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// 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>, ReadOnlyAccessors>
{
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;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = true,
PacketAccess = (PacketSize > 1),
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = true
};
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)
{ }
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
const Index numValues = internal::array_prod(m_impl.dimensions());
m_buffer = (CoeffReturnType*)m_device.allocate(numValues * sizeof(CoeffReturnType));
// Should initialize the memory in case we're dealing with non POD types.
if (NumTraits<CoeffReturnType>::RequireInitialization) {
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, const ArgType>::value;
internal::TensorExecutor<const EvalTo, Device, PacketAccess>::run(evalToTmp, m_device);
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 EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}
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
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