2 Stockfish, a UCI chess playing engine derived from Glaurung 2.1
3 Copyright (C) 2004-2020 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 // A class that converts the input features of the NNUE evaluation function
21 #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
22 #define NNUE_FEATURE_TRANSFORMER_H_INCLUDED
24 #include "nnue_common.h"
25 #include "nnue_architecture.h"
26 #include "features/index_list.h"
28 #include <cstring> // std::memset()
30 namespace Eval::NNUE {
32 // Input feature converter
33 class FeatureTransformer {
36 // Number of output dimensions for one side
37 static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
41 using OutputType = TransformedFeatureType;
43 // Number of input/output dimensions
44 static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
45 static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;
47 // Size of forward propagation buffer
48 static constexpr std::size_t kBufferSize =
49 kOutputDimensions * sizeof(OutputType);
51 // Hash value embedded in the evaluation file
52 static constexpr std::uint32_t GetHashValue() {
53 return RawFeatures::kHashValue ^ kOutputDimensions;
56 // Read network parameters
57 bool ReadParameters(std::istream& stream) {
58 stream.read(reinterpret_cast<char*>(biases_),
59 kHalfDimensions * sizeof(BiasType));
60 stream.read(reinterpret_cast<char*>(weights_),
61 kHalfDimensions * kInputDimensions * sizeof(WeightType));
62 return !stream.fail();
65 // Proceed with the difference calculation if possible
66 bool UpdateAccumulatorIfPossible(const Position& pos) const {
67 const auto now = pos.state();
68 if (now->accumulator.computed_accumulation) {
71 const auto prev = now->previous;
72 if (prev && prev->accumulator.computed_accumulation) {
73 UpdateAccumulator(pos);
79 // Convert input features
80 void Transform(const Position& pos, OutputType* output, bool refresh) const {
81 if (refresh || !UpdateAccumulatorIfPossible(pos)) {
82 RefreshAccumulator(pos);
84 const auto& accumulation = pos.state()->accumulator.accumulation;
87 constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
88 constexpr int kControl = 0b11011000;
89 const __m256i kZero = _mm256_setzero_si256();
91 #elif defined(USE_SSSE3)
92 constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
95 const __m128i kZero = _mm_setzero_si128();
97 const __m128i k0x80s = _mm_set1_epi8(-128);
100 #elif defined(USE_NEON)
101 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
102 const int8x8_t kZero = {0};
105 const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
106 for (IndexType p = 0; p < 2; ++p) {
107 const IndexType offset = kHalfDimensions * p;
109 #if defined(USE_AVX2)
110 auto out = reinterpret_cast<__m256i*>(&output[offset]);
111 for (IndexType j = 0; j < kNumChunks; ++j) {
112 __m256i sum0 = _mm256_loadA_si256(
113 &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
114 __m256i sum1 = _mm256_loadA_si256(
115 &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
116 _mm256_storeA_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
117 _mm256_packs_epi16(sum0, sum1), kZero), kControl));
120 #elif defined(USE_SSSE3)
121 auto out = reinterpret_cast<__m128i*>(&output[offset]);
122 for (IndexType j = 0; j < kNumChunks; ++j) {
123 __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
124 accumulation[perspectives[p]][0])[j * 2 + 0]);
125 __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
126 accumulation[perspectives[p]][0])[j * 2 + 1]);
127 const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
129 _mm_store_si128(&out[j],
132 _mm_max_epi8(packedbytes, kZero)
134 _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
140 #elif defined(USE_NEON)
141 const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
142 for (IndexType j = 0; j < kNumChunks; ++j) {
143 int16x8_t sum = reinterpret_cast<const int16x8_t*>(
144 accumulation[perspectives[p]][0])[j];
145 out[j] = vmax_s8(vqmovn_s16(sum), kZero);
149 for (IndexType j = 0; j < kHalfDimensions; ++j) {
150 BiasType sum = accumulation[static_cast<int>(perspectives[p])][0][j];
151 output[offset + j] = static_cast<OutputType>(
152 std::max<int>(0, std::min<int>(127, sum)));
160 // Calculate cumulative value without using difference calculation
161 void RefreshAccumulator(const Position& pos) const {
162 auto& accumulator = pos.state()->accumulator;
164 Features::IndexList active_indices[2];
165 RawFeatures::AppendActiveIndices(pos, kRefreshTriggers[i],
167 for (Color perspective : { WHITE, BLACK }) {
168 std::memcpy(accumulator.accumulation[perspective][i], biases_,
169 kHalfDimensions * sizeof(BiasType));
170 for (const auto index : active_indices[perspective]) {
171 const IndexType offset = kHalfDimensions * index;
173 #if defined(USE_AVX2)
174 auto accumulation = reinterpret_cast<__m256i*>(
175 &accumulator.accumulation[perspective][i][0]);
176 auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
177 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
178 for (IndexType j = 0; j < kNumChunks; ++j) {
179 _mm256_storeA_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadA_si256(&accumulation[j]), column[j]));
182 #elif defined(USE_SSE2)
183 auto accumulation = reinterpret_cast<__m128i*>(
184 &accumulator.accumulation[perspective][i][0]);
185 auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
186 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
187 for (IndexType j = 0; j < kNumChunks; ++j) {
188 accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
191 #elif defined(USE_NEON)
192 auto accumulation = reinterpret_cast<int16x8_t*>(
193 &accumulator.accumulation[perspective][i][0]);
194 auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
195 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
196 for (IndexType j = 0; j < kNumChunks; ++j) {
197 accumulation[j] = vaddq_s16(accumulation[j], column[j]);
201 for (IndexType j = 0; j < kHalfDimensions; ++j) {
202 accumulator.accumulation[perspective][i][j] += weights_[offset + j];
209 accumulator.computed_accumulation = true;
210 accumulator.computed_score = false;
213 // Calculate cumulative value using difference calculation
214 void UpdateAccumulator(const Position& pos) const {
215 const auto prev_accumulator = pos.state()->previous->accumulator;
216 auto& accumulator = pos.state()->accumulator;
218 Features::IndexList removed_indices[2], added_indices[2];
220 RawFeatures::AppendChangedIndices(pos, kRefreshTriggers[i],
221 removed_indices, added_indices, reset);
222 for (Color perspective : { WHITE, BLACK }) {
224 #if defined(USE_AVX2)
225 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
226 auto accumulation = reinterpret_cast<__m256i*>(
227 &accumulator.accumulation[perspective][i][0]);
229 #elif defined(USE_SSE2)
230 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
231 auto accumulation = reinterpret_cast<__m128i*>(
232 &accumulator.accumulation[perspective][i][0]);
234 #elif defined(USE_NEON)
235 constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
236 auto accumulation = reinterpret_cast<int16x8_t*>(
237 &accumulator.accumulation[perspective][i][0]);
240 if (reset[perspective]) {
241 std::memcpy(accumulator.accumulation[perspective][i], biases_,
242 kHalfDimensions * sizeof(BiasType));
244 std::memcpy(accumulator.accumulation[perspective][i],
245 prev_accumulator.accumulation[perspective][i],
246 kHalfDimensions * sizeof(BiasType));
247 // Difference calculation for the deactivated features
248 for (const auto index : removed_indices[perspective]) {
249 const IndexType offset = kHalfDimensions * index;
251 #if defined(USE_AVX2)
252 auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
253 for (IndexType j = 0; j < kNumChunks; ++j) {
254 accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
257 #elif defined(USE_SSE2)
258 auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
259 for (IndexType j = 0; j < kNumChunks; ++j) {
260 accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
263 #elif defined(USE_NEON)
264 auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
265 for (IndexType j = 0; j < kNumChunks; ++j) {
266 accumulation[j] = vsubq_s16(accumulation[j], column[j]);
270 for (IndexType j = 0; j < kHalfDimensions; ++j) {
271 accumulator.accumulation[perspective][i][j] -=
272 weights_[offset + j];
278 { // Difference calculation for the activated features
279 for (const auto index : added_indices[perspective]) {
280 const IndexType offset = kHalfDimensions * index;
282 #if defined(USE_AVX2)
283 auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
284 for (IndexType j = 0; j < kNumChunks; ++j) {
285 accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
288 #elif defined(USE_SSE2)
289 auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
290 for (IndexType j = 0; j < kNumChunks; ++j) {
291 accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
294 #elif defined(USE_NEON)
295 auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
296 for (IndexType j = 0; j < kNumChunks; ++j) {
297 accumulation[j] = vaddq_s16(accumulation[j], column[j]);
301 for (IndexType j = 0; j < kHalfDimensions; ++j) {
302 accumulator.accumulation[perspective][i][j] +=
303 weights_[offset + j];
311 accumulator.computed_accumulation = true;
312 accumulator.computed_score = false;
315 using BiasType = std::int16_t;
316 using WeightType = std::int16_t;
318 alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
319 alignas(kCacheLineSize)
320 WeightType weights_[kHalfDimensions * kInputDimensions];
323 } // namespace Eval::NNUE
325 #endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED