static_assert(std::is_same<InputType, std::int32_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType kInputDimensions =
- PreviousLayer::kOutputDimensions;
- static constexpr IndexType kOutputDimensions = kInputDimensions;
+ static constexpr IndexType InputDimensions =
+ PreviousLayer::OutputDimensions;
+ static constexpr IndexType OutputDimensions = InputDimensions;
// Size of forward propagation buffer used in this layer
- static constexpr std::size_t kSelfBufferSize =
- CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
+ static constexpr std::size_t SelfBufferSize =
+ ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
// Size of the forward propagation buffer used from the input layer to this layer
- static constexpr std::size_t kBufferSize =
- PreviousLayer::kBufferSize + kSelfBufferSize;
+ static constexpr std::size_t BufferSize =
+ PreviousLayer::BufferSize + SelfBufferSize;
// Hash value embedded in the evaluation file
- static constexpr std::uint32_t GetHashValue() {
- std::uint32_t hash_value = 0x538D24C7u;
- hash_value += PreviousLayer::GetHashValue();
- return hash_value;
+ static constexpr std::uint32_t get_hash_value() {
+ std::uint32_t hashValue = 0x538D24C7u;
+ hashValue += PreviousLayer::get_hash_value();
+ return hashValue;
}
// Read network parameters
- bool ReadParameters(std::istream& stream) {
- return previous_layer_.ReadParameters(stream);
+ bool read_parameters(std::istream& stream) {
+ return previousLayer.read_parameters(stream);
+ }
+
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ return previousLayer.write_parameters(stream);
}
// Forward propagation
- const OutputType* Propagate(
- const TransformedFeatureType* transformed_features, char* buffer) const {
- const auto input = previous_layer_.Propagate(
- transformed_features, buffer + kSelfBufferSize);
+ const OutputType* propagate(
+ const TransformedFeatureType* transformedFeatures, char* buffer) const {
+ const auto input = previousLayer.propagate(
+ transformedFeatures, buffer + SelfBufferSize);
const auto output = reinterpret_cast<OutputType*>(buffer);
#if defined(USE_AVX2)
- constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
- const __m256i kZero = _mm256_setzero_si256();
- const __m256i kOffsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
- const auto in = reinterpret_cast<const __m256i*>(input);
- const auto out = reinterpret_cast<__m256i*>(output);
- for (IndexType i = 0; i < kNumChunks; ++i) {
- const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
- _mm256_load_si256(&in[i * 4 + 0]),
- _mm256_load_si256(&in[i * 4 + 1])), kWeightScaleBits);
- const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
- _mm256_load_si256(&in[i * 4 + 2]),
- _mm256_load_si256(&in[i * 4 + 3])), kWeightScaleBits);
- _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
- _mm256_packs_epi16(words0, words1), kZero), kOffsets));
+ if constexpr (InputDimensions % SimdWidth == 0) {
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ const __m256i Zero = _mm256_setzero_si256();
+ const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
+ const auto in = reinterpret_cast<const __m256i*>(input);
+ const auto out = reinterpret_cast<__m256i*>(output);
+ for (IndexType i = 0; i < NumChunks; ++i) {
+ const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
+ _mm256_load_si256(&in[i * 4 + 0]),
+ _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
+ const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
+ _mm256_load_si256(&in[i * 4 + 2]),
+ _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
+ _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
+ _mm256_packs_epi16(words0, words1), Zero), Offsets));
+ }
+ } else {
+ constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
+ const __m128i Zero = _mm_setzero_si128();
+ const auto in = reinterpret_cast<const __m128i*>(input);
+ const auto out = reinterpret_cast<__m128i*>(output);
+ for (IndexType i = 0; i < NumChunks; ++i) {
+ const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
+ _mm_load_si128(&in[i * 4 + 0]),
+ _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
+ const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
+ _mm_load_si128(&in[i * 4 + 2]),
+ _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
+ const __m128i packedbytes = _mm_packs_epi16(words0, words1);
+ _mm_store_si128(&out[i], _mm_max_epi8(packedbytes, Zero));
+ }
}
- constexpr IndexType kStart = kNumChunks * kSimdWidth;
+ constexpr IndexType Start =
+ InputDimensions % SimdWidth == 0
+ ? InputDimensions / SimdWidth * SimdWidth
+ : InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);
#elif defined(USE_SSE2)
- constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
#ifdef USE_SSE41
- const __m128i kZero = _mm_setzero_si128();
+ const __m128i Zero = _mm_setzero_si128();
#else
const __m128i k0x80s = _mm_set1_epi8(-128);
#endif
const auto in = reinterpret_cast<const __m128i*>(input);
const auto out = reinterpret_cast<__m128i*>(output);
- for (IndexType i = 0; i < kNumChunks; ++i) {
+ for (IndexType i = 0; i < NumChunks; ++i) {
const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
_mm_load_si128(&in[i * 4 + 0]),
- _mm_load_si128(&in[i * 4 + 1])), kWeightScaleBits);
+ _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
_mm_load_si128(&in[i * 4 + 2]),
- _mm_load_si128(&in[i * 4 + 3])), kWeightScaleBits);
+ _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
const __m128i packedbytes = _mm_packs_epi16(words0, words1);
_mm_store_si128(&out[i],
#ifdef USE_SSE41
- _mm_max_epi8(packedbytes, kZero)
+ _mm_max_epi8(packedbytes, Zero)
#else
_mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
#endif
);
}
- constexpr IndexType kStart = kNumChunks * kSimdWidth;
+ constexpr IndexType Start = NumChunks * SimdWidth;
#elif defined(USE_MMX)
- constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
const auto in = reinterpret_cast<const __m64*>(input);
const auto out = reinterpret_cast<__m64*>(output);
- for (IndexType i = 0; i < kNumChunks; ++i) {
+ for (IndexType i = 0; i < NumChunks; ++i) {
const __m64 words0 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
- kWeightScaleBits);
+ WeightScaleBits);
const __m64 words1 = _mm_srai_pi16(
_mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
- kWeightScaleBits);
+ WeightScaleBits);
const __m64 packedbytes = _mm_packs_pi16(words0, words1);
out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
}
_mm_empty();
- constexpr IndexType kStart = kNumChunks * kSimdWidth;
+ constexpr IndexType Start = NumChunks * SimdWidth;
#elif defined(USE_NEON)
- constexpr IndexType kNumChunks = kInputDimensions / (kSimdWidth / 2);
- const int8x8_t kZero = {0};
+ constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
+ const int8x8_t Zero = {0};
const auto in = reinterpret_cast<const int32x4_t*>(input);
const auto out = reinterpret_cast<int8x8_t*>(output);
- for (IndexType i = 0; i < kNumChunks; ++i) {
+ for (IndexType i = 0; i < NumChunks; ++i) {
int16x8_t shifted;
const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
- pack[0] = vqshrn_n_s32(in[i * 2 + 0], kWeightScaleBits);
- pack[1] = vqshrn_n_s32(in[i * 2 + 1], kWeightScaleBits);
- out[i] = vmax_s8(vqmovn_s16(shifted), kZero);
+ pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
+ pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
+ out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
}
- constexpr IndexType kStart = kNumChunks * (kSimdWidth / 2);
+ constexpr IndexType Start = NumChunks * (SimdWidth / 2);
#else
- constexpr IndexType kStart = 0;
+ constexpr IndexType Start = 0;
#endif
- for (IndexType i = kStart; i < kInputDimensions; ++i) {
+ for (IndexType i = Start; i < InputDimensions; ++i) {
output[i] = static_cast<OutputType>(
- std::max(0, std::min(127, input[i] >> kWeightScaleBits)));
+ std::max(0, std::min(127, input[i] >> WeightScaleBits)));
}
return output;
}
private:
- PreviousLayer previous_layer_;
+ PreviousLayer previousLayer;
};
} // namespace Stockfish::Eval::NNUE::Layers
projects / stockfish / blobdiff