Cleanup comments

Tests used to derive some Elo worth comments:
https://tests.stockfishchess.org/tests/view/656a7f4e136acbc573555a31
https://tests.stockfishchess.org/tests/view/6585fb455457644dc984620f

closes https://github.com/official-stockfish/Stockfish/pull/4945

No functional change

src/nnue/features/half_ka_v2_hm.h

@@ -34,11 +34,11 @@ class Position;
 
 namespace Stockfish::Eval::NNUE::Features {
 
-// Feature HalfKAv2_hm: Combination of the position of own king
-// and the position of pieces. Position mirrored such that king always on e..h files.
+// Feature HalfKAv2_hm: Combination of the position of own king and the
+// position of pieces. Position mirrored such that king is always on e..h files.
 class HalfKAv2_hm {
 
-    // unique number for each piece type on each square
+    // Unique number for each piece type on each square
     enum {
         PS_NONE     = 0,
         PS_W_PAWN   = 0,
@@ -56,8 +56,8 @@ class HalfKAv2_hm {
     };
 
     static constexpr IndexType PieceSquareIndex[COLOR_NB][PIECE_NB] = {
-      // convention: W - us, B - them
-      // viewed from other side, W and B are reversed
+      // 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_KING, PS_NONE,
        PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_KING, PS_NONE},
       {PS_NONE, PS_B_PAWN, PS_B_KNIGHT, PS_B_BISHOP, PS_B_ROOK, PS_B_QUEEN, PS_KING, PS_NONE,
@@ -140,8 +140,8 @@ class HalfKAv2_hm {
     static int update_cost(const 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.
+    // Returns whether the change stored in this StateInfo means
+    // that a full accumulator refresh is required.
     static bool requires_refresh(const StateInfo* st, Color perspective);
 };
 

src/nnue/layers/affine_transform_sparse_input.h

@@ -235,7 +235,7 @@ class AffineTransformSparseInput {
 
         const auto input32 = reinterpret_cast<const std::int32_t*>(input);
 
-        // Find indices of nonzero 32bit blocks
+        // Find indices of nonzero 32-bit blocks
         find_nnz<NumChunks>(input32, nnz, count);
 
         const outvec_t* biasvec = reinterpret_cast<const outvec_t*>(biases);

src/nnue/layers/sqr_clipped_relu.h

@@ -91,7 +91,7 @@ class SqrClippedReLU {
         for (IndexType i = Start; i < InputDimensions; ++i)
         {
             output[i] = static_cast<OutputType>(
-              // Really should be /127 but we need to make it fast so we right shift
+              // Really should be /127 but we need to make it fast so we right-shift
               // by an extra 7 bits instead. Needs to be accounted for in the trainer.
               std::min(127ll, ((long long) (input[i]) * input[i]) >> (2 * WeightScaleBits + 7)));
         }

src/nnue/nnue_common.h

@@ -112,7 +112,7 @@ inline IntType read_little_endian(std::istream& stream) {
 
 // Utility to write an integer (signed or unsigned, any size)
 // to a stream in little-endian order. We swap the byte order before the write if
-// necessary to always write in little endian order, independently of the byte
+// necessary to always write in little-endian order, independently of the byte
 // ordering of the compiling machine.
 template<typename IntType>
 inline void write_little_endian(std::ostream& stream, IntType value) {
@@ -141,8 +141,8 @@ inline void write_little_endian(std::ostream& stream, IntType value) {
 }
 
 
-// Read integers in bulk from a little indian stream.
-// This reads N integers from stream s and put them in array out.
+// Read integers in bulk from a little-endian stream.
+// This reads N integers from stream s and puts them in array out.
 template<typename IntType>
 inline void read_little_endian(std::istream& stream, IntType* out, std::size_t count) {
     if (IsLittleEndian)
@@ -153,7 +153,7 @@ inline void read_little_endian(std::istream& stream, IntType* out, std::size_t c
 }
 
 
-// Write integers in bulk to a little indian stream.
+// Write integers in bulk to a little-endian stream.
 // This takes N integers from array values and writes them on stream s.
 template<typename IntType>
 inline void write_little_endian(std::ostream& stream, const IntType* values, std::size_t count) {
@@ -165,8 +165,8 @@ inline void write_little_endian(std::ostream& stream, const IntType* values, std
 }
 
 
-// Read N signed integers from the stream s, putting them in
-// the array out. The stream is assumed to be compressed using the signed LEB128 format.
+// Read N signed integers from the stream s, putting them in the array out.
+// The stream is assumed to be compressed using the signed LEB128 format.
 // See https://en.wikipedia.org/wiki/LEB128 for a description of the compression scheme.
 template<typename IntType>
 inline void read_leb_128(std::istream& stream, IntType* out, std::size_t count) {
@@ -216,8 +216,8 @@ inline void read_leb_128(std::istream& stream, IntType* out, std::size_t count)
 
 
 // Write signed integers to a stream with LEB128 compression.
-// This takes N integers from array values, compress them with the LEB128 algorithm and
-// writes the result on the stream s.
+// This takes N integers from array values, compresses them with
+// the LEB128 algorithm and writes the result on the stream s.
 // See https://en.wikipedia.org/wiki/LEB128 for a description of the compression scheme.
 template<typename IntType>
 inline void write_leb_128(std::ostream& stream, const IntType* values, std::size_t count) {

src/nnue/nnue_feature_transformer.h

@@ -366,14 +366,14 @@ class FeatureTransformer {
 
         // 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.
+        // feature set's update cost calculation to be correct and never allow
+        // updates with more added/removed features than MaxActiveDimensions.
         FeatureSet::IndexList removed[N - 1], added[N - 1];
 
         {
             int i =
               N
-              - 2;  // last potential state to update. Skip last element because it must be nullptr.
+              - 2;  // Last potential state to update. Skip last element because it must be nullptr.
             while (states_to_update[i] == nullptr)
                 --i;