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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Ke Yang <yangke@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_INFLATION_H
#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
namespace Eigen {
/** \class TensorInflation
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor inflation class.
*
*
*/
namespace internal {
template<typename Strides, typename XprType>
struct traits<TensorInflationOp<Strides, 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 Strides, typename XprType>
struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense>
{
typedef const TensorInflationOp<Strides, XprType>& type;
};
template<typename Strides, typename XprType>
struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type>
{
typedef TensorInflationOp<Strides, XprType> type;
};
} // end namespace internal
template<typename Strides, typename XprType>
class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
: m_xpr(expr), m_strides(strides) {}
EIGEN_DEVICE_FUNC
const Strides& strides() const { return m_strides; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const Strides m_strides;
};
// Eval as rvalue
template<typename Strides, typename ArgType, typename Device>
struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
{
typedef TensorInflationOp<Strides, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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_impl(op.expression(), device), m_strides(op.strides())
{
m_dimensions = m_impl.dimensions();
// Expand each dimension to the inflated dimension.
for (int i = 0; i < NumDims; ++i) {
m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
}
// Remember the strides for fast division.
for (int i = 0; i < NumDims; ++i) {
m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_outputStrides[0] = 1;
m_inputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
}
} else { // RowMajor
m_outputStrides[NumDims-1] = 1;
m_inputStrides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[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*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
// Computes the input index given the output index. Returns true if the output
// index doesn't fall into a hole.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const
{
eigen_assert(index < dimensions().TotalSize());
*inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (idx != idx / m_fastStrides[i] * m_strides[i]) {
return false;
}
*inputIndex += idx / m_strides[i] * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
if (index != index / m_fastStrides[0] * m_strides[0]) {
return false;
}
*inputIndex += index / m_strides[0];
return true;
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (idx != idx / m_fastStrides[i] * m_strides[i]) {
return false;
}
*inputIndex += idx / m_strides[i] * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) {
return false;
}
*inputIndex += index / m_strides[NumDims - 1];
}
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
Index inputIndex = 0;
if (getInputIndex(index, &inputIndex)) {
return m_impl.coeff(inputIndex);
} else {
return Scalar(0);
}
}
// TODO(yangke): optimize this function so that we can detect and produce
// all-zero packets
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
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 vectorized) const {
const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
3 * TensorOpCost::MulCost<Index>() +
2 * TensorOpCost::AddCost<Index>());
const double input_size = m_impl.dimensions().TotalSize();
const double output_size = m_dimensions.TotalSize();
if (output_size == 0)
return TensorOpCost();
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
const Strides m_strides;
array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H