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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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_GENERATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
namespace Eigen {
/** \class TensorGenerator
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor generator class.
*
*
*/
namespace internal {
template<typename Generator, typename XprType>
struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::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;
};
template<typename Generator, typename XprType>
struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
{
typedef const TensorGeneratorOp<Generator, XprType>& type;
};
template<typename Generator, typename XprType>
struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type>
{
typedef TensorGeneratorOp<Generator, XprType> type;
};
} // end namespace internal
template<typename Generator, typename XprType>
class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
: m_xpr(expr), m_generator(generator) {}
EIGEN_DEVICE_FUNC
const Generator& generator() const { return m_generator; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const Generator m_generator;
};
// Eval as rvalue
template<typename Generator, typename ArgType, typename Device>
struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
{
typedef TensorGeneratorOp<Generator, ArgType> XprType;
typedef typename XprType::Index Index;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
static const int NumDims = internal::array_size<Dimensions>::value;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
enum {
IsAligned = false,
PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1),
BlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_generator(op.generator())
{
TensorEvaluator<ArgType, Device> impl(op.expression(), device);
m_dimensions = impl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_strides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
}
} else {
m_strides[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
array<Index, NumDims> coords;
extract_coordinates(index, coords);
return m_generator(coords);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool) const {
// TODO(rmlarsen): This is just a placeholder. Define interface to make
// generators return their cost.
return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() +
TensorOpCost::MulCost<Scalar>());
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_strides[i];
index -= idx * m_strides[i];
coords[i] = idx;
}
coords[0] = index;
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_strides[i];
index -= idx * m_strides[i];
coords[i] = idx;
}
coords[NumDims-1] = index;
}
}
Dimensions m_dimensions;
array<Index, NumDims> m_strides;
Generator m_generator;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
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