| Current File : //usr/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h |
// 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_BROADCASTING_H
#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
namespace Eigen {
/** \class TensorBroadcasting
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor broadcasting class.
*
*
*/
namespace internal {
template<typename Broadcast, typename XprType>
struct traits<TensorBroadcastingOp<Broadcast, 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 typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename Broadcast, typename XprType>
struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
{
typedef const TensorBroadcastingOp<Broadcast, XprType>& type;
};
template<typename Broadcast, typename XprType>
struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
{
typedef TensorBroadcastingOp<Broadcast, XprType> type;
};
template <typename Dims>
struct is_input_scalar {
static const bool value = false;
};
template <>
struct is_input_scalar<Sizes<> > {
static const bool value = true;
};
#ifndef EIGEN_EMULATE_CXX11_META_H
template <typename std::size_t... Indices>
struct is_input_scalar<Sizes<Indices...> > {
static const bool value = (Sizes<Indices...>::total_size == 1);
};
#endif
} // end namespace internal
template<typename Broadcast, typename XprType>
class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
: m_xpr(expr), m_broadcast(broadcast) {}
EIGEN_DEVICE_FUNC
const Broadcast& broadcast() const { return m_broadcast; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const Broadcast m_broadcast;
};
// Eval as rvalue
template<typename Broadcast, typename ArgType, typename Device>
struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
{
typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = true,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_broadcast(op.broadcast()),m_impl(op.expression(), device)
{
// The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
// and store the result in a scalar. Instead one should reshape the scalar into a a N-D
// tensor with N >= 1 of 1 element first and then broadcast.
EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
const InputDimensions& input_dims = m_impl.dimensions();
const Broadcast& broadcast = op.broadcast();
for (int i = 0; i < NumDims; ++i) {
eigen_assert(input_dims[i] > 0);
m_dimensions[i] = input_dims[i] * broadcast[i];
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
} else {
m_inputStrides[NumDims-1] = 1;
m_outputStrides[NumDims-1] = 1;
for (int i = NumDims-2; i >= 0; --i) {
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
{
if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
return m_impl.coeff(0);
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return coeffColMajor(index);
} else {
return coeffRowMajor(index);
}
}
// TODO: attempt to speed this up. The integer divisions and modulo are slow
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
{
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
if (internal::index_statically_eq<Broadcast>(0, 1)) {
eigen_assert(index < m_impl.dimensions()[0]);
inputIndex += index;
} else {
if (internal::index_statically_eq<InputDimensions>(0, 1)) {
eigen_assert(index % m_impl.dimensions()[0] == 0);
} else {
inputIndex += (index % m_impl.dimensions()[0]);
}
}
return m_impl.coeff(inputIndex);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
{
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
eigen_assert(index < m_impl.dimensions()[NumDims-1]);
inputIndex += index;
} else {
if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
} else {
inputIndex += (index % m_impl.dimensions()[NumDims-1]);
}
}
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
{
if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
return internal::pset1<PacketReturnType>(m_impl.coeff(0));
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return packetColMajor<LoadMode>(index);
} else {
return packetRowMajor<LoadMode>(index);
}
}
// Ignore the LoadMode and always use unaligned loads since we can't guarantee
// the alignment at compile time.
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
{
EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
const Index originalIndex = index;
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
Index innermostLoc;
if (internal::index_statically_eq<Broadcast>(0, 1)) {
eigen_assert(index < m_impl.dimensions()[0]);
innermostLoc = index;
} else {
if (internal::index_statically_eq<InputDimensions>(0, 1)) {
eigen_assert(index % m_impl.dimensions()[0] == 0);
innermostLoc = 0;
} else {
innermostLoc = index % m_impl.dimensions()[0];
}
}
inputIndex += innermostLoc;
// Todo: this could be extended to the second dimension if we're not
// broadcasting alongside the first dimension, and so on.
if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) {
return m_impl.template packet<Unaligned>(inputIndex);
} else {
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < PacketSize; ++i) {
values[i] = coeffColMajor(originalIndex+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
{
EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
const Index originalIndex = index;
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
Index innermostLoc;
if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
eigen_assert(index < m_impl.dimensions()[NumDims-1]);
innermostLoc = index;
} else {
if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
innermostLoc = 0;
} else {
innermostLoc = index % m_impl.dimensions()[NumDims-1];
}
}
inputIndex += innermostLoc;
// Todo: this could be extended to the second dimension if we're not
// broadcasting alongside the first dimension, and so on.
if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) {
return m_impl.template packet<Unaligned>(inputIndex);
} else {
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < PacketSize; ++i) {
values[i] = coeffRowMajor(originalIndex+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
double compute_cost = TensorOpCost::AddCost<Index>();
if (NumDims > 0) {
for (int i = NumDims - 1; i > 0; --i) {
compute_cost += TensorOpCost::DivCost<Index>();
if (internal::index_statically_eq<Broadcast>(i, 1)) {
compute_cost +=
TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
} else {
if (!internal::index_statically_eq<InputDimensions>(i, 1)) {
compute_cost += TensorOpCost::MulCost<Index>() +
TensorOpCost::ModCost<Index>() +
TensorOpCost::AddCost<Index>();
}
}
compute_cost +=
TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
}
}
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
Broadcast functor() const { return m_broadcast; }
protected:
const Broadcast m_broadcast;
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H