10#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
11#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
23template<
typename PaddingDimensions,
typename XprType>
24struct traits<TensorPaddingOp<PaddingDimensions, XprType> > :
public traits<XprType>
26 typedef typename XprType::Scalar Scalar;
27 typedef traits<XprType> XprTraits;
28 typedef typename XprTraits::StorageKind StorageKind;
29 typedef typename XprTraits::Index
Index;
30 typedef typename XprType::Nested Nested;
31 typedef typename remove_reference<Nested>::type _Nested;
32 static const int NumDimensions = XprTraits::NumDimensions;
33 static const int Layout = XprTraits::Layout;
34 typedef typename XprTraits::PointerType PointerType;
37template<
typename PaddingDimensions,
typename XprType>
38struct eval<TensorPaddingOp<PaddingDimensions, XprType>,
Eigen::Dense>
40 typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
43template<
typename PaddingDimensions,
typename XprType>
44struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type>
46 typedef TensorPaddingOp<PaddingDimensions, XprType> type;
53template<
typename PaddingDimensions,
typename XprType>
54class TensorPaddingOp :
public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors>
57 typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
59 typedef typename XprType::CoeffReturnType CoeffReturnType;
60 typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
61 typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
62 typedef typename Eigen::internal::traits<TensorPaddingOp>::Index
Index;
64 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(
const XprType& expr,
const PaddingDimensions& padding_dims,
const Scalar padding_value)
65 : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {}
68 const PaddingDimensions& padding()
const {
return m_padding_dims; }
70 Scalar padding_value()
const {
return m_padding_value; }
73 const typename internal::remove_all<typename XprType::Nested>::type&
74 expression()
const {
return m_xpr; }
77 typename XprType::Nested m_xpr;
78 const PaddingDimensions m_padding_dims;
79 const Scalar m_padding_value;
84template<
typename PaddingDimensions,
typename ArgType,
typename Device>
85struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device>
87 typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
88 typedef typename XprType::Index Index;
89 static const int NumDims = internal::array_size<PaddingDimensions>::value;
90 typedef DSizes<Index, NumDims> Dimensions;
91 typedef typename XprType::Scalar Scalar;
92 typedef typename XprType::CoeffReturnType CoeffReturnType;
93 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
94 static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
95 typedef StorageMemory<CoeffReturnType, Device> Storage;
96 typedef typename Storage::Type EvaluatorPointerType;
100 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
101 BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
102 PreferBlockAccess =
true,
103 Layout = TensorEvaluator<ArgType, Device>::Layout,
108 typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
111 typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
112 typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
114 typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
119 EIGEN_STRONG_INLINE TensorEvaluator(
const XprType& op,
const Device& device)
120 : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device)
125 EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
128 m_dimensions = m_impl.dimensions();
129 for (
int i = 0; i < NumDims; ++i) {
130 m_dimensions[i] += m_padding[i].first + m_padding[i].second;
132 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
133 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
134 m_inputStrides[0] = 1;
135 m_outputStrides[0] = 1;
136 for (
int i = 1; i < NumDims; ++i) {
137 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
138 m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
140 m_outputStrides[NumDims] = m_outputStrides[NumDims-1] * m_dimensions[NumDims-1];
142 m_inputStrides[NumDims - 1] = 1;
143 m_outputStrides[NumDims] = 1;
144 for (
int i = NumDims - 2; i >= 0; --i) {
145 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
146 m_outputStrides[i+1] = m_outputStrides[i+2] * m_dimensions[i+1];
148 m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
152 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Dimensions& dimensions()
const {
return m_dimensions; }
154 EIGEN_STRONG_INLINE
bool evalSubExprsIfNeeded(EvaluatorPointerType) {
155 m_impl.evalSubExprsIfNeeded(NULL);
159#ifdef EIGEN_USE_THREADS
160 template <
typename EvalSubExprsCallback>
161 EIGEN_STRONG_INLINE
void evalSubExprsIfNeededAsync(
162 EvaluatorPointerType, EvalSubExprsCallback done) {
163 m_impl.evalSubExprsIfNeededAsync(
nullptr, [done](
bool) { done(
true); });
167 EIGEN_STRONG_INLINE
void cleanup() {
171 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index)
const
173 eigen_assert(index < dimensions().TotalSize());
174 Index inputIndex = 0;
175 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
177 for (
int i = NumDims - 1; i > 0; --i) {
178 const Index idx = index / m_outputStrides[i];
179 if (isPaddingAtIndexForDim(idx, i)) {
180 return m_paddingValue;
182 inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
183 index -= idx * m_outputStrides[i];
185 if (isPaddingAtIndexForDim(index, 0)) {
186 return m_paddingValue;
188 inputIndex += (index - m_padding[0].first);
191 for (
int i = 0; i < NumDims - 1; ++i) {
192 const Index idx = index / m_outputStrides[i+1];
193 if (isPaddingAtIndexForDim(idx, i)) {
194 return m_paddingValue;
196 inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
197 index -= idx * m_outputStrides[i+1];
199 if (isPaddingAtIndexForDim(index, NumDims-1)) {
200 return m_paddingValue;
202 inputIndex += (index - m_padding[NumDims-1].first);
204 return m_impl.coeff(inputIndex);
207 template<
int LoadMode>
208 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index)
const
210 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
211 return packetColMajor(index);
213 return packetRowMajor(index);
216 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(
bool vectorized)
const {
217 TensorOpCost cost = m_impl.costPerCoeff(vectorized);
218 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
220 for (
int i = 0; i < NumDims; ++i)
221 updateCostPerDimension(cost, i, i == 0);
224 for (
int i = NumDims - 1; i >= 0; --i)
225 updateCostPerDimension(cost, i, i == NumDims - 1);
230 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
231 internal::TensorBlockResourceRequirements getResourceRequirements()
const {
232 const size_t target_size = m_device.lastLevelCacheSize();
233 return internal::TensorBlockResourceRequirements::merge(
234 internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
235 m_impl.getResourceRequirements());
238 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
239 block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
240 bool =
false)
const {
242 if (desc.size() == 0) {
243 return TensorBlock(internal::TensorBlockKind::kView, NULL,
247 static const bool IsColMajor = Layout ==
static_cast<int>(
ColMajor);
248 const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1;
250 Index offset = desc.offset();
253 DSizes<Index, NumDims> output_offsets;
254 for (
int i = NumDims - 1; i > 0; --i) {
255 const int dim = IsColMajor ? i : NumDims - i - 1;
256 const int stride_dim = IsColMajor ? dim : dim + 1;
257 output_offsets[dim] = offset / m_outputStrides[stride_dim];
258 offset -= output_offsets[dim] * m_outputStrides[stride_dim];
260 output_offsets[inner_dim_idx] = offset;
263 DSizes<Index, NumDims> input_offsets = output_offsets;
264 for (
int i = 0; i < NumDims; ++i) {
265 const int dim = IsColMajor ? i : NumDims - i - 1;
266 input_offsets[dim] = input_offsets[dim] - m_padding[dim].first;
272 Index input_offset = 0;
273 for (
int i = 0; i < NumDims; ++i) {
274 const int dim = IsColMajor ? i : NumDims - i - 1;
275 input_offset += input_offsets[dim] * m_inputStrides[dim];
281 Index output_offset = 0;
282 const DSizes<Index, NumDims> output_strides =
283 internal::strides<Layout>(desc.dimensions());
293 array<BlockIteratorState, NumDims - 1> it;
294 for (
int i = 0; i < NumDims - 1; ++i) {
295 const int dim = IsColMajor ? i + 1 : NumDims - i - 2;
297 it[i].size = desc.dimension(dim);
299 it[i].input_stride = m_inputStrides[dim];
300 it[i].input_span = it[i].input_stride * (it[i].size - 1);
302 it[i].output_stride = output_strides[dim];
303 it[i].output_span = it[i].output_stride * (it[i].size - 1);
306 const Index input_inner_dim_size =
307 static_cast<Index>(m_impl.dimensions()[inner_dim_idx]);
310 const Index output_size = desc.size();
315 const Index output_inner_dim_size = desc.dimension(inner_dim_idx);
319 const Index output_inner_pad_before_size =
320 input_offsets[inner_dim_idx] < 0
321 ? numext::mini(numext::abs(input_offsets[inner_dim_idx]),
322 output_inner_dim_size)
326 const Index output_inner_copy_size = numext::mini(
328 (output_inner_dim_size - output_inner_pad_before_size),
330 numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] +
331 output_inner_pad_before_size),
334 eigen_assert(output_inner_copy_size >= 0);
338 const Index output_inner_pad_after_size =
339 (output_inner_dim_size - output_inner_copy_size -
340 output_inner_pad_before_size);
343 eigen_assert(output_inner_dim_size ==
344 (output_inner_pad_before_size + output_inner_copy_size +
345 output_inner_pad_after_size));
348 DSizes<Index, NumDims> output_coord = output_offsets;
349 DSizes<Index, NumDims> output_padded;
350 for (
int i = 0; i < NumDims; ++i) {
351 const int dim = IsColMajor ? i : NumDims - i - 1;
352 output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
355 typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy;
358 const typename TensorBlock::Storage block_storage =
359 TensorBlock::prepareStorage(desc, scratch);
367 const bool squeeze_writes =
370 (input_inner_dim_size == m_dimensions[inner_dim_idx]) &&
372 (input_inner_dim_size == output_inner_dim_size);
374 const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1;
377 const Index squeeze_max_coord =
378 squeeze_writes ? numext::mini(
380 static_cast<Index>(m_dimensions[squeeze_dim] -
381 m_padding[squeeze_dim].second),
383 static_cast<Index>(output_offsets[squeeze_dim] +
384 desc.dimension(squeeze_dim)))
385 : static_cast<
Index>(0);
388 for (Index size = 0; size < output_size;) {
390 bool is_padded =
false;
391 for (
int j = 1; j < NumDims; ++j) {
392 const int dim = IsColMajor ? j : NumDims - j - 1;
393 is_padded = output_padded[dim];
394 if (is_padded)
break;
399 size += output_inner_dim_size;
401 LinCopy::template Run<LinCopy::Kind::FillLinear>(
402 typename LinCopy::Dst(output_offset, 1, block_storage.data()),
403 typename LinCopy::Src(0, 0, &m_paddingValue),
404 output_inner_dim_size);
407 }
else if (squeeze_writes) {
409 const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim];
410 size += output_inner_dim_size * squeeze_num;
413 LinCopy::template Run<LinCopy::Kind::Linear>(
414 typename LinCopy::Dst(output_offset, 1, block_storage.data()),
415 typename LinCopy::Src(input_offset, 1, m_impl.data()),
416 output_inner_dim_size * squeeze_num);
422 it[0].count += (squeeze_num - 1);
423 input_offset += it[0].input_stride * (squeeze_num - 1);
424 output_offset += it[0].output_stride * (squeeze_num - 1);
425 output_coord[squeeze_dim] += (squeeze_num - 1);
429 size += output_inner_dim_size;
432 const Index out = output_offset;
434 LinCopy::template Run<LinCopy::Kind::FillLinear>(
435 typename LinCopy::Dst(out, 1, block_storage.data()),
436 typename LinCopy::Src(0, 0, &m_paddingValue),
437 output_inner_pad_before_size);
441 const Index out = output_offset + output_inner_pad_before_size;
442 const Index in = input_offset + output_inner_pad_before_size;
444 eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
446 LinCopy::template Run<LinCopy::Kind::Linear>(
447 typename LinCopy::Dst(out, 1, block_storage.data()),
448 typename LinCopy::Src(in, 1, m_impl.data()),
449 output_inner_copy_size);
453 const Index out = output_offset + output_inner_pad_before_size +
454 output_inner_copy_size;
456 LinCopy::template Run<LinCopy::Kind::FillLinear>(
457 typename LinCopy::Dst(out, 1, block_storage.data()),
458 typename LinCopy::Src(0, 0, &m_paddingValue),
459 output_inner_pad_after_size);
463 for (
int j = 0; j < NumDims - 1; ++j) {
464 const int dim = IsColMajor ? j + 1 : NumDims - j - 2;
466 if (++it[j].count < it[j].size) {
467 input_offset += it[j].input_stride;
468 output_offset += it[j].output_stride;
469 output_coord[dim] += 1;
470 output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
474 input_offset -= it[j].input_span;
475 output_offset -= it[j].output_span;
476 output_coord[dim] -= it[j].size - 1;
477 output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
481 return block_storage.AsTensorMaterializedBlock();
484 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data()
const {
return NULL; }
488 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void bind(cl::sycl::handler &cgh)
const {
494 struct BlockIteratorState {
511 EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool isPaddingAtIndexForDim(
512 Index index,
int dim_index)
const {
513#if defined(EIGEN_HAS_INDEX_LIST)
514 return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) &&
515 index < m_padding[dim_index].first) ||
516 (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) &&
517 index >= m_dimensions[dim_index] - m_padding[dim_index].second);
519 return (index < m_padding[dim_index].first) ||
520 (index >= m_dimensions[dim_index] - m_padding[dim_index].second);
524 EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool isLeftPaddingCompileTimeZero(
525 int dim_index)
const {
526#if defined(EIGEN_HAS_INDEX_LIST)
527 return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0);
529 EIGEN_UNUSED_VARIABLE(dim_index);
534 EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
bool isRightPaddingCompileTimeZero(
535 int dim_index)
const {
536#if defined(EIGEN_HAS_INDEX_LIST)
537 return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0);
539 EIGEN_UNUSED_VARIABLE(dim_index);
545 void updateCostPerDimension(TensorOpCost& cost,
int i,
bool first)
const {
546 const double in =
static_cast<double>(m_impl.dimensions()[i]);
547 const double out = in + m_padding[i].first + m_padding[i].second;
550 const double reduction = in / out;
553 cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
554 reduction * (1 * TensorOpCost::AddCost<Index>()));
556 cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
557 2 * TensorOpCost::MulCost<Index>() +
558 reduction * (2 * TensorOpCost::MulCost<Index>() +
559 1 * TensorOpCost::DivCost<Index>()));
565 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index)
const
567 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
568 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
570 const Index initialIndex = index;
571 Index inputIndex = 0;
573 for (
int i = NumDims - 1; i > 0; --i) {
574 const Index firstIdx = index;
575 const Index lastIdx = index + PacketSize - 1;
576 const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
577 const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
578 const Index lastPaddedRight = m_outputStrides[i+1];
580 if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
582 return internal::pset1<PacketReturnType>(m_paddingValue);
584 else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
586 return internal::pset1<PacketReturnType>(m_paddingValue);
588 else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
590 const Index idx = index / m_outputStrides[i];
591 inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
592 index -= idx * m_outputStrides[i];
596 return packetWithPossibleZero(initialIndex);
600 const Index lastIdx = index + PacketSize - 1;
601 const Index firstIdx = index;
602 const Index lastPaddedLeft = m_padding[0].first;
603 const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
604 const Index lastPaddedRight = m_outputStrides[1];
606 if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft) {
608 return internal::pset1<PacketReturnType>(m_paddingValue);
610 else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
612 return internal::pset1<PacketReturnType>(m_paddingValue);
614 else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
616 inputIndex += (index - m_padding[0].first);
617 return m_impl.template packet<Unaligned>(inputIndex);
620 return packetWithPossibleZero(initialIndex);
623 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index)
const
625 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
626 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
628 const Index initialIndex = index;
629 Index inputIndex = 0;
631 for (
int i = 0; i < NumDims - 1; ++i) {
632 const Index firstIdx = index;
633 const Index lastIdx = index + PacketSize - 1;
634 const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1];
635 const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1];
636 const Index lastPaddedRight = m_outputStrides[i];
638 if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
640 return internal::pset1<PacketReturnType>(m_paddingValue);
642 else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
644 return internal::pset1<PacketReturnType>(m_paddingValue);
646 else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
648 const Index idx = index / m_outputStrides[i+1];
649 inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
650 index -= idx * m_outputStrides[i+1];
654 return packetWithPossibleZero(initialIndex);
658 const Index lastIdx = index + PacketSize - 1;
659 const Index firstIdx = index;
660 const Index lastPaddedLeft = m_padding[NumDims-1].first;
661 const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second);
662 const Index lastPaddedRight = m_outputStrides[NumDims-1];
664 if (!isLeftPaddingCompileTimeZero(NumDims-1) && lastIdx < lastPaddedLeft) {
666 return internal::pset1<PacketReturnType>(m_paddingValue);
668 else if (!isRightPaddingCompileTimeZero(NumDims-1) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
670 return internal::pset1<PacketReturnType>(m_paddingValue);
672 else if ((isLeftPaddingCompileTimeZero(NumDims-1) && isRightPaddingCompileTimeZero(NumDims-1)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
674 inputIndex += (index - m_padding[NumDims-1].first);
675 return m_impl.template packet<Unaligned>(inputIndex);
678 return packetWithPossibleZero(initialIndex);
681 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index)
const
683 EIGEN_ALIGN_MAX
typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
685 for (
int i = 0; i < PacketSize; ++i) {
686 values[i] = coeff(index+i);
688 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
692 Dimensions m_dimensions;
693 array<Index, NumDims+1> m_outputStrides;
694 array<Index, NumDims> m_inputStrides;
695 TensorEvaluator<ArgType, Device> m_impl;
696 PaddingDimensions m_padding;
698 Scalar m_paddingValue;
700 const Device EIGEN_DEVICE_REF m_device;
Namespace containing all symbols from the Eigen library.
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index