stockfish/src/nnue/nnue_feature_transformer.h

/*
  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
  Copyright (C) 2004-2020 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
  the Free Software Foundation, either version 3 of the License, or
  (at your option) any later version.

  Stockfish is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

// A class that converts the input features of the NNUE evaluation function

#ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
#define NNUE_FEATURE_TRANSFORMER_H_INCLUDED

#include "nnue_common.h"
#include "nnue_architecture.h"
#include "features/index_list.h"

#include <cstring> // std::memset()

namespace Eval::NNUE {

  // Input feature converter
  class FeatureTransformer {

   private:
    // Number of output dimensions for one side
    static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions;

   public:
    // Output type
    using OutputType = TransformedFeatureType;

    // Number of input/output dimensions
    static constexpr IndexType kInputDimensions = RawFeatures::kDimensions;
    static constexpr IndexType kOutputDimensions = kHalfDimensions * 2;

    // Size of forward propagation buffer
    static constexpr std::size_t kBufferSize =
        kOutputDimensions * sizeof(OutputType);

    // Hash value embedded in the evaluation file
    static constexpr std::uint32_t GetHashValue() {
      return RawFeatures::kHashValue ^ kOutputDimensions;
    }

    // Read network parameters
    bool ReadParameters(std::istream& stream) {
      for (std::size_t i = 0; i < kHalfDimensions; ++i)
        biases_[i] = read_little_endian<BiasType>(stream);
      for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i)
        weights_[i] = read_little_endian<WeightType>(stream);
      return !stream.fail();
    }

    // Proceed with the difference calculation if possible
    bool UpdateAccumulatorIfPossible(const Position& pos) const {
      const auto now = pos.state();
      if (now->accumulator.computed_accumulation) {
        return true;
      }
      const auto prev = now->previous;
      if (prev && prev->accumulator.computed_accumulation) {
        UpdateAccumulator(pos);
        return true;
      }
      return false;
    }

    // Convert input features
    void Transform(const Position& pos, OutputType* output, bool refresh) const {
      if (refresh || !UpdateAccumulatorIfPossible(pos)) {
        RefreshAccumulator(pos);
      }
      const auto& accumulation = pos.state()->accumulator.accumulation;

  #if defined(USE_AVX2)
      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
      constexpr int kControl = 0b11011000;
      const __m256i kZero = _mm256_setzero_si256();

  #elif defined(USE_SSE2)
      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;

  #ifdef USE_SSE41
      const __m128i kZero = _mm_setzero_si128();
  #else
      const __m128i k0x80s = _mm_set1_epi8(-128);
  #endif

  #elif defined(USE_MMX)
      constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
      const __m64 k0x80s = _mm_set1_pi8(-128);

  #elif defined(USE_NEON)
      constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
      const int8x8_t kZero = {0};
  #endif

      const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
      for (IndexType p = 0; p < 2; ++p) {
        const IndexType offset = kHalfDimensions * p;

  #if defined(USE_AVX2)
        auto out = reinterpret_cast<__m256i*>(&output[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          __m256i sum0 = _mm256_loadA_si256(
              &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 0]);
          __m256i sum1 = _mm256_loadA_si256(
            &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]][0])[j * 2 + 1]);
          _mm256_storeA_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
              _mm256_packs_epi16(sum0, sum1), kZero), kControl));
        }

  #elif defined(USE_SSE2)
        auto out = reinterpret_cast<__m128i*>(&output[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
              accumulation[perspectives[p]][0])[j * 2 + 0]);
          __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
              accumulation[perspectives[p]][0])[j * 2 + 1]);
      const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);

          _mm_store_si128(&out[j],

  #ifdef USE_SSE41
            _mm_max_epi8(packedbytes, kZero)
  #else
            _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
  #endif

          );
        }

  #elif defined(USE_MMX)
        auto out = reinterpret_cast<__m64*>(&output[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          __m64 sum0 = *(&reinterpret_cast<const __m64*>(
              accumulation[perspectives[p]][0])[j * 2 + 0]);
          __m64 sum1 = *(&reinterpret_cast<const __m64*>(
              accumulation[perspectives[p]][0])[j * 2 + 1]);
          const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
          out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
        }

  #elif defined(USE_NEON)
        const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          int16x8_t sum = reinterpret_cast<const int16x8_t*>(
              accumulation[perspectives[p]][0])[j];
          out[j] = vmax_s8(vqmovn_s16(sum), kZero);
        }

  #else
        for (IndexType j = 0; j < kHalfDimensions; ++j) {
          BiasType sum = accumulation[static_cast<int>(perspectives[p])][0][j];
          output[offset + j] = static_cast<OutputType>(
              std::max<int>(0, std::min<int>(127, sum)));
        }
  #endif

      }
  #if defined(USE_MMX)
      _mm_empty();
  #endif
    }

   private:
    // Calculate cumulative value without using difference calculation
    void RefreshAccumulator(const Position& pos) const {
      auto& accumulator = pos.state()->accumulator;
      IndexType i = 0;
      Features::IndexList active_indices[2];
      RawFeatures::AppendActiveIndices(pos, kRefreshTriggers[i],
                                       active_indices);
      for (Color perspective : { WHITE, BLACK }) {
        std::memcpy(accumulator.accumulation[perspective][i], biases_,
                   kHalfDimensions * sizeof(BiasType));
        for (const auto index : active_indices[perspective]) {
          const IndexType offset = kHalfDimensions * index;
  #if defined(USE_AVX512)
          auto accumulation = reinterpret_cast<__m512i*>(
              &accumulator.accumulation[perspective][i][0]);
          auto column = reinterpret_cast<const __m512i*>(&weights_[offset]);
          constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth;
          for (IndexType j = 0; j < kNumChunks; ++j)
            _mm512_storeA_si512(&accumulation[j], _mm512_add_epi16(_mm512_loadA_si512(&accumulation[j]), column[j]));

  #elif defined(USE_AVX2)
          auto accumulation = reinterpret_cast<__m256i*>(
              &accumulator.accumulation[perspective][i][0]);
          auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
          constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
          for (IndexType j = 0; j < kNumChunks; ++j)
            _mm256_storeA_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadA_si256(&accumulation[j]), column[j]));

  #elif defined(USE_SSE2)
          auto accumulation = reinterpret_cast<__m128i*>(
              &accumulator.accumulation[perspective][i][0]);
          auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
          constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
          for (IndexType j = 0; j < kNumChunks; ++j)
            accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);

  #elif defined(USE_MMX)
          auto accumulation = reinterpret_cast<__m64*>(
              &accumulator.accumulation[perspective][i][0]);
          auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
          constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
          for (IndexType j = 0; j < kNumChunks; ++j) {
            accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
          }

  #elif defined(USE_NEON)
          auto accumulation = reinterpret_cast<int16x8_t*>(
              &accumulator.accumulation[perspective][i][0]);
          auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
          constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
          for (IndexType j = 0; j < kNumChunks; ++j)
            accumulation[j] = vaddq_s16(accumulation[j], column[j]);

  #else
          for (IndexType j = 0; j < kHalfDimensions; ++j)
            accumulator.accumulation[perspective][i][j] += weights_[offset + j];
  #endif

        }
      }
  #if defined(USE_MMX)
      _mm_empty();
  #endif

      accumulator.computed_accumulation = true;
      accumulator.computed_score = false;
    }

    // Calculate cumulative value using difference calculation
    void UpdateAccumulator(const Position& pos) const {
      const auto prev_accumulator = pos.state()->previous->accumulator;
      auto& accumulator = pos.state()->accumulator;
      IndexType i = 0;
      Features::IndexList removed_indices[2], added_indices[2];
      bool reset[2];
      RawFeatures::AppendChangedIndices(pos, kRefreshTriggers[i],
                                        removed_indices, added_indices, reset);
      for (Color perspective : { WHITE, BLACK }) {

  #if defined(USE_AVX2)
        constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
        auto accumulation = reinterpret_cast<__m256i*>(
            &accumulator.accumulation[perspective][i][0]);

  #elif defined(USE_SSE2)
        constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
        auto accumulation = reinterpret_cast<__m128i*>(
            &accumulator.accumulation[perspective][i][0]);

  #elif defined(USE_MMX)
        constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
        auto accumulation = reinterpret_cast<__m64*>(
            &accumulator.accumulation[perspective][i][0]);

  #elif defined(USE_NEON)
        constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
        auto accumulation = reinterpret_cast<int16x8_t*>(
            &accumulator.accumulation[perspective][i][0]);
  #endif

        if (reset[perspective]) {
          std::memcpy(accumulator.accumulation[perspective][i], biases_,
                      kHalfDimensions * sizeof(BiasType));
        } else {
          std::memcpy(accumulator.accumulation[perspective][i],
                      prev_accumulator.accumulation[perspective][i],
                      kHalfDimensions * sizeof(BiasType));
          // Difference calculation for the deactivated features
          for (const auto index : removed_indices[perspective]) {
            const IndexType offset = kHalfDimensions * index;

  #if defined(USE_AVX2)
            auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
            }

  #elif defined(USE_SSE2)
            auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
            }

  #elif defined(USE_MMX)
            auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
            }

  #elif defined(USE_NEON)
            auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = vsubq_s16(accumulation[j], column[j]);
            }

  #else
            for (IndexType j = 0; j < kHalfDimensions; ++j) {
              accumulator.accumulation[perspective][i][j] -=
                  weights_[offset + j];
            }
  #endif

          }
        }
        { // Difference calculation for the activated features
          for (const auto index : added_indices[perspective]) {
            const IndexType offset = kHalfDimensions * index;

  #if defined(USE_AVX2)
            auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
            }

  #elif defined(USE_SSE2)
            auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
            }

  #elif defined(USE_MMX)
            auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
            }

  #elif defined(USE_NEON)
            auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
            for (IndexType j = 0; j < kNumChunks; ++j) {
              accumulation[j] = vaddq_s16(accumulation[j], column[j]);
            }

  #else
            for (IndexType j = 0; j < kHalfDimensions; ++j) {
              accumulator.accumulation[perspective][i][j] +=
                  weights_[offset + j];
            }
  #endif

          }
        }
      }
  #if defined(USE_MMX)
      _mm_empty();
  #endif

      accumulator.computed_accumulation = true;
      accumulator.computed_score = false;
    }

    using BiasType = std::int16_t;
    using WeightType = std::int16_t;

    alignas(kCacheLineSize) BiasType biases_[kHalfDimensions];
    alignas(kCacheLineSize)
        WeightType weights_[kHalfDimensions * kInputDimensions];
  };

}  // namespace Eval::NNUE

#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED