2 Stockfish, a UCI chess playing engine derived from Glaurung 2.1
3 Copyright (C) 2004-2020 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 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 kInputDimensions =
39 PreviousLayer::kOutputDimensions;
40 static constexpr IndexType kOutputDimensions = kInputDimensions;
42 // Size of forward propagation buffer used in this layer
43 static constexpr std::size_t kSelfBufferSize =
44 CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
46 // Size of the forward propagation buffer used from the input layer to this layer
47 static constexpr std::size_t kBufferSize =
48 PreviousLayer::kBufferSize + kSelfBufferSize;
50 // Hash value embedded in the evaluation file
51 static constexpr std::uint32_t GetHashValue() {
52 std::uint32_t hash_value = 0x538D24C7u;
53 hash_value += PreviousLayer::GetHashValue();
57 // Read network parameters
58 bool ReadParameters(std::istream& stream) {
59 return previous_layer_.ReadParameters(stream);
62 // Forward propagation
63 const OutputType* Propagate(
64 const TransformedFeatureType* transformed_features, char* buffer) const {
65 const auto input = previous_layer_.Propagate(
66 transformed_features, buffer + kSelfBufferSize);
67 const auto output = reinterpret_cast<OutputType*>(buffer);
70 constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
71 const __m256i kZero = _mm256_setzero_si256();
72 const __m256i kOffsets = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0);
73 const auto in = reinterpret_cast<const __m256i*>(input);
74 const auto out = reinterpret_cast<__m256i*>(output);
75 for (IndexType i = 0; i < kNumChunks; ++i) {
76 const __m256i words0 = _mm256_srai_epi16(_mm256_packs_epi32(
77 _mm256_loadA_si256(&in[i * 4 + 0]),
78 _mm256_loadA_si256(&in[i * 4 + 1])), kWeightScaleBits);
79 const __m256i words1 = _mm256_srai_epi16(_mm256_packs_epi32(
80 _mm256_loadA_si256(&in[i * 4 + 2]),
81 _mm256_loadA_si256(&in[i * 4 + 3])), kWeightScaleBits);
82 _mm256_storeA_si256(&out[i], _mm256_permutevar8x32_epi32(_mm256_max_epi8(
83 _mm256_packs_epi16(words0, words1), kZero), kOffsets));
85 constexpr IndexType kStart = kNumChunks * kSimdWidth;
87 #elif defined(USE_SSE2)
88 constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
91 const __m128i kZero = _mm_setzero_si128();
93 const __m128i k0x80s = _mm_set1_epi8(-128);
96 const auto in = reinterpret_cast<const __m128i*>(input);
97 const auto out = reinterpret_cast<__m128i*>(output);
98 for (IndexType i = 0; i < kNumChunks; ++i) {
99 const __m128i words0 = _mm_srai_epi16(_mm_packs_epi32(
100 _mm_load_si128(&in[i * 4 + 0]),
101 _mm_load_si128(&in[i * 4 + 1])), kWeightScaleBits);
102 const __m128i words1 = _mm_srai_epi16(_mm_packs_epi32(
103 _mm_load_si128(&in[i * 4 + 2]),
104 _mm_load_si128(&in[i * 4 + 3])), kWeightScaleBits);
105 const __m128i packedbytes = _mm_packs_epi16(words0, words1);
106 _mm_store_si128(&out[i],
109 _mm_max_epi8(packedbytes, kZero)
111 _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
116 constexpr IndexType kStart = kNumChunks * kSimdWidth;
118 #elif defined(USE_MMX)
119 constexpr IndexType kNumChunks = kInputDimensions / kSimdWidth;
120 const __m64 k0x80s = _mm_set1_pi8(-128);
121 const auto in = reinterpret_cast<const __m64*>(input);
122 const auto out = reinterpret_cast<__m64*>(output);
123 for (IndexType i = 0; i < kNumChunks; ++i) {
124 const __m64 words0 = _mm_srai_pi16(
125 _mm_packs_pi32(in[i * 4 + 0], in[i * 4 + 1]),
127 const __m64 words1 = _mm_srai_pi16(
128 _mm_packs_pi32(in[i * 4 + 2], in[i * 4 + 3]),
130 const __m64 packedbytes = _mm_packs_pi16(words0, words1);
131 out[i] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
134 constexpr IndexType kStart = kNumChunks * kSimdWidth;
136 #elif defined(USE_NEON)
137 constexpr IndexType kNumChunks = kInputDimensions / (kSimdWidth / 2);
138 const int8x8_t kZero = {0};
139 const auto in = reinterpret_cast<const int32x4_t*>(input);
140 const auto out = reinterpret_cast<int8x8_t*>(output);
141 for (IndexType i = 0; i < kNumChunks; ++i) {
143 const auto pack = reinterpret_cast<int16x4_t*>(&shifted);
144 pack[0] = vqshrn_n_s32(in[i * 2 + 0], kWeightScaleBits);
145 pack[1] = vqshrn_n_s32(in[i * 2 + 1], kWeightScaleBits);
146 out[i] = vmax_s8(vqmovn_s16(shifted), kZero);
148 constexpr IndexType kStart = kNumChunks * (kSimdWidth / 2);
150 constexpr IndexType kStart = 0;
153 for (IndexType i = kStart; i < kInputDimensions; ++i) {
154 output[i] = static_cast<OutputType>(
155 std::max(0, std::min(127, input[i] >> kWeightScaleBits)));
161 PreviousLayer previous_layer_;
164 } // namespace Eval::NNUE::Layers
166 #endif // NNUE_LAYERS_CLIPPED_RELU_H_INCLUDED