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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_REDUCTION_H
#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
namespace Eigen {
/** \class TensorReduction
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reduction class.
*
*/
namespace internal {
template<typename Op, typename Dims, typename XprType>
struct traits<TensorReductionOp<Op, Dims, XprType> >
: traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
};
template<typename Op, typename Dims, typename XprType>
struct eval<TensorReductionOp<Op, Dims, XprType>, Eigen::Dense>
{
typedef const TensorReductionOp<Op, Dims, XprType>& type;
};
template<typename Op, typename Dims, typename XprType>
struct nested<TensorReductionOp<Op, Dims, XprType>, 1, typename eval<TensorReductionOp<Op, Dims, XprType> >::type>
{
typedef TensorReductionOp<Op, Dims, XprType> type;
};
} // end namespace internal
template <typename Op, typename Dims, typename XprType>
class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>, ReadOnlyAccessors> {
public:
typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorReductionOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested;
typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims)
{ }
TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const XprType& expression() const { return m_expr; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Dims& dims() const { return m_dims; }
const Op& reducer() const { return m_reducer; }
protected:
typename XprType::Nested m_expr;
const Dims m_dims;
const Op m_reducer;
};
// Eval as rvalue
template<typename Op, typename Dims, typename ArgType, typename Device>
struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
{
typedef TensorReductionOp<Op, Dims, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumReducedDims = internal::array_size<Dims>::value;
static const int NumDims = (NumInputDims==NumReducedDims) ? 1 : NumInputDims - NumReducedDims;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = false, // The code isn't vectorized properly yet
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_reducer(op.reducer())
{
EIGEN_STATIC_ASSERT(NumInputDims >= NumReducedDims, YOU_MADE_A_PROGRAMMING_MISTAKE);
array<bool, NumInputDims> reduced;
for (int i = 0; i < NumInputDims; ++i) {
reduced[i] = false;
}
for (int i = 0; i < NumReducedDims; ++i) {
eigen_assert(op.dims()[i] >= 0);
eigen_assert(op.dims()[i] < NumInputDims);
reduced[op.dims()[i]] = true;
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
int outputIndex = 0;
int reduceIndex = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (reduced[i]) {
m_reducedDims[reduceIndex] = input_dims[i];
++reduceIndex;
} else {
m_dimensions[outputIndex] = input_dims[i];
++outputIndex;
}
}
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
array<Index, NumInputDims> strides;
strides[0] = 1;
for (int i = 1; i < NumInputDims; ++i) {
strides[i] = strides[i-1] * input_dims[i-1];
}
outputIndex = 0;
reduceIndex = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (reduced[i]) {
m_reducedStrides[reduceIndex] = strides[i];
++reduceIndex;
} else {
m_preservedStrides[outputIndex] = strides[i];
++outputIndex;
}
}
// Special case for full reductions
if (NumInputDims == NumReducedDims) {
m_dimensions[0] = 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();
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
Op reducer(m_reducer);
reduce(firstInput(index), 0, reducer);
return reducer.finalize();
}
// TODO(bsteiner): provide a more efficient implementation.
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_DEFAULT CoeffReturnType values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
Scalar* data() const { return NULL; }
private:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
Index startInput = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
startInput += idx * m_preservedStrides[i];
index -= idx * m_outputStrides[i];
}
startInput += index * m_preservedStrides[0];
return startInput;
}
EIGEN_DEVICE_FUNC void reduce(Index firstIndex, int DimIndex, Op& reducer) const {
for (int j = 0; j < m_reducedDims[DimIndex]; ++j) {
const Index input = firstIndex + j * m_reducedStrides[DimIndex];
if (DimIndex < NumReducedDims-1) {
reduce(input, DimIndex+1, reducer);
} else {
reducer.reduce(m_impl.coeff(input));
}
}
}
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_preservedStrides;
array<Index, NumReducedDims> m_reducedStrides;
array<Index, NumReducedDims> m_reducedDims;
TensorEvaluator<ArgType, Device> m_impl;
Op m_reducer;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
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