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Added support for tensor reductions and concatenations
This commit is contained in:
Benoit Steiner
226
unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
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226
unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
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#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
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namespace Eigen {
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/** \class TensorReduction
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor reduction class.
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*
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*/
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namespace internal {
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template<typename Op, typename Dims, typename XprType>
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struct traits<TensorReductionOp<Op, Dims, XprType> >
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: traits<XprType>
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{
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typedef typename traits<XprType>::Scalar Scalar;
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typedef typename internal::packet_traits<Scalar>::type Packet;
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typedef typename traits<XprType>::StorageKind StorageKind;
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typedef typename traits<XprType>::Index Index;
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typedef typename XprType::Nested Nested;
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};
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template<typename Op, typename Dims, typename XprType>
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struct eval<TensorReductionOp<Op, Dims, XprType>, Eigen::Dense>
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{
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typedef const TensorReductionOp<Op, Dims, XprType>& type;
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};
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template<typename Op, typename Dims, typename XprType>
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struct nested<TensorReductionOp<Op, Dims, XprType>, 1, typename eval<TensorReductionOp<Op, Dims, XprType> >::type>
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{
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typedef TensorReductionOp<Op, Dims, XprType> type;
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};
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} // end namespace internal
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template <typename Op, typename Dims, typename XprType>
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class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>, ReadOnlyAccessors> {
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public:
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typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorReductionOp>::Packet Packet;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims)
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{ }
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TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer)
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{ }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const XprType& expression() const { return m_expr; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const Dims& dims() const { return m_dims; }
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const Op& reducer() const { return m_reducer; }
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protected:
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typename XprType::Nested m_expr;
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const Dims m_dims;
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const Op m_reducer;
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};
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// Eval as rvalue
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template<typename Op, typename Dims, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
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{
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typedef TensorReductionOp<Op, Dims, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumReducedDims = internal::array_size<Dims>::value;
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static const int NumDims = (NumInputDims==NumReducedDims) ? 1 : NumInputDims - NumReducedDims;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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enum {
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IsAligned = false,
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PacketAccess = false, // The code isn't vectorized properly yet
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_impl(op.expression(), device), m_reducer(op.reducer())
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{
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EIGEN_STATIC_ASSERT(NumInputDims >= NumReducedDims, YOU_MADE_A_PROGRAMMING_MISTAKE);
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array<bool, NumInputDims> reduced;
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for (int i = 0; i < NumInputDims; ++i) {
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reduced[i] = false;
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}
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for (int i = 0; i < NumReducedDims; ++i) {
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eigen_assert(op.dims()[i] >= 0);
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eigen_assert(op.dims()[i] < NumInputDims);
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reduced[op.dims()[i]] = true;
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}
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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int outputIndex = 0;
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int reduceIndex = 0;
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for (int i = 0; i < NumInputDims; ++i) {
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if (reduced[i]) {
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m_reducedDims[reduceIndex] = input_dims[i];
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++reduceIndex;
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} else {
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m_dimensions[outputIndex] = input_dims[i];
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++outputIndex;
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}
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}
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m_outputStrides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
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}
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array<Index, NumInputDims> strides;
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strides[0] = 1;
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for (int i = 1; i < NumInputDims; ++i) {
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strides[i] = strides[i-1] * input_dims[i-1];
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}
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outputIndex = 0;
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reduceIndex = 0;
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for (int i = 0; i < NumInputDims; ++i) {
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if (reduced[i]) {
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m_reducedStrides[reduceIndex] = strides[i];
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++reduceIndex;
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} else {
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m_preservedStrides[outputIndex] = strides[i];
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++outputIndex;
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}
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}
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// Special case for full reductions
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if (NumInputDims == NumReducedDims) {
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m_dimensions[0] = 1;
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
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m_impl.evalSubExprsIfNeeded(NULL);
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return true;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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Op reducer(m_reducer);
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reduce(firstInput(index), 0, reducer);
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return reducer.finalize();
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}
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// TODO(bsteiner): provide a more efficient implementation.
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
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{
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const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
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EIGEN_ALIGN_DEFAULT CoeffReturnType values[packetSize];
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for (int i = 0; i < packetSize; ++i) {
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values[i] = coeff(index+i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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}
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Scalar* data() const { return NULL; }
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private:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
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Index startInput = 0;
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx = index / m_outputStrides[i];
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startInput += idx * m_preservedStrides[i];
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index -= idx * m_outputStrides[i];
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}
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startInput += index * m_preservedStrides[0];
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return startInput;
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}
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EIGEN_DEVICE_FUNC void reduce(Index firstIndex, int DimIndex, Op& reducer) const {
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for (int j = 0; j < m_reducedDims[DimIndex]; ++j) {
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const Index input = firstIndex + j * m_reducedStrides[DimIndex];
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if (DimIndex < NumReducedDims-1) {
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reduce(input, DimIndex+1, reducer);
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} else {
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reducer.reduce(m_impl.coeff(input));
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}
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}
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}
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Dimensions m_dimensions;
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array<Index, NumDims> m_outputStrides;
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array<Index, NumDims> m_preservedStrides;
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array<Index, NumReducedDims> m_reducedStrides;
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array<Index, NumReducedDims> m_reducedDims;
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Op m_reducer;
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TensorEvaluator<ArgType, Device> m_impl;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
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