| // 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, | |
| BlockAccessV2 = TensorEvaluator<ArgType, Device>::BlockAccessV2, | |
| 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; | |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// | |
| typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; | |
| typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; | |
| typedef typename TensorEvaluator<const ArgType, Device>::TensorBlockV2 | |
| ArgTensorBlock; | |
| typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, | |
| Layout, Index> | |
| TensorBlockV2; | |
| //===--------------------------------------------------------------------===// | |
| 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; | |
| } | |
| #ifdef EIGEN_USE_THREADS | |
| template <typename EvalSubExprsCallback> | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync( | |
| EvaluatorPointerType, EvalSubExprsCallback done) { | |
| m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); | |
| } | |
| #endif // EIGEN_USE_THREADS | |
| 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 EIGEN_STRONG_INLINE TensorBlockV2 | |
| blockV2(TensorBlockDesc& desc, TensorBlockScratch& scratch, | |
| bool /*root_of_expr_ast*/ = false) const { | |
| BlockBroadcastingParams params = blockBroadcastingParams(desc); | |
| if (params.inner_dim_size == 0 || params.bcast_dim_size == 0) { | |
| return emptyBlock(); | |
| } | |
| // Prepare storage for the materialized broadcasting result. | |
| const typename TensorBlockV2::Storage block_storage = | |
| TensorBlockV2::prepareStorage(desc, scratch); | |
| ScalarNoConst* materialized_output = block_storage.data(); | |
| // We potentially will need to materialize input blocks. | |
| size_t materialized_input_size = 0; | |
| ScalarNoConst* materialized_input = NULL; | |
| // Initialize block broadcating iterator state for outer dimensions (outer | |
| // with regard to bcast dimension). Dimension in this array are always in | |
| // inner_most -> outer_most order (col major layout). | |
| array<BlockBroadcastingIteratorState, NumDims> it; | |
| int idx = 0; | |
| for (int i = params.inner_dim_count + 1; i < NumDims; ++i) { | |
| const Index dim = IsColMajor ? i : NumDims - 1 - i; | |
| it[idx].size = params.output_dims[dim]; | |
| it[idx].count = 0; | |
| it[idx].output_stride = m_outputStrides[dim]; | |
| it[idx].output_span = it[idx].output_stride * (it[idx].size - 1); | |
| idx++; | |
| } | |
| // Write output into the beginning of `materialized_output`. | |
| Index output_offset = 0; | |
| // We will fill output block by broadcasting along the bcast dim, and | |
| // iterating over outer dimension. | |
| const Index output_size = NumDims == 0 ? 1 : params.output_dims.TotalSize(); | |
| for (Index num_output_coeffs = 0; num_output_coeffs < output_size;) { | |
| ScalarNoConst* bcast_output = materialized_output + num_output_coeffs; | |
| Index bcast_offset = desc.offset() + output_offset; | |
| // Broadcast along the bcast dimension. | |
| num_output_coeffs += BroadcastBlockAlongBcastDim( | |
| params, bcast_offset, scratch, bcast_output, &materialized_input, | |
| &materialized_input_size); | |
| // Switch to the next outer dimension. | |
| for (int j = 0; j < idx; ++j) { | |
| if (++it[j].count < it[j].size) { | |
| output_offset += it[j].output_stride; | |
| break; | |
| } | |
| it[j].count = 0; | |
| output_offset -= it[j].output_span; | |
| } | |
| } | |
| return block_storage.AsTensorMaterializedBlock(); | |
| } | |
| 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: | |
| static const bool IsColMajor = | |
| static_cast<int>(Layout) == static_cast<int>(ColMajor); | |
| // We will build a general case block broadcasting on top of broadcasting | |
| // primitive that will do broadcasting only for the inner dimension(s) along | |
| // the first dimension smaller than the input size (it's called `bcast_dim`). | |
| // | |
| // Example: | |
| // dim: 0 1 2 (ColMajor) | |
| // input size: [9, 3, 6] | |
| // block size: [9, 2, 6] | |
| // | |
| // We will compute broadcasted block by iterating over the outer dimensions | |
| // before `bcast_dim` (only dimension `2` in this example) and computing | |
| // broadcasts along the `bcast_dim` (dimension `1` in this example). | |
| // BlockBroadcastingParams holds precomputed parameters for broadcasting a | |
| // single block along the broadcasting dimension. Sizes and strides along the | |
| // `bcast_dim` might be invalid, they will be adjusted later in | |
| // `BroadcastBlockAlongBcastDim`. | |
| struct BlockBroadcastingParams { | |
| Dimensions input_dims; // input expression dimensions | |
| Dimensions output_dims; // output block sizes | |
| Dimensions output_strides; // output block strides | |
| int inner_dim_count; // count inner dimensions matching in size | |
| int bcast_dim; // broadcasting dimension index | |
| Index bcast_dim_size; // broadcasting dimension size | |
| Index inner_dim_size; // inner dimensions size | |
| // Block sizes and strides for the input block where all dimensions before | |
| // `bcast_dim` are equal to `1`. | |
| Dimensions input_block_sizes; | |
| Dimensions input_block_strides; | |
| // Block sizes and strides for blocks with extra dimensions and strides `0`. | |
| BroadcastDimensions bcast_block_sizes; | |
| BroadcastDimensions bcast_block_strides; | |
| BroadcastDimensions bcast_input_strides; | |
| }; | |
| struct BlockBroadcastingIteratorState { | |
| Index size; | |
| Index count; | |
| Index output_stride; | |
| Index output_span; | |
| }; | |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlockBroadcastingParams | |
| blockBroadcastingParams(TensorBlockDesc& desc) const { | |
| BlockBroadcastingParams params; | |
| params.input_dims = Dimensions(m_impl.dimensions()); | |
| // Output block sizes and strides. | |
| params.output_dims = desc.dimensions(); | |
| params.output_strides = internal::strides<Layout>(params.output_dims); | |
| // Find the broadcasting dimension (first dimension with output size smaller | |
| // that the input size). | |
| params.bcast_dim = 0; | |
| params.bcast_dim_size = 1; | |
| params.inner_dim_size = 1; | |
| // Count the number of inner dimensions that have the same size in the block | |
| // and in the broadcast expression. | |
| params.inner_dim_count = 0; | |
| for (int i = 0; i < NumDims; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| if (params.output_dims[dim] == m_dimensions[dim]) { | |
| params.inner_dim_size *= params.output_dims[dim]; | |
| ++params.inner_dim_count; | |
| continue; | |
| } | |
| // First non-matching dimension is the broadcasting dimension. | |
| eigen_assert(params.output_dims[dim] < m_dimensions[dim]); | |
| params.bcast_dim = dim; | |
| params.bcast_dim_size = params.output_dims[dim]; | |
| break; | |
| } | |
| // Calculate the input block size for looking into the input. | |
| for (int i = 0; i < params.inner_dim_count; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| params.input_block_sizes[dim] = params.input_dims[dim]; | |
| } | |
| for (int i = params.inner_dim_count; i < NumDims; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| params.input_block_sizes[dim] = 1; | |
| } | |
| params.input_block_strides = | |
| internal::strides<Layout>(params.input_block_sizes); | |
| // Broadcast with the 0-stride trick: Create 1 extra dim for each | |
| // broadcast, set the input stride to 0. | |
| // | |
| // When ColMajor: | |
| // | |
| // - bcast_block_sizes: | |
| // [d_0, b_0, d_1, b_1, ...] | |
| // | |
| // - bcast_block_strides: | |
| // [output_block_strides[0], output_block_strides[0] * d_0, | |
| // output_block_strides[1], output_block_strides[1] * d_1, | |
| // ...] | |
| // | |
| // - bcast_input_strides: | |
| // [input_block_strides[0], 0, | |
| // input_block_strides[1], 0, | |
| // ...]. | |
| // | |
| for (int i = 0; i < params.inner_dim_count; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| const int copy_dim = IsColMajor ? 2 * i : 2 * NumDims - 2 * i - 1; | |
| const int broadcast_dim = IsColMajor ? copy_dim + 1 : copy_dim - 1; | |
| params.bcast_block_sizes[copy_dim] = params.input_dims[dim]; | |
| params.bcast_block_sizes[broadcast_dim] = m_broadcast[dim]; | |
| params.bcast_block_strides[copy_dim] = params.output_strides[dim]; | |
| params.bcast_block_strides[broadcast_dim] = | |
| params.output_strides[dim] * params.input_dims[dim]; | |
| params.bcast_input_strides[copy_dim] = params.input_block_strides[dim]; | |
| params.bcast_input_strides[broadcast_dim] = 0; | |
| } | |
| for (int i = 2 * params.inner_dim_count; i < 2 * NumDims; ++i) { | |
| const int dim = IsColMajor ? i : 2 * NumDims - i - 1; | |
| params.bcast_block_sizes[dim] = 1; | |
| params.bcast_block_strides[dim] = 0; | |
| params.bcast_input_strides[dim] = 0; | |
| } | |
| return params; | |
| } | |
| 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()); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlockV2 emptyBlock() const { | |
| DSizes<Index, NumDims> dimensions; | |
| for (int i = 0; i < NumDims; ++i) dimensions[i] = 0; | |
| return TensorBlockV2(internal::TensorBlockKind::kView, NULL, dimensions); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlockAlongBcastDim( | |
| BlockBroadcastingParams params, Index bcast_offset, | |
| TensorBlockScratch& scratch, ScalarNoConst* materialized_output, | |
| ScalarNoConst** materialized_input, | |
| size_t* materialized_input_size) const { | |
| if (params.bcast_dim_size == 1) { | |
| // We just need one block read using the ready-set values above. | |
| return BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, 0, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } else if (params.input_dims[params.bcast_dim] == 1) { | |
| // Broadcast bcast dimension (< NumDims) by bcast_dim_size. | |
| const int broadcast_bcast_dim = | |
| IsColMajor ? 2 * params.inner_dim_count + 1 | |
| : 2 * NumDims - 2 * params.inner_dim_count - 2; | |
| params.bcast_block_sizes[broadcast_bcast_dim] = params.bcast_dim_size; | |
| params.bcast_input_strides[broadcast_bcast_dim] = 0; | |
| params.bcast_block_strides[broadcast_bcast_dim] = | |
| params.output_strides[params.bcast_dim]; | |
| return BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, 0, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } else { | |
| // Keep track of the total number of the coefficients written to the | |
| // output block. | |
| Index num_output_coeffs = 0; | |
| // The general case. Let's denote the output block as | |
| // | |
| // x[..., a:a+bcast_dim_size, :, ..., :] | |
| // | |
| // where a:a+bcast_dim_size is a slice on the bcast_dim dimension | |
| // (< NumDims). We need to split the a:a+bcast_dim_size into possibly 3 | |
| // sub-blocks: | |
| // | |
| // (1) a:b, where b is the smallest multiple of | |
| // input_dims[bcast_dim_start] in [a, a+bcast_dim_size]. | |
| // | |
| // (2) b:c, where c is the largest multiple of input_dims[bcast_dim_start] | |
| // in [a, a+bcast_dim_size]. | |
| // | |
| // (3) c:a+bcast_dim_size . | |
| // | |
| // Or, when b and c do not exist, we just need to process the whole block | |
| // together. | |
| // Find a. | |
| const Index bcast_dim_left_index = | |
| bcast_offset / m_outputStrides[params.bcast_dim]; | |
| // Find b and c. | |
| const Index input_bcast_dim_size = params.input_dims[params.bcast_dim]; | |
| // First multiple after a. This is b when <= bcast_dim_left_index + | |
| // bcast_dim_size. | |
| const Index first_multiple = | |
| divup<Index>(bcast_dim_left_index, input_bcast_dim_size) * | |
| input_bcast_dim_size; | |
| if (first_multiple <= bcast_dim_left_index + params.bcast_dim_size) { | |
| // b exists, so does c. Find it. | |
| const Index last_multiple = | |
| (bcast_dim_left_index + params.bcast_dim_size) / | |
| input_bcast_dim_size * input_bcast_dim_size; | |
| const int copy_bcast_dim = | |
| IsColMajor ? 2 * params.inner_dim_count | |
| : 2 * NumDims - 2 * params.inner_dim_count - 1; | |
| const int broadcast_bcast_dim = | |
| IsColMajor ? 2 * params.inner_dim_count + 1 | |
| : 2 * NumDims - 2 * params.inner_dim_count - 2; | |
| if (first_multiple > bcast_dim_left_index) { | |
| const Index head_size = first_multiple - bcast_dim_left_index; | |
| params.input_block_sizes[params.bcast_dim] = head_size; | |
| params.bcast_block_sizes[copy_bcast_dim] = head_size; | |
| params.bcast_input_strides[copy_bcast_dim] = | |
| params.input_block_strides[params.bcast_dim]; | |
| params.bcast_block_strides[copy_bcast_dim] = | |
| params.output_strides[params.bcast_dim]; | |
| params.bcast_block_sizes[broadcast_bcast_dim] = 1; | |
| params.bcast_input_strides[broadcast_bcast_dim] = 0; | |
| params.bcast_block_strides[broadcast_bcast_dim] = | |
| params.output_strides[params.bcast_dim] * | |
| params.input_dims[params.bcast_dim]; | |
| num_output_coeffs += BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, 0, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } | |
| if (first_multiple < last_multiple) { | |
| params.input_block_sizes[params.bcast_dim] = input_bcast_dim_size; | |
| params.bcast_block_sizes[copy_bcast_dim] = input_bcast_dim_size; | |
| params.bcast_input_strides[copy_bcast_dim] = | |
| params.input_block_strides[params.bcast_dim]; | |
| params.bcast_block_strides[copy_bcast_dim] = | |
| params.output_strides[params.bcast_dim]; | |
| params.bcast_block_sizes[broadcast_bcast_dim] = | |
| (last_multiple - first_multiple) / input_bcast_dim_size; | |
| params.bcast_input_strides[broadcast_bcast_dim] = 0; | |
| params.bcast_block_strides[broadcast_bcast_dim] = | |
| params.output_strides[params.bcast_dim] * | |
| params.input_dims[params.bcast_dim]; | |
| const Index offset = (first_multiple - bcast_dim_left_index) * | |
| m_outputStrides[params.bcast_dim]; | |
| num_output_coeffs += BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, offset, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } | |
| if (last_multiple < bcast_dim_left_index + params.bcast_dim_size) { | |
| const Index tail_size = | |
| bcast_dim_left_index + params.bcast_dim_size - last_multiple; | |
| params.input_block_sizes[params.bcast_dim] = tail_size; | |
| params.bcast_block_sizes[copy_bcast_dim] = tail_size; | |
| params.bcast_input_strides[copy_bcast_dim] = | |
| params.input_block_strides[params.bcast_dim]; | |
| params.bcast_block_strides[copy_bcast_dim] = | |
| params.output_strides[params.bcast_dim]; | |
| params.bcast_block_sizes[broadcast_bcast_dim] = 1; | |
| params.bcast_input_strides[broadcast_bcast_dim] = 0; | |
| params.bcast_block_strides[broadcast_bcast_dim] = | |
| params.output_strides[params.bcast_dim] * | |
| params.input_dims[params.bcast_dim]; | |
| const Index offset = (last_multiple - bcast_dim_left_index) * | |
| m_outputStrides[params.bcast_dim]; | |
| num_output_coeffs += BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, offset, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } | |
| } else { | |
| // b and c do not exist. | |
| const int copy_bcast_dim = | |
| IsColMajor ? 2 * params.inner_dim_count | |
| : 2 * NumDims - 2 * params.inner_dim_count - 1; | |
| params.input_block_sizes[params.bcast_dim] = params.bcast_dim_size; | |
| params.bcast_block_sizes[copy_bcast_dim] = params.bcast_dim_size; | |
| params.bcast_input_strides[copy_bcast_dim] = | |
| params.input_block_strides[params.bcast_dim]; | |
| params.bcast_block_strides[copy_bcast_dim] = | |
| params.output_strides[params.bcast_dim]; | |
| num_output_coeffs += BroadcastBlockV2( | |
| params.input_block_sizes, params.input_block_strides, | |
| params.bcast_block_sizes, params.bcast_block_strides, | |
| params.bcast_input_strides, bcast_offset, 0, scratch, | |
| materialized_output, materialized_input, materialized_input_size); | |
| } | |
| return num_output_coeffs; | |
| } | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlockV2( | |
| const Dimensions& input_block_sizes, | |
| const Dimensions& input_block_strides, | |
| const BroadcastDimensions& bcast_block_sizes, | |
| const BroadcastDimensions& bcast_block_strides, | |
| const BroadcastDimensions& bcast_input_strides, Index bcast_offset, | |
| Index offset, TensorBlockScratch& scratch, | |
| ScalarNoConst* materialized_output, ScalarNoConst** materialized_input, | |
| size_t* materialized_input_size) const { | |
| // ---------------------------------------------------------------------- // | |
| // Tensor block descriptor for reading block from the input. | |
| const Index input_offset = bcast_offset + offset; | |
| TensorBlockDesc input_desc( | |
| IsColMajor ? indexColMajor(input_offset) : indexRowMajor(input_offset), | |
| input_block_sizes); | |
| ArgTensorBlock input_block = m_impl.blockV2(input_desc, scratch); | |
| // ---------------------------------------------------------------------- // | |
| // Materialize input block into a temporary memory buffer only if it's not | |
| // already available in the arg block. | |
| const ScalarNoConst* input_buffer = NULL; | |
| if (input_block.data() != NULL) { | |
| // Input block already has raw data, there is no need to materialize it. | |
| input_buffer = input_block.data(); | |
| } else { | |
| // Otherwise we have to do block assignment into a temporary buffer. | |
| // Maybe reuse previously allocated buffer, or allocate a new one with a | |
| // scratch allocator. | |
| const size_t input_total_size = input_block_sizes.TotalSize(); | |
| if (*materialized_input == NULL || | |
| *materialized_input_size < input_total_size) { | |
| *materialized_input_size = input_total_size; | |
| void* mem = scratch.allocate(*materialized_input_size * sizeof(Scalar)); | |
| *materialized_input = static_cast<ScalarNoConst*>(mem); | |
| } | |
| typedef internal::TensorBlockAssignment< | |
| ScalarNoConst, NumDims, typename ArgTensorBlock::XprType, Index> | |
| TensorBlockAssignment; | |
| TensorBlockAssignment::Run( | |
| TensorBlockAssignment::target(input_block_sizes, input_block_strides, | |
| *materialized_input), | |
| input_block.expr()); | |
| input_buffer = *materialized_input; | |
| } | |
| // ---------------------------------------------------------------------- // | |
| // Copy data from materialized input block to the materialized output, using | |
| // given broadcast strides (strides with zeroes). | |
| typedef internal::TensorBlockIOV2<ScalarNoConst, Index, 2 * NumDims, Layout> | |
| TensorBlockIOV2; | |
| typename TensorBlockIOV2::Src src(bcast_input_strides, input_buffer); | |
| typename TensorBlockIOV2::Dst dst(bcast_block_sizes, bcast_block_strides, | |
| materialized_output + offset); | |
| return TensorBlockIOV2::Copy(dst, src); | |
| } | |
| 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 |