// 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();
}
#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);
__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
}
- 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]);
+ #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) {
+ #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]);
#endif
}
+ #if defined(USE_MMX)
+ _mm_empty();
+ #endif
return output;
}
projects / stockfish / blobdiff