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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
//
// 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_SCAN_H
#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
namespace Eigen {
namespace internal {
template <typename Op, typename XprType>
struct traits<TensorScanOp<Op, XprType> >
: public traits<XprType> {
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
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 Op, typename XprType>
struct eval<TensorScanOp<Op, XprType>, Eigen::Dense>
{
typedef const TensorScanOp<Op, XprType>& type;
};
template<typename Op, typename XprType>
struct nested<TensorScanOp<Op, XprType>, 1,
typename eval<TensorScanOp<Op, XprType> >::type>
{
typedef TensorScanOp<Op, XprType> type;
};
} // end namespace internal
/** \class TensorScan
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor scan class.
*
*/
template <typename Op, typename XprType>
class TensorScanOp
: public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> {
public:
typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorScanOp>::type Nested;
typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(
const XprType& expr, const Index& axis, const Op& op = Op())
: m_expr(expr), m_axis(axis), m_accumulator(op) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Index axis() const { return m_axis; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const XprType& expression() const { return m_expr; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Op accumulator() const { return m_accumulator; }
protected:
typename XprType::Nested m_expr;
const Index m_axis;
const Op m_accumulator;
};
// Eval as rvalue
template <typename Op, typename ArgType, typename Device>
struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
typedef TensorScanOp<Op, 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;
enum {
IsAligned = false,
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
BlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false,
RawAccess = true
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
const Device& device)
: m_impl(op.expression(), device),
m_device(device),
m_axis(op.axis()),
m_accumulator(op.accumulator()),
m_dimensions(m_impl.dimensions()),
m_size(m_dimensions[m_axis]),
m_stride(1),
m_output(NULL) {
// Accumulating a scalar isn't supported.
EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(m_axis >= 0 && m_axis < NumDims);
// Compute stride of scan axis
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < m_axis; ++i) {
m_stride = m_stride * m_dimensions[i];
}
} else {
for (int i = NumDims - 1; i > m_axis; --i) {
m_stride = m_stride * m_dimensions[i];
}
}
}
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);
if (data) {
accumulateTo(data);
return false;
} else {
m_output = static_cast<CoeffReturnType*>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
accumulateTo(m_output);
return true;
}
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const
{
return m_output;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_output[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
if (m_output != NULL) {
m_device.deallocate(m_output);
m_output = NULL;
}
m_impl.cleanup();
}
protected:
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
const Index m_axis;
Op m_accumulator;
const Dimensions& m_dimensions;
const Index& m_size;
Index m_stride;
CoeffReturnType* m_output;
// TODO(ibab) Parallelize this single-threaded implementation if desired
EIGEN_DEVICE_FUNC void accumulateTo(Scalar* data) {
// We fix the index along the scan axis to 0 and perform an
// scan per remaining entry. The iteration is split into two nested
// loops to avoid an integer division by keeping track of each idx1 and idx2.
for (Index idx1 = 0; idx1 < dimensions().TotalSize() / m_size; idx1 += m_stride) {
for (Index idx2 = 0; idx2 < m_stride; idx2++) {
// Calculate the starting offset for the scan
Index offset = idx1 * m_size + idx2;
// Compute the prefix sum along the axis, starting at the calculated offset
CoeffReturnType accum = m_accumulator.initialize();
for (Index idx3 = 0; idx3 < m_size; idx3++) {
Index curr = offset + idx3 * m_stride;
m_accumulator.reduce(m_impl.coeff(curr), &accum);
data[curr] = m_accumulator.finalize(accum);
}
}
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
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