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Improved support for RowMajor tensors

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Misc fixes and API cleanups.
This commit is contained in:
Benoit Steiner
2015-01-14 15:38:48 -08:00
parent 6559d09c60
commit f697df7237
17 changed files with 1405 additions and 356 deletions
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208
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
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@@ -21,34 +21,61 @@ namespace Eigen {
*/
namespace internal {
template<std::size_t DimId, typename XprType>
template<DenseIndex DimId, typename XprType>
struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
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 - 1;
static const int Layout = XprTraits::Layout;
};
template<std::size_t DimId, typename XprType>
template<DenseIndex DimId, typename XprType>
struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
{
typedef const TensorChippingOp<DimId, XprType>& type;
};
template<std::size_t DimId, typename XprType>
template<DenseIndex DimId, typename XprType>
struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
{
typedef TensorChippingOp<DimId, XprType> type;
};
template <DenseIndex DimId>
struct DimensionId
{
DimensionId(DenseIndex dim) {
eigen_assert(dim == DimId);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
return DimId;
}
};
template <>
struct DimensionId<Dynamic>
{
DimensionId(DenseIndex dim) : actual_dim(dim) {
eigen_assert(dim >= 0);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
return actual_dim;
}
private:
const DenseIndex actual_dim;
};
} // end namespace internal
template<std::size_t DimId, typename XprType>
template<DenseIndex DimId, typename XprType>
class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
{
public:
@@ -61,34 +88,39 @@ class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset)
: m_xpr(expr), m_offset(offset) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
: m_xpr(expr), m_offset(offset), m_dim(dim) {
}
EIGEN_DEVICE_FUNC
const Index offset() const { return m_offset; }
EIGEN_DEVICE_FUNC
const Index offset() const { return m_offset; }
EIGEN_DEVICE_FUNC
const Index dim() const { return m_dim.actualDim(); }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
Assign assign(*this, other);
static const bool Vectorize = TensorEvaluator<const Assign, DefaultDevice>::PacketAccess;
internal::TensorExecutor<const Assign, DefaultDevice, Vectorize>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const Index m_offset;
const internal::DimensionId<DimId> m_dim;
};
// Eval as rvalue
template<std::size_t DimId, typename ArgType, typename Device>
template<DenseIndex DimId, typename ArgType, typename Device>
struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
typedef TensorChippingOp<DimId, ArgType> XprType;
@@ -96,41 +128,50 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
// Alignment can't be guaranteed at compile time since it depends on the
// slice offsets.
IsAligned = false,
PacketAccess = false, // not yet implemented
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_device(device)
: m_impl(op.expression(), device), m_dim(op.dim()), m_device(device)
{
// We could also support the case where NumInputDims==1 if needed.
EIGEN_STATIC_ASSERT(NumInputDims >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(NumInputDims > DimId, YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(NumInputDims > m_dim.actualDim());
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
int j = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (i != DimId) {
if (i != m_dim.actualDim()) {
m_dimensions[j] = input_dims[i];
++j;
}
}
m_stride = 1;
m_inputStride = 1;
for (int i = 0; i < DimId; ++i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
m_inputStride *= input_dims[DimId];
m_inputOffset = m_stride * op.offset();
m_stride = 1;
m_inputStride = 1;
if (Layout == ColMajor) {
for (int i = 0; i < m_dim.actualDim(); ++i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
} else {
for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
}
m_inputStride *= input_dims[m_dim.actualDim()];
m_inputOffset = m_stride * op.offset();
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
@@ -150,16 +191,52 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
return m_impl.coeff(srcCoeff(index));
}
/* to be done
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
}*/
if ((Layout == ColMajor && m_dim.actualDim() == 0) ||
(Layout == RowMajor && m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
Index inputIndex = index * m_inputStride + m_inputOffset;
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = m_impl.coeff(inputIndex);
inputIndex += m_inputStride;
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
} else if ((Layout == ColMajor && m_dim.actualDim() == NumInputDims - 1) ||
(Layout == RowMajor && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
return m_impl.template packet<LoadMode>(index + m_inputOffset);
} else {
const Index idx = index / m_stride;
const Index rem = index - idx * m_stride;
if (rem + packetSize <= m_stride) {
Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
return m_impl.template packet<LoadMode>(inputIndex);
} else {
// Cross the stride boundary. Fallback to slow path.
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index);
++index;
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const {
Scalar* result = m_impl.data();
if (DimId == NumDims && result) {
if (m_dim.actualDim() == NumDims && result) {
return result + m_inputOffset;
} else {
return NULL;
@@ -170,11 +247,13 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
{
Index inputIndex;
if (DimId == 0) {
if ((Layout == ColMajor && m_dim.actualDim() == 0) ||
(Layout == RowMajor && m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
inputIndex = index * m_inputStride + m_inputOffset;
} else if (DimId == NumInputDims-1) {
} else if ((Layout == ColMajor && m_dim.actualDim() == NumInputDims-1) ||
(Layout == RowMajor && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
inputIndex = index + m_inputOffset;
@@ -192,12 +271,13 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
Index m_inputOffset;
Index m_inputStride;
TensorEvaluator<ArgType, Device> m_impl;
const internal::DimensionId<DimId> m_dim;
const Device& m_device;
};
// Eval as lvalue
template<std::size_t DimId, typename ArgType, typename Device>
template<DenseIndex DimId, typename ArgType, typename Device>
struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
: public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
@@ -207,17 +287,17 @@ struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
@@ -226,11 +306,45 @@ struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
return this->m_impl.coeffRef(this->srcCoeff(index));
}
/* to be done
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
} */
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
if ((this->Layout == ColMajor && this->m_dim.actualDim() == 0) ||
(this->Layout == RowMajor && this->m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(this->m_stride == 1);
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
for (int i = 0; i < packetSize; ++i) {
this->m_impl.coeffRef(inputIndex) = values[i];
inputIndex += this->m_inputStride;
}
} else if ((this->Layout == ColMajor && this->m_dim.actualDim() == NumInputDims-1) ||
(this->Layout == RowMajor && this->m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(this->m_stride > index);
this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
} else {
const Index idx = index / this->m_stride;
const Index rem = index - idx * this->m_stride;
if (rem + packetSize <= this->m_stride) {
const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
this->m_impl.template writePacket<StoreMode>(inputIndex, x);
} else {
// Cross stride boundary. Fallback to slow path.
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
for (int i = 0; i < packetSize; ++i) {
this->coeffRef(index) = values[i];
++index;
}
}
}
}
};