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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 for (std::size_t i = 0; i < kOutputDimensions; ++i)
66 biases_[i] = read_le<BiasType>(stream);
67 for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
68 weights_[i] = read_le<WeightType>(stream);
69 return !stream.fail();
72 // Forward propagation
73 const OutputType* Propagate(
74 const TransformedFeatureType* transformed_features, char* buffer) const {
75 const auto input = previous_layer_.Propagate(
76 transformed_features, buffer + kSelfBufferSize);
77 const auto output = reinterpret_cast<OutputType*>(buffer);
79 #if defined(USE_AVX512)
80 constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2);
81 const auto input_vector = reinterpret_cast<const __m512i*>(input);
82 #if !defined(USE_VNNI)
83 const __m512i kOnes = _mm512_set1_epi16(1);
86 #elif defined(USE_AVX2)
87 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
88 const __m256i kOnes = _mm256_set1_epi16(1);
89 const auto input_vector = reinterpret_cast<const __m256i*>(input);
91 #elif defined(USE_SSE2)
92 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
94 const __m128i kZeros = _mm_setzero_si128();
96 const __m128i kOnes = _mm_set1_epi16(1);
98 const auto input_vector = reinterpret_cast<const __m128i*>(input);
100 #elif defined(USE_MMX)
101 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
102 const __m64 kZeros = _mm_setzero_si64();
103 const auto input_vector = reinterpret_cast<const __m64*>(input);
105 #elif defined(USE_NEON)
106 constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
107 const auto input_vector = reinterpret_cast<const int8x8_t*>(input);
110 for (IndexType i = 0; i < kOutputDimensions; ++i) {
111 const IndexType offset = i * kPaddedInputDimensions;
113 #if defined(USE_AVX512)
114 __m512i sum = _mm512_setzero_si512();
115 const auto row = reinterpret_cast<const __m512i*>(&weights_[offset]);
116 for (IndexType j = 0; j < kNumChunks; ++j) {
117 #if defined(USE_VNNI)
118 sum = _mm512_dpbusd_epi32(sum, _mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
120 __m512i product = _mm512_maddubs_epi16(_mm512_loadA_si512(&input_vector[j]), _mm512_load_si512(&row[j]));
121 product = _mm512_madd_epi16(product, kOnes);
122 sum = _mm512_add_epi32(sum, product);
126 // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks.
127 // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit)
128 // and we have to do one more 256bit chunk.
129 if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2)
131 const auto iv256 = reinterpret_cast<const __m256i*>(&input_vector[kNumChunks]);
132 const auto row256 = reinterpret_cast<const __m256i*>(&row[kNumChunks]);
133 #if defined(USE_VNNI)
134 __m256i product256 = _mm256_dpbusd_epi32(
135 _mm512_castsi512_si256(sum), _mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
136 sum = _mm512_inserti32x8(sum, product256, 0);
138 __m256i product256 = _mm256_maddubs_epi16(_mm256_loadA_si256(&iv256[0]), _mm256_load_si256(&row256[0]));
139 sum = _mm512_add_epi32(sum, _mm512_cvtepi16_epi32(product256));
142 output[i] = _mm512_reduce_add_epi32(sum) + biases_[i];
144 #elif defined(USE_AVX2)
145 __m256i sum = _mm256_setzero_si256();
146 const auto row = reinterpret_cast<const __m256i*>(&weights_[offset]);
147 for (IndexType j = 0; j < kNumChunks; ++j) {
148 __m256i product = _mm256_maddubs_epi16(_mm256_loadA_si256(&input_vector[j]), _mm256_load_si256(&row[j]));
149 product = _mm256_madd_epi16(product, kOnes);
150 sum = _mm256_add_epi32(sum, product);
152 __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
153 sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
154 sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
155 output[i] = _mm_cvtsi128_si32(sum128) + biases_[i];
157 #elif defined(USE_SSSE3)
158 __m128i sum = _mm_setzero_si128();
159 const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
160 for (int j = 0; j < (int)kNumChunks - 1; j += 2) {
161 __m128i product0 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j]));
162 product0 = _mm_madd_epi16(product0, kOnes);
163 sum = _mm_add_epi32(sum, product0);
164 __m128i product1 = _mm_maddubs_epi16(_mm_load_si128(&input_vector[j+1]), _mm_load_si128(&row[j+1]));
165 product1 = _mm_madd_epi16(product1, kOnes);
166 sum = _mm_add_epi32(sum, product1);
168 if (kNumChunks & 0x1) {
169 __m128i product = _mm_maddubs_epi16(_mm_load_si128(&input_vector[kNumChunks-1]), _mm_load_si128(&row[kNumChunks-1]));
170 product = _mm_madd_epi16(product, kOnes);
171 sum = _mm_add_epi32(sum, product);
173 sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
174 sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
175 output[i] = _mm_cvtsi128_si32(sum) + biases_[i];
177 #elif defined(USE_SSE2)
178 __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]);
179 __m128i sum_hi = kZeros;
180 const auto row = reinterpret_cast<const __m128i*>(&weights_[offset]);
181 for (IndexType j = 0; j < kNumChunks; ++j) {
182 __m128i row_j = _mm_load_si128(&row[j]);
183 __m128i input_j = _mm_load_si128(&input_vector[j]);
184 __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j);
185 __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs);
186 __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs);
187 __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros);
188 __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros);
189 __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo);
190 __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi);
191 sum_lo = _mm_add_epi32(sum_lo, product_lo);
192 sum_hi = _mm_add_epi32(sum_hi, product_hi);
194 __m128i sum = _mm_add_epi32(sum_lo, sum_hi);
195 __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
196 sum = _mm_add_epi32(sum, sum_high_64);
197 __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
198 sum = _mm_add_epi32(sum, sum_second_32);
199 output[i] = _mm_cvtsi128_si32(sum);
201 #elif defined(USE_MMX)
202 __m64 sum_lo = _mm_cvtsi32_si64(biases_[i]);
203 __m64 sum_hi = kZeros;
204 const auto row = reinterpret_cast<const __m64*>(&weights_[offset]);
205 for (IndexType j = 0; j < kNumChunks; ++j) {
206 __m64 row_j = row[j];
207 __m64 input_j = input_vector[j];
208 __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j);
209 __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs);
210 __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs);
211 __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros);
212 __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros);
213 __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo);
214 __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi);
215 sum_lo = _mm_add_pi32(sum_lo, product_lo);
216 sum_hi = _mm_add_pi32(sum_hi, product_hi);
218 __m64 sum = _mm_add_pi32(sum_lo, sum_hi);
219 sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
220 output[i] = _mm_cvtsi64_si32(sum);
222 #elif defined(USE_NEON)
223 int32x4_t sum = {biases_[i]};
224 const auto row = reinterpret_cast<const int8x8_t*>(&weights_[offset]);
225 for (IndexType j = 0; j < kNumChunks; ++j) {
226 int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]);
227 product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]);
228 sum = vpadalq_s16(sum, product);
230 output[i] = sum[0] + sum[1] + sum[2] + sum[3];
233 OutputType sum = biases_[i];
234 for (IndexType j = 0; j < kInputDimensions; ++j) {
235 sum += weights_[offset + j] * input[j];
248 using BiasType = OutputType;
249 using WeightType = std::int8_t;
251 PreviousLayer previous_layer_;
253 alignas(kCacheLineSize) BiasType biases_[kOutputDimensions];
254 alignas(kCacheLineSize)
255 WeightType weights_[kOutputDimensions * kPaddedInputDimensions];
258 } // namespace Eval::NNUE::Layers
260 #endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED