#include "nnue_common.h"
#include "nnue_architecture.h"
-#include "features/index_list.h"
#include <cstring> // std::memset()
-namespace Eval::NNUE {
+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
typedef __m512i vec_t;
+ typedef __m256i psqt_vec_t;
#define vec_load(a) _mm512_load_si512(a)
#define vec_store(a,b) _mm512_store_si512(a,b)
#define vec_add_16(a,b) _mm512_add_epi16(a,b)
#define vec_sub_16(a,b) _mm512_sub_epi16(a,b)
- static constexpr IndexType kNumRegs = 8; // only 8 are needed
+ #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
#elif USE_AVX2
typedef __m256i vec_t;
+ typedef __m256i psqt_vec_t;
#define vec_load(a) _mm256_load_si256(a)
#define vec_store(a,b) _mm256_store_si256(a,b)
#define vec_add_16(a,b) _mm256_add_epi16(a,b)
#define vec_sub_16(a,b) _mm256_sub_epi16(a,b)
- static constexpr IndexType kNumRegs = 16;
+ #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
#elif USE_SSE2
typedef __m128i vec_t;
+ typedef __m128i psqt_vec_t;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_epi16(a,b)
#define vec_sub_16(a,b) _mm_sub_epi16(a,b)
- static constexpr IndexType kNumRegs = Is64Bit ? 16 : 8;
+ #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)
#elif USE_MMX
typedef __m64 vec_t;
+ typedef __m64 psqt_vec_t;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_pi16(a,b)
#define vec_sub_16(a,b) _mm_sub_pi16(a,b)
- static constexpr IndexType kNumRegs = 8;
+ #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 NumRegistersSIMD 8
#elif USE_NEON
typedef int16x8_t vec_t;
+ typedef int32x4_t psqt_vec_t;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) vaddq_s16(a,b)
#define vec_sub_16(a,b) vsubq_s16(a,b)
- static constexpr IndexType kNumRegs = 16;
+ #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
#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.
+ #pragma GCC diagnostic push
+ #pragma GCC diagnostic ignored "-Wignored-attributes"
+
+ 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>();
+
+ #pragma GCC diagnostic pop
+
+ #endif
+
+
+
// Input feature converter
class FeatureTransformer {
private:
// Number of output dimensions for one side
- static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
+ static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
#ifdef VECTOR
- static constexpr IndexType kTileHeight = kNumRegs * sizeof(vec_t) / 2;
- static_assert(kHalfDimensions % kTileHeight == 0, "kTileHeight must divide kHalfDimensions");
+ 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:
using OutputType = TransformedFeatureType;
// Number of input/output dimensions
- static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
- static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;
+ static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
+ static constexpr IndexType OutputDimensions = HalfDimensions * 2;
// Size of forward propagation buffer
- static constexpr std::size_t kBufferSize =
- kOutputDimensions * sizeof(OutputType);
+ static constexpr std::size_t BufferSize =
+ OutputDimensions * sizeof(OutputType);
// Hash value embedded in the evaluation file
- static constexpr std::uint32_t GetHashValue() {
-
- return RawFeatures::kHashValue ^ kOutputDimensions;
+ static constexpr std::uint32_t get_hash_value() {
+ return FeatureSet::HashValue ^ OutputDimensions;
}
// Read network parameters
- bool ReadParameters(std::istream& stream) {
+ bool read_parameters(std::istream& stream) {
+
+ read_little_endian<BiasType >(stream, biases , HalfDimensions );
+ read_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ read_little_endian<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();
}
- // Convert input features
- void Transform(const Position& pos, OutputType* output) const {
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+
+ write_little_endian<BiasType >(stream, biases , HalfDimensions );
+ write_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ write_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
- UpdateAccumulator(pos, WHITE);
- UpdateAccumulator(pos, BLACK);
+ 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);
+ 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;
+
+ const auto psqt = (
+ psqtAccumulation[perspectives[0]][bucket]
+ - psqtAccumulation[perspectives[1]][bucket]
+ ) / 2;
+
#if defined(USE_AVX512)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth * 2);
- static_assert(kHalfDimensions % (kSimdWidth * 2) == 0);
- const __m512i kControl = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
- const __m512i kZero = _mm512_setzero_si512();
+
+ constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2);
+ static_assert(HalfDimensions % (SimdWidth * 2) == 0);
+ const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
+ const __m512i Zero = _mm512_setzero_si512();
+
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m512i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m512i sum0 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m512i sum1 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+
+ _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control,
+ _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero)));
+ }
+ }
+ return psqt;
#elif defined(USE_AVX2)
- constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
- constexpr int kControl = 0b11011000;
- const __m256i kZero = _mm256_setzero_si256();
+
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
+ constexpr int Control = 0b11011000;
+ const __m256i Zero = _mm256_setzero_si256();
+
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m256i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m256i sum0 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m256i sum1 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+
+ _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(
+ _mm256_max_epi8(_mm256_packs_epi16(sum0, sum1), Zero), Control));
+ }
+ }
+ return psqt;
#elif defined(USE_SSE2)
- constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
- #ifdef USE_SSE41
- const __m128i kZero = _mm_setzero_si128();
- #else
+ #ifdef USE_SSE41
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
+ const __m128i Zero = _mm_setzero_si128();
+ #else
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m128i k0x80s = _mm_set1_epi8(-128);
- #endif
+ #endif
+
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m128i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+ const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
+
+ #ifdef USE_SSE41
+ _mm_store_si128(&out[j], _mm_max_epi8(packedbytes, Zero));
+ #else
+ _mm_store_si128(&out[j], _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s));
+ #endif
+ }
+ }
+ return psqt;
#elif defined(USE_MMX)
- constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
- #elif defined(USE_NEON)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- const int8x8_t kZero = {0};
- #endif
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m64*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m64 sum0 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 0]);
+ __m64 sum1 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 1]);
+ const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
+ out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
+ }
+ }
+ _mm_empty();
+ return psqt;
- const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
- for (IndexType p = 0; p < 2; ++p) {
- const IndexType offset = kHalfDimensions * p;
+ #elif defined(USE_NEON)
- #if defined(USE_AVX512)
- auto out = reinterpret_cast<__m512i*>(&output[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- __m512i sum0 = _mm512_load_si512(
- &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m512i sum1 = _mm512_load_si512(
- &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
- _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(kControl,
- _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), kZero)));
- }
+ constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2);
+ const int8x8_t Zero = {0};
- #elif defined(USE_AVX2)
- auto out = reinterpret_cast<__m256i*>(&output[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- __m256i sum0 = _mm256_load_si256(
- &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m256i sum1 = _mm256_load_si256(
- &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
- _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
- _mm256_packs_epi16(sum0, sum1), kZero), kControl));
- }
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ int16x8_t sum = reinterpret_cast<const int16x8_t*>(accumulation[perspectives[p]])[j];
+ out[j] = vmax_s8(vqmovn_s16(sum), Zero);
+ }
+ }
+ return psqt;
- #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);
-
- _mm_store_si128(&out[j],
-
- #ifdef USE_SSE41
- _mm_max_epi8(packedbytes, kZero)
#else
- _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
- #endif
- );
- }
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ {
+ BiasType sum = accumulation[perspectives[p]][j];
+ output[offset + j] = static_cast<OutputType>(std::max<int>(0, std::min<int>(127, sum)));
+ }
+ }
+ return psqt;
- #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);
- }
+ #endif
- #elif defined(USE_NEON)
- const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- int16x8_t sum = reinterpret_cast<const int16x8_t*>(
- accumulation[perspectives[p]][0])[j];
- out[j] = vmax_s8(vqmovn_s16(sum), kZero);
- }
+ } // end of function transform()
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- BiasType sum = accumulation[static_cast<int>(perspectives[p])][0][j];
- output[offset + j] = static_cast<OutputType>(
- std::max<int>(0, std::min<int>(127, sum)));
- }
- #endif
- }
- #if defined(USE_MMX)
- _mm_empty();
- #endif
- }
private:
- void UpdateAccumulator(const Position& pos, const Color c) const {
+ 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[kNumRegs];
+ vec_t acc[NumRegs];
+ psqt_vec_t psqt[NumPsqtRegs];
#endif
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
StateInfo *st = pos.state(), *next = nullptr;
- int gain = pos.count<ALL_PIECES>() - 2;
- while (st->accumulator.state[c] == EMPTY)
+ int gain = FeatureSet::refresh_cost(pos);
+ while (st->previous && !st->accumulator.computed[perspective])
{
- auto& dp = st->dirtyPiece;
- // The first condition tests whether an incremental update is
- // possible at all: if this side's king has moved, it is not possible.
- static_assert(std::is_same_v<RawFeatures::SortedTriggerSet,
- Features::CompileTimeList<Features::TriggerEvent, Features::TriggerEvent::kFriendKingMoved>>,
- "Current code assumes that only kFriendlyKingMoved refresh trigger is being used.");
- if ( dp.piece[0] == make_piece(c, KING)
- || (gain -= dp.dirty_num + 1) < 0)
+ // 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;
}
- if (st->accumulator.state[c] == COMPUTED)
+ if (st->accumulator.computed[perspective])
{
if (next == nullptr)
return;
// Update incrementally in two steps. First, we update the "next"
// accumulator. Then, we update the current accumulator (pos.state()).
- // Gather all features to be updated. This code assumes HalfKP features
- // only and doesn't support refresh triggers.
- static_assert(std::is_same_v<Features::FeatureSet<Features::HalfKP<Features::Side::kFriend>>,
- RawFeatures>);
- Features::IndexList removed[2], added[2];
- Features::HalfKP<Features::Side::kFriend>::AppendChangedIndices(pos,
- next->dirtyPiece, c, &removed[0], &added[0]);
+ // Gather all features to be updated.
+ const Square ksq = pos.square<KING>(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)
- Features::HalfKP<Features::Side::kFriend>::AppendChangedIndices(pos,
- st2->dirtyPiece, c, &removed[1], &added[1]);
+ FeatureSet::append_changed_indices(
+ ksq, st2->dirtyPiece, perspective, removed[1], added[1]);
// Mark the accumulators as computed.
- next->accumulator.state[c] = COMPUTED;
- pos.state()->accumulator.state[c] = COMPUTED;
+ next->accumulator.computed[perspective] = true;
+ pos.state()->accumulator.computed[perspective] = true;
- // Now update the accumulators listed in info[], where the last element is a sentinel.
- StateInfo *info[3] =
+ // 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 < kHalfDimensions / kTileHeight; ++j)
+ for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
// Load accumulator
auto accTile = reinterpret_cast<vec_t*>(
- &st->accumulator.accumulation[c][0][j * kTileHeight]);
- for (IndexType k = 0; k < kNumRegs; ++k)
+ &st->accumulator.accumulation[perspective][j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTile[k]);
- for (IndexType i = 0; info[i]; ++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 = kHalfDimensions * index + j * kTileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
- for (IndexType k = 0; k < kNumRegs; ++k)
+ 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 = kHalfDimensions * index + j * kTileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
- for (IndexType k = 0; k < kNumRegs; ++k)
+ 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*>(
- &info[i]->accumulator.accumulation[c][0][j * kTileHeight]);
- for (IndexType k = 0; k < kNumRegs; ++k)
+ &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)
+ {
+ // 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]);
+ }
+ }
+
#else
- for (IndexType i = 0; info[i]; ++i)
+ for (IndexType i = 0; states_to_update[i]; ++i)
{
- std::memcpy(info[i]->accumulator.accumulation[c][0],
- st->accumulator.accumulation[c][0],
- kHalfDimensions * sizeof(BiasType));
- st = info[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 = kHalfDimensions * index;
+ const IndexType offset = HalfDimensions * index;
+
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ st->accumulator.accumulation[perspective][j] -= weights[offset + j];
- for (IndexType j = 0; j < kHalfDimensions; ++j)
- st->accumulator.accumulation[c][0][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 = kHalfDimensions * index;
+ const IndexType offset = HalfDimensions * index;
- for (IndexType j = 0; j < kHalfDimensions; ++j)
- st->accumulator.accumulation[c][0][j] += weights_[offset + j];
+ 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
{
// Refresh the accumulator
auto& accumulator = pos.state()->accumulator;
- accumulator.state[c] = COMPUTED;
- Features::IndexList active;
- Features::HalfKP<Features::Side::kFriend>::AppendActiveIndices(pos, c, &active);
+ accumulator.computed[perspective] = true;
+ FeatureSet::IndexList active;
+ FeatureSet::append_active_indices(pos, perspective, active);
#ifdef VECTOR
- for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j)
+ for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto biasesTile = reinterpret_cast<const vec_t*>(
- &biases_[j * kTileHeight]);
- for (IndexType k = 0; k < kNumRegs; ++k)
+ &biases[j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = biasesTile[k];
for (const auto index : active)
{
- const IndexType offset = kHalfDimensions * index + j * kTileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
+ const IndexType offset = HalfDimensions * index + j * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (unsigned k = 0; k < kNumRegs; ++k)
+ for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
auto accTile = reinterpret_cast<vec_t*>(
- &accumulator.accumulation[c][0][j * kTileHeight]);
- for (unsigned k = 0; k < kNumRegs; k++)
+ &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<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]);
+ }
+
+ 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
- std::memcpy(accumulator.accumulation[c][0], biases_,
- kHalfDimensions * sizeof(BiasType));
+ 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 = kHalfDimensions * index;
+ const IndexType offset = HalfDimensions * index;
+
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ accumulator.accumulation[perspective][j] += weights[offset + j];
- for (IndexType j = 0; j < kHalfDimensions; ++j)
- accumulator.accumulation[c][0][j] += weights_[offset + j];
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ accumulator.psqtAccumulation[perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
#endif
}
#endif
}
- using BiasType = std::int16_t;
- using WeightType = std::int16_t;
-
- alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
- alignas(kCacheLineSize)
- WeightType weights_[kHalfDimensions * kInputDimensions];
+ alignas(CacheLineSize) BiasType biases[HalfDimensions];
+ alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
+ alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
};
-} // namespace Eval::NNUE
+} // namespace Stockfish::Eval::NNUE
#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
projects / stockfish / blobdiff