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Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h')
| -rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h | 427 |
1 files changed, 427 insertions, 0 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h new file mode 100644 index 000000000..d084880de --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h @@ -0,0 +1,427 @@ +// 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_EVALUATOR_H +#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H + +namespace Eigen { + +/** \class TensorEvaluator + * \ingroup CXX11_Tensor_Module + * + * \brief The tensor evaluator classes. + * + * These classes are responsible for the evaluation of the tensor expression. + * + * TODO: add support for more types of expressions, in particular expressions + * leading to lvalues (slicing, reshaping, etc...) + */ + +// Generic evaluator +template<typename Derived, typename Device> +struct TensorEvaluator +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Packet Packet; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename Derived::Packet PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + // NumDimensions is -1 for variable dim tensors + static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? + internal::traits<Derived>::NumDimensions : 0; + + enum { + IsAligned = Derived::IsAligned, + PacketAccess = Derived::PacketAccess, + Layout = Derived::Layout, + CoordAccess = NumCoords > 0, + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device) + : m_data(const_cast<Scalar*>(m.data())), m_dims(m.dimensions()), m_device(device) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* dest) { + if (dest) { + m_device.memcpy((void*)dest, m_data, sizeof(Scalar) * m_dims.TotalSize()); + return false; + } + return true; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + eigen_assert(m_data); + return m_data[index]; + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { + eigen_assert(m_data); + return m_data[index]; + } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketReturnType packet(Index index) const + { + return internal::ploadt<Packet, LoadMode>(m_data + index); + } + + template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + void writePacket(Index index, const Packet& x) + { + return internal::pstoret<Scalar, Packet, StoreMode>(m_data + index, x); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const { + eigen_assert(m_data); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return m_data[m_dims.IndexOfColMajor(coords)]; + } else { + return m_data[m_dims.IndexOfRowMajor(coords)]; + } + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<DenseIndex, NumCoords>& coords) { + eigen_assert(m_data); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + return m_data[m_dims.IndexOfColMajor(coords)]; + } else { + return m_data[m_dims.IndexOfRowMajor(coords)]; + } + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return m_data; } + + protected: + Scalar* m_data; + Dimensions m_dims; + const Device& m_device; +}; + + +// Default evaluator for rvalues +template<typename Derived, typename Device> +struct TensorEvaluator<const Derived, Device> +{ + typedef typename Derived::Index Index; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Packet Packet; + typedef typename Derived::Scalar CoeffReturnType; + typedef typename Derived::Packet PacketReturnType; + typedef typename Derived::Dimensions Dimensions; + + // NumDimensions is -1 for variable dim tensors + static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? + internal::traits<Derived>::NumDimensions : 0; + + enum { + IsAligned = Derived::IsAligned, + PacketAccess = Derived::PacketAccess, + Layout = Derived::Layout, + CoordAccess = NumCoords > 0, + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device&) + : m_data(m.data()), m_dims(m.dimensions()) + { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { + eigen_assert(m_data); +#ifdef __CUDA_ARCH__ + return __ldg(m_data+index); +#else + return m_data[index]; +#endif + } + + template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE + PacketReturnType packet(Index index) const + { + return internal::ploadt_ro<Packet, LoadMode>(m_data + index); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const { + eigen_assert(m_data); + const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords) + : m_dims.IndexOfRowMajor(coords); +#ifdef __CUDA_ARCH__ + return __ldg(m_data+index); +#else + return m_data[index]; +#endif + } + + EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; } + + protected: + const Scalar* m_data; + Dimensions m_dims; +}; + + + + +// -------------------- CwiseNullaryOp -------------------- + +template<typename NullaryOp, typename ArgType, typename Device> +struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device> +{ + typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType; + + enum { + IsAligned = true, + PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + }; + + EIGEN_DEVICE_FUNC + TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename internal::traits<XprType>::Packet PacketReturnType; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(index); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(index); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + private: + const NullaryOp m_functor; + TensorEvaluator<ArgType, Device> m_argImpl; +}; + + + +// -------------------- CwiseUnaryOp -------------------- + +template<typename UnaryOp, typename ArgType, typename Device> +struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device> +{ + typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType; + + enum { + IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess & internal::functor_traits<UnaryOp>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = false, // to be implemented + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), + m_argImpl(op.nestedExpression(), device) + { } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename internal::traits<XprType>::Packet PacketReturnType; + typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) { + m_argImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_argImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(m_argImpl.coeff(index)); + } + + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + private: + const UnaryOp m_functor; + TensorEvaluator<ArgType, Device> m_argImpl; +}; + + +// -------------------- CwiseBinaryOp -------------------- + +template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device> +struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device> +{ + typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType; + + enum { + IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess & + internal::functor_traits<BinaryOp>::PacketAccess, + Layout = TensorEvaluator<LeftArgType, Device>::Layout, + CoordAccess = false, // to be implemented + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_functor(op.functor()), + m_leftImpl(op.lhsExpression(), device), + m_rightImpl(op.rhsExpression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions == 1), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename internal::traits<XprType>::Packet PacketReturnType; + typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // TODO: use right impl instead if right impl dimensions are known at compile time. + return m_leftImpl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_leftImpl.evalSubExprsIfNeeded(NULL); + m_rightImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_leftImpl.cleanup(); + m_rightImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index)); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const + { + return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + private: + const BinaryOp m_functor; + TensorEvaluator<LeftArgType, Device> m_leftImpl; + TensorEvaluator<RightArgType, Device> m_rightImpl; +}; + + +// -------------------- SelectOp -------------------- + +template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device> +struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device> +{ + typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType; + + enum { + IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned & TensorEvaluator<ElseArgType, Device>::IsAligned, + PacketAccess = TensorEvaluator<ThenArgType, Device>::PacketAccess & TensorEvaluator<ElseArgType, Device>::PacketAccess/* & + TensorEvaluator<IfArgType>::PacketAccess*/, + Layout = TensorEvaluator<IfArgType, Device>::Layout, + CoordAccess = false, // to be implemented + }; + + EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) + : m_condImpl(op.ifExpression(), device), + m_thenImpl(op.thenExpression(), device), + m_elseImpl(op.elseExpression(), device) + { + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions())); + eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions())); + } + + typedef typename XprType::Index Index; + typedef typename XprType::Scalar Scalar; + typedef typename internal::traits<XprType>::Scalar CoeffReturnType; + typedef typename internal::traits<XprType>::Packet PacketReturnType; + typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions; + + EIGEN_DEVICE_FUNC const Dimensions& dimensions() const + { + // TODO: use then or else impl instead if they happen to be known at compile time. + return m_condImpl.dimensions(); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { + m_condImpl.evalSubExprsIfNeeded(NULL); + m_thenImpl.evalSubExprsIfNeeded(NULL); + m_elseImpl.evalSubExprsIfNeeded(NULL); + return true; + } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { + m_condImpl.cleanup(); + m_thenImpl.cleanup(); + m_elseImpl.cleanup(); + } + + EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const + { + return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index); + } + template<int LoadMode> + EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const + { + static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; + internal::Selector<PacketSize> select; + for (Index i = 0; i < PacketSize; ++i) { + select.select[i] = m_condImpl.coeff(index+i); + } + return internal::pblend(select, + m_thenImpl.template packet<LoadMode>(index), + m_elseImpl.template packet<LoadMode>(index)); + } + + EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; } + + private: + TensorEvaluator<IfArgType, Device> m_condImpl; + TensorEvaluator<ThenArgType, Device> m_thenImpl; + TensorEvaluator<ElseArgType, Device> m_elseImpl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H |