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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_H
#define EIGEN_CXX11_TENSOR_TENSOR_META_H
namespace Eigen {
template<bool cond> struct Cond {};
template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
const T1& choose(Cond<true>, const T1& first, const T2&) {
return first;
}
template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
const T2& choose(Cond<false>, const T1&, const T2& second) {
return second;
}
template <typename T, typename X, typename Y>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T divup(const X x, const Y y) {
return static_cast<T>((x + y - 1) / y);
}
template <typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T divup(const T x, const T y) {
return static_cast<T>((x + y - 1) / y);
}
template <size_t n> struct max_n_1 {
static const size_t size = n;
};
template <> struct max_n_1<0> {
static const size_t size = 1;
};
// Default packet types
template <typename Scalar, typename Device>
struct PacketType : internal::packet_traits<Scalar> {
typedef typename internal::packet_traits<Scalar>::type type;
};
// For CUDA packet types when using a GpuDevice
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) && defined(EIGEN_HAS_CUDA_FP16)
template <>
struct PacketType<half, GpuDevice> {
typedef half2 type;
static const int size = 2;
enum {
HasAdd = 1,
HasSub = 1,
HasMul = 1,
HasNegate = 1,
HasAbs = 1,
HasArg = 0,
HasAbs2 = 0,
HasMin = 1,
HasMax = 1,
HasConj = 0,
HasSetLinear = 0,
HasBlend = 0,
HasDiv = 1,
HasSqrt = 1,
HasRsqrt = 1,
HasExp = 1,
HasLog = 1,
HasLog1p = 0,
HasLog10 = 0,
HasPow = 1,
};
};
#endif
#if defined(EIGEN_USE_SYCL)
template <typename T>
struct PacketType<T, SyclDevice> {
typedef T type;
static const int size = 1;
enum {
HasAdd = 0,
HasSub = 0,
HasMul = 0,
HasNegate = 0,
HasAbs = 0,
HasArg = 0,
HasAbs2 = 0,
HasMin = 0,
HasMax = 0,
HasConj = 0,
HasSetLinear = 0,
HasBlend = 0
};
};
#endif
// Tuple mimics std::pair but works on e.g. nvcc.
template <typename U, typename V> struct Tuple {
public:
U first;
V second;
typedef U first_type;
typedef V second_type;
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Tuple() : first(), second() {}
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Tuple(const U& f, const V& s) : first(f), second(s) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Tuple& operator= (const Tuple& rhs) {
if (&rhs == this) return *this;
first = rhs.first;
second = rhs.second;
return *this;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void swap(Tuple& rhs) {
using numext::swap;
swap(first, rhs.first);
swap(second, rhs.second);
}
};
template <typename U, typename V>
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
bool operator==(const Tuple<U, V>& x, const Tuple<U, V>& y) {
return (x.first == y.first && x.second == y.second);
}
template <typename U, typename V>
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
bool operator!=(const Tuple<U, V>& x, const Tuple<U, V>& y) {
return !(x == y);
}
// Can't use std::pairs on cuda devices
template <typename Idx> struct IndexPair {
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) {}
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Idx f, Idx s) : first(f), second(s) {}
EIGEN_DEVICE_FUNC void set(IndexPair<Idx> val) {
first = val.first;
second = val.second;
}
Idx first;
Idx second;
};
#ifdef EIGEN_HAS_SFINAE
namespace internal {
template<typename IndexType, Index... Is>
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
array<Index, sizeof...(Is)> customIndices2Array(IndexType& idx, numeric_list<Index, Is...>) {
return { idx[Is]... };
}
template<typename IndexType>
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
array<Index, 0> customIndices2Array(IndexType&, numeric_list<Index>) {
return array<Index, 0>();
}
/** Make an array (for index/dimensions) out of a custom index */
template<typename Index, std::size_t NumIndices, typename IndexType>
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
array<Index, NumIndices> customIndices2Array(IndexType& idx) {
return customIndices2Array(idx, typename gen_numeric_list<Index, NumIndices>::type{});
}
template <typename B, typename D>
struct is_base_of
{
typedef char (&yes)[1];
typedef char (&no)[2];
template <typename BB, typename DD>
struct Host
{
operator BB*() const;
operator DD*();
};
template<typename T>
static yes check(D*, T);
static no check(B*, int);
static const bool value = sizeof(check(Host<B,D>(), int())) == sizeof(yes);
};
}
#endif
} // namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_META_H
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