GitHub-Proxy/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2025-08-13 20:26:03 +08:00
Code Issues Packages Projects Releases Wiki Activity

Rebase to latest.

Browse Source
This commit is contained in:
Ville Kallioniemi 2016-02-01 19:32:31 -07:00
commit f0fdefa96f
113 changed files with 2894 additions and 1270 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Eigen/CholmodSupport
View File

@ -19,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup CholmodSupport_Module CholmodSupport module
*
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the two following main factorization classes:
* - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
* - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).

2
Eigen/SPQRSupport
View File

@ -17,7 +17,7 @@
/** \ingroup Support_modules
* \defgroup SPQRSupport_Module SuiteSparseQR module
*
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
*
* \code
* #include <Eigen/SPQRSupport>

2
Eigen/UmfPackSupport
View File

@ -19,7 +19,7 @@ extern "C" {
/** \ingroup Support_modules
* \defgroup UmfPackSupport_Module UmfPackSupport module
*
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
* This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
* It provides the following factorization class:
* - class UmfPackLU: a multifrontal sequential LU factorization.
*

5
Eigen/src/CholmodSupport/CholmodSupport.h
View File

@ -273,9 +273,10 @@ class CholmodBase : public SparseSolverBase<Derived>
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// Cholmod needs column-major stoarge without inner-stride, which corresponds to the default behavior of Ref.
Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
// note: cd stands for Cholmod Dense
Rhs& b_ref(b.const_cast_derived());
cholmod_dense b_cd = viewAsCholmod(b_ref);
cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
if(!x_cd)

16
Eigen/src/Core/ArrayBase.h
View File

@ -103,7 +103,7 @@ template<typename Derived> class ArrayBase
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const ArrayBase& other)
{
internal::call_assignment(derived(), other.derived());
@ -112,28 +112,28 @@ template<typename Derived> class ArrayBase
/** Set all the entries to \a value.
* \sa DenseBase::setConstant(), DenseBase::fill() */
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const Scalar &value)
{ Base::setConstant(value); return derived(); }
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const Scalar& scalar);
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const Scalar& scalar);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const ArrayBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const ArrayBase<OtherDerived>& other);
public:

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

@ -52,7 +52,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
EIGEN_DEVICE_FUNC
explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
@ -67,7 +67,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
@ -80,13 +80,13 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
@ -98,13 +98,13 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(index);
}
template<int LoadMode>
@ -116,7 +116,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
m_expression.template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@ -128,7 +128,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
m_expression.template writePacket<LoadMode>(index, val);
}
template<typename Dest>
@ -145,11 +145,11 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;
@ -195,7 +195,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
const Scalar
>::type ScalarWithConstIfNotLvalue;
typedef typename internal::ref_selector<ExpressionType>::type NestedExpressionType;
typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
EIGEN_DEVICE_FUNC
explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
@ -210,7 +210,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
inline Index innerStride() const { return m_expression.innerStride(); }
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return m_expression.data(); }
@ -223,7 +223,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index rowId, Index colId)
{
return m_expression.const_cast_derived().coeffRef(rowId, colId);
return m_expression.coeffRef(rowId, colId);
}
EIGEN_DEVICE_FUNC
@ -241,13 +241,13 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index index)
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(index);
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_expression.const_cast_derived().coeffRef(index);
return m_expression.coeffRef(index);
}
template<int LoadMode>
@ -259,7 +259,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val);
m_expression.template writePacket<LoadMode>(rowId, colId, val);
}
template<int LoadMode>
@ -271,7 +271,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_expression.const_cast_derived().template writePacket<LoadMode>(index, val);
m_expression.template writePacket<LoadMode>(index, val);
}
EIGEN_DEVICE_FUNC
@ -284,11 +284,11 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
EIGEN_DEVICE_FUNC
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
void resize(Index newSize) { m_expression.resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
EIGEN_DEVICE_FUNC
void resize(Index rows, Index cols) { m_expression.const_cast_derived().resize(rows,cols); }
void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
protected:
NestedExpressionType m_expression;

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

@ -637,7 +637,7 @@ protected:
***************************************************************************/
template<typename DstXprType, typename SrcXprType, typename Functor>
EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
@ -654,7 +654,7 @@ EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const S
}
template<typename DstXprType, typename SrcXprType>
EIGEN_DEVICE_FUNC void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src)
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>());
}
@ -688,26 +688,30 @@ struct Assignment;
// does not has to bother about these annoying details.
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& 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>());
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment(const Dst& dst, const Src& 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>());
}
// Deal with "assume-aliasing"
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if< evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
typename plain_matrix_type<Src>::type tmp(src);
call_assignment_no_alias(dst, tmp, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(Dst& dst, const Src& src, const Func& func, typename enable_if<!evaluator_assume_aliasing<Src>::value, void*>::type = 0)
{
call_assignment_no_alias(dst, src, func);
}
@ -715,14 +719,16 @@ EIGEN_DEVICE_FUNC void call_assignment(Dst& dst, const Src& src, const Func& fun
// by-pass "assume-aliasing"
// When there is no aliasing, we require that 'dst' has been properly resized
template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
{
call_assignment_no_alias(dst.expression(), src, func);
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
{
enum {
NeedToTranspose = ( (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
@ -747,13 +753,15 @@ EIGEN_DEVICE_FUNC void call_assignment_no_alias(Dst& dst, const Src& src, const
Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment_no_alias(Dst& dst, const Src& 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>());
}
template<typename Dst, typename Src, typename Func>
EIGEN_DEVICE_FUNC void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
{
Index dstRows = src.rows();
Index dstCols = src.cols();
@ -767,7 +775,8 @@ EIGEN_DEVICE_FUNC void call_assignment_no_alias_no_transpose(Dst& dst, const Src
Assignment<Dst,Src,Func>::run(dst, src, func);
}
template<typename Dst, typename Src>
EIGEN_DEVICE_FUNC void call_assignment_no_alias_no_transpose(Dst& dst, const Src& 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>());
}
@ -779,7 +788,8 @@ template<typename Dst, typename Src> void check_for_aliasing(const Dst &dst, con
template< typename DstXprType, typename SrcXprType, typename Functor, typename Scalar>
struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Scalar>
{
EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
{
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());

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

@ -129,8 +129,8 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class
: Impl(xpr, startRow, startCol)
{
EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
eigen_assert(startRow >= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols());
eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
&& startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
}
/** Dynamic-size constructor
@ -221,15 +221,13 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
inline Scalar& coeffRef(Index rowId, Index colId)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index rowId, Index colId) const
{
return m_xpr.derived()
.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
}
EIGEN_DEVICE_FUNC
@ -242,39 +240,34 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
inline Scalar& coeffRef(Index index)
{
EIGEN_STATIC_ASSERT_LVALUE(XprType)
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index index) const
{
return m_xpr.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
EIGEN_DEVICE_FUNC
inline const CoeffReturnType coeff(Index index) const
{
return m_xpr
.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
template<int LoadMode>
inline PacketScalar packet(Index rowId, Index colId) const
{
return m_xpr.template packet<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value());
return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
}
template<int LoadMode>
inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
(rowId + m_startRow.value(), colId + m_startCol.value(), val);
m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
}
template<int LoadMode>
@ -288,7 +281,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
template<int LoadMode>
inline void writePacket(Index index, const PacketScalar& val)
{
m_xpr.const_cast_derived().template writePacket<Unaligned>
m_xpr.template writePacket<Unaligned>
(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
}
@ -320,7 +313,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
protected:
const typename XprType::Nested m_xpr;
typename XprType::Nested m_xpr;
const internal::variable_if_dynamic<StorageIndex, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<StorageIndex, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;

199
Eigen/src/Core/CoreEvaluators.h
View File

@ -148,7 +148,8 @@ struct evaluator<PlainObjectBase<Derived> >
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
if (IsRowMajor)
return m_data[row * m_outerStride.value() + col];
@ -156,12 +157,14 @@ struct evaluator<PlainObjectBase<Derived> >
return m_data[row + col * m_outerStride.value()];
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_data[index];
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
if (IsRowMajor)
return const_cast<Scalar*>(m_data)[row * m_outerStride.value() + col];
@ -169,12 +172,14 @@ struct evaluator<PlainObjectBase<Derived> >
return const_cast<Scalar*>(m_data)[row + col * m_outerStride.value()];
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return const_cast<Scalar*>(m_data)[index];
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
if (IsRowMajor)
@ -184,12 +189,14 @@ struct evaluator<PlainObjectBase<Derived> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return ploadt<PacketType, LoadMode>(m_data + index);
}
template<int StoreMode,typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
if (IsRowMajor)
@ -201,6 +208,7 @@ struct evaluator<PlainObjectBase<Derived> >
}
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
return pstoret<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_data) + index, x);
@ -260,45 +268,53 @@ struct unary_evaluator<Transpose<ArgType>, IndexBased>
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_argImpl.coeff(col, row);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_argImpl.coeff(index);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_argImpl.coeffRef(col, row);
}
EIGEN_DEVICE_FUNC typename XprType::Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename XprType::Scalar& coeffRef(Index index)
{
return m_argImpl.coeffRef(index);
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_argImpl.template packet<LoadMode,PacketType>(col, row);
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_argImpl.template packet<LoadMode,PacketType>(index);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
m_argImpl.template writePacket<StoreMode,PacketType>(col, row, x);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
m_argImpl.template writePacket<StoreMode,PacketType>(index, x);
@ -338,23 +354,27 @@ struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_functor(row, col);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_functor(index);
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_functor.template packetOp<Index,PacketType>(row, col);
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_functor.template packetOp<Index,PacketType>(index);
@ -380,7 +400,8 @@ struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased >
Alignment = evaluator<ArgType>::Alignment
};
EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit unary_evaluator(const XprType& op)
: m_functor(op.functor()),
m_argImpl(op.nestedExpression())
{
@ -390,23 +411,27 @@ struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased >
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_functor(m_argImpl.coeff(row, col));
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_functor(m_argImpl.coeff(index));
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_functor.packetOp(m_argImpl.template packet<LoadMode, PacketType>(row, col));
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_functor.packetOp(m_argImpl.template packet<LoadMode, PacketType>(index));
@ -466,17 +491,20 @@ struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBase
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_functor(m_lhsImpl.coeff(row, col), m_rhsImpl.coeff(row, col));
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_functor(m_lhsImpl.coeff(index), m_rhsImpl.coeff(index));
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_functor.packetOp(m_lhsImpl.template packet<LoadMode,PacketType>(row, col),
@ -484,6 +512,7 @@ struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBase
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_functor.packetOp(m_lhsImpl.template packet<LoadMode,PacketType>(index),
@ -523,22 +552,26 @@ struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType>, IndexBased>
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_unaryOp(m_argImpl.coeff(row, col));
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_unaryOp(m_argImpl.coeff(index));
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_unaryOp(m_argImpl.coeffRef(row, col));
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_unaryOp(m_argImpl.coeffRef(index));
}
@ -578,47 +611,55 @@ struct mapbase_evaluator : evaluator_base<Derived>
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_data[col * m_xpr.colStride() + row * m_xpr.rowStride()];
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_data[index * m_xpr.innerStride()];
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_data[col * m_xpr.colStride() + row * m_xpr.rowStride()];
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_data[index * m_xpr.innerStride()];
}
template<int LoadMode, typename PacketType>
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();
return internal::ploadt<PacketType, LoadMode>(ptr);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return internal::ploadt<PacketType, LoadMode>(m_data + index * m_xpr.innerStride());
}
template<int StoreMode, typename PacketType>
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();
return internal::pstoret<Scalar, PacketType, StoreMode>(ptr, x);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_xpr.innerStride(), x);
@ -767,46 +808,54 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
RowsAtCompileTime = XprType::RowsAtCompileTime
};
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_argImpl.template packet<LoadMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return packet<LoadMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
RowsAtCompileTime == 1 ? index : 0);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
return m_argImpl.template writePacket<StoreMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col, x);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
return writePacket<StoreMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
@ -859,7 +908,7 @@ struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
};
inline EIGEN_DEVICE_FUNC explicit evaluator(const XprType& select)
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& select)
: m_conditionImpl(select.conditionMatrix()),
m_thenImpl(select.thenMatrix()),
m_elseImpl(select.elseMatrix())
@ -869,7 +918,8 @@ struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
typedef typename XprType::CoeffReturnType CoeffReturnType;
inline EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
if (m_conditionImpl.coeff(row, col))
return m_thenImpl.coeff(row, col);
@ -877,7 +927,8 @@ struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
return m_elseImpl.coeff(row, col);
}
inline EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
if (m_conditionImpl.coeff(index))
return m_thenImpl.coeff(index);
@ -921,7 +972,8 @@ struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
m_cols(replicate.nestedExpression().cols())
{}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
@ -934,7 +986,8 @@ struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
return m_argImpl.coeff(actual_row, actual_col);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
// try to avoid using modulo; this is a pure optimization strategy
const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
@ -945,6 +998,7 @@ struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
@ -958,6 +1012,7 @@ struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
@ -994,7 +1049,7 @@ struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
CoeffReadCost = TraversalSize==Dynamic ? HugeCost
: TraversalSize * evaluator<ArgType>::CoeffReadCost + int(CostOpType::value),
Flags = (traits<XprType>::Flags&RowMajorBit) | (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit))),
Flags = (traits<XprType>::Flags&RowMajorBit) | (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit))) | LinearAccessBit,
Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized
};
@ -1008,7 +1063,8 @@ struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Scalar coeff(Index i, Index j) const
{
if (Direction==Vertical)
return m_functor(m_arg.col(j));
@ -1016,7 +1072,8 @@ struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
return m_functor(m_arg.row(i));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Scalar coeff(Index index) const
{
if (Direction==Vertical)
return m_functor(m_arg.col(index));
@ -1051,45 +1108,53 @@ struct evaluator_wrapper_base
typedef typename ArgType::Scalar Scalar;
typedef typename ArgType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_argImpl.coeff(row, col);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_argImpl.coeff(index);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_argImpl.coeffRef(row, col);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_argImpl.coeffRef(index);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
return m_argImpl.template packet<LoadMode,PacketType>(row, col);
}
template<int LoadMode, typename PacketType>
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
return m_argImpl.template packet<LoadMode,PacketType>(index);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
m_argImpl.template writePacket<StoreMode>(row, col, x);
}
template<int StoreMode, typename PacketType>
template<int StoreMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
m_argImpl.template writePacket<StoreMode>(index, x);
@ -1164,29 +1229,34 @@ struct unary_evaluator<Reverse<ArgType, Direction> >
m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1)
{ }
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row,
ReverseCol ? m_cols.value() - col - 1 : col);
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index col)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row,
ReverseCol ? m_cols.value() - col - 1 : col);
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1);
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index row, Index col) const
{
enum {
@ -1201,6 +1271,7 @@ struct unary_evaluator<Reverse<ArgType, Direction> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
enum { PacketSize = unpacket_traits<PacketType>::size };
@ -1208,6 +1279,7 @@ struct unary_evaluator<Reverse<ArgType, Direction> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index row, Index col, const PacketType& x)
{
// FIXME we could factorize some code with packet(i,j)
@ -1224,6 +1296,7 @@ struct unary_evaluator<Reverse<ArgType, Direction> >
}
template<int LoadMode, typename PacketType>
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
enum { PacketSize = unpacket_traits<PacketType>::size };
@ -1267,22 +1340,26 @@ struct evaluator<Diagonal<ArgType, DiagIndex> >
typedef typename internal::conditional<!internal::is_same<typename ArgType::StorageKind,Sparse>::value,
typename XprType::CoeffReturnType,Scalar>::type CoeffReturnType;
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index row, Index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index) const
{
return m_argImpl.coeff(row + rowOffset(), row + colOffset());
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
return m_argImpl.coeff(index + rowOffset(), index + colOffset());
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index row, Index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index)
{
return m_argImpl.coeffRef(row + rowOffset(), row + colOffset());
}
EIGEN_DEVICE_FUNC Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
return m_argImpl.coeffRef(index + rowOffset(), index + colOffset());
}

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

@ -32,8 +32,8 @@ struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
// we still want to handle the case when the result type is different.
typedef typename result_of<
BinaryOp(
typename Lhs::Scalar,
typename Rhs::Scalar
const typename Lhs::Scalar&,
const typename Rhs::Scalar&
)
>::type Scalar;
typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,

29
Eigen/src/Core/CwiseUnaryOp.h
View File

@ -19,7 +19,7 @@ struct traits<CwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
typedef typename result_of<
UnaryOp(typename XprType::Scalar)
UnaryOp(const typename XprType::Scalar&)
>::type Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
@ -58,33 +58,34 @@ class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal
typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
typedef typename internal::ref_selector<XprType>::type XprTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
EIGEN_DEVICE_FUNC
explicit inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index cols() const { return m_xpr.cols(); }
/** \returns the functor representing the unary operation */
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<XprTypeNested>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
typename internal::remove_all<XprTypeNested>::type&
nestedExpression() { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
XprTypeNested m_xpr;
const UnaryOp m_functor;
};

9
Eigen/src/Core/CwiseUnaryView.h
View File

@ -18,7 +18,7 @@ struct traits<CwiseUnaryView<ViewOp, MatrixType> >
: traits<MatrixType>
{
typedef typename result_of<
ViewOp(typename traits<MatrixType>::Scalar)
ViewOp(const typename traits<MatrixType>::Scalar&)
>::type Scalar;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested;
@ -61,6 +61,7 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
explicit inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
@ -75,15 +76,15 @@ class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename in
const ViewOp& functor() const { return m_functor; }
/** \returns the nested expression */
const typename internal::remove_all<typename MatrixType::Nested>::type&
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
typename internal::remove_all<typename MatrixType::Nested>::type&
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
protected:
typename internal::ref_selector<MatrixType>::type m_matrix;
MatrixTypeNested m_matrix;
ViewOp m_functor;
};

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

@ -275,13 +275,13 @@ template<typename Derived> class DenseBase
/** Copies \a other into *this. \returns a reference to *this. */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase& other);
template<typename OtherDerived>
@ -388,10 +388,10 @@ template<typename Derived> class DenseBase
inline bool hasNaN() const;
inline bool allFinite() const;
EIGEN_DEVICE_FUNC
inline Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC
inline Derived& operator/=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const Scalar& other);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const Scalar& other);
typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType;
/** \returns the matrix or vector obtained by evaluating this expression.

14
Eigen/src/Core/Diagonal.h
View File

@ -103,21 +103,21 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
>::type ScalarWithConstIfNotLvalue;
EIGEN_DEVICE_FUNC
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline const Scalar* data() const { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); }
inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
EIGEN_DEVICE_FUNC
inline Scalar& coeffRef(Index row, Index)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index row, Index) const
{
return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset());
return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
}
EIGEN_DEVICE_FUNC
@ -130,13 +130,13 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
inline Scalar& coeffRef(Index idx)
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
inline const Scalar& coeffRef(Index idx) const
{
return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset());
return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
}
EIGEN_DEVICE_FUNC
@ -159,7 +159,7 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
}
protected:
typename MatrixType::Nested m_matrix;
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
private:

46
Eigen/src/Core/Dot.h
View File

@ -142,6 +142,52 @@ inline void MatrixBase<Derived>::normalize()
derived() /= numext::sqrt(z);
}
/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
*
* \only_for_vectors
*
* This method is analogue to the normalized() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0),
* then this function returns a copy of the input.
*
* \sa stableNorm(), stableNormalize(), normalized()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
_Nested n(derived());
RealScalar w = n.cwiseAbs().maxCoeff();
RealScalar z = (n/w).squaredNorm();
if(z>RealScalar(0))
return n / (numext::sqrt(z)*w);
else
return n;
}
/** Normalizes the vector while avoid underflow and overflow
*
* \only_for_vectors
*
* This method is analogue to the normalize() method, but it reduces the risk of
* underflow and overflow when computing the norm.
*
* \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
*
* \sa stableNorm(), stableNormalized(), normalize()
*/
template<typename Derived>
inline void MatrixBase<Derived>::stableNormalize()
{
RealScalar w = cwiseAbs().maxCoeff();
RealScalar z = (derived()/w).squaredNorm();
if(z>RealScalar(0))
derived() /= numext::sqrt(z)*w;
}
//---------- implementation of other norms ----------
namespace internal {

7
Eigen/src/Core/GenericPacketMath.h
View File

@ -75,6 +75,7 @@ struct default_packet_traits
HasCosh = 0,
HasTanh = 0,
HasLGamma = 0,
HasDiGamma = 0,
HasErf = 0,
HasErfc = 0,
@ -284,7 +285,7 @@ template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu
{ pstore(to, from); }
/** \internal tries to do cache prefetching of \a addr */
template<typename Scalar> inline void prefetch(const Scalar* addr)
template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
{
#ifdef __CUDA_ARCH__
#if defined(__LP64__)
@ -439,6 +440,10 @@ Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
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 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); }

1
Eigen/src/Core/GlobalFunctions.h
View File

@ -50,6 +50,7 @@ namespace Eigen
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(erf,scalar_erf_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op)

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

@ -748,9 +748,9 @@ template<typename T> EIGEN_DEVICE_FUNC bool isinf_msvc_helper(T x)
}
//MSVC defines a _isnan builtin function, but for double only
EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x); }
EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x) { return _isnan(x); }
EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x) { return _isnan(x); }
EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x)!=0; }
EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x) { return _isnan(x)!=0; }
EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x) { return _isnan(x)!=0; }
EIGEN_DEVICE_FUNC inline bool isinf_impl(const long double& x) { return isinf_msvc_helper(x); }
EIGEN_DEVICE_FUNC inline bool isinf_impl(const double& x) { return isinf_msvc_helper(x); }
@ -1080,21 +1080,21 @@ struct scalar_fuzzy_impl : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::
template<typename Scalar, typename OtherScalar> EIGEN_DEVICE_FUNC
inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
{
return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision);
}
template<typename Scalar> EIGEN_DEVICE_FUNC
inline bool isApprox(const Scalar& x, const Scalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
{
return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision);
}
template<typename Scalar> EIGEN_DEVICE_FUNC
inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y,
typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision())
const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
{
return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision);
}

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

@ -135,14 +135,14 @@ template<typename Derived> class MatrixBase
/** Special case of the template operator=, in order to prevent the compiler
* from generating a default operator= (issue hit with g++ 4.1)
*/
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const MatrixBase& other);
// We cannot inherit here via Base::operator= since it is causing
// trouble with MSVC.
template <typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator=(const DenseBase<OtherDerived>& other);
template <typename OtherDerived>
@ -154,10 +154,10 @@ template<typename Derived> class MatrixBase
Derived& operator=(const ReturnByValue<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const MatrixBase<OtherDerived>& other);
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const MatrixBase<OtherDerived>& other);
#ifdef __CUDACC__
@ -204,7 +204,9 @@ template<typename Derived> class MatrixBase
RealScalar blueNorm() const;
RealScalar hypotNorm() const;
EIGEN_DEVICE_FUNC const PlainObject normalized() const;
EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
EIGEN_DEVICE_FUNC void normalize();
EIGEN_DEVICE_FUNC void stableNormalize();
EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
EIGEN_DEVICE_FUNC void adjointInPlace();

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

@ -32,7 +32,7 @@ namespace internal {
template<typename MatrixType, unsigned int UpLo>
struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef MatrixType ExpressionType;
typedef typename MatrixType::PlainObject FullMatrixType;
@ -97,7 +97,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
{
EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
Base::check_coordinates_internal(row, col);
return m_matrix.const_cast_derived().coeffRef(row, col);
return m_matrix.coeffRef(row, col);
}
/** \internal */
@ -107,7 +107,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
EIGEN_DEVICE_FUNC
const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
EIGEN_DEVICE_FUNC
MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); }
MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
/** Efficient triangular matrix times vector/matrix product */
template<typename OtherDerived>

385
Eigen/src/Core/SpecialFunctions.h
View File

@ -13,79 +13,349 @@
namespace Eigen {
namespace internal {
// Parts of this code are based on the Cephes Math Library.
//
// Cephes Math Library Release 2.8: June, 2000
// Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
//
// Permission has been kindly provided by the original author
// to incorporate the Cephes software into the Eigen codebase:
//
// From: Stephen Moshier
// To: Eugene Brevdo
// Subject: Re: Permission to wrap several cephes functions in Eigen
//
// Hello Eugene,
//
// Thank you for writing.
//
// If your licensing is similar to BSD, the formal way that has been
// handled is simply to add a statement to the effect that you are incorporating
// the Cephes software by permission of the author.
//
// Good luck with your project,
// Steve
namespace cephes {
/* polevl (modified for Eigen)
*
* Evaluate polynomial
*
*
*
* SYNOPSIS:
*
* int N;
* Scalar x, y, coef[N+1];
*
* y = polevl<decltype(x), N>( x, coef);
*
*
*
* DESCRIPTION:
*
* Evaluates polynomial of degree N:
*
* 2 N
* y = C + C x + C x +...+ C x
* 0 1 2 N
*
* Coefficients are stored in reverse order:
*
* coef[0] = C , ..., coef[N] = C .
* N 0
*
* The function p1evl() assumes that coef[N] = 1.0 and is
* omitted from the array. Its calling arguments are
* otherwise the same as polevl().
*
*
* The Eigen implementation is templatized. For best speed, store
* coef as a const array (constexpr), e.g.
*
* const double coef[] = {1.0, 2.0, 3.0, ...};
*
*/
template <typename Scalar, int N>
struct polevl {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
static Scalar run(const Scalar x, const Scalar coef[]) {
EIGEN_STATIC_ASSERT((N > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
return polevl<Scalar, N - 1>::run(x, coef) * x + coef[N];
}
};
template <typename Scalar>
struct polevl<Scalar, 0> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
static Scalar run(const Scalar, const Scalar coef[]) {
return coef[0];
}
};
} // end namespace cephes
/****************************************************************************
* Implementation of lgamma *
****************************************************************************/
template<typename Scalar>
struct lgamma_impl
{
template <typename Scalar>
struct lgamma_impl {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Scalar&)
{
static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
THIS_TYPE_IS_NOT_SUPPORTED);
return Scalar(0);
}
};
template<typename Scalar>
struct lgamma_retval
{
template <typename Scalar>
struct lgamma_retval {
typedef Scalar type;
};
#ifdef EIGEN_HAS_C99_MATH
template<>
struct lgamma_impl<float>
{
template <>
struct lgamma_impl<float> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE double run(const float& x) { return ::lgammaf(x); }
static EIGEN_STRONG_INLINE float run(float x) { return ::lgammaf(x); }
};
template<>
struct lgamma_impl<double>
{
template <>
struct lgamma_impl<double> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE double run(const double& x) { return ::lgamma(x); }
static EIGEN_STRONG_INLINE double run(double x) { return ::lgamma(x); }
};
#endif
/****************************************************************************
* Implementation of digamma (psi) *
****************************************************************************/
#ifdef EIGEN_HAS_C99_MATH
/*
*
* Polynomial evaluation helper for the Psi (digamma) function.
*
* digamma_impl_maybe_poly::run(s) evaluates the asymptotic Psi expansion for
* input Scalar s, assuming s is above 10.0.
*
* If s is above a certain threshold for the given Scalar type, zero
* is returned. Otherwise the polynomial is evaluated with enough
* coefficients for results matching Scalar machine precision.
*
*
*/
template <typename Scalar>
struct digamma_impl_maybe_poly {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
THIS_TYPE_IS_NOT_SUPPORTED);
return Scalar(0);
}
};
template <>
struct digamma_impl_maybe_poly<float> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE float run(const float s) {
const float A[] = {
-4.16666666666666666667E-3,
3.96825396825396825397E-3,
-8.33333333333333333333E-3,
8.33333333333333333333E-2
};
float z;
if (s < 1.0e8f) {
z = 1.0f / (s * s);
return z * cephes::polevl<float, 3>::run(z, A);
} else return 0.0f;
}
};
template <>
struct digamma_impl_maybe_poly<double> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE double run(const double s) {
const double A[] = {
8.33333333333333333333E-2,
-2.10927960927960927961E-2,
7.57575757575757575758E-3,
-4.16666666666666666667E-3,
3.96825396825396825397E-3,
-8.33333333333333333333E-3,
8.33333333333333333333E-2
};
double z;
if (s < 1.0e17) {
z = 1.0 / (s * s);
return z * cephes::polevl<double, 6>::run(z, A);
}
else return 0.0;
}
};
#endif // EIGEN_HAS_C99_MATH
template <typename Scalar>
struct digamma_retval {
typedef Scalar type;
};
#ifdef EIGEN_HAS_C99_MATH
template <typename Scalar>
struct digamma_impl {
EIGEN_DEVICE_FUNC
static Scalar run(Scalar x) {
/*
*
* Psi (digamma) function (modified for Eigen)
*
*
* SYNOPSIS:
*
* double x, y, psi();
*
* y = psi( x );
*
*
* DESCRIPTION:
*
* d -
* psi(x) = -- ln | (x)
* dx
*
* is the logarithmic derivative of the gamma function.
* For integer x,
* n-1
* -
* psi(n) = -EUL + > 1/k.
* -
* k=1
*
* If x is negative, it is transformed to a positive argument by the
* reflection formula psi(1-x) = psi(x) + pi cot(pi x).
* For general positive x, the argument is made greater than 10
* using the recurrence psi(x+1) = psi(x) + 1/x.
* Then the following asymptotic expansion is applied:
*
* inf. B
* - 2k
* psi(x) = log(x) - 1/2x - > -------
* - 2k
* k=1 2k x
*
* where the B2k are Bernoulli numbers.
*
* ACCURACY (float):
* Relative error (except absolute when |psi| < 1):
* arithmetic domain # trials peak rms
* IEEE 0,30 30000 1.3e-15 1.4e-16
* IEEE -30,0 40000 1.5e-15 2.2e-16
*
* ACCURACY (double):
* Absolute error, relative when |psi| > 1 :
* arithmetic domain # trials peak rms
* IEEE -33,0 30000 8.2e-7 1.2e-7
* IEEE 0,33 100000 7.3e-7 7.7e-8
*
* ERROR MESSAGES:
* message condition value returned
* psi singularity x integer <=0 INFINITY
*/
Scalar p, q, nz, s, w, y;
bool negative;
const Scalar maxnum = std::numeric_limits<Scalar>::infinity();
const Scalar m_pi = 3.14159265358979323846;
negative = 0;
nz = 0.0;
const Scalar zero = 0.0;
const Scalar one = 1.0;
const Scalar half = 0.5;
if (x <= zero) {
negative = one;
q = x;
p = ::floor(q);
if (p == q) {
return maxnum;
}
/* Remove the zeros of tan(m_pi x)
* by subtracting the nearest integer from x
*/
nz = q - p;
if (nz != half) {
if (nz > half) {
p += one;
nz = q - p;
}
nz = m_pi / ::tan(m_pi * nz);
}
else {
nz = zero;
}
x = one - x;
}
/* use the recurrence psi(x+1) = psi(x) + 1/x. */
s = x;
w = zero;
while (s < Scalar(10)) {
w += one / s;
s += one;
}
y = digamma_impl_maybe_poly<Scalar>::run(s);
y = ::log(s) - (half / s) - y - w;
return (negative) ? y - nz : y;
}
};
#endif // EIGEN_HAS_C99_MATH
/****************************************************************************
* Implementation of erf *
****************************************************************************/
template<typename Scalar>
struct erf_impl
{
template <typename Scalar>
struct erf_impl {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Scalar&)
{
static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
THIS_TYPE_IS_NOT_SUPPORTED);
return Scalar(0);
}
};
template<typename Scalar>
struct erf_retval
{
template <typename Scalar>
struct erf_retval {
typedef Scalar type;
};
#ifdef EIGEN_HAS_C99_MATH
template<>
struct erf_impl<float>
{
template <>
struct erf_impl<float> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE float run(const float& x) { return ::erff(x); }
static EIGEN_STRONG_INLINE float run(float x) { return ::erff(x); }
};
template<>
struct erf_impl<double>
{
template <>
struct erf_impl<double> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE double run(const double& x) { return ::erf(x); }
static EIGEN_STRONG_INLINE double run(double x) { return ::erf(x); }
};
#endif // EIGEN_HAS_C99_MATH
@ -93,35 +363,30 @@ struct erf_impl<double>
* Implementation of erfc *
****************************************************************************/
template<typename Scalar>
struct erfc_impl
{
template <typename Scalar>
struct erfc_impl {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Scalar run(const Scalar&)
{
static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
THIS_TYPE_IS_NOT_SUPPORTED);
return Scalar(0);
}
};
template<typename Scalar>
struct erfc_retval
{
template <typename Scalar>
struct erfc_retval {
typedef Scalar type;
};
#ifdef EIGEN_HAS_C99_MATH
template<>
struct erfc_impl<float>
{
template <>
struct erfc_impl<float> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE float run(const float x) { return ::erfcf(x); }
};
template<>
struct erfc_impl<double>
{
template <>
struct erfc_impl<double> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE double run(const double x) { return ::erfc(x); }
};
@ -129,27 +394,29 @@ struct erfc_impl<double>
} // end namespace internal
namespace numext {
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar) lgamma(const Scalar& x)
{
template <typename Scalar>
EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar)
lgamma(const Scalar& x) {
return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(erf, Scalar) erf(const Scalar& x)
{
template <typename Scalar>
EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(digamma, Scalar)
digamma(const Scalar& x) {
return EIGEN_MATHFUNC_IMPL(digamma, Scalar)::run(x);
}
template <typename Scalar>
EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erf, Scalar)
erf(const Scalar& x) {
return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar) erfc(const Scalar& x)
{
template <typename Scalar>
EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar)
erfc(const Scalar& x) {
return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x);
}

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

@ -54,6 +54,8 @@ template<typename MatrixType> class Transpose
{
public:
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
@ -68,16 +70,16 @@ template<typename MatrixType> class Transpose
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename MatrixType::Nested>::type&
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
typename internal::remove_all<typename MatrixType::Nested>::type&
nestedExpression() { return m_matrix.const_cast_derived(); }
typename internal::remove_reference<MatrixTypeNested>::type&
nestedExpression() { return m_matrix; }
protected:
typename MatrixType::Nested m_matrix;
typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
};
namespace internal {

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

@ -325,7 +325,7 @@ class TranspositionsWrapper
protected:
const typename IndicesType::Nested m_indices;
typename IndicesType::Nested m_indices;
};

9
Eigen/src/Core/TriangularMatrix.h
View File

@ -168,7 +168,7 @@ namespace internal {
template<typename MatrixType, unsigned int _Mode>
struct traits<TriangularView<MatrixType, _Mode> > : traits<MatrixType>
{
typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
typedef typename MatrixType::PlainObject FullMatrixType;
@ -213,7 +213,6 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
IsVectorAtCompileTime = false
};
// FIXME This, combined with const_cast_derived in transpose() leads to a const-correctness loophole
EIGEN_DEVICE_FUNC
explicit inline TriangularView(MatrixType& matrix) : m_matrix(matrix)
{}
@ -235,7 +234,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
/** \returns a reference to the nested expression */
EIGEN_DEVICE_FUNC
NestedExpression& nestedExpression() { return *const_cast<NestedExpression*>(&m_matrix); }
NestedExpression& nestedExpression() { return m_matrix; }
typedef TriangularView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
/** \sa MatrixBase::conjugate() const */
@ -255,7 +254,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularView
inline TransposeReturnType transpose()
{
EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
typename MatrixType::TransposeReturnType tmp(m_matrix.const_cast_derived());
typename MatrixType::TransposeReturnType tmp(m_matrix);
return TransposeReturnType(tmp);
}
@ -418,7 +417,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
{
EIGEN_STATIC_ASSERT_LVALUE(TriangularViewType);
Base::check_coordinates_internal(row, col);
return derived().nestedExpression().const_cast_derived().coeffRef(row, col);
return derived().nestedExpression().coeffRef(row, col);
}
/** Assigns a triangular matrix to a triangular part of a dense matrix */

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

@ -124,7 +124,7 @@ struct member_lpnorm {
template <typename BinaryOp, typename Scalar>
struct member_redux {
typedef typename result_of<
BinaryOp(Scalar,Scalar)
BinaryOp(const Scalar&,const Scalar&)
>::type result_type;
template<typename _Scalar, int Size> struct Cost
{ enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };

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

@ -197,7 +197,7 @@ struct functor_traits<max_coeff_visitor<Scalar> > {
/** \returns the minimum of all coefficients of *this and puts in *row and *col its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visitor(), DenseBase::minCoeff()
* \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
*/
template<typename Derived>
template<typename IndexType>
@ -215,7 +215,7 @@ DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
/** \returns the minimum of all coefficients of *this and puts in *index its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::minCoeff()
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::minCoeff()
*/
template<typename Derived>
template<typename IndexType>
@ -233,7 +233,7 @@ DenseBase<Derived>::minCoeff(IndexType* index) const
/** \returns the maximum of all coefficients of *this and puts in *row and *col its location.
* \warning the result is undefined if \c *this contains NaN.
*
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
* \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
*/
template<typename Derived>
template<typename IndexType>

14
Eigen/src/Core/arch/CUDA/MathFunctions.h
View File

@ -78,6 +78,20 @@ 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 perf<float4>(const float4& a)
{

2
Eigen/src/Core/arch/CUDA/PacketMath.h
View File

@ -40,6 +40,7 @@ template<> struct packet_traits<float> : default_packet_traits
HasSqrt = 1,
HasRsqrt = 1,
HasLGamma = 1,
HasDiGamma = 1,
HasErf = 1,
HasErfc = 1,
@ -63,6 +64,7 @@ template<> struct packet_traits<double> : default_packet_traits
HasSqrt = 1,
HasRsqrt = 1,
HasLGamma = 1,
HasDiGamma = 1,
HasErf = 1,
HasErfc = 1,

63
Eigen/src/Core/functors/NullaryFunctors.h
View File

@ -37,7 +37,7 @@ template<typename Scalar>
struct functor_traits<scalar_identity_op<Scalar> >
{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
template <typename Scalar, typename Packet, bool RandomAccess> struct linspaced_op_impl;
template <typename Scalar, typename Packet, bool RandomAccess, bool IsInteger> struct linspaced_op_impl;
// linear access for packet ops:
// 1) initialization
@ -48,12 +48,12 @@ template <typename Scalar, typename Packet, bool RandomAccess> struct linspaced_
// TODO: Perhaps it's better to initialize lazily (so not in the constructor but in packetOp)
// in order to avoid the padd() in operator() ?
template <typename Scalar, typename Packet>
struct linspaced_op_impl<Scalar,Packet,false>
struct linspaced_op_impl<Scalar,Packet,/*RandomAccess*/false,/*IsInteger*/false>
{
linspaced_op_impl(const Scalar& low, const Scalar& step) :
m_low(low), m_step(step),
m_packetStep(pset1<Packet>(unpacket_traits<Packet>::size*step)),
m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(step),plset<Packet>(-unpacket_traits<Packet>::size)))) {}
linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
m_low(low), m_step(num_steps==1 ? Scalar() : (high-low)/Scalar(num_steps-1)),
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
@ -75,11 +75,11 @@ struct linspaced_op_impl<Scalar,Packet,false>
// 1) each step
// [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
template <typename Scalar, typename Packet>
struct linspaced_op_impl<Scalar,Packet,true>
struct linspaced_op_impl<Scalar,Packet,/*RandomAccess*/true,/*IsInteger*/false>
{
linspaced_op_impl(const Scalar& low, const Scalar& step) :
m_low(low), m_step(step),
m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Packet>(0)) {}
linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
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; }
@ -95,6 +95,31 @@ struct linspaced_op_impl<Scalar,Packet,true>
const Packet m_interPacket;
};
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))
{}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Scalar operator() (Index i) const {
return m_low + (m_length*Scalar(i))/m_divisor;
}
template<typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Packet packetOp(Index 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 Packet m_interPacket;
};
// ----- Linspace functor ----------------------------------------------------------------
// Forward declaration (we default to random access which does not really give
@ -102,10 +127,20 @@ struct linspaced_op_impl<Scalar,Packet,true>
// nested expressions).
template <typename Scalar, typename PacketType, bool RandomAccess = true> struct linspaced_op;
template <typename Scalar, typename PacketType, bool RandomAccess> struct functor_traits< linspaced_op<Scalar,PacketType,RandomAccess> >
{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasSetLinear, IsRepeatable = true }; };
{
enum
{
Cost = 1,
PacketAccess = packet_traits<Scalar>::HasSetLinear
&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),
IsRepeatable = true
};
};
template <typename Scalar, typename PacketType, bool RandomAccess> struct linspaced_op
{
linspaced_op(const Scalar& low, const Scalar& high, Index num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/Scalar(num_steps-1))) {}
linspaced_op(const Scalar& low, const Scalar& high, Index num_steps)
: 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); }
@ -134,7 +169,9 @@ template <typename Scalar, typename PacketType, bool RandomAccess> struct linspa
// This proxy object handles the actual required temporaries, the different
// implementations (random vs. sequential access) as well as the
// correct piping to size 2/4 packet operations.
const linspaced_op_impl<Scalar,PacketType,RandomAccess> impl;
// As long as we don't have a Bresenham-like implementation for linear-access and integer types,
// we have to by-pass RandomAccess for integer types. See bug 698.
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

24
Eigen/src/Core/functors/UnaryFunctors.h
View File

@ -427,6 +427,28 @@ struct functor_traits<scalar_lgamma_op<Scalar> >
};
};
/** \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 Gauss error function of a
* scalar
@ -644,7 +666,7 @@ struct functor_traits<scalar_floor_op<Scalar> >
template<typename Scalar> struct scalar_ceil_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_ceil_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::ceil(a); }
typedef typename packet_traits<Scalar>::type Packet;
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pceil(a); }
};
template<typename Scalar>

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

@ -252,7 +252,7 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
// we have both L2 and L3, and problem is small enough to be kept in L2
// Let's choose m such that lhs's block fit in 1/3 of L2
actual_lm = l2;
max_mc = 576;
max_mc = (std::min<Index>)(576,max_mc);
}
Index mc = (std::min<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
if (mc > Traits::mr) mc -= mc % Traits::mr;

3
Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -352,9 +352,8 @@ class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, M
}
else // no l3 blocking
{
Index m = this->m_mc;
Index n = this->m_nc;
computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, m, n, num_threads);
computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, n, num_threads);
}
m_sizeA = this->m_mc * this->m_kc;

48
Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -42,13 +42,14 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
{
typedef typename scalar_product_traits<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)
const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride,
const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
{
general_matrix_matrix_triangular_product<Index,
RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
ColMajor, UpLo==Lower?Upper:Lower>
::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha);
::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha,blocking);
}
};
@ -58,7 +59,8 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
{
typedef typename scalar_product_traits<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)
const RhsScalar* _rhs, Index rhsStride, ResScalar* _res, Index resStride,
const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
{
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
@ -69,16 +71,18 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
RhsMapper rhs(_rhs,rhsStride);
ResMapper res(_res, resStride);
Index kc = depth; // cache block size along the K direction
Index mc = size; // cache block size along the M direction
Index nc = size; // cache block size along the N direction
computeProductBlockingSizes<LhsScalar,RhsScalar>(kc, mc, nc, 1);
Index kc = blocking.kc();
Index mc = (std::min)(size,blocking.mc());
// !!! mc must be a multiple of nr:
if(mc > Traits::nr)
mc = (mc/Traits::nr)*Traits::nr;
ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, kc*mc, 0);
ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, kc*size, 0);
std::size_t sizeA = kc*mc;
std::size_t sizeB = kc*size;
ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
@ -136,7 +140,7 @@ struct tribb_kernel
typedef typename Traits::ResScalar ResScalar;
enum {
BlockSize = EIGEN_PLAIN_ENUM_MAX(mr,nr)
BlockSize = meta_least_common_multiple<EIGEN_PLAIN_ENUM_MAX(mr,nr),EIGEN_PLAIN_ENUM_MIN(mr,nr)>::ret
};
void operator()(ResScalar* _res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
{
@ -256,13 +260,27 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
enum {
IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
LhsIsRowMajor = _ActualLhs::Flags&RowMajorBit ? 1 : 0,
RhsIsRowMajor = _ActualRhs::Flags&RowMajorBit ? 1 : 0
};
Index size = mat.cols();
Index depth = actualLhs.cols();
typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,typename Lhs::Scalar,typename Rhs::Scalar,
MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualRhs::MaxColsAtCompileTime> BlockingType;
BlockingType blocking(size, size, depth, 1, false);
internal::general_matrix_matrix_triangular_product<Index,
typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo>
::run(mat.cols(), actualLhs.cols(),
typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
IsRowMajor ? RowMajor : ColMajor, UpLo>
::run(size, depth,
&actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(),
mat.data(), mat.outerStride(), actualAlpha);
mat.data(), mat.outerStride(), actualAlpha, blocking);
}
};

46
Eigen/src/Core/products/SelfadjointMatrixMatrix.h
View File

@ -291,7 +291,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,Co
const Scalar* lhs, Index lhsStride,
const Scalar* rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
product_selfadjoint_matrix<Scalar, Index,
EIGEN_LOGICAL_XOR(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
@ -299,7 +299,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,Co
EIGEN_LOGICAL_XOR(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
LhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsSelfAdjoint,ConjugateLhs),
ColMajor>
::run(cols, rows, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha);
::run(cols, rows, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha, blocking);
}
};
@ -314,7 +314,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha);
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index,
@ -325,7 +325,7 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,t
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* _res, Index resStride,
const Scalar& alpha)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
Index size = rows;
@ -340,17 +340,14 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,t
RhsMapper rhs(_rhs,rhsStride);
ResMapper res(_res, resStride);
Index kc = size; // cache block size along the K direction
Index mc = rows; // cache block size along the M direction
Index nc = cols; // cache block size along the N direction
computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc, 1);
// kc must smaller than mc
Index kc = blocking.kc(); // cache block size along the K direction
Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction
// kc must be smaller than mc
kc = (std::min)(kc,mc);
std::size_t sizeA = kc*mc;
std::size_t sizeB = kc*cols;
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0);
ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0);
Scalar* blockB = allocatedBlockB;
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
@ -410,7 +407,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* res, Index resStride,
const Scalar& alpha);
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
};
template <typename Scalar, typename Index,
@ -421,7 +418,7 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,f
const Scalar* _lhs, Index lhsStride,
const Scalar* _rhs, Index rhsStride,
Scalar* _res, Index resStride,
const Scalar& alpha)
const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
{
Index size = cols;
@ -432,14 +429,12 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,f
LhsMapper lhs(_lhs,lhsStride);
ResMapper res(_res,resStride);
Index kc = size; // cache block size along the K direction
Index mc = rows; // cache block size along the M direction
Index nc = cols; // cache block size along the N direction
computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc, 1);
Index kc = blocking.kc(); // cache block size along the K direction
Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction
std::size_t sizeA = kc*mc;
std::size_t sizeB = kc*cols;
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0);
ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0);
Scalar* blockB = allocatedBlockB;
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
@ -498,6 +493,11 @@ struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,1> BlockingType;
BlockingType blocking(lhs.rows(), rhs.cols(), lhs.cols(), 1, false);
internal::product_selfadjoint_matrix<Scalar, Index,
EIGEN_LOGICAL_XOR(LhsIsUpper,internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)),
@ -509,7 +509,7 @@ struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
&lhs.coeffRef(0,0), lhs.outerStride(), // lhs info
&rhs.coeffRef(0,0), rhs.outerStride(), // rhs info
&dst.coeffRef(0,0), dst.outerStride(), // result info
actualAlpha // alpha
actualAlpha, blocking // alpha
);
}
};

24
Eigen/src/Core/products/SelfadjointProduct.h
View File

@ -92,15 +92,27 @@ struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
enum {
IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
OtherIsRowMajor = _ActualOtherType::Flags&RowMajorBit ? 1 : 0
};
Index size = mat.cols();
Index depth = actualOther.cols();
typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,Scalar,Scalar,
MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, _ActualOtherType::MaxColsAtCompileTime> BlockingType;
BlockingType blocking(size, size, depth, 1, false);
internal::general_matrix_matrix_triangular_product<Index,
Scalar, _ActualOtherType::Flags&RowMajorBit ? RowMajor : ColMajor, OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
Scalar, _ActualOtherType::Flags&RowMajorBit ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex,
MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo>
::run(mat.cols(), actualOther.cols(),
Scalar, OtherIsRowMajor ? RowMajor : ColMajor, OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
Scalar, OtherIsRowMajor ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex,
IsRowMajor ? RowMajor : ColMajor, UpLo>
::run(size, depth,
&actualOther.coeffRef(0,0), actualOther.outerStride(), &actualOther.coeffRef(0,0), actualOther.outerStride(),
mat.data(), mat.outerStride(), actualAlpha);
mat.data(), mat.outerStride(), actualAlpha, blocking);
}
};

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

@ -126,6 +126,10 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
Index kc = blocking.kc(); // cache block size along the K direction
Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction
// The small panel size must not be larger than blocking size.
// Usually this should never be the case because SmallPanelWidth^2 is very small
// compared to L2 cache size, but let's be safe:
Index panelWidth = (std::min)(Index(SmallPanelWidth),(std::min)(kc,mc));
std::size_t sizeA = kc*mc;
std::size_t sizeB = kc*cols;
@ -169,9 +173,9 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
if(IsLower || actual_k2<rows)
{
// for each small vertical panels of lhs
for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth)
for (Index k1=0; k1<actual_kc; k1+=panelWidth)
{
Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth);
Index actualPanelWidth = std::min<Index>(actual_kc-k1, panelWidth);
Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1;
Index startBlock = actual_k2+k1;
Index blockBOffset = k1;

3
Eigen/src/Core/util/DisableStupidWarnings.h
View File

@ -15,10 +15,11 @@
// 4522 - 'class' : multiple assignment operators specified
// 4700 - uninitialized local variable 'xyz' used
// 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 )
#pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4717 4800)
#elif defined __INTEL_COMPILER
// 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
// ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body

1
Eigen/src/Core/util/Macros.h
View File

@ -336,7 +336,6 @@
// Do we support r-value references?
#if (__has_feature(cxx_rvalue_references) || \
(defined(__cplusplus) && __cplusplus >= 201103L) || \
defined(__GXX_EXPERIMENTAL_CXX0X__) || \
(EIGEN_COMP_MSVC >= 1600))
#define EIGEN_HAVE_RVALUE_REFERENCES
#endif

6
Eigen/src/Core/util/Memory.h
View File

@ -526,9 +526,9 @@ template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_align
template<int Alignment, typename Scalar, typename Index>
EIGEN_DEVICE_FUNC inline Index first_aligned(const Scalar* array, Index size)
{
static const Index ScalarSize = sizeof(Scalar);
static const Index AlignmentSize = Alignment / ScalarSize;
static const Index AlignmentMask = AlignmentSize-1;
const Index ScalarSize = sizeof(Scalar);
const Index AlignmentSize = Alignment / ScalarSize;
const Index AlignmentMask = AlignmentSize-1;
if(AlignmentSize<=1)
{

20
Eigen/src/Core/util/Meta.h
View File

@ -257,7 +257,7 @@ struct has_std_result_type {int a[2];};
struct has_tr1_result {int a[3];};
template<typename Func, typename ArgType, int SizeOf=sizeof(has_none)>
struct unary_result_of_select {typedef ArgType type;};
struct unary_result_of_select {typedef typename internal::remove_all<ArgType>::type type;};
template<typename Func, typename ArgType>
struct unary_result_of_select<Func, ArgType, sizeof(has_std_result_type)> {typedef typename Func::result_type type;};
@ -279,7 +279,7 @@ struct result_of<Func(ArgType)> {
};
template<typename Func, typename ArgType0, typename ArgType1, int SizeOf=sizeof(has_none)>
struct binary_result_of_select {typedef ArgType0 type;};
struct binary_result_of_select {typedef typename internal::remove_all<ArgType0>::type type;};
template<typename Func, typename ArgType0, typename ArgType1>
struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_std_result_type)>
@ -326,6 +326,22 @@ class meta_sqrt
template<int Y, int InfX, int SupX>
class meta_sqrt<Y, InfX, SupX, true> { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
/** \internal Computes the least common multiple of two positive integer A and B
* at compile-time. It implements a naive algorithm testing all multiples of A.
* It thus works better if A>=B.
*/
template<int A, int B, int K=1, bool Done = ((A*K)%B)==0>
struct meta_least_common_multiple
{
enum { ret = meta_least_common_multiple<A,B,K+1>::ret };
};
template<int A, int B, int K>
struct meta_least_common_multiple<A,B,K,true>
{
enum { ret = A*K };
};
/** \internal determines whether the product of two numeric types is allowed and what the return type is */
template<typename T, typename U> struct scalar_product_traits
{

2
Eigen/src/Core/util/StaticAssert.h
View File

@ -26,7 +26,7 @@
#ifndef EIGEN_NO_STATIC_ASSERT
#if defined(__GXX_EXPERIMENTAL_CXX0X__) || (EIGEN_COMP_MSVC >= 1600)
#if __has_feature(cxx_static_assert) || (defined(__cplusplus) && __cplusplus >= 201103L) || (EIGEN_COMP_MSVC >= 1600)
// if native static_assert is enabled, let's use it
#define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG);

33
Eigen/src/Core/util/XprHelper.h
View File

@ -466,17 +466,17 @@ struct special_scalar_op_base : public BaseType
template<typename Derived,typename Scalar,typename OtherScalar, typename BaseType>
struct special_scalar_op_base<Derived,Scalar,OtherScalar,BaseType,true> : public BaseType
{
const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, const Derived>
operator*(const OtherScalar& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
return CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, const Derived>
(*static_cast<const Derived*>(this), scalar_multiple2_op<Scalar,OtherScalar>(scalar));
}
inline friend const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived>
inline friend const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, const Derived>
operator*(const OtherScalar& scalar, const Derived& matrix)
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
@ -485,13 +485,13 @@ struct special_scalar_op_base<Derived,Scalar,OtherScalar,BaseType,true> : publi
return static_cast<const special_scalar_op_base&>(matrix).operator*(scalar);
}
const CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, Derived>
const CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, const Derived>
operator/(const OtherScalar& scalar) const
{
#ifdef EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
EIGEN_SPECIAL_SCALAR_MULTIPLE_PLUGIN
#endif
return CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, Derived>
return CwiseUnaryOp<scalar_quotient2_op<Scalar,OtherScalar>, const Derived>
(*static_cast<const Derived*>(this), scalar_quotient2_op<Scalar,OtherScalar>(scalar));
}
};
@ -526,22 +526,21 @@ template <typename A> struct promote_storage_type<const A, A>
* the functor.
* The default rules are as follows:
* \code
* A op A -> A
* A op dense -> dense
* dense op B -> dense
* A * dense -> A
* dense * B -> B
* A op A -> A
* A op dense -> dense
* dense op B -> dense
* sparse op dense -> sparse
* dense op sparse -> sparse
* \endcode
*/
template <typename A, typename B, typename Functor> struct cwise_promote_storage_type;
template <typename A, typename Functor> struct cwise_promote_storage_type<A,A,Functor> { typedef A ret; };
template <typename Functor> struct cwise_promote_storage_type<Dense,Dense,Functor> { typedef Dense ret; };
template <typename ScalarA, typename ScalarB> struct cwise_promote_storage_type<Dense,Dense,scalar_product_op<ScalarA,ScalarB> > { typedef Dense ret; };
template <typename A, typename Functor> struct cwise_promote_storage_type<A,Dense,Functor> { typedef Dense ret; };
template <typename B, typename Functor> struct cwise_promote_storage_type<Dense,B,Functor> { typedef Dense ret; };
template <typename A, typename ScalarA, typename ScalarB> struct cwise_promote_storage_type<A,Dense,scalar_product_op<ScalarA,ScalarB> > { typedef A ret; };
template <typename B, typename ScalarA, typename ScalarB> struct cwise_promote_storage_type<Dense,B,scalar_product_op<ScalarA,ScalarB> > { typedef B ret; };
template <typename A, typename Functor> struct cwise_promote_storage_type<A,A,Functor> { typedef A ret; };
template <typename Functor> struct cwise_promote_storage_type<Dense,Dense,Functor> { typedef Dense ret; };
template <typename A, typename Functor> struct cwise_promote_storage_type<A,Dense,Functor> { typedef Dense ret; };
template <typename B, typename Functor> struct cwise_promote_storage_type<Dense,B,Functor> { typedef Dense ret; };
template <typename Functor> struct cwise_promote_storage_type<Sparse,Dense,Functor> { typedef Sparse ret; };
template <typename Functor> struct cwise_promote_storage_type<Dense,Sparse,Functor> { typedef Sparse ret; };
/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
* The template parameter ProductTag permits to specialize the resulting storage kind wrt to

2
Eigen/src/Geometry/ParametrizedLine.h
View File

@ -129,7 +129,7 @@ public:
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const ParametrizedLine& other, typename NumTraits<Scalar>::Real prec = NumTraits<Scalar>::dummy_precision()) const
bool isApprox(const ParametrizedLine& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
{ return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
protected:

2
Eigen/src/Geometry/Translation.h
View File

@ -162,7 +162,7 @@ public:
* determined by \a prec.
*
* \sa MatrixBase::isApprox() */
bool isApprox(const Translation& other, typename NumTraits<Scalar>::Real prec = NumTraits<Scalar>::dummy_precision()) const
bool isApprox(const Translation& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
{ return m_coeffs.isApprox(other.m_coeffs, prec); }
};

192
Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
View File

@ -37,6 +37,8 @@ namespace Eigen {
* and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly performed
* on the matrix B. Otherwise, the factorization is performed on the shifted matrix \f$ B + (\sigma+|\beta| I \f$ where
* \f$ \sigma \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$ \sigma = 10^{-3} \f$.
* If the factorization fails, then the shift in doubled until it succeed or a maximum of ten attempts. If it still fails, as returned by
* the info() method, then you can either increase the initial shift, or better use another preconditioning technique.
*
*/
template <typename Scalar, int _UpLo = Lower, typename _OrderingType =
@ -185,6 +187,10 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol);
};
// Based on the following paper:
// C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
// Limited memory, SIAM J. Sci. Comput. 21(1), pp. 24-45, 1999
// http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
template<typename Scalar, int _UpLo, typename OrderingType>
template<typename _MatrixType>
void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType& mat)
@ -240,7 +246,7 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
else
m_scale(j) = 1;
// FIXME disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
// TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
// Scale and compute the shift for the matrix
RealScalar mindiag = NumTraits<RealScalar>::highest();
@ -251,96 +257,122 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
eigen_internal_assert(rowIdx[colPtr[j]]==j && "IncompleteCholesky: only the lower triangular part must be stored");
mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
}
FactorType L_save = m_L;
RealScalar shift = 0;
if(mindiag <= RealScalar(0.))
shift = m_initialShift - mindiag;
// Apply the shift to the diagonal elements of the matrix
for (Index j = 0; j < n; j++)
vals[colPtr[j]] += shift;
// jki version of the Cholesky factorization
for (Index j=0; j < n; ++j)
{
// Left-looking factorization of the j-th column
// First, load the j-th column into col_vals
Scalar diag = vals[colPtr[j]]; // It is assumed that only the lower part is stored
col_nnz = 0;
for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
m_info = NumericalIssue;
// Try to perform the incomplete factorization using the current shift
int iter = 0;
do
{
// Apply the shift to the diagonal elements of the matrix
for (Index j = 0; j < n; j++)
vals[colPtr[j]] += shift;
// jki version of the Cholesky factorization
Index j=0;
for (; j < n; ++j)
{
StorageIndex l = rowIdx[i];
col_vals(col_nnz) = vals[i];
col_irow(col_nnz) = l;
col_pattern(l) = col_nnz;
col_nnz++;
}
{
typename std::list<StorageIndex>::iterator k;
// Browse all previous columns that will update column j
for(k = listCol[j].begin(); k != listCol[j].end(); k++)
// Left-looking factorization of the j-th column
// First, load the j-th column into col_vals
Scalar diag = vals[colPtr[j]]; // It is assumed that only the lower part is stored
col_nnz = 0;
for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
{
Index jk = firstElt(*k); // First element to use in the column
eigen_internal_assert(rowIdx[jk]==j);
Scalar v_j_jk = numext::conj(vals[jk]);
jk += 1;
for (Index i = jk; i < colPtr[*k+1]; i++)
{
StorageIndex l = rowIdx[i];
if(col_pattern[l]<0)
{
col_vals(col_nnz) = vals[i] * v_j_jk;
col_irow[col_nnz] = l;
col_pattern(l) = col_nnz;
col_nnz++;
}
else
col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
}
updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
StorageIndex l = rowIdx[i];
col_vals(col_nnz) = vals[i];
col_irow(col_nnz) = l;
col_pattern(l) = col_nnz;
col_nnz++;
}
{
typename std::list<StorageIndex>::iterator k;
// Browse all previous columns that will update column j
for(k = listCol[j].begin(); k != listCol[j].end(); k++)
{
Index jk = firstElt(*k); // First element to use in the column
eigen_internal_assert(rowIdx[jk]==j);
Scalar v_j_jk = numext::conj(vals[jk]);
jk += 1;
for (Index i = jk; i < colPtr[*k+1]; i++)
{
StorageIndex l = rowIdx[i];
if(col_pattern[l]<0)
{
col_vals(col_nnz) = vals[i] * v_j_jk;
col_irow[col_nnz] = l;
col_pattern(l) = col_nnz;
col_nnz++;
}
else
col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
}
updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
}
}
// Scale the current column
if(numext::real(diag) <= 0)
{
if(++iter>=10)
return;
// increase shift
shift = numext::maxi(m_initialShift,RealScalar(2)*shift);
// restore m_L, col_pattern, and listCol
vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n+1);
col_pattern.fill(-1);
for(Index i=0; i<n; ++i)
listCol[i].clear();
break;
}
RealScalar rdiag = sqrt(numext::real(diag));
vals[colPtr[j]] = rdiag;
for (Index k = 0; k<col_nnz; ++k)
{
Index i = col_irow[k];
//Scale
col_vals(k) /= rdiag;
//Update the remaining diagonals with col_vals
vals[colPtr[i]] -= numext::abs2(col_vals(k));
}
// Select the largest p elements
// p is the original number of elements in the column (without the diagonal)
Index p = colPtr[j+1] - colPtr[j] - 1 ;
Ref<VectorSx> cvals = col_vals.head(col_nnz);
Ref<VectorIx> cirow = col_irow.head(col_nnz);
internal::QuickSplit(cvals,cirow, p);
// Insert the largest p elements in the matrix
Index cpt = 0;
for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
{
vals[i] = col_vals(cpt);
rowIdx[i] = col_irow(cpt);
// restore col_pattern:
col_pattern(col_irow(cpt)) = -1;
cpt++;
}
// Get the first smallest row index and put it after the diagonal element
Index jk = colPtr(j)+1;
updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
}
// Scale the current column
if(numext::real(diag) <= 0)
if(j==n)
{
m_info = NumericalIssue;
return;
m_factorizationIsOk = true;
m_info = Success;
}
RealScalar rdiag = sqrt(numext::real(diag));
vals[colPtr[j]] = rdiag;
for (Index k = 0; k<col_nnz; ++k)
{
Index i = col_irow[k];
//Scale
col_vals(k) /= rdiag;
//Update the remaining diagonals with col_vals
vals[colPtr[i]] -= numext::abs2(col_vals(k));
}
// Select the largest p elements
// p is the original number of elements in the column (without the diagonal)
Index p = colPtr[j+1] - colPtr[j] - 1 ;
Ref<VectorSx> cvals = col_vals.head(col_nnz);
Ref<VectorIx> cirow = col_irow.head(col_nnz);
internal::QuickSplit(cvals,cirow, p);
// Insert the largest p elements in the matrix
Index cpt = 0;
for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
{
vals[i] = col_vals(cpt);
rowIdx[i] = col_irow(cpt);
// restore col_pattern:
col_pattern(col_irow(cpt)) = -1;
cpt++;
}
// Get the first smallest row index and put it after the diagonal element
Index jk = colPtr(j)+1;
updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
}
m_factorizationIsOk = true;
m_info = Success;
} while(m_info!=Success);
}
template<typename Scalar, int _UpLo, typename OrderingType>

2
Eigen/src/OrderingMethods/Amd.h
View File

@ -8,7 +8,7 @@
NOTE: this routine has been adapted from the CSparse library:
Copyright (c) 2006, Timothy A. Davis.
http://www.cise.ufl.edu/research/sparse/CSparse
http://www.suitesparse.com
CSparse is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public

29
Eigen/src/OrderingMethods/Eigen_Colamd.h
View File

@ -41,12 +41,8 @@
//
// The colamd/symamd library is available at
//
// http://www.cise.ufl.edu/research/sparse/colamd/
// http://www.suitesparse.com
// This is the http://www.cise.ufl.edu/research/sparse/colamd/colamd.h
// file. It is required by the colamd.c, colamdmex.c, and symamdmex.c
// files, and by any C code that calls the routines whose prototypes are
// listed below, or that uses the colamd/symamd definitions listed below.
#ifndef EIGEN_COLAMD_H
#define EIGEN_COLAMD_H
@ -102,9 +98,6 @@ namespace internal {
/* === Definitions ========================================================== */
/* ========================================================================== */
#define COLAMD_MAX(a,b) (((a) > (b)) ? (a) : (b))
#define COLAMD_MIN(a,b) (((a) < (b)) ? (a) : (b))
#define ONES_COMPLEMENT(r) (-(r)-1)
/* -------------------------------------------------------------------------- */
@ -739,8 +732,8 @@ static void init_scoring
/* === Extract knobs ==================================================== */
dense_row_count = COLAMD_MAX (0, COLAMD_MIN (knobs [COLAMD_DENSE_ROW] * n_col, n_col)) ;
dense_col_count = COLAMD_MAX (0, COLAMD_MIN (knobs [COLAMD_DENSE_COL] * n_row, n_row)) ;
dense_row_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [COLAMD_DENSE_ROW] * n_col), n_col)) ;
dense_col_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [COLAMD_DENSE_COL] * n_row), n_row)) ;
COLAMD_DEBUG1 (("colamd: densecount: %d %d\n", dense_row_count, dense_col_count)) ;
max_deg = 0 ;
n_col2 = n_col ;
@ -804,7 +797,7 @@ static void init_scoring
else
{
/* keep track of max degree of remaining rows */
max_deg = COLAMD_MAX (max_deg, deg) ;
max_deg = numext::maxi(max_deg, deg) ;
}
}
COLAMD_DEBUG1 (("colamd: Dense and null rows killed: %d\n", n_row - n_row2)) ;
@ -842,7 +835,7 @@ static void init_scoring
/* add row's external degree */
score += Row [row].shared1.degree - 1 ;
/* guard against integer overflow */
score = COLAMD_MIN (score, n_col) ;
score = numext::mini(score, n_col) ;
}
/* determine pruned column length */
col_length = (IndexType) (new_cp - &A [Col [c].start]) ;
@ -914,7 +907,7 @@ static void init_scoring
head [score] = c ;
/* see if this score is less than current min */
min_score = COLAMD_MIN (min_score, score) ;
min_score = numext::mini(min_score, score) ;
}
@ -1040,7 +1033,7 @@ static IndexType find_ordering /* return the number of garbage collections */
/* === Garbage_collection, if necessary ============================= */
needed_memory = COLAMD_MIN (pivot_col_score, n_col - k) ;
needed_memory = numext::mini(pivot_col_score, n_col - k) ;
if (pfree + needed_memory >= Alen)
{
pfree = Eigen::internal::garbage_collection (n_row, n_col, Row, Col, A, &A [pfree]) ;
@ -1099,7 +1092,7 @@ static IndexType find_ordering /* return the number of garbage collections */
/* clear tag on pivot column */
Col [pivot_col].shared1.thickness = pivot_col_thickness ;
max_deg = COLAMD_MAX (max_deg, pivot_row_degree) ;
max_deg = numext::maxi(max_deg, pivot_row_degree) ;
/* === Kill all rows used to construct pivot row ==================== */
@ -1273,7 +1266,7 @@ static IndexType find_ordering /* return the number of garbage collections */
/* add set difference */
cur_score += row_mark - tag_mark ;
/* integer overflow... */
cur_score = COLAMD_MIN (cur_score, n_col) ;
cur_score = numext::mini(cur_score, n_col) ;
}
/* recompute the column's length */
@ -1386,7 +1379,7 @@ static IndexType find_ordering /* return the number of garbage collections */
cur_score -= Col [col].shared1.thickness ;
/* make sure score is less or equal than the max score */
cur_score = COLAMD_MIN (cur_score, max_score) ;
cur_score = numext::mini(cur_score, max_score) ;
COLAMD_ASSERT (cur_score >= 0) ;
/* store updated score */
@ -1409,7 +1402,7 @@ static IndexType find_ordering /* return the number of garbage collections */
head [cur_score] = col ;
/* see if this score is less than current min */
min_score = COLAMD_MIN (min_score, cur_score) ;
min_score = numext::mini(min_score, cur_score) ;
}

37
Eigen/src/SparseCore/SparseBlock.h
View File

@ -100,11 +100,11 @@ protected:
enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
public:
inline sparse_matrix_block_impl(const SparseMatrixType& xpr, Index i)
inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index i)
: m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
{}
inline sparse_matrix_block_impl(const SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
{}
@ -112,7 +112,7 @@ public:
inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other)
{
typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _NestedMatrixType;
_NestedMatrixType& matrix = const_cast<_NestedMatrixType&>(m_matrix);;
_NestedMatrixType& matrix = m_matrix;
// This assignment is slow if this vector set is not empty
// and/or it is not at the end of the nonzeros of the underlying matrix.
@ -209,28 +209,28 @@ public:
inline const Scalar* valuePtr() const
{ return m_matrix.valuePtr(); }
inline Scalar* valuePtr()
{ return m_matrix.const_cast_derived().valuePtr(); }
{ return m_matrix.valuePtr(); }
inline const StorageIndex* innerIndexPtr() const
{ return m_matrix.innerIndexPtr(); }
inline StorageIndex* innerIndexPtr()
{ return m_matrix.const_cast_derived().innerIndexPtr(); }
{ return m_matrix.innerIndexPtr(); }
inline const StorageIndex* outerIndexPtr() const
{ return m_matrix.outerIndexPtr() + m_outerStart; }
inline StorageIndex* outerIndexPtr()
{ return m_matrix.const_cast_derived().outerIndexPtr() + m_outerStart; }
{ return m_matrix.outerIndexPtr() + m_outerStart; }
inline const StorageIndex* innerNonZeroPtr() const
{ return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
inline StorageIndex* innerNonZeroPtr()
{ return isCompressed() ? 0 : (m_matrix.const_cast_derived().innerNonZeroPtr()+m_outerStart); }
{ return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
bool isCompressed() const { return m_matrix.innerNonZeroPtr()==0; }
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived().coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 : m_outerStart));
return m_matrix.coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 : m_outerStart));
}
inline const Scalar coeff(Index row, Index col) const
@ -264,7 +264,7 @@ public:
protected:
typename SparseMatrixType::Nested m_matrix;
typename internal::ref_selector<SparseMatrixType>::non_const_type m_matrix;
Index m_outerStart;
const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
@ -373,7 +373,7 @@ public:
/** Column or Row constructor
*/
inline BlockImpl(const XprType& xpr, Index i)
inline BlockImpl(XprType& xpr, Index i)
: m_matrix(xpr),
m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? convert_index(i) : 0),
m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? convert_index(i) : 0),
@ -383,7 +383,7 @@ public:
/** Dynamic-size constructor
*/
inline BlockImpl(const XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
: m_matrix(xpr), m_startRow(convert_index(startRow)), m_startCol(convert_index(startCol)), m_blockRows(convert_index(blockRows)), m_blockCols(convert_index(blockCols))
{}
@ -392,8 +392,7 @@ public:
inline Scalar& coeffRef(Index row, Index col)
{
return m_matrix.const_cast_derived()
.coeffRef(row + m_startRow.value(), col + m_startCol.value());
return m_matrix.coeffRef(row + m_startRow.value(), col + m_startCol.value());
}
inline const Scalar coeff(Index row, Index col) const
@ -403,16 +402,14 @@ public:
inline Scalar& coeffRef(Index index)
{
return m_matrix.const_cast_derived()
.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_matrix.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
inline const Scalar coeff(Index index) const
{
return m_matrix
.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
return m_matrix.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
}
inline const _MatrixTypeNested& nestedExpression() const { return m_matrix; }
@ -430,7 +427,7 @@ public:
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
typename XprType::Nested m_matrix;
typename internal::ref_selector<XprType>::non_const_type m_matrix;
const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;

21
Eigen/src/SparseCore/SparseCompressedBase.h
View File

@ -117,6 +117,24 @@ template<typename Derived>
class SparseCompressedBase<Derived>::InnerIterator
{
public:
InnerIterator()
: m_values(0), m_indices(0), m_outer(0), m_id(0), m_end(0)
{}
InnerIterator(const InnerIterator& other)
: m_values(other.m_values), m_indices(other.m_indices), m_outer(other.m_outer), m_id(other.m_id), m_end(other.m_end)
{}
InnerIterator& operator=(const InnerIterator& other)
{
m_values = other.m_values;
m_indices = other.m_indices;
const_cast<OuterType&>(m_outer).setValue(other.m_outer.value());
m_id = other.m_id;
m_end = other.m_end;
return *this;
}
InnerIterator(const SparseCompressedBase& mat, Index outer)
: m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
{
@ -162,7 +180,8 @@ class SparseCompressedBase<Derived>::InnerIterator
protected:
const Scalar* m_values;
const StorageIndex* m_indices;
const internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> m_outer;
typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
const OuterType m_outer;
Index m_id;
Index m_end;
private:

221
Eigen/src/SparseCore/SparseCwiseBinaryOp.h
View File

@ -49,17 +49,10 @@ class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
namespace internal {
template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived,
typename _LhsStorageMode = typename traits<Lhs>::StorageKind,
typename _RhsStorageMode = typename traits<Rhs>::StorageKind>
class sparse_cwise_binary_op_inner_iterator_selector;
} // end namespace internal
namespace internal {
// Generic "sparse OP sparse"
template<typename XprType> struct binary_sparse_evaluator;
template<typename BinaryOp, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
@ -153,6 +146,182 @@ protected:
evaluator<Rhs> m_rhsImpl;
};
// dense op sparse
template<typename BinaryOp, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IteratorBased>
: evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
protected:
typedef typename evaluator<Rhs>::InnerIterator RhsIterator;
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
typedef typename traits<XprType>::Scalar Scalar;
typedef typename XprType::StorageIndex StorageIndex;
public:
class ReverseInnerIterator;
class InnerIterator
{
enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
: m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_id(-1), m_innerSize(aEval.m_expr.rhs().innerSize())
{
this->operator++();
}
EIGEN_STRONG_INLINE InnerIterator& operator++()
{
++m_id;
if(m_id<m_innerSize)
{
Scalar lhsVal = m_lhsEval.coeff(IsRowMajor?m_rhsIter.outer():m_id,
IsRowMajor?m_id:m_rhsIter.outer());
if(m_rhsIter && m_rhsIter.index()==m_id)
{
m_value = m_functor(lhsVal, m_rhsIter.value());
++m_rhsIter;
}
else
m_value = m_functor(lhsVal, Scalar(0));
}
return *this;
}
EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
protected:
const evaluator<Lhs> &m_lhsEval;
RhsIterator m_rhsIter;
const BinaryOp& m_functor;
Scalar m_value;
StorageIndex m_id;
StorageIndex m_innerSize;
};
enum {
CoeffReadCost = evaluator<Lhs>::CoeffReadCost + evaluator<Rhs>::CoeffReadCost + functor_traits<BinaryOp>::Cost,
// Expose storage order of the sparse expression
Flags = (XprType::Flags & ~RowMajorBit) | (int(Rhs::Flags)&RowMajorBit)
};
explicit binary_evaluator(const XprType& xpr)
: m_functor(xpr.functor()),
m_lhsImpl(xpr.lhs()),
m_rhsImpl(xpr.rhs()),
m_expr(xpr)
{
EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
inline Index nonZerosEstimate() const {
return m_expr.size();
}
protected:
const BinaryOp m_functor;
evaluator<Lhs> m_lhsImpl;
evaluator<Rhs> m_rhsImpl;
const XprType &m_expr;
};
// sparse op dense
template<typename BinaryOp, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IndexBased>
: evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
{
protected:
typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
typedef typename traits<XprType>::Scalar Scalar;
typedef typename XprType::StorageIndex StorageIndex;
public:
class ReverseInnerIterator;
class InnerIterator
{
enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
public:
EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
: m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_id(-1), m_innerSize(aEval.m_expr.lhs().innerSize())
{
this->operator++();
}
EIGEN_STRONG_INLINE InnerIterator& operator++()
{
++m_id;
if(m_id<m_innerSize)
{
Scalar rhsVal = m_rhsEval.coeff(IsRowMajor?m_lhsIter.outer():m_id,
IsRowMajor?m_id:m_lhsIter.outer());
if(m_lhsIter && m_lhsIter.index()==m_id)
{
m_value = m_functor(m_lhsIter.value(), rhsVal);
++m_lhsIter;
}
else
m_value = m_functor(Scalar(0),rhsVal);
}
return *this;
}
EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
protected:
LhsIterator m_lhsIter;
const evaluator<Rhs> &m_rhsEval;
const BinaryOp& m_functor;
Scalar m_value;
StorageIndex m_id;
StorageIndex m_innerSize;
};
enum {
CoeffReadCost = evaluator<Lhs>::CoeffReadCost + evaluator<Rhs>::CoeffReadCost + functor_traits<BinaryOp>::Cost,
// Expose storage order of the sparse expression
Flags = (XprType::Flags & ~RowMajorBit) | (int(Lhs::Flags)&RowMajorBit)
};
explicit binary_evaluator(const XprType& xpr)
: m_functor(xpr.functor()),
m_lhsImpl(xpr.lhs()),
m_rhsImpl(xpr.rhs()),
m_expr(xpr)
{
EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
inline Index nonZerosEstimate() const {
return m_expr.size();
}
protected:
const BinaryOp m_functor;
evaluator<Lhs> m_lhsImpl;
evaluator<Rhs> m_rhsImpl;
const XprType &m_expr;
};
// "sparse .* sparse"
template<typename T, typename Lhs, typename Rhs>
struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T>, Lhs, Rhs>, IteratorBased, IteratorBased>
@ -287,7 +456,8 @@ public:
enum {
CoeffReadCost = evaluator<Lhs>::CoeffReadCost + evaluator<Rhs>::CoeffReadCost + functor_traits<BinaryOp>::Cost,
Flags = XprType::Flags
// Expose storage order of the sparse expression
Flags = (XprType::Flags & ~RowMajorBit) | (int(Rhs::Flags)&RowMajorBit)
};
explicit binary_evaluator(const XprType& xpr)
@ -360,7 +530,8 @@ public:
enum {
CoeffReadCost = evaluator<Lhs>::CoeffReadCost + evaluator<Rhs>::CoeffReadCost + functor_traits<BinaryOp>::Cost,
Flags = XprType::Flags
// Expose storage order of the sparse expression
Flags = (XprType::Flags & ~RowMajorBit) | (int(Lhs::Flags)&RowMajorBit)
};
explicit binary_evaluator(const XprType& xpr)
@ -428,6 +599,34 @@ SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) c
return typename CwiseProductDenseReturnType<OtherDerived>::Type(derived(), other.derived());
}
template<typename DenseDerived, typename SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>
operator+(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
}
template<typename SparseDerived, typename DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
operator+(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
}
template<typename DenseDerived, typename SparseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>
operator-(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
}
template<typename SparseDerived, typename DenseDerived>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
operator-(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
{
return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
}
} // end namespace Eigen
#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H

2
Eigen/src/SparseCore/SparseDenseProduct.h
View File

@ -48,7 +48,7 @@ struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, t
// It basically represents the minimal amount of work to be done to be worth it.
if(threads>1 && lhsEval.nonZerosEstimate() > 20000)
{
#pragma omp parallel for schedule(static) num_threads(threads)
#pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
for(Index i=0; i<n; ++i)
processRow(lhsEval,rhs,res,alpha,i,c);
}

7
Eigen/src/SparseCore/SparseMatrix.h
View File

@ -538,7 +538,12 @@ class SparseMatrix
}
/** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched.
* \sa reserve(), setZero()
*
* If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode
* and the storage of the out of bounds coefficients is kept and reserved.
* Call makeCompressed() to pack the entries and squeeze extra memory.
*
* \sa reserve(), setZero(), makeCompressed()
*/
void conservativeResize(Index rows, Index cols)
{

12
Eigen/src/SparseCore/SparseSelfAdjointView.h
View File

@ -55,10 +55,10 @@ template<typename MatrixType, unsigned int _Mode> class SparseSelfAdjointView
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<StorageIndex,Dynamic,1> VectorI;
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
explicit inline SparseSelfAdjointView(const MatrixType& matrix) : m_matrix(matrix)
explicit inline SparseSelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{
eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
}
@ -68,7 +68,7 @@ template<typename MatrixType, unsigned int _Mode> class SparseSelfAdjointView
/** \internal \returns a reference to the nested matrix */
const _MatrixTypeNested& matrix() const { return m_matrix; }
_MatrixTypeNested& matrix() { return m_matrix.const_cast_derived(); }
typename internal::remove_reference<MatrixTypeNested>::type& matrix() { return m_matrix; }
/** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs.
*
@ -158,7 +158,7 @@ template<typename MatrixType, unsigned int _Mode> class SparseSelfAdjointView
protected:
typename MatrixType::Nested m_matrix;
MatrixTypeNested m_matrix;
//mutable VectorI m_countPerRow;
//mutable VectorI m_countPerCol;
private:
@ -194,9 +194,9 @@ SparseSelfAdjointView<MatrixType,Mode>::rankUpdate(const SparseMatrixBase<Derive
{
SparseMatrix<Scalar,(MatrixType::Flags&RowMajorBit)?RowMajor:ColMajor> tmp = u * u.adjoint();
if(alpha==Scalar(0))
m_matrix.const_cast_derived() = tmp.template triangularView<Mode>();
m_matrix = tmp.template triangularView<Mode>();
else
m_matrix.const_cast_derived() += alpha * tmp.template triangularView<Mode>();
m_matrix += alpha * tmp.template triangularView<Mode>();
return *this;
}

31
Eigen/src/SparseCore/SparseVector.h
View File

@ -205,23 +205,54 @@ class SparseVector
inline void finalize() {}
/** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
{
m_data.prune(reference,epsilon);
}
/** Resizes the sparse vector to \a rows x \a cols
*
* This method is provided for compatibility with matrices.
* For a column vector, \a cols must be equal to 1.
* For a row vector, \a rows must be equal to 1.
*
* \sa resize(Index)
*/
void resize(Index rows, Index cols)
{
eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
resize(IsColVector ? rows : cols);
}
/** Resizes the sparse vector to \a newSize
* This method deletes all entries, thus leaving an empty sparse vector
*
* \sa conservativeResize(), setZero() */
void resize(Index newSize)
{
m_size = newSize;
m_data.clear();
}
/** Resizes the sparse vector to \a newSize, while leaving old values untouched.
*
* If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
* Call .data().squeeze() to free extra memory.
*
* \sa reserve(), setZero()
*/
void conservativeResize(Index newSize)
{
if (newSize < m_size)
{
Index i = 0;
while (i<m_data.size() && m_data.index(i)<newSize) ++i;
m_data.resize(i);
}
m_size = newSize;
}
void resizeNonZeros(Index size) { m_data.resize(size); }
inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); }

2
Eigen/src/SparseCore/SparseView.h
View File

@ -38,7 +38,7 @@ public:
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
RealScalar epsilon = NumTraits<Scalar>::dummy_precision())
const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
: m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
inline Index rows() const { return m_matrix.rows(); }

11
Eigen/src/SparseQR/SparseQR.h
View File

@ -128,6 +128,17 @@ class SparseQR : public SparseSolverBase<SparseQR<_MatrixType,_OrderingType> >
inline Index cols() const { return m_pmat.cols();}
/** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization.
* \warning The entries of the returned matrix are not sorted. This means that using it in algorithms
* expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()),
* and coefficient-wise operations. Matrix products and triangular solves are fine though.
*
* To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix
* is required, you can copy it again:
* \code
* SparseMatrix<double> R = qr.matrixR(); // column-major, not sorted!
* SparseMatrix<double,RowMajor> Rr = qr.matrixR(); // row-major, sorted
* SparseMatrix<double> Rc = Rr; // column-major, sorted
* \endcode
*/
const QRMatrixType& matrixR() const { return m_R; }

11
Eigen/src/plugins/ArrayCwiseUnaryOps.h
View File

@ -22,6 +22,7 @@ typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturn
typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaReturnType;
typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
typedef CwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived> PowReturnType;
@ -318,6 +319,16 @@ lgamma() const
return LgammaReturnType(derived());
}
/** \returns an expression of the coefficient-wise digamma (psi, derivative of lgamma).
*
* \sa cos(), sin(), tan()
*/
inline const DigammaReturnType
digamma() const
{
return DigammaReturnType(derived());
}
/** \returns an expression of the coefficient-wise Gauss error
* function of *this.
*

8
bench/tensors/README Normal file
View File

@ -0,0 +1,8 @@
Each benchmark comes in 2 flavors: one that runs on CPU, and one that runs on GPU.
To compile the CPU benchmarks, simply call:
g++ tensor_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu
To compile the GPU benchmarks, simply call:
nvcc tensor_benchmarks_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -arch compute_35 -o benchmarks_gpu

49
bench/tensors/benchmark.h Normal file
View File

@ -0,0 +1,49 @@
/*
* Copyright (C) 2012 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <stddef.h>
#include <stdint.h>
#include <vector>
namespace testing {
class Benchmark {
public:
Benchmark(const char* name, void (*fn)(int)) {
Register(name, fn, NULL);
}
Benchmark(const char* name, void (*fn_range)(int, int)) {
Register(name, NULL, fn_range);
}
Benchmark* Arg(int x);
Benchmark* Range(int lo, int hi);
const char* Name();
bool ShouldRun(int argc, char* argv[]);
void Run();
private:
const char* name_;
void (*fn_)(int);
void (*fn_range_)(int, int);
std::vector<int> args_;
void Register(const char* name, void (*fn)(int), void (*fn_range)(int, int));
void RunRepeatedlyWithArg(int iterations, int arg);
void RunWithArg(int arg);
};
} // namespace testing
void SetBenchmarkFlopsProcessed(int64_t);
void StopBenchmarkTiming();
void StartBenchmarkTiming();
#define BENCHMARK(f) \
static ::testing::Benchmark* _benchmark_##f __attribute__((unused)) = \
(new ::testing::Benchmark(#f, f))

237
bench/tensors/benchmark_main.cc Normal file
View File

@ -0,0 +1,237 @@
/*
* Copyright (C) 2012 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "benchmark.h"
#include <regex.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <string>
#include <inttypes.h>
#include <time.h>
#include <map>
static int64_t g_flops_processed;
static int64_t g_benchmark_total_time_ns;
static int64_t g_benchmark_start_time_ns;
typedef std::map<std::string, ::testing::Benchmark*> BenchmarkMap;
typedef BenchmarkMap::iterator BenchmarkMapIt;
BenchmarkMap& gBenchmarks() {
static BenchmarkMap g_benchmarks;
return g_benchmarks;
}
static int g_name_column_width = 20;
static int Round(int n) {
int base = 1;
while (base*10 < n) {
base *= 10;
}
if (n < 2*base) {
return 2*base;
}
if (n < 5*base) {
return 5*base;
}
return 10*base;
}
#ifdef __APPLE__
#include <mach/mach_time.h>
static mach_timebase_info_data_t g_time_info;
static void __attribute__((constructor)) init_info() {
mach_timebase_info(&g_time_info);
}
#endif
static int64_t NanoTime() {
#if defined(__APPLE__)
uint64_t t = mach_absolute_time();
return t * g_time_info.numer / g_time_info.denom;
#else
struct timespec t;
t.tv_sec = t.tv_nsec = 0;
clock_gettime(CLOCK_MONOTONIC, &t);
return static_cast<int64_t>(t.tv_sec) * 1000000000LL + t.tv_nsec;
#endif
}
namespace testing {
Benchmark* Benchmark::Arg(int arg) {
args_.push_back(arg);
return this;
}
Benchmark* Benchmark::Range(int lo, int hi) {
const int kRangeMultiplier = 8;
if (hi < lo) {
int temp = hi;
hi = lo;
lo = temp;
}
while (lo < hi) {
args_.push_back(lo);
lo *= kRangeMultiplier;
}
// We always run the hi number.
args_.push_back(hi);
return this;
}
const char* Benchmark::Name() {
return name_;
}
bool Benchmark::ShouldRun(int argc, char* argv[]) {
if (argc == 1) {
return true; // With no arguments, we run all benchmarks.
}
// Otherwise, we interpret each argument as a regular expression and
// see if any of our benchmarks match.
for (int i = 1; i < argc; i++) {
regex_t re;
if (regcomp(&re, argv[i], 0) != 0) {
fprintf(stderr, "couldn't compile \"%s\" as a regular expression!\n", argv[i]);
exit(EXIT_FAILURE);
}
int match = regexec(&re, name_, 0, NULL, 0);
regfree(&re);
if (match != REG_NOMATCH) {
return true;
}
}
return false;
}
void Benchmark::Register(const char* name, void (*fn)(int), void (*fn_range)(int, int)) {
name_ = name;
fn_ = fn;
fn_range_ = fn_range;
if (fn_ == NULL && fn_range_ == NULL) {
fprintf(stderr, "%s: missing function\n", name_);
exit(EXIT_FAILURE);
}
gBenchmarks().insert(std::make_pair(name, this));
}
void Benchmark::Run() {
if (fn_ != NULL) {
RunWithArg(0);
} else {
if (args_.empty()) {
fprintf(stderr, "%s: no args!\n", name_);
exit(EXIT_FAILURE);
}
for (size_t i = 0; i < args_.size(); ++i) {
RunWithArg(args_[i]);
}
}
}
void Benchmark::RunRepeatedlyWithArg(int iterations, int arg) {
g_flops_processed = 0;
g_benchmark_total_time_ns = 0;
g_benchmark_start_time_ns = NanoTime();
if (fn_ != NULL) {
fn_(iterations);
} else {
fn_range_(iterations, arg);
}
if (g_benchmark_start_time_ns != 0) {
g_benchmark_total_time_ns += NanoTime() - g_benchmark_start_time_ns;
}
}
void Benchmark::RunWithArg(int arg) {
// run once in case it's expensive
int iterations = 1;
RunRepeatedlyWithArg(iterations, arg);
while (g_benchmark_total_time_ns < 1e9 && iterations < 1e9) {
int last = iterations;
if (g_benchmark_total_time_ns/iterations == 0) {
iterations = 1e9;
} else {
iterations = 1e9 / (g_benchmark_total_time_ns/iterations);
}
iterations = std::max(last + 1, std::min(iterations + iterations/2, 100*last));
iterations = Round(iterations);
RunRepeatedlyWithArg(iterations, arg);
}
char throughput[100];
throughput[0] = '\0';
if (g_benchmark_total_time_ns > 0 && g_flops_processed > 0) {
double mflops_processed = static_cast<double>(g_flops_processed)/1e6;
double seconds = static_cast<double>(g_benchmark_total_time_ns)/1e9;
snprintf(throughput, sizeof(throughput), " %8.2f MFlops/s", mflops_processed/seconds);
}
char full_name[100];
if (fn_range_ != NULL) {
if (arg >= (1<<20)) {
snprintf(full_name, sizeof(full_name), "%s/%dM", name_, arg/(1<<20));
} else if (arg >= (1<<10)) {
snprintf(full_name, sizeof(full_name), "%s/%dK", name_, arg/(1<<10));
} else {
snprintf(full_name, sizeof(full_name), "%s/%d", name_, arg);
}
} else {
snprintf(full_name, sizeof(full_name), "%s", name_);
}
printf("%-*s %10d %10" PRId64 "%s\n", g_name_column_width, full_name,
iterations, g_benchmark_total_time_ns/iterations, throughput);
fflush(stdout);
}
} // namespace testing
void SetBenchmarkFlopsProcessed(int64_t x) {
g_flops_processed = x;
}
void StopBenchmarkTiming() {
if (g_benchmark_start_time_ns != 0) {
g_benchmark_total_time_ns += NanoTime() - g_benchmark_start_time_ns;
}
g_benchmark_start_time_ns = 0;
}
void StartBenchmarkTiming() {
if (g_benchmark_start_time_ns == 0) {
g_benchmark_start_time_ns = NanoTime();
}
}
int main(int argc, char* argv[]) {
if (gBenchmarks().empty()) {
fprintf(stderr, "No benchmarks registered!\n");
exit(EXIT_FAILURE);
}
for (BenchmarkMapIt it = gBenchmarks().begin(); it != gBenchmarks().end(); ++it) {
int name_width = static_cast<int>(strlen(it->second->Name()));
g_name_column_width = std::max(g_name_column_width, name_width);
}
bool need_header = true;
for (BenchmarkMapIt it = gBenchmarks().begin(); it != gBenchmarks().end(); ++it) {
::testing::Benchmark* b = it->second;
if (b->ShouldRun(argc, argv)) {
if (need_header) {
printf("%-*s %10s %10s\n", g_name_column_width, "", "iterations", "ns/op");
fflush(stdout);
need_header = false;
}
b->Run();
}
}
if (need_header) {
fprintf(stderr, "No matching benchmarks!\n");
fprintf(stderr, "Available benchmarks:\n");
for (BenchmarkMapIt it = gBenchmarks().begin(); it != gBenchmarks().end(); ++it) {
fprintf(stderr, " %s\n", it->second->Name());
}
exit(EXIT_FAILURE);
}
return 0;
}

199
bench/tensors/tensor_benchmarks.h
View File

@ -4,13 +4,15 @@
typedef int TensorIndex;
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "testing/base/public/benchmark.h"
#include "unsupported/Eigen/CXX11/Tensor"
#include "benchmark.h"
#define BENCHMARK_RANGE(bench, lo, hi) \
BENCHMARK(bench)->Range(lo, hi)
using Eigen::Tensor;
using Eigen::TensorMap;
// TODO(bsteiner): also templatize on the input type since we have users
// for int8 as well as floats.
template <typename Device> class BenchmarkSuite {
@ -38,12 +40,26 @@ template <typename Device> class BenchmarkSuite {
device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
}
// Record the number of values copied per second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void typeCasting(int num_iters) {
eigen_assert(m_ == n_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, k_}};
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> A(a_, sizes);
TensorMap<Tensor<int, 2, 0, TensorIndex>, Eigen::Aligned> B((int*)b_, sizes);
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
B.device(device_) = A.cast<int>();
}
// Record the number of values copied per second
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void random(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes(m_, m_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
StartBenchmarkTiming();
@ -51,21 +67,21 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = C.random();
}
// Record the number of random numbers generated per second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void slicing(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes(m_, m_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
const Eigen::DSizes<TensorIndex, 2> quarter_sizes(m_/2, m_/2);
const Eigen::DSizes<TensorIndex, 2> first_quadrant(0, 0);
const Eigen::DSizes<TensorIndex, 2> second_quadrant(0, m_/2);
const Eigen::DSizes<TensorIndex, 2> third_quadrant(m_/2, 0);
const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(m_/2, m_/2);
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
@ -80,31 +96,59 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of values copied from the rhs slice to the lhs slice
// each second
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void rowChip(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}};
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{n_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
C.device(device_) = B.chip(iter % k_, 0);
}
// Record the number of values copied from the rhs chip to the lhs.
finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
}
void colChip(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size= {{k_, n_}};
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{n_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
C.device(device_) = B.chip(iter % n_, 1);
}
// Record the number of values copied from the rhs chip to the lhs.
finalizeBenchmark(static_cast<int64_t>(n_) * num_iters);
}
void shuffling(int num_iters) {
eigen_assert(m_ == n_);
const Eigen::array<TensorIndex, 2> size_a(m_, k_);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
const Eigen::array<TensorIndex, 2> size_b(k_, m_);
const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
const Eigen::array<int, 2> shuffle(1, 0);
const Eigen::array<int, 2> shuffle = {{1, 0}};
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
B.device(device_) = A.shuffle(shuffle);
}
// Record the number of values shuffled from A and copied to B each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void padding(int num_iters) {
eigen_assert(m_ == k_);
const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_-3}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
const Eigen::array<TensorIndex, 2> size_b(k_, m_);
const Eigen::array<TensorIndex, 2> size_b = {{k_, m_}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
@ -116,35 +160,34 @@ template <typename Device> class BenchmarkSuite {
B.device(device_) = A.pad(paddings);
}
// Record the number of values copied from the padded tensor A each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void striding(int num_iters) {
eigen_assert(m_ == k_);
const Eigen::array<TensorIndex, 2> size_a(m_, k_);
const Eigen::array<TensorIndex, 2> size_a = {{m_, k_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
const Eigen::array<TensorIndex, 2> size_b = {{m_, k_ / 2}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
const Eigen::array<TensorIndex, 2> strides(1, 2);
const Eigen::array<TensorIndex, 2> strides = {{1, 2}};
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
B.device(device_) = A.stride(strides);
}
// Record the number of values copied from the padded tensor A each second
finalizeBenchmark(m_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * k_ * num_iters);
}
void broadcasting(int num_iters) {
const Eigen::array<TensorIndex, 2> size_a(m_, 1);
const Eigen::array<TensorIndex, 2> size_a = {{m_, 1}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
const Eigen::array<TensorIndex, 2> size_c(m_, n_);
const Eigen::array<TensorIndex, 2> size_c = {{m_, n_}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
#if defined(__CUDACC__)
// nvcc doesn't support cxx11
const Eigen::array<int, 2> broadcast(1, n_);
#ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<int, 2> broadcast = {{1, n_}};
#else
// Take advantage of cxx11 to give the compiler information it can use to
// optimize the code.
@ -157,12 +200,12 @@ template <typename Device> class BenchmarkSuite {
C.device(device_) = A.broadcast(broadcast);
}
// Record the number of values broadcasted from A and copied to C each second
finalizeBenchmark(m_ * n_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * n_ * num_iters);
}
void coeffWiseOp(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes(m_, m_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@ -173,12 +216,12 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (2 multiplications and
// 1 addition per value)
finalizeBenchmark(3 * m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(3) * m_ * m_ * num_iters);
}
void algebraicFunc(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes(m_, m_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@ -189,12 +232,12 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
void transcendentalFunc(int num_iters) {
eigen_assert(m_ == k_ && k_ == n_);
const Eigen::array<TensorIndex, 2> sizes(m_, m_);
const Eigen::array<TensorIndex, 2> sizes = {{m_, m_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
@ -205,17 +248,23 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(m_) * m_ * num_iters);
}
// Simple reduction
void reduction(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size(k_, n_);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
const Eigen::array<TensorIndex, 1> output_size(n_);
TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
// Row reduction
void rowReduction(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}};
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{n_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(c_, output_size);
const Eigen::array<TensorIndex, 1> sum_along_dim(0);
#ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<TensorIndex, 1> sum_along_dim = {{0}};
#else
// Take advantage of cxx11 to give the compiler information it can use to
// optimize the code.
Eigen::IndexList<Eigen::type2index<0>> sum_along_dim;
#endif
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
@ -223,21 +272,47 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(m_ * m_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
}
// Column reduction
void colReduction(int num_iters) {
const Eigen::array<TensorIndex, 2> input_size = {{k_, n_}};
const TensorMap<Tensor<float, 2, 0, TensorIndex>, Eigen::Aligned> B(
b_, input_size);
const Eigen::array<TensorIndex, 1> output_size = {{k_}};
TensorMap<Tensor<float, 1, 0, TensorIndex>, Eigen::Aligned> C(
c_, output_size);
#ifndef EIGEN_HAS_INDEX_LIST
const Eigen::array<TensorIndex, 1> sum_along_dim = {{1}};
#else
// Take advantage of cxx11 to give the compiler information it can use to
// optimize the code.
Eigen::IndexList<Eigen::type2index<1>> sum_along_dim;
#endif
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
C.device(device_) = B.sum(sum_along_dim);
}
// Record the number of FLOP executed per second (assuming one operation
// per value)
finalizeBenchmark(static_cast<int64_t>(k_) * n_ * num_iters);
}
// do a contraction which is equivalent to a matrix multiplication
void contraction(int num_iters) {
const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
const Eigen::array<TensorIndex, 2> sizeA = {{m_, k_}};
const Eigen::array<TensorIndex, 2> sizeB = {{k_, n_}};
const Eigen::array<TensorIndex, 2> sizeC = {{m_, n_}};
const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
typedef typename Tensor<float, 2>::DimensionPair DimPair;
const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
const Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}};
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
@ -245,18 +320,18 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (size_ multiplications and
// additions for each value in the resulting tensor)
finalizeBenchmark(static_cast<int64>(2) * m_ * n_ * k_ * num_iters);
finalizeBenchmark(static_cast<int64_t>(2) * m_ * n_ * k_ * num_iters);
}
void convolution(int num_iters, int kernel_x, int kernel_y) {
const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
const Eigen::array<TensorIndex, 2> input_sizes = {{m_, n_}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
const Eigen::array<TensorIndex, 2> kernel_sizes = {{kernel_x, kernel_y}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
const Eigen::array<TensorIndex, 2> result_sizes(
m_ - kernel_x + 1, n_ - kernel_y + 1);
const Eigen::array<TensorIndex, 2> result_sizes =
{{m_ - kernel_x + 1, n_ - kernel_y + 1}};
TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
Eigen::array<Tensor<float, 2>::Index, 2> dims = {{0, 1}};
StartBenchmarkTiming();
for (int iter = 0; iter < num_iters; ++iter) {
@ -264,8 +339,8 @@ template <typename Device> class BenchmarkSuite {
}
// Record the number of FLOP executed per second (kernel_size
// multiplications and additions for each value in the resulting tensor)
finalizeBenchmark(
(m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * 2 * num_iters);
finalizeBenchmark(static_cast<int64_t>(2) *
(m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * num_iters);
}
private:
@ -280,23 +355,23 @@ template <typename Device> class BenchmarkSuite {
device_.memset(b_, 23, k_ * n_ * sizeof(float));
device_.memset(c_, 31, m_ * n_ * sizeof(float));
BenchmarkUseRealTime();
//BenchmarkUseRealTime();
}
inline void finalizeBenchmark(int64 num_items) {
inline void finalizeBenchmark(int64_t num_items) {
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
device_.synchronize();
}
#endif
StopBenchmarkTiming();
SetBenchmarkItemsProcessed(num_items);
SetBenchmarkFlopsProcessed(num_items);
}
size_t m_;
size_t k_;
size_t n_;
TensorIndex m_;
TensorIndex k_;
TensorIndex n_;
float* a_;
float* b_;
float* c_;

48
bench/tensors/tensor_benchmarks_cpu.cc
View File

@ -1,19 +1,12 @@
#define EIGEN_USE_THREADS
#include "base/sysinfo.h"
#include "strings/strcat.h"
#include "third_party/eigen3/tensor_benchmarks.h"
#include "thread/threadpool.h"
#include <string>
#include "tensor_benchmarks.h"
#ifdef __ANDROID__
#define CREATE_THREAD_POOL(threads) \
Eigen::ThreadPoolDevice device(threads);
#else
#define CREATE_THREAD_POOL(threads) \
ThreadPool tp(threads); \
tp.StartWorkers(); \
Eigen::ThreadPoolDevice device(&tp, threads);
#endif
Eigen::ThreadPool pool(threads); \
Eigen::ThreadPoolDevice device(&pool, threads);
// Simple functions
#define BM_FuncCPU(FUNC, THREADS) \
@ -22,7 +15,6 @@ Eigen::ThreadPoolDevice device(&tp, threads);
CREATE_THREAD_POOL(THREADS); \
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \
suite.FUNC(iters); \
SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000);
@ -30,6 +22,10 @@ BM_FuncCPU(memcpy, 4);
BM_FuncCPU(memcpy, 8);
BM_FuncCPU(memcpy, 12);
BM_FuncCPU(typeCasting, 4);
BM_FuncCPU(typeCasting, 8);
BM_FuncCPU(typeCasting, 12);
BM_FuncCPU(random, 4);
BM_FuncCPU(random, 8);
BM_FuncCPU(random, 12);
@ -38,6 +34,14 @@ BM_FuncCPU(slicing, 4);
BM_FuncCPU(slicing, 8);
BM_FuncCPU(slicing, 12);
BM_FuncCPU(rowChip, 4);
BM_FuncCPU(rowChip, 8);
BM_FuncCPU(rowChip, 12);
BM_FuncCPU(colChip, 4);
BM_FuncCPU(colChip, 8);
BM_FuncCPU(colChip, 12);
BM_FuncCPU(shuffling, 4);
BM_FuncCPU(shuffling, 8);
BM_FuncCPU(shuffling, 12);
@ -66,9 +70,13 @@ BM_FuncCPU(transcendentalFunc, 4);
BM_FuncCPU(transcendentalFunc, 8);
BM_FuncCPU(transcendentalFunc, 12);
BM_FuncCPU(reduction, 4);
BM_FuncCPU(reduction, 8);
BM_FuncCPU(reduction, 12);
BM_FuncCPU(rowReduction, 4);
BM_FuncCPU(rowReduction, 8);
BM_FuncCPU(rowReduction, 12);
BM_FuncCPU(colReduction, 4);
BM_FuncCPU(colReduction, 8);
BM_FuncCPU(colReduction, 12);
// Contractions
@ -84,7 +92,6 @@ BM_FuncCPU(reduction, 12);
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, D1, D2, D3); \
suite.FUNC(iters); \
} \
SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T, 10, 5000);
@ -107,6 +114,12 @@ BM_FuncWithInputDimsCPU(contraction, N, 64, N, 8);
BM_FuncWithInputDimsCPU(contraction, N, 64, N, 12);
BM_FuncWithInputDimsCPU(contraction, N, 64, N, 16);
BM_FuncWithInputDimsCPU(contraction, N, N, 64, 1);
BM_FuncWithInputDimsCPU(contraction, N, N, 64, 4);
BM_FuncWithInputDimsCPU(contraction, N, N, 64, 8);
BM_FuncWithInputDimsCPU(contraction, N, N, 64, 12);
BM_FuncWithInputDimsCPU(contraction, N, N, 64, 16);
BM_FuncWithInputDimsCPU(contraction, 1, N, N, 1);
BM_FuncWithInputDimsCPU(contraction, 1, N, N, 4);
BM_FuncWithInputDimsCPU(contraction, 1, N, N, 8);
@ -127,7 +140,6 @@ BM_FuncWithInputDimsCPU(contraction, N, N, 1, 16);
CREATE_THREAD_POOL(THREADS); \
BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \
suite.FUNC(iters, DIM1, DIM2); \
SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T, 128, 5000);

25
bench/tensors/tensor_benchmarks_gpu.cc → bench/tensors/tensor_benchmarks_gpu.cu
View File

@ -3,47 +3,47 @@
#include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
#include "strings/strcat.h"
#include "third_party/eigen3/tensor_benchmarks.h"
#include "tensor_benchmarks.h"
// Simple functions
#define BM_FuncGPU(FUNC) \
static void BM_##FUNC(int iters, int N) { \
StopBenchmarkTiming(); \
cudaStream_t stream; \
cudaStreamCreate(&stream); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \
cudaDeviceSynchronize(); \
suite.FUNC(iters); \
cudaStreamDestroy(stream); \
} \
BENCHMARK_RANGE(BM_##FUNC, 10, 5000);
BM_FuncGPU(memcpy);
BM_FuncGPU(typeCasting);
BM_FuncGPU(random);
BM_FuncGPU(slicing);
BM_FuncGPU(rowChip);
BM_FuncGPU(colChip);
BM_FuncGPU(shuffling);
BM_FuncGPU(padding);
BM_FuncGPU(striding);
BM_FuncGPU(broadcasting);
BM_FuncGPU(coeffWiseOp);
BM_FuncGPU(reduction);
BM_FuncGPU(algebraicFunc);
BM_FuncGPU(transcendentalFunc);
BM_FuncGPU(rowReduction);
BM_FuncGPU(colReduction);
// Contractions
#define BM_FuncWithInputDimsGPU(FUNC, D1, D2, D3) \
static void BM_##FUNC##_##D1##x##D2##x##D3(int iters, int N) { \
StopBenchmarkTiming(); \
cudaStream_t stream; \
cudaStreamCreate(&stream); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, D1, D2, D3); \
cudaDeviceSynchronize(); \
suite.FUNC(iters); \
cudaStreamDestroy(stream); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3, 10, 5000);
@ -51,19 +51,18 @@ BM_FuncGPU(reduction);
BM_FuncWithInputDimsGPU(contraction, N, N, N);
BM_FuncWithInputDimsGPU(contraction, 64, N, N);
BM_FuncWithInputDimsGPU(contraction, N, 64, N);
BM_FuncWithInputDimsGPU(contraction, N, N, 64);
// Convolutions
#define BM_FuncWithKernelDimsGPU(FUNC, DIM1, DIM2) \
static void BM_##FUNC##_##DIM1##x##DIM2(int iters, int N) { \
StopBenchmarkTiming(); \
cudaStream_t stream; \
cudaStreamCreate(&stream); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \
cudaDeviceSynchronize(); \
suite.FUNC(iters, DIM1, DIM2); \
cudaStreamDestroy(stream); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2, 128, 5000);

95
blas/level2_cplx_impl.h
View File

@ -19,19 +19,12 @@
int EIGEN_BLAS_FUNC(hemv)(char *uplo, int *n, RealScalar *palpha, RealScalar *pa, int *lda, RealScalar *px, int *incx, RealScalar *pbeta, RealScalar *py, int *incy)
{
typedef void (*functype)(int, const Scalar*, int, const Scalar*, Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Upper,false,false>::run);
func[LO] = (internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Lower,false,false>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Upper,false,false>::run),
// array index: LO
(internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Lower,false,false>::run),
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* x = reinterpret_cast<Scalar*>(px);
@ -111,19 +104,12 @@ int EIGEN_BLAS_FUNC(hemv)(char *uplo, int *n, RealScalar *palpha, RealScalar *pa
int EIGEN_BLAS_FUNC(hpr)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *pap)
{
typedef void (*functype)(int, Scalar*, const Scalar*, RealScalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run);
func[LO] = (internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run),
// array index: LO
(internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* ap = reinterpret_cast<Scalar*>(pap);
@ -162,19 +148,12 @@ int EIGEN_BLAS_FUNC(hpr)(char *uplo, int *n, RealScalar *palpha, RealScalar *px,
int EIGEN_BLAS_FUNC(hpr2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *py, int *incy, RealScalar *pap)
{
typedef void (*functype)(int, Scalar*, const Scalar*, const Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::packed_rank2_update_selector<Scalar,int,Upper>::run);
func[LO] = (internal::packed_rank2_update_selector<Scalar,int,Lower>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::packed_rank2_update_selector<Scalar,int,Upper>::run),
// array index: LO
(internal::packed_rank2_update_selector<Scalar,int,Lower>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* y = reinterpret_cast<Scalar*>(py);
@ -217,19 +196,12 @@ int EIGEN_BLAS_FUNC(hpr2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px
int EIGEN_BLAS_FUNC(her)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *pa, int *lda)
{
typedef void (*functype)(int, Scalar*, int, const Scalar*, const Scalar*, const Scalar&);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (selfadjoint_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run);
func[LO] = (selfadjoint_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(selfadjoint_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run),
// array index: LO
(selfadjoint_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* a = reinterpret_cast<Scalar*>(pa);
@ -271,19 +243,12 @@ int EIGEN_BLAS_FUNC(her)(char *uplo, int *n, RealScalar *palpha, RealScalar *px,
int EIGEN_BLAS_FUNC(her2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *py, int *incy, RealScalar *pa, int *lda)
{
typedef void (*functype)(int, Scalar*, int, const Scalar*, const Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::rank2_update_selector<Scalar,int,Upper>::run);
func[LO] = (internal::rank2_update_selector<Scalar,int,Lower>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::rank2_update_selector<Scalar,int,Upper>::run),
// array index: LO
(internal::rank2_update_selector<Scalar,int,Lower>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* y = reinterpret_cast<Scalar*>(py);

303
blas/level2_impl.h
View File

@ -26,20 +26,15 @@ struct general_matrix_vector_product_wrapper
int EIGEN_BLAS_FUNC(gemv)(char *opa, int *m, int *n, RealScalar *palpha, RealScalar *pa, int *lda, RealScalar *pb, int *incb, RealScalar *pbeta, RealScalar *pc, int *incc)
{
typedef void (*functype)(int, int, const Scalar *, int, const Scalar *, int , Scalar *, int, Scalar);
static functype func[4];
static bool init = false;
if(!init)
{
for(int k=0; k<4; ++k)
func[k] = 0;
func[NOTR] = (general_matrix_vector_product_wrapper<int,Scalar,ColMajor,false,false>::run);
func[TR ] = (general_matrix_vector_product_wrapper<int,Scalar,RowMajor,false,false>::run);
func[ADJ ] = (general_matrix_vector_product_wrapper<int,Scalar,RowMajor,Conj ,false>::run);
init = true;
}
static const functype func[4] = {
// array index: NOTR
(general_matrix_vector_product_wrapper<int,Scalar,ColMajor,false,false>::run),
// array index: TR
(general_matrix_vector_product_wrapper<int,Scalar,RowMajor,false,false>::run),
// array index: ADJ
(general_matrix_vector_product_wrapper<int,Scalar,RowMajor,Conj ,false>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -90,32 +85,36 @@ int EIGEN_BLAS_FUNC(gemv)(char *opa, int *m, int *n, RealScalar *palpha, RealSca
int EIGEN_BLAS_FUNC(trsv)(char *uplo, char *opa, char *diag, int *n, RealScalar *pa, int *lda, RealScalar *pb, int *incb)
{
typedef void (*functype)(int, const Scalar *, int, Scalar *);
static functype func[16];
static bool init = false;
if(!init)
{
for(int k=0; k<16; ++k)
func[k] = 0;
func[NOTR | (UP << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,ColMajor>::run);
func[TR | (UP << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,RowMajor>::run);
func[ADJ | (UP << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, Conj, RowMajor>::run);
func[NOTR | (LO << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,ColMajor>::run);
func[TR | (LO << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,RowMajor>::run);
func[ADJ | (LO << 2) | (NUNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, Conj, RowMajor>::run);
func[NOTR | (UP << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,ColMajor>::run);
func[TR | (UP << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,RowMajor>::run);
func[ADJ | (UP << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,Conj, RowMajor>::run);
func[NOTR | (LO << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,ColMajor>::run);
func[TR | (LO << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,RowMajor>::run);
func[ADJ | (LO << 2) | (UNIT << 3)] = (internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,Conj, RowMajor>::run);
init = true;
}
static const functype func[16] = {
// array index: NOTR | (UP << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,ColMajor>::run),
// array index: TR | (UP << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, Conj, RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,ColMajor>::run),
// array index: TR | (LO << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (NUNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, Conj, RowMajor>::run),
0,
// array index: NOTR | (UP << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,ColMajor>::run),
// array index: TR | (UP << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,Conj, RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,ColMajor>::run),
// array index: TR | (LO << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (UNIT << 3)
(internal::triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,Conj, RowMajor>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -145,32 +144,36 @@ int EIGEN_BLAS_FUNC(trsv)(char *uplo, char *opa, char *diag, int *n, RealScalar
int EIGEN_BLAS_FUNC(trmv)(char *uplo, char *opa, char *diag, int *n, RealScalar *pa, int *lda, RealScalar *pb, int *incb)
{
typedef void (*functype)(int, int, const Scalar *, int, const Scalar *, int, Scalar *, int, const Scalar&);
static functype func[16];
static bool init = false;
if(!init)
{
for(int k=0; k<16; ++k)
func[k] = 0;
func[NOTR | (UP << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (UP << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (UP << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|0, Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (LO << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (LO << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (LO << 2) | (NUNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|0, Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (UP << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (UP << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (UP << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (LO << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (LO << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (LO << 2) | (UNIT << 3)] = (internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run);
init = true;
}
static const functype func[16] = {
// array index: NOTR | (UP << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (UP << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|0, Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (LO << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (NUNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|0, Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (UP << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (UP << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (LO << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (UNIT << 3)
(internal::triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -346,32 +349,36 @@ int EIGEN_BLAS_FUNC(tbmv)(char *uplo, char *opa, char *diag, int *n, int *k, Rea
int EIGEN_BLAS_FUNC(tbsv)(char *uplo, char *op, char *diag, int *n, int *k, RealScalar *pa, int *lda, RealScalar *px, int *incx)
{
typedef void (*functype)(int, int, const Scalar *, int, Scalar *);
static functype func[16];
static bool init = false;
if(!init)
{
for(int i=0; i<16; ++i)
func[i] = 0;
func[NOTR | (UP << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|0, Scalar,false,Scalar,ColMajor>::run);
func[TR | (UP << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|0, Scalar,false,Scalar,RowMajor>::run);
func[ADJ | (UP << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|0, Scalar,Conj, Scalar,RowMajor>::run);
func[NOTR | (LO << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|0, Scalar,false,Scalar,ColMajor>::run);
func[TR | (LO << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|0, Scalar,false,Scalar,RowMajor>::run);
func[ADJ | (LO << 2) | (NUNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|0, Scalar,Conj, Scalar,RowMajor>::run);
func[NOTR | (UP << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,false,Scalar,ColMajor>::run);
func[TR | (UP << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,false,Scalar,RowMajor>::run);
func[ADJ | (UP << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,Conj, Scalar,RowMajor>::run);
func[NOTR | (LO << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,false,Scalar,ColMajor>::run);
func[TR | (LO << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,false,Scalar,RowMajor>::run);
func[ADJ | (LO << 2) | (UNIT << 3)] = (internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,Conj, Scalar,RowMajor>::run);
init = true;
}
static const functype func[16] = {
// array index: NOTR | (UP << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|0, Scalar,false,Scalar,ColMajor>::run),
// array index: TR | (UP << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|0, Scalar,false,Scalar,RowMajor>::run),
// array index: ADJ | (UP << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|0, Scalar,Conj, Scalar,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|0, Scalar,false,Scalar,ColMajor>::run),
// array index: TR | (LO << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|0, Scalar,false,Scalar,RowMajor>::run),
// array index: ADJ | (LO << 2) | (NUNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|0, Scalar,Conj, Scalar,RowMajor>::run),
0,
// array index: NOTR | (UP << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,false,Scalar,ColMajor>::run),
// array index: TR | (UP << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,false,Scalar,RowMajor>::run),
// array index: ADJ | (UP << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,Conj, Scalar,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Lower|UnitDiag,Scalar,false,Scalar,ColMajor>::run),
// array index: TR | (LO << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,false,Scalar,RowMajor>::run),
// array index: ADJ | (LO << 2) | (UNIT << 3)
(internal::band_solve_triangular_selector<int,Upper|UnitDiag,Scalar,Conj, Scalar,RowMajor>::run),
0,
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* x = reinterpret_cast<Scalar*>(px);
@ -416,32 +423,36 @@ int EIGEN_BLAS_FUNC(tbsv)(char *uplo, char *op, char *diag, int *n, int *k, Real
int EIGEN_BLAS_FUNC(tpmv)(char *uplo, char *opa, char *diag, int *n, RealScalar *pap, RealScalar *px, int *incx)
{
typedef void (*functype)(int, const Scalar*, const Scalar*, Scalar*, Scalar);
static functype func[16];
static bool init = false;
if(!init)
{
for(int k=0; k<16; ++k)
func[k] = 0;
func[NOTR | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run);
func[NOTR | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run);
func[TR | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run);
func[ADJ | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run);
init = true;
}
static const functype func[16] = {
// array index: NOTR | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|0, Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|0, Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Lower|UnitDiag,Scalar,false,Scalar,false,ColMajor>::run),
// array index: TR | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,false,Scalar,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_matrix_vector_product<int,Upper|UnitDiag,Scalar,Conj, Scalar,false,RowMajor>::run),
0
};
Scalar* ap = reinterpret_cast<Scalar*>(pap);
Scalar* x = reinterpret_cast<Scalar*>(px);
@ -487,32 +498,36 @@ int EIGEN_BLAS_FUNC(tpmv)(char *uplo, char *opa, char *diag, int *n, RealScalar
int EIGEN_BLAS_FUNC(tpsv)(char *uplo, char *opa, char *diag, int *n, RealScalar *pap, RealScalar *px, int *incx)
{
typedef void (*functype)(int, const Scalar*, Scalar*);
static functype func[16];
static bool init = false;
if(!init)
{
for(int k=0; k<16; ++k)
func[k] = 0;
func[NOTR | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,ColMajor>::run);
func[TR | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,RowMajor>::run);
func[ADJ | (UP << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, Conj, RowMajor>::run);
func[NOTR | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,ColMajor>::run);
func[TR | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,RowMajor>::run);
func[ADJ | (LO << 2) | (NUNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, Conj, RowMajor>::run);
func[NOTR | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,ColMajor>::run);
func[TR | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,RowMajor>::run);
func[ADJ | (UP << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,Conj, RowMajor>::run);
func[NOTR | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,ColMajor>::run);
func[TR | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,RowMajor>::run);
func[ADJ | (LO << 2) | (UNIT << 3)] = (internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,Conj, RowMajor>::run);
init = true;
}
static const functype func[16] = {
// array index: NOTR | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,ColMajor>::run),
// array index: TR | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, Conj, RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|0, false,ColMajor>::run),
// array index: TR | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (NUNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|0, Conj, RowMajor>::run),
0,
// array index: NOTR | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,ColMajor>::run),
// array index: TR | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,RowMajor>::run),
// array index: ADJ | (UP << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,Conj, RowMajor>::run),
0,
// array index: NOTR | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Lower|UnitDiag,false,ColMajor>::run),
// array index: TR | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,false,RowMajor>::run),
// array index: ADJ | (LO << 2) | (UNIT << 3)
(internal::packed_triangular_solve_vector<Scalar,Scalar,int,OnTheLeft, Upper|UnitDiag,Conj, RowMajor>::run),
0
};
Scalar* ap = reinterpret_cast<Scalar*>(pap);
Scalar* x = reinterpret_cast<Scalar*>(px);

123
blas/level2_real_impl.h
View File

@ -13,19 +13,12 @@
int EIGEN_BLAS_FUNC(symv) (char *uplo, int *n, RealScalar *palpha, RealScalar *pa, int *lda, RealScalar *px, int *incx, RealScalar *pbeta, RealScalar *py, int *incy)
{
typedef void (*functype)(int, const Scalar*, int, const Scalar*, Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Upper,false,false>::run);
func[LO] = (internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Lower,false,false>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Upper,false,false>::run),
// array index: LO
(internal::selfadjoint_matrix_vector_product<Scalar,int,ColMajor,Lower,false,false>::run),
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* x = reinterpret_cast<Scalar*>(px);
@ -71,34 +64,13 @@ int EIGEN_BLAS_FUNC(symv) (char *uplo, int *n, RealScalar *palpha, RealScalar *p
int EIGEN_BLAS_FUNC(syr)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *pc, int *ldc)
{
// typedef void (*functype)(int, const Scalar *, int, Scalar *, int, Scalar);
// static functype func[2];
// static bool init = false;
// if(!init)
// {
// for(int k=0; k<2; ++k)
// func[k] = 0;
//
// func[UP] = (internal::selfadjoint_product<Scalar,ColMajor,ColMajor,false,UpperTriangular>::run);
// func[LO] = (internal::selfadjoint_product<Scalar,ColMajor,ColMajor,false,LowerTriangular>::run);
// init = true;
// }
typedef void (*functype)(int, Scalar*, int, const Scalar*, const Scalar*, const Scalar&);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (selfadjoint_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run);
func[LO] = (selfadjoint_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(selfadjoint_rank1_update<Scalar,int,ColMajor,Upper,false,Conj>::run),
// array index: LO
(selfadjoint_rank1_update<Scalar,int,ColMajor,Lower,false,Conj>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* c = reinterpret_cast<Scalar*>(pc);
@ -131,34 +103,13 @@ int EIGEN_BLAS_FUNC(syr)(char *uplo, int *n, RealScalar *palpha, RealScalar *px,
// C := alpha*x*y' + alpha*y*x' + C
int EIGEN_BLAS_FUNC(syr2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *py, int *incy, RealScalar *pc, int *ldc)
{
// typedef void (*functype)(int, const Scalar *, int, const Scalar *, int, Scalar *, int, Scalar);
// static functype func[2];
//
// static bool init = false;
// if(!init)
// {
// for(int k=0; k<2; ++k)
// func[k] = 0;
//
// func[UP] = (internal::selfadjoint_product<Scalar,ColMajor,ColMajor,false,UpperTriangular>::run);
// func[LO] = (internal::selfadjoint_product<Scalar,ColMajor,ColMajor,false,LowerTriangular>::run);
//
// init = true;
// }
typedef void (*functype)(int, Scalar*, int, const Scalar*, const Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::rank2_update_selector<Scalar,int,Upper>::run);
func[LO] = (internal::rank2_update_selector<Scalar,int,Lower>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::rank2_update_selector<Scalar,int,Upper>::run),
// array index: LO
(internal::rank2_update_selector<Scalar,int,Lower>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* y = reinterpret_cast<Scalar*>(py);
@ -234,19 +185,12 @@ int EIGEN_BLAS_FUNC(syr2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px
int EIGEN_BLAS_FUNC(spr)(char *uplo, int *n, Scalar *palpha, Scalar *px, int *incx, Scalar *pap)
{
typedef void (*functype)(int, Scalar*, const Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Upper,false,false>::run);
func[LO] = (internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Lower,false,false>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Upper,false,false>::run),
// array index: LO
(internal::selfadjoint_packed_rank1_update<Scalar,int,ColMajor,Lower,false,false>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* ap = reinterpret_cast<Scalar*>(pap);
@ -285,19 +229,12 @@ int EIGEN_BLAS_FUNC(spr)(char *uplo, int *n, Scalar *palpha, Scalar *px, int *in
int EIGEN_BLAS_FUNC(spr2)(char *uplo, int *n, RealScalar *palpha, RealScalar *px, int *incx, RealScalar *py, int *incy, RealScalar *pap)
{
typedef void (*functype)(int, Scalar*, const Scalar*, const Scalar*, Scalar);
static functype func[2];
static bool init = false;
if(!init)
{
for(int k=0; k<2; ++k)
func[k] = 0;
func[UP] = (internal::packed_rank2_update_selector<Scalar,int,Upper>::run);
func[LO] = (internal::packed_rank2_update_selector<Scalar,int,Lower>::run);
init = true;
}
static const functype func[2] = {
// array index: UP
(internal::packed_rank2_update_selector<Scalar,int,Upper>::run),
// array index: LO
(internal::packed_rank2_update_selector<Scalar,int,Lower>::run),
};
Scalar* x = reinterpret_cast<Scalar*>(px);
Scalar* y = reinterpret_cast<Scalar*>(py);

339
blas/level3_impl.h
View File

@ -13,24 +13,29 @@ int EIGEN_BLAS_FUNC(gemm)(char *opa, char *opb, int *m, int *n, int *k, RealScal
{
// std::cerr << "in gemm " << *opa << " " << *opb << " " << *m << " " << *n << " " << *k << " " << *lda << " " << *ldb << " " << *ldc << " " << *palpha << " " << *pbeta << "\n";
typedef void (*functype)(DenseIndex, DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, Scalar, internal::level3_blocking<Scalar,Scalar>&, Eigen::internal::GemmParallelInfo<DenseIndex>*);
static functype func[12];
static bool init = false;
if(!init)
{
for(int i=0; i<12; ++i)
func[i] = 0;
func[NOTR | (NOTR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,ColMajor,false,ColMajor>::run);
func[TR | (NOTR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,false,ColMajor>::run);
func[ADJ | (NOTR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor>::run);
func[NOTR | (TR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,false,ColMajor>::run);
func[TR | (TR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,RowMajor,false,ColMajor>::run);
func[ADJ | (TR << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,RowMajor,false,ColMajor>::run);
func[NOTR | (ADJ << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor>::run);
func[TR | (ADJ << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,RowMajor,Conj, ColMajor>::run);
func[ADJ | (ADJ << 2)] = (internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,RowMajor,Conj, ColMajor>::run);
init = true;
}
static const functype func[12] = {
// array index: NOTR | (NOTR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,ColMajor,false,ColMajor>::run),
// array index: TR | (NOTR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,false,ColMajor>::run),
// array index: ADJ | (NOTR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor>::run),
0,
// array index: NOTR | (TR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,false,ColMajor>::run),
// array index: TR | (TR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,RowMajor,false,ColMajor>::run),
// array index: ADJ | (TR << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,RowMajor,false,ColMajor>::run),
0,
// array index: NOTR | (ADJ << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor>::run),
// array index: TR | (ADJ << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,false,Scalar,RowMajor,Conj, ColMajor>::run),
// array index: ADJ | (ADJ << 2)
(internal::general_matrix_matrix_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,RowMajor,Conj, ColMajor>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -73,49 +78,64 @@ int EIGEN_BLAS_FUNC(trsm)(char *side, char *uplo, char *opa, char *diag, int *m,
{
// std::cerr << "in trsm " << *side << " " << *uplo << " " << *opa << " " << *diag << " " << *m << "," << *n << " " << *palpha << " " << *lda << " " << *ldb<< "\n";
typedef void (*functype)(DenseIndex, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, internal::level3_blocking<Scalar,Scalar>&);
static functype func[32];
static bool init = false;
if(!init)
{
for(int i=0; i<32; ++i)
func[i] = 0;
func[NOTR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, false,ColMajor,ColMajor>::run);
func[TR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, false,RowMajor,ColMajor>::run);
func[ADJ | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, Conj, RowMajor,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, false,ColMajor,ColMajor>::run);
func[TR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, false,RowMajor,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, Conj, RowMajor,ColMajor>::run);
func[NOTR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, false,ColMajor,ColMajor>::run);
func[TR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, false,RowMajor,ColMajor>::run);
func[ADJ | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, Conj, RowMajor,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, false,ColMajor,ColMajor>::run);
func[TR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, false,RowMajor,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, Conj, RowMajor,ColMajor>::run);
func[NOTR | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,false,ColMajor,ColMajor>::run);
func[TR | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,false,RowMajor,ColMajor>::run);
func[ADJ | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,Conj, RowMajor,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,false,ColMajor,ColMajor>::run);
func[TR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,false,RowMajor,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,Conj, RowMajor,ColMajor>::run);
func[NOTR | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,false,ColMajor,ColMajor>::run);
func[TR | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,false,RowMajor,ColMajor>::run);
func[ADJ | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,Conj, RowMajor,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,false,ColMajor,ColMajor>::run);
func[TR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,false,RowMajor,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,Conj, RowMajor,ColMajor>::run);
init = true;
}
static const functype func[32] = {
// array index: NOTR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, false,ColMajor,ColMajor>::run),
// array index: TR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, false,RowMajor,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, Conj, RowMajor,ColMajor>::run),\
0,
// array index: NOTR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, false,ColMajor,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, false,RowMajor,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|0, false,ColMajor,ColMajor>::run),
// array index: TR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, false,RowMajor,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|0, Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|0, false,ColMajor,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, false,RowMajor,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|0, Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,false,ColMajor,ColMajor>::run),
// array index: TR | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,false,RowMajor,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,false,ColMajor,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,false,RowMajor,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Lower|UnitDiag,false,ColMajor,ColMajor>::run),
// array index: TR | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,false,RowMajor,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheLeft, Upper|UnitDiag,Conj, RowMajor,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Lower|UnitDiag,false,ColMajor,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,false,RowMajor,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::triangular_solve_matrix<Scalar,DenseIndex,OnTheRight,Upper|UnitDiag,Conj, RowMajor,ColMajor>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -162,47 +182,64 @@ int EIGEN_BLAS_FUNC(trmm)(char *side, char *uplo, char *opa, char *diag, int *m,
{
// std::cerr << "in trmm " << *side << " " << *uplo << " " << *opa << " " << *diag << " " << *m << " " << *n << " " << *lda << " " << *ldb << " " << *palpha << "\n";
typedef void (*functype)(DenseIndex, DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, const Scalar&, internal::level3_blocking<Scalar,Scalar>&);
static functype func[32];
static bool init = false;
if(!init)
{
for(int k=0; k<32; ++k)
func[k] = 0;
func[NOTR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, RowMajor,false,ColMajor,false,ColMajor>::run);
func[ADJ | (LEFT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, RowMajor,Conj, ColMajor,false,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,RowMajor,false,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,RowMajor,Conj, ColMajor>::run);
func[NOTR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, RowMajor,false,ColMajor,false,ColMajor>::run);
func[ADJ | (LEFT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, RowMajor,Conj, ColMajor,false,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,RowMajor,false,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,RowMajor,Conj, ColMajor>::run);
func[NOTR | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, RowMajor,false,ColMajor,false,ColMajor>::run);
func[ADJ | (LEFT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, RowMajor,Conj, ColMajor,false,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,RowMajor,false,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (UP << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,RowMajor,Conj, ColMajor>::run);
func[NOTR | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, RowMajor,false,ColMajor,false,ColMajor>::run);
func[ADJ | (LEFT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, RowMajor,Conj, ColMajor,false,ColMajor>::run);
func[NOTR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,ColMajor,false,ColMajor>::run);
func[TR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,RowMajor,false,ColMajor>::run);
func[ADJ | (RIGHT << 2) | (LO << 3) | (UNIT << 4)] = (internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,RowMajor,Conj, ColMajor>::run);
init = true;
}
static const functype func[32] = {
// array index: NOTR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, RowMajor,false,ColMajor,false,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, RowMajor,Conj, ColMajor,false,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,RowMajor,false,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (UP << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,RowMajor,Conj, ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, true, ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, RowMajor,false,ColMajor,false,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, true, RowMajor,Conj, ColMajor,false,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|0, false,ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,RowMajor,false,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (LO << 3) | (NUNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|0, false,ColMajor,false,RowMajor,Conj, ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, RowMajor,false,ColMajor,false,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, RowMajor,Conj, ColMajor,false,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,RowMajor,false,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (UP << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,RowMajor,Conj, ColMajor>::run),
0,
// array index: NOTR | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,true, ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, RowMajor,false,ColMajor,false,ColMajor>::run),
// array index: ADJ | (LEFT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,true, RowMajor,Conj, ColMajor,false,ColMajor>::run),
0,
// array index: NOTR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Lower|UnitDiag,false,ColMajor,false,ColMajor,false,ColMajor>::run),
// array index: TR | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,RowMajor,false,ColMajor>::run),
// array index: ADJ | (RIGHT << 2) | (LO << 3) | (UNIT << 4)
(internal::product_triangular_matrix_matrix<Scalar,DenseIndex,Upper|UnitDiag,false,ColMajor,false,RowMajor,Conj, ColMajor>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* b = reinterpret_cast<Scalar*>(pb);
@ -275,9 +312,9 @@ int EIGEN_BLAS_FUNC(symm)(char *side, char *uplo, int *m, int *n, RealScalar *pa
return 1;
}
int size = (SIDE(*side)==LEFT) ? (*m) : (*n);
#if ISCOMPLEX
// FIXME add support for symmetric complex matrix
int size = (SIDE(*side)==LEFT) ? (*m) : (*n);
Matrix<Scalar,Dynamic,Dynamic,ColMajor> matA(size,size);
if(UPLO(*uplo)==UP)
{
@ -294,13 +331,15 @@ int EIGEN_BLAS_FUNC(symm)(char *side, char *uplo, int *m, int *n, RealScalar *pa
else if(SIDE(*side)==RIGHT)
matrix(c, *m, *n, *ldc) += alpha * matrix(b, *m, *n, *ldb) * matA;
#else
internal::gemm_blocking_space<ColMajor,Scalar,Scalar,Dynamic,Dynamic,Dynamic> blocking(*m,*n,size,1,false);
if(SIDE(*side)==LEFT)
if(UPLO(*uplo)==UP) internal::product_selfadjoint_matrix<Scalar, DenseIndex, RowMajor,true,false, ColMajor,false,false, ColMajor>::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha);
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,true,false, ColMajor,false,false, ColMajor>::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha);
if(UPLO(*uplo)==UP) internal::product_selfadjoint_matrix<Scalar, DenseIndex, RowMajor,true,false, ColMajor,false,false, ColMajor>::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha, blocking);
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,true,false, ColMajor,false,false, ColMajor>::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha, blocking);
else return 0;
else if(SIDE(*side)==RIGHT)
if(UPLO(*uplo)==UP) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,false,false, RowMajor,true,false, ColMajor>::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha);
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,false,false, ColMajor,true,false, ColMajor>::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha);
if(UPLO(*uplo)==UP) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,false,false, RowMajor,true,false, ColMajor>::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha, blocking);
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar, DenseIndex, ColMajor,false,false, ColMajor,true,false, ColMajor>::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha, blocking);
else return 0;
else
return 0;
@ -315,25 +354,23 @@ int EIGEN_BLAS_FUNC(syrk)(char *uplo, char *op, int *n, int *k, RealScalar *palp
{
// std::cerr << "in syrk " << *uplo << " " << *op << " " << *n << " " << *k << " " << *palpha << " " << *lda << " " << *pbeta << " " << *ldc << "\n";
#if !ISCOMPLEX
typedef void (*functype)(DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, const Scalar&);
static functype func[8];
static bool init = false;
if(!init)
{
for(int i=0; i<8; ++i)
func[i] = 0;
func[NOTR | (UP << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,ColMajor,Conj, Upper>::run);
func[TR | (UP << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,ColMajor,Conj, Upper>::run);
func[ADJ | (UP << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,ColMajor,false,Upper>::run);
func[NOTR | (LO << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,ColMajor,Conj, Lower>::run);
func[TR | (LO << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,ColMajor,Conj, Lower>::run);
func[ADJ | (LO << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,ColMajor,false,Lower>::run);
init = true;
}
typedef void (*functype)(DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, const Scalar&, internal::level3_blocking<Scalar,Scalar>&);
static const functype func[8] = {
// array index: NOTR | (UP << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,ColMajor,Conj, Upper>::run),
// array index: TR | (UP << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,ColMajor,Conj, Upper>::run),
// array index: ADJ | (UP << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,ColMajor,false,Upper>::run),
0,
// array index: NOTR | (LO << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,ColMajor,Conj, Lower>::run),
// array index: TR | (LO << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,false,Scalar,ColMajor,ColMajor,Conj, Lower>::run),
// array index: ADJ | (LO << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,ColMajor,false,Lower>::run),
0
};
#endif
Scalar* a = reinterpret_cast<Scalar*>(pa);
@ -381,8 +418,10 @@ int EIGEN_BLAS_FUNC(syrk)(char *uplo, char *op, int *n, int *k, RealScalar *palp
matrix(c, *n, *n, *ldc).triangularView<Lower>() += alpha * matrix(a,*k,*n,*lda).transpose() * matrix(a,*k,*n,*lda);
}
#else
internal::gemm_blocking_space<ColMajor,Scalar,Scalar,Dynamic,Dynamic,Dynamic> blocking(*n,*n,*k,1,false);
int code = OP(*op) | (UPLO(*uplo) << 2);
func[code](*n, *k, a, *lda, a, *lda, c, *ldc, alpha);
func[code](*n, *k, a, *lda, a, *lda, c, *ldc, alpha, blocking);
#endif
return 0;
@ -486,20 +525,23 @@ int EIGEN_BLAS_FUNC(hemm)(char *side, char *uplo, int *m, int *n, RealScalar *pa
return 1;
}
int size = (SIDE(*side)==LEFT) ? (*m) : (*n);
internal::gemm_blocking_space<ColMajor,Scalar,Scalar,Dynamic,Dynamic,Dynamic> blocking(*m,*n,size,1,false);
if(SIDE(*side)==LEFT)
{
if(UPLO(*uplo)==UP) internal::product_selfadjoint_matrix<Scalar,DenseIndex,RowMajor,true,Conj, ColMajor,false,false, ColMajor>
::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha);
::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha, blocking);
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar,DenseIndex,ColMajor,true,false, ColMajor,false,false, ColMajor>
::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha);
::run(*m, *n, a, *lda, b, *ldb, c, *ldc, alpha, blocking);
else return 0;
}
else if(SIDE(*side)==RIGHT)
{
if(UPLO(*uplo)==UP) matrix(c,*m,*n,*ldc) += alpha * matrix(b,*m,*n,*ldb) * matrix(a,*n,*n,*lda).selfadjointView<Upper>();/*internal::product_selfadjoint_matrix<Scalar,DenseIndex,ColMajor,false,false, RowMajor,true,Conj, ColMajor>
::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha);*/
::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha, blocking);*/
else if(UPLO(*uplo)==LO) internal::product_selfadjoint_matrix<Scalar,DenseIndex,ColMajor,false,false, ColMajor,true,false, ColMajor>
::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha);
::run(*m, *n, b, *ldb, a, *lda, c, *ldc, alpha, blocking);
else return 0;
}
else
@ -516,23 +558,21 @@ int EIGEN_BLAS_FUNC(herk)(char *uplo, char *op, int *n, int *k, RealScalar *palp
{
// std::cerr << "in herk " << *uplo << " " << *op << " " << *n << " " << *k << " " << *palpha << " " << *lda << " " << *pbeta << " " << *ldc << "\n";
typedef void (*functype)(DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, const Scalar&);
static functype func[8];
static bool init = false;
if(!init)
{
for(int i=0; i<8; ++i)
func[i] = 0;
func[NOTR | (UP << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor,Upper>::run);
func[ADJ | (UP << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor,Upper>::run);
func[NOTR | (LO << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor,Lower>::run);
func[ADJ | (LO << 2)] = (internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor,Lower>::run);
init = true;
}
typedef void (*functype)(DenseIndex, DenseIndex, const Scalar *, DenseIndex, const Scalar *, DenseIndex, Scalar *, DenseIndex, const Scalar&, internal::level3_blocking<Scalar,Scalar>&);
static const functype func[8] = {
// array index: NOTR | (UP << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor,Upper>::run),
0,
// array index: ADJ | (UP << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor,Upper>::run),
0,
// array index: NOTR | (LO << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,ColMajor,false,Scalar,RowMajor,Conj, ColMajor,Lower>::run),
0,
// array index: ADJ | (LO << 2)
(internal::general_matrix_matrix_triangular_product<DenseIndex,Scalar,RowMajor,Conj, Scalar,ColMajor,false,ColMajor,Lower>::run),
0
};
Scalar* a = reinterpret_cast<Scalar*>(pa);
Scalar* c = reinterpret_cast<Scalar*>(pc);
@ -571,7 +611,8 @@ int EIGEN_BLAS_FUNC(herk)(char *uplo, char *op, int *n, int *k, RealScalar *palp
if(*k>0 && alpha!=RealScalar(0))
{
func[code](*n, *k, a, *lda, a, *lda, c, *ldc, alpha);
internal::gemm_blocking_space<ColMajor,Scalar,Scalar,Dynamic,Dynamic,Dynamic> blocking(*n,*n,*k,1,false);
func[code](*n, *k, a, *lda, a, *lda, c, *ldc, alpha, blocking);
matrix(c, *n, *n, *ldc).diagonal().imag().setZero();
}
return 0;

5
doc/AsciiQuickReference.txt
View File

@ -44,7 +44,7 @@ C.setRandom(rows,cols) // C = rand(rows,cols)*2-1
VectorXd::LinSpaced(size,low,high) // linspace(low,high,size)'
v.setLinSpaced(size,low,high) // v = linspace(low,high,size)'
VectorXi::LinSpaced(((hi-low)/step)+1, // low:step:hi
low,low+step*(size-1))
low,low+step*(size-1)) //
// Matrix slicing and blocks. All expressions listed here are read/write.
@ -94,6 +94,8 @@ R.transpose() // R.' or conj(R') // Read-write
R.diagonal() // diag(R) // Read-write
x.asDiagonal() // diag(x)
R.transpose().colwise().reverse() // rot90(R) // Read-write
R.rowwise().reverse() // fliplr(R)
R.colwise().reverse() // flipud(R)
R.replicate(i,j) // repmat(P,i,j)
@ -139,6 +141,7 @@ R.cwiseAbs2() // abs(P.^2)
R.array().abs2() // abs(P.^2)
(R.array() < s).select(P,Q ); // (R < s ? P : Q)
R = (Q.array()==0).select(P,A) // R(Q==0) = P(Q==0)
R = P.unaryExpr(ptr_fun(func)) // R = arrayfun(func, P) // with: scalar func(const scalar &x);
// Reductions.

6
doc/SparseLinearSystems.dox
View File

@ -65,17 +65,17 @@ They are summarized in the following tables:
<td>Requires the <a href="http://pastix.gforge.inria.fr">PaStiX</a> package, \b CeCILL-C </td>
<td>optimized for tough problems and symmetric patterns</td></tr>
<tr><td>CholmodSupernodalLLT</td><td>\link CholmodSupport_Module CholmodSupport \endlink</td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing, Leverage fast dense algebra</td>
<td>Requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td>
<td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td>
<td></td></tr>
<tr><td>UmfPackLU</td><td>\link UmfPackSupport_Module UmfPackSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td>
<td>Requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td>
<td>Requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td>
<td></td></tr>
<tr><td>SuperLU</td><td>\link SuperLUSupport_Module SuperLUSupport \endlink</td><td>Direct LU factorization</td><td>Square</td><td>Fill-in reducing, Leverage fast dense algebra</td>
<td>Requires the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library, (BSD-like)</td>
<td></td></tr>
<tr><td>SPQR</td><td>\link SPQRSupport_Module SPQRSupport \endlink </td> <td> QR factorization </td>
<td> Any, rectangular</td><td>fill-in reducing, multithreaded, fast dense algebra</td>
<td> requires the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">SuiteSparse</a> package, \b GPL </td><td>recommended for linear least-squares problems, has a rank-revealing feature</tr>
<td> requires the <a href="http://www.suitesparse.com">SuiteSparse</a> package, \b GPL </td><td>recommended for linear least-squares problems, has a rank-revealing feature</tr>
</table>
Here \c SPD means symmetric positive definite.

30
doc/TopicAliasing.dox
View File

@ -153,10 +153,11 @@ not necessary to evaluate the right-hand side explicitly.
\section TopicAliasingMatrixMult Aliasing and matrix multiplication
Matrix multiplication is the only operation in %Eigen that assumes aliasing by default. Thus, if \c matA is a
matrix, then the statement <tt>matA = matA * matA;</tt> is safe. All other operations in %Eigen assume that
there are no aliasing problems, either because the result is assigned to a different matrix or because it is a
component-wise operation.
Matrix multiplication is the only operation in %Eigen that assumes aliasing by default, <strong>under the
condition that the destination matrix is not resized</strong>.
Thus, if \c matA is a \b squared matrix, then the statement <tt>matA = matA * matA;</tt> is safe.
All other operations in %Eigen assume that there are no aliasing problems,
either because the result is assigned to a different matrix or because it is a component-wise operation.
<table class="example">
<tr><th>Example</th><th>Output</th></tr>
@ -198,6 +199,27 @@ may get wrong results:
\verbinclude TopicAliasing_mult3.out
</td></tr></table>
Moreover, starting in Eigen 3.3, aliasing is \b not assumed if the destination matrix is resized and the product is not directly assigned to the destination.
Therefore, the following example is also wrong:
<table class="example">
<tr><th>Example</th><th>Output</th></tr>
<tr><td>
\include TopicAliasing_mult4.cpp
</td>
<td>
\verbinclude TopicAliasing_mult4.out
</td></tr></table>
As for any aliasing issue, you can resolve it by explicitly evaluating the expression prior to assignment:
<table class="example">
<tr><th>Example</th><th>Output</th></tr>
<tr><td>
\include TopicAliasing_mult5.cpp
</td>
<td>
\verbinclude TopicAliasing_mult5.out
</td></tr></table>
\section TopicAliasingSummary Summary

14
doc/TutorialReductionsVisitorsBroadcasting.dox
View File

@ -101,17 +101,16 @@ row and column position are to be stored. These variables should be of type
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_visitors.out
</td></tr></table>
Note that both functions also return the value of the minimum or maximum coefficient if needed,
as if it was a typical reduction operation.
Both functions also return the value of the minimum or maximum coefficient.
\section TutorialReductionsVisitorsBroadcastingPartialReductions Partial reductions
Partial reductions are reductions that can operate column- or row-wise on a Matrix or
Array, applying the reduction operation on each column or row and
returning a column or row-vector with the corresponding values. Partial reductions are applied
returning a column or row vector with the corresponding values. Partial reductions are applied
with \link DenseBase::colwise() colwise() \endlink or \link DenseBase::rowwise() rowwise() \endlink.
A simple example is obtaining the maximum of the elements
in each column in a given matrix, storing the result in a row-vector:
in each column in a given matrix, storing the result in a row vector:
<table class="example">
<tr><th>Example:</th><th>Output:</th></tr>
@ -133,8 +132,7 @@ The same operation can be performed row-wise:
\verbinclude Tutorial_ReductionsVisitorsBroadcasting_rowwise.out
</td></tr></table>
<b>Note that column-wise operations return a 'row-vector' while row-wise operations
return a 'column-vector'</b>
<b>Note that column-wise operations return a row vector, while row-wise operations return a column vector.</b>
\subsection TutorialReductionsVisitorsBroadcastingPartialReductionsCombined Combining partial reductions with other operations
It is also possible to use the result of a partial reduction to do further processing.
@ -176,7 +174,7 @@ The concept behind broadcasting is similar to partial reductions, with the diffe
constructs an expression where a vector (column or row) is interpreted as a matrix by replicating it in
one direction.
A simple example is to add a certain column-vector to each column in a matrix.
A simple example is to add a certain column vector to each column in a matrix.
This can be accomplished with:
<table class="example">
@ -253,7 +251,7 @@ is a new matrix whose size is the same as matrix <tt>m</tt>: \f[
\f]
- <tt>(m.colwise() - v).colwise().squaredNorm()</tt> is a partial reduction, computing the squared norm column-wise. The result of
this operation is a row-vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
this operation is a row vector where each coefficient is the squared Euclidean distance between each column in <tt>m</tt> and <tt>v</tt>: \f[
\mbox{(m.colwise() - v).colwise().squaredNorm()} =
\begin{bmatrix}
1 & 505 & 32 & 50

7
doc/TutorialSparse.dox
View File

@ -257,7 +257,14 @@ Binary coefficient wise operators can also mix sparse and dense expressions:
\code
sm2 = sm1.cwiseProduct(dm1);
dm2 = sm1 + dm1;
dm2 = dm1 - sm1;
\endcode
Performance-wise, the adding/subtracting sparse and dense matrices is better performed in two steps. For instance, instead of doing <tt>dm2 = sm1 + dm1</tt>, better write:
\code
dm2 = dm1;
dm2 += sm1;
\endcode
This version has the advantage to fully exploit the higher performance of dense storage (no indirection, SIMD, etc.), and to pay the cost of slow sparse evaluation on the few non-zeros of the sparse matrix only.
%Sparse expressions also support transposition:

2
doc/UsingIntelMKL.dox
View File

@ -52,7 +52,7 @@ When doing so, a number of Eigen's algorithms are silently substituted with call
These substitutions apply only for \b Dynamic \b or \b large enough objects with one of the following four standard scalar types: \c float, \c double, \c complex<float>, and \c complex<double>.
Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms.
In addition you can coarsely select choose which parts will be substituted by defining one or multiple of the following macros:
In addition you can choose which parts will be substituted by defining one or multiple of the following macros:
<table class="manual">
<tr><td>\c EIGEN_USE_BLAS </td><td>Enables the use of external BLAS level 2 and 3 routines (currently works with Intel MKL only)</td></tr>

5
doc/snippets/TopicAliasing_mult4.cpp Normal file
View File

@ -0,0 +1,5 @@
MatrixXf A(2,2), B(3,2);
B << 2, 0, 0, 3, 1, 1;
A << 2, 0, 0, -2;
A = (B * A).cwiseAbs();
cout << A;

5
doc/snippets/TopicAliasing_mult5.cpp Normal file
View File

@ -0,0 +1,5 @@
MatrixXf A(2,2), B(3,2);
B << 2, 0, 0, 3, 1, 1;
A << 2, 0, 0, -2;
A = (B * A).eval().cwiseAbs();
cout << A;

2
test/adjoint.cpp
View File

@ -45,12 +45,14 @@ template<> struct adjoint_specific<false> {
// check null inputs
VERIFY_IS_APPROX((v1*0).normalized(), (v1*0));
#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE)
RealScalar very_small = (std::numeric_limits<RealScalar>::min)();
VERIFY( (v1*very_small).norm() == 0 );
VERIFY_IS_APPROX((v1*very_small).normalized(), (v1*very_small));
v3 = v1*very_small;
v3.normalize();
VERIFY_IS_APPROX(v3, (v1*very_small));
#endif
// check compatibility of dot and adjoint
ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm()));

21
test/array.cpp
View File

@ -219,6 +219,7 @@ template<typename ArrayType> void array_real(const ArrayType& m)
VERIFY_IS_APPROX(m1.tanh(), tanh(m1));
#ifdef EIGEN_HAS_C99_MATH
VERIFY_IS_APPROX(m1.lgamma(), lgamma(m1));
VERIFY_IS_APPROX(m1.digamma(), digamma(m1));
VERIFY_IS_APPROX(m1.erf(), erf(m1));
VERIFY_IS_APPROX(m1.erfc(), erfc(m1));
#endif // EIGEN_HAS_C99_MATH
@ -309,7 +310,22 @@ template<typename ArrayType> void array_real(const ArrayType& m)
s1 += Scalar(tiny);
m1 += ArrayType::Constant(rows,cols,Scalar(tiny));
VERIFY_IS_APPROX(s1/m1, s1 * m1.inverse());
// check special functions (comparing against numpy implementation)
#ifdef EIGEN_HAS_C99_MATH
if (!NumTraits<Scalar>::IsComplex) {
VERIFY_IS_APPROX(numext::digamma(Scalar(1)), RealScalar(-0.5772156649015329));
VERIFY_IS_APPROX(numext::digamma(Scalar(1.5)), RealScalar(0.03648997397857645));
VERIFY_IS_APPROX(numext::digamma(Scalar(4)), RealScalar(1.2561176684318));
VERIFY_IS_APPROX(numext::digamma(Scalar(-10.5)), RealScalar(2.398239129535781));
VERIFY_IS_APPROX(numext::digamma(Scalar(10000.5)), RealScalar(9.210340372392849));
VERIFY_IS_EQUAL(numext::digamma(Scalar(0)),
std::numeric_limits<RealScalar>::infinity());
VERIFY_IS_EQUAL(numext::digamma(Scalar(-1)),
std::numeric_limits<RealScalar>::infinity());
}
#endif // EIGEN_HAS_C99_MATH
// check inplace transpose
m3 = m1;
m3.transposeInPlace();
@ -336,8 +352,6 @@ template<typename ArrayType> void array_complex(const ArrayType& m)
Array<RealScalar, -1, -1> m3(rows, cols);
Scalar s1 = internal::random<Scalar>();
for (Index i = 0; i < m.rows(); ++i)
for (Index j = 0; j < m.cols(); ++j)
m2(i,j) = sqrt(m1(i,j));
@ -410,6 +424,7 @@ template<typename ArrayType> void array_complex(const ArrayType& m)
VERIFY_IS_APPROX( m1.sign() * m1.abs(), m1);
// scalar by array division
Scalar s1 = internal::random<Scalar>();
const RealScalar tiny = sqrt(std::numeric_limits<RealScalar>::epsilon());
s1 += Scalar(tiny);
m1 += ArrayType::Constant(rows,cols,Scalar(tiny));

10
test/array_for_matrix.cpp
View File

@ -68,6 +68,16 @@ template<typename MatrixType> void array_for_matrix(const MatrixType& m)
const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0);
VERIFY(&ref_a1 == &ref_m1);
VERIFY(&ref_a2 == &ref_m2);
// Check write accessors:
m1.array().coeffRef(0,0) = 1;
VERIFY_IS_APPROX(m1(0,0),Scalar(1));
m1.array()(0,0) = 2;
VERIFY_IS_APPROX(m1(0,0),Scalar(2));
m1.array().matrix().coeffRef(0,0) = 3;
VERIFY_IS_APPROX(m1(0,0),Scalar(3));
m1.array().matrix()(0,0) = 4;
VERIFY_IS_APPROX(m1(0,0),Scalar(4));
}
template<typename MatrixType> void comparisons(const MatrixType& m)

7
test/diagonal.cpp
View File

@ -20,6 +20,8 @@ template<typename MatrixType> void diagonal(const MatrixType& m)
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols);
Scalar s1 = internal::random<Scalar>();
//check diagonal()
VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal());
m2.diagonal() = 2 * m1.diagonal();
@ -58,6 +60,11 @@ template<typename MatrixType> void diagonal(const MatrixType& m)
VERIFY_IS_APPROX(m2.template diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2));
m2.diagonal(N2)[0] *= 3;
VERIFY_IS_APPROX(m2.diagonal(N2)[0], static_cast<Scalar>(6) * m1.diagonal(N2)[0]);
m2.diagonal(N2).x() = s1;
VERIFY_IS_APPROX(m2.diagonal(N2).x(), s1);
m2.diagonal(N2).coeffRef(0) = Scalar(2)*s1;
VERIFY_IS_APPROX(m2.diagonal(N2).coeff(0), Scalar(2)*s1);
}
}

30
test/incomplete_cholesky.cpp
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2015-2016 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
@ -34,4 +34,32 @@ void test_incomplete_cholesky()
CALL_SUBTEST_1(( test_incomplete_cholesky_T<double,int>() ));
CALL_SUBTEST_2(( test_incomplete_cholesky_T<std::complex<double>, int>() ));
CALL_SUBTEST_3(( test_incomplete_cholesky_T<double,long int>() ));
#ifdef EIGEN_TEST_PART_1
// regression for bug 1150
for(int N = 1; N<20; ++N)
{
Eigen::MatrixXd b( N, N );
b.setOnes();
Eigen::SparseMatrix<double> m( N, N );
m.reserve(Eigen::VectorXi::Constant(N,4));
for( int i = 0; i < N; ++i )
{
m.insert( i, i ) = 1;
m.coeffRef( i, i / 2 ) = 2;
m.coeffRef( i, i / 3 ) = 2;
m.coeffRef( i, i / 4 ) = 2;
}
Eigen::SparseMatrix<double> A;
A = m * m.transpose();
Eigen::ConjugateGradient<Eigen::SparseMatrix<double>,
Eigen::Lower | Eigen::Upper,
Eigen::IncompleteCholesky<double> > solver( A );
VERIFY(solver.preconditioner().info() == Eigen::Success);
VERIFY(solver.info() == Eigen::Success);
}
#endif
}

41
test/mixingtypes.cpp
View File

@ -44,6 +44,7 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
Mat_d md = mf.template cast<double>();
Mat_cf mcf = Mat_cf::Random(size,size);
Mat_cd mcd = mcf.template cast<complex<double> >();
Mat_cd rcd = mcd;
Vec_f vf = Vec_f::Random(size,1);
Vec_d vd = vf.template cast<double>();
Vec_cf vcf = Vec_cf::Random(size,1);
@ -103,24 +104,23 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
VERIFY_IS_APPROX(mcd.array() *= md.array(), mcd2.array() *= md.array().template cast<std::complex<double> >());
// check matrix-matrix products
VERIFY_IS_APPROX(sd*md*mcd, (sd*md).template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(sd*mcd*md, sd*mcd*md.template cast<CD>());
VERIFY_IS_APPROX(scd*md*mcd, scd*md.template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(scd*mcd*md, scd*mcd*md.template cast<CD>());
VERIFY_IS_APPROX(sf*mf*mcf, sf*mf.template cast<CF>()*mcf);
VERIFY_IS_APPROX(sf*mcf*mf, sf*mcf*mf.template cast<CF>());
VERIFY_IS_APPROX(scf*mf*mcf, scf*mf.template cast<CF>()*mcf);
VERIFY_IS_APPROX(scf*mcf*mf, scf*mcf*mf.template cast<CF>());
VERIFY_IS_APPROX(sd*md.adjoint()*mcd, (sd*md).template cast<CD>().eval().adjoint()*mcd);
VERIFY_IS_APPROX(sd*mcd.adjoint()*md, sd*mcd.adjoint()*md.template cast<CD>());
VERIFY_IS_APPROX(sd*md.adjoint()*mcd.adjoint(), (sd*md).template cast<CD>().eval().adjoint()*mcd.adjoint());
VERIFY_IS_APPROX(sd*mcd.adjoint()*md.adjoint(), sd*mcd.adjoint()*md.template cast<CD>().adjoint());
VERIFY_IS_APPROX(sd*md*mcd.adjoint(), (sd*md).template cast<CD>().eval()*mcd.adjoint());
VERIFY_IS_APPROX(sd*mcd*md.adjoint(), sd*mcd*md.template cast<CD>().adjoint());
VERIFY_IS_APPROX(sf*mf.adjoint()*mcf, (sf*mf).template cast<CF>().eval().adjoint()*mcf);
VERIFY_IS_APPROX(sf*mcf.adjoint()*mf, sf*mcf.adjoint()*mf.template cast<CF>());
VERIFY_IS_APPROX(sf*mf.adjoint()*mcf.adjoint(), (sf*mf).template cast<CF>().eval().adjoint()*mcf.adjoint());
@ -147,6 +147,39 @@ template<int SizeAtCompileType> void mixingtypes(int size = SizeAtCompileType)
VERIFY_IS_APPROX(scd*vcd.adjoint()*md, scd*vcd.adjoint()*md.template cast<CD>().eval());
VERIFY_IS_APPROX(sd*vd.adjoint()*mcd, sd*vd.adjoint().template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(scd*vd.adjoint()*mcd, scd*vd.adjoint().template cast<CD>().eval()*mcd);
VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template triangularView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Upper>());
VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template triangularView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template triangularView<Lower>());
VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template triangularView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Lower>());
VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template triangularView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template triangularView<Upper>());
// Not supported yet: trmm
// VERIFY_IS_APPROX(sd*mcd*md.template triangularView<Lower>(), sd*mcd*md.template cast<CD>().eval().template triangularView<Lower>());
// VERIFY_IS_APPROX(scd*mcd*md.template triangularView<Upper>(), scd*mcd*md.template cast<CD>().eval().template triangularView<Upper>());
// VERIFY_IS_APPROX(sd*md*mcd.template triangularView<Lower>(), sd*md.template cast<CD>().eval()*mcd.template triangularView<Lower>());
// VERIFY_IS_APPROX(scd*md*mcd.template triangularView<Upper>(), scd*md.template cast<CD>().eval()*mcd.template triangularView<Upper>());
// Not supported yet: symv
// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Lower>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Lower>());
// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Lower>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Lower>());
// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
// Not supported yet: symm
// VERIFY_IS_APPROX(sd*vcd.adjoint()*md.template selfadjointView<Upper>(), sd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vcd.adjoint()*md.template selfadjointView<Upper>(), scd*vcd.adjoint()*md.template cast<CD>().eval().template selfadjointView<Upper>());
// VERIFY_IS_APPROX(sd*vd.adjoint()*mcd.template selfadjointView<Upper>(), sd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
// VERIFY_IS_APPROX(scd*vd.adjoint()*mcd.template selfadjointView<Upper>(), scd*vd.adjoint().template cast<CD>().eval()*mcd.template selfadjointView<Upper>());
rcd.setZero();
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * mcd * md),
Mat_cd((sd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = sd * md * mcd),
Mat_cd((sd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * mcd * md),
Mat_cd((scd * mcd * md.template cast<CD>().eval()).template triangularView<Upper>()));
VERIFY_IS_APPROX(Mat_cd(rcd.template triangularView<Upper>() = scd * md * mcd),
Mat_cd((scd * md.template cast<CD>().eval() * mcd).template triangularView<Upper>()));
}
void test_mixingtypes()

13
test/nomalloc.cpp
View File

@ -78,14 +78,15 @@ template<typename MatrixType> void nomalloc(const MatrixType& m)
VERIFY_IS_APPROX(m2,m2);
m2.template selfadjointView<Lower>().rankUpdate(m1.col(0),-1);
m2.template selfadjointView<Lower>().rankUpdate(m1.row(0),-1);
m2.template selfadjointView<Upper>().rankUpdate(m1.row(0),-1);
m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2
// The following fancy matrix-matrix products are not safe yet regarding static allocation
// m1 += m1.template triangularView<Upper>() * m2.col(;
// m1.template selfadjointView<Lower>().rankUpdate(m2);
// m1 += m1.template triangularView<Upper>() * m2;
// m1 += m1.template selfadjointView<Lower>() * m2;
// VERIFY_IS_APPROX(m1,m1);
m2.template selfadjointView<Lower>().rankUpdate(m1);
m2 += m2.template triangularView<Upper>() * m1;
m2.template triangularView<Upper>() = m2 * m2;
m1 += m1.template selfadjointView<Lower>() * m2;
VERIFY_IS_APPROX(m2,m2);
}
template<typename Scalar>

32
test/nullary.cpp
View File

@ -48,30 +48,32 @@ void testVectorType(const VectorType& base)
VectorType m(base);
m.setLinSpaced(size,low,high);
if(!NumTraits<Scalar>::IsInteger)
{
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = low+i*step;
VERIFY_IS_APPROX(m,n);
}
VectorType n(size);
for (int i=0; i<size; ++i)
n(i) = low+i*step;
n(i) = size==1 ? low : (low + ((high-low)*Scalar(i))/(size-1));
VERIFY_IS_APPROX(m,n);
// random access version
m = VectorType::LinSpaced(size,low,high);
VERIFY_IS_APPROX(m,n);
// Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79).
VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<Scalar>::epsilon() );
// These guys sometimes fail! This is not good. Any ideas how to fix them!?
//VERIFY( m(m.size()-1) == high );
//VERIFY( m(0) == low );
VERIFY( internal::isApprox(m(m.size()-1),high) );
VERIFY( size==1 || internal::isApprox(m(0),low) );
// sequential access version
m = VectorType::LinSpaced(Sequential,size,low,high);
VERIFY_IS_APPROX(m,n);
// These guys sometimes fail! This is not good. Any ideas how to fix them!?
//VERIFY( m(m.size()-1) == high );
//VERIFY( m(0) == low );
VERIFY( internal::isApprox(m(m.size()-1),high) );
VERIFY( size==1 || internal::isApprox(m(0),low) );
// check whether everything works with row and col major vectors
Matrix<Scalar,Dynamic,1> row_vector(size);
@ -126,5 +128,13 @@ void test_nullary()
CALL_SUBTEST_8( testVectorType(Vector4f()) );
CALL_SUBTEST_8( testVectorType(Matrix<float,8,1>()) );
CALL_SUBTEST_8( testVectorType(Matrix<float,1,1>()) );
CALL_SUBTEST_9( testVectorType(VectorXi(internal::random<int>(1,300))) );
CALL_SUBTEST_9( testVectorType(Matrix<int,1,1>()) );
}
#ifdef EIGEN_TEST_PART_6
// Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79).
VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<double>::epsilon() );
#endif
}

32
test/sparse_basic.cpp
View File

@ -192,6 +192,11 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
// test aliasing
VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
@ -455,6 +460,33 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
refMat1.setIdentity();
VERIFY_IS_APPROX(m1, refMat1);
}
// test array/vector of InnerIterator
{
typedef typename SparseMatrixType::InnerIterator IteratorType;
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
IteratorType static_array[2];
static_array[0] = IteratorType(m2,0);
static_array[1] = IteratorType(m2,m2.outerSize()-1);
VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 );
VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 );
if(static_array[0] && static_array[1])
{
++(static_array[1]);
static_array[1] = IteratorType(m2,0);
VERIFY( static_array[1] );
VERIFY( static_array[1].index() == static_array[0].index() );
VERIFY( static_array[1].outer() == static_array[0].outer() );
VERIFY( static_array[1].value() == static_array[0].value() );
}
std::vector<IteratorType> iters(2);
iters[0] = IteratorType(m2,0);
iters[1] = IteratorType(m2,m2.outerSize()-1);
}
}

54
test/sparse_vector.cpp
View File

@ -9,14 +9,14 @@
#include "sparse.h"
template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
{
double densityMat = (std::max)(8./(rows*cols), 0.01);
double densityVec = (std::max)(8./float(rows), 0.1);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseVector<Scalar,0,Index> SparseVectorType;
typedef SparseMatrix<Scalar,0,Index> SparseMatrixType;
typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
Scalar eps = 1e-6;
SparseMatrixType m1(rows,rows);
@ -87,8 +87,10 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
VERIFY_IS_APPROX(m1*v2, refM1*refV2);
VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
{
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
}
VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
@ -111,15 +113,51 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
// test conservative resize
{
std::vector<StorageIndex> inc;
if(rows > 3)
inc.push_back(-3);
inc.push_back(0);
inc.push_back(3);
inc.push_back(1);
inc.push_back(10);
for(std::size_t i = 0; i< inc.size(); i++) {
StorageIndex incRows = inc[i];
SparseVectorType vec1(rows);
DenseVector refVec1 = DenseVector::Zero(rows);
initSparse<Scalar>(densityVec, refVec1, vec1);
vec1.conservativeResize(rows+incRows);
refVec1.conservativeResize(rows+incRows);
if (incRows > 0) refVec1.tail(incRows).setZero();
VERIFY_IS_APPROX(vec1, refVec1);
// Insert new values
if (incRows > 0)
vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
VERIFY_IS_APPROX(vec1, refVec1);
}
}
}
void test_sparse_vector()
{
for(int i = 0; i < g_repeat; i++) {
int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
if(Eigen::internal::random<int>(0,4) == 0) {
r = c; // check square matrices in 25% of tries
}
EIGEN_UNUSED_VARIABLE(r+c);
CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(16, 16) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(299, 535) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(299, 535) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
}
}

15
test/stable_norm.cpp
View File

@ -163,6 +163,21 @@ template<typename MatrixType> void stable_norm(const MatrixType& m)
VERIFY(!(numext::isfinite)(v.blueNorm())); VERIFY((numext::isnan)(v.blueNorm()));
VERIFY(!(numext::isfinite)(v.hypotNorm())); VERIFY((numext::isnan)(v.hypotNorm()));
}
// stableNormalize[d]
{
VERIFY_IS_APPROX(vrand.stableNormalized(), vrand.normalized());
MatrixType vcopy(vrand);
vcopy.stableNormalize();
VERIFY_IS_APPROX(vcopy, vrand.normalized());
VERIFY_IS_APPROX((vrand.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX(vcopy.norm(), RealScalar(1));
VERIFY_IS_APPROX((vbig.stableNormalized()).norm(), RealScalar(1));
VERIFY_IS_APPROX((vsmall.stableNormalized()).norm(), RealScalar(1));
RealScalar big_scaling = ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4));
VERIFY_IS_APPROX(vbig/big_scaling, (vbig.stableNorm() * vbig.stableNormalized()).eval()/big_scaling);
VERIFY_IS_APPROX(vsmall, vsmall.stableNorm() * vsmall.stableNormalized());
}
}
void test_stable_norm()

3
test/vectorwiseop.cpp
View File

@ -210,6 +210,9 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
// regression for bug 1158
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());
// test normalized
m2 = m1.colwise().normalized();
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());

18
test/zerosized.cpp
View File

@ -25,6 +25,7 @@ template<typename MatrixType> void zeroReduction(const MatrixType& m) {
template<typename MatrixType> void zeroSizedMatrix()
{
MatrixType t1;
typedef typename MatrixType::Scalar Scalar;
if (MatrixType::SizeAtCompileTime == Dynamic || MatrixType::SizeAtCompileTime == 0)
{
@ -45,6 +46,23 @@ template<typename MatrixType> void zeroSizedMatrix()
VERIFY(t1==t2);
}
}
if(MatrixType::MaxColsAtCompileTime!=0 && MatrixType::MaxRowsAtCompileTime!=0)
{
Index rows = MatrixType::RowsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : MatrixType::RowsAtCompileTime;
Index cols = MatrixType::ColsAtCompileTime==Dynamic ? internal::random<Index>(1,10) : MatrixType::ColsAtCompileTime;
MatrixType m(rows,cols);
zeroReduction(m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols));
zeroReduction(m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0));
zeroReduction(m.template block<0,1>(0,0));
zeroReduction(m.template block<1,0>(0,0));
Matrix<Scalar,Dynamic,Dynamic> prod = m.template block<MatrixType::RowsAtCompileTime,0>(0,0,rows,0) * m.template block<0,MatrixType::ColsAtCompileTime>(0,0,0,cols);
VERIFY(prod.rows()==rows && prod.cols()==cols);
VERIFY(prod.isZero());
prod = m.template block<1,0>(0,0) * m.template block<0,1>(0,0);
VERIFY(prod.size()==1);
VERIFY(prod.isZero());
}
}
template<typename VectorType> void zeroSizedVector()

2
unsupported/Eigen/AlignedVector3
View File

@ -188,7 +188,7 @@ template<typename _Scalar> class AlignedVector3
}
template<typename Derived>
inline bool isApprox(const MatrixBase<Derived>& other, RealScalar eps=NumTraits<Scalar>::dummy_precision()) const
inline bool isApprox(const MatrixBase<Derived>& other, const RealScalar& eps=NumTraits<Scalar>::dummy_precision()) const
{
return m_coeffs.template head<3>().isApprox(other,eps);
}

39
unsupported/Eigen/CXX11/src/Core/util/EmulateArray.h
View File

@ -25,6 +25,16 @@ template <typename T, size_t n> class array {
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { return values[index]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& front() { return values[0]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& back() { return values[n-1]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& back() const { return values[n-1]; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
static std::size_t size() { return n; }
@ -123,13 +133,33 @@ template <typename T> class array<T, 0> {
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& operator[] (size_t) {
eigen_assert(false && "Can't index a zero size array");
return *static_cast<T*>(NULL);
return dummy;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& operator[] (size_t) const {
eigen_assert(false && "Can't index a zero size array");
return *static_cast<const T*>(NULL);
return dummy;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& front() {
eigen_assert(false && "Can't index a zero size array");
return dummy;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& front() const {
eigen_assert(false && "Can't index a zero size array");
return dummy;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& back() {
eigen_assert(false && "Can't index a zero size array");
return dummy;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& back() const {
eigen_assert(false && "Can't index a zero size array");
return dummy;
}
static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
@ -142,6 +172,9 @@ template <typename T> class array<T, 0> {
eigen_assert(l.size() == 0);
}
#endif
private:
T dummy;
};
namespace internal {

6
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
View File

@ -128,6 +128,12 @@ class TensorBase<Derived, ReadOnlyAccessors>
return unaryExpr(internal::scalar_lgamma_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived>
digamma() const {
return unaryExpr(internal::scalar_digamma_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived>
erf() const {

8
unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
View File

@ -378,7 +378,7 @@ struct TensorContractionEvaluatorBase
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalGemv(Scalar* buffer) const {
EIGEN_DEVICE_FUNC void evalGemv(Scalar* buffer) const {
const Index rows = m_i_size;
const Index cols = m_k_size;
@ -516,7 +516,7 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
Base(op, device) { }
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalProduct(Scalar* buffer) const {
EIGEN_DEVICE_FUNC void evalProduct(Scalar* buffer) const {
if (this->m_j_size == 1) {
this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
return;
@ -582,10 +582,8 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
OutputMapper output(buffer, m);
typedef typename internal::gemm_blocking_space<ColMajor, LhsScalar, RhsScalar, Dynamic, Dynamic, Dynamic> BlockingType;
// Sizes of the blocks to load in cache. See the Goto paper for details.
BlockingType blocking(m, n, k, 1, true);
internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, 1);
const Index kc = blocking.kc();
const Index mc = numext::mini(m, blocking.mc());
const Index nc = numext::mini(n, blocking.nc());

8
unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
View File

@ -28,7 +28,7 @@ class TensorContractionBlocking {
typedef typename LhsMapper::Scalar LhsScalar;
typedef typename RhsMapper::Scalar RhsScalar;
TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1) :
EIGEN_DEVICE_FUNC TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1) :
kc_(k), mc_(m), nc_(n)
{
if (ShardingType == ShardByCol) {
@ -41,9 +41,9 @@ class TensorContractionBlocking {
}
}
EIGEN_ALWAYS_INLINE Index kc() const { return kc_; }
EIGEN_ALWAYS_INLINE Index mc() const { return mc_; }
EIGEN_ALWAYS_INLINE Index nc() const { return nc_; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index kc() const { return kc_; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index mc() const { return mc_; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index nc() const { return nc_; }
private:
Index kc_;

5
unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
View File

@ -426,15 +426,16 @@ class TensorContractionSubMapper {
};
template<typename Scalar, typename Index, int side,
template<typename Scalar_, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
int packet_size,
bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment>
class TensorContractionInputMapper
: public BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> {
: public BaseTensorContractionMapper<Scalar_, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> {
public:
typedef Scalar_ Scalar;
typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Base;
typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper;
typedef SubMapper VectorMapper;

8
unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
View File

@ -176,10 +176,10 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
// compute block sizes (which depend on number of threads)
const Index num_threads = this->m_device.numThreads();
Index mc = m;
Index nc = n;
Index kc = k;
internal::computeProductBlockingSizes<LhsScalar,RhsScalar,1>(kc, mc, nc, num_threads);
internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, num_threads);
Index mc = blocking.mc();
Index nc = blocking.nc();
Index kc = blocking.kc();
eigen_assert(mc <= m);
eigen_assert(nc <= n);
eigen_assert(kc <= k);

8
unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
View File

@ -21,7 +21,7 @@ namespace Eigen {
*/
namespace internal {
template <typename Index, typename InputDims, size_t NumKernelDims, int Layout>
template <typename Index, typename InputDims, int NumKernelDims, int Layout>
class IndexMapper {
public:
IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
@ -123,7 +123,7 @@ class IndexMapper {
}
inputIndex += p * m_inputStrides[NumKernelDims];
} else {
int limit = 0;
std::ptrdiff_t limit = 0;
if (NumKernelDims < NumDims) {
limit = NumDims - NumKernelDims - 1;
}
@ -147,7 +147,7 @@ class IndexMapper {
}
outputIndex += p * m_outputStrides[NumKernelDims];
} else {
int limit = 0;
std::ptrdiff_t limit = 0;
if (NumKernelDims < NumDims) {
limit = NumDims - NumKernelDims - 1;
}
@ -206,7 +206,7 @@ class IndexMapper {
}
private:
static const size_t NumDims = internal::array_size<InputDims>::value;
static const int NumDims = internal::array_size<InputDims>::value;
array<Index, NumDims> m_inputStrides;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_cudaInputStrides;

12
unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
View File

@ -109,10 +109,12 @@ class CudaStreamDevice : public StreamInterface {
struct GpuDevice {
// The StreamInterface is not owned: the caller is
// responsible for its initialization and eventual destruction.
explicit GpuDevice(const StreamInterface* stream) : stream_(stream) {
explicit GpuDevice(const StreamInterface* stream) : stream_(stream), max_blocks_(INT_MAX) {
eigen_assert(stream);
}
explicit GpuDevice(const StreamInterface* stream, int num_blocks) : stream_(stream), max_blocks_(num_blocks) {
eigen_assert(stream);
}
// TODO(bsteiner): This is an internal API, we should not expose it.
EIGEN_STRONG_INLINE const cudaStream_t& stream() const {
return stream_->stream();
@ -246,6 +248,10 @@ struct GpuDevice {
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int maxBlocks() const {
return max_blocks_;
}
// This function checks if the CUDA runtime recorded an error for the
// underlying stream device.
inline bool ok() const {
@ -259,7 +265,7 @@ struct GpuDevice {
private:
const StreamInterface* stream_;
int max_blocks_;
};
#ifndef __CUDA_ARCH__

2
unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
View File

@ -136,7 +136,7 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return internal::ploadt<Packet, LoadMode>(m_buffer + index);
}

4
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
View File

@ -220,7 +220,7 @@ EIGEN_DEVICE_FUNC inline void TensorExecutor<Expression, GpuDevice, false>::run(
if (needs_assign)
{
const int block_size = device.maxCudaThreadsPerBlock();
const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size;
const int max_blocks = numext::maxi<int>(device.maxBlocks(), device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size);
const Index size = array_prod(evaluator.dimensions());
// Create a least one block to ensure we won't crash if we're called with tensors of size 0.
const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1);
@ -239,7 +239,7 @@ EIGEN_DEVICE_FUNC inline void TensorExecutor<Expression, GpuDevice, true>::run(c
if (needs_assign)
{
const int block_size = device.maxCudaThreadsPerBlock();
const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size;
const int max_blocks = numext::maxi<int>(device.maxBlocks(), device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size);
const Index size = array_prod(evaluator.dimensions());
// Create a least one block to ensure we won't crash if we're called with tensors of size 0.
const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, (size + block_size - 1) / block_size), 1);

2
unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
View File

@ -106,7 +106,6 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
m_impl.evalSubExprsIfNeeded(NULL);
const Index numValues = m_impl.dimensions().TotalSize();
m_buffer = (CoeffReturnType*)m_device.allocate(numValues * sizeof(CoeffReturnType));
// Should initialize the memory in case we're dealing with non POD types.
@ -119,7 +118,6 @@ struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
EvalTo evalToTmp(m_buffer, m_op);
const bool PacketAccess = internal::IsVectorizable<Device, const ArgType>::value;
internal::TensorExecutor<const EvalTo, Device, PacketAccess>::run(evalToTmp, m_device);
m_impl.cleanup();
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {

Some files were not shown because too many files have changed in this diff Show More