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Merge branch 'master' of github.com:official-stockfish/Stockfish into nnue-player-merge-2020-08-28

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# Conflicts:
#	README.md
#	src/Makefile
#	src/search.cpp
#	src/types.h
#	src/uci.cpp
#	src/ucioption.cpp
This commit is contained in:
nodchip
2020-08-28 11:26:11 +09:00
parent 5637996f79 242a7d9fea
commit 906c18eb46
35 changed files with 954 additions and 768 deletions
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src/nnue/layers/affine_transform.h
+106 -36
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@@ -70,11 +70,10 @@ namespace Eval::NNUE::Layers {
// Read network parameters
bool ReadParameters(std::istream& stream) {
if (!previous_layer_.ReadParameters(stream)) return false;
stream.read(reinterpret_cast<char*>(biases_),
kOutputDimensions * sizeof(BiasType));
stream.read(reinterpret_cast<char*>(weights_),
kOutputDimensions * kPaddedInputDimensions *
sizeof(WeightType));
for (std::size_t i = 0; i < kOutputDimensions; ++i)
biases_[i] = read_little_endian<BiasType>(stream);
for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
weights_[i] = read_little_endian<WeightType>(stream);
return !stream.fail();
}
@@ -98,19 +97,32 @@ 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;
const __m256i kOnes = _mm256_set1_epi16(1);
const auto input_vector = reinterpret_cast<const __m256i*>(input);
#if !defined(USE_VNNI)
const __m256i kOnes = _mm256_set1_epi16(1);
#endif
#elif defined(USE_SSSE3)
#elif defined(USE_SSE2)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
#ifndef USE_SSSE3
const __m128i kZeros = _mm_setzero_si128();
#else
const __m128i kOnes = _mm_set1_epi16(1);
#endif
const auto input_vector = reinterpret_cast<const __m128i*>(input);
#elif defined(USE_MMX)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const __m64 kZeros = _mm_setzero_si64();
const auto input_vector = reinterpret_cast<const __m64*>(input);
#elif defined(USE_NEON)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
@@ -123,60 +135,115 @@ 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) {
__m512i product = _mm512_maddubs_epi16(
_mm512_load_si512(&input_vector[j]), _mm512_load_si512(&row[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
}
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_load_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]);
#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];
#elif defined(USE_AVX2)
__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(
_mm256_load_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
#if defined(USE_VNNI)
sum = _mm256_dpbusd_epi32(sum, _mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
#else
__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);
#endif
}
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);
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_SSE2)
__m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
__m128i sum_hi = kZeros;
const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m128i row_j = _mm_load_si128(&row[j]);
__m128i input_j = _mm_load_si128(&input_vector[j]);
__m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
__m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
__m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
__m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
__m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
__m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
__m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
sum_lo = _mm_add_epi32(sum_lo, product_lo);
sum_hi = _mm_add_epi32(sum_hi, product_hi);
}
__m128i sum = _mm_add_epi32(sum_lo, sum_hi);
__m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
sum = _mm_add_epi32(sum, sum_high_64);
__m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
sum = _mm_add_epi32(sum, sum_second_32);
output[i] = _mm_cvtsi128_si32(sum);
#elif defined(USE_MMX)
__m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
__m64 sum_hi = kZeros;
const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
for (IndexType j = 0; j < kNumChunks; ++j) {
__m64 row_j = row[j];
__m64 input_j = input_vector[j];
__m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
__m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
__m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
__m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
__m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
__m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
__m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
sum_lo = _mm_add_pi32(sum_lo, product_lo);
sum_hi = _mm_add_pi32(sum_hi, product_hi);
}
__m64 sum = _mm_add_pi32(sum_lo, sum_hi);
sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
output[i] = _mm_cvtsi64_si32(sum);
#elif defined(USE_NEON)
int32x4_t sum = {biases_[i]};
const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
@@ -196,6 +263,9 @@ namespace Eval::NNUE::Layers {
#endif
}
#if defined(USE_MMX)
_mm_empty();
#endif
return output;
}