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add clang-format
This introduces clang-format to enforce a consistent code style for Stockfish. Having a documented and consistent style across the code will make contributing easier for new developers, and will make larger changes to the codebase easier to make. To facilitate formatting, this PR includes a Makefile target (`make format`) to format the code, this requires clang-format (version 17 currently) to be installed locally. Installing clang-format is straightforward on most OS and distros (e.g. with https://apt.llvm.org/, brew install clang-format, etc), as this is part of quite commonly used suite of tools and compilers (llvm / clang). Additionally, a CI action is present that will verify if the code requires formatting, and comment on the PR as needed. Initially, correct formatting is not required, it will be done by maintainers as part of the merge or in later commits, but obviously this is encouraged. fixes https://github.com/official-stockfish/Stockfish/issues/3608 closes https://github.com/official-stockfish/Stockfish/pull/4790 Co-Authored-By: Joost VandeVondele <Joost.VandeVondele@gmail.com>
This commit is contained in:
Disservin
committed by
Joost VandeVondele
Joost VandeVondele
parent
8366ec48ae
commit
2d0237db3f
@@ -39,97 +39,90 @@ using FeatureSet = Features::HalfKAv2_hm;
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// Number of input feature dimensions after conversion
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constexpr IndexType TransformedFeatureDimensions = 2560;
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constexpr IndexType PSQTBuckets = 8;
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constexpr IndexType LayerStacks = 8;
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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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struct Network {
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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::AffineTransformSparseInput<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
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Layers::SqrClippedReLU<FC_0_OUTPUTS + 1> ac_sqr_0;
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Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
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Layers::AffineTransform<FC_0_OUTPUTS * 2, 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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Layers::AffineTransformSparseInput<TransformedFeatureDimensions, FC_0_OUTPUTS + 1> fc_0;
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Layers::SqrClippedReLU<FC_0_OUTPUTS + 1> ac_sqr_0;
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Layers::ClippedReLU<FC_0_OUTPUTS + 1> ac_0;
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Layers::AffineTransform<FC_0_OUTPUTS * 2, 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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// 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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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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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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return fc_0.read_parameters(stream)
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&& ac_0.read_parameters(stream)
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&& fc_1.read_parameters(stream)
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&& ac_1.read_parameters(stream)
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&& fc_2.read_parameters(stream);
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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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return fc_0.read_parameters(stream) && ac_0.read_parameters(stream)
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&& fc_1.read_parameters(stream) && ac_1.read_parameters(stream)
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&& fc_2.read_parameters(stream);
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}
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// Write network parameters
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bool write_parameters(std::ostream& stream) const {
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return fc_0.write_parameters(stream)
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&& ac_0.write_parameters(stream)
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&& fc_1.write_parameters(stream)
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&& ac_1.write_parameters(stream)
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&& fc_2.write_parameters(stream);
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}
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// Write network parameters
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bool write_parameters(std::ostream& stream) const {
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return fc_0.write_parameters(stream) && ac_0.write_parameters(stream)
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&& fc_1.write_parameters(stream) && ac_1.write_parameters(stream)
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&& fc_2.write_parameters(stream);
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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_sqr_0)::OutputType ac_sqr_0_out[ceil_to_multiple<IndexType>(FC_0_OUTPUTS * 2, 32)];
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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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std::int32_t propagate(const TransformedFeatureType* transformedFeatures) {
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struct alignas(CacheLineSize) Buffer {
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alignas(CacheLineSize) decltype(fc_0)::OutputBuffer fc_0_out;
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alignas(CacheLineSize) decltype(ac_sqr_0)::OutputType
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ac_sqr_0_out[ceil_to_multiple<IndexType>(FC_0_OUTPUTS * 2, 32)];
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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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Buffer() { std::memset(this, 0, sizeof(*this)); }
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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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// 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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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_sqr_0.propagate(buffer.fc_0_out, buffer.ac_sqr_0_out);
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ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
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std::memcpy(buffer.ac_sqr_0_out + FC_0_OUTPUTS, buffer.ac_0_out, FC_0_OUTPUTS * sizeof(decltype(ac_0)::OutputType));
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fc_1.propagate(buffer.ac_sqr_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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fc_0.propagate(transformedFeatures, buffer.fc_0_out);
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ac_sqr_0.propagate(buffer.fc_0_out, buffer.ac_sqr_0_out);
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ac_0.propagate(buffer.fc_0_out, buffer.ac_0_out);
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std::memcpy(buffer.ac_sqr_0_out + FC_0_OUTPUTS, buffer.ac_0_out,
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FC_0_OUTPUTS * sizeof(decltype(ac_0)::OutputType));
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fc_1.propagate(buffer.ac_sqr_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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// 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 =
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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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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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#endif // #ifndef NNUE_ARCHITECTURE_H_INCLUDED
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