/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+ 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
namespace 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 {
// 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;
// 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) {
- stream.read(reinterpret_cast<char*>(biases_),
- kHalfDimensions * sizeof(BiasType));
- stream.read(reinterpret_cast<char*>(weights_),
- kHalfDimensions * kInputDimensions * sizeof(WeightType));
- return !stream.fail();
- }
- // Proceed with the difference calculation if possible
- bool UpdateAccumulatorIfPossible(const Position& pos) const {
- const auto now = pos.state();
- if (now->accumulator.computed_accumulation) {
- return true;
- }
- const auto prev = now->previous;
- if (prev && prev->accumulator.computed_accumulation) {
- UpdateAccumulator(pos);
- return true;
- }
- return false;
+ 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, bool refresh) const {
- if (refresh || !UpdateAccumulatorIfPossible(pos)) {
- RefreshAccumulator(pos);
- }
+ void Transform(const Position& pos, OutputType* output) const {
+
+ UpdateAccumulator(pos, WHITE);
+ UpdateAccumulator(pos, BLACK);
+
const auto& accumulation = pos.state()->accumulator.accumulation;
- #if defined(USE_AVX2)
+ #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_SSSE3)
+ #elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
#ifdef USE_SSE41
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};
for (IndexType p = 0; p < 2; ++p) {
const IndexType offset = kHalfDimensions * p;
- #if defined(USE_AVX2)
+ #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_loadA_si256(
+ __m256i sum0 = _mm256_load_si256(
&reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m256i sum1 = _mm256_loadA_si256(
- &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
- _mm256_storeA_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
+ __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_SSSE3)
+ #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*>(
_mm_store_si128(&out[j],
#ifdef USE_SSE41
- _mm_max_epi8(packedbytes, kZero)
+ _mm_max_epi8(packedbytes, kZero)
#else
- _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
+ _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) {
#endif
}
+ #if defined(USE_MMX)
+ _mm_empty();
+ #endif
}
private:
- // Calculate cumulative value without using difference calculation
- void RefreshAccumulator(const Position& pos) const {
- auto& accumulator = pos.state()->accumulator;
- IndexType i = 0;
- Features::IndexList active_indices[2];
- RawFeatures::AppendActiveIndices(pos, kRefreshTriggers[i],
- active_indices);
- for (Color perspective : { WHITE, BLACK }) {
- std::memcpy(accumulator.accumulation[perspective][i], biases_,
- kHalfDimensions * sizeof(BiasType));
- for (const auto index : active_indices[perspective]) {
- const IndexType offset = kHalfDimensions * index;
-
- #if defined(USE_AVX2)
- auto accumulation = reinterpret_cast<__m256i*>(
- &accumulator.accumulation[perspective][i][0]);
- auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- _mm256_storeA_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadA_si256(&accumulation[j]), column[j]));
- }
-
- #elif defined(USE_SSE2)
- auto accumulation = reinterpret_cast<__m128i*>(
- &accumulator.accumulation[perspective][i][0]);
- auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
- }
-
- #elif defined(USE_NEON)
- auto accumulation = reinterpret_cast<int16x8_t*>(
- &accumulator.accumulation[perspective][i][0]);
- auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = vaddq_s16(accumulation[j], column[j]);
- }
+ void UpdateAccumulator(const Position& pos, const Color c) const {
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- accumulator.accumulation[perspective][i][j] += weights_[offset + j];
- }
+ #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;
}
- accumulator.computed_accumulation = true;
- accumulator.computed_score = false;
- }
+ 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]);
+ }
- // Calculate cumulative value using difference calculation
- void UpdateAccumulator(const Position& pos) const {
- const auto prev_accumulator = pos.state()->previous->accumulator;
- auto& accumulator = pos.state()->accumulator;
- IndexType i = 0;
- Features::IndexList removed_indices[2], added_indices[2];
- bool reset[2];
- RawFeatures::AppendChangedIndices(pos, kRefreshTriggers[i],
- removed_indices, added_indices, reset);
- for (Color perspective : { WHITE, BLACK }) {
-
- #if defined(USE_AVX2)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- auto accumulation = reinterpret_cast<__m256i*>(
- &accumulator.accumulation[perspective][i][0]);
+ // 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]);
+ }
- #elif defined(USE_SSE2)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- auto accumulation = reinterpret_cast<__m128i*>(
- &accumulator.accumulation[perspective][i][0]);
+ // 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]);
+ }
+ }
- #elif defined(USE_NEON)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- auto accumulation = reinterpret_cast<int16x8_t*>(
- &accumulator.accumulation[perspective][i][0]);
- #endif
+ #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];
- if (reset[perspective]) {
- std::memcpy(accumulator.accumulation[perspective][i], biases_,
- kHalfDimensions * sizeof(BiasType));
- } else {
- std::memcpy(accumulator.accumulation[perspective][i],
- prev_accumulator.accumulation[perspective][i],
- kHalfDimensions * sizeof(BiasType));
// Difference calculation for the deactivated features
- for (const auto index : removed_indices[perspective]) {
+ for (const auto index : removed[i])
+ {
const IndexType offset = kHalfDimensions * index;
- #if defined(USE_AVX2)
- auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
- }
-
- #elif defined(USE_SSE2)
- auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
- }
-
- #elif defined(USE_NEON)
- auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = vsubq_s16(accumulation[j], column[j]);
- }
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
+ st->accumulator.accumulation[c][0][j] -= weights_[offset + j];
+ }
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- accumulator.accumulation[perspective][i][j] -=
- weights_[offset + j];
- }
- #endif
+ // 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];
}
}
- { // Difference calculation for the activated features
- for (const auto index : added_indices[perspective]) {
- const IndexType offset = kHalfDimensions * index;
-
- #if defined(USE_AVX2)
- auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
- }
-
- #elif defined(USE_SSE2)
- auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = _mm_add_epi16(accumulation[j], column[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]);
+ }
- #elif defined(USE_NEON)
- auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- accumulation[j] = vaddq_s16(accumulation[j], column[j]);
- }
+ 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
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- accumulator.accumulation[perspective][i][j] +=
- weights_[offset + j];
- }
- #endif
+ 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
}
- accumulator.computed_accumulation = true;
- accumulator.computed_score = false;
+ #if defined(USE_MMX)
+ _mm_empty();
+ #endif
}
using BiasType = std::int16_t;
projects / stockfish / blobdiff