// 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;
}
+
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};
#if defined(USE_AVX2)
auto out = reinterpret_cast<__m256i*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
- __m256i sum0 =
-
- #if defined(__MINGW32__) || defined(__MINGW64__)
- // HACK: Use _mm256_loadu_si256() instead of _mm256_load_si256. Because the binary
- // compiled with g++ in MSYS2 crashes here because the output memory is not aligned
- // even though alignas is specified.
- _mm256_loadu_si256
- #else
- _mm256_load_si256
- #endif
-
- (&reinterpret_cast<const __m256i*>(
- accumulation[perspectives[p]][0])[j * 2 + 0]);
- __m256i sum1 =
-
- #if defined(__MINGW32__) || defined(__MINGW64__)
- _mm256_loadu_si256
- #else
- _mm256_load_si256
- #endif
-
- (&reinterpret_cast<const __m256i*>(
- accumulation[perspectives[p]][0])[j * 2 + 1]);
-
- #if defined(__MINGW32__) || defined(__MINGW64__)
- _mm256_storeu_si256
- #else
- _mm256_store_si256
- #endif
-
- (&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
+ __m256i sum0 = _mm256_loadA_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(
_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];
kHalfDimensions * sizeof(BiasType));
for (const auto index : active_indices[perspective]) {
const IndexType offset = kHalfDimensions * index;
+ #if defined(USE_AVX512)
+ auto accumulation = reinterpret_cast<__m512i*>(
+ &accumulator.accumulation[perspective][i][0]);
+ auto column = reinterpret_cast<const __m512i*>(&weights_[offset]);
+ constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ _mm512_storeA_si512(&accumulation[j], _mm512_add_epi16(_mm512_loadA_si512(&accumulation[j]), column[j]));
- #if defined(USE_AVX2)
+ #elif 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) {
- #if defined(__MINGW32__) || defined(__MINGW64__)
- _mm256_storeu_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadu_si256(&accumulation[j]), column[j]));
- #else
- accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
- #endif
- }
+ 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) {
+ for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
- }
+
+ #elif defined(USE_MMX)
+ auto accumulation = reinterpret_cast<__m64*>(
+ &accumulator.accumulation[perspective][i][0]);
+ auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ accumulation[j] = _mm_add_pi16(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) {
+ for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = vaddq_s16(accumulation[j], column[j]);
- }
#else
- for (IndexType j = 0; j < kHalfDimensions; ++j) {
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
accumulator.accumulation[perspective][i][j] += weights_[offset + j];
- }
#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;
auto& accumulator = pos.state()->accumulator;
IndexType i = 0;
auto accumulation = reinterpret_cast<__m128i*>(
&accumulator.accumulation[perspective][i][0]);
+ #elif defined(USE_MMX)
+ constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
+ auto accumulation = reinterpret_cast<__m64*>(
+ &accumulator.accumulation[perspective][i][0]);
+
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
auto accumulation = reinterpret_cast<int16x8_t*>(
#if defined(USE_AVX2)
auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
+ 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) {
+ for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
- }
+
+ #elif defined(USE_MMX)
+ auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
+ 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];
- }
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
+ accumulator.accumulation[perspective][i][j] -= weights_[offset + j];
#endif
}
#if defined(USE_AVX2)
auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
+ 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) {
+ for (IndexType j = 0; j < kNumChunks; ++j)
accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
- }
+
+ #elif defined(USE_MMX)
+ auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
+ for (IndexType j = 0; j < kNumChunks; ++j)
+ accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
#elif defined(USE_NEON)
auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
+ 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];
- }
+ for (IndexType j = 0; j < kHalfDimensions; ++j)
+ accumulator.accumulation[perspective][i][j] += weights_[offset + j];
#endif
}
}
}
+ #if defined(USE_MMX)
+ _mm_empty();
+ #endif
accumulator.computed_accumulation = true;
- accumulator.computed_score = false;
}
using BiasType = std::int16_t;
projects / stockfish / blobdiff