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bug #1565: help MSVC to generatenot too bad ASM in reductions.

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This commit is contained in:
Gael Guennebaud 2018-07-05 09:21:26 +02:00
parent 6a241bd8ee
commit 0537123953
1 changed files with 33 additions and 29 deletions
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62
Eigen/src/Core/Redux.h
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@ -187,16 +187,17 @@ struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
{
typedef typename Evaluator::Scalar Scalar;
template<typename XprType>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
Scalar run(const Evaluator &eval, const Func& func)
Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
{
eigen_assert(eval.rows()>0 && eval.cols()>0 && "you are using an empty matrix");
eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
Scalar res;
res = eval.coeffByOuterInner(0, 0);
for(Index i = 1; i < eval.innerSize(); ++i)
for(Index i = 1; i < xpr.innerSize(); ++i)
res = func(res, eval.coeffByOuterInner(0, i));
for(Index i = 1; i < eval.outerSize(); ++i)
for(Index j = 0; j < eval.innerSize(); ++j)
for(Index i = 1; i < xpr.outerSize(); ++i)
for(Index j = 0; j < xpr.innerSize(); ++j)
res = func(res, eval.coeffByOuterInner(i, j));
return res;
}
@ -205,7 +206,16 @@ struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
template<typename Func, typename Evaluator>
struct redux_impl<Func,Evaluator, DefaultTraversal, CompleteUnrolling>
: redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime>
{};
{
typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
typedef typename Evaluator::Scalar Scalar;
template<typename XprType>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/)
{
return Base::run(eval,func);
}
};
template<typename Func, typename Evaluator>
struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
@ -213,9 +223,10 @@ struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
typedef typename Evaluator::Scalar Scalar;
typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
static Scalar run(const Evaluator &eval, const Func& func)
template<typename XprType>
static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
{
const Index size = eval.size();
const Index size = xpr.size();
const Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
const int packetAlignment = unpacket_traits<PacketScalar>::alignment;
@ -223,7 +234,7 @@ struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned),
alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Evaluator::Alignment)
};
const Index alignedStart = internal::first_default_aligned(eval.nestedExpression());
const Index alignedStart = internal::first_default_aligned(xpr);
const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
const Index alignedEnd2 = alignedStart + alignedSize2;
@ -272,11 +283,12 @@ struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling>
typedef typename Evaluator::Scalar Scalar;
typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func)
template<typename XprType>
EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
{
eigen_assert(eval.rows()>0 && eval.cols()>0 && "you are using an empty matrix");
const Index innerSize = eval.innerSize();
const Index outerSize = eval.outerSize();
eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
const Index innerSize = xpr.innerSize();
const Index outerSize = xpr.outerSize();
enum {
packetSize = redux_traits<Func, Evaluator>::PacketSize
};
@ -297,7 +309,7 @@ struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling>
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func);
res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func, xpr);
}
return res;
@ -316,10 +328,11 @@ struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling>
VectorizedSize = (Size / PacketSize) * PacketSize
};
template<typename XprType>
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
Scalar run(const Evaluator &eval, const Func& func)
Scalar run(const Evaluator &eval, const Func& func, const XprType &xpr)
{
eigen_assert(eval.rows()>0 && eval.cols()>0 && "you are using an empty matrix");
eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
if (VectorizedSize > 0) {
Scalar res = func.predux(redux_vec_unroller<Func, Evaluator, 0, Size / PacketSize>::run(eval,func));
if (VectorizedSize != Size)
@ -339,12 +352,11 @@ class redux_evaluator : public internal::evaluator<_XprType>
typedef internal::evaluator<_XprType> Base;
public:
typedef _XprType XprType;
EIGEN_DEVICE_FUNC explicit redux_evaluator(const XprType &xpr) : Base(xpr), m_xpr(xpr) {}
EIGEN_DEVICE_FUNC explicit redux_evaluator(const XprType &xpr) : Base(xpr) {}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketScalar PacketScalar;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
@ -356,12 +368,6 @@ public:
InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime
};
EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); }
EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); }
EIGEN_DEVICE_FUNC Index size() const { return m_xpr.size(); }
EIGEN_DEVICE_FUNC Index innerSize() const { return m_xpr.innerSize(); }
EIGEN_DEVICE_FUNC Index outerSize() const { return m_xpr.outerSize(); }
EIGEN_DEVICE_FUNC
CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
{ return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
@ -370,10 +376,6 @@ public:
PacketType packetByOuterInner(Index outer, Index inner) const
{ return Base::template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
const XprType & nestedExpression() const { return m_xpr; }
protected:
const XprType &m_xpr;
};
} // end namespace internal
@ -400,7 +402,9 @@ DenseBase<Derived>::redux(const Func& func) const
typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
ThisEvaluator thisEval(derived());
return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func);
// The initial expression is passed to the reducer as an additional argument instead of
// passing it as a member of redux_evaluator to help
return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func, derived());
}
/** \returns the minimum of all coefficients of \c *this.