2 Stockfish, a UCI chess playing engine derived from Glaurung 2.1
3 Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file)
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12 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
13 GNU General Public License for more details.
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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 if constexpr (InputDimensions % SimdWidth == 0) {
76 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
77 const __m256i Zero = _mm256_setzero_si256();
78 const __m256i Offsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
79 const auto in = reinterpret_cast<const __m256i*>(input);
80 const auto out = reinterpret_cast<__m256i*>(output);
81 for (IndexType i = 0; i < NumChunks; ++i) {
82 const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
83 _mm256_load_si256(&in[i * 4 + 0]),
84 _mm256_load_si256(&in[i * 4 + 1])), WeightScaleBits);
85 const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
86 _mm256_load_si256(&in[i * 4 + 2]),
87 _mm256_load_si256(&in[i * 4 + 3])), WeightScaleBits);
88 _mm256_store_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
89 _mm256_packs_epi16(words0, words1), Zero), Offsets));
92 constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
93 const __m128i Zero = _mm_setzero_si128();
94 const auto in = reinterpret_cast<const __m128i*>(input);
95 const auto out = reinterpret_cast<__m128i*>(output);
96 for (IndexType i = 0; i < NumChunks; ++i) {
97 const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
98 _mm_load_si128(&in[i * 4 + 0]),
99 _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
100 const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
101 _mm_load_si128(&in[i * 4 + 2]),
102 _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
103 const __m128i packedbytes = _mm_packs_epi16(words0, words1);
104 _mm_store_si128(&out[i], _mm_max_epi8(packedbytes, Zero));
107 constexpr IndexType Start =
108 InputDimensions % SimdWidth == 0
109 ? InputDimensions / SimdWidth * SimdWidth
110 : InputDimensions / (SimdWidth / 2) * (SimdWidth / 2);
112 #elif defined(USE_SSE2)
113 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
116 const __m128i Zero = _mm_setzero_si128();
118 const __m128i k0x80s = _mm_set1_epi8(-128);
121 const auto in = reinterpret_cast<const __m128i*>(input);
122 const auto out = reinterpret_cast<__m128i*>(output);
123 for (IndexType i = 0; i < NumChunks; ++i) {
124 const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
125 _mm_load_si128(&in[i * 4 + 0]),
126 _mm_load_si128(&in[i * 4 + 1])), WeightScaleBits);
127 const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
128 _mm_load_si128(&in[i * 4 + 2]),
129 _mm_load_si128(&in[i * 4 + 3])), WeightScaleBits);
130 const __m128i packedbytes = _mm_packs_epi16(words0, words1);
131 _mm_store_si128(&out[i],
134 _mm_max_epi8(packedbytes, Zero)
136 _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
141 constexpr IndexType Start = NumChunks * SimdWidth;
143 #elif defined(USE_MMX)
144 constexpr IndexType NumChunks = InputDimensions / SimdWidth;
145 const __m64 k0x80s = _mm_set1_pi8(-128);
146 const auto in = reinterpret_cast<const __m64*>(input);
147 const auto out = reinterpret_cast<__m64*>(output);
148 for (IndexType i = 0; i < NumChunks; ++i) {
149 const __m64 words0 = _mm_srai_pi16(
150 _mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
152 const __m64 words1 = _mm_srai_pi16(
153 _mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
155 const __m64 packedbytes = _mm_packs_pi16(words0, words1);
156 out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
159 constexpr IndexType Start = NumChunks * SimdWidth;
161 #elif defined(USE_NEON)
162 constexpr IndexType NumChunks = InputDimensions / (SimdWidth / 2);
163 const int8x8_t Zero = {0};
164 const auto in = reinterpret_cast<const int32x4_t*>(input);
165 const auto out = reinterpret_cast<int8x8_t*>(output);
166 for (IndexType i = 0; i < NumChunks; ++i) {
168 const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
169 pack[0] = vqshrn_n_s32(in[i * 2 + 0], WeightScaleBits);
170 pack[1] = vqshrn_n_s32(in[i * 2 + 1], WeightScaleBits);
171 out[i] = vmax_s8(vqmovn_s16(shifted), Zero);
173 constexpr IndexType Start = NumChunks * (SimdWidth / 2);
175 constexpr IndexType Start = 0;
178 for (IndexType i = Start; i < InputDimensions; ++i) {
179 output[i] = static_cast<OutputType>(
180 std::max(0, std::min(127, input[i] >> WeightScaleBits)));
186 PreviousLayer previousLayer;
189 } // namespace Stockfish::Eval::NNUE::Layers
191 #endif // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED