X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fnnue%2Fnnue_feature_transformer.h;h=56442bac9b17818ba74610c1213d21e853c512d8;hb=97f706ecc11459c8d0aa1901134d12fba00b4b15;hp=437076102310bc3d4446f98d92372bcb9a063db0;hpb=81d716f5ccff3f0898ae985b9ef69f79d014bdc5;p=stockfish diff --git a/src/nnue/nnue_feature_transformer.h b/src/nnue/nnue_feature_transformer.h index 43707610..56442bac 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) 2004-2023 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 @@ -21,358 +21,661 @@ #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED #define NNUE_FEATURE_TRANSFORMER_H_INCLUDED -#include "nnue_common.h" +#include +#include +#include +#include +#include +#include + +#include "../position.h" +#include "../types.h" +#include "nnue_accumulator.h" #include "nnue_architecture.h" -#include "features/index_list.h" +#include "nnue_common.h" + +namespace Stockfish::Eval::NNUE { + + using BiasType = std::int16_t; + using WeightType = std::int16_t; + using PSQTWeightType = std::int32_t; + + // 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 + + static_assert(PSQTBuckets % 8 == 0, + "Per feature PSQT values cannot be processed at granularity lower than 8 at a time."); + + #ifdef USE_AVX512 + using vec_t = __m512i; + using psqt_vec_t = __m256i; + #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) + #define vec_mul_16(a,b) _mm512_mullo_epi16(a,b) + #define vec_zero() _mm512_setzero_epi32() + #define vec_set_16(a) _mm512_set1_epi16(a) + #define vec_max_16(a,b) _mm512_max_epi16(a,b) + #define vec_min_16(a,b) _mm512_min_epi16(a,b) + inline vec_t vec_msb_pack_16(vec_t a, vec_t b){ + vec_t compacted = _mm512_packs_epi16(_mm512_srli_epi16(a,7),_mm512_srli_epi16(b,7)); + return _mm512_permutexvar_epi64(_mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7), compacted); + } + #define vec_load_psqt(a) _mm256_load_si256(a) + #define vec_store_psqt(a,b) _mm256_store_si256(a,b) + #define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b) + #define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b) + #define vec_zero_psqt() _mm256_setzero_si256() + #define NumRegistersSIMD 32 + #define MaxChunkSize 64 + + #elif USE_AVX2 + using vec_t = __m256i; + using psqt_vec_t = __m256i; + #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) + #define vec_mul_16(a,b) _mm256_mullo_epi16(a,b) + #define vec_zero() _mm256_setzero_si256() + #define vec_set_16(a) _mm256_set1_epi16(a) + #define vec_max_16(a,b) _mm256_max_epi16(a,b) + #define vec_min_16(a,b) _mm256_min_epi16(a,b) + inline vec_t vec_msb_pack_16(vec_t a, vec_t b){ + vec_t compacted = _mm256_packs_epi16(_mm256_srli_epi16(a,7), _mm256_srli_epi16(b,7)); + return _mm256_permute4x64_epi64(compacted, 0b11011000); + } + #define vec_load_psqt(a) _mm256_load_si256(a) + #define vec_store_psqt(a,b) _mm256_store_si256(a,b) + #define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b) + #define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b) + #define vec_zero_psqt() _mm256_setzero_si256() + #define NumRegistersSIMD 16 + #define MaxChunkSize 32 + + #elif USE_SSE2 + using vec_t = __m128i; + using psqt_vec_t = __m128i; + #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) + #define vec_mul_16(a,b) _mm_mullo_epi16(a,b) + #define vec_zero() _mm_setzero_si128() + #define vec_set_16(a) _mm_set1_epi16(a) + #define vec_max_16(a,b) _mm_max_epi16(a,b) + #define vec_min_16(a,b) _mm_min_epi16(a,b) + #define vec_msb_pack_16(a,b) _mm_packs_epi16(_mm_srli_epi16(a,7),_mm_srli_epi16(b,7)) + #define vec_load_psqt(a) (*(a)) + #define vec_store_psqt(a,b) *(a)=(b) + #define vec_add_psqt_32(a,b) _mm_add_epi32(a,b) + #define vec_sub_psqt_32(a,b) _mm_sub_epi32(a,b) + #define vec_zero_psqt() _mm_setzero_si128() + #define NumRegistersSIMD (Is64Bit ? 16 : 8) + #define MaxChunkSize 16 + + #elif USE_MMX + using vec_t = __m64; + using psqt_vec_t = __m64; + #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) + #define vec_mul_16(a,b) _mm_mullo_pi16(a,b) + #define vec_zero() _mm_setzero_si64() + #define vec_set_16(a) _mm_set1_pi16(a) + inline vec_t vec_max_16(vec_t a,vec_t b){ + vec_t comparison = _mm_cmpgt_pi16(a,b); + return _mm_or_si64(_mm_and_si64(comparison, a), _mm_andnot_si64(comparison, b)); + } + inline vec_t vec_min_16(vec_t a,vec_t b){ + vec_t comparison = _mm_cmpgt_pi16(a,b); + return _mm_or_si64(_mm_and_si64(comparison, b), _mm_andnot_si64(comparison, a)); + } + #define vec_msb_pack_16(a,b) _mm_packs_pi16(_mm_srli_pi16(a,7),_mm_srli_pi16(b,7)) + #define vec_load_psqt(a) (*(a)) + #define vec_store_psqt(a,b) *(a)=(b) + #define vec_add_psqt_32(a,b) _mm_add_pi32(a,b) + #define vec_sub_psqt_32(a,b) _mm_sub_pi32(a,b) + #define vec_zero_psqt() _mm_setzero_si64() + #define vec_cleanup() _mm_empty() + #define NumRegistersSIMD 8 + #define MaxChunkSize 8 + + #elif USE_NEON + using vec_t = int16x8_t; + using psqt_vec_t = int32x4_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) + #define vec_mul_16(a,b) vmulq_s16(a,b) + #define vec_zero() vec_t{0} + #define vec_set_16(a) vdupq_n_s16(a) + #define vec_max_16(a,b) vmaxq_s16(a,b) + #define vec_min_16(a,b) vminq_s16(a,b) + inline vec_t vec_msb_pack_16(vec_t a, vec_t b){ + const int8x8_t shifta = vshrn_n_s16(a, 7); + const int8x8_t shiftb = vshrn_n_s16(b, 7); + const int8x16_t compacted = vcombine_s8(shifta,shiftb); + return *reinterpret_cast (&compacted); + } + #define vec_load_psqt(a) (*(a)) + #define vec_store_psqt(a,b) *(a)=(b) + #define vec_add_psqt_32(a,b) vaddq_s32(a,b) + #define vec_sub_psqt_32(a,b) vsubq_s32(a,b) + #define vec_zero_psqt() psqt_vec_t{0} + #define NumRegistersSIMD 16 + #define MaxChunkSize 16 + + #else + #undef VECTOR + + #endif + + + #ifdef VECTOR + + // Compute optimal SIMD register count for feature transformer accumulation. + + // We use __m* types as template arguments, which causes GCC to emit warnings + // about losing some attribute information. This is irrelevant to us as we + // only take their size, so the following pragma are harmless. + #if defined(__GNUC__) + #pragma GCC diagnostic push + #pragma GCC diagnostic ignored "-Wignored-attributes" + #endif + + template + static constexpr int BestRegisterCount() + { + #define RegisterSize sizeof(SIMDRegisterType) + #define LaneSize sizeof(LaneType) + + static_assert(RegisterSize >= LaneSize); + static_assert(MaxRegisters <= NumRegistersSIMD); + static_assert(MaxRegisters > 0); + static_assert(NumRegistersSIMD > 0); + static_assert(RegisterSize % LaneSize == 0); + static_assert((NumLanes * LaneSize) % RegisterSize == 0); + + const int ideal = (NumLanes * LaneSize) / RegisterSize; + if (ideal <= MaxRegisters) + return ideal; + + // Look for the largest divisor of the ideal register count that is smaller than MaxRegisters + for (int divisor = MaxRegisters; divisor > 1; --divisor) + if (ideal % divisor == 0) + return divisor; + + return 1; + } + + static constexpr int NumRegs = BestRegisterCount(); + static constexpr int NumPsqtRegs = BestRegisterCount(); + #if defined(__GNUC__) + #pragma GCC diagnostic pop + #endif + #endif -#include // std::memset() -namespace Eval::NNUE { // 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 constexpr IndexType PsqtTileHeight = NumPsqtRegs * sizeof(psqt_vec_t) / 4; + static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions"); + static_assert(PSQTBuckets % PsqtTileHeight == 0, "PsqtTileHeight must divide PSQTBuckets"); + #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; // 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 * 2); } // Read network parameters - bool ReadParameters(std::istream& stream) { - for (std::size_t i = 0; i < kHalfDimensions; ++i) - biases_[i] = read_little_endian(stream); - for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i) - weights_[i] = read_little_endian(stream); + bool read_parameters(std::istream& stream) { + + read_leb_128(stream, biases , HalfDimensions ); + read_leb_128(stream, weights , HalfDimensions * InputDimensions); + read_leb_128(stream, psqtWeights, PSQTBuckets * InputDimensions); + 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 { + + write_leb_128(stream, biases , HalfDimensions ); + write_leb_128(stream, weights , HalfDimensions * InputDimensions); + write_leb_128(stream, psqtWeights, PSQTBuckets * InputDimensions); + + return !stream.fail(); } // Convert input features - void Transform(const Position& pos, OutputType* output, bool refresh) const { - if (refresh || !UpdateAccumulatorIfPossible(pos)) { - RefreshAccumulator(pos); - } + std::int32_t transform(const Position& pos, OutputType* output, int bucket) const { + update_accumulator(pos); + update_accumulator(pos); + + const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()}; const auto& accumulation = pos.state()->accumulator.accumulation; + const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation; - #if defined(USE_AVX2) - constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; - constexpr int kControl = 0b11011000; - const __m256i kZero = _mm256_setzero_si256(); + const auto psqt = ( + psqtAccumulation[perspectives[0]][bucket] + - psqtAccumulation[perspectives[1]][bucket] + ) / 2; - #elif defined(USE_SSE2) - constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; - #ifdef USE_SSE41 - const __m128i kZero = _mm_setzero_si128(); - #else - const __m128i k0x80s = _mm_set1_epi8(-128); - #endif + for (IndexType p = 0; p < 2; ++p) + { + const IndexType offset = (HalfDimensions / 2) * p; - #elif defined(USE_MMX) - constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; - const __m64 k0x80s = _mm_set1_pi8(-128); +#if defined(VECTOR) - #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - const int8x8_t kZero = {0}; - #endif + constexpr IndexType OutputChunkSize = MaxChunkSize; + static_assert((HalfDimensions / 2) % OutputChunkSize == 0); + constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize; - 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 = _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)); - } + vec_t Zero = vec_zero(); + vec_t One = vec_set_16(127); - #elif defined(USE_SSE2) - auto out = reinterpret_cast<__m128i*>(&output[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m128i sum0 = _mm_load_si128(&reinterpret_cast( - accumulation[perspectives[p]][0])[j * 2 + 0]); - __m128i sum1 = _mm_load_si128(&reinterpret_cast( - accumulation[perspectives[p]][0])[j * 2 + 1]); - const __m128i packedbytes = _mm_packs_epi16(sum0, sum1); + const vec_t* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0])); + const vec_t* in1 = reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2])); + vec_t* out = reinterpret_cast< vec_t*>(output + offset); - _mm_store_si128(&out[j], + for (IndexType j = 0; j < NumOutputChunks; j += 1) + { + const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero); + const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero); + const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero); + const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero); - #ifdef USE_SSE41 - _mm_max_epi8(packedbytes, kZero) - #else - _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s) + const vec_t pa = vec_mul_16(sum0a, sum1a); + const vec_t pb = vec_mul_16(sum0b, sum1b); + + out[j] = vec_msb_pack_16(pa, pb); + } + +#else + + for (IndexType j = 0; j < HalfDimensions / 2; ++j) { + BiasType sum0 = accumulation[static_cast(perspectives[p])][j + 0]; + BiasType sum1 = accumulation[static_cast(perspectives[p])][j + HalfDimensions / 2]; + sum0 = std::clamp(sum0, 0, 127); + sum1 = std::clamp(sum1, 0, 127); + output[offset + j] = static_cast(unsigned(sum0 * sum1) / 128); + } + +#endif + } + +#if defined(vec_cleanup) + vec_cleanup(); +#endif + + return psqt; + } // end of function transform() + + void hint_common_access(const Position& pos) const { + hint_common_access_for_perspective(pos); + hint_common_access_for_perspective(pos); + } + + private: + template + [[nodiscard]] std::pair try_find_computed_accumulator(const Position& pos) const { + // 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->previous && !st->accumulator.computed[Perspective]) + { + // 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; + } + return { st, next }; + } + + // NOTE: The parameter states_to_update is an array of position states, ending with nullptr. + // All states must be sequential, that is states_to_update[i] must either be reachable + // by repeatedly applying ->previous from states_to_update[i+1] or states_to_update[i] == nullptr. + // computed_st must be reachable by repeatedly applying ->previous on states_to_update[0], if not nullptr. + template + void update_accumulator_incremental(const Position& pos, StateInfo* computed_st, StateInfo* states_to_update[N]) const { + static_assert(N > 0); + assert(states_to_update[N-1] == nullptr); + + #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]; + psqt_vec_t psqt[NumPsqtRegs]; #endif - ); - } + if (states_to_update[0] == nullptr) + return; - #elif defined(USE_MMX) - auto out = reinterpret_cast<__m64*>(&output[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m64 sum0 = *(&reinterpret_cast( - accumulation[perspectives[p]][0])[j * 2 + 0]); - __m64 sum1 = *(&reinterpret_cast( - accumulation[perspectives[p]][0])[j * 2 + 1]); - const __m64 packedbytes = _mm_packs_pi16(sum0, sum1); - out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s); - } + // Update incrementally going back through states_to_update. - #elif defined(USE_NEON) - const auto out = reinterpret_cast(&output[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - int16x8_t sum = reinterpret_cast( - accumulation[perspectives[p]][0])[j]; - out[j] = vmax_s8(vqmovn_s16(sum), kZero); - } + // Gather all features to be updated. + const Square ksq = pos.square(Perspective); - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - BiasType sum = accumulation[static_cast(perspectives[p])][0][j]; - output[offset + j] = static_cast( - std::max(0, std::min(127, sum))); + // 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. + 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. + while (states_to_update[i] == nullptr) + --i; + + StateInfo *st2 = states_to_update[i]; + + for (; i >= 0; --i) + { + states_to_update[i]->accumulator.computed[Perspective] = true; + + StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1]; + + for (; st2 != end_state; st2 = st2->previous) + FeatureSet::append_changed_indices( + ksq, st2->dirtyPiece, removed[i], added[i]); } - #endif + } + + StateInfo* st = computed_st; + + // Now update the accumulators listed in states_to_update[], where the last element is a sentinel. +#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]); + } + // 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]); + } + + // 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]); + } } - #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_AVX512) - auto accumulation = reinterpret_cast<__m512i*>( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&weights_[offset]); - constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; - for (IndexType j = 0; j < kNumChunks; ++j) - _mm512_storeA_si512(&accumulation[j], _mm512_add_epi16(_mm512_loadA_si512(&accumulation[j]), column[j])); - - #elif 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_MMX) - auto accumulation = reinterpret_cast<__m64*>( - &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_pi16(accumulation[j], column[j]); + for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j) + { + // Load accumulator + auto accTilePsqt = reinterpret_cast( + &st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]); + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_load_psqt(&accTilePsqt[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 = PSQTBuckets * index + j * PsqtTileHeight; + auto columnPsqt = reinterpret_cast(&psqtWeights[offset]); + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]); } - #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]); + // Difference calculation for the activated features + for (const auto index : added[i]) + { + const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight; + auto columnPsqt = reinterpret_cast(&psqtWeights[offset]); + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]); + } - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) - accumulator.accumulation[perspective][i][j] += weights_[offset + j]; - #endif + // Store accumulator + accTilePsqt = reinterpret_cast( + &states_to_update[i]->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]); + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + vec_store_psqt(&accTilePsqt[k], psqt[k]); + } + } +#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)); + + for (std::size_t k = 0; k < PSQTBuckets; ++k) + states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k]; + + st = states_to_update[i]; + + // Difference calculation for the deactivated features + for (const auto index : removed[i]) + { + const IndexType offset = HalfDimensions * index; + + for (IndexType j = 0; j < HalfDimensions; ++j) + st->accumulator.accumulation[Perspective][j] -= weights[offset + j]; + + for (std::size_t k = 0; k < PSQTBuckets; ++k) + st->accumulator.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k]; + } + + // 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]; + + for (std::size_t k = 0; k < PSQTBuckets; ++k) + st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k]; } } +#endif + #if defined(USE_MMX) _mm_empty(); #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_MMX) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast<__m64*>( - &accumulator.accumulation[perspective][i][0]); - - #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast( - &accumulator.accumulation[perspective][i][0]); + template + void update_accumulator_refresh(const Position& pos) const { + #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]; + psqt_vec_t psqt[NumPsqtRegs]; #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(&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(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]); - } - - #elif defined(USE_MMX) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_sub_pi16(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]); - } + // Refresh the accumulator + // Could be extracted to a separate function because it's done in 2 places, + // but it's unclear if compilers would correctly handle register allocation. + auto& accumulator = pos.state()->accumulator; + accumulator.computed[Perspective] = true; + FeatureSet::IndexList active; + FeatureSet::append_active_indices(pos, 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]); + } - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - accumulator.accumulation[perspective][i][j] -= - weights_[offset + j]; - } - #endif + auto accTile = reinterpret_cast( + &accumulator.accumulation[Perspective][j * TileHeight]); + for (unsigned k = 0; k < NumRegs; k++) + vec_store(&accTile[k], acc[k]); + } - } - } - { // 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]); - } - - #elif defined(USE_MMX) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_add_pi16(accumulation[j], column[j]); - } - - #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]); - } + for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j) + { + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_zero_psqt(); - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - accumulator.accumulation[perspective][i][j] += - weights_[offset + j]; - } - #endif + for (const auto index : active) + { + const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight; + auto columnPsqt = reinterpret_cast(&psqtWeights[offset]); - } + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]); } + + auto accTilePsqt = reinterpret_cast( + &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]); + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + vec_store_psqt(&accTilePsqt[k], psqt[k]); + } + +#else + std::memcpy(accumulator.accumulation[Perspective], biases, + HalfDimensions * sizeof(BiasType)); + + for (std::size_t k = 0; k < PSQTBuckets; ++k) + accumulator.psqtAccumulation[Perspective][k] = 0; + + for (const auto index : active) + { + const IndexType offset = HalfDimensions * index; + + for (IndexType j = 0; j < HalfDimensions; ++j) + accumulator.accumulation[Perspective][j] += weights[offset + j]; + + for (std::size_t k = 0; k < PSQTBuckets; ++k) + accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k]; } +#endif + #if defined(USE_MMX) _mm_empty(); #endif + } + + template + void hint_common_access_for_perspective(const Position& pos) const { + + // Works like update_accumulator, but performs less work. + // Updates ONLY the accumulator for pos. - accumulator.computed_accumulation = true; - accumulator.computed_score = false; + // Look for a usable accumulator of an earlier position. We keep track + // of the estimated gain in terms of features to be added/subtracted. + // Fast early exit. + if (pos.state()->accumulator.computed[Perspective]) + return; + + auto [oldest_st, _] = try_find_computed_accumulator(pos); + + if (oldest_st->accumulator.computed[Perspective]) + { + // Only update current position accumulator to minimize work. + StateInfo* states_to_update[2] = { pos.state(), nullptr }; + update_accumulator_incremental(pos, oldest_st, states_to_update); + } + else + { + update_accumulator_refresh(pos); + } } - using BiasType = std::int16_t; - using WeightType = std::int16_t; + template + void update_accumulator(const Position& pos) const { + + auto [oldest_st, next] = try_find_computed_accumulator(pos); + + if (oldest_st->accumulator.computed[Perspective]) + { + if (next == nullptr) + return; + + // Now update the accumulators listed in states_to_update[], where the last element is a sentinel. + // Currently we update 2 accumulators. + // 1. for the current position + // 2. the next accumulator after the computed one + // The heuristic may change in the future. + StateInfo *states_to_update[3] = + { next, next == pos.state() ? nullptr : pos.state(), nullptr }; + + update_accumulator_incremental(pos, oldest_st, states_to_update); + } + else + { + update_accumulator_refresh(pos); + } + } - alignas(kCacheLineSize) BiasType biases_[kHalfDimensions]; - alignas(kCacheLineSize) - WeightType weights_[kHalfDimensions * kInputDimensions]; + alignas(CacheLineSize) BiasType biases[HalfDimensions]; + alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions]; + alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets]; }; -} // namespace Eval::NNUE +} // namespace Stockfish::Eval::NNUE #endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED