mirror of
https://github.com/opelly27/Stockfish.git
synced 2026-05-20 05:07:46 +00:00
Tomasz Sobczyk
174b038bf3
fixes #3946 an issue related with the toolchain as found in xcode 12 on macOS,
related to previous commit 5f781d36.
closes https://github.com/official-stockfish/Stockfish/pull/3950
No functional change
134 lines
4.5 KiB
C++
134 lines
4.5 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2022 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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// Input features and network structure used in NNUE evaluation function
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#ifndef NNUE_ARCHITECTURE_H_INCLUDED
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#define NNUE_ARCHITECTURE_H_INCLUDED
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#include <memory>
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#include "nnue_common.h"
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#include "features/half_ka_v2_hm.h"
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#include "layers/affine_transform.h"
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#include "layers/clipped_relu.h"
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#include "../misc.h"
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namespace Stockfish::Eval::NNUE {
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// Input features used in evaluation function
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using FeatureSet = Features::HalfKAv2_hm;
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// Number of input feature dimensions after conversion
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constexpr IndexType TransformedFeatureDimensions = 1024;
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constexpr IndexType PSQTBuckets = 8;
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constexpr IndexType LayerStacks = 8;
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struct Network
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{
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static constexpr int FC_0_OUTPUTS = 15;
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static constexpr int FC_1_OUTPUTS = 32;
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Layers::AffineTransform<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
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Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
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Layers::AffineTransform<FC_0_OUTPUTS, FC_1_OUTPUTS> fc_1;
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Layers::ClippedReLU<FC_1_OUTPUTS> ac_1;
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Layers::AffineTransform<FC_1_OUTPUTS, 1> fc_2;
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// Hash value embedded in the evaluation file
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static constexpr std::uint32_t get_hash_value() {
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// input slice hash
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std::uint32_t hashValue = 0xEC42E90Du;
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hashValue ^= TransformedFeatureDimensions * 2;
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hashValue = decltype(fc_0)::get_hash_value(hashValue);
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hashValue = decltype(ac_0)::get_hash_value(hashValue);
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hashValue = decltype(fc_1)::get_hash_value(hashValue);
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hashValue = decltype(ac_1)::get_hash_value(hashValue);
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hashValue = decltype(fc_2)::get_hash_value(hashValue);
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return hashValue;
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}
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// Read network parameters
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bool read_parameters(std::istream& stream) {
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if (!fc_0.read_parameters(stream)) return false;
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if (!ac_0.read_parameters(stream)) return false;
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if (!fc_1.read_parameters(stream)) return false;
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if (!ac_1.read_parameters(stream)) return false;
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if (!fc_2.read_parameters(stream)) return false;
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return true;
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}
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// Read network parameters
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bool write_parameters(std::ostream& stream) const {
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if (!fc_0.write_parameters(stream)) return false;
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if (!ac_0.write_parameters(stream)) return false;
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if (!fc_1.write_parameters(stream)) return false;
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if (!ac_1.write_parameters(stream)) return false;
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if (!fc_2.write_parameters(stream)) return false;
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return true;
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}
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std::int32_t propagate(const TransformedFeatureType* transformedFeatures)
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{
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struct alignas(CacheLineSize) Buffer
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{
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alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
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alignas(CacheLineSize) decltype(ac_0)::OutputBuffer ac_0_out;
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alignas(CacheLineSize) decltype(fc_1)::OutputBuffer fc_1_out;
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alignas(CacheLineSize) decltype(ac_1)::OutputBuffer ac_1_out;
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alignas(CacheLineSize) decltype(fc_2)::OutputBuffer fc_2_out;
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Buffer()
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{
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std::memset(this, 0, sizeof(*this));
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}
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};
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#if defined(__clang__) && (__APPLE__)
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// workaround for a bug reported with xcode 12
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static thread_local auto tlsBuffer = std::make_unique<Buffer>();
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// Access TLS only once, cache result.
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Buffer& buffer = *tlsBuffer;
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#else
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alignas(CacheLineSize) static thread_local Buffer buffer;
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#endif
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fc_0.propagate(transformedFeatures, buffer.fc_0_out);
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ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
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fc_1.propagate(buffer.ac_0_out, buffer.fc_1_out);
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ac_1.propagate(buffer.fc_1_out, buffer.ac_1_out);
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fc_2.propagate(buffer.ac_1_out, buffer.fc_2_out);
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// buffer.fc_0_out[FC_0_OUTPUTS] is such that 1.0 is equal to 127*(1<<WeightScaleBits) in quantized form
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// but we want 1.0 to be equal to 600*OutputScale
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std::int32_t fwdOut = int(buffer.fc_0_out[FC_0_OUTPUTS]) * (600*OutputScale) / (127*(1<<WeightScaleBits));
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std::int32_t outputValue = buffer.fc_2_out[0] + fwdOut;
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return outputValue;
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}
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};
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} // namespace Stockfish::Eval::NNUE
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#endif // #ifndef NNUE_ARCHITECTURE_H_INCLUDED
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