clipped_relu.h

/*
  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
  Copyright (C) 2004-2026 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 <algorithm>
#include <cstdint>
#include <iosfwd>

#include "../nnue_common.h"

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

// Clipped ReLU
template<IndexType InDims>
class ClippedReLU {
   public:
    // Input/output type
    using InputType  = std::int32_t;
    using OutputType = std::uint8_t;

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

    using OutputBuffer = OutputType[PaddedOutputDimensions];

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

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

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

    std::size_t get_content_hash() const {
        std::size_t h = 0;
        hash_combine(h, get_hash_value(0));
        return h;
    }

    // Forward propagation
    void propagate(const InputType* input, OutputType* output) const {

#if defined(USE_AVX2)
        if constexpr (InputDimensions % SimdWidth == 0)
        {
            constexpr IndexType NumChunks = InputDimensions / SimdWidth;
            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_srli_epi16(_mm256_packus_epi32(_mm256_load_si256(&in[i * 4 + 0]),
                                                        _mm256_load_si256(&in[i * 4 + 1])),
                                    WeightScaleBits);
                const __m256i words1 =
                  _mm256_srli_epi16(_mm256_packus_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_packs_epi16(words0, words1), Offsets));
            }
        }
        else
        {
            constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
            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_srli_epi16(
                  _mm_packus_epi32(_mm_load_si128(&in[i * 4 + 0]), _mm_load_si128(&in[i * 4 + 1])),
                  WeightScaleBits);
                const __m128i words1 = _mm_srli_epi16(
                  _mm_packus_epi32(_mm_load_si128(&in[i * 4 + 2]), _mm_load_si128(&in[i * 4 + 3])),
                  WeightScaleBits);
                _mm_store_si128(&out[i], _mm_packs_epi16(words0, words1));
            }
        }
        constexpr IndexType Start = InputDimensions % SimdWidth == 0
                                    ? InputDimensions / SimdWidth * SimdWidth
                                    : InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);

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

    #ifndef USE_SSE41
        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)
        {
    #if defined(USE_SSE41)
            const __m128i words0 = _mm_srli_epi16(
              _mm_packus_epi32(_mm_load_si128(&in[i * 4 + 0]), _mm_load_si128(&in[i * 4 + 1])),
              WeightScaleBits);
            const __m128i words1 = _mm_srli_epi16(
              _mm_packus_epi32(_mm_load_si128(&in[i * 4 + 2]), _mm_load_si128(&in[i * 4 + 3])),
              WeightScaleBits);
            _mm_store_si128(&out[i], _mm_packs_epi16(words0, words1));
    #else
            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_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s));
    #endif
        }
        constexpr IndexType Start = NumChunks * SimdWidth;

#elif defined(USE_NEON)
        constexpr IndexType    NumChunks = InputDimensions / (SimdWidth / 2);
        const SIMD::vec_i8x8_t Zero      = {0};
        const auto             in        = reinterpret_cast<const SIMD::vec_i32x4_t*>(input);
        const auto             out       = reinterpret_cast<SIMD::vec_i8x8_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::clamp(input[i] >> WeightScaleBits, 0, 127));
        }
    }
};

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

#endif  // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED