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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       for (std::size_t i = 0; i < kOutputDimensions; ++i)
  66         biases_[i] = read_little_endian<BiasType>(stream);
  67       for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
  68         weights_[i] = read_little_endian<WeightType>(stream);
  69       return !stream.fail();
  70     }
  71
  72     // Forward propagation
  73     const OutputType* Propagate(
  74         const TransformedFeatureType* transformed_features, char* buffer) const {
  75       const auto input = previous_layer_.Propagate(
  76           transformed_features, buffer + kSelfBufferSize);
  77       const auto output = reinterpret_cast<OutputType*>(buffer);
  78
  79   #if defined(USE_AVX512)
  80       constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
  81       const auto input_vector = reinterpret_cast<const __m512i*>(input);
  82   #if !defined(USE_VNNI)
  83       const __m512i kOnes = _mm512_set1_epi16(1);
  84   #endif
  85
  86   #elif defined(USE_AVX2)
  87       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
  88       const auto input_vector = reinterpret_cast<const __m256i*>(input);
  89   #if !defined(USE_VNNI)
  90       const __m256i kOnes = _mm256_set1_epi16(1);
  91   #endif
  92
  93   #elif defined(USE_SSE2)
  94       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
  95   #ifndef USE_SSSE3
  96       const __m128i kZeros = _mm_setzero_si128();
  97   #else
  98       const __m128i kOnes = _mm_set1_epi16(1);
  99   #endif
 100       const auto input_vector = reinterpret_cast<const __m128i*>(input);
 101
 102   #elif defined(USE_MMX)
 103       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
 104       const __m64 kZeros = _mm_setzero_si64();
 105       const auto input_vector = reinterpret_cast<const __m64*>(input);
 106
 107   #elif defined(USE_NEON)
 108       constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
 109       const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
 110   #endif
 111
 112       for (IndexType i = 0; i < kOutputDimensions; ++i) {
 113         const IndexType offset = i * kPaddedInputDimensions;
 114
 115   #if defined(USE_AVX512)
 116         __m512i sum = _mm512_setzero_si512();
 117         const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
 118         for (IndexType j = 0; j < kNumChunks; ++j) {
 119   #if defined(USE_VNNI)
 120             sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
 121   #else
 122             __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
 123             product = _mm512_madd_epi16(product, kOnes);
 124             sum = _mm512_add_epi32(sum, product);
 125   #endif
 126         }
 127
 128         // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
 129         // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
 130         // and we have to do one more 256bit chunk.
 131         if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
 132         {
 133             const auto iv256  = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
 134             const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
 135   #if defined(USE_VNNI)
 136             __m256i product256 = _mm256_dpbusd_epi32(
 137                 _mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
 138             sum = _mm512_inserti32x8(sum, product256, 0);
 139   #else
 140             __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
 141             sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
 142   #endif
 143         }
 144         output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
 145
 146   #elif defined(USE_AVX2)
 147         __m256i sum = _mm256_setzero_si256();
 148         const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
 149         for (IndexType j = 0; j < kNumChunks; ++j) {
 150   #if defined(USE_VNNI)
 151           sum = _mm256_dpbusd_epi32(sum, _mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
 152   #else
 153           __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
 154           product = _mm256_madd_epi16(product, kOnes);
 155           sum = _mm256_add_epi32(sum, product);
 156   #endif
 157         }
 158         __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
 159         sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
 160         sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
 161         output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
 162
 163   #elif defined(USE_SSSE3)
 164         __m128i sum = _mm_setzero_si128();
 165         const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
 166         for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
 167           __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
 168           product0 = _mm_madd_epi16(product0, kOnes);
 169           sum = _mm_add_epi32(sum, product0);
 170           __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
 171           product1 = _mm_madd_epi16(product1, kOnes);
 172           sum = _mm_add_epi32(sum, product1);
 173         }
 174         if (kNumChunks & 0x1) {
 175           __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
 176           product = _mm_madd_epi16(product, kOnes);
 177           sum = _mm_add_epi32(sum, product);
 178         }
 179         sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
 180         sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
 181         output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
 182
 183   #elif defined(USE_SSE2)
 184         __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
 185         __m128i sum_hi = kZeros;
 186         const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
 187         for (IndexType j = 0; j < kNumChunks; ++j) {
 188           __m128i row_j = _mm_load_si128(&row[j]);
 189           __m128i input_j = _mm_load_si128(&input_vector[j]);
 190           __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
 191           __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
 192           __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
 193           __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
 194           __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
 195           __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
 196           __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
 197           sum_lo = _mm_add_epi32(sum_lo, product_lo);
 198           sum_hi = _mm_add_epi32(sum_hi, product_hi);
 199         }
 200         __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
 201         __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
 202         sum = _mm_add_epi32(sum, sum_high_64);
 203         __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
 204         sum = _mm_add_epi32(sum, sum_second_32);
 205         output[i] = _mm_cvtsi128_si32(sum);
 206
 207   #elif defined(USE_MMX)
 208         __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
 209         __m64 sum_hi = kZeros;
 210         const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
 211         for (IndexType j = 0; j < kNumChunks; ++j) {
 212           __m64 row_j = row[j];
 213           __m64 input_j = input_vector[j];
 214           __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
 215           __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
 216           __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
 217           __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
 218           __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
 219           __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
 220           __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
 221           sum_lo = _mm_add_pi32(sum_lo, product_lo);
 222           sum_hi = _mm_add_pi32(sum_hi, product_hi);
 223         }
 224         __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
 225         sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
 226         output[i] = _mm_cvtsi64_si32(sum);
 227
 228   #elif defined(USE_NEON)
 229         int32x4_t sum = {biases_[i]};
 230         const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
 231         for (IndexType j = 0; j < kNumChunks; ++j) {
 232           int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
 233           product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
 234           sum = vpadalq_s16(sum, product);
 235         }
 236         output[i] = sum[0] + sum[1] + sum[2] + sum[3];
 237
 238   #else
 239         OutputType sum = biases_[i];
 240         for (IndexType j = 0; j < kInputDimensions; ++j) {
 241           sum += weights_[offset + j] * input[j];
 242         }
 243         output[i] = sum;
 244   #endif
 245
 246       }
 247   #if defined(USE_MMX)
 248       _mm_empty();
 249   #endif
 250       return output;
 251     }
 252
 253    private:
 254     using BiasType = OutputType;
 255     using WeightType = std::int8_t;
 256
 257     PreviousLayer previous_layer_;
 258
 259     alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
 260     alignas(kCacheLineSize)
 261         WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
 262   };
 263
 264 }  // namespace Eval::NNUE::Layers
 265
 266 #endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED