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10 #ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
23 template<
typename XprType,
template <
class>
class MakePointer_>
31 typedef typename XprType::Nested
Nested;
33 static const int NumDimensions = XprTraits::NumDimensions;
34 static const int Layout = XprTraits::Layout;
50 template<
typename XprType,
template <
class>
class MakePointer_>
56 template<
typename XprType,
template <
class>
class MakePointer_>
67 template<
typename XprType,
template <
class>
class MakePointer_>
97 template<
typename ArgType,
typename Device,
template <
class>
class MakePointer_>
135 : m_impl(op.expression(), device), m_buffer(device.
get(op.
buffer())), m_expression(op.expression()){}
147 return m_impl.evalSubExprsIfNeeded(m_buffer);
150 #ifdef EIGEN_USE_THREADS
151 template <
typename EvalSubExprsCallback>
156 m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
161 m_buffer[
i] = m_impl.coeff(
i);
169 return m_impl.getResourceRequirements();
175 desc.template AddDestinationBuffer<Layout>(
176 m_buffer +
desc.offset(),
177 internal::strides<Layout>(m_impl.dimensions()));
180 m_impl.block(
desc, scratch,
true);
185 TensorBlockAssignment::Run(
186 TensorBlockAssignment::target(
187 desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
188 m_buffer,
desc.offset()),
200 return m_buffer[index];
203 template<
int LoadMode>
206 return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
212 return m_impl.costPerCoeff(vectorized) +
218 #ifdef EIGEN_USE_SYCL
236 #endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
EIGEN_DEVICE_FUNC PointerType buffer() const
#define EIGEN_DEVICE_FUNC
Namespace containing all symbols from the Eigen library.
Storage::Type EvaluatorPointerType
TensorEvaluator< ArgType, Device > m_impl
internal::TensorBlockScratchAllocator< Device > TensorBlockScratch
Generic expression where a coefficient-wise binary operator is applied to two expressions.
XprTraits::StorageKind StorageKind
ArgType expression() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
TensorEvalToOp< ArgType, MakePointer_ > XprType
traits< XprType > XprTraits
Eigen::internal::traits< TensorEvalToOp >::Index Index
const typedef TensorEvalToOp< XprType, MakePointer_ > & type
MakePointer_< Scalar >::Type PointerType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType &expr)
static const int PacketSize
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
TensorEvaluator< const ArgType, Device >::TensorBlock ArgTensorBlock
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const
TensorEvalToOp< XprType, MakePointer_ > type
internal::TensorBlockAssignment< CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index > TensorBlockAssignment
#define EIGEN_UNUSED_VARIABLE(var)
MakePointer_< CoeffReturnType >::Type PointerType
mxArray * scalar(mxClassID classid)
Eigen::internal::traits< TensorEvalToOp >::StorageKind StorageKind
EIGEN_STRONG_INLINE void cleanup()
#define EIGEN_STRONG_INLINE
Eigen::NumTraits< Scalar >::Real RealScalar
Eigen::internal::traits< TensorEvalToOp >::Scalar Scalar
StorageMemory< CoeffReturnType, Device > Storage
const EIGEN_DEVICE_FUNC internal::remove_all< typename XprType::Nested >::type & expression() const
Eigen::internal::traits< XprType >::PointerType TensorPointerType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i)
internal::TensorBlockDescriptor< NumDims, Index > TensorBlockDesc
CwiseBinaryOp< internal::scalar_sum_op< double, double >, const CpyMatrixXd, const CpyMatrixXd > XprType
Eigen::internal::nested< TensorEvalToOp >::type Nested
EvaluatorPointerType m_buffer
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
Storage::Type EvaluatorPointerType
const ArgType m_expression
A cost model used to limit the number of threads used for evaluating tensor expression.
EIGEN_STRONG_INLINE ~TensorEvaluator()
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar)
const XprType & expr() const
Derived::Scalar CoeffReturnType
const EIGEN_DEVICE_FUNC Dimensions & dimensions() const
MakePointer_< T > MakePointerT
remove_reference< Nested >::type _Nested
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool=false) const
internal::remove_const< typename XprType::CoeffReturnType >::type CoeffReturnType
internal::remove_const< typename XprType::CoeffReturnType >::type CoeffReturnType
TensorEvaluator< ArgType, Device >::Dimensions Dimensions
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlockDesc &desc, TensorBlockScratch &scratch)
Container::iterator get(Container &c, Position position)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i)
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
TensorBlockKind kind() const
gtsam
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autogenerated on Fri Nov 1 2024 03:37:35