os: ubuntu-20.04,
compiler: g++,
comp: gcc,
- run_expensive_tests: true
+ run_expensive_tests: true,
+ run_32bit_tests: true,
+ run_64bit_tests: true,
+ shell: 'bash {0}'
}
- {
name: "Ubuntu 20.04 Clang",
os: ubuntu-20.04,
compiler: clang++,
comp: clang,
- run_expensive_tests: false
+ run_expensive_tests: false,
+ run_32bit_tests: true,
+ run_64bit_tests: true,
+ shell: 'bash {0}'
+ }
+ - {
+ name: "MacOS 10.15 Apple Clang",
+ os: macos-10.15,
+ compiler: clang++,
+ comp: clang,
+ run_expensive_tests: false,
+ run_32bit_tests: false,
+ run_64bit_tests: true,
+ shell: 'bash {0}'
+ }
+ - {
+ name: "MacOS 10.15 GCC 10",
+ os: macos-10.15,
+ compiler: g++-10,
+ comp: gcc,
+ run_expensive_tests: false,
+ run_32bit_tests: false,
+ run_64bit_tests: true,
+ shell: 'bash {0}'
+ }
+ - {
+ name: "Windows 2019 Mingw-w64 GCC x86_64",
+ os: windows-2019,
+ compiler: g++,
+ comp: gcc,
+ run_expensive_tests: false,
+ run_32bit_tests: false,
+ run_64bit_tests: true,
+ msys_sys: 'mingw64',
+ msys_env: 'x86_64',
+ shell: 'msys2 {0}'
+ }
+ - {
+ name: "Windows 2019 Mingw-w64 GCC i686",
+ os: windows-2019,
+ compiler: g++,
+ comp: gcc,
+ run_expensive_tests: false,
+ run_32bit_tests: true,
+ run_64bit_tests: false,
+ msys_sys: 'mingw32',
+ msys_env: 'i686',
+ shell: 'msys2 {0}'
}
defaults:
run:
working-directory: src
+ shell: ${{ matrix.config.shell }}
steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0
- - name: Download required packages
+ - name: Download required linux packages
+ if: runner.os == 'Linux'
run: |
sudo apt update
sudo apt install expect valgrind g++-multilib
+ - name: Setup msys and install required packages
+ if: runner.os == 'Windows'
+ uses: msys2/setup-msys2@v2
+ with:
+ msystem: ${{matrix.config.msys_sys}}
+ install: mingw-w64-${{matrix.config.msys_env}}-gcc make git expect
+
- name: Download the used network from the fishtest framework
run: |
make net
# x86-32 tests
- name: Test debug x86-32 build
+ if: ${{ matrix.config.run_32bit_tests }}
run: |
export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
make clean
../tests/signature.sh $benchref
- name: Test x86-32 build
+ if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32 build
../tests/signature.sh $benchref
- name: Test x86-32-sse41-popcnt build
+ if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse41-popcnt build
../tests/signature.sh $benchref
- name: Test x86-32-sse2 build
+ if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=x86-32-sse2 build
../tests/signature.sh $benchref
- name: Test general-32 build
+ if: ${{ matrix.config.run_32bit_tests }}
run: |
make clean
make -j2 ARCH=general-32 build
# x86-64 tests
- name: Test debug x86-64-modern build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
export CXXFLAGS="-Werror -D_GLIBCXX_DEBUG"
make clean
../tests/signature.sh $benchref
- name: Test x86-64-modern build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
../tests/signature.sh $benchref
- name: Test x86-64-ssse3 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-ssse3 build
../tests/signature.sh $benchref
- name: Test x86-64-sse3-popcnt build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-sse3-popcnt build
../tests/signature.sh $benchref
- name: Test x86-64 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64 build
../tests/signature.sh $benchref
- name: Test general-64 build
+ if: matrix.config.run_64bit_tests
run: |
make clean
make -j2 ARCH=general-64 build
# x86-64 with newer extensions tests
- name: Compile x86-64-avx2 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-avx2 build
- name: Compile x86-64-bmi2 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-bmi2 build
- name: Compile x86-64-avx512 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-avx512 build
- name: Compile x86-64-vnni512 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-vnni512 build
- name: Compile x86-64-vnni256 build
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-vnni256 build
# Other tests
- name: Check perft and search reproducibility
+ if: ${{ matrix.config.run_64bit_tests }}
run: |
make clean
make -j2 ARCH=x86-64-modern build
-# List of authors for Stockfish, as of June 14, 2021
+# List of authors for Stockfish
# Founders of the Stockfish project and fishtest infrastructure
Tord Romstad (romstad)
Gary Heckman (gheckman)
George Sobala (gsobala)
gguliash
+Giacomo Lorenzetti (G-Lorenz)
Gian-Carlo Pascutto (gcp)
Gontran Lemaire (gonlem)
Goodkov Vasiliy Aleksandrovich (goodkov)
Krystian Kuzniarek (kuzkry)
Leonardo Ljubičić (ICCF World Champion)
Leonid Pechenik (lp--)
+Liam Keegan (lkeegan)
Linus Arver (listx)
loco-loco
Lub van den Berg (ElbertoOne)
Tom Vijlbrief (tomtor)
Tomasz Sobczyk (Sopel97)
Torsten Franz (torfranz, tfranzer)
+Torsten Hellwig (Torom)
Tracey Emery (basepr1me)
tttak
Unai Corzo (unaiic)
SRCS = benchmark.cpp bitbase.cpp bitboard.cpp endgame.cpp evaluate.cpp main.cpp \
material.cpp misc.cpp movegen.cpp movepick.cpp pawns.cpp position.cpp psqt.cpp \
search.cpp thread.cpp timeman.cpp tt.cpp uci.cpp ucioption.cpp tune.cpp syzygy/tbprobe.cpp \
- nnue/evaluate_nnue.cpp nnue/features/half_ka_v2.cpp \
+ nnue/evaluate_nnue.cpp nnue/features/half_ka_v2_hm.cpp \
hashprobe.grpc.pb.cc hashprobe.pb.cc
CLISRCS = client.cpp hashprobe.grpc.pb.cc hashprobe.pb.cc uci.cpp
CXX=g++
endif
- CXXFLAGS += -Wextra -Wshadow
+ CXXFLAGS += -pedantic -Wextra -Wshadow
LDFLAGS += -static
endif
endif
ifeq ($(KERNEL),Darwin)
- CXXFLAGS += -arch $(arch) -mmacosx-version-min=10.14
- LDFLAGS += -arch $(arch) -mmacosx-version-min=10.14
+ CXXFLAGS += -mmacosx-version-min=10.14
+ LDFLAGS += -mmacosx-version-min=10.14
+ ifneq ($(arch),any)
+ CXXFLAGS += -arch $(arch)
+ LDFLAGS += -arch $(arch)
+ endif
XCRUN = xcrun
endif
# Other stuff
-.depend:
+.depend: $(SRCS)
-@$(CXX) $(DEPENDFLAGS) -MM $(SRCS) > $@ 2> /dev/null
-include .depend
return;
string eval_file = string(Options["EvalFile"]);
+ if (eval_file.empty())
+ eval_file = EvalFileDefaultName;
#if defined(DEFAULT_NNUE_DIRECTORY)
#define stringify2(x) #x
void NNUE::verify() {
string eval_file = string(Options["EvalFile"]);
+ if (eval_file.empty())
+ eval_file = EvalFileDefaultName;
if (useNNUE && eval_file_loaded != eval_file)
{
- UCI::OptionsMap defaults;
- UCI::init(defaults);
string msg1 = "If the UCI option \"Use NNUE\" is set to true, network evaluation parameters compatible with the engine must be available.";
string msg2 = "The option is set to true, but the network file " + eval_file + " was not loaded successfully.";
string msg3 = "The UCI option EvalFile might need to specify the full path, including the directory name, to the network file.";
- string msg4 = "The default net can be downloaded from: https://tests.stockfishchess.org/api/nn/" + string(defaults["EvalFile"]);
+ string msg4 = "The default net can be downloaded from: https://tests.stockfishchess.org/api/nn/" + std::string(EvalFileDefaultName);
string msg5 = "The engine will be terminated now.";
sync_cout << "info string ERROR: " << msg1 << sync_endl;
// Scale and shift NNUE for compatibility with search and classical evaluation
auto adjusted_NNUE = [&]()
{
- int scale = 903
+ int scale = 883
+ 32 * pos.count<PAWN>()
+ 32 * pos.non_pawn_material() / 1024;
// NNUE eval faster when shuffling or if the material on the board is high.
int r50 = pos.rule50_count();
Value psq = Value(abs(eg_value(pos.psq_score())));
- bool classical = psq * 5 > (750 + pos.non_pawn_material() / 64) * (5 + r50);
+ bool classical = psq * 5 > (850 + pos.non_pawn_material() / 64) * (5 + r50);
v = classical ? Evaluation<NO_TRACE>(pos).value() // classical
: adjusted_NNUE(); // NNUE
// The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
// for the build process (profile-build and fishtest) to work. Do not change the
// name of the macro, as it is used in the Makefile.
- #define EvalFileDefaultName "nn-9e3c6298299a.nnue"
+ #define EvalFileDefaultName "nn-6762d36ad265.nnue"
namespace NNUE {
static Logger l;
- if (!fname.empty() && !l.file.is_open())
+ if (l.file.is_open())
+ {
+ cout.rdbuf(l.out.buf);
+ cin.rdbuf(l.in.buf);
+ l.file.close();
+ }
+
+ if (!fname.empty())
{
l.file.open(fname, ifstream::out);
cin.rdbuf(&l.in);
cout.rdbuf(&l.out);
}
- else if (fname.empty() && l.file.is_open())
- {
- cout.rdbuf(l.out.buf);
- cin.rdbuf(l.in.buf);
- l.file.close();
- }
}
};
static void* aligned_large_pages_alloc_windows(size_t allocSize) {
#if !defined(_WIN64)
+ (void)allocSize; // suppress unused-parameter compiler warning
return nullptr;
#else
constexpr Direction UpRight = (Us == WHITE ? NORTH_EAST : SOUTH_WEST);
constexpr Direction UpLeft = (Us == WHITE ? NORTH_WEST : SOUTH_EAST);
- const Bitboard emptySquares = Type == QUIETS || Type == QUIET_CHECKS ? target : ~pos.pieces();
- const Bitboard enemies = Type == EVASIONS ? pos.checkers()
- : Type == CAPTURES ? target : pos.pieces(Them);
+ const Bitboard emptySquares = ~pos.pieces();
+ const Bitboard enemies = Type == EVASIONS ? pos.checkers()
+ : pos.pieces(Them);
Bitboard pawnsOn7 = pos.pieces(Us, PAWN) & TRank7BB;
Bitboard pawnsNotOn7 = pos.pieces(Us, PAWN) & ~TRank7BB;
+ (*continuationHistory[1])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[3])[pos.moved_piece(m)][to_sq(m)]
+ (*continuationHistory[5])[pos.moved_piece(m)][to_sq(m)]
- + (ply < MAX_LPH ? std::min(4, depth / 3) * (*lowPlyHistory)[ply][from_to(m)] : 0);
+ + (ply < MAX_LPH ? 6 * (*lowPlyHistory)[ply][from_to(m)] : 0);
else // Type == EVASIONS
{
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
-//Definition of input features HalfKAv2 of NNUE evaluation function
+//Definition of input features HalfKAv2_hm of NNUE evaluation function
-#include "half_ka_v2.h"
+#include "half_ka_v2_hm.h"
#include "../../position.h"
namespace Stockfish::Eval::NNUE::Features {
// Orient a square according to perspective (rotates by 180 for black)
- inline Square HalfKAv2::orient(Color perspective, Square s) {
- return Square(int(s) ^ (bool(perspective) * 56));
+ inline Square HalfKAv2_hm::orient(Color perspective, Square s, Square ksq) {
+ return Square(int(s) ^ (bool(perspective) * SQ_A8) ^ ((file_of(ksq) < FILE_E) * SQ_H1));
}
// Index of a feature for a given king position and another piece on some square
- inline IndexType HalfKAv2::make_index(Color perspective, Square s, Piece pc, Square ksq) {
- return IndexType(orient(perspective, s) + PieceSquareIndex[perspective][pc] + PS_NB * ksq);
+ inline IndexType HalfKAv2_hm::make_index(Color perspective, Square s, Piece pc, Square ksq) {
+ Square o_ksq = orient(perspective, ksq, ksq);
+ return IndexType(orient(perspective, s, ksq) + PieceSquareIndex[perspective][pc] + PS_NB * KingBuckets[o_ksq]);
}
// Get a list of indices for active features
- void HalfKAv2::append_active_indices(
+ void HalfKAv2_hm::append_active_indices(
const Position& pos,
Color perspective,
ValueListInserter<IndexType> active
) {
- Square ksq = orient(perspective, pos.square<KING>(perspective));
+ Square ksq = pos.square<KING>(perspective);
Bitboard bb = pos.pieces();
while (bb)
{
// append_changed_indices() : get a list of indices for recently changed features
- void HalfKAv2::append_changed_indices(
+ void HalfKAv2_hm::append_changed_indices(
Square ksq,
StateInfo* st,
Color perspective,
ValueListInserter<IndexType> added
) {
const auto& dp = st->dirtyPiece;
- Square oriented_ksq = orient(perspective, ksq);
for (int i = 0; i < dp.dirty_num; ++i) {
Piece pc = dp.piece[i];
if (dp.from[i] != SQ_NONE)
- removed.push_back(make_index(perspective, dp.from[i], pc, oriented_ksq));
+ removed.push_back(make_index(perspective, dp.from[i], pc, ksq));
if (dp.to[i] != SQ_NONE)
- added.push_back(make_index(perspective, dp.to[i], pc, oriented_ksq));
+ added.push_back(make_index(perspective, dp.to[i], pc, ksq));
}
}
- int HalfKAv2::update_cost(StateInfo* st) {
+ int HalfKAv2_hm::update_cost(StateInfo* st) {
return st->dirtyPiece.dirty_num;
}
- int HalfKAv2::refresh_cost(const Position& pos) {
+ int HalfKAv2_hm::refresh_cost(const Position& pos) {
return pos.count<ALL_PIECES>();
}
- bool HalfKAv2::requires_refresh(StateInfo* st, Color perspective) {
+ bool HalfKAv2_hm::requires_refresh(StateInfo* st, Color perspective) {
return st->dirtyPiece.piece[0] == make_piece(perspective, KING);
}
//Definition of input features HalfKP of NNUE evaluation function
-#ifndef NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
-#define NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
+#ifndef NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
+#define NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
#include "../nnue_common.h"
namespace Stockfish::Eval::NNUE::Features {
- // Feature HalfKAv2: Combination of the position of own king
- // and the position of pieces
- class HalfKAv2 {
+ // Feature HalfKAv2_hm: Combination of the position of own king
+ // and the position of pieces. Position mirrored such that king always on e..h files.
+ class HalfKAv2_hm {
// unique number for each piece type on each square
enum {
};
// Orient a square according to perspective (rotates by 180 for black)
- static Square orient(Color perspective, Square s);
+ static Square orient(Color perspective, Square s, Square ksq);
// Index of a feature for a given king position and another piece on some square
static IndexType make_index(Color perspective, Square s, Piece pc, Square ksq);
public:
// Feature name
- static constexpr const char* Name = "HalfKAv2(Friend)";
+ static constexpr const char* Name = "HalfKAv2_hm(Friend)";
// Hash value embedded in the evaluation file
- static constexpr std::uint32_t HashValue = 0x5f234cb8u;
+ static constexpr std::uint32_t HashValue = 0x7f234cb8u;
// Number of feature dimensions
static constexpr IndexType Dimensions =
- static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_NB);
+ static_cast<IndexType>(SQUARE_NB) * static_cast<IndexType>(PS_NB) / 2;
+
+ static constexpr int KingBuckets[64] = {
+ -1, -1, -1, -1, 31, 30, 29, 28,
+ -1, -1, -1, -1, 27, 26, 25, 24,
+ -1, -1, -1, -1, 23, 22, 21, 20,
+ -1, -1, -1, -1, 19, 18, 17, 16,
+ -1, -1, -1, -1, 15, 14, 13, 12,
+ -1, -1, -1, -1, 11, 10, 9, 8,
+ -1, -1, -1, -1, 7, 6, 5, 4,
+ -1, -1, -1, -1, 3, 2, 1, 0
+ };
// Maximum number of simultaneously active features.
static constexpr IndexType MaxActiveDimensions = 32;
} // namespace Stockfish::Eval::NNUE::Features
-#endif // #ifndef NNUE_FEATURES_HALF_KA_V2_H_INCLUDED
+#endif // #ifndef NNUE_FEATURES_HALF_KA_V2_HM_H_INCLUDED
#define NNUE_LAYERS_AFFINE_TRANSFORM_H_INCLUDED
#include <iostream>
+#include <algorithm>
+#include <type_traits>
#include "../nnue_common.h"
+#include "../../simd.h"
+
+/*
+ This file contains the definition for a fully connected layer (aka affine transform).
+ Two approaches are employed, depending on the sizes of the transform.
+
+ Approach 1:
+ - used when the PaddedInputDimensions >= 128
+ - uses AVX512 if possible
+ - processes inputs in batches of 2*InputSimdWidth
+ - so in batches of 128 for AVX512
+ - the weight blocks of size InputSimdWidth are transposed such that
+ access is sequential
+ - N columns of the weight matrix are processed a time, where N
+ depends on the architecture (the amount of registers)
+ - accumulate + hadd is used
+
+ Approach 2:
+ - used when the PaddedInputDimensions < 128
+ - does not use AVX512
+ - expected use-case is for when PaddedInputDimensions == 32 and InputDimensions <= 32.
+ - that's why AVX512 is hard to implement
+ - expected use-case is small layers
+ - not optimized as well as the approach 1
+ - inputs are processed in chunks of 4, weights are respectively transposed
+ - accumulation happens directly to int32s
+*/
namespace Stockfish::Eval::NNUE::Layers {
- // Affine transformation layer
+// Fallback implementation for older/other architectures.
+// Identical for both approaches. Requires the input to be padded to at least 16 values.
+#if !defined(USE_SSSE3)
+ template <IndexType InputDimensions, IndexType PaddedInputDimensions, IndexType OutputDimensions>
+ 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)
+ {
+# if defined(USE_SSE2)
+ // At least a multiple of 16, with SSE2.
+ static_assert(PaddedInputDimensions % 16 == 0);
+ constexpr IndexType NumChunks = PaddedInputDimensions / 16;
+ const __m128i Zeros = _mm_setzero_si128();
+ const auto inputVector = reinterpret_cast<const __m128i*>(input);
+
+# elif defined(USE_MMX)
+ static_assert(InputDimensions % 8 == 0);
+ constexpr IndexType NumChunks = InputDimensions / 8;
+ const __m64 Zeros = _mm_setzero_si64();
+ const auto inputVector = reinterpret_cast<const __m64*>(input);
+
+# elif defined(USE_NEON)
+ static_assert(PaddedInputDimensions % 16 == 0);
+ constexpr IndexType NumChunks = PaddedInputDimensions / 16;
+ const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
+# endif
+
+ for (IndexType i = 0; i < OutputDimensions; ++i) {
+ const IndexType offset = i * PaddedInputDimensions;
+
+# if defined(USE_SSE2)
+ __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
+ __m128i sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j) {
+ __m128i row_j = _mm_load_si128(&row[j]);
+ __m128i input_j = _mm_load_si128(&inputVector[j]);
+ __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
+ __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
+ __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
+ __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
+ __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
+ __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_epi32(sumLo, productLo);
+ sumHi = _mm_add_epi32(sumHi, productHi);
+ }
+ __m128i sum = _mm_add_epi32(sumLo, sumHi);
+ __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sumHigh_64);
+ __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
+ sum = _mm_add_epi32(sum, sum_second_32);
+ output[i] = _mm_cvtsi128_si32(sum);
+
+# elif defined(USE_MMX)
+ __m64 sumLo = _mm_cvtsi32_si64(biases[i]);
+ __m64 sumHi = Zeros;
+ const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j) {
+ __m64 row_j = row[j];
+ __m64 input_j = inputVector[j];
+ __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
+ __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
+ __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
+ __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
+ __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
+ __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
+ sumLo = _mm_add_pi32(sumLo, productLo);
+ sumHi = _mm_add_pi32(sumHi, productHi);
+ }
+ __m64 sum = _mm_add_pi32(sumLo, sumHi);
+ sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
+ output[i] = _mm_cvtsi64_si32(sum);
+
+# elif defined(USE_NEON)
+ int32x4_t sum = {biases[i]};
+ const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
+ for (IndexType j = 0; j < NumChunks; ++j) {
+ int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
+ product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
+ sum = vpadalq_s16(sum, product);
+ }
+ output[i] = sum[0] + sum[1] + sum[2] + sum[3];
+
+# else
+ std::int32_t sum = biases[i];
+ for (IndexType j = 0; j < InputDimensions; ++j) {
+ sum += weights[offset + j] * input[j];
+ }
+ output[i] = sum;
+# endif
+ }
+
+# if defined(USE_MMX)
+ _mm_empty();
+# endif
+ }
+#endif
+
+ template <typename PreviousLayer, IndexType OutDims, typename Enabled = void>
+ class AffineTransform;
+
+ // A specialization for large inputs.
template <typename PreviousLayer, IndexType OutDims>
- class AffineTransform {
+ class AffineTransform<PreviousLayer, OutDims, std::enable_if_t<(PreviousLayer::OutputDimensions >= 2*64-1)>> {
public:
// Input/output type
using InputType = typename PreviousLayer::OutputType;
static_assert(std::is_same<InputType, std::uint8_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType InputDimensions =
- PreviousLayer::OutputDimensions;
+ static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions;
static constexpr IndexType OutputDimensions = OutDims;
+
static constexpr IndexType PaddedInputDimensions =
- ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+
+ static_assert(PaddedInputDimensions >= 128, "Something went wrong. This specialization should not have been chosen.");
+
#if defined (USE_AVX512)
- static constexpr const IndexType OutputSimdWidth = SimdWidth / 2;
+ static constexpr const IndexType InputSimdWidth = 64;
+ static constexpr const IndexType MaxNumOutputRegs = 16;
+#elif defined (USE_AVX2)
+ static constexpr const IndexType InputSimdWidth = 32;
+ static constexpr const IndexType MaxNumOutputRegs = 8;
#elif defined (USE_SSSE3)
- static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
+ static constexpr const IndexType InputSimdWidth = 16;
+ static constexpr const IndexType MaxNumOutputRegs = 8;
+#else
+ // The fallback implementation will not have permuted weights.
+ // We define these to avoid a lot of ifdefs later.
+ static constexpr const IndexType InputSimdWidth = 1;
+ static constexpr const IndexType MaxNumOutputRegs = 1;
#endif
+ // A big block is a region in the weight matrix of the size [PaddedInputDimensions, NumOutputRegs].
+ // A small block is a region of size [InputSimdWidth, 1]
+
+ static constexpr const IndexType NumOutputRegs = std::min(MaxNumOutputRegs, OutputDimensions);
+ static constexpr const IndexType SmallBlockSize = InputSimdWidth;
+ static constexpr const IndexType BigBlockSize = NumOutputRegs * PaddedInputDimensions;
+ static constexpr const IndexType NumSmallBlocksInBigBlock = BigBlockSize / SmallBlockSize;
+ static constexpr const IndexType NumSmallBlocksPerOutput = PaddedInputDimensions / SmallBlockSize;
+ static constexpr const IndexType NumBigBlocks = OutputDimensions / NumOutputRegs;
+
+ static_assert(OutputDimensions % NumOutputRegs == 0);
+
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
- ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
+ ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
// Size of the forward propagation buffer used from the input layer to this layer
static constexpr std::size_t BufferSize =
- PreviousLayer::BufferSize + SelfBufferSize;
+ PreviousLayer::BufferSize + SelfBufferSize;
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
return hashValue;
}
+ /*
+ Transposes the small blocks within a block.
+ Effectively means that weights can be traversed sequentially during inference.
+ */
+ static IndexType get_weight_index(IndexType i)
+ {
+ const IndexType smallBlock = (i / SmallBlockSize) % NumSmallBlocksInBigBlock;
+ const IndexType smallBlockCol = smallBlock / NumSmallBlocksPerOutput;
+ const IndexType smallBlockRow = smallBlock % NumSmallBlocksPerOutput;
+ const IndexType bigBlock = i / BigBlockSize;
+ const IndexType rest = i % SmallBlockSize;
+
+ const IndexType idx =
+ bigBlock * BigBlockSize
+ + smallBlockRow * SmallBlockSize * NumOutputRegs
+ + smallBlockCol * SmallBlockSize
+ + rest;
+
+ return idx;
+ }
+
// Read network parameters
bool read_parameters(std::istream& stream) {
if (!previousLayer.read_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
biases[i] = read_little_endian<BiasType>(stream);
+
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
-#if !defined (USE_SSSE3)
- weights[i] = read_little_endian<WeightType>(stream);
-#else
- weights[
- (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
- i / PaddedInputDimensions * 4 +
- i % 4
- ] = read_little_endian<WeightType>(stream);
-#endif
+ weights[get_weight_index(i)] = read_little_endian<WeightType>(stream);
return !stream.fail();
}
if (!previousLayer.write_parameters(stream)) return false;
for (std::size_t i = 0; i < OutputDimensions; ++i)
write_little_endian<BiasType>(stream, biases[i]);
-#if !defined (USE_SSSE3)
- for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
- write_little_endian<WeightType>(stream, weights[i]);
-#else
- std::unique_ptr<WeightType[]> unscrambledWeights = std::make_unique<WeightType[]>(OutputDimensions * PaddedInputDimensions);
- for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i) {
- unscrambledWeights[i] =
- weights[
- (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
- i / PaddedInputDimensions * 4 +
- i % 4
- ];
- }
for (std::size_t i = 0; i < OutputDimensions * PaddedInputDimensions; ++i)
- write_little_endian<WeightType>(stream, unscrambledWeights[i]);
-#endif
+ write_little_endian<WeightType>(stream, weights[get_weight_index(i)]);
return !stream.fail();
}
const OutputType* propagate(
const TransformedFeatureType* transformedFeatures, char* buffer) const {
const auto input = previousLayer.propagate(
- transformedFeatures, buffer + SelfBufferSize);
-
-#if defined (USE_AVX512)
-
- [[maybe_unused]] const __m512i Ones512 = _mm512_set1_epi16(1);
-
- [[maybe_unused]] auto m512_hadd = [](__m512i sum, int bias) -> int {
- return _mm512_reduce_add_epi32(sum) + bias;
- };
-
- [[maybe_unused]] auto m512_add_dpbusd_epi32 = [=](__m512i& acc, __m512i a, __m512i b) {
-#if defined (USE_VNNI)
- acc = _mm512_dpbusd_epi32(acc, a, b);
-#else
- __m512i product0 = _mm512_maddubs_epi16(a, b);
- product0 = _mm512_madd_epi16(product0, Ones512);
- acc = _mm512_add_epi32(acc, product0);
-#endif
- };
-
- [[maybe_unused]] auto m512_add_dpbusd_epi32x4 = [=](__m512i& acc, __m512i a0, __m512i b0, __m512i a1, __m512i b1,
- __m512i a2, __m512i b2, __m512i a3, __m512i b3) {
-#if defined (USE_VNNI)
- acc = _mm512_dpbusd_epi32(acc, a0, b0);
- acc = _mm512_dpbusd_epi32(acc, a1, b1);
- acc = _mm512_dpbusd_epi32(acc, a2, b2);
- acc = _mm512_dpbusd_epi32(acc, a3, b3);
-#else
- __m512i product0 = _mm512_maddubs_epi16(a0, b0);
- __m512i product1 = _mm512_maddubs_epi16(a1, b1);
- __m512i product2 = _mm512_maddubs_epi16(a2, b2);
- __m512i product3 = _mm512_maddubs_epi16(a3, b3);
- product0 = _mm512_adds_epi16(product0, product1);
- product0 = _mm512_madd_epi16(product0, Ones512);
- product2 = _mm512_adds_epi16(product2, product3);
- product2 = _mm512_madd_epi16(product2, Ones512);
- acc = _mm512_add_epi32(acc, _mm512_add_epi32(product0, product2));
-#endif
- };
-
-#endif
-#if defined (USE_AVX2)
-
- [[maybe_unused]] const __m256i Ones256 = _mm256_set1_epi16(1);
-
- [[maybe_unused]] auto m256_hadd = [](__m256i sum, int bias) -> int {
- __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
- sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
- return _mm_cvtsi128_si32(sum128) + bias;
- };
-
- [[maybe_unused]] auto m256_add_dpbusd_epi32 = [=](__m256i& acc, __m256i a, __m256i b) {
-#if defined (USE_VNNI)
- acc = _mm256_dpbusd_epi32(acc, a, b);
-#else
- __m256i product0 = _mm256_maddubs_epi16(a, b);
- product0 = _mm256_madd_epi16(product0, Ones256);
- acc = _mm256_add_epi32(acc, product0);
-#endif
- };
-
- [[maybe_unused]] auto m256_add_dpbusd_epi32x4 = [=](__m256i& acc, __m256i a0, __m256i b0, __m256i a1, __m256i b1,
- __m256i a2, __m256i b2, __m256i a3, __m256i b3) {
-#if defined (USE_VNNI)
- acc = _mm256_dpbusd_epi32(acc, a0, b0);
- acc = _mm256_dpbusd_epi32(acc, a1, b1);
- acc = _mm256_dpbusd_epi32(acc, a2, b2);
- acc = _mm256_dpbusd_epi32(acc, a3, b3);
-#else
- __m256i product0 = _mm256_maddubs_epi16(a0, b0);
- __m256i product1 = _mm256_maddubs_epi16(a1, b1);
- __m256i product2 = _mm256_maddubs_epi16(a2, b2);
- __m256i product3 = _mm256_maddubs_epi16(a3, b3);
- product0 = _mm256_adds_epi16(product0, product1);
- product0 = _mm256_madd_epi16(product0, Ones256);
- product2 = _mm256_adds_epi16(product2, product3);
- product2 = _mm256_madd_epi16(product2, Ones256);
- acc = _mm256_add_epi32(acc, _mm256_add_epi32(product0, product2));
-#endif
- };
-
-#endif
-#if defined (USE_SSSE3)
-
- [[maybe_unused]] const __m128i Ones128 = _mm_set1_epi16(1);
-
- [[maybe_unused]] auto m128_hadd = [](__m128i sum, int bias) -> int {
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
- sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
- return _mm_cvtsi128_si32(sum) + bias;
- };
-
- [[maybe_unused]] auto m128_add_dpbusd_epi32 = [=](__m128i& acc, __m128i a, __m128i b) {
- __m128i product0 = _mm_maddubs_epi16(a, b);
- product0 = _mm_madd_epi16(product0, Ones128);
- acc = _mm_add_epi32(acc, product0);
- };
-
- [[maybe_unused]] auto m128_add_dpbusd_epi32x4 = [=](__m128i& acc, __m128i a0, __m128i b0, __m128i a1, __m128i b1,
- __m128i a2, __m128i b2, __m128i a3, __m128i b3) {
- __m128i product0 = _mm_maddubs_epi16(a0, b0);
- __m128i product1 = _mm_maddubs_epi16(a1, b1);
- __m128i product2 = _mm_maddubs_epi16(a2, b2);
- __m128i product3 = _mm_maddubs_epi16(a3, b3);
- product0 = _mm_adds_epi16(product0, product1);
- product0 = _mm_madd_epi16(product0, Ones128);
- product2 = _mm_adds_epi16(product2, product3);
- product2 = _mm_madd_epi16(product2, Ones128);
- acc = _mm_add_epi32(acc, _mm_add_epi32(product0, product2));
- };
-
-#endif
+ transformedFeatures, buffer + SelfBufferSize);
+ OutputType* output = reinterpret_cast<OutputType*>(buffer);
#if defined (USE_AVX512)
using vec_t = __m512i;
#define vec_setzero _mm512_setzero_si512
#define vec_set_32 _mm512_set1_epi32
- auto& vec_add_dpbusd_32 = m512_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m512_add_dpbusd_epi32x4;
- auto& vec_hadd = m512_hadd;
+ #define vec_add_dpbusd_32 Simd::m512_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m512_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m512_hadd
+ #define vec_haddx4 Simd::m512_haddx4
#elif defined (USE_AVX2)
using vec_t = __m256i;
#define vec_setzero _mm256_setzero_si256
#define vec_set_32 _mm256_set1_epi32
- auto& vec_add_dpbusd_32 = m256_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m256_add_dpbusd_epi32x4;
- auto& vec_hadd = m256_hadd;
+ #define vec_add_dpbusd_32 Simd::m256_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m256_hadd
+ #define vec_haddx4 Simd::m256_haddx4
#elif defined (USE_SSSE3)
using vec_t = __m128i;
#define vec_setzero _mm_setzero_si128
#define vec_set_32 _mm_set1_epi32
- auto& vec_add_dpbusd_32 = m128_add_dpbusd_epi32;
- auto& vec_add_dpbusd_32x4 = m128_add_dpbusd_epi32x4;
- auto& vec_hadd = m128_hadd;
+ #define vec_add_dpbusd_32 Simd::m128_add_dpbusd_epi32
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_hadd Simd::m128_hadd
+ #define vec_haddx4 Simd::m128_haddx4
#endif
#if defined (USE_SSSE3)
- // Different layout, we process 4 inputs at a time, always.
- static_assert(InputDimensions % 4 == 0);
+ const vec_t* invec = reinterpret_cast<const vec_t*>(input);
- const auto output = reinterpret_cast<OutputType*>(buffer);
- const auto inputVector = reinterpret_cast<const vec_t*>(input);
- static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
-
- // OutputDimensions is either 1 or a multiple of SimdWidth
- // because then it is also an input dimension.
- if constexpr (OutputDimensions % OutputSimdWidth == 0)
+ // Perform accumulation to registers for each big block
+ for (IndexType bigBlock = 0; bigBlock < NumBigBlocks; ++bigBlock)
{
- constexpr IndexType NumChunks = InputDimensions / 4;
+ vec_t acc[NumOutputRegs] = { vec_setzero() };
+
+ // Each big block has NumOutputRegs small blocks in each "row", one per register.
+ // We process two small blocks at a time to save on one addition without VNNI.
+ for (IndexType smallBlock = 0; smallBlock < NumSmallBlocksPerOutput; smallBlock += 2)
+ {
+ const vec_t* weightvec =
+ reinterpret_cast<const vec_t*>(
+ weights
+ + bigBlock * BigBlockSize
+ + smallBlock * SmallBlockSize * NumOutputRegs);
+
+ const vec_t in0 = invec[smallBlock + 0];
+ const vec_t in1 = invec[smallBlock + 1];
+
+ for (IndexType k = 0; k < NumOutputRegs; ++k)
+ vec_add_dpbusd_32x2(acc[k], in0, weightvec[k], in1, weightvec[k + NumOutputRegs]);
+ }
- const auto input32 = reinterpret_cast<const std::int32_t*>(input);
- vec_t* outptr = reinterpret_cast<vec_t*>(output);
- std::memcpy(output, biases, OutputDimensions * sizeof(OutputType));
+ // Horizontally add all accumulators.
+ if constexpr (NumOutputRegs % 4 == 0)
+ {
+ __m128i* outputvec = reinterpret_cast<__m128i*>(output);
+ const __m128i* biasvec = reinterpret_cast<const __m128i*>(biases);
- for (int i = 0; i < (int)NumChunks - 3; i += 4)
- {
- const vec_t in0 = vec_set_32(input32[i + 0]);
- const vec_t in1 = vec_set_32(input32[i + 1]);
- const vec_t in2 = vec_set_32(input32[i + 2]);
- const vec_t in3 = vec_set_32(input32[i + 3]);
- const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
- const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
- const auto col2 = reinterpret_cast<const vec_t*>(&weights[(i + 2) * OutputDimensions * 4]);
- const auto col3 = reinterpret_cast<const vec_t*>(&weights[(i + 3) * OutputDimensions * 4]);
- for (int j = 0; j * OutputSimdWidth < OutputDimensions; ++j)
- vec_add_dpbusd_32x4(outptr[j], in0, col0[j], in1, col1[j], in2, col2[j], in3, col3[j]);
- }
- }
- else if constexpr (OutputDimensions == 1)
- {
-#if defined (USE_AVX512)
- if constexpr (PaddedInputDimensions % (SimdWidth * 2) != 0)
+ for (IndexType k = 0; k < NumOutputRegs; k += 4)
{
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
- const auto inputVector256 = reinterpret_cast<const __m256i*>(input);
-
- __m256i sum0 = _mm256_setzero_si256();
- const auto row0 = reinterpret_cast<const __m256i*>(&weights[0]);
-
- for (int j = 0; j < (int)NumChunks; ++j)
- {
- const __m256i in = inputVector256[j];
- m256_add_dpbusd_epi32(sum0, in, row0[j]);
- }
- output[0] = m256_hadd(sum0, biases[0]);
+ const IndexType idx = (bigBlock * NumOutputRegs + k) / 4;
+ outputvec[idx] = vec_haddx4(acc[k+0], acc[k+1], acc[k+2], acc[k+3], biasvec[idx]);
}
- else
-#endif
+ }
+ else
+ {
+ for (IndexType k = 0; k < NumOutputRegs; ++k)
{
-#if defined (USE_AVX512)
- constexpr IndexType NumChunks = PaddedInputDimensions / (SimdWidth * 2);
-#else
- constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
-#endif
- vec_t sum0 = vec_setzero();
- const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
-
- for (int j = 0; j < (int)NumChunks; ++j)
- {
- const vec_t in = inputVector[j];
- vec_add_dpbusd_32(sum0, in, row0[j]);
- }
- output[0] = vec_hadd(sum0, biases[0]);
+ const IndexType idx = (bigBlock * NumOutputRegs + k);
+ output[idx] = vec_hadd(acc[k], biases[idx]);
}
+ }
}
+# undef vec_setzero
+# undef vec_set_32
+# undef vec_add_dpbusd_32
+# undef vec_add_dpbusd_32x2
+# undef vec_hadd
+# undef vec_haddx4
#else
+ // Use old implementation for the other architectures.
+ affine_transform_non_ssse3<
+ InputDimensions,
+ PaddedInputDimensions,
+ OutputDimensions>(output, weights, biases, input);
+
+#endif
-// Use old implementation for the other architectures.
+ return output;
+ }
- auto output = reinterpret_cast<OutputType*>(buffer);
+ private:
+ using BiasType = OutputType;
+ using WeightType = std::int8_t;
-#if defined(USE_SSE2)
- // At least a multiple of 16, with SSE2.
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
- const __m128i Zeros = _mm_setzero_si128();
- const auto inputVector = reinterpret_cast<const __m128i*>(input);
+ PreviousLayer previousLayer;
-#elif defined(USE_MMX)
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
- const __m64 Zeros = _mm_setzero_si64();
- const auto inputVector = reinterpret_cast<const __m64*>(input);
+ alignas(CacheLineSize) BiasType biases[OutputDimensions];
+ alignas(CacheLineSize) WeightType weights[OutputDimensions * PaddedInputDimensions];
+ };
-#elif defined(USE_NEON)
- static_assert(InputDimensions % SimdWidth == 0);
- constexpr IndexType NumChunks = InputDimensions / SimdWidth;
- const auto inputVector = reinterpret_cast<const int8x8_t*>(input);
+ template <typename PreviousLayer, IndexType OutDims>
+ class AffineTransform<PreviousLayer, OutDims, std::enable_if_t<(PreviousLayer::OutputDimensions < 2*64-1)>> {
+ public:
+ // Input/output type
+ using InputType = typename PreviousLayer::OutputType;
+ using OutputType = std::int32_t;
+ static_assert(std::is_same<InputType, std::uint8_t>::value, "");
+
+ // Number of input/output dimensions
+ static constexpr IndexType InputDimensions =
+ PreviousLayer::OutputDimensions;
+ static constexpr IndexType OutputDimensions = OutDims;
+ static constexpr IndexType PaddedInputDimensions =
+ ceil_to_multiple<IndexType>(InputDimensions, MaxSimdWidth);
+
+ static_assert(PaddedInputDimensions < 128, "Something went wrong. This specialization should not have been chosen.");
+
+#if defined (USE_SSSE3)
+ static constexpr const IndexType OutputSimdWidth = SimdWidth / 4;
+ static constexpr const IndexType InputSimdWidth = SimdWidth;
#endif
- for (IndexType i = 0; i < OutputDimensions; ++i) {
- const IndexType offset = i * PaddedInputDimensions;
-
-#if defined(USE_SSE2)
- __m128i sumLo = _mm_cvtsi32_si128(biases[i]);
- __m128i sumHi = Zeros;
- const auto row = reinterpret_cast<const __m128i*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m128i row_j = _mm_load_si128(&row[j]);
- __m128i input_j = _mm_load_si128(&inputVector[j]);
- __m128i extendedRowLo = _mm_srai_epi16(_mm_unpacklo_epi8(row_j, row_j), 8);
- __m128i extendedRowHi = _mm_srai_epi16(_mm_unpackhi_epi8(row_j, row_j), 8);
- __m128i extendedInputLo = _mm_unpacklo_epi8(input_j, Zeros);
- __m128i extendedInputHi = _mm_unpackhi_epi8(input_j, Zeros);
- __m128i productLo = _mm_madd_epi16(extendedRowLo, extendedInputLo);
- __m128i productHi = _mm_madd_epi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_epi32(sumLo, productLo);
- sumHi = _mm_add_epi32(sumHi, productHi);
- }
- __m128i sum = _mm_add_epi32(sumLo, sumHi);
- __m128i sumHigh_64 = _mm_shuffle_epi32(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sumHigh_64);
- __m128i sum_second_32 = _mm_shufflelo_epi16(sum, _MM_SHUFFLE(1, 0, 3, 2));
- sum = _mm_add_epi32(sum, sum_second_32);
- output[i] = _mm_cvtsi128_si32(sum);
-
-#elif defined(USE_MMX)
- __m64 sumLo = _mm_cvtsi32_si64(biases[i]);
- __m64 sumHi = Zeros;
- const auto row = reinterpret_cast<const __m64*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- __m64 row_j = row[j];
- __m64 input_j = inputVector[j];
- __m64 extendedRowLo = _mm_srai_pi16(_mm_unpacklo_pi8(row_j, row_j), 8);
- __m64 extendedRowHi = _mm_srai_pi16(_mm_unpackhi_pi8(row_j, row_j), 8);
- __m64 extendedInputLo = _mm_unpacklo_pi8(input_j, Zeros);
- __m64 extendedInputHi = _mm_unpackhi_pi8(input_j, Zeros);
- __m64 productLo = _mm_madd_pi16(extendedRowLo, extendedInputLo);
- __m64 productHi = _mm_madd_pi16(extendedRowHi, extendedInputHi);
- sumLo = _mm_add_pi32(sumLo, productLo);
- sumHi = _mm_add_pi32(sumHi, productHi);
- }
- __m64 sum = _mm_add_pi32(sumLo, sumHi);
- sum = _mm_add_pi32(sum, _mm_unpackhi_pi32(sum, sum));
- output[i] = _mm_cvtsi64_si32(sum);
-
-#elif defined(USE_NEON)
- int32x4_t sum = {biases[i]};
- const auto row = reinterpret_cast<const int8x8_t*>(&weights[offset]);
- for (IndexType j = 0; j < NumChunks; ++j) {
- int16x8_t product = vmull_s8(inputVector[j * 2], row[j * 2]);
- product = vmlal_s8(product, inputVector[j * 2 + 1], row[j * 2 + 1]);
- sum = vpadalq_s16(sum, product);
- }
- output[i] = sum[0] + sum[1] + sum[2] + sum[3];
+ // Size of forward propagation buffer used in this layer
+ static constexpr std::size_t SelfBufferSize =
+ ceil_to_multiple(OutputDimensions * sizeof(OutputType), CacheLineSize);
+
+ // Size of the forward propagation buffer used from the input layer to this layer
+ static constexpr std::size_t BufferSize =
+ PreviousLayer::BufferSize + SelfBufferSize;
+
+ // Hash value embedded in the evaluation file
+ static constexpr std::uint32_t get_hash_value() {
+ std::uint32_t hashValue = 0xCC03DAE4u;
+ hashValue += OutputDimensions;
+ hashValue ^= PreviousLayer::get_hash_value() >> 1;
+ hashValue ^= PreviousLayer::get_hash_value() << 31;
+ return hashValue;
+ }
+ static IndexType get_weight_index_scrambled(IndexType i)
+ {
+ return
+ (i / 4) % (PaddedInputDimensions / 4) * OutputDimensions * 4 +
+ i / PaddedInputDimensions * 4 +
+ i % 4;
+ }
+
+ static IndexType get_weight_index(IndexType i)
+ {
+#if defined (USE_SSSE3)
+ return get_weight_index_scrambled(i);
#else
- OutputType sum = biases[i];
- for (IndexType j = 0; j < InputDimensions; ++j) {
- sum += weights[offset + j] * input[j];
- }
- output[i] = sum;
+ return i;
#endif
+ }
- }
-#if defined(USE_MMX)
- _mm_empty();
+ // Read network parameters
+ bool read_parameters(std::istream& stream) {
+ if (!previousLayer.read_parameters(stream)) return false;
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ biases[i] = read_little_endian<BiasType>(stream);
+ for (std::size_t 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 {
+ if (!previousLayer.write_parameters(stream)) return false;
+ for (std::size_t i = 0; i < OutputDimensions; ++i)
+ write_little_endian<BiasType>(stream, biases[i]);
+
+ for (std::size_t 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 TransformedFeatureType* transformedFeatures, char* buffer) const {
+ const auto input = previousLayer.propagate(
+ transformedFeatures, buffer + SelfBufferSize);
+ const auto output = reinterpret_cast<OutputType*>(buffer);
+
+#if 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
+ #define vec_add_dpbusd_32x2 Simd::m256_add_dpbusd_epi32x2
+ #define vec_add_dpbusd_32x4 Simd::m256_add_dpbusd_epi32x4
+ #define vec_hadd Simd::m256_hadd
+ #define vec_haddx4 Simd::m256_haddx4
+#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
+ #define vec_add_dpbusd_32x2 Simd::m128_add_dpbusd_epi32x2
+ #define vec_add_dpbusd_32x4 Simd::m128_add_dpbusd_epi32x4
+ #define vec_hadd Simd::m128_hadd
+ #define vec_haddx4 Simd::m128_haddx4
#endif
+#if defined (USE_SSSE3)
+ const auto inputVector = reinterpret_cast<const vec_t*>(input);
+
+ static_assert(InputDimensions % 8 == 0);
+ static_assert(OutputDimensions % OutputSimdWidth == 0 || OutputDimensions == 1);
+
+ if constexpr (OutputDimensions % OutputSimdWidth == 0)
+ {
+ constexpr IndexType NumChunks = InputDimensions / 4;
+ constexpr IndexType NumRegs = OutputDimensions / OutputSimdWidth;
+
+ const auto input32 = reinterpret_cast<const std::int32_t*>(input);
+ 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 i = 0; i < NumChunks; i += 2)
+ {
+ const vec_t in0 = vec_set_32(input32[i + 0]);
+ const vec_t in1 = vec_set_32(input32[i + 1]);
+ const auto col0 = reinterpret_cast<const vec_t*>(&weights[(i + 0) * OutputDimensions * 4]);
+ const auto col1 = reinterpret_cast<const vec_t*>(&weights[(i + 1) * OutputDimensions * 4]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_add_dpbusd_32x2(acc[k], in0, col0[k], in1, col1[k]);
+ }
+
+ vec_t* outptr = reinterpret_cast<vec_t*>(output);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ outptr[k] = acc[k];
+ }
+ else if constexpr (OutputDimensions == 1)
+ {
+ constexpr IndexType NumChunks = PaddedInputDimensions / SimdWidth;
+ vec_t sum0 = vec_setzero();
+ const auto row0 = reinterpret_cast<const vec_t*>(&weights[0]);
+
+ for (int j = 0; j < (int)NumChunks; ++j)
+ {
+ const vec_t in = inputVector[j];
+ vec_add_dpbusd_32(sum0, in, row0[j]);
+ }
+ output[0] = vec_hadd(sum0, biases[0]);
+ }
+
+# undef vec_setzero
+# undef vec_set_32
+# undef vec_add_dpbusd_32
+# undef vec_add_dpbusd_32x2
+# undef vec_add_dpbusd_32x4
+# undef vec_hadd
+# undef vec_haddx4
+#else
+ // Use old implementation for the other architectures.
+ affine_transform_non_ssse3<
+ InputDimensions,
+ PaddedInputDimensions,
+ OutputDimensions>(output, weights, biases, input);
#endif
return output;
static_assert(std::is_same<InputType, std::int32_t>::value, "");
// Number of input/output dimensions
- static constexpr IndexType InputDimensions =
- PreviousLayer::OutputDimensions;
+ static constexpr IndexType InputDimensions = PreviousLayer::OutputDimensions;
static constexpr IndexType OutputDimensions = InputDimensions;
+ static constexpr IndexType PaddedOutputDimensions =
+ ceil_to_multiple<IndexType>(OutputDimensions, 32);
// Size of forward propagation buffer used in this layer
static constexpr std::size_t SelfBufferSize =
output[i] = static_cast<OutputType>(
std::max(0, std::min(127, input[i] >> WeightScaleBits)));
}
+
+ // Affine transform layers expect that there is at least
+ // ceil_to_multiple(OutputDimensions, 32) initialized values.
+ // We cannot do this in the affine transform because it requires
+ // preallocating space here.
+ for (IndexType i = OutputDimensions; i < PaddedOutputDimensions; ++i) {
+ output[i] = 0;
+ }
+
return output;
}
#include "nnue_common.h"
-#include "features/half_ka_v2.h"
+#include "features/half_ka_v2_hm.h"
#include "layers/input_slice.h"
#include "layers/affine_transform.h"
namespace Stockfish::Eval::NNUE {
// Input features used in evaluation function
- using FeatureSet = Features::HalfKAv2;
+ using FeatureSet = Features::HalfKAv2_hm;
// Number of input feature dimensions after conversion
- constexpr IndexType TransformedFeatureDimensions = 512;
+ constexpr IndexType TransformedFeatureDimensions = 1024;
constexpr IndexType PSQTBuckets = 8;
constexpr IndexType LayerStacks = 8;
// Define network structure
using InputLayer = InputSlice<TransformedFeatureDimensions * 2>;
- using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 16>>;
+ using HiddenLayer1 = ClippedReLU<AffineTransform<InputLayer, 8>>;
using HiddenLayer2 = ClippedReLU<AffineTransform<HiddenLayer1, 32>>;
using OutputLayer = AffineTransform<HiddenLayer2, 1>;
? ss->staticEval > (ss-4)->staticEval || (ss-4)->staticEval == VALUE_NONE
: ss->staticEval > (ss-2)->staticEval;
- // Step 7. Futility pruning: child node (~50 Elo)
+ // Step 7. Futility pruning: child node (~50 Elo).
+ // The depth condition is important for mate finding.
if ( !PvNode
+ && depth < 9
&& eval - futility_margin(depth, improving) >= beta
&& eval < VALUE_KNOWN_WIN) // Do not return unproven wins
return eval;
assert(eval - beta >= 0);
// Null move dynamic reduction based on depth and value
- Depth R = (1090 + 81 * depth) / 256 + std::min(int(eval - beta) / 205, 3);
+ Depth R = std::min(int(eval - beta) / 205, 3) + depth / 3 + 4;
ss->currentMove = MOVE_NULL;
ss->continuationHistory = &thisThread->continuationHistory[0][0][NO_PIECE][0];
&& !ttMove)
depth -= 2;
-moves_loop: // When in check, search starts from here
+moves_loop: // When in check, search starts here
ttCapture = ttMove && pos.capture_or_promotion(ttMove);
// Calculate new depth for this move
newDepth = depth - 1;
- // Step 13. Pruning at shallow depth (~200 Elo)
+ // Step 13. Pruning at shallow depth (~200 Elo). Depth conditions are important for mate finding.
if ( !rootNode
&& pos.non_pawn_material(us)
&& bestValue > VALUE_TB_LOSS_IN_MAX_PLY)
else
{
// Continuation history based pruning (~20 Elo)
- if ( lmrDepth < 5
- && (*contHist[0])[movedPiece][to_sq(move)] < CounterMovePruneThreshold
- && (*contHist[1])[movedPiece][to_sq(move)] < CounterMovePruneThreshold)
+ if (lmrDepth < 5
+ && (*contHist[0])[movedPiece][to_sq(move)]
+ + (*contHist[1])[movedPiece][to_sq(move)]
+ + (*contHist[3])[movedPiece][to_sq(move)] < -3000 * depth + 3000)
continue;
// Futility pruning: parent node (~5 Elo)
if ( !ss->inCheck
- && ss->staticEval + 174 + 157 * lmrDepth <= alpha
- && (*contHist[0])[movedPiece][to_sq(move)]
- + (*contHist[1])[movedPiece][to_sq(move)]
- + (*contHist[3])[movedPiece][to_sq(move)]
- + (*contHist[5])[movedPiece][to_sq(move)] / 3 < 28255)
+ && lmrDepth < 7
+ && ss->staticEval + 174 + 157 * lmrDepth <= alpha)
continue;
// Prune moves with negative SEE (~20 Elo)
- if (!pos.see_ge(move, Value(-(30 - std::min(lmrDepth, 18)) * lmrDepth * lmrDepth)))
+ if (!pos.see_ge(move, Value(-21 * lmrDepth * lmrDepth - 21 * lmrDepth)))
continue;
}
}
return beta;
}
}
+
+ // Capture extensions for PvNodes and cutNodes
+ else if ( (PvNode || cutNode)
+ && captureOrPromotion
+ && moveCount != 1)
+ extension = 1;
+
+ // Check extensions
else if ( givesCheck
&& depth > 6
&& abs(ss->staticEval) > Value(100))
if (cutNode && move != ss->killers[0])
r += 2;
- if (!captureOrPromotion)
- {
- // Increase reduction if ttMove is a capture (~3 Elo)
- if (ttCapture)
- r++;
-
- ss->statScore = thisThread->mainHistory[us][from_to(move)]
- + (*contHist[0])[movedPiece][to_sq(move)]
- + (*contHist[1])[movedPiece][to_sq(move)]
- + (*contHist[3])[movedPiece][to_sq(move)]
- - 4923;
-
- // Decrease/increase reduction for moves with a good/bad history (~30 Elo)
- if (!ss->inCheck)
- r -= ss->statScore / 14721;
- }
+ // Increase reduction if ttMove is a capture (~3 Elo)
+ if (ttCapture)
+ r++;
+
+ ss->statScore = thisThread->mainHistory[us][from_to(move)]
+ + (*contHist[0])[movedPiece][to_sq(move)]
+ + (*contHist[1])[movedPiece][to_sq(move)]
+ + (*contHist[3])[movedPiece][to_sq(move)]
+ - 4923;
+
+ // Decrease/increase reduction for moves with a good/bad history (~30 Elo)
+ r -= ss->statScore / 14721;
// In general we want to cap the LMR depth search at newDepth. But if
// reductions are really negative and movecount is low, we allow this move
- // to be searched deeper than the first move, unless ttMove was extended by 2.
- Depth d = std::clamp(newDepth - r, 1, newDepth + (r < -1 && moveCount <= 5 && !doubleExtension));
+ // to be searched deeper than the first move in specific cases.
+ Depth d = std::clamp(newDepth - r, 1, newDepth + (r < -1 && (moveCount <= 5 || (depth > 6 && PvNode)) && !doubleExtension));
value = -search<NonPV>(pos, ss+1, -(alpha+1), -alpha, d, true);
// Bonus for prior countermove that caused the fail low
else if ( (depth >= 3 || PvNode)
&& !priorCapture)
- update_continuation_histories(ss-1, pos.piece_on(prevSq), prevSq, stat_bonus(depth));
+ update_continuation_histories(ss-1, pos.piece_on(prevSq), prevSq, stat_bonus(depth) * (1 + (PvNode || cutNode)));
if (PvNode)
bestValue = std::min(bestValue, maxValue);
{
assert(is_ok(move));
+ // Check for legality
+ if (!pos.legal(move))
+ continue;
+
givesCheck = pos.gives_check(move);
captureOrPromotion = pos.capture_or_promotion(move);
// Speculative prefetch as early as possible
prefetch(TT.first_entry(pos.key_after(move)));
- // Check for legality just before making the move
- if (!pos.legal(move))
- {
- moveCount--;
- continue;
- }
-
ss->currentMove = move;
ss->continuationHistory = &thisThread->continuationHistory[ss->inCheck]
[captureOrPromotion]
--- /dev/null
+/*
+ Stockfish, a UCI chess playing engine derived from Glaurung 2.1
+ Copyright (C) 2004-2021 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/>.
+*/
+
+#ifndef STOCKFISH_SIMD_H_INCLUDED
+#define STOCKFISH_SIMD_H_INCLUDED
+
+#if defined(USE_AVX2)
+# include <immintrin.h>
+
+#elif defined(USE_SSE41)
+# include <smmintrin.h>
+
+#elif defined(USE_SSSE3)
+# include <tmmintrin.h>
+
+#elif defined(USE_SSE2)
+# include <emmintrin.h>
+
+#elif defined(USE_MMX)
+# include <mmintrin.h>
+
+#elif defined(USE_NEON)
+# include <arm_neon.h>
+#endif
+
+// The inline asm is only safe for GCC, where it is necessary to get good codegen.
+// See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=101693
+// Clang does fine without it.
+// Play around here: https://godbolt.org/z/7EWqrYq51
+#if (defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER))
+#define USE_INLINE_ASM
+#endif
+
+namespace Stockfish::Simd {
+
+#if defined (USE_AVX512)
+
+ [[maybe_unused]] static int m512_hadd(__m512i sum, int bias) {
+ return _mm512_reduce_add_epi32(sum) + bias;
+ }
+
+ /*
+ Parameters:
+ sum0 = [zmm0.i128[0], zmm0.i128[1], zmm0.i128[2], zmm0.i128[3]]
+ sum1 = [zmm1.i128[0], zmm1.i128[1], zmm1.i128[2], zmm1.i128[3]]
+ sum2 = [zmm2.i128[0], zmm2.i128[1], zmm2.i128[2], zmm2.i128[3]]
+ sum3 = [zmm3.i128[0], zmm3.i128[1], zmm3.i128[2], zmm3.i128[3]]
+
+ Returns:
+ ret = [
+ reduce_add_epi32(zmm0.i128[0]), reduce_add_epi32(zmm1.i128[0]), reduce_add_epi32(zmm2.i128[0]), reduce_add_epi32(zmm3.i128[0]),
+ reduce_add_epi32(zmm0.i128[1]), reduce_add_epi32(zmm1.i128[1]), reduce_add_epi32(zmm2.i128[1]), reduce_add_epi32(zmm3.i128[1]),
+ reduce_add_epi32(zmm0.i128[2]), reduce_add_epi32(zmm1.i128[2]), reduce_add_epi32(zmm2.i128[2]), reduce_add_epi32(zmm3.i128[2]),
+ reduce_add_epi32(zmm0.i128[3]), reduce_add_epi32(zmm1.i128[3]), reduce_add_epi32(zmm2.i128[3]), reduce_add_epi32(zmm3.i128[3])
+ ]
+ */
+ [[maybe_unused]] static __m512i m512_hadd128x16_interleave(
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3) {
+
+ __m512i sum01a = _mm512_unpacklo_epi32(sum0, sum1);
+ __m512i sum01b = _mm512_unpackhi_epi32(sum0, sum1);
+
+ __m512i sum23a = _mm512_unpacklo_epi32(sum2, sum3);
+ __m512i sum23b = _mm512_unpackhi_epi32(sum2, sum3);
+
+ __m512i sum01 = _mm512_add_epi32(sum01a, sum01b);
+ __m512i sum23 = _mm512_add_epi32(sum23a, sum23b);
+
+ __m512i sum0123a = _mm512_unpacklo_epi64(sum01, sum23);
+ __m512i sum0123b = _mm512_unpackhi_epi64(sum01, sum23);
+
+ return _mm512_add_epi32(sum0123a, sum0123b);
+ }
+
+ [[maybe_unused]] static __m128i m512_haddx4(
+ __m512i sum0, __m512i sum1, __m512i sum2, __m512i sum3,
+ __m128i bias) {
+
+ __m512i sum = m512_hadd128x16_interleave(sum0, sum1, sum2, sum3);
+
+ __m256i sum256lo = _mm512_castsi512_si256(sum);
+ __m256i sum256hi = _mm512_extracti64x4_epi64(sum, 1);
+
+ sum256lo = _mm256_add_epi32(sum256lo, sum256hi);
+
+ __m128i sum128lo = _mm256_castsi256_si128(sum256lo);
+ __m128i sum128hi = _mm256_extracti128_si256(sum256lo, 1);
+
+ return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
+ }
+
+ [[maybe_unused]] static void m512_add_dpbusd_epi32(
+ __m512i& acc,
+ __m512i a,
+ __m512i b) {
+
+# if defined (USE_VNNI)
+# if defined (USE_INLINE_ASM)
+ asm(
+ "vpdpbusd %[b], %[a], %[acc]\n\t"
+ : [acc]"+v"(acc)
+ : [a]"v"(a), [b]"vm"(b)
+ );
+# else
+ acc = _mm512_dpbusd_epi32(acc, a, b);
+# endif
+# else
+# if defined (USE_INLINE_ASM)
+ __m512i tmp = _mm512_maddubs_epi16(a, b);
+ asm(
+ "vpmaddwd %[tmp], %[ones], %[tmp]\n\t"
+ "vpaddd %[acc], %[tmp], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp]"+&v"(tmp)
+ : [ones]"v"(_mm512_set1_epi16(1))
+ );
+# else
+ __m512i product0 = _mm512_maddubs_epi16(a, b);
+ product0 = _mm512_madd_epi16(product0, _mm512_set1_epi16(1));
+ acc = _mm512_add_epi32(acc, product0);
+# endif
+# endif
+ }
+
+ [[maybe_unused]] static void m512_add_dpbusd_epi32x2(
+ __m512i& acc,
+ __m512i a0, __m512i b0,
+ __m512i a1, __m512i b1) {
+
+# if defined (USE_VNNI)
+# if defined (USE_INLINE_ASM)
+ asm(
+ "vpdpbusd %[b0], %[a0], %[acc]\n\t"
+ "vpdpbusd %[b1], %[a1], %[acc]\n\t"
+ : [acc]"+v"(acc)
+ : [a0]"v"(a0), [b0]"vm"(b0), [a1]"v"(a1), [b1]"vm"(b1)
+ );
+# else
+ acc = _mm512_dpbusd_epi32(acc, a0, b0);
+ acc = _mm512_dpbusd_epi32(acc, a1, b1);
+# endif
+# else
+# if defined (USE_INLINE_ASM)
+ __m512i tmp0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i tmp1 = _mm512_maddubs_epi16(a1, b1);
+ asm(
+ "vpaddsw %[tmp0], %[tmp1], %[tmp0]\n\t"
+ "vpmaddwd %[tmp0], %[ones], %[tmp0]\n\t"
+ "vpaddd %[acc], %[tmp0], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
+ : [tmp1]"v"(tmp1), [ones]"v"(_mm512_set1_epi16(1))
+ );
+# else
+ __m512i product0 = _mm512_maddubs_epi16(a0, b0);
+ __m512i product1 = _mm512_maddubs_epi16(a1, b1);
+ product0 = _mm512_adds_epi16(product0, product1);
+ product0 = _mm512_madd_epi16(product0, _mm512_set1_epi16(1));
+ acc = _mm512_add_epi32(acc, product0);
+# endif
+# endif
+ }
+
+#endif
+
+#if defined (USE_AVX2)
+
+ [[maybe_unused]] static int m256_hadd(__m256i sum, int bias) {
+ __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(sum), _mm256_extracti128_si256(sum, 1));
+ sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_BADC));
+ sum128 = _mm_add_epi32(sum128, _mm_shuffle_epi32(sum128, _MM_PERM_CDAB));
+ return _mm_cvtsi128_si32(sum128) + bias;
+ }
+
+ [[maybe_unused]] static __m128i m256_haddx4(
+ __m256i sum0, __m256i sum1, __m256i sum2, __m256i sum3,
+ __m128i bias) {
+
+ sum0 = _mm256_hadd_epi32(sum0, sum1);
+ sum2 = _mm256_hadd_epi32(sum2, sum3);
+
+ sum0 = _mm256_hadd_epi32(sum0, sum2);
+
+ __m128i sum128lo = _mm256_castsi256_si128(sum0);
+ __m128i sum128hi = _mm256_extracti128_si256(sum0, 1);
+
+ return _mm_add_epi32(_mm_add_epi32(sum128lo, sum128hi), bias);
+ }
+
+ [[maybe_unused]] static void m256_add_dpbusd_epi32(
+ __m256i& acc,
+ __m256i a,
+ __m256i b) {
+
+# if defined (USE_VNNI)
+# if defined (USE_INLINE_ASM)
+ asm(
+ "vpdpbusd %[b], %[a], %[acc]\n\t"
+ : [acc]"+v"(acc)
+ : [a]"v"(a), [b]"vm"(b)
+ );
+# else
+ acc = _mm256_dpbusd_epi32(acc, a, b);
+# endif
+# else
+# if defined (USE_INLINE_ASM)
+ __m256i tmp = _mm256_maddubs_epi16(a, b);
+ asm(
+ "vpmaddwd %[tmp], %[ones], %[tmp]\n\t"
+ "vpaddd %[acc], %[tmp], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp]"+&v"(tmp)
+ : [ones]"v"(_mm256_set1_epi16(1))
+ );
+# else
+ __m256i product0 = _mm256_maddubs_epi16(a, b);
+ product0 = _mm256_madd_epi16(product0, _mm256_set1_epi16(1));
+ acc = _mm256_add_epi32(acc, product0);
+# endif
+# endif
+ }
+
+ [[maybe_unused]] static void m256_add_dpbusd_epi32x2(
+ __m256i& acc,
+ __m256i a0, __m256i b0,
+ __m256i a1, __m256i b1) {
+
+# if defined (USE_VNNI)
+# if defined (USE_INLINE_ASM)
+ asm(
+ "vpdpbusd %[b0], %[a0], %[acc]\n\t"
+ "vpdpbusd %[b1], %[a1], %[acc]\n\t"
+ : [acc]"+v"(acc)
+ : [a0]"v"(a0), [b0]"vm"(b0), [a1]"v"(a1), [b1]"vm"(b1)
+ );
+# else
+ acc = _mm256_dpbusd_epi32(acc, a0, b0);
+ acc = _mm256_dpbusd_epi32(acc, a1, b1);
+# endif
+# else
+# if defined (USE_INLINE_ASM)
+ __m256i tmp0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i tmp1 = _mm256_maddubs_epi16(a1, b1);
+ asm(
+ "vpaddsw %[tmp0], %[tmp1], %[tmp0]\n\t"
+ "vpmaddwd %[tmp0], %[ones], %[tmp0]\n\t"
+ "vpaddd %[acc], %[tmp0], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
+ : [tmp1]"v"(tmp1), [ones]"v"(_mm256_set1_epi16(1))
+ );
+# else
+ __m256i product0 = _mm256_maddubs_epi16(a0, b0);
+ __m256i product1 = _mm256_maddubs_epi16(a1, b1);
+ product0 = _mm256_adds_epi16(product0, product1);
+ product0 = _mm256_madd_epi16(product0, _mm256_set1_epi16(1));
+ acc = _mm256_add_epi32(acc, product0);
+# endif
+# endif
+ }
+
+#endif
+
+#if defined (USE_SSSE3)
+
+ [[maybe_unused]] static int m128_hadd(__m128i sum, int bias) {
+ sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0x4E)); //_MM_PERM_BADC
+ sum = _mm_add_epi32(sum, _mm_shuffle_epi32(sum, 0xB1)); //_MM_PERM_CDAB
+ return _mm_cvtsi128_si32(sum) + bias;
+ }
+
+ [[maybe_unused]] static __m128i m128_haddx4(
+ __m128i sum0, __m128i sum1, __m128i sum2, __m128i sum3,
+ __m128i bias) {
+
+ sum0 = _mm_hadd_epi32(sum0, sum1);
+ sum2 = _mm_hadd_epi32(sum2, sum3);
+ sum0 = _mm_hadd_epi32(sum0, sum2);
+ return _mm_add_epi32(sum0, bias);
+ }
+
+ [[maybe_unused]] static void m128_add_dpbusd_epi32(
+ __m128i& acc,
+ __m128i a,
+ __m128i b) {
+
+# if defined (USE_INLINE_ASM)
+ __m128i tmp = _mm_maddubs_epi16(a, b);
+ asm(
+ "pmaddwd %[ones], %[tmp]\n\t"
+ "paddd %[tmp], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp]"+&v"(tmp)
+ : [ones]"v"(_mm_set1_epi16(1))
+ );
+# else
+ __m128i product0 = _mm_maddubs_epi16(a, b);
+ product0 = _mm_madd_epi16(product0, _mm_set1_epi16(1));
+ acc = _mm_add_epi32(acc, product0);
+# endif
+ }
+
+ [[maybe_unused]] static void m128_add_dpbusd_epi32x2(
+ __m128i& acc,
+ __m128i a0, __m128i b0,
+ __m128i a1, __m128i b1) {
+
+# if defined (USE_INLINE_ASM)
+ __m128i tmp0 = _mm_maddubs_epi16(a0, b0);
+ __m128i tmp1 = _mm_maddubs_epi16(a1, b1);
+ asm(
+ "paddsw %[tmp1], %[tmp0]\n\t"
+ "pmaddwd %[ones], %[tmp0]\n\t"
+ "paddd %[tmp0], %[acc]\n\t"
+ : [acc]"+v"(acc), [tmp0]"+&v"(tmp0)
+ : [tmp1]"v"(tmp1), [ones]"v"(_mm_set1_epi16(1))
+ );
+# else
+ __m128i product0 = _mm_maddubs_epi16(a0, b0);
+ __m128i product1 = _mm_maddubs_epi16(a1, b1);
+ product0 = _mm_adds_epi16(product0, product1);
+ product0 = _mm_madd_epi16(product0, _mm_set1_epi16(1));
+ acc = _mm_add_epi32(acc, product0);
+# endif
+ }
+
+#endif
+
+}
+
+#endif // STOCKFISH_SIMD_H_INCLUDED
assert(!type.empty());
- if ( (type != "button" && v.empty())
+ if ( (type != "button" && type != "string" && v.empty())
|| (type == "check" && v != "true" && v != "false")
|| (type == "spin" && (stof(v) < min || stof(v) > max)))
return *this;
projects / stockfish / commitdiff