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19 // Definition of layer AffineTransform of NNUE evaluation function
21 #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
22 #define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
25 #include "../nnue_common.h"
27 namespace Eval::NNUE::Layers {
29 // Affine transformation layer
30 template <typename PreviousLayer, IndexType OutputDimensions>
31 class AffineTransform {
34 using InputType = typename PreviousLayer::OutputType;
35 using OutputType = std::int32_t;
36 static_assert(std::is_same<InputType, std::uint8_t>::value, "");
38 // Number of input/output dimensions
39 static constexpr IndexType kInputDimensions =
40 PreviousLayer::kOutputDimensions;
41 static constexpr IndexType kOutputDimensions = OutputDimensions;
42 static constexpr IndexType kPaddedInputDimensions =
43 CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
45 // Size of forward propagation buffer used in this layer
46 static constexpr std::size_t kSelfBufferSize =
47 CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
49 // Size of the forward propagation buffer used from the input layer to this layer
50 static constexpr std::size_t kBufferSize =
51 PreviousLayer::kBufferSize + kSelfBufferSize;
53 // Hash value embedded in the evaluation file
54 static constexpr std::uint32_t GetHashValue() {
55 std::uint32_t hash_value = 0xCC03DAE4u;
56 hash_value += kOutputDimensions;
57 hash_value ^= PreviousLayer::GetHashValue() >> 1;
58 hash_value ^= PreviousLayer::GetHashValue() << 31;
62 // Read network parameters
63 bool ReadParameters(std::istream& stream) {
64 if (!previous_layer_.ReadParameters(stream)) return false;
65 stream.read(reinterpret_cast<char*>(biases_),
66 kOutputDimensions * sizeof(BiasType));
67 stream.read(reinterpret_cast<char*>(weights_),
68 kOutputDimensions * kPaddedInputDimensions *
70 return !stream.fail();
73 // Forward propagation
74 const OutputType* Propagate(
75 const TransformedFeatureType* transformed_features, char* buffer) const {
76 const auto input = previous_layer_.Propagate(
77 transformed_features, buffer + kSelfBufferSize);
78 const auto output = reinterpret_cast<OutputType*>(buffer);
80 #if defined(USE_AVX512)
81 constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
82 const __m512i kOnes = _mm512_set1_epi16(1);
83 const auto input_vector = reinterpret_cast<const __m512i*>(input);
85 #elif defined(USE_AVX2)
86 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
87 const __m256i kOnes = _mm256_set1_epi16(1);
88 const auto input_vector = reinterpret_cast<const __m256i*>(input);
90 #elif defined(USE_SSSE3)
91 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
92 const __m128i kOnes = _mm_set1_epi16(1);
93 const auto input_vector = reinterpret_cast<const __m128i*>(input);
95 #elif defined(USE_NEON)
96 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
97 const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
100 for (IndexType i = 0; i < kOutputDimensions; ++i) {
101 const IndexType offset = i * kPaddedInputDimensions;
103 #if defined(USE_AVX512)
104 __m512i sum = _mm512_setzero_si512();
105 const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
106 for (IndexType j = 0; j < kNumChunks; ++j) {
107 __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
108 product = _mm512_madd_epi16(product, kOnes);
109 sum = _mm512_add_epi32(sum, product);
112 // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
113 // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
114 // and we have to do one more 256bit chunk.
115 if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
117 const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
118 const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
119 __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
120 product256 = _mm256_madd_epi16(product256, _mm256_set1_epi16(1));
121 sum = _mm512_add_epi32(sum, _mm512_zextsi256_si512(product256));
123 output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
125 #elif defined(USE_AVX2)
126 __m256i sum = _mm256_setzero_si256();
127 const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
128 for (IndexType j = 0; j < kNumChunks; ++j) {
129 __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
130 product = _mm256_madd_epi16(product, kOnes);
131 sum = _mm256_add_epi32(sum, product);
133 __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
134 sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
135 sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
136 output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
138 #elif defined(USE_SSSE3)
139 __m128i sum = _mm_setzero_si128();
140 const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
141 for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
142 __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
143 product0 = _mm_madd_epi16(product0, kOnes);
144 sum = _mm_add_epi32(sum, product0);
145 __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
146 product1 = _mm_madd_epi16(product1, kOnes);
147 sum = _mm_add_epi32(sum, product1);
149 if (kNumChunks & 0x1) {
150 __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
151 product = _mm_madd_epi16(product, kOnes);
152 sum = _mm_add_epi32(sum, product);
154 sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
155 sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
156 output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
158 #elif defined(USE_NEON)
159 int32x4_t sum = {biases_[i]};
160 const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
161 for (IndexType j = 0; j < kNumChunks; ++j) {
162 int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
163 product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
164 sum = vpadalq_s16(sum, product);
166 output[i] = sum[0] + sum[1] + sum[2] + sum[3];
169 OutputType sum = biases_[i];
170 for (IndexType j = 0; j < kInputDimensions; ++j) {
171 sum += weights_[offset + j] * input[j];
181 using BiasType = OutputType;
182 using WeightType = std::int8_t;
184 PreviousLayer previous_layer_;
186 alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
187 alignas(kCacheLineSize)
188 WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
191 } // namespace Eval::NNUE::Layers
193 #endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED