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Merged eigen/eigen into default

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This commit is contained in:
tillahoffmann 2016-04-05 18:21:05 +01:00
commit 726bd5f077
6 changed files with 36 additions and 11 deletions
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2
Eigen/src/Core/MathFunctions.h
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@ -1034,7 +1034,7 @@ double tan(const double &x) { return ::tan(x); }
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T abs(const T &x) {
typename NumTraits<T>::Real abs(const T &x) {
EIGEN_USING_STD_MATH(abs);
return abs(x);
}

24
Eigen/src/Core/arch/NEON/PacketMath.h
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@ -177,7 +177,9 @@ template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, co
return pset1<Packet4i>(0);
}
#ifdef __ARM_FEATURE_FMA
// Clang/ARM wrongly advertises __ARM_FEATURE_FMA even when it's not available,
// then implements a slow software scalar fallback calling fmaf()!
#if (defined __ARM_FEATURE_FMA) && !(EIGEN_COMP_CLANG && EIGEN_ARCH_ARM)
// See bug 936.
// FMA is available on VFPv4 i.e. when compiling with -mfpu=neon-vfpv4.
// FMA is a true fused multiply-add i.e. only 1 rounding at the end, no intermediate rounding.
@ -186,7 +188,25 @@ template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, co
// MLA: 10 GFlop/s ; FMA: 12 GFlops/s.
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vfmaq_f32(c,a,b); }
#else
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vmlaq_f32(c,a,b); }
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
#if EIGEN_COMP_CLANG && EIGEN_ARCH_ARM
// Clang/ARM will replace VMLA by VMUL+VADD at least for some values of -mcpu,
// at least -mcpu=cortex-a8 and -mcpu=cortex-a7. Since the former is the default on
// -march=armv7-a, that is a very common case.
// See e.g. this thread:
// http://lists.llvm.org/pipermail/llvm-dev/2013-December/068806.html
Packet4f r = c;
asm volatile(
"vmla.f32 %q[r], %q[a], %q[b]"
: [r] "+w" (r)
: [a] "w" (a),
[b] "w" (b)
: );
return r;
#else
return vmlaq_f32(c,a,b);
#endif
}
#endif
// No FMA instruction for int, so use MLA unconditionally.

2
Eigen/src/Core/functors/UnaryFunctors.h
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@ -41,7 +41,7 @@ struct functor_traits<scalar_opposite_op<Scalar> >
template<typename Scalar> struct scalar_abs_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { using std::abs; return abs(a); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs(a); }
template<typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pabs(a); }

13
unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
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@ -589,23 +589,24 @@ EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(log,
return ReturnType(log(x.value()),x.derivatives() * (Scalar(1)/x.value()));)
template<typename DerType>
inline const Eigen::AutoDiffScalar<Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<typename Eigen::internal::traits<DerType>::Scalar>, const DerType> >
pow(const Eigen::AutoDiffScalar<DerType>& x, typename Eigen::internal::traits<DerType>::Scalar y)
inline const Eigen::AutoDiffScalar<Eigen::CwiseUnaryOp<Eigen::internal::scalar_multiple_op<typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar>, const typename internal::remove_all<DerType>::type> >
pow(const Eigen::AutoDiffScalar<DerType>& x, typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar y)
{
using namespace Eigen;
typedef typename Eigen::internal::traits<DerType>::Scalar Scalar;
return AutoDiffScalar<CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const DerType> >(
typedef typename internal::remove_all<DerType>::type DerTypeCleaned;
typedef typename Eigen::internal::traits<DerTypeCleaned>::Scalar Scalar;
return AutoDiffScalar<CwiseUnaryOp<Eigen::internal::scalar_multiple_op<Scalar>, const DerTypeCleaned> >(
std::pow(x.value(),y),
x.derivatives() * (y * std::pow(x.value(),y-1)));
}
template<typename DerTypeA,typename DerTypeB>
inline const AutoDiffScalar<Matrix<typename internal::traits<DerTypeA>::Scalar,Dynamic,1> >
inline const AutoDiffScalar<Matrix<typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar,Dynamic,1> >
atan2(const AutoDiffScalar<DerTypeA>& a, const AutoDiffScalar<DerTypeB>& b)
{
using std::atan2;
typedef typename internal::traits<DerTypeA>::Scalar Scalar;
typedef typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar Scalar;
typedef AutoDiffScalar<Matrix<Scalar,Dynamic,1> > PlainADS;
PlainADS ret;
ret.value() = atan2(a.value(), b.value());

2
unsupported/test/autodiff.cpp
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@ -16,7 +16,7 @@ EIGEN_DONT_INLINE Scalar foo(const Scalar& x, const Scalar& y)
using namespace std;
// return x+std::sin(y);
EIGEN_ASM_COMMENT("mybegin");
return static_cast<Scalar>(x*2 - pow(x,2) + 2*sqrt(y*y) - 4 * sin(x) + 2 * cos(y) - exp(-0.5*x*x));
return static_cast<Scalar>(x*2 - 1 + pow(1+x,2) + 2*sqrt(y*y+0) - 4 * sin(0+x) + 2 * cos(y+0) - exp(-0.5*x*x+0));
//return x+2*y*x;//x*2 -std::pow(x,2);//(2*y/x);// - y*2;
EIGEN_ASM_COMMENT("myend");
}

4
unsupported/test/autodiff_scalar.cpp
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@ -30,6 +30,10 @@ template<typename Scalar> void check_atan2()
VERIFY_IS_APPROX(res.value(), x.value());
VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
res = atan2(r*s+0, r*c+0);
VERIFY_IS_APPROX(res.value(), x.value());
VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
}