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