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Cleanup and optimize SSE/AVX code 

AVX512 +4% faster
AVX2 +1% faster
SSSE3 +5% faster

passed non-regression STC:
STC https://tests.stockfishchess.org/tests/view/5f31249f90816720665374f6
LLR: 2.96 (-2.94,2.94) {-1.50,0.50}
Total: 17576 W: 2344 L: 2245 D: 12987
Ptnml(0-2): 127, 1570, 5292, 1675, 124

closes https://github.com/official-stockfish/Stockfish/pull/2962

No functional change
pull/2956/head
mstembera 2020-08-09 16:23:33 -07:00 committed by Joost VandeVondele
parent cb0504028e
commit f948cd008d
4 changed files with 41 additions and 34 deletions
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46
src/nnue/layers/affine_transform.h
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@ -108,24 +108,19 @@ namespace Eval::NNUE::Layers {
product = _mm512_madd_epi16(product, kOnes);
sum = _mm512_add_epi32(sum, product);
}
output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
// Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
// As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
// and we have to do one more 256bit chunk.
if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
{
const auto iv_256 = reinterpret_cast<const __m256i*>(input);
const auto row_256 = reinterpret_cast<const __m256i*>(&weights_[offset]);
int j = kNumChunks * 2;
__m256i sum256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv_256[j]), _mm256_load_si256(&row_256[j]));
sum256 = _mm256_madd_epi16(sum256, _mm256_set1_epi16(1));
sum256 = _mm256_hadd_epi32(sum256, sum256);
sum256 = _mm256_hadd_epi32(sum256, sum256);
const __m128i lo = _mm256_extracti128_si256(sum256, 0);
const __m128i hi = _mm256_extracti128_si256(sum256, 1);
output[i] += _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi);
const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
__m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
product256 = _mm256_madd_epi16(product256, _mm256_set1_epi16(1));
sum = _mm512_add_epi32(sum, _mm512_zextsi256_si512(product256));
}
output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
#elif defined(USE_AVX2)
__m256i sum = _mm256_setzero_si256();
@ -135,23 +130,30 @@ namespace Eval::NNUE::Layers {
product = _mm256_madd_epi16(product, kOnes);
sum = _mm256_add_epi32(sum, product);
}
sum = _mm256_hadd_epi32(sum, sum);
sum = _mm256_hadd_epi32(sum, sum);
const __m128i lo = _mm256_extracti128_si256(sum, 0);
const __m128i hi = _mm256_extracti128_si256(sum, 1);
output[i] = _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi) + biases_[i];
__m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
#elif defined(USE_SSSE3)
__m128i sum = _mm_cvtsi32_si128(biases_[i]);
__m128i sum = _mm_setzero_si128();
const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
__m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
product0 = _mm_madd_epi16(product0, kOnes);
sum = _mm_add_epi32(sum, product0);
__m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
product1 = _mm_madd_epi16(product1, kOnes);
sum = _mm_add_epi32(sum, product1);
}
if (kNumChunks & 0x1) {
__m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
product = _mm_madd_epi16(product, kOnes);
sum = _mm_add_epi32(sum, product);
}
sum = _mm_hadd_epi32(sum, sum);
sum = _mm_hadd_epi32(sum, sum);
output[i] = _mm_cvtsi128_si32(sum);
sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
#elif defined(USE_NEON)
int32x4_t sum = {biases_[i]};

2
src/nnue/nnue_accumulator.h
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@ -26,7 +26,7 @@
namespace Eval::NNUE {
// Class that holds the result of affine transformation of input features
struct alignas(32) Accumulator {
struct alignas(kCacheLineSize) Accumulator {
std::int16_t
accumulation[2][kRefreshTriggers.size()][kTransformedFeatureDimensions];
Value score;

6
src/nnue/nnue_common.h
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@ -52,9 +52,11 @@
#if defined(USE_AVX512)
#if defined(__GNUC__ ) && (__GNUC__ < 9)
#define _mm512_loadA_si512 _mm512_loadu_si512
#define _mm512_loadA_si512 _mm512_loadu_si512
#define _mm512_storeA_si512 _mm512_storeu_si512
#else
#define _mm512_loadA_si512 _mm512_load_si512
#define _mm512_loadA_si512 _mm512_load_si512
#define _mm512_storeA_si512 _mm512_store_si512
#endif
#endif

21
src/nnue/nnue_feature_transformer.h
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@ -169,38 +169,41 @@ namespace Eval::NNUE {
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) {
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_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
}