devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Pulled latest updates from upstream

Browse Source
This commit is contained in:
Benoit Steiner
2016-04-29 13:41:26 -07:00
parent fa5a8f055a 0f3c4c8ff4
commit 07a247dcf4
113 changed files with 3595 additions and 972 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

@@ -29,13 +29,10 @@ struct copy_using_evaluator_traits
{
typedef typename DstEvaluator::XprType Dst;
typedef typename Dst::Scalar DstScalar;
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type PacketType;
enum {
DstFlags = DstEvaluator::Flags,
SrcFlags = SrcEvaluator::Flags,
RequiredAlignment = unpacket_traits<PacketType>::alignment
SrcFlags = SrcEvaluator::Flags
};
public:
@@ -55,10 +52,25 @@ private:
: int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
: int(Dst::MaxRowsAtCompileTime),
OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
PacketSize = unpacket_traits<PacketType>::size
MaxSizeAtCompileTime = Dst::SizeAtCompileTime
};
// TODO distinguish between linear traversal and inner-traversals
typedef typename find_best_packet<DstScalar,Dst::SizeAtCompileTime>::type LinearPacketType;
typedef typename find_best_packet<DstScalar,InnerSize>::type InnerPacketType;
enum {
LinearPacketSize = unpacket_traits<LinearPacketType>::size,
InnerPacketSize = unpacket_traits<InnerPacketType>::size
};
public:
enum {
LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
};
private:
enum {
DstIsRowMajor = DstFlags&RowMajorBit,
SrcIsRowMajor = SrcFlags&RowMajorBit,
@@ -67,16 +79,16 @@ private:
&& (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
&& (functor_traits<AssignFunc>::PacketAccess),
MayInnerVectorize = MightVectorize
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(PacketSize)==0
&& int(JointAlignment)>=int(RequiredAlignment),
&& int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& int(JointAlignment)>=int(InnerRequiredAlignment),
MayLinearize = StorageOrdersAgree && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess
&& ((int(DstAlignment)>=int(RequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
&& ((int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */
MaySliceVectorize = MightVectorize && DstHasDirectAccess
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize)
&& (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*InnerPacketSize)
/* slice vectorization can be slow, so we only want it if the slices are big, which is
indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
in a fixed-size matrix */
@@ -84,7 +96,8 @@ private:
public:
enum {
Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
Traversal = int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize) ? int(LinearVectorizedTraversal)
: int(MayInnerVectorize) ? int(InnerVectorizedTraversal)
: int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
: int(MaySliceVectorize) ? int(SliceVectorizedTraversal)
: int(MayLinearize) ? int(LinearTraversal)
@@ -94,9 +107,14 @@ public:
|| int(Traversal) == SliceVectorizedTraversal
};
typedef typename conditional<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType>::type PacketType;
private:
enum {
UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1),
ActualPacketSize = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
: Vectorized ? InnerPacketSize
: 1,
UnrollingLimit = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
&& int(Dst::SizeAtCompileTime) * int(SrcEvaluator::CoeffReadCost) <= int(UnrollingLimit),
MayUnrollInner = int(InnerSize) != Dynamic
@@ -112,7 +130,7 @@ public:
: int(NoUnrolling)
)
: int(Traversal) == int(LinearVectorizedTraversal)
? ( bool(MayUnrollCompletely) && (int(DstAlignment)>=int(RequiredAlignment)) ? int(CompleteUnrolling)
? ( bool(MayUnrollCompletely) && (int(DstAlignment)>=int(LinearRequiredAlignment)) ? int(CompleteUnrolling)
: int(NoUnrolling) )
: int(Traversal) == int(LinearTraversal)
? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
@@ -131,11 +149,13 @@ public:
std::cerr.unsetf(std::ios::hex);
EIGEN_DEBUG_VAR(DstAlignment)
EIGEN_DEBUG_VAR(SrcAlignment)
EIGEN_DEBUG_VAR(RequiredAlignment)
EIGEN_DEBUG_VAR(LinearRequiredAlignment)
EIGEN_DEBUG_VAR(InnerRequiredAlignment)
EIGEN_DEBUG_VAR(JointAlignment)
EIGEN_DEBUG_VAR(InnerSize)
EIGEN_DEBUG_VAR(InnerMaxSize)
EIGEN_DEBUG_VAR(PacketSize)
EIGEN_DEBUG_VAR(LinearPacketSize)
EIGEN_DEBUG_VAR(InnerPacketSize)
EIGEN_DEBUG_VAR(StorageOrdersAgree)
EIGEN_DEBUG_VAR(MightVectorize)
EIGEN_DEBUG_VAR(MayLinearize)
@@ -370,7 +390,7 @@ struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
typedef typename Kernel::Scalar Scalar;
typedef typename Kernel::PacketType PacketType;
enum {
requestedAlignment = Kernel::AssignmentTraits::RequiredAlignment,
requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
packetSize = unpacket_traits<PacketType>::size,
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
@@ -484,7 +504,7 @@ struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
typedef typename Kernel::PacketType PacketType;
enum {
packetSize = unpacket_traits<PacketType>::size,
requestedAlignment = int(Kernel::AssignmentTraits::RequiredAlignment),
requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
dstAlignment = alignable ? int(requestedAlignment)

166
Eigen/src/Core/ConditionEstimator.h Normal file
View File

@@ -0,0 +1,166 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CONDITIONESTIMATOR_H
#define EIGEN_CONDITIONESTIMATOR_H
namespace Eigen {
namespace internal {
template <typename Vector, typename RealVector, bool IsComplex>
struct rcond_compute_sign {
static inline Vector run(const Vector& v) {
const RealVector v_abs = v.cwiseAbs();
return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
.select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
}
};
// Partial specialization to avoid elementwise division for real vectors.
template <typename Vector>
struct rcond_compute_sign<Vector, Vector, false> {
static inline Vector run(const Vector& v) {
return (v.array() < static_cast<typename Vector::RealScalar>(0))
.select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
}
};
/** \brief Reciprocal condition number estimator.
*
* Computing a decomposition of a dense matrix takes O(n^3) operations, while
* this method estimates the condition number quickly and reliably in O(n^2)
* operations.
*
* \returns an estimate of the reciprocal condition number
* (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
* its decomposition. Supports the following decompositions: FullPivLU,
* PartialPivLU, LDLT, and LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar
rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return RealScalar(1);
if (matrix_norm == RealScalar(0)) return RealScalar(0);
if (dec.rows() == 1) return RealScalar(1);
const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
return (inverse_matrix_norm == RealScalar(0) ? RealScalar(0)
: (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
}
/**
* \returns an estimate of ||inv(matrix)||_1 given a decomposition of
* \a matrix that implements .solve() and .adjoint().solve() methods.
*
* This function implements Algorithms 4.1 and 5.1 from
* http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
* which also forms the basis for the condition number estimators in
* LAPACK. Since at most 10 calls to the solve method of dec are
* performed, the total cost is O(dims^2), as opposed to O(dims^3)
* needed to compute the inverse matrix explicitly.
*
* The most common usage is in estimating the condition number
* ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
* computed directly in O(n^2) operations.
*
* Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
* LLT.
*
* \sa FullPivLU, PartialPivLU, LDLT, LLT.
*/
template <typename Decomposition>
typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
{
typedef typename Decomposition::MatrixType MatrixType;
typedef typename Decomposition::Scalar Scalar;
typedef typename Decomposition::RealScalar RealScalar;
typedef typename internal::plain_col_type<MatrixType>::type Vector;
typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
eigen_assert(dec.rows() == dec.cols());
const Index n = dec.rows();
if (n == 0)
return 0;
Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
// lower_bound is a lower bound on
// ||inv(matrix)||_1 = sup_v ||inv(matrix) v||_1 / ||v||_1
// and is the objective maximized by the ("super-") gradient ascent
// algorithm below.
RealScalar lower_bound = v.template lpNorm<1>();
if (n == 1)
return lower_bound;
// Gradient ascent algorithm follows: We know that the optimum is achieved at
// one of the simplices v = e_i, so in each iteration we follow a
// super-gradient to move towards the optimal one.
RealScalar old_lower_bound = lower_bound;
Vector sign_vector(n);
Vector old_sign_vector;
Index v_max_abs_index = -1;
Index old_v_max_abs_index = v_max_abs_index;
for (int k = 0; k < 4; ++k)
{
sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
// Break if the solution stagnated.
break;
}
// v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
v = dec.adjoint().solve(sign_vector);
v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {
// Break if the solution stagnated.
break;
}
// Move to the new simplex e_j, where j = v_max_abs_index.
v = dec.solve(Vector::Unit(n, v_max_abs_index)); // v = inv(matrix) * e_j.
lower_bound = v.template lpNorm<1>();
if (lower_bound <= old_lower_bound) {
// Break if the gradient step did not increase the lower_bound.
break;
}
if (!is_complex) {
old_sign_vector = sign_vector;
}
old_v_max_abs_index = v_max_abs_index;
old_lower_bound = lower_bound;
}
// The following calculates an independent estimate of ||matrix||_1 by
// multiplying matrix by a vector with entries of slowly increasing
// magnitude and alternating sign:
// v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
// This improvement to Hager's algorithm above is due to Higham. It was
// added to make the algorithm more robust in certain corner cases where
// large elements in the matrix might otherwise escape detection due to
// exact cancellation (especially when op and op_adjoint correspond to a
// sequence of backsubstitutions and permutations), which could cause
// Hager's algorithm to vastly underestimate ||matrix||_1.
Scalar alternating_sign(RealScalar(1));
for (Index i = 0; i < n; ++i) {
v[i] = alternating_sign * (RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
alternating_sign = -alternating_sign;
}
v = dec.solve(v);
const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
return numext::maxi(lower_bound, alternate_lower_bound);
}
} // namespace internal
} // namespace Eigen
#endif

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

@@ -81,6 +81,8 @@ public:
* This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
// FIXME I'm not sure the current mapping is the ideal one.
template<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; };
template<int M> struct product_type_selector<M, 1, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int N> struct product_type_selector<1, N, 1> { enum { ret = LazyCoeffBasedProductMode }; };
template<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; };
template<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; };

150
Eigen/src/Core/MathFunctions.h
View File

@@ -1023,21 +1023,6 @@ template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double log(const double &x) { return ::log(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T tan(const T &x) {
EIGEN_USING_STD_MATH(tan);
return tan(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tan(const float &x) { return ::tanf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double tan(const double &x) { return ::tan(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
typename NumTraits<T>::Real abs(const T &x) {
@@ -1068,6 +1053,141 @@ template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double exp(const double &x) { return ::exp(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T cos(const T &x) {
EIGEN_USING_STD_MATH(cos);
return cos(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float cos(const float &x) { return ::cosf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double cos(const double &x) { return ::cos(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T sin(const T &x) {
EIGEN_USING_STD_MATH(sin);
return sin(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float sin(const float &x) { return ::sinf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double sin(const double &x) { return ::sin(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T tan(const T &x) {
EIGEN_USING_STD_MATH(tan);
return tan(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tan(const float &x) { return ::tanf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double tan(const double &x) { return ::tan(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T acos(const T &x) {
EIGEN_USING_STD_MATH(acos);
return acos(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float acos(const float &x) { return ::acosf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double acos(const double &x) { return ::acos(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T asin(const T &x) {
EIGEN_USING_STD_MATH(asin);
return asin(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float asin(const float &x) { return ::asinf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double asin(const double &x) { return ::asin(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T atan(const T &x) {
EIGEN_USING_STD_MATH(atan);
return atan(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float atan(const float &x) { return ::atanf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double atan(const double &x) { return ::atan(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T cosh(const T &x) {
EIGEN_USING_STD_MATH(cosh);
return cosh(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float cosh(const float &x) { return ::coshf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double cosh(const double &x) { return ::cosh(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T sinh(const T &x) {
EIGEN_USING_STD_MATH(sinh);
return sinh(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float sinh(const float &x) { return ::sinhf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double sinh(const double &x) { return ::sinh(x); }
#endif
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T tanh(const T &x) {
EIGEN_USING_STD_MATH(tanh);
return tanh(x);
}
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float tanh(const float &x) { return ::tanhf(x); }
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double tanh(const double &x) { return ::tanh(x); }
#endif
template <typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE

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

@@ -410,8 +410,6 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
typedef Product<Lhs, Rhs, LazyProduct> XprType;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketScalar PacketScalar;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
explicit product_evaluator(const XprType& xpr)
@@ -437,16 +435,20 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
typedef evaluator<LhsNestedCleaned> LhsEtorType;
typedef evaluator<RhsNestedCleaned> RhsEtorType;
enum {
RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime,
PacketSize = packet_traits<Scalar>::size,
MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
};
typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
enum {
LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
@@ -459,19 +461,23 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
LhsFlags = LhsEtorType::Flags,
RhsFlags = RhsEtorType::Flags,
LhsAlignment = LhsEtorType::Alignment,
RhsAlignment = RhsEtorType::Alignment,
LhsRowMajor = LhsFlags & RowMajorBit,
RhsRowMajor = RhsFlags & RowMajorBit,
LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
// Here, we don't care about alignment larger than the usable packet size.
LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
CanVectorizeRhs = RhsRowMajor && (RhsFlags & PacketAccessBit)
&& (ColsAtCompileTime == Dynamic || ((ColsAtCompileTime % PacketSize) == 0) ),
&& (ColsAtCompileTime == Dynamic || ((ColsAtCompileTime % RhsVecPacketSize) == 0) ),
CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit)
&& (RowsAtCompileTime == Dynamic || ((RowsAtCompileTime % PacketSize) == 0) ),
&& (RowsAtCompileTime == Dynamic || ((RowsAtCompileTime % LhsVecPacketSize) == 0) ),
EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
: (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
@@ -491,10 +497,10 @@ struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape,
: 0,
/* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
* of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
* loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
* the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
*/
* of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
* loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
* the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
*/
CanVectorizeInner = SameType
&& LhsRowMajor
&& (!RhsRowMajor)
@@ -1000,7 +1006,7 @@ struct transposition_matrix_product
const Index size = tr.size();
StorageIndex j = 0;
if(!(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat)))
if(!is_same_dense(dst,mat))
dst = mat;
for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)

30
Eigen/src/Core/Redux.h
View File

@@ -27,8 +27,9 @@ template<typename Func, typename Derived>
struct redux_traits
{
public:
typedef typename find_best_packet<typename Derived::Scalar,Derived::SizeAtCompileTime>::type PacketType;
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = unpacket_traits<PacketType>::size,
InnerMaxSize = int(Derived::IsRowMajor)
? Derived::MaxColsAtCompileTime
: Derived::MaxRowsAtCompileTime
@@ -137,12 +138,12 @@ template<typename Func, typename Derived, int Start, int Length>
struct redux_vec_unroller
{
enum {
PacketSize = packet_traits<typename Derived::Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
HalfLength = Length/2
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func)
{
@@ -156,14 +157,14 @@ template<typename Func, typename Derived, int Start>
struct redux_vec_unroller<Func, Derived, Start, 1>
{
enum {
index = Start * packet_traits<typename Derived::Scalar>::size,
index = Start * redux_traits<Func, Derived>::PacketSize,
outer = index / int(Derived::InnerSizeAtCompileTime),
inner = index % int(Derived::InnerSizeAtCompileTime),
alignment = Derived::Alignment
};
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&)
{
@@ -209,13 +210,13 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
static Scalar run(const Derived &mat, const Func& func)
{
const Index size = mat.size();
const Index packetSize = packet_traits<Scalar>::size;
const Index packetSize = redux_traits<Func, Derived>::PacketSize;
const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
enum {
alignment0 = (bool(Derived::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
@@ -268,7 +269,7 @@ template<typename Func, typename Derived, int Unrolling>
struct redux_impl<Func, Derived, SliceVectorizedTraversal, Unrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketType;
typedef typename redux_traits<Func, Derived>::PacketType PacketType;
EIGEN_DEVICE_FUNC static Scalar run(const Derived &mat, const Func& func)
{
@@ -276,7 +277,7 @@ struct redux_impl<Func, Derived, SliceVectorizedTraversal, Unrolling>
const Index innerSize = mat.innerSize();
const Index outerSize = mat.outerSize();
enum {
packetSize = packet_traits<Scalar>::size
packetSize = redux_traits<Func, Derived>::PacketSize
};
const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
Scalar res;
@@ -306,9 +307,10 @@ template<typename Func, typename Derived>
struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename packet_traits<Scalar>::type PacketScalar;
typedef typename redux_traits<Func, Derived>::PacketType PacketScalar;
enum {
PacketSize = packet_traits<Scalar>::size,
PacketSize = redux_traits<Func, Derived>::PacketSize,
Size = Derived::SizeAtCompileTime,
VectorizedSize = (Size / PacketSize) * PacketSize
};
@@ -367,11 +369,11 @@ public:
{ return m_evaluator.coeff(index); }
template<int LoadMode, typename PacketType>
PacketReturnType packet(Index row, Index col) const
PacketType packet(Index row, Index col) const
{ return m_evaluator.template packet<LoadMode,PacketType>(row, col); }
template<int LoadMode, typename PacketType>
PacketReturnType packet(Index index) const
PacketType packet(Index index) const
{ return m_evaluator.template packet<LoadMode,PacketType>(index); }
EIGEN_DEVICE_FUNC
@@ -379,7 +381,7 @@ public:
{ return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
template<int LoadMode, typename PacketType>
PacketReturnType packetByOuterInner(Index outer, Index inner) const
PacketType packetByOuterInner(Index outer, Index inner) const
{ return m_evaluator.template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
const XprType & nestedExpression() const { return m_xpr; }

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

@@ -213,7 +213,7 @@ template<int Side, typename TriangularType, typename Rhs> struct triangular_solv
template<typename Dest> inline void evalTo(Dest& dst) const
{
if(!(is_same<RhsNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_rhs)))
if(!is_same_dense(dst,m_rhs))
dst = m_rhs;
m_triangularMatrix.template solveInPlace<Side>(dst);
}

104
Eigen/src/Core/SpecialFunctions.h
View File

@@ -281,20 +281,18 @@ struct digamma_impl {
*/
Scalar p, q, nz, s, w, y;
bool negative;
bool negative = false;
const Scalar maxnum = NumTraits<Scalar>::infinity();
const Scalar m_pi = EIGEN_PI;
const Scalar m_pi(EIGEN_PI);
negative = 0;
nz = 0.0;
const Scalar zero = 0.0;
const Scalar one = 1.0;
const Scalar half = 0.5;
const Scalar zero = Scalar(0);
const Scalar one = Scalar(1);
const Scalar half = Scalar(0.5);
nz = zero;
if (x <= zero) {
negative = one;
negative = true;
q = x;
p = numext::floor(q);
if (p == q) {
@@ -463,7 +461,7 @@ template <typename Scalar>
struct igammac_impl {
EIGEN_DEVICE_FUNC
static Scalar run(Scalar a, Scalar x) {
/* igamc()
/* igamc()
*
* Incomplete gamma integral (modified for Eigen)
*
@@ -519,26 +517,51 @@ struct igammac_impl {
*/
const Scalar zero = 0;
const Scalar one = 1;
const Scalar two = 2;
const Scalar machep = igamma_helper<Scalar>::machep();
const Scalar maxlog = numext::log(NumTraits<Scalar>::highest());
const Scalar big = igamma_helper<Scalar>::big();
const Scalar biginv = 1 / big;
const Scalar nan = NumTraits<Scalar>::quiet_NaN();
const Scalar inf = NumTraits<Scalar>::infinity();
Scalar ans, ax, c, yc, r, t, y, z;
Scalar pk, pkm1, pkm2, qk, qkm1, qkm2;
if ((x < zero) || ( a <= zero)) {
if ((x < zero) || (a <= zero)) {
// domain error
return nan;
}
if ((x < one) || (x < a)) {
return (one - igamma_impl<Scalar>::run(a, x));
/* The checks above ensure that we meet the preconditions for
* igamma_impl::Impl(), so call it, rather than igamma_impl::Run().
* Calling Run() would also work, but in that case the compiler may not be
* able to prove that igammac_impl::Run and igamma_impl::Run are not
* mutually recursive. This leads to worse code, particularly on
* platforms like nvptx, where recursion is allowed only begrudgingly.
*/
return (one - igamma_impl<Scalar>::Impl(a, x));
}
return Impl(a, x);
}
private:
/* igamma_impl calls igammac_impl::Impl. */
friend struct igamma_impl<Scalar>;
/* Actually computes igamc(a, x).
*
* Preconditions:
* a > 0
* x >= 1
* x >= a
*/
EIGEN_DEVICE_FUNC static Scalar Impl(Scalar a, Scalar x) {
const Scalar zero = 0;
const Scalar one = 1;
const Scalar two = 2;
const Scalar machep = igamma_helper<Scalar>::machep();
const Scalar maxlog = numext::log(NumTraits<Scalar>::highest());
const Scalar big = igamma_helper<Scalar>::big();
const Scalar biginv = 1 / big;
const Scalar inf = NumTraits<Scalar>::infinity();
Scalar ans, ax, c, yc, r, t, y, z;
Scalar pk, pkm1, pkm2, qk, qkm1, qkm2;
if (x == inf) return zero; // std::isinf crashes on CUDA
/* Compute x**a * exp(-x) / gamma(a) */
@@ -618,7 +641,7 @@ template <typename Scalar>
struct igamma_impl {
EIGEN_DEVICE_FUNC
static Scalar run(Scalar a, Scalar x) {
/* igam()
/* igam()
* Incomplete gamma integral
*
*
@@ -680,22 +703,47 @@ struct igamma_impl {
*/
const Scalar zero = 0;
const Scalar one = 1;
const Scalar machep = igamma_helper<Scalar>::machep();
const Scalar maxlog = numext::log(NumTraits<Scalar>::highest());
const Scalar nan = NumTraits<Scalar>::quiet_NaN();
double ans, ax, c, r;
if (x == zero) return zero;
if ((x < zero) || ( a <= zero)) { // domain error
if ((x < zero) || (a <= zero)) { // domain error
return nan;
}
if ((x > one) && (x > a)) {
return (one - igammac_impl<Scalar>::run(a, x));
/* The checks above ensure that we meet the preconditions for
* igammac_impl::Impl(), so call it, rather than igammac_impl::Run().
* Calling Run() would also work, but in that case the compiler may not be
* able to prove that igammac_impl::Run and igamma_impl::Run are not
* mutually recursive. This leads to worse code, particularly on
* platforms like nvptx, where recursion is allowed only begrudgingly.
*/
return (one - igammac_impl<Scalar>::Impl(a, x));
}
return Impl(a, x);
}
private:
/* igammac_impl calls igamma_impl::Impl. */
friend struct igammac_impl<Scalar>;
/* Actually computes igam(a, x).
*
* Preconditions:
* x > 0
* a > 0
* !(x > 1 && x > a)
*/
EIGEN_DEVICE_FUNC static Scalar Impl(Scalar a, Scalar x) {
const Scalar zero = 0;
const Scalar one = 1;
const Scalar machep = igamma_helper<Scalar>::machep();
const Scalar maxlog = numext::log(NumTraits<Scalar>::highest());
double ans, ax, c, r;
/* Compute x**a * exp(-x) / gamma(a) */
ax = a * numext::log(x) - x - lgamma_impl<Scalar>::run(a);
if (ax < -maxlog) {

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

@@ -168,11 +168,12 @@ MatrixBase<Derived>::stableNorm() const
DerivedCopy copy(derived());
enum {
CanAlign = (int(Flags)&DirectAccessBit) || (int(internal::evaluator<DerivedCopyClean>::Alignment)>0) // FIXME
CanAlign = ( (int(DerivedCopyClean::Flags)&DirectAccessBit)
|| (int(internal::evaluator<DerivedCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT) // ifwe cannot allocate on the stack, then let's not bother about this optimization
};
typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<DerivedCopyClean>::Alignment>,
typename DerivedCopyClean
::ConstSegmentReturnType>::type SegmentWrapper;
typename DerivedCopyClean::ConstSegmentReturnType>::type SegmentWrapper;
Index n = size();
if(n==1)

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

@@ -532,7 +532,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
template<typename RhsType, typename DstType>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
if(!(internal::is_same<RhsType,DstType>::value && internal::extract_data(dst) == internal::extract_data(rhs)))
if(!internal::is_same_dense(dst,rhs))
dst = rhs;
this->solveInPlace(dst);
}

69
Eigen/src/Core/arch/CUDA/Half.h
View File

@@ -70,12 +70,18 @@ struct half : public __half {
explicit EIGEN_DEVICE_FUNC half(bool b)
: __half(internal::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
explicit EIGEN_DEVICE_FUNC half(unsigned int ui)
: __half(internal::float_to_half_rtne(static_cast<float>(ui))) {}
explicit EIGEN_DEVICE_FUNC half(int i)
: __half(internal::float_to_half_rtne(static_cast<float>(i))) {}
explicit EIGEN_DEVICE_FUNC half(unsigned long ul)
: __half(internal::float_to_half_rtne(static_cast<float>(ul))) {}
explicit EIGEN_DEVICE_FUNC half(long l)
: __half(internal::float_to_half_rtne(static_cast<float>(l))) {}
explicit EIGEN_DEVICE_FUNC half(long long ll)
: __half(internal::float_to_half_rtne(static_cast<float>(ll))) {}
explicit EIGEN_DEVICE_FUNC half(unsigned long long ull)
: __half(internal::float_to_half_rtne(static_cast<float>(ull))) {}
explicit EIGEN_DEVICE_FUNC half(float f)
: __half(internal::float_to_half_rtne(f)) {}
explicit EIGEN_DEVICE_FUNC half(double d)
@@ -401,6 +407,7 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const Eigen::half& a)
static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const Eigen::half& a) {
return !(Eigen::numext::isinf)(a) && !(Eigen::numext::isnan)(a);
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half abs(const Eigen::half& a) {
Eigen::half result;
result.x = a.x & 0x7FFF;
@@ -418,6 +425,18 @@ template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sqrt(const Eigen::h
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half pow(const Eigen::half& a, const Eigen::half& b) {
return Eigen::half(::powf(float(a), float(b)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half sin(const Eigen::half& a) {
return Eigen::half(::sinf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half cos(const Eigen::half& a) {
return Eigen::half(::cosf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half tan(const Eigen::half& a) {
return Eigen::half(::tanf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half tanh(const Eigen::half& a) {
return Eigen::half(::tanhf(float(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half floor(const Eigen::half& a) {
return Eigen::half(::floorf(float(a)));
}
@@ -425,6 +444,51 @@ template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ceil(const Eigen::h
return Eigen::half(::ceilf(float(a)));
}
template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half mini(const Eigen::half& a, const Eigen::half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(b, a) ? b : a;
#else
const float f1 = static_cast<float>(a);
const float f2 = static_cast<float>(b);
return f2 < f1 ? b : a;
#endif
}
template <> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half maxi(const Eigen::half& a, const Eigen::half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(a, b) ? b : a;
#else
const float f1 = static_cast<float>(a);
const float f2 = static_cast<float>(b);
return f1 < f2 ? b : a;
#endif
}
#ifdef EIGEN_HAS_C99_MATH
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) {
return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) {
return Eigen::half(Eigen::numext::digamma(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half zeta(const Eigen::half& x, const Eigen::half& q) {
return Eigen::half(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half polygamma(const Eigen::half& n, const Eigen::half& x) {
return Eigen::half(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erf(const Eigen::half& a) {
return Eigen::half(Eigen::numext::erf(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erfc(const Eigen::half& a) {
return Eigen::half(Eigen::numext::erfc(static_cast<float>(a)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma(const Eigen::half& a, const Eigen::half& x) {
return Eigen::half(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
}
template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igammac(const Eigen::half& a, const Eigen::half& x) {
return Eigen::half(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
}
#endif
} // end namespace numext
} // end namespace Eigen
@@ -466,6 +530,11 @@ static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC int (isfinite)(const Eigen::half& a
namespace std {
EIGEN_ALWAYS_INLINE ostream& operator << (ostream& os, const Eigen::half& v) {
os << static_cast<float>(v);
return os;
}
#if __cplusplus > 199711L
template <>
struct hash<Eigen::half> {

16
Eigen/src/Core/functors/BinaryFunctors.h
View File

@@ -344,6 +344,22 @@ template<> struct functor_traits<scalar_boolean_or_op> {
};
};
/** \internal
* \brief Template functor to compute the xor of two booleans
*
* \sa class CwiseBinaryOp, ArrayBase::operator^
*/
struct scalar_boolean_xor_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; }
};
template<> struct functor_traits<scalar_boolean_xor_op> {
enum {
Cost = NumTraits<bool>::AddCost,
PacketAccess = false
};
};
/** \internal
* \brief Template functor to compute the incomplete gamma function igamma(a, x)
*

106
Eigen/src/Core/functors/UnaryFunctors.h
View File

@@ -234,9 +234,33 @@ template<typename Scalar> struct scalar_exp_op {
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
};
template<typename Scalar>
struct functor_traits<scalar_exp_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasExp }; };
template <typename Scalar>
struct functor_traits<scalar_exp_op<Scalar> > {
enum {
PacketAccess = packet_traits<Scalar>::HasExp,
// The following numbers are based on the AVX implementation.
#ifdef EIGEN_VECTORIZE_FMA
// Haswell can issue 2 add/mul/madd per cycle.
Cost =
(sizeof(Scalar) == 4
// float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other
? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost)
// double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other
: (14 * NumTraits<Scalar>::AddCost +
6 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost))
#else
Cost =
(sizeof(Scalar) == 4
// float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other
? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost)
// double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div, 13 other
: (23 * NumTraits<Scalar>::AddCost +
12 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost))
#endif
};
};
/** \internal
*
@@ -250,9 +274,24 @@ template<typename Scalar> struct scalar_log_op {
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
};
template<typename Scalar>
struct functor_traits<scalar_log_op<Scalar> >
{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
template <typename Scalar>
struct functor_traits<scalar_log_op<Scalar> > {
enum {
PacketAccess = packet_traits<Scalar>::HasLog,
Cost =
(PacketAccess
// The following numbers are based on the AVX implementation.
#ifdef EIGEN_VECTORIZE_FMA
// 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle.
? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost)
#else
// 8 pmadd, 6 pmul, 8 padd/psub, 20 other
? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost)
#endif
// Measured cost of std::log.
: sizeof(Scalar)==4 ? 40 : 85)
};
};
/** \internal
*
@@ -280,10 +319,19 @@ template<typename Scalar> struct scalar_sqrt_op {
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
};
template<typename Scalar>
struct functor_traits<scalar_sqrt_op<Scalar> >
{ enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
template <typename Scalar>
struct functor_traits<scalar_sqrt_op<Scalar> > {
enum {
#if EIGEN_FAST_MATH
// The following numbers are based on the AVX implementation.
Cost = (sizeof(Scalar) == 8 ? 28
// 4 pmul, 1 pmadd, 3 other
: (3 * NumTraits<Scalar>::AddCost +
5 * NumTraits<Scalar>::MulCost)),
#else
// The following numbers are based on min VSQRT throughput on Haswell.
Cost = (sizeof(Scalar) == 8 ? 28 : 14),
#endif
PacketAccess = packet_traits<Scalar>::HasSqrt
};
};
@@ -313,7 +361,7 @@ struct functor_traits<scalar_rsqrt_op<Scalar> >
*/
template<typename Scalar> struct scalar_cos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op)
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { using std::cos; return cos(a); }
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::cos(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
};
@@ -332,7 +380,7 @@ struct functor_traits<scalar_cos_op<Scalar> >
*/
template<typename Scalar> struct scalar_sin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::sin; return sin(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sin(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
};
@@ -352,7 +400,7 @@ struct functor_traits<scalar_sin_op<Scalar> >
*/
template<typename Scalar> struct scalar_tan_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::tan; return tan(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tan(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
};
@@ -371,7 +419,7 @@ struct functor_traits<scalar_tan_op<Scalar> >
*/
template<typename Scalar> struct scalar_acos_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::acos; return acos(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::acos(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
};
@@ -390,7 +438,7 @@ struct functor_traits<scalar_acos_op<Scalar> >
*/
template<typename Scalar> struct scalar_asin_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::asin; return asin(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::asin(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
};
@@ -546,7 +594,7 @@ struct functor_traits<scalar_erfc_op<Scalar> >
*/
template<typename Scalar> struct scalar_atan_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_atan_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::atan; return atan(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::atan(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::patan(a); }
};
@@ -566,7 +614,7 @@ struct functor_traits<scalar_atan_op<Scalar> >
*/
template<typename Scalar> struct scalar_tanh_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::tanh; return tanh(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tanh(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptanh(a); }
};
@@ -574,8 +622,24 @@ template<typename Scalar>
struct functor_traits<scalar_tanh_op<Scalar> >
{
enum {
Cost = 5 * NumTraits<Scalar>::MulCost,
PacketAccess = packet_traits<Scalar>::HasTanh
PacketAccess = packet_traits<Scalar>::HasTanh,
Cost =
(PacketAccess
// The following numbers are based on the AVX implementation,
#ifdef EIGEN_VECTORIZE_FMA
// Haswell can issue 2 add/mul/madd per cycle.
// 9 pmadd, 2 pmul, 1 div, 2 other
? (2 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost)
#else
? (11 * NumTraits<Scalar>::AddCost +
11 * NumTraits<Scalar>::MulCost +
NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost)
#endif
// This number assumes a naive implementation of tanh
: (6 * NumTraits<Scalar>::AddCost + 3 * NumTraits<Scalar>::MulCost +
2 * NumTraits<Scalar>::template Div<packet_traits<Scalar>::HasDiv>::Cost +
functor_traits<scalar_exp_op<Scalar> >::Cost))
};
};
@@ -585,7 +649,7 @@ struct functor_traits<scalar_tanh_op<Scalar> >
*/
template<typename Scalar> struct scalar_sinh_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_sinh_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::sinh; return sinh(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sinh(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psinh(a); }
};
@@ -604,7 +668,7 @@ struct functor_traits<scalar_sinh_op<Scalar> >
*/
template<typename Scalar> struct scalar_cosh_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_cosh_op)
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::cosh; return cosh(a); }
EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::cosh(a); }
template <typename Packet>
EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcosh(a); }
};

79
Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@@ -11,8 +11,8 @@
#define EIGEN_GENERAL_BLOCK_PANEL_H
namespace Eigen {
namespace Eigen {
namespace internal {
template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false>
@@ -36,7 +36,7 @@ const std::ptrdiff_t defaultL3CacheSize = 512*1024;
#endif
/** \internal */
struct CacheSizes {
struct CacheSizes {
CacheSizes(): m_l1(-1),m_l2(-1),m_l3(-1) {
int l1CacheSize, l2CacheSize, l3CacheSize;
queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize);
@@ -89,7 +89,7 @@ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff
*
* \sa setCpuCacheSizes */
template<typename LhsScalar, typename RhsScalar, int KcFactor>
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1)
{
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
@@ -107,21 +107,17 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
enum {
kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
ksub = Traits::mr * Traits::nr * sizeof(ResScalar),
k_mask = -8,
kr = 8,
mr = Traits::mr,
mr_mask = -mr,
nr = Traits::nr,
nr_mask = -nr
nr = Traits::nr
};
// Increasing k gives us more time to prefetch the content of the "C"
// registers. However once the latency is hidden there is no point in
// increasing the value of k, so we'll cap it at 320 (value determined
// experimentally).
const Index k_cache = (std::min<Index>)((l1-ksub)/kdiv, 320);
const Index k_cache = (numext::mini<Index>)((l1-ksub)/kdiv, 320);
if (k_cache < k) {
k = k_cache & k_mask;
k = k_cache - (k_cache % kr);
eigen_internal_assert(k > 0);
}
@@ -130,10 +126,10 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
if (n_cache <= n_per_thread) {
// Don't exceed the capacity of the l2 cache.
eigen_internal_assert(n_cache >= static_cast<Index>(nr));
n = n_cache & nr_mask;
n = n_cache - (n_cache % nr);
eigen_internal_assert(n > 0);
} else {
n = (std::min<Index>)(n, (n_per_thread + nr - 1) & nr_mask);
n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
}
if (l3 > l2) {
@@ -141,10 +137,10 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads);
const Index m_per_thread = numext::div_ceil(m, num_threads);
if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
m = m_cache & mr_mask;
m = m_cache - (m_cache % mr);
eigen_internal_assert(m > 0);
} else {
m = (std::min<Index>)(m, (m_per_thread + mr - 1) & mr_mask);
m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
}
}
}
@@ -156,29 +152,29 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
l2 = 32*1024;
l3 = 512*1024;
#endif
// Early return for small problems because the computation below are time consuming for small problems.
// Perhaps it would make more sense to consider k*n*m??
// Note that for very tiny problem, this function should be bypassed anyway
// because we use the coefficient-based implementation for them.
if((std::max)(k,(std::max)(m,n))<48)
if((numext::maxi)(k,(numext::maxi)(m,n))<48)
return;
typedef typename Traits::ResScalar ResScalar;
enum {
k_peeling = 8,
k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
k_sub = Traits::mr * Traits::nr * sizeof(ResScalar)
};
// ---- 1st level of blocking on L1, yields kc ----
// Blocking on the third dimension (i.e., k) is chosen so that an horizontal panel
// of size mr x kc of the lhs plus a vertical panel of kc x nr of the rhs both fits within L1 cache.
// We also include a register-level block of the result (mx x nr).
// (In an ideal world only the lhs panel would stay in L1)
// Moreover, kc has to be a multiple of 8 to be compatible with loop peeling, leading to a maximum blocking size of:
const Index max_kc = std::max<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
const Index max_kc = numext::maxi<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
const Index old_k = k;
if(k>max_kc)
{
@@ -187,12 +183,12 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
// while keeping the same number of sweeps over the result.
k = (k%max_kc)==0 ? max_kc
: max_kc - k_peeling * ((max_kc-1-(k%max_kc))/(k_peeling*(k/max_kc+1)));
eigen_internal_assert(((old_k/k) == (old_k/max_kc)) && "the number of sweeps has to remain the same");
}
// ---- 2nd level of blocking on max(L2,L3), yields nc ----
// TODO find a reliable way to get the actual amount of cache per core to use for 2nd level blocking, that is:
// actual_l2 = max(l2, l3/nb_core_sharing_l3)
// The number below is quite conservative: it is better to underestimate the cache size rather than overestimating it)
@@ -202,7 +198,7 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
#else
const Index actual_l2 = 1572864; // == 1.5 MB
#endif
// Here, nc is chosen such that a block of kc x nc of the rhs fit within half of L2.
// The second half is implicitly reserved to access the result and lhs coefficients.
// When k<max_kc, then nc can arbitrarily growth. In practice, it seems to be fruitful
@@ -223,7 +219,7 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
max_nc = (3*actual_l2)/(2*2*max_kc*sizeof(RhsScalar));
}
// WARNING Below, we assume that Traits::nr is a power of two.
Index nc = std::min<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
Index nc = numext::mini<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
if(n>nc)
{
// We are really blocking over the columns:
@@ -252,9 +248,9 @@ 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 = (std::min<Index>)(576,max_mc);
max_mc = (numext::mini<Index>)(576,max_mc);
}
Index mc = (std::min<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
Index mc = (numext::mini<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
if (mc > Traits::mr) mc -= mc % Traits::mr;
else if (mc==0) return;
m = (m%mc)==0 ? mc
@@ -263,13 +259,14 @@ void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index n
}
}
template <typename Index>
inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n)
{
#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
k = std::min<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
m = std::min<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
n = std::min<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
k = numext::mini<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
m = numext::mini<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
n = numext::mini<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
return true;
}
#else
@@ -296,11 +293,11 @@ inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n)
*
* \sa setCpuCacheSizes */
template<typename LhsScalar, typename RhsScalar, int KcFactor>
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
{
if (!useSpecificBlockingSizes(k, m, n)) {
evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor>(k, m, n, num_threads);
evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
}
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
@@ -314,10 +311,10 @@ void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads
if (n > nr) n -= n % nr;
}
template<typename LhsScalar, typename RhsScalar>
template<typename LhsScalar, typename RhsScalar, typename Index>
inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
{
computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n, num_threads);
computeProductBlockingSizes<LhsScalar,RhsScalar,1,Index>(k, m, n, num_threads);
}
#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD
@@ -2225,6 +2222,16 @@ inline std::ptrdiff_t l2CacheSize()
return l2;
}
/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size paramete\
rs.
* \sa setCpuCacheSize */
inline std::ptrdiff_t l3CacheSize()
{
std::ptrdiff_t l1, l2, l3;
internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
return l3;
}
/** Set the cpu L1 and L2 cache sizes (in bytes).
* These values are use to adjust the size of the blocks
* for the algorithms working per blocks.

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

@@ -43,7 +43,7 @@ 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, level3_blocking<LhsScalar,RhsScalar>& blocking)
const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
{
general_matrix_matrix_triangular_product<Index,
RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,

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

@@ -27,13 +27,13 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
};
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha);
};
template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha)
{
static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
Index size = (std::min)(_rows,_cols);

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

@@ -83,7 +83,7 @@ EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conju
// coherence when accessing the rhs elements
std::ptrdiff_t l1, l2, l3;
manage_caching_sizes(GetAction, &l1, &l2, &l3);
Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * otherStride) : 0;
Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * std::max<Index>(otherStride,size)) : 0;
subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr);
for(Index k2=IsLower ? 0 : size;

14
Eigen/src/Core/util/Macros.h
View File

@@ -371,10 +371,10 @@
// Does the compiler support const expressions?
#ifdef __CUDACC__
// Const expressions are supported provided that c++11 is enabled and we're using either clang or nvcc 7.5 or above
#if __cplusplus > 199711L && defined(__CUDACC_VER__) && (defined(__clang__) || __CUDACC_VER__ >= 70500)
#if __cplusplus > 199711L && defined(__CUDACC_VER__) && (EIGEN_COMP_CLANG || __CUDACC_VER__ >= 70500)
#define EIGEN_HAS_CONSTEXPR 1
#endif
#elif (defined(__cplusplus) && __cplusplus >= 201402L) || \
#elif __has_feature(cxx_relaxed_constexpr) || (defined(__cplusplus) && __cplusplus >= 201402L) || \
EIGEN_GNUC_AT_LEAST(4,8)
#define EIGEN_HAS_CONSTEXPR 1
#endif
@@ -572,12 +572,12 @@ namespace Eigen {
//------------------------------------------------------------------------------------------
// Static and dynamic alignment control
//
//
// The main purpose of this section is to define EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES
// as the maximal boundary in bytes on which dynamically and statically allocated data may be alignment respectively.
// The values of EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES can be specified by the user. If not,
// a default value is automatically computed based on architecture, compiler, and OS.
//
//
// This section also defines macros EIGEN_ALIGN_TO_BOUNDARY(N) and the shortcuts EIGEN_ALIGN{8,16,32,_MAX}
// to be used to declare statically aligned buffers.
//------------------------------------------------------------------------------------------
@@ -640,7 +640,7 @@ namespace Eigen {
#ifndef EIGEN_MAX_STATIC_ALIGN_BYTES
// Try to automatically guess what is the best default value for EIGEN_MAX_STATIC_ALIGN_BYTES
// 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable
// 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always
// enable alignment, but it can be a cause of problems on some platforms, so we just disable it in
@@ -667,13 +667,13 @@ namespace Eigen {
#else
#define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0
#endif
#if EIGEN_ARCH_WANTS_STACK_ALIGNMENT
#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
#else
#define EIGEN_MAX_STATIC_ALIGN_BYTES 0
#endif
#endif
// If EIGEN_MAX_ALIGN_BYTES is defined, then it is considered as an upper bound for EIGEN_MAX_ALIGN_BYTES