| // 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 |
| |
| // clang is incompatible with the CUDA syntax wrt making a kernel a class friend, |
| // so we'll use a macro to make clang happy. |
| #ifndef KERNEL_FRIEND |
| #if defined(__clang__) && (defined(__CUDA__) || defined(__HIP__)) |
| #define KERNEL_FRIEND friend __global__ |
| #else |
| #define KERNEL_FRIEND friend |
| #endif |
| #endif |
| |
| |
| 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; |
| typedef typename XprTraits::PointerType PointerType; |
| |
| 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 && Self::ReducerTraits::PacketAccess), |
| bool UseTreeReduction = (!Self::ReducerTraits::IsStateful && |
| !Self::ReducerTraits::IsExactlyAssociative)> |
| 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, false> { |
| 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 typename Self::Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size; |
| const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize; |
| typename Self::PacketReturnType paccum = 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), &paccum); |
| } |
| 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, paccum); |
| } |
| }; |
| |
| #if !defined(EIGEN_HIPCC) |
| static const int kLeafSize = 1024; |
| |
| template <typename Self, typename Op> |
| struct InnerMostDimReducer<Self, Op, false, 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) { |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| if (numValuesToReduce > kLeafSize) { |
| const typename Self::Index half = numValuesToReduce / 2; |
| reducer.reduce(reduce(self, firstIndex, half, reducer), &accum); |
| reducer.reduce( |
| reduce(self, firstIndex + half, numValuesToReduce - half, reducer), |
| &accum); |
| } else { |
| 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, 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 typename Self::Index packetSize = |
| internal::unpacket_traits<typename Self::PacketReturnType>::size; |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| if (numValuesToReduce > packetSize * kLeafSize) { |
| // Make sure the split point is aligned on a packet boundary. |
| const typename Self::Index split = |
| packetSize * |
| divup(firstIndex + divup(numValuesToReduce, typename Self::Index(2)), |
| packetSize); |
| const typename Self::Index num_left = |
| numext::mini(split - firstIndex, numValuesToReduce); |
| reducer.reduce(reduce(self, firstIndex, num_left, reducer), &accum); |
| if (num_left < numValuesToReduce) { |
| reducer.reduce( |
| reduce(self, split, numValuesToReduce - num_left, reducer), &accum); |
| } |
| return reducer.finalize(accum); |
| } else { |
| const typename Self::Index VectorizedSize = |
| (numValuesToReduce / packetSize) * packetSize; |
| typename Self::PacketReturnType paccum = |
| 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), &paccum); |
| } |
| for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; |
| ++j) { |
| reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum); |
| } |
| return reducer.finalizeBoth(accum, paccum); |
| } |
| } |
| }; |
| #endif |
| |
| template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::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 && Self::ReducerTraits::PacketAccess)> |
| struct FullReducer { |
| static const bool HasOptimizedImplementation = false; |
| |
| static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::EvaluatorPointerType 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 && Self::ReducerTraits::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 = !Self::ReducerTraits::IsStateful; |
| static const Index 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; |
| } |
| }; |
| |
| #ifdef EIGEN_USE_SYCL |
| // Default Generic reducer |
| template <typename Self, typename Op, typename Device> |
| struct GenericReducer { |
| 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; |
| } |
| }; |
| #endif |
| |
| #if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC)) |
| template <int B, int N, typename S, typename R, typename I_> |
| __global__ void FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*); |
| |
| |
| #if defined(EIGEN_HAS_GPU_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 |
| |
| /** |
| * For SYCL, the return type of the reduction is deduced from the initialize method of the given Op. |
| * This allows the reduction to have a different type for the accumulator than the input data type. |
| * If this is the case, the functor needs to have two reduce method: one for reducing an element of the input |
| * with the accumulator and the other for reducing two accumulators. |
| * Such a reducer can be useful for instance when the accumulator is a boolean or a bitset that checks for |
| * some properties of the input. |
| */ |
| template <typename Op, typename CoeffReturnType> |
| struct ReductionReturnType { |
| #if EIGEN_HAS_CXX11 && defined(EIGEN_USE_SYCL) |
| typedef typename remove_const<decltype(std::declval<Op>().initialize())>::type type; |
| #else |
| typedef typename remove_const<CoeffReturnType>::type type; |
| #endif |
| }; |
| |
| template <typename Self, typename Op, |
| bool Vectorizable = |
| (Self::InputPacketAccess & Self::ReducerTraits::PacketAccess)> |
| class BlockReducer { |
| public: |
| typedef typename Self::Index Index; |
| typedef typename Self::Scalar Scalar; |
| typedef typename Self::CoeffReturnType CoeffReturnType; |
| typedef typename Self::PacketReturnType PacketReturnType; |
| explicit BlockReducer(const Op& reducer) : op_(reducer) { |
| accum_ = op_.initialize(); |
| } |
| void Reduce(Index index, Index num_values_to_reduce, Scalar* data) { |
| for (Index i = 0; i < num_values_to_reduce; ++i) { |
| op_.reduce(data[index + i], &accum_); |
| } |
| } |
| CoeffReturnType Finalize() { return op_.finalize(accum_); } |
| PacketReturnType FinalizePacket() { |
| // TODO(andydavis) This function should not be called for Scalar |
| // reductions: clean this up or add an assert here. |
| return PacketReturnType(); |
| } |
| |
| private: |
| CoeffReturnType accum_; |
| Op op_; |
| }; |
| |
| template <typename Self, typename Op> |
| class BlockReducer<Self, Op, true> { |
| public: |
| typedef typename Self::Index Index; |
| typedef typename Self::Scalar Scalar; |
| typedef typename Self::CoeffReturnType CoeffReturnType; |
| typedef typename Self::PacketReturnType PacketReturnType; |
| static const Index PacketSize = |
| internal::unpacket_traits<PacketReturnType>::size; |
| |
| explicit BlockReducer(const Op& reducer) : op_(reducer) { |
| vaccum_ = op_.template initializePacket<PacketReturnType>(); |
| accum_ = op_.initialize(); |
| } |
| void Reduce(Index index, Index num_values_to_reduce, Scalar* data) { |
| const Index vectorized_size = |
| (num_values_to_reduce / PacketSize) * PacketSize; |
| for (Index i = 0; i < vectorized_size; i += PacketSize) { |
| op_.reducePacket( |
| internal::ploadt<PacketReturnType, Unaligned>(&data[index + i]), |
| &vaccum_); |
| } |
| for (Index i = vectorized_size; i < num_values_to_reduce; ++i) { |
| op_.reduce(data[index + i], &accum_); |
| } |
| } |
| CoeffReturnType Finalize() { return op_.finalizeBoth(accum_, vaccum_); } |
| PacketReturnType FinalizePacket() { return op_.finalizePacket(vaccum_); } |
| |
| private: |
| PacketReturnType vaccum_; |
| CoeffReturnType accum_; |
| Op op_; |
| }; |
| |
| } // 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; |
| }; |
| |
| template<typename ArgType, typename Device> |
| struct TensorReductionEvaluatorBase; |
| |
| // Eval as rvalue |
| template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> |
| struct TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> |
| { |
| typedef internal::reducer_traits<Op, Device> ReducerTraits; |
| typedef Dims ReducedDims; |
| 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 TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self; |
| static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess; |
| typedef typename internal::ReductionReturnType<Op, typename XprType::CoeffReturnType>::type CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static const Index PacketSize = PacketType<CoeffReturnType, Device>::size; |
| |
| typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| // 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; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = Self::InputPacketAccess && ReducerTraits::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumOutputDims, Layout> |
| OutputTensorBlock; |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumInputDims, Layout> |
| InputTensorBlock; |
| |
| 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 TensorReductionEvaluatorBase(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device) |
| { |
| 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]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); |
| } |
| } else { |
| m_outputStrides[NumOutputDims - 1] = 1; |
| for (int i = NumOutputDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); |
| } |
| } |
| } |
| |
| // 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]; |
| m_output_to_input_dim_map[outputIndex] = i; |
| ++outputIndex; |
| } |
| } |
| } |
| |
| // Special case for full reductions |
| if (NumOutputDims == 0) { |
| m_preservedStrides[0] = internal::array_prod(input_dims); |
| } |
| |
| m_numValuesToReduce = |
| NumOutputDims == 0 |
| ? internal::array_prod(input_dims) |
| : (static_cast<int>(Layout) == static_cast<int>(ColMajor)) |
| ? m_preservedStrides[0] |
| : m_preservedStrides[NumOutputDims - 1]; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE |
| #if !defined(EIGEN_HIPCC) |
| // Marking this as EIGEN_DEVICE_FUNC for HIPCC requires also doing the same for all the functions |
| // being called within here, which then leads to proliferation of EIGEN_DEVICE_FUNC markings, one |
| // of which will eventually result in an NVCC error |
| EIGEN_DEVICE_FUNC |
| #endif |
| bool evalSubExprsIfNeeded(EvaluatorPointerType 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<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(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; |
| } |
| |
| // Attempt to use an optimized reduction. |
| else if ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || (RunningOnSycl)) { |
| 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) || (RunningOnSycl)) { |
| data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve))); |
| m_result = data; |
| } |
| else { |
| return true; |
| } |
| } |
| Op reducer(m_reducer); |
| // For SYCL this if always return false |
| 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_temp(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) || (RunningOnSycl)) { |
| data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve))); |
| m_result = data; |
| } |
| else { |
| return true; |
| } |
| } |
| Op reducer(m_reducer); |
| // For SYCL this if always return false |
| 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_temp(m_result); |
| m_result = NULL; |
| } |
| return true; |
| } else { |
| return (m_result != NULL); |
| } |
| } |
| #if defined(EIGEN_USE_SYCL) |
| // If there is no Optimised version for SYCL, the reduction expression |
| // must break into two subexpression and use the SYCL generic Reducer on the device. |
| 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<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve))); |
| m_result = data; |
| } |
| Op reducer(m_reducer); |
| internal::GenericReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve); |
| return (m_result != NULL); |
| } |
| #endif |
| } |
| return true; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| if (m_result) { |
| m_device.deallocate_temp(m_result); |
| m_result = NULL; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| if (( 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 EIGEN_STRONG_INLINE void getResourceRequirements( |
| std::vector<internal::TensorOpResourceRequirements>* resources) const { |
| Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>( |
| 1, m_device.lastLevelCacheSize() / sizeof(Scalar)); |
| resources->push_back(internal::TensorOpResourceRequirements( |
| internal::kSkewedInnerDims, block_total_size_max)); |
| m_impl.getResourceRequirements(resources); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void block( |
| OutputTensorBlock* output_block) const { |
| // Special case full reductions to avoid input block copy below. |
| if (NumInputDims == NumReducedDims) { |
| eigen_assert(output_block->first_coeff_index() == 0); |
| eigen_assert(output_block->block_sizes().TotalSize() == 1); |
| Op reducer(m_reducer); |
| output_block->data()[0] = internal::InnerMostDimReducer<Self, Op>::reduce( |
| *this, 0, m_numValuesToReduce, reducer); |
| return; |
| } |
| |
| // Calculate input tensor 'slice' required to reduce output block coeffs. |
| DSizes<Index, NumInputDims> input_slice_sizes(m_impl.dimensions()); |
| for (int i = 0; i < NumOutputDims; ++i) { |
| // Clip preserved input dimensions by output block size. |
| input_slice_sizes[m_output_to_input_dim_map[i]] = |
| output_block->block_sizes()[i]; |
| } |
| |
| // Shard input tensor slice into blocks (because it could be large if we |
| // need to reduce along several dimensions to calculate required output |
| // coefficients). |
| const Index max_coeff_count = |
| numext::mini<Index>(((m_device.firstLevelCacheSize()) / sizeof(Scalar)), |
| input_slice_sizes.TotalSize()); |
| |
| // Calculate max output shard size needed to keep working set of reducers |
| // in L1, while leaving enough space for reducer overhead and 'PacketSize' |
| // reductions. |
| DSizes<Index, NumInputDims> target_input_block_sizes; |
| CalculateTargetInputBlockShape(max_coeff_count, input_slice_sizes, |
| &target_input_block_sizes); |
| // Calculate indices for first preserved dimension. |
| const Index first_preserved_dim_output_index = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 0 |
| : NumOutputDims - 1; |
| const Index first_preserved_dim_input_index = |
| m_output_to_input_dim_map[first_preserved_dim_output_index]; |
| const bool inner_most_dim_preserved = |
| PreservingInnerMostDims || |
| (first_preserved_dim_input_index == |
| (static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? 0 |
| : NumInputDims - 1)); |
| |
| // Calculate output block inner/outer dimension sizes. |
| const Index output_block_inner_dim_size = |
| output_block->block_sizes()[first_preserved_dim_output_index]; |
| const Index output_block_outer_dim_size = |
| output_block->block_sizes().TotalSize() / output_block_inner_dim_size; |
| // Calculate shard size for first preserved dimension. |
| const Index output_shard_size = |
| target_input_block_sizes[first_preserved_dim_input_index]; |
| const Index num_output_shards = |
| (output_block_inner_dim_size + output_shard_size - 1) / |
| output_shard_size; |
| |
| // Initialize 'tensor_slice_offsets' from input coords of output index. |
| DSizes<Index, NumInputDims> tensor_slice_offsets; |
| GetInputCoordsForOutputIndex(output_block->first_coeff_index(), |
| &tensor_slice_offsets); |
| |
| // Store tensor slice offset in first preserved dimension to be used |
| // to update tensor slice extents in loop below. |
| const Index first_preserved_dim_offset_start = |
| tensor_slice_offsets[first_preserved_dim_input_index]; |
| |
| array<BlockIteratorState, NumOutputDims> block_iter_state; |
| |
| // Initialize state used to iterate through output coefficients |
| // and update 'tensor_slice_offsets' in outer preserved dims. |
| for (int i = 0; i < NumOutputDims - 1; ++i) { |
| const int dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i + 1 |
| : NumOutputDims - i - 2; |
| block_iter_state[i].input_dim = m_output_to_input_dim_map[dim]; |
| block_iter_state[i].output_size = output_block->block_sizes()[dim]; |
| block_iter_state[i].output_count = 0; |
| } |
| |
| // Allocate input block memory. |
| ScalarNoConst* input_block_data = static_cast<ScalarNoConst*>( |
| m_device.allocate(max_coeff_count * sizeof(Scalar))); |
| // Allocate reducer memory. |
| const bool packet_reductions_enabled = |
| (Self::InputPacketAccess & Self::ReducerTraits::PacketAccess); |
| const Index num_reducers = |
| (inner_most_dim_preserved && packet_reductions_enabled) |
| ? (output_shard_size / PacketSize + output_shard_size % PacketSize + |
| PacketSize) |
| : output_shard_size; |
| typedef internal::BlockReducer<Self, Op> BlockReducer; |
| BlockReducer* reducers = static_cast<BlockReducer*>( |
| m_device.allocate(num_reducers * sizeof(BlockReducer))); |
| |
| InputDimensions input_tensor_dims(m_impl.dimensions()); |
| for (Index output_outer_index = 0; |
| output_outer_index < output_block_outer_dim_size; |
| ++output_outer_index) { |
| for (Index output_shard_index = 0; output_shard_index < num_output_shards; |
| ++output_shard_index) { |
| // Initialize 'tensor_slice_extents' for this output shard. |
| DSizes<Index, NumInputDims> tensor_slice_extents(input_slice_sizes); |
| for (int i = 0; i < NumInputDims; ++i) { |
| if (i == first_preserved_dim_input_index) { |
| // Clip first preserved dim size to output shard size. |
| tensor_slice_extents[i] = numext::mini( |
| output_shard_size, |
| input_slice_sizes[i] - (tensor_slice_offsets[i] - |
| first_preserved_dim_offset_start)); |
| |
| } else if (!m_reduced[i]) { |
| // Clip outer preserved dims to size 1, so that we reduce a |
| // contiguous set of output coefficients. |
| tensor_slice_extents[i] = 1; |
| } |
| } |
| |
| // Initialize output coefficient reducers. |
| for (int i = 0; i < num_reducers; ++i) { |
| new (&reducers[i]) BlockReducer(m_reducer); |
| } |
| |
| typedef internal::TensorSliceBlockMapper<ScalarNoConst, Index, |
| NumInputDims, Layout> |
| TensorSliceBlockMapper; |
| |
| // TODO(andydavis) Consider removing 'input_block_stride_order' if we |
| // find that scattered reads are not worth supporting in |
| // TensorSliceBlockMapper. |
| TensorSliceBlockMapper block_mapper( |
| typename TensorSliceBlockMapper::Dimensions(input_tensor_dims), |
| tensor_slice_offsets, tensor_slice_extents, |
| target_input_block_sizes, DimensionList<Index, NumInputDims>()); |
| |
| const Index num_outputs_to_update = |
| tensor_slice_extents[first_preserved_dim_input_index]; |
| const Index preserved_dim_vector_reducer_count = |
| (inner_most_dim_preserved && packet_reductions_enabled) |
| ? num_outputs_to_update / PacketSize |
| : 0; |
| const Index preserved_dim_vector_coeff_count = |
| inner_most_dim_preserved |
| ? preserved_dim_vector_reducer_count * PacketSize |
| : 0; |
| const Index preserved_dim_reducer_limit = |
| (inner_most_dim_preserved && packet_reductions_enabled) |
| ? (preserved_dim_vector_reducer_count + |
| num_outputs_to_update % PacketSize) |
| : num_outputs_to_update; |
| |
| const Index total_block_count = block_mapper.total_block_count(); |
| for (Index b = 0; b < total_block_count; ++b) { |
| InputTensorBlock input_block = |
| block_mapper.GetBlockForIndex(b, input_block_data); |
| // Read. |
| m_impl.block(&input_block); |
| |
| Index num_values_to_reduce = 1; |
| for (Index i = 0; i < NumInputDims; ++i) { |
| if (m_reduced[i]) { |
| num_values_to_reduce *= input_block.block_sizes()[i]; |
| } |
| } |
| // Reduce. |
| if (inner_most_dim_preserved) { |
| const Index input_outer_dim_size = |
| input_block.block_sizes().TotalSize() / num_outputs_to_update; |
| for (Index input_outer_dim_index = 0; |
| input_outer_dim_index < input_outer_dim_size; |
| ++input_outer_dim_index) { |
| const Index input_outer_dim_base = |
| input_outer_dim_index * num_outputs_to_update; |
| for (Index i = 0; i < preserved_dim_vector_reducer_count; ++i) { |
| reducers[i].Reduce(input_outer_dim_base + i * PacketSize, |
| PacketSize, input_block.data()); |
| } |
| const Index scalar_reducer_base = |
| input_outer_dim_base + preserved_dim_vector_coeff_count; |
| for (Index i = preserved_dim_vector_reducer_count; |
| i < preserved_dim_reducer_limit; ++i) { |
| reducers[i].Reduce(scalar_reducer_base + i - |
| preserved_dim_vector_reducer_count, |
| 1, input_block.data()); |
| } |
| } |
| } else { |
| for (Index i = 0; i < num_outputs_to_update; ++i) { |
| reducers[i].Reduce(i * num_values_to_reduce, num_values_to_reduce, |
| input_block.data()); |
| } |
| } |
| } |
| |
| // Finalize all reducers for this output shard. |
| const Index output_base_index = |
| output_outer_index * output_block_inner_dim_size + |
| output_shard_index * output_shard_size; |
| if (inner_most_dim_preserved) { |
| EIGEN_ALIGN_MAX |
| typename internal::remove_const<CoeffReturnType>::type |
| values[PacketSize]; |
| for (Index i = 0; i < preserved_dim_vector_reducer_count; ++i) { |
| const Index reducer_base = output_base_index + i * PacketSize; |
| internal::pstore<CoeffReturnType, PacketReturnType>( |
| values, reducers[i].FinalizePacket()); |
| for (Index j = 0; j < PacketSize; ++j) { |
| output_block->data()[reducer_base + j] = values[j]; |
| } |
| } |
| const Index scalar_reducer_base = |
| output_base_index + preserved_dim_vector_coeff_count; |
| |
| for (Index i = preserved_dim_vector_reducer_count; |
| i < preserved_dim_reducer_limit; ++i) { |
| output_block->data()[scalar_reducer_base + i - |
| preserved_dim_vector_reducer_count] = |
| reducers[i].Finalize(); |
| } |
| } else { |
| for (int i = 0; i < num_outputs_to_update; ++i) { |
| output_block->data()[output_base_index + i] = |
| reducers[i].Finalize(); |
| } |
| } |
| |
| // Update 'tensor_slice_offsets' by num outputs for this output shard. |
| tensor_slice_offsets[first_preserved_dim_input_index] += |
| num_outputs_to_update; |
| } |
| // Update slice offset for inner preserved dim. |
| tensor_slice_offsets[first_preserved_dim_input_index] -= |
| output_block_inner_dim_size; |
| // Update slice offsets for remaining output dims. |
| for (int i = 0; i < NumOutputDims - 1; ++i) { |
| BlockIteratorState& b = block_iter_state[i]; |
| if (++b.output_count < b.output_size) { |
| ++tensor_slice_offsets[b.input_dim]; |
| break; |
| } |
| b.output_count = 0; |
| tensor_slice_offsets[b.input_dim] -= b.output_size - 1; |
| } |
| } |
| |
| // Free memory. |
| m_device.deallocate(input_block_data); |
| m_device.deallocate(reducers); |
| } |
| |
| EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; } |
| EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| EIGEN_DEVICE_FUNC const Device& device() const { return m_device; } |
| #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); |
| m_result.bind(cgh); |
| } |
| #endif |
| |
| private: |
| template <int, typename, typename> friend struct internal::GenericDimReducer; |
| template <typename, typename, bool, 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(EIGEN_GPUCC)) |
| template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*); |
| #if defined(EIGEN_HAS_GPU_FP16) |
| template <typename S, typename R, typename I_> KERNEL_FRIEND void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I_, half2*); |
| template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernelHalfFloat(R, const S, I_, half*, half2*); |
| template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernelHalfFloat(R, const S, I_, I_, half*); |
| #endif |
| template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*); |
| |
| template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*); |
| #endif |
| |
| #if defined(EIGEN_USE_SYCL) |
| template < typename Evaluator_, typename Op__> friend class TensorSycl::internal::ReductionFunctor; |
| // SYCL need the Generic reducer for the case the recution algorithm is neither inner, outer, and full reducer |
| template <typename, typename, typename> friend struct internal::GenericReducer; |
| #endif |
| |
| |
| template <typename S, typename O, typename D> friend struct internal::InnerReducer; |
| |
| struct BlockIteratorState { |
| Index input_dim; |
| Index output_size; |
| Index output_count; |
| }; |
| |
| // 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; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void GetInputCoordsForOutputIndex( |
| Index index, |
| DSizes<Index, NumInputDims>* coords) const { |
| for (int i = 0; i < NumInputDims; ++i) { |
| (*coords)[i] = 0; |
| } |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumOutputDims - 1; i > 0; --i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| (*coords)[m_output_to_input_dim_map[i]] = idx; |
| index -= idx * m_outputStrides[i]; |
| } |
| (*coords)[m_output_to_input_dim_map[0]] = index; |
| } else { |
| for (int i = 0; i < NumOutputDims - 1; ++i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| (*coords)[m_output_to_input_dim_map[i]] = idx; |
| index -= idx * m_outputStrides[i]; |
| } |
| (*coords)[m_output_to_input_dim_map[NumOutputDims-1]] = index; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void CalculateTargetInputBlockShape( |
| const Index max_coeff_count, |
| const DSizes<Index, NumInputDims>& input_slice_sizes, |
| DSizes<Index, NumInputDims>* target_input_block_sizes) const { |
| typedef internal::BlockReducer<Self, Op> BlockReducer; |
| // TODO(andydavis) Compute reducer overhead correctly for the case where |
| // we are preserving the inner most dimension, and a single reducer |
| // reduces a packet's worth of output coefficients. |
| const Index reducer_overhead = sizeof(BlockReducer) / sizeof(Scalar); |
| |
| Index coeff_to_allocate = max_coeff_count; |
| bool first_preserved_dim_allocated = false; |
| bool first_reduced_dim_allocated = false; |
| for (int i = 0; i < NumInputDims; ++i) { |
| const int dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i |
| : NumInputDims - i - 1; |
| (*target_input_block_sizes)[dim] = 1; |
| if (m_reduced[dim]) { |
| // TODO(andydavis) Consider allocating to multiple reduced dimensions. |
| // Watch out for cases where reduced dimensions are not contiguous, |
| // which induces scattered reads. |
| if (!first_reduced_dim_allocated) { |
| (*target_input_block_sizes)[dim] = |
| numext::mini(input_slice_sizes[dim], coeff_to_allocate); |
| coeff_to_allocate /= (*target_input_block_sizes)[dim]; |
| first_reduced_dim_allocated = true; |
| } |
| } else if (!first_preserved_dim_allocated) { |
| // TODO(andydavis) Include output block size in this L1 working set |
| // calculation. |
| const Index alloc_size = numext::maxi( |
| static_cast<Index>(1), coeff_to_allocate / reducer_overhead); |
| (*target_input_block_sizes)[dim] = |
| numext::mini(input_slice_sizes[dim], alloc_size); |
| coeff_to_allocate = numext::maxi( |
| static_cast<Index>(1), |
| coeff_to_allocate / |
| ((*target_input_block_sizes)[dim] * reducer_overhead)); |
| first_preserved_dim_allocated = true; |
| } |
| } |
| } |
| |
| // 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; |
| array<internal::TensorIntDivisor<Index>, NumOutputDims> m_fastOutputStrides; |
| array<Index, NumPreservedStrides> m_preservedStrides; |
| // Map from output to input dimension index. |
| array<Index, NumOutputDims> m_output_to_input_dim_map; |
| // How many values go into each reduction |
| Index m_numValuesToReduce; |
| |
| // 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(EIGEN_GPUCC)) |
| 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 |
| EvaluatorPointerType m_result; |
| |
| const Device EIGEN_DEVICE_REF m_device; |
| }; |
| |
| template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device> |
| struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> |
| : public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> { |
| typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Base; |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Device& device) : Base(op, device){} |
| }; |
| |
| |
| template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_> |
| struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> |
| : public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> { |
| |
| typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> Base; |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Eigen::SyclDevice& device) : Base(op, device){} |
| // The coeff function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel |
| //Therefore the coeff function should be overridden by for SYCL kernel |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::CoeffReturnType coeff(typename Base::Index index) const { |
| return *(this->data() + index); |
| } |
| // The packet function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel |
| //Therefore the packet function should be overridden by for SYCL kernel |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::PacketReturnType packet(typename Base::Index index) const { |
| return internal::pload<typename Base::PacketReturnType>(this->data() + index); |
| } |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H |
