BadFish/src/nnue/layers/clipped_relu.h

/*
  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
  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
  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/>.
*/

// Definition of layer ClippedReLU of NNUE evaluation function

#ifndef NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
#define NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED

#include "../nnue_common.h"

namespace Stockfish::Eval::NNUE::Layers {

  // Clipped ReLU
  template <typename PreviousLayer>
  class ClippedReLU {
   public:
    // Input/output type
    using InputType = typename PreviousLayer::OutputType;
    using OutputType = std::uint8_t;
    static_assert(std::is_same<InputType, std::int32_t>::value, "");

    // Number of input/output dimensions
    static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions;
    static constexpr IndexType OutputDimensions = InputDimensions;
    static constexpr IndexType PaddedOutputDimensions =
        ceil_to_multiple<IndexType>(OutputDimensions, 32);

    // Size of forward propagation buffer used in this layer
    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 BufferSize =
        PreviousLayer::BufferSize + SelfBufferSize;

    // Hash value embedded in the evaluation file
    static constexpr std::uint32_t get_hash_value() {
      std::uint32_t hashValue = 0x538D24C7u;
      hashValue += PreviousLayer::get_hash_value();
      return hashValue;
    }

    // Read network parameters
    bool read_parameters(std::istream& stream) {
      return previousLayer.read_parameters(stream);
    }

    // Write network parameters
    bool write_parameters(std::ostream& stream) const {
      return previousLayer.write_parameters(stream);
    }

    // Forward propagation
    const OutputType* propagate(
        const TransformedFeatureType* transformedFeatures, char* buffer) const {
      const auto input = previousLayer.propagate(
          transformedFeatures, buffer + SelfBufferSize);
      const auto output = reinterpret_cast<OutputType*>(buffer);

  #if defined(USE_AVX2)
      if constexpr (InputDimensions % SimdWidth == 0) {
        constexpr IndexType NumChunks = InputDimensions / SimdWidth;
        const __m256i Zero = _mm256_setzero_si256();
        const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
        const auto in = reinterpret_cast<const __m256i*>(input);
        const auto out = reinterpret_cast<__m256i*>(output);
        for (IndexType i = 0; i < NumChunks; ++i) {
          const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
              _mm256_load_si256(&in[i * 4 + 0]),
              _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
          const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
              _mm256_load_si256(&in[i * 4 + 2]),
              _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
          _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
              _mm256_packs_epi16(words0, words1), Zero), Offsets));
        }
      } else {
        constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
        const __m128i Zero = _mm_setzero_si128();
        const auto in = reinterpret_cast<const __m128i*>(input);
        const auto out = reinterpret_cast<__m128i*>(output);
        for (IndexType i = 0; i < NumChunks; ++i) {
          const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
              _mm_load_si128(&in[i * 4 + 0]),
              _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
          const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
              _mm_load_si128(&in[i * 4 + 2]),
              _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
          const __m128i packedbytes = _mm_packs_epi16(words0, words1);
          _mm_store_si128(&out[i], _mm_max_epi8(packedbytes, Zero));
        }
      }
      constexpr IndexType Start =
        InputDimensions % SimdWidth == 0
        ? InputDimensions / SimdWidth * SimdWidth
        : InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);

  #elif defined(USE_SSE2)
      constexpr IndexType NumChunks = InputDimensions / SimdWidth;

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

      const auto in = reinterpret_cast<const __m128i*>(input);
      const auto out = reinterpret_cast<__m128i*>(output);
      for (IndexType i = 0; i < NumChunks; ++i) {
        const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
            _mm_load_si128(&in[i * 4 + 0]),
            _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
        const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
            _mm_load_si128(&in[i * 4 + 2]),
            _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
        const __m128i packedbytes = _mm_packs_epi16(words0, words1);
        _mm_store_si128(&out[i],

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

        );
      }
      constexpr IndexType Start = NumChunks * SimdWidth;

  #elif defined(USE_MMX)
      constexpr IndexType NumChunks = InputDimensions / SimdWidth;
      const __m64 k0x80s = _mm_set1_pi8(-128);
      const auto in = reinterpret_cast<const __m64*>(input);
      const auto out = reinterpret_cast<__m64*>(output);
      for (IndexType i = 0; i < NumChunks; ++i) {
        const __m64 words0 = _mm_srai_pi16(
            _mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
            WeightScaleBits);
        const __m64 words1 = _mm_srai_pi16(
            _mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
            WeightScaleBits);
        const __m64 packedbytes = _mm_packs_pi16(words0, words1);
        out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
      }
      _mm_empty();
      constexpr IndexType Start = NumChunks * SimdWidth;

  #elif defined(USE_NEON)
      constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
      const int8x8_t Zero = {0};
      const auto in = reinterpret_cast<const int32x4_t*>(input);
      const auto out = reinterpret_cast<int8x8_t*>(output);
      for (IndexType i = 0; i < NumChunks; ++i) {
        int16x8_t shifted;
        const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
        pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
        pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
        out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
      }
      constexpr IndexType Start = NumChunks * (SimdWidth / 2);
  #else
      constexpr IndexType Start = 0;
  #endif

      for (IndexType i = Start; i < InputDimensions; ++i) {
        output[i] = static_cast<OutputType>(
            std::max(0, std::min(127, input[i] >> WeightScaleBits)));
      }

      // Affine transform layers expect that there is at least
      // ceil_to_multiple(OutputDimensions, 32) initialized values.
      // We cannot do this in the affine transform because it requires
      // preallocating space here.
      for (IndexType i = OutputDimensions; i < PaddedOutputDimensions; ++i) {
        output[i] = 0;
      }

      return output;
    }

   private:
    PreviousLayer previousLayer;
  };

}  // namespace Stockfish::Eval::NNUE::Layers

#endif // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED