mirror of
https://github.com/sockspls/badfish
synced 2025-05-02 09:39:36 +00:00
247 lines
8.6 KiB
C++
247 lines
8.6 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 AffineTransformSparseInput of NNUE evaluation function
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#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
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#define NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
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#include <iostream>
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#include <algorithm>
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#include <array>
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#include <type_traits>
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#include "../nnue_common.h"
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#include "affine_transform.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) with block sparse input.
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*/
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namespace Stockfish::Eval::NNUE::Layers {
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#if defined(USE_SSSE3)
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alignas(CacheLineSize) static inline const std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = [](){
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std::array<std::array<std::uint16_t, 8>, 256> v{};
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for (unsigned i = 0; i < 256; ++i)
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{
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std::uint64_t j = i, k = 0;
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while(j)
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v[i][k++] = pop_lsb(j);
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}
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return v;
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}();
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// Find indices of nonzero numbers in an int32_t array
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template<const IndexType InputDimensions>
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void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
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#if defined (USE_AVX512)
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using vec_t = __m512i;
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#define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
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#elif defined (USE_AVX2)
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using vec_t = __m256i;
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#if defined(USE_VNNI) && !defined(USE_AVXVNNI)
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#define vec_nnz(a) _mm256_cmpgt_epi32_mask(a, _mm256_setzero_si256())
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#else
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#define vec_nnz(a) _mm256_movemask_ps(_mm256_castsi256_ps(_mm256_cmpgt_epi32(a, _mm256_setzero_si256())))
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#endif
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#elif defined (USE_SSSE3)
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using vec_t = __m128i;
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#define vec_nnz(a) _mm_movemask_ps(_mm_castsi128_ps(_mm_cmpgt_epi32(a, _mm_setzero_si128())))
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#endif
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constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t);
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// Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8)
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constexpr IndexType ChunkSize = std::max<IndexType>(InputSimdWidth, 8);
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constexpr IndexType NumChunks = InputDimensions / ChunkSize;
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constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth;
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constexpr IndexType OutputsPerChunk = ChunkSize / 8;
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const auto inputVector = reinterpret_cast<const vec_t*>(input);
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IndexType count = 0;
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__m128i base = _mm_setzero_si128();
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const __m128i increment = _mm_set1_epi16(8);
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for (IndexType i = 0; i < NumChunks; ++i)
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{
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// bitmask of nonzero values in this chunk
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unsigned nnz = 0;
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for (IndexType j = 0; j < InputsPerChunk; ++j)
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{
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const vec_t inputChunk = inputVector[i * InputsPerChunk + j];
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nnz |= (unsigned)vec_nnz(inputChunk) << (j * InputSimdWidth);
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}
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for (IndexType j = 0; j < OutputsPerChunk; ++j)
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{
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const auto lookup = (nnz >> (j * 8)) & 0xFF;
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const auto offsets = _mm_loadu_si128(reinterpret_cast<const __m128i*>(&lookup_indices[lookup]));
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_mm_storeu_si128(reinterpret_cast<__m128i*>(out + count), _mm_add_epi16(base, offsets));
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count += popcount(lookup);
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base = _mm_add_epi16(base, increment);
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}
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}
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count_out = count;
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}
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# undef vec_nnz
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#endif
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// Sparse input implementation
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template <IndexType InDims, IndexType OutDims>
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class AffineTransformSparseInput {
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public:
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// Input/output type
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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_assert(OutputDimensions % 16 == 0, "Only implemented for OutputDimensions divisible by 16.");
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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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#if defined (USE_SSSE3)
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static constexpr IndexType ChunkSize = 4;
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#else
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static constexpr IndexType ChunkSize = 1;
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#endif
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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 IndexType get_weight_index_scrambled(IndexType i)
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{
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return
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(i / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize +
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i / PaddedInputDimensions * ChunkSize +
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i % ChunkSize;
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}
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static 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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const OutputType* propagate(
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const InputType* input, OutputType* output) const {
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#if defined (USE_SSSE3)
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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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#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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#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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#endif
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static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
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constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
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constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
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std::uint16_t nnz[NumChunks];
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IndexType count;
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const auto input32 = reinterpret_cast<const std::int32_t*>(input);
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// Find indices of nonzero 32bit blocks
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find_nnz<NumChunks>(input32, nnz, count);
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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 j = 0; j < count; ++j)
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{
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const auto i = nnz[j];
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const vec_t in = vec_set_32(input32[i]);
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const auto col = reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * ChunkSize]);
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for (IndexType k = 0; k < NumRegs; ++k)
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vec_add_dpbusd_32(acc[k], in, col[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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# undef vec_setzero
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# undef vec_set_32
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# undef vec_add_dpbusd_32
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#else
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// Use dense 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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return output;
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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_SPARSE_INPUT_H_INCLUDED
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