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https://github.com/sockspls/badfish
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Implement AffineTransformSparseInput for armv8
Implements AffineTransformSparseInput layer for the NNUE evaluation for the armv8 and armv8-dotprod architectures. We measured some nice speed improvements via 10 runs of our benchmark: armv8, Cortex-X1 : 18.5% speed-up armv8, Cortex-A76 : 13.2% speed-up armv8-dotprod, Cortex-X1 : 27.1% speed-up armv8-dotprod, Cortex-A76 : 12.1% speed-up armv8, Cortex-A72, Raspberry Pi 4 : 8.2% speed-up (thanks Torom!) closes https://github.com/official-stockfish/Stockfish/pull/4719 No functional change
This commit is contained in:
AndrovT
Stéphane Nicolet
parent
4c43e1e27c
commit
a6d9a302b8
2 changed files with 83 additions and 35 deletions
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@ -35,7 +35,7 @@
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namespace Stockfish::Eval::NNUE::Layers {
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#if defined(USE_SSSE3)
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#if (USE_SSSE3 | (USE_NEON >= 8))
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alignas(CacheLineSize) static inline const std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = [](){
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std::array<std::array<std::uint16_t, 8>, 256> v{};
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for (unsigned i = 0; i < 256; ++i)
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@ -50,19 +50,37 @@ namespace Stockfish::Eval::NNUE::Layers {
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// Find indices of nonzero numbers in an int32_t array
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template<const IndexType InputDimensions>
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void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
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#if defined (USE_AVX512)
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using vec_t = __m512i;
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#define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
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#elif defined (USE_AVX2)
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using vec_t = __m256i;
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#if defined(USE_VNNI) && !defined(USE_AVXVNNI)
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#define vec_nnz(a) _mm256_cmpgt_epi32_mask(a, _mm256_setzero_si256())
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#else
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#define vec_nnz(a) _mm256_movemask_ps(_mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
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#if defined (USE_SSSE3)
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#if defined (USE_AVX512)
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using vec_t = __m512i;
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#define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
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#elif defined (USE_AVX2)
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using vec_t = __m256i;
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#if defined(USE_VNNI) && !defined(USE_AVXVNNI)
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#define vec_nnz(a) _mm256_cmpgt_epi32_mask(a, _mm256_setzero_si256())
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#else
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#define vec_nnz(a) _mm256_movemask_ps(_mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
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#endif
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#elif defined (USE_SSSE3)
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using vec_t = __m128i;
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#define vec_nnz(a) _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
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#endif
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#elif defined (USE_SSSE3)
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using vec_t = __m128i;
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#define vec_nnz(a) _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
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using vec128_t = __m128i;
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#define vec128_zero _mm_setzero_si128()
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#define vec128_set_16(a) _mm_set1_epi16(a)
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#define vec128_load(a) _mm_load_si128(a)
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#define vec128_storeu(a, b) _mm_storeu_si128(a, b)
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#define vec128_add(a, b) _mm_add_epi16(a, b)
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#elif defined (USE_NEON)
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using vec_t = int32x4_t;
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static const std::uint32_t Mask[4] = {1, 2, 4, 8};
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#define vec_nnz(a) vaddvq_u32(vandq_u32(vtstq_u32(a, a), vld1q_u32(Mask)))
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using vec128_t = int16x8_t;
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#define vec128_zero vdupq_n_u16(0)
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#define vec128_set_16(a) vdupq_n_u16(a)
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#define vec128_load(a) vld1q_u16(reinterpret_cast<const std::uint16_t*>(a))
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#define vec128_storeu(a, b) vst1q_u16(reinterpret_cast<std::uint16_t*>(a), b)
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#define vec128_add(a, b) vaddq_u16(a, b)
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#endif
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constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t);
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// Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8)
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@ -73,8 +91,8 @@ namespace Stockfish::Eval::NNUE::Layers {
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const auto inputVector = reinterpret_cast<const vec_t*>(input);
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IndexType count = 0;
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__m128i base = _mm_setzero_si128();
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const __m128i increment = _mm_set1_epi16(8);
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vec128_t base = vec128_zero;
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const vec128_t increment = vec128_set_16(8);
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for (IndexType i = 0; i < NumChunks; ++i)
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{
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// bitmask of nonzero values in this chunk
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@ -87,15 +105,20 @@ namespace Stockfish::Eval::NNUE::Layers {
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for (IndexType j = 0; j < OutputsPerChunk; ++j)
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{
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const auto lookup = (nnz >> (j * 8)) & 0xFF;
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const auto offsets = _mm_loadu_si128(reinterpret_cast<const __m128i*>(&lookup_indices[lookup]));
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_mm_storeu_si128(reinterpret_cast<__m128i*>(out + count), _mm_add_epi16(base, offsets));
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const auto offsets = vec128_load(reinterpret_cast<const vec128_t*>(&lookup_indices[lookup]));
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vec128_storeu(reinterpret_cast<vec128_t*>(out + count), vec128_add(base, offsets));
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count += popcount(lookup);
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base = _mm_add_epi16(base, increment);
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base = vec128_add(base, increment);
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}
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}
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count_out = count;
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}
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# undef vec_nnz
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# undef vec128_zero
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# undef vec128_set_16
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# undef vec128_load
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# undef vec128_storeu
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# undef vec128_add
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#endif
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// Sparse input implementation
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@ -117,7 +140,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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static constexpr IndexType PaddedOutputDimensions =
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ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
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#if defined (USE_SSSE3)
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#if (USE_SSSE3 | (USE_NEON >= 8))
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static constexpr IndexType ChunkSize = 4;
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#else
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static constexpr IndexType ChunkSize = 1;
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@ -144,7 +167,7 @@ namespace Stockfish::Eval::NNUE::Layers {
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static constexpr IndexType get_weight_index(IndexType i)
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{
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#if defined (USE_SSSE3)
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#if (USE_SSSE3 | (USE_NEON >= 8))
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return get_weight_index_scrambled(i);
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#else
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return i;
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@ -173,24 +196,34 @@ namespace Stockfish::Eval::NNUE::Layers {
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void propagate(
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const InputType* input, OutputType* output) const {
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#if defined (USE_SSSE3)
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#if (USE_SSSE3 | (USE_NEON >= 8))
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#if defined (USE_AVX512)
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using vec_t = __m512i;
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#define vec_setzero _mm512_setzero_si512
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using invec_t = __m512i;
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using outvec_t = __m512i;
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#define vec_set_32 _mm512_set1_epi32
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#define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
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#elif defined (USE_AVX2)
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using vec_t = __m256i;
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#define vec_setzero _mm256_setzero_si256
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using invec_t = __m256i;
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using outvec_t = __m256i;
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#define vec_set_32 _mm256_set1_epi32
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#define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
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#elif defined (USE_SSSE3)
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using vec_t = __m128i;
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#define vec_setzero _mm_setzero_si128
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using invec_t = __m128i;
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using outvec_t = __m128i;
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#define vec_set_32 _mm_set1_epi32
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#define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
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#elif defined (USE_NEON_DOTPROD)
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using invec_t = int8x16_t;
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using outvec_t = int32x4_t;
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#define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
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#define vec_add_dpbusd_32 Simd::dotprod_m128_add_dpbusd_epi32
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#elif defined (USE_NEON)
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using invec_t = int8x16_t;
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using outvec_t = int32x4_t;
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#define vec_set_32(a) vreinterpretq_s8_u32(vdupq_n_u32(a))
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#define vec_add_dpbusd_32 Simd::neon_m128_add_dpbusd_epi32
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#endif
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static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
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static constexpr IndexType OutputSimdWidth = sizeof(outvec_t) / sizeof(OutputType);
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
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constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
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@ -202,24 +235,23 @@ namespace Stockfish::Eval::NNUE::Layers {
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// Find indices of nonzero 32bit blocks
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find_nnz<NumChunks>(input32, nnz, count);
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const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
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vec_t acc[NumRegs];
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const outvec_t* biasvec = reinterpret_cast<const outvec_t*>(biases);
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outvec_t acc[NumRegs];
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for (IndexType k = 0; k < NumRegs; ++k)
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acc[k] = biasvec[k];
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for (IndexType j = 0; j < count; ++j)
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{
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const auto i = nnz[j];
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const vec_t in = vec_set_32(input32[i]);
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const auto col = reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * ChunkSize]);
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const invec_t in = vec_set_32(input32[i]);
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const auto col = reinterpret_cast<const invec_t*>(&weights[i * OutputDimensions * ChunkSize]);
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for (IndexType k = 0; k < NumRegs; ++k)
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vec_add_dpbusd_32(acc[k], in, col[k]);
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}
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vec_t* outptr = reinterpret_cast<vec_t*>(output);
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outvec_t* outptr = reinterpret_cast<outvec_t*>(output);
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for (IndexType k = 0; k < NumRegs; ++k)
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outptr[k] = acc[k];
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# undef vec_setzero
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# undef vec_set_32
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# undef vec_add_dpbusd_32
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#else
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@ -239,6 +239,12 @@ namespace Stockfish::Simd {
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acc = vdotq_s32(acc, a1, b1);
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}
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[[maybe_unused]] static void dotprod_m128_add_dpbusd_epi32(
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int32x4_t& acc,
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int8x16_t a, int8x16_t b) {
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acc = vdotq_s32(acc, a, b);
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}
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#endif
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#if defined (USE_NEON)
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@ -277,9 +283,19 @@ namespace Stockfish::Simd {
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product = vmlal_s8(product, a1, b1);
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acc = vpadalq_s16(acc, product);
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}
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#endif
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#if USE_NEON >= 8
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[[maybe_unused]] static void neon_m128_add_dpbusd_epi32(
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int32x4_t& acc,
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int8x16_t a, int8x16_t b) {
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int16x8_t product0 = vmull_s8(vget_low_s8(a), vget_low_s8(b));
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int16x8_t product1 = vmull_high_s8(a, b);
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int16x8_t sum = vpaddq_s16(product0, product1);
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acc = vpadalq_s16(acc, sum);
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}
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#endif
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}
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#endif // STOCKFISH_SIMD_H_INCLUDED
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