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2d0237db3f
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>
104 lines
3.7 KiB
C++
104 lines
3.7 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2023 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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// Definition of layer ClippedReLU of NNUE evaluation function
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#ifndef NNUE_LAYERS_SQR_CLIPPED_RELU_H_INCLUDED
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#define NNUE_LAYERS_SQR_CLIPPED_RELU_H_INCLUDED
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#include <algorithm>
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#include <cstdint>
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#include <iosfwd>
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#include "../nnue_common.h"
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namespace Stockfish::Eval::NNUE::Layers {
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// Clipped ReLU
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template<IndexType InDims>
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class SqrClippedReLU {
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public:
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// Input/output type
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using InputType = std::int32_t;
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using OutputType = std::uint8_t;
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// Number of input/output dimensions
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static constexpr IndexType InputDimensions = InDims;
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static constexpr IndexType OutputDimensions = InputDimensions;
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static constexpr IndexType PaddedOutputDimensions =
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ceil_to_multiple<IndexType>(OutputDimensions, 32);
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using OutputBuffer = OutputType[PaddedOutputDimensions];
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// Hash value embedded in the evaluation file
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static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
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std::uint32_t hashValue = 0x538D24C7u;
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hashValue += prevHash;
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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&) { return true; }
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// Write network parameters
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bool write_parameters(std::ostream&) const { return true; }
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// Forward propagation
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void propagate(const InputType* input, OutputType* output) const {
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#if defined(USE_SSE2)
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constexpr IndexType NumChunks = InputDimensions / 16;
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static_assert(WeightScaleBits == 6);
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const auto in = reinterpret_cast<const __m128i*>(input);
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const auto out = reinterpret_cast<__m128i*>(output);
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for (IndexType i = 0; i < NumChunks; ++i)
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{
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__m128i words0 =
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_mm_packs_epi32(_mm_load_si128(&in[i * 4 + 0]), _mm_load_si128(&in[i * 4 + 1]));
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__m128i words1 =
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_mm_packs_epi32(_mm_load_si128(&in[i * 4 + 2]), _mm_load_si128(&in[i * 4 + 3]));
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// We shift by WeightScaleBits * 2 = 12 and divide by 128
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// which is an additional shift-right of 7, meaning 19 in total.
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// MulHi strips the lower 16 bits so we need to shift out 3 more to match.
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words0 = _mm_srli_epi16(_mm_mulhi_epi16(words0, words0), 3);
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words1 = _mm_srli_epi16(_mm_mulhi_epi16(words1, words1), 3);
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_mm_store_si128(&out[i], _mm_packs_epi16(words0, words1));
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}
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constexpr IndexType Start = NumChunks * 16;
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#else
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constexpr IndexType Start = 0;
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#endif
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for (IndexType i = Start; i < InputDimensions; ++i)
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{
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output[i] = static_cast<OutputType>(
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// Really should be /127 but we need to make it fast so we right shift
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// by an extra 7 bits instead. Needs to be accounted for in the trainer.
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std::min(127ll, ((long long) input[i] * input[i]) >> (2 * WeightScaleBits + 7)));
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}
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}
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};
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} // namespace Stockfish::Eval::NNUE::Layers
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#endif // NNUE_LAYERS_SQR_CLIPPED_RELU_H_INCLUDED
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