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10 #ifndef EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
23 template<
typename NewDimensions,
typename XprType>
30 typedef typename XprType::Nested
Nested;
33 static const int Layout = XprTraits::Layout;
37 template<
typename NewDimensions,
typename XprType>
43 template<
typename NewDimensions,
typename XprType>
53 template<
typename NewDimensions,
typename XprType>
83 template<
typename NewDimensions,
typename ArgType,
typename Device>
110 #if defined(EIGEN_HAS_INDEX_LIST)
111 (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(0, 1)) ? OneByN
112 : (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(1, 1)) ?
NByOne
126 NumInputDims > 0 && NumOutputDims > 0,
146 : m_impl(op.expression(), device), m_dimensions(op.
dimensions())
155 #ifdef EIGEN_USE_THREADS
156 template <
typename EvalSubExprsCallback>
159 m_impl.evalSubExprsIfNeededAsync(
data, std::move(done));
164 return m_impl.evalSubExprsIfNeeded(
data);
172 return m_impl.coeff(index);
175 template<
int LoadMode>
178 return m_impl.template packet<LoadMode>(index);
182 return m_impl.costPerCoeff(vectorized);
192 struct BlockIteratorState {
201 bool =
false)
const {
204 (kind == OneByN &&
desc.dimensions()[0] == 1) ||
205 (kind ==
NByOne &&
desc.dimensions()[1] == 1));
207 if (kind == OneByN || kind ==
NByOne) {
211 m_impl.data() +
desc.offset(),
desc.dimensions());
226 #ifdef EIGEN_USE_SYCL
239 template<
typename NewDimensions,
typename ArgType,
typename Device>
241 :
public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
274 return this->m_impl.coeffRef(index);
280 this->m_impl.template writePacket<StoreMode>(index,
x);
283 template <
typename TensorBlock>
286 assert(this->m_impl.data() !=
NULL);
290 Scalar, TensorEvaluator::NumOutputDims, TensorBlockExpr,
Index>
293 TensorBlockAssign::Run(
294 TensorBlockAssign::target(
desc.dimensions(),
295 internal::strides<Layout>(
this->dimensions()),
296 this->m_impl.data(),
desc.offset()),
310 template<
typename StartIndices,
typename Sizes,
typename XprType>
320 static const int Layout = XprTraits::Layout;
324 template<
typename StartIndices,
typename Sizes,
typename XprType>
330 template<
typename StartIndices,
typename Sizes,
typename XprType>
340 template<
typename StartIndices,
typename Sizes,
typename XprType>
374 template <
typename Index,
typename Device,
bool BlockAccess>
struct MemcpyTriggerForSlicing {
377 const bool prefer_block_evaluation = BlockAccess && total > 32*1024;
378 return !prefer_block_evaluation && contiguous >
threshold_;
388 template <
typename Index,
bool BlockAccess>
struct MemcpyTriggerForSlicing<
Index, GpuDevice, BlockAccess> {
396 #ifdef EIGEN_USE_SYCL
397 template <
typename Index,
bool BlockAccess>
struct MemcpyTriggerForSlicing<
Index,
Eigen::SyclDevice, BlockAccess> {
406 template<
typename StartIndices,
typename Sizes,
typename ArgType,
typename Device>
447 : m_impl(op.expression(), device),
m_device(device), m_dimensions(op.
sizes()), m_offsets(op.startIndices())
449 m_is_identity =
true;
453 if (m_impl.dimensions()[
i] != op.
sizes()[
i] ||
455 m_is_identity =
false;
460 if (NumDims == 0)
return;
465 m_inputStrides[0] = 1;
466 for (
int i = 1;
i < NumDims; ++
i) {
467 m_inputStrides[
i] = m_inputStrides[
i-1] * input_dims[
i-1];
471 m_outputStrides[0] = 1;
472 for (
int i = 1;
i < NumDims; ++
i) {
473 m_outputStrides[
i] = m_outputStrides[
i-1] * output_dims[
i-1];
477 m_inputStrides[NumDims-1] = 1;
478 for (
int i = NumDims - 2;
i >= 0; --
i) {
479 m_inputStrides[
i] = m_inputStrides[
i+1] * input_dims[
i+1];
483 m_outputStrides[NumDims-1] = 1;
484 for (
int i = NumDims - 2;
i >= 0; --
i) {
485 m_outputStrides[
i] = m_outputStrides[
i+1] * output_dims[
i+1];
494 m_impl.evalSubExprsIfNeeded(
NULL);
496 &&
data && m_impl.data()) {
497 Index contiguous_values = 1;
499 for (
int i = 0;
i < NumDims; ++
i) {
506 for (
int i = NumDims-1;
i >= 0; --
i) {
514 const MemcpyTriggerForSlicing<Index, Device, BlockAccess> trigger(
m_device);
527 #ifdef EIGEN_USE_THREADS
528 template <
typename EvalSubExprsCallback>
531 m_impl.evalSubExprsIfNeededAsync(
nullptr, [done](
bool) { done(
true); });
533 #endif // EIGEN_USE_THREADS
542 return m_impl.coeff(index);
544 return m_impl.coeff(srcCoeff(index));
548 template<
int LoadMode>
556 return m_impl.template packet<LoadMode>(index);
559 Index inputIndices[] = {0, 0};
563 for (
int i = NumDims - 1;
i > 0; --
i) {
566 inputIndices[0] += (idx0 + m_offsets[
i]) * m_inputStrides[
i];
567 inputIndices[1] += (idx1 + m_offsets[
i]) * m_inputStrides[
i];
568 indices[0] -= idx0 * m_outputStrides[
i];
569 indices[1] -= idx1 * m_outputStrides[
i];
571 inputIndices[0] += (
indices[0] + m_offsets[0]);
572 inputIndices[1] += (
indices[1] + m_offsets[0]);
575 for (
int i = 0;
i < NumDims - 1; ++
i) {
578 inputIndices[0] += (idx0 + m_offsets[
i]) * m_inputStrides[
i];
579 inputIndices[1] += (idx1 + m_offsets[
i]) * m_inputStrides[
i];
580 indices[0] -= idx0 * m_outputStrides[
i];
581 indices[1] -= idx1 * m_outputStrides[
i];
583 inputIndices[0] += (
indices[0] + m_offsets[NumDims-1]);
584 inputIndices[1] += (
indices[1] + m_offsets[NumDims-1]);
586 if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
592 values[0] = m_impl.coeff(inputIndices[0]);
593 values[packetSize-1] = m_impl.coeff(inputIndices[1]);
595 for (
int i = 1;
i < packetSize-1; ++
i) {
604 return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
609 const size_t target_size =
m_device.lastLevelCacheSize();
611 internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
612 m_impl.getResourceRequirements());
617 bool =
false)
const {
629 for (
int i = 0;
i < NumDims; ++
i) {
630 if (m_dimensions[
i] != m_impl.dimensions()[
i]) {
631 offset += m_offsets[
i] * m_inputStrides[
i];
632 for (
int j =
i+1;
j < NumDims; ++
j) {
633 if (m_dimensions[
j] > 1) {
636 offset += m_offsets[
j] * m_inputStrides[
j];
642 for (
int i = NumDims - 1;
i >= 0; --
i) {
643 if (m_dimensions[
i] != m_impl.dimensions()[
i]) {
644 offset += m_offsets[
i] * m_inputStrides[
i];
645 for (
int j =
i-1;
j >= 0; --
j) {
646 if (m_dimensions[
j] > 1) {
649 offset += m_offsets[
j] * m_inputStrides[
j];
659 #ifdef EIGEN_USE_SYCL
669 Index inputIndex = 0;
672 for (
int i = NumDims - 1;
i > 0; --
i) {
673 const Index idx = index / m_fastOutputStrides[
i];
674 inputIndex += (idx + m_offsets[
i]) * m_inputStrides[
i];
675 index -= idx * m_outputStrides[
i];
677 inputIndex += (index + m_offsets[0]);
680 for (
int i = 0;
i < NumDims - 1; ++
i) {
681 const Index idx = index / m_fastOutputStrides[
i];
682 inputIndex += (idx + m_offsets[
i]) * m_inputStrides[
i];
683 index -= idx * m_outputStrides[
i];
685 inputIndex += (index + m_offsets[NumDims-1]);
702 template<
typename StartIndices,
typename Sizes,
typename ArgType,
typename Device>
704 :
public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
739 if (this->m_is_identity) {
740 return this->m_impl.coeffRef(index);
742 return this->m_impl.coeffRef(this->srcCoeff(index));
749 if (this->m_is_identity) {
750 this->m_impl.template writePacket<StoreMode>(index,
x);
755 Index inputIndices[] = {0, 0};
759 for (
int i = NumDims - 1;
i > 0; --
i) {
760 const Index idx0 =
indices[0] / this->m_fastOutputStrides[
i];
761 const Index idx1 =
indices[1] / this->m_fastOutputStrides[
i];
762 inputIndices[0] += (idx0 + this->m_offsets[
i]) * this->m_inputStrides[
i];
763 inputIndices[1] += (idx1 + this->m_offsets[
i]) * this->m_inputStrides[
i];
764 indices[0] -= idx0 * this->m_outputStrides[
i];
765 indices[1] -= idx1 * this->m_outputStrides[
i];
767 inputIndices[0] += (
indices[0] + this->m_offsets[0]);
768 inputIndices[1] += (
indices[1] + this->m_offsets[0]);
771 for (
int i = 0;
i < NumDims - 1; ++
i) {
772 const Index idx0 =
indices[0] / this->m_fastOutputStrides[
i];
773 const Index idx1 =
indices[1] / this->m_fastOutputStrides[
i];
774 inputIndices[0] += (idx0 + this->m_offsets[
i]) * this->m_inputStrides[
i];
775 inputIndices[1] += (idx1 + this->m_offsets[
i]) * this->m_inputStrides[
i];
776 indices[0] -= idx0 * this->m_outputStrides[
i];
777 indices[1] -= idx1 * this->m_outputStrides[
i];
779 inputIndices[0] += (
indices[0] + this->m_offsets[NumDims-1]);
780 inputIndices[1] += (
indices[1] + this->m_offsets[NumDims-1]);
782 if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
783 this->m_impl.template writePacket<StoreMode>(inputIndices[0],
x);
787 internal::pstore<CoeffReturnType, PacketReturnType>(
values,
x);
788 this->m_impl.coeffRef(inputIndices[0]) =
values[0];
789 this->m_impl.coeffRef(inputIndices[1]) =
values[packetSize-1];
791 for (
int i = 1;
i < packetSize-1; ++
i) {
797 template<
typename TensorBlock>
801 this->m_impl.writeBlock(arg_desc,
block);
806 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename XprType>
816 static const int Layout = XprTraits::Layout;
820 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename XprType>
826 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename XprType>
835 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename XprType>
873 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename ArgType,
typename Device>
902 : m_impl(op.expression(), device),
910 if (m_strides[
i] > 0) {
911 startIndicesClamped[
i] =
913 stopIndicesClamped[
i] =
917 startIndicesClamped[
i] =
919 stopIndicesClamped[
i] =
922 m_startIndices[
i] = startIndicesClamped[
i];
926 const InputDimensions& input_dims = m_impl.
dimensions();
929 m_is_identity =
true;
930 for (
int i = 0;
i < NumDims;
i++) {
931 Index interval = stopIndicesClamped[
i] - startIndicesClamped[
i];
932 if (interval == 0 || ((interval < 0) != (m_strides[
i] < 0))) {
936 (interval / m_strides[
i]) + (interval % m_strides[
i] != 0 ? 1 : 0);
939 if (m_strides[
i] != 1 || interval != m_impl.dimensions()[
i]) {
940 m_is_identity =
false;
944 Strides output_dims = m_dimensions;
947 m_inputStrides[0] = m_strides[0];
948 m_offsets[0] = startIndicesClamped[0];
949 Index previousDimProduct = 1;
950 for (
int i = 1;
i < NumDims; ++
i) {
951 previousDimProduct *= input_dims[
i-1];
952 m_inputStrides[
i] = previousDimProduct * m_strides[
i];
953 m_offsets[
i] = startIndicesClamped[
i] * previousDimProduct;
957 m_outputStrides[0] = 1;
958 for (
int i = 1;
i < NumDims; ++
i) {
959 m_outputStrides[
i] = m_outputStrides[
i-1] * output_dims[
i-1];
963 m_inputStrides[NumDims-1] = m_strides[NumDims-1];
964 m_offsets[NumDims-1] = startIndicesClamped[NumDims-1];
965 Index previousDimProduct = 1;
966 for (
int i = NumDims - 2;
i >= 0; --
i) {
967 previousDimProduct *= input_dims[
i+1];
968 m_inputStrides[
i] = previousDimProduct * m_strides[
i];
969 m_offsets[
i] = startIndicesClamped[
i] * previousDimProduct;
972 m_outputStrides[NumDims-1] = 1;
973 for (
int i = NumDims - 2;
i >= 0; --
i) {
974 m_outputStrides[
i] = m_outputStrides[
i+1] * output_dims[
i+1];
984 m_impl.evalSubExprsIfNeeded(
NULL);
995 return m_impl.coeff(index);
997 return m_impl.coeff(srcCoeff(index));
1002 return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
1008 #ifdef EIGEN_USE_SYCL
1017 Index inputIndex = 0;
1020 for (
int i = NumDims - 1;
i >= 0; --
i) {
1021 const Index idx = index / m_fastOutputStrides[
i];
1022 inputIndex += idx * m_inputStrides[
i] + m_offsets[
i];
1023 index -= idx * m_outputStrides[
i];
1027 for (
int i = 0;
i < NumDims; ++
i) {
1028 const Index idx = index / m_fastOutputStrides[
i];
1029 inputIndex += idx * m_inputStrides[
i] + m_offsets[
i];
1030 index -= idx * m_outputStrides[
i];
1037 #ifndef SYCL_DEVICE_ONLY
1057 template<
typename StartIndices,
typename StopIndices,
typename Str
ides,
typename ArgType,
typename Device>
1059 :
public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
1091 if (this->m_is_identity) {
1092 return this->m_impl.coeffRef(index);
1094 return this->m_impl.coeffRef(this->srcCoeff(index));
1102 #endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
Eigen::internal::traits< TensorSlicingOp >::StorageKind StorageKind
#define EIGEN_DEVICE_FUNC
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType &x)
EIGEN_STRONG_INLINE void cleanup()
Namespace containing all symbols from the Eigen library.
static EIGEN_STRONG_INLINE TensorMaterializedBlock materialize(const Scalar *data, const DataDimensions &data_dims, TensorBlockDesc &desc, TensorBlockScratch &scratch)
Eigen::internal::traits< TensorReshapingOp >::Index Index
const EIGEN_DEVICE_FUNC internal::remove_all< typename XprType::Nested >::type & expression() const
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
Eigen::internal::traits< TensorSlicingOp >::Scalar Scalar
CleanedUpDerType< DerType >::type() max(const AutoDiffScalar< DerType > &x, const T &y)
EIGEN_ALWAYS_INLINE DSizes< IndexType, NumDims > strides(const DSizes< IndexType, NumDims > &dimensions)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
TensorReshapingOp< NewDimensions, ArgType > XprType
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
EIGEN_STRONG_INLINE void cleanup()
Generic expression where a coefficient-wise binary operator is applied to two expressions.
array< Index, NumDims > m_inputStrides
TensorEvaluator< const TensorSlicingOp< StartIndices, Sizes, ArgType >, Device > Base
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
internal::TensorBlockDescriptor< NumDims, Index > TensorBlockDesc
internal::remove_const< Scalar >::type ScalarNoConst
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType & coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
array< Index, NumDims > m_inputStrides
set noclip points set clip one set noclip two set bar set border lt lw set xdata set ydata set zdata set x2data set y2data set boxwidth set dummy x
internal::TensorMaterializedBlock< ScalarNoConst, NumCoords, Layout, Index > TensorBlock
XprTraits::StorageKind StorageKind
StorageMemory< CoeffReturnType, Device > Storage
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType & coeffRef(Index index)
const StartIndices m_indices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool=false) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
TensorEvaluator< const ArgType, Device >::TensorBlock TensorBlock
XprTraits::PointerType PointerType
std::vector< Array2i > sizes
TensorEvaluator< ArgType, Device > m_impl
XprType::CoeffReturnType CoeffReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool=false) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Storage::Type data() const
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
TensorStridingSlicingOp< StartIndices, StopIndices, Strides, XprType > type
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlockResourceRequirements merge(const TensorBlockResourceRequirements &lhs, const TensorBlockResourceRequirements &rhs)
const EIGEN_DEVICE_FUNC NewDimensions & dimensions() const
Eigen::internal::nested< TensorSlicingOp >::type Nested
remove_reference< Nested >::type _Nested
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp(const XprType &expr, const StartIndices &startIndices, const StopIndices &stopIndices, const Strides &strides)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType &expr, const NewDimensions &dims)
XprTraits::PointerType PointerType
internal::traits< TensorStridingSlicingOp >::StorageKind StorageKind
const StopIndices m_stopIndices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType & coeffRef(Index index)
array< internal::TensorIntDivisor< Index >, NumDims > m_fastOutputStrides
const EIGEN_DEVICE_FUNC internal::remove_all< typename XprType::Nested >::type & expression() const
XprTraits::PointerType PointerType
StorageMemory< CoeffReturnType, Device > Storage
Eigen::internal::traits< TensorReshapingOp >::StorageKind StorageKind
const EIGEN_DEVICE_FUNC internal::remove_all< typename XprType::Nested >::type & expression() const
traits< XprType > XprTraits
PacketType< CoeffReturnType, Device >::type PacketReturnType
const EIGEN_DEVICE_FUNC StartIndices & strides() const
const EIGEN_DEVICE_FUNC StartIndices & startIndices() const
TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType > XprType
internal::TensorBlockScratchAllocator< Device > TensorBlockScratch
const Device EIGEN_DEVICE_REF m_device
internal::TensorBlockScratchAllocator< Device > TensorBlockScratch
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
XprType::CoeffReturnType CoeffReturnType
Eigen::IndexList< Index, Eigen::type2index< 1 > > NByOne(Index n)
PacketType< CoeffReturnType, Device >::type PacketReturnType
remove_reference< Nested >::type _Nested
const StartIndices m_offsets
internal::remove_const< typename XprType::CoeffReturnType >::type CoeffReturnType
internal::traits< TensorStridingSlicingOp >::Index Index
const EIGEN_DEVICE_FUNC TensorEvaluator< ArgType, Device > & impl() const
EIGEN_STRONG_INLINE void cleanup()
XprType::CoeffReturnType CoeffReturnType
internal::TensorBlockDescriptor< NumDims, Index > TensorBlockDesc
const typedef TensorReshapingOp< NewDimensions, XprType > EIGEN_DEVICE_REF type
internal::enable_if< internal::valid_indexed_view_overload< RowIndices, ColIndices >::value &&internal::traits< typename EIGEN_INDEXED_VIEW_METHOD_TYPE< RowIndices, ColIndices >::type >::ReturnAsIndexedView, typename EIGEN_INDEXED_VIEW_METHOD_TYPE< RowIndices, ColIndices >::type >::type operator()(const RowIndices &rowIndices, const ColIndices &colIndices) EIGEN_INDEXED_VIEW_METHOD_CONST
Eigen::internal::traits< TensorSlicingOp >::Index Index
TensorEvaluator< const TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType >, Device > Base
TensorBase< TensorSlicingOp< StartIndices, Sizes, XprType > > Base
XprTraits::StorageKind StorageKind
internal::TensorBlockScratchAllocator< Device > TensorBlockScratch
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T mini(const T &x, const T &y)
TensorBase< TensorReshapingOp< NewDimensions, XprType >, WriteAccessors > Base
internal::remove_const< Scalar >::type ScalarNoConst
internal::TensorMaterializedBlock< ScalarNoConst, NumOutputDims, Layout, Index > TensorBlock
XprType::CoeffReturnType CoeffReturnType
#define EIGEN_STRONG_INLINE
#define EIGEN_UNROLL_LOOP
traits< XprType > XprTraits
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
TensorReshapingOp< NewDimensions, XprType > type
Storage::Type EvaluatorPointerType
array< Index, NumDims > m_outputStrides
internal::remove_const< Scalar >::type ScalarNoConst
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc &desc, const TensorBlock &block)
StorageMemory< CoeffReturnType, Device > Storage
PacketType< CoeffReturnType, Device >::type PacketReturnType
const StartIndices m_startIndices
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
internal::remove_const< Scalar >::type ScalarNoConst
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
internal::TensorBlockDescriptor< NumOutputDims, Index > TensorBlockDesc
XprType::CoeffReturnType CoeffReturnType
const Device EIGEN_DEVICE_REF m_device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
internal::TensorBlockDescriptor< TensorEvaluator::NumOutputDims, Index > TensorBlockDesc
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes< Indices... > &)
const Device EIGEN_DEVICE_REF m_device
Storage::Type EvaluatorPointerType
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TensorSlicingOp< StartIndices, Sizes, ArgType > XprType
const EIGEN_DEVICE_FUNC StartIndices & startIndices() const
XprType::CoeffReturnType CoeffReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType & coeffRef(Index index)
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
const EIGEN_DEVICE_FUNC StartIndices & stopIndices() const
bool HasDestinationBuffer() const
DSizes< Index, NumDims > m_dimensions
#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(Derived)
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
DSizes< Index, NumDims > m_offsets
#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
const typedef TensorSlicingOp< StartIndices, Sizes, XprType > EIGEN_DEVICE_REF type
XprType::CoeffReturnType CoeffReturnType
Storage::Type EvaluatorPointerType
Storage::Type EvaluatorPointerType
traits< XprType > XprTraits
StorageMemory< typename internal::remove_const< CoeffReturnType >::type, Device > ConstCastStorage
internal::TensorBlockNotImplemented TensorBlock
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
DSizes< Index, NumDims > m_startIndices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
TensorEvaluator< const TensorReshapingOp< NewDimensions, ArgType >, Device > Base
PacketType< CoeffReturnType, Device >::type PacketReturnType
TensorSlicingOp< StartIndices, Sizes, XprType > type
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
const EIGEN_DEVICE_FUNC Sizes & sizes() const
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
A cost model used to limit the number of threads used for evaluating tensor expression.
Eigen::internal::nested< TensorReshapingOp >::type Nested
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc &desc, const TensorBlock &block)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType &expr, const StartIndices &indices, const Sizes &sizes)
TensorBase< TensorStridingSlicingOp< StartIndices, StopIndices, Strides, XprType > > Base
const XprType & expr() const
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
internal::traits< TensorStridingSlicingOp >::Scalar Scalar
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T maxi(const T &x, const T &y)
CleanedUpDerType< DerType >::type() min(const AutoDiffScalar< DerType > &x, const T &y)
TensorEvaluator< ArgType, Device > m_impl
const NewDimensions m_dims
TensorSlicingOp< StartIndices, Sizes, ArgType > XprType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType &x)
TensorEvaluator< ArgType, Device > m_impl
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool=false) const
array< Index, NumDims > m_outputStrides
internal::TensorBlockNotImplemented TensorBlock
array< internal::TensorIntDivisor< Index >, NumDims > m_fastOutputStrides
NewDimensions m_dimensions
const typedef TensorStridingSlicingOp< StartIndices, StopIndices, Strides, XprType > EIGEN_DEVICE_REF type
XprType::CoeffReturnType CoeffReturnType
TensorReshapingOp< NewDimensions, ArgType > XprType
XprTraits::StorageKind StorageKind
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_DEVICE_FUNC Storage::Type data() const
Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T * constCast(const T *data)
Eigen::internal::traits< TensorReshapingOp >::Scalar Scalar
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max)
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
StorageMemory< typename internal::remove_const< CoeffReturnType >::type, Device > ConstCastStorage
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlockResourceRequirements any()
TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType > XprType
internal::nested< TensorStridingSlicingOp >::type Nested
remove_reference< Nested >::type _Nested
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Storage::Type data() const
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autogenerated on Fri Nov 1 2024 03:38:20