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BadFish/src/nnue/nnue_feature_transformer.h
mstembera 95fe2b9a9d Reduce SIMD register count from 32 to 16 
in the case of avx512 and vnni512 archs.

Up to 17% speedup, depending on the compiler, e.g.

```
AMD pro 7840u (zen4 phoenix apu 4nm)
bash bench_parallel.sh ./stockfish_avx512_gcc13 ./stockfish_avx512_pr_gcc13 20 10
sf_base =  1077737 +/-   8446 (95%)
sf_test =  1264268 +/-   8543 (95%)
diff    =   186531 +/-   4280 (95%)
speedup =  17.308% +/- 0.397% (95%)
```

Prior to this patch, it appears gcc spills registers.

closes https://github.com/official-stockfish/Stockfish/pull/4796

No functional change
2023-09-22 19:15:34 +02:00

681 lines
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/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
// A class that converts the input features of the NNUE evaluation function
#ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
#define NNUE_FEATURE_TRANSFORMER_H_INCLUDED
#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 "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
// Input feature converter
class FeatureTransformer {
private:
// Number of output dimensions for one side
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 InputDimensions = FeatureSet::Dimensions;
static constexpr IndexType OutputDimensions = HalfDimensions;
// Size of forward propagation buffer
static constexpr std::size_t BufferSize =
OutputDimensions * sizeof(OutputType);
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
return FeatureSet::HashValue ^ (OutputDimensions * 2);
}
// Read network parameters
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);
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
write_leb_128<BiasType >(stream, biases , HalfDimensions );
write_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
return !stream.fail();
}
// Convert input features
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[perspectives[0]][bucket]
- psqtAccumulation[perspectives[1]][bucket]
) / 2;
for (IndexType p = 0; p < 2; ++p)
{
const IndexType offset = (HalfDimensions / 2) * p;
#if defined(VECTOR)
constexpr IndexType OutputChunkSize = MaxChunkSize;
static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
vec_t Zero = vec_zero();
vec_t One = vec_set_16(127);
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 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);
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;
// 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];
for (; st2 != end_state; st2 = st2->previous)
FeatureSet::append_changed_indices<Perspective>(
ksq, st2->dirtyPiece, removed[i], added[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<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]);
}
}
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; states_to_update[i]; ++i)
{
std::memcpy(states_to_update[i]->accumulator.accumulation[Perspective],
st->accumulator.accumulation[Perspective],
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k];
st = states_to_update[i];
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
st->accumulator.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
st->accumulator.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
}
#endif
#if defined(USE_MMX)
_mm_empty();
#endif
}
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
// 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<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]);
}
auto accTile = reinterpret_cast<vec_t*>(
&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[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<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;
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);
}
}
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(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
};
} // namespace Stockfish::Eval::NNUE
#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
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