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BadFish/src/nnue/nnue_accumulator.h
2024-11-13 20:11:03 +01:00

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3.5 KiB
C++

/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
// Class for difference calculation of NNUE evaluation function
#ifndef NNUE_ACCUMULATOR_H_INCLUDED
#define NNUE_ACCUMULATOR_H_INCLUDED
#include <cstdint>
#include "nnue_architecture.h"
#include "nnue_common.h"
namespace Stockfish::Eval::NNUE {
using BiasType = std::int16_t;
using PSQTWeightType = std::int32_t;
using IndexType = std::uint32_t;
// Class that holds the result of affine transformation of input features
template<IndexType Size>
struct alignas(CacheLineSize) Accumulator {
std::int16_t accumulation[COLOR_NB][Size];
std::int32_t psqtAccumulation[COLOR_NB][PSQTBuckets];
bool computed[COLOR_NB];
};
// AccumulatorCaches struct provides per-thread accumulator caches, where each
// cache contains multiple entries for each of the possible king squares.
// When the accumulator needs to be refreshed, the cached entry is used to more
// efficiently update the accumulator, instead of rebuilding it from scratch.
// This idea, was first described by Luecx (author of Koivisto) and
// is commonly referred to as "Finny Tables".
struct AccumulatorCaches {
template<typename Networks>
AccumulatorCaches(const Networks& networks) {
clear(networks);
}
template<IndexType Size>
struct alignas(CacheLineSize) Cache {
struct alignas(CacheLineSize) Entry {
BiasType accumulation[Size];
PSQTWeightType psqtAccumulation[PSQTBuckets];
Bitboard byColorBB[COLOR_NB];
Bitboard byTypeBB[PIECE_TYPE_NB];
// To initialize a refresh entry, we set all its bitboards empty,
// so we put the biases in the accumulation, without any weights on top
void clear(const BiasType* biases) {
std::memcpy(accumulation, biases, sizeof(accumulation));
std::memset((uint8_t*) this + offsetof(Entry, psqtAccumulation), 0,
sizeof(Entry) - offsetof(Entry, psqtAccumulation));
}
};
template<typename Network>
void clear(const Network& network) {
for (auto& entries1D : entries)
for (auto& entry : entries1D)
entry.clear(network.featureTransformer->biases);
}
std::array<Entry, COLOR_NB>& operator[](Square sq) { return entries[sq]; }
std::array<std::array<Entry, COLOR_NB>, SQUARE_NB> entries;
};
template<typename Networks>
void clear(const Networks& networks) {
big.clear(networks.big);
small.clear(networks.small);
}
Cache<TransformedFeatureDimensionsBig> big;
Cache<TransformedFeatureDimensionsSmall> small;
};
} // namespace Stockfish::Eval::NNUE
#endif // NNUE_ACCUMULATOR_H_INCLUDED