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 TILING
+
+ #ifdef USE_AVX512
+ typedef __m512i vec_t;
+ #define vec_load(a) _mm512_loadA_si512(a)
+ #define vec_store(a,b) _mm512_storeA_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_loadA_si256(a)
+ #define vec_store(a,b) _mm256_storeA_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 TILING
+
+ #endif
+
// Input feature converter
class FeatureTransformer {
// Number of output dimensions for one side
static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;
+ #ifdef TILING
+ 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));
+
+ 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();
}
// Proceed with the difference calculation if possible
bool UpdateAccumulatorIfPossible(const Position& pos) const {
+
const auto now = pos.state();
- if (now->accumulator.computed_accumulation) {
+ if (now->accumulator.computed_accumulation)
return true;
- }
+
const auto prev = now->previous;
- if (prev && prev->accumulator.computed_accumulation) {
- UpdateAccumulator(pos);
- return true;
+ if (prev) {
+ if (prev->accumulator.computed_accumulation) {
+ UpdateAccumulator(pos);
+ return true;
+ } else if (prev->previous && prev->previous->accumulator.computed_accumulation) {
+ UpdateAccumulator(pos);
+ return true;
+ }
}
+
return false;
}
// Convert input features
- void Transform(const Position& pos, OutputType* output, bool refresh) const {
- if (refresh || !UpdateAccumulatorIfPossible(pos)) {
+ void Transform(const Position& pos, OutputType* output) const {
+
+ if (!UpdateAccumulatorIfPossible(pos))
RefreshAccumulator(pos);
- }
+
const auto& accumulation = pos.state()->accumulator.accumulation;
#if defined(USE_AVX2)
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};
_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*>(
);
}
+ #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]));
+ #ifdef TILING
+ for (unsigned j = 0; j < kHalfDimensions / kTileHeight; ++j) {
+ auto biasesTile = reinterpret_cast<const vec_t*>(
+ &biases_[j * kTileHeight]);
+ auto accTile = reinterpret_cast<vec_t*>(
+ &accumulator.accumulation[perspective][i][j * kTileHeight]);
+ vec_t acc[kNumRegs];
+
+ for (unsigned k = 0; k < kNumRegs; ++k)
+ acc[k] = biasesTile[k];
+
+ for (const auto index : active_indices[perspective]) {
+ 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_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]);
- }
+ for (unsigned k = 0; k < kNumRegs; k++)
+ vec_store(&accTile[k], acc[k]);
+ }
+ #else
+ std::memcpy(accumulator.accumulation[perspective][i], biases_,
+ kHalfDimensions * sizeof(BiasType));
- #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]);
- }
+ for (const auto index : active_indices[perspective]) {
+ const IndexType offset = kHalfDimensions * index;
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
accumulator.accumulation[perspective][i][j] += weights_[offset + j];
- }
- #endif
-
}
+ #endif
}
+ #if defined(USE_MMX)
+ _mm_empty();
+ #endif
+
accumulator.computed_accumulation = true;
- accumulator.computed_score = false;
}
// Calculate cumulative value using difference calculation
void UpdateAccumulator(const Position& pos) const {
- const auto prev_accumulator = pos.state()->previous->accumulator;
+
+ Accumulator* prev_accumulator;
+ assert(pos.state()->previous);
+ if (pos.state()->previous->accumulator.computed_accumulation) {
+ prev_accumulator = &pos.state()->previous->accumulator;
+ }
+ else {
+ assert(pos.state()->previous->previous);
+ assert(pos.state()->previous->previous->accumulator.computed_accumulation);
+ prev_accumulator = &pos.state()->previous->previous->accumulator;
+ }
+
auto& accumulator = pos.state()->accumulator;
IndexType i = 0;
Features::IndexList removed_indices[2], added_indices[2];
- bool reset[2];
+ bool reset[2] = { false, false };
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]);
+ #ifdef TILING
+ for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j) {
+ for (Color perspective : { WHITE, BLACK }) {
+ auto accTile = reinterpret_cast<vec_t*>(
+ &accumulator.accumulation[perspective][i][j * kTileHeight]);
+ vec_t acc[kNumRegs];
+
+ if (reset[perspective]) {
+ auto biasesTile = reinterpret_cast<const vec_t*>(
+ &biases_[j * kTileHeight]);
+ for (unsigned k = 0; k < kNumRegs; ++k)
+ acc[k] = biasesTile[k];
+ } else {
+ auto prevAccTile = reinterpret_cast<const vec_t*>(
+ &prev_accumulator->accumulation[perspective][i][j * kTileHeight]);
+ for (IndexType k = 0; k < kNumRegs; ++k)
+ acc[k] = vec_load(&prevAccTile[k]);
+
+ // Difference calculation for the deactivated features
+ for (const auto index : removed_indices[perspective]) {
+ 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_indices[perspective]) {
+ const IndexType offset = kHalfDimensions * index + j * kTileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights_[offset]);
- #elif defined(USE_SSE2)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- auto accumulation = reinterpret_cast<__m128i*>(
- &accumulator.accumulation[perspective][i][0]);
+ for (IndexType k = 0; k < kNumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], column[k]);
+ }
+ }
- #elif defined(USE_NEON)
- constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
- auto accumulation = reinterpret_cast<int16x8_t*>(
- &accumulator.accumulation[perspective][i][0]);
+ for (IndexType k = 0; k < kNumRegs; ++k)
+ vec_store(&accTile[k], acc[k]);
+ }
+ }
+ #if defined(USE_MMX)
+ _mm_empty();
#endif
+ #else
+ for (Color perspective : { WHITE, BLACK }) {
+
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],
+ prev_accumulator->accumulation[perspective][i],
kHalfDimensions * sizeof(BiasType));
// Difference calculation for the deactivated features
for (const auto index : removed_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_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]);
- }
-
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- accumulator.accumulation[perspective][i][j] -=
- weights_[offset + j];
- }
- #endif
-
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
+ accumulator.accumulation[perspective][i][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]);
- }
-
- #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]);
- }
-
- #else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
- accumulator.accumulation[perspective][i][j] +=
- weights_[offset + j];
- }
- #endif
-
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
+ accumulator.accumulation[perspective][i][j] += weights_[offset + j];
}
}
}
+ #endif
accumulator.computed_accumulation = true;
- accumulator.computed_score = false;
}
using BiasType = std::int16_t;
projects / stockfish / blobdiff