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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
// 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_ARG_MAX_H
#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
namespace Eigen {
namespace internal {
/** \class TensorIndexTuple
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor + Index Tuple class.
*
*
*/
template<typename XprType>
struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
{
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
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 XprType>
struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
{
typedef const TensorIndexTupleOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorIndexTupleOp<XprType>, 1,
typename eval<TensorIndexTupleOp<XprType> >::type>
{
typedef TensorIndexTupleOp<XprType> type;
};
} // end namespace internal
template<typename XprType>
class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
: m_xpr(expr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
};
// Eval as rvalue
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
{
typedef TensorIndexTupleOp<ArgType> XprType;
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
static const int NumDims = internal::array_size<Dimensions>::value;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
BlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
return m_impl.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_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return CoeffReturnType(index, m_impl.coeff(index));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const {
return m_impl;
}
#endif
protected:
TensorEvaluator<ArgType, Device> m_impl;
};
namespace internal {
/** \class TensorTupleIndex
* \ingroup CXX11_Tensor_Module
*
* \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>.
*
*/
template<typename ReduceOp, typename Dims, typename XprType>
struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
{
typedef traits<XprType> XprTraits;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef Index Scalar;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
static const int Layout = XprTraits::Layout;
};
template<typename ReduceOp, typename Dims, typename XprType>
struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
{
typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type;
};
template<typename ReduceOp, typename Dims, typename XprType>
struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
{
typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
};
} // end namespace internal
template<typename ReduceOp, typename Dims, typename XprType>
class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
typedef Index CoeffReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
const ReduceOp& reduce_op,
const Index return_dim,
const Dims& reduce_dims)
: m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
const ReduceOp& reduce_op() const { return m_reduce_op; }
EIGEN_DEVICE_FUNC
const Dims& reduce_dims() const { return m_reduce_dims; }
EIGEN_DEVICE_FUNC
Index return_dim() const { return m_return_dim; }
protected:
typename XprType::Nested m_xpr;
const ReduceOp m_reduce_op;
const Index m_return_dim;
const Dims m_reduce_dims;
};
// Eval as rvalue
template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
{
typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
static const int NumDims = internal::array_size<InputDimensions>::value;
typedef array<Index, NumDims> StrideDims;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
BlockAccess = false,
Layout = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_orig_impl(op.expression(), device),
m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
m_return_dim(op.return_dim())
#ifdef EIGEN_USE_SYCL
,m_device(device)
#endif
{
gen_strides(m_orig_impl.dimensions(), m_strides);
if (Layout == static_cast<int>(ColMajor)) {
const Index total_size = internal::array_prod(m_orig_impl.dimensions());
m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size;
} else {
const Index total_size = internal::array_prod(m_orig_impl.dimensions());
m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size;
}
m_stride_div = m_strides[m_return_dim];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
return m_impl.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_STRONG_INLINE CoeffReturnType coeff(Index index) const {
const TupleType v = m_impl.coeff(index);
return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
}
#ifndef EIGEN_USE_SYCL
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
#else // following functions are required by sycl
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TupleType* data() const { return m_impl.data(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index return_dim() const {return m_return_dim;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const StrideDims& strides() const {return m_strides;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride_mod() const {return m_stride_mod;}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride_div() const {return m_stride_div;}
const Device& device() const{return m_device;}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double compute_cost = 1.0 +
(m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>()));
return m_orig_impl.costPerCoeff(vectorized) +
m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
}
private:
EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) {
if (m_return_dim < 0) {
return; // Won't be using the strides.
}
eigen_assert(m_return_dim < NumDims &&
"Asking to convert index to a dimension outside of the rank");
// Calculate m_stride_div and m_stride_mod, which are used to
// calculate the value of an index w.r.t. the m_return_dim.
if (Layout == static_cast<int>(ColMajor)) {
strides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
strides[i] = strides[i-1] * dims[i-1];
}
} else {
strides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
strides[i] = strides[i+1] * dims[i+1];
}
}
}
protected:
TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
const Index m_return_dim;
StrideDims m_strides;
Index m_stride_mod;
Index m_stride_div;
#ifdef EIGEN_USE_SYCL
const Device& m_device;
#endif
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
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