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AVX-512 for smaller affine and feature transforms.

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For the feature transformer the code is analogical to AVX2 since there was room for easy adaptation of wider simd registers.

For the smaller affine transforms that have 32 byte stride we keep 2 columns in one zmm register. We also unroll more aggressively so that in the end we have to do 16 parallel horizontal additions on ymm slices each consisting of 4 32-bit integers. The slices are embedded in 8 zmm registers.

These changes provide about 1.5% speedup for AVX-512 builds.

Closes https://github.com/official-stockfish/Stockfish/pull/3218

No functional change.
This commit is contained in:
Tomasz Sobczyk
2020-11-03 22:49:10 +01:00
committed by Joost VandeVondele
parent 7fc47eeb6f
commit ba35c88ab8
2 changed files with 148 additions and 8 deletions
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src/nnue/nnue_feature_transformer.h
+22 -5
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@@ -127,7 +127,13 @@ namespace Eval::NNUE {
const auto& accumulation = pos.state()->accumulator.accumulation;
#if defined(USE_AVX2)
#if defined(USE_AVX512)
constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth * 2);
static_assert(kHalfDimensions % (kSimdWidth * 2) == 0);
const __m512i kControl = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
const __m512i kZero = _mm512_setzero_si512();
#elif defined(USE_AVX2)
constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
constexpr int kControl = 0b11011000;
const __m256i kZero = _mm256_setzero_si256();
@@ -154,13 +160,24 @@ namespace Eval::NNUE {
for (IndexType p = 0; p < 2; ++p) {
const IndexType offset = kHalfDimensions * p;
#if defined(USE_AVX2)
#if defined(USE_AVX512)
auto out = reinterpret_cast<__m512i*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m512i sum0 = _mm512_load_si512(
&reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
__m512i sum1 = _mm512_load_si512(
&reinterpret_cast<const __m512i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
_mm512_store_si512(&out[j], _mm512_permutexvar_epi64(kControl,
_mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), kZero)));
}
#elif defined(USE_AVX2)
auto out = reinterpret_cast<__m256i*>(&output[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m256i sum0 = _mm256_load_si256(
&reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
__m256i sum1 = _mm256_load_si256(
&reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
&reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
_mm256_store_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
_mm256_packs_epi16(sum0, sum1), kZero), kControl));
}
@@ -177,9 +194,9 @@ namespace Eval::NNUE {
_mm_store_si128(&out[j],
#ifdef USE_SSE41
_mm_max_epi8(packedbytes, kZero)
_mm_max_epi8(packedbytes, kZero)
#else
_mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
_mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
#endif
);