| // 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; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename Broadcast, typename XprType> |
| struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense> |
| { |
| typedef const TensorBroadcastingOp<Broadcast, XprType> EIGEN_DEVICE_REF 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::ptrdiff_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 = PacketType<CoeffReturnType, Device>::size; |
| protected: // all the non-static fields must have the same access control, otherwise the TensorEvaluator wont be standard layout; |
| bool isCopy, nByOne, oneByN; |
| public: |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| enum { |
| IsAligned = true, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| RawAccess = false |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| // Block based access to the XprType (input) tensor. |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlock; |
| typedef internal::TensorBlockReader<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlockReader; |
| |
| // We do block based broadcasting using a trick with 2x tensor rank and 0 |
| // strides. See block method implementation for details. |
| typedef DSizes<Index, 2 * NumDims> BroadcastDimensions; |
| typedef internal::TensorBlock<ScalarNoConst, Index, 2 * NumDims, Layout> |
| BroadcastTensorBlock; |
| typedef internal::TensorBlockReader<ScalarNoConst, Index, 2 * NumDims, Layout> |
| BroadcastTensorBlockReader; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, |
| const Device& device) |
| : isCopy(false), nByOne(false), oneByN(false), |
| m_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(); |
| isCopy = true; |
| for (int i = 0; i < NumDims; ++i) { |
| eigen_assert(input_dims[i] > 0); |
| m_dimensions[i] = input_dims[i] * m_broadcast[i]; |
| if (m_broadcast[i] != 1) { |
| isCopy = false; |
| } |
| } |
| |
| 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]; |
| } |
| } |
| |
| if (input_dims[0] == 1) { |
| oneByN = true; |
| for (int i = 1; i < NumDims; ++i) { |
| if (m_broadcast[i] != 1) { |
| oneByN = false; |
| break; |
| } |
| } |
| } else if (input_dims[NumDims-1] == 1) { |
| nByOne = true; |
| for (int i = 0; i < NumDims-1; ++i) { |
| if (m_broadcast[i] != 1) { |
| nByOne = false; |
| break; |
| } |
| } |
| } |
| |
| // Handle special format like NCHW, its input shape is '[1, N..., 1]' and |
| // broadcast shape is '[N, 1..., N]' |
| if (!oneByN && !nByOne) { |
| if (input_dims[0] == 1 && input_dims[NumDims-1] == 1 && NumDims > 2) { |
| nByOne = true; |
| oneByN = true; |
| for (int i = 1; i < NumDims-1; ++i) { |
| if (m_broadcast[i] != 1) { |
| nByOne = false; |
| oneByN = false; |
| break; |
| } |
| } |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { |
| 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)) { |
| if (isCopy) { |
| return m_impl.coeff(index); |
| } else { |
| return coeffColMajor(index); |
| } |
| } else { |
| if (isCopy) { |
| return m_impl.coeff(index); |
| } else { |
| return coeffRowMajor(index); |
| } |
| } |
| } |
| |
| // TODO: attempt to speed this up. The integer divisions and modulo are slow |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexColMajor(Index index) const { |
| Index inputIndex = 0; |
| EIGEN_UNROLL_LOOP |
| 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 inputIndex; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const |
| { |
| return m_impl.coeff(indexColMajor(index)); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexRowMajor(Index index) const { |
| Index inputIndex = 0; |
| EIGEN_UNROLL_LOOP |
| 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 inputIndex; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const |
| { |
| return m_impl.coeff(indexRowMajor(index)); |
| } |
| |
| 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)) { |
| if (isCopy) { |
| #ifdef EIGEN_GPU_COMPILE_PHASE |
| // See PR 437: on NVIDIA P100 and K20m we observed a x3-4 speed up by enforcing |
| // unaligned loads here. The reason is unclear though. |
| return m_impl.template packet<Unaligned>(index); |
| #else |
| return m_impl.template packet<LoadMode>(index); |
| #endif |
| } else if (oneByN && !nByOne) { |
| return packetNByOne<LoadMode>(index); |
| } else if (!oneByN && nByOne) { |
| return packetOneByN<LoadMode>(index); |
| } else if (oneByN && nByOne) { |
| return packetOneByNByOne<LoadMode>(index); |
| } else { |
| return packetColMajor<LoadMode>(index); |
| } |
| } else { |
| if (isCopy) { |
| #ifdef EIGEN_GPU_COMPILE_PHASE |
| // See above. |
| return m_impl.template packet<Unaligned>(index); |
| #else |
| return m_impl.template packet<LoadMode>(index); |
| #endif |
| } else if (oneByN && !nByOne) { |
| return packetOneByN<LoadMode>(index); |
| } else if (!oneByN && nByOne) { |
| return packetNByOne<LoadMode>(index); |
| } else if (oneByN && nByOne) { |
| return packetOneByNByOne<LoadMode>(index); |
| } else { |
| return packetRowMajor<LoadMode>(index); |
| } |
| } |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByNByOne |
| (Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| Index startDim, endDim; |
| Index inputIndex, outputOffset, batchedIndex; |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| startDim = NumDims - 1; |
| endDim = 1; |
| } else { |
| startDim = 0; |
| endDim = NumDims - 2; |
| } |
| |
| batchedIndex = index % m_outputStrides[startDim]; |
| inputIndex = batchedIndex / m_outputStrides[endDim]; |
| outputOffset = batchedIndex % m_outputStrides[endDim]; |
| |
| if (outputOffset + PacketSize <= m_outputStrides[endDim]) { |
| values[0] = m_impl.coeff(inputIndex); |
| return internal::pload1<PacketReturnType>(values); |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) { |
| if (outputOffset + cur < m_outputStrides[endDim]) { |
| values[i] = m_impl.coeff(inputIndex); |
| } else { |
| ++inputIndex; |
| inputIndex = (inputIndex == m_inputStrides[startDim] ? 0 : inputIndex); |
| values[i] = m_impl.coeff(inputIndex); |
| outputOffset = 0; |
| cur = 0; |
| } |
| } |
| return internal::pload<PacketReturnType>(values); |
| } |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| Index dim, inputIndex; |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| dim = NumDims - 1; |
| } else { |
| dim = 0; |
| } |
| |
| inputIndex = index % m_inputStrides[dim]; |
| if (inputIndex + PacketSize <= m_inputStrides[dim]) { |
| return m_impl.template packet<Unaligned>(inputIndex); |
| } else { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| if (inputIndex > m_inputStrides[dim]-1) { |
| inputIndex = 0; |
| } |
| values[i] = m_impl.coeff(inputIndex++); |
| } |
| return internal::pload<PacketReturnType>(values); |
| } |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetNByOne(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| Index dim, inputIndex, outputOffset; |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| dim = 1; |
| } else { |
| dim = NumDims - 2; |
| } |
| |
| inputIndex = index / m_outputStrides[dim]; |
| outputOffset = index % m_outputStrides[dim]; |
| if (outputOffset + PacketSize <= m_outputStrides[dim]) { |
| values[0] = m_impl.coeff(inputIndex); |
| return internal::pload1<PacketReturnType>(values); |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) { |
| if (outputOffset + cur < m_outputStrides[dim]) { |
| values[i] = m_impl.coeff(inputIndex); |
| } else { |
| values[i] = m_impl.coeff(++inputIndex); |
| outputOffset = 0; |
| cur = 0; |
| } |
| } |
| return internal::pload<PacketReturnType>(values); |
| } |
| } |
| |
| // 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; |
| EIGEN_UNROLL_LOOP |
| 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); |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < PacketSize; ++i) { |
| if (innermostLoc + i < m_impl.dimensions()[0]) { |
| values[i] = m_impl.coeff(inputIndex+i); |
| } else { |
| 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; |
| EIGEN_UNROLL_LOOP |
| 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); |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < PacketSize; ++i) { |
| if (innermostLoc + i < m_impl.dimensions()[NumDims-1]) { |
| values[i] = m_impl.coeff(inputIndex+i); |
| } else { |
| 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 (!isCopy && NumDims > 0) { |
| EIGEN_UNROLL_LOOP |
| 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 EIGEN_STRONG_INLINE void getResourceRequirements( |
| std::vector<internal::TensorOpResourceRequirements>* resources) const { |
| // TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large |
| // tensors. But this might need further tuning. |
| Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>( |
| 1, m_device.firstLevelCacheSize() / sizeof(Scalar)); |
| |
| resources->push_back(internal::TensorOpResourceRequirements( |
| internal::kSkewedInnerDims, block_total_size_max)); |
| |
| m_impl.getResourceRequirements(resources); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( |
| TensorBlock* output_block) const { |
| if (NumDims <= 0) { |
| output_block->data()[0] = m_impl.coeff(0); |
| return; |
| } |
| |
| // Because we only support kSkewedInnerDims blocking, block size should be |
| // equal to m_dimensions for inner dims, a smaller than m_dimensions[i] size |
| // for the first outer dim, and 1 for other outer dims. This is guaranteed |
| // by MergeResourceRequirements() in TensorBlock.h. |
| const Dimensions& output_block_sizes = output_block->block_sizes(); |
| const Dimensions& output_block_strides = output_block->block_strides(); |
| |
| // Find where outer dims start. |
| int outer_dim_start = 0; |
| Index outer_dim_size = 1, inner_dim_size = 1; |
| for (int i = 0; i < NumDims; ++i) { |
| const int dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i |
| : NumDims - i - 1; |
| if (i > outer_dim_start) { |
| eigen_assert(output_block_sizes[dim] == 1); |
| } else if (output_block_sizes[dim] != m_dimensions[dim]) { |
| eigen_assert(output_block_sizes[dim] < m_dimensions[dim]); |
| outer_dim_size = output_block_sizes[dim]; |
| } else { |
| inner_dim_size *= output_block_sizes[dim]; |
| ++outer_dim_start; |
| } |
| } |
| |
| if (inner_dim_size == 0 || outer_dim_size == 0) { |
| return; |
| } |
| |
| const Dimensions& input_dims = Dimensions(m_impl.dimensions()); |
| |
| // Pre-fill input_block_sizes, broadcast_block_sizes, |
| // broadcast_block_strides, and broadcast_tensor_strides. Later on we will |
| // only modify the outer_dim_start-th dimension on these arrays. |
| |
| // Calculate the input block size for looking into the input. |
| Dimensions input_block_sizes; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = 0; i < outer_dim_start; ++i) { |
| input_block_sizes[i] = input_dims[i]; |
| } |
| for (int i = outer_dim_start; i < NumDims; ++i) { |
| input_block_sizes[i] = 1; |
| } |
| } else { |
| for (int i = 0; i < outer_dim_start; ++i) { |
| input_block_sizes[NumDims - i - 1] = input_dims[NumDims - i - 1]; |
| } |
| for (int i = outer_dim_start; i < NumDims; ++i) { |
| input_block_sizes[NumDims - i - 1] = 1; |
| } |
| } |
| |
| // Broadcast with the 0-stride trick: Create 1 extra dim for each |
| // broadcast, set the input stride to 0. |
| // |
| // When ColMajor: |
| // - broadcast_block_sizes is [d_0, b_0, d_1, b_1, ...]. |
| // |
| // - broadcast_block_strides is [output_block_strides[0], |
| // output_block_strides[0] * d_0, |
| // output_block_strides[1], |
| // output_block_strides[1] * d_1, |
| // ...]. |
| // |
| // - broadcast_tensor_strides is [output_block_strides[0], |
| // 0, |
| // output_block_strides[1], |
| // 0, |
| // ...]. |
| BroadcastDimensions broadcast_block_sizes, broadcast_block_strides, |
| broadcast_tensor_strides; |
| |
| for (int i = 0; i < outer_dim_start; ++i) { |
| const int dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i |
| : NumDims - i - 1; |
| const int copy_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 2 * i |
| : 2 * NumDims - 2 * i - 1; |
| const int broadcast_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) ? copy_dim + 1 |
| : copy_dim - 1; |
| broadcast_block_sizes[copy_dim] = input_dims[dim]; |
| broadcast_block_sizes[broadcast_dim] = m_broadcast[dim]; |
| broadcast_block_strides[copy_dim] = output_block_strides[dim]; |
| broadcast_block_strides[broadcast_dim] = |
| output_block_strides[dim] * input_dims[dim]; |
| broadcast_tensor_strides[copy_dim] = m_inputStrides[dim]; |
| broadcast_tensor_strides[broadcast_dim] = 0; |
| } |
| for (int i = 2 * outer_dim_start; i < 2 * NumDims; ++i) { |
| const int dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i |
| : 2 * NumDims - i - 1; |
| broadcast_block_sizes[dim] = 1; |
| broadcast_block_strides[dim] = 0; |
| broadcast_tensor_strides[dim] = 0; |
| } |
| |
| const int outer_dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? outer_dim_start |
| : NumDims - outer_dim_start - 1; |
| |
| if (outer_dim_size == 1) { |
| // We just need one block read using the ready-set values above. |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, 0, |
| output_block); |
| } else if (input_dims[outer_dim] == 1) { |
| // Broadcast outer_dim_start-th dimension (< NumDims) by outer_dim_size. |
| const int broadcast_outer_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 2 * outer_dim_start + 1 |
| : 2 * NumDims - 2 * outer_dim_start - 2; |
| broadcast_block_sizes[broadcast_outer_dim] = outer_dim_size; |
| broadcast_tensor_strides[broadcast_outer_dim] = 0; |
| broadcast_block_strides[broadcast_outer_dim] = |
| output_block_strides[outer_dim]; |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, 0, |
| output_block); |
| } else { |
| // The general case. Let's denote the output block as x[..., |
| // a:a+outer_dim_size, :, ..., :], where a:a+outer_dim_size is a slice on |
| // the outer_dim_start-th dimension (< NumDims). We need to split the |
| // a:a+outer_dim_size into possibly 3 sub-blocks: |
| // |
| // (1) a:b, where b is the smallest multiple of |
| // input_dims[outer_dim_start] in [a, a+outer_dim_size]. |
| // |
| // (2) b:c, where c is the largest multiple of input_dims[outer_dim_start] |
| // in [a, a+outer_dim_size]. |
| // |
| // (3) c:a+outer_dim_size . |
| // |
| // Or, when b and c do not exist, we just need to process the whole block |
| // together. |
| |
| // Find a. |
| const Index outer_dim_left_index = |
| output_block->first_coeff_index() / m_outputStrides[outer_dim]; |
| |
| // Find b and c. |
| const Index input_outer_dim_size = input_dims[outer_dim]; |
| |
| // First multiple after a. This is b when <= outer_dim_left_index + |
| // outer_dim_size. |
| const Index first_multiple = |
| divup<Index>(outer_dim_left_index, input_outer_dim_size) * |
| input_outer_dim_size; |
| |
| if (first_multiple <= outer_dim_left_index + outer_dim_size) { |
| // b exists, so does c. Find it. |
| const Index last_multiple = (outer_dim_left_index + outer_dim_size) / |
| input_outer_dim_size * input_outer_dim_size; |
| const int copy_outer_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 2 * outer_dim_start |
| : 2 * NumDims - 2 * outer_dim_start - 1; |
| const int broadcast_outer_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 2 * outer_dim_start + 1 |
| : 2 * NumDims - 2 * outer_dim_start - 2; |
| if (first_multiple > outer_dim_left_index) { |
| const Index head_size = first_multiple - outer_dim_left_index; |
| input_block_sizes[outer_dim] = head_size; |
| broadcast_block_sizes[copy_outer_dim] = head_size; |
| broadcast_tensor_strides[copy_outer_dim] = m_inputStrides[outer_dim]; |
| broadcast_block_strides[copy_outer_dim] = |
| output_block_strides[outer_dim]; |
| broadcast_block_sizes[broadcast_outer_dim] = 1; |
| broadcast_tensor_strides[broadcast_outer_dim] = 0; |
| broadcast_block_strides[broadcast_outer_dim] = |
| output_block_strides[outer_dim] * input_dims[outer_dim]; |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, 0, |
| output_block); |
| } |
| if (first_multiple < last_multiple) { |
| input_block_sizes[outer_dim] = input_outer_dim_size; |
| broadcast_block_sizes[copy_outer_dim] = input_outer_dim_size; |
| broadcast_tensor_strides[copy_outer_dim] = m_inputStrides[outer_dim]; |
| broadcast_block_strides[copy_outer_dim] = |
| output_block_strides[outer_dim]; |
| broadcast_block_sizes[broadcast_outer_dim] = |
| (last_multiple - first_multiple) / input_outer_dim_size; |
| broadcast_tensor_strides[broadcast_outer_dim] = 0; |
| broadcast_block_strides[broadcast_outer_dim] = |
| output_block_strides[outer_dim] * input_dims[outer_dim]; |
| const Index offset = (first_multiple - outer_dim_left_index) * |
| m_outputStrides[outer_dim]; |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, |
| offset, output_block); |
| } |
| if (last_multiple < outer_dim_left_index + outer_dim_size) { |
| const Index tail_size = |
| outer_dim_left_index + outer_dim_size - last_multiple; |
| input_block_sizes[outer_dim] = tail_size; |
| broadcast_block_sizes[copy_outer_dim] = tail_size; |
| broadcast_tensor_strides[copy_outer_dim] = m_inputStrides[outer_dim]; |
| broadcast_block_strides[copy_outer_dim] = |
| output_block_strides[outer_dim]; |
| broadcast_block_sizes[broadcast_outer_dim] = 1; |
| broadcast_tensor_strides[broadcast_outer_dim] = 0; |
| broadcast_block_strides[broadcast_outer_dim] = |
| output_block_strides[outer_dim] * input_dims[outer_dim]; |
| const Index offset = (last_multiple - outer_dim_left_index) * |
| m_outputStrides[outer_dim]; |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, |
| offset, output_block); |
| } |
| } else { |
| // b and c do not exist. |
| const int copy_outer_dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 2 * outer_dim_start |
| : 2 * NumDims - 2 * outer_dim_start - 1; |
| input_block_sizes[outer_dim] = outer_dim_size; |
| broadcast_block_sizes[copy_outer_dim] = outer_dim_size; |
| broadcast_tensor_strides[copy_outer_dim] = m_inputStrides[outer_dim]; |
| broadcast_block_strides[copy_outer_dim] = |
| output_block_strides[outer_dim]; |
| BroadcastBlock(input_block_sizes, broadcast_block_sizes, |
| broadcast_block_strides, broadcast_tensor_strides, 0, |
| output_block); |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; } |
| |
| const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| |
| Broadcast functor() const { return m_broadcast; } |
| #ifdef EIGEN_USE_SYCL |
| // binding placeholder accessors to a command group handler for SYCL |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const { |
| m_impl.bind(cgh); |
| } |
| #endif |
| private: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void BroadcastBlock( |
| const Dimensions& input_block_sizes, |
| const BroadcastDimensions& broadcast_block_sizes, |
| const BroadcastDimensions& broadcast_block_strides, |
| const BroadcastDimensions& broadcast_tensor_strides, Index offset, |
| TensorBlock* output_block) const { |
| TensorBlock input_view_block( |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? indexColMajor(output_block->first_coeff_index() + offset) |
| : indexRowMajor(output_block->first_coeff_index() + offset), |
| input_block_sizes, Dimensions(m_inputStrides), |
| Dimensions(m_inputStrides), NULL); |
| |
| internal::TensorBlockView<ArgType, Device> input_block(m_device, m_impl, |
| input_view_block); |
| BroadcastTensorBlock broadcast_block( |
| 0, broadcast_block_sizes, broadcast_block_strides, |
| broadcast_tensor_strides, output_block->data() + offset); |
| |
| BroadcastTensorBlockReader::Run(&broadcast_block, input_block.data()); |
| } |
| |
| protected: |
| const Device EIGEN_DEVICE_REF m_device; |
| const typename internal::remove_reference<Broadcast>::type 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 |
