| // 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_BROADCASTING_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H |
| |
| namespace Eigen { |
| |
| /** \class TensorBroadcasting |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor broadcasting class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename Broadcast, typename XprType> |
| struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType> |
| { |
| typedef typename XprType::Scalar Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef typename remove_reference<Nested>::type _Nested; |
| static const int NumDimensions = XprTraits::NumDimensions; |
| static const int Layout = XprTraits::Layout; |
| }; |
| |
| template<typename Broadcast, typename XprType> |
| struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense> |
| { |
| typedef const TensorBroadcastingOp<Broadcast, XprType>& type; |
| }; |
| |
| template<typename Broadcast, typename XprType> |
| struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type> |
| { |
| typedef TensorBroadcastingOp<Broadcast, XprType> type; |
| }; |
| |
| template <typename Dims> |
| struct is_input_scalar { |
| static const bool value = false; |
| }; |
| template <> |
| struct is_input_scalar<Sizes<> > { |
| static const bool value = true; |
| }; |
| #ifndef EIGEN_EMULATE_CXX11_META_H |
| template <typename std::size_t... Indices> |
| struct is_input_scalar<Sizes<Indices...> > { |
| static const bool value = (Sizes<Indices...>::total_size == 1); |
| }; |
| #endif |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename Broadcast, typename XprType> |
| class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors> |
| { |
| public: |
| typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast) |
| : m_xpr(expr), m_broadcast(broadcast) {} |
| |
| EIGEN_DEVICE_FUNC |
| const Broadcast& broadcast() const { return m_broadcast; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const Broadcast m_broadcast; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Broadcast, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device> |
| { |
| typedef TensorBroadcastingOp<Broadcast, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; |
| |
| enum { |
| IsAligned = true, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| RawAccess = false |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_broadcast(op.broadcast()),m_impl(op.expression(), device) |
| { |
| // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar |
| // and store the result in a scalar. Instead one should reshape the scalar into a a N-D |
| // tensor with N >= 1 of 1 element first and then broadcast. |
| EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| const InputDimensions& input_dims = m_impl.dimensions(); |
| const Broadcast& broadcast = op.broadcast(); |
| for (int i = 0; i < NumDims; ++i) { |
| eigen_assert(input_dims[i] > 0); |
| m_dimensions[i] = input_dims[i] * broadcast[i]; |
| } |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_inputStrides[0] = 1; |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; |
| m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; |
| } |
| } else { |
| m_inputStrides[NumDims-1] = 1; |
| m_outputStrides[NumDims-1] = 1; |
| for (int i = NumDims-2; i >= 0; --i) { |
| m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; |
| m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const |
| { |
| if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) { |
| return m_impl.coeff(0); |
| } |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| return coeffColMajor(index); |
| } else { |
| return coeffRowMajor(index); |
| } |
| } |
| |
| // TODO: attempt to speed this up. The integer divisions and modulo are slow |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const |
| { |
| Index inputIndex = 0; |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_outputStrides[i]; |
| if (internal::index_statically_eq<Broadcast>(i, 1)) { |
| eigen_assert(idx < m_impl.dimensions()[i]); |
| inputIndex += idx * m_inputStrides[i]; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(i, 1)) { |
| eigen_assert(idx % m_impl.dimensions()[i] == 0); |
| } else { |
| inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; |
| } |
| } |
| index -= idx * m_outputStrides[i]; |
| } |
| if (internal::index_statically_eq<Broadcast>(0, 1)) { |
| eigen_assert(index < m_impl.dimensions()[0]); |
| inputIndex += index; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(0, 1)) { |
| eigen_assert(index % m_impl.dimensions()[0] == 0); |
| } else { |
| inputIndex += (index % m_impl.dimensions()[0]); |
| } |
| } |
| return m_impl.coeff(inputIndex); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const |
| { |
| Index inputIndex = 0; |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_outputStrides[i]; |
| if (internal::index_statically_eq<Broadcast>(i, 1)) { |
| eigen_assert(idx < m_impl.dimensions()[i]); |
| inputIndex += idx * m_inputStrides[i]; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(i, 1)) { |
| eigen_assert(idx % m_impl.dimensions()[i] == 0); |
| } else { |
| inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; |
| } |
| } |
| index -= idx * m_outputStrides[i]; |
| } |
| if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) { |
| eigen_assert(index < m_impl.dimensions()[NumDims-1]); |
| inputIndex += index; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) { |
| eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0); |
| } else { |
| inputIndex += (index % m_impl.dimensions()[NumDims-1]); |
| } |
| } |
| return m_impl.coeff(inputIndex); |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const |
| { |
| if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) { |
| return internal::pset1<PacketReturnType>(m_impl.coeff(0)); |
| } |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| return packetColMajor<LoadMode>(index); |
| } else { |
| return packetRowMajor<LoadMode>(index); |
| } |
| } |
| |
| // Ignore the LoadMode and always use unaligned loads since we can't guarantee |
| // the alignment at compile time. |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| const Index originalIndex = index; |
| |
| Index inputIndex = 0; |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_outputStrides[i]; |
| if (internal::index_statically_eq<Broadcast>(i, 1)) { |
| eigen_assert(idx < m_impl.dimensions()[i]); |
| inputIndex += idx * m_inputStrides[i]; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(i, 1)) { |
| eigen_assert(idx % m_impl.dimensions()[i] == 0); |
| } else { |
| inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; |
| } |
| } |
| index -= idx * m_outputStrides[i]; |
| } |
| Index innermostLoc; |
| if (internal::index_statically_eq<Broadcast>(0, 1)) { |
| eigen_assert(index < m_impl.dimensions()[0]); |
| innermostLoc = index; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(0, 1)) { |
| eigen_assert(index % m_impl.dimensions()[0] == 0); |
| innermostLoc = 0; |
| } else { |
| innermostLoc = index % m_impl.dimensions()[0]; |
| } |
| } |
| inputIndex += innermostLoc; |
| |
| // Todo: this could be extended to the second dimension if we're not |
| // broadcasting alongside the first dimension, and so on. |
| if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) { |
| return m_impl.template packet<Unaligned>(inputIndex); |
| } else { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| values[0] = m_impl.coeff(inputIndex); |
| for (int i = 1; i < PacketSize; ++i) { |
| values[i] = coeffColMajor(originalIndex+i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| const Index originalIndex = index; |
| |
| Index inputIndex = 0; |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_outputStrides[i]; |
| if (internal::index_statically_eq<Broadcast>(i, 1)) { |
| eigen_assert(idx < m_impl.dimensions()[i]); |
| inputIndex += idx * m_inputStrides[i]; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(i, 1)) { |
| eigen_assert(idx % m_impl.dimensions()[i] == 0); |
| } else { |
| inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i]; |
| } |
| } |
| index -= idx * m_outputStrides[i]; |
| } |
| Index innermostLoc; |
| if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) { |
| eigen_assert(index < m_impl.dimensions()[NumDims-1]); |
| innermostLoc = index; |
| } else { |
| if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) { |
| eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0); |
| innermostLoc = 0; |
| } else { |
| innermostLoc = index % m_impl.dimensions()[NumDims-1]; |
| } |
| } |
| inputIndex += innermostLoc; |
| |
| // Todo: this could be extended to the second dimension if we're not |
| // broadcasting alongside the first dimension, and so on. |
| if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) { |
| return m_impl.template packet<Unaligned>(inputIndex); |
| } else { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| values[0] = m_impl.coeff(inputIndex); |
| for (int i = 1; i < PacketSize; ++i) { |
| values[i] = coeffRowMajor(originalIndex+i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost |
| costPerCoeff(bool vectorized) const { |
| double compute_cost = TensorOpCost::AddCost<Index>(); |
| if (NumDims > 0) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| compute_cost += TensorOpCost::DivCost<Index>(); |
| if (internal::index_statically_eq<Broadcast>(i, 1)) { |
| compute_cost += |
| TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>(); |
| } else { |
| if (!internal::index_statically_eq<InputDimensions>(i, 1)) { |
| compute_cost += TensorOpCost::MulCost<Index>() + |
| TensorOpCost::ModCost<Index>() + |
| TensorOpCost::AddCost<Index>(); |
| } |
| } |
| compute_cost += |
| TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>(); |
| } |
| } |
| return m_impl.costPerCoeff(vectorized) + |
| TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); |
| } |
| |
| EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } |
| |
| const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| |
| Broadcast functor() const { return m_broadcast; } |
| |
| protected: |
| const Broadcast m_broadcast; |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_outputStrides; |
| array<Index, NumDims> m_inputStrides; |
| TensorEvaluator<ArgType, Device> m_impl; |
| }; |
| |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H |