| // 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 | |
| // IWYU pragma: private | |
| #include "./InternalHeaderCheck.h" | |
| namespace Eigen { | |
| 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 * | |
| numext::div_ceil(firstIndex + numext::div_ceil(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 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) { | |
| auto run_shard = [i, blocksize, &self, &barrier, &shards, &reducer]() { | |
| shards[i] = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, i * blocksize, blocksize, reducer); | |
| barrier.Notify(); | |
| }; | |
| device.enqueue(std::move(run_shard)); | |
| } | |
| 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 | |
| /** | |
| * \ingroup CXX11_Tensor_Module | |
| * | |
| * \brief Tensor reduction class. | |
| * | |
| */ | |
| 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 = static_cast<Index>(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 always returns 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 = static_cast<Index>(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 always returns 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 = static_cast<Index>(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 to 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; } | |
| 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; | |
| #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 needs the generic reducer when the reduction is neither inner, outer, nor full. | |
| 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. | |
| // Avoid zero-sized arrays, since element access fails to compile on GPU. | |
| array<Index, (std::max)(NumOutputDims, 1)> m_outputStrides; | |
| array<internal::TensorIntDivisor<Index>, (std::max)(NumOutputDims, 1)> m_fastOutputStrides; | |
| array<Index, (std::max)(NumPreservedStrides, 1)> m_preservedStrides; | |
| // Map from output to input dimension index. | |
| array<Index, (std::max)(NumOutputDims, 1)> 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 base coeff function uses a recursive method that is not standard layout and cannot be used in | |
| // SYCL kernels, so it must be overridden. | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::CoeffReturnType coeff(typename Base::Index index) const { | |
| return *(this->data() + index); | |
| } | |
| // The base packet function uses a recursive method that is not standard layout and cannot be used in | |
| // SYCL kernels, so it must be overridden. | |
| 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 |