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TensorEval are now typed on the device: this will make it possible to use partial template specialization to optimize the strategy of each evaluator for each device type.

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Started work on partial evaluations.
This commit is contained in:
Benoit Steiner
2014-06-10 09:14:44 -07:00
parent a77458a8ff
commit 925fb6b937
9 changed files with 129 additions and 102 deletions
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14
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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@@ -198,19 +198,25 @@ class TensorBase<Derived, ReadOnlyAccessors>
}
// Coefficient-wise ternary operators.
template<typename ThenDerived, typename ElseDerived>
inline const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const {
return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
}
// Morphing operators (slicing tbd).
template <typename NewDimensions>
inline const TensorReshapingOp<const Derived, const NewDimensions>
template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReshapingOp<const Derived, const NewDimensions>
reshape(const NewDimensions& newDimensions) const {
return TensorReshapingOp<const Derived, const NewDimensions>(derived(), newDimensions);
}
// Force the evaluation of the expression.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorForcedEvalOp<const Derived> eval() const {
return TensorForcedEvalOp<const Derived>(derived());
}
protected:
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC