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Workaround using unaligned loads for gcc < 9

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despite usage of alignas, the generated (avx2/avx512) code with older compilers needs to use
unaligned loads with older gcc (e.g. confirmed crash with gcc 7.3/mingw on abrok).

Better performance thus requires gcc >= 9 on hardware supporting avx2/avx512

closes https://github.com/official-stockfish/Stockfish/pull/2969

No functional change
This commit is contained in:
mstembera
2020-08-09 23:50:59 -07:00
committed by Joost VandeVondele
parent a54f9011c3
commit 875183b310
4 changed files with 36 additions and 107 deletions
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src/nnue/layers/affine_transform.h
+4 -28
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@@ -104,13 +104,7 @@ namespace Eval::NNUE::Layers {
__m512i sum = _mm512_setzero_si512();
const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
#if defined(__MINGW32__) || defined(__MINGW64__)
__m512i product = _mm512_maddubs_epi16(_mm512_loadu_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
#else
__m512i product = _mm512_maddubs_epi16(_mm512_load_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
#endif
__m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
product = _mm512_madd_epi16(product, kOnes);
sum = _mm512_add_epi32(sum, product);
}
@@ -124,13 +118,7 @@ namespace Eval::NNUE::Layers {
const auto iv_256 = reinterpret_cast<const __m256i*>(input);
const auto row_256 = reinterpret_cast<const __m256i*>(&weights_[offset]);
int j = kNumChunks * 2;
#if defined(__MINGW32__) || defined(__MINGW64__) // See HACK comment below in AVX2.
__m256i sum256 = _mm256_maddubs_epi16(_mm256_loadu_si256(&iv_256[j]), _mm256_load_si256(&row_256[j]));
#else
__m256i sum256 = _mm256_maddubs_epi16(_mm256_load_si256(&iv_256[j]), _mm256_load_si256(&row_256[j]));
#endif
__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);
@@ -143,18 +131,7 @@ namespace Eval::NNUE::Layers {
__m256i sum = _mm256_setzero_si256();
const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m256i product = _mm256_maddubs_epi16(
#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
(&input_vector[j]), _mm256_load_si256(&row[j]));
__m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
product = _mm256_madd_epi16(product, kOnes);
sum = _mm256_add_epi32(sum, product);
}
@@ -168,8 +145,7 @@ namespace Eval::NNUE::Layers {
__m128i sum = _mm_cvtsi32_si128(biases_[i]);
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]));
__m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
product = _mm_madd_epi16(product, kOnes);
sum = _mm_add_epi32(sum, product);
}