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eigen/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
Rasmus Munk Larsen 350544eb01 Clean up TensorDeviceThreadPool.h
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// 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 = 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; }
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 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.
// 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 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
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