| // 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__ EIGEN_HIP_LAUNCH_BOUNDS_1024 |
| #else |
| #define KERNEL_FRIEND friend |
| #endif |
| #endif |
| |
| #include "./InternalHeaderCheck.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 constexpr int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value; |
| static constexpr 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; |
| }; |
| |
| 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; |
| }; |
| |
| |
| 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 && |
| // GPU threads can quickly run out of stack space |
| // for moderately sized inputs. |
| !Self::RunningOnGPU |
| )> |
| 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& reducer0) { |
| using Index = typename Self::Index; |
| constexpr Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size; |
| Index start = 0; |
| typename Self::PacketReturnType paccum0 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| if (!Self::ReducerTraits::IsStateful && numValuesToReduce >= 4*packetSize) { |
| const Index VectorizedSize4 = (numValuesToReduce / (4*packetSize)) * (4*packetSize); |
| typename Self::PacketReturnType paccum1 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| typename Self::PacketReturnType paccum2 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| typename Self::PacketReturnType paccum3 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| const Index offset0 = firstIndex; |
| const Index offset1 = firstIndex + packetSize; |
| const Index offset2 = firstIndex + 2*packetSize; |
| const Index offset3 = firstIndex + 3*packetSize; |
| for (Index j = 0; j < VectorizedSize4; j += 4*packetSize) { |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset0 + j), &paccum0); |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset1 + j), &paccum1); |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset2 + j), &paccum2); |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(offset3 + j), &paccum3); |
| } |
| reducer0.reducePacket(paccum1, &paccum0); |
| reducer0.reducePacket(paccum2, &paccum0); |
| reducer0.reducePacket(paccum3, &paccum0); |
| start = VectorizedSize4; |
| } |
| if (start <= (numValuesToReduce - packetSize)) { |
| const Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize; |
| for (Index j = start; j < VectorizedSize; j += packetSize) { |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum0); |
| } |
| start = VectorizedSize; |
| } |
| typename Self::CoeffReturnType accum = reducer0.initialize(); |
| for (Index j = start; j < numValuesToReduce; ++j) { |
| reducer0.reduce(self.m_impl.coeff(firstIndex + j), &accum); |
| } |
| return reducer0.finalizeBoth(accum, paccum0); |
| } |
| }; |
| |
| |
| #if !defined(EIGEN_HIPCC) |
| |
| // The following implements tree-based reduction, which improves the accuracy |
| // of sum and mean reductions, since each of the n inputs only participates in |
| // O(log n) additions. |
| template <typename T> |
| EIGEN_DEVICE_FUNC inline Index LeafSize() { return 1024; } |
| template <> |
| EIGEN_DEVICE_FUNC inline Index LeafSize<half>() { return 200; } |
| template <> |
| EIGEN_DEVICE_FUNC inline Index LeafSize<bfloat16>() { return 128; } |
| |
| 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) { |
| const Index kLeafSize = LeafSize<typename Self::CoeffReturnType>(); |
| typename Self::CoeffReturnType accum = reducer.initialize(); |
| if (numValuesToReduce > kLeafSize) { |
| const typename Self::Index half = numValuesToReduce / 2; |
| // Recursively reduce the two halves. |
| reducer.reduce(reduce(self, firstIndex, half, reducer), &accum); |
| reducer.reduce( |
| reduce(self, firstIndex + half, numValuesToReduce - half, reducer), |
| &accum); |
| return reducer.finalize(accum); |
| } else { |
| return InnerMostDimReducer<Self, Op, false, false>::reduce(self, firstIndex, numValuesToReduce, reducer); |
| } |
| } |
| }; |
| |
| 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 Index kLeafSize = LeafSize<typename Self::CoeffReturnType>(); |
| 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 { |
| return InnerMostDimReducer<Self, Op, true, false>::reduce(self, firstIndex, numValuesToReduce, reducer); |
| } |
| } |
| }; |
| #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& reducer0, typename Self::PacketReturnType* accum0) { |
| using Index = typename Self::Index; |
| const Index stride = self.m_reducedStrides[0]; |
| const Index size = self.m_reducedDims[0]; |
| if (!Self::ReducerTraits::IsStateful && size >= 16) { |
| const Index unrolled_size4 = (size / 4) * 4; |
| typename Self::PacketReturnType accum1 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| typename Self::PacketReturnType accum2 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| typename Self::PacketReturnType accum3 = reducer0.template initializePacket<typename Self::PacketReturnType>(); |
| for (Index j = 0; j < unrolled_size4; j += 4) { |
| const Index input0 = firstIndex + j * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input0), accum0); |
| const Index input1 = firstIndex + (j+1) * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input1), &accum1); |
| const Index input2 = firstIndex + (j+2) * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input2), &accum2); |
| const Index input3 = firstIndex + (j+3) * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input3), &accum3); |
| } |
| reducer0.reducePacket(accum1, accum0); |
| reducer0.reducePacket(accum2, accum0); |
| reducer0.reducePacket(accum3, accum0); |
| for (Index j = unrolled_size4; j < size; ++j) { |
| Index input = firstIndex + j * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input), accum0); |
| } |
| } else { |
| for (Index j = 0; j < size; ++j) { |
| Index input = firstIndex + j * stride; |
| reducer0.reducePacket(self.m_impl.template packet<Unaligned>(input), accum0); |
| } |
| } |
| } |
| }; |
| 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 constexpr 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 constexpr bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful; |
| static constexpr 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 Index 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 = 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 constexpr 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 constexpr 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 constexpr 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__ EIGEN_HIP_LAUNCH_BOUNDS_1024 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__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(R, const S, I_, internal::packet_traits<half>::type*); |
| template <int B, int N, typename S, typename R, typename I_> |
| __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<half>::type*); |
| template <int NPT, typename S, typename R, typename I_> |
| __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(R, const S, I_, I_, half*); |
| |
| #endif |
| |
| template <int NPT, typename S, typename R, typename I_> |
| __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*); |
| |
| template <int NPT, typename S, typename R, typename I_> |
| __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 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 defined(EIGEN_USE_SYCL) |
| typedef std::remove_const_t<decltype(std::declval<Op>().initialize())> type; |
| #else |
| typedef std::remove_const_t<CoeffReturnType> type; |
| #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 std::remove_const_t<typename XprType::CoeffReturnType> 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 constexpr int NumInputDims = internal::array_size<InputDimensions>::value; |
| static constexpr int NumReducedDims = internal::array_size<Dims>::value; |
| static constexpr int NumOutputDims = NumInputDims - NumReducedDims; |
| typedef std::conditional_t<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> > Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self; |
| static constexpr bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess; |
| typedef typename internal::ReductionReturnType<Op, typename XprType::CoeffReturnType>::type CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static constexpr 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 constexpr int NumPreservedStrides = max_n_1<NumOutputDims>::size; |
| |
| // For full reductions |
| #if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC)) |
| static constexpr bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value; |
| static constexpr bool RunningOnSycl = false; |
| #elif defined(EIGEN_USE_SYCL) |
| static constexpr bool RunningOnSycl = internal::is_same<internal::remove_all_t<Device>, Eigen::SyclDevice>::value; |
| static constexpr bool RunningOnGPU = false; |
| #else |
| static constexpr bool RunningOnGPU = false; |
| static constexpr bool RunningOnSycl = false; |
| #endif |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = Self::InputPacketAccess && ReducerTraits::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = true, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| typedef std::remove_const_t<Scalar> ScalarNoConst; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| static constexpr bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value; |
| static constexpr bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value; |
| static constexpr bool RunningFullReduction = (NumOutputDims==0); |
| |
| 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[static_cast<size_t>(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[static_cast<size_t>(NumOutputDims - 1)]; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE |
| bool evalSubExprsIfNeededCommon(EvaluatorPointerType data) { |
| // 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; |
| } |
| |
| #ifdef EIGEN_USE_THREADS |
| template <typename EvalSubExprsCallback> |
| EIGEN_STRONG_INLINE |
| void |
| evalSubExprsIfNeededAsync(EvaluatorPointerType data, |
| EvalSubExprsCallback done) { |
| m_impl.evalSubExprsIfNeededAsync(NULL, [this, data, done](bool) { |
| done(evalSubExprsIfNeededCommon(data)); |
| }); |
| } |
| #endif |
| |
| EIGEN_STRONG_INLINE |
| bool evalSubExprsIfNeeded(EvaluatorPointerType data) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return evalSubExprsIfNeededCommon(data); |
| } |
| |
| 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_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions()))); |
| |
| if (RunningOnGPU && m_result) { |
| return internal::pload<PacketReturnType>(m_result + index); |
| } |
| |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> 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 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); |
| if(m_result) 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_, internal::packet_traits<Eigen::half>::type*); |
| template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<Eigen::half>::type*); |
| 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::GenericNondeterministicReducer; |
| // 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; |
| } |
| |
| // 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; |
| |
| 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_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_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 |
