X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fnnue%2Fnnue_feature_transformer.h;h=a4a8e98f9c5e8f579cea140b77126f9763184421;hb=58054fd0fa6294510fc8cf76b0ba9673d5094c10;hp=cbcc26f3efae9f592eead48230d153c93ddd1301;hpb=875183b310a8249922c2155e82cb4cecfae2097e;p=stockfish diff --git a/src/nnue/nnue_feature_transformer.h b/src/nnue/nnue_feature_transformer.h index cbcc26f3..a4a8e98f 100644 --- a/src/nnue/nnue_feature_transformer.h +++ b/src/nnue/nnue_feature_transformer.h @@ -1,6 +1,6 @@ /* Stockfish, a UCI chess playing engine derived from Glaurung 2.1 - Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file) + 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 @@ -23,303 +23,401 @@ #include "nnue_common.h" #include "nnue_architecture.h" -#include "features/index_list.h" + +#include "../misc.h" #include // std::memset() -namespace Eval::NNUE { +namespace Stockfish::Eval::NNUE { + + // If vector instructions are enabled, we update and refresh the + // accumulator tile by tile such that each tile fits in the CPU's + // vector registers. + #define VECTOR + + #ifdef USE_AVX512 + typedef __m512i vec_t; + #define vec_load(a) _mm512_load_si512(a) + #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 NumRegs = 8; // only 8 are needed + + #elif USE_AVX2 + typedef __m256i vec_t; + #define vec_load(a) _mm256_load_si256(a) + #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 NumRegs = 16; + + #elif USE_SSE2 + typedef __m128i vec_t; + #define vec_load(a) (*(a)) + #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 NumRegs = Is64Bit ? 16 : 8; + + #elif USE_MMX + typedef __m64 vec_t; + #define vec_load(a) (*(a)) + #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 NumRegs = 8; + + #elif USE_NEON + typedef int16x8_t vec_t; + #define vec_load(a) (*(a)) + #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 NumRegs = 16; + + #else + #undef VECTOR + + #endif // Input feature converter class FeatureTransformer { private: // Number of output dimensions for one side - static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions; + static constexpr IndexType HalfDimensions = TransformedFeatureDimensions; + + #ifdef VECTOR + static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2; + static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions"); + #endif public: // Output type using OutputType = TransformedFeatureType; // Number of input/output dimensions - static constexpr IndexType kInputDimensions = RawFeatures::kDimensions; - static constexpr IndexType kOutputDimensions = kHalfDimensions * 2; + static constexpr IndexType InputDimensions = FeatureSet::Dimensions; + static constexpr IndexType OutputDimensions = HalfDimensions * 2; // Size of forward propagation buffer - static constexpr std::size_t kBufferSize = - kOutputDimensions * sizeof(OutputType); + static constexpr std::size_t BufferSize = + OutputDimensions * sizeof(OutputType); // Hash value embedded in the evaluation file - static constexpr std::uint32_t GetHashValue() { - return RawFeatures::kHashValue ^ kOutputDimensions; + static constexpr std::uint32_t get_hash_value() { + return FeatureSet::HashValue ^ OutputDimensions; } // Read network parameters - bool ReadParameters(std::istream& stream) { - stream.read(reinterpret_cast(biases_), - kHalfDimensions * sizeof(BiasType)); - stream.read(reinterpret_cast(weights_), - kHalfDimensions * kInputDimensions * sizeof(WeightType)); + bool read_parameters(std::istream& stream) { + for (std::size_t i = 0; i < HalfDimensions; ++i) + biases[i] = read_little_endian(stream); + for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i) + weights[i] = read_little_endian(stream); 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; + // Write network parameters + bool write_parameters(std::ostream& stream) const { + for (std::size_t i = 0; i < HalfDimensions; ++i) + write_little_endian(stream, biases[i]); + for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i) + write_little_endian(stream, weights[i]); + return !stream.fail(); } // Convert input features - void Transform(const Position& pos, OutputType* output, bool refresh) const { - if (refresh || !UpdateAccumulatorIfPossible(pos)) { - RefreshAccumulator(pos); - } + void transform(const Position& pos, OutputType* output) const { + update_accumulator(pos, WHITE); + update_accumulator(pos, BLACK); + 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(); + #if defined(USE_AVX512) + constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2); + static_assert(HalfDimensions % (SimdWidth * 2) == 0); + const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7); + const __m512i Zero = _mm512_setzero_si512(); - #elif defined(USE_SSSE3) - constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; + #elif defined(USE_AVX2) + constexpr IndexType NumChunks = HalfDimensions / SimdWidth; + constexpr int Control = 0b11011000; + const __m256i Zero = _mm256_setzero_si256(); + + #elif defined(USE_SSE2) + 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 NumChunks = HalfDimensions / SimdWidth; + const __m64 k0x80s = _mm_set1_pi8(-128); + #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - const int8x8_t kZero = {0}; + constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2); + 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 < NumChunks; ++j) { + __m512i sum0 = _mm512_load_si512( + &reinterpret_cast(accumulation[perspectives[p]])[j * 2 + 0]); + __m512i sum1 = _mm512_load_si512( + &reinterpret_cast(accumulation[perspectives[p]])[j * 2 + 1]); + _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control, + _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero))); + } - #if defined(USE_AVX2) + #elif defined(USE_AVX2) auto out = reinterpret_cast<__m256i*>(&output[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m256i sum0 = _mm256_loadA_si256( - &reinterpret_cast(accumulation[perspectives[p]][0])[j * 2 + 0]); - __m256i sum1 = _mm256_loadA_si256( - &reinterpret_cast(accumulation[perspectives[p]][0])[j * 2 + 1]); - _mm256_storeA_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8( - _mm256_packs_epi16(sum0, sum1), kZero), kControl)); + for (IndexType j = 0; j < NumChunks; ++j) { + __m256i sum0 = _mm256_load_si256( + &reinterpret_cast(accumulation[perspectives[p]])[j * 2 + 0]); + __m256i sum1 = _mm256_load_si256( + &reinterpret_cast(accumulation[perspectives[p]])[j * 2 + 1]); + _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8( + _mm256_packs_epi16(sum0, sum1), Zero), Control)); } - #elif defined(USE_SSSE3) + #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( - accumulation[perspectives[p]][0])[j * 2 + 0]); + accumulation[perspectives[p]])[j * 2 + 0]); __m128i sum1 = _mm_load_si128(&reinterpret_cast( - 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) + _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s) #endif ); } + #elif defined(USE_MMX) + auto out = reinterpret_cast<__m64*>(&output[offset]); + for (IndexType j = 0; j < NumChunks; ++j) { + __m64 sum0 = *(&reinterpret_cast( + accumulation[perspectives[p]])[j * 2 + 0]); + __m64 sum1 = *(&reinterpret_cast( + 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(&output[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { + for (IndexType j = 0; j < NumChunks; ++j) { int16x8_t sum = reinterpret_cast( - accumulation[perspectives[p]][0])[j]; - out[j] = vmax_s8(vqmovn_s16(sum), kZero); + accumulation[perspectives[p]])[j]; + out[j] = vmax_s8(vqmovn_s16(sum), Zero); } #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - BiasType sum = accumulation[static_cast(perspectives[p])][0][j]; + for (IndexType j = 0; j < HalfDimensions; ++j) { + BiasType sum = accumulation[static_cast(perspectives[p])][j]; output[offset + j] = static_cast( std::max(0, std::min(127, sum))); } #endif } + #if defined(USE_MMX) + _mm_empty(); + #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(&weights_[offset]); - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - for (IndexType j = 0; j < kNumChunks; ++j) { - _mm256_storeA_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadA_si256(&accumulation[j]), column[j])); - } - - #elif defined(USE_SSE2) - auto accumulation = reinterpret_cast<__m128i*>( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&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( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&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]; - } + 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; + + #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[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 = FeatureSet::refresh_cost(pos); + while (st->accumulator.state[perspective] == EMPTY) + { + // This governs when a full feature refresh is needed and how many + // updates are better than just one full refresh. + if ( FeatureSet::requires_refresh(st, perspective) + || (gain -= FeatureSet::update_cost(st) + 1) < 0) + break; + next = st; + st = st->previous; } - accumulator.computed_accumulation = true; - accumulator.computed_score = false; - } + if (st->accumulator.state[perspective] == COMPUTED) + { + if (next == nullptr) + return; + + // 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. + const Square ksq = pos.square(perspective); + IndexList removed[2], added[2]; + FeatureSet::append_changed_indices( + ksq, next, perspective, removed[0], added[0]); + for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous) + FeatureSet::append_changed_indices( + ksq, st2, perspective, removed[1], added[1]); + + // Mark the accumulators as computed. + next->accumulator.state[perspective] = COMPUTED; + pos.state()->accumulator.state[perspective] = COMPUTED; + + // Now update the accumulators listed in states_to_update[], where the last element is a sentinel. + StateInfo *states_to_update[3] = + { next, next == pos.state() ? nullptr : pos.state(), nullptr }; + #ifdef VECTOR + for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j) + { + // Load accumulator + auto accTile = reinterpret_cast( + &st->accumulator.accumulation[perspective][j * TileHeight]); + for (IndexType k = 0; k < NumRegs; ++k) + acc[k] = vec_load(&accTile[k]); + + for (IndexType i = 0; states_to_update[i]; ++i) + { + // Difference calculation for the deactivated features + for (const auto index : removed[i]) + { + const IndexType offset = HalfDimensions * index + j * TileHeight; + auto column = reinterpret_cast(&weights[offset]); + for (IndexType k = 0; k < NumRegs; ++k) + acc[k] = vec_sub_16(acc[k], column[k]); + } - // 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]); + // Difference calculation for the activated features + for (const auto index : added[i]) + { + const IndexType offset = HalfDimensions * index + j * TileHeight; + auto column = reinterpret_cast(&weights[offset]); + for (IndexType k = 0; k < NumRegs; ++k) + acc[k] = vec_add_16(acc[k], column[k]); + } - #elif defined(USE_SSE2) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast<__m128i*>( - &accumulator.accumulation[perspective][i][0]); + // Store accumulator + accTile = reinterpret_cast( + &states_to_update[i]->accumulator.accumulation[perspective][j * TileHeight]); + for (IndexType k = 0; k < NumRegs; ++k) + vec_store(&accTile[k], acc[k]); + } + } - #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast( - &accumulator.accumulation[perspective][i][0]); - #endif + #else + for (IndexType i = 0; states_to_update[i]; ++i) + { + std::memcpy(states_to_update[i]->accumulator.accumulation[perspective], + st->accumulator.accumulation[perspective], + HalfDimensions * sizeof(BiasType)); + st = states_to_update[i]; - 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(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]); - } + for (const auto index : removed[i]) + { + const IndexType offset = HalfDimensions * index; - #elif defined(USE_SSE2) - auto column = reinterpret_cast(&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(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = vsubq_s16(accumulation[j], column[j]); - } + for (IndexType j = 0; j < HalfDimensions; ++j) + st->accumulator.accumulation[perspective][j] -= weights[offset + 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[i]) + { + const IndexType offset = HalfDimensions * index; + for (IndexType j = 0; j < HalfDimensions; ++j) + st->accumulator.accumulation[perspective][j] += weights[offset + j]; } } - { // 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(&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(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_add_epi16(accumulation[j], column[j]); - } + #endif + } + else + { + // Refresh the accumulator + auto& accumulator = pos.state()->accumulator; + accumulator.state[perspective] = COMPUTED; + IndexList active; + FeatureSet::append_active_indices(pos, perspective, active); + + #ifdef VECTOR + for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j) + { + auto biasesTile = reinterpret_cast( + &biases[j * TileHeight]); + for (IndexType k = 0; k < NumRegs; ++k) + acc[k] = biasesTile[k]; + + for (const auto index : active) + { + const IndexType offset = HalfDimensions * index + j * TileHeight; + auto column = reinterpret_cast(&weights[offset]); + + for (unsigned k = 0; k < NumRegs; ++k) + acc[k] = vec_add_16(acc[k], column[k]); + } - #elif defined(USE_NEON) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = vaddq_s16(accumulation[j], column[j]); - } + auto accTile = reinterpret_cast( + &accumulator.accumulation[perspective][j * TileHeight]); + for (unsigned k = 0; k < NumRegs; k++) + vec_store(&accTile[k], acc[k]); + } #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - accumulator.accumulation[perspective][i][j] += - weights_[offset + j]; - } - #endif + std::memcpy(accumulator.accumulation[perspective], biases, + HalfDimensions * sizeof(BiasType)); - } + for (const auto index : active) + { + const IndexType offset = HalfDimensions * index; + + for (IndexType j = 0; j < HalfDimensions; ++j) + accumulator.accumulation[perspective][j] += weights[offset + j]; } + #endif } - accumulator.computed_accumulation = true; - accumulator.computed_score = false; + #if defined(USE_MMX) + _mm_empty(); + #endif } using BiasType = std::int16_t; using WeightType = std::int16_t; - alignas(kCacheLineSize) BiasType biases_[kHalfDimensions]; - alignas(kCacheLineSize) - WeightType weights_[kHalfDimensions * kInputDimensions]; + alignas(CacheLineSize) BiasType biases[HalfDimensions]; + alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions]; }; -} // namespace Eval::NNUE +} // namespace Stockfish::Eval::NNUE #endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED