2 Stockfish, a UCI chess playing engine derived from Glaurung 2.1
3 Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
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.
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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.
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16 along with this program. If not, see <http://www.gnu.org/licenses/>.
19 // Definition of layer ClippedReLU of NNUE evaluation function
21 #ifndef NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
22 #define NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED
24 #include "../nnue_common.h"
26 namespace Stockfish::Eval::NNUE::Layers {
29 template <typename PreviousLayer>
33 using InputType = typename PreviousLayer::OutputType;
34 using OutputType = std::uint8_t;
35 static_assert(std::is_same<InputType, std::int32_t>::value, "");
37 // Number of input/output dimensions
38 static constexpr IndexType InputDimensions =
39 PreviousLayer::OutputDimensions;
40 static constexpr IndexType OutputDimensions = InputDimensions;
42 // Size of forward propagation buffer used in this layer
43 static constexpr std::size_t SelfBufferSize =
44 ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
46 // Size of the forward propagation buffer used from the input layer to this layer
47 static constexpr std::size_t BufferSize =
48 PreviousLayer::BufferSize + SelfBufferSize;
50 // Hash value embedded in the evaluation file
51 static constexpr std::uint32_t get_hash_value() {
52 std::uint32_t hashValue = 0x538D24C7u;
53 hashValue += PreviousLayer::get_hash_value();
57 // Read network parameters
58 bool read_parameters(std::istream& stream) {
59 return previousLayer.read_parameters(stream);
62 // Write network parameters
63 bool write_parameters(std::ostream& stream) const {
64 return previousLayer.write_parameters(stream);
67 // Forward propagation
68 const OutputType* propagate(
69 const TransformedFeatureType* transformedFeatures, char* buffer) const {
70 const auto input = previousLayer.propagate(
71 transformedFeatures, buffer + SelfBufferSize);
72 const auto output = reinterpret_cast<OutputType*>(buffer);
75 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
76 const __m256i Zero = _mm256_setzero_si256();
77 const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
78 const auto in = reinterpret_cast<const __m256i*>(input);
79 const auto out = reinterpret_cast<__m256i*>(output);
80 for (IndexType i = 0; i < NumChunks; ++i) {
81 const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
82 _mm256_load_si256(&in[i * 4 + 0]),
83 _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
84 const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
85 _mm256_load_si256(&in[i * 4 + 2]),
86 _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
87 _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
88 _mm256_packs_epi16(words0, words1), Zero), Offsets));
90 constexpr IndexType Start = NumChunks * SimdWidth;
92 #elif defined(USE_SSE2)
93 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
96 const __m128i Zero = _mm_setzero_si128();
98 const __m128i k0x80s = _mm_set1_epi8(-128);
101 const auto in = reinterpret_cast<const __m128i*>(input);
102 const auto out = reinterpret_cast<__m128i*>(output);
103 for (IndexType i = 0; i < NumChunks; ++i) {
104 const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
105 _mm_load_si128(&in[i * 4 + 0]),
106 _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
107 const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
108 _mm_load_si128(&in[i * 4 + 2]),
109 _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
110 const __m128i packedbytes = _mm_packs_epi16(words0, words1);
111 _mm_store_si128(&out[i],
114 _mm_max_epi8(packedbytes, Zero)
116 _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
121 constexpr IndexType Start = NumChunks * SimdWidth;
123 #elif defined(USE_MMX)
124 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
125 const __m64 k0x80s = _mm_set1_pi8(-128);
126 const auto in = reinterpret_cast<const __m64*>(input);
127 const auto out = reinterpret_cast<__m64*>(output);
128 for (IndexType i = 0; i < NumChunks; ++i) {
129 const __m64 words0 = _mm_srai_pi16(
130 _mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
132 const __m64 words1 = _mm_srai_pi16(
133 _mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
135 const __m64 packedbytes = _mm_packs_pi16(words0, words1);
136 out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
139 constexpr IndexType Start = NumChunks * SimdWidth;
141 #elif defined(USE_NEON)
142 constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
143 const int8x8_t Zero = {0};
144 const auto in = reinterpret_cast<const int32x4_t*>(input);
145 const auto out = reinterpret_cast<int8x8_t*>(output);
146 for (IndexType i = 0; i < NumChunks; ++i) {
148 const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
149 pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
150 pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
151 out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
153 constexpr IndexType Start = NumChunks * (SimdWidth / 2);
155 constexpr IndexType Start = 0;
158 for (IndexType i = Start; i < InputDimensions; ++i) {
159 output[i] = static_cast<OutputType>(
160 std::max(0, std::min(127, input[i] >> WeightScaleBits)));
166 PreviousLayer previousLayer;
169 } // namespace Stockfish::Eval::NNUE::Layers
171 #endif // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED