/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2022 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
#include "nnue_common.h"
#include "nnue_architecture.h"
-#include "../misc.h"
-
#include <cstring> // std::memset()
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, "Assumed by the current choice of constants.");
+ 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;
#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()
- static constexpr IndexType NumRegs = 8; // only 8 are needed
- static constexpr IndexType NumPsqtRegs = 1;
+ #define NumRegistersSIMD 32
+ #define MaxChunkSize 64
#elif USE_AVX2
typedef __m256i vec_t;
#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()
- static constexpr IndexType NumRegs = 16;
- static constexpr IndexType NumPsqtRegs = 1;
+ #define NumRegistersSIMD 16
+ #define MaxChunkSize 32
#elif USE_SSE2
typedef __m128i vec_t;
#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()
- static constexpr IndexType NumRegs = Is64Bit ? 16 : 8;
- static constexpr IndexType NumPsqtRegs = 2;
+ #define NumRegistersSIMD (Is64Bit ? 16 : 8)
+ #define MaxChunkSize 16
#elif USE_MMX
typedef __m64 vec_t;
#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()
- static constexpr IndexType NumRegs = 8;
- static constexpr IndexType NumPsqtRegs = 4;
+ #define vec_cleanup() _mm_empty()
+ #define NumRegistersSIMD 8
+ #define MaxChunkSize 8
#elif USE_NEON
typedef int16x8_t vec_t;
#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}
- static constexpr IndexType NumRegs = 16;
- static constexpr IndexType NumPsqtRegs = 2;
+ #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
+
+
+
// Input feature converter
class FeatureTransformer {
// Number of input/output dimensions
static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
- static constexpr IndexType OutputDimensions = HalfDimensions * 2;
+ static constexpr IndexType OutputDimensions = HalfDimensions;
// Size of forward propagation buffer
static constexpr std::size_t BufferSize =
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
- return FeatureSet::HashValue ^ OutputDimensions;
+ return FeatureSet::HashValue ^ (OutputDimensions * 2);
}
// Read network parameters
bool read_parameters(std::istream& stream) {
- for (std::size_t i = 0; i < HalfDimensions; ++i)
- biases[i] = read_little_endian<BiasType>(stream);
- for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
- weights[i] = read_little_endian<WeightType>(stream);
- for (std::size_t i = 0; i < PSQTBuckets * InputDimensions; ++i)
- psqtWeights[i] = read_little_endian<PSQTWeightType>(stream);
+
+ read_little_endian<BiasType >(stream, biases , HalfDimensions );
+ read_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ read_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
- for (std::size_t i = 0; i < HalfDimensions; ++i)
- write_little_endian<BiasType>(stream, biases[i]);
- for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
- write_little_endian<WeightType>(stream, weights[i]);
+
+ write_little_endian<BiasType >(stream, biases , HalfDimensions );
+ write_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ write_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+
return !stream.fail();
}
// Convert input features
- std::pair<std::int32_t, bool> transform(const Position& pos, OutputType* output, int bucket, Value lazyThreshold) const {
- update_accumulator(pos, WHITE);
- update_accumulator(pos, BLACK);
+ 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;
const auto psqt = (
- psqtAccumulation[static_cast<int>(perspectives[0])][bucket]
- - psqtAccumulation[static_cast<int>(perspectives[1])][bucket]
+ psqtAccumulation[perspectives[0]][bucket]
+ - psqtAccumulation[perspectives[1]][bucket]
) / 2;
- if (abs(psqt) > (int)lazyThreshold * OutputScale)
- return { psqt, true };
- #if defined(USE_AVX512)
- constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2);
- static_assert(HalfDimensions % (SimdWidth * 2) == 0);
- const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
- const __m512i Zero = _mm512_setzero_si512();
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = (HalfDimensions / 2) * p;
- #elif defined(USE_AVX2)
- constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
- constexpr int Control = 0b11011000;
- const __m256i Zero = _mm256_setzero_si256();
+#if defined(VECTOR)
- #elif defined(USE_SSE2)
- constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
+ constexpr IndexType OutputChunkSize = MaxChunkSize;
+ static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
+ constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
- #ifdef USE_SSE41
- const __m128i Zero = _mm_setzero_si128();
- #else
- const __m128i k0x80s = _mm_set1_epi8(-128);
- #endif
+ vec_t Zero = vec_zero();
+ vec_t One = vec_set_16(127);
- #elif defined(USE_MMX)
- constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
- const __m64 k0x80s = _mm_set1_pi8(-128);
-
- #elif defined(USE_NEON)
- constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2);
- const int8x8_t Zero = {0};
- #endif
+ 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);
- for (IndexType p = 0; p < 2; ++p) {
- const IndexType offset = HalfDimensions * p;
-
- #if defined(USE_AVX512)
- auto out = reinterpret_cast<__m512i*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m512i sum0 = _mm512_load_si512(
- &reinterpret_cast<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)));
- }
+ 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);
- #elif defined(USE_AVX2)
- 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));
- }
+ const vec_t pa = vec_mul_16(sum0a, sum1a);
+ const vec_t pb = vec_mul_16(sum0b, sum1b);
- #elif defined(USE_SSE2)
- 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);
+ out[j] = vec_msb_pack_16(pa, pb);
+ }
- _mm_store_si128(&out[j],
+#else
- #ifdef USE_SSE41
- _mm_max_epi8(packedbytes, Zero)
- #else
- _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
- #endif
+ 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::max<int>(0, std::min<int>(127, sum0));
+ sum1 = std::max<int>(0, std::min<int>(127, sum1));
+ output[offset + j] = static_cast<OutputType>(sum0 * sum1 / 128);
+ }
- );
- }
+#endif
+ }
- #elif defined(USE_MMX)
- 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);
- }
+#if defined(vec_cleanup)
+ vec_cleanup();
+#endif
- #elif defined(USE_NEON)
- 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;
- #else
- for (IndexType j = 0; j < HalfDimensions; ++j) {
- BiasType sum = accumulation[static_cast<int>(perspectives[p])][j];
- output[offset + j] = static_cast<OutputType>(
- std::max<int>(0, std::min<int>(127, sum)));
- }
- #endif
+ } // end of function transform()
- }
- #if defined(USE_MMX)
- _mm_empty();
- #endif
- return { psqt, false };
- }
private:
- void update_accumulator(const Position& pos, const Color perspective) const {
+ template<Color Perspective>
+ void update_accumulator(const Position& pos) const {
// The size must be enough to contain the largest possible update.
// That might depend on the feature set and generally relies on the
// feature set's update cost calculation to be correct and never
// allow updates with more added/removed features than MaxActiveDimensions.
- using IndexList = ValueList<IndexType, FeatureSet::MaxActiveDimensions>;
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
// of the estimated gain in terms of features to be added/subtracted.
StateInfo *st = pos.state(), *next = nullptr;
int gain = FeatureSet::refresh_cost(pos);
- while (st->accumulator.state[perspective] == EMPTY)
+ 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;
}
- if (st->accumulator.state[perspective] == COMPUTED)
+ if (st->accumulator.computed[Perspective])
{
if (next == nullptr)
return;
// accumulator. Then, we update the current accumulator (pos.state()).
// Gather all features to be updated.
- const Square ksq = pos.square<KING>(perspective);
- IndexList removed[2], added[2];
- FeatureSet::append_changed_indices(
- ksq, next, perspective, removed[0], added[0]);
+ const Square ksq = pos.square<KING>(Perspective);
+ FeatureSet::IndexList removed[2], added[2];
+ FeatureSet::append_changed_indices<Perspective>(
+ ksq, next->dirtyPiece, removed[0], added[0]);
for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous)
- FeatureSet::append_changed_indices(
- ksq, st2, perspective, removed[1], added[1]);
+ FeatureSet::append_changed_indices<Perspective>(
+ ksq, st2->dirtyPiece, removed[1], added[1]);
// Mark the accumulators as computed.
- next->accumulator.state[perspective] = COMPUTED;
- pos.state()->accumulator.state[perspective] = COMPUTED;
+ next->accumulator.computed[Perspective] = true;
+ pos.state()->accumulator.computed[Perspective] = true;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
StateInfo *states_to_update[3] =
{
// Load accumulator
auto accTile = reinterpret_cast<vec_t*>(
- &st->accumulator.accumulation[perspective][j * TileHeight]);
+ &st->accumulator.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTile[k]);
// Store accumulator
accTile = reinterpret_cast<vec_t*>(
- &states_to_update[i]->accumulator.accumulation[perspective][j * TileHeight]);
+ &states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_store(&accTile[k], acc[k]);
}
{
// Load accumulator
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &st->accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
+ &st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_load_psqt(&accTilePsqt[k]);
// Store accumulator
accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &states_to_update[i]->accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
+ &states_to_update[i]->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
#else
for (IndexType i = 0; states_to_update[i]; ++i)
{
- std::memcpy(states_to_update[i]->accumulator.accumulation[perspective],
- st->accumulator.accumulation[perspective],
+ 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];
+ states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k];
st = states_to_update[i];
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[perspective][j] -= weights[offset + 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];
+ st->accumulator.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
}
// Difference calculation for the activated features
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[perspective][j] += weights[offset + 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];
+ st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
}
#endif
{
// Refresh the accumulator
auto& accumulator = pos.state()->accumulator;
- accumulator.state[perspective] = COMPUTED;
- IndexList active;
- FeatureSet::append_active_indices(pos, perspective, active);
+ 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 accTile = reinterpret_cast<vec_t*>(
- &accumulator.accumulation[perspective][j * TileHeight]);
+ &accumulator.accumulation[Perspective][j * TileHeight]);
for (unsigned k = 0; k < NumRegs; k++)
vec_store(&accTile[k], acc[k]);
}
}
auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &accumulator.psqtAccumulation[perspective][j * PsqtTileHeight]);
+ &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,
+ std::memcpy(accumulator.accumulation[Perspective], biases,
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[perspective][k] = 0;
+ 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];
+ accumulator.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[perspective][k] += psqtWeights[index * PSQTBuckets + k];
+ accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
#endif
}
#endif
}
- using BiasType = std::int16_t;
- using WeightType = std::int16_t;
- using PSQTWeightType = std::int32_t;
-
alignas(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
projects / stockfish / blobdiff