/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2024 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 .
*/
// 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
#include
#include
#include
#include
#include
#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)
// Inverse permuted at load time
#define vec_msb_pack_16(a, b) \
_mm512_packs_epi16(_mm512_srli_epi16(a, 7), _mm512_srli_epi16(b, 7))
#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)
// Inverse permuted at load time
#define vec_msb_pack_16(a, b) \
_mm256_packs_epi16(_mm256_srli_epi16(a, 7), _mm256_srli_epi16(b, 7))
#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_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(&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
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;
}
#if defined(__GNUC__)
#pragma GCC diagnostic pop
#endif
#endif
// Input feature converter
template StateInfo::*accPtr>
class FeatureTransformer {
// Number of output dimensions for one side
static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
private:
#ifdef VECTOR
static constexpr int NumRegs =
BestRegisterCount();
static constexpr int NumPsqtRegs =
BestRegisterCount();
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);
}
static constexpr void order_packs([[maybe_unused]] uint64_t* v) {
#if defined(USE_AVX512) // _mm512_packs_epi16 ordering
uint64_t tmp0 = v[2], tmp1 = v[3];
v[2] = v[8], v[3] = v[9];
v[8] = v[4], v[9] = v[5];
v[4] = tmp0, v[5] = tmp1;
tmp0 = v[6], tmp1 = v[7];
v[6] = v[10], v[7] = v[11];
v[10] = v[12], v[11] = v[13];
v[12] = tmp0, v[13] = tmp1;
#elif defined(USE_AVX2) // _mm256_packs_epi16 ordering
std::swap(v[2], v[4]);
std::swap(v[3], v[5]);
#endif
}
static constexpr void inverse_order_packs([[maybe_unused]] uint64_t* v) {
#if defined(USE_AVX512) // Inverse _mm512_packs_epi16 ordering
uint64_t tmp0 = v[2], tmp1 = v[3];
v[2] = v[4], v[3] = v[5];
v[4] = v[8], v[5] = v[9];
v[8] = tmp0, v[9] = tmp1;
tmp0 = v[6], tmp1 = v[7];
v[6] = v[12], v[7] = v[13];
v[12] = v[10], v[13] = v[11];
v[10] = tmp0, v[11] = tmp1;
#elif defined(USE_AVX2) // Inverse _mm256_packs_epi16 ordering
std::swap(v[2], v[4]);
std::swap(v[3], v[5]);
#endif
}
void permute_weights([[maybe_unused]] void (*order_fn)(uint64_t*)) const {
#if defined(USE_AVX2)
#if defined(USE_AVX512)
constexpr IndexType di = 16;
#else
constexpr IndexType di = 8;
#endif
uint64_t* b = reinterpret_cast(const_cast(&biases[0]));
for (IndexType i = 0; i < HalfDimensions * sizeof(BiasType) / sizeof(uint64_t); i += di)
order_fn(&b[i]);
for (IndexType j = 0; j < InputDimensions; ++j)
{
uint64_t* w =
reinterpret_cast(const_cast(&weights[j * HalfDimensions]));
for (IndexType i = 0; i < HalfDimensions * sizeof(WeightType) / sizeof(uint64_t);
i += di)
order_fn(&w[i]);
}
#endif
}
// Read network parameters
bool read_parameters(std::istream& stream) {
read_leb_128(stream, biases, HalfDimensions);
read_leb_128(stream, weights, HalfDimensions * InputDimensions);
read_leb_128(stream, psqtWeights, PSQTBuckets * InputDimensions);
permute_weights(inverse_order_packs);
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
permute_weights(order_packs);
write_leb_128(stream, biases, HalfDimensions);
write_leb_128(stream, weights, HalfDimensions * InputDimensions);
write_leb_128(stream, psqtWeights, PSQTBuckets * InputDimensions);
permute_weights(inverse_order_packs);
return !stream.fail();
}
// Convert input features
std::int32_t transform(const Position& pos,
AccumulatorCaches::Cache* cache,
OutputType* output,
int bucket,
bool psqtOnly) const {
update_accumulator(pos, cache, psqtOnly);
update_accumulator(pos, cache, psqtOnly);
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
const auto& psqtAccumulation = (pos.state()->*accPtr).psqtAccumulation;
const auto psqt =
(psqtAccumulation[perspectives[0]][bucket] - psqtAccumulation[perspectives[1]][bucket])
/ 2;
if (psqtOnly)
return psqt;
const auto& accumulation = (pos.state()->*accPtr).accumulation;
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;
const vec_t Zero = vec_zero();
const vec_t One = vec_set_16(127);
const vec_t* in0 = reinterpret_cast(&(accumulation[perspectives[p]][0]));
const vec_t* in1 =
reinterpret_cast(&(accumulation[perspectives[p]][HalfDimensions / 2]));
vec_t* out = reinterpret_cast(output + offset);
for (IndexType j = 0; j < NumOutputChunks; ++j)
{
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(perspectives[p])][j + 0];
BiasType sum1 =
accumulation[static_cast(perspectives[p])][j + HalfDimensions / 2];
sum0 = std::clamp(sum0, 0, 127);
sum1 = std::clamp(sum1, 0, 127);
output[offset + j] = static_cast(unsigned(sum0 * sum1) / 128);
}
#endif
}
return psqt;
} // end of function transform()
void hint_common_access(const Position& pos,
AccumulatorCaches::Cache* cache,
bool psqtOnly) const {
hint_common_access_for_perspective(pos, cache, psqtOnly);
hint_common_access_for_perspective(pos, cache, psqtOnly);
}
private:
template
[[nodiscard]] std::pair
try_find_computed_accumulator(const Position& pos, bool psqtOnly) 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->*accPtr).computedPSQT[Perspective]
|| (!psqtOnly && !(st->*accPtr).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
void update_accumulator_incremental(const Position& pos,
StateInfo* computed_st,
StateInfo* states_to_update[N],
bool psqtOnly) 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(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]->*accPtr).computed[Perspective] = !psqtOnly;
(states_to_update[i]->*accPtr).computedPSQT[Perspective] = true;
const StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
for (; st2 != end_state; st2 = st2->previous)
FeatureSet::append_changed_indices(ksq, st2->dirtyPiece,
removed[i], added[i]);
}
}
StateInfo* st = computed_st;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
#ifdef VECTOR
if (states_to_update[1] == nullptr && (removed[0].size() == 1 || removed[0].size() == 2)
&& added[0].size() == 1)
{
assert(states_to_update[0]);
if (!psqtOnly)
{
auto accIn =
reinterpret_cast(&(st->*accPtr).accumulation[Perspective][0]);
auto accOut = reinterpret_cast(
&(states_to_update[0]->*accPtr).accumulation[Perspective][0]);
const IndexType offsetR0 = HalfDimensions * removed[0][0];
auto columnR0 = reinterpret_cast(&weights[offsetR0]);
const IndexType offsetA = HalfDimensions * added[0][0];
auto columnA = reinterpret_cast(&weights[offsetA]);
if (removed[0].size() == 1)
{
for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
++k)
accOut[k] = vec_add_16(vec_sub_16(accIn[k], columnR0[k]), columnA[k]);
}
else
{
const IndexType offsetR1 = HalfDimensions * removed[0][1];
auto columnR1 = reinterpret_cast(&weights[offsetR1]);
for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
++k)
accOut[k] = vec_sub_16(vec_add_16(accIn[k], columnA[k]),
vec_add_16(columnR0[k], columnR1[k]));
}
}
auto accPsqtIn =
reinterpret_cast(&(st->*accPtr).psqtAccumulation[Perspective][0]);
auto accPsqtOut = reinterpret_cast(
&(states_to_update[0]->*accPtr).psqtAccumulation[Perspective][0]);
const IndexType offsetPsqtR0 = PSQTBuckets * removed[0][0];
auto columnPsqtR0 = reinterpret_cast(&psqtWeights[offsetPsqtR0]);
const IndexType offsetPsqtA = PSQTBuckets * added[0][0];
auto columnPsqtA = reinterpret_cast(&psqtWeights[offsetPsqtA]);
if (removed[0].size() == 1)
{
for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
++k)
accPsqtOut[k] = vec_add_psqt_32(vec_sub_psqt_32(accPsqtIn[k], columnPsqtR0[k]),
columnPsqtA[k]);
}
else
{
const IndexType offsetPsqtR1 = PSQTBuckets * removed[0][1];
auto columnPsqtR1 = reinterpret_cast(&psqtWeights[offsetPsqtR1]);
for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
++k)
accPsqtOut[k] =
vec_sub_psqt_32(vec_add_psqt_32(accPsqtIn[k], columnPsqtA[k]),
vec_add_psqt_32(columnPsqtR0[k], columnPsqtR1[k]));
}
}
else
{
if (!psqtOnly)
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
// Load accumulator
auto accTileIn = reinterpret_cast(
&(st->*accPtr).accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_load(&accTileIn[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(&weights[offset]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_sub_16(acc[k], column[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast(&weights[offset]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
// Store accumulator
auto accTileOut =
reinterpret_cast(&(states_to_update[i]->*accPtr)
.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
vec_store(&accTileOut[k], acc[k]);
}
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
// Load accumulator
auto accTilePsqtIn = reinterpret_cast(
&(st->*accPtr).psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_load_psqt(&accTilePsqtIn[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(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
// Store accumulator
auto accTilePsqtOut = reinterpret_cast(
&(states_to_update[i]->*accPtr)
.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqtOut[k], psqt[k]);
}
}
}
#else
for (IndexType i = 0; states_to_update[i]; ++i)
{
if (!psqtOnly)
std::memcpy((states_to_update[i]->*accPtr).accumulation[Perspective],
(st->*accPtr).accumulation[Perspective],
HalfDimensions * sizeof(BiasType));
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(states_to_update[i]->*accPtr).psqtAccumulation[Perspective][k] =
(st->*accPtr).psqtAccumulation[Perspective][k];
st = states_to_update[i];
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
if (!psqtOnly)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
(st->*accPtr).accumulation[Perspective][j] -= weights[offset + j];
}
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(st->*accPtr).psqtAccumulation[Perspective][k] -=
psqtWeights[index * PSQTBuckets + k];
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
if (!psqtOnly)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
(st->*accPtr).accumulation[Perspective][j] += weights[offset + j];
}
for (std::size_t k = 0; k < PSQTBuckets; ++k)
(st->*accPtr).psqtAccumulation[Perspective][k] +=
psqtWeights[index * PSQTBuckets + k];
}
}
#endif
}
template
void update_accumulator_refresh_cache(const Position& pos,
AccumulatorCaches::Cache* cache) const {
assert(cache != nullptr);
Square ksq = pos.square(Perspective);
auto& entry = (*cache)[ksq];
auto& accumulator = pos.state()->*accPtr;
accumulator.computed[Perspective] = true;
accumulator.computedPSQT[Perspective] = true;
FeatureSet::IndexList removed, added;
for (Color c : {WHITE, BLACK})
{
for (PieceType pt = PAWN; pt <= KING; ++pt)
{
const Piece piece = make_piece(c, pt);
const Bitboard oldBB =
entry.byColorBB[Perspective][c] & entry.byTypeBB[Perspective][pt];
const Bitboard newBB = pos.pieces(c, pt);
Bitboard toRemove = oldBB & ~newBB;
Bitboard toAdd = newBB & ~oldBB;
while (toRemove)
{
Square sq = pop_lsb(toRemove);
removed.push_back(FeatureSet::make_index(sq, piece, ksq));
}
while (toAdd)
{
Square sq = pop_lsb(toAdd);
added.push_back(FeatureSet::make_index(sq, piece, ksq));
}
}
}
#ifdef VECTOR
vec_t acc[NumRegs];
psqt_vec_t psqt[NumPsqtRegs];
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto entryTile =
reinterpret_cast(&entry.accumulation[Perspective][j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = entryTile[k];
for (int i = 0; i < int(added.size()); ++i)
{
IndexType index = added[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast(&weights[offset]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
for (int i = 0; i < int(removed.size()); ++i)
{
IndexType index = removed[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast(&weights[offset]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_sub_16(acc[k], column[k]);
}
for (IndexType k = 0; k < NumRegs; k++)
vec_store(&entryTile[k], acc[k]);
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
auto entryTilePsqt = reinterpret_cast(
&entry.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = entryTilePsqt[k];
for (int i = 0; i < int(added.size()); ++i)
{
IndexType index = added[i];
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
for (int i = 0; i < int(removed.size()); ++i)
{
IndexType index = removed[i];
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
}
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&entryTilePsqt[k], psqt[k]);
}
#else
for (const auto index : added)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
entry.accumulation[Perspective][j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
entry.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
}
for (const auto index : removed)
{
const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
entry.accumulation[Perspective][j] -= weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
entry.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
}
#endif
// The accumulator of the refresh entry has been updated.
// Now copy its content to the actual accumulator we were refreshing
std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation[Perspective],
sizeof(int32_t) * PSQTBuckets);
std::memcpy(accumulator.accumulation[Perspective], entry.accumulation[Perspective],
sizeof(BiasType) * HalfDimensions);
for (Color c : {WHITE, BLACK})
entry.byColorBB[Perspective][c] = pos.pieces(c);
for (PieceType pt = PAWN; pt <= KING; ++pt)
entry.byTypeBB[Perspective][pt] = pos.pieces(pt);
}
template
void
update_accumulator_refresh(const Position& pos,
[[maybe_unused]] AccumulatorCaches::Cache* cache,
bool psqtOnly) const {
// When we are refreshing the accumulator of the big net,
// redirect to the version of refresh that uses the refresh table.
// Using the cache for the small net is not beneficial.
if constexpr (HalfDimensions == Eval::NNUE::TransformedFeatureDimensionsBig)
{
update_accumulator_refresh_cache(pos, cache);
return;
}
#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()->*accPtr;
accumulator.computed[Perspective] = !psqtOnly;
accumulator.computedPSQT[Perspective] = true;
FeatureSet::IndexList active;
FeatureSet::append_active_indices(pos, active);
#ifdef VECTOR
if (!psqtOnly)
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
auto biasesTile = reinterpret_cast(&biases[j * TileHeight]);
for (IndexType k = 0; k < NumRegs; ++k)
acc[k] = biasesTile[k];
int i = 0;
for (; i < int(active.size()) - 1; i += 2)
{
IndexType index0 = active[i];
IndexType index1 = active[i + 1];
const IndexType offset0 = HalfDimensions * index0 + j * TileHeight;
const IndexType offset1 = HalfDimensions * index1 + j * TileHeight;
auto column0 = reinterpret_cast(&weights[offset0]);
auto column1 = reinterpret_cast(&weights[offset1]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], vec_add_16(column0[k], column1[k]));
}
for (; i < int(active.size()); ++i)
{
IndexType index = active[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast(&weights[offset]);
for (unsigned k = 0; k < NumRegs; ++k)
acc[k] = vec_add_16(acc[k], column[k]);
}
auto accTile =
reinterpret_cast(&accumulator.accumulation[Perspective][j * TileHeight]);
for (unsigned k = 0; k < NumRegs; k++)
vec_store(&accTile[k], acc[k]);
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_zero_psqt();
int i = 0;
for (; i < int(active.size()) - 1; i += 2)
{
IndexType index0 = active[i];
IndexType index1 = active[i + 1];
const IndexType offset0 = PSQTBuckets * index0 + j * PsqtTileHeight;
const IndexType offset1 = PSQTBuckets * index1 + j * PsqtTileHeight;
auto columnPsqt0 = reinterpret_cast(&psqtWeights[offset0]);
auto columnPsqt1 = reinterpret_cast(&psqtWeights[offset1]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] =
vec_add_psqt_32(psqt[k], vec_add_psqt_32(columnPsqt0[k], columnPsqt1[k]));
}
for (; i < int(active.size()); ++i)
{
IndexType index = active[i];
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast(&psqtWeights[offset]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
auto accTilePsqt = reinterpret_cast(
&accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
#else
if (!psqtOnly)
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)
{
if (!psqtOnly)
{
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
}
template
void hint_common_access_for_perspective(const Position& pos,
AccumulatorCaches::Cache* cache,
bool psqtOnly) 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()->*accPtr).computed[Perspective]
|| (psqtOnly && (pos.state()->*accPtr).computedPSQT[Perspective]))
return;
auto [oldest_st, _] = try_find_computed_accumulator(pos, psqtOnly);
if ((oldest_st->*accPtr).computed[Perspective]
|| (psqtOnly && (oldest_st->*accPtr).computedPSQT[Perspective]))
{
// Only update current position accumulator to minimize work.
StateInfo* states_to_update[2] = {pos.state(), nullptr};
update_accumulator_incremental(pos, oldest_st, states_to_update,
psqtOnly);
}
else
update_accumulator_refresh(pos, cache, psqtOnly);
}
template
void update_accumulator(const Position& pos,
AccumulatorCaches::Cache* cache,
bool psqtOnly) const {
auto [oldest_st, next] = try_find_computed_accumulator(pos, psqtOnly);
if ((oldest_st->*accPtr).computed[Perspective]
|| (psqtOnly && (oldest_st->*accPtr).computedPSQT[Perspective]))
{
if (next == nullptr)
return;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
// Currently we update 2 accumulators.
// 1. for the current position
// 2. the next accumulator after the computed one
// The heuristic may change in the future.
StateInfo* states_to_update[3] = {next, next == pos.state() ? nullptr : pos.state(),
nullptr};
update_accumulator_incremental(pos, oldest_st, states_to_update,
psqtOnly);
}
else
update_accumulator_refresh(pos, cache, psqtOnly);
}
template
friend struct AccumulatorCaches::Cache;
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