/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
#include <iostream>
#include "../nnue_common.h"
-namespace Eval::NNUE::Layers {
+namespace Stockfish::Eval::NNUE::Layers {
// Affine transformation layer
- template <typename PreviousLayer, IndexType OutputDimensions>
+ template <typename PreviousLayer, IndexType OutDims>
class AffineTransform {
public:
// Input/output type
static_assert(std::is_same<InputType, std::uint8_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType kInputDimensions =
- PreviousLayer::kOutputDimensions;
- static constexpr IndexType kOutputDimensions = OutputDimensions;
- static constexpr IndexType kPaddedInputDimensions =
- CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
+ static constexpr IndexType InputDimensions =
+ PreviousLayer::OutputDimensions;
+ static constexpr IndexType OutputDimensions = OutDims;
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+#if defined (USE_AVX512)
+ static constexpr const IndexType OutputSimdWidth = SimdWidth / 2;
+#elif defined (USE_SSSE3)
+ static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
+#endif
// 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 = 0xCC03DAE4u;
- hash_value += kOutputDimensions;
- hash_value ^= PreviousLayer::GetHashValue() >> 1;
- hash_value ^= PreviousLayer::GetHashValue() << 31;
- return hash_value;
+ static constexpr std::uint32_t get_hash_value() {
+ std::uint32_t hashValue = 0xCC03DAE4u;
+ hashValue += OutputDimensions;
+ hashValue ^= PreviousLayer::get_hash_value() >> 1;
+ hashValue ^= PreviousLayer::get_hash_value() << 31;
+ return hashValue;
}
- // 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));
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ if (!previousLayer.read_parameters(stream)) return false;
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ biases[i] = read_little_endian<BiasType>(stream);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+#if !defined (USE_SSSE3)
+ weights[i] = read_little_endian<WeightType>(stream);
+#else
+ weights[
+ (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+ i / PaddedInputDimensions * 4 +
+ i % 4
+ ] = read_little_endian<WeightType>(stream);
+#endif
+
return !stream.fail();
}
- // Forward propagation
- const OutputType* Propagate(
- const TransformedFeatureType* transformed_features, char* buffer) const {
- const auto input = previous_layer_.Propagate(
- transformed_features, buffer + kSelfBufferSize);
- const auto output = reinterpret_cast<OutputType*>(buffer);
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ if (!previousLayer.write_parameters(stream)) return false;
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ write_little_endian<BiasType>(stream, biases[i]);
+#if !defined (USE_SSSE3)
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[i]);
+#else
+ std::unique_ptr<WeightType[]> unscrambledWeights = std::make_unique<WeightType[]>(OutputDimensions * PaddedInputDimensions);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
+ unscrambledWeights[i] =
+ weights[
+ (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+ i / PaddedInputDimensions * 4 +
+ i % 4
+ ];
+ }
- #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);
-
- #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);
-
- #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);
- #endif
-
- for (IndexType i = 0; i < kOutputDimensions; ++i) {
- const IndexType offset = i * kPaddedInputDimensions;
-
- #if defined(USE_AVX512)
- __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_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
- product = _mm512_madd_epi16(product, kOnes);
- sum = _mm512_add_epi32(sum, product);
- }
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, unscrambledWeights[i]);
+#endif
- // 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 iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
- const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
- __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));
- }
- 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_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
- product = _mm256_madd_epi16(product, kOnes);
- sum = _mm256_add_epi32(sum, product);
- }
+ return !stream.fail();
+ }
+
+ // Forward propagation
+ const OutputType* propagate(
+ const TransformedFeatureType* transformedFeatures, char* buffer) const {
+ const auto input = previousLayer.propagate(
+ transformedFeatures, buffer + SelfBufferSize);
+
+#if defined (USE_AVX512)
+
+ [[maybe_unused]] const __m512i Ones512 = _mm512_set1_epi16(1);
+
+ [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
+ return _mm512_reduce_add_epi32(sum) + bias;
+ };
+
+ [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a, b);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a, b);
+ product0 = _mm512_madd_epi16(product0, Ones512);
+ acc = _mm512_add_epi32(acc, product0);
+#endif
+ };
+
+ [[maybe_unused]] auto m512_add_dpbusd_epi32x4 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1,
+ __m512i a2, __m512i b2, __m512i a3, __m512i b3) {
+#if defined (USE_VNNI)
+ acc = _mm512_dpbusd_epi32(acc, a0, b0);
+ acc = _mm512_dpbusd_epi32(acc, a1, b1);
+ acc = _mm512_dpbusd_epi32(acc, a2, b2);
+ acc = _mm512_dpbusd_epi32(acc, a3, b3);
+#else
+ __m512i product0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i product1 = _mm512_maddubs_epi16(a1, b1);
+ __m512i product2 = _mm512_maddubs_epi16(a2, b2);
+ __m512i product3 = _mm512_maddubs_epi16(a3, b3);
+ product0 = _mm512_adds_epi16(product0, product1);
+ product0 = _mm512_madd_epi16(product0, Ones512);
+ product2 = _mm512_adds_epi16(product2, product3);
+ product2 = _mm512_madd_epi16(product2, Ones512);
+ acc = _mm512_add_epi32(acc, _mm512_add_epi32(product0, product2));
+#endif
+ };
+
+#endif
+#if defined (USE_AVX2)
+
+ [[maybe_unused]] const __m256i Ones256 = _mm256_set1_epi16(1);
+
+ [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
__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_setzero_si128();
- const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
- 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);
- }
+ return _mm_cvtsi128_si32(sum128) + bias;
+ };
+
+ [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a, b);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a, b);
+ product0 = _mm256_madd_epi16(product0, Ones256);
+ acc = _mm256_add_epi32(acc, product0);
+#endif
+ };
+
+ [[maybe_unused]] auto m256_add_dpbusd_epi32x4 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1,
+ __m256i a2, __m256i b2, __m256i a3, __m256i b3) {
+#if defined (USE_VNNI)
+ acc = _mm256_dpbusd_epi32(acc, a0, b0);
+ acc = _mm256_dpbusd_epi32(acc, a1, b1);
+ acc = _mm256_dpbusd_epi32(acc, a2, b2);
+ acc = _mm256_dpbusd_epi32(acc, a3, b3);
+#else
+ __m256i product0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i product1 = _mm256_maddubs_epi16(a1, b1);
+ __m256i product2 = _mm256_maddubs_epi16(a2, b2);
+ __m256i product3 = _mm256_maddubs_epi16(a3, b3);
+ product0 = _mm256_adds_epi16(product0, product1);
+ product0 = _mm256_madd_epi16(product0, Ones256);
+ product2 = _mm256_adds_epi16(product2, product3);
+ product2 = _mm256_madd_epi16(product2, Ones256);
+ acc = _mm256_add_epi32(acc, _mm256_add_epi32(product0, product2));
+#endif
+ };
+
+#endif
+#if defined (USE_SSSE3)
+
+ [[maybe_unused]] const __m128i Ones128 = _mm_set1_epi16(1);
+
+ [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
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];
+ return _mm_cvtsi128_si32(sum) + bias;
+ };
+
+ [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
+ __m128i product0 = _mm_maddubs_epi16(a, b);
+ product0 = _mm_madd_epi16(product0, Ones128);
+ acc = _mm_add_epi32(acc, product0);
+ };
+
+ [[maybe_unused]] auto m128_add_dpbusd_epi32x4 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1,
+ __m128i a2, __m128i b2, __m128i a3, __m128i b3) {
+ __m128i product0 = _mm_maddubs_epi16(a0, b0);
+ __m128i product1 = _mm_maddubs_epi16(a1, b1);
+ __m128i product2 = _mm_maddubs_epi16(a2, b2);
+ __m128i product3 = _mm_maddubs_epi16(a3, b3);
+ product0 = _mm_adds_epi16(product0, product1);
+ product0 = _mm_madd_epi16(product0, Ones128);
+ product2 = _mm_adds_epi16(product2, product3);
+ product2 = _mm_madd_epi16(product2, Ones128);
+ acc = _mm_add_epi32(acc, _mm_add_epi32(product0, product2));
+ };
+
+#endif
+
+#if defined (USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_setzero _mm512_setzero_si512
+ #define vec_set_32 _mm512_set1_epi32
+ auto& vec_add_dpbusd_32 = m512_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m512_add_dpbusd_epi32x4;
+ auto& vec_hadd = m512_hadd;
+#elif defined (USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ auto& vec_add_dpbusd_32 = m256_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m256_add_dpbusd_epi32x4;
+ auto& vec_hadd = m256_hadd;
+#elif defined (USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ auto& vec_add_dpbusd_32 = m128_add_dpbusd_epi32;
+ auto& vec_add_dpbusd_32x4 = m128_add_dpbusd_epi32x4;
+ auto& vec_hadd = m128_hadd;
+#endif
+
+#if defined (USE_SSSE3)
+ // Different layout, we process 4 inputs at a time, always.
+ static_assert(InputDimensions % 4 == 0);
- #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) {
+ const auto output = reinterpret_cast<OutputType*>(buffer);
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+
+ static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
+
+ // OutputDimensions is either 1 or a multiple of SimdWidth
+ // because then it is also an input dimension.
+ if constexpr (OutputDimensions % OutputSimdWidth == 0)
+ {
+ constexpr IndexType NumChunks = InputDimensions / 4;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
+
+ for (int i = 0; i < (int)NumChunks - 3; i += 4)
+ {
+ const vec_t in0 = vec_set_32(input32[i + 0]);
+ const vec_t in1 = vec_set_32(input32[i + 1]);
+ const vec_t in2 = vec_set_32(input32[i + 2]);
+ const vec_t in3 = vec_set_32(input32[i + 3]);
+ const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
+ const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
+ const auto col2 = reinterpret_cast<const vec_t*>(&weights[(i + 2) * OutputDimensions * 4]);
+ const auto col3 = reinterpret_cast<const vec_t*>(&weights[(i + 3) * OutputDimensions * 4]);
+ for (int j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
+ vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
+ }
+ }
+ else if constexpr (OutputDimensions == 1)
+ {
+#if defined (USE_AVX512)
+ if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
+ {
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
+
+ __m256i sum0 = _mm256_setzero_si256();
+ const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
+
+ for (int j = 0; j < (int)NumChunks; ++j)
+ {
+ const __m256i in = inputVector256[j];
+ m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ }
+ output[0] = m256_hadd(sum0, biases[0]);
+ }
+ else
+#endif
+ {
+#if defined (USE_AVX512)
+ constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
+#else
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+#endif
+ vec_t sum0 = vec_setzero();
+ const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
+
+ for (int j = 0; j < (int)NumChunks; ++j)
+ {
+ const vec_t in = inputVector[j];
+ vec_add_dpbusd_32(sum0, in, row0[j]);
+ }
+ output[0] = vec_hadd(sum0, biases[0]);
+ }
+ }
+
+#else
+
+// Use old implementation for the other architectures.
+
+ auto output = reinterpret_cast<OutputType*>(buffer);
+
+#if defined(USE_SSE2)
+ // At least a multiple of 16, with SSE2.
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ const __m128i Zeros = _mm_setzero_si128();
+ const auto inputVector = reinterpret_cast<const __m128i*>(input);
+
+#elif defined(USE_MMX)
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ const __m64 Zeros = _mm_setzero_si64();
+ const auto inputVector = reinterpret_cast<const __m64*>(input);
+
+#elif defined(USE_NEON)
+ static_assert(InputDimensions % SimdWidth == 0);
+ constexpr IndexType NumChunks = InputDimensions / SimdWidth;
+ const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
+#endif
+
+ for (IndexType i = 0; i < OutputDimensions; ++i) {
+ const IndexType offset = i * PaddedInputDimensions;
+
+#if defined(USE_SSE2)
+ __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
+ __m128i sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++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 input_j = _mm_load_si128(&inputVector[j]);
+ __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+ __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
+ __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
+ __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
+ __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
+ __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_epi32(sumLo, productLo);
+ sumHi = _mm_add_epi32(sumHi, productHi);
}
- __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 = _mm_add_epi32(sumLo, sumHi);
+ __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sumHigh_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) {
+#elif defined(USE_MMX)
+ __m64 sumLo = _mm_cvtsi32_si64(biases[i]);
+ __m64 sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++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 input_j = inputVector[j];
+ __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
+ __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
+ __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
+ __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
+ __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
+ __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_pi32(sumLo, productLo);
+ sumHi = _mm_add_pi32(sumHi, productHi);
}
- __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
+ __m64 sum = _mm_add_pi32(sumLo, sumHi);
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]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
- product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
+#elif defined(USE_NEON)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j) {
+ int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
+ product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
sum = vpadalq_s16(sum, product);
}
output[i] = sum[0] + sum[1] + sum[2] + sum[3];
- #else
- OutputType sum = biases_[i];
- for (IndexType j = 0; j < kInputDimensions; ++j) {
- sum += weights_[offset + j] * input[j];
+#else
+ OutputType sum = biases[i];
+ for (IndexType j = 0; j < InputDimensions; ++j) {
+ sum += weights[offset + j] * input[j];
}
output[i] = sum;
- #endif
+#endif
}
- #if defined(USE_MMX)
+#if defined(USE_MMX)
_mm_empty();
- #endif
+#endif
+
+#endif
+
return output;
}
using BiasType = OutputType;
using WeightType = std::int8_t;
- PreviousLayer previous_layer_;
+ PreviousLayer previousLayer;
- alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
- alignas(kCacheLineSize)
- WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
+ alignas(CacheLineSize) BiasType biases[OutputDimensions];
+ alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
};
-} // namespace Eval::NNUE::Layers
+} // namespace Stockfish::Eval::NNUE::Layers
#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
projects / stockfish / blobdiff