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Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h')
-rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h | 150 |
1 files changed, 150 insertions, 0 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h new file mode 100644 index 0000000000..1d1ce47174 --- /dev/null +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h @@ -0,0 +1,150 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H +#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H + +namespace Eigen { + +/** \class TensorForcedEval + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reshaping class. + * + * + */ +namespace internal { +template<typename XprType> +struct traits<TensorForcedEvalOp<XprType> > +{ + // Type promotion to handle the case where the types of the lhs and the rhs are different. + typedef typename XprType::Scalar Scalar; + typedef traits<XprType> XprTraits; + typedef typename traits<XprType>::StorageKind StorageKind; + typedef typename traits<XprType>::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions; + static const int Layout = XprTraits::Layout; + + enum { + Flags = 0, + }; +}; + +template<typename XprType> +struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense> +{ + typedef const TensorForcedEvalOp<XprType>& type; +}; + +template<typename XprType> +struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type> +{ + typedef TensorForcedEvalOp<XprType> type; +}; + +} // end namespace internal + + + +template<typename XprType> +class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType> > +{ + public: + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; + typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested; + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind; + typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr) + : m_xpr(expr) {} + + EIGEN_DEVICE_FUNC + const typename internal::remove_all<typename XprType::Nested>::type& + expression() const { return m_xpr; } + + protected: + typename XprType::Nested m_xpr; +}; + + +template<typename ArgType, typename Device> +struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device> +{ + typedef TensorForcedEvalOp<ArgType> XprType; + typedef typename ArgType::Scalar Scalar; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + + enum { + IsAligned = true, + PacketAccess = (internal::packet_traits<Scalar>::size > 1), + BlockAccess = false, + Layout = TensorEvaluator<ArgType, Device>::Layout, + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device), m_op(op.expression()), m_device(device), m_buffer(NULL) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_impl.evalSubExprsIfNeeded(NULL); + const Index numValues = m_impl.dimensions().TotalSize(); + m_buffer = (CoeffReturnType*)m_device.allocate(numValues * sizeof(CoeffReturnType)); + // Should initialize the memory in case we're dealing with non POD types. + if (!internal::is_arithmetic<CoeffReturnType>::value) { + for (Index i = 0; i < numValues; ++i) { + new(m_buffer+i) CoeffReturnType(); + } + } + typedef TensorEvalToOp<const ArgType> EvalTo; + EvalTo evalToTmp(m_buffer, m_op); + const bool PacketAccess = internal::IsVectorizable<Device, ArgType>::value; + const bool BlockAccess = false; + internal::TensorExecutor<const EvalTo, Device, PacketAccess, BlockAccess>::run(evalToTmp, m_device); + m_impl.cleanup(); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_device.deallocate(m_buffer); + m_buffer = NULL; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const + { + return m_buffer[index]; + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index); + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return m_buffer; } + + private: + TensorEvaluator<ArgType, Device> m_impl; + const ArgType m_op; + const Device& m_device; + CoeffReturnType* m_buffer; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H |