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Merged eigen/eigen into default

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This commit is contained in:
Benoit Steiner
2016-10-12 22:42:33 -07:00
parent 5c68051cd7 e74612b9a0
commit 7e4a6754b2
15 changed files with 131 additions and 73 deletions
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2
Eigen/src/CholmodSupport/CholmodSupport.h
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@@ -53,7 +53,7 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_StorageIndex>& mat)
{
cholmod_sparse res;
res.nzmax = mat.nonZeros();
res.nrow = mat.rows();;
res.nrow = mat.rows();
res.ncol = mat.cols();
res.p = mat.outerIndexPtr();
res.i = mat.innerIndexPtr();

33
Eigen/src/Core/arch/AVX/MathFunctions.h
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@@ -355,30 +355,27 @@ pexp<Packet4d>(const Packet4d& _x) {
// Functions for sqrt.
// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
// exact solution. The main advantage of this approach is not just speed, but
// also the fact that it can be inlined and pipelined with other computations,
// further reducing its effective latency.
// exact solution. It does not handle +inf, or denormalized numbers correctly.
// The main advantage of this approach is not just speed, but also the fact that
// it can be inlined and pipelined with other computations, further reducing its
// effective latency. This is similar to Quake3's fast inverse square root.
// For detail see here: http://www.beyond3d.com/content/articles/8/
#if EIGEN_FAST_MATH
template <>
EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f
psqrt<Packet8f>(const Packet8f& _x) {
_EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f);
_EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f);
_EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000);
Packet8f neg_half = pmul(_x, p8f_minus_half);
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
Packet8f non_zero_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_GE_OQ);
Packet8f x = _mm256_and_ps(non_zero_mask, _mm256_rsqrt_ps(_x));
Packet8f half = pmul(_x, pset1<Packet8f>(.5f));
Packet8f denormal_mask = _mm256_and_ps(
_mm256_cmp_ps(_x, pset1<Packet8f>((std::numeric_limits<float>::min)()),
_CMP_LT_OQ),
_mm256_cmp_ps(_x, _mm256_setzero_ps(), _CMP_GE_OQ));
// Compute approximate reciprocal sqrt.
Packet8f x = _mm256_rsqrt_ps(_x);
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p8f_one_point_five));
// Multiply the original _x by it's reciprocal square root to extract the
// square root.
return pmul(_x, x);
x = pmul(x, psub(pset1<Packet8f>(1.5f), pmul(half, pmul(x,x))));
// Flush results for denormals to zero.
return _mm256_andnot_ps(denormal_mask, pmul(_x,x));
}
#else
template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED

15
Eigen/src/Core/arch/NEON/Complex.h
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@@ -16,8 +16,14 @@ namespace Eigen {
namespace internal {
inline uint32x4_t p4ui_CONJ_XOR() {
// See bug 1325, clang fails to call vld1q_u64.
#if EIGEN_COMP_CLANG
uint32x4_t ret = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
return ret;
#else
static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
return vld1q_u32( conj_XOR_DATA );
#endif
}
inline uint32x2_t p2ui_CONJ_XOR() {
@@ -282,8 +288,13 @@ ptranspose(PacketBlock<Packet2cf,2>& kernel) {
//---------- double ----------
#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
const uint64_t p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
// See bug 1325, clang fails to call vld1q_u64.
#if EIGEN_COMP_CLANG
static uint64x2_t p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
#else
const uint64_t p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
#endif
struct Packet1cd
{

28
Eigen/src/Core/arch/SSE/MathFunctions.h
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@@ -32,7 +32,7 @@ Packet4f plog<Packet4f>(const Packet4f& _x)
/* the smallest non denormalized float number */
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000);
_EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000);//-1.f/0.f);
/* natural logarithm computed for 4 simultaneous float
return NaN for x <= 0
*/
@@ -444,25 +444,33 @@ Packet4f pcos<Packet4f>(const Packet4f& _x)
#if EIGEN_FAST_MATH
// This is based on Quake3's fast inverse square root.
// Functions for sqrt.
// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step
// of Newton's method, at a cost of 1-2 bits of precision as opposed to the
// exact solution. It does not handle +inf, or denormalized numbers correctly.
// The main advantage of this approach is not just speed, but also the fact that
// it can be inlined and pipelined with other computations, further reducing its
// effective latency. This is similar to Quake3's fast inverse square root.
// For detail see here: http://www.beyond3d.com/content/articles/8/
// It lacks 1 (or 2 bits in some rare cases) of precision, and does not handle negative, +inf, or denormalized numbers correctly.
template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f psqrt<Packet4f>(const Packet4f& _x)
{
Packet4f half = pmul(_x, pset1<Packet4f>(.5f));
Packet4f denormal_mask = _mm_and_ps(
_mm_cmpge_ps(_x, _mm_setzero_ps()),
_mm_cmplt_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)())));
/* select only the inverse sqrt of non-zero inputs */
Packet4f non_zero_mask = _mm_cmpge_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)()));
Packet4f x = _mm_and_ps(non_zero_mask, _mm_rsqrt_ps(_x));
// Compute approximate reciprocal sqrt.
Packet4f x = _mm_rsqrt_ps(_x);
// Do a single step of Newton's iteration.
x = pmul(x, psub(pset1<Packet4f>(1.5f), pmul(half, pmul(x,x))));
return pmul(_x,x);
// Flush results for denormals to zero.
return _mm_andnot_ps(denormal_mask, pmul(_x,x));
}
#else
template<>EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
template<>EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
#endif
@@ -491,7 +499,7 @@ Packet4f prsqrt<Packet4f>(const Packet4f& _x) {
Packet4f neg_mask = _mm_cmplt_ps(_x, _mm_setzero_ps());
Packet4f zero_mask = _mm_andnot_ps(neg_mask, le_zero_mask);
Packet4f infs_and_nans = _mm_or_ps(_mm_and_ps(neg_mask, p4f_nan),
_mm_and_ps(zero_mask, p4f_inf));
_mm_and_ps(zero_mask, p4f_inf));
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p4f_one_point_five));

4
Eigen/src/Core/util/Macros.h
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@@ -392,8 +392,8 @@
// Does the compiler support variadic templates?
#ifndef EIGEN_HAS_VARIADIC_TEMPLATES
#if EIGEN_MAX_CPP_VER>=11 && (__cplusplus > 199711L || EIGEN_COMP_MSVC >= 1900) \
&& ( !defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 )
// ^^ Disable the use of variadic templates when compiling with nvcc on ARM devices:
&& ( !defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 || (defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000) )
// ^^ Disable the use of variadic templates when compiling with versions of nvcc older than 8.0 on ARM devices:
// this prevents nvcc from crashing when compiling Eigen on Tegra X1
#define EIGEN_HAS_VARIADIC_TEMPLATES 1
#else

12
Eigen/src/Geometry/Scaling.h Normal file → Executable file
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@@ -118,28 +118,28 @@ operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s)
{ return matrix.derived() * s.factor(); }
/** Constructs a uniform scaling from scale factor \a s */
static inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
/** Constructs a uniform scaling from scale factor \a s */
static inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
/** Constructs a uniform scaling from scale factor \a s */
template<typename RealScalar>
static inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
{ return UniformScaling<std::complex<RealScalar> >(s); }
/** Constructs a 2D axis aligned scaling */
template<typename Scalar>
static inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy)
inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy)
{ return DiagonalMatrix<Scalar,2>(sx, sy); }
/** Constructs a 3D axis aligned scaling */
template<typename Scalar>
static inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); }
/** Constructs an axis aligned scaling expression from vector expression \a coeffs
* This is an alias for coeffs.asDiagonal()
*/
template<typename Derived>
static inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs)
inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs)
{ return coeffs.asDiagonal(); }
/** \deprecated */

7
Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
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@@ -119,16 +119,16 @@ class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
max2Norm = RealScalar(1);
pivotThreshold = 20 * (mat.rows() + mat.cols()) * max2Norm * NumTraits<RealScalar>::epsilon();
}
cholmod_sparse A;
A = viewAsCholmod(mat);
m_rows = matrix.rows();
Index col = matrix.cols();
m_rank = SuiteSparseQR<Scalar>(m_ordering, pivotThreshold, col, &A,
&m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
if (!m_cR)
{
m_info = NumericalIssue;
m_info = NumericalIssue;
m_isInitialized = false;
return;
}
@@ -139,7 +139,7 @@ class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
/**
* Get the number of rows of the input matrix and the Q matrix
*/
inline Index rows() const {return m_cR->nrow; }
inline Index rows() const {return m_rows; }
/**
* Get the number of columns of the input matrix.
@@ -245,6 +245,7 @@ class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
mutable Index m_rank; // The rank of the matrix
mutable cholmod_common m_cc; // Workspace and parameters
bool m_useDefaultThreshold; // Use default threshold
Index m_rows;
template<typename ,typename > friend struct SPQR_QProduct;
};