#include "nnue_common.h"
#include "nnue_architecture.h"
-#include "../misc.h"
-
#include <cstring> // std::memset()
namespace Stockfish::Eval::NNUE {
#elif USE_MMX
typedef __m64 vec_t;
- typedef std::int32_t psqt_vec_t;
+ typedef __m64 psqt_vec_t;
#define vec_load(a) (*(a))
#define vec_store(a,b) *(a)=(b)
#define vec_add_16(a,b) _mm_add_pi16(a,b)
#define vec_sub_16(a,b) _mm_sub_pi16(a,b)
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a,b) *(a)=(b)
- #define vec_add_psqt_32(a,b) a+b
- #define vec_sub_psqt_32(a,b) a-b
- #define vec_zero_psqt() 0
+ #define vec_add_psqt_32(a,b) _mm_add_pi32(a,b)
+ #define vec_sub_psqt_32(a,b) _mm_sub_pi32(a,b)
+ #define vec_zero_psqt() _mm_setzero_si64()
static constexpr IndexType NumRegs = 8;
- static constexpr IndexType NumPsqtRegs = 8;
+ static constexpr IndexType NumPsqtRegs = 4;
#elif USE_NEON
typedef int16x8_t vec_t;
// Number of output dimensions for one side
static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
- static constexpr int LazyThreshold = 1400;
-
#ifdef VECTOR
static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
static constexpr IndexType PsqtTileHeight = NumPsqtRegs * sizeof(psqt_vec_t) / 4;
// Read network parameters
bool read_parameters(std::istream& stream) {
- for (std::size_t i = 0; i < HalfDimensions; ++i)
- biases[i] = read_little_endian<BiasType>(stream);
- for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
- weights[i] = read_little_endian<WeightType>(stream);
- for (std::size_t i = 0; i < PSQTBuckets * InputDimensions; ++i)
- psqtWeights[i] = read_little_endian<PSQTWeightType>(stream);
+
+ read_little_endian<BiasType >(stream, biases , HalfDimensions );
+ read_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ read_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
- for (std::size_t i = 0; i < HalfDimensions; ++i)
- write_little_endian<BiasType>(stream, biases[i]);
- for (std::size_t i = 0; i < HalfDimensions * InputDimensions; ++i)
- write_little_endian<WeightType>(stream, weights[i]);
+
+ write_little_endian<BiasType >(stream, biases , HalfDimensions );
+ write_little_endian<WeightType >(stream, weights , HalfDimensions * InputDimensions);
+ write_little_endian<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+
return !stream.fail();
}
// Convert input features
- std::pair<std::int32_t, bool> transform(const Position& pos, OutputType* output, int bucket) const {
+ std::int32_t transform(const Position& pos, OutputType* output, int bucket) const {
update_accumulator(pos, WHITE);
update_accumulator(pos, BLACK);
const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation;
const auto psqt = (
- psqtAccumulation[static_cast<int>(perspectives[0])][bucket]
- - psqtAccumulation[static_cast<int>(perspectives[1])][bucket]
+ psqtAccumulation[perspectives[0]][bucket]
+ - psqtAccumulation[perspectives[1]][bucket]
) / 2;
- if (abs(psqt) > LazyThreshold * OutputScale)
- return { psqt, true };
#if defined(USE_AVX512)
+
constexpr IndexType NumChunks = HalfDimensions / (SimdWidth * 2);
static_assert(HalfDimensions % (SimdWidth * 2) == 0);
const __m512i Control = _mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7);
const __m512i Zero = _mm512_setzero_si512();
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m512i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m512i sum0 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m512i sum1 = _mm512_load_si512(&reinterpret_cast<const __m512i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+
+ _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control,
+ _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero)));
+ }
+ }
+ return psqt;
+
#elif defined(USE_AVX2)
+
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
constexpr int Control = 0b11011000;
const __m256i Zero = _mm256_setzero_si256();
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m256i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m256i sum0 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m256i sum1 = _mm256_load_si256(&reinterpret_cast<const __m256i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+
+ _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(
+ _mm256_max_epi8(_mm256_packs_epi16(sum0, sum1), Zero), Control));
+ }
+ }
+ return psqt;
+
#elif defined(USE_SSE2)
- constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
- #ifdef USE_SSE41
+ #ifdef USE_SSE41
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m128i Zero = _mm_setzero_si128();
- #else
+ #else
+ constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m128i k0x80s = _mm_set1_epi8(-128);
- #endif
+ #endif
+
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m128i*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>
+ (accumulation[perspectives[p]])[j * 2 + 0]);
+ __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>
+ (accumulation[perspectives[p]])[j * 2 + 1]);
+ const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
+
+ #ifdef USE_SSE41
+ _mm_store_si128(&out[j], _mm_max_epi8(packedbytes, Zero));
+ #else
+ _mm_store_si128(&out[j], _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s));
+ #endif
+ }
+ }
+ return psqt;
#elif defined(USE_MMX)
+
constexpr IndexType NumChunks = HalfDimensions / SimdWidth;
const __m64 k0x80s = _mm_set1_pi8(-128);
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ auto out = reinterpret_cast<__m64*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ __m64 sum0 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 0]);
+ __m64 sum1 = *(&reinterpret_cast<const __m64*>(accumulation[perspectives[p]])[j * 2 + 1]);
+ const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
+ out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
+ }
+ }
+ _mm_empty();
+ return psqt;
+
#elif defined(USE_NEON)
+
constexpr IndexType NumChunks = HalfDimensions / (SimdWidth / 2);
const int8x8_t Zero = {0};
- #endif
- for (IndexType p = 0; p < 2; ++p) {
- const IndexType offset = HalfDimensions * p;
-
- #if defined(USE_AVX512)
- auto out = reinterpret_cast<__m512i*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m512i sum0 = _mm512_load_si512(
- &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]])[j * 2 + 0]);
- __m512i sum1 = _mm512_load_si512(
- &reinterpret_cast<const __m512i*>(accumulation[perspectives[p]])[j * 2 + 1]);
- _mm512_store_si512(&out[j], _mm512_permutexvar_epi64(Control,
- _mm512_max_epi8(_mm512_packs_epi16(sum0, sum1), Zero)));
- }
-
- #elif defined(USE_AVX2)
- auto out = reinterpret_cast<__m256i*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m256i sum0 = _mm256_load_si256(
- &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]])[j * 2 + 0]);
- __m256i sum1 = _mm256_load_si256(
- &reinterpret_cast<const __m256i*>(accumulation[perspectives[p]])[j * 2 + 1]);
- _mm256_store_si256(&out[j], _mm256_permute4x64_epi64(_mm256_max_epi8(
- _mm256_packs_epi16(sum0, sum1), Zero), Control));
- }
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j)
+ {
+ int16x8_t sum = reinterpret_cast<const int16x8_t*>(accumulation[perspectives[p]])[j];
+ out[j] = vmax_s8(vqmovn_s16(sum), Zero);
+ }
+ }
+ return psqt;
- #elif defined(USE_SSE2)
- auto out = reinterpret_cast<__m128i*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m128i sum0 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
- accumulation[perspectives[p]])[j * 2 + 0]);
- __m128i sum1 = _mm_load_si128(&reinterpret_cast<const __m128i*>(
- accumulation[perspectives[p]])[j * 2 + 1]);
- const __m128i packedbytes = _mm_packs_epi16(sum0, sum1);
-
- _mm_store_si128(&out[j],
-
- #ifdef USE_SSE41
- _mm_max_epi8(packedbytes, Zero)
#else
- _mm_subs_epi8(_mm_adds_epi8(packedbytes, k0x80s), k0x80s)
- #endif
-
- );
- }
- #elif defined(USE_MMX)
- auto out = reinterpret_cast<__m64*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m64 sum0 = *(&reinterpret_cast<const __m64*>(
- accumulation[perspectives[p]])[j * 2 + 0]);
- __m64 sum1 = *(&reinterpret_cast<const __m64*>(
- accumulation[perspectives[p]])[j * 2 + 1]);
- const __m64 packedbytes = _mm_packs_pi16(sum0, sum1);
- out[j] = _mm_subs_pi8(_mm_adds_pi8(packedbytes, k0x80s), k0x80s);
- }
-
- #elif defined(USE_NEON)
- const auto out = reinterpret_cast<int8x8_t*>(&output[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- int16x8_t sum = reinterpret_cast<const int16x8_t*>(
- accumulation[perspectives[p]])[j];
- out[j] = vmax_s8(vqmovn_s16(sum), Zero);
- }
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = HalfDimensions * p;
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ {
+ BiasType sum = accumulation[perspectives[p]][j];
+ output[offset + j] = static_cast<OutputType>(std::max<int>(0, std::min<int>(127, sum)));
+ }
+ }
+ return psqt;
- #else
- for (IndexType j = 0; j < HalfDimensions; ++j) {
- BiasType sum = accumulation[static_cast<int>(perspectives[p])][j];
- output[offset + j] = static_cast<OutputType>(
- std::max<int>(0, std::min<int>(127, sum)));
- }
#endif
- }
- #if defined(USE_MMX)
- _mm_empty();
- #endif
+ } // end of function transform()
+
- return { psqt, false };
- }
private:
void update_accumulator(const Position& pos, const Color perspective) const {