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https://gitlab.com/libeigen/eigen.git
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Added support for tensor chips
This commit is contained in:
Benoit Steiner
parent
4b36c3591f
commit
2ed1838aeb
@ -47,6 +47,7 @@
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h"
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#include "unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h"
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@ -254,6 +254,11 @@ class TensorBase<Derived, ReadOnlyAccessors>
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slice(const StartIndices& startIndices, const Sizes& sizes) const {
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return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
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}
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template <std::size_t DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorChippingOp<DimId, const Derived>
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chip(const Index offset) const {
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return TensorChippingOp<DimId, const Derived>(derived(), offset);
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}
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template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const TensorPaddingOp<const PaddingDimensions, const Derived>
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pad(const PaddingDimensions& padding) const {
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@ -327,7 +332,7 @@ class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyA
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template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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TensorReshapingOp<const NewDimensions, Derived>
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reshape(const NewDimensions& newDimensions) {
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reshape(const NewDimensions& newDimensions) const {
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return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
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}
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template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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@ -335,6 +340,11 @@ class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyA
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slice(const StartIndices& startIndices, const Sizes& sizes) const {
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return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
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}
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template <std::size_t DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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TensorChippingOp<DimId, Derived>
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chip(const Index offset) const {
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return TensorChippingOp<DimId, Derived>(derived(), offset);
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}
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template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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TensorShufflingOp<const Shuffle, Derived>
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shuffle(const Shuffle& shuffle) const {
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232
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
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232
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
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@ -0,0 +1,232 @@
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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_CHIPPING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
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namespace Eigen {
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/** \class TensorKChippingReshaping
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* \ingroup CXX11_Tensor_Module
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*
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* \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
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*
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*
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*/
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namespace internal {
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template<std::size_t DimId, typename XprType>
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struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
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{
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typedef typename 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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typedef typename remove_reference<Nested>::type _Nested;
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};
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template<std::size_t DimId, typename XprType>
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struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
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{
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typedef const TensorChippingOp<DimId, XprType>& type;
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};
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template<std::size_t DimId, typename XprType>
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struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
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{
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typedef TensorChippingOp<DimId, XprType> type;
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};
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} // end namespace internal
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template<std::size_t DimId, typename XprType>
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class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
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{
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public:
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typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorChippingOp>::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<TensorChippingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset)
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: m_xpr(expr), m_offset(offset) {}
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EIGEN_DEVICE_FUNC
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const Index offset() const { return m_offset; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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expression() const { return m_xpr; }
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template<typename OtherDerived>
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
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return *this;
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}
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protected:
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typename XprType::Nested m_xpr;
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const Index m_offset;
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};
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// Eval as rvalue
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template<std::size_t DimId, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
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{
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typedef TensorChippingOp<DimId, ArgType> XprType;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims-1;
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typedef typename XprType::Index Index;
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typedef DSizes<Index, NumDims> Dimensions;
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enum {
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// Alignment can't be guaranteed at compile time since it depends on the
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// slice offsets.
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IsAligned = false,
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PacketAccess = false, // not yet implemented
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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_device(device)
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{
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// We could also support the case where NumInputDims==1 if needed.
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EIGEN_STATIC_ASSERT(NumInputDims >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
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EIGEN_STATIC_ASSERT(NumInputDims > DimId, YOU_MADE_A_PROGRAMMING_MISTAKE);
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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int j = 0;
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for (int i = 0; i < NumInputDims; ++i) {
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if (i != DimId) {
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m_dimensions[j] = input_dims[i];
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++j;
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}
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}
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m_stride = 1;
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m_inputStride = 1;
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for (int i = 0; i < DimId; ++i) {
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m_stride *= input_dims[i];
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m_inputStride *= input_dims[i];
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}
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m_inputStride *= input_dims[DimId];
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m_inputOffset = m_stride * op.offset();
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}
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typedef typename XprType::Scalar Scalar;
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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 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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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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return m_impl.coeff(srcCoeff(index));
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}
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/* to be done
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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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}*/
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const { return NULL; }
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
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{
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Index inputIndex;
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if (DimId == 0) {
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// m_stride is equal to 1, so let's avoid the integer division.
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eigen_assert(m_stride == 1);
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inputIndex = index * m_inputStride + m_inputOffset;
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} else if (DimId == NumInputDims-1) {
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// m_stride is aways greater than index, so let's avoid the integer division.
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eigen_assert(m_stride > index);
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inputIndex = index + m_inputOffset;
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} else {
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const Index idx = index / m_stride;
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inputIndex = idx * m_inputStride + m_inputOffset;
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index -= idx * m_stride;
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inputIndex += index;
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}
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return inputIndex;
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}
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Dimensions m_dimensions;
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Index m_stride;
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Index m_inputOffset;
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Index m_inputStride;
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TensorEvaluator<ArgType, Device> m_impl;
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const Device& m_device;
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};
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// Eval as lvalue
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template<std::size_t DimId, typename ArgType, typename Device>
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struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
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: public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
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typedef TensorChippingOp<DimId, ArgType> XprType;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims-1;
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typedef typename XprType::Index Index;
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typedef DSizes<Index, NumDims> Dimensions;
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enum {
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IsAligned = false,
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PacketAccess = false,
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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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: Base(op, device)
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{ }
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typedef typename XprType::Scalar Scalar;
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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& coeffRef(Index index)
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{
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return this->m_impl.coeffRef(this->srcCoeff(index));
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}
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/* to be done
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template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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void writePacket(Index index, const PacketReturnType& x)
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{
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} */
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
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@ -21,11 +21,12 @@ template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryO
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template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
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template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
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template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
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template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
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template<typename Op, typename Dims, typename XprType> class TensorReductionOp;
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template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
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template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
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template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
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template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
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template<std::size_t DimId, typename XprType> class TensorChippingOp;
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template<typename NewDimensions, typename XprType> class TensorReshapingOp;
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template<typename StartIndices, typename Sizes, typename XprType> class TensorSlicingOp;
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template<typename PaddingDimensions, typename XprType> class TensorPaddingOp;
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@ -115,6 +115,7 @@ if(EIGEN_TEST_CXX11)
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ei_add_test(cxx11_tensor_lvalue "-std=c++0x")
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ei_add_test(cxx11_tensor_map "-std=c++0x")
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ei_add_test(cxx11_tensor_broadcasting "-std=c++0x")
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ei_add_test(cxx11_tensor_chipping "-std=c++0x")
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ei_add_test(cxx11_tensor_concatenation "-std=c++0x")
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ei_add_test(cxx11_tensor_morphing "-std=c++0x")
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ei_add_test(cxx11_tensor_padding "-std=c++0x")
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244
unsupported/test/cxx11_tensor_chipping.cpp
Normal file
244
unsupported/test/cxx11_tensor_chipping.cpp
Normal file
@ -0,0 +1,244 @@
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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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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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static void test_simple_chip()
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{
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Tensor<float, 5> tensor(2,3,5,7,11);
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tensor.setRandom();
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Tensor<float, 4> chip1;
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chip1 = tensor.chip<0>(1);
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VERIFY_IS_EQUAL(chip1.dimension(0), 3);
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VERIFY_IS_EQUAL(chip1.dimension(1), 5);
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VERIFY_IS_EQUAL(chip1.dimension(2), 7);
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VERIFY_IS_EQUAL(chip1.dimension(3), 11);
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip1(i,j,k,l), tensor(1,i,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4> chip2 = tensor.chip<1>(1);
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VERIFY_IS_EQUAL(chip2.dimension(0), 2);
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VERIFY_IS_EQUAL(chip2.dimension(1), 5);
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VERIFY_IS_EQUAL(chip2.dimension(2), 7);
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VERIFY_IS_EQUAL(chip2.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip2(i,j,k,l), tensor(i,1,j,k,l));
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}
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}
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}
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}
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Tensor<float, 4> chip3 = tensor.chip<2>(2);
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VERIFY_IS_EQUAL(chip3.dimension(0), 2);
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VERIFY_IS_EQUAL(chip3.dimension(1), 3);
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VERIFY_IS_EQUAL(chip3.dimension(2), 7);
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VERIFY_IS_EQUAL(chip3.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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VERIFY_IS_EQUAL(chip3(i,j,k,l), tensor(i,j,2,k,l));
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}
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}
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}
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}
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Tensor<float, 4> chip4(tensor.chip<3>(5));
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VERIFY_IS_EQUAL(chip4.dimension(0), 2);
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VERIFY_IS_EQUAL(chip4.dimension(1), 3);
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VERIFY_IS_EQUAL(chip4.dimension(2), 5);
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VERIFY_IS_EQUAL(chip4.dimension(3), 11);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(chip4(i,j,k,l), tensor(i,j,k,5,l));
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}
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}
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}
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}
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Tensor<float, 4> chip5(tensor.chip<4>(7));
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VERIFY_IS_EQUAL(chip5.dimension(0), 2);
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VERIFY_IS_EQUAL(chip5.dimension(1), 3);
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VERIFY_IS_EQUAL(chip5.dimension(2), 5);
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VERIFY_IS_EQUAL(chip5.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(chip5(i,j,k,l), tensor(i,j,k,l,7));
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}
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}
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}
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}
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}
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static void test_chip_in_expr() {
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Tensor<float, 5> input1(2,3,5,7,11);
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input1.setRandom();
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Tensor<float, 4> input2(3,5,7,11);
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input2.setRandom();
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Tensor<float, 4> result = input1.chip<0>(0) + input2;
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for (int i = 0; i < 3; ++i) {
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for (int j = 0; j < 5; ++j) {
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for (int k = 0; k < 7; ++k) {
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for (int l = 0; l < 11; ++l) {
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float expected = input1(0,i,j,k,l) + input2(i,j,k,l);
|
||||
VERIFY_IS_EQUAL(result(i,j,k,l), expected);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Tensor<float, 3> input3(3,7,11);
|
||||
input3.setRandom();
|
||||
Tensor<float, 3> result2 = input1.chip<0>(0).chip<1>(2) + input3;
|
||||
for (int i = 0; i < 3; ++i) {
|
||||
for (int j = 0; j < 7; ++j) {
|
||||
for (int k = 0; k < 11; ++k) {
|
||||
float expected = input1(0,i,2,j,k) + input3(i,j,k);
|
||||
VERIFY_IS_EQUAL(result2(i,j,k), expected);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
static void test_chip_as_lvalue()
|
||||
{
|
||||
Tensor<float, 5> input1(2,3,5,7,11);
|
||||
input1.setRandom();
|
||||
|
||||
Tensor<float, 4> input2(3,5,7,11);
|
||||
input2.setRandom();
|
||||
Tensor<float, 5> tensor = input1;
|
||||
tensor.chip<0>(1) = input2;
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
for (int j = 0; j < 3; ++j) {
|
||||
for (int k = 0; k < 5; ++k) {
|
||||
for (int l = 0; l < 7; ++l) {
|
||||
for (int m = 0; m < 11; ++m) {
|
||||
if (i != 1) {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
|
||||
} else {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input2(j,k,l,m));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Tensor<float, 4> input3(2,5,7,11);
|
||||
input3.setRandom();
|
||||
tensor = input1;
|
||||
tensor.chip<1>(1) = input3;
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
for (int j = 0; j < 3; ++j) {
|
||||
for (int k = 0; k < 5; ++k) {
|
||||
for (int l = 0; l < 7; ++l) {
|
||||
for (int m = 0; m < 11; ++m) {
|
||||
if (j != 1) {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
|
||||
} else {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input3(i,k,l,m));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Tensor<float, 4> input4(2,3,7,11);
|
||||
input4.setRandom();
|
||||
tensor = input1;
|
||||
tensor.chip<2>(3) = input4;
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
for (int j = 0; j < 3; ++j) {
|
||||
for (int k = 0; k < 5; ++k) {
|
||||
for (int l = 0; l < 7; ++l) {
|
||||
for (int m = 0; m < 11; ++m) {
|
||||
if (k != 3) {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
|
||||
} else {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input4(i,j,l,m));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Tensor<float, 4> input5(2,3,5,11);
|
||||
input5.setRandom();
|
||||
tensor = input1;
|
||||
tensor.chip<3>(4) = input5;
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
for (int j = 0; j < 3; ++j) {
|
||||
for (int k = 0; k < 5; ++k) {
|
||||
for (int l = 0; l < 7; ++l) {
|
||||
for (int m = 0; m < 11; ++m) {
|
||||
if (l != 4) {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
|
||||
} else {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input5(i,j,k,m));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Tensor<float, 4> input6(2,3,5,7);
|
||||
input6.setRandom();
|
||||
tensor = input1;
|
||||
tensor.chip<4>(5) = input6;
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
for (int j = 0; j < 3; ++j) {
|
||||
for (int k = 0; k < 5; ++k) {
|
||||
for (int l = 0; l < 7; ++l) {
|
||||
for (int m = 0; m < 11; ++m) {
|
||||
if (m != 5) {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input1(i,j,k,l,m));
|
||||
} else {
|
||||
VERIFY_IS_EQUAL(tensor(i,j,k,l,m), input6(i,j,k,l));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void test_cxx11_tensor_chipping()
|
||||
{
|
||||
CALL_SUBTEST(test_simple_chip());
|
||||
CALL_SUBTEST(test_chip_in_expr());
|
||||
CALL_SUBTEST(test_chip_as_lvalue());
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user