// 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_SSE2)
constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
__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.
{
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]));
- product256 = _mm256_madd_epi16(product256, _mm256_set1_epi16(1));
- sum = _mm512_add_epi32(sum, _mm512_zextsi256_si512(product256));
+ sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
+ #endif
}
output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
__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
}
__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));