/*
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
#ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
#define NNUE_FEATURE_TRANSFORMER_H_INCLUDED
-#include "nnue_common.h"
+#include <algorithm>
+#include <cassert>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <utility>
+
+#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 16
+ #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<const vec_t*> (&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 <typename SIMDRegisterType,
+ typename LaneType,
+ int NumLanes,
+ int MaxRegisters>
+ 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<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
+ static constexpr int NumPsqtRegs = BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
+ #if defined(__GNUC__)
+ #pragma GCC diagnostic pop
+ #endif
+ #endif
-#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;
+ 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) {
+ bool read_parameters(std::istream& stream) {
+
+ read_leb_128<BiasType >(stream, biases , HalfDimensions );
+ read_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
- for (std::size_t i = 0; i < kHalfDimensions; ++i)
- biases_[i] = read_little_endian<BiasType>(stream);
- for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i)
- weights_[i] = read_little_endian<WeightType>(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;
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
- const auto prev = now->previous;
- if (prev && prev->accumulator.computed_accumulation) {
- UpdateAccumulator(pos);
- return true;
- }
+ write_leb_128<BiasType >(stream, biases , HalfDimensions );
+ write_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
- return false;
+ return !stream.fail();
}
// Convert input features
- void Transform(const Position& pos, OutputType* output) const {
-
- if (!UpdateAccumulatorIfPossible(pos))
- RefreshAccumulator(pos);
+ std::int32_t transform(const Position& pos, OutputType* output, int bucket) const {
+ update_accumulator<WHITE>(pos);
+ update_accumulator<BLACK>(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<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m256i sum1 = _mm256_loadA_si256(
- &reinterpret_cast<const __m256i*>(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<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);
+ const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
+ const vec_t* in1 = reinterpret_cast<const vec_t*>(&(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<int>(perspectives[p])][j + 0];
+ BiasType sum1 = accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
+ sum0 = std::clamp<BiasType>(sum0, 0, 127);
+ sum1 = std::clamp<BiasType>(sum1, 0, 127);
+ output[offset + j] = static_cast<OutputType>(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<WHITE>(pos);
+ hint_common_access_for_perspective<BLACK>(pos);
+ }
+
+ private:
+ template<Color Perspective>
+ [[nodiscard]] std::pair<StateInfo*, StateInfo*> 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<Color Perspective, size_t N>
+ 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<const __m64*>(
- accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m64 sum1 = *(&reinterpret_cast<const __m64*>(
- 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.
+
+ // Gather all features to be updated.
+ const Square ksq = pos.square<KING>(Perspective);
+
+ // 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];
- #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);
+ for (; st2 != end_state; st2 = st2->previous)
+ FeatureSet::append_changed_indices<Perspective>(
+ ksq, st2->dirtyPiece, removed[i], added[i]);
}
+ }
- #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)));
+ 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<vec_t*>(
+ &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<const vec_t*>(&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<const vec_t*>(&weights[offset]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], column[k]);
+ }
+
+ // Store accumulator
+ accTile = reinterpret_cast<vec_t*>(
+ &states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_store(&accTile[k], acc[k]);
}
- #endif
+ }
+
+ for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
+ {
+ // Load accumulator
+ auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
+ &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<const psqt_vec_t*>(&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<const psqt_vec_t*>(&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<psqt_vec_t*>(
+ &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]);
+ }
}
- #if defined(USE_MMX)
- _mm_empty();
- #endif
- }
- private:
- // Calculate cumulative value without using difference calculation
- void RefreshAccumulator(const Position& pos) const {
+#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));
- 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<const __m512i*>(&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<const __m256i*>(&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<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_MMX)
- auto accumulation = reinterpret_cast<__m64*>(
- &accumulator.accumulation[perspective][i][0]);
- auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- for (IndexType j = 0; j < kNumChunks; ++j)
- accumulation[j] = _mm_add_pi16(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]);
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k];
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j)
- accumulator.accumulation[perspective][i][j] += weights_[offset + j];
- #endif
+ 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;
}
- // Calculate cumulative value using difference calculation
- void UpdateAccumulator(const Position& pos) const {
+ template<Color Perspective>
+ 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
- const auto prev_accumulator = pos.state()->previous->accumulator;
+ // 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;
- 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<int16x8_t*>(
- &accumulator.accumulation[perspective][i][0]);
- #endif
+ accumulator.computed[Perspective] = true;
+ FeatureSet::IndexList active;
+ FeatureSet::append_active_indices<Perspective>(pos, active);
+
+#ifdef VECTOR
+ for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
+ {
+ auto biasesTile = reinterpret_cast<const vec_t*>(
+ &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<const vec_t*>(&weights[offset]);
+
+ for (unsigned k = 0; k < NumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], column[k]);
+ }
- 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;
+ auto accTile = reinterpret_cast<vec_t*>(
+ &accumulator.accumulation[Perspective][j * TileHeight]);
+ for (unsigned k = 0; k < NumRegs; k++)
+ vec_store(&accTile[k], acc[k]);
+ }
- #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]);
+ for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
+ {
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_zero_psqt();
- #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]);
+ for (const auto index : active)
+ {
+ const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- #elif defined(USE_MMX)
- auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j)
- accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
+ }
- #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]);
+ auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
+ &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ vec_store_psqt(&accTilePsqt[k], psqt[k]);
+ }
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j)
- accumulator.accumulation[perspective][i][j] -= weights_[offset + j];
- #endif
+#else
+ std::memcpy(accumulator.accumulation[Perspective], biases,
+ HalfDimensions * sizeof(BiasType));
- }
- }
- { // 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_MMX)
- auto column = reinterpret_cast<const __m64*>(&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<const int16x8_t*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j)
- accumulation[j] = vaddq_s16(accumulation[j], column[j]);
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ accumulator.psqtAccumulation[Perspective][k] = 0;
- #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 = 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<Color Perspective>
+ 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;
- accumulator.computed_accumulation = true;
+ auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(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<Perspective, 2>(pos, oldest_st, states_to_update);
+ }
+ else
+ {
+ update_accumulator_refresh<Perspective>(pos);
+ }
}
- using BiasType = std::int16_t;
- using WeightType = std::int16_t;
+ template<Color Perspective>
+ void update_accumulator(const Position& pos) const {
+
+ auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(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<Perspective, 3>(pos, oldest_st, states_to_update);
+ }
+ else
+ {
+ update_accumulator_refresh<Perspective>(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
projects / stockfish / blobdiff