// Fallback implementation for older/other architectures.
// Requires the input to be padded to at least 16 values.
#if !defined(USE_SSSE3)
- template <IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
- static void affine_transform_non_ssse3(std::int32_t* output, const std::int8_t* weights, const std::int32_t* biases, const std::uint8_t* input)
- {
-# if defined(USE_SSE2) || defined(USE_NEON_DOTPROD) || defined(USE_NEON)
-# if defined(USE_SSE2)
+template<IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
+static void affine_transform_non_ssse3(std::int32_t* output,
+ const std::int8_t* weights,
+ const std::int32_t* biases,
+ const std::uint8_t* input) {
+ #if defined(USE_SSE2) || defined(USE_NEON_DOTPROD) || defined(USE_NEON)
+ #if defined(USE_SSE2)
// At least a multiple of 16, with SSE2.
- constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
- const __m128i Zeros = _mm_setzero_si128();
- const auto inputVector = reinterpret_cast<const __m128i*>(input);
-
-# elif defined(USE_NEON_DOTPROD)
- constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
- const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
-
-# elif defined(USE_NEON)
- constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
- const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
-# endif
-
- for (IndexType i = 0; i < OutputDimensions; ++i) {
- const IndexType offset = i * PaddedInputDimensions;
-
-# if defined(USE_SSE2)
- __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
- __m128i sumHi = Zeros;
- const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m128i row_j = _mm_load_si128(&row[j]);
- __m128i input_j = _mm_load_si128(&inputVector[j]);
- __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
- __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
- __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
- __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
- __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
- __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_epi32(sumLo, productLo);
- sumHi = _mm_add_epi32(sumHi, productHi);
- }
- __m128i sum = _mm_add_epi32(sumLo, sumHi);
- __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sumHigh_64);
- __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sum_second_32);
- output[i] = _mm_cvtsi128_si32(sum);
-
-# elif defined(USE_NEON_DOTPROD)
- int32x4_t sum = {biases[i]};
- const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- sum = vdotq_s32(sum, inputVector[j], row[j]);
- }
- output[i] = vaddvq_s32(sum);
-
-# elif defined(USE_NEON)
- int32x4_t sum = {biases[i]};
- const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
- product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
- sum = vpadalq_s16(sum, product);
- }
- output[i] = sum[0] + sum[1] + sum[2] + sum[3];
-
-# endif
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const __m128i Zeros = _mm_setzero_si128();
+ const auto inputVector = reinterpret_cast<const __m128i*>(input);
+
+ #elif defined(USE_NEON_DOTPROD)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
+
+ #elif defined(USE_NEON)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
+ #endif
+
+ for (IndexType i = 0; i < OutputDimensions; ++i)
+ {
+ const IndexType offset = i * PaddedInputDimensions;
+
+ #if defined(USE_SSE2)
+ __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
+ __m128i sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m128i row_j = _mm_load_si128(&row[j]);
+ __m128i input_j = _mm_load_si128(&inputVector[j]);
+ __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+ __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
+ __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
+ __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
+ __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
+ __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_epi32(sumLo, productLo);
+ sumHi = _mm_add_epi32(sumHi, productHi);
+ }
+ __m128i sum = _mm_add_epi32(sumLo, sumHi);
+ __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sumHigh_64);
+ __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sum_second_32);
+ output[i] = _mm_cvtsi128_si32(sum);
+
+ #elif defined(USE_NEON_DOTPROD)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ sum = vdotq_s32(sum, inputVector[j], row[j]);
+ }
+ output[i] = vaddvq_s32(sum);
+
+ #elif defined(USE_NEON)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
+ product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
+ sum = vpadalq_s16(sum, product);
+ }
+ output[i] = sum[0] + sum[1] + sum[2] + sum[3];
+
+ #endif
}
-# else
- std::memcpy(output, biases, sizeof(std::int32_t) * OutputDimensions);
-
- // Traverse weights in transpose order to take advantage of input sparsity
- for (IndexType i = 0; i < InputDimensions; ++i)
- if (input[i]) {
- const std::int8_t* w = &weights[i];
- const int in = input[i];
- for (IndexType j = 0; j < OutputDimensions; ++j)
- output[j] += w[j * PaddedInputDimensions] * in;
- }
-# endif
- }
+ #else
+ std::memcpy(output, biases, sizeof(std::int32_t) * OutputDimensions);
+
+ // Traverse weights in transpose order to take advantage of input sparsity
+ for (IndexType i = 0; i < InputDimensions; ++i)
+ if (input[i])
+ {
+ const std::int8_t* w = &weights[i];
+ const int in = input[i];
+ for (IndexType j = 0; j < OutputDimensions; ++j)
+ output[j] += w[j * PaddedInputDimensions] * in;
+ }
+ #endif
+}
#endif
- template <IndexType InDims, IndexType OutDims>
- class AffineTransform {
+template<IndexType InDims, IndexType OutDims>
+class AffineTransform {
public:
// Input/output type
- using InputType = std::uint8_t;
+ using InputType = std::uint8_t;
using OutputType = std::int32_t;
// Number of input/output dimensions
- static constexpr IndexType InputDimensions = InDims;
+ static constexpr IndexType InputDimensions = InDims;
static constexpr IndexType OutputDimensions = OutDims;
static constexpr IndexType PaddedInputDimensions =
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
- std::uint32_t hashValue = 0xCC03DAE4u;
- hashValue += OutputDimensions;
- hashValue ^= prevHash >> 1;
- hashValue ^= prevHash << 31;
- return hashValue;
+ std::uint32_t hashValue = 0xCC03DAE4u;
+ hashValue += OutputDimensions;
+ hashValue ^= prevHash >> 1;
+ hashValue ^= prevHash << 31;
+ return hashValue;
}
- static constexpr IndexType get_weight_index_scrambled(IndexType i)
- {
- return
- (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
- i / PaddedInputDimensions * 4 +
- i % 4;
+ static constexpr IndexType get_weight_index_scrambled(IndexType i) {
+ return (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4
+ + i / PaddedInputDimensions * 4 + i % 4;
}
- static constexpr IndexType get_weight_index(IndexType i)
- {
-#if defined (USE_SSSE3)
- return get_weight_index_scrambled(i);
+ static constexpr IndexType get_weight_index(IndexType i) {
+#if defined(USE_SSSE3)
+ return get_weight_index_scrambled(i);
#else
- return i;
+ return i;
#endif
}
// Read network parameters
bool read_parameters(std::istream& stream) {
- read_little_endian<BiasType>(stream, biases, OutputDimensions);
- for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
- weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
+ read_little_endian<BiasType>(stream, biases, OutputDimensions);
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
- return !stream.fail();
+ return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
- write_little_endian<BiasType>(stream, biases, OutputDimensions);
+ write_little_endian<BiasType>(stream, biases, OutputDimensions);
- for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
- write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
- return !stream.fail();
+ return !stream.fail();
}
// Forward propagation
- void propagate(
- const InputType* input, OutputType* output) const {
-
-#if defined (USE_SSSE3)
-
- if constexpr (OutputDimensions > 1)
- {
-
-#if defined (USE_AVX512)
- using vec_t = __m512i;
- #define vec_setzero _mm512_setzero_si512
- #define vec_set_32 _mm512_set1_epi32
- #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
- #define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
- #define vec_hadd Simd::m512_hadd
-#elif defined (USE_AVX2)
- using vec_t = __m256i;
- #define vec_setzero _mm256_setzero_si256
- #define vec_set_32 _mm256_set1_epi32
- #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
- #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
- #define vec_hadd Simd::m256_hadd
-#elif defined (USE_SSSE3)
- using vec_t = __m128i;
- #define vec_setzero _mm_setzero_si128
- #define vec_set_32 _mm_set1_epi32
- #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
- #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
- #define vec_hadd Simd::m128_hadd
-#endif
-
- static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
-
- static_assert(OutputDimensions % OutputSimdWidth == 0);
+ void propagate(const InputType* input, OutputType* output) const {
- constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
- constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+#if defined(USE_SSSE3)
- const auto input32 = reinterpret_cast<const std::int32_t*>(input);
- const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
- vec_t acc[NumRegs];
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = biasvec[k];
-
- for (IndexType i = 0; i < NumChunks; i += 2)
+ if constexpr (OutputDimensions > 1)
{
- const vec_t in0 = vec_set_32(input32[i + 0]);
- const vec_t in1 = vec_set_32(input32[i + 1]);
- const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
- const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
- for (IndexType k = 0; k < NumRegs; ++k)
- vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]);
- }
-
- vec_t* outptr = reinterpret_cast<vec_t*>(output);
- for (IndexType k = 0; k < NumRegs; ++k)
- outptr[k] = acc[k];
-
-# undef vec_setzero
-# undef vec_set_32
-# undef vec_add_dpbusd_32
-# undef vec_add_dpbusd_32x2
-# undef vec_hadd
-
- }
- else if constexpr (OutputDimensions == 1)
- {
-
-// We cannot use AVX512 for the last layer because there's only 32 inputs and the buffer is not padded to 64 elements.
-#if defined (USE_AVX2)
- using vec_t = __m256i;
- #define vec_setzero _mm256_setzero_si256
- #define vec_set_32 _mm256_set1_epi32
- #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
- #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
- #define vec_hadd Simd::m256_hadd
-#elif defined (USE_SSSE3)
- using vec_t = __m128i;
- #define vec_setzero _mm_setzero_si128
- #define vec_set_32 _mm_set1_epi32
- #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
- #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
- #define vec_hadd Simd::m128_hadd
-#endif
- const auto inputVector = reinterpret_cast<const vec_t*>(input);
-
- static constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(InputType);
-
- static_assert(PaddedInputDimensions % InputSimdWidth == 0);
-
- constexpr IndexType NumChunks = PaddedInputDimensions / InputSimdWidth;
- vec_t sum0 = vec_setzero();
- const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
-
- for (int j = 0; j < int(NumChunks); ++j)
- {
- const vec_t in = inputVector[j];
- vec_add_dpbusd_32(sum0, in, row0[j]);
+ #if defined(USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_setzero _mm512_setzero_si512
+ #define vec_set_32 _mm512_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m512_hadd
+ #elif defined(USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m256_hadd
+ #elif defined(USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m128_hadd
+ #endif
+
+ static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
+
+ static_assert(OutputDimensions % OutputSimdWidth == 0);
+
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
+ vec_t acc[NumRegs];
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = biasvec[k];
+
+ for (IndexType i = 0; i < NumChunks; i += 2)
+ {
+ const vec_t in0 = vec_set_32(input32[i + 0]);
+ const vec_t in1 = vec_set_32(input32[i + 1]);
+ const auto col0 =
+ reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
+ const auto col1 =
+ reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]);
+ }
+
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ outptr[k] = acc[k];
+
+ #undef vec_setzero
+ #undef vec_set_32
+ #undef vec_add_dpbusd_32
+ #undef vec_add_dpbusd_32x2
+ #undef vec_hadd
}
- output[0] = vec_hadd(sum0, biases[0]);
-
-# undef vec_setzero
-# undef vec_set_32
-# undef vec_add_dpbusd_32
-# undef vec_add_dpbusd_32x2
-# undef vec_hadd
+ else if constexpr (OutputDimensions == 1)
+ {
- }
+ // We cannot use AVX512 for the last layer because there's only 32 inputs and the buffer is not padded to 64 elements.
+ #if defined(USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m256_hadd
+ #elif defined(USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m128_hadd
+ #endif
+
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+
+ static constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(InputType);
+
+ static_assert(PaddedInputDimensions % InputSimdWidth == 0);
+
+ constexpr IndexType NumChunks = PaddedInputDimensions / InputSimdWidth;
+ vec_t sum0 = vec_setzero();
+ const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
+
+ for (int j = 0; j < int(NumChunks); ++j)
+ {
+ const vec_t in = inputVector[j];
+ vec_add_dpbusd_32(sum0, in, row0[j]);
+ }
+ output[0] = vec_hadd(sum0, biases[0]);
+
+ #undef vec_setzero
+ #undef vec_set_32
+ #undef vec_add_dpbusd_32
+ #undef vec_add_dpbusd_32x2
+ #undef vec_hadd
+ }
#else
- // Use old implementation for the other architectures.
- affine_transform_non_ssse3<
- InputDimensions,
- PaddedInputDimensions,
- OutputDimensions>(output, weights, biases, input);
+ // Use old implementation for the other architectures.
+ affine_transform_non_ssse3<InputDimensions, PaddedInputDimensions, OutputDimensions>(
+ output, weights, biases, input);
#endif
}
private:
- using BiasType = OutputType;
+ using BiasType = OutputType;
using WeightType = std::int8_t;
alignas(CacheLineSize) BiasType biases[OutputDimensions];
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
- };
+};
} // namespace Stockfish::Eval::NNUE::Layers
-#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
+#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
projects / stockfish / blobdiff