mirror of
https://github.com/HChaZZY/Stockfish.git
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3c0e86a91e
Reorder a few includes, include "position.h" where it was previously missing and apply include-what-you-use suggestions. Also make the order of the includes consistent, in the following way: 1. Related header (for .cpp files) 2. A blank line 3. C/C++ headers 4. A blank line 5. All other header files closes https://github.com/official-stockfish/Stockfish/pull/4763 fixes https://github.com/official-stockfish/Stockfish/issues/4707 No functional change
307 lines
11 KiB
C++
307 lines
11 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 AffineTransform of NNUE evaluation function
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#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
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#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
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#include <cstdint>
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#include <iostream>
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#include "../nnue_common.h"
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#include "simd.h"
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/*
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This file contains the definition for a fully connected layer (aka affine transform).
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- expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32.
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- that's why AVX512 is hard to implement
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- expected use-case is small layers
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- inputs are processed in chunks of 4, weights are respectively transposed
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- accumulation happens directly to int32s
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*/
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namespace Stockfish::Eval::NNUE::Layers {
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// Fallback implementation for older/other architectures.
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// Requires the input to be padded to at least 16 values.
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#if !defined(USE_SSSE3)
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template <IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
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static void affine_transform_non_ssse3(std::int32_t* output, const std::int8_t* weights, const std::int32_t* biases, const std::uint8_t* input)
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{
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# if defined(USE_SSE2)
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// At least a multiple of 16, with SSE2.
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
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const __m128i Zeros = _mm_setzero_si128();
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const auto inputVector = reinterpret_cast<const __m128i*>(input);
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# elif defined(USE_MMX)
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 8;
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const __m64 Zeros = _mm_setzero_si64();
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const auto inputVector = reinterpret_cast<const __m64*>(input);
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# elif defined(USE_NEON_DOTPROD)
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
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const auto inputVector = reinterpret_cast<const int8x16_t*>(input);
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# elif defined(USE_NEON)
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 16) / 16;
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const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
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# endif
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for (IndexType i = 0; i < OutputDimensions; ++i) {
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const IndexType offset = i * PaddedInputDimensions;
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# if defined(USE_SSE2)
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__m128i sumLo = _mm_cvtsi32_si128(biases[i]);
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__m128i sumHi = Zeros;
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const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
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for (IndexType j = 0; j < NumChunks; ++j) {
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__m128i row_j = _mm_load_si128(&row[j]);
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__m128i input_j = _mm_load_si128(&inputVector[j]);
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__m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
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__m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
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__m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
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__m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
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__m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
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__m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
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sumLo = _mm_add_epi32(sumLo, productLo);
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sumHi = _mm_add_epi32(sumHi, productHi);
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}
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__m128i sum = _mm_add_epi32(sumLo, sumHi);
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__m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
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sum = _mm_add_epi32(sum, sumHigh_64);
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__m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
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sum = _mm_add_epi32(sum, sum_second_32);
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output[i] = _mm_cvtsi128_si32(sum);
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# elif defined(USE_MMX)
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__m64 sumLo = _mm_cvtsi32_si64(biases[i]);
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__m64 sumHi = Zeros;
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const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
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for (IndexType j = 0; j < NumChunks; ++j) {
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__m64 row_j = row[j];
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__m64 input_j = inputVector[j];
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__m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
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__m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
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__m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
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__m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
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__m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
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__m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
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sumLo = _mm_add_pi32(sumLo, productLo);
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sumHi = _mm_add_pi32(sumHi, productHi);
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}
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__m64 sum = _mm_add_pi32(sumLo, sumHi);
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sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
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output[i] = _mm_cvtsi64_si32(sum);
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# elif defined(USE_NEON_DOTPROD)
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int32x4_t sum = {biases[i]};
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const auto row = reinterpret_cast<const int8x16_t*>(&weights[offset]);
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for (IndexType j = 0; j < NumChunks; ++j) {
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sum = vdotq_s32(sum, inputVector[j], row[j]);
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}
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output[i] = vaddvq_s32(sum);
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# elif defined(USE_NEON)
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int32x4_t sum = {biases[i]};
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const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
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for (IndexType j = 0; j < NumChunks; ++j) {
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int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
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product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
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sum = vpadalq_s16(sum, product);
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}
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output[i] = sum[0] + sum[1] + sum[2] + sum[3];
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# else
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std::int32_t sum = biases[i];
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for (IndexType j = 0; j < InputDimensions; ++j) {
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sum += weights[offset + j] * input[j];
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}
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output[i] = sum;
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# endif
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}
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# if defined(USE_MMX)
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_mm_empty();
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# endif
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}
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#endif
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template <IndexType InDims, IndexType OutDims>
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class AffineTransform {
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public:
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// Input/output type
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using InputType = std::uint8_t;
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using OutputType = std::int32_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 = OutDims;
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static constexpr IndexType PaddedInputDimensions =
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ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
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static constexpr IndexType PaddedOutputDimensions =
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ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
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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 = 0xCC03DAE4u;
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hashValue += OutputDimensions;
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hashValue ^= prevHash >> 1;
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hashValue ^= prevHash << 31;
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return hashValue;
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}
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static constexpr IndexType get_weight_index_scrambled(IndexType i)
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{
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return
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(i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
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i / PaddedInputDimensions * 4 +
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i % 4;
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}
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static constexpr IndexType get_weight_index(IndexType i)
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{
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#if defined (USE_SSSE3)
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return get_weight_index_scrambled(i);
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#else
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return i;
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#endif
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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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read_little_endian<BiasType>(stream, biases, OutputDimensions);
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for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
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weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
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return !stream.fail();
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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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write_little_endian<BiasType>(stream, biases, OutputDimensions);
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for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
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write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
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return !stream.fail();
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}
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// Forward propagation
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void propagate(
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const InputType* input, OutputType* output) const {
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#if defined (USE_AVX512)
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using vec_t = __m512i;
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#define vec_setzero _mm512_setzero_si512
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#define vec_set_32 _mm512_set1_epi32
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#define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
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#define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
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#define vec_hadd Simd::m512_hadd
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#elif defined (USE_AVX2)
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using vec_t = __m256i;
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#define vec_setzero _mm256_setzero_si256
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#define vec_set_32 _mm256_set1_epi32
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#define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
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#define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
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#define vec_hadd Simd::m256_hadd
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#elif defined (USE_SSSE3)
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using vec_t = __m128i;
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#define vec_setzero _mm_setzero_si128
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#define vec_set_32 _mm_set1_epi32
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#define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
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#define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
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#define vec_hadd Simd::m128_hadd
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#endif
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#if defined (USE_SSSE3)
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const auto inputVector = reinterpret_cast<const vec_t*>(input);
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static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
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static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
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if constexpr (OutputDimensions % OutputSimdWidth == 0)
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{
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / 4;
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constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
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const auto input32 = reinterpret_cast<const std::int32_t*>(input);
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const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
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vec_t acc[NumRegs];
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for (IndexType k = 0; k < NumRegs; ++k)
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acc[k] = biasvec[k];
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for (IndexType i = 0; i < NumChunks; i += 2)
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{
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const vec_t in0 = vec_set_32(input32[i + 0]);
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const vec_t in1 = vec_set_32(input32[i + 1]);
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const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
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const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
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for (IndexType k = 0; k < NumRegs; ++k)
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vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]);
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}
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vec_t* outptr = reinterpret_cast<vec_t*>(output);
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for (IndexType k = 0; k < NumRegs; ++k)
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outptr[k] = acc[k];
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}
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else if constexpr (OutputDimensions == 1)
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{
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constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
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vec_t sum0 = vec_setzero();
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const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
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for (int j = 0; j < (int)NumChunks; ++j)
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{
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const vec_t in = inputVector[j];
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vec_add_dpbusd_32(sum0, in, row0[j]);
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}
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output[0] = vec_hadd(sum0, biases[0]);
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}
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# undef vec_setzero
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# undef vec_set_32
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# undef vec_add_dpbusd_32
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# undef vec_add_dpbusd_32x2
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# undef vec_hadd
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#else
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// Use old implementation for the other architectures.
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affine_transform_non_ssse3<
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InputDimensions,
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PaddedInputDimensions,
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OutputDimensions>(output, weights, biases, input);
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#endif
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}
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private:
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using BiasType = OutputType;
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using WeightType = std::int8_t;
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alignas(CacheLineSize) BiasType biases[OutputDimensions];
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alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
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};
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} // namespace Stockfish::Eval::NNUE::Layers
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#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
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