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Upgraded the bundled Eigen to 3.7

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bubnikv
2019-01-14 10:27:28 +01:00
parent 1ee0f8cc85
commit 8696c70af4
33 changed files with 273 additions and 174 deletions
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4
src/eigen/Eigen/Core
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@@ -53,9 +53,9 @@
#endif
#define EIGEN_DEVICE_FUNC __host__ __device__
// We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro
// We need cuda_runtime.h to ensure that that EIGEN_USING_STD_MATH macro
// works properly on the device side
#include <math_functions.hpp>
#include <cuda_runtime.h>
#else
#define EIGEN_DEVICE_FUNC
#endif

3
src/eigen/Eigen/src/Cholesky/LDLT.h
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@@ -305,7 +305,8 @@ template<> struct ldlt_inplace<Lower>
if (size <= 1)
{
transpositions.setIdentity();
if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
if(size==0) sign = ZeroSign;
else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
else sign = ZeroSign;
return true;

2
src/eigen/Eigen/src/Core/Array.h
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@@ -153,8 +153,6 @@ class Array
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)

2
src/eigen/Eigen/src/Core/ConditionEstimator.h
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@@ -160,7 +160,7 @@ rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Deco
{
typedef typename Decomposition::RealScalar RealScalar;
eigen_assert(dec.rows() == dec.cols());
if (dec.rows() == 0) return RealScalar(1);
if (dec.rows() == 0) return NumTraits<RealScalar>::infinity();
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);

6
src/eigen/Eigen/src/Core/MapBase.h
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@@ -43,6 +43,7 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
enum {
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
SizeAtCompileTime = Base::SizeAtCompileTime
};
@@ -187,8 +188,11 @@ template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
void checkSanity(typename internal::enable_if<(internal::traits<T>::Alignment>0),void*>::type = 0) const
{
#if EIGEN_MAX_ALIGN_BYTES>0
// innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
eigen_assert(( ((internal::UIntPtr(m_data) % internal::traits<Derived>::Alignment) == 0)
|| (cols() * rows() * innerStride() * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
|| (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
#endif
}

36
src/eigen/Eigen/src/Core/MathFunctions.h
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@@ -616,21 +616,28 @@ template<typename Scalar>
struct random_default_impl<Scalar, false, true>
{
static inline Scalar run(const Scalar& x, const Scalar& y)
{
typedef typename conditional<NumTraits<Scalar>::IsSigned,std::ptrdiff_t,std::size_t>::type ScalarX;
if(y<x)
{
if (y <= x)
return x;
// the following difference might overflow on a 32 bits system,
// but since y>=x the result converted to an unsigned long is still correct.
std::size_t range = ScalarX(y)-ScalarX(x);
std::size_t offset = 0;
// rejection sampling
std::size_t divisor = 1;
std::size_t multiplier = 1;
if(range<RAND_MAX) divisor = (std::size_t(RAND_MAX)+1)/(range+1);
else multiplier = 1 + range/(std::size_t(RAND_MAX)+1);
// ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself.
typedef typename make_unsigned<Scalar>::type ScalarU;
// ScalarX is the widest of ScalarU and unsigned int.
// We'll deal only with ScalarX and unsigned int below thus avoiding signed
// types and arithmetic and signed overflows (which are undefined behavior).
typedef typename conditional<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned>::type ScalarX;
// The following difference doesn't overflow, provided our integer types are two's
// complement and have the same number of padding bits in signed and unsigned variants.
// This is the case in most modern implementations of C++.
ScalarX range = ScalarX(y) - ScalarX(x);
ScalarX offset = 0;
ScalarX divisor = 1;
ScalarX multiplier = 1;
const unsigned rand_max = RAND_MAX;
if (range <= rand_max) divisor = (rand_max + 1) / (range + 1);
else multiplier = 1 + range / (rand_max + 1);
// Rejection sampling.
do {
offset = (std::size_t(std::rand()) * multiplier) / divisor;
offset = (unsigned(std::rand()) * multiplier) / divisor;
} while (offset > range);
return Scalar(ScalarX(x) + offset);
}
@@ -1006,7 +1013,8 @@ inline int log2(int x)
/** \returns the square root of \a x.
*
* It is essentially equivalent to \code using std::sqrt; return sqrt(x); \endcode,
* It is essentially equivalent to
* \code using std::sqrt; return sqrt(x); \endcode
* but slightly faster for float/double and some compilers (e.g., gcc), thanks to
* specializations when SSE is enabled.
*

2
src/eigen/Eigen/src/Core/Matrix.h
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@@ -274,8 +274,6 @@ class Matrix
: Base(std::move(other))
{
Base::_check_template_params();
if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
Base::_set_noalias(other);
}
EIGEN_DEVICE_FUNC
Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)

26
src/eigen/Eigen/src/Core/MatrixBase.h
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@@ -444,16 +444,24 @@ template<typename Derived> class MatrixBase
///////// MatrixFunctions module /////////
typedef typename internal::stem_function<Scalar>::type StemFunction;
const MatrixExponentialReturnValue<Derived> exp() const;
#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \
/** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
const ReturnType<Derived> Name() const;
#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \
/** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
const ReturnType<Derived> Name(Argument) const;
EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
/** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>.*/
const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
const MatrixFunctionReturnValue<Derived> cosh() const;
const MatrixFunctionReturnValue<Derived> sinh() const;
const MatrixFunctionReturnValue<Derived> cos() const;
const MatrixFunctionReturnValue<Derived> sin() const;
const MatrixSquareRootReturnValue<Derived> sqrt() const;
const MatrixLogarithmReturnValue<Derived> log() const;
const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const;
const MatrixComplexPowerReturnValue<Derived> pow(const std::complex<RealScalar>& p) const;
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue, pow, power to \c p, const RealScalar& p)
EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex<RealScalar>& p)
protected:
EIGEN_DEVICE_FUNC MatrixBase() : Base() {}

3
src/eigen/Eigen/src/Core/SolveTriangular.h
View File

@@ -169,6 +169,9 @@ void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<Ot
OtherDerived& other = _other.const_cast_derived();
eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
// If solving for a 0x0 matrix, nothing to do, simply return.
if (derived().cols() == 0)
return;
enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit) && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
typedef typename internal::conditional<copy,

13
src/eigen/Eigen/src/Core/arch/AVX/PacketMath.h
View File

@@ -159,11 +159,12 @@ template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& /*a*/, co
#ifdef __FMA__
template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
#if ( EIGEN_COMP_GNUC_STRICT || (EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<308)) )
// clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and gcc stupidly generates a vfmadd132ps instruction,
// so let's enforce it to generate a vfmadd231ps instruction since the most common use case is to accumulate
// the result of the product.
#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
// Clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers,
// and even register spilling with clang>=6.0 (bug 1637).
// Gcc stupidly generates a vfmadd132ps instruction.
// So let's enforce it to generate a vfmadd231ps instruction since the most common use
// case is to accumulate the result of the product.
Packet8f res = c;
__asm__("vfmadd231ps %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));
return res;
@@ -172,7 +173,7 @@ template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f&
#endif
}
template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
#if ( EIGEN_COMP_GNUC_STRICT || (EIGEN_COMP_CLANG && (EIGEN_COMP_CLANG<308)) )
#if ( (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC<80) || (EIGEN_COMP_CLANG) )
// see above
Packet4d res = c;
__asm__("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b));

6
src/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h
View File

@@ -648,13 +648,13 @@ template<> EIGEN_STRONG_INLINE Packet8d preverse(const Packet8d& a)
template<> EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a)
{
// _mm512_abs_ps intrinsic not found, so hack around it
return (__m512)_mm512_and_si512((__m512i)a, _mm512_set1_epi32(0x7fffffff));
return _mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(a), _mm512_set1_epi32(0x7fffffff)));
}
template <>
EIGEN_STRONG_INLINE Packet8d pabs(const Packet8d& a) {
// _mm512_abs_ps intrinsic not found, so hack around it
return (__m512d)_mm512_and_si512((__m512i)a,
_mm512_set1_epi64(0x7fffffffffffffff));
return _mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(a),
_mm512_set1_epi64(0x7fffffffffffffff)));
}
#ifdef EIGEN_VECTORIZE_AVX512DQ

193
src/eigen/Eigen/src/Core/arch/CUDA/Half.h
View File

@@ -29,7 +29,7 @@
// type Eigen::half (inheriting from CUDA's __half struct) with
// operator overloads such that it behaves basically as an arithmetic
// type. It will be quite slow on CPUs (so it is recommended to stay
// in fp32 for CPUs, except for simple parameter conversions, I/O
// in float32_bits for CPUs, except for simple parameter conversions, I/O
// to disk and the likes), but fast on GPUs.
@@ -50,38 +50,45 @@ struct half;
namespace half_impl {
#if !defined(EIGEN_HAS_CUDA_FP16)
// Make our own __half definition that is similar to CUDA's.
struct __half {
EIGEN_DEVICE_FUNC __half() {}
explicit EIGEN_DEVICE_FUNC __half(unsigned short raw) : x(raw) {}
// Make our own __half_raw definition that is similar to CUDA's.
struct __half_raw {
EIGEN_DEVICE_FUNC __half_raw() : x(0) {}
explicit EIGEN_DEVICE_FUNC __half_raw(unsigned short raw) : x(raw) {}
unsigned short x;
};
#elif defined(EIGEN_CUDACC_VER) && EIGEN_CUDACC_VER < 90000
// In CUDA < 9.0, __half is the equivalent of CUDA 9's __half_raw
typedef __half __half_raw;
#endif
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw raw_uint16_to_half(unsigned short x);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h);
struct half_base : public __half {
struct half_base : public __half_raw {
EIGEN_DEVICE_FUNC half_base() {}
EIGEN_DEVICE_FUNC half_base(const half_base& h) : __half(h) {}
EIGEN_DEVICE_FUNC half_base(const __half& h) : __half(h) {}
EIGEN_DEVICE_FUNC half_base(const half_base& h) : __half_raw(h) {}
EIGEN_DEVICE_FUNC half_base(const __half_raw& h) : __half_raw(h) {}
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDACC_VER) && EIGEN_CUDACC_VER >= 90000
EIGEN_DEVICE_FUNC half_base(const __half& h) : __half_raw(*(__half_raw*)&h) {}
#endif
};
} // namespace half_impl
// Class definition.
struct half : public half_impl::half_base {
#if !defined(EIGEN_HAS_CUDA_FP16)
typedef half_impl::__half __half;
#if !defined(EIGEN_HAS_CUDA_FP16) || (defined(EIGEN_CUDACC_VER) && EIGEN_CUDACC_VER < 90000)
typedef half_impl::__half_raw __half_raw;
#endif
EIGEN_DEVICE_FUNC half() {}
EIGEN_DEVICE_FUNC half(const __half& h) : half_impl::half_base(h) {}
EIGEN_DEVICE_FUNC half(const __half_raw& h) : half_impl::half_base(h) {}
EIGEN_DEVICE_FUNC half(const half& h) : half_impl::half_base(h) {}
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDACC_VER) && EIGEN_CUDACC_VER >= 90000
EIGEN_DEVICE_FUNC half(const __half& h) : half_impl::half_base(h) {}
#endif
explicit EIGEN_DEVICE_FUNC half(bool b)
: half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
@@ -138,12 +145,66 @@ struct half : public half_impl::half_base {
}
};
} // end namespace Eigen
namespace std {
template<>
struct numeric_limits<Eigen::half> {
static const bool is_specialized = true;
static const bool is_signed = true;
static const bool is_integer = false;
static const bool is_exact = false;
static const bool has_infinity = true;
static const bool has_quiet_NaN = true;
static const bool has_signaling_NaN = true;
static const float_denorm_style has_denorm = denorm_present;
static const bool has_denorm_loss = false;
static const std::float_round_style round_style = std::round_to_nearest;
static const bool is_iec559 = false;
static const bool is_bounded = false;
static const bool is_modulo = false;
static const int digits = 11;
static const int digits10 = 3; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int max_digits10 = 5; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int radix = 2;
static const int min_exponent = -13;
static const int min_exponent10 = -4;
static const int max_exponent = 16;
static const int max_exponent10 = 4;
static const bool traps = true;
static const bool tinyness_before = false;
static Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x400); }
static Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
static Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
static Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x0800); }
static Eigen::half round_error() { return Eigen::half(0.5); }
static Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
static Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x1); }
};
// If std::numeric_limits<T> is specialized, should also specialize
// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
// std::numeric_limits<const volatile T>
// https://stackoverflow.com/a/16519653/
template<>
struct numeric_limits<const Eigen::half> : numeric_limits<Eigen::half> {};
template<>
struct numeric_limits<volatile Eigen::half> : numeric_limits<Eigen::half> {};
template<>
struct numeric_limits<const volatile Eigen::half> : numeric_limits<Eigen::half> {};
} // end namespace std
namespace Eigen {
namespace half_impl {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
// Intrinsics for native fp16 support. Note that on current hardware,
// these are no faster than fp32 arithmetic (you need to use the half2
// these are no faster than float32_bits arithmetic (you need to use the half2
// versions to get the ALU speed increased), but you do save the
// conversion steps back and forth.
@@ -202,7 +263,7 @@ EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
#else // Emulate support for half floats
// Definitions for CPUs and older CUDA, mostly working through conversion
// to/from fp32.
// to/from float32_bits.
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
return half(float(a) + float(b));
@@ -269,34 +330,35 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
// these in hardware. If we need more performance on older/other CPUs, they are
// also possible to vectorize directly.
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half raw_uint16_to_half(unsigned short x) {
__half h;
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw raw_uint16_to_half(unsigned short x) {
__half_raw h;
h.x = x;
return h;
}
union FP32 {
union float32_bits {
unsigned int u;
float f;
};
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
return __float2half(ff);
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300
__half tmp_ff = __float2half(ff);
return *(__half_raw*)&tmp_ff;
#elif defined(EIGEN_HAS_FP16_C)
__half h;
__half_raw h;
h.x = _cvtss_sh(ff, 0);
return h;
#else
FP32 f; f.f = ff;
float32_bits f; f.f = ff;
const FP32 f32infty = { 255 << 23 };
const FP32 f16max = { (127 + 16) << 23 };
const FP32 denorm_magic = { ((127 - 15) + (23 - 10) + 1) << 23 };
const float32_bits f32infty = { 255 << 23 };
const float32_bits f16max = { (127 + 16) << 23 };
const float32_bits denorm_magic = { ((127 - 15) + (23 - 10) + 1) << 23 };
unsigned int sign_mask = 0x80000000u;
__half o;
__half_raw o;
o.x = static_cast<unsigned short>(0x0u);
unsigned int sign = f.u & sign_mask;
@@ -335,17 +397,17 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half float_to_half_rtne(float ff) {
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half h) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300
return __half2float(h);
#elif defined(EIGEN_HAS_FP16_C)
return _cvtsh_ss(h.x);
#else
const FP32 magic = { 113 << 23 };
const float32_bits magic = { 113 << 23 };
const unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift
FP32 o;
float32_bits o;
o.u = (h.x & 0x7fff) << 13; // exponent/mantissa bits
unsigned int exp = shifted_exp & o.u; // just the exponent
@@ -370,7 +432,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const half& a) {
return (a.x & 0x7fff) == 0x7c00;
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const half& a) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
return __hisnan(a);
#else
return (a.x & 0x7fff) > 0x7c00;
@@ -443,7 +505,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
return __hlt(b, a) ? b : a;
#else
const float f1 = static_cast<float>(a);
@@ -452,7 +514,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (max)(const half& a, const half& b) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
return __hlt(a, b) ? b : a;
#else
const float f1 = static_cast<float>(a);
@@ -490,49 +552,6 @@ template<> struct is_arithmetic<half> { enum { value = true }; };
} // end namespace internal
} // end namespace Eigen
namespace std {
template<>
struct numeric_limits<Eigen::half> {
static const bool is_specialized = true;
static const bool is_signed = true;
static const bool is_integer = false;
static const bool is_exact = false;
static const bool has_infinity = true;
static const bool has_quiet_NaN = true;
static const bool has_signaling_NaN = true;
static const float_denorm_style has_denorm = denorm_present;
static const bool has_denorm_loss = false;
static const std::float_round_style round_style = std::round_to_nearest;
static const bool is_iec559 = false;
static const bool is_bounded = false;
static const bool is_modulo = false;
static const int digits = 11;
static const int digits10 = 3; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int max_digits10 = 5; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int radix = 2;
static const int min_exponent = -13;
static const int min_exponent10 = -4;
static const int max_exponent = 16;
static const int max_exponent10 = 4;
static const bool traps = true;
static const bool tinyness_before = false;
static Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x400); }
static Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
static Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
static Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x0800); }
static Eigen::half round_error() { return Eigen::half(0.5); }
static Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
static Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x1); }
};
}
namespace Eigen {
template<> struct NumTraits<Eigen::half>
: GenericNumTraits<Eigen::half>
{
@@ -607,14 +626,18 @@ struct hash<Eigen::half> {
// Add the missing shfl_xor intrinsic
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 300
#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300
__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width=warpSize) {
#if EIGEN_CUDACC_VER < 90000
return static_cast<Eigen::half>(__shfl_xor(static_cast<float>(var), laneMask, width));
#else
return static_cast<Eigen::half>(__shfl_xor_sync(0xFFFFFFFF, static_cast<float>(var), laneMask, width));
#endif
}
#endif
// ldg() has an overload for __half, but we also need one for Eigen::half.
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350
// ldg() has an overload for __half_raw, but we also need one for Eigen::half.
#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half __ldg(const Eigen::half* ptr) {
return Eigen::half_impl::raw_uint16_to_half(
__ldg(reinterpret_cast<const unsigned short*>(ptr)));
@@ -622,7 +645,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half __ldg(const Eigen::half* ptr)
#endif
#if defined(__CUDA_ARCH__)
#if defined(EIGEN_CUDA_ARCH)
namespace Eigen {
namespace numext {

3
src/eigen/Eigen/src/Core/arch/CUDA/PacketMathHalf.h
View File

@@ -99,7 +99,8 @@ template<> __device__ EIGEN_STRONG_INLINE Eigen::half pfirst<half2>(const half2&
template<> __device__ EIGEN_STRONG_INLINE half2 pabs<half2>(const half2& a) {
half2 result;
result.x = a.x & 0x7FFF7FFF;
unsigned temp = *(reinterpret_cast<const unsigned*>(&(a)));
*(reinterpret_cast<unsigned*>(&(result))) = temp & 0x7FFF7FFF;
return result;
}

2
src/eigen/Eigen/src/Core/arch/SSE/PacketMath.h
View File

@@ -28,7 +28,7 @@ namespace internal {
#endif
#endif
#if (defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW) && (__GXX_ABI_VERSION < 1004)
#if ((defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW) && (__GXX_ABI_VERSION < 1004)) || EIGEN_OS_QNX
// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
// have overloads for both types without linking error.
// One solution is to increase ABI version using -fabi-version=4 (or greater).

15
src/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h
View File

@@ -1197,10 +1197,16 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
RhsPacket B_0, B1, B2, B3, T0;
#define EIGEN_GEBGP_ONESTEP(K) \
// NOTE: the begin/end asm comments below work around bug 935!
// but they are not enough for gcc>=6 without FMA (bug 1637)
#if EIGEN_GNUC_AT_LEAST(6,0) && defined(EIGEN_VECTORIZE_SSE)
#define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND __asm__ ("" : [a0] "+x,m" (A0),[a1] "+x,m" (A1));
#else
#define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND
#endif
#define EIGEN_GEBGP_ONESTEP(K) \
do { \
EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4"); \
EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \
traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \
traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \
@@ -1212,6 +1218,7 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
traits.madd(A1, B2, C6, B2); \
traits.madd(A0, B3, C3, T0); \
traits.madd(A1, B3, C7, B3); \
EIGEN_GEBP_2PX4_SPILLING_WORKAROUND \
EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4"); \
} while(false)
@@ -1526,10 +1533,10 @@ void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,Conjuga
// The following piece of code wont work for 512 bit registers
// Moreover, if LhsProgress==8 it assumes that there is a half packet of the same size
// as nr (which is currently 4) for the return type.
typedef typename unpacket_traits<SResPacket>::half SResPacketHalf;
const int SResPacketHalfSize = unpacket_traits<typename unpacket_traits<SResPacket>::half>::size;
if ((SwappedTraits::LhsProgress % 4) == 0 &&
(SwappedTraits::LhsProgress <= 8) &&
(SwappedTraits::LhsProgress!=8 || unpacket_traits<SResPacketHalf>::size==nr))
(SwappedTraits::LhsProgress!=8 || SResPacketHalfSize==nr))
{
SAccPacket C0, C1, C2, C3;
straits.initAcc(C0);

2
src/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
View File

@@ -52,7 +52,7 @@ struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,Con
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) \
{ \
if ( lhs==rhs && ((UpLo&(Lower|Upper)==UpLo)) ) { \
if ( lhs==rhs && ((UpLo&(Lower|Upper))==UpLo) ) { \
general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha,blocking); \
} else { \

14
src/eigen/Eigen/src/Core/util/DisableStupidWarnings.h
View File

@@ -43,12 +43,20 @@
#endif
#pragma clang diagnostic ignored "-Wconstant-logical-operand"
#elif defined __GNUC__ && __GNUC__>=6
#elif defined __GNUC__
#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
#if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
#pragma GCC diagnostic push
#endif
#pragma GCC diagnostic ignored "-Wignored-attributes"
// g++ warns about local variables shadowing member functions, which is too strict
#pragma GCC diagnostic ignored "-Wshadow"
#if __GNUC__ == 4 && __GNUC_MINOR__ < 8
// Until g++-4.7 there are warnings when comparing unsigned int vs 0, even in templated functions:
#pragma GCC diagnostic ignored "-Wtype-limits"
#endif
#if __GNUC__>=6
#pragma GCC diagnostic ignored "-Wignored-attributes"
#endif
#endif

2
src/eigen/Eigen/src/Core/util/Macros.h
View File

@@ -13,7 +13,7 @@
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 3
#define EIGEN_MINOR_VERSION 5
#define EIGEN_MINOR_VERSION 7
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \

10
src/eigen/Eigen/src/Core/util/Memory.h
View File

@@ -747,7 +747,15 @@ public:
pointer allocate(size_type num, const void* /*hint*/ = 0)
{
internal::check_size_for_overflow<T>(num);
return static_cast<pointer>( internal::aligned_malloc(num * sizeof(T)) );
size_type size = num * sizeof(T);
#if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_LEAST(7,0)
// workaround gcc bug https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
// It triggered eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807
if(size>=std::size_t((std::numeric_limits<std::ptrdiff_t>::max)()))
return 0;
else
#endif
return static_cast<pointer>( internal::aligned_malloc(size) );
}
void deallocate(pointer p, size_type /*num*/)

22
src/eigen/Eigen/src/Core/util/Meta.h
View File

@@ -109,6 +109,28 @@ template<> struct is_integral<unsigned int> { enum { value = true }; };
template<> struct is_integral<signed long> { enum { value = true }; };
template<> struct is_integral<unsigned long> { enum { value = true }; };
#if EIGEN_HAS_CXX11
using std::make_unsigned;
#else
// TODO: Possibly improve this implementation of make_unsigned.
// It is currently used only by
// template<typename Scalar> struct random_default_impl<Scalar, false, true>.
template<typename> struct make_unsigned;
template<> struct make_unsigned<char> { typedef unsigned char type; };
template<> struct make_unsigned<signed char> { typedef unsigned char type; };
template<> struct make_unsigned<unsigned char> { typedef unsigned char type; };
template<> struct make_unsigned<signed short> { typedef unsigned short type; };
template<> struct make_unsigned<unsigned short> { typedef unsigned short type; };
template<> struct make_unsigned<signed int> { typedef unsigned int type; };
template<> struct make_unsigned<unsigned int> { typedef unsigned int type; };
template<> struct make_unsigned<signed long> { typedef unsigned long type; };
template<> struct make_unsigned<unsigned long> { typedef unsigned long type; };
#if EIGEN_COMP_MSVC
template<> struct make_unsigned<signed __int64> { typedef unsigned __int64 type; };
template<> struct make_unsigned<unsigned __int64> { typedef unsigned __int64 type; };
#endif
#endif
template <typename T> struct add_const { typedef const T type; };
template <typename T> struct add_const<T&> { typedef T& type; };

2
src/eigen/Eigen/src/Core/util/ReenableStupidWarnings.h
View File

@@ -8,7 +8,7 @@
#pragma warning pop
#elif defined __clang__
#pragma clang diagnostic pop
#elif defined __GNUC__ && __GNUC__>=6
#elif defined __GNUC__ && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
#pragma GCC diagnostic pop
#endif

2
src/eigen/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
View File

@@ -66,7 +66,6 @@ template<typename Derived>
inline typename MatrixBase<Derived>::EigenvaluesReturnType
MatrixBase<Derived>::eigenvalues() const
{
typedef typename internal::traits<Derived>::Scalar Scalar;
return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
}
@@ -88,7 +87,6 @@ template<typename MatrixType, unsigned int UpLo>
inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
{
typedef typename SelfAdjointView<MatrixType, UpLo>::PlainObject PlainObject;
PlainObject thisAsMatrix(*this);
return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
}

5
src/eigen/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
View File

@@ -50,7 +50,8 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
tol_error = 0;
return;
}
RealScalar threshold = tol*tol*rhsNorm2;
const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
RealScalar threshold = numext::maxi(tol*tol*rhsNorm2,considerAsZero);
RealScalar residualNorm2 = residual.squaredNorm();
if (residualNorm2 < threshold)
{
@@ -58,7 +59,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
tol_error = sqrt(residualNorm2 / rhsNorm2);
return;
}
VectorType p(n);
p = precond.solve(residual); // initial search direction

13
src/eigen/Eigen/src/Jacobi/Jacobi.h
View File

@@ -65,11 +65,11 @@ template<typename Scalar> class JacobiRotation
bool makeJacobi(const MatrixBase<Derived>&, Index p, Index q);
bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z);
void makeGivens(const Scalar& p, const Scalar& q, Scalar* z=0);
void makeGivens(const Scalar& p, const Scalar& q, Scalar* r=0);
protected:
void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::true_type);
void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::false_type);
void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type);
void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type);
Scalar m_c, m_s;
};
@@ -84,7 +84,6 @@ bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, co
{
using std::sqrt;
using std::abs;
typedef typename NumTraits<Scalar>::Real RealScalar;
RealScalar deno = RealScalar(2)*abs(y);
if(deno < (std::numeric_limits<RealScalar>::min)())
{
@@ -133,7 +132,7 @@ inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Ind
* \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields:
* \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$.
*
* The value of \a z is returned if \a z is not null (the default is null).
* The value of \a r is returned if \a r is not null (the default is null).
* Also note that G is built such that the cosine is always real.
*
* Example: \include Jacobi_makeGivens.cpp
@@ -146,9 +145,9 @@ inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Ind
* \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
*/
template<typename Scalar>
void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* z)
void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r)
{
makeGivens(p, q, z, typename internal::conditional<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>::type());
makeGivens(p, q, r, typename internal::conditional<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>::type());
}

4
src/eigen/Eigen/src/SVD/SVDBase.h
View File

@@ -180,8 +180,10 @@ public:
RealScalar threshold() const
{
eigen_assert(m_isInitialized || m_usePrescribedThreshold);
// this temporary is needed to workaround a MSVC issue
Index diagSize = (std::max<Index>)(1,m_diagSize);
return m_usePrescribedThreshold ? m_prescribedThreshold
: (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon();
: diagSize*NumTraits<Scalar>::epsilon();
}
/** \returns true if \a U (full or thin) is asked for in this SVD decomposition */

6
src/eigen/Eigen/src/SparseCore/SparseMatrix.h
View File

@@ -893,7 +893,7 @@ public:
Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++;
m_data.index(p) = convert_index(inner);
return (m_data.value(p) = 0);
return (m_data.value(p) = Scalar(0));
}
private:
@@ -1274,7 +1274,7 @@ EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar&
m_innerNonZeros[outer]++;
m_data.index(p) = inner;
return (m_data.value(p) = 0);
return (m_data.value(p) = Scalar(0));
}
template<typename _Scalar, int _Options, typename _StorageIndex>
@@ -1381,7 +1381,7 @@ EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_StorageIndex>::Scalar&
}
m_data.index(p) = inner;
return (m_data.value(p) = 0);
return (m_data.value(p) = Scalar(0));
}
namespace internal {

2
src/eigen/Eigen/src/SparseLU/SparseLU.h
View File

@@ -499,8 +499,6 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
typedef typename IndexVector::Scalar StorageIndex;
m_isInitialized = true;

2
src/eigen/Eigen/src/SparseLU/SparseLU_panel_bmod.h
View File

@@ -146,7 +146,7 @@ void SparseLUImpl<Scalar,StorageIndex>::panel_bmod(const Index m, const Index w,
Index ldl = internal::first_multiple<Index>(nrow, PacketSize);
Index offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
auto L = MappedMatrixBlock(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
MappedMatrixBlock L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
L.setZero();
internal::sparselu_gemm<Scalar>(L.rows(), L.cols(), B.cols(), B.data(), B.outerStride(), U.data(), U.outerStride(), L.data(), L.outerStride());

4
src/eigen/Eigen/src/SuperLUSupport/SuperLUSupport.h
View File

@@ -297,8 +297,8 @@ SluMatrix asSluMatrix(MatrixType& mat)
template<typename Scalar, int Flags, typename Index>
MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
{
eigen_assert((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR
|| (Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC);
eigen_assert(((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR)
|| ((Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC));
Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;