions / SimdWidth;
-      const __m256i kZero = _mm256_setzero_si256();
+      const __m256i Zero = _mm256_setzero_si256();
-      const __m256i kOffsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
+      const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
       const auto in = reinterpret_cast<const __m256i*>(input);
       const auto out = reinterpret_cast<__m256i*>(output);
-      for (IndexType i = 0; i < kNumChunks; ++i) {
+      for (IndexType i = 0; i < NumChunks; ++i) {
         const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
             _mm256_load_si256(&in[i * 4 + 0]),
-            _mm256_load_si256(&in[i * 4 + 1])), kWeightScaleBits);
+            _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
         const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
             _mm256_load_si256(&in[i * 4 + 2]),
-            _mm256_load_si256(&in[i * 4 + 3])), kWeightScaleBits);
+            _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
         _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
-            _mm256_packs_epi16(words0, words1), kZero), kOffsets));
+            _mm256_packs_epi16(words0, words1), Zero), Offsets));
       }
-      constexpr IndexType kStart = kNumChunks * kSimdWidth;
+      constexpr IndexType Start = NumChunks * SimdWidth;
 
   #elif defined(USE_SSE2)
-      constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = InputDimensions / SimdWidth;
 
   #ifdef USE_SSE41
-      const __m128i kZero = _mm_setzero_si128();
+      const __m128i Zero = _mm_setzero_si128();
   #else
       const __m128i k0x80s = _mm_set1_epi8(-128);
   #endif
 
       const auto in = reinterpret_cast<const __m128i*>(input);
       const auto out = reinterpret_cast<__m128i*>(output);
-      for (IndexType i = 0; i < kNumChunks; ++i) {
+      for (IndexType i = 0; i < NumChunks; ++i) {
         const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
             _mm_load_si128(&in[i * 4 + 0]),
-            _mm_load_si128(&in[i * 4 + 1])), kWeightScaleBits);
+            _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
         const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
             _mm_load_si128(&in[i * 4 + 2]),
-            _mm_load_si128(&in[i * 4 + 3])), kWeightScaleBits);
+            _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
         const __m128i packedbytes = _mm_packs_epi16(words0, words1);
         _mm_store_si128(&out[i],
 
   #ifdef USE_SSE41
-          _mm_max_epi8(packedbytes, kZero)
+          _mm_max_epi8(packedbytes, Zero)
   #else
           _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
   #endif
 
         );
       }
-      constexpr IndexType kStart = kNumChunks * kSimdWidth;
+      constexpr IndexType Start = NumChunks * SimdWidth;
 
   #elif defined(USE_MMX)
-      constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = InputDimensions / SimdWidth;
       const __m64 k0x80s = _mm_set1_pi8(-128);
       const auto in = reinterpret_cast<const __m64*>(input);
       const auto out = reinterpret_cast<__m64*>(output);
-      for (IndexType i = 0; i < kNumChunks; ++i) {
+      for (IndexType i = 0; i < NumChunks; ++i) {
         const __m64 words0 = _mm_srai_pi16(
             _mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
-            kWeightScaleBits);
+            WeightScaleBits);
         const __m64 words1 = _mm_srai_pi16(
             _mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
-            kWeightScaleBits);
+            WeightScaleBits);
         const __m64 packedbytes = _mm_packs_pi16(words0, words1);
         out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
       }
       _mm_empty();
-      constexpr IndexType kStart = kNumChunks * kSimdWidth;
+      constexpr IndexType Start = NumChunks * SimdWidth;
 
   #elif defined(USE_NEON)
-      constexpr IndexType kNumChunks = kInputDimensions / (kSimdWidth / 2);
+      constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
-      const int8x8_t kZero = {0};
+      const int8x8_t Zero = {0};
       const auto in = reinterpret_cast<const int32x4_t*>(input);
       const auto out = reinterpret_cast<int8x8_t*>(output);
-      for (IndexType i = 0; i < kNumChunks; ++i) {
+      for (IndexType i = 0; i < NumChunks; ++i) {
         int16x8_t shifted;
         const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
-        pack[0] = vqshrn_n_s32(in[i * 2 + 0], kWeightScaleBits);
+        pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
-        pack[1] = vqshrn_n_s32(in[i * 2 + 1], kWeightScaleBits);
+        pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
-        out[i] = vmax_s8(vqmovn_s16(shifted), kZero);
+        out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
       }
-      constexpr IndexType kStart = kNumChunks * (kSimdWidth / 2);
+      constexpr IndexType Start = NumChunks * (SimdWidth / 2);
   #else
-      constexpr IndexType kStart = 0;
+      constexpr IndexType Start = 0;
   #endif
 
-      for (IndexType i = kStart; i < kInputDimensions; ++i) {
+      for (IndexType i = Start; i < InputDimensions; ++i) {
         output[i] = static_cast<OutputType>(
-            std::max(0, std::min(127, input[i] >> kWeightScaleBits)));
+            std::max(0, std::min(127, input[i] >> WeightScaleBits)));
       }
       return output;
     }
 
    private:
-    PreviousLayer previous_layer_;
+    PreviousLayer previousLayer;
   };
 
 }  // namespace Stockfish::Eval::NNUE::Layers

src/nnue/layers/input_slice.h

@@ -26,37 +26,42 @@
 namespace Stockfish::Eval::NNUE::Layers {
 
 // Input layer
-template <IndexType OutputDimensions, IndexType Offset = 0>
+template <IndexType OutDims, IndexType Offset = 0>
 class InputSlice {
  public:
   // Need to maintain alignment
-  static_assert(Offset % kMaxSimdWidth == 0, "");
+  static_assert(Offset % MaxSimdWidth == 0, "");
 
   // Output type
   using OutputType = TransformedFeatureType;
 
   // Output dimensionality
-  static constexpr IndexType kOutputDimensions = OutputDimensions;
+  static constexpr IndexType OutputDimensions = OutDims;
 
   // Size of forward propagation buffer used from the input layer to this layer
-  static constexpr std::size_t kBufferSize = 0;
+  static constexpr std::size_t BufferSize = 0;
 
   // Hash value embedded in the evaluation file
-  static constexpr std::uint32_t GetHashValue() {
+  static constexpr std::uint32_t get_hash_value() {
-    std::uint32_t hash_value = 0xEC42E90Du;
+    std::uint32_t hashValue = 0xEC42E90Du;
-    hash_value ^= kOutputDimensions ^ (Offset << 10);
+    hashValue ^= OutputDimensions ^ (Offset << 10);
-    return hash_value;
+    return hashValue;
   }
 
   // Read network parameters
-  bool ReadParameters(std::istream& /*stream*/) {
+  bool read_parameters(std::istream& /*stream*/) {
+    return true;
+  }
+
+  // Read network parameters
+  bool write_parameters(std::ostream& /*stream*/) const {
     return true;
   }
   // Forward propagation
-  const OutputType* Propagate(
+  const OutputType* propagate(
-      const TransformedFeatureType* transformed_features,
+      const TransformedFeatureType* transformedFeatures,
       char* /*buffer*/) const {
-    return transformed_features + Offset;
+    return transformedFeatures + Offset;
   }
 
  private:

src/nnue/nnue_accumulator.h

@@ -29,9 +29,9 @@ namespace Stockfish::Eval::NNUE {
   enum AccumulatorState { EMPTY, COMPUTED, INIT };
 
   // Class that holds the result of affine transformation of input features
-  struct alignas(kCacheLineSize) Accumulator {
+  struct alignas(CacheLineSize) Accumulator {
     std::int16_t
-        accumulation[2][kRefreshTriggers.size()][kTransformedFeatureDimensions];
+        accumulation[2][TransformedFeatureDimensions];
     AccumulatorState state[2];
   };
 

src/nnue/nnue_architecture.h

@@ -21,17 +21,37 @@
 #ifndef NNUE_ARCHITECTURE_H_INCLUDED
 #define NNUE_ARCHITECTURE_H_INCLUDED
 
-// Defines the network structure
+#include "nnue_common.h"
-#include "architectures/halfkp_256x2-32-32.h"
+
+#include "features/half_kp.h"
+
+#include "layers/input_slice.h"
+#include "layers/affine_transform.h"
+#include "layers/clipped_relu.h"
 
 namespace Stockfish::Eval::NNUE {
 
-  static_assert(kTransformedFeatureDimensions % kMaxSimdWidth == 0, "");
+  // Input features used in evaluation function
-  static_assert(Network::kOutputDimensions == 1, "");
+  using FeatureSet = Features::HalfKP;
-  static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
 
-  // Trigger for full calculation instead of difference calculation
+  // Number of input feature dimensions after conversion
-  constexpr auto kRefreshTriggers = RawFeatures::kRefreshTriggers;
+  constexpr IndexType TransformedFeatureDimensions = 256;
+
+  namespace Layers {
+
+    // Define network structure
+    using InputLayer = InputSlice<TransformedFeatureDimensions * 2>;
+    using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 32>>;
+    using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
+    using OutputLayer = AffineTransform<HiddenLayer2, 1>;
+
+  }  // namespace Layers
+
+  using Network = Layers::OutputLayer;
+
+  static_assert(TransformedFeatureDimensions % MaxSimdWidth == 0, "");
+  static_assert(Network::OutputDimensions == 1, "");
+  static_assert(std::is_same<Network::OutputType, std::int32_t>::value, "");
 
 }  // namespace Stockfish::Eval::NNUE
 

src/nnue/nnue_common.h

@@ -48,58 +48,30 @@
 namespace Stockfish::Eval::NNUE {
 
   // Version of the evaluation file
-  constexpr std::uint32_t kVersion = 0x7AF32F16u;
+  constexpr std::uint32_t Version = 0x7AF32F16u;
 
   // Constant used in evaluation value calculation
-  constexpr int FV_SCALE = 16;
+  constexpr int OutputScale = 16;
-  constexpr int kWeightScaleBits = 6;
+  constexpr int WeightScaleBits = 6;
 
   // Size of cache line (in bytes)
-  constexpr std::size_t kCacheLineSize = 64;
+  constexpr std::size_t CacheLineSize = 64;
 
   // SIMD width (in bytes)
   #if defined(USE_AVX2)
-  constexpr std::size_t kSimdWidth = 32;
+  constexpr std::size_t SimdWidth = 32;
 
   #elif defined(USE_SSE2)
-  constexpr std::size_t kSimdWidth = 16;
+  constexpr std::size_t SimdWidth = 16;
 
   #elif defined(USE_MMX)
-  constexpr std::size_t kSimdWidth = 8;
+  constexpr std::size_t SimdWidth = 8;
 
   #elif defined(USE_NEON)
-  constexpr std::size_t kSimdWidth = 16;
+  constexpr std::size_t SimdWidth = 16;
   #endif
 
-  constexpr std::size_t kMaxSimdWidth = 32;
+  constexpr std::size_t MaxSimdWidth = 32;
-
-  // unique number for each piece type on each square
-  enum {
-    PS_NONE     =  0,
-    PS_W_PAWN   =  1,
-    PS_B_PAWN   =  1 * SQUARE_NB + 1,
-    PS_W_KNIGHT =  2 * SQUARE_NB + 1,
-    PS_B_KNIGHT =  3 * SQUARE_NB + 1,
-    PS_W_BISHOP =  4 * SQUARE_NB + 1,
-    PS_B_BISHOP =  5 * SQUARE_NB + 1,
-    PS_W_ROOK   =  6 * SQUARE_NB + 1,
-    PS_B_ROOK   =  7 * SQUARE_NB + 1,
-    PS_W_QUEEN  =  8 * SQUARE_NB + 1,
-    PS_B_QUEEN  =  9 * SQUARE_NB + 1,
-    PS_W_KING   = 10 * SQUARE_NB + 1,
-    PS_END      = PS_W_KING, // pieces without kings (pawns included)
-    PS_B_KING   = 11 * SQUARE_NB + 1,
-    PS_END2     = 12 * SQUARE_NB + 1
-  };
-
-  constexpr uint32_t kpp_board_index[COLOR_NB][PIECE_NB] = {
-    // convention: W - us, B - them
-    // viewed from other side, W and B are reversed
-    { PS_NONE, PS_W_PAWN, PS_W_KNIGHT, PS_W_BISHOP, PS_W_ROOK, PS_W_QUEEN, PS_W_KING, PS_NONE,
-      PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_B_KING, PS_NONE },
-    { PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_B_KING, PS_NONE,
-      PS_NONE, PS_W_PAWN, PS_W_KNIGHT, PS_W_BISHOP, PS_W_ROOK, PS_W_QUEEN, PS_W_KING, PS_NONE }
-  };
 
   // Type of input feature after conversion
   using TransformedFeatureType = std::uint8_t;
@@ -107,7 +79,7 @@ namespace Stockfish::Eval::NNUE {
 
   // Round n up to be a multiple of base
   template <typename IntType>
-  constexpr IntType CeilToMultiple(IntType n, IntType base) {
+  constexpr IntType ceil_to_multiple(IntType n, IntType base) {
       return (n + base - 1) / base * base;
   }
 
@@ -129,6 +101,24 @@ namespace Stockfish::Eval::NNUE {
       return result;
   }
 
+  template <typename IntType>
+  inline void write_little_endian(std::ostream& stream, IntType value) {
+
+      std::uint8_t u[sizeof(IntType)];
+      typename std::make_unsigned<IntType>::type v = value;
+
+      std::size_t i = 0;
+      // if constexpr to silence the warning about shift by 8
+      if constexpr (sizeof(IntType) > 1) {
+        for (; i + 1 < sizeof(IntType); ++i) {
+            u[i] = v;
+            v >>= 8;
+        }
+      }
+      u[i] = v;
+
+      stream.write(reinterpret_cast<char*>(u), sizeof(IntType));
+  }
 }  // namespace Stockfish::Eval::NNUE
 
 #endif // #ifndef NNUE_COMMON_H_INCLUDED

src/nnue/nnue_feature_transformer.h

@@ -23,7 +23,8 @@
 
 #include "nnue_common.h"
 #include "nnue_architecture.h"
-#include "features/index_list.h"
+
+#include "../misc.h"
 
 #include <cstring> // std::memset()
 
@@ -40,7 +41,7 @@ namespace Stockfish::Eval::NNUE {
   #define vec_store(a,b) _mm512_store_si512(a,b)
   #define vec_add_16(a,b) _mm512_add_epi16(a,b)
   #define vec_sub_16(a,b) _mm512_sub_epi16(a,b)
-  static constexpr IndexType kNumRegs = 8; // only 8 are needed
+  static constexpr IndexType NumRegs = 8; // only 8 are needed
 
   #elif USE_AVX2
   typedef __m256i vec_t;
@@ -48,7 +49,7 @@ namespace Stockfish::Eval::NNUE {
   #define vec_store(a,b) _mm256_store_si256(a,b)
   #define vec_add_16(a,b) _mm256_add_epi16(a,b)
   #define vec_sub_16(a,b) _mm256_sub_epi16(a,b)
-  static constexpr IndexType kNumRegs = 16;
+  static constexpr IndexType NumRegs = 16;
 
   #elif USE_SSE2
   typedef __m128i vec_t;
@@ -56,7 +57,7 @@ namespace Stockfish::Eval::NNUE {
   #define vec_store(a,b) *(a)=(b)
   #define vec_add_16(a,b) _mm_add_epi16(a,b)
   #define vec_sub_16(a,b) _mm_sub_epi16(a,b)
-  static constexpr IndexType kNumRegs = Is64Bit ? 16 : 8;
+  static constexpr IndexType NumRegs = Is64Bit ? 16 : 8;
 
   #elif USE_MMX
   typedef __m64 vec_t;
@@ -64,7 +65,7 @@ namespace Stockfish::Eval::NNUE {
   #define vec_store(a,b) *(a)=(b)
   #define vec_add_16(a,b) _mm_add_pi16(a,b)
   #define vec_sub_16(a,b) _mm_sub_pi16(a,b)
-  static constexpr IndexType kNumRegs = 8;
+  static constexpr IndexType NumRegs = 8;
 
   #elif USE_NEON
   typedef int16x8_t vec_t;
@@ -72,7 +73,7 @@ namespace Stockfish::Eval::NNUE {
   #define vec_store(a,b) *(a)=(b)
   #define vec_add_16(a,b) vaddq_s16(a,b)
   #define vec_sub_16(a,b) vsubq_s16(a,b)
-  static constexpr IndexType kNumRegs = 16;
+  static constexpr IndexType NumRegs = 16;
 
   #else
   #undef VECTOR
@@ -84,11 +85,11 @@ namespace Stockfish::Eval::NNUE {
 
    private:
     // Number of output dimensions for one side
-    static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
+    static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
 
     #ifdef VECTOR
-    static constexpr IndexType kTileHeight = kNumRegs * sizeof(vec_t) / 2;
+    static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
-    static_assert(kHalfDimensions % kTileHeight == 0, "kTileHeight must divide kHalfDimensions");
+    static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions");
     #endif
 
    public:
@@ -96,105 +97,111 @@ namespace Stockfish::Eval::NNUE {
     using OutputType = TransformedFeatureType;
 
     // Number of input/output dimensions
-    static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
+    static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
-    static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;
+    static constexpr IndexType OutputDimensions = HalfDimensions * 2;
 
     // Size of forward propagation buffer
-    static constexpr std::size_t kBufferSize =
+    static constexpr std::size_t BufferSize =
-        kOutputDimensions * sizeof(OutputType);
+        OutputDimensions * sizeof(OutputType);
 
     // Hash value embedded in the evaluation file
-    static constexpr std::uint32_t GetHashValue() {
+    static constexpr std::uint32_t get_hash_value() {
-
+      return FeatureSet::HashValue ^ OutputDimensions;
-      return RawFeatures::kHashValue ^ kOutputDimensions;
     }
 
     // Read network parameters
-    bool ReadParameters(std::istream& stream) {
+    bool read_parameters(std::istream& stream) {
+      for (std::size_t i = 0; i < HalfDimensions; ++i)
+        biases[i] = read_little_endian<BiasType>(stream);
+      for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
+        weights[i] = read_little_endian<WeightType>(stream);
+      return !stream.fail();
+    }
 
-      for (std::size_t i = 0; i < kHalfDimensions; ++i)
+    // Write network parameters
-        biases_[i] = read_little_endian<BiasType>(stream);
+    bool write_parameters(std::ostream& stream) const {
-      for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i)
+      for (std::size_t i = 0; i < HalfDimensions; ++i)
-        weights_[i] = read_little_endian<WeightType>(stream);
+        write_little_endian<BiasType>(stream, biases[i]);
+      for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
+        write_little_endian<WeightType>(stream, weights[i]);
       return !stream.fail();
     }
 
     // Convert input features
-    void Transform(const Position& pos, OutputType* output) const {
+    void transform(const Position& pos, OutputType* output) const {
-
+      update_accumulator(pos, WHITE);
-      UpdateAccumulator(pos, WHITE);
+      update_accumulator(pos, BLACK);
-      UpdateAccumulator(pos, BLACK);
 
       const auto& accumulation = pos.state()->accumulator.accumulation;
 
   #if defined(USE_AVX512)
-      constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth * 2);
+      constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2);
-      static_assert(kHalfDimensions % (kSimdWidth * 2) == 0);
+      static_assert(HalfDimensions % (SimdWidth * 2) == 0);
-      const __m512i kControl = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
+      const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
-      const __m512i kZero = _mm512_setzero_si512();
+      const __m512i Zero = _mm512_setzero_si512();
 
   #elif defined(USE_AVX2)
-      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
-      constexpr int kControl = 0b11011000;
+      constexpr int Control = 0b11011000;
-      const __m256i kZero = _mm256_setzero_si256();
+      const __m256i Zero = _mm256_setzero_si256();
 
   #elif defined(USE_SSE2)
-      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
 
   #ifdef USE_SSE41
-      const __m128i kZero = _mm_setzero_si128();
+      const __m128i Zero = _mm_setzero_si128();
   #else
       const __m128i k0x80s = _mm_set1_epi8(-128);
   #endif
 
   #elif defined(USE_MMX)
-      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+      constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
       const __m64 k0x80s = _mm_set1_pi8(-128);
 
   #elif defined(USE_NEON)
-      constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+      constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2);
-      const int8x8_t kZero = {0};
+      const int8x8_t Zero = {0};
   #endif
 
       const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
       for (IndexType p = 0; p < 2; ++p) {
-        const IndexType offset = kHalfDimensions * p;
+        const IndexType offset = HalfDimensions * p;
 
   #if defined(USE_AVX512)
         auto out = reinterpret_cast<__m512i*>(&output[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m512i sum0 = _mm512_load_si512(
-              &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
+              &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]])[j * 2 + 0]);
           __m512i sum1 = _mm512_load_si512(
-              &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
+              &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]])[j * 2 + 1]);
-          _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(kControl,
+          _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control,
-              _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), kZero)));
+              _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero)));
         }
 
   #elif defined(USE_AVX2)
         auto out = reinterpret_cast<__m256i*>(&output[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m256i sum0 = _mm256_load_si256(
-              &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
+              &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]])[j * 2 + 0]);
           __m256i sum1 = _mm256_load_si256(
-              &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
+              &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]])[j * 2 + 1]);
           _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
-              _mm256_packs_epi16(sum0, sum1), kZero), kControl));
+              _mm256_packs_epi16(sum0, sum1), Zero), Control));
         }
 
   #elif defined(USE_SSE2)
         auto out = reinterpret_cast<__m128i*>(&output[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
-              accumulation[perspectives[p]][0])[j * 2 + 0]);
+              accumulation[perspectives[p]])[j * 2 + 0]);
           __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
-              accumulation[perspectives[p]][0])[j * 2 + 1]);
+              accumulation[perspectives[p]])[j * 2 + 1]);
       const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
 
           _mm_store_si128(&out[j],
 
   #ifdef USE_SSE41
-              _mm_max_epi8(packedbytes, kZero)
+              _mm_max_epi8(packedbytes, Zero)
   #else
               _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
   #endif
@@ -204,26 +211,26 @@ namespace Stockfish::Eval::NNUE {
 
   #elif defined(USE_MMX)
         auto out = reinterpret_cast<__m64*>(&output[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           __m64 sum0 = *(&reinterpret_cast<const __m64*>(
-              accumulation[perspectives[p]][0])[j * 2 + 0]);
+              accumulation[perspectives[p]])[j * 2 + 0]);
           __m64 sum1 = *(&reinterpret_cast<const __m64*>(
-              accumulation[perspectives[p]][0])[j * 2 + 1]);
+              accumulation[perspectives[p]])[j * 2 + 1]);
           const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
           out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
         }
 
   #elif defined(USE_NEON)
         const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
-        for (IndexType j = 0; j < kNumChunks; ++j) {
+        for (IndexType j = 0; j < NumChunks; ++j) {
           int16x8_t sum = reinterpret_cast<const int16x8_t*>(
-              accumulation[perspectives[p]][0])[j];
+              accumulation[perspectives[p]])[j];
-          out[j] = vmax_s8(vqmovn_s16(sum), kZero);
+          out[j] = vmax_s8(vqmovn_s16(sum), Zero);
         }
 
   #else
-        for (IndexType j = 0; j < kHalfDimensions; ++j) {
+        for (IndexType j = 0; j < HalfDimensions; ++j) {
-          BiasType sum = accumulation[static_cast<int>(perspectives[p])][0][j];
+          BiasType sum = accumulation[static_cast<int>(perspectives[p])][j];
           output[offset + j] = static_cast<OutputType>(
               std::max<int>(0, std::min<int>(127, sum)));
         }
@@ -236,34 +243,36 @@ namespace Stockfish::Eval::NNUE {
     }
 
    private:
-    void UpdateAccumulator(const Position& pos, const Color c) const {
+    void update_accumulator(const Position& pos, const Color perspective) const {
+
+      // The size must be enough to contain the largest possible update.
+      // 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.
+      using IndexList = ValueList<IndexType, FeatureSet::MaxActiveDimensions>;
 
   #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[kNumRegs];
+      vec_t acc[NumRegs];
   #endif
 
       // 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 = pos.count<ALL_PIECES>() - 2;
+      int gain = FeatureSet::refresh_cost(pos);
-      while (st->accumulator.state[c] == EMPTY)
+      while (st->accumulator.state[perspective] == EMPTY)
       {
-        auto& dp = st->dirtyPiece;
+        // This governs when a full feature refresh is needed and how many
-        // The first condition tests whether an incremental update is
+        // updates are better than just one full refresh.
-        // possible at all: if this side's king has moved, it is not possible.
+        if (   FeatureSet::requires_refresh(st, perspective)
-        static_assert(std::is_same_v<RawFeatures::SortedTriggerSet,
+            || (gain -= FeatureSet::update_cost(st) + 1) < 0)
-              Features::CompileTimeList<Features::TriggerEvent, Features::TriggerEvent::kFriendKingMoved>>,
-              "Current code assumes that only kFriendlyKingMoved refresh trigger is being used.");
-        if (   dp.piece[0] == make_piece(c, KING)
-            || (gain -= dp.dirty_num + 1) < 0)
           break;
         next = st;
         st = st->previous;
       }
 
-      if (st->accumulator.state[c] == COMPUTED)
+      if (st->accumulator.state[perspective] == COMPUTED)
       {
         if (next == nullptr)
           return;
@@ -271,85 +280,83 @@ namespace Stockfish::Eval::NNUE {
         // Update incrementally in two steps. First, we update the "next"
         // accumulator. Then, we update the current accumulator (pos.state()).
 
-        // Gather all features to be updated. This code assumes HalfKP features
+        // Gather all features to be updated.
-        // only and doesn't support refresh triggers.
+        const Square ksq = pos.square<KING>(perspective);
-        static_assert(std::is_same_v<Features::FeatureSet<Features::HalfKP<Features::Side::kFriend>>,
+        IndexList removed[2], added[2];
-                                     RawFeatures>);
+        FeatureSet::append_changed_indices(
-        Features::IndexList removed[2], added[2];
+          ksq, next, perspective, removed[0], added[0]);
-        Features::HalfKP<Features::Side::kFriend>::AppendChangedIndices(pos,
-            next->dirtyPiece, c, &removed[0], &added[0]);
         for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous)
-          Features::HalfKP<Features::Side::kFriend>::AppendChangedIndices(pos,
+          FeatureSet::append_changed_indices(
-              st2->dirtyPiece, c, &removed[1], &added[1]);
+            ksq, st2, perspective, removed[1], added[1]);
 
         // Mark the accumulators as computed.
-        next->accumulator.state[c] = COMPUTED;
+        next->accumulator.state[perspective] = COMPUTED;
-        pos.state()->accumulator.state[c] = COMPUTED;
+        pos.state()->accumulator.state[perspective] = COMPUTED;
 
-        // Now update the accumulators listed in info[], where the last element is a sentinel.
+        // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
-        StateInfo *info[3] =
+        StateInfo *states_to_update[3] =
           { next, next == pos.state() ? nullptr : pos.state(), nullptr };
   #ifdef VECTOR
-        for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j)
+        for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
         {
           // Load accumulator
           auto accTile = reinterpret_cast<vec_t*>(
-            &st->accumulator.accumulation[c][0][j * kTileHeight]);
+            &st->accumulator.accumulation[perspective][j * TileHeight]);
-          for (IndexType k = 0; k < kNumRegs; ++k)
+          for (IndexType k = 0; k < NumRegs; ++k)
             acc[k] = vec_load(&accTile[k]);
 
-          for (IndexType i = 0; info[i]; ++i)
+          for (IndexType i = 0; states_to_update[i]; ++i)
           {
             // Difference calculation for the deactivated features
             for (const auto index : removed[i])
             {
-              const IndexType offset = kHalfDimensions * index + j * kTileHeight;
+              const IndexType offset = HalfDimensions * index + j * TileHeight;
-              auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
+              auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
-              for (IndexType k = 0; k < kNumRegs; ++k)
+              for (IndexType k = 0; k < NumRegs; ++k)
                 acc[k] = vec_sub_16(acc[k], column[k]);
             }
 
             // Difference calculation for the activated features
             for (const auto index : added[i])
             {
-              const IndexType offset = kHalfDimensions * index + j * kTileHeight;
+              const IndexType offset = HalfDimensions * index + j * TileHeight;
-              auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
+              auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
-              for (IndexType k = 0; k < kNumRegs; ++k)
+              for (IndexType k = 0; k < NumRegs; ++k)
                 acc[k] = vec_add_16(acc[k], column[k]);
             }
 
             // Store accumulator
             accTile = reinterpret_cast<vec_t*>(
-              &info[i]->accumulator.accumulation[c][0][j * kTileHeight]);
+              &states_to_update[i]->accumulator.accumulation[perspective][j * TileHeight]);
-            for (IndexType k = 0; k < kNumRegs; ++k)
+            for (IndexType k = 0; k < NumRegs; ++k)
               vec_store(&accTile[k], acc[k]);
           }
         }
 
   #else
-        for (IndexType i = 0; info[i]; ++i)
+        for (IndexType i = 0; states_to_update[i]; ++i)
         {
-          std::memcpy(info[i]->accumulator.accumulation[c][0],
+          std::memcpy(states_to_update[i]->accumulator.accumulation[perspective],
-              st->accumulator.accumulation[c][0],
+              st->accumulator.accumulation[perspective],
-              kHalfDimensions * sizeof(BiasType));
+              HalfDimensions * sizeof(BiasType));
-          st = info[i];
+          st = states_to_update[i];
 
           // Difference calculation for the deactivated features
           for (const auto index : removed[i])
           {
-            const IndexType offset = kHalfDimensions * index;
+            const IndexType offset = HalfDimensions * index;
 
-            for (IndexType j = 0; j < kHalfDimensions; ++j)
+            for (IndexType j = 0; j < HalfDimensions; ++j)
-              st->accumulator.accumulation[c][0][j] -= weights_[offset + j];
+              st->accumulator.accumulation[perspective][j] -= weights[offset + j];
           }
 
           // Difference calculation for the activated features
           for (const auto index : added[i])
           {
-            const IndexType offset = kHalfDimensions * index;
+            const IndexType offset = HalfDimensions * index;
 
-            for (IndexType j = 0; j < kHalfDimensions; ++j)
+            for (IndexType j = 0; j < HalfDimensions; ++j)
-              st->accumulator.accumulation[c][0][j] += weights_[offset + j];
+              st->accumulator.accumulation[perspective][j] += weights[offset + j];
           }
         }
   #endif
@@ -358,43 +365,43 @@ namespace Stockfish::Eval::NNUE {
       {
         // Refresh the accumulator
         auto& accumulator = pos.state()->accumulator;
-        accumulator.state[c] = COMPUTED;
+        accumulator.state[perspective] = COMPUTED;
-        Features::IndexList active;
+        IndexList active;
-        Features::HalfKP<Features::Side::kFriend>::AppendActiveIndices(pos, c, &active);
+        FeatureSet::append_active_indices(pos, perspective, active);
 
   #ifdef VECTOR
-        for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j)
+        for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
         {
           auto biasesTile = reinterpret_cast<const vec_t*>(
-              &biases_[j * kTileHeight]);
+              &biases[j * TileHeight]);
-          for (IndexType k = 0; k < kNumRegs; ++k)
+          for (IndexType k = 0; k < NumRegs; ++k)
             acc[k] = biasesTile[k];
 
           for (const auto index : active)
           {
-            const IndexType offset = kHalfDimensions * index + j * kTileHeight;
+            const IndexType offset = HalfDimensions * index + j * TileHeight;
-            auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
+            auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
 
-            for (unsigned k = 0; k < kNumRegs; ++k)
+            for (unsigned k = 0; k < NumRegs; ++k)
               acc[k] = vec_add_16(acc[k], column[k]);
           }
 
           auto accTile = reinterpret_cast<vec_t*>(
-              &accumulator.accumulation[c][0][j * kTileHeight]);
+              &accumulator.accumulation[perspective][j * TileHeight]);
-          for (unsigned k = 0; k < kNumRegs; k++)
+          for (unsigned k = 0; k < NumRegs; k++)
             vec_store(&accTile[k], acc[k]);
         }
 
   #else
-        std::memcpy(accumulator.accumulation[c][0], biases_,
+        std::memcpy(accumulator.accumulation[perspective], biases,
-            kHalfDimensions * sizeof(BiasType));
+            HalfDimensions * sizeof(BiasType));
 
         for (const auto index : active)
         {
-          const IndexType offset = kHalfDimensions * index;
+          const IndexType offset = HalfDimensions * index;
 
-          for (IndexType j = 0; j < kHalfDimensions; ++j)
+          for (IndexType j = 0; j < HalfDimensions; ++j)
-            accumulator.accumulation[c][0][j] += weights_[offset + j];
+            accumulator.accumulation[perspective][j] += weights[offset + j];
         }
   #endif
       }
@@ -407,9 +414,8 @@ namespace Stockfish::Eval::NNUE {
     using BiasType = std::int16_t;
     using WeightType = std::int16_t;
 
-    alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
+    alignas(CacheLineSize) BiasType biases[HalfDimensions];
-    alignas(kCacheLineSize)
+    alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
-        WeightType weights_[kHalfDimensions * kInputDimensions];
   };
 
 }  // namespace Stockfish::Eval::NNUE

src/position.cpp

@@ -84,7 +84,7 @@ std::ostream& operator<<(std::ostream& os, const Position& pos) {
       && !pos.can_castle(ANY_CASTLING))
   {
       StateInfo st;
-      ASSERT_ALIGNED(&st, Eval::NNUE::kCacheLineSize);
+      ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);
 
       Position p;
       p.set(pos.fen(), pos.is_chess960(), &st, pos.this_thread());
@@ -1337,7 +1337,7 @@ bool Position::pos_is_ok() const {
               assert(0 && "pos_is_ok: Bitboards");
 
   StateInfo si = *st;
-  ASSERT_ALIGNED(&si, Eval::NNUE::kCacheLineSize);
+  ASSERT_ALIGNED(&si, Eval::NNUE::CacheLineSize);
 
   set_state(&si);
   if (std::memcmp(&si, st, sizeof(StateInfo)))

src/position.h

@@ -118,7 +118,6 @@ public:
   Bitboard blockers_for_king(Color c) const;
   Bitboard check_squares(PieceType pt) const;
   Bitboard pinners(Color c) const;
-  bool is_discovered_check_on_king(Color c, Move m) const;
 
   // Attacks to/from a given square
   Bitboard attackers_to(Square s) const;
@@ -328,10 +327,6 @@ inline Bitboard Position::check_squares(PieceType pt) const {
   return st->checkSquares[pt];
 }
 
-inline bool Position::is_discovered_check_on_king(Color c, Move m) const {
-  return st->blockersForKing[c] & from_sq(m);
-}
-
 inline bool Position::pawn_passed(Color c, Square s) const {
   return !(pieces(~c, PAWN) & passed_pawn_span(c, s));
 }

src/search.cpp

@@ -96,49 +96,6 @@ namespace {
     Move best = MOVE_NONE;
   };
 
-  // Breadcrumbs are used to mark nodes as being searched by a given thread
-  struct Breadcrumb {
-    std::atomic<Thread*> thread;
-    std::atomic<Key> key;
-  };
-  std::array<Breadcrumb, 1024> breadcrumbs;
-
-  // ThreadHolding structure keeps track of which thread left breadcrumbs at the given
-  // node for potential reductions. A free node will be marked upon entering the moves
-  // loop by the constructor, and unmarked upon leaving that loop by the destructor.
-  struct ThreadHolding {
-    explicit ThreadHolding(Thread* thisThread, Key posKey, int ply) {
-       location = ply < 8 ? &breadcrumbs[posKey & (breadcrumbs.size() - 1)] : nullptr;
-       otherThread = false;
-       owning = false;
-       if (location)
-       {
-          // See if another already marked this location, if not, mark it ourselves
-          Thread* tmp = (*location).thread.load(std::memory_order_relaxed);
-          if (tmp == nullptr)
-          {
-              (*location).thread.store(thisThread, std::memory_order_relaxed);
-              (*location).key.store(posKey, std::memory_order_relaxed);
-              owning = true;
-          }
-          else if (   tmp != thisThread
-                   && (*location).key.load(std::memory_order_relaxed) == posKey)
-              otherThread = true;
-       }
-    }
-
-    ~ThreadHolding() {
-       if (owning) // Free the marked location
-           (*location).thread.store(nullptr, std::memory_order_relaxed);
-    }
-
-    bool marked() { return otherThread; }
-
-    private:
-    Breadcrumb* location;
-    bool otherThread, owning;
-  };
-
   template <NodeType NT>
   Value search(Position& pos, Stack* ss, Value alpha, Value beta, Depth depth, bool cutNode);
 
@@ -159,7 +116,7 @@ namespace {
   uint64_t perft(Position& pos, Depth depth) {
 
     StateInfo st;
-    ASSERT_ALIGNED(&st, Eval::NNUE::kCacheLineSize);
+    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);
 
     uint64_t cnt, nodes = 0;
     const bool leaf = (depth == 2);
@@ -594,7 +551,7 @@ namespace {
 
     Move pv[MAX_PLY+1], capturesSearched[32], quietsSearched[64];
     StateInfo st;
-    ASSERT_ALIGNED(&st, Eval::NNUE::kCacheLineSize);
+    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);
 
     TTEntry* tte;
     Key posKey;
@@ -1003,8 +960,12 @@ moves_loop: // When in check, search starts from here
     value = bestValue;
     singularQuietLMR = moveCountPruning = false;
 
-    // Mark this node as being searched
+    // Indicate PvNodes that will probably fail low if the node was searched
-    ThreadHolding th(thisThread, posKey, ss->ply);
+    // at a depth equal or greater than the current depth, and the result of this search was a fail low.
+    bool likelyFailLow =    PvNode
+                         && ttMove
+                         && (tte->bound() & BOUND_UPPER)
+                         && tte->depth() >= depth;
 
     // Step 12. Loop through all pseudo-legal moves until no moves remain
     // or a beta cutoff occurs.
@@ -1043,14 +1004,6 @@ moves_loop: // When in check, search starts from here
       movedPiece = pos.moved_piece(move);
       givesCheck = pos.gives_check(move);
 
-      // Indicate PvNodes that will probably fail low if node was searched with non-PV search
-      // at depth equal or greater to current depth and result of this search was far below alpha
-      bool likelyFailLow =    PvNode
-                           && ttMove
-                           && (tte->bound() & BOUND_UPPER)
-                           && ttValue < alpha + 200 + 100 * depth
-                           && tte->depth() >= depth;
-
       // Calculate new depth for this move
       newDepth = depth - 1;
 
@@ -1130,6 +1083,8 @@ moves_loop: // When in check, search starts from here
           {
               extension = 1;
               singularQuietLMR = !ttCapture;
+              if (!PvNode && value < singularBeta - 140)
+                  extension = 2;
           }
 
           // Multi-cut pruning
@@ -1153,11 +1108,6 @@ moves_loop: // When in check, search starts from here
           }
       }
 
-      // Check extension (~2 Elo)
-      else if (    givesCheck
-               && (pos.is_discovered_check_on_king(~us, move) || pos.see_ge(move)))
-          extension = 1;
-
       // Add extension to new depth
       newDepth += extension;
 
@@ -1185,7 +1135,8 @@ moves_loop: // When in check, search starts from here
               || ss->staticEval + PieceValue[EG][pos.captured_piece()] <= alpha
               || cutNode
               || (!PvNode && !formerPv && captureHistory[movedPiece][to_sq(move)][type_of(pos.captured_piece())] < 3678)
-              || thisThread->ttHitAverage < 432 * TtHitAverageResolution * TtHitAverageWindow / 1024))
+              || thisThread->ttHitAverage < 432 * TtHitAverageResolution * TtHitAverageWindow / 1024)
+          && (!PvNode || ss->ply > 1 || thisThread->id() % 4 != 3))
       {
           Depth r = reduction(improving, depth, moveCount);
 
@@ -1193,10 +1144,6 @@ moves_loop: // When in check, search starts from here
           if (thisThread->ttHitAverage > 537 * TtHitAverageResolution * TtHitAverageWindow / 1024)
               r--;
 
-          // Increase reduction if other threads are searching this position
-          if (th.marked())
-              r++;
-
           // Decrease reduction if position is or has been on the PV
           // and node is not likely to fail low. (~10 Elo)
           if (   ss->ttPv
@@ -1209,29 +1156,17 @@ moves_loop: // When in check, search starts from here
               && thisThread->bestMoveChanges <= 2)
               r++;
 
-          // More reductions for late moves if position was not in previous PV
+          // Decrease reduction if opponent's move count is high (~1 Elo)
-          if (   moveCountPruning
-              && !formerPv)
-              r++;
-
-          // Decrease reduction if opponent's move count is high (~5 Elo)
           if ((ss-1)->moveCount > 13)
               r--;
 
-          // Decrease reduction if ttMove has been singularly extended (~3 Elo)
+          // Decrease reduction if ttMove has been singularly extended (~1 Elo)
           if (singularQuietLMR)
               r--;
 
-          if (captureOrPromotion)
+          if (!captureOrPromotion)
           {
-              // Increase reduction for non-checking captures likely to be bad
+              // Increase reduction if ttMove is a capture (~3 Elo)
-              if (   !givesCheck
-                  && ss->staticEval + PieceValue[EG][pos.captured_piece()] + 210 * depth <= alpha)
-                  r++;
-          }
-          else
-          {
-              // Increase reduction if ttMove is a capture (~5 Elo)
               if (ttCapture)
                   r++;
 
@@ -1242,13 +1177,6 @@ moves_loop: // When in check, search starts from here
               if (cutNode)
                   r += 2;
 
-              // Decrease reduction for moves that escape a capture. Filter out
-              // castling moves, because they are coded as "king captures rook" and
-              // hence break reverse_move() (~2 Elo)
-              else if (    type_of(move) == NORMAL
-                       && !pos.see_ge(reverse_move(move)))
-                  r -= 2 + ss->ttPv - (type_of(movedPiece) == PAWN);
-
               ss->statScore =  thisThread->mainHistory[us][from_to(move)]
                              + (*contHist[0])[movedPiece][to_sq(move)]
                              + (*contHist[1])[movedPiece][to_sq(move)]
@@ -1458,7 +1386,7 @@ moves_loop: // When in check, search starts from here
 
     Move pv[MAX_PLY+1];
     StateInfo st;
-    ASSERT_ALIGNED(&st, Eval::NNUE::kCacheLineSize);
+    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);
 
     TTEntry* tte;
     Key posKey;
@@ -1964,7 +1892,7 @@ string UCI::pv(const Position& pos, Depth depth, Value alpha, Value beta) {
 bool RootMove::extract_ponder_from_tt(Position& pos) {
 
     StateInfo st;
-    ASSERT_ALIGNED(&st, Eval::NNUE::kCacheLineSize);
+    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);
 
     bool ttHit;
 

src/thread.h

@@ -74,6 +74,7 @@ public:
   void idle_loop();
   void start_searching();
   void wait_for_search_finished();
+  int id() const { return idx; }
   void wait_for_worker_finished();
   size_t thread_idx() const { return idx; }
 

src/timeman.cpp

@@ -94,6 +94,14 @@ void TimeManagement::init(Search::LimitsType& limits, Color us, int ply) {
   optimumTime = TimePoint(optScale * timeLeft);
   maximumTime = TimePoint(std::min(0.8 * limits.time[us] - moveOverhead, maxScale * optimumTime));
 
+  if (Stockfish::Search::Limits.use_time_management())
+  {
+      int strength = std::log( std::max(1, int(optimumTime * Threads.size() / 10))) * 60;
+      tempoNNUE = std::clamp( (strength + 264) / 24, 18, 30);
+  }
+  else
+      tempoNNUE = 28; // default for no time given
+
   if (Options["Ponder"])
       optimumTime += optimumTime / 4;
 }

src/timeman.h

@@ -37,6 +37,7 @@ public:
                                      TimePoint(Threads.nodes_searched()) : now() - startTime; }
 
   int64_t availableNodes; // When in 'nodes as time' mode
+  int tempoNNUE;
 
 private:
   TimePoint startTime;

src/uci.cpp

@@ -319,7 +319,14 @@ void UCI::loop(int argc, char* argv[]) {
       else if (token == "d")        sync_cout << pos << sync_endl;
       else if (token == "eval")     trace_eval(pos);
       else if (token == "compiler") sync_cout << compiler_info() << sync_endl;
-
+      else if (token == "export_net") {
+          std::optional<std::string> filename;
+          std::string f;
+          if (is >> skipws >> f) {
+            filename = f;
+          }
+          Eval::NNUE::export_net(filename);
+      }
       else if (token == "generate_training_data") Tools::generate_training_data(is);
       else if (token == "generate_training_data") Tools::generate_training_data_nonpv(is);
       else if (token == "convert") Tools::convert(is);