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Made two specializations for affine transform easier to understand.

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Added AVX-512 for the specialization for small inputs

closes https://github.com/official-stockfish/Stockfish/pull/4502

No functional change
This commit is contained in:
Maxim Masiutin 2023-04-06 14:08:50 +03:00 • committed by Joost VandeVondele
parent a5643b89fd
commit 2f2f45f9f4
1 changed files with 16 additions and 12 deletions
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28
src/nnue/layers/affine_transform.h
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@ -31,7 +31,7 @@
This file contains the definition for a fully connected layer (aka affine transform). This file contains the definition for a fully connected layer (aka affine transform).
Two approaches are employed, depending on the sizes of the transform. Two approaches are employed, depending on the sizes of the transform.
Approach 1: Approach 1 (a specialization for large inputs):
- used when the PaddedInputDimensions >= 128 - used when the PaddedInputDimensions >= 128
- uses AVX512 if possible - uses AVX512 if possible
- processes inputs in batches of 2*InputSimdWidth - processes inputs in batches of 2*InputSimdWidth
@ -42,9 +42,8 @@
depends on the architecture (the amount of registers) depends on the architecture (the amount of registers)
- accumulate + hadd is used - accumulate + hadd is used
Approach 2: Approach 2 (a specialization for small inputs):
- used when the PaddedInputDimensions < 128 - used when the PaddedInputDimensions < 128
- does not use AVX512
- expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32. - expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32.
- that's why AVX512 is hard to implement - that's why AVX512 is hard to implement
- expected use-case is small layers - expected use-case is small layers
@ -169,7 +168,7 @@ namespace Stockfish::Eval::NNUE::Layers {
constexpr IndexType LargeInputSize = std::numeric_limits<IndexType>::max(); constexpr IndexType LargeInputSize = std::numeric_limits<IndexType>::max();
#endif #endif
// A specialization for large inputs. // A specialization for large inputs
template <IndexType InDims, IndexType OutDims> template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= LargeInputSize)>> { class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) >= LargeInputSize)>> {
public: public:
@ -188,7 +187,7 @@ namespace Stockfish::Eval::NNUE::Layers {
using OutputBuffer = OutputType[PaddedOutputDimensions]; using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions >= LargeInputSize, "Something went wrong. This specialization should not have been chosen."); static_assert(PaddedInputDimensions >= LargeInputSize, "Something went wrong. This specialization (for large inputs) should not have been chosen.");
#if defined (USE_AVX512) #if defined (USE_AVX512)
static constexpr IndexType InputSimdWidth = 64; static constexpr IndexType InputSimdWidth = 64;
@ -396,6 +395,7 @@ namespace Stockfish::Eval::NNUE::Layers {
alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions]; alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
}; };
// A specialization for small inputs
template <IndexType InDims, IndexType OutDims> template <IndexType InDims, IndexType OutDims>
class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < LargeInputSize)>> { class AffineTransform<InDims, OutDims, std::enable_if_t<(ceil_to_multiple<IndexType>(InDims, MaxSimdWidth) < LargeInputSize)>> {
public: public:
@ -415,12 +415,7 @@ namespace Stockfish::Eval::NNUE::Layers {
using OutputBuffer = OutputType[PaddedOutputDimensions]; using OutputBuffer = OutputType[PaddedOutputDimensions];
static_assert(PaddedInputDimensions < LargeInputSize, "Something went wrong. This specialization should not have been chosen."); static_assert(PaddedInputDimensions < LargeInputSize, "Something went wrong. This specialization (for small inputs) should not have been chosen.");
#if defined (USE_SSSE3)
static constexpr IndexType OutputSimdWidth = SimdWidth / 4;
static constexpr IndexType InputSimdWidth = SimdWidth;
#endif
// Hash value embedded in the evaluation file // Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) { static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
@ -470,7 +465,14 @@ namespace Stockfish::Eval::NNUE::Layers {
const OutputType* propagate( const OutputType* propagate(
const InputType* input, OutputType* output) const { const InputType* input, OutputType* output) const {
#if defined (USE_AVX2) #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; using vec_t = __m256i;
#define vec_setzero _mm256_setzero_si256 #define vec_setzero _mm256_setzero_si256
#define vec_set_32 _mm256_set1_epi32 #define vec_set_32 _mm256_set1_epi32
@ -489,6 +491,8 @@ namespace Stockfish::Eval::NNUE::Layers {
#if defined (USE_SSSE3) #if defined (USE_SSSE3)
const auto inputVector = reinterpret_cast<const vec_t*>(input); const auto inputVector = reinterpret_cast<const vec_t*>(input);
static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1); static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
if constexpr (OutputDimensions % OutputSimdWidth == 0) if constexpr (OutputDimensions % OutputSimdWidth == 0)