X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fnnue%2Flayers%2Faffine_transform.h;h=b28712780b2684868bc2c2937628e4112b72b69c;hb=18dcf1f09754284325157f2d270df10a09297958;hp=b585bc87819d23c808ce66a472c4ffba59e47072;hpb=84f3e867903f62480c33243dd0ecbffd342796fc;p=stockfish diff --git a/src/nnue/layers/affine_transform.h b/src/nnue/layers/affine_transform.h index b585bc87..b2871278 100644 --- a/src/nnue/layers/affine_transform.h +++ b/src/nnue/layers/affine_transform.h @@ -1,6 +1,6 @@ /* Stockfish, a UCI chess playing engine derived from Glaurung 2.1 - Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file) + Copyright (C) 2004-2021 The Stockfish developers (see AUTHORS file) Stockfish is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -22,13 +22,141 @@ #define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED #include +#include +#include #include "../nnue_common.h" +#include "../../simd.h" -namespace Eval::NNUE::Layers { +/* + This file contains the definition for a fully connected layer (aka affine transform). + Two approaches are employed, depending on the sizes of the transform. + + Approach 1: + - used when the PaddedInputDimensions >= 128 + - uses AVX512 if possible + - processes inputs in batches of 2*InputSimdWidth + - so in batches of 128 for AVX512 + - the weight blocks of size InputSimdWidth are transposed such that + access is sequential + - N columns of the weight matrix are processed a time, where N + depends on the architecture (the amount of registers) + - accumulate + hadd is used + + Approach 2: + - used when the PaddedInputDimensions < 128 + - does not use AVX512 + - expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32. + - that's why AVX512 is hard to implement + - expected use-case is small layers + - not optimized as well as the approach 1 + - inputs are processed in chunks of 4, weights are respectively transposed + - accumulation happens directly to int32s +*/ + +namespace Stockfish::Eval::NNUE::Layers { + +// Fallback implementation for older/other architectures. +// Identical for both approaches. Requires the input to be padded to at least 16 values. +#if !defined(USE_SSSE3) + template + 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) + // At least a multiple of 16, with SSE2. + static_assert(PaddedInputDimensions % 16 == 0); + constexpr IndexType NumChunks = PaddedInputDimensions / 16; + const __m128i Zeros = _mm_setzero_si128(); + const auto inputVector = reinterpret_cast(input); + +# elif defined(USE_MMX) + static_assert(InputDimensions % 8 == 0); + constexpr IndexType NumChunks = InputDimensions / 8; + const __m64 Zeros = _mm_setzero_si64(); + const auto inputVector = reinterpret_cast(input); + +# elif defined(USE_NEON) + static_assert(PaddedInputDimensions % 16 == 0); + constexpr IndexType NumChunks = PaddedInputDimensions / 16; + const auto inputVector = reinterpret_cast(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(&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_MMX) + __m64 sumLo = _mm_cvtsi32_si64(biases[i]); + __m64 sumHi = Zeros; + const auto row = reinterpret_cast(&weights[offset]); + for (IndexType j = 0; j < NumChunks; ++j) { + __m64 row_j = row[j]; + __m64 input_j = inputVector[j]; + __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8); + __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8); + __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros); + __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros); + __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo); + __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi); + sumLo = _mm_add_pi32(sumLo, productLo); + sumHi = _mm_add_pi32(sumHi, productHi); + } + __m64 sum = _mm_add_pi32(sumLo, sumHi); + sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum)); + output[i] = _mm_cvtsi64_si32(sum); + +# elif defined(USE_NEON) + int32x4_t sum = {biases[i]}; + const auto row = reinterpret_cast(&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]; + +# else + std::int32_t sum = biases[i]; + for (IndexType j = 0; j < InputDimensions; ++j) { + sum += weights[offset + j] * input[j]; + } + output[i] = sum; +# endif + } + +# if defined(USE_MMX) + _mm_empty(); +# endif + } +#endif - // Affine transformation layer - template - class AffineTransform { + template + class AffineTransform; + + // A specialization for large inputs. + template + class AffineTransform= 2*64-1)>> { public: // Input/output type using InputType = typename PreviousLayer::OutputType; @@ -36,166 +164,378 @@ namespace Eval::NNUE::Layers { static_assert(std::is_same::value, ""); // Number of input/output dimensions - static constexpr IndexType kInputDimensions = - PreviousLayer::kOutputDimensions; - static constexpr IndexType kOutputDimensions = OutputDimensions; - static constexpr IndexType kPaddedInputDimensions = - CeilToMultiple(kInputDimensions, kMaxSimdWidth); + static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions; + static constexpr IndexType OutputDimensions = OutDims; + + static constexpr IndexType PaddedInputDimensions = + ceil_to_multiple(InputDimensions, MaxSimdWidth); + + static_assert(PaddedInputDimensions >= 128, "Something went wrong. This specialization should not have been chosen."); + +#if defined (USE_AVX512) + static constexpr const IndexType InputSimdWidth = 64; + static constexpr const IndexType MaxNumOutputRegs = 16; +#elif defined (USE_AVX2) + static constexpr const IndexType InputSimdWidth = 32; + static constexpr const IndexType MaxNumOutputRegs = 8; +#elif defined (USE_SSSE3) + static constexpr const IndexType InputSimdWidth = 16; + static constexpr const IndexType MaxNumOutputRegs = 8; +#else + // The fallback implementation will not have permuted weights. + // We define these to avoid a lot of ifdefs later. + static constexpr const IndexType InputSimdWidth = 1; + static constexpr const IndexType MaxNumOutputRegs = 1; +#endif + + // A big block is a region in the weight matrix of the size [PaddedInputDimensions, NumOutputRegs]. + // A small block is a region of size [InputSimdWidth, 1] + + static constexpr const IndexType NumOutputRegs = std::min(MaxNumOutputRegs, OutputDimensions); + static constexpr const IndexType SmallBlockSize = InputSimdWidth; + static constexpr const IndexType BigBlockSize = NumOutputRegs * PaddedInputDimensions; + static constexpr const IndexType NumSmallBlocksInBigBlock = BigBlockSize / SmallBlockSize; + static constexpr const IndexType NumSmallBlocksPerOutput = PaddedInputDimensions / SmallBlockSize; + static constexpr const IndexType NumBigBlocks = OutputDimensions / NumOutputRegs; + + static_assert(OutputDimensions % NumOutputRegs == 0); // Size of forward propagation buffer used in this layer - static constexpr std::size_t kSelfBufferSize = - CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize); + static constexpr std::size_t SelfBufferSize = + ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize); // Size of the forward propagation buffer used from the input layer to this layer - static constexpr std::size_t kBufferSize = - PreviousLayer::kBufferSize + kSelfBufferSize; + static constexpr std::size_t BufferSize = + PreviousLayer::BufferSize + SelfBufferSize; // Hash value embedded in the evaluation file - static constexpr std::uint32_t GetHashValue() { - std::uint32_t hash_value = 0xCC03DAE4u; - hash_value += kOutputDimensions; - hash_value ^= PreviousLayer::GetHashValue() >> 1; - hash_value ^= PreviousLayer::GetHashValue() << 31; - return hash_value; + static constexpr std::uint32_t get_hash_value() { + std::uint32_t hashValue = 0xCC03DAE4u; + hashValue += OutputDimensions; + hashValue ^= PreviousLayer::get_hash_value() >> 1; + hashValue ^= PreviousLayer::get_hash_value() << 31; + return hashValue; + } + + /* + Transposes the small blocks within a block. + Effectively means that weights can be traversed sequentially during inference. + */ + static IndexType get_weight_index(IndexType i) + { + const IndexType smallBlock = (i / SmallBlockSize) % NumSmallBlocksInBigBlock; + const IndexType smallBlockCol = smallBlock / NumSmallBlocksPerOutput; + const IndexType smallBlockRow = smallBlock % NumSmallBlocksPerOutput; + const IndexType bigBlock = i / BigBlockSize; + const IndexType rest = i % SmallBlockSize; + + const IndexType idx = + bigBlock * BigBlockSize + + smallBlockRow * SmallBlockSize * NumOutputRegs + + smallBlockCol * SmallBlockSize + + rest; + + return idx; } - // Read network parameters - bool ReadParameters(std::istream& stream) { - if (!previous_layer_.ReadParameters(stream)) return false; - stream.read(reinterpret_cast(biases_), - kOutputDimensions * sizeof(BiasType)); - stream.read(reinterpret_cast(weights_), - kOutputDimensions * kPaddedInputDimensions * - sizeof(WeightType)); + // Read network parameters + bool read_parameters(std::istream& stream) { + if (!previousLayer.read_parameters(stream)) return false; + for (std::size_t i = 0; i < OutputDimensions; ++i) + biases[i] = read_little_endian(stream); + + for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + weights[get_weight_index(i)] = read_little_endian(stream); + return !stream.fail(); } - // Forward propagation - const OutputType* Propagate( - const TransformedFeatureType* transformed_features, char* buffer) const { - const auto input = previous_layer_.Propagate( - transformed_features, buffer + kSelfBufferSize); - const auto output = reinterpret_cast(buffer); + // Write network parameters + bool write_parameters(std::ostream& stream) const { + if (!previousLayer.write_parameters(stream)) return false; + for (std::size_t i = 0; i < OutputDimensions; ++i) + write_little_endian(stream, biases[i]); - #if defined(USE_AVX512) - constexpr IndexType kNumChunks = kPaddedInputDimensions / (kSimdWidth * 2); - const __m512i kOnes = _mm512_set1_epi16(1); - const auto input_vector = reinterpret_cast(input); - - #elif defined(USE_AVX2) - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - const __m256i kOnes = _mm256_set1_epi16(1); - const auto input_vector = reinterpret_cast(input); - - #elif defined(USE_SSSE3) - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - const __m128i kOnes = _mm_set1_epi16(1); - const auto input_vector = reinterpret_cast(input); - - #elif defined(USE_NEON) - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - const auto input_vector = reinterpret_cast(input); - #endif - - for (IndexType i = 0; i < kOutputDimensions; ++i) { - const IndexType offset = i * kPaddedInputDimensions; - - #if defined(USE_AVX512) - __m512i sum = _mm512_setzero_si512(); - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - - #if defined(__MINGW32__) || defined(__MINGW64__) - __m512i product = _mm512_maddubs_epi16(_mm512_loadu_si512(&input_vector[j]), _mm512_load_si512(&row[j])); - #else - __m512i product = _mm512_maddubs_epi16(_mm512_load_si512(&input_vector[j]), _mm512_load_si512(&row[j])); - #endif - - product = _mm512_madd_epi16(product, kOnes); - sum = _mm512_add_epi32(sum, product); - } - output[i] = _mm512_reduce_add_epi32(sum) + biases_[i]; + for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + write_little_endian(stream, weights[get_weight_index(i)]); - // Note: Changing kMaxSimdWidth from 32 to 64 breaks loading existing networks. - // As a result kPaddedInputDimensions may not be an even multiple of 64(512bit) - // and we have to do one more 256bit chunk. - if (kPaddedInputDimensions != kNumChunks * kSimdWidth * 2) + return !stream.fail(); + } + + // Forward propagation + const OutputType* propagate( + const TransformedFeatureType* transformedFeatures, char* buffer) const { + const auto input = previousLayer.propagate( + transformedFeatures, buffer + SelfBufferSize); + OutputType* output = reinterpret_cast(buffer); + +#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 + #define vec_haddx4 Simd::m512_haddx4 +#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 + #define vec_haddx4 Simd::m256_haddx4 +#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 + #define vec_haddx4 Simd::m128_haddx4 +#endif + +#if defined (USE_SSSE3) + const vec_t* invec = reinterpret_cast(input); + + + // Perform accumulation to registers for each big block + for (IndexType bigBlock = 0; bigBlock < NumBigBlocks; ++bigBlock) + { + vec_t acc[NumOutputRegs] = { vec_setzero() }; + + // Each big block has NumOutputRegs small blocks in each "row", one per register. + // We process two small blocks at a time to save on one addition without VNNI. + for (IndexType smallBlock = 0; smallBlock < NumSmallBlocksPerOutput; smallBlock += 2) { - const auto iv_256 = reinterpret_cast(input); - const auto row_256 = reinterpret_cast(&weights_[offset]); - int j = kNumChunks * 2; - - #if defined(__MINGW32__) || defined(__MINGW64__) // See HACK comment below in AVX2. - __m256i sum256 = _mm256_maddubs_epi16(_mm256_loadu_si256(&iv_256[j]), _mm256_load_si256(&row_256[j])); - #else - __m256i sum256 = _mm256_maddubs_epi16(_mm256_load_si256(&iv_256[j]), _mm256_load_si256(&row_256[j])); - #endif - - sum256 = _mm256_madd_epi16(sum256, _mm256_set1_epi16(1)); - sum256 = _mm256_hadd_epi32(sum256, sum256); - sum256 = _mm256_hadd_epi32(sum256, sum256); - const __m128i lo = _mm256_extracti128_si256(sum256, 0); - const __m128i hi = _mm256_extracti128_si256(sum256, 1); - output[i] += _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi); - } + const vec_t* weightvec = + reinterpret_cast( + weights + + bigBlock * BigBlockSize + + smallBlock * SmallBlockSize * NumOutputRegs); - #elif defined(USE_AVX2) - __m256i sum = _mm256_setzero_si256(); - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m256i product = _mm256_maddubs_epi16( - - #if defined(__MINGW32__) || defined(__MINGW64__) - // HACK: Use _mm256_loadu_si256() instead of _mm256_load_si256. Because the binary - // compiled with g++ in MSYS2 crashes here because the output memory is not aligned - // even though alignas is specified. - _mm256_loadu_si256 - #else - _mm256_load_si256 - #endif - - (&input_vector[j]), _mm256_load_si256(&row[j])); - product = _mm256_madd_epi16(product, kOnes); - sum = _mm256_add_epi32(sum, product); + const vec_t in0 = invec[smallBlock + 0]; + const vec_t in1 = invec[smallBlock + 1]; + + for (IndexType k = 0; k < NumOutputRegs; ++k) + vec_add_dpbusd_32x2(acc[k], in0, weightvec[k], in1, weightvec[k + NumOutputRegs]); } - sum = _mm256_hadd_epi32(sum, sum); - sum = _mm256_hadd_epi32(sum, sum); - const __m128i lo = _mm256_extracti128_si256(sum, 0); - const __m128i hi = _mm256_extracti128_si256(sum, 1); - output[i] = _mm_cvtsi128_si32(lo) + _mm_cvtsi128_si32(hi) + biases_[i]; - - #elif defined(USE_SSSE3) - __m128i sum = _mm_cvtsi32_si128(biases_[i]); - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m128i product = _mm_maddubs_epi16( - _mm_load_si128(&input_vector[j]), _mm_load_si128(&row[j])); - product = _mm_madd_epi16(product, kOnes); - sum = _mm_add_epi32(sum, product); + + // Horizontally add all accumulators. + if constexpr (NumOutputRegs % 4 == 0) + { + __m128i* outputvec = reinterpret_cast<__m128i*>(output); + const __m128i* biasvec = reinterpret_cast(biases); + + for (IndexType k = 0; k < NumOutputRegs; k += 4) + { + const IndexType idx = (bigBlock * NumOutputRegs + k) / 4; + outputvec[idx] = vec_haddx4(acc[k+0], acc[k+1], acc[k+2], acc[k+3], biasvec[idx]); + } } - sum = _mm_hadd_epi32(sum, sum); - sum = _mm_hadd_epi32(sum, sum); - output[i] = _mm_cvtsi128_si32(sum); - - #elif defined(USE_NEON) - int32x4_t sum = {biases_[i]}; - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - int16x8_t product = vmull_s8(input_vector[j * 2], row[j * 2]); - product = vmlal_s8(product, input_vector[j * 2 + 1], row[j * 2 + 1]); - sum = vpadalq_s16(sum, product); + else + { + for (IndexType k = 0; k < NumOutputRegs; ++k) + { + const IndexType idx = (bigBlock * NumOutputRegs + k); + output[idx] = vec_hadd(acc[k], biases[idx]); + } } - output[i] = sum[0] + sum[1] + sum[2] + sum[3]; + } + +# undef vec_setzero +# undef vec_set_32 +# undef vec_add_dpbusd_32 +# undef vec_add_dpbusd_32x2 +# undef vec_hadd +# undef vec_haddx4 +#else + // Use old implementation for the other architectures. + affine_transform_non_ssse3< + InputDimensions, + PaddedInputDimensions, + OutputDimensions>(output, weights, biases, input); + +#endif + + return output; + } + + private: + using BiasType = OutputType; + using WeightType = std::int8_t; + + PreviousLayer previousLayer; + + alignas(CacheLineSize) BiasType biases[OutputDimensions]; + alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions]; + }; - #else - OutputType sum = biases_[i]; - for (IndexType j = 0; j < kInputDimensions; ++j) { - sum += weights_[offset + j] * input[j]; + template + class AffineTransform> { + public: + // Input/output type + using InputType = typename PreviousLayer::OutputType; + using OutputType = std::int32_t; + static_assert(std::is_same::value, ""); + + // Number of input/output dimensions + static constexpr IndexType InputDimensions = + PreviousLayer::OutputDimensions; + static constexpr IndexType OutputDimensions = OutDims; + static constexpr IndexType PaddedInputDimensions = + ceil_to_multiple(InputDimensions, MaxSimdWidth); + + static_assert(PaddedInputDimensions < 128, "Something went wrong. This specialization should not have been chosen."); + +#if defined (USE_SSSE3) + static constexpr const IndexType OutputSimdWidth = SimdWidth / 4; + static constexpr const IndexType InputSimdWidth = SimdWidth; +#endif + + // Size of forward propagation buffer used in this layer + static constexpr std::size_t SelfBufferSize = + ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize); + + // Size of the forward propagation buffer used from the input layer to this layer + static constexpr std::size_t BufferSize = + PreviousLayer::BufferSize + SelfBufferSize; + + // Hash value embedded in the evaluation file + static constexpr std::uint32_t get_hash_value() { + std::uint32_t hashValue = 0xCC03DAE4u; + hashValue += OutputDimensions; + hashValue ^= PreviousLayer::get_hash_value() >> 1; + hashValue ^= PreviousLayer::get_hash_value() << 31; + return hashValue; + } + + static IndexType get_weight_index_scrambled(IndexType i) + { + return + (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 + + i / PaddedInputDimensions * 4 + + i % 4; + } + + static IndexType get_weight_index(IndexType i) + { +#if defined (USE_SSSE3) + return get_weight_index_scrambled(i); +#else + return i; +#endif + } + + // Read network parameters + bool read_parameters(std::istream& stream) { + if (!previousLayer.read_parameters(stream)) return false; + for (std::size_t i = 0; i < OutputDimensions; ++i) + biases[i] = read_little_endian(stream); + for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + weights[get_weight_index(i)] = read_little_endian(stream); + + return !stream.fail(); + } + + // Write network parameters + bool write_parameters(std::ostream& stream) const { + if (!previousLayer.write_parameters(stream)) return false; + for (std::size_t i = 0; i < OutputDimensions; ++i) + write_little_endian(stream, biases[i]); + + for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + write_little_endian(stream, weights[get_weight_index(i)]); + + return !stream.fail(); + } + // Forward propagation + const OutputType* propagate( + const TransformedFeatureType* transformedFeatures, char* buffer) const { + const auto input = previousLayer.propagate( + transformedFeatures, buffer + SelfBufferSize); + const auto output = reinterpret_cast(buffer); + +#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_add_dpbusd_32x4 Simd::m256_add_dpbusd_epi32x4 + #define vec_hadd Simd::m256_hadd + #define vec_haddx4 Simd::m256_haddx4 +#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_add_dpbusd_32x4 Simd::m128_add_dpbusd_epi32x4 + #define vec_hadd Simd::m128_hadd + #define vec_haddx4 Simd::m128_haddx4 +#endif + +#if defined (USE_SSSE3) + const auto inputVector = reinterpret_cast(input); + + static_assert(InputDimensions % 8 == 0); + static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1); + + if constexpr (OutputDimensions % OutputSimdWidth == 0) + { + constexpr IndexType NumChunks = InputDimensions / 4; + constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth; + + const auto input32 = reinterpret_cast(input); + const vec_t* biasvec = reinterpret_cast(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(&weights[(i + 0) * OutputDimensions * 4]); + const auto col1 = reinterpret_cast(&weights[(i + 1) * OutputDimensions * 4]); + for (IndexType k = 0; k < NumRegs; ++k) + vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]); } - output[i] = sum; - #endif + vec_t* outptr = reinterpret_cast(output); + for (IndexType k = 0; k < NumRegs; ++k) + outptr[k] = acc[k]; } + else if constexpr (OutputDimensions == 1) + { + constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth; + vec_t sum0 = vec_setzero(); + const auto row0 = reinterpret_cast(&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_add_dpbusd_32x4 +# undef vec_hadd +# undef vec_haddx4 +#else + // Use old implementation for the other architectures. + affine_transform_non_ssse3< + InputDimensions, + PaddedInputDimensions, + OutputDimensions>(output, weights, biases, input); +#endif + return output; } @@ -203,13 +543,12 @@ namespace Eval::NNUE::Layers { using BiasType = OutputType; using WeightType = std::int8_t; - PreviousLayer previous_layer_; + PreviousLayer previousLayer; - alignas(kCacheLineSize) BiasType biases_[kOutputDimensions]; - alignas(kCacheLineSize) - WeightType weights_[kOutputDimensions * kPaddedInputDimensions]; + alignas(CacheLineSize) BiasType biases[OutputDimensions]; + alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions]; }; -} // namespace Eval::NNUE::Layers +} // namespace Stockfish::Eval::NNUE::Layers #endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED