#include "nnue_common.h"
#include "nnue_architecture.h"
-#include "../misc.h"
-
#include <cstring> // std::memset()
namespace Stockfish::Eval::NNUE {
+ using BiasType = std::int16_t;
+ using WeightType = std::int16_t;
+ using PSQTWeightType = std::int32_t;
+
// If vector instructions are enabled, we update and refresh the
// accumulator tile by tile such that each tile fits in the CPU's
// vector registers.
#define VECTOR
- static_assert(PSQTBuckets == 8, "Assumed by the current choice of constants.");
+ static_assert(PSQTBuckets % 8 == 0,
+ "Per feature PSQT values cannot be processed at granularity lower than 8 at a time.");
#ifdef USE_AVX512
typedef __m512i vec_t;
#define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
#define vec_zero_psqt() _mm256_setzero_si256()
- static constexpr IndexType NumRegs = 8; // only 8 are needed
- static constexpr IndexType NumPsqtRegs = 1;
+ #define NumRegistersSIMD 32
#elif USE_AVX2
typedef __m256i vec_t;
#define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
#define vec_zero_psqt() _mm256_setzero_si256()
- static constexpr IndexType NumRegs = 16;
- static constexpr IndexType NumPsqtRegs = 1;
+ #define NumRegistersSIMD 16
#elif USE_SSE2
typedef __m128i vec_t;
#define vec_add_psqt_32(a,b) _mm_add_epi32(a,b)
#define vec_sub_psqt_32(a,b) _mm_sub_epi32(a,b)
#define vec_zero_psqt() _mm_setzero_si128()
- static constexpr IndexType NumRegs = Is64Bit ? 16 : 8;
- static constexpr IndexType NumPsqtRegs = 2;
+ #define NumRegistersSIMD (Is64Bit ? 16 : 8)
#elif USE_MMX
typedef __m64 vec_t;
#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 = 4;
+ #define NumRegistersSIMD 8
#elif USE_NEON
typedef int16x8_t vec_t;
#define vec_add_psqt_32(a,b) vaddq_s32(a,b)
#define vec_sub_psqt_32(a,b) vsubq_s32(a,b)
#define vec_zero_psqt() psqt_vec_t{0}
- static constexpr IndexType NumRegs = 16;
- static constexpr IndexType NumPsqtRegs = 2;
+ #define NumRegistersSIMD 16
#else
#undef VECTOR
#endif
+
+ #ifdef VECTOR
+
+ // Compute optimal SIMD register count for feature transformer accumulation.
+
+ // We use __m* types as template arguments, which causes GCC to emit warnings
+ // about losing some attribute information. This is irrelevant to us as we
+ // only take their size, so the following pragma are harmless.
+ #pragma GCC diagnostic push
+ #pragma GCC diagnostic ignored "-Wignored-attributes"
+
+ template <typename SIMDRegisterType,
+ typename LaneType,
+ int NumLanes,
+ int MaxRegisters>
+ static constexpr int BestRegisterCount()
+ {
+ #define RegisterSize sizeof(SIMDRegisterType)
+ #define LaneSize sizeof(LaneType)
+
+ static_assert(RegisterSize >= LaneSize);
+ static_assert(MaxRegisters <= NumRegistersSIMD);
+ static_assert(MaxRegisters > 0);
+ static_assert(NumRegistersSIMD > 0);
+ static_assert(RegisterSize % LaneSize == 0);
+ static_assert((NumLanes * LaneSize) % RegisterSize == 0);
+
+ const int ideal = (NumLanes * LaneSize) / RegisterSize;
+ if (ideal <= MaxRegisters)
+ return ideal;
+
+ // Look for the largest divisor of the ideal register count that is smaller than MaxRegisters
+ for (int divisor = MaxRegisters; divisor > 1; --divisor)
+ if (ideal % divisor == 0)
+ return divisor;
+
+ return 1;
+ }
+
+ static constexpr int NumRegs = BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
+ static constexpr int NumPsqtRegs = BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
+
+ #pragma GCC diagnostic pop
+
+ #endif
+
+
+
// Input feature converter
class FeatureTransformer {
// 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]);
- for (std::size_t i = 0; i < PSQTBuckets * InputDimensions; ++i)
- write_little_endian<PSQTWeightType>(stream, psqtWeights[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();
}
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 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;
- }
private:
void update_accumulator(const Position& pos, const Color perspective) const {
// That might depend on the feature set and generally relies on the
// feature set's update cost calculation to be correct and never
// allow updates with more added/removed features than MaxActiveDimensions.
- using IndexList = ValueList<IndexType, FeatureSet::MaxActiveDimensions>;
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
// of the estimated gain in terms of features to be added/subtracted.
StateInfo *st = pos.state(), *next = nullptr;
int gain = FeatureSet::refresh_cost(pos);
- while (st->accumulator.state[perspective] == EMPTY)
+ while (st->previous && !st->accumulator.computed[perspective])
{
// This governs when a full feature refresh is needed and how many
// updates are better than just one full refresh.
st = st->previous;
}
- if (st->accumulator.state[perspective] == COMPUTED)
+ if (st->accumulator.computed[perspective])
{
if (next == nullptr)
return;
// Gather all features to be updated.
const Square ksq = pos.square<KING>(perspective);
- IndexList removed[2], added[2];
+ FeatureSet::IndexList removed[2], added[2];
FeatureSet::append_changed_indices(
- ksq, next, perspective, removed[0], added[0]);
+ ksq, next->dirtyPiece, perspective, removed[0], added[0]);
for (StateInfo *st2 = pos.state(); st2 != next; st2 = st2->previous)
FeatureSet::append_changed_indices(
- ksq, st2, perspective, removed[1], added[1]);
+ ksq, st2->dirtyPiece, perspective, removed[1], added[1]);
// Mark the accumulators as computed.
- next->accumulator.state[perspective] = COMPUTED;
- pos.state()->accumulator.state[perspective] = COMPUTED;
+ next->accumulator.computed[perspective] = true;
+ pos.state()->accumulator.computed[perspective] = true;
// Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
StateInfo *states_to_update[3] =
{
// Refresh the accumulator
auto& accumulator = pos.state()->accumulator;
- accumulator.state[perspective] = COMPUTED;
- IndexList active;
+ accumulator.computed[perspective] = true;
+ FeatureSet::IndexList active;
FeatureSet::append_active_indices(pos, perspective, active);
#ifdef VECTOR
#endif
}
- using BiasType = std::int16_t;
- using WeightType = std::int16_t;
- using PSQTWeightType = std::int32_t;
-
alignas(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
projects / stockfish / blobdiff