145 lines
4.6 KiB
C++
145 lines
4.6 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2021 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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// Code for calculating NNUE evaluation function
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#include <iostream>
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#include <set>
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#include "../evaluate.h"
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#include "../position.h"
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#include "../misc.h"
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#include "../uci.h"
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#include "../types.h"
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#include "evaluate_nnue.h"
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namespace Stockfish::Eval::NNUE {
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// Input feature converter
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LargePagePtr<FeatureTransformer> feature_transformer;
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// Evaluation function
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AlignedPtr<Network> network;
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// Evaluation function file name
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std::string fileName;
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namespace Detail {
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// Initialize the evaluation function parameters
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template <typename T>
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void Initialize(AlignedPtr<T>& pointer) {
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pointer.reset(reinterpret_cast<T*>(std_aligned_alloc(alignof(T), sizeof(T))));
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std::memset(pointer.get(), 0, sizeof(T));
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}
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template <typename T>
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void Initialize(LargePagePtr<T>& pointer) {
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static_assert(alignof(T) <= 4096, "aligned_large_pages_alloc() may fail for such a big alignment requirement of T");
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pointer.reset(reinterpret_cast<T*>(aligned_large_pages_alloc(sizeof(T))));
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std::memset(pointer.get(), 0, sizeof(T));
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}
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// Read evaluation function parameters
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template <typename T>
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bool ReadParameters(std::istream& stream, T& reference) {
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std::uint32_t header;
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header = read_little_endian<std::uint32_t>(stream);
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if (!stream || header != T::GetHashValue()) return false;
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return reference.ReadParameters(stream);
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}
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} // namespace Detail
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// Initialize the evaluation function parameters
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void Initialize() {
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Detail::Initialize(feature_transformer);
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Detail::Initialize(network);
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}
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// Read network header
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bool ReadHeader(std::istream& stream, std::uint32_t* hash_value, std::string* architecture)
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{
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std::uint32_t version, size;
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version = read_little_endian<std::uint32_t>(stream);
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*hash_value = read_little_endian<std::uint32_t>(stream);
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size = read_little_endian<std::uint32_t>(stream);
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if (!stream || version != kVersion) return false;
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architecture->resize(size);
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stream.read(&(*architecture)[0], size);
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return !stream.fail();
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}
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// Read network parameters
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bool ReadParameters(std::istream& stream) {
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std::uint32_t hash_value;
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std::string architecture;
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if (!ReadHeader(stream, &hash_value, &architecture)) return false;
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if (hash_value != kHashValue) return false;
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if (!Detail::ReadParameters(stream, *feature_transformer)) return false;
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if (!Detail::ReadParameters(stream, *network)) return false;
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return stream && stream.peek() == std::ios::traits_type::eof();
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}
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// Evaluation function. Perform differential calculation.
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Value evaluate(const Position& pos) {
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// We manually align the arrays on the stack because with gcc < 9.3
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// overaligning stack variables with alignas() doesn't work correctly.
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constexpr uint64_t alignment = kCacheLineSize;
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#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
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TransformedFeatureType transformed_features_unaligned[
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FeatureTransformer::kBufferSize + alignment / sizeof(TransformedFeatureType)];
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char buffer_unaligned[Network::kBufferSize + alignment];
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auto* transformed_features = align_ptr_up<alignment>(&transformed_features_unaligned[0]);
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auto* buffer = align_ptr_up<alignment>(&buffer_unaligned[0]);
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#else
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alignas(alignment)
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TransformedFeatureType transformed_features[FeatureTransformer::kBufferSize];
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alignas(alignment) char buffer[Network::kBufferSize];
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#endif
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ASSERT_ALIGNED(transformed_features, alignment);
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ASSERT_ALIGNED(buffer, alignment);
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feature_transformer->Transform(pos, transformed_features);
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const auto output = network->Propagate(transformed_features, buffer);
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return static_cast<Value>(output[0] / FV_SCALE);
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}
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// Load eval, from a file stream or a memory stream
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bool load_eval(std::string name, std::istream& stream) {
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Initialize();
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fileName = name;
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return ReadParameters(stream);
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}
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} // namespace Stockfish::Eval::NNUE
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