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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_FORCED_EVAL_H
#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
namespace Eigen {
/** \class TensorForcedEval
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reshaping class.
*
*
*/
namespace internal {
template<typename XprType>
struct traits<TensorForcedEvalOp<XprType> >
{
// 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 traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::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 XprTraits::PointerType PointerType;
enum {
Flags = 0
};
};
template<typename XprType>
struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense>
{
typedef const TensorForcedEvalOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type>
{
typedef TensorForcedEvalOp<XprType> type;
};
} // end namespace internal
template<typename XprType>
class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested;
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr)
: m_xpr(expr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
};
namespace internal {
template <typename Device, typename CoeffReturnType>
struct non_integral_type_placement_new{
template <typename StorageType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index numValues, StorageType m_buffer) {
// Initialize non-trivially constructible types.
if (!internal::is_arithmetic<CoeffReturnType>::value) {
for (Index i = 0; i < numValues; ++i) new (m_buffer + i) CoeffReturnType();
}
}
};
// SYCL does not support non-integral types
// having new (m_buffer + i) CoeffReturnType() causes the following compiler error for SYCL Devices
// no matching function for call to 'operator new'
template <typename CoeffReturnType>
struct non_integral_type_placement_new<Eigen::SyclDevice, CoeffReturnType> {
template <typename StorageType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index, StorageType) {
}
};
} // end namespace internal
template<typename ArgType_, typename Device>
struct TensorEvaluator<const TensorForcedEvalOp<ArgType_>, Device>
{
typedef const typename internal::remove_all<ArgType_>::type ArgType;
typedef TensorForcedEvalOp<ArgType> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType 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 = true,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = internal::is_arithmetic<CoeffReturnType>::value,
PreferBlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
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 internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
Layout, Index>
TensorBlock;
//===--------------------------------------------------------------------===//
TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_op(op.expression()),
m_device(device), m_buffer(NULL)
{ }
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
const Index numValues = internal::array_prod(m_impl.dimensions());
m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(numValues * sizeof(CoeffReturnType)));
internal::non_integral_type_placement_new<Device, CoeffReturnType>()(numValues, m_buffer);
typedef TensorEvalToOp< const typename internal::remove_const<ArgType>::type > EvalTo;
EvalTo evalToTmp(m_device.get(m_buffer), m_op);
internal::TensorExecutor<
const EvalTo, typename internal::remove_const<Device>::type,
/*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
/*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
run(evalToTmp, m_device);
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
const Index numValues = internal::array_prod(m_impl.dimensions());
m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(
numValues * sizeof(CoeffReturnType)));
typedef TensorEvalToOp<const typename internal::remove_const<ArgType>::type>
EvalTo;
EvalTo evalToTmp(m_device.get(m_buffer), m_op);
auto on_done = std::bind([](EvalSubExprsCallback done_) { done_(true); },
std::move(done));
internal::TensorAsyncExecutor<
const EvalTo, typename internal::remove_const<Device>::type,
decltype(on_done),
/*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
/*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
runAsync(evalToTmp, m_device, std::move(on_done));
}
#endif
EIGEN_STRONG_INLINE void cleanup() {
m_device.deallocate_temp(m_buffer);
m_buffer = NULL;
}
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
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return internal::TensorBlockResourceRequirements::any();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
assert(m_buffer != NULL);
return TensorBlock::materialize(m_buffer, m_impl.dimensions(), desc, scratch);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
EvaluatorPointerType data() const { return m_buffer; }
#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_buffer.bind(cgh);
m_impl.bind(cgh);
}
#endif
private:
TensorEvaluator<ArgType, Device> m_impl;
const ArgType m_op;
const Device EIGEN_DEVICE_REF m_device;
EvaluatorPointerType m_buffer;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
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