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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_SHUFFLING_H
#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class TensorShuffling
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor shuffling class.
*
*
*/
namespace internal {
template <typename Shuffle, typename XprType>
struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType> {
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef std::remove_reference_t<Nested> Nested_;
static constexpr int NumDimensions = XprTraits::NumDimensions;
static constexpr int Layout = XprTraits::Layout;
typedef typename XprTraits::PointerType PointerType;
};
template <typename Shuffle, typename XprType>
struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense> {
typedef const TensorShufflingOp<Shuffle, XprType>& type;
};
template <typename Shuffle, typename XprType>
struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type> {
typedef TensorShufflingOp<Shuffle, XprType> type;
};
} // end namespace internal
template <typename Shuffle, typename XprType>
class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > {
public:
typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base;
typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl)
: m_xpr(expr), m_shuffle(shfl) {}
EIGEN_DEVICE_FUNC const Shuffle& shufflePermutation() const { return m_shuffle; }
EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp)
protected:
typename XprType::Nested m_xpr;
const Shuffle m_shuffle;
};
// Eval as rvalue
template <typename Shuffle, typename ArgType, typename Device>
struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> {
typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self;
typedef TensorShufflingOp<Shuffle, ArgType> XprType;
typedef typename XprType::Index Index;
static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
enum {
IsAligned = false,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
PreferBlockAccess = true,
CoordAccess = false, // to be implemented
RawAccess = false
};
typedef std::remove_const_t<Scalar> ScalarNoConst;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_device(device), m_impl(op.expression(), device) {
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const Shuffle& shuffle = op.shufflePermutation();
m_is_identity = true;
for (int i = 0; i < NumDims; ++i) {
m_shuffle[i] = static_cast<int>(shuffle[i]);
m_dimensions[i] = input_dims[shuffle[i]];
m_inverseShuffle[shuffle[i]] = i;
if (m_is_identity && shuffle[i] != i) {
m_is_identity = false;
}
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_unshuffledInputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_unshuffledInputStrides[i] = m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
m_fastOutputStrides[i] =
internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
}
} else {
m_unshuffledInputStrides[NumDims - 1] = 1;
m_outputStrides[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_unshuffledInputStrides[i] = m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
m_fastOutputStrides[i] =
internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
}
}
for (int i = 0; i < NumDims; ++i) {
m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]];
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
if (m_is_identity) {
return m_impl.coeff(index);
} else {
return m_impl.coeff(srcCoeff(index));
}
}
template <int LoadMode, typename Self, bool ImplPacketAccess>
struct PacketLoader {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType Run(const Self& self, Index index) {
EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
values[i] = self.coeff(index + i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
};
template <int LoadMode, typename Self>
struct PacketLoader<LoadMode, Self, true> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType Run(const Self& self, Index index) {
if (self.m_is_identity) {
return self.m_impl.template packet<LoadMode>(index);
} else {
EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
values[i] = self.coeff(index + i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
};
template <int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
static const int inner_dim = Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
const size_t target_size = m_device.firstLevelCacheSize();
const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
// Shuffled inner dimensions leads to a random memory access, which is not
// captured by default cost model bytes loaded/stored. We add this cost
// explicitly. The number of cycles picked based on the benchmarks.
// TODO(ezhulenev): This number was picked based on a very questionable
// benchmarks, add benchmarks that are representative of real workloads.
using BlockRequirements = internal::TensorBlockResourceRequirements;
if (inner_dim_shuffled) {
return BlockRequirements::uniform<Scalar>(target_size).addCostPerCoeff({0, 0, NumDims * 28});
} else {
return BlockRequirements::skewed<Scalar>(target_size);
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool root_of_expr_ast = false) const {
eigen_assert(m_impl.data() != NULL);
typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
typedef typename TensorBlockIO::Dst TensorBlockIODst;
typedef typename TensorBlockIO::Src TensorBlockIOSrc;
const typename TensorBlock::Storage block_storage =
TensorBlock::prepareStorage(desc, scratch, /*allow_strided_storage=*/root_of_expr_ast);
typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides);
TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset()));
TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(), block_storage.data());
typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle);
TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
return block_storage.AsTensorMaterializedBlock();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
const double compute_cost = m_is_identity
? TensorOpCost::AddCost<Index>()
: NumDims * (2 * TensorOpCost::AddCost<Index>() +
2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize);
}
EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index
GetBlockOutputIndex(Index input_index, const DSizes<Index, NumDims>& input_block_strides,
const DSizes<Index, NumDims>& output_block_strides,
const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const {
Index output_index = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = input_index / fast_input_block_strides[i];
output_index += idx * output_block_strides[m_inverseShuffle[i]];
input_index -= idx * input_block_strides[i];
}
return output_index + input_index * output_block_strides[m_inverseShuffle[0]];
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = input_index / fast_input_block_strides[i];
output_index += idx * output_block_strides[m_inverseShuffle[i]];
input_index -= idx * input_block_strides[i];
}
return output_index + input_index * output_block_strides[m_inverseShuffle[NumDims - 1]];
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_fastOutputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
return inputIndex + index * m_inputStrides[0];
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_fastOutputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
return inputIndex + index * m_inputStrides[NumDims - 1];
}
}
Dimensions m_dimensions;
bool m_is_identity;
array<int, NumDims> m_shuffle;
array<Index, NumDims> m_inverseShuffle; // TODO(ezhulenev): Make it int type.
array<Index, NumDims> m_outputStrides;
array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
array<Index, NumDims> m_inputStrides;
array<Index, NumDims> m_unshuffledInputStrides;
const Device EIGEN_DEVICE_REF m_device;
TensorEvaluator<ArgType, Device> m_impl;
};
// Eval as lvalue
template <typename Shuffle, typename ArgType, typename Device>
struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
: public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> {
typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
typedef TensorShufflingOp<Shuffle, ArgType> XprType;
typedef typename XprType::Index Index;
static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
enum {
IsAligned = false,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess,
PreferBlockAccess = true,
RawAccess = false
};
typedef std::remove_const_t<Scalar> ScalarNoConst;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
return this->m_impl.coeffRef(this->srcCoeff(index));
}
template <int StoreMode>
EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
EIGEN_UNROLL_LOOP
for (int i = 0; i < PacketSize; ++i) {
this->coeffRef(index + i) = values[i];
}
}
template <typename TensorBlock>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
eigen_assert(this->m_impl.data() != NULL);
typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
typedef typename TensorBlockIO::Dst TensorBlockIODst;
typedef typename TensorBlockIO::Src TensorBlockIOSrc;
const Scalar* block_buffer = block.data();
// TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen
// expression with coefficient and packet access as `src`.
void* mem = NULL;
if (block_buffer == NULL) {
mem = this->m_device.allocate(desc.size() * sizeof(Scalar));
ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem);
typedef internal::TensorBlockAssignment<ScalarNoConst, NumDims, typename TensorBlock::XprType, Index>
TensorBlockAssignment;
TensorBlockAssignment::Run(
TensorBlockAssignment::target(desc.dimensions(), internal::strides<Layout>(desc.dimensions()), buf),
block.expr());
block_buffer = buf;
}
// Read from block.
TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()), block_buffer);
// Write to the output buffer.
typename TensorBlockIO::Dimensions output_strides(this->m_unshuffledInputStrides);
typename TensorBlockIO::Dimensions output_dimensions;
for (int i = 0; i < NumDims; ++i) {
output_dimensions[this->m_shuffle[i]] = desc.dimension(i);
}
TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(), this->srcCoeff(desc.offset()));
// Reorder dimensions according to the shuffle.
typename TensorBlockIO::DimensionsMap dst_to_src_dim_map;
for (int i = 0; i < NumDims; ++i) {
dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]);
}
TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
// Deallocate temporary buffer used for the block materialization.
if (mem != NULL) this->m_device.deallocate(mem);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H