+/*
+ Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+ Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+
+ Stockfish is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ Stockfish is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>.
+*/
+
+// A class that converts the input features of the NNUE evaluation function
+
+#ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
+#define NNUE_FEATURE_TRANSFORMER_H_INCLUDED
+
+#include "nnue_common.h"
+#include "nnue_architecture.h"
+#include "features/index_list.h"
+
+#include <cstring> // std::memset()
+
+namespace Eval::NNUE {
+
+ // Input feature converter
+ class FeatureTransformer {
+
+ private:
+ // Number of output dimensions for one side
+ static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
+
+ public:
+ // Output type
+ using OutputType = TransformedFeatureType;
+
+ // Number of input/output dimensions
+ static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
+ static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;
+
+ // Size of forward propagation buffer
+ static constexpr std::size_t kBufferSize =
+ kOutputDimensions * sizeof(OutputType);
+
+ // Hash value embedded in the evaluation file
+ static constexpr std::uint32_t GetHashValue() {
+ return RawFeatures::kHashValue ^ kOutputDimensions;
+ }
+
+ // Read network parameters
+ bool ReadParameters(std::istream& stream) {
+ stream.read(reinterpret_cast<char*>(biases_),
+ kHalfDimensions * sizeof(BiasType));
+ stream.read(reinterpret_cast<char*>(weights_),
+ kHalfDimensions * kInputDimensions * sizeof(WeightType));
+ return !stream.fail();
+ }
+
+ // Proceed with the difference calculation if possible
+ bool UpdateAccumulatorIfPossible(const Position& pos) const {
+ const auto now = pos.state();
+ if (now->accumulator.computed_accumulation) {
+ return true;
+ }
+ const auto prev = now->previous;
+ if (prev && prev->accumulator.computed_accumulation) {
+ UpdateAccumulator(pos);
+ return true;
+ }
+ return false;
+ }
+
+ // Convert input features
+ void Transform(const Position& pos, OutputType* output, bool refresh) const {
+ if (refresh || !UpdateAccumulatorIfPossible(pos)) {
+ RefreshAccumulator(pos);
+ }
+ const auto& accumulation = pos.state()->accumulator.accumulation;
+
+ #if defined(USE_AVX2)
+ constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+ constexpr int kControl = 0b11011000;
+ const __m256i kZero = _mm256_setzero_si256();
+
+ #elif defined(USE_SSSE3)
+ constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+
+ #ifdef USE_SSE41
+ const __m128i kZero = _mm_setzero_si128();
+ #else
+ const __m128i k0x80s = _mm_set1_epi8(-128);
+ #endif
+
+ #elif defined(USE_NEON)
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ const int8x8_t kZero = {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;
+
+ #if defined(USE_AVX2)
+ auto out = reinterpret_cast<__m256i*>(&output[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ __m256i sum0 =
+
+ #if defined(__MINGW32__) || defined(__MINGW64__)
+ // HACK: Use _mm256_loadu_si256() instead of _mm256_load_si256. Because the binary
+ // compiled with g++ in MSYS2 crashes here because the output memory is not aligned
+ // even though alignas is specified.
+ _mm256_loadu_si256
+ #else
+ _mm256_load_si256
+ #endif
+
+ (&reinterpret_cast<const __m256i*>(
+ accumulation[perspectives[p]][0])[j * 2 + 0]);
+ __m256i sum1 =
+
+ #if defined(__MINGW32__) || defined(__MINGW64__)
+ _mm256_loadu_si256
+ #else
+ _mm256_load_si256
+ #endif
+
+ (&reinterpret_cast<const __m256i*>(
+ accumulation[perspectives[p]][0])[j * 2 + 1]);
+
+ #if defined(__MINGW32__) || defined(__MINGW64__)
+ _mm256_storeu_si256
+ #else
+ _mm256_store_si256
+ #endif
+
+ (&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
+ _mm256_packs_epi16(sum0, sum1), kZero), kControl));
+ }
+
+ #elif defined(USE_SSSE3)
+ auto out = reinterpret_cast<__m128i*>(&output[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
+ accumulation[perspectives[p]][0])[j * 2 + 0]);
+ __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
+ accumulation[perspectives[p]][0])[j * 2 + 1]);
+ const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
+
+ _mm_store_si128(&out[j],
+
+ #ifdef USE_SSE41
+ _mm_max_epi8(packedbytes, kZero)
+ #else
+ _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
+ #endif
+
+ );
+ }
+
+ #elif defined(USE_NEON)
+ const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ int16x8_t sum = reinterpret_cast<const int16x8_t*>(
+ accumulation[perspectives[p]][0])[j];
+ out[j] = vmax_s8(vqmovn_s16(sum), kZero);
+ }
+
+ #else
+ for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ BiasType sum = accumulation[static_cast<int>(perspectives[p])][0][j];
+ output[offset + j] = static_cast<OutputType>(
+ std::max<int>(0, std::min<int>(127, sum)));
+ }
+ #endif
+
+ }
+ }
+
+ private:
+ // Calculate cumulative value without using difference calculation
+ void RefreshAccumulator(const Position& pos) const {
+ auto& accumulator = pos.state()->accumulator;
+ IndexType i = 0;
+ Features::IndexList active_indices[2];
+ RawFeatures::AppendActiveIndices(pos, kRefreshTriggers[i],
+ active_indices);
+ for (Color perspective : { WHITE, BLACK }) {
+ std::memcpy(accumulator.accumulation[perspective][i], biases_,
+ kHalfDimensions * sizeof(BiasType));
+ for (const auto index : active_indices[perspective]) {
+ const IndexType offset = kHalfDimensions * index;
+
+ #if defined(USE_AVX2)
+ auto accumulation = reinterpret_cast<__m256i*>(
+ &accumulator.accumulation[perspective][i][0]);
+ auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ #if defined(__MINGW32__) || defined(__MINGW64__)
+ _mm256_storeu_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadu_si256(&accumulation[j]), column[j]));
+ #else
+ accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
+ #endif
+ }
+
+ #elif defined(USE_SSE2)
+ auto accumulation = reinterpret_cast<__m128i*>(
+ &accumulator.accumulation[perspective][i][0]);
+ auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
+ }
+
+ #elif defined(USE_NEON)
+ auto accumulation = reinterpret_cast<int16x8_t*>(
+ &accumulator.accumulation[perspective][i][0]);
+ auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = vaddq_s16(accumulation[j], column[j]);
+ }
+
+ #else
+ for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ accumulator.accumulation[perspective][i][j] += weights_[offset + j];
+ }
+ #endif
+
+ }
+ }
+
+ accumulator.computed_accumulation = true;
+ accumulator.computed_score = false;
+ }
+
+ // Calculate cumulative value using difference calculation
+ void UpdateAccumulator(const Position& pos) const {
+ const auto prev_accumulator = pos.state()->previous->accumulator;
+ auto& accumulator = pos.state()->accumulator;
+ IndexType i = 0;
+ Features::IndexList removed_indices[2], added_indices[2];
+ bool reset[2];
+ RawFeatures::AppendChangedIndices(pos, kRefreshTriggers[i],
+ removed_indices, added_indices, reset);
+ for (Color perspective : { WHITE, BLACK }) {
+
+ #if defined(USE_AVX2)
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ auto accumulation = reinterpret_cast<__m256i*>(
+ &accumulator.accumulation[perspective][i][0]);
+
+ #elif defined(USE_SSE2)
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ auto accumulation = reinterpret_cast<__m128i*>(
+ &accumulator.accumulation[perspective][i][0]);
+
+ #elif defined(USE_NEON)
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ auto accumulation = reinterpret_cast<int16x8_t*>(
+ &accumulator.accumulation[perspective][i][0]);
+ #endif
+
+ if (reset[perspective]) {
+ std::memcpy(accumulator.accumulation[perspective][i], biases_,
+ kHalfDimensions * sizeof(BiasType));
+ } else {
+ std::memcpy(accumulator.accumulation[perspective][i],
+ prev_accumulator.accumulation[perspective][i],
+ kHalfDimensions * sizeof(BiasType));
+ // Difference calculation for the deactivated features
+ for (const auto index : removed_indices[perspective]) {
+ const IndexType offset = kHalfDimensions * index;
+
+ #if defined(USE_AVX2)
+ auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
+ }
+
+ #elif defined(USE_SSE2)
+ auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
+ }
+
+ #elif defined(USE_NEON)
+ auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = vsubq_s16(accumulation[j], column[j]);
+ }
+
+ #else
+ for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ accumulator.accumulation[perspective][i][j] -=
+ weights_[offset + j];
+ }
+ #endif
+
+ }
+ }
+ { // Difference calculation for the activated features
+ for (const auto index : added_indices[perspective]) {
+ const IndexType offset = kHalfDimensions * index;
+
+ #if defined(USE_AVX2)
+ auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
+ }
+
+ #elif defined(USE_SSE2)
+ auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
+ }
+
+ #elif defined(USE_NEON)
+ auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j) {
+ accumulation[j] = vaddq_s16(accumulation[j], column[j]);
+ }
+
+ #else
+ for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ accumulator.accumulation[perspective][i][j] +=
+ weights_[offset + j];
+ }
+ #endif
+
+ }
+ }
+ }
+
+ accumulator.computed_accumulation = true;
+ accumulator.computed_score = false;
+ }
+
+ using BiasType = std::int16_t;
+ using WeightType = std::int16_t;
+
+ alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
+ alignas(kCacheLineSize)
+ WeightType weights_[kHalfDimensions * kInputDimensions];
+ };
+
+} // namespace Eval::NNUE
+
+#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED