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Cleanup and optimize SSE/AVX code

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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
This commit is contained in:
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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src/nnue/layers/affine_transform.h
+24 -22
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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]};