namespace Stockfish::Eval::NNUE::Layers {
#if (USE_SSSE3 | (USE_NEON >= 8))
- alignas(CacheLineSize) static inline const std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = [](){
- std::array<std::array<std::uint16_t, 8>, 256> v{};
- for (unsigned i = 0; i < 256; ++i)
- {
- std::uint64_t j = i, k = 0;
- while(j)
- v[i][k++] = pop_lsb(j);
- }
- return v;
+alignas(CacheLineSize) static inline const
+ std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = []() {
+ std::array<std::array<std::uint16_t, 8>, 256> v{};
+ for (unsigned i = 0; i < 256; ++i)
+ {
+ std::uint64_t j = i, k = 0;
+ while (j)
+ v[i][k++] = pop_lsb(j);
+ }
+ return v;
}();
- // Find indices of nonzero numbers in an int32_t array
- template<const IndexType InputDimensions>
- void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
-#if defined (USE_SSSE3)
- #if defined (USE_AVX512)
- using vec_t = __m512i;
- #define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
- #elif defined (USE_AVX2)
- using vec_t = __m256i;
- #if defined(USE_VNNI) && !defined(USE_AVXVNNI)
- #define vec_nnz(a) _mm256_cmpgt_epi32_mask(a, _mm256_setzero_si256())
- #else
- #define vec_nnz(a) _mm256_movemask_ps(_mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
+// Find indices of nonzero numbers in an int32_t array
+template<const IndexType InputDimensions>
+void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
+ #if defined(USE_SSSE3)
+ #if defined(USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
+ #elif defined(USE_AVX2)
+ using vec_t = __m256i;
+ #if defined(USE_VNNI) && !defined(USE_AVXVNNI)
+ #define vec_nnz(a) _mm256_cmpgt_epi32_mask(a, _mm256_setzero_si256())
+ #else
+ #define vec_nnz(a) \
+ _mm256_movemask_ps( \
+ _mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
+ #endif
+ #elif defined(USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_nnz(a) \
+ _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
#endif
- #elif defined (USE_SSSE3)
- using vec_t = __m128i;
- #define vec_nnz(a) _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
- #endif
using vec128_t = __m128i;
- #define vec128_zero _mm_setzero_si128()
- #define vec128_set_16(a) _mm_set1_epi16(a)
- #define vec128_load(a) _mm_load_si128(a)
- #define vec128_storeu(a, b) _mm_storeu_si128(a, b)
- #define vec128_add(a, b) _mm_add_epi16(a, b)
-#elif defined (USE_NEON)
- using vec_t = uint32x4_t;
+ #define vec128_zero _mm_setzero_si128()
+ #define vec128_set_16(a) _mm_set1_epi16(a)
+ #define vec128_load(a) _mm_load_si128(a)
+ #define vec128_storeu(a, b) _mm_storeu_si128(a, b)
+ #define vec128_add(a, b) _mm_add_epi16(a, b)
+ #elif defined(USE_NEON)
+ using vec_t = uint32x4_t;
static const std::uint32_t Mask[4] = {1, 2, 4, 8};
- #define vec_nnz(a) vaddvq_u32(vandq_u32(vtstq_u32(a, a), vld1q_u32(Mask)))
- using vec128_t = uint16x8_t;
- #define vec128_zero vdupq_n_u16(0)
- #define vec128_set_16(a) vdupq_n_u16(a)
- #define vec128_load(a) vld1q_u16(reinterpret_cast<const std::uint16_t*>(a))
- #define vec128_storeu(a, b) vst1q_u16(reinterpret_cast<std::uint16_t*>(a), b)
- #define vec128_add(a, b) vaddq_u16(a, b)
-#endif
+ #define vec_nnz(a) vaddvq_u32(vandq_u32(vtstq_u32(a, a), vld1q_u32(Mask)))
+ using vec128_t = uint16x8_t;
+ #define vec128_zero vdupq_n_u16(0)
+ #define vec128_set_16(a) vdupq_n_u16(a)
+ #define vec128_load(a) vld1q_u16(reinterpret_cast<const std::uint16_t*>(a))
+ #define vec128_storeu(a, b) vst1q_u16(reinterpret_cast<std::uint16_t*>(a), b)
+ #define vec128_add(a, b) vaddq_u16(a, b)
+ #endif
constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t);
// Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8)
- constexpr IndexType ChunkSize = std::max<IndexType>(InputSimdWidth, 8);
- constexpr IndexType NumChunks = InputDimensions / ChunkSize;
- constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth;
+ constexpr IndexType ChunkSize = std::max<IndexType>(InputSimdWidth, 8);
+ constexpr IndexType NumChunks = InputDimensions / ChunkSize;
+ constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth;
constexpr IndexType OutputsPerChunk = ChunkSize / 8;
- const auto inputVector = reinterpret_cast<const vec_t*>(input);
- IndexType count = 0;
- vec128_t base = vec128_zero;
- const vec128_t increment = vec128_set_16(8);
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+ IndexType count = 0;
+ vec128_t base = vec128_zero;
+ const vec128_t increment = vec128_set_16(8);
for (IndexType i = 0; i < NumChunks; ++i)
{
- // bitmask of nonzero values in this chunk
- unsigned nnz = 0;
- for (IndexType j = 0; j < InputsPerChunk; ++j)
- {
- const vec_t inputChunk = inputVector[i * InputsPerChunk + j];
- nnz |= unsigned(vec_nnz(inputChunk)) << (j * InputSimdWidth);
- }
- for (IndexType j = 0; j < OutputsPerChunk; ++j)
- {
- const auto lookup = (nnz >> (j * 8)) & 0xFF;
- const auto offsets = vec128_load(reinterpret_cast<const vec128_t*>(&lookup_indices[lookup]));
- vec128_storeu(reinterpret_cast<vec128_t*>(out + count), vec128_add(base, offsets));
- count += popcount(lookup);
- base = vec128_add(base, increment);
- }
+ // bitmask of nonzero values in this chunk
+ unsigned nnz = 0;
+ for (IndexType j = 0; j < InputsPerChunk; ++j)
+ {
+ const vec_t inputChunk = inputVector[i * InputsPerChunk + j];
+ nnz |= unsigned(vec_nnz(inputChunk)) << (j * InputSimdWidth);
+ }
+ for (IndexType j = 0; j < OutputsPerChunk; ++j)
+ {
+ const auto lookup = (nnz >> (j * 8)) & 0xFF;
+ const auto offsets =
+ vec128_load(reinterpret_cast<const vec128_t*>(&lookup_indices[lookup]));
+ vec128_storeu(reinterpret_cast<vec128_t*>(out + count), vec128_add(base, offsets));
+ count += popcount(lookup);
+ base = vec128_add(base, increment);
+ }
}
count_out = count;
- }
-# undef vec_nnz
-# undef vec128_zero
-# undef vec128_set_16
-# undef vec128_load
-# undef vec128_storeu
-# undef vec128_add
+}
+ #undef vec_nnz
+ #undef vec128_zero
+ #undef vec128_set_16
+ #undef vec128_load
+ #undef vec128_storeu
+ #undef vec128_add
#endif
- // Sparse input implementation
- template <IndexType InDims, IndexType OutDims>
- class AffineTransformSparseInput {
+// Sparse input implementation
+template<IndexType InDims, IndexType OutDims>
+class AffineTransformSparseInput {
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_assert(OutputDimensions % 16 == 0, "Only implemented for OutputDimensions divisible by 16.");
+ static_assert(OutputDimensions % 16 == 0,
+ "Only implemented for OutputDimensions divisible by 16.");
static constexpr IndexType PaddedInputDimensions =
ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
// 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 / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize +
- i / PaddedInputDimensions * ChunkSize +
- i % ChunkSize;
+ static constexpr IndexType get_weight_index_scrambled(IndexType i) {
+ return (i / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize
+ + i / PaddedInputDimensions * ChunkSize + i % ChunkSize;
}
- static constexpr IndexType get_weight_index(IndexType i)
- {
+ static constexpr IndexType get_weight_index(IndexType i) {
#if (USE_SSSE3 | (USE_NEON >= 8))
- return get_weight_index_scrambled(i);
+ 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 {
+ void propagate(const InputType* input, OutputType* output) const {
#if (USE_SSSE3 | (USE_NEON >= 8))
-#if defined (USE_AVX512)
- using invec_t = __m512i;
- using outvec_t = __m512i;
- #define vec_set_32 _mm512_set1_epi32
- #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
-#elif defined (USE_AVX2)
- using invec_t = __m256i;
- using outvec_t = __m256i;
- #define vec_set_32 _mm256_set1_epi32
- #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
-#elif defined (USE_SSSE3)
- using invec_t = __m128i;
- using outvec_t = __m128i;
- #define vec_set_32 _mm_set1_epi32
- #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
-#elif defined (USE_NEON_DOTPROD)
- using invec_t = int8x16_t;
- using outvec_t = int32x4_t;
- #define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
- #define vec_add_dpbusd_32 Simd::dotprod_m128_add_dpbusd_epi32
-#elif defined (USE_NEON)
- using invec_t = int8x16_t;
- using outvec_t = int32x4_t;
- #define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
- #define vec_add_dpbusd_32 Simd::neon_m128_add_dpbusd_epi32
-#endif
- static constexpr IndexType OutputSimdWidth = sizeof(outvec_t) / sizeof(OutputType);
-
- constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
- constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
- std::uint16_t nnz[NumChunks];
- IndexType count;
+ #if defined(USE_AVX512)
+ using invec_t = __m512i;
+ using outvec_t = __m512i;
+ #define vec_set_32 _mm512_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
+ #elif defined(USE_AVX2)
+ using invec_t = __m256i;
+ using outvec_t = __m256i;
+ #define vec_set_32 _mm256_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+ #elif defined(USE_SSSE3)
+ using invec_t = __m128i;
+ using outvec_t = __m128i;
+ #define vec_set_32 _mm_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+ #elif defined(USE_NEON_DOTPROD)
+ using invec_t = int8x16_t;
+ using outvec_t = int32x4_t;
+ #define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
+ #define vec_add_dpbusd_32 Simd::dotprod_m128_add_dpbusd_epi32
+ #elif defined(USE_NEON)
+ using invec_t = int8x16_t;
+ using outvec_t = int32x4_t;
+ #define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
+ #define vec_add_dpbusd_32 Simd::neon_m128_add_dpbusd_epi32
+ #endif
+ static constexpr IndexType OutputSimdWidth = sizeof(outvec_t) / sizeof(OutputType);
- const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+ std::uint16_t nnz[NumChunks];
+ IndexType count;
- // Find indices of nonzero 32bit blocks
- find_nnz<NumChunks>(input32, nnz, count);
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
- const outvec_t* biasvec = reinterpret_cast<const outvec_t*>(biases);
- outvec_t acc[NumRegs];
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = biasvec[k];
+ // Find indices of nonzero 32bit blocks
+ find_nnz<NumChunks>(input32, nnz, count);
- for (IndexType j = 0; j < count; ++j)
- {
- const auto i = nnz[j];
- const invec_t in = vec_set_32(input32[i]);
- const auto col = reinterpret_cast<const invec_t*>(&weights[i * OutputDimensions * ChunkSize]);
+ const outvec_t* biasvec = reinterpret_cast<const outvec_t*>(biases);
+ outvec_t acc[NumRegs];
for (IndexType k = 0; k < NumRegs; ++k)
- vec_add_dpbusd_32(acc[k], in, col[k]);
- }
-
- outvec_t* outptr = reinterpret_cast<outvec_t*>(output);
- for (IndexType k = 0; k < NumRegs; ++k)
- outptr[k] = acc[k];
-# undef vec_set_32
-# undef vec_add_dpbusd_32
+ acc[k] = biasvec[k];
+
+ for (IndexType j = 0; j < count; ++j)
+ {
+ const auto i = nnz[j];
+ const invec_t in = vec_set_32(input32[i]);
+ const auto col =
+ reinterpret_cast<const invec_t*>(&weights[i * OutputDimensions * ChunkSize]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_add_dpbusd_32(acc[k], in, col[k]);
+ }
+
+ outvec_t* outptr = reinterpret_cast<outvec_t*>(output);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ outptr[k] = acc[k];
+ #undef vec_set_32
+ #undef vec_add_dpbusd_32
#else
- // Use dense implementation for the other architectures.
- affine_transform_non_ssse3<
- InputDimensions,
- PaddedInputDimensions,
- OutputDimensions>(output, weights, biases, input);
+ // Use dense 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_SPARSE_INPUT_H_INCLUDED
+#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
projects / stockfish / blobdiff