| // 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 | |
| // IWYU pragma: private | |
| #include "./InternalHeaderCheck.h" | |
| namespace Eigen { | |
| 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 std::remove_reference_t<Nested> Nested_; | |
| static constexpr int NumDimensions = XprTraits::NumDimensions; | |
| static constexpr int Layout = XprTraits::Layout; | |
| typedef typename XprTraits::PointerType PointerType; | |
| enum { | |
| // Broadcast is read-only. | |
| Flags = traits<XprType>::Flags & ~LvalueBit | |
| }; | |
| }; | |
| 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; | |
| }; | |
| template <typename std::ptrdiff_t... Indices> | |
| struct is_input_scalar<Sizes<Indices...>> { | |
| static constexpr bool value = (Sizes<Indices...>::total_size == 1); | |
| }; | |
| } // end namespace internal | |
| /** Tensor broadcasting class. | |
| * \ingroup CXX11_Tensor_Module | |
| */ | |
| 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 internal::remove_all_t<typename XprType::Nested>& 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 constexpr 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 constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; | |
| protected: // all the non-static fields must have the same access control, otherwise the TensorEvaluator won't be | |
| // standard layout; | |
| bool isCopy, nByOne, oneByN; | |
| public: | |
| typedef StorageMemory<CoeffReturnType, Device> Storage; | |
| typedef typename Storage::Type EvaluatorPointerType; | |
| enum { | |
| IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, | |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, | |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, | |
| PreferBlockAccess = true, | |
| RawAccess = false | |
| }; | |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; | |
| typedef std::remove_const_t<Scalar> ScalarNoConst; | |
| // 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; | |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// | |
| typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; | |
| typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; | |
| typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock; | |
| typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock; | |
| //===--------------------------------------------------------------------===// | |
| 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 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_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { | |
| m_impl.evalSubExprsIfNeeded(NULL); | |
| return true; | |
| } | |
| #ifdef EIGEN_USE_THREADS | |
| template <typename EvalSubExprsCallback> | |
| EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) { | |
| m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); | |
| } | |
| #endif // EIGEN_USE_THREADS | |
| EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); } | |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const { | |
| if (internal::is_input_scalar<internal::remove_all_t<InputDimensions>>::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<internal::remove_all_t<InputDimensions>>::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_assert(index + PacketSize - 1 < dimensions().TotalSize()); | |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> 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 { | |
| // Consider the flattened tensor [v0, ..., vN], | |
| // Concatenates m_broadcast[dim] copies, | |
| // [v0, ..., vN, v0, ..., vN, ... ] | |
| // with dim == NumDims - 1 for col-major, dim == 0 for row-major. | |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); | |
| // Size of flattened tensor. | |
| const Index M = | |
| (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_inputStrides[NumDims - 1] : m_inputStrides[0]; | |
| Index inputIndex = index % M; | |
| if (inputIndex + PacketSize <= M) { | |
| return m_impl.template packet<Unaligned>(inputIndex); | |
| } else { | |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize]; | |
| EIGEN_UNROLL_LOOP | |
| for (int i = 0; i < PacketSize; ++i) { | |
| if (inputIndex > M - 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 { | |
| // Consider the flattened tensor [v0, ..., vN], | |
| // Interleaves m_broadcast[dim] copies, | |
| // [v0, v0, ..., v1, v1, ..., vN, vN, ... ] | |
| // with dim == 0 for col-major, dim == NumDims - 1 for row-major. | |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); | |
| const Index M = | |
| (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_broadcast[0] : m_broadcast[NumDims - 1]; | |
| Index inputIndex = index / M; | |
| Index outputOffset = index % M; | |
| if (outputOffset + PacketSize <= M) { | |
| return internal::pset1<PacketReturnType>(m_impl.coeff(inputIndex)); | |
| } else { | |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize]; | |
| EIGEN_UNROLL_LOOP | |
| for (int i = 0; i < PacketSize; ++i) { | |
| if (outputOffset < M) { | |
| values[i] = m_impl.coeff(inputIndex); | |
| ++outputOffset; | |
| } else { | |
| values[i] = m_impl.coeff(++inputIndex); | |
| outputOffset = 1; // Next offset. | |
| } | |
| } | |
| 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_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 std::remove_const_t<CoeffReturnType> 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_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 std::remove_const_t<CoeffReturnType> 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 internal::TensorBlockResourceRequirements getResourceRequirements() const { | |
| // TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large | |
| // tensors. But this might need further tuning. | |
| const size_t target_size = m_device.firstLevelCacheSize(); | |
| return internal::TensorBlockResourceRequirements::merge( | |
| m_impl.getResourceRequirements(), internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size)); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(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 TensorBlock::Storage block_storage = TensorBlock::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; } | |
| private: | |
| static constexpr 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 TensorBlock emptyBlock() const { | |
| DSizes<Index, NumDims> dimensions; | |
| for (int i = 0; i < NumDims; ++i) dimensions[i] = 0; | |
| return TensorBlock(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 BroadcastBlock(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 BroadcastBlock(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 = | |
| numext::div_ceil<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 += | |
| BroadcastBlock(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 += | |
| BroadcastBlock(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 += | |
| BroadcastBlock(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 += | |
| BroadcastBlock(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 BroadcastBlock( | |
| 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.block(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::TensorBlockIO<ScalarNoConst, Index, 2 * NumDims, Layout> TensorBlockIO; | |
| typename TensorBlockIO::Src src(bcast_input_strides, input_buffer); | |
| typename TensorBlockIO::Dst dst(bcast_block_sizes, bcast_block_strides, materialized_output + offset); | |
| return TensorBlockIO::Copy(dst, src); | |
| } | |
| protected: | |
| const Device EIGEN_DEVICE_REF m_device; | |
| const std::remove_reference_t<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 |