/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
+ Copyright (C) 2004-2024 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 vec_store(a, b) _mm512_store_si512(a, b)
#define vec_add_16(a, b) _mm512_add_epi16(a, b)
#define vec_sub_16(a, b) _mm512_sub_epi16(a, b)
- #define vec_mul_16(a, b) _mm512_mullo_epi16(a, b)
+ #define vec_mulhi_16(a, b) _mm512_mulhi_epi16(a, b)
#define vec_zero() _mm512_setzero_epi32()
#define vec_set_16(a) _mm512_set1_epi16(a)
#define vec_max_16(a, b) _mm512_max_epi16(a, b)
#define vec_min_16(a, b) _mm512_min_epi16(a, b)
-inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
- vec_t compacted = _mm512_packs_epi16(_mm512_srli_epi16(a, 7), _mm512_srli_epi16(b, 7));
- return _mm512_permutexvar_epi64(_mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7), compacted);
-}
+ #define vec_slli_16(a, b) _mm512_slli_epi16(a, b)
+ // Inverse permuted at load time
+ #define vec_packus_16(a, b) _mm512_packus_epi16(a, b)
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a, b) _mm256_store_si256(a, b)
#define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
#define vec_store(a, b) _mm256_store_si256(a, b)
#define vec_add_16(a, b) _mm256_add_epi16(a, b)
#define vec_sub_16(a, b) _mm256_sub_epi16(a, b)
- #define vec_mul_16(a, b) _mm256_mullo_epi16(a, b)
+ #define vec_mulhi_16(a, b) _mm256_mulhi_epi16(a, b)
#define vec_zero() _mm256_setzero_si256()
#define vec_set_16(a) _mm256_set1_epi16(a)
#define vec_max_16(a, b) _mm256_max_epi16(a, b)
#define vec_min_16(a, b) _mm256_min_epi16(a, b)
-inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
- vec_t compacted = _mm256_packs_epi16(_mm256_srli_epi16(a, 7), _mm256_srli_epi16(b, 7));
- return _mm256_permute4x64_epi64(compacted, 0b11011000);
-}
+ #define vec_slli_16(a, b) _mm256_slli_epi16(a, b)
+ // Inverse permuted at load time
+ #define vec_packus_16(a, b) _mm256_packus_epi16(a, b)
#define vec_load_psqt(a) _mm256_load_si256(a)
#define vec_store_psqt(a, b) _mm256_store_si256(a, b)
#define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
#define vec_store(a, b) *(a) = (b)
#define vec_add_16(a, b) _mm_add_epi16(a, b)
#define vec_sub_16(a, b) _mm_sub_epi16(a, b)
- #define vec_mul_16(a, b) _mm_mullo_epi16(a, b)
+ #define vec_mulhi_16(a, b) _mm_mulhi_epi16(a, b)
#define vec_zero() _mm_setzero_si128()
#define vec_set_16(a) _mm_set1_epi16(a)
#define vec_max_16(a, b) _mm_max_epi16(a, b)
#define vec_min_16(a, b) _mm_min_epi16(a, b)
- #define vec_msb_pack_16(a, b) _mm_packs_epi16(_mm_srli_epi16(a, 7), _mm_srli_epi16(b, 7))
+ #define vec_slli_16(a, b) _mm_slli_epi16(a, b)
+ #define vec_packus_16(a, b) _mm_packus_epi16(a, b)
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a, b) *(a) = (b)
#define vec_add_psqt_32(a, b) _mm_add_epi32(a, b)
#define vec_store(a, b) *(a) = (b)
#define vec_add_16(a, b) vaddq_s16(a, b)
#define vec_sub_16(a, b) vsubq_s16(a, b)
- #define vec_mul_16(a, b) vmulq_s16(a, b)
+ #define vec_mulhi_16(a, b) vqdmulhq_s16(a, b)
#define vec_zero() \
vec_t { 0 }
#define vec_set_16(a) vdupq_n_s16(a)
#define vec_max_16(a, b) vmaxq_s16(a, b)
#define vec_min_16(a, b) vminq_s16(a, b)
-inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
- const int8x8_t shifta = vshrn_n_s16(a, 7);
- const int8x8_t shiftb = vshrn_n_s16(b, 7);
- const int8x16_t compacted = vcombine_s8(shifta, shiftb);
- return *reinterpret_cast<const vec_t*>(&compacted);
-}
+ #define vec_slli_16(a, b) vshlq_s16(a, vec_set_16(b))
+ #define vec_packus_16(a, b) reinterpret_cast<vec_t>(vcombine_u8(vqmovun_s16(a), vqmovun_s16(b)))
#define vec_load_psqt(a) (*(a))
#define vec_store_psqt(a, b) *(a) = (b)
#define vec_add_psqt_32(a, b) vaddq_s32(a, b)
return 1;
}
-
-static constexpr int NumRegs =
- BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
-static constexpr int NumPsqtRegs =
- BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
#if defined(__GNUC__)
#pragma GCC diagnostic pop
#endif
// Input feature converter
+template<IndexType TransformedFeatureDimensions,
+ Accumulator<TransformedFeatureDimensions> StateInfo::*accPtr>
class FeatureTransformer {
- private:
// Number of output dimensions for one side
static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
+ private:
#ifdef VECTOR
+ static constexpr int NumRegs =
+ BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
+ static constexpr int NumPsqtRegs =
+ BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
+
static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
static constexpr IndexType PsqtTileHeight = NumPsqtRegs * sizeof(psqt_vec_t) / 4;
static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions");
return FeatureSet::HashValue ^ (OutputDimensions * 2);
}
+ static constexpr void order_packs([[maybe_unused]] uint64_t* v) {
+#if defined(USE_AVX512) // _mm512_packs_epi16 ordering
+ uint64_t tmp0 = v[2], tmp1 = v[3];
+ v[2] = v[8], v[3] = v[9];
+ v[8] = v[4], v[9] = v[5];
+ v[4] = tmp0, v[5] = tmp1;
+ tmp0 = v[6], tmp1 = v[7];
+ v[6] = v[10], v[7] = v[11];
+ v[10] = v[12], v[11] = v[13];
+ v[12] = tmp0, v[13] = tmp1;
+#elif defined(USE_AVX2) // _mm256_packs_epi16 ordering
+ std::swap(v[2], v[4]);
+ std::swap(v[3], v[5]);
+#endif
+ }
+
+ static constexpr void inverse_order_packs([[maybe_unused]] uint64_t* v) {
+#if defined(USE_AVX512) // Inverse _mm512_packs_epi16 ordering
+ uint64_t tmp0 = v[2], tmp1 = v[3];
+ v[2] = v[4], v[3] = v[5];
+ v[4] = v[8], v[5] = v[9];
+ v[8] = tmp0, v[9] = tmp1;
+ tmp0 = v[6], tmp1 = v[7];
+ v[6] = v[12], v[7] = v[13];
+ v[12] = v[10], v[13] = v[11];
+ v[10] = tmp0, v[11] = tmp1;
+#elif defined(USE_AVX2) // Inverse _mm256_packs_epi16 ordering
+ std::swap(v[2], v[4]);
+ std::swap(v[3], v[5]);
+#endif
+ }
+
+ void permute_weights([[maybe_unused]] void (*order_fn)(uint64_t*)) const {
+#if defined(USE_AVX2)
+ #if defined(USE_AVX512)
+ constexpr IndexType di = 16;
+ #else
+ constexpr IndexType di = 8;
+ #endif
+ uint64_t* b = reinterpret_cast<uint64_t*>(const_cast<BiasType*>(&biases[0]));
+ for (IndexType i = 0; i < HalfDimensions * sizeof(BiasType) / sizeof(uint64_t); i += di)
+ order_fn(&b[i]);
+
+ for (IndexType j = 0; j < InputDimensions; ++j)
+ {
+ uint64_t* w =
+ reinterpret_cast<uint64_t*>(const_cast<WeightType*>(&weights[j * HalfDimensions]));
+ for (IndexType i = 0; i < HalfDimensions * sizeof(WeightType) / sizeof(uint64_t);
+ i += di)
+ order_fn(&w[i]);
+ }
+#endif
+ }
+
+ inline void scale_weights(bool read) const {
+ for (IndexType j = 0; j < InputDimensions; ++j)
+ {
+ WeightType* w = const_cast<WeightType*>(&weights[j * HalfDimensions]);
+ for (IndexType i = 0; i < HalfDimensions; ++i)
+ w[i] = read ? w[i] * 2 : w[i] / 2;
+ }
+
+ BiasType* b = const_cast<BiasType*>(biases);
+ for (IndexType i = 0; i < HalfDimensions; ++i)
+ b[i] = read ? b[i] * 2 : b[i] / 2;
+ }
+
// Read network parameters
bool read_parameters(std::istream& stream) {
read_leb_128<WeightType>(stream, weights, HalfDimensions * InputDimensions);
read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+ permute_weights(inverse_order_packs);
+ scale_weights(true);
return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
+ permute_weights(order_packs);
+ scale_weights(false);
+
write_leb_128<BiasType>(stream, biases, HalfDimensions);
write_leb_128<WeightType>(stream, weights, HalfDimensions * InputDimensions);
write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+ permute_weights(inverse_order_packs);
+ scale_weights(true);
return !stream.fail();
}
// Convert input features
- std::int32_t transform(const Position& pos, OutputType* output, int bucket) const {
- update_accumulator<WHITE>(pos);
- update_accumulator<BLACK>(pos);
+ std::int32_t transform(const Position& pos,
+ AccumulatorCaches::Cache<HalfDimensions>* cache,
+ OutputType* output,
+ int bucket) const {
+ update_accumulator<WHITE>(pos, cache);
+ update_accumulator<BLACK>(pos, cache);
const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
- const auto& accumulation = pos.state()->accumulator.accumulation;
- const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation;
-
- const auto psqt =
+ const auto& psqtAccumulation = (pos.state()->*accPtr).psqtAccumulation;
+ const auto psqt =
(psqtAccumulation[perspectives[0]][bucket] - psqtAccumulation[perspectives[1]][bucket])
/ 2;
+ const auto& accumulation = (pos.state()->*accPtr).accumulation;
for (IndexType p = 0; p < 2; ++p)
{
static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
- vec_t Zero = vec_zero();
- vec_t One = vec_set_16(127);
+ const vec_t Zero = vec_zero();
+ const vec_t One = vec_set_16(127 * 2);
const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
const vec_t* in1 =
reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][HalfDimensions / 2]));
vec_t* out = reinterpret_cast<vec_t*>(output + offset);
- for (IndexType j = 0; j < NumOutputChunks; j += 1)
+ // Per the NNUE architecture, here we want to multiply pairs of
+ // clipped elements and divide the product by 128. To do this,
+ // we can naively perform min/max operation to clip each of the
+ // four int16 vectors, mullo pairs together, then pack them into
+ // one int8 vector. However, there exists a faster way.
+
+ // The idea here is to use the implicit clipping from packus to
+ // save us two vec_max_16 instructions. This clipping works due
+ // to the fact that any int16 integer below zero will be zeroed
+ // on packus.
+
+ // Consider the case where the second element is negative.
+ // If we do standard clipping, that element will be zero, which
+ // means our pairwise product is zero. If we perform packus and
+ // remove the lower-side clip for the second element, then our
+ // product before packus will be negative, and is zeroed on pack.
+ // The two operation produce equivalent results, but the second
+ // one (using packus) saves one max operation per pair.
+
+ // But here we run into a problem: mullo does not preserve the
+ // sign of the multiplication. We can get around this by doing
+ // mulhi, which keeps the sign. But that requires an additional
+ // tweak.
+
+ // mulhi cuts off the last 16 bits of the resulting product,
+ // which is the same as performing a rightward shift of 16 bits.
+ // We can use this to our advantage. Recall that we want to
+ // divide the final product by 128, which is equivalent to a
+ // 7-bit right shift. Intuitively, if we shift the clipped
+ // value left by 9, and perform mulhi, which shifts the product
+ // right by 16 bits, then we will net a right shift of 7 bits.
+ // However, this won't work as intended. Since we clip the
+ // values to have a maximum value of 127, shifting it by 9 bits
+ // might occupy the signed bit, resulting in some positive
+ // values being interpreted as negative after the shift.
+
+ // There is a way, however, to get around this limitation. When
+ // loading the network, scale accumulator weights and biases by
+ // 2. To get the same pairwise multiplication result as before,
+ // we need to divide the product by 128 * 2 * 2 = 512, which
+ // amounts to a right shift of 9 bits. So now we only have to
+ // shift left by 7 bits, perform mulhi (shifts right by 16 bits)
+ // and net a 9 bit right shift. Since we scaled everything by
+ // two, the values are clipped at 127 * 2 = 254, which occupies
+ // 8 bits. Shifting it by 7 bits left will no longer occupy the
+ // signed bit, so we are safe.
+
+ // Note that on NEON processors, we shift left by 6 instead
+ // because the instruction "vqdmulhq_s16" also doubles the
+ // return value after the multiplication, adding an extra shift
+ // to the left by 1, so we compensate by shifting less before
+ // the multiplication.
+
+ constexpr int shift =
+ #if defined(USE_SSE2)
+ 7;
+ #else
+ 6;
+ #endif
+
+ for (IndexType j = 0; j < NumOutputChunks; ++j)
{
- const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero);
- const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero);
- const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero);
- const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero);
+ const vec_t sum0a =
+ vec_slli_16(vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero), shift);
+ const vec_t sum0b =
+ vec_slli_16(vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero), shift);
+ const vec_t sum1a = vec_min_16(in1[j * 2 + 0], One);
+ const vec_t sum1b = vec_min_16(in1[j * 2 + 1], One);
- const vec_t pa = vec_mul_16(sum0a, sum1a);
- const vec_t pb = vec_mul_16(sum0b, sum1b);
+ const vec_t pa = vec_mulhi_16(sum0a, sum1a);
+ const vec_t pb = vec_mulhi_16(sum0b, sum1b);
- out[j] = vec_msb_pack_16(pa, pb);
+ out[j] = vec_packus_16(pa, pb);
}
#else
BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
BiasType sum1 =
accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
- sum0 = std::clamp<BiasType>(sum0, 0, 127);
- sum1 = std::clamp<BiasType>(sum1, 0, 127);
- output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 128);
+ sum0 = std::clamp<BiasType>(sum0, 0, 127 * 2);
+ sum1 = std::clamp<BiasType>(sum1, 0, 127 * 2);
+ output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 512);
}
#endif
return psqt;
} // end of function transform()
- void hint_common_access(const Position& pos) const {
- hint_common_access_for_perspective<WHITE>(pos);
- hint_common_access_for_perspective<BLACK>(pos);
+ void hint_common_access(const Position& pos,
+ AccumulatorCaches::Cache<HalfDimensions>* cache) const {
+ hint_common_access_for_perspective<WHITE>(pos, cache);
+ hint_common_access_for_perspective<BLACK>(pos, cache);
}
private:
template<Color Perspective>
- [[nodiscard]] std::pair<StateInfo*, StateInfo*>
- try_find_computed_accumulator(const Position& pos) const {
+ StateInfo* try_find_computed_accumulator(const Position& pos) const {
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
- StateInfo *st = pos.state(), *next = nullptr;
+ StateInfo* st = pos.state();
int gain = FeatureSet::refresh_cost(pos);
- while (st->previous && !st->accumulator.computed[Perspective])
+ while (st->previous && !(st->*accPtr).computed[Perspective])
{
// This governs when a full feature refresh is needed and how many
// updates are better than just one full refresh.
if (FeatureSet::requires_refresh(st, Perspective)
|| (gain -= FeatureSet::update_cost(st) + 1) < 0)
break;
- next = st;
- st = st->previous;
+ st = st->previous;
}
- return {st, next};
+ return st;
}
- // NOTE: The parameter states_to_update is an array of position states, ending with nullptr.
- // All states must be sequential, that is states_to_update[i] must either be reachable
- // by repeatedly applying ->previous from states_to_update[i+1] or
- // states_to_update[i] == nullptr.
- // computed_st must be reachable by repeatedly applying ->previous on
- // states_to_update[0], if not nullptr.
- template<Color Perspective, size_t N>
- void update_accumulator_incremental(const Position& pos,
- StateInfo* computed_st,
- StateInfo* states_to_update[N]) const {
- static_assert(N > 0);
- assert(states_to_update[N - 1] == nullptr);
+ // It computes the accumulator of the next position, or updates the
+ // current position's accumulator if CurrentOnly is true.
+ template<Color Perspective, bool CurrentOnly>
+ void update_accumulator_incremental(const Position& pos, StateInfo* computed) const {
+ assert((computed->*accPtr).computed[Perspective]);
+ assert(computed->next != nullptr);
#ifdef VECTOR
// Gcc-10.2 unnecessarily spills AVX2 registers if this array
- // is defined in the VECTOR code below, once in each branch
+ // is defined in the VECTOR code below, once in each branch.
vec_t acc[NumRegs];
psqt_vec_t psqt[NumPsqtRegs];
#endif
- if (states_to_update[0] == nullptr)
- return;
-
- // Update incrementally going back through states_to_update.
-
- // Gather all features to be updated.
const Square ksq = pos.square<KING>(Perspective);
// The size must be enough to contain the largest possible update.
// 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.
- FeatureSet::IndexList removed[N - 1], added[N - 1];
-
- {
- int i =
- N
- - 2; // last potential state to update. Skip last element because it must be nullptr.
- while (states_to_update[i] == nullptr)
- --i;
-
- StateInfo* st2 = states_to_update[i];
-
- for (; i >= 0; --i)
- {
- states_to_update[i]->accumulator.computed[Perspective] = true;
-
- const StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
-
- for (; st2 != end_state; st2 = st2->previous)
- FeatureSet::append_changed_indices<Perspective>(ksq, st2->dirtyPiece,
- removed[i], added[i]);
- }
- }
+ // feature set's update cost calculation to be correct and never allow
+ // updates with more added/removed features than MaxActiveDimensions.
+ FeatureSet::IndexList removed, added;
+
+ if constexpr (CurrentOnly)
+ for (StateInfo* st = pos.state(); st != computed; st = st->previous)
+ FeatureSet::append_changed_indices<Perspective>(ksq, st->dirtyPiece, removed,
+ added);
+ else
+ FeatureSet::append_changed_indices<Perspective>(ksq, computed->next->dirtyPiece,
+ removed, added);
- StateInfo* st = computed_st;
+ StateInfo* next = CurrentOnly ? pos.state() : computed->next;
+ assert(!(next->*accPtr).computed[Perspective]);
- // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
#ifdef VECTOR
-
- if (states_to_update[1] == nullptr && (removed[0].size() == 1 || removed[0].size() == 2)
- && added[0].size() == 1)
+ if ((removed.size() == 1 || removed.size() == 2) && added.size() == 1)
{
- assert(states_to_update[0]);
-
auto accIn =
- reinterpret_cast<const vec_t*>(&st->accumulator.accumulation[Perspective][0]);
- auto accOut = reinterpret_cast<vec_t*>(
- &states_to_update[0]->accumulator.accumulation[Perspective][0]);
+ reinterpret_cast<const vec_t*>(&(computed->*accPtr).accumulation[Perspective][0]);
+ auto accOut = reinterpret_cast<vec_t*>(&(next->*accPtr).accumulation[Perspective][0]);
- const IndexType offsetR0 = HalfDimensions * removed[0][0];
+ const IndexType offsetR0 = HalfDimensions * removed[0];
auto columnR0 = reinterpret_cast<const vec_t*>(&weights[offsetR0]);
- const IndexType offsetA = HalfDimensions * added[0][0];
+ const IndexType offsetA = HalfDimensions * added[0];
auto columnA = reinterpret_cast<const vec_t*>(&weights[offsetA]);
- if (removed[0].size() == 1)
+ if (removed.size() == 1)
{
- for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
- ++k)
- accOut[k] = vec_add_16(vec_sub_16(accIn[k], columnR0[k]), columnA[k]);
+ for (IndexType i = 0; i < HalfDimensions * sizeof(WeightType) / sizeof(vec_t); ++i)
+ accOut[i] = vec_add_16(vec_sub_16(accIn[i], columnR0[i]), columnA[i]);
}
else
{
- const IndexType offsetR1 = HalfDimensions * removed[0][1];
+ const IndexType offsetR1 = HalfDimensions * removed[1];
auto columnR1 = reinterpret_cast<const vec_t*>(&weights[offsetR1]);
- for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
- ++k)
- accOut[k] = vec_sub_16(vec_add_16(accIn[k], columnA[k]),
- vec_add_16(columnR0[k], columnR1[k]));
+ for (IndexType i = 0; i < HalfDimensions * sizeof(WeightType) / sizeof(vec_t); ++i)
+ accOut[i] = vec_sub_16(vec_add_16(accIn[i], columnA[i]),
+ vec_add_16(columnR0[i], columnR1[i]));
}
auto accPsqtIn = reinterpret_cast<const psqt_vec_t*>(
- &st->accumulator.psqtAccumulation[Perspective][0]);
- auto accPsqtOut = reinterpret_cast<psqt_vec_t*>(
- &states_to_update[0]->accumulator.psqtAccumulation[Perspective][0]);
+ &(computed->*accPtr).psqtAccumulation[Perspective][0]);
+ auto accPsqtOut =
+ reinterpret_cast<psqt_vec_t*>(&(next->*accPtr).psqtAccumulation[Perspective][0]);
- const IndexType offsetPsqtR0 = PSQTBuckets * removed[0][0];
+ const IndexType offsetPsqtR0 = PSQTBuckets * removed[0];
auto columnPsqtR0 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtR0]);
- const IndexType offsetPsqtA = PSQTBuckets * added[0][0];
+ const IndexType offsetPsqtA = PSQTBuckets * added[0];
auto columnPsqtA = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtA]);
- if (removed[0].size() == 1)
+ if (removed.size() == 1)
{
- for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
- ++k)
- accPsqtOut[k] = vec_add_psqt_32(vec_sub_psqt_32(accPsqtIn[k], columnPsqtR0[k]),
- columnPsqtA[k]);
+ for (std::size_t i = 0;
+ i < PSQTBuckets * sizeof(PSQTWeightType) / sizeof(psqt_vec_t); ++i)
+ accPsqtOut[i] = vec_add_psqt_32(vec_sub_psqt_32(accPsqtIn[i], columnPsqtR0[i]),
+ columnPsqtA[i]);
}
else
{
- const IndexType offsetPsqtR1 = PSQTBuckets * removed[0][1];
+ const IndexType offsetPsqtR1 = PSQTBuckets * removed[1];
auto columnPsqtR1 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtR1]);
- for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
- ++k)
- accPsqtOut[k] =
- vec_sub_psqt_32(vec_add_psqt_32(accPsqtIn[k], columnPsqtA[k]),
- vec_add_psqt_32(columnPsqtR0[k], columnPsqtR1[k]));
+ for (std::size_t i = 0;
+ i < PSQTBuckets * sizeof(PSQTWeightType) / sizeof(psqt_vec_t); ++i)
+ accPsqtOut[i] =
+ vec_sub_psqt_32(vec_add_psqt_32(accPsqtIn[i], columnPsqtA[i]),
+ vec_add_psqt_32(columnPsqtR0[i], columnPsqtR1[i]));
}
}
else
{
- for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
+ for (IndexType i = 0; i < HalfDimensions / TileHeight; ++i)
{
// Load accumulator
auto accTileIn = reinterpret_cast<const vec_t*>(
- &st->accumulator.accumulation[Perspective][j * TileHeight]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_load(&accTileIn[k]);
+ &(computed->*accPtr).accumulation[Perspective][i * TileHeight]);
+ for (IndexType j = 0; j < NumRegs; ++j)
+ acc[j] = vec_load(&accTileIn[j]);
+
+ // Difference calculation for the deactivated features
+ for (const auto index : removed)
+ {
+ const IndexType offset = HalfDimensions * index + i * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumRegs; ++j)
+ acc[j] = vec_sub_16(acc[j], column[j]);
+ }
- for (IndexType i = 0; states_to_update[i]; ++i)
+ // Difference calculation for the activated features
+ for (const auto index : added)
{
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = HalfDimensions * index + j * TileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_sub_16(acc[k], column[k]);
- }
-
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = HalfDimensions * index + j * TileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_add_16(acc[k], column[k]);
- }
-
- // Store accumulator
- auto accTileOut = reinterpret_cast<vec_t*>(
- &states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
- for (IndexType k = 0; k < NumRegs; ++k)
- vec_store(&accTileOut[k], acc[k]);
+ const IndexType offset = HalfDimensions * index + i * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumRegs; ++j)
+ acc[j] = vec_add_16(acc[j], column[j]);
}
+
+ // Store accumulator
+ auto accTileOut = reinterpret_cast<vec_t*>(
+ &(next->*accPtr).accumulation[Perspective][i * TileHeight]);
+ for (IndexType j = 0; j < NumRegs; ++j)
+ vec_store(&accTileOut[j], acc[j]);
}
- for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
+ for (IndexType i = 0; i < PSQTBuckets / PsqtTileHeight; ++i)
{
// Load accumulator
auto accTilePsqtIn = reinterpret_cast<const psqt_vec_t*>(
- &st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_load_psqt(&accTilePsqtIn[k]);
+ &(computed->*accPtr).psqtAccumulation[Perspective][i * PsqtTileHeight]);
+ for (std::size_t j = 0; j < NumPsqtRegs; ++j)
+ psqt[j] = vec_load_psqt(&accTilePsqtIn[j]);
- for (IndexType i = 0; states_to_update[i]; ++i)
+ // Difference calculation for the deactivated features
+ for (const auto index : removed)
{
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
- auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
- }
-
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
- auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
- }
-
- // Store accumulator
- auto accTilePsqtOut = reinterpret_cast<psqt_vec_t*>(
- &states_to_update[i]
- ->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- vec_store_psqt(&accTilePsqtOut[k], psqt[k]);
+ const IndexType offset = PSQTBuckets * index + i * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+ for (std::size_t j = 0; j < NumPsqtRegs; ++j)
+ psqt[j] = vec_sub_psqt_32(psqt[j], columnPsqt[j]);
}
+
+ // Difference calculation for the activated features
+ for (const auto index : added)
+ {
+ const IndexType offset = PSQTBuckets * index + i * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+ for (std::size_t j = 0; j < NumPsqtRegs; ++j)
+ psqt[j] = vec_add_psqt_32(psqt[j], columnPsqt[j]);
+ }
+
+ // Store accumulator
+ auto accTilePsqtOut = reinterpret_cast<psqt_vec_t*>(
+ &(next->*accPtr).psqtAccumulation[Perspective][i * PsqtTileHeight]);
+ for (std::size_t j = 0; j < NumPsqtRegs; ++j)
+ vec_store_psqt(&accTilePsqtOut[j], psqt[j]);
}
}
#else
- for (IndexType i = 0; states_to_update[i]; ++i)
+ std::memcpy((next->*accPtr).accumulation[Perspective],
+ (computed->*accPtr).accumulation[Perspective],
+ HalfDimensions * sizeof(BiasType));
+ std::memcpy((next->*accPtr).psqtAccumulation[Perspective],
+ (computed->*accPtr).psqtAccumulation[Perspective],
+ PSQTBuckets * sizeof(PSQTWeightType));
+
+ // Difference calculation for the deactivated features
+ for (const auto index : removed)
{
- std::memcpy(states_to_update[i]->accumulator.accumulation[Perspective],
- st->accumulator.accumulation[Perspective],
- HalfDimensions * sizeof(BiasType));
+ const IndexType offset = HalfDimensions * index;
+ for (IndexType i = 0; i < HalfDimensions; ++i)
+ (next->*accPtr).accumulation[Perspective][i] -= weights[offset + i];
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] =
- st->accumulator.psqtAccumulation[Perspective][k];
+ for (std::size_t i = 0; i < PSQTBuckets; ++i)
+ (next->*accPtr).psqtAccumulation[Perspective][i] -=
+ psqtWeights[index * PSQTBuckets + i];
+ }
- st = states_to_update[i];
+ // Difference calculation for the activated features
+ for (const auto index : added)
+ {
+ const IndexType offset = HalfDimensions * index;
+ for (IndexType i = 0; i < HalfDimensions; ++i)
+ (next->*accPtr).accumulation[Perspective][i] += weights[offset + i];
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = HalfDimensions * index;
+ for (std::size_t i = 0; i < PSQTBuckets; ++i)
+ (next->*accPtr).psqtAccumulation[Perspective][i] +=
+ psqtWeights[index * PSQTBuckets + i];
+ }
+#endif
- for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
+ (next->*accPtr).computed[Perspective] = true;
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- st->accumulator.psqtAccumulation[Perspective][k] -=
- psqtWeights[index * PSQTBuckets + k];
- }
+ if (!CurrentOnly && next != pos.state())
+ update_accumulator_incremental<Perspective, false>(pos, next);
+ }
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = HalfDimensions * index;
+ template<Color Perspective>
+ void update_accumulator_refresh_cache(const Position& pos,
+ AccumulatorCaches::Cache<HalfDimensions>* cache) const {
+ assert(cache != nullptr);
- for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[Perspective][j] += weights[offset + j];
+ Square ksq = pos.square<KING>(Perspective);
+ auto& entry = (*cache)[ksq][Perspective];
+ FeatureSet::IndexList removed, added;
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- st->accumulator.psqtAccumulation[Perspective][k] +=
- psqtWeights[index * PSQTBuckets + k];
+ for (Color c : {WHITE, BLACK})
+ {
+ for (PieceType pt = PAWN; pt <= KING; ++pt)
+ {
+ const Piece piece = make_piece(c, pt);
+ const Bitboard oldBB = entry.byColorBB[c] & entry.byTypeBB[pt];
+ const Bitboard newBB = pos.pieces(c, pt);
+ Bitboard toRemove = oldBB & ~newBB;
+ Bitboard toAdd = newBB & ~oldBB;
+
+ while (toRemove)
+ {
+ Square sq = pop_lsb(toRemove);
+ removed.push_back(FeatureSet::make_index<Perspective>(sq, piece, ksq));
+ }
+ while (toAdd)
+ {
+ Square sq = pop_lsb(toAdd);
+ added.push_back(FeatureSet::make_index<Perspective>(sq, piece, ksq));
+ }
}
}
-#endif
- }
- template<Color Perspective>
- void update_accumulator_refresh(const Position& pos) const {
+ auto& accumulator = pos.state()->*accPtr;
+ accumulator.computed[Perspective] = true;
+
#ifdef VECTOR
- // Gcc-10.2 unnecessarily spills AVX2 registers if this array
- // is defined in the VECTOR code below, once in each branch
vec_t acc[NumRegs];
psqt_vec_t psqt[NumPsqtRegs];
-#endif
-
- // Refresh the accumulator
- // Could be extracted to a separate function because it's done in 2 places,
- // but it's unclear if compilers would correctly handle register allocation.
- auto& accumulator = pos.state()->accumulator;
- accumulator.computed[Perspective] = true;
- FeatureSet::IndexList active;
- FeatureSet::append_active_indices<Perspective>(pos, active);
-#ifdef VECTOR
for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
- auto biasesTile = reinterpret_cast<const vec_t*>(&biases[j * TileHeight]);
+ auto accTile =
+ reinterpret_cast<vec_t*>(&accumulator.accumulation[Perspective][j * TileHeight]);
+ auto entryTile = reinterpret_cast<vec_t*>(&entry.accumulation[j * TileHeight]);
+
for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = biasesTile[k];
+ acc[k] = entryTile[k];
+
+ int i = 0;
+ for (; i < int(std::min(removed.size(), added.size())); ++i)
+ {
+ IndexType indexR = removed[i];
+ const IndexType offsetR = HalfDimensions * indexR + j * TileHeight;
+ auto columnR = reinterpret_cast<const vec_t*>(&weights[offsetR]);
+ IndexType indexA = added[i];
+ const IndexType offsetA = HalfDimensions * indexA + j * TileHeight;
+ auto columnA = reinterpret_cast<const vec_t*>(&weights[offsetA]);
+
+ for (unsigned k = 0; k < NumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], vec_sub_16(columnA[k], columnR[k]));
+ }
+ for (; i < int(removed.size()); ++i)
+ {
+ IndexType index = removed[i];
+ const IndexType offset = HalfDimensions * index + j * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (const auto index : active)
+ for (unsigned k = 0; k < NumRegs; ++k)
+ acc[k] = vec_sub_16(acc[k], column[k]);
+ }
+ for (; i < int(added.size()); ++i)
{
+ IndexType index = added[i];
const IndexType offset = HalfDimensions * index + j * TileHeight;
auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
acc[k] = vec_add_16(acc[k], column[k]);
}
- auto accTile =
- reinterpret_cast<vec_t*>(&accumulator.accumulation[Perspective][j * TileHeight]);
- for (unsigned k = 0; k < NumRegs; k++)
+ for (IndexType k = 0; k < NumRegs; k++)
+ vec_store(&entryTile[k], acc[k]);
+ for (IndexType k = 0; k < NumRegs; k++)
vec_store(&accTile[k], acc[k]);
}
for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
+ auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
+ &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ auto entryTilePsqt =
+ reinterpret_cast<psqt_vec_t*>(&entry.psqtAccumulation[j * PsqtTileHeight]);
+
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_zero_psqt();
+ psqt[k] = entryTilePsqt[k];
- for (const auto index : active)
+ for (int i = 0; i < int(removed.size()); ++i)
+ {
+ IndexType index = removed[i];
+ const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
+ }
+ for (int i = 0; i < int(added.size()); ++i)
{
+ IndexType index = added[i];
const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
}
- auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ vec_store_psqt(&entryTilePsqt[k], psqt[k]);
for (std::size_t k = 0; k < NumPsqtRegs; ++k)
vec_store_psqt(&accTilePsqt[k], psqt[k]);
}
#else
- std::memcpy(accumulator.accumulation[Perspective], biases,
- HalfDimensions * sizeof(BiasType));
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[Perspective][k] = 0;
-
- for (const auto index : active)
+ for (const auto index : removed)
{
const IndexType offset = HalfDimensions * index;
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ entry.accumulation[j] -= weights[offset + j];
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ entry.psqtAccumulation[k] -= psqtWeights[index * PSQTBuckets + k];
+ }
+ for (const auto index : added)
+ {
+ const IndexType offset = HalfDimensions * index;
for (IndexType j = 0; j < HalfDimensions; ++j)
- accumulator.accumulation[Perspective][j] += weights[offset + j];
+ entry.accumulation[j] += weights[offset + j];
for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[Perspective][k] +=
- psqtWeights[index * PSQTBuckets + k];
+ entry.psqtAccumulation[k] += psqtWeights[index * PSQTBuckets + k];
}
+
+ // The accumulator of the refresh entry has been updated.
+ // Now copy its content to the actual accumulator we were refreshing.
+
+ std::memcpy(accumulator.accumulation[Perspective], entry.accumulation,
+ sizeof(BiasType) * HalfDimensions);
+
+ std::memcpy(accumulator.psqtAccumulation[Perspective], entry.psqtAccumulation,
+ sizeof(int32_t) * PSQTBuckets);
#endif
+
+ for (Color c : {WHITE, BLACK})
+ entry.byColorBB[c] = pos.pieces(c);
+
+ for (PieceType pt = PAWN; pt <= KING; ++pt)
+ entry.byTypeBB[pt] = pos.pieces(pt);
}
template<Color Perspective>
- void hint_common_access_for_perspective(const Position& pos) const {
+ void hint_common_access_for_perspective(const Position& pos,
+ AccumulatorCaches::Cache<HalfDimensions>* cache) const {
// Works like update_accumulator, but performs less work.
// Updates ONLY the accumulator for pos.
// Look for a usable accumulator of an earlier position. We keep track
// of the estimated gain in terms of features to be added/subtracted.
// Fast early exit.
- if (pos.state()->accumulator.computed[Perspective])
+ if ((pos.state()->*accPtr).computed[Perspective])
return;
-
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
+
StateInfo* oldest = try_find_computed_accumulator<Perspective>(pos);
- if (oldest_st->accumulator.computed[Perspective])
- {
- // Only update current position accumulator to minimize work.
- StateInfo* states_to_update[2] = {pos.state(), nullptr};
- update_accumulator_incremental<Perspective, 2>(pos, oldest_st, states_to_update);
- }
+ if ((oldest->*accPtr).computed[Perspective] && oldest != pos.state())
+ update_accumulator_incremental<Perspective, true>(pos, oldest);
else
- {
- update_accumulator_refresh<Perspective>(pos);
- }
+ update_accumulator_refresh_cache<Perspective>(pos, cache);
}
template<Color Perspective>
- void update_accumulator(const Position& pos) const {
+ void update_accumulator(const Position& pos,
+ AccumulatorCaches::Cache<HalfDimensions>* cache) const {
-
auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
+
StateInfo* oldest = try_find_computed_accumulator<Perspective>(pos);
- if (oldest_st->accumulator.computed[Perspective])
- {
- if (next == nullptr)
- return;
-
- // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
- // Currently we update 2 accumulators.
- // 1. for the current position
- // 2. the next accumulator after the computed one
- // The heuristic may change in the future.
- StateInfo* states_to_update[3] = {next, next == pos.state() ? nullptr : pos.state(),
- nullptr};
-
- update_accumulator_incremental<Perspective, 3>(pos, oldest_st, states_to_update);
- }
+ if ((oldest->*accPtr).computed[Perspective] && oldest != pos.state())
+ // Start from the oldest computed accumulator, update all the
+ // accumulators up to the current position.
+ update_accumulator_incremental<Perspective, false>(pos, oldest);
else
- {
- update_accumulator_refresh<Perspective>(pos);
- }
+ update_accumulator_refresh_cache<Perspective>(pos, cache);
}
+ template<IndexType Size>
+ friend struct AccumulatorCaches::Cache;
+
alignas(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
projects / stockfish / blobdiff