deepcrayon
/
stockfish
1
0
Fork 0
Code Releases Activity
stockfish/src/nnue/nnue_feature_transformer.h

418 lines
15 KiB
C++
Raw Blame History

/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 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 "nnue_common.h"
#include "nnue_architecture.h"
#include "features/index_list.h"
#include <cstring> // std::memset()
namespace Stockfish::Eval::NNUE {
// 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
#ifdef USE_AVX512
typedef __m512i 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
#elif USE_AVX2
typedef __m256i 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;
#elif USE_SSE2
typedef __m128i 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;
#elif USE_MMX
typedef __m64 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;
#elif USE_NEON
typedef int16x8_t 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;
#else
#undef VECTOR
#endif
// Input feature converter
class FeatureTransformer {
private:
// Number of output dimensions for one side
static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
#ifdef VECTOR
static constexpr IndexType kTileHeight = kNumRegs * sizeof(vec_t) / 2;
static_assert(kHalfDimensions % kTileHeight == 0, "kTileHeight must divide kHalfDimensions");
#endif
public:
// Output type
using OutputType = TransformedFeatureType;
// Number of input/output dimensions
static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;
// Size of forward propagation buffer
static constexpr std::size_t kBufferSize =
kOutputDimensions * sizeof(OutputType);
// Hash value embedded in the evaluation file
static constexpr std::uint32_t GetHashValue() {
return RawFeatures::kHashValue ^ kOutputDimensions;
}
// Read network parameters
bool ReadParameters(std::istream& stream) {
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 {
UpdateAccumulator(pos, WHITE);
UpdateAccumulator(pos, BLACK);
const auto& accumulation = pos.state()->accumulator.accumulation;
#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();
#elif defined(USE_AVX2)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
constexpr int kControl = 0b11011000;
const __m256i kZero = _mm256_setzero_si256();
#elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
#ifdef USE_SSE41
const __m128i kZero = _mm_setzero_si128();
#else
const __m128i k0x80s = _mm_set1_epi8(-128);
#endif
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
const int8x8_t kZero = {0};
#endif
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
for (IndexType p = 0; p < 2; ++p) {
const IndexType offset = kHalfDimensions * p;
#if defined(USE_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)));
}
#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));
}
#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
);
}
#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);
}
#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);
}
#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 {
#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];
#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)
{
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)
break;
next = st;
st = st->previous;
}
if (st->accumulator.state[c] == COMPUTED)
{
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]);
for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous)
Features::HalfKP<Features::Side::kFriend>::AppendChangedIndices(pos,
st2->dirtyPiece, c, &removed[1], &added[1]);
// Mark the accumulators as computed.
next->accumulator.state[c] = COMPUTED;
pos.state()->accumulator.state[c] = COMPUTED;
// Now update the accumulators listed in info[], where the last element is a sentinel.
StateInfo *info[3] =
{ next, next == pos.state() ? nullptr : pos.state(), nullptr };
#ifdef VECTOR
for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j)
{
// Load accumulator
auto accTile = reinterpret_cast<vec_t*>(
&st->accumulator.accumulation[c][0][j * kTileHeight]);
for (IndexType k = 0; k < kNumRegs; ++k)
acc[k] = vec_load(&accTile[k]);
for (IndexType i = 0; info[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)
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)
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)
vec_store(&accTile[k], acc[k]);
}
}
#else
for (IndexType i = 0; info[i]; ++i)
{
std::memcpy(info[i]->accumulator.accumulation[c][0],
st->accumulator.accumulation[c][0],
kHalfDimensions * sizeof(BiasType));
st = info[i];
// Difference calculation for the deactivated features
for (const auto index : removed[i])
{
const IndexType offset = kHalfDimensions * index;
for (IndexType j = 0; j < kHalfDimensions; ++j)
st->accumulator.accumulation[c][0][j] -= weights_[offset + j];
}
// Difference calculation for the activated features
for (const auto index : added[i])
{
const IndexType offset = kHalfDimensions * index;
for (IndexType j = 0; j < kHalfDimensions; ++j)
st->accumulator.accumulation[c][0][j] += weights_[offset + j];
}
}
#endif
}
else
{
// Refresh the accumulator
auto& accumulator = pos.state()->accumulator;
accumulator.state[c] = COMPUTED;
Features::IndexList active;
Features::HalfKP<Features::Side::kFriend>::AppendActiveIndices(pos, c, &active);
#ifdef VECTOR
for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j)
{
auto biasesTile = reinterpret_cast<const vec_t*>(
&biases_[j * kTileHeight]);
for (IndexType k = 0; k < kNumRegs; ++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]);
for (unsigned k = 0; k < kNumRegs; ++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++)
vec_store(&accTile[k], acc[k]);
}
#else
std::memcpy(accumulator.accumulation[c][0], biases_,
kHalfDimensions * sizeof(BiasType));
for (const auto index : active)
{
const IndexType offset = kHalfDimensions * index;
for (IndexType j = 0; j < kHalfDimensions; ++j)
accumulator.accumulation[c][0][j] += weights_[offset + j];
}
#endif
}
#if defined(USE_MMX)
_mm_empty();
#endif
}
using BiasType = std::int16_t;
using WeightType = std::int16_t;
alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
alignas(kCacheLineSize)
WeightType weights_[kHalfDimensions * kInputDimensions];
};
} // namespace Stockfish::Eval::NNUE
#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
View Git Blame Copy Permalink