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eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h

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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, typename OutputKernelType>
struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >
{
// 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<LhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value;
static const int Layout = traits<LhsXprType>::Layout;
typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
typename traits<LhsXprType>::PointerType,
typename traits<RhsXprType>::PointerType>::type
PointerType;
enum {
Flags = 0
};
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, Eigen::Dense>
{
typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>& type;
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >::type>
{
typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> type;
};
template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename OutputKernelType_, typename Device_>
struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_, OutputKernelType_>, Device_> > {
typedef Indices_ Indices;
typedef LeftArgType_ LeftArgType;
typedef RightArgType_ RightArgType;
typedef OutputKernelType_ OutputKernelType;
typedef Device_ Device;
// From NumDims below.
static const int NumDimensions = traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value;
};
// Helper class to allocate and deallocate temporary memory for packed buffers.
template <typename LhsScalar, typename RhsScalar>
struct TensorContractionBlockMemAllocator {
typedef void* BlockMemHandle;
template <typename Device>
EIGEN_DEVICE_FUNC static BlockMemHandle allocate(Device& d, const Index bm,
const Index bk,
const Index bn,
LhsScalar** lhs_block,
RhsScalar** rhs_block) {
eigen_assert(lhs_block);
eigen_assert(rhs_block);
BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
char* block_mem = static_cast<char*>(d.allocate(sz.lhs_size + sz.rhs_size));
eigen_assert(block_mem);
*lhs_block = reinterpret_cast<LhsScalar*>(block_mem);
*rhs_block = reinterpret_cast<RhsScalar*>(block_mem + sz.lhs_size);
return block_mem;
}
template <typename Device>
EIGEN_DEVICE_FUNC static BlockMemHandle allocateSlices(
Device& d, const Index bm, const Index bk, const Index bn,
const Index num_lhs, const Index num_rhs, const Index num_slices,
std::vector<LhsScalar*>* lhs_blocks,
std::vector<RhsScalar*>* rhs_blocks) {
eigen_assert(num_slices > 0);
eigen_assert(num_lhs >= 0 && num_rhs >= 0);
eigen_assert(num_lhs == 0 || lhs_blocks);
eigen_assert(num_rhs == 0 || rhs_blocks);
BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
void* block_mem = d.allocate(
(num_lhs * sz.lhs_size + num_rhs * sz.rhs_size) * num_slices);
eigen_assert(block_mem);
char* mem = static_cast<char*>(block_mem);
for (Index x = 0; x < num_slices; x++) {
if (num_lhs > 0) lhs_blocks[x].resize(num_lhs);
for (Index m = 0; m < num_lhs; m++) {
lhs_blocks[x][m] = reinterpret_cast<LhsScalar*>(mem);
mem += sz.lhs_size;
}
if (num_rhs > 0) rhs_blocks[x].resize(num_rhs);
for (Index n = 0; n < num_rhs; n++) {
rhs_blocks[x][n] = reinterpret_cast<RhsScalar*>(mem);
mem += sz.rhs_size;
}
}
return block_mem;
}
template <typename Device>
EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
d.deallocate(handle);
}
private:
struct BlockSizes {
Index lhs_size;
Index rhs_size;
};
EIGEN_DEVICE_FUNC static BlockSizes ComputeLhsRhsBlockSizes(const Index bm,
const Index bk,
const Index bn) {
Index align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
BlockSizes sz;
sz.lhs_size = divup<Index>(bm * bk * sizeof(LhsScalar), align) * align;
sz.rhs_size = divup<Index>(bn * bk * sizeof(RhsScalar), align) * align;
return sz;
}
};
// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in
// ColMajor storage order. This property is guaranteed by the
// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack
// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix
// multiplication for these blocks. Default tensor contraction uses
// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see
// GeneralBlocPanelKernel.h for details).
//
// By specializing contraction kernels we can use other low level libraries to
// perform matrix multiplication, and still rely on Eigen contraction evaluator.
// This also includes full support in TensorContractionThreadPool, assuming that
// underlying gemm do not use it's own threading.
//
// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of
// multiplication, lhs tensor and rhs tensor respectively.
//
// - StorageIndex - index type for the tensor expressions. In practice almost
// always is Eigen::Index.
//
// - OutputMapper provides access to the memory of the output matrix. In
// practice it's always column major blas_data_mapper (it must be of ResScalar
// type).
//
// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional
// view into the Lhs/Rhs tensor expressions. In practice it's
// TensorContractionInputMapper, or some specialization of it based on the
// type of tensor expression (e.g. TensorImagePatchOp has optimized input
// mapper).
template <typename ResScalar, typename LhsScalar, typename RhsScalar,
typename StorageIndex, typename OutputMapper, typename LhsMapper,
typename RhsMapper>
struct TensorContractionKernel {
EIGEN_DEVICE_FUNC
TensorContractionKernel(StorageIndex m_, StorageIndex k_, StorageIndex n_,
StorageIndex bm_, StorageIndex bk_, StorageIndex bn_)
: m(m_), k(k_), n(n_), bm(bm_), bk(bk_), bn(bn_) {}
// Pack blocks of Lhs and Rhs into contiguous blocks in memory.
typedef LhsScalar* LhsBlock;
typedef RhsScalar* RhsBlock;
// Packed Lhs/Rhs block memory allocator.
typedef TensorContractionBlockMemAllocator<LhsScalar, RhsScalar>
BlockMemAllocator;
typedef typename BlockMemAllocator::BlockMemHandle BlockMemHandle;
typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
typedef internal::gemm_pack_lhs<
LhsScalar, StorageIndex, typename LhsMapper::SubMapper, Traits::mr,
Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor>
LhsPacker;
typedef internal::gemm_pack_rhs<RhsScalar, StorageIndex,
typename RhsMapper::SubMapper, Traits::nr,
ColMajor>
RhsPacker;
typedef internal::gebp_kernel<LhsScalar, RhsScalar, StorageIndex,
OutputMapper, Traits::mr, Traits::nr,
/*ConjugateLhs*/ false, /*ConjugateRhs*/ false>
GebpKernel;
template <typename Device>
EIGEN_DEVICE_FUNC BlockMemHandle allocate(Device& d, LhsBlock* lhs_block,
RhsBlock* rhs_block) {
return BlockMemAllocator::allocate(d, bm, bk, bn, lhs_block, rhs_block);
}
template <typename Device>
EIGEN_DEVICE_FUNC BlockMemHandle allocateSlices(
Device& d, const StorageIndex num_lhs, const StorageIndex num_rhs,
const StorageIndex num_slices, std::vector<LhsBlock>* lhs_blocks,
std::vector<RhsBlock>* rhs_blocks) {
return BlockMemAllocator::allocateSlices(
d, bm, bk, bn, num_lhs, num_rhs, num_slices, lhs_blocks, rhs_blocks);
}
template <typename Device>
EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
BlockMemAllocator::deallocate(d, handle);
}
EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packLhs(
LhsBlock* lhsBlock, const typename LhsMapper::SubMapper& data_mapper,
const StorageIndex depth, const StorageIndex rows) {
LhsPacker()(*lhsBlock, data_mapper, depth, rows, /*stride*/ 0,
/*offset*/ 0);
}
EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packRhs(
RhsBlock* rhsBlock, const typename RhsMapper::SubMapper& data_mapper,
const StorageIndex depth, const StorageIndex cols) {
RhsPacker()(*rhsBlock, data_mapper, depth, cols);
}
EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void invoke(
const OutputMapper& output_mapper, const LhsBlock& lhsBlock,
const RhsBlock& rhsBlock, const StorageIndex rows,
const StorageIndex depth, const StorageIndex cols,
const ResScalar alpha) {
static const int kComputeStrideFromBlockDimensions = -1;
GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
/*strideA*/ kComputeStrideFromBlockDimensions,
/*strideB*/ kComputeStrideFromBlockDimensions,
/*offsetA*/ 0, /*offsetB*/ 0);
}
private:
// These are dimensions of the original Tensors, and selected block sizes. The
// actual block sizes passed to all function above might be smaller because of
// the partial blocks at the end.
const StorageIndex m;
const StorageIndex k;
const StorageIndex n;
const StorageIndex bm;
const StorageIndex bk;
const StorageIndex bn;
};
} // end namespace internal
// Tensor contraction params that should enable to get from output matrix
// 2-dimensional coordinates to the output tensor dimensions.
struct TensorContractionParams {
// TensorContraction evaluator assumes that both tensors are in ColMajor
// layout, if tensors are in RowMajor evaluator swap lhs with rhs.
bool swapped_arguments;
};
// Output kernel allows to fuse operations into the tensor contraction.
//
// Examples:
// 1. Elementwise Relu transformation following Conv2D.
// 2. AddBias to the Conv2D output channels dimension.
//
// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
struct NoOpOutputKernel {
/**
* Tensor contraction evaluator calls this kernel after finishing each block
* of output matrix. Output blocks belong to the 2-dimensional output tensor.
*
* TensorContractionParams contains contraction dimensions information
* required to map output 2-d space into the expected output tensor space
* (potentially higher dimensional).
*
* \param[in] output_mapper Access to output tensor memory
* \param[in] params Tensor contraction parameters
* \param[in] i Index of a first row available through output_mapper
* \param[in] j Index of a first column available through output_mapper
* \param[in] num_rows Number of available rows
* \param[in] num_cols Number of available columns
*/
template <typename Index, typename Scalar>
EIGEN_ALWAYS_INLINE void operator()(
const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
const TensorContractionParams& params, Index i,
Index j, Index num_rows, Index num_cols) const {
EIGEN_UNUSED_VARIABLE(output_mapper);
EIGEN_UNUSED_VARIABLE(params);
EIGEN_UNUSED_VARIABLE(i);
EIGEN_UNUSED_VARIABLE(j);
EIGEN_UNUSED_VARIABLE(num_rows);
EIGEN_UNUSED_VARIABLE(num_cols);
}
};
template<typename Indices, typename LhsXprType, typename RhsXprType, typename OutputKernelType = const NoOpOutputKernel>
class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType, OutputKernelType>, 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,
const OutputKernelType& output_kernel = OutputKernelType())
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims),
m_output_kernel(output_kernel) {}
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; }
EIGEN_DEVICE_FUNC
const OutputKernelType& outputKernel() const { return m_output_kernel; }
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const Indices m_indices;
const OutputKernelType m_output_kernel;
};
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>::OutputKernelType OutputKernelType;
typedef typename internal::traits<Derived>::Device Device;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> 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;
typedef StorageMemory<Scalar, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
enum {
IsAligned = true,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = false,
BlockAccessV2 = false,
PreferBlockAccess = false,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = true
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlockV2;
//===--------------------------------------------------------------------===//
// 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;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluatorType;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluatorType;
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_output_kernel(op.outputKernel()),
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;
Index 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]);
}
}
// A set of parameters that will allow output kernel to get from output
// tensor dimensions (i, j) into the original tensor dimensions.
// TODO(ezhulenev): Add parameters required to infer output tensor index for
// more complex contractions than 2x2 on internal dimension.
m_tensor_contraction_params.swapped_arguments = static_cast<int>(Layout) == RowMajor;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
if (data) {
evalTo(data);
return false;
} else {
m_result = static_cast<EvaluatorPointerType>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
evalTo(m_result);
return true;
}
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType dest, EvalSubExprsCallback done) {
m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
if (dest) {
evalToAsync(dest, [done]() { done(false); });
} else {
m_result = static_cast<EvaluatorPointerType>(
m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
evalToAsync(m_result, [done]() { done(true); });
}
});
});
}
#endif // EIGEN_USE_THREADS
#define TENSOR_CONTRACTION_DISPATCH(METHOD, ALIGNMENT, ARGS) \
if (this->m_lhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_reordered) { \
METHOD<true, true, true, ALIGNMENT> ARGS; \
} else { \
METHOD<true, true, false, ALIGNMENT> ARGS; \
} \
} else { \
if (this->m_rhs_inner_dim_reordered) { \
METHOD<true, false, true, ALIGNMENT> ARGS; \
} else { \
METHOD<true, false, false, ALIGNMENT> ARGS; \
} \
} \
} else { \
if (this->m_rhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_reordered) { \
METHOD<false, true, true, ALIGNMENT> ARGS; \
} else { \
METHOD<false, true, false, ALIGNMENT> ARGS; \
} \
} else { \
if (this->m_rhs_inner_dim_reordered) { \
METHOD<false, false, true, ALIGNMENT> ARGS; \
} else { \
METHOD<false, false, false, ALIGNMENT> ARGS; \
} \
} \
}
#define TENSOR_CONTRACTION_ASYNC_DISPATCH(METHOD, DONE, ALIGNMENT, ARGS, FN) \
if (this->m_lhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_reordered) { \
(new METHOD<DONE, true, true, true, ALIGNMENT> ARGS)->FN; \
} else { \
(new METHOD<DONE, true, true, false, ALIGNMENT> ARGS)->FN; \
} \
} else { \
if (this->m_rhs_inner_dim_reordered) { \
(new METHOD<DONE, true, false, true, ALIGNMENT> ARGS)->FN; \
} else { \
(new METHOD<DONE, true, false, false, ALIGNMENT> ARGS)->FN; \
} \
} \
} else { \
if (this->m_rhs_inner_dim_contiguous) { \
if (this->m_rhs_inner_dim_reordered) { \
(new METHOD<DONE, false, true, true, ALIGNMENT> ARGS)->FN; \
} else { \
(new METHOD<DONE, false, true, false, ALIGNMENT> ARGS)->FN; \
} \
} else { \
if (this->m_rhs_inner_dim_reordered) { \
(new METHOD<DONE, false, false, true, ALIGNMENT> ARGS)->FN; \
} else { \
(new METHOD<DONE, false, false, false, ALIGNMENT> ARGS)->FN; \
} \
} \
}
EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
static_cast<const Derived*>(this)->template evalProduct<Unaligned>(buffer);
}
#ifdef EIGEN_USE_THREADS
template <typename EvalToCallback>
void evalToAsync(Scalar* buffer, EvalToCallback done) const {
static_cast<const Derived*>(this)
->template evalProductAsync<EvalToCallback, Unaligned>(buffer,
std::move(done));
}
#endif // EIGEN_USE_THREADS
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
bool rhs_inner_dim_reordered, int Alignment>
void evalProductSequential(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);
} else {
this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Alignment>(buffer);
}
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
#if !defined(EIGEN_HIPCC)
EIGEN_DEVICE_FUNC
#endif
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);
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
m_output_kernel(OutputMapper(buffer, rows), m_tensor_contraction_params,
static_cast<Index>(0), static_cast<Index>(0), rows,
static_cast<Index>(1));
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
#if !defined(EIGEN_HIPCC)
EIGEN_DEVICE_FUNC
#endif
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));
this->template evalGemmPartial<lhs_inner_dim_contiguous,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered,
Alignment, true>(buffer, 0, k, 1);
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
bool rhs_inner_dim_reordered, int Alignment>
EIGEN_DEVICE_FUNC void evalGemmPartialWithoutOutputKernel(
Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Alignment,
/*use_output_kernel*/ false>(buffer, k_start, k_end,
num_threads);
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment, bool use_output_kernel>
EIGEN_DEVICE_FUNC void evalGemmPartial(Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
eigen_assert(k_end >= k_start && k_start >= 0 && k_end <= this->m_k_size);
// columns in slice on left side, rows on right side
const Index k_slice = k_end - k_start;
// rows in left side
const Index m = this->m_i_size;
// columns in right side
const Index n = this->m_j_size;
// define data mappers for Lhs and Rhs
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;
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;
typedef internal::TensorContractionKernel<
Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
TensorContractionKernel;
// 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<Scalar, LhsScalar, RhsScalar,
Index, internal::ShardByCol>
blocking(k_slice, m, n, num_threads);
const Index kc = blocking.kc();
const Index mc = numext::mini(m, blocking.mc());
const Index nc = numext::mini(n, blocking.nc());
typedef typename TensorContractionKernel::LhsBlock LhsBlock;
typedef typename TensorContractionKernel::RhsBlock RhsBlock;
LhsBlock blockA;
RhsBlock blockB;
TensorContractionKernel kernel(m, k_slice, n, mc, kc, nc);
typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
const BlockMemHandle packed_mem =
kernel.allocate(this->m_device, &blockA, &blockB);
for(Index i2=0; i2<m; i2+=mc)
{
const Index actual_mc = numext::mini(i2+mc,m)-i2;
for (Index k2 = k_start; k2 < k_end; 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_end) - k2;
kernel.packLhs(&blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
// 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;
kernel.packRhs(&blockB, rhs.getSubMapper(k2, j2), actual_kc,
actual_nc);
// call gebp (matrix kernel)
// The parameters here are copied from Eigen's GEMM implementation
const OutputMapper output_mapper = output.getSubMapper(i2, j2);
kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,
actual_nc, Scalar(1));
// We are done with this [i2, j2] output block.
if (use_output_kernel && k2 + kc >= k_end) {
m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2,
actual_mc, actual_nc);
}
}
}
}
kernel.deallocate(this->m_device, packed_mem);
}
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 EvaluatorPointerType 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;
TensorContractionParams m_tensor_contraction_params;
TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
const Device EIGEN_DEVICE_REF m_device;
OutputKernelType m_output_kernel;
EvaluatorPointerType m_result;
};
// evaluator for default device
template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType, typename Device>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> :
public TensorContractionEvaluatorBase<
TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> > {
typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
typedef TensorContractionEvaluatorBase<Self> Base;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> 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 <int Alignment>
void evalProduct(Scalar* buffer) const {
TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential, Alignment, (buffer));
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
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