2 Stockfish, a UCI chess playing engine derived from Glaurung 2.1
3 Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
5 Stockfish is free software: you can redistribute it and/or modify
6 it under the terms of the GNU General Public License as published by
7 the Free Software Foundation, either version 3 of the License, or
8 (at your option) any later version.
10 Stockfish is distributed in the hope that it will be useful,
11 but WITHOUT ANY WARRANTY; without even the implied warranty of
12 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
13 GNU General Public License for more details.
15 You should have received a copy of the GNU General Public License
16 along with this program. If not, see <http://www.gnu.org/licenses/>.
19 // Code for calculating NNUE evaluation function
27 #include "../evaluate.h"
28 #include "../position.h"
33 #include "evaluate_nnue.h"
35 namespace Stockfish::Eval::NNUE {
37 // Input feature converter
38 LargePagePtr<FeatureTransformer> featureTransformer;
40 // Evaluation function
41 AlignedPtr<Network> network[LayerStacks];
43 // Evaluation function file name
45 std::string netDescription;
49 // Initialize the evaluation function parameters
51 void initialize(AlignedPtr<T>& pointer) {
53 pointer.reset(reinterpret_cast<T*>(std_aligned_alloc(alignof(T), sizeof(T))));
54 std::memset(pointer.get(), 0, sizeof(T));
58 void initialize(LargePagePtr<T>& pointer) {
60 static_assert(alignof(T) <= 4096, "aligned_large_pages_alloc() may fail for such a big alignment requirement of T");
61 pointer.reset(reinterpret_cast<T*>(aligned_large_pages_alloc(sizeof(T))));
62 std::memset(pointer.get(), 0, sizeof(T));
65 // Read evaluation function parameters
67 bool read_parameters(std::istream& stream, T& reference) {
70 header = read_little_endian<std::uint32_t>(stream);
71 if (!stream || header != T::get_hash_value()) return false;
72 return reference.read_parameters(stream);
75 // Write evaluation function parameters
77 bool write_parameters(std::ostream& stream, const T& reference) {
79 write_little_endian<std::uint32_t>(stream, T::get_hash_value());
80 return reference.write_parameters(stream);
85 // Initialize the evaluation function parameters
88 Detail::initialize(featureTransformer);
89 for (std::size_t i = 0; i < LayerStacks; ++i)
90 Detail::initialize(network[i]);
93 // Read network header
94 bool read_header(std::istream& stream, std::uint32_t* hashValue, std::string* desc)
96 std::uint32_t version, size;
98 version = read_little_endian<std::uint32_t>(stream);
99 *hashValue = read_little_endian<std::uint32_t>(stream);
100 size = read_little_endian<std::uint32_t>(stream);
101 if (!stream || version != Version) return false;
103 stream.read(&(*desc)[0], size);
104 return !stream.fail();
107 // Write network header
108 bool write_header(std::ostream& stream, std::uint32_t hashValue, const std::string& desc)
110 write_little_endian<std::uint32_t>(stream, Version);
111 write_little_endian<std::uint32_t>(stream, hashValue);
112 write_little_endian<std::uint32_t>(stream, (std::uint32_t)desc.size());
113 stream.write(&desc[0], desc.size());
114 return !stream.fail();
117 // Read network parameters
118 bool read_parameters(std::istream& stream) {
120 std::uint32_t hashValue;
121 if (!read_header(stream, &hashValue, &netDescription)) return false;
122 if (hashValue != HashValue) return false;
123 if (!Detail::read_parameters(stream, *featureTransformer)) return false;
124 for (std::size_t i = 0; i < LayerStacks; ++i)
125 if (!Detail::read_parameters(stream, *(network[i]))) return false;
126 return stream && stream.peek() == std::ios::traits_type::eof();
129 // Write network parameters
130 bool write_parameters(std::ostream& stream) {
132 if (!write_header(stream, HashValue, netDescription)) return false;
133 if (!Detail::write_parameters(stream, *featureTransformer)) return false;
134 for (std::size_t i = 0; i < LayerStacks; ++i)
135 if (!Detail::write_parameters(stream, *(network[i]))) return false;
139 // Evaluation function. Perform differential calculation.
140 Value evaluate(const Position& pos, bool adjusted) {
142 // We manually align the arrays on the stack because with gcc < 9.3
143 // overaligning stack variables with alignas() doesn't work correctly.
145 constexpr uint64_t alignment = CacheLineSize;
146 int delta = 10 - pos.non_pawn_material() / 1515;
148 #if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
149 TransformedFeatureType transformedFeaturesUnaligned[
150 FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
152 auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
155 TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
158 ASSERT_ALIGNED(transformedFeatures, alignment);
160 const int bucket = (pos.count<ALL_PIECES>() - 1) / 4;
161 const auto psqt = featureTransformer->transform(pos, transformedFeatures, bucket);
162 const auto positional = network[bucket]->propagate(transformedFeatures);
164 // Give more value to positional evaluation when adjusted flag is set
166 return static_cast<Value>(((128 - delta) * psqt + (128 + delta) * positional) / 128 / OutputScale);
168 return static_cast<Value>((psqt + positional) / OutputScale);
171 struct NnueEvalTrace {
172 static_assert(LayerStacks == PSQTBuckets);
174 Value psqt[LayerStacks];
175 Value positional[LayerStacks];
176 std::size_t correctBucket;
179 static NnueEvalTrace trace_evaluate(const Position& pos) {
181 // We manually align the arrays on the stack because with gcc < 9.3
182 // overaligning stack variables with alignas() doesn't work correctly.
184 constexpr uint64_t alignment = CacheLineSize;
186 #if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
187 TransformedFeatureType transformedFeaturesUnaligned[
188 FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
190 auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
193 TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
196 ASSERT_ALIGNED(transformedFeatures, alignment);
199 t.correctBucket = (pos.count<ALL_PIECES>() - 1) / 4;
200 for (IndexType bucket = 0; bucket < LayerStacks; ++bucket) {
201 const auto materialist = featureTransformer->transform(pos, transformedFeatures, bucket);
202 const auto positional = network[bucket]->propagate(transformedFeatures);
204 t.psqt[bucket] = static_cast<Value>( materialist / OutputScale );
205 t.positional[bucket] = static_cast<Value>( positional / OutputScale );
211 static const std::string PieceToChar(" PNBRQK pnbrqk");
214 // format_cp_compact() converts a Value into (centi)pawns and writes it in a buffer.
215 // The buffer must have capacity for at least 5 chars.
216 static void format_cp_compact(Value v, char* buffer) {
218 buffer[0] = (v < 0 ? '-' : v > 0 ? '+' : ' ');
220 int cp = std::abs(100 * v / PawnValueEg);
223 buffer[1] = '0' + cp / 10000; cp %= 10000;
224 buffer[2] = '0' + cp / 1000; cp %= 1000;
225 buffer[3] = '0' + cp / 100;
230 buffer[1] = '0' + cp / 1000; cp %= 1000;
231 buffer[2] = '0' + cp / 100; cp %= 100;
233 buffer[4] = '0' + cp / 10;
237 buffer[1] = '0' + cp / 100; cp %= 100;
239 buffer[3] = '0' + cp / 10; cp %= 10;
240 buffer[4] = '0' + cp / 1;
245 // format_cp_aligned_dot() converts a Value into (centi)pawns and writes it in a buffer,
246 // always keeping two decimals. The buffer must have capacity for at least 7 chars.
247 static void format_cp_aligned_dot(Value v, char* buffer) {
249 buffer[0] = (v < 0 ? '-' : v > 0 ? '+' : ' ');
251 double cp = 1.0 * std::abs(int(v)) / PawnValueEg;
252 sprintf(&buffer[1], "%6.2f", cp);
256 // trace() returns a string with the value of each piece on a board,
257 // and a table for (PSQT, Layers) values bucket by bucket.
259 std::string trace(Position& pos) {
261 std::stringstream ss;
263 char board[3*8+1][8*8+2];
264 std::memset(board, ' ', sizeof(board));
265 for (int row = 0; row < 3*8+1; ++row)
266 board[row][8*8+1] = '\0';
268 // A lambda to output one box of the board
269 auto writeSquare = [&board](File file, Rank rank, Piece pc, Value value) {
271 const int x = ((int)file) * 8;
272 const int y = (7 - (int)rank) * 3;
273 for (int i = 1; i < 8; ++i)
274 board[y][x+i] = board[y+3][x+i] = '-';
275 for (int i = 1; i < 3; ++i)
276 board[y+i][x] = board[y+i][x+8] = '|';
277 board[y][x] = board[y][x+8] = board[y+3][x+8] = board[y+3][x] = '+';
279 board[y+1][x+4] = PieceToChar[pc];
280 if (value != VALUE_NONE)
281 format_cp_compact(value, &board[y+2][x+2]);
284 // We estimate the value of each piece by doing a differential evaluation from
285 // the current base eval, simulating the removal of the piece from its square.
286 Value base = evaluate(pos);
287 base = pos.side_to_move() == WHITE ? base : -base;
289 for (File f = FILE_A; f <= FILE_H; ++f)
290 for (Rank r = RANK_1; r <= RANK_8; ++r)
292 Square sq = make_square(f, r);
293 Piece pc = pos.piece_on(sq);
294 Value v = VALUE_NONE;
296 if (pc != NO_PIECE && type_of(pc) != KING)
298 auto st = pos.state();
300 pos.remove_piece(sq);
301 st->accumulator.computed[WHITE] = false;
302 st->accumulator.computed[BLACK] = false;
304 Value eval = evaluate(pos);
305 eval = pos.side_to_move() == WHITE ? eval : -eval;
308 pos.put_piece(pc, sq);
309 st->accumulator.computed[WHITE] = false;
310 st->accumulator.computed[BLACK] = false;
313 writeSquare(f, r, pc, v);
316 ss << " NNUE derived piece values:\n";
317 for (int row = 0; row < 3*8+1; ++row)
318 ss << board[row] << '\n';
321 auto t = trace_evaluate(pos);
323 ss << " NNUE network contributions "
324 << (pos.side_to_move() == WHITE ? "(White to move)" : "(Black to move)") << std::endl
325 << "+------------+------------+------------+------------+\n"
326 << "| Bucket | Material | Positional | Total |\n"
327 << "| | (PSQT) | (Layers) | |\n"
328 << "+------------+------------+------------+------------+\n";
330 for (std::size_t bucket = 0; bucket < LayerStacks; ++bucket)
333 std::memset(buffer, '\0', sizeof(buffer));
335 format_cp_aligned_dot(t.psqt[bucket], buffer[0]);
336 format_cp_aligned_dot(t.positional[bucket], buffer[1]);
337 format_cp_aligned_dot(t.psqt[bucket] + t.positional[bucket], buffer[2]);
339 ss << "| " << bucket << " "
340 << " | " << buffer[0] << " "
341 << " | " << buffer[1] << " "
342 << " | " << buffer[2] << " "
344 if (bucket == t.correctBucket)
345 ss << " <-- this bucket is used";
349 ss << "+------------+------------+------------+------------+\n";
355 // Load eval, from a file stream or a memory stream
356 bool load_eval(std::string name, std::istream& stream) {
360 return read_parameters(stream);
363 // Save eval, to a file stream or a memory stream
364 bool save_eval(std::ostream& stream) {
366 if (fileName.empty())
369 return write_parameters(stream);
372 /// Save eval, to a file given by its name
373 bool save_eval(const std::optional<std::string>& filename) {
375 std::string actualFilename;
378 if (filename.has_value())
379 actualFilename = filename.value();
382 if (currentEvalFileName != EvalFileDefaultName)
384 msg = "Failed to export a net. A non-embedded net can only be saved if the filename is specified";
386 sync_cout << msg << sync_endl;
389 actualFilename = EvalFileDefaultName;
392 std::ofstream stream(actualFilename, std::ios_base::binary);
393 bool saved = save_eval(stream);
395 msg = saved ? "Network saved successfully to " + actualFilename
396 : "Failed to export a net";
398 sync_cout << msg << sync_endl;
403 } // namespace Stockfish::Eval::NNUE