+++ /dev/null
-/*
- Stockfish, a UCI chess playing engine derived from Glaurung 2.1
- Copyright (C) 2004-2021 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/>.
-*/
-
-// Code for calculating NNUE evaluation function
-
-#include <iostream>
-#include <set>
-
-#include "../evaluate.h"
-#include "../position.h"
-#include "../misc.h"
-#include "../uci.h"
-#include "../types.h"
-
-#include "evaluate_nnue.h"
-
-namespace Stockfish::Eval::NNUE {
-
- // Input feature converter
- LargePagePtr<FeatureTransformer> featureTransformer;
-
- // Evaluation function
- AlignedPtr<Network> network[LayerStacks];
-
- // Evaluation function file name
- std::string fileName;
- std::string netDescription;
-
- namespace Detail {
-
- // Initialize the evaluation function parameters
- template <typename T>
- void initialize(AlignedPtr<T>& pointer) {
-
- pointer.reset(reinterpret_cast<T*>(std_aligned_alloc(alignof(T), sizeof(T))));
- std::memset(pointer.get(), 0, sizeof(T));
- }
-
- template <typename T>
- void initialize(LargePagePtr<T>& pointer) {
-
- static_assert(alignof(T) <= 4096, "aligned_large_pages_alloc() may fail for such a big alignment requirement of T");
- pointer.reset(reinterpret_cast<T*>(aligned_large_pages_alloc(sizeof(T))));
- std::memset(pointer.get(), 0, sizeof(T));
- }
-
- // Read evaluation function parameters
- template <typename T>
- bool read_parameters(std::istream& stream, T& reference) {
-
- std::uint32_t header;
- header = read_little_endian<std::uint32_t>(stream);
- if (!stream || header != T::get_hash_value()) return false;
- return reference.read_parameters(stream);
- }
-
- // Write evaluation function parameters
- template <typename T>
- bool write_parameters(std::ostream& stream, const T& reference) {
-
- write_little_endian<std::uint32_t>(stream, T::get_hash_value());
- return reference.write_parameters(stream);
- }
-
- } // namespace Detail
-
- // Initialize the evaluation function parameters
- void initialize() {
-
- Detail::initialize(featureTransformer);
- for (std::size_t i = 0; i < LayerStacks; ++i)
- Detail::initialize(network[i]);
- }
-
- // Read network header
- bool read_header(std::istream& stream, std::uint32_t* hashValue, std::string* desc)
- {
- std::uint32_t version, size;
-
- version = read_little_endian<std::uint32_t>(stream);
- *hashValue = read_little_endian<std::uint32_t>(stream);
- size = read_little_endian<std::uint32_t>(stream);
- if (!stream || version != Version) return false;
- desc->resize(size);
- stream.read(&(*desc)[0], size);
- return !stream.fail();
- }
-
- // Write network header
- bool write_header(std::ostream& stream, std::uint32_t hashValue, const std::string& desc)
- {
- write_little_endian<std::uint32_t>(stream, Version);
- write_little_endian<std::uint32_t>(stream, hashValue);
- write_little_endian<std::uint32_t>(stream, desc.size());
- stream.write(&desc[0], desc.size());
- return !stream.fail();
- }
-
- // Read network parameters
- bool read_parameters(std::istream& stream) {
-
- std::uint32_t hashValue;
- if (!read_header(stream, &hashValue, &netDescription)) return false;
- if (hashValue != HashValue) return false;
- if (!Detail::read_parameters(stream, *featureTransformer)) return false;
- for (std::size_t i = 0; i < LayerStacks; ++i)
- if (!Detail::read_parameters(stream, *(network[i]))) return false;
- return stream && stream.peek() == std::ios::traits_type::eof();
- }
-
- // Write network parameters
- bool write_parameters(std::ostream& stream) {
-
- if (!write_header(stream, HashValue, netDescription)) return false;
- if (!Detail::write_parameters(stream, *featureTransformer)) return false;
- for (std::size_t i = 0; i < LayerStacks; ++i)
- if (!Detail::write_parameters(stream, *(network[i]))) return false;
- return (bool)stream;
- }
-
- // Evaluation function. Perform differential calculation.
- Value evaluate(const Position& pos) {
-
- // We manually align the arrays on the stack because with gcc < 9.3
- // overaligning stack variables with alignas() doesn't work correctly.
-
- constexpr uint64_t alignment = CacheLineSize;
-
-#if defined(ALIGNAS_ON_STACK_VARIABLES_BROKEN)
- TransformedFeatureType transformedFeaturesUnaligned[
- FeatureTransformer::BufferSize + alignment / sizeof(TransformedFeatureType)];
- char bufferUnaligned[Network::BufferSize + alignment];
-
- auto* transformedFeatures = align_ptr_up<alignment>(&transformedFeaturesUnaligned[0]);
- auto* buffer = align_ptr_up<alignment>(&bufferUnaligned[0]);
-#else
- alignas(alignment)
- TransformedFeatureType transformedFeatures[FeatureTransformer::BufferSize];
- alignas(alignment) char buffer[Network::BufferSize];
-#endif
-
- ASSERT_ALIGNED(transformedFeatures, alignment);
- ASSERT_ALIGNED(buffer, alignment);
-
- const std::size_t bucket = (pos.count<ALL_PIECES>() - 1) / 4;
-
- const auto [psqt, lazy] = featureTransformer->transform(pos, transformedFeatures, bucket);
- if (lazy) {
- return static_cast<Value>(psqt / OutputScale);
- } else {
- const auto output = network[bucket]->propagate(transformedFeatures, buffer);
- return static_cast<Value>((output[0] + psqt) / OutputScale);
- }
- }
-
- // Load eval, from a file stream or a memory stream
- bool load_eval(std::string name, std::istream& stream) {
-
- initialize();
- fileName = name;
- return read_parameters(stream);
- }
-
- // Save eval, to a file stream or a memory stream
- bool save_eval(std::ostream& stream) {
-
- if (fileName.empty())
- return false;
-
- return write_parameters(stream);
- }
-
-} // namespace Stockfish::Eval::NNUE
projects / stockfish / blobdiff