/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2020 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2022 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
#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
#include <iostream>
+#include <algorithm>
+#include <type_traits>
#include "../nnue_common.h"
+#include "../../simd.h"
-namespace Eval::NNUE::Layers {
-
- // Affine transformation layer
- template <typename PreviousLayer, IndexType OutputDimensions>
- class AffineTransform {
- public:
- // Input/output type
- using InputType = typename PreviousLayer::OutputType;
- using OutputType = std::int32_t;
- static_assert(std::is_same<InputType, std::uint8_t>::value, "");
-
- // Number of input/output dimensions
- static constexpr IndexType kInputDimensions =
- PreviousLayer::kOutputDimensions;
- static constexpr IndexType kOutputDimensions = OutputDimensions;
- static constexpr IndexType kPaddedInputDimensions =
- CeilToMultiple<IndexType>(kInputDimensions, kMaxSimdWidth);
-
- // Size of forward propagation buffer used in this layer
- static constexpr std::size_t kSelfBufferSize =
- CeilToMultiple(kOutputDimensions * sizeof(OutputType), kCacheLineSize);
-
- // Size of the forward propagation buffer used from the input layer to this layer
- static constexpr std::size_t kBufferSize =
- PreviousLayer::kBufferSize + kSelfBufferSize;
+/*
+ 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
+*/
- // 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;
- }
+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 <IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
+ 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.
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const __m128i Zeros = _mm_setzero_si128();
+ const auto inputVector = reinterpret_cast<const __m128i*>(input);
+
+# elif defined(USE_MMX)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 8;
+ const __m64 Zeros = _mm_setzero_si64();
+ const auto inputVector = reinterpret_cast<const __m64*>(input);
+
+# elif defined(USE_NEON)
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
+ const auto inputVector = reinterpret_cast<const int8x8_t*>(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<const __m128i*>(&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<const __m64*>(&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<const int8x8_t*>(&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];
- // 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<BiasType>(stream);
- for (std::size_t i = 0; i < kOutputDimensions * kPaddedInputDimensions; ++i)
- weights_[i] = read_little_endian<WeightType>(stream);
- return !stream.fail();
+# else
+ std::int32_t sum = biases[i];
+ for (IndexType j = 0; j < InputDimensions; ++j) {
+ sum += weights[offset + j] * input[j];
+ }
+ output[i] = sum;
+# endif
}
- // 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;
- };
-
- // This function takes
- // sum0 = [xmm0a, xmm0b, xmm0c, xmm0d]
- // sum1 = [xmm1a, xmm1b, xmm1c, xmm1d]
- // sum2 = [xmm2a, xmm2b, xmm2c, xmm2d]
- // sum3 = [xmm3a, xmm3b, xmm3c, xmm3d]
- // and returns
- // ret = [
- // reduce_add_epi32(xmm0a), reduce_add_epi32(xmm1a), reduce_add_epi32(xmm2a), reduce_add_epi32(xmm3a),
- // reduce_add_epi32(xmm0b), reduce_add_epi32(xmm1b), reduce_add_epi32(xmm2b), reduce_add_epi32(xmm3b),
- // reduce_add_epi32(xmm0c), reduce_add_epi32(xmm1c), reduce_add_epi32(xmm2c), reduce_add_epi32(xmm3c),
- // reduce_add_epi32(xmm0d), reduce_add_epi32(xmm1d), reduce_add_epi32(xmm2d), reduce_add_epi32(xmm3d)
- // ]
- [[maybe_unused]] auto m512_hadd128x16_interleave = [](
- __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3) -> __m512i {
-
- __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);
-
- return _mm512_add_epi32(sum0123a, sum0123b);
- };
-
- [[maybe_unused]] auto m512_haddx4 = [m512_hadd128x16_interleave](
- __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m128i bias) -> __m128i {
-
- __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
-
- __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_haddx8 = [m512_hadd128x16_interleave](
- __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3,
- __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m256i bias) -> __m256i {
-
- __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
- __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7);
-
- __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13);
- __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15);
- __m512i x = _mm512_add_epi32(
- _mm512_permutex2var_epi64(suma, indices0, sumb),
- _mm512_permutex2var_epi64(suma, indices1, sumb));
-
- __m256i sum256lo = _mm512_castsi512_si256(x);
- __m256i sum256hi = _mm512_extracti64x4_epi64(x, 1);
-
- return _mm256_add_epi32(_mm256_add_epi32(sum256lo, sum256hi), bias);
- };
-
- [[maybe_unused]] auto m512_hadd256x8 =[m512_hadd128x16_interleave](
- __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3, __m256i bias) -> __m256i {
-
- __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
-
- __m512i indices = _mm512_setr_epi32(
- 0, 4, 8, 12, 2, 6, 10, 14,
- 1, 5, 9, 13, 3, 7, 11, 15);
- sum = _mm512_permutexvar_epi32(indices, sum);
+# if defined(USE_MMX)
+ _mm_empty();
+# endif
+ }
+#endif
- __m256i sum256lo = _mm512_castsi512_si256(sum);
- __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1);
+ template <IndexType InDims, IndexType OutDims, typename Enabled = void>
+ class AffineTransform;
- return _mm256_add_epi32(_mm256_hadd_epi32(sum256lo, sum256hi), bias);
- };
+ // A specialization for large inputs.
+ template <IndexType InDims, IndexType OutDims>
+ class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= 2*64)>> {
+ public:
+ // Input/output type
+ using InputType = std::uint8_t;
+ using OutputType = std::int32_t;
- [[maybe_unused]] auto m512_hadd256x16 = [m512_hadd128x16_interleave](
- __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3,
- __m512i sum4, __m512i sum5, __m512i sum6, __m512i sum7, __m512i bias) -> __m512i {
+ // Number of input/output dimensions
+ static constexpr IndexType InputDimensions = InDims;
+ static constexpr IndexType OutputDimensions = OutDims;
- __m512i suma = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
- __m512i sumb = m512_hadd128x16_interleave(sum4, sum5, sum6, sum7);
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
- __m512i indices0 = _mm512_setr_epi64(0, 1, 8, 9, 4, 5, 12, 13);
- __m512i indices1 = _mm512_setr_epi64(2, 3, 10, 11, 6, 7, 14, 15);
- __m512i x = _mm512_add_epi32(
- _mm512_permutex2var_epi64(suma, indices0, sumb),
- _mm512_permutex2var_epi64(suma, indices1, sumb));
+ using OutputBuffer = OutputType[PaddedOutputDimensions];
- __m512i indices = _mm512_setr_epi32(0, 8, 1, 9, 2, 10, 3, 11, 4, 12, 5, 13, 6, 14, 7, 15);
- return _mm512_add_epi32(_mm512_permutexvar_epi32(indices, x), bias);
- };
+ static_assert(PaddedInputDimensions >= 128, "Something went wrong. This specialization should not have been chosen.");
-#if defined (USE_VNNI)
- [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
- acc = _mm512_dpbusd_epi32(acc, a, b);
+#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;
+#elif defined (USE_NEON)
+ static constexpr const IndexType InputSimdWidth = 8;
+ static constexpr const IndexType MaxNumOutputRegs = 8;
#else
- [[maybe_unused]] auto m512_dpbusd_epi32 = [=](__m512i a, __m512i b) -> __m512i {
- __m512i product0 = _mm512_maddubs_epi16(a, b);
- return _mm512_madd_epi16(product0, kOnes512);
-#endif
- };
-
+ // 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
-#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);
+ // 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]
- sum0 = _mm256_hadd_epi32(sum0, sum2);
+ 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;
- __m128i sum128lo = _mm256_castsi256_si128(sum0);
- __m128i sum128hi = _mm256_extracti128_si256(sum0, 1);
+ static_assert(OutputDimensions % NumOutputRegs == 0);
- return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
- };
-#if defined (USE_VNNI)
- [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
- acc = _mm256_dpbusd_epi32(acc, a, b);
-#else
- [[maybe_unused]] auto m256_dpbusd_epi32 = [=](__m256i a, __m256i b) -> __m256i {
- __m256i product0 = _mm256_maddubs_epi16(a, b);
- return _mm256_madd_epi16(product0, kOnes256);
-#endif
- };
+ // 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;
+ }
-#endif
+ /*
+ 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;
+ }
-#if defined (USE_SSSE3)
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ biases[i] = read_little_endian<BiasType>(stream);
- [[maybe_unused]] const __m128i kOnes128 = _mm_set1_epi16(1);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
- [[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;
- };
+ return !stream.fail();
+ }
- [[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);
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ write_little_endian<BiasType>(stream, biases[i]);
- sum0 = _mm_hadd_epi32(sum0, sum2);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
- return _mm_add_epi32(sum0, bias);
- };
+ return !stream.fail();
+ }
- [[maybe_unused]] auto m128_dpbusd_epi32 = [=](__m128i a, __m128i b) -> __m128i {
- __m128i product0 = _mm_maddubs_epi16(a, b);
- return _mm_madd_epi16(product0, kOnes128);
- };
+ // Forward propagation
+ const OutputType* propagate(
+ const InputType* input, OutputType* output) const {
+#if defined (USE_AVX512)
+ using acc_vec_t = __m512i;
+ using bias_vec_t = __m128i;
+ using weight_vec_t = __m512i;
+ using in_vec_t = __m512i;
+ #define vec_zero _mm512_setzero_si512()
+ #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 acc_vec_t = __m256i;
+ using bias_vec_t = __m128i;
+ using weight_vec_t = __m256i;
+ using in_vec_t = __m256i;
+ #define vec_zero _mm256_setzero_si256()
+ #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 acc_vec_t = __m128i;
+ using bias_vec_t = __m128i;
+ using weight_vec_t = __m128i;
+ using in_vec_t = __m128i;
+ #define vec_zero _mm_setzero_si128()
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m128_hadd
+ #define vec_haddx4 Simd::m128_haddx4
+#elif defined (USE_NEON)
+ using acc_vec_t = int32x4_t;
+ using bias_vec_t = int32x4_t;
+ using weight_vec_t = int8x8_t;
+ using in_vec_t = int8x8_t;
+ #define vec_zero {0}
+ #define vec_add_dpbusd_32x2 Simd::neon_m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::neon_m128_hadd
+ #define vec_haddx4 Simd::neon_m128_haddx4
#endif
-#if defined (USE_AVX512)
+#if defined (USE_SSSE3) || defined (USE_NEON)
+ const in_vec_t* invec = reinterpret_cast<const in_vec_t*>(input);
- constexpr IndexType kNumChunks512 = kPaddedInputDimensions / (kSimdWidth * 2);
- constexpr IndexType kNumChunks256 = kPaddedInputDimensions / kSimdWidth;
-
- const auto output = reinterpret_cast<OutputType*>(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<const __m512i*>(input);
- [[maybe_unused]] const auto input_vector256 = reinterpret_cast<const __m256i*>(input);
-
- // kOutputDimensions is either 1 or a multiple of kSimdWidth
- // because then it is also an input dimension.
- if constexpr (kOutputDimensions % 16 == 0 && kNumChunks256 == 1)
+ // Perform accumulation to registers for each big block
+ for (IndexType bigBlock = 0; bigBlock < NumBigBlocks; ++bigBlock)
{
- for (IndexType i = 0; i < kOutputDimensions; i += 16)
- {
- const IndexType offset01a = (i + 0) * kPaddedInputDimensions;
- const IndexType offset23a = (i + 2) * kPaddedInputDimensions;
- const IndexType offset45a = (i + 4) * kPaddedInputDimensions;
- const IndexType offset67a = (i + 6) * kPaddedInputDimensions;
- const IndexType offset01b = (i + 8) * kPaddedInputDimensions;
- const IndexType offset23b = (i + 10) * kPaddedInputDimensions;
- const IndexType offset45b = (i + 12) * kPaddedInputDimensions;
- const IndexType offset67b = (i + 14) * kPaddedInputDimensions;
-
- const __m512i bias = *reinterpret_cast<const __m512i*>(&biases_[i]);
- __m512i* outptr = reinterpret_cast<__m512i*>(&output[i]);
-
- const auto row01a = *reinterpret_cast<const __m512i*>(&weights_[offset01a]);
- const auto row23a = *reinterpret_cast<const __m512i*>(&weights_[offset23a]);
- const auto row45a = *reinterpret_cast<const __m512i*>(&weights_[offset45a]);
- const auto row67a = *reinterpret_cast<const __m512i*>(&weights_[offset67a]);
- const auto row01b = *reinterpret_cast<const __m512i*>(&weights_[offset01b]);
- const auto row23b = *reinterpret_cast<const __m512i*>(&weights_[offset23b]);
- const auto row45b = *reinterpret_cast<const __m512i*>(&weights_[offset45b]);
- const auto row67b = *reinterpret_cast<const __m512i*>(&weights_[offset67b]);
-
- const __m256i in256 = input_vector256[0];
- const __m512i in = _mm512_inserti64x4(_mm512_castsi256_si512(in256), in256, 1);
-
-#if defined (USE_VNNI)
- __m512i sum01a = _mm512_setzero_si512();
- __m512i sum23a = _mm512_setzero_si512();
- __m512i sum45a = _mm512_setzero_si512();
- __m512i sum67a = _mm512_setzero_si512();
- __m512i sum01b = _mm512_setzero_si512();
- __m512i sum23b = _mm512_setzero_si512();
- __m512i sum45b = _mm512_setzero_si512();
- __m512i sum67b = _mm512_setzero_si512();
-
- m512_add_dpbusd_epi32(sum01a, in, row01a);
- m512_add_dpbusd_epi32(sum23a, in, row23a);
- m512_add_dpbusd_epi32(sum45a, in, row45a);
- m512_add_dpbusd_epi32(sum67a, in, row67a);
- m512_add_dpbusd_epi32(sum01b, in, row01b);
- m512_add_dpbusd_epi32(sum23b, in, row23b);
- m512_add_dpbusd_epi32(sum45b, in, row45b);
- m512_add_dpbusd_epi32(sum67b, in, row67b);
-#else
- __m512i sum01a = m512_dpbusd_epi32(in, row01a);
- __m512i sum23a = m512_dpbusd_epi32(in, row23a);
- __m512i sum45a = m512_dpbusd_epi32(in, row45a);
- __m512i sum67a = m512_dpbusd_epi32(in, row67a);
- __m512i sum01b = m512_dpbusd_epi32(in, row01b);
- __m512i sum23b = m512_dpbusd_epi32(in, row23b);
- __m512i sum45b = m512_dpbusd_epi32(in, row45b);
- __m512i sum67b = m512_dpbusd_epi32(in, row67b);
-#endif
+ acc_vec_t acc[NumOutputRegs] = { vec_zero };
- *outptr = m512_hadd256x16(
- sum01a, sum23a, sum45a, sum67a,
- sum01b, sum23b, sum45b, sum67b, bias);
- }
- }
- else if constexpr (kOutputDimensions % 4 == 0)
- {
- for (IndexType i = 0; i < kOutputDimensions; i += 4)
+ // 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 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<const __m128i*>(&biases_[i]);
- __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
+ const weight_vec_t* weightvec =
+ reinterpret_cast<const weight_vec_t*>(
+ weights
+ + bigBlock * BigBlockSize
+ + smallBlock * SmallBlockSize * NumOutputRegs);
- if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
- {
- const auto row0 = reinterpret_cast<const __m512i*>(&weights_[offset0]);
- const auto row1 = reinterpret_cast<const __m512i*>(&weights_[offset1]);
- const auto row2 = reinterpret_cast<const __m512i*>(&weights_[offset2]);
- const auto row3 = reinterpret_cast<const __m512i*>(&weights_[offset3]);
-
-#if defined (USE_VNNI)
- __m512i sum0 = _mm512_setzero_si512();
- __m512i sum1 = _mm512_setzero_si512();
- __m512i sum2 = _mm512_setzero_si512();
- __m512i sum3 = _mm512_setzero_si512();
- const IndexType kStart = 0;
-#else
- __m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
- __m512i sum1 = m512_dpbusd_epi32(input_vector512[0], row1[0]);
- __m512i sum2 = m512_dpbusd_epi32(input_vector512[0], row2[0]);
- __m512i sum3 = m512_dpbusd_epi32(input_vector512[0], row3[0]);
- const IndexType kStart = 1;
-#endif
+ const in_vec_t in0 = invec[smallBlock + 0];
+ const in_vec_t in1 = invec[smallBlock + 1];
- for (IndexType j = kStart; j < kNumChunks512; ++j)
- {
- const __m512i in = input_vector512[j];
+ for (IndexType k = 0; k < NumOutputRegs; ++k)
+ vec_add_dpbusd_32x2(acc[k], in0, weightvec[k], in1, weightvec[k + NumOutputRegs]);
+ }
-#if defined (USE_VNNI)
- 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]);
-#else
- sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
- sum1 = _mm512_add_epi32(sum1, m512_dpbusd_epi32(in, row1[j]));
- sum2 = _mm512_add_epi32(sum2, m512_dpbusd_epi32(in, row2[j]));
- sum3 = _mm512_add_epi32(sum3, m512_dpbusd_epi32(in, row3[j]));
-#endif
- }
+ // Horizontally add all accumulators.
+ if constexpr (NumOutputRegs % 4 == 0)
+ {
+ bias_vec_t* outputvec = reinterpret_cast<bias_vec_t*>(output);
+ const bias_vec_t* biasvec = reinterpret_cast<const bias_vec_t*>(biases);
- *outptr = m512_haddx4(sum0, sum1, sum2, sum3, bias);
- }
- else
+ for (IndexType k = 0; k < NumOutputRegs; k += 4)
{
- const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
- const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
- const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
- const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
-
-#if defined (USE_VNNI)
- __m256i sum0 = _mm256_setzero_si256();
- __m256i sum1 = _mm256_setzero_si256();
- __m256i sum2 = _mm256_setzero_si256();
- __m256i sum3 = _mm256_setzero_si256();
- const IndexType kStart = 0;
-#else
- __m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
- __m256i sum1 = m256_dpbusd_epi32(input_vector256[0], row1[0]);
- __m256i sum2 = m256_dpbusd_epi32(input_vector256[0], row2[0]);
- __m256i sum3 = m256_dpbusd_epi32(input_vector256[0], row3[0]);
- const IndexType kStart = 1;
-#endif
-
- for (IndexType j = kStart; j < kNumChunks256; ++j)
- {
- const __m256i in = input_vector256[j];
-
-#if defined (USE_VNNI)
- 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]);
-#else
- sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
- sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
- sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
- sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
-#endif
- }
-
- *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
+ 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]);
}
}
- }
- else if constexpr (kOutputDimensions == 1)
- {
- if constexpr (kPaddedInputDimensions % (kSimdWidth * 2) == 0)
+ else
{
- const auto row0 = reinterpret_cast<const __m512i*>(&weights_[0]);
-
-#if defined (USE_VNNI)
- __m512i sum0 = _mm512_setzero_si512();
- const IndexType kStart = 0;
-#else
- __m512i sum0 = m512_dpbusd_epi32(input_vector512[0], row0[0]);
- const IndexType kStart = 1;
-#endif
-
- for (IndexType j = kStart; j < kNumChunks512; ++j)
+ for (IndexType k = 0; k < NumOutputRegs; ++k)
{
- const __m512i in = input_vector512[j];
-
-#if defined (USE_VNNI)
- m512_add_dpbusd_epi32(sum0, in, row0[j]);
-#else
- sum0 = _mm512_add_epi32(sum0, m512_dpbusd_epi32(in, row0[j]));
-#endif
+ const IndexType idx = (bigBlock * NumOutputRegs + k);
+ output[idx] = vec_hadd(acc[k], biases[idx]);
}
-
- output[0] = m512_hadd(sum0, biases_[0]);
}
- else
- {
- const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+ }
-#if defined (USE_VNNI)
- __m256i sum0 = _mm256_setzero_si256();
- const IndexType kStart = 0;
+# undef vec_zero
+# undef vec_add_dpbusd_32x2
+# undef vec_hadd
+# undef vec_haddx4
#else
- __m256i sum0 = m256_dpbusd_epi32(input_vector256[0], row0[0]);
- const IndexType kStart = 1;
-#endif
-
- for (IndexType j = kStart; j < kNumChunks256; ++j)
- {
- const __m256i in = input_vector256[j];
+ // Use old implementation for the other architectures.
+ affine_transform_non_ssse3<
+ InputDimensions,
+ PaddedInputDimensions,
+ OutputDimensions>(output, weights, biases, input);
-#if defined (USE_VNNI)
- m256_add_dpbusd_epi32(sum0, in, row0[j]);
-#else
- sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
#endif
- }
- 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);
- }
+ return output;
+ }
-#elif defined (USE_AVX2)
+ private:
+ using BiasType = OutputType;
+ using WeightType = std::int8_t;
- constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+ alignas(CacheLineSize) BiasType biases[OutputDimensions];
+ alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
+ };
- const auto output = reinterpret_cast<OutputType*>(buffer);
- const auto input_vector = reinterpret_cast<const __m256i*>(input);
+ template <IndexType InDims, IndexType OutDims>
+ class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < 2*64)>> {
+ public:
+ // Input/output type
+ // Input/output type
+ using InputType = std::uint8_t;
+ using OutputType = std::int32_t;
- // 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<const __m128i*>(&biases_[i]);
- __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
-
- const auto row0 = reinterpret_cast<const __m256i*>(&weights_[offset0]);
- const auto row1 = reinterpret_cast<const __m256i*>(&weights_[offset1]);
- const auto row2 = reinterpret_cast<const __m256i*>(&weights_[offset2]);
- const auto row3 = reinterpret_cast<const __m256i*>(&weights_[offset3]);
-
-#if defined (USE_VNNI)
- __m256i sum0 = _mm256_setzero_si256();
- __m256i sum1 = _mm256_setzero_si256();
- __m256i sum2 = _mm256_setzero_si256();
- __m256i sum3 = _mm256_setzero_si256();
- const IndexType kStart = 0;
-#else
- __m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
- __m256i sum1 = m256_dpbusd_epi32(input_vector[0], row1[0]);
- __m256i sum2 = m256_dpbusd_epi32(input_vector[0], row2[0]);
- __m256i sum3 = m256_dpbusd_epi32(input_vector[0], row3[0]);
- const IndexType kStart = 1;
-#endif
+ // Number of input/output dimensions
+ static constexpr IndexType InputDimensions = InDims;
+ static constexpr IndexType OutputDimensions = OutDims;
- for (IndexType j = kStart; j < kNumChunks; ++j)
- {
- const __m256i in = input_vector[j];
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
-#if defined (USE_VNNI)
- 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]);
-#else
- sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
- sum1 = _mm256_add_epi32(sum1, m256_dpbusd_epi32(in, row1[j]));
- sum2 = _mm256_add_epi32(sum2, m256_dpbusd_epi32(in, row2[j]));
- sum3 = _mm256_add_epi32(sum3, m256_dpbusd_epi32(in, row3[j]));
-#endif
- }
+ using OutputBuffer = OutputType[PaddedOutputDimensions];
- *outptr = m256_haddx4(sum0, sum1, sum2, sum3, bias);
- }
- }
- else if constexpr (kOutputDimensions == 1)
- {
- const auto row0 = reinterpret_cast<const __m256i*>(&weights_[0]);
+ static_assert(PaddedInputDimensions < 128, "Something went wrong. This specialization should not have been chosen.");
-#if defined (USE_VNNI)
- __m256i sum0 = _mm256_setzero_si256();
- const IndexType kStart = 0;
-#else
- __m256i sum0 = m256_dpbusd_epi32(input_vector[0], row0[0]);
- const IndexType kStart = 1;
+#if defined (USE_SSSE3)
+ static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
+ static constexpr const IndexType InputSimdWidth = SimdWidth;
#endif
- for (IndexType j = kStart; j < kNumChunks; ++j)
- {
- const __m256i in = input_vector[j];
+ // 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;
+ }
+
+ static IndexType get_weight_index_scrambled(IndexType i)
+ {
+ return
+ (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+ i / PaddedInputDimensions * 4 +
+ i % 4;
+ }
-#if defined (USE_VNNI)
- m256_add_dpbusd_epi32(sum0, in, row0[j]);
+ static IndexType get_weight_index(IndexType i)
+ {
+#if defined (USE_SSSE3)
+ return get_weight_index_scrambled(i);
#else
- sum0 = _mm256_add_epi32(sum0, m256_dpbusd_epi32(in, row0[j]));
+ return i;
#endif
- }
-
- 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);
- }
-
-#elif defined (USE_SSSE3)
+ }
- constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ biases[i] = read_little_endian<BiasType>(stream);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
- auto output = reinterpret_cast<OutputType*>(buffer);
- const auto input_vector = reinterpret_cast<const __m128i*>(input);
+ return !stream.fail();
+ }
- // 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;
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ write_little_endian<BiasType>(stream, biases[i]);
- const __m128i bias = *reinterpret_cast<const __m128i*>(&biases_[i]);
- __m128i* outptr = reinterpret_cast<__m128i*>(&output[i]);
+ for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
- const auto row0 = reinterpret_cast<const __m128i*>(&weights_[offset0]);
- const auto row1 = reinterpret_cast<const __m128i*>(&weights_[offset1]);
- const auto row2 = reinterpret_cast<const __m128i*>(&weights_[offset2]);
- const auto row3 = reinterpret_cast<const __m128i*>(&weights_[offset3]);
+ return !stream.fail();
+ }
+ // Forward propagation
+ const OutputType* propagate(
+ const InputType* input, OutputType* output) const {
- __m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
- __m128i sum1 = m128_dpbusd_epi32(input_vector[0], row1[0]);
- __m128i sum2 = m128_dpbusd_epi32(input_vector[0], row2[0]);
- __m128i sum3 = m128_dpbusd_epi32(input_vector[0], row3[0]);
+#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
- for (int j = 1; j < (int)kNumChunks; ++j)
- {
- const __m128i in = input_vector[j];
+#if defined (USE_SSSE3)
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
- sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(in, row0[j]));
- sum1 = _mm_add_epi32(sum1, m128_dpbusd_epi32(in, row1[j]));
- sum2 = _mm_add_epi32(sum2, m128_dpbusd_epi32(in, row2[j]));
- sum3 = _mm_add_epi32(sum3, m128_dpbusd_epi32(in, row3[j]));
- }
+ static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
- *outptr = m128_haddx4(sum0, sum1, sum2, sum3, bias);
- }
- }
- else if constexpr (kOutputDimensions == 1)
+ if constexpr (OutputDimensions % OutputSimdWidth == 0)
{
- const auto row0 = reinterpret_cast<const __m128i*>(&weights_[0]);
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
- __m128i sum0 = m128_dpbusd_epi32(input_vector[0], row0[0]);
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
+ vec_t acc[NumRegs];
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = biasvec[k];
- for (int j = 1; j < (int)kNumChunks; ++j)
- sum0 = _mm_add_epi32(sum0, m128_dpbusd_epi32(input_vector[j], row0[j]));
+ 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<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
+ const auto col1 = reinterpret_cast<const vec_t*>(&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[0] = m128_hadd(sum0, biases_[0]);
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ outptr[k] = acc[k];
}
- else
+ else if constexpr (OutputDimensions == 1)
{
- // 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<OutputType*>(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<const __m128i*>(input);
-
-#elif defined(USE_MMX)
- constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
- const __m64 kZeros = _mm_setzero_si64();
- const auto input_vector = reinterpret_cast<const __m64*>(input);
-
-#elif defined(USE_NEON)
- constexpr IndexType kNumChunks = kPaddedInputDimensions / kSimdWidth;
- const auto input_vector = reinterpret_cast<const int8x8_t*>(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<const __m128i*>(&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 extended_row_lo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
- __m128i extended_row_hi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
- __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<const __m64*>(&weights_[offset]);
- for (IndexType j = 0; j < kNumChunks; ++j) {
- __m64 row_j = row[j];
- __m64 input_j = input_vector[j];
- __m64 extended_row_lo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
- __m64 extended_row_hi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
- __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<const int8x8_t*>(&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];
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ vec_t sum0 = vec_setzero();
+ const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
-#else
- OutputType sum = biases_[i];
- for (IndexType j = 0; j < kInputDimensions; ++j) {
- sum += weights_[offset + j] * input[j];
+ for (int j = 0; j < (int)NumChunks; ++j)
+ {
+ const vec_t in = inputVector[j];
+ vec_add_dpbusd_32(sum0, in, row0[j]);
}
- output[i] = sum;
-#endif
-
+ output[0] = vec_hadd(sum0, biases[0]);
}
-#if defined(USE_MMX)
- _mm_empty();
-#endif
+# 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;
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
projects / stockfish / blobdiff