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Merged latest updates from trunk

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This commit is contained in:
Benoit Steiner 2016-10-05 18:48:55 -07:00
commit 78b569f685
474 changed files with 33986 additions and 6837 deletions
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22
CMakeLists.txt
View File

@ -1,4 +1,4 @@
project(Eigen)
project(Eigen3)
cmake_minimum_required(VERSION 2.8.5)
@ -8,6 +8,11 @@ if(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_BINARY_DIR})
message(FATAL_ERROR "In-source builds not allowed. Please make a new directory (called a build directory) and run CMake from there. You may need to remove CMakeCache.txt. ")
endif()
# Alias Eigen_*_DIR to Eigen3_*_DIR:
set(Eigen_SOURCE_DIR ${Eigen3_SOURCE_DIR})
set(Eigen_BINARY_DIR ${Eigen3_BINARY_DIR})
# guard against bad build-type strings
if (NOT CMAKE_BUILD_TYPE)
@ -93,9 +98,11 @@ else()
endif()
option(EIGEN_BUILD_BTL "Build benchmark suite" OFF)
if(NOT WIN32)
# Disable pkgconfig only for native Windows builds
if(NOT WIN32 OR NOT CMAKE_HOST_SYSTEM_NAME MATCHES Windows)
option(EIGEN_BUILD_PKGCONFIG "Build pkg-config .pc file for Eigen" ON)
endif(NOT WIN32)
endif()
set(CMAKE_INCLUDE_CURRENT_DIR ON)
@ -120,7 +127,7 @@ endmacro(ei_add_cxx_compiler_flag)
if(NOT MSVC)
# We assume that other compilers are partly compatible with GNUCC
# clang outputs some warnings for unknwon flags that are not caught by check_cxx_compiler_flag
# clang outputs some warnings for unknown flags that are not caught by check_cxx_compiler_flag
# adding -Werror turns such warnings into errors
check_cxx_compiler_flag("-Werror" COMPILER_SUPPORT_WERROR)
if(COMPILER_SUPPORT_WERROR)
@ -142,8 +149,11 @@ if(NOT MSVC)
ei_add_cxx_compiler_flag("-Wwrite-strings")
ei_add_cxx_compiler_flag("-Wformat-security")
ei_add_cxx_compiler_flag("-Wshorten-64-to-32")
ei_add_cxx_compiler_flag("-Wlogical-op")
ei_add_cxx_compiler_flag("-Wenum-conversion")
ei_add_cxx_compiler_flag("-Wc++11-extensions")
ei_add_cxx_compiler_flag("-Wdouble-promotion")
# ei_add_cxx_compiler_flag("-Wconversion")
# -Wshadow is insanely too strict with gcc, hopefully it will become usable with gcc 6
# if(NOT CMAKE_COMPILER_IS_GNUCXX OR (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.0.0"))
@ -159,7 +169,7 @@ if(NOT MSVC)
ei_add_cxx_compiler_flag("-fno-common")
ei_add_cxx_compiler_flag("-fstrict-aliasing")
ei_add_cxx_compiler_flag("-wd981") # disable ICC's "operands are evaluated in unspecified order" remark
ei_add_cxx_compiler_flag("-wd2304") # disbale ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
ei_add_cxx_compiler_flag("-wd2304") # disable ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
# The -ansi flag must be added last, otherwise it is also used as a linker flag by check_cxx_compiler_flag making it fails
@ -402,7 +412,7 @@ if(EIGEN_BUILD_PKGCONFIG)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/eigen3.pc
DESTINATION ${PKGCONFIG_INSTALL_DIR}
)
endif(EIGEN_BUILD_PKGCONFIG)
endif()
add_subdirectory(Eigen)

2
Eigen/CMakeLists.txt
View File

@ -16,4 +16,4 @@ install(FILES
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
)
add_subdirectory(src)
install(DIRECTORY src DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel FILES_MATCHING PATTERN "*.h")

3
Eigen/Cholesky
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@ -31,7 +31,8 @@
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Cholesky/LLT_MKL.h"
#include "src/misc/lapacke.h"
#include "src/Cholesky/LLT_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

19
Eigen/Core
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@ -164,6 +164,7 @@
#if EIGEN_COMP_ICC >= 1110
#include <immintrin.h>
#else
#include <mmintrin.h>
#include <emmintrin.h>
#include <xmmintrin.h>
#ifdef EIGEN_VECTORIZE_SSE3
@ -259,6 +260,11 @@
// for min/max:
#include <algorithm>
// for std::is_nothrow_move_assignable
#ifdef EIGEN_INCLUDE_TYPE_TRAITS
#include <type_traits>
#endif
// for outputting debug info
#ifdef EIGEN_DEBUG_ASSIGN
#include <iostream>
@ -332,8 +338,8 @@ using std::ptrdiff_t;
#include "src/Core/NumTraits.h"
#include "src/Core/MathFunctions.h"
#include "src/Core/SpecialFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.h"
#if defined EIGEN_VECTORIZE_AVX512
#include "src/Core/arch/SSE/PacketMath.h"
@ -368,23 +374,29 @@ using std::ptrdiff_t;
#include "src/Core/arch/ZVector/Complex.h"
#endif
// Half float support
#include "src/Core/arch/CUDA/Half.h"
#include "src/Core/arch/CUDA/PacketMathHalf.h"
#include "src/Core/arch/CUDA/TypeCasting.h"
#if defined EIGEN_VECTORIZE_CUDA
#include "src/Core/arch/CUDA/PacketMath.h"
#include "src/Core/arch/CUDA/PacketMathHalf.h"
#include "src/Core/arch/CUDA/MathFunctions.h"
#include "src/Core/arch/CUDA/TypeCasting.h"
#endif
#include "src/Core/arch/Default/Settings.h"
#include "src/Core/functors/TernaryFunctors.h"
#include "src/Core/functors/BinaryFunctors.h"
#include "src/Core/functors/UnaryFunctors.h"
#include "src/Core/functors/NullaryFunctors.h"
#include "src/Core/functors/StlFunctors.h"
#include "src/Core/functors/AssignmentFunctors.h"
// Specialized functors to enable the processing of complex numbers
// on CUDA devices
#include "src/Core/arch/CUDA/Complex.h"
#include "src/Core/DenseCoeffsBase.h"
#include "src/Core/DenseBase.h"
#include "src/Core/MatrixBase.h"
@ -411,6 +423,7 @@ using std::ptrdiff_t;
#include "src/Core/PlainObjectBase.h"
#include "src/Core/Matrix.h"
#include "src/Core/Array.h"
#include "src/Core/CwiseTernaryOp.h"
#include "src/Core/CwiseBinaryOp.h"
#include "src/Core/CwiseUnaryOp.h"
#include "src/Core/CwiseNullaryOp.h"

8
Eigen/Eigenvalues
View File

@ -32,6 +32,7 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/Eigenvalues/Tridiagonalization.h"
#include "src/Eigenvalues/RealSchur.h"
#include "src/Eigenvalues/EigenSolver.h"
@ -44,9 +45,10 @@
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/Eigenvalues/RealSchur_MKL.h"
#include "src/Eigenvalues/ComplexSchur_MKL.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_MKL.h"
#include "src/misc/lapacke.h"
#include "src/Eigenvalues/RealSchur_LAPACKE.h"
#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

3
Eigen/LU
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@ -28,7 +28,8 @@
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/LU/PartialPivLU_MKL.h"
#include "src/misc/lapacke.h"
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"
#include "src/LU/InverseImpl.h"

5
Eigen/QR
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@ -36,8 +36,9 @@
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#include "src/QR/HouseholderQR_MKL.h"
#include "src/QR/ColPivHouseholderQR_MKL.h"
#include "src/misc/lapacke.h"
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

4
Eigen/SVD
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@ -31,12 +31,14 @@
* \endcode
*/
#include "src/misc/RealSvd2x2.h"
#include "src/SVD/UpperBidiagonalization.h"
#include "src/SVD/SVDBase.h"
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#include "src/SVD/JacobiSVD_MKL.h"
#include "src/misc/lapacke.h"
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif
#include "src/Core/util/ReenableStupidWarnings.h"

2
Eigen/SuperLUSupport
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@ -43,7 +43,7 @@ namespace Eigen { struct SluMatrix; }
* - class SuperLU: a supernodal sequential LU factorization.
* - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
*
* \warning This wrapper is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
* \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
*
* \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
*

7
Eigen/src/CMakeLists.txt
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@ -1,7 +0,0 @@
file(GLOB Eigen_src_subdirectories "*")
escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
foreach(f ${Eigen_src_subdirectories})
if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
add_subdirectory(${f})
endif()
endforeach()

6
Eigen/src/Cholesky/CMakeLists.txt
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@ -1,6 +0,0 @@
FILE(GLOB Eigen_Cholesky_SRCS "*.h")
INSTALL(FILES
${Eigen_Cholesky_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
)

54
Eigen/src/Cholesky/LDLT.h
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@ -43,6 +43,8 @@ namespace internal {
* Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
* decomposition to determine whether a system of equations has a solution.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
*/
template<typename _MatrixType, int _UpLo> class LDLT
@ -52,7 +54,6 @@ template<typename _MatrixType, int _UpLo> class LDLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here!
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
UpLo = _UpLo
@ -61,7 +62,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
@ -97,6 +98,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
/** \brief Constructor with decomposition
*
* This calculates the decomposition for the input \a matrix.
*
* \sa LDLT(Index size)
*/
template<typename InputType>
@ -110,6 +112,23 @@ template<typename _MatrixType, int _UpLo> class LDLT
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
*
* \sa LDLT(const EigenBase&)
*/
template<typename InputType>
explicit LDLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** Clear any existing decomposition
* \sa rankUpdate(w,sigma)
*/
@ -234,7 +253,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return Success;
return m_info;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -262,6 +281,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
TmpMatrixType m_temporary;
internal::SignMatrix m_sign;
bool m_isInitialized;
ComputationInfo m_info;
};
namespace internal {
@ -279,6 +299,8 @@ template<> struct ldlt_inplace<Lower>
typedef typename TranspositionType::StorageIndex IndexType;
eigen_assert(mat.rows()==mat.cols());
const Index size = mat.rows();
bool found_zero_pivot = false;
bool ret = true;
if (size <= 1)
{
@ -337,9 +359,27 @@ template<> struct ldlt_inplace<Lower>
// we should only make sure that we do not introduce INF or NaN values.
// Remark that LAPACK also uses 0 as the cutoff value.
RealScalar realAkk = numext::real(mat.coeffRef(k,k));
if((rs>0) && (abs(realAkk) > RealScalar(0)))
bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
if(k==0 && !pivot_is_valid)
{
// The entire diagonal is zero, there is nothing more to do
// except filling the transpositions, and checking whether the matrix is zero.
sign = ZeroSign;
for(Index j = 0; j<size; ++j)
{
transpositions.coeffRef(j) = IndexType(j);
ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
}
return ret;
}
if((rs>0) && pivot_is_valid)
A21 /= realAkk;
if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
else if(!pivot_is_valid) found_zero_pivot = true;
if (sign == PositiveSemiDef) {
if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
} else if (sign == NegativeSemiDef) {
@ -350,7 +390,7 @@ template<> struct ldlt_inplace<Lower>
}
}
return true;
return ret;
}
// Reference for the algorithm: Davis and Hager, "Multiple Rank
@ -474,7 +514,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputTyp
m_temporary.resize(size);
m_sign = internal::ZeroSign;
internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign);
m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
m_isInitialized = true;
return *this;
@ -602,7 +642,6 @@ MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const
return res;
}
#ifndef __CUDACC__
/** \cholesky_module
* \returns the Cholesky decomposition with full pivoting without square root of \c *this
* \sa MatrixBase::ldlt()
@ -624,7 +663,6 @@ MatrixBase<Derived>::ldlt() const
{
return LDLT<PlainObject>(derived());
}
#endif // __CUDACC__
} // end namespace Eigen

20
Eigen/src/Cholesky/LLT.h
View File

@ -41,6 +41,8 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
/* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
@ -54,7 +56,6 @@ template<typename _MatrixType, int _UpLo> class LLT
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
Options = MatrixType::Options,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
typedef typename MatrixType::Scalar Scalar;
@ -95,6 +96,21 @@ template<typename _MatrixType, int _UpLo> class LLT
compute(matrix.derived());
}
/** \brief Constructs a LDLT factorization from a given matrix
*
* This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
* \c MatrixType is a Eigen::Ref.
*
* \sa LLT(const EigenBase&)
*/
template<typename InputType>
explicit LLT(EigenBase<InputType>& matrix)
: m_matrix(matrix.derived()),
m_isInitialized(false)
{
compute(matrix.derived());
}
/** \returns a view of the upper triangular matrix U */
inline typename Traits::MatrixU matrixU() const
{
@ -491,7 +507,6 @@ MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const
return matrixL() * matrixL().adjoint().toDenseMatrix();
}
#ifndef __CUDACC__
/** \cholesky_module
* \returns the LLT decomposition of \c *this
* \sa SelfAdjointView::llt()
@ -513,7 +528,6 @@ SelfAdjointView<MatrixType, UpLo>::llt() const
{
return LLT<PlainObject,UpLo>(m_matrix);
}
#endif // __CUDACC__
} // end namespace Eigen

35
Eigen/src/Cholesky/LLT_MKL.h → Eigen/src/Cholesky/LLT_LAPACKE.h
View File

@ -25,25 +25,22 @@
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************
* Content : Eigen bindings to Intel(R) MKL
* Content : Eigen bindings to LAPACKe
* LLt decomposition based on LAPACKE_?potrf function.
********************************************************************************
*/
#ifndef EIGEN_LLT_MKL_H
#define EIGEN_LLT_MKL_H
#include "Eigen/src/Core/util/MKL_support.h"
#include <iostream>
#ifndef EIGEN_LLT_LAPACKE_H
#define EIGEN_LLT_LAPACKE_H
namespace Eigen {
namespace internal {
template<typename Scalar> struct mkl_llt;
template<typename Scalar> struct lapacke_llt;
#define EIGEN_MKL_LLT(EIGTYPE, MKLTYPE, MKLPREFIX) \
template<> struct mkl_llt<EIGTYPE> \
#define EIGEN_LAPACKE_LLT(EIGTYPE, BLASTYPE, LAPACKE_PREFIX) \
template<> struct lapacke_llt<EIGTYPE> \
{ \
template<typename MatrixType> \
static inline Index potrf(MatrixType& m, char uplo) \
@ -53,13 +50,13 @@ template<> struct mkl_llt<EIGTYPE> \
EIGTYPE* a; \
eigen_assert(m.rows()==m.cols()); \
/* Set up parameters for ?potrf */ \
size = m.rows(); \
size = convert_index<lapack_int>(m.rows()); \
StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
a = &(m.coeffRef(0,0)); \
lda = m.outerStride(); \
lda = convert_index<lapack_int>(m.outerStride()); \
\
info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \
info = LAPACKE_##LAPACKE_PREFIX##potrf( matrix_order, uplo, size, (BLASTYPE*)a, lda ); \
info = (info==0) ? -1 : info>0 ? info-1 : size; \
return info; \
} \
@ -69,7 +66,7 @@ template<> struct llt_inplace<EIGTYPE, Lower> \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return mkl_llt<EIGTYPE>::potrf(m, 'L'); \
return lapacke_llt<EIGTYPE>::potrf(m, 'L'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
@ -80,7 +77,7 @@ template<> struct llt_inplace<EIGTYPE, Upper> \
template<typename MatrixType> \
static Index blocked(MatrixType& m) \
{ \
return mkl_llt<EIGTYPE>::potrf(m, 'U'); \
return lapacke_llt<EIGTYPE>::potrf(m, 'U'); \
} \
template<typename MatrixType, typename VectorType> \
static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
@ -90,13 +87,13 @@ template<> struct llt_inplace<EIGTYPE, Upper> \
} \
};
EIGEN_MKL_LLT(double, double, d)
EIGEN_MKL_LLT(float, float, s)
EIGEN_MKL_LLT(dcomplex, MKL_Complex16, z)
EIGEN_MKL_LLT(scomplex, MKL_Complex8, c)
EIGEN_LAPACKE_LLT(double, double, d)
EIGEN_LAPACKE_LLT(float, float, s)
EIGEN_LAPACKE_LLT(dcomplex, lapack_complex_double, z)
EIGEN_LAPACKE_LLT(scomplex, lapack_complex_float, c)
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_LLT_MKL_H
#endif // EIGEN_LLT_LAPACKE_H

6
Eigen/src/CholmodSupport/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_CholmodSupport_SRCS "*.h")
INSTALL(FILES
${Eigen_CholmodSupport_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel
)

8
Eigen/src/Core/Array.h
View File

@ -37,7 +37,7 @@ struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : tra
* storage layout.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
*
* \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
*/
@ -147,9 +147,9 @@ class Array
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Array(Array&& other)
Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
@ -157,7 +157,7 @@ class Array
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other)
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;

12
Eigen/src/Core/ArrayBase.h
View File

@ -32,7 +32,7 @@ template<typename ExpressionType> class MatrixWrapper;
* \tparam Derived is the derived type, e.g., an array or an expression type.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
*
* \sa class MatrixBase, \ref TopicClassHierarchy
*/
@ -52,8 +52,6 @@ template<typename Derived> class ArrayBase
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef DenseBase<Derived> Base;
using Base::operator*;
using Base::operator/;
using Base::RowsAtCompileTime;
using Base::ColsAtCompileTime;
using Base::SizeAtCompileTime;
@ -89,6 +87,7 @@ template<typename Derived> class ArrayBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
# include "../plugins/ArrayCwiseUnaryOps.h"
@ -99,6 +98,7 @@ template<typename Derived> class ArrayBase
# include EIGEN_ARRAYBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
@ -178,7 +178,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -191,7 +191,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -217,7 +217,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

117
Eigen/src/Core/AssignEvaluator.h
View File

@ -75,23 +75,24 @@ private:
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
MightVectorize = StorageOrdersAgree
MightVectorize = bool(StorageOrdersAgree)
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& (functor_traits<AssignFunc>::PacketAccess),
&& bool(functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& int(JointAlignment)>=int(InnerRequiredAlignment),
MayLinearize = StorageOrdersAgree && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& ((int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
&& (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)),
MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess
&& (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = MightVectorize && DstHasDirectAccess
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*InnerPacketSize)
MaySliceVectorize = bool(MightVectorize) && bool(DstHasDirectAccess)
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix */
in a fixed-size matrix
However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
};
public:
@ -116,9 +117,9 @@ private:
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
&& int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
&& int(InnerSize) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit)
&& int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
};
public:
@ -130,11 +131,17 @@ public:
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && (int(DstAlignment)>=int(LinearRequiredAlignment)) ? int(CompleteUnrolling)
: int(NoUnrolling) )
? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
: int(NoUnrolling) )
#if EIGEN_UNALIGNED_VECTORIZE
: int(Traversal) == int(SliceVectorizedTraversal)
? ( bool(MayUnrollInner) ? int(InnerUnrolling)
: int(NoUnrolling) )
#endif
: int(NoUnrolling)
};
@ -156,6 +163,7 @@ public:
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(LinearPacketSize)
EIGEN_DEBUG_VAR(InnerPacketSize)
EIGEN_DEBUG_VAR(ActualPacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
@ -163,6 +171,7 @@ public:
EIGEN_DEBUG_VAR(MayLinearVectorize)
EIGEN_DEBUG_VAR(MaySliceVectorize)
std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
EIGEN_DEBUG_VAR(UnrollingLimit)
EIGEN_DEBUG_VAR(MayUnrollCompletely)
EIGEN_DEBUG_VAR(MayUnrollInner)
@ -256,13 +265,13 @@ struct copy_using_evaluator_innervec_CompleteUnrolling
enum {
outer = Index / DstXprType::InnerSizeAtCompileTime,
inner = Index % DstXprType::InnerSizeAtCompileTime,
JointAlignment = Kernel::AssignmentTraits::JointAlignment,
DefaultAlignment = unpacket_traits<PacketType>::alignment
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
kernel.template assignPacketByOuterInner<DefaultAlignment, JointAlignment, PacketType>(outer, inner);
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
enum { NextIndex = Index + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
}
@ -274,23 +283,20 @@ struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
};
template<typename Kernel, int Index_, int Stop>
template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling
{
typedef typename Kernel::PacketType PacketType;
enum {
DefaultAlignment = unpacket_traits<PacketType>::alignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
{
kernel.template assignPacketByOuterInner<DefaultAlignment, DefaultAlignment, PacketType>(outer, Index_);
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop>::run(kernel, outer);
copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
}
};
template<typename Kernel, int Stop>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop>
template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
};
@ -419,9 +425,10 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrollin
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::SizeAtCompileTime,
packetSize = packet_traits<typename Kernel::Scalar>::size,
packetSize =unpacket_traits<PacketType>::size,
alignedSize = (size/packetSize)*packetSize };
copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
@ -438,7 +445,8 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
{
typedef typename Kernel::PacketType PacketType;
enum {
DefaultAlignment = unpacket_traits<PacketType>::alignment
SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
DstAlignment = Kernel::AssignmentTraits::DstAlignment
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
@ -447,7 +455,7 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
const Index packetSize = unpacket_traits<PacketType>::size;
for(Index outer = 0; outer < outerSize; ++outer)
for(Index inner = 0; inner < innerSize; inner+=packetSize)
kernel.template assignPacketByOuterInner<DefaultAlignment, DefaultAlignment, PacketType>(outer, inner);
kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
}
};
@ -467,9 +475,11 @@ struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::AssignmentTraits Traits;
const Index outerSize = kernel.outerSize();
for(Index outer = 0; outer < outerSize; ++outer)
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
}
};
@ -518,7 +528,7 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
: int(Kernel::AssignmentTraits::DstAlignment)
};
const Scalar *dst_ptr = &kernel.dstEvaluator().coeffRef(0,0);
if((!bool(dstIsAligned)) && (size_t(dst_ptr) % sizeof(Scalar))>0)
if((!bool(dstIsAligned)) && (UIntPtr(dst_ptr) % sizeof(Scalar))>0)
{
// the pointer is not aligend-on scalar, so alignment is not possible
return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
@ -549,6 +559,29 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
}
};
#if EIGEN_UNALIGNED_VECTORIZE
template<typename Kernel>
struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
{
EIGEN_DEVICE_FUNC static inline void run(Kernel &kernel)
{
typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
typedef typename Kernel::PacketType PacketType;
enum { size = DstXprType::InnerSizeAtCompileTime,
packetSize =unpacket_traits<PacketType>::size,
vectorizableSize = (size/packetSize)*packetSize };
for(Index outer = 0; outer < kernel.outerSize(); ++outer)
{
copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, size>::run(kernel, outer);
}
}
};
#endif
/***************************************************************************
* Part 4 : Generic dense assignment kernel
***************************************************************************/
@ -683,7 +716,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(const DstX
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar>());
call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
/***************************************************************************
@ -705,7 +738,7 @@ template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Ki
// This is the main assignment class
template< typename DstXprType, typename SrcXprType, typename Functor,
typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
typename Scalar = typename DstXprType::Scalar>
typename EnableIf = void>
struct Assignment;
@ -718,13 +751,13 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(const Dst& dst, const Src& src)
{
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// Deal with "assume-aliasing"
@ -783,7 +816,7 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src)
{
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
template<typename Dst, typename Src, typename Func>
@ -805,15 +838,17 @@ template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
{
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar>());
call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
}
// forward declaration
template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, const Src &src);
// Generic Dense to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Scalar>
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
@ -830,11 +865,13 @@ struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Scalar>
// Generic assignment through evalTo.
// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Scalar>
// Note that the last template argument "Weak" is needed to make it possible to perform
// both partial specialization+SFINAE without ambiguous specialization
template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
{
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
src.evalTo(dst);

26
Eigen/src/Core/Assign_MKL.h Normal file → Executable file
View File

@ -81,10 +81,10 @@ class vml_assign_traits
#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml,EIGENTYPE>::type> { \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE> &/*func*/) { \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &/*func*/) { \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) { \
VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \
@ -138,22 +138,24 @@ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE) \
template< typename DstXprType, typename SrcXprNested> \
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml,EIGENTYPE>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE> &/*func*/) { \
template< typename DstXprType, typename SrcXprNested, typename Plain> \
struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &/*func*/) { \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.functor().m_exponent); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \
{ \
VMLOP( dst.size(), (const VMLTYPE*)src.nestedExpression().data(), exponent, \
VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent, \
(VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE) ); \
} else { \
const Index outerSize = dst.outerSize(); \
for(Index outer = 0; outer < outerSize; ++outer) { \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : \
&(src.nestedExpression().coeffRef(0, outer)); \
const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) : \
&(src.lhs().coeffRef(0, outer)); \
EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); \
VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent, \
(VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_##VMLMODE)); \

11
Eigen/src/Core/CMakeLists.txt
View File

@ -1,11 +0,0 @@
FILE(GLOB Eigen_Core_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
)
ADD_SUBDIRECTORY(products)
ADD_SUBDIRECTORY(util)
ADD_SUBDIRECTORY(arch)
ADD_SUBDIRECTORY(functors)

25
Eigen/src/Core/CommaInitializer.h
View File

@ -80,9 +80,7 @@ struct CommaInitializer
EIGEN_DEVICE_FUNC
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if(other.cols()==0 || other.rows()==0)
return *this;
if (m_col==m_xpr.cols())
if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
{
m_row+=m_currentBlockRows;
m_col = 0;
@ -90,15 +88,11 @@ struct CommaInitializer
eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
&& "Too many rows passed to comma initializer (operator<<)");
}
eigen_assert(m_col<m_xpr.cols()
eigen_assert((m_col + other.cols() <= m_xpr.cols())
&& "Too many coefficients passed to comma initializer (operator<<)");
eigen_assert(m_currentBlockRows==other.rows());
if (OtherDerived::SizeAtCompileTime != Dynamic)
m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1,
OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1>
(m_row, m_col) = other;
else
m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other;
m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
(m_row, m_col, other.rows(), other.cols()) = other;
m_col += other.cols();
return *this;
}
@ -109,9 +103,7 @@ struct CommaInitializer
EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
#endif
{
eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows()
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
finished();
}
/** \returns the built matrix once all its coefficients have been set.
@ -122,7 +114,12 @@ struct CommaInitializer
* \endcode
*/
EIGEN_DEVICE_FUNC
inline XprType& finished() { return m_xpr; }
inline XprType& finished() {
eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
&& m_col == m_xpr.cols()
&& "Too few coefficients passed to comma initializer (operator<<)");
return m_xpr;
}
XprType& m_xpr; // target expression
Index m_row; // current row id

65
Eigen/src/Core/ConditionEstimator.h
View File

@ -32,33 +32,6 @@ struct rcond_compute_sign<Vector, Vector, false> {
}
};
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return RealScalar(1);
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
/**
* \returns an estimate of ||inv(matrix)||_1 given a decomposition of
* \a matrix that implements .solve() and .adjoint().solve() methods.
@ -94,7 +67,15 @@ typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomp
if (n == 0)
return 0;
// Disable Index to float conversion warning
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
// lower_bound is a lower bound on
// ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1
@ -151,7 +132,8 @@ typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomp
// Hager's algorithm to vastly underestimate ||matrix||_1.
Scalar alternating_sign(RealScalar(1));
for (Index i = 0; i < n; ++i) {
v[i] = alternating_sign * (RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
// The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
alternating_sign = -alternating_sign;
}
v = dec.solve(v);
@ -159,6 +141,33 @@ typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomp
return numext::maxi(lower_bound, alternate_lower_bound);
}
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return RealScalar(1);
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
} // namespace internal
} // namespace Eigen

274
Eigen/src/Core/CoreEvaluators.h
View File

@ -41,10 +41,19 @@ template<> struct storage_kind_to_shape<TranspositionsStorage> { typedef Transp
// We currently distinguish the following kind of evaluators:
// - unary_evaluator for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose, MatrixWrapper, ArrayWrapper, Reverse, Replicate)
// - binary_evaluator for expression taking two arguments (CwiseBinaryOp)
// - ternary_evaluator for expression taking three arguments (CwiseTernaryOp)
// - product_evaluator for linear algebra products (Product); special case of binary_evaluator because it requires additional tags for dispatching.
// - mapbase_evaluator for Map, Block, Ref
// - block_evaluator for Block (special dispatching to a mapbase_evaluator or unary_evaluator)
template< typename T,
typename Arg1Kind = typename evaluator_traits<typename T::Arg1>::Kind,
typename Arg2Kind = typename evaluator_traits<typename T::Arg2>::Kind,
typename Arg3Kind = typename evaluator_traits<typename T::Arg3>::Kind,
typename Arg1Scalar = typename traits<typename T::Arg1>::Scalar,
typename Arg2Scalar = typename traits<typename T::Arg2>::Scalar,
typename Arg3Scalar = typename traits<typename T::Arg3>::Scalar> struct ternary_evaluator;
template< typename T,
typename LhsKind = typename evaluator_traits<typename T::Lhs>::Kind,
typename RhsKind = typename evaluator_traits<typename T::Rhs>::Kind,
@ -328,6 +337,120 @@ protected:
// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator.
// Likewise, there is not need to more sophisticated dispatching here.
template<typename Scalar,typename NullaryOp,
bool has_nullary = has_nullary_operator<NullaryOp>::value,
bool has_unary = has_unary_operator<NullaryOp>::value,
bool has_binary = has_binary_operator<NullaryOp>::value>
struct nullary_wrapper
{
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { return op(i,j); }
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { return op.template packetOp<T>(i,j); }
template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
};
template<typename Scalar,typename NullaryOp>
struct nullary_wrapper<Scalar,NullaryOp,true,false,false>
{
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType=0, IndexType=0) const { return op(); }
template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType=0, IndexType=0) const { return op.template packetOp<T>(); }
};
template<typename Scalar,typename NullaryOp>
struct nullary_wrapper<Scalar,NullaryOp,false,false,true>
{
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j=0) const { return op(i,j); }
template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j=0) const { return op.template packetOp<T>(i,j); }
};
// We need the following specialization for vector-only functors assigned to a runtime vector,
// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd.
// In this case, i==0 and j is used for the actual iteration.
template<typename Scalar,typename NullaryOp>
struct nullary_wrapper<Scalar,NullaryOp,false,true,false>
{
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
eigen_assert(i==0 || j==0);
return op(i+j);
}
template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
eigen_assert(i==0 || j==0);
return op.template packetOp<T>(i+j);
}
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
template <typename T, typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
};
template<typename Scalar,typename NullaryOp>
struct nullary_wrapper<Scalar,NullaryOp,false,false,false> {};
#if 0 && EIGEN_COMP_MSVC>0
// Disable this ugly workaround. This is now handled in traits<Ref>::match,
// but this piece of code might still become handly if some other weird compilation
// erros pop up again.
// MSVC exhibits a weird compilation error when
// compiling:
// Eigen::MatrixXf A = MatrixXf::Random(3,3);
// Ref<const MatrixXf> R = 2.f*A;
// and that has_*ary_operator<scalar_constant_op<float>> have not been instantiated yet.
// The "problem" is that evaluator<2.f*A> is instantiated by traits<Ref>::match<2.f*A>
// and at that time has_*ary_operator<T> returns true regardless of T.
// Then nullary_wrapper is badly instantiated as nullary_wrapper<.,.,true,true,true>.
// The trick is thus to defer the proper instantiation of nullary_wrapper when coeff(),
// and packet() are really instantiated as implemented below:
// This is a simple wrapper around Index to enforce the re-instantiation of
// has_*ary_operator when needed.
template<typename T> struct nullary_wrapper_workaround_msvc {
nullary_wrapper_workaround_msvc(const T&);
operator T()const;
};
template<typename Scalar,typename NullaryOp>
struct nullary_wrapper<Scalar,NullaryOp,true,true,true>
{
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
return nullary_wrapper<Scalar,NullaryOp,
has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i,j);
}
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
return nullary_wrapper<Scalar,NullaryOp,
has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i);
}
template <typename T, typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
return nullary_wrapper<Scalar,NullaryOp,
has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i,j);
}
template <typename T, typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
return nullary_wrapper<Scalar,NullaryOp,
has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i);
}
};
#endif // MSVC workaround
template<typename NullaryOp, typename PlainObjectType>
struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
: evaluator_base<CwiseNullaryOp<NullaryOp,PlainObjectType> >
@ -347,41 +470,44 @@ struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
};
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& n)
: m_functor(n.functor())
: m_functor(n.functor()), m_wrapper()
{
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
CoeffReturnType coeff(IndexType row, IndexType col) const
{
return m_functor(row, col);
return m_wrapper(m_functor, row, col);
}
template <typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
CoeffReturnType coeff(IndexType index) const
{
return m_functor(index);
return m_wrapper(m_functor,index);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType, typename IndexType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
PacketType packet(IndexType row, IndexType col) const
{
return m_functor.template packetOp<Index,PacketType>(row, col);
return m_wrapper.template packetOp<PacketType>(m_functor, row, col);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType, typename IndexType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
PacketType packet(IndexType index) const
{
return m_functor.template packetOp<Index,PacketType>(index);
return m_wrapper.template packetOp<PacketType>(m_functor, index);
}
protected:
const NullaryOp m_functor;
const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
};
// -------------------- CwiseUnaryOp --------------------
@ -442,6 +568,96 @@ protected:
evaluator<ArgType> m_argImpl;
};
// -------------------- CwiseTernaryOp --------------------
// this is a ternary expression
template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
: public ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
{
typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
typedef ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
};
template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased, IndexBased>
: evaluator_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
{
typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
enum {
CoeffReadCost = evaluator<Arg1>::CoeffReadCost + evaluator<Arg2>::CoeffReadCost + evaluator<Arg3>::CoeffReadCost + functor_traits<TernaryOp>::Cost,
Arg1Flags = evaluator<Arg1>::Flags,
Arg2Flags = evaluator<Arg2>::Flags,
Arg3Flags = evaluator<Arg3>::Flags,
SameType = is_same<typename Arg1::Scalar,typename Arg2::Scalar>::value && is_same<typename Arg1::Scalar,typename Arg3::Scalar>::value,
StorageOrdersAgree = (int(Arg1Flags)&RowMajorBit)==(int(Arg2Flags)&RowMajorBit) && (int(Arg1Flags)&RowMajorBit)==(int(Arg3Flags)&RowMajorBit),
Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) & (
HereditaryBits
| (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) &
( (StorageOrdersAgree ? LinearAccessBit : 0)
| (functor_traits<TernaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
)
)
),
Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
Alignment = EIGEN_PLAIN_ENUM_MIN(
EIGEN_PLAIN_ENUM_MIN(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
evaluator<Arg3>::Alignment)
};
EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr)
: m_functor(xpr.functor()),
m_arg1Impl(xpr.arg1()),
m_arg2Impl(xpr.arg2()),
m_arg3Impl(xpr.arg3())
{
EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<TernaryOp>::Cost);
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_functor(m_arg1Impl.coeff(row, col), m_arg2Impl.coeff(row, col), m_arg3Impl.coeff(row, col));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_functor.packetOp(m_arg1Impl.template packet<LoadMode,PacketType>(row, col),
m_arg2Impl.template packet<LoadMode,PacketType>(row, col),
m_arg3Impl.template packet<LoadMode,PacketType>(row, col));
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_functor.packetOp(m_arg1Impl.template packet<LoadMode,PacketType>(index),
m_arg2Impl.template packet<LoadMode,PacketType>(index),
m_arg3Impl.template packet<LoadMode,PacketType>(index));
}
protected:
const TernaryOp m_functor;
evaluator<Arg1> m_arg1Impl;
evaluator<Arg2> m_arg2Impl;
evaluator<Arg3> m_arg3Impl;
};
// -------------------- CwiseBinaryOp --------------------
// this is a binary expression
@ -604,7 +820,8 @@ struct mapbase_evaluator : evaluator_base<Derived>
EIGEN_DEVICE_FUNC explicit mapbase_evaluator(const XprType& map)
: m_data(const_cast<PointerType>(map.data())),
m_xpr(map)
m_innerStride(map.innerStride()),
m_outerStride(map.outerStride())
{
EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(evaluator<Derived>::Flags&PacketAccessBit, internal::inner_stride_at_compile_time<Derived>::ret==1),
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
@ -614,32 +831,32 @@ struct mapbase_evaluator : evaluator_base<Derived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_data[col * m_xpr.colStride() + row * m_xpr.rowStride()];
return m_data[col * colStride() + row * rowStride()];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_data[index * m_xpr.innerStride()];
return m_data[index * m_innerStride.value()];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_data[col * m_xpr.colStride() + row * m_xpr.rowStride()];
return m_data[col * colStride() + row * rowStride()];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_data[index * m_xpr.innerStride()];
return m_data[index * m_innerStride.value()];
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
PointerType ptr = m_data + row * m_xpr.rowStride() + col * m_xpr.colStride();
PointerType ptr = m_data + row * rowStride() + col * colStride();
return internal::ploadt<PacketType, LoadMode>(ptr);
}
@ -647,14 +864,14 @@ struct mapbase_evaluator : evaluator_base<Derived>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return internal::ploadt<PacketType, LoadMode>(m_data + index * m_xpr.innerStride());
return internal::ploadt<PacketType, LoadMode>(m_data + index * m_innerStride.value());
}
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
PointerType ptr = m_data + row * m_xpr.rowStride() + col * m_xpr.colStride();
PointerType ptr = m_data + row * rowStride() + col * colStride();
return internal::pstoret<Scalar, PacketType, StoreMode>(ptr, x);
}
@ -662,12 +879,17 @@ struct mapbase_evaluator : evaluator_base<Derived>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_xpr.innerStride(), x);
internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x);
}
protected:
EIGEN_DEVICE_FUNC
inline Index rowStride() const { return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value(); }
EIGEN_DEVICE_FUNC
inline Index colStride() const { return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value(); }
PointerType m_data;
const XprType& m_xpr;
const internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_innerStride;
const internal::variable_if_dynamic<Index, XprType::OuterStrideAtCompileTime> m_outerStride;
};
template<typename PlainObjectType, int MapOptions, typename StrideType>
@ -755,9 +977,7 @@ struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
OuterStrideAtCompileTime = HasSameStorageOrderAsArgType
? int(outer_stride_at_compile_time<ArgType>::ret)
: int(inner_stride_at_compile_time<ArgType>::ret),
MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
&& (InnerStrideAtCompileTime == 1)
? PacketAccessBit : 0,
MaskPacketAccessBit = (InnerStrideAtCompileTime == 1) ? PacketAccessBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator<ArgType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
FlagsRowMajorBit = XprType::Flags&RowMajorBit,
@ -884,7 +1104,7 @@ struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAc
: mapbase_evaluator<XprType, typename XprType::PlainObject>(block)
{
// TODO: for the 3.3 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
eigen_assert(((size_t(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
eigen_assert(((internal::UIntPtr(block.data()) % EIGEN_PLAIN_ENUM_MAX(1,evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
}
};
@ -1325,7 +1545,7 @@ struct evaluator<Diagonal<ArgType, DiagIndex> >
enum {
CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
Flags = (unsigned int)evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit) & ~RowMajorBit,
Flags = (unsigned int)(evaluator<ArgType>::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit,
Alignment = 0
};

4
Eigen/src/Core/CwiseBinaryOp.h
View File

@ -160,7 +160,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -173,7 +173,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

49
Eigen/src/Core/CwiseNullaryOp.h
View File

@ -20,7 +20,8 @@ struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectT
Flags = traits<PlainObjectType>::Flags & RowMajorBit
};
};
}
} // namespace internal
/** \class CwiseNullaryOp
* \ingroup Core_Module
@ -37,7 +38,23 @@ struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectT
* However, if you want to write a function returning such an expression, you
* will need to use this class.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr()
* The functor NullaryOp must expose one of the following method:
<table class="manual">
<tr ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
<tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
<tr ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
</table>
* It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
*
* See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
* C++11 random number generators.
*
* A nullary expression can also be used to implement custom sophisticated matrix manipulations
* that cannot be covered by the existing set of natively supported matrix manipulations.
* See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
* on the behavior of CwiseNullaryOp.
*
* \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
*/
template<typename NullaryOp, typename PlainObjectType>
class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
@ -62,30 +79,6 @@ class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const
{
return m_functor(rowId, colId);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
{
return m_functor.packetOp(rowId, colId);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return m_functor(index);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
{
return m_functor.packetOp(index);
}
/** \returns the functor representing the nullary operation */
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
@ -227,7 +220,7 @@ DenseBase<Derived>::Constant(const Scalar& value)
*
* The function generates 'size' equally spaced values in the closed interval [low,high].
* This particular version of LinSpaced() uses sequential access, i.e. vector access is
* assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization
* assumed to be a(0), a(1), ..., a(size-1). This assumption allows for better vectorization
* and yields faster code than the random access version.
*
* When size is set to 1, a vector of length 1 containing 'high' is returned.
@ -396,7 +389,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, con
/**
* \brief Sets a linearly spaced vector.
*
* The function fill *this with equally spaced values in the closed interval [low,high].
* The function fills *this with equally spaced values in the closed interval [low,high].
* When size is set to 1, a vector of length 1 containing 'high' is returned.
*
* \only_for_vectors

197
Eigen/src/Core/CwiseTernaryOp.h Normal file
View File

@ -0,0 +1,197 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CWISE_TERNARY_OP_H
#define EIGEN_CWISE_TERNARY_OP_H
namespace Eigen {
namespace internal {
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
// we must not inherit from traits<Arg1> since it has
// the potential to cause problems with MSVC
typedef typename remove_all<Arg1>::type Ancestor;
typedef typename traits<Ancestor>::XprKind XprKind;
enum {
RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
};
// even though we require Arg1, Arg2, and Arg3 to have the same scalar type
// (see CwiseTernaryOp constructor),
// we still want to handle the case when the result type is different.
typedef typename result_of<TernaryOp(
const typename Arg1::Scalar&, const typename Arg2::Scalar&,
const typename Arg3::Scalar&)>::type Scalar;
typedef typename internal::traits<Arg1>::StorageKind StorageKind;
typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
typedef typename Arg1::Nested Arg1Nested;
typedef typename Arg2::Nested Arg2Nested;
typedef typename Arg3::Nested Arg3Nested;
typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
enum { Flags = _Arg1Nested::Flags & RowMajorBit };
};
} // end namespace internal
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl;
/** \class CwiseTernaryOp
* \ingroup Core_Module
*
* \brief Generic expression where a coefficient-wise ternary operator is
* applied to two expressions
*
* \tparam TernaryOp template functor implementing the operator
* \tparam Arg1Type the type of the first argument
* \tparam Arg2Type the type of the second argument
* \tparam Arg3Type the type of the third argument
*
* This class represents an expression where a coefficient-wise ternary
* operator is applied to three expressions.
* It is the return type of ternary operators, by which we mean only those
* ternary operators where
* all three arguments are Eigen expressions.
* For example, the return type of betainc(matrix1, matrix2, matrix3) is a
* CwiseTernaryOp.
*
* Most of the time, this is the only way that it is used, so you typically
* don't have to name
* CwiseTernaryOp types explicitly.
*
* \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
* MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
* class CwiseUnaryOp, class CwiseNullaryOp
*/
template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
typename Arg3Type>
class CwiseTernaryOp : public CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>,
internal::no_assignment_operator
{
public:
typedef typename internal::remove_all<Arg1Type>::type Arg1;
typedef typename internal::remove_all<Arg2Type>::type Arg2;
typedef typename internal::remove_all<Arg3Type>::type Arg3;
typedef typename CwiseTernaryOpImpl<
TernaryOp, Arg1Type, Arg2Type, Arg3Type,
typename internal::traits<Arg1Type>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
typedef typename internal::remove_reference<Arg1Nested>::type _Arg1Nested;
typedef typename internal::remove_reference<Arg2Nested>::type _Arg2Nested;
typedef typename internal::remove_reference<Arg3Nested>::type _Arg3Nested;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
const Arg3& a3,
const TernaryOp& func = TernaryOp())
: m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
// require the sizes to match
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
// The index types should match
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<
typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
a1.rows() == a3.rows() && a1.cols() == a3.cols());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg3.rows();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
RowsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
RowsAtCompileTime == Dynamic)
return m_arg2.rows();
else
return m_arg1.rows();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const {
// return the fixed size type if available to enable compile time
// optimizations
if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg2Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg3.cols();
else if (internal::traits<typename internal::remove_all<Arg1Nested>::type>::
ColsAtCompileTime == Dynamic &&
internal::traits<typename internal::remove_all<Arg3Nested>::type>::
ColsAtCompileTime == Dynamic)
return m_arg2.cols();
else
return m_arg1.cols();
}
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg1Nested& arg1() const { return m_arg1; }
/** \returns the first argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg2Nested& arg2() const { return m_arg2; }
/** \returns the third argument nested expression */
EIGEN_DEVICE_FUNC
const _Arg3Nested& arg3() const { return m_arg3; }
/** \returns the functor representing the ternary operation */
EIGEN_DEVICE_FUNC
const TernaryOp& functor() const { return m_functor; }
protected:
Arg1Nested m_arg1;
Arg2Nested m_arg2;
Arg3Nested m_arg3;
const TernaryOp m_functor;
};
// Generic API dispatcher
template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
typename StorageKind>
class CwiseTernaryOpImpl
: public internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
public:
typedef typename internal::generic_xpr_base<
CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
};
} // end namespace Eigen
#endif // EIGEN_CWISE_TERNARY_OP_H

17
Eigen/src/Core/DenseBase.h
View File

@ -34,17 +34,15 @@ static inline void check_DenseIndex_is_signed() {
* \tparam Derived is the derived type, e.g., a matrix type or an expression.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
*
* \sa \blank \ref TopicClassHierarchy
*/
template<typename Derived> class DenseBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
: public internal::special_scalar_op_base<Derived, typename internal::traits<Derived>::Scalar,
typename NumTraits<typename internal::traits<Derived>::Scalar>::Real,
DenseCoeffsBase<Derived> >
#else
: public DenseCoeffsBase<Derived>
#else
: public DenseCoeffsBase<Derived,DirectWriteAccessors>
#endif // not EIGEN_PARSED_BY_DOXYGEN
{
public:
@ -73,10 +71,8 @@ template<typename Derived> class DenseBase
typedef Scalar value_type;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef internal::special_scalar_op_base<Derived,Scalar,RealScalar, DenseCoeffsBase<Derived> > Base;
typedef DenseCoeffsBase<Derived> Base;
using Base::operator*;
using Base::operator/;
using Base::derived;
using Base::const_cast_derived;
using Base::rows;
@ -562,12 +558,15 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC void reverseInPlace();
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
# include "../plugins/BlockMethods.h"
# ifdef EIGEN_DENSEBASE_PLUGIN
# include EIGEN_DENSEBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
// disable the use of evalTo for dense objects with a nice compilation error
template<typename Dest>

22
Eigen/src/Core/DenseStorage.h
View File

@ -67,13 +67,13 @@ struct plain_array
template<typename PtrType>
EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; }
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
eigen_assert((internal::UIntPtr(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
#else
#define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
eigen_assert((reinterpret_cast<size_t>(array) & (sizemask)) == 0 \
eigen_assert((internal::UIntPtr(array) & (sizemask)) == 0 \
&& "this assertion is explained here: " \
"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
" **** READ THIS WEB PAGE !!! ****");
@ -362,9 +362,9 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
}
return *this;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
, m_cols(std::move(other.m_cols))
@ -374,7 +374,7 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@ -441,9 +441,9 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
}
return *this;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_cols(std::move(other.m_cols))
{
@ -451,7 +451,7 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
other.m_cols = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);
@ -514,9 +514,9 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
}
return *this;
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
DenseStorage(DenseStorage&& other)
DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
: m_data(std::move(other.m_data))
, m_rows(std::move(other.m_rows))
{
@ -524,7 +524,7 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
other.m_rows = 0;
}
EIGEN_DEVICE_FUNC
DenseStorage& operator=(DenseStorage&& other)
DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_data, other.m_data);

19
Eigen/src/Core/DiagonalMatrix.h
View File

@ -71,18 +71,17 @@ class DiagonalBase : public EigenBase<Derived>
return InverseReturnType(diagonal().cwiseInverse());
}
typedef DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > ScalarMultipleReturnType;
EIGEN_DEVICE_FUNC
inline const ScalarMultipleReturnType
inline const DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >
operator*(const Scalar& scalar) const
{
return ScalarMultipleReturnType(diagonal() * scalar);
return DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType,Scalar,product) >(diagonal() * scalar);
}
EIGEN_DEVICE_FUNC
friend inline const ScalarMultipleReturnType
friend inline const DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return ScalarMultipleReturnType(other.diagonal() * scalar);
return DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,DiagonalVectorType,product) >(scalar * other.diagonal());
}
};
@ -317,19 +316,19 @@ struct Diagonal2Dense {};
template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
// Diagonal matrix to Dense assignment
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense, Scalar>
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
{
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{
dst.setZero();
dst.diagonal() = src.diagonal();
}
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() += src.diagonal(); }
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar> &/*func*/)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
{ dst.diagonal() -= src.diagonal(); }
};

19
Eigen/src/Core/Dot.h
View File

@ -28,22 +28,24 @@ template<typename T, typename U,
>
struct dot_nocheck
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.template binaryExpr<conj_prod>(b).sum();
}
};
template<typename T, typename U>
struct dot_nocheck<T, U, true>
{
typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar;
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum();
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
};
@ -62,7 +64,7 @@ struct dot_nocheck<T, U, true>
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
@ -227,9 +229,12 @@ struct lpNorm_selector<Derived, 2>
template<typename Derived>
struct lpNorm_selector<Derived, Infinity>
{
typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
static inline RealScalar run(const MatrixBase<Derived>& m)
{
if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
return RealScalar(0);
return m.cwiseAbs().maxCoeff();
}
};
@ -240,6 +245,8 @@ struct lpNorm_selector<Derived, Infinity>
* of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of \c *this.
*
* In all cases, if \c *this is empty, then the value 0 is returned.
*
* \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
*
* \sa norm()

4
Eigen/src/Core/EigenBase.h
View File

@ -138,7 +138,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -146,7 +146,7 @@ template<typename Derived>
template<typename OtherDerived>
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar>());
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}

18
Eigen/src/Core/GeneralProduct.h
View File

@ -159,20 +159,20 @@ struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
template<typename Scalar,int Size,int MaxSize>
struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
{
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
enum {
ForceAlignment = internal::packet_traits<Scalar>::Vectorizable,
PacketSize = internal::packet_traits<Scalar>::size
};
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data;
#if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0,EIGEN_PLAIN_ENUM_MIN(AlignedMax,PacketSize)> m_data;
EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
// Some architectures cannot align on the stack,
// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
EIGEN_STRONG_INLINE Scalar* data() {
return ForceAlignment
? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
: m_data.array;
}
#endif
@ -207,7 +207,7 @@ template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);

56
Eigen/src/Core/GenericPacketMath.h
View File

@ -62,6 +62,7 @@ struct default_packet_traits
HasRsqrt = 0,
HasExp = 0,
HasLog = 0,
HasLog1p = 0,
HasLog10 = 0,
HasPow = 0,
@ -82,6 +83,7 @@ struct default_packet_traits
HasErfc = 0,
HasIGamma = 0,
HasIGammac = 0,
HasBetaInc = 0,
HasRound = 0,
HasFloor = 0,
@ -304,7 +306,7 @@ template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* a
// 32-bit pointer operand constraint for inlined asm
asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
#endif
#elif !EIGEN_COMP_MSVC
#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
__builtin_prefetch(addr);
#endif
}
@ -346,22 +348,6 @@ template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Pack
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
{ return a; }
template<size_t offset, typename Packet>
struct protate_impl
{
// Empty so attempts to use this unimplemented path will fail to compile.
// Only specializations of this template should be used.
};
/** \internal \returns a packet with the coefficients rotated to the right in little-endian convention,
* by the given offset, e.g. for offset == 1:
* (packet[3], packet[2], packet[1], packet[0]) becomes (packet[0], packet[3], packet[2], packet[1])
*/
template<size_t offset, typename Packet> EIGEN_DEVICE_FUNC inline Packet protate(const Packet& a)
{
return offset ? protate_impl<offset, Packet>::run(a) : a;
}
/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
{
@ -419,6 +405,10 @@ Packet pexp(const Packet& a) { using std::exp; return exp(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog(const Packet& a) { using std::log; return log(a); }
/** \internal \returns the log1p of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog1p(const Packet& a) { return numext::log1p(a); }
/** \internal \returns the log10 of \a a (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plog10(const Packet& a) { using std::log10; return log10(a); }
@ -445,38 +435,6 @@ Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
/** \internal \returns the ln(|gamma(\a a)|) (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet plgamma(const Packet& a) { using numext::lgamma; return lgamma(a); }
/** \internal \returns the derivative of lgamma, psi(\a a) (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pdigamma(const Packet& a) { using numext::digamma; return digamma(a); }
/** \internal \returns the zeta function of two arguments (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet pzeta(const Packet& x, const Packet& q) { using numext::zeta; return zeta(x, q); }
/** \internal \returns the polygamma function (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet ppolygamma(const Packet& n, const Packet& x) { using numext::polygamma; return polygamma(n, x); }
/** \internal \returns the erf(\a a) (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet perf(const Packet& a) { using numext::erf; return erf(a); }
/** \internal \returns the erfc(\a a) (coeff-wise) */
template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
Packet perfc(const Packet& a) { using numext::erfc; return erfc(a); }
/** \internal \returns the incomplete gamma function igamma(\a a, \a x) */
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Packet pigamma(const Packet& a, const Packet& x) { using numext::igamma; return igamma(a, x); }
/** \internal \returns the complementary incomplete gamma function igammac(\a a, \a x) */
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Packet pigammac(const Packet& a, const Packet& x) { using numext::igammac; return igammac(a, x); }
/***************************************************************************
* The following functions might not have to be overwritten for vectorized types
***************************************************************************/

191
Eigen/src/Core/GlobalFunctions.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
@ -11,13 +11,30 @@
#ifndef EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_GLOBAL_FUNCTIONS_H
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR) \
#ifdef EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
/** \returns an expression of the coefficient-wise DOC_OP of \a x
DOC_DETAILS
\sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
*/ \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
NAME(const Eigen::ArrayBase<Derived>& x);
#else
#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
template<typename Derived> \
inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
(NAME)(const Eigen::ArrayBase<Derived>& x) { \
return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
}
#endif // EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
\
template<typename Derived> \
@ -36,47 +53,68 @@
namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(zeta,scalar_zeta_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(polygamma,scalar_polygamma_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
/** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
*
* \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression (\c Derived::Scalar).
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Derived,typename ScalarExponent>
inline const CwiseBinaryOp<internal::scalar_pow_op<Derived::Scalar,ScalarExponent>,Derived,Constant<ScalarExponent> >
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
#else
template<typename Derived,typename ScalarExponent>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,ScalarExponent>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,typename Derived::Scalar,ScalarExponent),
const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,ScalarExponent,pow) >::type
pow(const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
return x.derived().pow(exponent);
}
template<typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived>
inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename Derived::Scalar,pow)
pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) {
return x.derived().pow(exponent);
}
#endif
/** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
*
@ -86,12 +124,14 @@ namespace Eigen
* Output: \verbinclude Cwise_array_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
x.derived(),
exponents.derived()
);
@ -100,66 +140,39 @@ namespace Eigen
/** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
*
* This function computes the coefficient-wise power between a scalar and an array of exponents.
* Beaware that the scalar type of the input scalar \a x and the exponents \a exponents must be the same.
*
* \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
*
* Example: \include Cwise_scalar_power_array.cpp
* Output: \verbinclude Cwise_scalar_power_array.out
*
* \sa ArrayBase::pow()
*
* \relates ArrayBase
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
template<typename Scalar,typename Derived>
inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
#else
template<typename Scalar, typename Derived>
inline typename internal::enable_if< !(internal::is_same<typename Derived::Scalar,Scalar>::value) && EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar),
const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow) >::type
pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
template<typename Derived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>
inline const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)
pow(const typename Derived::Scalar& x, const Eigen::ArrayBase<Derived>& exponents)
{
typename Derived::ConstantReturnType constant_x(exponents.rows(), exponents.cols(), x);
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const typename Derived::ConstantReturnType, const Derived>(
constant_x,
exponents.derived()
);
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename Derived::Scalar,Derived,pow)(
typename internal::plain_constant_type<Derived,typename Derived::Scalar>::type(exponents.rows(), exponents.cols(), x), exponents.derived() );
}
#endif
/**
* \brief Component-wise division of a scalar by array elements.
**/
template <typename Derived>
inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>
operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a)
{
return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>(
a.derived(),
Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>(s)
);
}
/** \returns an expression of the coefficient-wise igamma(\a a, \a x) to the given arrays.
*
* This function computes the coefficient-wise incomplete gamma function.
*
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
igamma(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
a.derived(),
x.derived()
);
}
/** \returns an expression of the coefficient-wise igammac(\a a, \a x) to the given arrays.
*
* This function computes the coefficient-wise complementary incomplete gamma function.
*
*/
template<typename Derived,typename ExponentDerived>
inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
igammac(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
{
return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
a.derived(),
x.derived()
);
}
namespace internal
{

32
Eigen/src/Core/IO.h
View File

@ -125,31 +125,17 @@ DenseBase<Derived>::format(const IOFormat& fmt) const
namespace internal {
template<typename Scalar, bool IsInteger>
struct significant_decimals_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline int run()
{
using std::ceil;
using std::log;
return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10))));
}
};
template<typename Scalar>
struct significant_decimals_default_impl<Scalar, true>
{
static inline int run()
{
return 0;
}
};
// NOTE: This helper is kept for backward compatibility with previous code specializing
// this internal::significant_decimals_impl structure. In the future we should directly
// call digits10() which has been introduced in July 2016 in 3.3.
template<typename Scalar>
struct significant_decimals_impl
: significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger>
{};
{
static inline int run()
{
return NumTraits<Scalar>::digits10();
}
};
/** \internal
* print the matrix \a _m to the output stream \a s using the output format \a fmt */

2
Eigen/src/Core/Inverse.h
View File

@ -50,7 +50,7 @@ public:
typedef typename internal::ref_selector<Inverse>::type Nested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
explicit Inverse(const XprType &xpr)
explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
: m_xpr(xpr)
{}

39
Eigen/src/Core/MapBase.h
View File

@ -17,10 +17,20 @@
namespace Eigen {
/** \class MapBase
* \ingroup Core_Module
/** \ingroup Core_Module
*
* \brief Base class for Map and Block expression with direct access
* \brief Base class for dense Map and Block expression with direct access
*
* This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
* Map and Block objects with direct access.
* Typical users do not have to directly deal with this class.
*
* This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
* See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
*
* The \c Derived class has to provide the following two methods describing the memory layout:
* \code Index innerStride() const; \endcode
* \code Index outerStride() const; \endcode
*
* \sa class Map, class Block
*/
@ -75,7 +85,9 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
typedef typename Base::CoeffReturnType CoeffReturnType;
/** \copydoc DenseBase::rows() */
EIGEN_DEVICE_FUNC inline Index rows() const { return m_rows.value(); }
/** \copydoc DenseBase::cols() */
EIGEN_DEVICE_FUNC inline Index cols() const { return m_cols.value(); }
/** Returns a pointer to the first coefficient of the matrix or vector.
@ -86,12 +98,14 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
*/
EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
/** \copydoc PlainObjectBase::coeff(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index rowId, Index colId) const
{
return m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeff(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeff(Index index) const
{
@ -99,12 +113,14 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return m_data[index * innerStride()];
}
/** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return this->m_data[colId * colStride() + rowId * rowStride()];
}
/** \copydoc PlainObjectBase::coeffRef(Index) const */
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
@ -112,6 +128,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return this->m_data[index * innerStride()];
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
@ -119,6 +136,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
(m_data + (colId * colStride() + rowId * rowStride()));
}
/** \internal */
template<int LoadMode>
inline PacketScalar packet(Index index) const
{
@ -126,6 +144,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
}
/** \internal Constructor for fixed size matrices or vectors */
EIGEN_DEVICE_FUNC
explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
{
@ -133,6 +152,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
checkSanity<Derived>();
}
/** \internal Constructor for dynamically sized vectors */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index vecSize)
: m_data(dataPtr),
@ -145,6 +165,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
checkSanity<Derived>();
}
/** \internal Constructor for dynamically sized matrices */
EIGEN_DEVICE_FUNC
inline MapBase(PointerType dataPtr, Index rows, Index cols)
: m_data(dataPtr), m_rows(rows), m_cols(cols)
@ -166,7 +187,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
{
#if EIGEN_MAX_ALIGN_BYTES>0
eigen_assert(( ((size_t(m_data) % internal::traits<Derived>::Alignment) == 0)
eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
|| (cols() * rows() * innerStride() * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
#endif
}
@ -181,6 +202,16 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
};
/** \ingroup Core_Module
*
* \brief Base class for non-const dense Map and Block expression with direct access
*
* This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
* dense Map and Block objects with direct access.
* It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
*
* \sa class Map, class Block
*/
template<typename Derived> class MapBase<Derived, WriteAccessors>
: public MapBase<Derived, ReadOnlyAccessors>
{

132
Eigen/src/Core/MathFunctions.h
View File

@ -11,7 +11,9 @@
#define EIGEN_MATHFUNCTIONS_H
// source: http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html
#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406
// TODO this should better be moved to NumTraits
#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406L
namespace Eigen {
@ -95,6 +97,19 @@ struct real_default_impl<Scalar,true>
template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
#ifdef __CUDA_ARCH__
template<typename T>
struct real_impl<std::complex<T> >
{
typedef T RealScalar;
EIGEN_DEVICE_FUNC
static inline T run(const std::complex<T>& x)
{
return x.real();
}
};
#endif
template<typename Scalar>
struct real_retval
{
@ -130,6 +145,19 @@ struct imag_default_impl<Scalar,true>
template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
#ifdef __CUDA_ARCH__
template<typename T>
struct imag_impl<std::complex<T> >
{
typedef T RealScalar;
EIGEN_DEVICE_FUNC
static inline T run(const std::complex<T>& x)
{
return x.imag();
}
};
#endif
template<typename Scalar>
struct imag_retval
{
@ -457,30 +485,33 @@ struct arg_retval
/****************************************************************************
* Implementation of log1p *
****************************************************************************/
template<typename Scalar, bool isComplex = NumTraits<Scalar>::IsComplex >
struct log1p_impl
{
static inline Scalar run(const Scalar& x)
{
namespace std_fallback {
// fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar,
// or that there is no suitable std::log1p function available
template<typename Scalar>
EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) {
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_USING_STD_MATH(log);
Scalar x1p = RealScalar(1) + x;
return ( x1p == Scalar(1) ) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) );
}
};
}
#if EIGEN_HAS_CXX11_MATH
template<typename Scalar>
struct log1p_impl<Scalar, false> {
struct log1p_impl {
static inline Scalar run(const Scalar& x)
{
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
#if EIGEN_HAS_CXX11_MATH
using std::log1p;
#endif
using std_fallback::log1p;
return log1p(x);
}
};
#endif
template<typename Scalar>
struct log1p_retval
@ -492,24 +523,26 @@ struct log1p_retval
* Implementation of pow *
****************************************************************************/
template<typename Scalar, bool IsInteger>
struct pow_default_impl
template<typename ScalarX,typename ScalarY, bool IsInteger = NumTraits<ScalarX>::IsInteger&&NumTraits<ScalarY>::IsInteger>
struct pow_impl
{
typedef Scalar retval;
static EIGEN_DEVICE_FUNC inline Scalar run(const Scalar& x, const Scalar& y)
//typedef Scalar retval;
typedef typename ScalarBinaryOpTraits<ScalarX,ScalarY,internal::scalar_pow_op<ScalarX,ScalarY> >::ReturnType result_type;
static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y)
{
EIGEN_USING_STD_MATH(pow);
return pow(x, y);
}
};
template<typename Scalar>
struct pow_default_impl<Scalar, true>
template<typename ScalarX,typename ScalarY>
struct pow_impl<ScalarX,ScalarY, true>
{
static EIGEN_DEVICE_FUNC inline Scalar run(Scalar x, Scalar y)
typedef ScalarX result_type;
static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y)
{
Scalar res(1);
eigen_assert(!NumTraits<Scalar>::IsSigned || y >= 0);
ScalarX res(1);
eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
if(y & 1) res *= x;
y >>= 1;
while(y)
@ -522,15 +555,6 @@ struct pow_default_impl<Scalar, true>
}
};
template<typename Scalar>
struct pow_impl : pow_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {};
template<typename Scalar>
struct pow_retval
{
typedef Scalar type;
};
/****************************************************************************
* Implementation of random *
****************************************************************************/
@ -620,16 +644,18 @@ struct random_default_impl<Scalar, false, true>
typedef typename conditional<NumTraits<Scalar>::IsSigned,std::ptrdiff_t,std::size_t>::type ScalarX;
if(y<x)
return x;
// the following difference might overflow on a 32 bits system,
// but since y>=x the result converted to an unsigned long is still correct.
std::size_t range = ScalarX(y)-ScalarX(x);
std::size_t offset = 0;
// rejection sampling
std::size_t divisor = (range+RAND_MAX-1)/(range+1);
std::size_t multiplier = (range+RAND_MAX-1)/std::size_t(RAND_MAX);
std::size_t divisor = 1;
std::size_t multiplier = 1;
if(range<RAND_MAX) divisor = (std::size_t(RAND_MAX)+1)/(range+1);
else multiplier = 1 + range/(std::size_t(RAND_MAX)+1);
do {
offset = ( (std::size_t(std::rand()) * multiplier) / divisor );
offset = (std::size_t(std::rand()) * multiplier) / divisor;
} while (offset > range);
return Scalar(ScalarX(x) + offset);
}
@ -790,6 +816,8 @@ template<typename T> EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>&
template<typename T> EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x);
template<typename T> EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x);
template<typename T> T generic_fast_tanh_float(const T& a_x);
} // end namespace internal
/****************************************************************************
@ -825,7 +853,7 @@ template<>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y)
{
return fmin(x, y);
return fminf(x, y);
}
template<typename T>
EIGEN_DEVICE_FUNC
@ -837,7 +865,7 @@ template<>
EIGEN_DEVICE_FUNC
EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y)
{
return fmax(x, y);
return fmaxf(x, y);
}
#endif
@ -926,11 +954,19 @@ inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
}
template<typename Scalar>
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float log1p(const float &x) { return ::log1pf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double log1p(const double &x) { return ::log1p(x); }
#endif
template<typename ScalarX,typename ScalarY>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const ScalarX& x, const ScalarY& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
}
template<typename T> EIGEN_DEVICE_FUNC bool (isnan) (const T &x) { return internal::isnan_impl(x); }
@ -1036,6 +1072,16 @@ float abs(const float &x) { return ::fabsf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double abs(const double &x) { return ::fabs(x); }
template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float abs(const std::complex<float>& x) {
return ::hypotf(x.real(), x.imag());
}
template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double abs(const std::complex<double>& x) {
return ::hypot(x.real(), x.imag());
}
#endif
template<typename T>
@ -1181,6 +1227,11 @@ T tanh(const T &x) {
return tanh(x);
}
#if (!defined(__CUDACC__)) && EIGEN_FAST_MATH
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tanh(float x) { return internal::generic_fast_tanh_float(x); }
#endif
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tanh(const float &x) { return ::tanhf(x); }
@ -1192,7 +1243,7 @@ double tanh(const double &x) { return ::tanh(x); }
template <typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T fmod(const T& a, const T& b) {
EIGEN_USING_STD_MATH(floor);
EIGEN_USING_STD_MATH(fmod);
return fmod(a, b);
}
@ -1287,11 +1338,12 @@ template<typename Scalar>
struct scalar_fuzzy_default_impl<Scalar, true, false>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
template<typename OtherScalar>
template<typename OtherScalar> EIGEN_DEVICE_FUNC
static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
{
return numext::abs2(x) <= numext::abs2(y) * prec * prec;
}
EIGEN_DEVICE_FUNC
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec;

78
Eigen/src/Core/MathFunctionsImpl.h Normal file
View File

@ -0,0 +1,78 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_MATHFUNCTIONSIMPL_H
#define EIGEN_MATHFUNCTIONSIMPL_H
namespace Eigen {
namespace internal {
/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
Doesn't do anything fancy, just a 13/6-degree rational interpolant which
is accurate up to a couple of ulp in the range [-9, 9], outside of which
the tanh(x) = +/-1.
This implementation works on both scalars and packets.
*/
template<typename T>
T generic_fast_tanh_float(const T& a_x)
{
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
const T plus_9 = pset1<T>(9.f);
const T minus_9 = pset1<T>(-9.f);
// NOTE GCC prior to 6.3 might improperly optimize this max/min
// step such that if a_x is nan, x will be either 9 or -9,
// and tanh will return 1 or -1 instead of nan.
// This is supposed to be fixed in gcc6.3,
// see: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
const T x = pmax(minus_9,pmin(plus_9,a_x));
// The monomial coefficients of the numerator polynomial (odd).
const T alpha_1 = pset1<T>(4.89352455891786e-03f);
const T alpha_3 = pset1<T>(6.37261928875436e-04f);
const T alpha_5 = pset1<T>(1.48572235717979e-05f);
const T alpha_7 = pset1<T>(5.12229709037114e-08f);
const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
const T alpha_11 = pset1<T>(2.00018790482477e-13f);
const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
// The monomial coefficients of the denominator polynomial (even).
const T beta_0 = pset1<T>(4.89352518554385e-03f);
const T beta_2 = pset1<T>(2.26843463243900e-03f);
const T beta_4 = pset1<T>(1.18534705686654e-04f);
const T beta_6 = pset1<T>(1.19825839466702e-06f);
// Since the polynomials are odd/even, we need x^2.
const T x2 = pmul(x, x);
// Evaluate the numerator polynomial p.
T p = pmadd(x2, alpha_13, alpha_11);
p = pmadd(x2, p, alpha_9);
p = pmadd(x2, p, alpha_7);
p = pmadd(x2, p, alpha_5);
p = pmadd(x2, p, alpha_3);
p = pmadd(x2, p, alpha_1);
p = pmul(x, p);
// Evaluate the denominator polynomial p.
T q = pmadd(x2, beta_6, beta_4);
q = pmadd(x2, q, beta_2);
q = pmadd(x2, q, beta_0);
// Divide the numerator by the denominator.
return pdiv(p, q);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_MATHFUNCTIONSIMPL_H

10
Eigen/src/Core/Matrix.h
View File

@ -27,7 +27,7 @@ private:
default_alignment = compute_default_alignment<_Scalar,max_size>::value,
actual_alignment = ((_Options&DontAlign)==0) ? default_alignment : 0,
required_alignment = unpacket_traits<PacketScalar>::alignment,
packet_access_bit = packet_traits<_Scalar>::Vectorizable && (actual_alignment>=required_alignment) ? PacketAccessBit : 0
packet_access_bit = (packet_traits<_Scalar>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
};
public:
@ -106,7 +106,7 @@ public:
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
*
* <i><b>Some notes:</b></i>
*
@ -268,9 +268,9 @@ class Matrix
: Base(internal::constructor_without_unaligned_array_assert())
{ Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
Matrix(Matrix&& other)
Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
: Base(std::move(other))
{
Base::_check_template_params();
@ -278,7 +278,7 @@ class Matrix
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other)
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
{
other.swap(*this);
return *this;

20
Eigen/src/Core/MatrixBase.h
View File

@ -41,7 +41,7 @@ namespace Eigen {
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
*
* \sa \blank \ref TopicClassHierarchy
*/
@ -80,8 +80,6 @@ template<typename Derived> class MatrixBase
using Base::operator-=;
using Base::operator*=;
using Base::operator/=;
using Base::operator*;
using Base::operator/;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
@ -100,7 +98,7 @@ template<typename Derived> class MatrixBase
/** \returns the size of the main diagonal, which is min(rows(),cols()).
* \sa rows(), cols(), SizeAtCompileTime. */
EIGEN_DEVICE_FUNC
inline Index diagonalSize() const { return (std::min)(rows(),cols()); }
inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
typedef typename Base::PlainObject PlainObject;
@ -123,6 +121,7 @@ template<typename Derived> class MatrixBase
#endif // not EIGEN_PARSED_BY_DOXYGEN
#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
#define EIGEN_DOC_UNARY_ADDONS(X,Y)
# include "../plugins/CommonCwiseUnaryOps.h"
# include "../plugins/CommonCwiseBinaryOps.h"
# include "../plugins/MatrixCwiseUnaryOps.h"
@ -131,6 +130,7 @@ template<typename Derived> class MatrixBase
# include EIGEN_MATRIXBASE_PLUGIN
# endif
#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
#undef EIGEN_DOC_UNARY_ADDONS
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
@ -195,7 +195,7 @@ template<typename Derived> class MatrixBase
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
dot(const MatrixBase<OtherDerived>& other) const;
EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
@ -330,15 +330,11 @@ template<typename Derived> class MatrixBase
/////////// LU module ///////////
EIGEN_DEVICE_FUNC
inline const FullPivLU<PlainObject> fullPivLu() const;
EIGEN_DEVICE_FUNC
inline const PartialPivLU<PlainObject> partialPivLu() const;
EIGEN_DEVICE_FUNC
inline const PartialPivLU<PlainObject> lu() const;
EIGEN_DEVICE_FUNC
inline const Inverse<Derived> inverse() const;
template<typename ResultType>
@ -383,7 +379,7 @@ template<typename Derived> class MatrixBase
#ifndef EIGEN_PARSED_BY_DOXYGEN
/// \internal helper struct to form the return type of the cross product
template<typename OtherDerived> struct cross_product_return_type {
typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type;
};
#endif // EIGEN_PARSED_BY_DOXYGEN
@ -405,7 +401,6 @@ template<typename Derived> class MatrixBase
inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
inline ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const;
// put this as separate enum value to work around possible GCC 4.3 bug (?)
enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
: ColsAtCompileTime==1 ? Vertical : Horizontal };
@ -418,8 +413,7 @@ template<typename Derived> class MatrixBase
typedef Block<const Derived,
internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>,
const ConstStartMinusOne > HNormalizedReturnType;
typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
inline const HNormalizedReturnType hnormalized() const;

6
Eigen/src/Core/NoAlias.h
View File

@ -39,7 +39,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
@ -47,7 +47,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}
@ -55,7 +55,7 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
{
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar>());
call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
return m_expression;
}

98
Eigen/src/Core/NumTraits.h
View File

@ -12,6 +12,37 @@
namespace Eigen {
namespace internal {
// default implementation of digits10(), based on numeric_limits if specialized,
// 0 for integer types, and log10(epsilon()) otherwise.
template< typename T,
bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
bool is_integer = NumTraits<T>::IsInteger>
struct default_digits10_impl
{
static int run() { return std::numeric_limits<T>::digits10; }
};
template<typename T>
struct default_digits10_impl<T,false,false> // Floating point
{
static int run() {
using std::log10;
using std::ceil;
typedef typename NumTraits<T>::Real Real;
return int(ceil(-log10(NumTraits<Real>::epsilon())));
}
};
template<typename T>
struct default_digits10_impl<T,false,true> // Integer
{
static int run() { return 0; }
};
} // end namespace internal
/** \class NumTraits
* \ingroup Core_Module
*
@ -22,14 +53,16 @@ namespace Eigen {
* This class stores enums, typedefs and static methods giving information about a numeric type.
*
* The provided data consists of:
* \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
* then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
* \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
* then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
* is a typedef to \a U.
* \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
* \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
* such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
* \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
* take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
* only intended as a helper for code that needs to explicitly promote types.
* \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
* Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
* \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
* this means, just use \a T here.
* \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
@ -42,10 +75,14 @@ namespace Eigen {
* \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
* \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
* be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
* \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T.
* \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
* it returns a \a Real instead of a \a T.
* \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
* value by the fuzzy comparison operators.
* \li highest() and lowest() functions returning the highest and lowest possible values respectively.
* \li digits10() function returning the number of decimal digits that can be represented without change. This is
* the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
* which is used as the default implementation if specialized.
*/
template<typename T> struct GenericNumTraits
@ -60,23 +97,6 @@ template<typename T> struct GenericNumTraits
MulCost = 1
};
// Division is messy but important, because it is expensive and throughput
// varies significantly. The following numbers are based on min division
// throughput on Haswell.
template<bool Vectorized>
struct Div {
enum {
#ifdef EIGEN_VECTORIZE_AVX
AVX = true,
#else
AVX = false,
#endif
Cost = IsInteger ? (sizeof(T) == 8 ? (IsSigned ? 24 : 21) : (IsSigned ? 8 : 9)):
Vectorized ? (sizeof(T) == 8 ? (AVX ? 16 : 8) : (AVX ? 14 : 7)) : 8
};
};
typedef T Real;
typedef typename internal::conditional<
IsInteger,
@ -84,12 +104,20 @@ template<typename T> struct GenericNumTraits
T
>::type NonInteger;
typedef T Nested;
typedef T Literal;
EIGEN_DEVICE_FUNC
static inline Real epsilon()
{
return numext::numeric_limits<T>::epsilon();
}
EIGEN_DEVICE_FUNC
static inline int digits10()
{
return internal::default_digits10_impl<T>::run();
}
EIGEN_DEVICE_FUNC
static inline Real dummy_precision()
{
@ -145,6 +173,7 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
: GenericNumTraits<std::complex<_Real> >
{
typedef _Real Real;
typedef typename NumTraits<_Real>::Literal Literal;
enum {
IsComplex = 1,
RequireInitialization = NumTraits<_Real>::RequireInitialization,
@ -157,6 +186,8 @@ template<typename _Real> struct NumTraits<std::complex<_Real> >
static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
EIGEN_DEVICE_FUNC
static inline int digits10() { return NumTraits<Real>::digits10(); }
};
template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
@ -168,6 +199,7 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
typedef ArrayType & Nested;
typedef typename NumTraits<Scalar>::Literal Literal;
enum {
IsComplex = NumTraits<Scalar>::IsComplex,
@ -185,6 +217,30 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
};
template<> struct NumTraits<std::string>
: GenericNumTraits<std::string>
{
enum {
RequireInitialization = 1,
ReadCost = HugeCost,
AddCost = HugeCost,
MulCost = HugeCost
};
static inline int digits10() { return 0; }
private:
static inline std::string epsilon();
static inline std::string dummy_precision();
static inline std::string lowest();
static inline std::string highest();
static inline std::string infinity();
static inline std::string quiet_NaN();
};
// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
template<> struct NumTraits<void> {};
} // end namespace Eigen
#endif // EIGEN_NUMTRAITS_H

49
Eigen/src/Core/PlainObjectBase.h
View File

@ -59,33 +59,34 @@ template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct m
} // end namespace internal
/** \class PlainObjectBase
* \ingroup Core_Module
* \brief %Dense storage base class for matrices and arrays.
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
*
* \sa \ref TopicClassHierarchy
*/
#ifdef EIGEN_PARSED_BY_DOXYGEN
namespace internal {
namespace doxygen {
// this is a workaround to doxygen not being able to understand the inheritance logic
// when it is hidden by the dense_xpr_base helper struct.
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename Derived> struct dense_xpr_base_dispatcher_for_doxygen;// : public MatrixBase<Derived> {};
template<typename Derived> struct dense_xpr_base_dispatcher;
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
struct dense_xpr_base_dispatcher<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
/** This class is just a workaround for Doxygen and it does not not actually exist. */
template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
struct dense_xpr_base_dispatcher_for_doxygen<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
struct dense_xpr_base_dispatcher<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
: public ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {};
} // namespace internal
} // namespace doxygen
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base_dispatcher_for_doxygen<Derived>
class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
#else
template<typename Derived>
class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
@ -145,6 +146,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); }
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const
{
@ -154,12 +159,20 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
return m_storage.data()[index];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId)
{
@ -169,12 +182,18 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
* provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
*
* See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_storage.data()[index];
}
/** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const
{
@ -184,6 +203,8 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
return m_storage.data()[rowId + colId * m_storage.rows()];
}
/** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
* It is provided for convenience. */
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const
{
@ -471,15 +492,15 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
#endif
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC
PlainObjectBase(PlainObjectBase&& other)
PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
: m_storage( std::move(other.m_storage) )
{
}
EIGEN_DEVICE_FUNC
PlainObjectBase& operator=(PlainObjectBase&& other)
PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
{
using std::swap;
swap(m_storage, other.m_storage);
@ -697,7 +718,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
//_resize_to_match(other);
// the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
// it wouldn't allow to copy a row-vector into a column-vector.
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar>());
internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return this->derived();
}
@ -713,11 +734,11 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
template<typename T0, typename T1>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
m_storage.data()[0] = val0;
m_storage.data()[1] = val1;
m_storage.data()[0] = Scalar(val0);
m_storage.data()[1] = Scalar(val1);
}
template<typename T0, typename T1>

35
Eigen/src/Core/Product.h
View File

@ -16,39 +16,6 @@ template<typename Lhs, typename Rhs, int Option, typename StorageKind> class Pro
namespace internal {
// Determine the scalar of Product<Lhs, Rhs>. This is normally the same as Lhs::Scalar times
// Rhs::Scalar, but product with permutation matrices inherit the scalar of the other factor.
template<typename Lhs, typename Rhs, typename LhsShape = typename evaluator_traits<Lhs>::Shape,
typename RhsShape = typename evaluator_traits<Rhs>::Shape >
struct product_result_scalar
{
typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, PermutationShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, PermutationShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename RhsShape>
struct product_result_scalar<Lhs, Rhs, TranspositionsShape, RhsShape>
{
typedef typename Rhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, typename LhsShape>
struct product_result_scalar<Lhs, Rhs, LhsShape, TranspositionsShape>
{
typedef typename Lhs::Scalar Scalar;
};
template<typename Lhs, typename Rhs, int Option>
struct traits<Product<Lhs, Rhs, Option> >
{
@ -59,7 +26,7 @@ struct traits<Product<Lhs, Rhs, Option> >
typedef MatrixXpr XprKind;
typedef typename product_result_scalar<LhsCleaned,RhsCleaned>::Scalar Scalar;
typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
typename RhsTraits::StorageKind,
internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;

189
Eigen/src/Core/ProductEvaluators.h
View File

@ -35,22 +35,28 @@ struct evaluator<Product<Lhs, Rhs, Options> >
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
};
// Catch scalar * ( A * B ) and transform it to (A*scalar) * B
// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
// TODO we should apply that rule only if that's really helpful
template<typename Lhs, typename Rhs, typename Scalar>
struct evaluator_assume_aliasing<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > >
template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > >
{
static const bool value = true;
};
template<typename Lhs, typename Rhs, typename Scalar>
struct evaluator<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > >
: public evaluator<Product<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,const Lhs>, Rhs, DefaultProduct> >
template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > >
: public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
{
typedef CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Product<Lhs, Rhs, DefaultProduct> > XprType;
typedef evaluator<Product<CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,const Lhs>, Rhs, DefaultProduct> > Base;
typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
const Product<Lhs, Rhs, DefaultProduct> > XprType;
typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
: Base(xpr.functor().m_other * xpr.nestedExpression().lhs() * xpr.nestedExpression().rhs())
: Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
{}
};
@ -122,13 +128,17 @@ protected:
PlainObject m_result;
};
// The following three shortcuts are enabled only if the scalar types match excatly.
// TODO: we could enable them for different scalar types when the product is not vectorized.
// Dense = Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
@ -137,11 +147,12 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scal
// Dense += Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar> &)
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
@ -150,11 +161,12 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<
// Dense -= Product
template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct),Scalar>::type>
struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
{
typedef Product<Lhs,Rhs,Options> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar> &)
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
{
// FIXME shall we handle nested_eval here?
generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
@ -165,55 +177,57 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<
// Dense ?= scalar * Product
// TODO we should apply that rule if that's really helpful
// for instance, this is not good for inner products
template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis>
struct Assignment<DstXprType, CwiseUnaryOp<internal::scalar_multiple_op<ScalarBis>,
const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense, Scalar>
template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
{
typedef CwiseUnaryOp<internal::scalar_multiple_op<ScalarBis>,
const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
{
call_assignment_no_alias(dst, (src.functor().m_other * src.nestedExpression().lhs())*src.nestedExpression().rhs(), func);
call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
}
};
//----------------------------------------
// Catch "Dense ?= xpr + Product<>" expression to save one temporary
// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
// TODO enable it for "Dense ?= xpr - Product<>" as well.
template<typename OtherXpr, typename Lhs, typename Rhs>
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar>, const OtherXpr,
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
static const bool value = true;
};
template<typename DstXprType, typename OtherXpr, typename ProductType, typename Scalar, typename Func1, typename Func2>
struct assignment_from_xpr_plus_product
template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
struct assignment_from_xpr_op_product
{
typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr, const ProductType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const Func1& func)
template<typename SrcXprType, typename InitialFunc>
static EIGEN_STRONG_INLINE
void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
{
call_assignment_no_alias(dst, src.lhs(), func);
call_assignment_no_alias(dst, src.lhs(), Func1());
call_assignment_no_alias(dst, src.rhs(), Func2());
}
};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::assign_op<Scalar>, internal::add_assign_op<Scalar> >
{};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::add_assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::add_assign_op<Scalar>, internal::add_assign_op<Scalar> >
{};
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, internal::sub_assign_op<Scalar>, Dense2Dense>
: assignment_from_xpr_plus_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, Scalar, internal::sub_assign_op<Scalar>, internal::sub_assign_op<Scalar> >
{};
#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
: assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
{}
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_sum_op,add_assign_op);
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op, scalar_difference_op,sub_assign_op);
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
//----------------------------------------
template<typename Lhs, typename Rhs>
@ -243,7 +257,7 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
// Column major result
template<typename Dst, typename Lhs, typename Rhs, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
{
evaluator<Rhs> rhsEval(rhs);
typename nested_eval<Lhs,Rhs::SizeAtCompileTime>::type actual_lhs(lhs);
@ -251,12 +265,12 @@ EIGEN_DONT_INLINE void outer_product_selector_run(Dst& dst, const Lhs &lhs, cons
// FIXME not very good if rhs is real and lhs complex while alpha is real too
const Index cols = dst.cols();
for (Index j=0; j<cols; ++j)
func(dst.col(j), rhsEval.coeff(0,j) * actual_lhs);
func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
}
// Row major result
template<typename Dst, typename Lhs, typename Rhs, typename Func>
EIGEN_DONT_INLINE void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
void outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
{
evaluator<Lhs> lhsEval(lhs);
typename nested_eval<Rhs,Lhs::SizeAtCompileTime>::type actual_rhs(rhs);
@ -264,7 +278,7 @@ EIGEN_DONT_INLINE void outer_product_selector_run(Dst& dst, const Lhs &lhs, cons
// FIXME not very good if lhs is real and rhs complex while alpha is real too
const Index rows = dst.rows();
for (Index i=0; i<rows; ++i)
func(dst.row(i), lhsEval.coeff(i,0) * actual_rhs);
func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
}
template<typename Lhs, typename Rhs>
@ -319,19 +333,19 @@ struct generic_product_impl_base
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
template<typename Dst>
static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{ dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
template<typename Dst>
static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{ scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
template<typename Dst>
static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{ scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
template<typename Dst>
static void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
static EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
{ Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
};
@ -345,7 +359,7 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
typedef typename internal::conditional<int(Side)==OnTheRight,Lhs,Rhs>::type MatrixType;
template<typename Dest>
static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
static EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
{
internal::gemv_dense_selector<Side,
(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
@ -360,25 +374,25 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
typedef typename Product<Lhs,Rhs>::Scalar Scalar;
template<typename Dst>
static inline void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
// Same as: dst.noalias() = lhs.lazyProduct(rhs);
// but easier on the compiler side
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
}
template<typename Dst>
static inline void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
// dst.noalias() += lhs.lazyProduct(rhs);
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
}
template<typename Dst>
static inline void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
// dst.noalias() -= lhs.lazyProduct(rhs);
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<Scalar>());
call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
}
// template<typename Dst>
@ -423,6 +437,18 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
#if 0
std::cerr << "LhsOuterStrideBytes= " << LhsOuterStrideBytes << "\n";
std::cerr << "RhsOuterStrideBytes= " << RhsOuterStrideBytes << "\n";
std::cerr << "LhsAlignment= " << LhsAlignment << "\n";
std::cerr << "RhsAlignment= " << RhsAlignment << "\n";
std::cerr << "CanVectorizeLhs= " << CanVectorizeLhs << "\n";
std::cerr << "CanVectorizeRhs= " << CanVectorizeRhs << "\n";
std::cerr << "CanVectorizeInner= " << CanVectorizeInner << "\n";
std::cerr << "EvalToRowMajor= " << EvalToRowMajor << "\n";
std::cerr << "Alignment= " << Alignment << "\n";
std::cerr << "Flags= " << Flags << "\n";
#endif
}
// Everything below here is taken from CoeffBasedProduct.h
@ -473,15 +499,12 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
CanVectorizeRhs = RhsRowMajor && (RhsFlags & PacketAccessBit)
&& (ColsAtCompileTime == Dynamic || ((ColsAtCompileTime % RhsVecPacketSize) == 0) ),
CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit)
&& (RowsAtCompileTime == Dynamic || ((RowsAtCompileTime % LhsVecPacketSize) == 0) ),
CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
: (RhsRowMajor && !CanVectorizeLhs),
: (bool(RhsRowMajor) && !CanVectorizeLhs),
Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & ~RowMajorBit)
| (EvalToRowMajor ? RowMajorBit : 0)
@ -492,8 +515,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
Alignment = CanVectorizeLhs ? (LhsOuterStrideBytes<0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
: CanVectorizeRhs ? (RhsOuterStrideBytes<0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
: bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
: 0,
/* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
@ -519,8 +542,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
*/
EIGEN_DEVICE_FUNC const CoeffReturnType coeff(Index index) const
{
const Index row = RowsAtCompileTime == 1 ? 0 : index;
const Index col = RowsAtCompileTime == 1 ? index : 0;
const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
}
@ -538,8 +561,8 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
template<int LoadMode, typename PacketType>
const PacketType packet(Index index) const
{
const Index row = RowsAtCompileTime == 1 ? 0 : index;
const Index col = RowsAtCompileTime == 1 ? index : 0;
const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
return packet<LoadMode,PacketType>(row,col);
}
@ -579,7 +602,7 @@ struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, Load
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
{
etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
res = pmadd(pset1<Packet>(lhs.coeff(row, UnrollingIndex-1)), rhs.template packet<LoadMode,Packet>(UnrollingIndex-1, col), res);
res = pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
}
};
@ -589,7 +612,7 @@ struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, Load
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
{
etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
res = pmadd(lhs.template packet<LoadMode,Packet>(row, UnrollingIndex-1), pset1<Packet>(rhs.coeff(UnrollingIndex-1, col)), res);
res = pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
}
};
@ -598,7 +621,7 @@ struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
{
res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode,Packet>(0, col));
res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
}
};
@ -607,7 +630,7 @@ struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
{
res = pmul(lhs.template packet<LoadMode,Packet>(row, 0), pset1<Packet>(rhs.coeff(0, col)));
res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
}
};
@ -616,7 +639,7 @@ struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
{
res = pset1<Packet>(0);
res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
}
};
@ -625,7 +648,7 @@ struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
{
res = pset1<Packet>(0);
res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
}
};
@ -634,7 +657,7 @@ struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
{
res = pset1<Packet>(0);
res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
for(Index i = 0; i < innerDim; ++i)
res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
}
@ -645,7 +668,7 @@ struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
{
static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
{
res = pset1<Packet>(0);
res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
for(Index i = 0; i < innerDim; ++i)
res = pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
}
@ -730,7 +753,7 @@ template<typename MatrixType, typename DiagonalType, typename Derived, int Produ
struct diagonal_product_evaluator_base
: evaluator_base<Derived>
{
typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
public:
enum {
CoeffReadCost = NumTraits<Scalar>::MulCost + evaluator<MatrixType>::CoeffReadCost + evaluator<DiagonalType>::CoeffReadCost,

3
Eigen/src/Core/Random.h
View File

@ -16,8 +16,7 @@ namespace internal {
template<typename Scalar> struct scalar_random_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op)
template<typename Index>
inline const Scalar operator() (Index, Index = 0) const { return random<Scalar>(); }
inline const Scalar operator() () const { return random<Scalar>(); }
};
template<typename Scalar>

23
Eigen/src/Core/Redux.h
View File

@ -38,8 +38,8 @@ public:
enum {
MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit)
&& (functor_traits<Func>::PacketAccess),
MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize
MayLinearVectorize = bool(MightVectorize) && (int(Derived::Flags)&LinearAccessBit),
MaySliceVectorize = bool(MightVectorize) && int(InnerMaxSize)>=3*PacketSize
};
public:
@ -425,7 +425,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::minCoeff() const
{
return derived().redux(Eigen::internal::scalar_min_op<Scalar>());
return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar>());
}
/** \returns the maximum of all coefficients of \c *this.
@ -435,10 +435,12 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::maxCoeff() const
{
return derived().redux(Eigen::internal::scalar_max_op<Scalar>());
return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar>());
}
/** \returns the sum of all coefficients of *this
/** \returns the sum of all coefficients of \c *this
*
* If \c *this is empty, then the value 0 is returned.
*
* \sa trace(), prod(), mean()
*/
@ -448,7 +450,7 @@ DenseBase<Derived>::sum() const
{
if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
return Scalar(0);
return derived().redux(Eigen::internal::scalar_sum_op<Scalar>());
return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
}
/** \returns the mean of all coefficients of *this
@ -459,7 +461,14 @@ template<typename Derived>
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::mean() const
{
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning push
#pragma warning ( disable : 2259 )
#endif
return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
#ifdef __INTEL_COMPILER
#pragma warning pop
#endif
}
/** \returns the product of all coefficients of *this

10
Eigen/src/Core/Ref.h
View File

@ -35,7 +35,13 @@ struct traits<Ref<_PlainObjectType, _Options, _StrideType> >
|| (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
OuterStrideMatch = Derived::IsVectorAtCompileTime
|| int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (int(evaluator<Derived>::Alignment) >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
// NOTE, this indirection of evaluator<Derived>::Alignment is needed
// to workaround a very strange bug in MSVC related to the instantiation
// of has_*ary_operator in evaluator<CwiseNullaryOp>.
// This line is surprisingly very sensitive. For instance, simply adding parenthesis
// as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
DerivedAlignment = int(evaluator<Derived>::Alignment),
AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
};
@ -262,7 +268,7 @@ template<typename TPlainObjectType, int Options, typename StrideType> class Ref<
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
{
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar>());
internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
Base::construct(m_object);
}

38
Eigen/src/Core/SelfAdjointView.h
View File

@ -55,6 +55,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
typedef TriangularBase<SelfAdjointView> Base;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
typedef MatrixTypeNestedCleaned NestedExpression;
/** \brief The type of coefficients in this matrix */
typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
@ -128,7 +129,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
}
friend EIGEN_DEVICE_FUNC
const SelfAdjointView<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>,MatrixType>,UpLo>
const SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),UpLo>
operator*(const Scalar& s, const SelfAdjointView& mat)
{
return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
@ -162,6 +163,41 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
EIGEN_DEVICE_FUNC
SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
/** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
*
* The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
* \c #Lower, \c #StrictlyLower, \c #UnitLower.
*
* If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView of the nested expression,
* otherwise, the nested expression is first transposed, thus returning a \c TriangularView<Transpose<MatrixType>> object.
*
* \sa MatrixBase::triangularView(), class TriangularView
*/
template<unsigned int TriMode>
EIGEN_DEVICE_FUNC
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type
triangularView() const
{
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType>::type tmp1(m_matrix);
typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType>::type tmp2(tmp1);
return typename internal::conditional<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
TriangularView<MatrixType,TriMode>,
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
}
/** \returns a const expression of the main diagonal of the matrix \c *this
*
* This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
*
* \sa MatrixBase::diagonal(), class Diagonal */
EIGEN_DEVICE_FUNC
typename MatrixType::ConstDiagonalReturnType diagonal() const
{
return typename MatrixType::ConstDiagonalReturnType(m_matrix);
}
/////////// Cholesky module ///////////
const LLT<PlainObject, UpLo> llt() const;

10
Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@ -12,11 +12,13 @@
namespace Eigen {
// TODO generalize the scalar type of 'other'
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
return derived();
}
@ -24,7 +26,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
return derived();
}
@ -32,7 +34,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
return derived();
}
@ -40,7 +42,7 @@ template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar>());
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
return derived();
}

13
Eigen/src/Core/Solve.h
View File

@ -134,10 +134,10 @@ protected:
// Specialization for "dst = dec.solve(rhs)"
// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
{
typedef Solve<DecType,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
// FIXME shall we resize dst here?
src.dec()._solve_impl(src.rhs(), dst);
@ -146,10 +146,10 @@ struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar
// Specialization for "dst = dec.transpose().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
{
typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
}
@ -157,10 +157,11 @@ struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal:
// Specialization for "dst = dec.adjoint().solve(rhs)"
template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>, internal::assign_op<Scalar>, Dense2Dense, Scalar>
struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
internal::assign_op<Scalar,Scalar>, Dense2Dense>
{
typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
{
src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
}

2
Eigen/src/Core/SolveTriangular.h
View File

@ -169,7 +169,7 @@ void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<Ot
eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime };
enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit) && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
typedef typename internal::conditional<copy,
typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
OtherCopy otherCopy(other);

34
Eigen/src/Core/TriangularMatrix.h
View File

@ -367,14 +367,14 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
template<typename Other>
EIGEN_DEVICE_FUNC
TriangularViewType& operator+=(const DenseBase<Other>& other) {
internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename Other::Scalar>());
return derived();
}
/** \sa MatrixBase::operator-=() */
template<typename Other>
EIGEN_DEVICE_FUNC
TriangularViewType& operator-=(const DenseBase<Other>& other) {
internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename Other::Scalar>());
return derived();
}
@ -552,7 +552,7 @@ template<typename OtherDerived>
inline TriangularView<MatrixType, Mode>&
TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const MatrixBase<OtherDerived>& other)
{
internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar>());
internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
return derived();
}
@ -794,7 +794,7 @@ void call_triangular_assignment_loop(const DstXprType& dst, const SrcXprType& sr
enum {
unroll = DstXprType::SizeAtCompileTime != Dynamic
&& SrcEvaluatorType::CoeffReadCost < HugeCost
&& DstXprType::SizeAtCompileTime * SrcEvaluatorType::CoeffReadCost / 2 <= EIGEN_UNROLLING_LIMIT
&& DstXprType::SizeAtCompileTime * (DstEvaluatorType::CoeffReadCost+SrcEvaluatorType::CoeffReadCost) / 2 <= EIGEN_UNROLLING_LIMIT
};
triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(kernel);
@ -804,7 +804,7 @@ template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_triangular_assignment_loop(const DstXprType& dst, const SrcXprType& src)
{
call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar>());
call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
}
template<> struct AssignmentKind<TriangularShape,TriangularShape> { typedef Triangular2Triangular Kind; };
@ -812,8 +812,8 @@ template<> struct AssignmentKind<DenseShape,TriangularShape> { typedef Tria
template<> struct AssignmentKind<TriangularShape,DenseShape> { typedef Dense2Triangular Kind; };
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular, Scalar>
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
@ -823,8 +823,8 @@ struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular, Scalar
}
};
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense, Scalar>
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
@ -832,8 +832,8 @@ struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense, Scalar>
}
};
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular, Scalar>
template< typename DstXprType, typename SrcXprType, typename Functor>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
@ -933,10 +933,10 @@ namespace internal {
// Triangular = Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst.setZero();
dst._assignProduct(src, 1);
@ -945,10 +945,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_
// Triangular += Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst._assignProduct(src, 1);
}
@ -956,10 +956,10 @@ struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_ass
// Triangular -= Product
template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar>, Dense2Triangular, Scalar>
struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
{
typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar> &)
static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
{
dst._assignProduct(src, -1);
}

15
Eigen/src/Core/VectorwiseOp.h
View File

@ -284,6 +284,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
typedef typename ReturnType<internal::member_any>::Type AnyReturnType;
typedef PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> CountReturnType;
typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
typedef Reverse<ExpressionType, Direction> ReverseReturnType;
template<int p> struct LpNormReturnType {
@ -456,7 +457,15 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
*
* \sa DenseBase::reverse() */
EIGEN_DEVICE_FUNC
const ReverseReturnType reverse() const
const ConstReverseReturnType reverse() const
{ return ConstReverseReturnType( _expression() ); }
/** \returns a writable matrix expression
* where each column (or row) are reversed.
*
* \sa reverse() const */
EIGEN_DEVICE_FUNC
ReverseReturnType reverse()
{ return ReverseReturnType( _expression() ); }
typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
@ -540,7 +549,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator+(const DenseBase<OtherDerived>& other) const
@ -553,7 +562,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
/** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
CwiseBinaryOp<internal::scalar_difference_op<Scalar>,
CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
const ExpressionTypeNestedCleaned,
const typename ExtendedType<OtherDerived>::Type>
operator-(const DenseBase<OtherDerived>& other) const

6
Eigen/src/Core/arch/AVX/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_AVX_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_AVX_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/AVX COMPONENT Devel
)

46
Eigen/src/Core/arch/AVX/MathFunctions.h
View File

@ -266,52 +266,10 @@ pexp<Packet8f>(const Packet8f& _x) {
}
// Hyperbolic Tangent function.
// Doesn't do anything fancy, just a 13/6-degree rational interpolant which
// is accurate up to a couple of ulp in the range [-9, 9], outside of which the
// fl(tanh(x)) = +/-1.
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
ptanh<Packet8f>(const Packet8f& _x) {
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
_EIGEN_DECLARE_CONST_Packet8f(plus_9, 9.0f);
_EIGEN_DECLARE_CONST_Packet8f(minus_9, -9.0f);
const Packet8f x = pmax(p8f_minus_9, pmin(p8f_plus_9, _x));
// The monomial coefficients of the numerator polynomial (odd).
_EIGEN_DECLARE_CONST_Packet8f(alpha_1, 4.89352455891786e-03f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_3, 6.37261928875436e-04f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_5, 1.48572235717979e-05f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_7, 5.12229709037114e-08f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_9, -8.60467152213735e-11f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_11, 2.00018790482477e-13f);
_EIGEN_DECLARE_CONST_Packet8f(alpha_13, -2.76076847742355e-16f);
// The monomial coefficients of the denominator polynomial (even).
_EIGEN_DECLARE_CONST_Packet8f(beta_0, 4.89352518554385e-03f);
_EIGEN_DECLARE_CONST_Packet8f(beta_2, 2.26843463243900e-03f);
_EIGEN_DECLARE_CONST_Packet8f(beta_4, 1.18534705686654e-04f);
_EIGEN_DECLARE_CONST_Packet8f(beta_6, 1.19825839466702e-06f);
// Since the polynomials are odd/even, we need x^2.
const Packet8f x2 = pmul(x, x);
// Evaluate the numerator polynomial p.
Packet8f p = pmadd(x2, p8f_alpha_13, p8f_alpha_11);
p = pmadd(x2, p, p8f_alpha_9);
p = pmadd(x2, p, p8f_alpha_7);
p = pmadd(x2, p, p8f_alpha_5);
p = pmadd(x2, p, p8f_alpha_3);
p = pmadd(x2, p, p8f_alpha_1);
p = pmul(x, p);
// Evaluate the denominator polynomial p.
Packet8f q = pmadd(x2, p8f_beta_6, p8f_beta_4);
q = pmadd(x2, q, p8f_beta_2);
q = pmadd(x2, q, p8f_beta_0);
// Divide the numerator by the denominator.
return pdiv(p, q);
ptanh<Packet8f>(const Packet8f& x) {
return internal::generic_fast_tanh_float(x);
}
template <>

7
Eigen/src/Core/arch/AVX/PacketMath.h
View File

@ -97,6 +97,9 @@ template<> struct packet_traits<double> : default_packet_traits
};
#endif
template<> struct scalar_div_cost<float,true> { enum { value = 14 }; };
template<> struct scalar_div_cost<double,true> { enum { value = 16 }; };
/* Proper support for integers is only provided by AVX2. In the meantime, we'll
use SSE instructions and packets to deal with integers.
template<> struct packet_traits<int> : default_packet_traits
@ -156,7 +159,7 @@ template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, co
#ifdef __FMA__
template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
#if ( EIGEN_COMP_GNUC_STRICT || (EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<308)) )
// clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and gcc stupidly generates a vfmadd132ps instruction,
// so let's enforce it to generate a vfmadd231ps instruction since the most common use case is to accumulate
@ -169,7 +172,7 @@ template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f&
#endif
}
template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
#if ( EIGEN_COMP_GNUC_STRICT || (EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<308)) )
// see above
Packet4d res = c;
__asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));

6
Eigen/src/Core/arch/AltiVec/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_AltiVec_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_AltiVec_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/AltiVec COMPONENT Devel
)

183
Eigen/src/Core/arch/AltiVec/Complex.h
View File

@ -2,6 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2016 Konstantinos Margaritis <markos@freevec.org>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -15,18 +16,20 @@ namespace Eigen {
namespace internal {
static Packet4ui p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_ZERO_);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
#ifdef _BIG_ENDIAN
#ifdef __VSX__
#if defined(_BIG_ENDIAN)
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#else
static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
#endif
#endif
//---------- float ----------
struct Packet2cf
{
EIGEN_STRONG_INLINE Packet2cf() {}
EIGEN_STRONG_INLINE explicit Packet2cf() : v(p4f_ZERO) {}
EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
Packet4f v;
};
@ -39,6 +42,7 @@ template<> struct packet_traits<std::complex<float> > : default_packet_traits
Vectorizable = 1,
AlignedOnScalar = 1,
size = 2,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
@ -49,6 +53,9 @@ template<> struct packet_traits<std::complex<float> > : default_packet_traits
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
#ifdef __VSX__
HasBlend = 1,
#endif
HasSetLinear = 0
};
};
@ -58,7 +65,6 @@ template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type;
template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
{
Packet2cf res;
/* On AltiVec we cannot load 64-bit registers, so wa have to take care of alignment */
if((ptrdiff_t(&from) % 16) == 0)
res.v = pload<Packet4f>((const float *)&from);
else
@ -67,26 +73,32 @@ template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<flo
return res;
}
template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) { return Packet2cf(pload<Packet4f>((const float *) from)); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { return Packet2cf(ploadu<Packet4f>((const float*) from)); }
template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { pstore((float*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { pstoreu((float*)to, from.v); }
template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
{
std::complex<float> EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return Packet2cf(vec_ld(0, (const float*)af));
return pload<Packet2cf>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
{
std::complex<float> EIGEN_ALIGN16 af[2];
vec_st(from.v, 0, (float*)af);
pstore<std::complex<float> >((std::complex<float> *) af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_add(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_sub(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v + b.v); }
template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v - b.v); }
template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf((Packet4f)vec_xor((Packet4ui)a.v, p4ui_CONJ_XOR)); }
template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
@ -100,30 +112,19 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
v1 = vec_madd(v1, b.v, p4f_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(v2, b.v, p4f_ZERO);
v2 = (Packet4f) vec_xor((Packet4ui)v2, p4ui_CONJ_XOR);
v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR)));
// permute back to a proper order
v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
return Packet2cf(vec_add(v1, v2));
return Packet2cf(padd<Packet4f>(v1, v2));
}
template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_and(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_or(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_xor(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_and(a.v, vec_nor(b.v,b.v))); }
template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v, b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v, b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v, b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v, b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); }
template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from)
{
return pset1<Packet2cf>(*from);
}
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { vec_dstt((float *)addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { EIGEN_PPC_PREFETCH(addr); }
template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
{
@ -143,23 +144,23 @@ template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
{
Packet4f b;
b = (Packet4f) vec_sld(a.v, a.v, 8);
b = padd(a.v, b);
return pfirst(Packet2cf(b));
b = vec_sld(a.v, a.v, 8);
b = padd<Packet4f>(a.v, b);
return pfirst<Packet2cf>(Packet2cf(b));
}
template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs)
{
Packet4f b1, b2;
#ifdef _BIG_ENDIAN
b1 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
b2 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
b1 = vec_sld(vecs[0].v, vecs[1].v, 8);
b2 = vec_sld(vecs[1].v, vecs[0].v, 8);
#else
b1 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8);
b2 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8);
b1 = vec_sld(vecs[1].v, vecs[0].v, 8);
b2 = vec_sld(vecs[0].v, vecs[1].v, 8);
#endif
b2 = (Packet4f) vec_sld(b2, b2, 8);
b2 = padd(b1, b2);
b2 = vec_sld(b2, b2, 8);
b2 = padd<Packet4f>(b1, b2);
return Packet2cf(b2);
}
@ -168,10 +169,10 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const P
{
Packet4f b;
Packet2cf prod;
b = (Packet4f) vec_sld(a.v, a.v, 8);
prod = pmul(a, Packet2cf(b));
b = vec_sld(a.v, a.v, 8);
prod = pmul<Packet2cf>(a, Packet2cf(b));
return pfirst(prod);
return pfirst<Packet2cf>(prod);
}
template<int Offset>
@ -223,12 +224,30 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
}
};
template<> struct conj_helper<Packet4f, Packet2cf, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet4f& x, const Packet2cf& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet4f& x, const Packet2cf& y) const
{ return Packet2cf(internal::pmul<Packet4f>(x, y.v)); }
};
template<> struct conj_helper<Packet2cf, Packet4f, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet4f& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& x, const Packet4f& y) const
{ return Packet2cf(internal::pmul<Packet4f>(x.v, y)); }
};
template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
// TODO optimize it for AltiVec
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s = vec_madd(b.v, b.v, p4f_ZERO);
return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX32_REV))));
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a, b);
Packet4f s = pmul<Packet4f>(b.v, b.v);
return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
}
template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
@ -243,6 +262,14 @@ EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
kernel.packet[0].v = tmp;
}
#ifdef __VSX__
template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
Packet2cf result;
result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
return result;
}
#endif
//---------- double ----------
#ifdef __VSX__
struct Packet1cd
@ -277,10 +304,10 @@ template<> struct packet_traits<std::complex<double> > : default_packet_traits
template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16}; typedef Packet1cd half; };
template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { return Packet1cd(pload<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { return Packet1cd(ploadu<Packet2d>((const double*)from)); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
@ -300,10 +327,10 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1c
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_add(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_sub(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR2))); }
template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
@ -317,23 +344,20 @@ template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, con
v1 = vec_madd(a_re, b.v, p2d_ZERO);
// multiply a_im * b and get the conjugate result
v2 = vec_madd(a_im, b.v, p2d_ZERO);
v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
v2 = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(v2), reinterpret_cast<Packet4ui>(v2), 8));
v2 = pxor(v2, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR1));
return Packet1cd(vec_add(v1, v2));
return Packet1cd(padd<Packet2d>(v1, v2));
}
template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pand(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(por(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pxor(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pandnot(a.v, b.v)); }
template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
{
return pset1<Packet1cd>(*from);
}
template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { vec_dstt((long *)addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { EIGEN_PPC_PREFETCH(addr); }
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
{
@ -345,20 +369,10 @@ template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Pac
template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs) { return vecs[0]; }
template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs)
{
return vecs[0];
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
{
return pfirst(a);
}
template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
template<int Offset>
struct palign_impl<Offset,Packet1cd>
@ -402,13 +416,30 @@ template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
return pconj(internal::pmul(a, b));
}
};
template<> struct conj_helper<Packet2d, Packet1cd, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet2d& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet2d& x, const Packet1cd& y) const
{ return Packet1cd(internal::pmul<Packet2d>(x, y.v)); }
};
template<> struct conj_helper<Packet1cd, Packet2d, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet2d& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& x, const Packet2d& y) const
{ return Packet1cd(internal::pmul<Packet2d>(x.v, y)); }
};
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for AltiVec
Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b);
Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
return Packet1cd(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_REVERSE64))));
Packet2d s = pmul<Packet2d>(b.v, b.v);
return Packet1cd(pdiv(res.v, padd<Packet2d>(s, vec_perm(s, s, p16uc_REVERSE64))));
}
EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)

188
Eigen/src/Core/arch/AltiVec/MathFunctions.h
View File

@ -3,6 +3,7 @@
//
// Copyright (C) 2007 Julien Pommier
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -19,38 +20,79 @@ namespace Eigen {
namespace internal {
static _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
static _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
static _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
static _EIGEN_DECLARE_CONST_Packet4i(23, 23);
static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
/* the smallest non denormalized float number */
static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000); // -1.f/0.f
static _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan, 0xffffffff);
/* natural logarithm computed for 4 simultaneous float
return NaN for x <= 0
*/
static _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
static _EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f);
static _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
static _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
#ifdef __VSX__
static _EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
static _EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
static _EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
static _EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437);
static _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
static _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
static Packet2l p2l_1023 = { 1023, 1023 };
static Packet2ul p2ul_52 = { 52, 52 };
#endif
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f plog<Packet4f>(const Packet4f& _x)
{
Packet4f x = _x;
_EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
_EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
_EIGEN_DECLARE_CONST_Packet4i(23, 23);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
/* the smallest non denormalized float number */
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000); // -1.f/0.f
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan, 0xffffffff);
/* natural logarithm computed for 4 simultaneous float
return NaN for x <= 0
*/
_EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
Packet4i emm0;
@ -112,36 +154,17 @@ template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f pexp<Packet4f>(const Packet4f& _x)
{
Packet4f x = _x;
_EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
_EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
_EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
_EIGEN_DECLARE_CONST_Packet4i(23, 23);
_EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f);
_EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
_EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
Packet4f tmp, fx;
Packet4i emm0;
// clamp x
x = vec_max(vec_min(x, p4f_exp_hi), p4f_exp_lo);
x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo);
/* express exp(x) as exp(g + n*log(2)) */
// express exp(x) as exp(g + n*log(2))
fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
fx = vec_floor(fx);
fx = pfloor(fx);
tmp = pmul(fx, p4f_cephes_exp_C1);
Packet4f z = pmul(fx, p4f_cephes_exp_C2);
@ -171,14 +194,44 @@ Packet4f pexp<Packet4f>(const Packet4f& _x)
isnumber_mask);
}
#ifndef EIGEN_COMP_CLANG
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f prsqrt<Packet4f>(const Packet4f& x)
{
return vec_rsqrt(x);
}
#endif
#ifdef __VSX__
#ifndef EIGEN_COMP_CLANG
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d prsqrt<Packet2d>(const Packet2d& x)
{
return vec_rsqrt(x);
}
#endif
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f psqrt<Packet4f>(const Packet4f& x)
{
return vec_sqrt(x);
}
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet2d psqrt<Packet2d>(const Packet2d& x)
{
return vec_sqrt(x);
}
// VSX support varies between different compilers and even different
// versions of the same compiler. For gcc version >= 4.9.3, we can use
// vec_cts to efficiently convert Packet2d to Packet2l. Otherwise, use
// a slow version that works with older compilers.
// Update: apparently vec_cts/vec_ctf intrinsics for 64-bit doubles
// are buggy, https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70963
static inline Packet2l ConvertToPacket2l(const Packet2d& x) {
#if EIGEN_GNUC_AT_LEAST(5, 0) || \
(EIGEN_GNUC_AT(4, 9) && __GNUC_PATCHLEVEL__ >= 3)
#if EIGEN_GNUC_AT_LEAST(5, 4) || \
(EIGEN_GNUC_AT(6, 1) && __GNUC_PATCHLEVEL__ >= 1)
return vec_cts(x, 0); // TODO: check clang version.
#else
double tmp[2];
@ -194,36 +247,16 @@ Packet2d pexp<Packet2d>(const Packet2d& _x)
{
Packet2d x = _x;
_EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
_EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
_EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
_EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437);
_EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
_EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
_EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
Packet2d tmp, fx;
Packet2l emm0;
// clamp x
x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
fx = vec_floor(fx);
/* express exp(x) as exp(g + n*log(2)) */
fx = pmadd(x, p2d_cephes_LOG2EF, p2d_half);
fx = pfloor(fx);
tmp = pmul(fx, p2d_cephes_exp_C1);
Packet2d z = pmul(fx, p2d_cephes_exp_C2);
@ -249,9 +282,6 @@ Packet2d pexp<Packet2d>(const Packet2d& _x)
emm0 = ConvertToPacket2l(fx);
#ifdef __POWER8_VECTOR__
static const Packet2l p2l_1023 = { 1023, 1023 };
static const Packet2ul p2ul_52 = { 52, 52 };
emm0 = vec_add(emm0, p2l_1023);
emm0 = vec_sl(emm0, p2ul_52);
#else

414
Eigen/src/Core/arch/AltiVec/PacketMath.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2014 Konstantinos Margaritis <markos@freevec.org>
// Copyright (C) 2008-2016 Konstantinos Margaritis <markos@freevec.org>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -42,7 +42,7 @@ typedef __vector unsigned char Packet16uc;
// and it doesn't really work to declare them global, so we define macros instead
#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
Packet4f p4f_##NAME = (Packet4f) vec_splat_s32(X)
Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(vec_splat_s32(X))
#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
Packet4i p4i_##NAME = vec_splat_s32(X)
@ -69,13 +69,13 @@ typedef __vector unsigned char Packet16uc;
// These constants are endian-agnostic
static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
#ifndef __VSX__
static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
#endif
static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
static Packet4f p4f_ZERO_ = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
#ifndef __VSX__
static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
#endif
static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
@ -96,7 +96,9 @@ static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
// Define global static constants:
#ifdef _BIG_ENDIAN
static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
#ifdef __VSX__
static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#endif
static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
@ -110,8 +112,8 @@ static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i
static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16); //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16); //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
static Packet16uc p16uc_TRANSPOSE64_HI = p16uc_PSET64_HI + p16uc_HALF64_0_16; //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
static Packet16uc p16uc_TRANSPOSE64_LO = p16uc_PSET64_LO + p16uc_HALF64_0_16; //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8); //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
@ -121,6 +123,12 @@ static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8
static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
#endif // _BIG_ENDIAN
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
#define EIGEN_PPC_PREFETCH(ADDR) __builtin_prefetch(ADDR);
#else
#define EIGEN_PPC_PREFETCH(ADDR) asm( " dcbt [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
#endif
template<> struct packet_traits<float> : default_packet_traits
{
typedef Packet4f type;
@ -129,15 +137,35 @@ template<> struct packet_traits<float> : default_packet_traits
Vectorizable = 1,
AlignedOnScalar = 1,
size=4,
HasHalfPacket=0,
HasHalfPacket = 1,
// FIXME check the Has*
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasMin = 1,
HasMax = 1,
HasAbs = 1,
HasSin = 0,
HasCos = 0,
HasLog = 1,
HasLog = 0,
HasExp = 1,
HasSqrt = 0
#ifdef __VSX__
HasSqrt = 1,
#if !EIGEN_COMP_CLANG
HasRsqrt = 1,
#else
HasRsqrt = 0,
#endif
#else
HasSqrt = 0,
HasRsqrt = 0,
#endif
HasRound = 1,
HasFloor = 1,
HasCeil = 1,
HasNegate = 1,
HasBlend = 1
};
};
template<> struct packet_traits<int> : default_packet_traits
@ -145,10 +173,16 @@ template<> struct packet_traits<int> : default_packet_traits
typedef Packet4i type;
typedef Packet4i half;
enum {
// FIXME check the Has*
Vectorizable = 1,
AlignedOnScalar = 1,
size=4
size = 4,
HasHalfPacket = 0,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 0,
HasBlend = 1
};
};
@ -200,41 +234,56 @@ inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}
/*
inline std::ostream & operator <<(std::ostream & s, const Packetbi & v)
{
union {
Packet4bi v;
unsigned int n[4];
} vt;
vt.v = v;
s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
return s;
}*/
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); }
template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
#ifdef __VSX__
return vec_vsx_ld(0, from);
#else
return vec_ld(0, from);
#endif
}
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); }
template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
#ifdef __VSX__
return vec_vsx_ld(0, from);
#else
return vec_ld(0, from);
#endif
}
template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
{
EIGEN_DEBUG_ALIGNED_STORE
#ifdef __VSX__
vec_vsx_st(from, 0, to);
#else
vec_st(from, 0, to);
#endif
}
template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from)
{
EIGEN_DEBUG_ALIGNED_STORE
#ifdef __VSX__
vec_vsx_st(from, 0, to);
#else
vec_st(from, 0, to);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
// Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
float EIGEN_ALIGN16 af[4];
af[0] = from;
Packet4f vc = pload<Packet4f>(af);
vc = vec_splat(vc, 0);
return vc;
Packet4f v = {from, from, from, from};
return v;
}
template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
int EIGEN_ALIGN16 ai[4];
ai[0] = from;
Packet4i vc = pload<Packet4i>(ai);
vc = vec_splat(vc, 0);
return vc;
Packet4i v = {from, from, from, from};
return v;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet4f>(const float *a,
@ -294,58 +343,24 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const
to[3*stride] = ai[3];
}
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return vec_add(pset1<Packet4f>(a), p4f_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return vec_add(pset1<Packet4i>(a), p4i_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return pset1<Packet4f>(a) + p4f_COUNTDOWN; }
template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return pset1<Packet4i>(a) + p4i_COUNTDOWN; }
template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return a + b; }
template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return a + b; }
template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_sub(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_sub(a,b); }
template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return a - b; }
template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return a - b; }
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return psub<Packet4f>(p4f_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return psub<Packet4i>(p4i_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return p4f_ZERO - a; }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return p4i_ZERO - a; }
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b,p4f_ZERO); }
/* Commented out: it's actually slower than processing it scalar
*
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)
{
// Detailed in: http://freevec.org/content/32bit_signed_integer_multiplication_altivec
//Set up constants, variables
Packet4i a1, b1, bswap, low_prod, high_prod, prod, prod_, v1sel;
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b, p4f_ZERO); }
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return a * b; }
// Get the absolute values
a1 = vec_abs(a);
b1 = vec_abs(b);
// Get the signs using xor
Packet4bi sgn = (Packet4bi) vec_cmplt(vec_xor(a, b), p4i_ZERO);
// Do the multiplication for the asbolute values.
bswap = (Packet4i) vec_rl((Packet4ui) b1, (Packet4ui) p4i_MINUS16 );
low_prod = vec_mulo((Packet8i) a1, (Packet8i)b1);
high_prod = vec_msum((Packet8i) a1, (Packet8i) bswap, p4i_ZERO);
high_prod = (Packet4i) vec_sl((Packet4ui) high_prod, (Packet4ui) p4i_MINUS16);
prod = vec_add( low_prod, high_prod );
// NOR the product and select only the negative elements according to the sign mask
prod_ = vec_nor(prod, prod);
prod_ = vec_sel(p4i_ZERO, prod_, sgn);
// Add 1 to the result to get the negative numbers
v1sel = vec_sel(p4i_ZERO, p4i_ONE, sgn);
prod_ = vec_add(prod_, v1sel);
// Merge the results back to the final vector.
prod = vec_sel(prod, prod_, sgn);
return prod;
}
*/
template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
{
#ifndef __VSX__ // VSX actually provides a div instruction
@ -370,8 +385,8 @@ template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, co
}
// for some weird raisons, it has to be overloaded for packet of integers
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a, b, c); }
template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); }
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a,b,c); }
template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return a*b + c; }
template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
@ -391,6 +406,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) { return vec_round(a); }
template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) { return vec_ceil(a); }
template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return vec_floor(a); }
#ifdef _BIG_ENDIAN
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
@ -418,12 +437,12 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX
template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
EIGEN_DEBUG_UNALIGNED_LOAD
return (Packet4i) vec_vsx_ld((long)from & 15, (const int*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
EIGEN_DEBUG_UNALIGNED_LOAD
return (Packet4f) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
}
#endif
@ -494,16 +513,19 @@ template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f&
}
#endif
#ifndef __VSX__
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); }
#endif
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_PPC_PREFETCH(addr); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { EIGEN_PPC_PREFETCH(addr); }
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; }
template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x; vec_ste(a, 0, &x); return x; }
template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x; vec_ste(a, 0, &x); return x; }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); }
template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
{
return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
}
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
{
return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32)); }
template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
@ -511,10 +533,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs
template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
{
Packet4f b, sum;
b = (Packet4f) vec_sld(a, a, 8);
sum = vec_add(a, b);
b = (Packet4f) vec_sld(sum, sum, 4);
sum = vec_add(sum, b);
b = vec_sld(a, a, 8);
sum = a + b;
b = vec_sld(sum, sum, 4);
sum += b;
return pfirst(sum);
}
@ -537,11 +559,11 @@ template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs)
// Now do the summation:
// Lines 0+1
sum[0] = vec_add(sum[0], sum[1]);
sum[0] = sum[0] + sum[1];
// Lines 2+3
sum[1] = vec_add(sum[2], sum[3]);
sum[1] = sum[2] + sum[3];
// Add the results
sum[0] = vec_add(sum[0], sum[1]);
sum[0] = sum[0] + sum[1];
return sum[0];
}
@ -577,11 +599,11 @@ template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs)
// Now do the summation:
// Lines 0+1
sum[0] = vec_add(sum[0], sum[1]);
sum[0] = sum[0] + sum[1];
// Lines 2+3
sum[1] = vec_add(sum[2], sum[3]);
sum[1] = sum[2] + sum[3];
// Add the results
sum[0] = vec_add(sum[0], sum[1]);
sum[0] = sum[0] + sum[1];
return sum[0];
}
@ -591,8 +613,8 @@ template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs)
template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
{
Packet4f prod;
prod = pmul(a, (Packet4f)vec_sld(a, a, 8));
return pfirst(pmul(prod, (Packet4f)vec_sld(prod, prod, 4)));
prod = pmul(a, vec_sld(a, a, 8));
return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
}
template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
@ -716,33 +738,52 @@ ptranspose(PacketBlock<Packet4i,4>& kernel) {
kernel.packet[3] = vec_mergel(t1, t3);
}
template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
Packet4ui mask = reinterpret_cast<Packet4ui>(vec_cmpeq(reinterpret_cast<Packet4ui>(select), reinterpret_cast<Packet4ui>(p4i_ONE)));
return vec_sel(elsePacket, thenPacket, mask);
}
template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
Packet4ui mask = reinterpret_cast<Packet4ui>(vec_cmpeq(reinterpret_cast<Packet4ui>(select), reinterpret_cast<Packet4ui>(p4i_ONE)));
return vec_sel(elsePacket, thenPacket, mask);
}
//---------- double ----------
#ifdef __VSX__
typedef __vector double Packet2d;
typedef __vector unsigned long long Packet2ul;
typedef __vector long long Packet2l;
static Packet2l p2l_ZERO = (Packet2l) p4i_ZERO;
static Packet2d p2d_ONE = { 1.0, 1.0 };
static Packet2d p2d_ZERO = (Packet2d) p4f_ZERO;
static Packet2d p2d_ZERO_ = { -0.0, -0.0 };
#ifdef _BIG_ENDIAN
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ZERO, (Packet16uc) p2d_ONE, 8);
#if EIGEN_COMP_CLANG
typedef Packet2ul Packet2bl;
#else
static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ONE, (Packet16uc) p2d_ZERO, 8);
typedef __vector __bool long Packet2bl;
#endif
static EIGEN_STRONG_INLINE Packet2d vec_splat_dbl(Packet2d& a, int index)
static Packet2l p2l_ONE = { 1, 1 };
static Packet2l p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
static Packet2d p2d_ONE = { 1.0, 1.0 };
static Packet2d p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
static Packet2d p2d_ZERO_ = { -0.0, -0.0 };
#ifdef _BIG_ENDIAN
static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
#else
static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ONE), reinterpret_cast<Packet4f>(p2d_ZERO), 8));
#endif
template<int index> Packet2d vec_splat_dbl(Packet2d& a);
template<> EIGEN_STRONG_INLINE Packet2d vec_splat_dbl<0>(Packet2d& a)
{
switch (index) {
case 0:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_HI);
case 1:
return (Packet2d) vec_perm(a, a, p16uc_PSET64_LO);
}
return a;
return reinterpret_cast<Packet2d>(vec_perm(a, a, p16uc_PSET64_HI));
}
template<> EIGEN_STRONG_INLINE Packet2d vec_splat_dbl<1>(Packet2d& a)
{
return reinterpret_cast<Packet2d>(vec_perm(a, a, p16uc_PSET64_LO));
}
template<> struct packet_traits<double> : default_packet_traits
@ -753,16 +794,41 @@ template<> struct packet_traits<double> : default_packet_traits
Vectorizable = 1,
AlignedOnScalar = 1,
size=2,
HasHalfPacket = 0,
HasHalfPacket = 1,
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasDiv = 1,
HasMin = 1,
HasMax = 1,
HasAbs = 1,
HasSin = 0,
HasCos = 0,
HasLog = 0,
HasExp = 1,
HasSqrt = 0
HasSqrt = 1,
HasRsqrt = 1,
HasRound = 1,
HasFloor = 1,
HasCeil = 1,
HasNegate = 1,
HasBlend = 1
};
};
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16}; typedef Packet2d half; };
inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
{
union {
Packet2l v;
int64_t n[2];
} vt;
vt.v = v;
s << vt.n[0] << ", " << vt.n[1];
return s;
}
inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
{
@ -776,28 +842,43 @@ inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
}
// Need to define them first or we get specialization after instantiation errors
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return (Packet2d) vec_ld(0, (const float *) from); } //FIXME
template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
#ifdef __VSX__
return vec_vsx_ld(0, from);
#else
return vec_ld(0, from);
#endif
}
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st((Packet4f)from, 0, (float *)to); }
template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from)
{
EIGEN_DEBUG_ALIGNED_STORE
#ifdef __VSX__
vec_vsx_st(from, 0, to);
#else
vec_st(from, 0, to);
#endif
}
template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
double EIGEN_ALIGN16 af[2];
af[0] = from;
Packet2d vc = pload<Packet2d>(af);
vc = vec_splat_dbl(vc, 0);
return vc;
Packet2d v = {from, from};
return v;
}
template<> EIGEN_STRONG_INLINE void
pbroadcast4<Packet2d>(const double *a,
Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
{
a1 = pload<Packet2d>(a);
a0 = vec_splat_dbl(a1, 0);
a1 = vec_splat_dbl(a1, 1);
a0 = vec_splat_dbl<0>(a1);
a1 = vec_splat_dbl<1>(a1);
a3 = pload<Packet2d>(a+2);
a2 = vec_splat_dbl(a3, 0);
a3 = vec_splat_dbl(a3, 1);
a2 = vec_splat_dbl<0>(a3);
a3 = vec_splat_dbl<1>(a3);
}
template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
{
double EIGEN_ALIGN16 af[2];
@ -812,13 +893,14 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to,
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return vec_add(pset1<Packet2d>(a), p2d_COUNTDOWN); }
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_add(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return pset1<Packet2d>(a) + p2d_COUNTDOWN; }
template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_sub(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return a + b; }
template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return psub<Packet2d>(p2d_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return a - b; }
template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return p2d_ZERO - a; }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
@ -840,17 +922,22 @@ template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const
template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) { return vec_round(a); }
template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) { return vec_ceil(a); }
template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
{
EIGEN_DEBUG_ALIGNED_LOAD
return (Packet2d) vec_vsx_ld((long)from & 15, (const float*) _EIGEN_ALIGNED_PTR(from));
return (Packet2d) vec_vsx_ld((long)from & 15, (const double*) _EIGEN_ALIGNED_PTR(from));
}
template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from)
{
Packet2d p;
if((ptrdiff_t(from) % 16) == 0) p = pload<Packet2d>(from);
else p = ploadu<Packet2d>(from);
return vec_perm(p, p, p16uc_PSET64_HI);
return vec_splat_dbl<0>(p);
}
template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
@ -859,32 +946,34 @@ template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d&
vec_vsx_st((Packet4f)from, (long)to & 15, (float*) _EIGEN_ALIGNED_PTR(to));
}
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { vec_dstt((const float *) addr, DST_CTRL(2,2,32), DST_CHAN); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_PPC_PREFETCH(addr); }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return (Packet2d)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE64); }
template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore<double>(x, a); return x[0]; }
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
{
return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
}
template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
{
Packet2d b, sum;
b = (Packet2d) vec_sld((Packet4ui) a, (Packet4ui)a, 8);
sum = vec_add(a, b);
return pfirst(sum);
b = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(a), reinterpret_cast<Packet4f>(a), 8));
sum = a + b;
return pfirst<Packet2d>(sum);
}
template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
{
Packet2d v[2], sum;
v[0] = vec_add(vecs[0], (Packet2d) vec_sld((Packet4ui) vecs[0], (Packet4ui) vecs[0], 8));
v[1] = vec_add(vecs[1], (Packet2d) vec_sld((Packet4ui) vecs[1], (Packet4ui) vecs[1], 8));
v[0] = vecs[0] + reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(vecs[0]), reinterpret_cast<Packet4f>(vecs[0]), 8));
v[1] = vecs[1] + reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(vecs[1]), reinterpret_cast<Packet4f>(vecs[1]), 8));
#ifdef _BIG_ENDIAN
sum = (Packet2d) vec_sld((Packet4ui) v[0], (Packet4ui) v[1], 8);
sum = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(v[0]), reinterpret_cast<Packet4f>(v[1]), 8));
#else
sum = (Packet2d) vec_sld((Packet4ui) v[1], (Packet4ui) v[0], 8);
sum = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(v[1]), reinterpret_cast<Packet4f>(v[0]), 8));
#endif
return sum;
@ -893,19 +982,19 @@ template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs)
// mul
template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
{
return pfirst(pmul(a, (Packet2d)vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
}
// min
template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
{
return pfirst(vec_min(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
return pfirst(pmin(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
}
// max
template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
{
return pfirst(vec_max(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8)));
return pfirst(pmax(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
}
template<int Offset>
@ -915,9 +1004,9 @@ struct palign_impl<Offset,Packet2d>
{
if (Offset == 1)
#ifdef _BIG_ENDIAN
first = (Packet2d) vec_sld((Packet4ui) first, (Packet4ui) second, 8);
first = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(first), reinterpret_cast<Packet4ui>(second), 8));
#else
first = (Packet2d) vec_sld((Packet4ui) second, (Packet4ui) first, 8);
first = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(second), reinterpret_cast<Packet4ui>(first), 8));
#endif
}
};
@ -931,6 +1020,11 @@ ptranspose(PacketBlock<Packet2d,2>& kernel) {
kernel.packet[1] = t1;
}
template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
Packet2l select = { ifPacket.select[0], ifPacket.select[1] };
Packet2bl mask = vec_cmpeq(reinterpret_cast<Packet2d>(select), reinterpret_cast<Packet2d>(p2l_ONE));
return vec_sel(elsePacket, thenPacket, mask);
}
#endif // __VSX__
} // end namespace internal

10
Eigen/src/Core/arch/CMakeLists.txt
View File

@ -1,10 +0,0 @@
ADD_SUBDIRECTORY(AltiVec)
ADD_SUBDIRECTORY(AVX)
ADD_SUBDIRECTORY(AVX512)
ADD_SUBDIRECTORY(CUDA)
ADD_SUBDIRECTORY(Default)
ADD_SUBDIRECTORY(NEON)
ADD_SUBDIRECTORY(SSE)

6
Eigen/src/Core/arch/CUDA/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_CUDA_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_CUDA_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/CUDA COMPONENT Devel
)

103
Eigen/src/Core/arch/CUDA/Complex.h Normal file
View File

@ -0,0 +1,103 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_COMPLEX_CUDA_H
#define EIGEN_COMPLEX_CUDA_H
// clang-format off
namespace Eigen {
namespace internal {
#if defined(__CUDACC__) && defined(EIGEN_USE_GPU)
// Many std::complex methods such as operator+, operator-, operator* and
// operator/ are not constexpr. Due to this, clang does not treat them as device
// functions and thus Eigen functors making use of these operators fail to
// compile. Here, we manually specialize these functors for complex types when
// building for CUDA to avoid non-constexpr methods.
// Sum
template<typename T> struct scalar_sum_op<const std::complex<T>, const std::complex<T> > : binary_op_base<const std::complex<T>, const std::complex<T> > {
typedef typename std::complex<T> result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator() (const std::complex<T>& a, const std::complex<T>& b) const {
return std::complex<T>(numext::real(a) + numext::real(b),
numext::imag(a) + numext::imag(b));
}
};
template<typename T> struct scalar_sum_op<std::complex<T>, std::complex<T> > : scalar_sum_op<const std::complex<T>, const std::complex<T> > {};
// Difference
template<typename T> struct scalar_difference_op<const std::complex<T>, const std::complex<T> > : binary_op_base<const std::complex<T>, const std::complex<T> > {
typedef typename std::complex<T> result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator() (const std::complex<T>& a, const std::complex<T>& b) const {
return std::complex<T>(numext::real(a) - numext::real(b),
numext::imag(a) - numext::imag(b));
}
};
template<typename T> struct scalar_difference_op<std::complex<T>, std::complex<T> > : scalar_difference_op<const std::complex<T>, const std::complex<T> > {};
// Product
template<typename T> struct scalar_product_op<const std::complex<T>, const std::complex<T> > : binary_op_base<const std::complex<T>, const std::complex<T> > {
enum {
Vectorizable = packet_traits<std::complex<T>>::HasMul
};
typedef typename std::complex<T> result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator() (const std::complex<T>& a, const std::complex<T>& b) const {
const T a_real = numext::real(a);
const T a_imag = numext::imag(a);
const T b_real = numext::real(b);
const T b_imag = numext::imag(b);
return std::complex<T>(a_real * b_real - a_imag * b_imag,
a_real * b_imag + a_imag * b_real);
}
};
template<typename T> struct scalar_product_op<std::complex<T>, std::complex<T> > : scalar_product_op<const std::complex<T>, const std::complex<T> > {};
// Quotient
template<typename T> struct scalar_quotient_op<const std::complex<T>, const std::complex<T> > : binary_op_base<const std::complex<T>, const std::complex<T> > {
enum {
Vectorizable = packet_traits<std::complex<T>>::HasDiv
};
typedef typename std::complex<T> result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<T> operator() (const std::complex<T>& a, const std::complex<T>& b) const {
const T a_real = numext::real(a);
const T a_imag = numext::imag(a);
const T b_real = numext::real(b);
const T b_imag = numext::imag(b);
const T norm = T(1) / (b_real * b_real + b_imag * b_imag);
return std::complex<T>((a_real * b_real + a_imag * b_imag) * norm,
(a_imag * b_real - a_real * b_imag) * norm);
}
};
template<typename T> struct scalar_quotient_op<std::complex<T>, std::complex<T> > : scalar_quotient_op<const std::complex<T>, const std::complex<T> > {};
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_COMPLEX_CUDA_H

386
Eigen/src/Core/arch/CUDA/Half.h
View File

@ -1,11 +1,3 @@
// Standard 16-bit float type, mostly useful for GPUs. Defines a new
// class Eigen::half (inheriting from CUDA's __half struct) with
// operator overloads such that it behaves basically as an arithmetic
// type. It will be quite slow on CPUs (so it is recommended to stay
// in fp32 for CPUs, except for simple parameter conversions, I/O
// to disk and the likes), but fast on GPUs.
//
//
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
@ -32,6 +24,15 @@
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
// Standard 16-bit float type, mostly useful for GPUs. Defines a new
// type Eigen::half (inheriting from CUDA's __half struct) with
// operator overloads such that it behaves basically as an arithmetic
// type. It will be quite slow on CPUs (so it is recommended to stay
// in fp32 for CPUs, except for simple parameter conversions, I/O
// to disk and the likes), but fast on GPUs.
#ifndef EIGEN_HALF_CUDA_H
#define EIGEN_HALF_CUDA_H
@ -42,92 +43,93 @@
#endif
namespace Eigen {
struct half;
namespace half_impl {
#if !defined(EIGEN_HAS_CUDA_FP16)
// Make our own __half definition that is similar to CUDA's.
struct __half {
__half() {}
explicit __half(unsigned short raw) : x(raw) {}
EIGEN_DEVICE_FUNC __half() {}
explicit EIGEN_DEVICE_FUNC __half(unsigned short raw) : x(raw) {}
unsigned short x;
};
#endif
namespace Eigen {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h);
namespace internal {
struct half_base : public __half {
EIGEN_DEVICE_FUNC half_base() {}
EIGEN_DEVICE_FUNC half_base(const half_base& h) : __half(h) {}
EIGEN_DEVICE_FUNC half_base(const __half& h) : __half(h) {}
};
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x);
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff);
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h);
} // end namespace internal
} // namespace half_impl
// Class definition.
struct half : public __half {
struct half : public half_impl::half_base {
#if !defined(EIGEN_HAS_CUDA_FP16)
typedef half_impl::__half __half;
#endif
EIGEN_DEVICE_FUNC half() {}
EIGEN_DEVICE_FUNC half(const __half& h) : __half(h) {}
EIGEN_DEVICE_FUNC half(const half& h) : __half(h) {}
EIGEN_DEVICE_FUNC half(const __half& h) : half_impl::half_base(h) {}
EIGEN_DEVICE_FUNC half(const half& h) : half_impl::half_base(h) {}
explicit EIGEN_DEVICE_FUNC half(bool b)
: __half(internal::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
explicit EIGEN_DEVICE_FUNC half(unsigned int ui)
: __half(internal::float_to_half_rtne(static_cast<float>(ui))) {}
explicit EIGEN_DEVICE_FUNC half(int i)
: __half(internal::float_to_half_rtne(static_cast<float>(i))) {}
explicit EIGEN_DEVICE_FUNC half(unsigned long ul)
: __half(internal::float_to_half_rtne(static_cast<float>(ul))) {}
explicit EIGEN_DEVICE_FUNC half(long l)
: __half(internal::float_to_half_rtne(static_cast<float>(l))) {}
explicit EIGEN_DEVICE_FUNC half(long long ll)
: __half(internal::float_to_half_rtne(static_cast<float>(ll))) {}
explicit EIGEN_DEVICE_FUNC half(unsigned long long ull)
: __half(internal::float_to_half_rtne(static_cast<float>(ull))) {}
: half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
template<class T>
explicit EIGEN_DEVICE_FUNC half(const T& val)
: half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(val))) {}
explicit EIGEN_DEVICE_FUNC half(float f)
: __half(internal::float_to_half_rtne(f)) {}
explicit EIGEN_DEVICE_FUNC half(double d)
: __half(internal::float_to_half_rtne(static_cast<float>(d))) {}
: half_impl::half_base(half_impl::float_to_half_rtne(f)) {}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(bool) const {
// +0.0 and -0.0 become false, everything else becomes true.
return (x & 0x7fff) != 0;
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(signed char) const {
return static_cast<signed char>(internal::half_to_float(*this));
return static_cast<signed char>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(unsigned char) const {
return static_cast<unsigned char>(internal::half_to_float(*this));
return static_cast<unsigned char>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(short) const {
return static_cast<short>(internal::half_to_float(*this));
return static_cast<short>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(unsigned short) const {
return static_cast<unsigned short>(internal::half_to_float(*this));
return static_cast<unsigned short>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(int) const {
return static_cast<int>(internal::half_to_float(*this));
return static_cast<int>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(unsigned int) const {
return static_cast<unsigned int>(internal::half_to_float(*this));
return static_cast<unsigned int>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(long) const {
return static_cast<long>(internal::half_to_float(*this));
return static_cast<long>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(unsigned long) const {
return static_cast<unsigned long>(internal::half_to_float(*this));
return static_cast<unsigned long>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(long long) const {
return static_cast<long long>(internal::half_to_float(*this));
return static_cast<long long>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(unsigned long long) const {
return static_cast<unsigned long long>(internal::half_to_float(*this));
return static_cast<unsigned long long>(half_to_float(*this));
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(float) const {
return internal::half_to_float(*this);
return half_impl::half_to_float(*this);
}
EIGEN_DEVICE_FUNC EIGEN_EXPLICIT_CAST(double) const {
return static_cast<double>(internal::half_to_float(*this));
return static_cast<double>(half_impl::half_to_float(*this));
}
EIGEN_DEVICE_FUNC half& operator=(const half& other) {
@ -136,6 +138,8 @@ struct half : public __half {
}
};
namespace half_impl {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
// Intrinsics for native fp16 support. Note that on current hardware,
@ -200,55 +204,55 @@ __device__ bool operator >= (const half& a, const half& b) {
// Definitions for CPUs and older CUDA, mostly working through conversion
// to/from fp32.
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
return half(float(a) + float(b));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
return half(float(a) * float(b));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
return half(float(a) - float(b));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
return half(float(a) / float(b));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
half result;
result.x = a.x ^ 0x8000;
return result;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
a = half(float(a) + float(b));
return a;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
a = half(float(a) * float(b));
return a;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
a = half(float(a) - float(b));
return a;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
a = half(float(a) / float(b));
return a;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
return float(a) == float(b);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
return float(a) != float(b);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
return float(a) < float(b);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
return float(a) <= float(b);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
return float(a) > float(b);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
return float(a) >= float(b);
}
@ -256,8 +260,8 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, co
// Division by an index. Do it in full float precision to avoid accuracy
// issues in converting the denominator to half.
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
return Eigen::half(static_cast<float>(a) / static_cast<float>(b));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
return half(static_cast<float>(a) / static_cast<float>(b));
}
// Conversion routines, including fallbacks for the host or older CUDA.
@ -265,9 +269,7 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Ind
// these in hardware. If we need more performance on older/other CPUs, they are
// also possible to vectorize directly.
namespace internal {
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x) {
__half h;
h.x = x;
return h;
@ -278,7 +280,7 @@ union FP32 {
float f;
};
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __float2half(ff);
@ -333,7 +335,7 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff)
#endif
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __half2float(h);
@ -362,92 +364,69 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h) {
#endif
}
} // end namespace internal
// --- standard functions ---
// Traits.
namespace internal {
template<> struct is_arithmetic<half> { enum { value = true }; };
} // end namespace internal
template<> struct NumTraits<Eigen::half>
: GenericNumTraits<Eigen::half>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half epsilon() {
return internal::raw_uint16_to_half(0x0800);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half dummy_precision() { return half(1e-3f); }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half highest() {
return internal::raw_uint16_to_half(0x7bff);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half lowest() {
return internal::raw_uint16_to_half(0xfbff);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half infinity() {
return internal::raw_uint16_to_half(0x7c00);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
return internal::raw_uint16_to_half(0x7c01);
}
};
// Infinity/NaN checks.
namespace numext {
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const half& a) {
return (a.x & 0x7fff) == 0x7c00;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const half& a) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hisnan(a);
#else
return (a.x & 0x7fff) > 0x7c00;
#endif
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const Eigen::half& a) {
return !(Eigen::numext::isinf)(a) && !(Eigen::numext::isnan)(a);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const half& a) {
return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half abs(const Eigen::half& a) {
Eigen::half result;
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
half result;
result.x = a.x & 0x7FFF;
return result;
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half exp(const Eigen::half& a) {
return Eigen::half(::expf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
return half(::expf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half log(const Eigen::half& a) {
return Eigen::half(::logf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return Eigen::half(::hlog(a));
#else
return half(::logf(float(a)));
#endif
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sqrt(const Eigen::half& a) {
return Eigen::half(::sqrtf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log1p(const half& a) {
return half(numext::log1p(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half pow(const Eigen::half& a, const Eigen::half& b) {
return Eigen::half(::powf(float(a), float(b)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) {
return half(::log10f(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sin(const Eigen::half& a) {
return Eigen::half(::sinf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
return half(::sqrtf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half cos(const Eigen::half& a) {
return Eigen::half(::cosf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
return half(::powf(float(a), float(b)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half tan(const Eigen::half& a) {
return Eigen::half(::tanf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sin(const half& a) {
return half(::sinf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half tanh(const Eigen::half& a) {
return Eigen::half(::tanhf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half cos(const half& a) {
return half(::cosf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half floor(const Eigen::half& a) {
return Eigen::half(::floorf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tan(const half& a) {
return half(::tanf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ceil(const Eigen::half& a) {
return Eigen::half(::ceilf(float(a)));
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) {
return half(::tanhf(float(a)));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
return half(::floorf(float(a)));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
return half(::ceilf(float(a)));
}
template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half mini(const Eigen::half& a, const Eigen::half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(b, a) ? b : a;
#else
@ -456,7 +435,7 @@ template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half mini(const Eigen::
return f2 < f1 ? b : a;
#endif
}
template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half maxi(const Eigen::half& a, const Eigen::half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (max)(const half& a, const half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(a, b) ? b : a;
#else
@ -466,78 +445,89 @@ template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half maxi(const Eigen::
#endif
}
#ifdef EIGEN_HAS_C99_MATH
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) {
return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a)));
EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const half& v) {
os << static_cast<float>(v);
return os;
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) {
return Eigen::half(Eigen::numext::digamma(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half zeta(const Eigen::half& x, const Eigen::half& q) {
return Eigen::half(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half polygamma(const Eigen::half& n, const Eigen::half& x) {
return Eigen::half(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erf(const Eigen::half& a) {
return Eigen::half(Eigen::numext::erf(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erfc(const Eigen::half& a) {
return Eigen::half(Eigen::numext::erfc(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma(const Eigen::half& a, const Eigen::half& x) {
return Eigen::half(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igammac(const Eigen::half& a, const Eigen::half& x) {
return Eigen::half(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
}
#endif
} // end namespace numext
} // end namespace half_impl
// import Eigen::half_impl::half into Eigen namespace
// using half_impl::half;
namespace internal {
template<>
struct random_default_impl<half, false, false>
{
static inline half run(const half& x, const half& y)
{
return x + (y-x) * half(float(std::rand()) / float(RAND_MAX));
}
static inline half run()
{
return run(half(-1.f), half(1.f));
}
};
template<> struct is_arithmetic<half> { enum { value = true }; };
} // end namespace internal
template<> struct NumTraits<Eigen::half>
: GenericNumTraits<Eigen::half>
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half epsilon() {
return half_impl::raw_uint16_to_half(0x0800);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half dummy_precision() { return Eigen::half(1e-2f); }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half highest() {
return half_impl::raw_uint16_to_half(0x7bff);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half lowest() {
return half_impl::raw_uint16_to_half(0xfbff);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half infinity() {
return half_impl::raw_uint16_to_half(0x7c00);
}
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
return half_impl::raw_uint16_to_half(0x7c01);
}
};
} // end namespace Eigen
// Standard mathematical functions and trancendentals.
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half fabsh(const Eigen::half& a) {
// C-like standard mathematical functions and trancendentals.
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half fabsh(const Eigen::half& a) {
Eigen::half result;
result.x = a.x & 0x7FFF;
return result;
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half exph(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half exph(const Eigen::half& a) {
return Eigen::half(::expf(float(a)));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half logh(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half logh(const Eigen::half& a) {
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return Eigen::half(::hlog(a));
#else
return Eigen::half(::logf(float(a)));
#endif
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sqrth(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sqrth(const Eigen::half& a) {
return Eigen::half(::sqrtf(float(a)));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half powh(const Eigen::half& a, const Eigen::half& b) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half powh(const Eigen::half& a, const Eigen::half& b) {
return Eigen::half(::powf(float(a), float(b)));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half floorh(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half floorh(const Eigen::half& a) {
return Eigen::half(::floorf(float(a)));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ceilh(const Eigen::half& a) {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ceilh(const Eigen::half& a) {
return Eigen::half(::ceilf(float(a)));
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC int (isnan)(const Eigen::half& a) {
return (Eigen::numext::isnan)(a);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC int (isinf)(const Eigen::half& a) {
return (Eigen::numext::isinf)(a);
}
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC int (isfinite)(const Eigen::half& a) {
return !(Eigen::numext::isinf)(a) && !(Eigen::numext::isnan)(a);
}
namespace std {
EIGEN_ALWAYS_INLINE ostream& operator << (ostream& os, const Eigen::half& v) {
os << static_cast<float>(v);
return os;
}
#if __cplusplus > 199711L
template <>
struct hash<Eigen::half> {
@ -551,19 +541,45 @@ struct hash<Eigen::half> {
// Add the missing shfl_xor intrinsic
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width=warpSize) {
return static_cast<Eigen::half>(__shfl_xor(static_cast<float>(var), laneMask, width));
}
#endif
// ldg() has an overload for __half, but we also need one for Eigen::half.
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 320
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half __ldg(const Eigen::half* ptr) {
return Eigen::internal::raw_uint16_to_half(
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half __ldg(const Eigen::half* ptr) {
return Eigen::half_impl::raw_uint16_to_half(
__ldg(reinterpret_cast<const unsigned short*>(ptr)));
}
#endif
#if defined(__CUDA_ARCH__)
namespace Eigen {
namespace numext {
template<>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool (isnan)(const Eigen::half& h) {
return (half_impl::isnan)(h);
}
template<>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool (isinf)(const Eigen::half& h) {
return (half_impl::isinf)(h);
}
template<>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool (isfinite)(const Eigen::half& h) {
return (half_impl::isfinite)(h);
}
} // namespace Eigen
} // namespace numext
#endif
#endif // EIGEN_HALF_CUDA_H

129
Eigen/src/Core/arch/CUDA/MathFunctions.h
View File

@ -27,9 +27,22 @@ float4 plog<float4>(const float4& a)
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 plog<double2>(const double2& a)
{
using ::log;
return make_double2(log(a.x), log(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 plog1p<float4>(const float4& a)
{
return make_float4(log1pf(a.x), log1pf(a.y), log1pf(a.z), log1pf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 plog1p<double2>(const double2& a)
{
return make_double2(log1p(a.x), log1p(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pexp<float4>(const float4& a)
{
@ -39,6 +52,7 @@ float4 pexp<float4>(const float4& a)
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pexp<double2>(const double2& a)
{
using ::exp;
return make_double2(exp(a.x), exp(a.y));
}
@ -51,6 +65,7 @@ float4 psqrt<float4>(const float4& a)
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 psqrt<double2>(const double2& a)
{
using ::sqrt;
return make_double2(sqrt(a.x), sqrt(a.y));
}
@ -66,120 +81,6 @@ double2 prsqrt<double2>(const double2& a)
return make_double2(rsqrt(a.x), rsqrt(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 plgamma<float4>(const float4& a)
{
return make_float4(lgammaf(a.x), lgammaf(a.y), lgammaf(a.z), lgammaf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 plgamma<double2>(const double2& a)
{
return make_double2(lgamma(a.x), lgamma(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pdigamma<float4>(const float4& a)
{
using numext::digamma;
return make_float4(digamma(a.x), digamma(a.y), digamma(a.z), digamma(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pdigamma<double2>(const double2& a)
{
using numext::digamma;
return make_double2(digamma(a.x), digamma(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pzeta<float4>(const float4& x, const float4& q)
{
using numext::zeta;
return make_float4(zeta(x.x, q.x), zeta(x.y, q.y), zeta(x.z, q.z), zeta(x.w, q.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pzeta<double2>(const double2& x, const double2& q)
{
using numext::zeta;
return make_double2(zeta(x.x, q.x), zeta(x.y, q.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 ppolygamma<float4>(const float4& n, const float4& x)
{
using numext::polygamma;
return make_float4(polygamma(n.x, x.x), polygamma(n.y, x.y), polygamma(n.z, x.z), polygamma(n.w, x.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 ppolygamma<double2>(const double2& n, const double2& x)
{
using numext::polygamma;
return make_double2(polygamma(n.x, x.x), polygamma(n.y, x.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 perf<float4>(const float4& a)
{
return make_float4(erf(a.x), erf(a.y), erf(a.z), erf(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 perf<double2>(const double2& a)
{
return make_double2(erf(a.x), erf(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 perfc<float4>(const float4& a)
{
return make_float4(erfc(a.x), erfc(a.y), erfc(a.z), erfc(a.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 perfc<double2>(const double2& a)
{
return make_double2(erfc(a.x), erfc(a.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pigamma<float4>(const float4& a, const float4& x)
{
using numext::igamma;
return make_float4(
igamma(a.x, x.x),
igamma(a.y, x.y),
igamma(a.z, x.z),
igamma(a.w, x.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pigamma<double2>(const double2& a, const double2& x)
{
using numext::igamma;
return make_double2(igamma(a.x, x.x), igamma(a.y, x.y));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
float4 pigammac<float4>(const float4& a, const float4& x)
{
using numext::igammac;
return make_float4(
igammac(a.x, x.x),
igammac(a.y, x.y),
igammac(a.z, x.z),
igammac(a.w, x.w));
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
double2 pigammac<double2>(const double2& a, const double2& x)
{
using numext::igammac;
return make_double2(igammac(a.x, x.x), igammac(a.y, x.y));
}
#endif

35
Eigen/src/Core/arch/CUDA/PacketMath.h
View File

@ -44,8 +44,9 @@ template<> struct packet_traits<float> : default_packet_traits
HasPolygamma = 1,
HasErf = 1,
HasErfc = 1,
HasIgamma = 1,
HasIGamma = 1,
HasIGammac = 1,
HasBetaInc = 1,
HasBlend = 0,
};
@ -68,10 +69,13 @@ template<> struct packet_traits<double> : default_packet_traits
HasRsqrt = 1,
HasLGamma = 1,
HasDiGamma = 1,
HasZeta = 1,
HasPolygamma = 1,
HasErf = 1,
HasErfc = 1,
HasIGamma = 1,
HasIGammac = 1,
HasBetaInc = 1,
HasBlend = 0,
};
@ -278,35 +282,6 @@ template<> EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a)
return a.x * a.y;
}
template<size_t offset>
struct protate_impl<offset, float4>
{
static float4 run(const float4& a) {
if (offset == 0) {
return make_float4(a.x, a.y, a.z, a.w);
}
if (offset == 1) {
return make_float4(a.w, a.x, a.y, a.z);
}
if (offset == 2) {
return make_float4(a.z, a.w, a.x, a.y);
}
return make_float4(a.y, a.z, a.w, a.x);
}
};
template<size_t offset>
struct protate_impl<offset, double2>
{
static double2 run(const double2& a) {
if (offset == 0) {
return make_double2(a.x, a.y);
}
return make_double2(a.y, a.x);
}
};
template<> EIGEN_DEVICE_FUNC inline float4 pabs<float4>(const float4& a) {
return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
}

653
Eigen/src/Core/arch/CUDA/PacketMathHalf.h
View File

@ -10,22 +10,16 @@
#ifndef EIGEN_PACKET_MATH_HALF_CUDA_H
#define EIGEN_PACKET_MATH_HALF_CUDA_H
#if defined(EIGEN_HAS_CUDA_FP16)
// Make sure this is only available when targeting a GPU: we don't want to
// introduce conflicts between these packet_traits definitions and the ones
// we'll use on the host side (SSE, AVX, ...)
#if defined(__CUDACC__) && defined(EIGEN_USE_GPU)
// Most of the following operations require arch >= 5.3
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
namespace Eigen {
namespace internal {
// Most of the following operations require arch >= 3.0
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
template<> struct is_arithmetic<half2> { enum { value = true }; };
template<> struct packet_traits<half> : default_packet_traits
template<> struct packet_traits<Eigen::half> : default_packet_traits
{
typedef half2 type;
typedef half2 half;
@ -34,105 +28,172 @@ template<> struct packet_traits<half> : default_packet_traits
AlignedOnScalar = 1,
size=2,
HasHalfPacket = 0,
HasDiv = 1
HasAdd = 1,
HasMul = 1,
HasDiv = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasExp = 1,
HasLog = 1,
HasLog1p = 1
};
};
template<> struct unpacket_traits<half2> { typedef Eigen::half type; enum {size=2, alignment=Aligned16}; typedef half2 half; };
template<> struct unpacket_traits<half2> { typedef half type; enum {size=2, alignment=Aligned16}; typedef half2 half; };
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const half& from) {
template<> __device__ EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
return __half2half2(from);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload<half2>(const half* from) {
template<> __device__ EIGEN_STRONG_INLINE half2 pload<half2>(const Eigen::half* from) {
return *reinterpret_cast<const half2*>(from);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu<half2>(const half* from) {
template<> __device__ EIGEN_STRONG_INLINE half2 ploadu<half2>(const Eigen::half* from) {
return __halves2half2(from[0], from[1]);
}
template<> EIGEN_STRONG_INLINE half2 ploaddup<half2>(const half* from) {
template<> EIGEN_STRONG_INLINE half2 ploaddup<half2>(const Eigen::half* from) {
return __halves2half2(from[0], from[0]);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<half>(half* to, const half2& from) {
template<> __device__ EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const half2& from) {
*reinterpret_cast<half2*>(to) = from;
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<half>(half* to, const half2& from) {
template<> __device__ EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const half2& from) {
to[0] = __low2half(from);
to[1] = __high2half(from);
}
template<>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro<half2, Aligned>(const half* from) {
return __ldg((const half2*)from);
__device__ EIGEN_ALWAYS_INLINE half2 ploadt_ro<half2, Aligned>(const Eigen::half* from) {
#if __CUDA_ARCH__ >= 350
return __ldg((const half2*)from);
#else
return __halves2half2(*(from+0), *(from+1));
#endif
}
template<>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro<half2, Unaligned>(const half* from) {
return __halves2half2(__ldg(from+0), __ldg(from+1));
__device__ EIGEN_ALWAYS_INLINE half2 ploadt_ro<half2, Unaligned>(const Eigen::half* from) {
#if __CUDA_ARCH__ >= 350
return __halves2half2(__ldg(from+0), __ldg(from+1));
#else
return __halves2half2(*(from+0), *(from+1));
#endif
}
template<> EIGEN_DEVICE_FUNC inline half2 pgather<half, half2>(const half* from, Index stride) {
template<> __device__ EIGEN_STRONG_INLINE half2 pgather<Eigen::half, half2>(const Eigen::half* from, Index stride) {
return __halves2half2(from[0*stride], from[1*stride]);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<half, half2>(half* to, const half2& from, Index stride) {
template<> __device__ EIGEN_STRONG_INLINE void pscatter<Eigen::half, half2>(Eigen::half* to, const half2& from, Index stride) {
to[stride*0] = __low2half(from);
to[stride*1] = __high2half(from);
}
template<> EIGEN_DEVICE_FUNC inline half pfirst<half2>(const half2& a) {
template<> __device__ EIGEN_STRONG_INLINE Eigen::half pfirst<half2>(const half2& a) {
return __low2half(a);
}
template<> EIGEN_DEVICE_FUNC inline half2 pabs<half2>(const half2& a) {
template<> __device__ EIGEN_STRONG_INLINE half2 pabs<half2>(const half2& a) {
half2 result;
result.x = a.x & 0x7FFF7FFF;
return result;
}
EIGEN_DEVICE_FUNC inline void
__device__ EIGEN_STRONG_INLINE void
ptranspose(PacketBlock<half2,2>& kernel) {
half a1 = __low2half(kernel.packet[0]);
half a2 = __high2half(kernel.packet[0]);
half b1 = __low2half(kernel.packet[1]);
half b2 = __high2half(kernel.packet[1]);
__half a1 = __low2half(kernel.packet[0]);
__half a2 = __high2half(kernel.packet[0]);
__half b1 = __low2half(kernel.packet[1]);
__half b2 = __high2half(kernel.packet[1]);
kernel.packet[0] = __halves2half2(a1, b1);
kernel.packet[1] = __halves2half2(a2, b2);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset<half2>(const half& a) {
template<> __device__ EIGEN_STRONG_INLINE half2 plset<half2>(const Eigen::half& a) {
#if __CUDA_ARCH__ >= 530
return __halves2half2(a, __hadd(a, __float2half(1.0f)));
#else
float f = __half2float(a) + 1.0f;
return __halves2half2(a, __float2half(f));
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a, const half2& b) {
#if __CUDA_ARCH__ >= 530
return __hadd2(a, b);
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
float r1 = a1 + b1;
float r2 = a2 + b2;
return __floats2half2_rn(r1, r2);
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psub<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 psub<half2>(const half2& a, const half2& b) {
#if __CUDA_ARCH__ >= 530
return __hsub2(a, b);
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
float r1 = a1 - b1;
float r2 = a2 - b2;
return __floats2half2_rn(r1, r2);
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
template<> __device__ EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
#if __CUDA_ARCH__ >= 530
return __hneg2(a);
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
return __floats2half2_rn(-a1, -a2);
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
template<> __device__ EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a, const half2& b) {
#if __CUDA_ARCH__ >= 530
return __hmul2(a, b);
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
float r1 = a1 * b1;
float r2 = a2 * b2;
return __floats2half2_rn(r1, r2);
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmadd<half2>(const half2& a, const half2& b, const half2& c) {
template<> __device__ EIGEN_STRONG_INLINE half2 pmadd<half2>(const half2& a, const half2& b, const half2& c) {
#if __CUDA_ARCH__ >= 530
return __hfma2(a, b, c);
}
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
float c1 = __low2float(c);
float c2 = __high2float(c);
float r1 = a1 * b1 + c1;
float r2 = a2 * b2 + c2;
return __floats2half2_rn(r1, r2);
#endif
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a, const half2& b) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
@ -142,51 +203,529 @@ template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2&
return __floats2half2_rn(r1, r2);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a, const half2& b) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
__half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
__half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
return __halves2half2(r1, r2);
}
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a, const half2& b) {
template<> __device__ EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a, const half2& b) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float b1 = __low2float(b);
float b2 = __high2float(b);
half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
__half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
__half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
return __halves2half2(r1, r2);
}
template<> EIGEN_DEVICE_FUNC inline half predux<half2>(const half2& a) {
template<> __device__ EIGEN_STRONG_INLINE Eigen::half predux<half2>(const half2& a) {
#if __CUDA_ARCH__ >= 530
return __hadd(__low2half(a), __high2half(a));
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
return Eigen::half(half_impl::raw_uint16_to_half(__float2half_rn(a1 + a2)));
#endif
}
template<> EIGEN_DEVICE_FUNC inline half predux_max<half2>(const half2& a) {
half first = __low2half(a);
half second = __high2half(a);
template<> __device__ EIGEN_STRONG_INLINE Eigen::half predux_max<half2>(const half2& a) {
#if __CUDA_ARCH__ >= 530
__half first = __low2half(a);
__half second = __high2half(a);
return __hgt(first, second) ? first : second;
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
return a1 > a2 ? __low2half(a) : __high2half(a);
#endif
}
template<> EIGEN_DEVICE_FUNC inline half predux_min<half2>(const half2& a) {
half first = __low2half(a);
half second = __high2half(a);
template<> __device__ EIGEN_STRONG_INLINE Eigen::half predux_min<half2>(const half2& a) {
#if __CUDA_ARCH__ >= 530
__half first = __low2half(a);
__half second = __high2half(a);
return __hlt(first, second) ? first : second;
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
return a1 < a2 ? __low2half(a) : __high2half(a);
#endif
}
template<> EIGEN_DEVICE_FUNC inline half predux_mul<half2>(const half2& a) {
template<> __device__ EIGEN_STRONG_INLINE Eigen::half predux_mul<half2>(const half2& a) {
#if __CUDA_ARCH__ >= 530
return __hmul(__low2half(a), __high2half(a));
#else
float a1 = __low2float(a);
float a2 = __high2float(a);
return Eigen::half(half_impl::raw_uint16_to_half(__float2half_rn(a1 * a2)));
#endif
}
} // end namespace internal
template<> __device__ EIGEN_STRONG_INLINE half2 plog1p<half2>(const half2& a) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float r1 = log1pf(a1);
float r2 = log1pf(a2);
return __floats2half2_rn(r1, r2);
}
} // end namespace Eigen
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 530
template<> __device__ EIGEN_STRONG_INLINE
half2 plog<half2>(const half2& a) {
return h2log(a);
}
template<> __device__ EIGEN_STRONG_INLINE
half2 pexp<half2>(const half2& a) {
return h2exp(a);
}
template<> __device__ EIGEN_STRONG_INLINE
half2 psqrt<half2>(const half2& a) {
return h2sqrt(a);
}
template<> __device__ EIGEN_STRONG_INLINE
half2 prsqrt<half2>(const half2& a) {
return h2rsqrt(a);
}
#else
template<> __device__ EIGEN_STRONG_INLINE half2 plog<half2>(const half2& a) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float r1 = logf(a1);
float r2 = logf(a2);
return __floats2half2_rn(r1, r2);
}
template<> __device__ EIGEN_STRONG_INLINE half2 pexp<half2>(const half2& a) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float r1 = expf(a1);
float r2 = expf(a2);
return __floats2half2_rn(r1, r2);
}
template<> __device__ EIGEN_STRONG_INLINE half2 psqrt<half2>(const half2& a) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float r1 = sqrtf(a1);
float r2 = sqrtf(a2);
return __floats2half2_rn(r1, r2);
}
template<> __device__ EIGEN_STRONG_INLINE half2 prsqrt<half2>(const half2& a) {
float a1 = __low2float(a);
float a2 = __high2float(a);
float r1 = rsqrtf(a1);
float r2 = rsqrtf(a2);
return __floats2half2_rn(r1, r2);
}
#endif
#elif defined EIGEN_VECTORIZE_AVX
typedef struct {
__m128i x;
} Packet8h;
template<> struct is_arithmetic<Packet8h> { enum { value = true }; };
template <>
struct packet_traits<Eigen::half> : default_packet_traits {
typedef Packet8h type;
// There is no half-size packet for Packet8h.
typedef Packet8h half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size = 8,
HasHalfPacket = 0,
HasAdd = 0,
HasSub = 0,
HasMul = 0,
HasNegate = 0,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasConj = 0,
HasSetLinear = 0,
HasDiv = 0,
HasSqrt = 0,
HasRsqrt = 0,
HasExp = 0,
HasLog = 0,
HasBlend = 0
};
};
template<> struct unpacket_traits<Packet8h> { typedef Eigen::half type; enum {size=8, alignment=Aligned16}; typedef Packet8h half; };
template<> EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
Packet8h result;
result.x = _mm_set1_epi16(from.x);
return result;
}
template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm_extract_epi16(from.x, 0)));
}
template<> EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
Packet8h result;
result.x = _mm_load_si128(reinterpret_cast<const __m128i*>(from));
return result;
}
template<> EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
Packet8h result;
result.x = _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
return result;
}
template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8h& from) {
_mm_store_si128(reinterpret_cast<__m128i*>(to), from.x);
}
template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8h& from) {
_mm_storeu_si128(reinterpret_cast<__m128i*>(to), from.x);
}
template<> EIGEN_STRONG_INLINE Packet8h
ploadquad<Packet8h>(const Eigen::half* from) {
Packet8h result;
unsigned short a = from[0].x;
unsigned short b = from[1].x;
result.x = _mm_set_epi16(b, b, b, b, a, a, a, a);
return result;
}
EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
#ifdef EIGEN_HAS_FP16_C
return _mm256_cvtph_ps(a.x);
#else
EIGEN_ALIGN32 Eigen::half aux[8];
pstore(aux, a);
float f0(aux[0]);
float f1(aux[1]);
float f2(aux[2]);
float f3(aux[3]);
float f4(aux[4]);
float f5(aux[5]);
float f6(aux[6]);
float f7(aux[7]);
return _mm256_set_ps(f7, f6, f5, f4, f3, f2, f1, f0);
#endif
}
EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
#ifdef EIGEN_HAS_FP16_C
Packet8h result;
result.x = _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
return result;
#else
EIGEN_ALIGN32 float aux[8];
pstore(aux, a);
Eigen::half h0(aux[0]);
Eigen::half h1(aux[1]);
Eigen::half h2(aux[2]);
Eigen::half h3(aux[3]);
Eigen::half h4(aux[4]);
Eigen::half h5(aux[5]);
Eigen::half h6(aux[6]);
Eigen::half h7(aux[7]);
Packet8h result;
result.x = _mm_set_epi16(h7.x, h6.x, h5.x, h4.x, h3.x, h2.x, h1.x, h0.x);
return result;
#endif
}
template<> EIGEN_STRONG_INLINE Packet8h pconj(const Packet8h& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
Packet8f af = half2float(a);
Packet8f bf = half2float(b);
Packet8f rf = padd(af, bf);
return float2half(rf);
}
template<> EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
Packet8f af = half2float(a);
Packet8f bf = half2float(b);
Packet8f rf = pmul(af, bf);
return float2half(rf);
}
template<> EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride)
{
Packet8h result;
result.x = _mm_set_epi16(from[7*stride].x, from[6*stride].x, from[5*stride].x, from[4*stride].x, from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
return result;
}
template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride)
{
EIGEN_ALIGN32 Eigen::half aux[8];
pstore(aux, from);
to[stride*0].x = aux[0].x;
to[stride*1].x = aux[1].x;
to[stride*2].x = aux[2].x;
to[stride*3].x = aux[3].x;
to[stride*4].x = aux[4].x;
to[stride*5].x = aux[5].x;
to[stride*6].x = aux[6].x;
to[stride*7].x = aux[7].x;
}
EIGEN_STRONG_INLINE void
ptranspose(PacketBlock<Packet8h,8>& kernel) {
__m128i a = kernel.packet[0].x;
__m128i b = kernel.packet[1].x;
__m128i c = kernel.packet[2].x;
__m128i d = kernel.packet[3].x;
__m128i e = kernel.packet[4].x;
__m128i f = kernel.packet[5].x;
__m128i g = kernel.packet[6].x;
__m128i h = kernel.packet[7].x;
__m128i a03b03 = _mm_unpacklo_epi16(a, b);
__m128i c03d03 = _mm_unpacklo_epi16(c, d);
__m128i e03f03 = _mm_unpacklo_epi16(e, f);
__m128i g03h03 = _mm_unpacklo_epi16(g, h);
__m128i a47b47 = _mm_unpackhi_epi16(a, b);
__m128i c47d47 = _mm_unpackhi_epi16(c, d);
__m128i e47f47 = _mm_unpackhi_epi16(e, f);
__m128i g47h47 = _mm_unpackhi_epi16(g, h);
__m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
__m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
__m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
__m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
__m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
__m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
__m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
__m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
__m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
__m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
__m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
__m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
__m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
__m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
__m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
__m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
kernel.packet[0].x = a0b0c0d0e0f0g0h0;
kernel.packet[1].x = a1b1c1d1e1f1g1h1;
kernel.packet[2].x = a2b2c2d2e2f2g2h2;
kernel.packet[3].x = a3b3c3d3e3f3g3h3;
kernel.packet[4].x = a4b4c4d4e4f4g4h4;
kernel.packet[5].x = a5b5c5d5e5f5g5h5;
kernel.packet[6].x = a6b6c6d6e6f6g6h6;
kernel.packet[7].x = a7b7c7d7e7f7g7h7;
}
EIGEN_STRONG_INLINE void
ptranspose(PacketBlock<Packet8h,4>& kernel) {
EIGEN_ALIGN32 Eigen::half in[4][8];
pstore<Eigen::half>(in[0], kernel.packet[0]);
pstore<Eigen::half>(in[1], kernel.packet[1]);
pstore<Eigen::half>(in[2], kernel.packet[2]);
pstore<Eigen::half>(in[3], kernel.packet[3]);
EIGEN_ALIGN32 Eigen::half out[4][8];
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 4; ++j) {
out[i][j] = in[j][2*i];
}
for (int j = 0; j < 4; ++j) {
out[i][j+4] = in[j][2*i+1];
}
}
kernel.packet[0] = pload<Packet8h>(out[0]);
kernel.packet[1] = pload<Packet8h>(out[1]);
kernel.packet[2] = pload<Packet8h>(out[2]);
kernel.packet[3] = pload<Packet8h>(out[3]);
}
// Disable the following code since it's broken on too many platforms / compilers.
//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
#elif 0
typedef struct {
__m64 x;
} Packet4h;
template<> struct is_arithmetic<Packet4h> { enum { value = true }; };
template <>
struct packet_traits<Eigen::half> : default_packet_traits {
typedef Packet4h type;
// There is no half-size packet for Packet4h.
typedef Packet4h half;
enum {
Vectorizable = 1,
AlignedOnScalar = 1,
size = 4,
HasHalfPacket = 0,
HasAdd = 0,
HasSub = 0,
HasMul = 0,
HasNegate = 0,
HasAbs = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasConj = 0,
HasSetLinear = 0,
HasDiv = 0,
HasSqrt = 0,
HasRsqrt = 0,
HasExp = 0,
HasLog = 0,
HasBlend = 0
};
};
template<> struct unpacket_traits<Packet4h> { typedef Eigen::half type; enum {size=4, alignment=Aligned16}; typedef Packet4h half; };
template<> EIGEN_STRONG_INLINE Packet4h pset1<Packet4h>(const Eigen::half& from) {
Packet4h result;
result.x = _mm_set1_pi16(from.x);
return result;
}
template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h>(const Packet4h& from) {
return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm_cvtsi64_si32(from.x)));
}
template<> EIGEN_STRONG_INLINE Packet4h pconj(const Packet4h& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4h padd<Packet4h>(const Packet4h& a, const Packet4h& b) {
__int64_t a64 = _mm_cvtm64_si64(a.x);
__int64_t b64 = _mm_cvtm64_si64(b.x);
Eigen::half h[4];
Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
h[0] = ha + hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
h[1] = ha + hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
h[2] = ha + hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
h[3] = ha + hb;
Packet4h result;
result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
return result;
}
template<> EIGEN_STRONG_INLINE Packet4h pmul<Packet4h>(const Packet4h& a, const Packet4h& b) {
__int64_t a64 = _mm_cvtm64_si64(a.x);
__int64_t b64 = _mm_cvtm64_si64(b.x);
Eigen::half h[4];
Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
h[0] = ha * hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
h[1] = ha * hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
h[2] = ha * hb;
ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
h[3] = ha * hb;
Packet4h result;
result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
return result;
}
template<> EIGEN_STRONG_INLINE Packet4h pload<Packet4h>(const Eigen::half* from) {
Packet4h result;
result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
return result;
}
template<> EIGEN_STRONG_INLINE Packet4h ploadu<Packet4h>(const Eigen::half* from) {
Packet4h result;
result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
return result;
}
template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4h& from) {
__int64_t r = _mm_cvtm64_si64(from.x);
*(reinterpret_cast<__int64_t*>(to)) = r;
}
template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4h& from) {
__int64_t r = _mm_cvtm64_si64(from.x);
*(reinterpret_cast<__int64_t*>(to)) = r;
}
template<> EIGEN_STRONG_INLINE Packet4h
ploadquad<Packet4h>(const Eigen::half* from) {
return pset1<Packet4h>(*from);
}
template<> EIGEN_STRONG_INLINE Packet4h pgather<Eigen::half, Packet4h>(const Eigen::half* from, Index stride)
{
Packet4h result;
result.x = _mm_set_pi16(from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
return result;
}
template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h>(Eigen::half* to, const Packet4h& from, Index stride)
{
__int64_t a = _mm_cvtm64_si64(from.x);
to[stride*0].x = static_cast<unsigned short>(a);
to[stride*1].x = static_cast<unsigned short>(a >> 16);
to[stride*2].x = static_cast<unsigned short>(a >> 32);
to[stride*3].x = static_cast<unsigned short>(a >> 48);
}
EIGEN_STRONG_INLINE void
ptranspose(PacketBlock<Packet4h,4>& kernel) {
__m64 T0 = _mm_unpacklo_pi16(kernel.packet[0].x, kernel.packet[1].x);
__m64 T1 = _mm_unpacklo_pi16(kernel.packet[2].x, kernel.packet[3].x);
__m64 T2 = _mm_unpackhi_pi16(kernel.packet[0].x, kernel.packet[1].x);
__m64 T3 = _mm_unpackhi_pi16(kernel.packet[2].x, kernel.packet[3].x);
kernel.packet[0].x = _mm_unpacklo_pi32(T0, T1);
kernel.packet[1].x = _mm_unpackhi_pi32(T0, T1);
kernel.packet[2].x = _mm_unpacklo_pi32(T2, T3);
kernel.packet[3].x = _mm_unpackhi_pi32(T2, T3);
}
#endif
}
}
#endif // EIGEN_PACKET_MATH_HALF_CUDA_H

121
Eigen/src/Core/arch/CUDA/TypeCasting.h
View File

@ -14,50 +14,48 @@ namespace Eigen {
namespace internal {
#if defined(EIGEN_HAS_CUDA_FP16)
template<>
struct scalar_cast_op<float, half> {
struct scalar_cast_op<float, Eigen::half> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
typedef half result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half operator() (const float& a) const {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
typedef Eigen::half result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const float& a) const {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __float2half(a);
#else
return half(a);
return Eigen::half(a);
#endif
}
};
template<>
struct functor_traits<scalar_cast_op<float, half> >
struct functor_traits<scalar_cast_op<float, Eigen::half> >
{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
template<>
struct scalar_cast_op<int, half> {
struct scalar_cast_op<int, Eigen::half> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
typedef half result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half operator() (const int& a) const {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
typedef Eigen::half result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half operator() (const int& a) const {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __float2half(static_cast<float>(a));
#else
return half(static_cast<float>(a));
return Eigen::half(static_cast<float>(a));
#endif
}
};
template<>
struct functor_traits<scalar_cast_op<int, half> >
struct functor_traits<scalar_cast_op<int, Eigen::half> >
{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
template<>
struct scalar_cast_op<half, float> {
struct scalar_cast_op<Eigen::half, float> {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op)
typedef float result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const half& a) const {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator() (const Eigen::half& a) const {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __half2float(a);
#else
return static_cast<float>(a);
@ -66,15 +64,15 @@ struct scalar_cast_op<half, float> {
};
template<>
struct functor_traits<scalar_cast_op<half, float> >
struct functor_traits<scalar_cast_op<Eigen::half, float> >
{ enum { Cost = NumTraits<float>::AddCost, PacketAccess = false }; };
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
template <>
struct type_casting_traits<half, float> {
struct type_casting_traits<Eigen::half, float> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 2,
@ -89,7 +87,7 @@ template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<half2, float4>(con
}
template <>
struct type_casting_traits<float, half> {
struct type_casting_traits<float, Eigen::half> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 1,
@ -97,12 +95,87 @@ struct type_casting_traits<float, half> {
};
};
template<> EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
// Simply discard the second half of the input
return __float22half2_rn(make_float2(a.x, a.y));
return __floats2half2_rn(a.x, a.y);
}
#elif defined EIGEN_VECTORIZE_AVX
template <>
struct type_casting_traits<Eigen::half, float> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
return half2float(a);
}
template <>
struct type_casting_traits<float, Eigen::half> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template<> EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
return float2half(a);
}
// Disable the following code since it's broken on too many platforms / compilers.
//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
#elif 0
template <>
struct type_casting_traits<Eigen::half, float> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4h, Packet4f>(const Packet4h& a) {
__int64_t a64 = _mm_cvtm64_si64(a.x);
Eigen::half h = raw_uint16_to_half(static_cast<unsigned short>(a64));
float f1 = static_cast<float>(h);
h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
float f2 = static_cast<float>(h);
h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
float f3 = static_cast<float>(h);
h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
float f4 = static_cast<float>(h);
return _mm_set_ps(f4, f3, f2, f1);
}
template <>
struct type_casting_traits<float, Eigen::half> {
enum {
VectorizedCast = 1,
SrcCoeffRatio = 1,
TgtCoeffRatio = 1
};
};
template<> EIGEN_STRONG_INLINE Packet4h pcast<Packet4f, Packet4h>(const Packet4f& a) {
EIGEN_ALIGN16 float aux[4];
pstore(aux, a);
Eigen::half h0(aux[0]);
Eigen::half h1(aux[1]);
Eigen::half h2(aux[2]);
Eigen::half h3(aux[3]);
Packet4h result;
result.x = _mm_set_pi16(h3.x, h2.x, h1.x, h0.x);
return result;
}
#endif
#endif
} // end namespace internal

6
Eigen/src/Core/arch/Default/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_Default_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_Default_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/Default COMPONENT Devel
)

6
Eigen/src/Core/arch/NEON/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_NEON_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_NEON_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/NEON COMPONENT Devel
)

21
Eigen/src/Core/arch/NEON/Complex.h
View File

@ -2,6 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -14,8 +15,15 @@ namespace Eigen {
namespace internal {
static uint32x4_t p4ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET4(0x00000000, 0x80000000, 0x00000000, 0x80000000);
static uint32x2_t p2ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x00000000, 0x80000000);
inline uint32x4_t p4ui_CONJ_XOR() {
static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
return vld1q_u32( conj_XOR_DATA );
}
inline uint32x2_t p2ui_CONJ_XOR() {
static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000 };
return vld1_u32( conj_XOR_DATA );
}
//---------- float ----------
struct Packet2cf
@ -64,7 +72,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Pa
template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
{
Packet4ui b = vreinterpretq_u32_f32(a.v);
return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR)));
return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
}
template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
@ -80,7 +88,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
// Multiply the imag a with b
v2 = vmulq_f32(v2, b.v);
// Conjugate v2
v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR));
v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR()));
// Swap real/imag elements in v2.
v2 = vrev64q_f32(v2);
// Add and return the result
@ -195,7 +203,7 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const P
// Multiply the imag a with b
v2 = vmul_f32(v2, a2);
// Conjugate v2
v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR));
v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
// Swap real/imag elements in v2.
v2 = vrev64_f32(v2);
// Add v1, v2
@ -274,7 +282,8 @@ ptranspose(PacketBlock<Packet2cf,2>& kernel) {
//---------- double ----------
#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
static uint64x2_t p2ul_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x0, 0x8000000000000000);
const uint64_t p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
struct Packet1cd
{

46
Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -2,7 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Konstantinos Margaritis <markos@codex.gr>
// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
// Heavily based on Gael's SSE version.
//
// This Source Code Form is subject to the terms of the Mozilla
@ -49,17 +49,6 @@ typedef uint32x4_t Packet4ui;
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
const Packet4i p4i_##NAME = pset1<Packet4i>(X)
#if EIGEN_COMP_LLVM && !EIGEN_COMP_CLANG
//Special treatment for Apple's llvm-gcc, its NEON packet types are unions
#define EIGEN_INIT_NEON_PACKET2(X, Y) {{X, Y}}
#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {{X, Y, Z, W}}
#else
//Default initializer for packets
#define EIGEN_INIT_NEON_PACKET2(X, Y) {X, Y}
#define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {X, Y, Z, W}
#endif
// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
// which available on LLVM and GCC (at least)
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
@ -122,12 +111,14 @@ template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) {
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
{
Packet4f countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
const float32_t f[] = {0, 1, 2, 3};
Packet4f countdown = vld1q_f32(f);
return vaddq_f32(pset1<Packet4f>(a), countdown);
}
template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a)
{
Packet4i countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3);
const int32_t i[] = {0, 1, 2, 3};
Packet4i countdown = vld1q_s32(i);
return vaddq_s32(pset1<Packet4i>(a), countdown);
}
@ -334,22 +325,6 @@ template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
return vcombine_s32(a_hi, a_lo);
}
template<size_t offset>
struct protate_impl<offset, Packet4f>
{
static Packet4f run(const Packet4f& a) {
return vextq_f32(a, a, offset);
}
};
template<size_t offset>
struct protate_impl<offset, Packet4i>
{
static Packet4i run(const Packet4i& a) {
return vextq_s32(a, a, offset);
}
};
template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vabsq_f32(a); }
template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s32(a); }
@ -601,7 +576,8 @@ template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { r
template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a)
{
Packet2d countdown = EIGEN_INIT_NEON_PACKET2(0, 1);
const double countdown_raw[] = {0.0,1.0};
const Packet2d countdown = vld1q_f64(countdown_raw);
return vaddq_f64(pset1<Packet2d>(a), countdown);
}
template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }
@ -679,14 +655,6 @@ template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { retu
template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); }
template<size_t offset>
struct protate_impl<offset, Packet2d>
{
static Packet2d run(const Packet2d& a) {
return vextq_f64(a, a, offset);
}
};
template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); }
#if EIGEN_COMP_CLANG && defined(__apple_build_version__)

6
Eigen/src/Core/arch/SSE/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_SSE_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_SSE_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/SSE COMPONENT Devel
)

46
Eigen/src/Core/arch/SSE/MathFunctions.h
View File

@ -517,52 +517,10 @@ Packet2d prsqrt<Packet2d>(const Packet2d& x) {
}
// Hyperbolic Tangent function.
// Doesn't do anything fancy, just a 13/6-degree rational interpolant which
// is accurate up to a couple of ulp in the range [-9, 9], outside of which the
// fl(tanh(x)) = +/-1.
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4f
ptanh<Packet4f>(const Packet4f& _x) {
// Clamp the inputs to the range [-9, 9] since anything outside
// this range is +/-1.0f in single-precision.
_EIGEN_DECLARE_CONST_Packet4f(plus_9, 9.0f);
_EIGEN_DECLARE_CONST_Packet4f(minus_9, -9.0f);
const Packet4f x = pmax(p4f_minus_9, pmin(p4f_plus_9, _x));
// The monomial coefficients of the numerator polynomial (odd).
_EIGEN_DECLARE_CONST_Packet4f(alpha_1, 4.89352455891786e-03f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_3, 6.37261928875436e-04f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_5, 1.48572235717979e-05f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_7, 5.12229709037114e-08f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_9, -8.60467152213735e-11f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_11, 2.00018790482477e-13f);
_EIGEN_DECLARE_CONST_Packet4f(alpha_13, -2.76076847742355e-16f);
// The monomial coefficients of the denominator polynomial (even).
_EIGEN_DECLARE_CONST_Packet4f(beta_0, 4.89352518554385e-03f);
_EIGEN_DECLARE_CONST_Packet4f(beta_2, 2.26843463243900e-03f);
_EIGEN_DECLARE_CONST_Packet4f(beta_4, 1.18534705686654e-04f);
_EIGEN_DECLARE_CONST_Packet4f(beta_6, 1.19825839466702e-06f);
// Since the polynomials are odd/even, we need x^2.
const Packet4f x2 = pmul(x, x);
// Evaluate the numerator polynomial p.
Packet4f p = pmadd(x2, p4f_alpha_13, p4f_alpha_11);
p = pmadd(x2, p, p4f_alpha_9);
p = pmadd(x2, p, p4f_alpha_7);
p = pmadd(x2, p, p4f_alpha_5);
p = pmadd(x2, p, p4f_alpha_3);
p = pmadd(x2, p, p4f_alpha_1);
p = pmul(x, p);
// Evaluate the denominator polynomial p.
Packet4f q = pmadd(x2, p4f_beta_6, p4f_beta_4);
q = pmadd(x2, q, p4f_beta_2);
q = pmadd(x2, q, p4f_beta_0);
// Divide the numerator by the denominator.
return pdiv(p, q);
ptanh<Packet4f>(const Packet4f& x) {
return internal::generic_fast_tanh_float(x);
}
} // end namespace internal

39
Eigen/src/Core/arch/SSE/PacketMath.h
View File

@ -162,6 +162,11 @@ template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4,
template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16}; typedef Packet2d half; };
template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4, alignment=Aligned16}; typedef Packet4i half; };
#ifndef EIGEN_VECTORIZE_AVX
template<> struct scalar_div_cost<float,true> { enum { value = 7 }; };
template<> struct scalar_div_cost<double,true> { enum { value = 8 }; };
#endif
#if EIGEN_COMP_MSVC==1500
// Workaround MSVC 9 internal compiler error.
// TODO: It has been detected with win64 builds (amd64), so let's check whether it also happens in 32bits+SSE mode
@ -434,30 +439,6 @@ template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
{ return _mm_shuffle_epi32(a,0x1B); }
template<size_t offset>
struct protate_impl<offset, Packet4f>
{
static Packet4f run(const Packet4f& a) {
return vec4f_swizzle1(a, offset, (offset + 1) % 4, (offset + 2) % 4, (offset + 3) % 4);
}
};
template<size_t offset>
struct protate_impl<offset, Packet4i>
{
static Packet4i run(const Packet4i& a) {
return vec4i_swizzle1(a, offset, (offset + 1) % 4, (offset + 2) % 4, (offset + 3) % 4);
}
};
template<size_t offset>
struct protate_impl<offset, Packet2d>
{
static Packet2d run(const Packet2d& a) {
return vec2d_swizzle1(a, offset, (offset + 1) % 2);
}
};
template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a)
{
const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
@ -837,6 +818,16 @@ template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, cons
#endif
}
// Scalar path for pmadd with FMA to ensure consistency with vectorized path.
#ifdef __FMA__
template<> EIGEN_STRONG_INLINE float pmadd(const float& a, const float& b, const float& c) {
return ::fmaf(a,b,c);
}
template<> EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, const double& c) {
return ::fma(a,b,c);
}
#endif
} // end namespace internal
} // end namespace Eigen

6
Eigen/src/Core/arch/ZVector/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_arch_ZVector_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_arch_ZVector_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/ZVector COMPONENT Devel
)

15
Eigen/src/Core/arch/ZVector/Complex.h
View File

@ -57,21 +57,6 @@ template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<
template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
af[0] = from[0*stride];
af[1] = from[1*stride];
return pload<Packet1cd>(af);
}
template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index stride)
{
std::complex<double> EIGEN_ALIGN16 af[2];
pstore<std::complex<double> >(af, from);
to[0*stride] = af[0];
to[1*stride] = af[1];
}
template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }

70
Eigen/src/Core/functors/AssignmentFunctors.h
View File

@ -18,20 +18,24 @@ namespace internal {
* \brief Template functor for scalar/packet assignment
*
*/
template<typename Scalar> struct assign_op {
template<typename DstScalar,typename SrcScalar> struct assign_op {
EIGEN_EMPTY_STRUCT_CTOR(assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a = b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,b); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,b); }
};
template<typename Scalar>
struct functor_traits<assign_op<Scalar> > {
// Empty overload for void type (used by PermutationMatrix
template<typename DstScalar> struct assign_op<DstScalar,void> {};
template<typename DstScalar,typename SrcScalar>
struct functor_traits<assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost,
PacketAccess = packet_traits<Scalar>::Vectorizable
Cost = NumTraits<DstScalar>::ReadCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable
};
};
@ -39,20 +43,20 @@ struct functor_traits<assign_op<Scalar> > {
* \brief Template functor for scalar/packet assignment with addition
*
*/
template<typename Scalar> struct add_assign_op {
template<typename DstScalar,typename SrcScalar> struct add_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(add_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a += b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a += b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<add_assign_op<Scalar> > {
template<typename DstScalar,typename SrcScalar>
struct functor_traits<add_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd
};
};
@ -60,20 +64,20 @@ struct functor_traits<add_assign_op<Scalar> > {
* \brief Template functor for scalar/packet assignment with subtraction
*
*/
template<typename Scalar> struct sub_assign_op {
template<typename DstScalar,typename SrcScalar> struct sub_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a -= b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a -= b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<sub_assign_op<Scalar> > {
template<typename DstScalar,typename SrcScalar>
struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasSub
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub
};
};
@ -98,30 +102,28 @@ struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
};
};
template<typename DstScalar,typename SrcScalar> struct functor_is_product_like<mul_assign_op<DstScalar,SrcScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor for scalar/packet assignment with diviving
*
*/
template<typename Scalar> struct div_assign_op {
template<typename DstScalar, typename SrcScalar=DstScalar> struct div_assign_op {
EIGEN_EMPTY_STRUCT_CTOR(div_assign_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a /= b; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a /= b; }
template<int Alignment, typename Packet>
EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const
{ internal::pstoret<Scalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); }
EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
{ internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); }
};
template<typename Scalar>
struct functor_traits<div_assign_op<Scalar> > {
template<typename DstScalar, typename SrcScalar>
struct functor_traits<div_assign_op<DstScalar,SrcScalar> > {
enum {
Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasDiv
Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv
};
};
/** \internal
* \brief Template functor for scalar/packet assignment with swapping
*

471
Eigen/src/Core/functors/BinaryFunctors.h
View File

@ -16,27 +16,43 @@ namespace internal {
//---------- associative binary functors ----------
template<typename Arg1, typename Arg2>
struct binary_op_base
{
typedef Arg1 first_argument_type;
typedef Arg2 second_argument_type;
};
/** \internal
* \brief Template functor to compute the sum of two scalars
*
* \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
*/
template<typename Scalar> struct scalar_sum_op {
// typedef Scalar result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; }
#else
scalar_sum_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::padd(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sum_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasAdd
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2, // rough estimate!
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
// TODO vectorize mixed sum
};
};
@ -45,7 +61,7 @@ struct functor_traits<scalar_sum_op<Scalar> > {
* This is required to solve Bug 426.
* \sa DenseBase::count(), DenseBase::any(), ArrayBase::cast(), MatrixBase::cast()
*/
template<> struct scalar_sum_op<bool> : scalar_sum_op<int> {
template<> struct scalar_sum_op<bool,bool> : scalar_sum_op<int,int> {
EIGEN_DEPRECATED
scalar_sum_op() {}
};
@ -56,13 +72,17 @@ template<> struct scalar_sum_op<bool> : scalar_sum_op<int> {
*
* \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_product_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_product_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op)
#else
scalar_product_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
@ -75,7 +95,8 @@ template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
enum {
Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate!
PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
// TODO vectorize mixed product
};
};
@ -84,13 +105,15 @@ struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
*
* This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_conj_product_op : binary_op_base<LhsScalar,RhsScalar>
{
enum {
Conj = NumTraits<LhsScalar>::IsComplex
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
@ -113,21 +136,24 @@ struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
*/
template<typename Scalar> struct scalar_min_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::mini(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::mini(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_min(a); }
};
template<typename Scalar>
struct functor_traits<scalar_min_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_min_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMin
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
};
};
@ -136,21 +162,24 @@ struct functor_traits<scalar_min_op<Scalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
*/
template<typename Scalar> struct scalar_max_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::maxi(a, b); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return numext::maxi(a, b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const
{ return internal::predux_max(a); }
};
template<typename Scalar>
struct functor_traits<scalar_max_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_max_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasMax
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
};
};
@ -158,56 +187,70 @@ struct functor_traits<scalar_max_op<Scalar> > {
* \brief Template functors for comparison of two scalars
* \todo Implement packet-comparisons
*/
template<typename Scalar, ComparisonName cmp> struct scalar_cmp_op;
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp> struct scalar_cmp_op;
template<typename Scalar, ComparisonName cmp>
struct functor_traits<scalar_cmp_op<Scalar, cmp> > {
template<typename LhsScalar, typename RhsScalar, ComparisonName cmp>
struct functor_traits<scalar_cmp_op<LhsScalar,RhsScalar, cmp> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = false
};
};
template<ComparisonName Cmp, typename Scalar>
struct result_of<scalar_cmp_op<Scalar, Cmp>(Scalar,Scalar)> {
template<ComparisonName Cmp, typename LhsScalar, typename RhsScalar>
struct result_of<scalar_cmp_op<LhsScalar, RhsScalar, Cmp>(LhsScalar,RhsScalar)> {
typedef bool type;
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_EQ> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_EQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a==b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a==b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LT> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LE> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_LE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a<=b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GT> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GT> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GE> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_GE> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a>=b;}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_UNORD> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_UNORD> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return !(a<=b || b<=a);}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return !(a<=b || b<=a);}
};
template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_NEQ> {
template<typename LhsScalar, typename RhsScalar>
struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,RhsScalar>
{
typedef bool result_type;
EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a!=b;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const LhsScalar& a, const RhsScalar& b) const {return a!=b;}
};
@ -216,7 +259,9 @@ template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_NEQ> {
*
* \sa MatrixBase::stableNorm(), class Redux
*/
template<typename Scalar> struct scalar_hypot_op {
template<typename Scalar>
struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
// typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
@ -237,12 +282,12 @@ template<typename Scalar> struct scalar_hypot_op {
}
};
template<typename Scalar>
struct functor_traits<scalar_hypot_op<Scalar> > {
struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
enum
{
Cost = 3 * NumTraits<Scalar>::AddCost +
2 * NumTraits<Scalar>::MulCost +
2 * NumTraits<Scalar>::template Div<false>::Cost,
2 * scalar_div_cost<Scalar,false>::value,
PacketAccess = false
};
};
@ -250,13 +295,24 @@ struct functor_traits<scalar_hypot_op<Scalar> > {
/** \internal
* \brief Template functor to compute the pow of two scalars
*/
template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op)
template<typename Scalar, typename Exponent>
struct scalar_pow_op : binary_op_base<Scalar,Exponent>
{
typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_pow_op)
#else
scalar_pow_op() {
typedef Scalar LhsScalar;
typedef Exponent RhsScalar;
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); }
inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
};
template<typename Scalar, typename OtherScalar>
struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
template<typename Scalar, typename Exponent>
struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
};
@ -269,18 +325,27 @@ struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
*
* \sa class CwiseBinaryOp, MatrixBase::operator-
*/
template<typename Scalar> struct scalar_difference_op {
template<typename LhsScalar,typename RhsScalar>
struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; }
#else
scalar_difference_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::psub(a,b); }
};
template<typename Scalar>
struct functor_traits<scalar_difference_op<Scalar> > {
template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
enum {
Cost = NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasSub
Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
};
};
@ -289,13 +354,17 @@ struct functor_traits<scalar_difference_op<Scalar> > {
*
* \sa class CwiseBinaryOp, Cwise::operator/()
*/
template<typename LhsScalar,typename RhsScalar> struct scalar_quotient_op {
enum {
// TODO vectorize mixed product
Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv
};
typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type;
template<typename LhsScalar,typename RhsScalar>
struct scalar_quotient_op : binary_op_base<LhsScalar,RhsScalar>
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
#ifndef EIGEN_SCALAR_BINARY_OP_PLUGIN
EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op)
#else
scalar_quotient_op() {
EIGEN_SCALAR_BINARY_OP_PLUGIN
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
@ -305,8 +374,8 @@ template<typename LhsScalar,typename RhsScalar>
struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
enum {
PacketAccess = scalar_quotient_op<LhsScalar,RhsScalar>::Vectorizable,
Cost = NumTraits<result_type>::template Div<PacketAccess>::Cost
PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
Cost = scalar_div_cost<result_type,PacketAccess>::value
};
};
@ -360,236 +429,50 @@ template<> struct functor_traits<scalar_boolean_xor_op> {
};
};
/** \internal
* \brief Template functor to compute the incomplete gamma function igamma(a, x)
*
* \sa class CwiseBinaryOp, Cwise::igamma
*/
template<typename Scalar> struct scalar_igamma_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
using numext::igamma; return igamma(a, x);
}
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
return internal::pigammac(a, x);
}
};
template<typename Scalar>
struct functor_traits<scalar_igamma_op<Scalar> > {
enum {
// Guesstimate
Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasIGamma
};
};
/** \internal
* \brief Template functor to compute the complementary incomplete gamma function igammac(a, x)
*
* \sa class CwiseBinaryOp, Cwise::igammac
*/
template<typename Scalar> struct scalar_igammac_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_igammac_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
using numext::igammac; return igammac(a, x);
}
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const
{
return internal::pigammac(a, x);
}
};
template<typename Scalar>
struct functor_traits<scalar_igammac_op<Scalar> > {
enum {
// Guesstimate
Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasIGammac
};
};
//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
/** \internal
* \brief Template functor to multiply a scalar by a fixed other one
*
* \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/
*/
/* NOTE why doing the pset1() in packetOp *is* an optimization ?
* indeed it seems better to declare m_other as a Packet and do the pset1() once
* in the constructor. However, in practice:
* - GCC does not like m_other as a Packet and generate a load every time it needs it
* - on the other hand GCC is able to moves the pset1() outside the loop :)
* - simpler code ;)
* (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y)
*/
template<typename Scalar>
struct scalar_multiple_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_multiple_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
// They are analogues to std::binder1st/binder2nd but with the following differences:
// - they are compatible with packetOp
// - they are portable across C++ versions (the std::binder* are deprecated in C++11)
template<typename BinaryOp> struct bind1st_op : BinaryOp {
template<typename Scalar1, typename Scalar2>
struct scalar_multiple2_op {
typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
};
template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> >
{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
/** \internal
* \brief Template functor to divide a scalar by a fixed other one
*
* This functor is used to implement the quotient of a matrix by
* a scalar where the scalar type is not necessarily a floating point type.
*
* \sa class CwiseUnaryOp, MatrixBase::operator/
*/
template<typename Scalar>
struct scalar_quotient1_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const scalar_quotient1_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : m_other(other) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pdiv(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_op<Scalar> >
{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; };
bind1st_op(const first_argument_type &val) : m_value(val) {}
template<typename Scalar1, typename Scalar2>
struct scalar_quotient2_op {
typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient2_op(const scalar_quotient2_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient2_op(const Scalar2& other) : m_other(other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a / m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other;
};
template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_quotient2_op<Scalar1,Scalar2> >
{ enum { Cost = 2 * NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
// In Eigen, any binary op (Product, CwiseBinaryOp) require the Lhs and Rhs to have the same scalar type, except for multiplication
// where the mixing of different types is handled by scalar_product_traits
// In particular, real * complex<real> is allowed.
// FIXME move this to functor_traits adding a functor_default
template<typename Functor> struct functor_is_product_like { enum { ret = 0 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_quotient_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; };
/** \internal
* \brief Template functor to add a scalar to a fixed other one
* \sa class CwiseUnaryOp, Array::operator+
*/
/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */
template<typename Scalar>
struct scalar_add_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_add_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a + m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::padd(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_add_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to subtract a fixed scalar to another one
* \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_rsub_op
*/
template<typename Scalar>
struct scalar_sub_op {
EIGEN_DEVICE_FUNC inline scalar_sub_op(const scalar_sub_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_sub_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a - m_other; }
template <typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::psub(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_sub_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to subtract a scalar to fixed another one
* \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_sub_op
*/
template<typename Scalar>
struct scalar_rsub_op {
EIGEN_DEVICE_FUNC inline scalar_rsub_op(const scalar_rsub_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC inline scalar_rsub_op(const Scalar& other) : m_other(other) { }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other - a; }
template <typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::psub(pset1<Packet>(m_other), a); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_rsub_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; };
/** \internal
* \brief Template functor to raise a scalar to a power
* \sa class CwiseUnaryOp, Cwise::pow
*/
template<typename Scalar>
struct scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_DEVICE_FUNC inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
EIGEN_DEVICE_FUNC inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); }
const Scalar m_exponent;
};
template<typename Scalar>
struct functor_traits<scalar_pow_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to compute the quotient between a scalar and array entries.
* \sa class CwiseUnaryOp, Cwise::inverse()
*/
template<typename Scalar>
struct scalar_inverse_mult_op {
EIGEN_DEVICE_FUNC scalar_inverse_mult_op(const Scalar& other) : m_other(other) {}
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other / a; }
template<typename Packet>
EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
{ return internal::pdiv(pset1<Packet>(m_other),a); }
Scalar m_other;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
{ return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
first_argument_type m_value;
};
template<typename Scalar>
struct functor_traits<scalar_inverse_mult_op<Scalar> >
{ enum { PacketAccess = packet_traits<Scalar>::HasDiv, Cost = NumTraits<Scalar>::template Div<PacketAccess>::Cost }; };
template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
template<typename BinaryOp> struct bind2nd_op : BinaryOp {
typedef typename BinaryOp::first_argument_type first_argument_type;
typedef typename BinaryOp::second_argument_type second_argument_type;
typedef typename BinaryOp::result_type result_type;
bind2nd_op(const second_argument_type &val) : m_value(val) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
second_argument_type m_value;
};
template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
} // end namespace internal

6
Eigen/src/Core/functors/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_Functor_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_Functor_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/functors COMPONENT Devel
)

78
Eigen/src/Core/functors/NullaryFunctors.h
View File

@ -18,20 +18,20 @@ template<typename Scalar>
struct scalar_constant_op {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; }
template<typename Index, typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp(Index, Index = 0) const { return internal::pset1<PacketType>(m_other); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() () const { return m_other; }
template<typename PacketType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp() const { return internal::pset1<PacketType>(m_other); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_constant_op<Scalar> >
{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
{ enum { Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
template<typename Scalar> struct scalar_identity_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op)
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); }
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType row, IndexType col) const { return row==col ? Scalar(1) : Scalar(0); }
};
template<typename Scalar>
struct functor_traits<scalar_identity_op<Scalar> >
@ -55,15 +55,15 @@ struct linspaced_op_impl<Scalar,Packet,/*RandomAccess*/false,/*IsInteger*/false>
m_packetStep(pset1<Packet>(unpacket_traits<Packet>::size*m_step)),
m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(m_step),plset<Packet>(-unpacket_traits<Packet>::size)))) {}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const
{
m_base = padd(m_base, pset1<Packet>(m_step));
return m_low+Scalar(i)*m_step;
}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); }
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType) const { return m_base = padd(m_base,m_packetStep); }
const Scalar m_low;
const Scalar m_step;
@ -81,11 +81,11 @@ struct linspaced_op_impl<Scalar,Packet,/*RandomAccess*/true,/*IsInteger*/false>
m_low(low), m_step(num_steps==1 ? Scalar() : (high-low)/Scalar(num_steps-1)),
m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Packet>(0)) {}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; }
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return m_low+i*m_step; }
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index i) const
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const
{ return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1<Packet>(Scalar(i)),m_interPacket))); }
const Scalar m_low;
@ -99,24 +99,24 @@ template <typename Scalar, typename Packet>
struct linspaced_op_impl<Scalar,Packet,/*RandomAccess*/true,/*IsInteger*/true>
{
linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
m_low(low), m_length(high-low), m_divisor(num_steps==1?1:num_steps-1), m_interPacket(plset<Packet>(0))
m_low(low), m_length(high-low), m_divisor(convert_index<Scalar>(num_steps==1?1:num_steps-1)), m_interPacket(plset<Packet>(0))
{}
template<typename Index>
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Scalar operator() (Index i) const {
const Scalar operator() (IndexType i) const {
return m_low + (m_length*Scalar(i))/m_divisor;
}
template<typename Index>
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Packet packetOp(Index i) const {
const Packet packetOp(IndexType i) const {
return internal::padd(pset1<Packet>(m_low), pdiv(pmul(pset1<Packet>(m_length), padd(pset1<Packet>(Scalar(i)),m_interPacket)),
pset1<Packet>(m_divisor))); }
const Scalar m_low;
const Scalar m_length;
const Index m_divisor;
const Scalar m_divisor;
const Packet m_interPacket;
};
@ -142,29 +142,11 @@ template <typename Scalar, typename PacketType, bool RandomAccess> struct linspa
: impl((num_steps==1 ? high : low),high,num_steps)
{}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); }
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return impl(i); }
// We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
// there row==0 and col is used for the actual iteration.
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const
{
eigen_assert(col==0 || row==0);
return impl(col + row);
}
template<typename Index, typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); }
// We need this function when assigning e.g. a RowVectorXd to a MatrixXd since
// there row==0 and col is used for the actual iteration.
template<typename Index, typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const
{
eigen_assert(col==0 || row==0);
return impl.packetOp(col + row);
}
template<typename Packet,typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const { return impl.packetOp(i); }
// This proxy object handles the actual required temporaries, the different
// implementations (random vs. sequential access) as well as the
@ -174,11 +156,11 @@ template <typename Scalar, typename PacketType, bool RandomAccess> struct linspa
const linspaced_op_impl<Scalar,PacketType,(NumTraits<Scalar>::IsInteger?true:RandomAccess),NumTraits<Scalar>::IsInteger> impl;
};
// all functors allow linear access, except scalar_identity_op. So we fix here a quick meta
// to indicate whether a functor allows linear access, just always answering 'yes' except for
// scalar_identity_op.
template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; };
template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; };
// Linear access is automatically determined from the operator() prototypes available for the given functor.
// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently
// and linear access is not possible. In all other cases, linear access is enabled.
// Users should not have to deal with this struture.
template<typename Functor> struct functor_has_linear_access { enum { ret = !has_binary_operator<Functor>::value }; };
} // end namespace internal

25
Eigen/src/Core/functors/TernaryFunctors.h Normal file
View File

@ -0,0 +1,25 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_TERNARY_FUNCTORS_H
#define EIGEN_TERNARY_FUNCTORS_H
namespace Eigen {
namespace internal {
//---------- associative ternary functors ----------
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_TERNARY_FUNCTORS_H

211
Eigen/src/Core/functors/UnaryFunctors.h
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -248,7 +248,7 @@ struct functor_traits<scalar_exp_op<Scalar> > {
// double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other
: (14 * NumTraits<Scalar>::AddCost +
6 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost))
scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
#else
Cost =
(sizeof(Scalar) == 4
@ -257,7 +257,7 @@ struct functor_traits<scalar_exp_op<Scalar> > {
// double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other
: (23 * NumTraits<Scalar>::AddCost +
12 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost))
scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
#endif
};
};
@ -266,7 +266,7 @@ struct functor_traits<scalar_exp_op<Scalar> > {
*
* \brief Template functor to compute the logarithm of a scalar
*
* \sa class CwiseUnaryOp, Cwise::log()
* \sa class CwiseUnaryOp, ArrayBase::log()
*/
template<typename Scalar> struct scalar_log_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op)
@ -293,6 +293,26 @@ struct functor_traits<scalar_log_op<Scalar> > {
};
};
/** \internal
*
* \brief Template functor to compute the logarithm of 1 plus a scalar value
*
* \sa class CwiseUnaryOp, ArrayBase::log1p()
*/
template<typename Scalar> struct scalar_log1p_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_log1p_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log1p(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog1p(a); }
};
template <typename Scalar>
struct functor_traits<scalar_log1p_op<Scalar> > {
enum {
PacketAccess = packet_traits<Scalar>::HasLog1p,
Cost = functor_traits<scalar_log_op<Scalar> >::Cost // TODO measure cost of log1p
};
};
/** \internal
*
* \brief Template functor to compute the base-10 logarithm of a scalar
@ -452,142 +472,6 @@ struct functor_traits<scalar_asin_op<Scalar> >
};
/** \internal
* \brief Template functor to compute the natural log of the absolute
* value of Gamma of a scalar
* \sa class CwiseUnaryOp, Cwise::lgamma()
*/
template<typename Scalar> struct scalar_lgamma_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_lgamma_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const {
using numext::lgamma; return lgamma(a);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plgamma(a); }
};
template<typename Scalar>
struct functor_traits<scalar_lgamma_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasLGamma
};
};
/** \internal
* \brief Template functor to compute psi, the derivative of lgamma of a scalar.
* \sa class CwiseUnaryOp, Cwise::digamma()
*/
template<typename Scalar> struct scalar_digamma_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_digamma_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const {
using numext::digamma; return digamma(a);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pdigamma(a); }
};
template<typename Scalar>
struct functor_traits<scalar_digamma_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasDiGamma
};
};
/** \internal
* \brief Template functor to compute the Riemann Zeta function of two arguments.
* \sa class CwiseUnaryOp, Cwise::zeta()
*/
template<typename Scalar> struct scalar_zeta_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_zeta_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& x, const Scalar& q) const {
using numext::zeta; return zeta(x, q);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x, const Packet& q) const { return internal::pzeta(x, q); }
};
template<typename Scalar>
struct functor_traits<scalar_zeta_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasZeta
};
};
/** \internal
* \brief Template functor to compute the polygamma function.
* \sa class CwiseUnaryOp, Cwise::polygamma()
*/
template<typename Scalar> struct scalar_polygamma_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_polygamma_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& n, const Scalar& x) const {
using numext::polygamma; return polygamma(n, x);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& n, const Packet& x) const { return internal::ppolygamma(n, x); }
};
template<typename Scalar>
struct functor_traits<scalar_polygamma_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasPolygamma
};
};
/** \internal
* \brief Template functor to compute the Gauss error function of a
* scalar
* \sa class CwiseUnaryOp, Cwise::erf()
*/
template<typename Scalar> struct scalar_erf_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_erf_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const {
using numext::erf; return erf(a);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::perf(a); }
};
template<typename Scalar>
struct functor_traits<scalar_erf_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasErf
};
};
/** \internal
* \brief Template functor to compute the Complementary Error Function
* of a scalar
* \sa class CwiseUnaryOp, Cwise::erfc()
*/
template<typename Scalar> struct scalar_erfc_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_erfc_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const {
using numext::erfc; return erfc(a);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::perfc(a); }
};
template<typename Scalar>
struct functor_traits<scalar_erfc_op<Scalar> >
{
enum {
// Guesstimate
Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
PacketAccess = packet_traits<Scalar>::HasErfc
};
};
/** \internal
* \brief Template functor to compute the atan of a scalar
* \sa class CwiseUnaryOp, ArrayBase::atan()
@ -607,39 +491,40 @@ struct functor_traits<scalar_atan_op<Scalar> >
};
};
/** \internal
* \brief Template functor to compute the tanh of a scalar
* \sa class CwiseUnaryOp, ArrayBase::tanh()
*/
template<typename Scalar> struct scalar_tanh_op {
template <typename Scalar>
struct scalar_tanh_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tanh(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::tanh(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptanh(a); }
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return ptanh(x); }
};
template<typename Scalar>
struct functor_traits<scalar_tanh_op<Scalar> >
{
template <typename Scalar>
struct functor_traits<scalar_tanh_op<Scalar> > {
enum {
PacketAccess = packet_traits<Scalar>::HasTanh,
Cost =
(PacketAccess
// The following numbers are based on the AVX implementation,
Cost = ( (EIGEN_FAST_MATH && is_same<Scalar,float>::value)
// The following numbers are based on the AVX implementation,
#ifdef EIGEN_VECTORIZE_FMA
// Haswell can issue 2 add/mul/madd per cycle.
// 9 pmadd, 2 pmul, 1 div, 2 other
? (2 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost)
// Haswell can issue 2 add/mul/madd per cycle.
// 9 pmadd, 2 pmul, 1 div, 2 other
? (2 * NumTraits<Scalar>::AddCost +
6 * NumTraits<Scalar>::MulCost +
scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
#else
? (11 * NumTraits<Scalar>::AddCost +
11 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost)
? (11 * NumTraits<Scalar>::AddCost +
11 * NumTraits<Scalar>::MulCost +
scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
#endif
// This number assumes a naive implementation of tanh
: (6 * NumTraits<Scalar>::AddCost + 3 * NumTraits<Scalar>::MulCost +
2 * NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost +
functor_traits<scalar_exp_op<Scalar> >::Cost))
// This number assumes a naive implementation of tanh
: (6 * NumTraits<Scalar>::AddCost +
3 * NumTraits<Scalar>::MulCost +
2 * scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value +
functor_traits<scalar_exp_op<Scalar> >::Cost))
};
};
@ -880,9 +765,9 @@ struct scalar_sign_op<Scalar,true> {
{
typedef typename NumTraits<Scalar>::Real real_type;
real_type aa = numext::abs(a);
if (aa==0)
if (aa==real_type(0))
return Scalar(0);
aa = 1./aa;
aa = real_type(1)/aa;
return Scalar(real(a)*aa, imag(a)*aa );
}
//TODO

6
Eigen/src/Core/products/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_Product_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_Product_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/products COMPONENT Devel
)

130
Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@ -299,16 +299,6 @@ void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads
if (!useSpecificBlockingSizes(k, m, n)) {
evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
}
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
enum {
kr = 8,
mr = Traits::mr,
nr = Traits::nr
};
if (k > kr) k -= k % kr;
if (m > mr) m -= m % mr;
if (n > nr) n -= n % nr;
}
template<typename LhsScalar, typename RhsScalar, typename Index>
@ -363,7 +353,7 @@ class gebp_traits
public:
typedef _LhsScalar LhsScalar;
typedef _RhsScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
@ -444,15 +434,16 @@ public:
template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, AccPacketType& tmp) const
{
conj_helper<LhsPacketType,RhsPacketType,ConjLhs,ConjRhs> cj;
// It would be a lot cleaner to call pmadd all the time. Unfortunately if we
// let gcc allocate the register in which to store the result of the pmul
// (in the case where there is no FMA) gcc fails to figure out how to avoid
// spilling register.
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
EIGEN_UNUSED_VARIABLE(tmp);
c = pmadd(a,b,c);
c = cj.pmadd(a,b,c);
#else
tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);
tmp = b; tmp = cj.pmul(a,tmp); c = padd(c,tmp);
#endif
}
@ -467,9 +458,6 @@ public:
r = pmadd(c,alpha,r);
}
protected:
// conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
// conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj;
};
template<typename RealScalar, bool _ConjLhs>
@ -478,7 +466,7 @@ class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false>
public:
typedef std::complex<RealScalar> LhsScalar;
typedef RealScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
@ -860,80 +848,6 @@ protected:
conj_helper<ResPacket,ResPacket,false,ConjRhs> cj;
};
// helper for the rotating kernel below
template <typename GebpKernel, bool UseRotatingKernel = GebpKernel::UseRotatingKernel>
struct PossiblyRotatingKernelHelper
{
// default implementation, not rotating
typedef typename GebpKernel::Traits Traits;
typedef typename Traits::RhsScalar RhsScalar;
typedef typename Traits::RhsPacket RhsPacket;
typedef typename Traits::AccPacket AccPacket;
const Traits& traits;
PossiblyRotatingKernelHelper(const Traits& t) : traits(t) {}
template <size_t K, size_t Index>
void loadOrRotateRhs(RhsPacket& to, const RhsScalar* from) const
{
traits.loadRhs(from + (Index+4*K)*Traits::RhsProgress, to);
}
void unrotateResult(AccPacket&,
AccPacket&,
AccPacket&,
AccPacket&)
{
}
};
// rotating implementation
template <typename GebpKernel>
struct PossiblyRotatingKernelHelper<GebpKernel, true>
{
typedef typename GebpKernel::Traits Traits;
typedef typename Traits::RhsScalar RhsScalar;
typedef typename Traits::RhsPacket RhsPacket;
typedef typename Traits::AccPacket AccPacket;
const Traits& traits;
PossiblyRotatingKernelHelper(const Traits& t) : traits(t) {}
template <size_t K, size_t Index>
void loadOrRotateRhs(RhsPacket& to, const RhsScalar* from) const
{
if (Index == 0) {
to = pload<RhsPacket>(from + 4*K*Traits::RhsProgress);
} else {
EIGEN_ASM_COMMENT("Do not reorder code, we're very tight on registers");
to = protate<1>(to);
}
}
void unrotateResult(AccPacket& res0,
AccPacket& res1,
AccPacket& res2,
AccPacket& res3)
{
PacketBlock<AccPacket> resblock;
resblock.packet[0] = res0;
resblock.packet[1] = res1;
resblock.packet[2] = res2;
resblock.packet[3] = res3;
ptranspose(resblock);
resblock.packet[3] = protate<1>(resblock.packet[3]);
resblock.packet[2] = protate<2>(resblock.packet[2]);
resblock.packet[1] = protate<3>(resblock.packet[1]);
ptranspose(resblock);
res0 = resblock.packet[0];
res1 = resblock.packet[1];
res2 = resblock.packet[2];
res3 = resblock.packet[3];
}
};
/* optimized GEneral packed Block * packed Panel product kernel
*
* Mixing type logic: C += A * B
@ -967,16 +881,6 @@ struct gebp_kernel
ResPacketSize = Traits::ResPacketSize
};
static const bool UseRotatingKernel =
EIGEN_ARCH_ARM &&
internal::is_same<LhsScalar, float>::value &&
internal::is_same<RhsScalar, float>::value &&
internal::is_same<ResScalar, float>::value &&
Traits::LhsPacketSize == 4 &&
Traits::RhsPacketSize == 4 &&
Traits::ResPacketSize == 4;
EIGEN_DONT_INLINE
void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
Index rows, Index depth, Index cols, ResScalar alpha,
@ -1010,8 +914,6 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
// Usually, make sense only with FMA
if(mr>=3*Traits::LhsProgress)
{
PossiblyRotatingKernelHelper<gebp_kernel> possiblyRotatingKernelHelper(traits);
// Here, the general idea is to loop on each largest micro horizontal panel of the lhs (3*Traits::LhsProgress x depth)
// and on each largest micro vertical panel of the rhs (depth * nr).
// Blocking sizes, i.e., 'depth' has been computed so that the micro horizontal panel of the lhs fit in L1.
@ -1074,19 +976,19 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \
traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \
possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 0>(B_0, blB); \
traits.loadRhs(blB + (0+4*K)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C0, T0); \
traits.madd(A1, B_0, C4, T0); \
traits.madd(A2, B_0, C8, B_0); \
possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 1>(B_0, blB); \
traits.loadRhs(blB + (1+4*K)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C1, T0); \
traits.madd(A1, B_0, C5, T0); \
traits.madd(A2, B_0, C9, B_0); \
possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 2>(B_0, blB); \
traits.loadRhs(blB + (2+4*K)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C2, T0); \
traits.madd(A1, B_0, C6, T0); \
traits.madd(A2, B_0, C10, B_0); \
possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 3>(B_0, blB); \
traits.loadRhs(blB + (3+4*K)*Traits::RhsProgress, B_0); \
traits.madd(A0, B_0, C3 , T0); \
traits.madd(A1, B_0, C7, T0); \
traits.madd(A2, B_0, C11, B_0); \
@ -1120,10 +1022,6 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
#undef EIGEN_GEBP_ONESTEP
possiblyRotatingKernelHelper.unrotateResult(C0, C1, C2, C3);
possiblyRotatingKernelHelper.unrotateResult(C4, C5, C6, C7);
possiblyRotatingKernelHelper.unrotateResult(C8, C9, C10, C11);
ResPacket R0, R1, R2;
ResPacket alphav = pset1<ResPacket>(alpha);
@ -1625,9 +1523,13 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
prefetch(&blA[0]);
const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
// NOTE The following piece of code doesn't work for 512 bit registers,
// so we don't call it for registers that contain more than 8 values.
if( ((SwappedTraits::LhsProgress % 4)==0) && (SwappedTraits::LhsProgress <= 8))
// The following piece of code wont work for 512 bit registers
// Moreover, if LhsProgress==8 it assumes that there is a half packet of the same size
// as nr (which is currently 4) for the return type.
typedef typename unpacket_traits<SResPacket>::half SResPacketHalf;
if ((SwappedTraits::LhsProgress % 4) == 0 &&
(SwappedTraits::LhsProgress <= 8) &&
(SwappedTraits::LhsProgress!=8 || unpacket_traits<SResPacketHalf>::size==nr))
{
SAccPacket C0, C1, C2, C3;
straits.initAcc(C0);

8
Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -25,7 +25,7 @@ struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLh
{
typedef gebp_traits<RhsScalar,LhsScalar> Traits;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(
Index rows, Index cols, Index depth,
const LhsScalar* lhs, Index lhsStride,
@ -55,7 +55,7 @@ struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLh
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static void run(Index rows, Index cols, Index depth,
const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsStride,
@ -309,8 +309,8 @@ class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, M
this->m_blockA = m_staticA;
this->m_blockB = m_staticB;
#else
this->m_blockA = reinterpret_cast<LhsScalar*>((std::size_t(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
this->m_blockB = reinterpret_cast<RhsScalar*>((std::size_t(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
this->m_blockA = reinterpret_cast<LhsScalar*>((internal::UIntPtr(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
this->m_blockB = reinterpret_cast<RhsScalar*>((internal::UIntPtr(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
#endif
}

4
Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -40,7 +40,7 @@ template <typename Index, typename LhsScalar, int LhsStorageOrder, bool Conjugat
typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,UpLo,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride,
const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
@ -57,7 +57,7 @@ template <typename Index, typename LhsScalar, int LhsStorageOrder, bool Conjugat
typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version>
struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsStride, ResScalar* _res, Index resStride,
const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)

14
Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -58,7 +58,7 @@ namespace internal {
template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable
@ -140,7 +140,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
// find how many columns do we have to skip to be aligned with the result (if possible)
Index skipColumns = 0;
// if the data cannot be aligned (TODO add some compile time tests when possible, e.g. for floats)
if( (lhsAlignmentOffset < 0) || (lhsAlignmentOffset == size) || (size_t(res)%sizeof(ResScalar)) )
if( (lhsAlignmentOffset < 0) || (lhsAlignmentOffset == size) || (UIntPtr(res)%sizeof(ResScalar)) )
{
alignedSize = 0;
alignedStart = 0;
@ -183,8 +183,8 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
alignmentPattern = AllAligned;
}
const Index offset1 = (FirstAligned && alignmentStep==1?3:1);
const Index offset3 = (FirstAligned && alignmentStep==1?1:3);
const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;
Index columnBound = ((cols-skipColumns)/columnsAtOnce)*columnsAtOnce + skipColumns;
for (Index i=skipColumns; i<columnBound; i+=columnsAtOnce)
@ -334,7 +334,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable
@ -457,8 +457,8 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,R
alignmentPattern = AllAligned;
}
const Index offset1 = (FirstAligned && alignmentStep==1?3:1);
const Index offset3 = (FirstAligned && alignmentStep==1?1:3);
const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;
Index rowBound = ((rows-skipRows)/rowsAtOnce)*rowsAtOnce + skipRows;
for (Index i=skipRows; i<rowBound; i+=rowsAtOnce)

4
Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
View File

@ -122,7 +122,7 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLh
Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \
a_tmp = lhs.conjugate(); \
a = a_tmp.data(); \
lda = a_tmp.outerStride(); \
lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
} else a = _lhs; \
if (LhsStorageOrder==RowMajor) uplo='U'; \
\
@ -256,7 +256,7 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateL
b_tmp = lhs.transpose(); \
} \
b = b_tmp.data(); \
ldb = b_tmp.outerStride(); \
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} \
\
BLASPREFIX##hemm_(&side, &uplo, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \

2
Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -179,7 +179,7 @@ struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true>
{
typedef typename Dest::Scalar ResScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols());

6
Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -20,7 +20,7 @@ struct triangular_matrix_vector_product;
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = ((Mode&Lower)==Lower),
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
@ -91,7 +91,7 @@ EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,Con
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
{
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
IsLower = ((Mode&Lower)==Lower),
HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
@ -216,7 +216,7 @@ template<int Mode> struct trmv_selector<Mode,ColMajor>
typedef internal::blas_traits<Rhs> RhsBlasTraits;
typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);

53
Eigen/src/Core/util/BlasUtil.h
View File

@ -44,16 +44,29 @@ template<bool Conjugate> struct conj_if;
template<> struct conj_if<true> {
template<typename T>
inline T operator()(const T& x) { return numext::conj(x); }
inline T operator()(const T& x) const { return numext::conj(x); }
template<typename T>
inline T pconj(const T& x) { return internal::pconj(x); }
inline T pconj(const T& x) const { return internal::pconj(x); }
};
template<> struct conj_if<false> {
template<typename T>
inline const T& operator()(const T& x) { return x; }
inline const T& operator()(const T& x) const { return x; }
template<typename T>
inline const T& pconj(const T& x) { return x; }
inline const T& pconj(const T& x) const { return x; }
};
// Generic implementation for custom complex types.
template<typename LhsScalar, typename RhsScalar, bool ConjLhs, bool ConjRhs>
struct conj_helper
{
typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar>::ReturnType Scalar;
EIGEN_STRONG_INLINE Scalar pmadd(const LhsScalar& x, const RhsScalar& y, const Scalar& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Scalar pmul(const LhsScalar& x, const RhsScalar& y) const
{ return conj_if<ConjLhs>()(x) * conj_if<ConjRhs>()(y); }
};
template<typename Scalar> struct conj_helper<Scalar,Scalar,false,false>
@ -111,7 +124,7 @@ template<typename RealScalar,bool Conj> struct conj_helper<RealScalar, std::comp
};
template<typename From,typename To> struct get_factor {
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return x; }
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return To(x); }
};
template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
@ -135,7 +148,7 @@ class BlasVectorMapper {
template <typename Packet>
EIGEN_DEVICE_FUNC bool aligned(Index i) const {
return (size_t(m_data+i)%sizeof(Packet))==0;
return (UIntPtr(m_data+i)%sizeof(Packet))==0;
}
protected:
@ -227,7 +240,7 @@ class blas_data_mapper {
EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC Index firstAligned(Index size) const {
if (size_t(m_data)%sizeof(Scalar)) {
if (UIntPtr(m_data)%sizeof(Scalar)) {
return -1;
}
return internal::first_default_aligned(m_data, size);
@ -293,17 +306,33 @@ struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> >
};
// pop scalar multiple
template<typename Scalar, typename NestedXpr>
struct blas_traits<CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> >
template<typename Scalar, typename NestedXpr, typename Plain>
struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> >
: blas_traits<NestedXpr>
{
typedef blas_traits<NestedXpr> Base;
typedef CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> XprType;
typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> XprType;
typedef typename Base::ExtractType ExtractType;
static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
static inline ExtractType extract(const XprType& x) { return Base::extract(x.rhs()); }
static inline Scalar extractScalarFactor(const XprType& x)
{ return x.functor().m_other * Base::extractScalarFactor(x.nestedExpression()); }
{ return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs()); }
};
template<typename Scalar, typename NestedXpr, typename Plain>
struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > >
: blas_traits<NestedXpr>
{
typedef blas_traits<NestedXpr> Base;
typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > XprType;
typedef typename Base::ExtractType ExtractType;
static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
static inline Scalar extractScalarFactor(const XprType& x)
{ return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other; }
};
template<typename Scalar, typename Plain1, typename Plain2>
struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1>,
const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain2> > >
: blas_traits<CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1> >
{};
// pop opposite
template<typename Scalar, typename NestedXpr>

6
Eigen/src/Core/util/CMakeLists.txt
View File

@ -1,6 +0,0 @@
FILE(GLOB Eigen_Core_util_SRCS "*.h")
INSTALL(FILES
${Eigen_Core_util_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/util COMPONENT Devel
)

23
Eigen/src/Core/util/Constants.h
View File

@ -199,7 +199,7 @@ const unsigned int HereditaryBits = RowMajorBit
/** \ingroup enums
* Enum containing possible values for the \c Mode or \c UpLo parameter of
* MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. */
enum {
enum UpLoType {
/** View matrix as a lower triangular matrix. */
Lower=0x1,
/** View matrix as an upper triangular matrix. */
@ -224,7 +224,7 @@ enum {
/** \ingroup enums
* Enum for indicating whether a buffer is aligned or not. */
enum {
enum AlignmentType {
Unaligned=0, /**< Data pointer has no specific alignment. */
Aligned8=8, /**< Data pointer is aligned on a 8 bytes boundary. */
Aligned16=16, /**< Data pointer is aligned on a 16 bytes boundary. */
@ -273,7 +273,7 @@ enum DirectionType {
/** \internal \ingroup enums
* Enum to specify how to traverse the entries of a matrix. */
enum {
enum TraversalType {
/** \internal Default traversal, no vectorization, no index-based access */
DefaultTraversal,
/** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */
@ -295,7 +295,7 @@ enum {
/** \internal \ingroup enums
* Enum to specify whether to unroll loops when traversing over the entries of a matrix. */
enum {
enum UnrollingType {
/** \internal Do not unroll loops. */
NoUnrolling,
/** \internal Unroll only the inner loop, but not the outer loop. */
@ -307,7 +307,7 @@ enum {
/** \internal \ingroup enums
* Enum to specify whether to use the default (built-in) implementation or the specialization. */
enum {
enum SpecializedType {
Specialized,
BuiltIn
};
@ -315,7 +315,7 @@ enum {
/** \ingroup enums
* Enum containing possible values for the \p _Options template parameter of
* Matrix, Array and BandMatrix. */
enum {
enum StorageOptions {
/** Storage order is column major (see \ref TopicStorageOrders). */
ColMajor = 0,
/** Storage order is row major (see \ref TopicStorageOrders). */
@ -328,7 +328,7 @@ enum {
/** \ingroup enums
* Enum for specifying whether to apply or solve on the left or right. */
enum {
enum SideType {
/** Apply transformation on the left. */
OnTheLeft = 1,
/** Apply transformation on the right. */
@ -353,7 +353,7 @@ enum Default_t { Default };
/** \internal \ingroup enums
* Used in AmbiVector. */
enum {
enum AmbiVectorMode {
IsDense = 0,
IsSparse
};
@ -479,8 +479,9 @@ namespace Architecture
}
/** \internal \ingroup enums
* Enum used as template parameter in Product and product evalautors. */
enum { DefaultProduct=0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct };
* Enum used as template parameter in Product and product evaluators. */
enum ProductImplType
{ DefaultProduct=0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct };
/** \internal \ingroup enums
* Enum used in experimental parallel implementation. */
@ -492,7 +493,7 @@ struct Dense {};
/** The type used to identify a general sparse storage. */
struct Sparse {};
/** The type used to identify a general solver (foctored) storage. */
/** The type used to identify a general solver (factored) storage. */
struct SolverStorage {};
/** The type used to identify a permutation storage. */

21
Eigen/src/Core/util/DisableStupidWarnings.h
View File

@ -14,12 +14,13 @@
// 4512 - assignment operator could not be generated
// 4522 - 'class' : multiple assignment operators specified
// 4700 - uninitialized local variable 'xyz' used
// 4714 - function marked as __forceinline not inlined
// 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow
// 4800 - 'type' : forcing value to bool 'true' or 'false' (performance warning)
#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
#pragma warning( push )
#endif
#pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4717 4800)
#pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
#elif defined __INTEL_COMPILER
// 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
@ -41,6 +42,14 @@
#pragma clang diagnostic push
#endif
#pragma clang diagnostic ignored "-Wconstant-logical-operand"
#elif defined __GNUC__ && __GNUC__>=6
#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
#pragma GCC diagnostic push
#endif
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
#if defined __NVCC__
@ -48,11 +57,19 @@
#pragma diag_suppress code_is_unreachable
// Disable the "dynamic initialization in unreachable code" message
#pragma diag_suppress initialization_not_reachable
// Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are 4 of them)
// Disable the "invalid error number" message that we get with older versions of nvcc
#pragma diag_suppress 1222
// Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are many of them and they seem to change with every version of the compiler)
#pragma diag_suppress 2527
#pragma diag_suppress 2529
#pragma diag_suppress 2651
#pragma diag_suppress 2653
#pragma diag_suppress 2668
#pragma diag_suppress 2669
#pragma diag_suppress 2670
#pragma diag_suppress 2671
#pragma diag_suppress 2735
#pragma diag_suppress 2737
#endif
#endif // not EIGEN_WARNINGS_DISABLED

32
Eigen/src/Core/util/ForwardDeclarations.h
View File

@ -91,6 +91,7 @@ template<typename NullaryOp, typename MatrixType> class CwiseNullaryOp;
template<typename UnaryOp, typename MatrixType> class CwiseUnaryOp;
template<typename ViewOp, typename MatrixType> class CwiseUnaryView;
template<typename BinaryOp, typename Lhs, typename Rhs> class CwiseBinaryOp;
template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3> class CwiseTernaryOp;
template<typename Decomposition, typename Rhstype> class Solve;
template<typename XprType> class Inverse;
@ -174,9 +175,11 @@ namespace internal {
// with optional conjugation of the arguments.
template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper;
template<typename Scalar> struct scalar_sum_op;
template<typename Scalar> struct scalar_difference_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_sum_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_difference_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_conj_product_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_min_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_max_op;
template<typename Scalar> struct scalar_opposite_op;
template<typename Scalar> struct scalar_conjugate_op;
template<typename Scalar> struct scalar_real_op;
@ -192,27 +195,28 @@ template<typename Scalar> struct scalar_sin_op;
template<typename Scalar> struct scalar_acos_op;
template<typename Scalar> struct scalar_asin_op;
template<typename Scalar> struct scalar_tan_op;
template<typename Scalar> struct scalar_pow_op;
template<typename Scalar> struct scalar_inverse_op;
template<typename Scalar> struct scalar_square_op;
template<typename Scalar> struct scalar_cube_op;
template<typename Scalar, typename NewType> struct scalar_cast_op;
template<typename Scalar> struct scalar_multiple_op;
template<typename Scalar> struct scalar_quotient1_op;
template<typename Scalar> struct scalar_min_op;
template<typename Scalar> struct scalar_max_op;
template<typename Scalar> struct scalar_random_op;
template<typename Scalar> struct scalar_add_op;
template<typename Scalar> struct scalar_constant_op;
template<typename Scalar> struct scalar_identity_op;
template<typename Scalar,bool iscpx> struct scalar_sign_op;
template<typename Scalar,typename ScalarExponent> struct scalar_pow_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_hypot_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
// SpecialFunctions module
template<typename Scalar> struct scalar_lgamma_op;
template<typename Scalar> struct scalar_digamma_op;
template<typename Scalar> struct scalar_erf_op;
template<typename Scalar> struct scalar_erfc_op;
template<typename Scalar> struct scalar_igamma_op;
template<typename Scalar> struct scalar_igammac_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_multiple2_op;
template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
template<typename LhsScalar,typename RhsScalar> struct scalar_quotient2_op;
template<typename Scalar> struct scalar_zeta_op;
template<typename Scalar> struct scalar_betainc_op;
} // end namespace internal

4
Eigen/src/Core/util/MKL_support.h Normal file → Executable file
View File

@ -49,7 +49,7 @@
#define EIGEN_USE_LAPACKE
#endif
#if defined(EIGEN_USE_LAPACKE) || defined(EIGEN_USE_MKL_VML)
#if defined(EIGEN_USE_MKL_VML)
#define EIGEN_USE_MKL
#endif
@ -72,7 +72,7 @@
#endif
#if defined EIGEN_USE_MKL
#include <mkl_lapacke.h>
#define EIGEN_MKL_VML_THRESHOLD 128
/* MKL_DOMAIN_BLAS, etc are defined only in 10.3 update 7 */

170
Eigen/src/Core/util/Macros.h
View File

@ -13,7 +13,7 @@
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 2
#define EIGEN_MINOR_VERSION 92
#define EIGEN_MINOR_VERSION 94
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@ -28,9 +28,9 @@
#define EIGEN_COMP_GNUC 0
#endif
/// \internal EIGEN_COMP_CLANG set to 1 if the compiler is clang (alias for __clang__)
/// \internal EIGEN_COMP_CLANG set to major+minor version (e.g., 307 for clang 3.7) if the compiler is clang
#if defined(__clang__)
#define EIGEN_COMP_CLANG 1
#define EIGEN_COMP_CLANG (__clang_major__*100+__clang_minor__)
#else
#define EIGEN_COMP_CLANG 0
#endif
@ -71,6 +71,15 @@
#define EIGEN_COMP_MSVC 0
#endif
// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC:
// name ver MSC_VER
// 2008 9 1500
// 2010 10 1600
// 2012 11 1700
// 2013 12 1800
// 2015 14 1900
// "15" 15 1900
/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC
#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC)
#define EIGEN_COMP_MSVC_STRICT _MSC_VER
@ -340,50 +349,82 @@
# define __has_feature(x) 0
#endif
// Upperbound on the C++ version to use.
// Expected values are 03, 11, 14, 17, etc.
// By default, let's use an arbitrarily large C++ version.
#ifndef EIGEN_MAX_CPP_VER
#define EIGEN_MAX_CPP_VER 99
#endif
// Do we support r-value references?
#if (__has_feature(cxx_rvalue_references) || \
#ifndef EIGEN_HAS_RVALUE_REFERENCES
#if EIGEN_MAX_CPP_VER>=11 && \
(__has_feature(cxx_rvalue_references) || \
(defined(__cplusplus) && __cplusplus >= 201103L) || \
(EIGEN_COMP_MSVC >= 1600))
#define EIGEN_HAVE_RVALUE_REFERENCES
#define EIGEN_HAS_RVALUE_REFERENCES 1
#else
#define EIGEN_HAS_RVALUE_REFERENCES 0
#endif
#endif
// Does the compiler support C99?
#if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901)) \
#ifndef EIGEN_HAS_C99_MATH
#if EIGEN_MAX_CPP_VER>=11 && \
((defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901)) \
|| (defined(__GNUC__) && defined(_GLIBCXX_USE_C99)) \
|| (defined(_LIBCPP_VERSION) && !defined(_MSC_VER))
#define EIGEN_HAS_C99_MATH 1
|| (defined(_LIBCPP_VERSION) && !defined(_MSC_VER)))
#define EIGEN_HAS_C99_MATH 1
#else
#define EIGEN_HAS_C99_MATH 0
#endif
#endif
// Does the compiler support result_of?
#if (__has_feature(cxx_lambdas) || (defined(__cplusplus) && __cplusplus >= 201103L))
#ifndef EIGEN_HAS_STD_RESULT_OF
#if EIGEN_MAX_CPP_VER>=11 && ((__has_feature(cxx_lambdas) || (defined(__cplusplus) && __cplusplus >= 201103L)))
#define EIGEN_HAS_STD_RESULT_OF 1
#else
#define EIGEN_HAS_STD_RESULT_OF 0
#endif
#endif
// Does the compiler support variadic templates?
#if __cplusplus > 199711L || EIGEN_COMP_MSVC >= 1900
// Disable the use of variadic templates when compiling with nvcc on ARM devices:
// this prevents nvcc from crashing when compiling Eigen on Tegra X1
#if !defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64
#ifndef EIGEN_HAS_VARIADIC_TEMPLATES
#if EIGEN_MAX_CPP_VER>=11 && (__cplusplus > 199711L || EIGEN_COMP_MSVC >= 1900) \
&& ( !defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 )
// ^^ Disable the use of variadic templates when compiling with nvcc on ARM devices:
// this prevents nvcc from crashing when compiling Eigen on Tegra X1
#define EIGEN_HAS_VARIADIC_TEMPLATES 1
#else
#define EIGEN_HAS_VARIADIC_TEMPLATES 0
#endif
#endif
// Does the compiler support const expressions?
// Does the compiler fully support const expressions? (as in c++14)
#ifndef EIGEN_HAS_CONSTEXPR
#ifdef __CUDACC__
// Const expressions are supported provided that c++11 is enabled and we're using either clang or nvcc 7.5 or above
#if __cplusplus > 199711L && defined(__CUDACC_VER__) && (EIGEN_COMP_CLANG || __CUDACC_VER__ >= 70500)
#if EIGEN_MAX_CPP_VER>=14 && (__cplusplus > 199711L && defined(__CUDACC_VER__) && (EIGEN_COMP_CLANG || __CUDACC_VER__ >= 70500))
#define EIGEN_HAS_CONSTEXPR 1
#endif
#elif __has_feature(cxx_relaxed_constexpr) || (defined(__cplusplus) && __cplusplus >= 201402L) || \
(EIGEN_GNUC_AT_LEAST(4,8) && (__cplusplus > 199711L))
#elif EIGEN_MAX_CPP_VER>=14 && (__has_feature(cxx_relaxed_constexpr) || (defined(__cplusplus) && __cplusplus >= 201402L) || \
(EIGEN_GNUC_AT_LEAST(4,8) && (__cplusplus > 199711L)))
#define EIGEN_HAS_CONSTEXPR 1
#endif
#ifndef EIGEN_HAS_CONSTEXPR
#define EIGEN_HAS_CONSTEXPR 0
#endif
#endif
// Does the compiler support C++11 math?
// Let's be conservative and enable the default C++11 implementation only if we are sure it exists
#ifndef EIGEN_HAS_CXX11_MATH
#if (__cplusplus > 201103L) || (__cplusplus >= 201103L) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC) \
&& (EIGEN_ARCH_i386_OR_x86_64) && (EIGEN_OS_GNULINUX || EIGEN_OS_WIN_STRICT || EIGEN_OS_MAC)
#if EIGEN_MAX_CPP_VER>=11 && ((__cplusplus > 201103L) || (__cplusplus >= 201103L) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC || EIGEN_COMP_ICC) \
&& (EIGEN_ARCH_i386_OR_x86_64) && (EIGEN_OS_GNULINUX || EIGEN_OS_WIN_STRICT || EIGEN_OS_MAC))
#define EIGEN_HAS_CXX11_MATH 1
#else
#define EIGEN_HAS_CXX11_MATH 0
@ -392,9 +433,10 @@
// Does the compiler support proper C++11 containers?
#ifndef EIGEN_HAS_CXX11_CONTAINERS
#if (__cplusplus > 201103L) \
#if EIGEN_MAX_CPP_VER>=11 && \
((__cplusplus > 201103L) \
|| ((__cplusplus >= 201103L) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_ICC>=1400)) \
|| EIGEN_COMP_MSVC >= 1900
|| EIGEN_COMP_MSVC >= 1900)
#define EIGEN_HAS_CXX11_CONTAINERS 1
#else
#define EIGEN_HAS_CXX11_CONTAINERS 0
@ -403,9 +445,11 @@
// Does the compiler support C++11 noexcept?
#ifndef EIGEN_HAS_CXX11_NOEXCEPT
#if (__cplusplus > 201103L) \
#if EIGEN_MAX_CPP_VER>=11 && \
(__has_feature(cxx_noexcept) \
|| (__cplusplus > 201103L) \
|| ((__cplusplus >= 201103L) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_CLANG || EIGEN_COMP_ICC>=1400)) \
|| EIGEN_COMP_MSVC >= 1900
|| EIGEN_COMP_MSVC >= 1900)
#define EIGEN_HAS_CXX11_NOEXCEPT 1
#else
#define EIGEN_HAS_CXX11_NOEXCEPT 0
@ -427,6 +471,8 @@
#define EIGEN_CAT2(a,b) a ## b
#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b)
#define EIGEN_COMMA ,
// convert a token to a string
#define EIGEN_MAKESTRING2(a) #a
#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
@ -725,6 +771,11 @@ namespace Eigen {
#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
#endif
#ifndef EIGEN_UNALIGNED_VECTORIZE
#define EIGEN_UNALIGNED_VECTORIZE 1
#endif
//----------------------------------------------------------------------
@ -839,18 +890,10 @@ namespace Eigen {
#define EIGEN_IMPLIES(a,b) (!(a) || (b))
#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> \
(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); \
}
// the expression type of a cwise product
#define EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS) \
// the expression type of a standard coefficient wise binary operation
#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME) \
CwiseBinaryOp< \
internal::scalar_product_op< \
EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)< \
typename internal::traits<LHS>::Scalar, \
typename internal::traits<RHS>::Scalar \
>, \
@ -858,6 +901,55 @@ namespace Eigen {
const RHS \
>
#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,OPNAME) \
template<typename OtherDerived> \
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME) \
(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME)(derived(), other.derived()); \
}
#define EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,TYPEA,TYPEB) \
(Eigen::internal::has_ReturnType<Eigen::ScalarBinaryOpTraits<TYPEA,TYPEB,EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,OPNAME),_op)<TYPEA,TYPEB> > >::value)
#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR,SCALAR,OPNAME) \
CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<typename internal::traits<EXPR>::Scalar,SCALAR>, const EXPR, \
const typename internal::plain_constant_type<EXPR,SCALAR>::type>
#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR,EXPR,OPNAME) \
CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<SCALAR,typename internal::traits<EXPR>::Scalar>, \
const typename internal::plain_constant_type<EXPR,SCALAR>::type, const EXPR>
// Workaround for MSVC 2010 (see ML thread "patch with compile for for MSVC 2010")
#if EIGEN_COMP_MSVC_STRICT<=1600
#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) typename internal::enable_if<true,X>::type
#else
#define EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(X) X
#endif
#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME) \
template <typename T> EIGEN_DEVICE_FUNC inline \
EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type,OPNAME))\
(METHOD)(const T& scalar) const { \
typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type PromotedT; \
return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedT,OPNAME)(derived(), \
typename internal::plain_constant_type<Derived,PromotedT>::type(derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar))); \
}
#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
template <typename T> EIGEN_DEVICE_FUNC inline friend \
EIGEN_MSVC10_WORKAROUND_BINARYOP_RETURN_TYPE(const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type,Derived,OPNAME)) \
(METHOD)(const T& scalar, const StorageBaseType& matrix) { \
typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type PromotedT; \
return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT,Derived,OPNAME)( \
typename internal::plain_constant_type<Derived,PromotedT>::type(matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)), matrix.derived()); \
}
#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD,OPNAME) \
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME)
#ifdef EIGEN_EXCEPTIONS
# define EIGEN_THROW_X(X) throw X
# define EIGEN_THROW throw
@ -865,8 +957,8 @@ namespace Eigen {
# define EIGEN_CATCH(X) catch (X)
#else
# ifdef __CUDA_ARCH__
# define EIGEN_THROW_X(X) asm("trap;") return {}
# define EIGEN_THROW asm("trap;"); return {}
# define EIGEN_THROW_X(X) asm("trap;")
# define EIGEN_THROW asm("trap;")
# else
# define EIGEN_THROW_X(X) std::abort()
# define EIGEN_THROW std::abort()
@ -875,10 +967,16 @@ namespace Eigen {
# define EIGEN_CATCH(X) else
#endif
#if EIGEN_HAS_CXX11_NOEXCEPT
# define EIGEN_INCLUDE_TYPE_TRAITS
# define EIGEN_NOEXCEPT noexcept
# define EIGEN_NOEXCEPT_IF(x) noexcept(x)
# define EIGEN_NO_THROW noexcept(true)
# define EIGEN_EXCEPTION_SPEC(X) noexcept(false)
#else
# define EIGEN_NOEXCEPT
# define EIGEN_NOEXCEPT_IF(x)
# define EIGEN_NO_THROW throw()
# define EIGEN_EXCEPTION_SPEC(X) throw(X)
#endif

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