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342 lines
12 KiB
C++
342 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_BROADCASTING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
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namespace Eigen {
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/** \class TensorBroadcasting
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor broadcasting 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 Broadcast, typename XprType>
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struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename packet_traits<Scalar>::type Packet;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::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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static const int NumDimensions = XprTraits::NumDimensions;
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static const int Layout = XprTraits::Layout;
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};
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template<typename Broadcast, typename XprType>
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struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
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{
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typedef const TensorBroadcastingOp<Broadcast, XprType>& type;
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};
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template<typename Broadcast, typename XprType>
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struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
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{
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typedef TensorBroadcastingOp<Broadcast, XprType> type;
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};
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} // end namespace internal
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template<typename Broadcast, typename XprType>
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class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorBroadcastingOp>::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<TensorBroadcastingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
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: m_xpr(expr), m_broadcast(broadcast) {}
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EIGEN_DEVICE_FUNC
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const Broadcast& broadcast() const { return m_broadcast; }
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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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protected:
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typename XprType::Nested m_xpr;
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const Broadcast m_broadcast;
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};
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// Eval as rvalue
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template<typename Broadcast, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
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{
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typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
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enum {
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IsAligned = false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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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)
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{
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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const Broadcast& broadcast = op.broadcast();
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for (int i = 0; i < NumDims; ++i) {
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eigen_assert(input_dims[i] > 0);
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m_dimensions[i] = input_dims[i] * broadcast[i];
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}
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_inputStrides[0] = 1;
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m_outputStrides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
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m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
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}
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} else {
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m_inputStrides[NumDims-1] = 1;
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m_outputStrides[NumDims-1] = 1;
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for (int i = NumDims-2; i >= 0; --i) {
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m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
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m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
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}
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}
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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 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_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
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{
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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return coeffColMajor(index);
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} else {
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return coeffRowMajor(index);
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}
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}
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// TODO: attempt to speed this up. The integer divisions and modulo are slow
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
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{
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Index inputIndex = 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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if (internal::index_statically_eq<Broadcast>()(i, 1)) {
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eigen_assert(idx < m_impl.dimensions()[i]);
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inputIndex += idx * m_inputStrides[i];
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} else {
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if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
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eigen_assert(idx % m_impl.dimensions()[i] == 0);
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} else {
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inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
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}
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}
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index -= idx * m_outputStrides[i];
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}
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if (internal::index_statically_eq<Broadcast>()(0, 1)) {
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eigen_assert(index < m_impl.dimensions()[0]);
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inputIndex += index;
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} else {
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if (internal::index_statically_eq<InputDimensions>()(0, 1)) {
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eigen_assert(index % m_impl.dimensions()[0] == 0);
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} else {
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inputIndex += (index % m_impl.dimensions()[0]);
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}
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}
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return m_impl.coeff(inputIndex);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
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{
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Index inputIndex = 0;
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_outputStrides[i];
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if (internal::index_statically_eq<Broadcast>()(i, 1)) {
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eigen_assert(idx < m_impl.dimensions()[i]);
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inputIndex += idx * m_inputStrides[i];
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} else {
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if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
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eigen_assert(idx % m_impl.dimensions()[i] == 0);
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} else {
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inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
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}
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}
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index -= idx * m_outputStrides[i];
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}
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if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) {
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eigen_assert(index < m_impl.dimensions()[NumDims-1]);
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inputIndex += index;
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} else {
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if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) {
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eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
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} else {
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inputIndex += (index % m_impl.dimensions()[NumDims-1]);
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}
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}
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return m_impl.coeff(inputIndex);
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
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{
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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return packetColMajor<LoadMode>(index);
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} else {
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return packetRowMajor<LoadMode>(index);
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}
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}
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// Ignore the LoadMode and always use unaligned loads since we can't guarantee
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// the alignment at compile time.
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(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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const Index originalIndex = index;
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Index inputIndex = 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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if (internal::index_statically_eq<Broadcast>()(i, 1)) {
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eigen_assert(idx < m_impl.dimensions()[i]);
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inputIndex += idx * m_inputStrides[i];
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} else {
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if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
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eigen_assert(idx % m_impl.dimensions()[i] == 0);
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} else {
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inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
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}
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}
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index -= idx * m_outputStrides[i];
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}
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Index innermostLoc;
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if (internal::index_statically_eq<Broadcast>()(0, 1)) {
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eigen_assert(index < m_impl.dimensions()[0]);
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innermostLoc = index;
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} else {
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if (internal::index_statically_eq<InputDimensions>()(0, 1)) {
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eigen_assert(innermostLoc % m_impl.dimensions()[0] == 0);
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innermostLoc = 0;
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} else {
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innermostLoc = index % m_impl.dimensions()[0];
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}
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}
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inputIndex += innermostLoc;
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// Todo: this could be extended to the second dimension if we're not
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// broadcasting alongside the first dimension, and so on.
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if (innermostLoc + packetSize <= m_impl.dimensions()[0]) {
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return m_impl.template packet<Unaligned>(inputIndex);
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} else {
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EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
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values[0] = m_impl.coeff(inputIndex);
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for (int i = 1; i < packetSize; ++i) {
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values[i] = coeffColMajor(originalIndex+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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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(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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const Index originalIndex = index;
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Index inputIndex = 0;
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_outputStrides[i];
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if (internal::index_statically_eq<Broadcast>()(i, 1)) {
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eigen_assert(idx < m_impl.dimensions()[i]);
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inputIndex += idx * m_inputStrides[i];
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} else {
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if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
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eigen_assert(idx % m_impl.dimensions()[i] == 0);
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} else {
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inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
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}
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}
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index -= idx * m_outputStrides[i];
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}
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Index innermostLoc;
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if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) {
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eigen_assert(index < m_impl.dimensions()[NumDims-1]);
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innermostLoc = index;
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} else {
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if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) {
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eigen_assert(innermostLoc % m_impl.dimensions()[NumDims-1] == 0);
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innermostLoc = 0;
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} else {
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innermostLoc = index % m_impl.dimensions()[NumDims-1];
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}
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}
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inputIndex += innermostLoc;
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// Todo: this could be extended to the second dimension if we're not
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// broadcasting alongside the first dimension, and so on.
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if (innermostLoc + packetSize <= m_impl.dimensions()[NumDims-1]) {
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return m_impl.template packet<Unaligned>(inputIndex);
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} else {
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EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
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values[0] = m_impl.coeff(inputIndex);
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for (int i = 1; i < packetSize; ++i) {
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values[i] = coeffRowMajor(originalIndex+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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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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protected:
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Dimensions m_dimensions;
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array<Index, NumDims> m_outputStrides;
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array<Index, NumDims> m_inputStrides;
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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_BROADCASTING_H
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