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Add support for VNNI

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Adds support for Vector Neural Network Instructions (avx512), as available on Intel Cascade Lake

The _mm512_dpbusd_epi32() intrinsic (vpdpbusd instruction) is taylor made for NNUE.

on a cascade lake CPU (AWS C5.24x.large, gcc 10) NNUE eval is at roughly 78% nps of classical
(single core test)

bench 1024 1 24 default depth:
target 	classical 	NNUE 	ratio
vnni 	2207232 	1725987 	78.20
avx512 	2216789 	1671734 	75.41
avx2 	2194006 	1611263 	73.44
modern 	2185001 	1352469 	61.90

closes https://github.com/official-stockfish/Stockfish/pull/2987

No functional change
This commit is contained in:
mstembera
2020-08-11 12:59:39 -07:00
committed by Joost VandeVondele
parent 6bc0256292
commit dd63b98fb0
3 changed files with 41 additions and 1 deletions
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src/nnue/layers/affine_transform.h
+13 -1
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@@ -79,8 +79,10 @@ namespace Eval::NNUE::Layers {
#if defined(USE_AVX512)
constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
const __m512i kOnes = _mm512_set1_epi16(1);
const auto input_vector = reinterpret_cast<const __m512i*>(input);
#if !defined(USE_VNNI)
const __m512i kOnes = _mm512_set1_epi16(1);
#endif
#elif defined(USE_AVX2)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
@@ -113,9 +115,13 @@ 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(USE_VNNI)
sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
#else
__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);
#endif
}
// Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
@@ -125,8 +131,14 @@ namespace Eval::NNUE::Layers {
{
const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
#if defined(USE_VNNI)
__m256i product256 = _mm256_dpbusd_epi32(
_mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
sum = _mm512_inserti32x8(sum, product256, 0);
#else
__m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
#endif
}
output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];