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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_EVAL_TO_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
namespace Eigen {
/** \class TensorForcedEval
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reshaping class.
*
*
*/
namespace internal {
template<typename XprType, template <class> class MakePointer_>
struct traits<TensorEvalToOp<XprType, MakePointer_> >
{
// 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 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;
typedef typename MakePointer_<Scalar>::Type PointerType;
enum {
Flags = 0
};
template <class T>
struct MakePointer {
// Intermediate typedef to workaround MSVC issue.
typedef MakePointer_<T> MakePointerT;
typedef typename MakePointerT::Type Type;
};
};
template<typename XprType, template <class> class MakePointer_>
struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
{
typedef const TensorEvalToOp<XprType, MakePointer_>& type;
};
template<typename XprType, template <class> class MakePointer_>
struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
{
typedef TensorEvalToOp<XprType, MakePointer_> type;
};
} // end namespace internal
template<typename XprType, template <class> class MakePointer_>
class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
static const int NumDims = Eigen::internal::traits<TensorEvalToOp>::NumDimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
: m_xpr(expr), m_buffer(buffer) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }
protected:
typename XprType::Nested m_xpr;
PointerType m_buffer;
};
template<typename ArgType, typename Device, template <class> class MakePointer_>
struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
{
typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef typename XprType::Index Index;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = true,
PreferBlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = true
};
static const int NumDims = internal::traits<ArgType>::NumDimensions;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
ArgTensorBlock;
typedef internal::TensorBlockAssignment<
CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index>
TensorBlockAssignment;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_buffer(device.get(op.buffer())), m_expression(op.expression()){}
EIGEN_STRONG_INLINE ~TensorEvaluator() {
}
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar) {
EIGEN_UNUSED_VARIABLE(scalar);
eigen_assert(scalar == NULL);
return m_impl.evalSubExprsIfNeeded(m_buffer);
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType scalar, EvalSubExprsCallback done) {
EIGEN_UNUSED_VARIABLE(scalar);
eigen_assert(scalar == NULL);
m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
m_buffer[i] = m_impl.coeff(i);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(m_buffer + i, m_impl.template packet<TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned>(i));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return m_impl.getResourceRequirements();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
TensorBlockDesc& desc, TensorBlockScratch& scratch) {
// Add `m_buffer` as destination buffer to the block descriptor.
desc.template AddDestinationBuffer<Layout>(
/*dst_base=*/m_buffer + desc.offset(),
/*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()));
ArgTensorBlock block =
m_impl.block(desc, scratch, /*root_of_expr_ast=*/true);
// If block was evaluated into a destination buffer, there is no need to do
// an assignment.
if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
TensorBlockAssignment::Run(
TensorBlockAssignment::target(
desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
m_buffer, desc.offset()),
block.expr());
}
block.cleanup();
}
EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
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 {
// We assume that evalPacket or evalScalar is called to perform the
// assignment and account for the cost of the write here.
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_buffer; }
ArgType expression() const { return m_expression; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_impl.bind(cgh);
m_buffer.bind(cgh);
}
#endif
private:
TensorEvaluator<ArgType, Device> m_impl;
EvaluatorPointerType m_buffer;
const ArgType m_expression;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
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