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11 #ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
12 #define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
22 template<
typename ReverseDimensions,
typename XprType>
30 typedef typename XprType::Nested
Nested;
32 static const int NumDimensions = XprTraits::NumDimensions;
33 static const int Layout = XprTraits::Layout;
37 template<
typename ReverseDimensions,
typename XprType>
43 template<
typename ReverseDimensions,
typename XprType>
52 template<
typename ReverseDimensions,
typename XprType>
54 XprType>, WriteAccessors>
67 const XprType& expr,
const ReverseDimensions& reverse_dims)
86 template<
typename ReverseDimensions,
typename ArgType,
typename Device>
125 : m_impl(op.expression(), device),
133 m_dimensions = m_impl.dimensions();
136 for (
int i = 1;
i < NumDims; ++
i) {
137 m_strides[
i] = m_strides[
i-1] * m_dimensions[
i-1];
141 m_strides[NumDims-1] = 1;
142 for (
int i = NumDims - 2;
i >= 0; --
i) {
143 m_strides[
i] = m_strides[
i+1] * m_dimensions[
i+1];
153 m_impl.evalSubExprsIfNeeded(
NULL);
157 #ifdef EIGEN_USE_THREADS
158 template <
typename EvalSubExprsCallback>
161 m_impl.evalSubExprsIfNeededAsync(
nullptr, [done](
bool) { done(
true); });
163 #endif // EIGEN_USE_THREADS
172 Index inputIndex = 0;
175 for (
int i = NumDims - 1;
i > 0; --
i) {
176 Index idx = index / m_fastStrides[
i];
177 index -= idx * m_strides[
i];
179 idx = m_dimensions[
i] - idx - 1;
181 inputIndex += idx * m_strides[
i] ;
184 inputIndex += (m_dimensions[0] - index - 1);
190 for (
int i = 0;
i < NumDims - 1; ++
i) {
191 Index idx = index / m_fastStrides[
i];
192 index -= idx * m_strides[
i];
194 idx = m_dimensions[
i] - idx - 1;
196 inputIndex += idx * m_strides[
i] ;
198 if (m_reverse[NumDims-1]) {
199 inputIndex += (m_dimensions[NumDims-1] - index - 1);
209 return m_impl.coeff(reverseIndex(index));
212 template<
int LoadMode>
233 const size_t target_size =
m_device.lastLevelCacheSize();
236 return internal::TensorBlockResourceRequirements::skewed<Scalar>(
238 .addCostPerCoeff({0, 0, 24});
243 bool =
false)
const {
251 static const bool isColMajor =
254 static const Index inner_dim_idx = isColMajor ? 0 : NumDims - 1;
255 const bool inner_dim_reversed = m_reverse[inner_dim_idx];
258 Index block_offset = 0;
261 Index input_offset = reverseIndex(
desc.offset());
266 for (
int i = 0;
i < NumDims; ++
i) {
267 const int dim = isColMajor ?
i : NumDims - 1 -
i;
270 it[
i].reverse = m_reverse[dim];
273 i == 0 ? 1 : (it[
i - 1].
size * it[
i - 1].block_stride);
274 it[
i].block_span = it[
i].block_stride * (it[
i].
size - 1);
276 it[
i].input_stride = m_strides[dim];
277 it[
i].input_span = it[
i].input_stride * (it[
i].
size - 1);
280 it[
i].input_stride = -1 * it[
i].input_stride;
281 it[
i].input_span = -1 * it[
i].input_span;
287 int effective_inner_dim = 0;
288 for (
int i = 1;
i < NumDims; ++
i) {
290 if (it[
i].block_stride != it[effective_inner_dim].
size)
break;
291 if (it[
i].block_stride !=
numext::abs(it[
i].input_stride))
break;
295 it[
i].block_stride = 1;
296 it[
i].input_stride = (inner_dim_reversed ? -1 : 1);
298 it[
i].block_span = it[
i].block_stride * (it[
i].
size - 1);
299 it[
i].input_span = it[
i].input_stride * (it[
i].
size - 1);
301 effective_inner_dim =
i;
304 eigen_assert(it[effective_inner_dim].block_stride == 1);
306 (inner_dim_reversed ? -1 : 1));
308 const Index inner_dim_size = it[effective_inner_dim].
size;
315 while (it[NumDims - 1].count < it[NumDims - 1].
size) {
317 Index dst = block_offset;
318 Index src = input_offset;
322 if (inner_dim_reversed) {
323 for (
Index i = 0;
i < inner_dim_size; ++
i) {
324 block_buffer[dst] = m_impl.coeff(src);
329 for (
Index i = 0;
i < inner_dim_size; ++
i) {
330 block_buffer[dst] = m_impl.coeff(src);
337 if ((NumDims - effective_inner_dim) == 1)
break;
340 for (
Index i = effective_inner_dim + 1;
i < NumDims; ++
i) {
341 if (++it[
i].count < it[
i].
size) {
342 block_offset += it[
i].block_stride;
343 input_offset += it[
i].input_stride;
346 if (
i != NumDims - 1) it[
i].count = 0;
347 block_offset -= it[
i].block_span;
348 input_offset -= it[
i].input_span;
356 double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
357 2 * TensorOpCost::MulCost<Index>() +
358 TensorOpCost::DivCost<Index>());
359 for (
int i = 0;
i < NumDims; ++
i) {
361 compute_cost += 2 * TensorOpCost::AddCost<Index>();
364 return m_impl.costPerCoeff(vectorized) +
370 #ifdef EIGEN_USE_SYCL
386 struct BlockIteratorState {
408 template <
typename ReverseDimensions,
typename ArgType,
typename Device>
410 :
public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
429 :
Base(op, device) {}
444 return this->m_impl.coeffRef(this->reverseIndex(index));
454 internal::pstore<CoeffReturnType, PacketReturnType>(
values,
x);
465 #endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
internal::TensorBlockScratchAllocator< Device > TensorBlockScratch
#define EIGEN_DEVICE_FUNC
TensorEvaluator< const ArgType, Device >::TensorBlock ArgTensorBlock
Namespace containing all symbols from the Eigen library.
TensorReverseOp< ReverseDimensions, ArgType > XprType
Storage::Type EvaluatorPointerType
const EIGEN_DEVICE_FUNC internal::remove_all< typename XprType::Nested >::type & expression() const
Generic expression where a coefficient-wise binary operator is applied to two expressions.
DSizes< Index, NumDims > Dimensions
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
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internal::TensorMaterializedBlock< ScalarNoConst, NumCoords, Layout, Index > TensorBlock
internal::TensorIntDivisor< Index > IndexDivisor
const Device EIGEN_DEVICE_REF m_device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(const XprType &expr, const ReverseDimensions &reverse_dims)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType &x)
const ReverseDimensions m_reverse_dims
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
XprTraits::StorageKind StorageKind
static const int PacketSize
const typedef TensorReverseOp< ReverseDimensions, XprType > & type
TensorEvaluator< const TensorReverseOp< ReverseDimensions, ArgType >, Device > Base
EIGEN_DEVICE_FUNC Storage::Type data() const
Eigen::NumTraits< Scalar >::Real RealScalar
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
DSizes< Index, NumDims > Dimensions
internal::TensorBlockDescriptor< NumDims, Index > TensorBlockDesc
internal::TensorBlockNotImplemented TensorBlock
TensorEvaluator< ArgType, Device > m_impl
EIGEN_DEVICE_FUNC const EIGEN_STRONG_INLINE Dimensions & dimensions() const
#define EIGEN_STRONG_INLINE
ReverseDimensions m_reverse
#define EIGEN_UNROLL_LOOP
TensorReverseOp< ReverseDimensions, XprType > type
array< Index, NumDims > m_strides
Eigen::internal::traits< TensorReverseOp >::Index Index
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
const Device EIGEN_DEVICE_REF m_device
Eigen::internal::traits< TensorReverseOp >::Scalar Scalar
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType & coeffRef(Index index)
Eigen::internal::nested< TensorReverseOp >::type Nested
#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(Derived)
#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
const EIGEN_DEVICE_FUNC ReverseDimensions & reverse() const
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
EIGEN_STRONG_INLINE void cleanup()
Storage::Type EvaluatorPointerType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool=false) const
EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE std::size_t size()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
traits< XprType > XprTraits
remove_reference< Nested >::type _Nested
A cost model used to limit the number of threads used for evaluating tensor expression.
XprType::CoeffReturnType CoeffReturnType
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE internal::enable_if< NumTraits< T >::IsSigned||NumTraits< T >::IsComplex, typename NumTraits< T >::Real >::type abs(const T &x)
Eigen::internal::traits< TensorReverseOp >::StorageKind StorageKind
void reverse(const MatrixType &m)
Derived::Scalar CoeffReturnType
static EIGEN_STRONG_INLINE Storage prepareStorage(TensorBlockDesc &desc, TensorBlockScratch &scratch, bool allow_strided_storage=false)
StorageMemory< CoeffReturnType, Device > Storage
TensorMaterializedBlock AsTensorMaterializedBlock() const
TensorBase< TensorReverseOp< ReverseDimensions, XprType >, WriteAccessors > Base
EIGEN_STRONG_INLINE TensorEvaluator(const XprType &op, const Device &device)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar & coeffRef(Index index)
XprType::CoeffReturnType CoeffReturnType
internal::TensorMaterializedBlock< CoeffReturnType, NumDims, Layout, Index > TensorBlock
array< IndexDivisor, NumDims > m_fastStrides
XprType::CoeffReturnType CoeffReturnType
XprTraits::PointerType PointerType
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
TensorReverseOp< ReverseDimensions, ArgType > XprType
PacketType< CoeffReturnType, Device >::type PacketReturnType
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autogenerated on Fri Nov 1 2024 03:39:08