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https://github.com/sockspls/badfish
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8ee9905d8b
Passed STC: LLR: 2.94 (-2.94,2.94) <-1.75,0.25> Total: 94208 W: 24270 L: 24112 D: 45826 Ptnml(0-2): 286, 11186, 24009, 11330, 293 https://tests.stockfishchess.org/tests/view/6635ddd773559a8aa8582826 Passed LTC: LLR: 2.95 (-2.94,2.94) <-1.75,0.25> Total: 114960 W: 29107 L: 28982 D: 56871 Ptnml(0-2): 37, 12683, 31924, 12790, 46 https://tests.stockfishchess.org/tests/view/663604a973559a8aa85881ed closes #5214 Bench 1653939
105 lines
3.6 KiB
C++
105 lines
3.6 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2024 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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// Class for difference calculation of NNUE evaluation function
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#ifndef NNUE_ACCUMULATOR_H_INCLUDED
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#define NNUE_ACCUMULATOR_H_INCLUDED
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#include <cstdint>
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#include "nnue_architecture.h"
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#include "nnue_common.h"
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namespace Stockfish::Eval::NNUE {
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using BiasType = std::int16_t;
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using PSQTWeightType = std::int32_t;
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using IndexType = std::uint32_t;
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// Class that holds the result of affine transformation of input features
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template<IndexType Size>
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struct alignas(CacheLineSize) Accumulator {
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std::int16_t accumulation[COLOR_NB][Size];
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std::int32_t psqtAccumulation[COLOR_NB][PSQTBuckets];
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bool computed[COLOR_NB];
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};
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// AccumulatorCaches struct provides per-thread accumulator caches, where each
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// cache contains multiple entries for each of the possible king squares.
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// When the accumulator needs to be refreshed, the cached entry is used to more
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// efficiently update the accumulator, instead of rebuilding it from scratch.
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// This idea, was first described by Luecx (author of Koivisto) and
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// is commonly referred to as "Finny Tables".
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struct AccumulatorCaches {
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template<typename Networks>
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AccumulatorCaches(const Networks& networks) {
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clear(networks);
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}
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template<IndexType Size>
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struct alignas(CacheLineSize) Cache {
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struct alignas(CacheLineSize) Entry {
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BiasType accumulation[Size];
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PSQTWeightType psqtAccumulation[PSQTBuckets];
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Bitboard byColorBB[COLOR_NB];
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Bitboard byTypeBB[PIECE_TYPE_NB];
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// To initialize a refresh entry, we set all its bitboards empty,
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// so we put the biases in the accumulation, without any weights on top
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void clear(const BiasType* biases) {
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std::memcpy(accumulation, biases, sizeof(accumulation));
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std::memset((uint8_t*) this + offsetof(Entry, psqtAccumulation), 0,
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sizeof(Entry) - offsetof(Entry, psqtAccumulation));
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}
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};
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template<typename Network>
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void clear(const Network& network) {
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for (auto& entries1D : entries)
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for (auto& entry : entries1D)
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entry.clear(network.featureTransformer->biases);
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}
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void clear(const BiasType* biases) {
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for (auto& entry : entries)
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entry.clear(biases);
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}
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std::array<Entry, COLOR_NB>& operator[](Square sq) { return entries[sq]; }
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std::array<std::array<Entry, COLOR_NB>, SQUARE_NB> entries;
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};
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template<typename Networks>
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void clear(const Networks& networks) {
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big.clear(networks.big);
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small.clear(networks.small);
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}
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Cache<TransformedFeatureDimensionsBig> big;
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Cache<TransformedFeatureDimensionsSmall> small;
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};
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} // namespace Stockfish::Eval::NNUE
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#endif // NNUE_ACCUMULATOR_H_INCLUDED
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