X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fnnue%2Fnnue_feature_transformer.h;h=0af0ed96cc5a1446a98150f1d9d6b1ba9f3b4c65;hb=3c0e86a91e48baea273306e45fb6cf13a59373cf;hp=855980182fccafd731879a91b6458f93e3c6570d;hpb=5f781d366e0f4369ec12e36c9978ad63ffa32235;p=stockfish diff --git a/src/nnue/nnue_feature_transformer.h b/src/nnue/nnue_feature_transformer.h index 85598018..0af0ed96 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) 2004-2022 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 @@ -21,10 +21,18 @@ #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED #define NNUE_FEATURE_TRANSFORMER_H_INCLUDED -#include "nnue_common.h" -#include "nnue_architecture.h" +#include +#include +#include +#include +#include +#include -#include // std::memset() +#include "../position.h" +#include "../types.h" +#include "nnue_accumulator.h" +#include "nnue_architecture.h" +#include "nnue_common.h" namespace Stockfish::Eval::NNUE { @@ -41,74 +49,127 @@ namespace Stockfish::Eval::NNUE { "Per feature PSQT values cannot be processed at granularity lower than 8 at a time."); #ifdef USE_AVX512 - typedef __m512i vec_t; - typedef __m256i psqt_vec_t; + 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 - typedef __m256i vec_t; - typedef __m256i psqt_vec_t; + 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 - typedef __m128i vec_t; - typedef __m128i psqt_vec_t; + 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 - typedef __m64 vec_t; - typedef __m64 psqt_vec_t; + 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 - typedef int16x8_t vec_t; - typedef int32x4_t psqt_vec_t; + 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 @@ -199,9 +260,9 @@ namespace Stockfish::Eval::NNUE { // Read network parameters bool read_parameters(std::istream& stream) { - read_little_endian(stream, biases , HalfDimensions ); - read_little_endian(stream, weights , HalfDimensions * InputDimensions); - read_little_endian(stream, psqtWeights, PSQTBuckets * InputDimensions); + read_leb_128(stream, biases , HalfDimensions ); + read_leb_128(stream, weights , HalfDimensions * InputDimensions); + read_leb_128(stream, psqtWeights, PSQTBuckets * InputDimensions); return !stream.fail(); } @@ -209,17 +270,17 @@ namespace Stockfish::Eval::NNUE { // Write network parameters bool write_parameters(std::ostream& stream) const { - write_little_endian(stream, biases , HalfDimensions ); - write_little_endian(stream, weights , HalfDimensions * InputDimensions); - write_little_endian(stream, psqtWeights, PSQTBuckets * InputDimensions); + 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 std::int32_t transform(const Position& pos, OutputType* output, int bucket) const { - update_accumulator(pos, WHITE); - update_accumulator(pos, BLACK); + 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; @@ -235,110 +296,30 @@ namespace Stockfish::Eval::NNUE { { const IndexType offset = (HalfDimensions / 2) * p; -#if defined(USE_AVX512) - - constexpr IndexType OutputChunkSize = 512 / 8; - static_assert((HalfDimensions / 2) % OutputChunkSize == 0); - constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize; - - const __m512i Zero = _mm512_setzero_si512(); - const __m512i One = _mm512_set1_epi16(127); - const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7); - - const __m512i* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0])); - const __m512i* in1 = reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2])); - __m512i* out = reinterpret_cast< __m512i*>(output + offset); - - for (IndexType j = 0; j < NumOutputChunks; j += 1) - { - const __m512i sum0a = _mm512_max_epi16(_mm512_min_epi16(in0[j * 2 + 0], One), Zero); - const __m512i sum0b = _mm512_max_epi16(_mm512_min_epi16(in0[j * 2 + 1], One), Zero); - const __m512i sum1a = _mm512_max_epi16(_mm512_min_epi16(in1[j * 2 + 0], One), Zero); - const __m512i sum1b = _mm512_max_epi16(_mm512_min_epi16(in1[j * 2 + 1], One), Zero); - - const __m512i pa = _mm512_srli_epi16(_mm512_mullo_epi16(sum0a, sum1a), 7); - const __m512i pb = _mm512_srli_epi16(_mm512_mullo_epi16(sum0b, sum1b), 7); - - out[j] = _mm512_permutexvar_epi64(Control, _mm512_packs_epi16(pa, pb)); - } - -#elif defined(USE_AVX2) - - constexpr IndexType OutputChunkSize = 256 / 8; - static_assert((HalfDimensions / 2) % OutputChunkSize == 0); - constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize; - - const __m256i Zero = _mm256_setzero_si256(); - const __m256i One = _mm256_set1_epi16(127); - constexpr int Control = 0b11011000; - - const __m256i* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0])); - const __m256i* in1 = reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2])); - __m256i* out = reinterpret_cast< __m256i*>(output + offset); - - for (IndexType j = 0; j < NumOutputChunks; j += 1) - { - const __m256i sum0a = _mm256_max_epi16(_mm256_min_epi16(in0[j * 2 + 0], One), Zero); - const __m256i sum0b = _mm256_max_epi16(_mm256_min_epi16(in0[j * 2 + 1], One), Zero); - const __m256i sum1a = _mm256_max_epi16(_mm256_min_epi16(in1[j * 2 + 0], One), Zero); - const __m256i sum1b = _mm256_max_epi16(_mm256_min_epi16(in1[j * 2 + 1], One), Zero); - - const __m256i pa = _mm256_srli_epi16(_mm256_mullo_epi16(sum0a, sum1a), 7); - const __m256i pb = _mm256_srli_epi16(_mm256_mullo_epi16(sum0b, sum1b), 7); - - out[j] = _mm256_permute4x64_epi64(_mm256_packs_epi16(pa, pb), Control); - } - -#elif defined(USE_SSE2) +#if defined(VECTOR) - constexpr IndexType OutputChunkSize = 128 / 8; + constexpr IndexType OutputChunkSize = MaxChunkSize; static_assert((HalfDimensions / 2) % OutputChunkSize == 0); constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize; - const __m128i Zero = _mm_setzero_si128(); - const __m128i One = _mm_set1_epi16(127); + vec_t Zero = vec_zero(); + vec_t One = vec_set_16(127); - const __m128i* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0])); - const __m128i* in1 = reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2])); - __m128i* out = reinterpret_cast< __m128i*>(output + offset); + 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); for (IndexType j = 0; j < NumOutputChunks; j += 1) { - const __m128i sum0a = _mm_max_epi16(_mm_min_epi16(in0[j * 2 + 0], One), Zero); - const __m128i sum0b = _mm_max_epi16(_mm_min_epi16(in0[j * 2 + 1], One), Zero); - const __m128i sum1a = _mm_max_epi16(_mm_min_epi16(in1[j * 2 + 0], One), Zero); - const __m128i sum1b = _mm_max_epi16(_mm_min_epi16(in1[j * 2 + 1], One), Zero); + 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); - const __m128i pa = _mm_srli_epi16(_mm_mullo_epi16(sum0a, sum1a), 7); - const __m128i pb = _mm_srli_epi16(_mm_mullo_epi16(sum0b, sum1b), 7); + const vec_t pa = vec_mul_16(sum0a, sum1a); + const vec_t pb = vec_mul_16(sum0b, sum1b); - out[j] = _mm_packs_epi16(pa, pb); - } - -#elif defined(USE_NEON) - - constexpr IndexType OutputChunkSize = 128 / 8; - static_assert((HalfDimensions / 2) % OutputChunkSize == 0); - constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize; - - const int16x8_t Zero = vdupq_n_s16(0); - const int16x8_t One = vdupq_n_s16(127); - - const int16x8_t* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0])); - const int16x8_t* in1 = reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2])); - int8x16_t* out = reinterpret_cast< int8x16_t*>(output + offset); - - for (IndexType j = 0; j < NumOutputChunks; j += 1) - { - const int16x8_t sum0a = vmaxq_s16(vminq_s16(in0[j * 2 + 0], One), Zero); - const int16x8_t sum0b = vmaxq_s16(vminq_s16(in0[j * 2 + 1], One), Zero); - const int16x8_t sum1a = vmaxq_s16(vminq_s16(in1[j * 2 + 0], One), Zero); - const int16x8_t sum1b = vmaxq_s16(vminq_s16(in1[j * 2 + 1], One), Zero); - - const int8x8_t pa = vshrn_n_s16(vmulq_s16(sum0a, sum1a), 7); - const int8x8_t pb = vshrn_n_s16(vmulq_s16(sum0b, sum1b), 7); - - out[j] = vcombine_s8(pa, pb); + out[j] = vec_msb_pack_16(pa, pb); } #else @@ -354,253 +335,342 @@ namespace Stockfish::Eval::NNUE { #endif } - return psqt; - - } // end of function transform() +#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: - 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. - - #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 - + 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]) + 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) + if ( FeatureSet::requires_refresh(st, Perspective) || (gain -= FeatureSet::update_cost(st) + 1) < 0) break; next = st; st = st->previous; } + return { st, next }; + } - if (st->accumulator.computed[perspective]) - { - if (next == nullptr) - return; + // 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); - // Update incrementally in two steps. First, we update the "next" - // accumulator. Then, we update the current accumulator (pos.state()). + #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 - // Gather all features to be updated. - const Square ksq = pos.square(perspective); - FeatureSet::IndexList removed[2], added[2]; - FeatureSet::append_changed_indices( - ksq, next->dirtyPiece, perspective, removed[0], added[0]); - for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous) - FeatureSet::append_changed_indices( - ksq, st2->dirtyPiece, perspective, removed[1], added[1]); + if (states_to_update[0] == nullptr) + return; - // Mark the accumulators as computed. - next->accumulator.computed[perspective] = true; - pos.state()->accumulator.computed[perspective] = true; + // Update incrementally going back through states_to_update. - // 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]); + // Gather all features to be updated. + const Square ksq = pos.square(Perspective); - 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]); - } - } + // 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]; - 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]); + { + int i = N-2; // last potential state to update. Skip last element because it must be nullptr. + while (states_to_update[i] == nullptr) + --i; - 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]); - } - - // 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]); - } - - // 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]); - } - } + StateInfo *st2 = states_to_update[i]; - #else - for (IndexType i = 0; states_to_update[i]; ++i) + for (; i >= 0; --i) { - std::memcpy(states_to_update[i]->accumulator.accumulation[perspective], - st->accumulator.accumulation[perspective], - HalfDimensions * sizeof(BiasType)); + states_to_update[i]->accumulator.computed[Perspective] = true; - for (std::size_t k = 0; k < PSQTBuckets; ++k) - states_to_update[i]->accumulator.psqtAccumulation[perspective][k] = st->accumulator.psqtAccumulation[perspective][k]; + 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]); + } + } - st = states_to_update[i]; + 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; - - 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]; + 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; - - 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 - } - else - { - // Refresh the accumulator - auto& accumulator = pos.state()->accumulator; - accumulator.computed[perspective] = true; - FeatureSet::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) + for (IndexType k = 0; k < NumRegs; ++k) acc[k] = vec_add_16(acc[k], column[k]); } - auto accTile = reinterpret_cast( - &accumulator.accumulation[perspective][j * TileHeight]); - for (unsigned k = 0; k < NumRegs; 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]); } + } - for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j) - { - for (std::size_t k = 0; k < NumPsqtRegs; ++k) - psqt[k] = vec_zero_psqt(); + 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 (const auto index : active) + 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]); + } + // 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]); } - auto accTilePsqt = reinterpret_cast( - &accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]); + // 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 - std::memcpy(accumulator.accumulation[perspective], biases, +#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) - accumulator.psqtAccumulation[perspective][k] = 0; + states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k]; - for (const auto index : active) + 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) - accumulator.accumulation[perspective][j] += weights[offset + j]; + st->accumulator.accumulation[Perspective][j] += weights[offset + j]; for (std::size_t k = 0; k < PSQTBuckets; ++k) - accumulator.psqtAccumulation[perspective][k] += psqtWeights[index * PSQTBuckets + k]; + st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k]; } + } +#endif + + #if defined(USE_MMX) + _mm_empty(); #endif + } + + 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 + + // 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]); + } + + auto accTile = reinterpret_cast( + &accumulator.accumulation[Perspective][j * TileHeight]); + for (unsigned k = 0; k < NumRegs; k++) + vec_store(&accTile[k], acc[k]); + } + + for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j) + { + for (std::size_t k = 0; k < NumPsqtRegs; ++k) + psqt[k] = vec_zero_psqt(); + + 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. + + // 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); + } + } + + 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(CacheLineSize) BiasType biases[HalfDimensions]; alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions]; alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];