--- /dev/null
+/*
+ Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+ Copyright (C) 2004-2023 The Stockfish developers (see AUTHORS file)
+
+ Stockfish is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ Stockfish is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <http://www.gnu.org/licenses/>.
+*/
+
+// Definition of layer AffineTransformSparseInput of NNUE evaluation function
+
+#ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
+#define NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
+
+#include <iostream>
+#include <algorithm>
+#include <array>
+#include <type_traits>
+#include "../nnue_common.h"
+#include "affine_transform.h"
+#include "simd.h"
+
+/*
+ This file contains the definition for a fully connected layer (aka affine transform) with block sparse input.
+*/
+
+namespace Stockfish::Eval::NNUE::Layers {
+#if defined(__GNUC__) // GCC, Clang, ICC
+
+ static inline IndexType lsb_(std::uint32_t b) {
+ assert(b);
+ return IndexType(__builtin_ctzl(b));
+ }
+
+#elif defined(_MSC_VER) // MSVC
+
+ static inline IndexType lsb_(std::uint32_t b) {
+ assert(b);
+ unsigned long idx;
+ _BitScanForward(&idx, b);
+ return (IndexType) idx;
+ }
+
+#else // Compiler is neither GCC nor MSVC compatible
+
+#error "Compiler not supported."
+
+#endif
+
+
+#if defined(USE_SSSE3)
+ alignas(CacheLineSize) static inline const std::array<std::array<std::uint16_t, 8>, 256> lookup_indices = [](){
+ std::array<std::array<std::uint16_t, 8>, 256> v{};
+ for (int i = 0; i < 256; ++i)
+ {
+ int j = i;
+ int k = 0;
+ while(j)
+ {
+ const IndexType lsbIndex = lsb_(std::uint32_t(j));
+ j &= j - 1;
+ v[i][k] = lsbIndex;
+ ++k;
+ }
+ }
+ return v;
+ }();
+ alignas(CacheLineSize) static inline const std::array<unsigned, 256> lookup_count = [](){
+ std::array<unsigned, 256> v;
+ for (int i = 0; i < 256; ++i)
+ {
+ int j = i;
+ int k = 0;
+ while(j)
+ {
+ j &= j - 1;
+ ++k;
+ }
+ v[i] = k;
+ }
+ return v;
+ }();
+
+ // Find indices of nonzero numbers in an int32_t array
+ template<const IndexType InputDimensions>
+ void find_nnz(const std::int32_t* input, std::uint16_t* out, IndexType& count_out) {
+#if defined (USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_nnz(a) _mm512_cmpgt_epi32_mask(a, _mm512_setzero_si512())
+#elif defined (USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_nnz(a) _mm256_movemask_ps((__m256)_mm256_cmpgt_epi32(a, _mm256_setzero_si256()))
+#elif defined (USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_nnz(a) _mm_movemask_ps((__m128)_mm_cmpgt_epi32(a, _mm_setzero_si128()))
+#endif
+ constexpr IndexType InputSimdWidth = sizeof(vec_t) / sizeof(std::int32_t);
+ // Inputs are processed InputSimdWidth at a time and outputs are processed 8 at a time so we process in chunks of max(InputSimdWidth, 8)
+ constexpr IndexType ChunkSize = std::max<IndexType>(InputSimdWidth, 8);
+ constexpr IndexType NumChunks = InputDimensions / ChunkSize;
+ constexpr IndexType InputsPerChunk = ChunkSize / InputSimdWidth;
+ constexpr IndexType OutputsPerChunk = ChunkSize / 8;
+
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+ IndexType count = 0;
+ __m128i base = _mm_set1_epi16(0);
+ __m128i increment = _mm_set1_epi16(8);
+ for (IndexType i = 0; i < NumChunks; ++i)
+ {
+ // bitmask of nonzero values in this chunk
+ unsigned nnz = 0;
+ for (IndexType j = 0; j < InputsPerChunk; ++j)
+ {
+ const vec_t inputChunk = inputVector[i * InputsPerChunk + j];
+ nnz |= (unsigned)vec_nnz(inputChunk) << (j * InputSimdWidth);
+ }
+ for (IndexType j = 0; j < OutputsPerChunk; ++j)
+ {
+ const auto lookup = (nnz >> (j * 8)) & 0xFF;
+ const auto offsets = _mm_loadu_si128(reinterpret_cast<const __m128i*>(&lookup_indices[lookup]));
+ _mm_storeu_si128(reinterpret_cast<__m128i*>(out + count), _mm_add_epi16(base, offsets));
+ count += lookup_count[lookup];
+ base = _mm_add_epi16(base, increment);
+ }
+ }
+ count_out = count;
+ }
+# undef vec_nnz
+#endif
+
+ // Sparse input implementation
+ template <IndexType InDims, IndexType OutDims>
+ class AffineTransformSparseInput {
+ public:
+ // Input/output type
+ // Input/output type
+ using InputType = std::uint8_t;
+ using OutputType = std::int32_t;
+
+ // Number of input/output dimensions
+ static constexpr IndexType InputDimensions = InDims;
+ static constexpr IndexType OutputDimensions = OutDims;
+
+ static_assert(OutputDimensions % 16 == 0, "Only implemented for OutputDimensions divisible by 16.");
+
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, MaxSimdWidth);
+
+#if defined (USE_SSSE3)
+ static constexpr IndexType ChunkSize = 4;
+#else
+ static constexpr IndexType ChunkSize = 1;
+#endif
+
+ using OutputBuffer = OutputType[PaddedOutputDimensions];
+
+ // Hash value embedded in the evaluation file
+ static constexpr std::uint32_t get_hash_value(std::uint32_t prevHash) {
+ std::uint32_t hashValue = 0xCC03DAE4u;
+ hashValue += OutputDimensions;
+ hashValue ^= prevHash >> 1;
+ hashValue ^= prevHash << 31;
+ return hashValue;
+ }
+
+ static IndexType get_weight_index_scrambled(IndexType i)
+ {
+ return
+ (i / ChunkSize) % (PaddedInputDimensions / ChunkSize) * OutputDimensions * ChunkSize +
+ i / PaddedInputDimensions * ChunkSize +
+ i % ChunkSize;
+ }
+
+ static IndexType get_weight_index(IndexType i)
+ {
+#if defined (USE_SSSE3)
+ return get_weight_index_scrambled(i);
+#else
+ return i;
+#endif
+ }
+
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ read_little_endian<BiasType>(stream, biases, OutputDimensions);
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
+
+ return !stream.fail();
+ }
+
+ // Write network parameters
+ bool write_parameters(std::ostream& stream) const {
+ write_little_endian<BiasType>(stream, biases, OutputDimensions);
+
+ for (IndexType i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
+
+ return !stream.fail();
+ }
+ // Forward propagation
+ const OutputType* propagate(
+ const InputType* input, OutputType* output) const {
+
+#if defined (USE_SSSE3)
+#if defined (USE_AVX512)
+ using vec_t = __m512i;
+ #define vec_setzero _mm512_setzero_si512
+ #define vec_set_32 _mm512_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
+#elif defined (USE_AVX2)
+ using vec_t = __m256i;
+ #define vec_setzero _mm256_setzero_si256
+ #define vec_set_32 _mm256_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+#elif defined (USE_SSSE3)
+ using vec_t = __m128i;
+ #define vec_setzero _mm_setzero_si128
+ #define vec_set_32 _mm_set1_epi32
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+#endif
+ static constexpr IndexType OutputSimdWidth = sizeof(vec_t) / sizeof(OutputType);
+
+ constexpr IndexType NumChunks = ceil_to_multiple<IndexType>(InputDimensions, 8) / ChunkSize;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+ std::uint16_t nnz[NumChunks];
+ IndexType count;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+
+ // Find indices of nonzero 32bit blocks
+ find_nnz<NumChunks>(input32, nnz, count);
+
+ const vec_t* biasvec = reinterpret_cast<const vec_t*>(biases);
+ vec_t acc[NumRegs];
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = biasvec[k];
+
+ for (IndexType j = 0; j < count; ++j)
+ {
+ const auto i = nnz[j];
+ const vec_t in = vec_set_32(input32[i]);
+ const auto col = reinterpret_cast<const vec_t*>(&weights[i * OutputDimensions * ChunkSize]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_add_dpbusd_32(acc[k], in, col[k]);
+ }
+
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ outptr[k] = acc[k];
+# undef vec_setzero
+# undef vec_set_32
+# undef vec_add_dpbusd_32
+#else
+ // Use dense implementation for the other architectures.
+ affine_transform_non_ssse3<
+ InputDimensions,
+ PaddedInputDimensions,
+ OutputDimensions>(output, weights, biases, input);
+#endif
+
+ return output;
+ }
+
+ private:
+ using BiasType = OutputType;
+ using WeightType = std::int8_t;
+
+ alignas(CacheLineSize) BiasType biases[OutputDimensions];
+ alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
+ };
+
+} // namespace Stockfish::Eval::NNUE::Layers
+
+#endif // #ifndef NNUE_LAYERS_AFFINE_TRANSFORM_SPARSE_INPUT_H_INCLUDED
projects / stockfish / blobdiff