X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=src%2Fnnue%2Flayers%2Faffine_transform.h;h=fc65c34339ff35bb44f69dee3a85285de69eb51b;hb=8a912951de6d4bff78d3ff5258213a0c7e6f494e;hp=f0292e453c14237e59cd86717c06158103308bbe;hpb=75e06a1c89ebac9c9ec4247bc82ec728a2bffe1e;p=stockfish diff --git a/src/nnue/layers/affine_transform.h b/src/nnue/layers/affine_transform.h index f0292e45..fc65c343 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) 2004-2023 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 @@ -21,536 +21,296 @@ #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED #define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED +#include #include + #include "../nnue_common.h" +#include "simd.h" + +/* + This file contains the definition for a fully connected layer (aka affine transform). + + - 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 + - inputs are processed in chunks of 4, weights are respectively transposed + - accumulation happens directly to int32s +*/ -namespace Eval::NNUE::Layers { +namespace Stockfish::Eval::NNUE::Layers { + +// Fallback implementation for older/other architectures. +// 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) || 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(InputDimensions, 16) / 16; + const __m128i Zeros = _mm_setzero_si128(); + const auto inputVector = reinterpret_cast(input); + +# elif defined(USE_NEON_DOTPROD) + constexpr IndexType NumChunks = ceil_to_multiple(InputDimensions, 16) / 16; + const auto inputVector = reinterpret_cast(input); + +# elif defined(USE_NEON) + constexpr IndexType NumChunks = ceil_to_multiple(InputDimensions, 16) / 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_NEON_DOTPROD) + int32x4_t sum = {biases[i]}; + const auto row = reinterpret_cast(&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(&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]; - // Affine transformation layer - template +# 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 class AffineTransform { public: // Input/output type - using InputType = typename PreviousLayer::OutputType; + using InputType = std::uint8_t; using OutputType = std::int32_t; - 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 = InDims; + static constexpr IndexType OutputDimensions = OutDims; - // Size of forward propagation buffer used in this layer - static constexpr std::size_t kSelfBufferSize = - CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize); + static constexpr IndexType PaddedInputDimensions = + ceil_to_multiple(InputDimensions, MaxSimdWidth); + static constexpr IndexType PaddedOutputDimensions = + ceil_to_multiple(OutputDimensions, MaxSimdWidth); - // Size of the forward propagation buffer used from the input layer to this layer - static constexpr std::size_t kBufferSize = - PreviousLayer::kBufferSize + kSelfBufferSize; + using OutputBuffer = OutputType[PaddedOutputDimensions]; // 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 prevHash) { + std::uint32_t hashValue = 0xCC03DAE4u; + hashValue += OutputDimensions; + hashValue ^= prevHash >> 1; + hashValue ^= prevHash << 31; + return hashValue; } - // Read network parameters - bool ReadParameters(std::istream& stream) { - if (!previous_layer_.ReadParameters(stream)) return false; - for (std::size_t i = 0; i < kOutputDimensions; ++i) - biases_[i] = read_little_endian(stream); - for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i) - weights_[i] = read_little_endian(stream); - return !stream.fail(); + static constexpr IndexType get_weight_index_scrambled(IndexType i) + { + return + (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 + + i / PaddedInputDimensions * 4 + + i % 4; } - // Forward propagation - const OutputType* Propagate( - const TransformedFeatureType* transformed_features, char* buffer) const { - const auto input = previous_layer_.Propagate( - transformed_features, buffer + kSelfBufferSize); - -#if defined (USE_AVX512) - - [[maybe_unused]] const __m512i kOnes512 = _mm512_set1_epi16(1); - - [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int { - return _mm512_reduce_add_epi32(sum) + bias; - }; - - [[maybe_unused]] auto m512_haddx4 = [](__m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i { - __m512i sum01a = _mm512_unpacklo_epi32(sum0, sum1); - __m512i sum01b = _mm512_unpackhi_epi32(sum0, sum1); - - __m512i sum23a = _mm512_unpacklo_epi32(sum2, sum3); - __m512i sum23b = _mm512_unpackhi_epi32(sum2, sum3); - - __m512i sum01 = _mm512_add_epi32(sum01a, sum01b); - __m512i sum23 = _mm512_add_epi32(sum23a, sum23b); - - __m512i sum0123a = _mm512_unpacklo_epi64(sum01, sum23); - __m512i sum0123b = _mm512_unpackhi_epi64(sum01, sum23); - - __m512i sum = _mm512_add_epi32(sum0123a, sum0123b); - - __m256i sum256lo = _mm512_castsi512_si256(sum); - __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1); - - sum256lo = _mm256_add_epi32(sum256lo, sum256hi); - - __m128i sum128lo = _mm256_castsi256_si128(sum256lo); - __m128i sum128hi = _mm256_extracti128_si256(sum256lo, 1); - - return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias); - }; - - [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) { -#if defined (USE_VNNI) - acc = _mm512_dpbusd_epi32(acc, a, b); + static constexpr IndexType get_weight_index(IndexType i) + { +#if defined (USE_SSSE3) + return get_weight_index_scrambled(i); #else - __m512i product0 = _mm512_maddubs_epi16(a, b); - product0 = _mm512_madd_epi16(product0, kOnes512); - acc = _mm512_add_epi32(acc, product0); + return i; #endif - }; - -#endif -#if defined (USE_AVX2) - - [[maybe_unused]] const __m256i kOnes256 = _mm256_set1_epi16(1); - - [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int { - __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1)); - sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC)); - sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB)); - return _mm_cvtsi128_si32(sum128) + bias; - }; + } - [[maybe_unused]] auto m256_haddx4 = [](__m256i sum0, __m256i sum1, __m256i sum2, __m256i sum3, __m128i bias) -> __m128i { - sum0 = _mm256_hadd_epi32(sum0, sum1); - sum2 = _mm256_hadd_epi32(sum2, sum3); + // Read network parameters + bool read_parameters(std::istream& stream) { + read_little_endian(stream, biases, OutputDimensions); + for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + weights[get_weight_index(i)] = read_little_endian(stream); - sum0 = _mm256_hadd_epi32(sum0, sum2); + return !stream.fail(); + } - __m128i sum128lo = _mm256_castsi256_si128(sum0); - __m128i sum128hi = _mm256_extracti128_si256(sum0, 1); + // Write network parameters + bool write_parameters(std::ostream& stream) const { + write_little_endian(stream, biases, OutputDimensions); - return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias); - }; + for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) + write_little_endian(stream, weights[get_weight_index(i)]); - [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) { -#if defined (USE_VNNI) - acc = _mm256_dpbusd_epi32(acc, a, b); -#else - __m256i product0 = _mm256_maddubs_epi16(a, b); - product0 = _mm256_madd_epi16(product0, kOnes256); - acc = _mm256_add_epi32(acc, product0); -#endif - }; - -#endif + return !stream.fail(); + } + // Forward propagation + void propagate( + const InputType* input, OutputType* output) const { #if defined (USE_SSSE3) - [[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1); - - [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int { - sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC - sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB - return _mm_cvtsi128_si32(sum) + bias; - }; - - [[maybe_unused]] auto m128_haddx4 = [](__m128i sum0, __m128i sum1, __m128i sum2, __m128i sum3, __m128i bias) -> __m128i { - sum0 = _mm_hadd_epi32(sum0, sum1); - sum2 = _mm_hadd_epi32(sum2, sum3); - - sum0 = _mm_hadd_epi32(sum0, sum2); - - return _mm_add_epi32(sum0, bias); - }; - - [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) { - __m128i product0 = _mm_maddubs_epi16(a, b); - product0 = _mm_madd_epi16(product0, kOnes128); - acc = _mm_add_epi32(acc, product0); - }; - -#endif - -#if defined (USE_AVX512) - - constexpr IndexType kNumChunks512 = kPaddedInputDimensions / (kSimdWidth * 2); - constexpr IndexType kNumChunks256 = kPaddedInputDimensions / kSimdWidth; - - const auto output = reinterpret_cast(buffer); - - // Since to saturate a zmm register it takes 64 bytes we - // cannot use AVX512 for the smaller affine transforms. - // Instead we fallback to a AVX2 implementation if the - // kInputDimensions isn't a multiple of 64. - // Note that this means that for example for - // kInputDimensions of 96 we fallback to AVX2 even though - // the first 64 elements could be processed with AVX512. - // This is caused by mixing the __m256 and __m512 variables - // required to better handle that case and it would - // require handling more cases statically not to lose performance. - // This should be revisited if such input dimensions are to be considered. - [[maybe_unused]] const auto input_vector512 = reinterpret_cast(input); - [[maybe_unused]] const auto input_vector256 = reinterpret_cast(input); - - // kOutputDimensions is either 1 or a multiple of kSimdWidth - // because then it is also an input dimension. - if constexpr (kOutputDimensions % 4 == 0) + if constexpr (OutputDimensions > 1) { - for (IndexType i = 0; i < kOutputDimensions; i += 4) - { - const IndexType offset0 = (i + 0) * kPaddedInputDimensions; - const IndexType offset1 = (i + 1) * kPaddedInputDimensions; - const IndexType offset2 = (i + 2) * kPaddedInputDimensions; - const IndexType offset3 = (i + 3) * kPaddedInputDimensions; - - const __m128i bias = *reinterpret_cast(&biases_[i]); - __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); - - if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0) - { - __m512i sum0 = _mm512_setzero_si512(); - __m512i sum1 = _mm512_setzero_si512(); - __m512i sum2 = _mm512_setzero_si512(); - __m512i sum3 = _mm512_setzero_si512(); - - const auto row0 = reinterpret_cast(&weights_[offset0]); - const auto row1 = reinterpret_cast(&weights_[offset1]); - const auto row2 = reinterpret_cast(&weights_[offset2]); - const auto row3 = reinterpret_cast(&weights_[offset3]); - - for (IndexType j = 0; j < kNumChunks512; ++j) - { - const __m512i in = input_vector512[j]; - - m512_add_dpbusd_epi32(sum0, in, row0[j]); - m512_add_dpbusd_epi32(sum1, in, row1[j]); - m512_add_dpbusd_epi32(sum2, in, row2[j]); - m512_add_dpbusd_epi32(sum3, in, row3[j]); - } - - *outptr = m512_haddx4(sum0, sum1, sum2, sum3, bias); - } - else - { - __m256i sum0 = _mm256_setzero_si256(); - __m256i sum1 = _mm256_setzero_si256(); - __m256i sum2 = _mm256_setzero_si256(); - __m256i sum3 = _mm256_setzero_si256(); - - const auto row0 = reinterpret_cast(&weights_[offset0]); - const auto row1 = reinterpret_cast(&weights_[offset1]); - const auto row2 = reinterpret_cast(&weights_[offset2]); - const auto row3 = reinterpret_cast(&weights_[offset3]); - - for (IndexType j = 0; j < kNumChunks256; ++j) - { - const __m256i in = input_vector256[j]; - - m256_add_dpbusd_epi32(sum0, in, row0[j]); - m256_add_dpbusd_epi32(sum1, in, row1[j]); - m256_add_dpbusd_epi32(sum2, in, row2[j]); - m256_add_dpbusd_epi32(sum3, in, row3[j]); - } - - *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias); - } - } - } - else if constexpr (kOutputDimensions == 1) - { - if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0) - { - __m512i sum0 = _mm512_setzero_si512(); - const auto row0 = reinterpret_cast(&weights_[0]); - - for (IndexType j = 0; j < kNumChunks512; ++j) - { - const __m512i in = input_vector512[j]; - - m512_add_dpbusd_epi32(sum0, in, row0[j]); - } - - output[0] = m512_hadd(sum0, biases_[0]); - } - else - { - __m256i sum0 = _mm256_setzero_si256(); - - const auto row0 = reinterpret_cast(&weights_[0]); +#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 - for (IndexType j = 0; j < kNumChunks256; ++j) - { - const __m256i in = input_vector256[j]; + static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType); - m256_add_dpbusd_epi32(sum0, in, row0[j]); - } + static_assert(OutputDimensions % OutputSimdWidth == 0); - output[0] = m256_hadd(sum0, biases_[0]); - } - } - else - { - // This case can never happen because kOutputDimensions - // is always 1 or a multiple of kSimdWidth. - assert(false); - } + constexpr IndexType NumChunks = ceil_to_multiple(InputDimensions, 8) / 4; + constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth; -#elif defined (USE_AVX2) + 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]; - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - - const auto output = reinterpret_cast(buffer); - const auto input_vector = reinterpret_cast(input); - - // kOutputDimensions is either 1 or a multiple of kSimdWidth - // because then it is also an input dimension. - if constexpr (kOutputDimensions % 4 == 0) - { - for (IndexType i = 0; i < kOutputDimensions; i += 4) + for (IndexType i = 0; i < NumChunks; i += 2) { - const IndexType offset0 = (i + 0) * kPaddedInputDimensions; - const IndexType offset1 = (i + 1) * kPaddedInputDimensions; - const IndexType offset2 = (i + 2) * kPaddedInputDimensions; - const IndexType offset3 = (i + 3) * kPaddedInputDimensions; - - const __m128i bias = *reinterpret_cast(&biases_[i]); - __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); - - __m256i sum0 = _mm256_setzero_si256(); - __m256i sum1 = _mm256_setzero_si256(); - __m256i sum2 = _mm256_setzero_si256(); - __m256i sum3 = _mm256_setzero_si256(); - - const auto row0 = reinterpret_cast(&weights_[offset0]); - const auto row1 = reinterpret_cast(&weights_[offset1]); - const auto row2 = reinterpret_cast(&weights_[offset2]); - const auto row3 = reinterpret_cast(&weights_[offset3]); - - for (IndexType j = 0; j < kNumChunks; ++j) - { - const __m256i in = input_vector[j]; - - m256_add_dpbusd_epi32(sum0, in, row0[j]); - m256_add_dpbusd_epi32(sum1, in, row1[j]); - m256_add_dpbusd_epi32(sum2, in, row2[j]); - m256_add_dpbusd_epi32(sum3, in, row3[j]); - } - - *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias); + 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]); } - } - else if constexpr (kOutputDimensions == 1) - { - __m256i sum0 = _mm256_setzero_si256(); - - const auto row0 = reinterpret_cast(&weights_[0]); - for (IndexType j = 0; j < kNumChunks; ++j) - { - const __m256i in = input_vector[j]; + vec_t* outptr = reinterpret_cast(output); + for (IndexType k = 0; k < NumRegs; ++k) + outptr[k] = acc[k]; - m256_add_dpbusd_epi32(sum0, in, row0[j]); - } +# undef vec_setzero +# undef vec_set_32 +# undef vec_add_dpbusd_32 +# undef vec_add_dpbusd_32x2 +# undef vec_hadd - output[0] = m256_hadd(sum0, biases_[0]); } - else + else if constexpr (OutputDimensions == 1) { - // This case can never happen because kOutputDimensions - // is always 1 or a multiple of kSimdWidth. - assert(false); - } +// 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 - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; + const auto inputVector = reinterpret_cast(input); - auto output = reinterpret_cast(buffer); - const auto input_vector = reinterpret_cast(input); + static constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(InputType); - // kOutputDimensions is either 1 or a multiple of kSimdWidth - // because then it is also an input dimension. - if constexpr (kOutputDimensions % 4 == 0) - { - for (IndexType i = 0; i < kOutputDimensions; i += 4) - { - const IndexType offset0 = (i + 0) * kPaddedInputDimensions; - const IndexType offset1 = (i + 1) * kPaddedInputDimensions; - const IndexType offset2 = (i + 2) * kPaddedInputDimensions; - const IndexType offset3 = (i + 3) * kPaddedInputDimensions; - - const __m128i bias = *reinterpret_cast(&biases_[i]); - __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]); - - __m128i sum0 = _mm_setzero_si128(); - __m128i sum1 = _mm_setzero_si128(); - __m128i sum2 = _mm_setzero_si128(); - __m128i sum3 = _mm_setzero_si128(); - - const auto row0 = reinterpret_cast(&weights_[offset0]); - const auto row1 = reinterpret_cast(&weights_[offset1]); - const auto row2 = reinterpret_cast(&weights_[offset2]); - const auto row3 = reinterpret_cast(&weights_[offset3]); - - for (int j = 0; j < (int)kNumChunks; j += 1) - { - const __m128i in = input_vector[j]; - - m128_add_dpbusd_epi32(sum0, in, row0[j]); - m128_add_dpbusd_epi32(sum1, in, row1[j]); - m128_add_dpbusd_epi32(sum2, in, row2[j]); - m128_add_dpbusd_epi32(sum3, in, row3[j]); - } - - *outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias); - } - } - else if constexpr (kOutputDimensions == 1) - { - __m128i sum0 = _mm_setzero_si128(); + static_assert(PaddedInputDimensions % InputSimdWidth == 0); - const auto row0 = reinterpret_cast(&weights_[0]); + constexpr IndexType NumChunks = PaddedInputDimensions / InputSimdWidth; + vec_t sum0 = vec_setzero(); + const auto row0 = reinterpret_cast(&weights[0]); - for (int j = 0; j < (int)kNumChunks; j += 1) + for (int j = 0; j < int(NumChunks); ++j) { - const __m128i in = input_vector[j]; - - m128_add_dpbusd_epi32(sum0, in, row0[j]); + const vec_t in = inputVector[j]; + vec_add_dpbusd_32(sum0, in, row0[j]); } + output[0] = vec_hadd(sum0, biases[0]); - output[0] = m128_hadd(sum0, biases_[0]); - } - else - { - // This case can never happen because kOutputDimensions - // is always 1 or a multiple of kSimdWidth. - assert(false); - } - -#else - -// Use old implementation for the other architectures. - - auto output = reinterpret_cast(buffer); - -#if defined(USE_SSE2) - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; -#ifndef USE_SSSE3 - const __m128i kZeros = _mm_setzero_si128(); -#else - const __m128i kOnes = _mm_set1_epi16(1); -#endif - const auto input_vector = reinterpret_cast(input); - -#elif defined(USE_MMX) - constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth; - const __m64 kZeros = _mm_setzero_si64(); - 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_SSE2) - __m128i sum_lo = _mm_cvtsi32_si128(biases_[i]); - __m128i sum_hi = kZeros; - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m128i row_j = _mm_load_si128(&row[j]); - __m128i input_j = _mm_load_si128(&input_vector[j]); - __m128i row_signs = _mm_cmpgt_epi8(kZeros, row_j); - __m128i extended_row_lo = _mm_unpacklo_epi8(row_j, row_signs); - __m128i extended_row_hi = _mm_unpackhi_epi8(row_j, row_signs); - __m128i extended_input_lo = _mm_unpacklo_epi8(input_j, kZeros); - __m128i extended_input_hi = _mm_unpackhi_epi8(input_j, kZeros); - __m128i product_lo = _mm_madd_epi16(extended_row_lo, extended_input_lo); - __m128i product_hi = _mm_madd_epi16(extended_row_hi, extended_input_hi); - sum_lo = _mm_add_epi32(sum_lo, product_lo); - sum_hi = _mm_add_epi32(sum_hi, product_hi); - } - __m128i sum = _mm_add_epi32(sum_lo, sum_hi); - __m128i sum_high_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2)); - sum = _mm_add_epi32(sum, sum_high_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 sum_lo = _mm_cvtsi32_si64(biases_[i]); - __m64 sum_hi = kZeros; - const auto row = reinterpret_cast(&weights_[offset]); - for (IndexType j = 0; j < kNumChunks; ++j) { - __m64 row_j = row[j]; - __m64 input_j = input_vector[j]; - __m64 row_signs = _mm_cmpgt_pi8(kZeros, row_j); - __m64 extended_row_lo = _mm_unpacklo_pi8(row_j, row_signs); - __m64 extended_row_hi = _mm_unpackhi_pi8(row_j, row_signs); - __m64 extended_input_lo = _mm_unpacklo_pi8(input_j, kZeros); - __m64 extended_input_hi = _mm_unpackhi_pi8(input_j, kZeros); - __m64 product_lo = _mm_madd_pi16(extended_row_lo, extended_input_lo); - __m64 product_hi = _mm_madd_pi16(extended_row_hi, extended_input_hi); - sum_lo = _mm_add_pi32(sum_lo, product_lo); - sum_hi = _mm_add_pi32(sum_hi, product_hi); - } - __m64 sum = _mm_add_pi32(sum_lo, sum_hi); - 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 < 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); - } - output[i] = sum[0] + sum[1] + sum[2] + sum[3]; - -#else - OutputType sum = biases_[i]; - for (IndexType j = 0; j < kInputDimensions; ++j) { - sum += weights_[offset + j] * input[j]; - } - output[i] = sum; -#endif +# undef vec_setzero +# undef vec_set_32 +# undef vec_add_dpbusd_32 +# undef vec_add_dpbusd_32x2 +# undef vec_hadd } -#if defined(USE_MMX) - _mm_empty(); -#endif - +#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 previous_layer_; - - 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