contrib/Eigen/src/Tensor/TensorForwardDeclarations.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.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/.
// SPDX-License-Identifier: MPL-2.0

#ifndef EIGEN_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
#define EIGEN_TENSOR_TENSOR_FORWARD_DECLARATIONS_H

// IWYU pragma: private
#include "./InternalHeaderCheck.h"

namespace Eigen {

// MakePointer class is used as a container of the address space of the pointer
// on the host and on the device. From the host side it generates the T* pointer
// and when EIGEN_USE_SYCL is used it constructs a buffer with a map_allocator to
// T* m_data on the host. It is always called on the device.
// Specialisation of MakePointer class for creating the sycl buffer with
// map_allocator.
template <typename T>
struct MakePointer {
  typedef T* Type;
  typedef const T* ConstType;
};

template <typename T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* constCast(const T* data) {
  return const_cast<T*>(data);
}

// The StorageMemory class is a container of the device specific pointer
// used for referring to a Pointer on TensorEvaluator class. While the TensorExpression
// is a device-agnostic type and need MakePointer class for type conversion,
// the TensorEvaluator class can be specialized for a device, hence it is possible
// to construct different types of temporary storage memory in TensorEvaluator
// for different devices by specializing the following StorageMemory class.
template <typename T, typename device>
struct StorageMemory : MakePointer<T> {};

namespace internal {
template <typename A, typename B>
struct Pointer_type_promotion {
  static constexpr bool val = false;
};
template <typename A>
struct Pointer_type_promotion<A, A> {
  static constexpr bool val = true;
};
template <typename A, typename B>
struct TypeConversion {
  typedef A* type;
};
}  // namespace internal

template <typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer>
class TensorMap;
template <typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex>
class Tensor;
template <typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex>
class TensorFixedSize;
template <typename PlainObjectType>
class TensorRef;
template <typename Derived, int AccessLevel>
class TensorBase;

template <typename NullaryOp, typename PlainObjectType>
class TensorCwiseNullaryOp;
template <typename UnaryOp, typename XprType>
class TensorCwiseUnaryOp;
template <typename BinaryOp, typename LeftXprType, typename RightXprType>
class TensorCwiseBinaryOp;
template <typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
class TensorCwiseTernaryOp;
template <typename IfXprType, typename ThenXprType, typename ElseXprType>
class TensorSelectOp;
template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer>
class TensorReductionOp;
template <typename XprType>
class TensorIndexPairOp;
template <typename ReduceOp, typename Dims, typename XprType>
class TensorPairReducerOp;
template <typename Axis, typename LeftXprType, typename RightXprType>
class TensorConcatenationOp;
template <typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType>
class TensorContractionOp;
template <typename TargetType, typename XprType>
class TensorConversionOp;
template <typename Dimensions, typename InputXprType, typename KernelXprType>
class TensorConvolutionOp;
template <typename FFT, typename XprType, int FFTDataType, int FFTDirection>
class TensorFFTOp;
template <typename PatchDim, typename XprType>
class TensorPatchOp;
template <DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp;
template <DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorVolumePatchOp;
template <typename Broadcast, typename XprType>
class TensorBroadcastingOp;
template <DenseIndex DimId, typename XprType>
class TensorChippingOp;
template <typename NewDimensions, typename XprType>
class TensorReshapingOp;
template <typename XprType>
class TensorLayoutSwapOp;
template <typename StartIndices, typename Sizes, typename XprType>
class TensorSlicingOp;
template <typename ReverseDimensions, typename XprType>
class TensorReverseOp;
template <typename Rolls, typename XprType>
class TensorRollOp;
template <typename PaddingDimensions, typename XprType>
class TensorPaddingOp;
template <typename Shuffle, typename XprType>
class TensorShufflingOp;
template <typename Strides, typename XprType>
class TensorStridingOp;
template <typename StartIndices, typename StopIndices, typename Strides, typename XprType>
class TensorStridingSlicingOp;
template <typename Strides, typename XprType>
class TensorInflationOp;
template <typename Generator, typename XprType>
class TensorGeneratorOp;
template <typename LeftXprType, typename RightXprType>
class TensorAssignOp;
template <typename Op, typename XprType>
class TensorScanOp;
template <typename Dims, typename XprType>
class TensorTraceOp;

template <typename CustomUnaryFunc, typename XprType>
class TensorCustomUnaryOp;
template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
class TensorCustomBinaryOp;

template <typename XprType, template <class> class MakePointer_ = MakePointer>
class TensorEvalToOp;
template <typename XprType>
class TensorForcedEvalOp;

template <typename ExpressionType, typename DeviceType>
class TensorDevice;
template <typename ExpressionType, typename DeviceType, typename DoneCallback>
class TensorAsyncDevice;
template <typename Derived, typename Device>
struct TensorEvaluator;

struct NoOpOutputKernel;

struct DefaultDevice;
struct ThreadPoolDevice;
struct GpuDevice;
struct SyclDevice;

#ifdef EIGEN_USE_SYCL
namespace TensorSycl {
namespace internal {
template <typename Evaluator, typename Op>
class GenericNondeterministicReducer;
}
}  // namespace TensorSycl
#endif

enum FFTResultType { RealPart = 0, ImagPart = 1, BothParts = 2 };

enum FFTDirection { FFT_FORWARD = 0, FFT_REVERSE = 1 };

namespace internal {

template <typename Device, typename Expression>
struct IsVectorizable {
  static constexpr bool value = TensorEvaluator<Expression, Device>::PacketAccess;
};

template <typename Expression>
struct IsVectorizable<GpuDevice, Expression> {
  static constexpr bool value =
      TensorEvaluator<Expression, GpuDevice>::PacketAccess && TensorEvaluator<Expression, GpuDevice>::IsAligned;
};

// Tiled evaluation strategy.
enum TiledEvaluation {
  Off = 0,  // tiled evaluation is not supported
  On = 1,   // still work in progress (see TensorBlock.h)
};

template <typename Device, typename Expression>
struct IsTileable {
  // Check that block evaluation is supported and it's a preferred option (at
  // least one sub-expression has much faster block evaluation, e.g.
  // broadcasting).
  static constexpr bool BlockAccess =
      TensorEvaluator<Expression, Device>::BlockAccess && TensorEvaluator<Expression, Device>::PreferBlockAccess;

  static constexpr TiledEvaluation value = BlockAccess ? TiledEvaluation::On : TiledEvaluation::Off;
};

template <typename Expression, typename Device, bool Vectorizable = IsVectorizable<Device, Expression>::value,
          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
class TensorExecutor;

template <typename Expression, typename Device, typename DoneCallback,
          bool Vectorizable = IsVectorizable<Device, Expression>::value,
          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
class TensorAsyncExecutor;

}  // end namespace internal

}  // end namespace Eigen

#endif  // EIGEN_TENSOR_TENSOR_FORWARD_DECLARATIONS_H