| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> |
| // Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.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_REDUCTION_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H |
| |
| namespace Eigen { |
| |
| /** \class TensorReduction |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor reduction class. |
| * |
| */ |
| |
| namespace internal { |
| template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ > |
| struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> > |
| : traits<XprType> |
| { |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::Scalar Scalar; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value; |
| static const int Layout = XprTraits::Layout; |
| |
| template <class T> struct MakePointer { |
| // Intermediate typedef to workaround MSVC issue. |
| typedef MakePointer_<T> MakePointerT; |
| typedef typename MakePointerT::Type Type; |
| }; |
| }; |
| |
| template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_> |
| struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense> |
| { |
| typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type; |
| }; |
| |
| template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_> |
| struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type> |
| { |
| typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type; |
| }; |
| |
| |
| template <typename OutputDims> struct DimInitializer { |
| template <typename InputDims, typename ReducedDims> EIGEN_DEVICE_FUNC |
| static void run(const InputDims& input_dims, |
| const array<bool, internal::array_size<InputDims>::value>& reduced, |
| OutputDims* output_dims, ReducedDims* reduced_dims) { |
| const int NumInputDims = internal::array_size<InputDims>::value; |
| int outputIndex = 0; |
| int reduceIndex = 0; |
| for (int i = 0; i < NumInputDims; ++i) { |
| if (reduced[i]) { |
| (*reduced_dims)[reduceIndex] = input_dims[i]; |
| ++reduceIndex; |
| } else { |
| (*output_dims)[outputIndex] = input_dims[i]; |
| ++outputIndex; |
| } |
| } |
| } |
| }; |
| |
| template <> struct DimInitializer<Sizes<> > { |
| template <typename InputDims, typename Index, size_t Rank> EIGEN_DEVICE_FUNC |
| static void run(const InputDims& input_dims, const array<bool, Rank>&, |
| Sizes<>*, array<Index, Rank>* reduced_dims) { |
| const int NumInputDims = internal::array_size<InputDims>::value; |
| for (int i = 0; i < NumInputDims; ++i) { |
| (*reduced_dims)[i] = input_dims[i]; |
| } |
| } |
| }; |
| |
| |
| template <typename ReducedDims, int NumTensorDims, int Layout> |
| struct are_inner_most_dims { |
| static const bool value = false; |
| }; |
| template <typename ReducedDims, int NumTensorDims, int Layout> |
| struct preserve_inner_most_dims { |
| static const bool value = false; |
| }; |
| |
| #if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES |
| template <typename ReducedDims, int NumTensorDims> |
| struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{ |
| static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); |
| static const bool tmp2 = index_statically_eq<ReducedDims>(0, 0); |
| static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value-1, array_size<ReducedDims>::value-1); |
| static const bool value = tmp1 & tmp2 & tmp3; |
| }; |
| template <typename ReducedDims, int NumTensorDims> |
| struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{ |
| static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); |
| static const bool tmp2 = index_statically_eq<ReducedDims>(0, NumTensorDims - array_size<ReducedDims>::value); |
| static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1); |
| static const bool value = tmp1 & tmp2 & tmp3; |
| |
| }; |
| template <typename ReducedDims, int NumTensorDims> |
| struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{ |
| static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); |
| static const bool tmp2 = index_statically_gt<ReducedDims>(0, 0); |
| static const bool value = tmp1 & tmp2; |
| |
| }; |
| template <typename ReducedDims, int NumTensorDims> |
| struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{ |
| static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>(); |
| static const bool tmp2 = index_statically_lt<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1); |
| static const bool value = tmp1 & tmp2; |
| }; |
| #endif |
| |
| |
| template <int DimIndex, typename Self, typename Op> |
| struct GenericDimReducer { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) { |
| EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| for (int j = 0; j < self.m_reducedDims[DimIndex]; ++j) { |
| const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex]; |
| GenericDimReducer<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum); |
| } |
| } |
| }; |
| template <typename Self, typename Op> |
| struct GenericDimReducer<0, Self, Op> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) { |
| for (int j = 0; j < self.m_reducedDims[0]; ++j) { |
| const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0]; |
| reducer.reduce(self.m_impl.coeff(input), accum); |
| } |
| } |
| }; |
| template <typename Self, typename Op> |
| struct GenericDimReducer<-1, Self, Op> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index index, Op& reducer, typename Self::CoeffReturnType* accum) { |
| reducer.reduce(self.m_impl.coeff(index), accum); |
| } |
| }; |
| |
| template <typename Self, typename Op, bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> |
| struct InnerMostDimReducer { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) { |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| for (typename Self::Index j = 0; j < numValuesToReduce; ++j) { |
| reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum); |
| } |
| return reducer.finalize(accum); |
| } |
| }; |
| |
| template <typename Self, typename Op> |
| struct InnerMostDimReducer<Self, Op, true> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) { |
| const int packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size; |
| const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize; |
| typename Self::PacketReturnType p = reducer.template initializePacket<typename Self::PacketReturnType>(); |
| for (typename Self::Index j = 0; j < VectorizedSize; j += packetSize) { |
| reducer.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &p); |
| } |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; ++j) { |
| reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum); |
| } |
| return reducer.finalizeBoth(accum, p); |
| } |
| }; |
| |
| template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> |
| struct InnerMostDimPreserver { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) { |
| eigen_assert(false && "should never be called"); |
| } |
| }; |
| |
| template <int DimIndex, typename Self, typename Op> |
| struct InnerMostDimPreserver<DimIndex, Self, Op, true> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) { |
| EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| for (typename Self::Index j = 0; j < self.m_reducedDims[DimIndex]; ++j) { |
| const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex]; |
| InnerMostDimPreserver<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum); |
| } |
| } |
| }; |
| |
| template <typename Self, typename Op> |
| struct InnerMostDimPreserver<0, Self, Op, true> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) { |
| for (typename Self::Index j = 0; j < self.m_reducedDims[0]; ++j) { |
| const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0]; |
| reducer.reducePacket(self.m_impl.template packet<Unaligned>(input), accum); |
| } |
| } |
| }; |
| template <typename Self, typename Op> |
| struct InnerMostDimPreserver<-1, Self, Op, true> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) { |
| eigen_assert(false && "should never be called"); |
| } |
| }; |
| |
| // Default full reducer |
| template <typename Self, typename Op, typename Device, bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> |
| struct FullReducer { |
| static const bool HasOptimizedImplementation = false; |
| |
| static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::CoeffReturnType* output) { |
| const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions()); |
| *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); |
| } |
| }; |
| |
| |
| #ifdef EIGEN_USE_THREADS |
| // Multithreaded full reducers |
| template <typename Self, typename Op, |
| bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> |
| struct FullReducerShard { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Self& self, typename Self::Index firstIndex, |
| typename Self::Index numValuesToReduce, Op& reducer, |
| typename Self::CoeffReturnType* output) { |
| *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( |
| self, firstIndex, numValuesToReduce, reducer); |
| } |
| }; |
| |
| // Multithreaded full reducer |
| template <typename Self, typename Op, bool Vectorizable> |
| struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> { |
| static const bool HasOptimizedImplementation = !Op::IsStateful; |
| static const int PacketSize = |
| unpacket_traits<typename Self::PacketReturnType>::size; |
| |
| // launch one reducer per thread and accumulate the result. |
| static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device, |
| typename Self::CoeffReturnType* output) { |
| typedef typename Self::Index Index; |
| const Index num_coeffs = array_prod(self.m_impl.dimensions()); |
| if (num_coeffs == 0) { |
| *output = reducer.finalize(reducer.initialize()); |
| return; |
| } |
| const TensorOpCost cost = |
| self.m_impl.costPerCoeff(Vectorizable) + |
| TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable, |
| PacketSize); |
| const int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( |
| num_coeffs, cost, device.numThreads()); |
| if (num_threads == 1) { |
| *output = |
| InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); |
| return; |
| } |
| const Index blocksize = |
| std::floor<Index>(static_cast<float>(num_coeffs) / num_threads); |
| const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0; |
| eigen_assert(num_coeffs >= numblocks * blocksize); |
| |
| Barrier barrier(internal::convert_index<unsigned int>(numblocks)); |
| MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize()); |
| for (Index i = 0; i < numblocks; ++i) { |
| device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, Vectorizable>::run, |
| self, i * blocksize, blocksize, reducer, |
| &shards[i]); |
| } |
| typename Self::CoeffReturnType finalShard; |
| if (numblocks * blocksize < num_coeffs) { |
| finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( |
| self, numblocks * blocksize, num_coeffs - numblocks * blocksize, |
| reducer); |
| } else { |
| finalShard = reducer.initialize(); |
| } |
| barrier.Wait(); |
| |
| for (Index i = 0; i < numblocks; ++i) { |
| reducer.reduce(shards[i], &finalShard); |
| } |
| *output = reducer.finalize(finalShard); |
| } |
| }; |
| |
| #endif |
| |
| |
| // Default inner reducer |
| template <typename Self, typename Op, typename Device> |
| struct InnerReducer { |
| static const bool HasOptimizedImplementation = false; |
| |
| EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) { |
| eigen_assert(false && "Not implemented"); |
| return true; |
| } |
| }; |
| |
| // Default outer reducer |
| template <typename Self, typename Op, typename Device> |
| struct OuterReducer { |
| static const bool HasOptimizedImplementation = false; |
| |
| EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) { |
| eigen_assert(false && "Not implemented"); |
| return true; |
| } |
| }; |
| |
| |
| #if defined(EIGEN_USE_GPU) && defined(__CUDACC__) |
| template <int B, int N, typename S, typename R, typename I> |
| __global__ void FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*); |
| |
| |
| #ifdef EIGEN_HAS_CUDA_FP16 |
| template <typename S, typename R, typename I> |
| __global__ void ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*); |
| template <int B, int N, typename S, typename R, typename I> |
| __global__ void FullReductionKernelHalfFloat(R, const S, I, half*, half2*); |
| template <int NPT, typename S, typename R, typename I> |
| __global__ void InnerReductionKernelHalfFloat(R, const S, I, I, half*); |
| |
| #endif |
| |
| template <int NPT, typename S, typename R, typename I> |
| __global__ void InnerReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); |
| |
| template <int NPT, typename S, typename R, typename I> |
| __global__ void OuterReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); |
| #endif |
| |
| } // end namespace internal |
| |
| |
| template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_> |
| class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> { |
| public: |
| typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims) |
| { } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer) |
| { } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| const XprType& expression() const { return m_expr; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| const Dims& dims() const { return m_dims; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| const Op& reducer() const { return m_reducer; } |
| |
| protected: |
| typename XprType::Nested m_expr; |
| const Dims m_dims; |
| const Op m_reducer; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> |
| struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> |
| { |
| typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType; |
| typedef typename XprType::Index Index; |
| typedef ArgType ChildType; |
| typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; |
| static const int NumInputDims = internal::array_size<InputDimensions>::value; |
| static const int NumReducedDims = internal::array_size<Dims>::value; |
| static const int NumOutputDims = NumInputDims - NumReducedDims; |
| typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self; |
| static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess; |
| typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = Self::InputPacketAccess && Op::PacketAccess, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| static const bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value; |
| static const bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value; |
| static const bool RunningFullReduction = (NumOutputDims==0); |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device), m_xpr_dims(op.dims()) |
| { |
| EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)), |
| YOU_MADE_A_PROGRAMMING_MISTAKE); |
| |
| // Build the bitmap indicating if an input dimension is reduced or not. |
| for (int i = 0; i < NumInputDims; ++i) { |
| m_reduced[i] = false; |
| } |
| for (int i = 0; i < NumReducedDims; ++i) { |
| eigen_assert(op.dims()[i] >= 0); |
| eigen_assert(op.dims()[i] < NumInputDims); |
| m_reduced[op.dims()[i]] = true; |
| } |
| |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| internal::DimInitializer<Dimensions>::run(input_dims, m_reduced, &m_dimensions, &m_reducedDims); |
| |
| // Precompute output strides. |
| if (NumOutputDims > 0) { |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumOutputDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; |
| } |
| } else { |
| m_outputStrides.back() = 1; |
| for (int i = NumOutputDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; |
| } |
| } |
| } |
| |
| // Precompute input strides. |
| if (NumInputDims > 0) { |
| array<Index, NumInputDims> input_strides; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| input_strides[0] = 1; |
| for (int i = 1; i < NumInputDims; ++i) { |
| input_strides[i] = input_strides[i-1] * input_dims[i-1]; |
| } |
| } else { |
| input_strides.back() = 1; |
| for (int i = NumInputDims - 2; i >= 0; --i) { |
| input_strides[i] = input_strides[i + 1] * input_dims[i + 1]; |
| } |
| } |
| |
| int outputIndex = 0; |
| int reduceIndex = 0; |
| for (int i = 0; i < NumInputDims; ++i) { |
| if (m_reduced[i]) { |
| m_reducedStrides[reduceIndex] = input_strides[i]; |
| ++reduceIndex; |
| } else { |
| m_preservedStrides[outputIndex] = input_strides[i]; |
| ++outputIndex; |
| } |
| } |
| } |
| |
| // Special case for full reductions |
| if (NumOutputDims == 0) { |
| m_preservedStrides[0] = internal::array_prod(input_dims); |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(typename MakePointer_<CoeffReturnType>::Type data) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| |
| // Use the FullReducer if possible. |
| if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction && |
| internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation && |
| ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || |
| !RunningOnGPU))) { |
| bool need_assign = false; |
| if (!data) { |
| m_result = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType))); |
| data = m_result; |
| need_assign = true; |
| } |
| Op reducer(m_reducer); |
| internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data); |
| return need_assign; |
| } |
| else if(RunningOnSycl){ |
| const Index num_values_to_reduce = internal::array_prod(m_reducedDims); |
| const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); |
| if (!data) { |
| data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); |
| m_result = data; |
| } |
| Op reducer(m_reducer); |
| internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve); |
| return (m_result != NULL); |
| } |
| |
| // Attempt to use an optimized reduction. |
| else if (RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) { |
| bool reducing_inner_dims = true; |
| for (int i = 0; i < NumReducedDims; ++i) { |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| reducing_inner_dims &= m_reduced[i]; |
| } else { |
| reducing_inner_dims &= m_reduced[NumInputDims - 1 - i]; |
| } |
| } |
| if (internal::InnerReducer<Self, Op, Device>::HasOptimizedImplementation && |
| (reducing_inner_dims || ReducingInnerMostDims)) { |
| const Index num_values_to_reduce = internal::array_prod(m_reducedDims); |
| const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); |
| if (!data) { |
| if (num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 128) { |
| data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); |
| m_result = data; |
| } |
| else { |
| return true; |
| } |
| } |
| Op reducer(m_reducer); |
| if (internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) { |
| if (m_result) { |
| m_device.deallocate(m_result); |
| m_result = NULL; |
| } |
| return true; |
| } else { |
| return (m_result != NULL); |
| } |
| } |
| |
| bool preserving_inner_dims = true; |
| for (int i = 0; i < NumReducedDims; ++i) { |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| preserving_inner_dims &= m_reduced[NumInputDims - 1 - i]; |
| } else { |
| preserving_inner_dims &= m_reduced[i]; |
| } |
| } |
| if (internal::OuterReducer<Self, Op, Device>::HasOptimizedImplementation && |
| preserving_inner_dims) { |
| const Index num_values_to_reduce = internal::array_prod(m_reducedDims); |
| const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions); |
| if (!data) { |
| if (num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 32) { |
| data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve)); |
| m_result = data; |
| } |
| else { |
| return true; |
| } |
| } |
| Op reducer(m_reducer); |
| if (internal::OuterReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) { |
| if (m_result) { |
| m_device.deallocate(m_result); |
| m_result = NULL; |
| } |
| return true; |
| } else { |
| return (m_result != NULL); |
| } |
| } |
| } |
| return true; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| if (m_result) { |
| m_device.deallocate(m_result); |
| m_result = NULL; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| if ((RunningOnSycl || RunningFullReduction || RunningOnGPU) && m_result) { |
| return *(m_result + index); |
| } |
| Op reducer(m_reducer); |
| if (ReducingInnerMostDims || RunningFullReduction) { |
| const Index num_values_to_reduce = |
| (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1]; |
| return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index), |
| num_values_to_reduce, reducer); |
| } else { |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| internal::GenericDimReducer<NumReducedDims-1, Self, Op>::reduce(*this, firstInput(index), reducer, &accum); |
| return reducer.finalize(accum); |
| } |
| } |
| |
| // TODO(bsteiner): provide a more efficient implementation. |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions()))); |
| |
| if (RunningOnGPU && m_result) { |
| return internal::pload<PacketReturnType>(m_result + index); |
| } |
| |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| if (ReducingInnerMostDims) { |
| const Index num_values_to_reduce = |
| (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1]; |
| const Index firstIndex = firstInput(index); |
| for (Index i = 0; i < PacketSize; ++i) { |
| Op reducer(m_reducer); |
| values[i] = internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstIndex + i * num_values_to_reduce, |
| num_values_to_reduce, reducer); |
| } |
| } else if (PreservingInnerMostDims) { |
| const Index firstIndex = firstInput(index); |
| const int innermost_dim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : NumOutputDims - 1; |
| // TBD: extend this the the n innermost dimensions that we preserve. |
| if (((firstIndex % m_dimensions[innermost_dim]) + PacketSize - 1) < m_dimensions[innermost_dim]) { |
| Op reducer(m_reducer); |
| typename Self::PacketReturnType accum = reducer.template initializePacket<typename Self::PacketReturnType>(); |
| internal::InnerMostDimPreserver<NumReducedDims-1, Self, Op>::reduce(*this, firstIndex, reducer, &accum); |
| return reducer.finalizePacket(accum); |
| } else { |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = coeff(index + i); |
| } |
| } |
| } else { |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = coeff(index + i); |
| } |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| |
| // Must be called after evalSubExprsIfNeeded(). |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| if (RunningFullReduction && m_result) { |
| return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize); |
| } else { |
| const Index num_values_to_reduce = internal::array_prod(m_reducedDims); |
| const double compute_cost = num_values_to_reduce * internal::functor_traits<Op>::Cost; |
| return m_impl.costPerCoeff(vectorized) * num_values_to_reduce + |
| TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC typename MakePointer_<Scalar>::Type data() const { return m_result; } |
| /// required by sycl in order to extract the accessor |
| const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| /// added for sycl in order to construct the buffer from the sycl device |
| const Device& device() const{return m_device;} |
| /// added for sycl in order to re-construct the reduction eval on the device for the sub-kernel |
| const Dims& xprDims() const {return m_xpr_dims;} |
| |
| |
| private: |
| template <int, typename, typename> friend struct internal::GenericDimReducer; |
| template <typename, typename, bool> friend struct internal::InnerMostDimReducer; |
| template <int, typename, typename, bool> friend struct internal::InnerMostDimPreserver; |
| template <typename S, typename O, typename D, bool V> friend struct internal::FullReducer; |
| #ifdef EIGEN_USE_THREADS |
| template <typename S, typename O, bool V> friend struct internal::FullReducerShard; |
| #endif |
| #if defined(EIGEN_USE_GPU) && defined(__CUDACC__) |
| template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernel(R, const S, I, typename S::CoeffReturnType*, unsigned int*); |
| #ifdef EIGEN_HAS_CUDA_FP16 |
| template <typename S, typename R, typename I> friend void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*); |
| template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernelHalfFloat(R, const S, I, half*, half2*); |
| template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernelHalfFloat(R, const S, I, I, half*); |
| #endif |
| template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); |
| |
| template <int NPT, typename S, typename R, typename I> friend void internal::OuterReductionKernel(R, const S, I, I, typename S::CoeffReturnType*); |
| #endif |
| |
| template <typename S, typename O, typename D> friend struct internal::InnerReducer; |
| |
| // Returns the Index in the input tensor of the first value that needs to be |
| // used to compute the reduction at output index "index". |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const { |
| if (ReducingInnerMostDims) { |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| return index * m_preservedStrides[0]; |
| } else { |
| return index * m_preservedStrides[NumPreservedStrides - 1]; |
| } |
| } |
| // TBD: optimize the case where we preserve the innermost dimensions. |
| Index startInput = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumOutputDims - 1; i > 0; --i) { |
| // This is index_i in the output tensor. |
| const Index idx = index / m_outputStrides[i]; |
| startInput += idx * m_preservedStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| if (PreservingInnerMostDims) { |
| eigen_assert(m_preservedStrides[0] == 1); |
| startInput += index; |
| } else { |
| startInput += index * m_preservedStrides[0]; |
| } |
| } else { |
| for (int i = 0; i < NumOutputDims - 1; ++i) { |
| // This is index_i in the output tensor. |
| const Index idx = index / m_outputStrides[i]; |
| startInput += idx * m_preservedStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| if (PreservingInnerMostDims) { |
| eigen_assert(m_preservedStrides[NumPreservedStrides - 1] == 1); |
| startInput += index; |
| } else { |
| startInput += index * m_preservedStrides[NumPreservedStrides - 1]; |
| } |
| } |
| return startInput; |
| } |
| |
| // Bitmap indicating if an input dimension is reduced or not. |
| array<bool, NumInputDims> m_reduced; |
| // Dimensions of the output of the operation. |
| Dimensions m_dimensions; |
| // Precomputed strides for the output tensor. |
| array<Index, NumOutputDims> m_outputStrides; |
| // Subset of strides of the input tensor for the non-reduced dimensions. |
| // Indexed by output dimensions. |
| static const int NumPreservedStrides = max_n_1<NumOutputDims>::size; |
| array<Index, NumPreservedStrides> m_preservedStrides; |
| |
| // Subset of strides of the input tensor for the reduced dimensions. |
| // Indexed by reduced dimensions. |
| array<Index, NumReducedDims> m_reducedStrides; |
| // Size of the input dimensions that are reduced. |
| // Indexed by reduced dimensions. |
| array<Index, NumReducedDims> m_reducedDims; |
| |
| // Evaluator for the input expression. |
| TensorEvaluator<ArgType, Device> m_impl; |
| |
| // Operation to apply for computing the reduction. |
| Op m_reducer; |
| |
| // For full reductions |
| #if defined(EIGEN_USE_GPU) && defined(__CUDACC__) |
| static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value; |
| static const bool RunningOnSycl = false; |
| #elif defined(EIGEN_USE_SYCL) |
| static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value; |
| static const bool RunningOnGPU = false; |
| #else |
| static const bool RunningOnGPU = false; |
| static const bool RunningOnSycl = false; |
| #endif |
| typename MakePointer_<CoeffReturnType>::Type m_result; |
| |
| const Device& m_device; |
| const Dims& m_xpr_dims; |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H |
