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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_CONTRACTION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
namespace Eigen {
/** \class TensorContraction
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor contraction class.
*
*
*/
namespace internal {
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename gebp_traits<typename remove_const<typename LhsXprType::Scalar>::type,
typename remove_const<typename RhsXprType::Scalar>::type>::ResScalar Scalar;
typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
typename traits<RhsXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
// From NumDims below.
static const int NumDimensions = traits<RhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value;
static const int Layout = traits<LhsXprType>::Layout;
enum {
Flags = 0
};
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType>& type;
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >::type>
{
typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType> type;
};
template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename Device_>
struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_>, Device_> > {
typedef Indices_ Indices;
typedef LeftArgType_ LeftArgType;
typedef RightArgType_ RightArgType;
typedef Device_ Device;
// From NumDims below.
static const int NumDimensions = traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value;
};
} // end namespace internal
template<typename Indices, typename LhsXprType, typename RhsXprType>
class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
typedef typename internal::gebp_traits<typename LhsXprType::CoeffReturnType,
typename RhsXprType::CoeffReturnType>::ResScalar CoeffReturnType;
typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested;
typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(
const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims) {}
EIGEN_DEVICE_FUNC
const Indices& indices() const { return m_indices; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return m_lhs_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const Indices m_indices;
};
template<typename Derived>
struct TensorContractionEvaluatorBase
{
typedef typename internal::traits<Derived>::Indices Indices;
typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
typedef typename internal::traits<Derived>::RightArgType RightArgType;
typedef typename internal::traits<Derived>::Device Device;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
enum {
IsAligned = true,
PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1),
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = true
};
// Most of the code is assuming that both input tensors are ColMajor. If the
// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
// If we want to compute A * B = C, where A is LHS and B is RHS, the code
// will pretend B is LHS and A is RHS.
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
static const int LDims =
internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
static const int RDims =
internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
static const int ContractDims = internal::array_size<Indices>::value;
static const int NumDims = LDims + RDims - 2 * ContractDims;
typedef array<Index, ContractDims> contract_t;
typedef array<Index, LDims - ContractDims> left_nocontract_t;
typedef array<Index, RDims - ContractDims> right_nocontract_t;
typedef DSizes<Index, NumDims> Dimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorContractionEvaluatorBase(const XprType& op, const Device& device)
: m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
op.lhsExpression(), op.rhsExpression()), device),
m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
op.rhsExpression(), op.lhsExpression()), device),
m_device(device),
m_result(NULL) {
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
YOU_MADE_A_PROGRAMMING_MISTAKE);
DSizes<Index, LDims> eval_left_dims;
DSizes<Index, RDims> eval_right_dims;
array<IndexPair<Index>, ContractDims> eval_op_indices;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
// For ColMajor, we keep using the existing dimensions
for (int i = 0; i < LDims; i++) {
eval_left_dims[i] = m_leftImpl.dimensions()[i];
}
for (int i = 0; i < RDims; i++) {
eval_right_dims[i] = m_rightImpl.dimensions()[i];
}
// We keep the pairs of contracting indices.
for (int i = 0; i < ContractDims; i++) {
eval_op_indices[i].first = op.indices()[i].first;
eval_op_indices[i].second = op.indices()[i].second;
}
} else {
// For RowMajor, we need to reverse the existing dimensions
for (int i = 0; i < LDims; i++) {
eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];
}
for (int i = 0; i < RDims; i++) {
eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];
}
// We need to flip all the pairs of contracting indices as well as
// reversing the dimensions.
for (int i = 0; i < ContractDims; i++) {
eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second;
eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first;
}
}
// Check for duplicate axes and make sure the first index in eval_op_indices
// is increasing. Using O(n^2) sorting is OK since ContractDims is small
for (int i = 0; i < ContractDims; i++) {
for (int j = i + 1; j < ContractDims; j++) {
eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first &&
eval_op_indices[j].second != eval_op_indices[i].second &&
"contraction axes should be unique");
if (eval_op_indices[j].first < eval_op_indices[i].first) {
numext::swap(eval_op_indices[j], eval_op_indices[i]);
}
}
}
array<Index, LDims> lhs_strides;
lhs_strides[0] = 1;
for (int i = 0; i < LDims-1; ++i) {
lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i];
}
array<Index, RDims> rhs_strides;
rhs_strides[0] = 1;
for (int i = 0; i < RDims-1; ++i) {
rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i];
}
if (m_i_strides.size() > 0) m_i_strides[0] = 1;
if (m_j_strides.size() > 0) m_j_strides[0] = 1;
if (m_k_strides.size() > 0) m_k_strides[0] = 1;
m_i_size = 1;
m_j_size = 1;
m_k_size = 1;
// To compute the dimension, we simply concatenate the non-contracting
// dimensions of the left and then the right tensor. Additionally, we also
// compute the strides corresponding to the left non-contracting
// dimensions and right non-contracting dimensions.
m_lhs_inner_dim_contiguous = true;
int dim_idx = 0;
unsigned int nocontract_idx = 0;
for (int i = 0; i < LDims; i++) {
// find if we are contracting on index i of left tensor
bool contracting = false;
for (int j = 0; j < ContractDims; j++) {
if (eval_op_indices[j].first == i) {
contracting = true;
break;
}
}
if (!contracting) {
// add dimension size to output dimensions
m_dimensions[dim_idx] = eval_left_dims[i];
m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];
if (dim_idx != i) {
m_lhs_inner_dim_contiguous = false;
}
if (nocontract_idx+1 < internal::array_size<left_nocontract_t>::value) {
m_i_strides[nocontract_idx+1] =
m_i_strides[nocontract_idx] * eval_left_dims[i];
} else {
m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];
}
dim_idx++;
nocontract_idx++;
}
}
nocontract_idx = 0;
for (int i = 0; i < RDims; i++) {
bool contracting = false;
// find if we are contracting on index i of right tensor
for (int j = 0; j < ContractDims; j++) {
if (eval_op_indices[j].second == i) {
contracting = true;
break;
}
}
if (!contracting) {
m_dimensions[dim_idx] = eval_right_dims[i];
if (nocontract_idx+1 < internal::array_size<right_nocontract_t>::value) {
m_j_strides[nocontract_idx+1] =
m_j_strides[nocontract_idx] * eval_right_dims[i];
} else {
m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];
}
m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];
dim_idx++;
nocontract_idx++;
}
}
// Now compute the strides corresponding to the contracting dimensions. We
// assumed above that non-contracting axes are represented in the same order
// in the matrix as they are in the tensor. This is not the case for
// contracting axes. As the contracting axes must be of the same size in
// each tensor, we'll only look at the first tensor here.
m_rhs_inner_dim_contiguous = true;
m_rhs_inner_dim_reordered = false;
for (int i = 0; i < ContractDims; i++) {
Index left = eval_op_indices[i].first;
Index right = eval_op_indices[i].second;
Index size = eval_left_dims[left];
eigen_assert(size == eval_right_dims[right] &&
"Contraction axes must be same size");
if (i+1 < static_cast<int>(internal::array_size<contract_t>::value)) {
m_k_strides[i+1] = m_k_strides[i] * size;
} else {
m_k_size = m_k_strides[i] * size;
}
m_left_contracting_strides[i] = lhs_strides[left];
m_right_contracting_strides[i] = rhs_strides[right];
if (i > 0 && right < eval_op_indices[i-1].second) {
m_rhs_inner_dim_reordered = true;
}
if (right != i) {
m_rhs_inner_dim_contiguous = false;
}
}
// If the layout is RowMajor, we need to reverse the m_dimensions
if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) {
for (int i = 0, j = NumDims - 1; i < j; i++, j--) {
numext::swap(m_dimensions[i], m_dimensions[j]);
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
if (data) {
evalTo(data);
return false;
} else {
m_result = static_cast<Scalar *>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
evalTo(m_result);
return true;
}
}
EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
if (this->m_lhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<true, true, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<true, true, false, Unaligned>(buffer);
}
}
else {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<true, false, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<true, false, false, Unaligned>(buffer);
}
}
}
else {
if (this->m_rhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<false, true, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<false, true, false, Unaligned>(buffer);
}
}
else {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<false, false, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<false, false, false, Unaligned>(buffer);
}
}
}
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
EIGEN_DEVICE_FUNC void evalGemv(Scalar* buffer) const {
const Index rows = m_i_size;
const Index cols = m_k_size;
typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
const int lhs_alignment = LeftEvaluator::IsAligned ? Aligned : Unaligned;
const int rhs_alignment = RightEvaluator::IsAligned ? Aligned : Unaligned;
typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
LeftEvaluator, left_nocontract_t,
contract_t, lhs_packet_size,
lhs_inner_dim_contiguous,
false, lhs_alignment> LhsMapper;
typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
RightEvaluator, right_nocontract_t,
contract_t, rhs_packet_size,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, rhs_alignment> RhsMapper;
LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides,
m_left_contracting_strides, m_k_strides);
RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides,
m_right_contracting_strides, m_k_strides);
const Scalar alpha(1);
const Index resIncr(1);
// zero out the result buffer (which must be of size at least rows * sizeof(Scalar)
m_device.memset(buffer, 0, rows * sizeof(Scalar));
internal::general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false>::run(
rows, cols, lhs, rhs,
buffer, resIncr, alpha);
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
EIGEN_DEVICE_FUNC void evalGemm(Scalar* buffer) const {
// columns in left side, rows in right side
const Index k = this->m_k_size;
// rows in left side
const Index m = this->m_i_size;
// columns in right side
const Index n = this->m_j_size;
// zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
// define mr, nr, and all of my data mapper types
typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
const Index nr = Traits::nr;
const Index mr = Traits::mr;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
LeftEvaluator, left_nocontract_t,
contract_t, lhs_packet_size,
lhs_inner_dim_contiguous,
false, Unaligned> LhsMapper;
typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
RightEvaluator, right_nocontract_t,
contract_t, rhs_packet_size,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Unaligned> RhsMapper;
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
// Declare GEBP packing and kernel structs
internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, mr, Traits::LhsProgress, ColMajor> pack_lhs;
internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, nr, ColMajor> pack_rhs;
internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper, mr, nr, false, false> gebp;
// initialize data mappers
LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
this->m_left_contracting_strides, this->m_k_strides);
RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
this->m_right_contracting_strides, this->m_k_strides);
OutputMapper output(buffer, m);
// Sizes of the blocks to load in cache. See the Goto paper for details.
internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, 1);
const Index kc = blocking.kc();
const Index mc = numext::mini(m, blocking.mc());
const Index nc = numext::mini(n, blocking.nc());
const Index sizeA = mc * kc;
const Index sizeB = kc * nc;
LhsScalar* blockA = static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar)));
RhsScalar* blockB = static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar)));
for(Index i2=0; i2<m; i2+=mc)
{
const Index actual_mc = numext::mini(i2+mc,m)-i2;
for (Index k2 = 0; k2 < k; k2 += kc) {
// make sure we don't overshoot right edge of left matrix, then pack vertical panel
const Index actual_kc = numext::mini(k2 + kc, k) - k2;
pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc, 0, 0);
// series of horizontal blocks
for (Index j2 = 0; j2 < n; j2 += nc) {
// make sure we don't overshoot right edge of right matrix, then pack block
const Index actual_nc = numext::mini(j2 + nc, n) - j2;
pack_rhs(blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc, 0, 0);
// call gebp (matrix kernel)
// The parameters here are copied from Eigen's GEMM implementation
gebp(output.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, Scalar(1), -1, -1, 0, 0);
}
}
}
this->m_device.deallocate(blockA);
this->m_device.deallocate(blockB);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
if (m_result != NULL) {
m_device.deallocate(m_result);
m_result = NULL;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
return m_result[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { return m_result; }
protected:
// Prevent assignment
TensorContractionEvaluatorBase& operator = (const TensorContractionEvaluatorBase&);
Dimensions m_dimensions;
contract_t m_k_strides;
contract_t m_left_contracting_strides;
contract_t m_right_contracting_strides;
bool m_lhs_inner_dim_contiguous;
bool m_rhs_inner_dim_contiguous;
bool m_rhs_inner_dim_reordered;
left_nocontract_t m_i_strides;
right_nocontract_t m_j_strides;
left_nocontract_t m_left_nocontract_strides;
right_nocontract_t m_right_nocontract_strides;
Index m_i_size;
Index m_j_size;
Index m_k_size;
TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
const Device& m_device;
Scalar* m_result;
};
// evaluator for default device
template<typename Indices, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> :
public TensorContractionEvaluatorBase<
TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> > {
typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
typedef TensorContractionEvaluatorBase<Self> Base;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
enum {
Layout = TensorEvaluator<LeftArgType, Device>::Layout
};
// Most of the code is assuming that both input tensors are ColMajor. If the
// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
// If we want to compute A * B = C, where A is LHS and B is RHS, the code
// will pretend B is LHS and A is RHS.
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
static const int LDims =
internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
static const int RDims =
internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
static const int ContractDims = internal::array_size<Indices>::value;
typedef array<Index, ContractDims> contract_t;
typedef array<Index, LDims - ContractDims> left_nocontract_t;
typedef array<Index, RDims - ContractDims> right_nocontract_t;
static const int NumDims = LDims + RDims - 2 * ContractDims;
// Could we use NumDimensions here?
typedef DSizes<Index, NumDims> Dimensions;
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
Base(op, device) { }
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
EIGEN_DEVICE_FUNC void evalProduct(Scalar* buffer) const {
if (this->m_j_size == 1) {
this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
return;
}
this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H