X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=src%2Fnnue%2Fnnue_feature_transformer.h;h=2f86d20a639b712d6a0bcc51e4d70f5c1c373dd0;hp=cbcc26f3efae9f592eead48230d153c93ddd1301;hb=c065abd;hpb=875183b310a8249922c2155e82cb4cecfae2097e diff --git a/src/nnue/nnue_feature_transformer.h b/src/nnue/nnue_feature_transformer.h index cbcc26f3..2f86d20a 100644 --- a/src/nnue/nnue_feature_transformer.h +++ b/src/nnue/nnue_feature_transformer.h @@ -29,6 +29,56 @@ namespace Eval::NNUE { + // If vector instructions are enabled, we update and refresh the + // accumulator tile by tile such that each tile fits in the CPU's + // vector registers. + #define TILING + + #ifdef USE_AVX512 + typedef __m512i vec_t; + #define vec_load(a) _mm512_loadA_si512(a) + #define vec_store(a,b) _mm512_storeA_si512(a,b) + #define vec_add_16(a,b) _mm512_add_epi16(a,b) + #define vec_sub_16(a,b) _mm512_sub_epi16(a,b) + static constexpr IndexType kNumRegs = 8; // only 8 are needed + + #elif USE_AVX2 + typedef __m256i vec_t; + #define vec_load(a) _mm256_loadA_si256(a) + #define vec_store(a,b) _mm256_storeA_si256(a,b) + #define vec_add_16(a,b) _mm256_add_epi16(a,b) + #define vec_sub_16(a,b) _mm256_sub_epi16(a,b) + static constexpr IndexType kNumRegs = 16; + + #elif USE_SSE2 + typedef __m128i vec_t; + #define vec_load(a) (*(a)) + #define vec_store(a,b) *(a)=(b) + #define vec_add_16(a,b) _mm_add_epi16(a,b) + #define vec_sub_16(a,b) _mm_sub_epi16(a,b) + static constexpr IndexType kNumRegs = Is64Bit ? 16 : 8; + + #elif USE_MMX + typedef __m64 vec_t; + #define vec_load(a) (*(a)) + #define vec_store(a,b) *(a)=(b) + #define vec_add_16(a,b) _mm_add_pi16(a,b) + #define vec_sub_16(a,b) _mm_sub_pi16(a,b) + static constexpr IndexType kNumRegs = 8; + + #elif USE_NEON + typedef int16x8_t vec_t; + #define vec_load(a) (*(a)) + #define vec_store(a,b) *(a)=(b) + #define vec_add_16(a,b) vaddq_s16(a,b) + #define vec_sub_16(a,b) vsubq_s16(a,b) + static constexpr IndexType kNumRegs = 16; + + #else + #undef TILING + + #endif + // Input feature converter class FeatureTransformer { @@ -36,6 +86,11 @@ namespace Eval::NNUE { // Number of output dimensions for one side static constexpr IndexType kHalfDimensions = kTransformedFeatureDimensions; + #ifdef TILING + static constexpr IndexType kTileHeight = kNumRegs * sizeof(vec_t) / 2; + static_assert(kHalfDimensions % kTileHeight == 0, "kTileHeight must divide kHalfDimensions"); + #endif + public: // Output type using OutputType = TransformedFeatureType; @@ -50,37 +105,47 @@ namespace Eval::NNUE { // Hash value embedded in the evaluation file static constexpr std::uint32_t GetHashValue() { + return RawFeatures::kHashValue ^ kOutputDimensions; } // Read network parameters bool ReadParameters(std::istream& stream) { - stream.read(reinterpret_cast(biases_), - kHalfDimensions * sizeof(BiasType)); - stream.read(reinterpret_cast(weights_), - kHalfDimensions * kInputDimensions * sizeof(WeightType)); + + for (std::size_t i = 0; i < kHalfDimensions; ++i) + biases_[i] = read_little_endian(stream); + for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i) + weights_[i] = read_little_endian(stream); return !stream.fail(); } // Proceed with the difference calculation if possible bool UpdateAccumulatorIfPossible(const Position& pos) const { + const auto now = pos.state(); - if (now->accumulator.computed_accumulation) { + if (now->accumulator.computed_accumulation) return true; - } + const auto prev = now->previous; - if (prev && prev->accumulator.computed_accumulation) { - UpdateAccumulator(pos); - return true; + if (prev) { + if (prev->accumulator.computed_accumulation) { + UpdateAccumulator(pos); + return true; + } else if (prev->previous && prev->previous->accumulator.computed_accumulation) { + UpdateAccumulator(pos); + return true; + } } + return false; } // Convert input features - void Transform(const Position& pos, OutputType* output, bool refresh) const { - if (refresh || !UpdateAccumulatorIfPossible(pos)) { + void Transform(const Position& pos, OutputType* output) const { + + if (!UpdateAccumulatorIfPossible(pos)) RefreshAccumulator(pos); - } + const auto& accumulation = pos.state()->accumulator.accumulation; #if defined(USE_AVX2) @@ -88,7 +153,7 @@ namespace Eval::NNUE { constexpr int kControl = 0b11011000; const __m256i kZero = _mm256_setzero_si256(); - #elif defined(USE_SSSE3) + #elif defined(USE_SSE2) constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; #ifdef USE_SSE41 @@ -97,6 +162,10 @@ namespace Eval::NNUE { const __m128i k0x80s = _mm_set1_epi8(-128); #endif + #elif defined(USE_MMX) + constexpr IndexType kNumChunks = kHalfDimensions / kSimdWidth; + const __m64 k0x80s = _mm_set1_pi8(-128); + #elif defined(USE_NEON) constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); const int8x8_t kZero = {0}; @@ -117,7 +186,7 @@ namespace Eval::NNUE { _mm256_packs_epi16(sum0, sum1), kZero), kControl)); } - #elif defined(USE_SSSE3) + #elif defined(USE_SSE2) auto out = reinterpret_cast<__m128i*>(&output[offset]); for (IndexType j = 0; j < kNumChunks; ++j) { __m128i sum0 = _mm_load_si128(&reinterpret_cast( @@ -137,6 +206,17 @@ namespace Eval::NNUE { ); } + #elif defined(USE_MMX) + auto out = reinterpret_cast<__m64*>(&output[offset]); + for (IndexType j = 0; j < kNumChunks; ++j) { + __m64 sum0 = *(&reinterpret_cast( + accumulation[perspectives[p]][0])[j * 2 + 0]); + __m64 sum1 = *(&reinterpret_cast( + accumulation[perspectives[p]][0])[j * 2 + 1]); + const __m64 packedbytes = _mm_packs_pi16(sum0, sum1); + out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s); + } + #elif defined(USE_NEON) const auto out = reinterpret_cast(&output[offset]); for (IndexType j = 0; j < kNumChunks; ++j) { @@ -154,162 +234,159 @@ namespace Eval::NNUE { #endif } + #if defined(USE_MMX) + _mm_empty(); + #endif } private: // Calculate cumulative value without using difference calculation void RefreshAccumulator(const Position& pos) const { + auto& accumulator = pos.state()->accumulator; IndexType i = 0; Features::IndexList active_indices[2]; RawFeatures::AppendActiveIndices(pos, kRefreshTriggers[i], active_indices); for (Color perspective : { WHITE, BLACK }) { - std::memcpy(accumulator.accumulation[perspective][i], biases_, - kHalfDimensions * sizeof(BiasType)); - for (const auto index : active_indices[perspective]) { - const IndexType offset = kHalfDimensions * index; - - #if defined(USE_AVX2) - auto accumulation = reinterpret_cast<__m256i*>( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&weights_[offset]); - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - for (IndexType j = 0; j < kNumChunks; ++j) { - _mm256_storeA_si256(&accumulation[j], _mm256_add_epi16(_mm256_loadA_si256(&accumulation[j]), column[j])); + #ifdef TILING + for (unsigned j = 0; j < kHalfDimensions / kTileHeight; ++j) { + auto biasesTile = reinterpret_cast( + &biases_[j * kTileHeight]); + auto accTile = reinterpret_cast( + &accumulator.accumulation[perspective][i][j * kTileHeight]); + vec_t acc[kNumRegs]; + + for (unsigned k = 0; k < kNumRegs; ++k) + acc[k] = biasesTile[k]; + + for (const auto index : active_indices[perspective]) { + const IndexType offset = kHalfDimensions * index + j * kTileHeight; + auto column = reinterpret_cast(&weights_[offset]); + + for (unsigned k = 0; k < kNumRegs; ++k) + acc[k] = vec_add_16(acc[k], column[k]); } - #elif defined(USE_SSE2) - auto accumulation = reinterpret_cast<__m128i*>( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&weights_[offset]); - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_add_epi16(accumulation[j], column[j]); - } + for (unsigned k = 0; k < kNumRegs; k++) + vec_store(&accTile[k], acc[k]); + } + #else + std::memcpy(accumulator.accumulation[perspective][i], biases_, + kHalfDimensions * sizeof(BiasType)); - #elif defined(USE_NEON) - auto accumulation = reinterpret_cast( - &accumulator.accumulation[perspective][i][0]); - auto column = reinterpret_cast(&weights_[offset]); - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = vaddq_s16(accumulation[j], column[j]); - } + for (const auto index : active_indices[perspective]) { + const IndexType offset = kHalfDimensions * index; - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { + for (IndexType j = 0; j < kHalfDimensions; ++j) accumulator.accumulation[perspective][i][j] += weights_[offset + j]; - } - #endif - } + #endif } + #if defined(USE_MMX) + _mm_empty(); + #endif + accumulator.computed_accumulation = true; - accumulator.computed_score = false; } // Calculate cumulative value using difference calculation void UpdateAccumulator(const Position& pos) const { - const auto prev_accumulator = pos.state()->previous->accumulator; + + Accumulator* prev_accumulator; + assert(pos.state()->previous); + if (pos.state()->previous->accumulator.computed_accumulation) { + prev_accumulator = &pos.state()->previous->accumulator; + } + else { + assert(pos.state()->previous->previous); + assert(pos.state()->previous->previous->accumulator.computed_accumulation); + prev_accumulator = &pos.state()->previous->previous->accumulator; + } + auto& accumulator = pos.state()->accumulator; IndexType i = 0; Features::IndexList removed_indices[2], added_indices[2]; - bool reset[2]; + bool reset[2] = { false, false }; RawFeatures::AppendChangedIndices(pos, kRefreshTriggers[i], removed_indices, added_indices, reset); - for (Color perspective : { WHITE, BLACK }) { - #if defined(USE_AVX2) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast<__m256i*>( - &accumulator.accumulation[perspective][i][0]); + #ifdef TILING + for (IndexType j = 0; j < kHalfDimensions / kTileHeight; ++j) { + for (Color perspective : { WHITE, BLACK }) { + auto accTile = reinterpret_cast( + &accumulator.accumulation[perspective][i][j * kTileHeight]); + vec_t acc[kNumRegs]; + + if (reset[perspective]) { + auto biasesTile = reinterpret_cast( + &biases_[j * kTileHeight]); + for (unsigned k = 0; k < kNumRegs; ++k) + acc[k] = biasesTile[k]; + } else { + auto prevAccTile = reinterpret_cast( + &prev_accumulator->accumulation[perspective][i][j * kTileHeight]); + for (IndexType k = 0; k < kNumRegs; ++k) + acc[k] = vec_load(&prevAccTile[k]); + + // Difference calculation for the deactivated features + for (const auto index : removed_indices[perspective]) { + const IndexType offset = kHalfDimensions * index + j * kTileHeight; + auto column = reinterpret_cast(&weights_[offset]); + + for (IndexType k = 0; k < kNumRegs; ++k) + acc[k] = vec_sub_16(acc[k], column[k]); + } + } + { // Difference calculation for the activated features + for (const auto index : added_indices[perspective]) { + const IndexType offset = kHalfDimensions * index + j * kTileHeight; + auto column = reinterpret_cast(&weights_[offset]); - #elif defined(USE_SSE2) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast<__m128i*>( - &accumulator.accumulation[perspective][i][0]); + for (IndexType k = 0; k < kNumRegs; ++k) + acc[k] = vec_add_16(acc[k], column[k]); + } + } - #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2); - auto accumulation = reinterpret_cast( - &accumulator.accumulation[perspective][i][0]); + for (IndexType k = 0; k < kNumRegs; ++k) + vec_store(&accTile[k], acc[k]); + } + } + #if defined(USE_MMX) + _mm_empty(); #endif + #else + for (Color perspective : { WHITE, BLACK }) { + if (reset[perspective]) { std::memcpy(accumulator.accumulation[perspective][i], biases_, kHalfDimensions * sizeof(BiasType)); } else { std::memcpy(accumulator.accumulation[perspective][i], - prev_accumulator.accumulation[perspective][i], + prev_accumulator->accumulation[perspective][i], kHalfDimensions * sizeof(BiasType)); // Difference calculation for the deactivated features for (const auto index : removed_indices[perspective]) { const IndexType offset = kHalfDimensions * index; - #if defined(USE_AVX2) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]); - } - - #elif defined(USE_SSE2) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]); - } - - #elif defined(USE_NEON) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = vsubq_s16(accumulation[j], column[j]); - } - - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - accumulator.accumulation[perspective][i][j] -= - weights_[offset + j]; - } - #endif - + for (IndexType j = 0; j < kHalfDimensions; ++j) + accumulator.accumulation[perspective][i][j] -= weights_[offset + j]; } } { // Difference calculation for the activated features for (const auto index : added_indices[perspective]) { const IndexType offset = kHalfDimensions * index; - #if defined(USE_AVX2) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]); - } - - #elif defined(USE_SSE2) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = _mm_add_epi16(accumulation[j], column[j]); - } - - #elif defined(USE_NEON) - auto column = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - accumulation[j] = vaddq_s16(accumulation[j], column[j]); - } - - #else - for (IndexType j = 0; j < kHalfDimensions; ++j) { - accumulator.accumulation[perspective][i][j] += - weights_[offset + j]; - } - #endif - + for (IndexType j = 0; j < kHalfDimensions; ++j) + accumulator.accumulation[perspective][i][j] += weights_[offset + j]; } } } + #endif accumulator.computed_accumulation = true; - accumulator.computed_score = false; } using BiasType = std::int16_t;