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337 lines
12 KiB
C++
337 lines
12 KiB
C++
// 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_CUSTOM_OP_H
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#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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/** \class TensorCustomUnaryOp
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor custom class.
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*
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*
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*/
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namespace internal {
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template<typename CustomUnaryFunc, typename XprType>
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struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
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{
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::StorageKind StorageKind;
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typedef typename XprType::Index Index;
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typedef typename XprType::Nested Nested;
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typedef std::remove_reference_t<Nested> Nested_;
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static constexpr int NumDimensions = traits<XprType>::NumDimensions;
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static constexpr int Layout = traits<XprType>::Layout;
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typedef typename traits<XprType>::PointerType PointerType;
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};
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template<typename CustomUnaryFunc, typename XprType>
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struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
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{
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typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>EIGEN_DEVICE_REF type;
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};
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template<typename CustomUnaryFunc, typename XprType>
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struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
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{
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
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};
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} // end namespace internal
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template<typename CustomUnaryFunc, typename XprType>
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class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
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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 internal::nested<TensorCustomUnaryOp>::type Nested;
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typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind;
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typedef typename internal::traits<TensorCustomUnaryOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func)
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: m_expr(expr), m_func(func) {}
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EIGEN_DEVICE_FUNC
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const CustomUnaryFunc& func() const { return m_func; }
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EIGEN_DEVICE_FUNC
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const internal::remove_all_t<typename XprType::Nested>&
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expression() const { return m_expr; }
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protected:
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typename XprType::Nested m_expr;
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const CustomUnaryFunc m_func;
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};
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// Eval as rvalue
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template<typename CustomUnaryFunc, typename XprType, typename Device>
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struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
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{
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typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
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typedef typename internal::traits<ArgType>::Index Index;
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static constexpr int NumDims = internal::traits<ArgType>::NumDimensions;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef std::remove_const_t<typename ArgType::Scalar> Scalar;
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typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
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typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
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typedef StorageMemory<CoeffReturnType, Device> Storage;
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typedef typename Storage::Type EvaluatorPointerType;
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static constexpr int Layout = TensorEvaluator<XprType, Device>::Layout;
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enum {
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IsAligned = false,
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PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
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BlockAccess = false,
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PreferBlockAccess = false,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device)
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: m_op(op), m_device(device), m_result(NULL)
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{
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m_dimensions = op.func().dimensions(op.expression());
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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_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
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if (data) {
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evalTo(data);
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return false;
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} else {
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m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
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m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))));
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evalTo(m_result);
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return true;
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}
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}
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EIGEN_STRONG_INLINE void cleanup() {
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if (m_result) {
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m_device.deallocate_temp(m_result);
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m_result = NULL;
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
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return m_result[index];
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
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return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
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// TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
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return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
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}
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EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
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protected:
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void evalTo(EvaluatorPointerType data) {
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TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(m_device.get(data), m_dimensions);
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m_op.func().eval(m_op.expression(), result, m_device);
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}
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Dimensions m_dimensions;
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const ArgType m_op;
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const Device EIGEN_DEVICE_REF m_device;
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EvaluatorPointerType m_result;
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};
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/** \class TensorCustomBinaryOp
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor custom class.
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*
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*
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*/
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namespace internal {
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template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
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struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
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{
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typedef typename internal::promote_storage_type<typename LhsXprType::Scalar,
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typename RhsXprType::Scalar>::ret Scalar;
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typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
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typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
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typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
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typename traits<RhsXprType>::StorageKind>::ret StorageKind;
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typedef typename promote_index_type<typename traits<LhsXprType>::Index,
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typename traits<RhsXprType>::Index>::type Index;
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typedef typename LhsXprType::Nested LhsNested;
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typedef typename RhsXprType::Nested RhsNested;
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typedef std::remove_reference_t<LhsNested> LhsNested_;
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typedef std::remove_reference_t<RhsNested> RhsNested_;
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static constexpr int NumDimensions = traits<LhsXprType>::NumDimensions;
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static constexpr int Layout = traits<LhsXprType>::Layout;
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typedef std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
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typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType> PointerType;
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};
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template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
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struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense>
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{
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typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
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};
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template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
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struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
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{
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typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
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};
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} // end namespace internal
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template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
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class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType;
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typedef typename internal::nested<TensorCustomBinaryOp>::type Nested;
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typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind;
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typedef typename internal::traits<TensorCustomBinaryOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const CustomBinaryFunc& func)
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: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func) {}
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EIGEN_DEVICE_FUNC
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const CustomBinaryFunc& func() const { return m_func; }
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EIGEN_DEVICE_FUNC
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const internal::remove_all_t<typename LhsXprType::Nested>&
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lhsExpression() const { return m_lhs_xpr; }
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EIGEN_DEVICE_FUNC
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const internal::remove_all_t<typename RhsXprType::Nested>&
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rhsExpression() const { return m_rhs_xpr; }
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protected:
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typename LhsXprType::Nested m_lhs_xpr;
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typename RhsXprType::Nested m_rhs_xpr;
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const CustomBinaryFunc m_func;
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};
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// Eval as rvalue
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template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
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struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device>
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{
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typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
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typedef typename internal::traits<XprType>::Index Index;
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static constexpr int NumDims = internal::traits<XprType>::NumDimensions;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
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typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
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typedef StorageMemory<CoeffReturnType, Device> Storage;
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typedef typename Storage::Type EvaluatorPointerType;
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static constexpr int Layout = TensorEvaluator<LhsXprType, Device>::Layout;
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enum {
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IsAligned = false,
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PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
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BlockAccess = false,
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PreferBlockAccess = false,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_op(op), m_device(device), m_result(NULL)
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{
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m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression());
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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_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
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if (data) {
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evalTo(data);
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return false;
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} else {
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m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
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m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))));
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evalTo(m_result);
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return true;
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}
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}
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EIGEN_STRONG_INLINE void cleanup() {
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if (m_result != NULL) {
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m_device.deallocate_temp(m_result);
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m_result = NULL;
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
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return m_result[index];
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
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return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
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// TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
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return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
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}
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EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
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protected:
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void evalTo(EvaluatorPointerType data) {
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TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(m_device.get(data), m_dimensions);
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m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
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}
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Dimensions m_dimensions;
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const XprType m_op;
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const Device EIGEN_DEVICE_REF m_device;
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EvaluatorPointerType m_result;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
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