/*
  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/>.
*/

// Definition of layer AffineTransform of NNUE evaluation function

#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED

#include <iostream>
#include "../nnue_common.h"

namespace Eval::NNUE::Layers {

  // Affine transformation layer
  template <typename PreviousLayer, IndexType OutputDimensions>
  class AffineTransform {
   public:
    // Input/output type
    using InputType = typename PreviousLayer::OutputType;
    using OutputType = std::int32_t;
    static_assert(std::is_same<InputType, std::uint8_t>::value, "");

    // Number of input/output dimensions
    static constexpr IndexType kInputDimensions =
        PreviousLayer::kOutputDimensions;
    static constexpr IndexType kOutputDimensions = OutputDimensions;
    static constexpr IndexType kPaddedInputDimensions =
        CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);

    // Size of forward propagation buffer used in this layer
    static constexpr std::size_t kSelfBufferSize =
        CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);

    // Size of the forward propagation buffer used from the input layer to this layer
    static constexpr std::size_t kBufferSize =
        PreviousLayer::kBufferSize + kSelfBufferSize;

    // Hash value embedded in the evaluation file
    static constexpr std::uint32_t GetHashValue() {
      std::uint32_t hash_value = 0xCC03DAE4u;
      hash_value += kOutputDimensions;
      hash_value ^= PreviousLayer::GetHashValue() >> 1;
      hash_value ^= PreviousLayer::GetHashValue() << 31;
      return hash_value;
    }

    // A string that represents the structure from the input layer to this layer
    static std::string GetStructureString() {
      return "AffineTransform[" +
        std::to_string(kOutputDimensions) + "<-" +
        std::to_string(kInputDimensions) + "](" +
        PreviousLayer::GetStructureString() + ")";
    }
    
   // Read network parameters
    bool ReadParameters(std::istream& stream) {
      if (!previous_layer_.ReadParameters(stream)) return false;
      for (std::size_t i = 0; i < kOutputDimensions; ++i)
        biases_[i] = read_little_endian<BiasType>(stream);
      for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
        weights_[i] = read_little_endian<WeightType>(stream);
      return !stream.fail();
    }

    // write parameters
    bool WriteParameters(std::ostream& stream) const {
      if (!previous_layer_.WriteParameters(stream)) return false;
      stream.write(reinterpret_cast<const char*>(biases_),
        kOutputDimensions * sizeof(BiasType));
      stream.write(reinterpret_cast<const char*>(weights_),
        kOutputDimensions * kPaddedInputDimensions *
        sizeof(WeightType));
      return !stream.fail();
    }

    // Forward propagation
    const OutputType* Propagate(
        const TransformedFeatureType* transformed_features, char* buffer) const {
      const auto input = previous_layer_.Propagate(
          transformed_features, buffer + kSelfBufferSize);
      const auto output = reinterpret_cast<OutputType*>(buffer);

  #if defined(USE_AVX512)
      constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
      const auto input_vector = reinterpret_cast<const __m512i*>(input);
  #if !defined(USE_VNNI)
      const __m512i kOnes = _mm512_set1_epi16(1);
  #endif

  #elif defined(USE_AVX2)
      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
      const auto input_vector = reinterpret_cast<const __m256i*>(input);
  #if !defined(USE_VNNI)
      const __m256i kOnes = _mm256_set1_epi16(1);
  #endif

  #elif defined(USE_SSE2)
      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
  #ifndef USE_SSSE3
      const __m128i kZeros = _mm_setzero_si128();
  #else
      const __m128i kOnes = _mm_set1_epi16(1);
  #endif
      const auto input_vector = reinterpret_cast<const __m128i*>(input);

  #elif defined(USE_MMX)
      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
      const __m64 kZeros = _mm_setzero_si64();
      const auto input_vector = reinterpret_cast<const __m64*>(input);

  #elif defined(USE_NEON)
      constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
      const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
  #endif

      for (IndexType i = 0; i < kOutputDimensions; ++i) {
        const IndexType offset = i * kPaddedInputDimensions;

  #if defined(USE_AVX512)
        __m512i sum = _mm512_setzero_si512();
        const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
  #if defined(USE_VNNI)
            sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
  #else
            __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
            product = _mm512_madd_epi16(product, kOnes);
            sum = _mm512_add_epi32(sum, product);
  #endif
        }

        // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
        // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
        // and we have to do one more 256bit chunk.
        if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
        {
            const auto iv256  = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
            const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
  #if defined(USE_VNNI)
            __m256i product256 = _mm256_dpbusd_epi32(
                _mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
            sum = _mm512_inserti32x8(sum, product256, 0);
  #else
            __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
            sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
  #endif
        }
        output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];

  #elif defined(USE_AVX2)
        __m256i sum = _mm256_setzero_si256();
        const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
  #if defined(USE_VNNI)
          sum = _mm256_dpbusd_epi32(sum, _mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
  #else
          __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
          product = _mm256_madd_epi16(product, kOnes);
          sum = _mm256_add_epi32(sum, product);
  #endif
        }
        __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
        sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
        sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
        output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];

  #elif defined(USE_SSSE3)
        __m128i sum = _mm_setzero_si128();
        const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
        for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
          __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
          product0 = _mm_madd_epi16(product0, kOnes);
          sum = _mm_add_epi32(sum, product0);
          __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
          product1 = _mm_madd_epi16(product1, kOnes);
          sum = _mm_add_epi32(sum, product1);
        }
        if (kNumChunks & 0x1) {
          __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
          product = _mm_madd_epi16(product, kOnes);
          sum = _mm_add_epi32(sum, product);
        }
        sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
        sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
        output[i] = _mm_cvtsi128_si32(sum) + biases_[i];

  #elif defined(USE_SSE2)
        __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
        __m128i sum_hi = kZeros;
        const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          __m128i row_j = _mm_load_si128(&row[j]);
          __m128i input_j = _mm_load_si128(&input_vector[j]);
          __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
          __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
          __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
          __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
          __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
          __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
          __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
          sum_lo = _mm_add_epi32(sum_lo, product_lo);
          sum_hi = _mm_add_epi32(sum_hi, product_hi);
        }
        __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
        __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
        sum = _mm_add_epi32(sum, sum_high_64);
        __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
        sum = _mm_add_epi32(sum, sum_second_32);
        output[i] = _mm_cvtsi128_si32(sum);

  #elif defined(USE_MMX)
        __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
        __m64 sum_hi = kZeros;
        const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          __m64 row_j = row[j];
          __m64 input_j = input_vector[j];
          __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
          __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
          __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
          __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
          __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
          __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
          __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
          sum_lo = _mm_add_pi32(sum_lo, product_lo);
          sum_hi = _mm_add_pi32(sum_hi, product_hi);
        }
        __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
        sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
        output[i] = _mm_cvtsi64_si32(sum);

  #elif defined(USE_NEON)
        int32x4_t sum = {biases_[i]};
        const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
        for (IndexType j = 0; j < kNumChunks; ++j) {
          int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
          product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
          sum = vpadalq_s16(sum, product);
        }
        output[i] = sum[0] + sum[1] + sum[2] + sum[3];

  #else
        OutputType sum = biases_[i];
        for (IndexType j = 0; j < kInputDimensions; ++j) {
          sum += weights_[offset + j] * input[j];
        }
        output[i] = sum;
  #endif

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

   private:
    using BiasType = OutputType;
    using WeightType = std::int8_t;

    // Make the learning class a friend
    friend class Trainer<AffineTransform>;

    PreviousLayer previous_layer_;

    alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
    alignas(kCacheLineSize)
        WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
  };

}  // namespace Eval::NNUE::Layers

#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED