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   1 /*
   2   Stockfish, a UCI chess playing engine derived from Glaurung 2.1
   3   Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
   4
   5   Stockfish is free software: you can redistribute it and/or modify
   6   it under the terms of the GNU General Public License as published by
   7   the Free Software Foundation, either version 3 of the License, or
   8   (at your option) any later version.
   9
  10   Stockfish is distributed in the hope that it will be useful,
  11   but WITHOUT ANY WARRANTY; without even the implied warranty of
  12   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  13   GNU General Public License for more details.
  14
  15   You should have received a copy of the GNU General Public License
  16   along with this program.  If not, see <http://www.gnu.org/licenses/>.
  17 */
  18
  19 // Definition of layer AffineTransform of NNUE evaluation function
  20
  21 #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
  22 #define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
  23
  24 #include <iostream>
  25 #include "../nnue_common.h"
  26
  27 namespace Eval::NNUE::Layers {
  28
  29   // Affine transformation layer
  30   template <typename PreviousLayer, IndexType OutputDimensions>
  31   class AffineTransform {
  32    public:
  33     // Input/output type
  34     using InputType = typename PreviousLayer::OutputType;
  35     using OutputType = std::int32_t;
  36     static_assert(std::is_same<InputType, std::uint8_t>::value, "");
  37
  38     // Number of input/output dimensions
  39     static constexpr IndexType kInputDimensions =
  40         PreviousLayer::kOutputDimensions;
  41     static constexpr IndexType kOutputDimensions = OutputDimensions;
  42     static constexpr IndexType kPaddedInputDimensions =
  43         CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
  44
  45     // Size of forward propagation buffer used in this layer
  46     static constexpr std::size_t kSelfBufferSize =
  47         CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
  48
  49     // Size of the forward propagation buffer used from the input layer to this layer
  50     static constexpr std::size_t kBufferSize =
  51         PreviousLayer::kBufferSize + kSelfBufferSize;
  52
  53     // Hash value embedded in the evaluation file
  54     static constexpr std::uint32_t GetHashValue() {
  55       std::uint32_t hash_value = 0xCC03DAE4u;
  56       hash_value += kOutputDimensions;
  57       hash_value ^= PreviousLayer::GetHashValue() >> 1;
  58       hash_value ^= PreviousLayer::GetHashValue() << 31;
  59       return hash_value;
  60     }
  61
  62    // Read network parameters
  63     bool ReadParameters(std::istream& stream) {
  64       if (!previous_layer_.ReadParameters(stream)) return false;
  65       stream.read(reinterpret_cast<char*>(biases_),
  66                   kOutputDimensions * sizeof(BiasType));
  67       stream.read(reinterpret_cast<char*>(weights_),
  68                   kOutputDimensions * kPaddedInputDimensions *
  69                   sizeof(WeightType));
  70       return !stream.fail();
  71     }
  72
  73     // Forward propagation
  74     const OutputType* Propagate(
  75         const TransformedFeatureType* transformed_features, char* buffer) const {
  76       const auto input = previous_layer_.Propagate(
  77           transformed_features, buffer + kSelfBufferSize);
  78       const auto output = reinterpret_cast<OutputType*>(buffer);
  79
  80   #if defined(USE_AVX512)
  81       constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
  82       const __m512i kOnes = _mm512_set1_epi16(1);
  83       const auto input_vector = reinterpret_cast<const __m512i*>(input);
  84
  85   #elif defined(USE_AVX2)
  86       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
  87       const __m256i kOnes = _mm256_set1_epi16(1);
  88       const auto input_vector = reinterpret_cast<const __m256i*>(input);
  89
  90   #elif defined(USE_SSE2)
  91       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
  92   #ifndef USE_SSSE3
  93       const __m128i kZeros = _mm_setzero_si128();
  94   #else
  95       const __m128i kOnes = _mm_set1_epi16(1);
  96   #endif
  97       const auto input_vector = reinterpret_cast<const __m128i*>(input);
  98
  99   #elif defined(USE_MMX)
 100       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
 101       const __m64 kZeros = _mm_setzero_si64();
 102       const auto input_vector = reinterpret_cast<const __m64*>(input);
 103
 104   #elif defined(USE_NEON)
 105       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
 106       const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
 107   #endif
 108
 109       for (IndexType i = 0; i < kOutputDimensions; ++i) {
 110         const IndexType offset = i * kPaddedInputDimensions;
 111
 112   #if defined(USE_AVX512)
 113         __m512i sum = _mm512_setzero_si512();
 114         const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
 115         for (IndexType j = 0; j < kNumChunks; ++j) {
 116             __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
 117             product = _mm512_madd_epi16(product, kOnes);
 118             sum = _mm512_add_epi32(sum, product);
 119         }
 120
 121         // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
 122         // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
 123         // and we have to do one more 256bit chunk.
 124         if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
 125         {
 126             const auto iv256  = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
 127             const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
 128             __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
 129             sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
 130         }
 131         output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
 132
 133   #elif defined(USE_AVX2)
 134         __m256i sum = _mm256_setzero_si256();
 135         const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
 136         for (IndexType j = 0; j < kNumChunks; ++j) {
 137           __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
 138           product = _mm256_madd_epi16(product, kOnes);
 139           sum = _mm256_add_epi32(sum, product);
 140         }
 141         __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
 142         sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
 143         sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
 144         output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
 145
 146   #elif defined(USE_SSSE3)
 147         __m128i sum = _mm_setzero_si128();
 148         const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
 149         for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
 150           __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
 151           product0 = _mm_madd_epi16(product0, kOnes);
 152           sum = _mm_add_epi32(sum, product0);
 153           __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
 154           product1 = _mm_madd_epi16(product1, kOnes);
 155           sum = _mm_add_epi32(sum, product1);
 156         }
 157         if (kNumChunks & 0x1) {
 158           __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
 159           product = _mm_madd_epi16(product, kOnes);
 160           sum = _mm_add_epi32(sum, product);
 161         }
 162         sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
 163         sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
 164         output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
 165
 166   #elif defined(USE_SSE2)
 167         __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
 168         __m128i sum_hi = kZeros;
 169         const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
 170         for (IndexType j = 0; j < kNumChunks; ++j) {
 171           __m128i row_j = _mm_load_si128(&row[j]);
 172           __m128i input_j = _mm_load_si128(&input_vector[j]);
 173           __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
 174           __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
 175           __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
 176           __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
 177           __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
 178           __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
 179           __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
 180           sum_lo = _mm_add_epi32(sum_lo, product_lo);
 181           sum_hi = _mm_add_epi32(sum_hi, product_hi);
 182         }
 183         __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
 184         __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
 185         sum = _mm_add_epi32(sum, sum_high_64);
 186         __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
 187         sum = _mm_add_epi32(sum, sum_second_32);
 188         output[i] = _mm_cvtsi128_si32(sum);
 189
 190   #elif defined(USE_MMX)
 191         __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
 192         __m64 sum_hi = kZeros;
 193         const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
 194         for (IndexType j = 0; j < kNumChunks; ++j) {
 195           __m64 row_j = row[j];
 196           __m64 input_j = input_vector[j];
 197           __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
 198           __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
 199           __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
 200           __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
 201           __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
 202           __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
 203           __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
 204           sum_lo = _mm_add_pi32(sum_lo, product_lo);
 205           sum_hi = _mm_add_pi32(sum_hi, product_hi);
 206         }
 207         __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
 208         sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
 209         output[i] = _mm_cvtsi64_si32(sum);
 210
 211   #elif defined(USE_NEON)
 212         int32x4_t sum = {biases_[i]};
 213         const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
 214         for (IndexType j = 0; j < kNumChunks; ++j) {
 215           int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
 216           product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
 217           sum = vpadalq_s16(sum, product);
 218         }
 219         output[i] = sum[0] + sum[1] + sum[2] + sum[3];
 220
 221   #else
 222         OutputType sum = biases_[i];
 223         for (IndexType j = 0; j < kInputDimensions; ++j) {
 224           sum += weights_[offset + j] * input[j];
 225         }
 226         output[i] = sum;
 227   #endif
 228
 229       }
 230   #if defined(USE_MMX)
 231       _mm_empty();
 232   #endif
 233       return output;
 234     }
 235
 236    private:
 237     using BiasType = OutputType;
 238     using WeightType = std::int8_t;
 239
 240     PreviousLayer previous_layer_;
 241
 242     alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
 243     alignas(kCacheLineSize)
 244         WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
 245   };
 246
 247 }  // namespace Eval::NNUE::Layers
 248
 249 #endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED