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merge Tensor module within Eigen/unsupported and update gemv BLAS wrapper

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This commit is contained in:
Gael Guennebaud
2015-02-12 21:48:41 +01:00
parent 409547a0c8 4470c99975
commit 0918c51e60
119 changed files with 24100 additions and 816 deletions
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15
unsupported/Eigen/CXX11/Core
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@@ -2,6 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
// 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
@@ -21,20 +22,26 @@
* module. Note that at this stage, you should not need to include
* this module directly.
*
* It also provides a limited fallback for compilers that don't support
* CXX11 yet, such as nvcc.
*
* \code
* #include <Eigen/CXX11/Core>
* \endcode
*/
#include <array>
#include <vector>
// Emulate the cxx11 functionality that we need if the compiler doesn't support it.
#if __cplusplus <= 199711L
#include "src/Core/util/EmulateCXX11Meta.h"
#else
#include <array>
#include "src/Core/util/CXX11Workarounds.h"
#include "src/Core/util/CXX11Meta.h"
#endif
#include <Eigen/src/Core/util/ReenableStupidWarnings.h>
#endif // EIGEN_CXX11_CORE_MODULE
/*
* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
*/

75
unsupported/Eigen/CXX11/Tensor
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@@ -1,6 +1,7 @@
// 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) 2013 Christian Seiler <christian@iwakd.de>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -10,9 +11,10 @@
#ifndef EIGEN_CXX11_TENSOR_MODULE
#define EIGEN_CXX11_TENSOR_MODULE
#include <unsupported/Eigen/CXX11/Core>
#include "Eigen/src/Core/util/StaticAssert.h"
#include "unsupported/Eigen/CXX11/Core"
#include <Eigen/src/Core/util/DisableStupidWarnings.h>
#include "Eigen/src/Core/util/DisableStupidWarnings.h"
/** \defgroup CXX11_Tensor_Module Tensor Module
*
@@ -26,14 +28,69 @@
#include <cstddef>
#include <cstring>
#include <stdint.h>
#include "src/Tensor/TensorStorage.h"
#include "src/Tensor/Tensor.h"
#if __cplusplus > 199711
#include <random>
#endif
#include <Eigen/src/Core/util/ReenableStupidWarnings.h>
#ifdef EIGEN_USE_THREADS
#include <future>
#endif
#ifdef EIGEN_USE_GPU
#include <cuda_runtime.h>
#if defined(__CUDACC__)
#include <curand_kernel.h>
#endif
#endif
#include "Eigen/Core"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorDeviceType.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorBase.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h"
#include "unsupported/Eigen/CXX11/src/Tensor/Tensor.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorMap.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorRef.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorIO.h"
#include "Eigen/src/Core/util/ReenableStupidWarnings.h"
#endif // EIGEN_CXX11_TENSOR_MODULE
/*
* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
*/

32
unsupported/Eigen/CXX11/src/Core/util/CXX11Meta.h
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@@ -317,7 +317,7 @@ constexpr inline decltype(reduce<sum_op, Ts...>::run((*((Ts*)0))...)) arg_sum(Ts
template<typename Array, int... n>
constexpr inline Array h_array_reverse(Array arr, numeric_list<int, n...>)
{
return {{std_array_get<sizeof...(n) - n - 1>(arr)...}};
return {{array_get<sizeof...(n) - n - 1>(arr)...}};
}
template<typename T, std::size_t N>
@@ -335,9 +335,9 @@ constexpr inline std::array<T, N> array_reverse(std::array<T, N> arr)
// an infinite loop)
template<typename Reducer, typename T, std::size_t N, std::size_t n = N - 1>
struct h_array_reduce {
constexpr static inline auto run(std::array<T, N> arr) -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr), std_array_get<n>(arr)))
constexpr static inline auto run(std::array<T, N> arr) -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr), array_get<n>(arr)))
{
return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr), std_array_get<n>(arr));
return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr), array_get<n>(arr));
}
};
@@ -346,7 +346,7 @@ struct h_array_reduce<Reducer, T, N, 0>
{
constexpr static inline T run(std::array<T, N> arr)
{
return std_array_get<0>(arr);
return array_get<0>(arr);
}
};
@@ -370,12 +370,20 @@ constexpr inline auto array_prod(std::array<T, N> arr) -> decltype(array_reduce<
return array_reduce<product_op, T, N>(arr);
}
template<typename t>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
eigen_assert(a.size() > 0);
t prod = 1;
for (size_t i = 0; i < a.size(); ++i) { prod *= a[i]; }
return prod;
}
/* zip an array */
template<typename Op, typename A, typename B, std::size_t N, int... n>
constexpr inline std::array<decltype(Op::run(A(), B())),N> h_array_zip(std::array<A, N> a, std::array<B, N> b, numeric_list<int, n...>)
{
return std::array<decltype(Op::run(A(), B())),N>{{ Op::run(std_array_get<n>(a), std_array_get<n>(b))... }};
return std::array<decltype(Op::run(A(), B())),N>{{ Op::run(array_get<n>(a), array_get<n>(b))... }};
}
template<typename Op, typename A, typename B, std::size_t N>
@@ -387,9 +395,9 @@ constexpr inline std::array<decltype(Op::run(A(), B())),N> array_zip(std::array<
/* zip an array and reduce the result */
template<typename Reducer, typename Op, typename A, typename B, std::size_t N, int... n>
constexpr inline auto h_array_zip_and_reduce(std::array<A, N> a, std::array<B, N> b, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(std_array_get<n>(a), std_array_get<n>(b))...))
constexpr inline auto h_array_zip_and_reduce(std::array<A, N> a, std::array<B, N> b, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...))
{
return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(std_array_get<n>(a), std_array_get<n>(b))...);
return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...);
}
template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
@@ -403,7 +411,7 @@ constexpr inline auto array_zip_and_reduce(std::array<A, N> a, std::array<B, N>
template<typename Op, typename A, std::size_t N, int... n>
constexpr inline std::array<decltype(Op::run(A())),N> h_array_apply(std::array<A, N> a, numeric_list<int, n...>)
{
return std::array<decltype(Op::run(A())),N>{{ Op::run(std_array_get<n>(a))... }};
return std::array<decltype(Op::run(A())),N>{{ Op::run(array_get<n>(a))... }};
}
template<typename Op, typename A, std::size_t N>
@@ -415,9 +423,9 @@ constexpr inline std::array<decltype(Op::run(A())),N> array_apply(std::array<A,
/* apply stuff to an array and reduce */
template<typename Reducer, typename Op, typename A, std::size_t N, int... n>
constexpr inline auto h_array_apply_and_reduce(std::array<A, N> arr, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(std_array_get<n>(arr))...))
constexpr inline auto h_array_apply_and_reduce(std::array<A, N> arr, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...))
{
return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(std_array_get<n>(arr))...);
return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...);
}
template<typename Reducer, typename Op, typename A, std::size_t N>
@@ -497,7 +505,3 @@ InstType instantiate_by_c_array(ArrType* arr)
} // end namespace Eigen
#endif // EIGEN_CXX11META_H
/*
* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
*/

31
unsupported/Eigen/CXX11/src/Core/util/CXX11Workarounds.h
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@@ -17,9 +17,6 @@
#error Intel Compiler only supports required C++ features since version 13.1.
// note that most stuff in principle works with 13.0 but when combining
// some features, at some point 13.0 will just fail with an internal assertion
#elif defined(__clang__) && (__clang_major__ < 3 || (__clang_major__ == 3 && __clang_minor__ < 1))
// note that it _should_ work with 3.1 but it was only tested with 3.2
#error Clang C++ Compiler (clang++) only supports required C++ features since version 3.1.
#elif defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 6))
// G++ < 4.6 by default will continue processing the source files - even if we use #error to make
// it error out. For this reason, we use the pragma to make sure G++ aborts at the first error
@@ -42,32 +39,46 @@
namespace Eigen {
// Use std::array as Eigen array
template <typename T, std::size_t N> using array = std::array<T, N>;
namespace internal {
/* std::get is only constexpr in C++14, not yet in C++11
* - libstdc++ from version 4.7 onwards has it nevertheless,
* so use that
* - libstdc++ older versions: use _M_instance directly
* - libc++ from version 3.4 onwards has it IF compiled with
* -std=c++1y
* - libc++ older versions or -std=c++11: use __elems_ directly
* - libc++ all versions so far: use __elems_ directly
* - all other libs: use std::get to be portable, but
* this may not be constexpr
*/
#if defined(__GLIBCXX__) && __GLIBCXX__ < 20120322
#define STD_GET_ARR_HACK a._M_instance[I]
#elif defined(_LIBCPP_VERSION) && (!defined(_LIBCPP_STD_VER) || _LIBCPP_STD_VER <= 11)
#elif defined(_LIBCPP_VERSION)
#define STD_GET_ARR_HACK a.__elems_[I]
#else
#define STD_GET_ARR_HACK std::template get<I, T, N>(a)
#endif
template<std::size_t I, class T, std::size_t N> constexpr inline T& std_array_get(std::array<T,N>& a) { return (T&) STD_GET_ARR_HACK; }
template<std::size_t I, class T, std::size_t N> constexpr inline T&& std_array_get(std::array<T,N>&& a) { return (T&&) STD_GET_ARR_HACK; }
template<std::size_t I, class T, std::size_t N> constexpr inline T const& std_array_get(std::array<T,N> const& a) { return (T const&) STD_GET_ARR_HACK; }
template<std::size_t I, class T, std::size_t N> constexpr inline T& array_get(std::array<T,N>& a) { return (T&) STD_GET_ARR_HACK; }
template<std::size_t I, class T, std::size_t N> constexpr inline T&& array_get(std::array<T,N>&& a) { return (T&&) STD_GET_ARR_HACK; }
template<std::size_t I, class T, std::size_t N> constexpr inline T const& array_get(std::array<T,N> const& a) { return (T const&) STD_GET_ARR_HACK; }
template<std::size_t I, class T> constexpr inline T& array_get(std::vector<T>& a) { return a[I]; }
template<std::size_t I, class T> constexpr inline T&& array_get(std::vector<T>&& a) { return a[I]; }
template<std::size_t I, class T> constexpr inline T const& array_get(std::vector<T> const& a) { return a[I]; }
#undef STD_GET_ARR_HACK
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<const std::array<T,N> > {
static const size_t value = N;
};
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<std::array<T,N> > {
static const size_t value = N;
};
/* Suppose you have a template of the form
* template<typename T> struct X;
* And you want to specialize it in such a way:

435
unsupported/Eigen/CXX11/src/Core/util/EmulateCXX11Meta.h Normal file
View File

@@ -0,0 +1,435 @@
// 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/.
#ifndef EIGEN_EMULATE_CXX11_META_H
#define EIGEN_EMULATE_CXX11_META_H
namespace Eigen {
// The array class is only available starting with cxx11. Emulate our own here
// if needed
template <typename T, size_t n> class array {
public:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T& operator[] (size_t index) { return values[index]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { return values[index]; }
static const std::size_t size = n;
T values[n];
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array() { }
explicit EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v) {
EIGEN_STATIC_ASSERT(n==1, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2) {
EIGEN_STATIC_ASSERT(n==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3) {
EIGEN_STATIC_ASSERT(n==3, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3,
const T& v4) {
EIGEN_STATIC_ASSERT(n==4, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
values[3] = v4;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
const T& v5) {
EIGEN_STATIC_ASSERT(n==5, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
values[3] = v4;
values[4] = v5;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
const T& v5, const T& v6) {
EIGEN_STATIC_ASSERT(n==6, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
values[3] = v4;
values[4] = v5;
values[5] = v6;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
const T& v5, const T& v6, const T& v7) {
EIGEN_STATIC_ASSERT(n==7, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
values[3] = v4;
values[4] = v5;
values[5] = v6;
values[6] = v7;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE array(
const T& v1, const T& v2, const T& v3, const T& v4,
const T& v5, const T& v6, const T& v7, const T& v8) {
EIGEN_STATIC_ASSERT(n==8, YOU_MADE_A_PROGRAMMING_MISTAKE)
values[0] = v1;
values[1] = v2;
values[2] = v3;
values[3] = v4;
values[4] = v5;
values[5] = v6;
values[6] = v7;
values[7] = v8;
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
array(std::initializer_list<T> l) {
eigen_assert(l.size() == n);
std::copy(l.begin(), l.end(), values);
}
#endif
};
namespace internal {
/** \internal
* \file CXX11/Core/util/EmulateCXX11Meta.h
* This file emulates a subset of the functionality provided by CXXMeta.h for
* compilers that don't yet support cxx11 such as nvcc.
*/
struct empty_list { static const std::size_t count = 0; };
template<typename T, typename Tail=empty_list> struct type_list {
typedef T HeadType;
typedef Tail TailType;
static const T head;
static const Tail tail;
static const std::size_t count = 1 + Tail::count;
};
struct null_type { };
template<typename T1 = null_type, typename T2 = null_type, typename T3 = null_type,
typename T4 = null_type, typename T5 = null_type, typename T6 = null_type,
typename T7 = null_type, typename T8 = null_type>
struct make_type_list {
typedef typename make_type_list<T2, T3, T4, T5, T6, T7, T8>::type tailresult;
typedef type_list<T1, tailresult> type;
};
template<> struct make_type_list<> {
typedef empty_list type;
};
template <std::size_t index, class TList> struct get_type;
template <class Head, class Tail>
struct get_type<0, type_list<Head, Tail> >
{
typedef Head type;
};
template <std::size_t i, class Head, class Tail>
struct get_type<i, type_list<Head, Tail> >
{
typedef typename get_type<i-1, Tail>::type type;
};
/* numeric list */
template <typename T, T n>
struct type2val {
typedef T type;
static const T value = n;
};
template<typename T, size_t n, T V> struct gen_numeric_list_repeated;
template<typename T, T V> struct gen_numeric_list_repeated<T, 1, V> {
typedef typename make_type_list<type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 2, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 3, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 4, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V>, type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 5, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V>, type2val<T, V>, type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 6, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V>,
type2val<T, V>, type2val<T, V>, type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 7, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V>,
type2val<T, V>, type2val<T, V>, type2val<T, V>,
type2val<T, V> >::type type;
};
template<typename T, T V> struct gen_numeric_list_repeated<T, 8, V> {
typedef typename make_type_list<type2val<T, V>, type2val<T, V>, type2val<T, V>,
type2val<T, V>, type2val<T, V>, type2val<T, V>,
type2val<T, V>, type2val<T, V> >::type type;
};
template <std::size_t index, class NList> struct get;
template <std::size_t i>
struct get<i, empty_list>
{
get() { eigen_assert(false && "index overflow"); }
typedef void type;
static const char value = '\0';
};
template <std::size_t i, class Head>
struct get<i, type_list<Head, empty_list> >
{
get() { eigen_assert(false && "index overflow"); }
typedef void type;
static const char value = '\0';
};
template <class Head>
struct get<0, type_list<Head, empty_list> >
{
typedef typename Head::type type;
static const type value = Head::value;
};
template <class Head, class Tail>
struct get<0, type_list<Head, Tail> >
{
typedef typename Head::type type;
static const type value = Head::value;
};
template <std::size_t i, class Head, class Tail>
struct get<i, type_list<Head, Tail> >
{
typedef typename Tail::HeadType::type type;
static const type value = get<i-1, Tail>::value;
};
template <class NList> struct arg_prod {
static const typename NList::HeadType::type value = get<0, NList>::value * arg_prod<typename NList::TailType>::value;
};
template <> struct arg_prod<empty_list> {
static const int value = 1;
};
template<int n, typename t>
array<t, n> repeat(t v) {
array<t, n> array;
array.fill(v);
return array;
}
template<std::size_t I, class Head, class Tail>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Head::type array_get(type_list<Head, Tail>& a) {
return get<I, type_list<Head, Tail> >::value;
}
template<std::size_t I, class Head, class Tail>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Head::type array_get(const type_list<Head, Tail>& a) {
return get<I, type_list<Head, Tail> >::value;
}
template <class NList>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NList::HeadType::type array_prod(const NList& l) {
return arg_prod<NList>::value;
};
template<std::size_t n, typename t>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const array<t, n>& a) {
t prod = 1;
for (size_t i = 0; i < n; ++i) { prod *= a[i]; }
return prod;
}
template<typename t>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const array<t, 0>& /*a*/) {
return 0;
}
template<typename t>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
eigen_assert(a.size() > 0);
t prod = 1;
for (size_t i = 0; i < a.size(); ++i) { prod *= a[i]; }
return prod;
}
template<std::size_t I, class T, std::size_t N>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(array<T,N>& a) {
return a[I];
}
template<std::size_t I, class T, std::size_t N>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const array<T,N>& a) {
return a[I];
}
template<std::size_t I, class T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(std::vector<T>& a) {
return a[I];
}
template<std::size_t I, class T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const std::vector<T>& a) {
return a[I];
}
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<array<T,N> > {
static const size_t value = N;
};
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<array<T,N>& > {
static const size_t value = N;
};
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<const array<T,N> > {
static const size_t value = N;
};
template <typename T> struct array_size;
template<class T, std::size_t N> struct array_size<const array<T,N>& > {
static const size_t value = N;
};
struct sum_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a + b; }
};
struct product_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a * b; }
};
struct logical_and_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a && b; }
};
struct logical_or_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a || b; }
};
struct equal_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a == b; }
};
struct not_equal_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a != b; }
};
struct lesser_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a < b; }
};
struct lesser_equal_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a <= b; }
};
struct greater_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a > b; }
};
struct greater_equal_op {
template<typename A, typename B> static inline bool run(A a, B b) { return a >= b; }
};
struct not_op {
template<typename A> static inline bool run(A a) { return !a; }
};
struct negation_op {
template<typename A> static inline bool run(A a) { return -a; }
};
struct greater_equal_zero_op {
template<typename A> static inline bool run(A a) { return a >= 0; }
};
template<typename Reducer, typename Op, typename A, std::size_t N>
struct ArrayApplyAndReduce {
static inline bool run(const array<A, N>& a) {
EIGEN_STATIC_ASSERT(N >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
bool result = Reducer::run(Op::run(a[0]), Op::run(a[1]));
for (size_t i = 2; i < N; ++i) {
result = Reducer::run(result, Op::run(a[i]));
}
return result;
}
};
template<typename Reducer, typename Op, typename A>
struct ArrayApplyAndReduce<Reducer, Op, A, 1> {
static inline bool run(const array<A, 1>& a) {
return Op::run(a[0]);
}
};
template<typename Reducer, typename Op, typename A, std::size_t N>
inline bool array_apply_and_reduce(const array<A, N>& a) {
return ArrayApplyAndReduce<Reducer, Op, A, N>::run(a);
}
template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
struct ArrayZipAndReduce {
static inline bool run(const array<A, N>& a, const array<B, N>& b) {
EIGEN_STATIC_ASSERT(N >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
bool result = Reducer::run(Op::run(a[0], b[0]), Op::run(a[1], b[1]));
for (size_t i = 2; i < N; ++i) {
result = Reducer::run(result, Op::run(a[i], b[i]));
}
return result;
}
};
template<typename Reducer, typename Op, typename A, typename B>
struct ArrayZipAndReduce<Reducer, Op, A, B, 1> {
static inline bool run(const array<A, 1>& a, const array<B, 1>& b) {
return Op::run(a[0], b[0]);
}
};
template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
inline bool array_zip_and_reduce(const array<A, N>& a, const array<B, N>& b) {
return ArrayZipAndReduce<Reducer, Op, A, B, N>::run(a, b);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_EMULATE_CXX11_META_H

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@@ -1,6 +1,7 @@
// 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) 2013 Christian Seiler <christian@iwakd.de>
//
// This Source Code Form is subject to the terms of the Mozilla
@@ -55,70 +56,46 @@ namespace Eigen {
* change dramatically.</dd>
* </dl>
*
* \ref TopicStorageOrders
* \ref TopicStorageOrders
*/
template<typename Scalar_, std::size_t NumIndices_, int Options_ = 0>
class Tensor;
namespace internal {
template<typename Scalar_, std::size_t NumIndices_, int Options_>
struct traits<Tensor<Scalar_, NumIndices_, Options_>>
{
typedef Scalar_ Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
enum {
Options = Options_
};
};
template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
struct tensor_index_linearization_helper
{
constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const& dimensions)
{
return std_array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
std_array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
}
};
template<typename Index, std::size_t NumIndices, bool RowMajor>
struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
{
constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const&)
{
return std_array_get<RowMajor ? 0 : NumIndices - 1>(indices);
}
};
} // end namespace internal
template<typename Scalar_, std::size_t NumIndices_, int Options_>
class Tensor
class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_> >
{
static_assert(NumIndices_ >= 1, "A tensor must have at least one index.");
public:
typedef Tensor<Scalar_, NumIndices_, Options_> Self;
typedef TensorBase<Tensor<Scalar_, NumIndices_, Options_> > Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<Self>::StorageKind StorageKind;
typedef typename internal::traits<Self>::Index Index;
typedef typename internal::traits<Self>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef Scalar_ Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Self DenseType;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef typename Base::PacketReturnType PacketReturnType;
constexpr static int Options = Options_;
constexpr static std::size_t NumIndices = NumIndices_;
enum {
IsAligned = bool(EIGEN_ALIGN) & !(Options_&DontAlign),
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = Options_ & RowMajor ? RowMajor : ColMajor,
CoordAccess = true,
};
static const int Options = Options_;
static const std::size_t NumIndices = NumIndices_;
typedef DSizes<Index, NumIndices_> Dimensions;
protected:
TensorStorage<Scalar, NumIndices, Dynamic, Options> m_storage;
public:
EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
EIGEN_STRONG_INLINE std::array<Index, NumIndices> dimensions() const { return m_storage.dimensions(); }
EIGEN_STRONG_INLINE Index size() const { return internal::array_prod(m_storage.dimensions()); }
EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); }
EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); }
// Metadata
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<DenseIndex, NumIndices_>& dimensions() const { return m_storage.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); }
// This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
// work, because that uses base().coeffRef() - and we don't yet
@@ -126,146 +103,254 @@ class Tensor
inline Self& base() { return *this; }
inline const Self& base() const { return *this; }
void setZero()
{
// FIXME: until we have implemented packet access and the
// expression engine w.r.t. nullary ops, use this
// as a kludge. Only works with POD types, but for
// any standard usage, this shouldn't be a problem
memset((void *)data(), 0, size() * sizeof(Scalar));
}
inline Self& operator=(Self const& other)
{
m_storage = other.m_storage;
return *this;
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
{
static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
return coeff(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#endif
inline const Scalar& coeff(const std::array<Index, NumIndices>& indices) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
inline const Scalar& coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
{
static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
return coeffRef(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#endif
inline Scalar& coeffRef(const std::array<Index, NumIndices>& indices)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
inline Scalar& coeffRef(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
{
static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
return this->operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
{
return coeff(array<Index, 2>(i0, i1));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
{
return coeff(array<Index, 3>(i0, i1, i2));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
{
return coeff(array<Index, 4>(i0, i1, i2, i3));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
return coeff(array<Index, 5>(i0, i1, i2, i3, i4));
}
#endif
inline const Scalar& operator()(const std::array<Index, NumIndices>& indices) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
{
eigen_assert(checkIndexRange(indices));
return coeff(indices);
}
inline const Scalar& operator()(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return coeff(index);
}
inline const Scalar& operator[](Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
{
static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
// The bracket operator is only for vectors, use the parenthesis operator instead.
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
return coeff(index);
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
{
static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
return operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
{
return coeffRef(array<Index, 2>(i0, i1));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
{
return coeffRef(array<Index, 3>(i0, i1, i2));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
return coeffRef(array<Index, 4>(i0, i1, i2, i3));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
{
return coeffRef(array<Index, 5>(i0, i1, i2, i3, i4));
}
#endif
inline Scalar& operator()(const std::array<Index, NumIndices>& indices)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
{
eigen_assert(checkIndexRange(indices));
return coeffRef(indices);
}
inline Scalar& operator()(Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return coeffRef(index);
}
inline Scalar& operator[](Index index)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index)
{
static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
// The bracket operator is only for vectors, use the parenthesis operator instead
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(index);
}
inline Tensor()
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor()
: m_storage()
{
}
inline Tensor(const Self& other)
: m_storage(other.m_storage)
{
}
inline Tensor(Self&& other)
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const Self& other)
: m_storage(other.m_storage)
{
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Tensor(Index firstDimension, IndexTypes... otherDimensions)
: m_storage()
: m_storage(internal::array_prod(array<Index, NumIndices>{{firstDimension, otherDimensions...}}), array<Index, NumIndices>{{firstDimension, otherDimensions...}})
{
static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to construct a tensor must be equal to the rank of the tensor.");
resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#else
inline explicit Tensor(Index dim1)
: m_storage(dim1, array<Index, 1>(dim1))
{
EIGEN_STATIC_ASSERT(1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
inline explicit Tensor(Index dim1, Index dim2)
: m_storage(dim1*dim2, array<Index, 2>(dim1, dim2))
{
EIGEN_STATIC_ASSERT(2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
inline explicit Tensor(Index dim1, Index dim2, Index dim3)
: m_storage(dim1*dim2*dim3, array<Index, 3>(dim1, dim2, dim3))
{
EIGEN_STATIC_ASSERT(3 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
inline explicit Tensor(Index dim1, Index dim2, Index dim3, Index dim4)
: m_storage(dim1*dim2*dim3*dim4, array<Index, 4>(dim1, dim2, dim3, dim4))
{
EIGEN_STATIC_ASSERT(4 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
inline explicit Tensor(Index dim1, Index dim2, Index dim3, Index dim4, Index dim5)
: m_storage(dim1*dim2*dim3*dim4*dim5, array<Index, 4>(dim1, dim2, dim3, dim4, dim5))
{
EIGEN_STATIC_ASSERT(5 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#endif
inline Tensor(std::array<Index, NumIndices> dimensions)
: m_storage(internal::array_prod(dimensions), dimensions)
inline explicit Tensor(const array<Index, NumIndices>& dimensions)
: m_storage(internal::array_prod(dimensions), dimensions)
{
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other.derived());
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other.derived());
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other)
{
typedef TensorAssignOp<Tensor, const Tensor> Assign;
Assign assign(*this, other);
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other);
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
void resize(Index firstDimension, IndexTypes... otherDimensions)
{
static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to resize a tensor must be equal to the rank of the tensor.");
resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
// The number of dimensions used to resize a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}});
}
#endif
void resize(const std::array<Index, NumIndices>& dimensions)
EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions)
{
std::size_t i;
Index size = Index(1);
@@ -282,8 +367,17 @@ class Tensor
#endif
}
EIGEN_DEVICE_FUNC void resize(const DSizes<Index, NumIndices>& dimensions) {
array<Index, NumIndices> dims;
for (std::size_t i = 0; i < NumIndices; ++i) {
dims[i] = dimensions[i];
}
resize(dims);
}
protected:
bool checkIndexRange(const std::array<Index, NumIndices>& indices) const
bool checkIndexRange(const array<Index, NumIndices>& indices) const
{
using internal::array_apply_and_reduce;
using internal::array_zip_and_reduce;
@@ -298,16 +392,16 @@ class Tensor
array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
}
inline Index linearizedIndex(const std::array<Index, NumIndices>& indices) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
{
return internal::tensor_index_linearization_helper<Index, NumIndices, NumIndices - 1, Options&RowMajor>::run(indices, m_storage.dimensions());
if (Options&RowMajor) {
return m_storage.dimensions().IndexOfRowMajor(indices);
} else {
return m_storage.dimensions().IndexOfColMajor(indices);
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_H
/*
* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
*/

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unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h Normal file
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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>
//
// 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_ASSIGN_H
#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
namespace Eigen {
/** \class TensorAssign
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor assignment class.
*
* This class is represents the assignment of the values resulting from the evaluation of
* the rhs expression to the memory locations denoted by the lhs expression.
*/
namespace internal {
template<typename LhsXprType, typename RhsXprType>
struct traits<TensorAssignOp<LhsXprType, RhsXprType> >
{
typedef typename LhsXprType::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename traits<LhsXprType>::StorageKind StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
static const int Layout = internal::traits<LhsXprType>::Layout;
enum {
Flags = 0,
};
};
template<typename LhsXprType, typename RhsXprType>
struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
};
template<typename LhsXprType, typename RhsXprType>
struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type>
{
typedef TensorAssignOp<LhsXprType, RhsXprType> type;
};
} // end namespace internal
template<typename LhsXprType, typename RhsXprType>
class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorAssignOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
typedef typename LhsXprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
protected:
typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr;
const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr;
};
template<typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
{
typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
enum {
IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
m_leftImpl(op.lhsExpression(), device),
m_rightImpl(op.rhsExpression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
// The dimensions of the lhs and the rhs tensors should be equal to prevent
// overflows and ensure the result is fully initialized.
eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_leftImpl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use left impl instead if right impl dimensions are known at compile time.
return m_rightImpl.dimensions();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
m_leftImpl.evalSubExprsIfNeeded(NULL);
// If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
// null value), attempt to evaluate the rhs expression in place. Returns true iff in place
// evaluation isn't supported and the caller still needs to manually assign the values generated
// by the rhs to the lhs.
return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_leftImpl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
{
return m_leftImpl.template packet<LoadMode>(index);
}
private:
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
};
}
#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_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>
//
// 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_BASE_H
#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H
namespace Eigen {
/** \class TensorBase
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor base class.
*
* This class is the common parent of the Tensor and TensorMap class, thus
* making it possible to use either class interchangably in expressions.
*/
template<typename Derived>
class TensorBase<Derived, ReadOnlyAccessors>
{
public:
typedef internal::traits<Derived> DerivedTraits;
typedef typename DerivedTraits::Scalar Scalar;
typedef typename DerivedTraits::Index Index;
typedef typename internal::remove_const<Scalar>::type CoeffReturnType;
typedef typename internal::packet_traits<CoeffReturnType>::type PacketReturnType;
static const int NumDimensions = DerivedTraits::NumDimensions;
// Generic nullary operation support.
template <typename CustomNullaryOp> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<CustomNullaryOp, const Derived>
nullaryExpr(const CustomNullaryOp& func) const {
return TensorCwiseNullaryOp<CustomNullaryOp, const Derived>(derived(), func);
}
// Coefficient-wise nullary operators
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
constant(const Scalar& value) const {
return nullaryExpr(internal::scalar_constant_op<Scalar>(value));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::UniformRandomGenerator<Scalar>, const Derived>
random() const {
return nullaryExpr(internal::UniformRandomGenerator<Scalar>());
}
template <typename RandomGenerator> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<RandomGenerator, const Derived>
random() const {
return nullaryExpr(RandomGenerator());
}
// Generic unary operation support.
template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived>
unaryExpr(const CustomUnaryOp& func) const {
return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
}
// Coefficient-wise unary operators
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived>
operator-() const {
return unaryExpr(internal::scalar_opposite_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived>
sqrt() const {
return unaryExpr(internal::scalar_sqrt_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived>
square() const {
return unaryExpr(internal::scalar_square_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived>
cube() const {
return unaryExpr(internal::scalar_cube_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived>
inverse() const {
return unaryExpr(internal::scalar_inverse_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived>
exp() const {
return unaryExpr(internal::scalar_exp_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived>
log() const {
return unaryExpr(internal::scalar_log_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived>
abs() const {
return unaryExpr(internal::scalar_abs_op<Scalar>());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived>
pow(Scalar exponent) const {
return unaryExpr(internal::scalar_pow_op<Scalar>(exponent));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>
operator+ (Scalar rhs) const {
return unaryExpr(internal::scalar_add_op<Scalar>(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived>
operator- (Scalar rhs) const {
EIGEN_STATIC_ASSERT((std::numeric_limits<Scalar>::is_signed || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
return unaryExpr(internal::scalar_sub_op<Scalar>(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived>
operator* (Scalar rhs) const {
return unaryExpr(internal::scalar_multiple_op<Scalar>(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived>
operator/ (Scalar rhs) const {
// EIGEN_STATIC_ASSERT(!std::numeric_limits<Scalar>::is_integer, YOU_MADE_A_PROGRAMMING_MISTAKE);
return unaryExpr(internal::scalar_quotient1_op<Scalar>(rhs));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
cwiseMax(Scalar threshold) const {
return cwiseMax(constant(threshold));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
cwiseMin(Scalar threshold) const {
return cwiseMin(constant(threshold));
}
template <typename NewType> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cast_op<Scalar, NewType>, const Derived>
cast() const {
return unaryExpr(internal::scalar_cast_op<Scalar, NewType>());
}
// Generic binary operation support.
template <typename CustomBinaryOp, typename OtherDerived> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
binaryExpr(const OtherDerived& other, const CustomBinaryOp& func) const {
return TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other, func);
}
// Coefficient-wise binary operators.
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived>
operator+(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_sum_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived>
operator-(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_difference_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>
operator*(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_product_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
operator/(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_quotient_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>
cwiseMax(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_max_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>
cwiseMin(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_min_op<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>
operator&&(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_boolean_and_op());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>
operator||(const OtherDerived& other) const {
return binaryExpr(other.derived(), internal::scalar_boolean_or_op());
}
// Comparisons and tests.
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::less<Scalar>, const Derived, const OtherDerived>
operator<(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::less<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::less_equal<Scalar>, const Derived, const OtherDerived>
operator<=(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::less_equal<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::greater<Scalar>, const Derived, const OtherDerived>
operator>(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::greater<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::greater_equal<Scalar>, const Derived, const OtherDerived>
operator>=(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::greater_equal<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::equal_to<Scalar>, const Derived, const OtherDerived>
operator==(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::equal_to<Scalar>());
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorCwiseBinaryOp<std::not_equal_to<Scalar>, const Derived, const OtherDerived>
operator!=(const OtherDerived& other) const {
return binaryExpr(other.derived(), std::not_equal_to<Scalar>());
}
// Coefficient-wise ternary operators.
template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const {
return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
}
// Contractions.
typedef Eigen::IndexPair<Index> DimensionPair;
template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorContractionOp<const Dimensions, const Derived, const OtherDerived>
contract(const OtherDerived& other, const Dimensions& dims) const {
return TensorContractionOp<const Dimensions, const Derived, const OtherDerived>(derived(), other.derived(), dims);
}
// Convolutions.
template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>
convolve(const KernelDerived& kernel, const Dimensions& dims) const {
return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims);
}
// Reductions.
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>
sum(const Dims& dims) const {
return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::SumReducer<CoeffReturnType>());
}
const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>
sum() const {
array<Index, NumDimensions> in_dims;
for (int i = 0; i < NumDimensions; ++i) in_dims[i] = i;
return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::SumReducer<CoeffReturnType>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>
mean(const Dims& dims) const {
return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MeanReducer<CoeffReturnType>());
}
const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>
mean() const {
array<Index, NumDimensions> in_dims;
for (int i = 0; i < NumDimensions; ++i) in_dims[i] = i;
return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MeanReducer<CoeffReturnType>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>
prod(const Dims& dims) const {
return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::ProdReducer<CoeffReturnType>());
}
const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>
prod() const {
array<Index, NumDimensions> in_dims;
for (int i = 0; i < NumDimensions; ++i) in_dims[i] = i;
return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived>
maximum(const Dims& dims) const {
return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType>());
}
const TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>
maximum() const {
array<Index, NumDimensions> in_dims;
for (int i = 0; i < NumDimensions; ++i) in_dims[i] = i;
return TensorReductionOp<internal::MaxReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType>());
}
template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived>
minimum(const Dims& dims) const {
return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType>());
}
const TensorReductionOp<internal::MinReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>
minimum() const {
array<Index, NumDimensions> in_dims;
for (int i = 0; i < NumDimensions; ++i) in_dims[i] = i;
return TensorReductionOp<internal::MinReducer<CoeffReturnType>, const array<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType>());
}
template <typename Reducer, typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReductionOp<Reducer, const Dims, const Derived>
reduce(const Dims& dims, const Reducer& reducer) const {
return TensorReductionOp<Reducer, const Dims, const Derived>(derived(), dims, reducer);
}
template <typename Broadcast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorBroadcastingOp<const Broadcast, const Derived>
broadcast(const Broadcast& broadcast) const {
return TensorBroadcastingOp<const Broadcast, const Derived>(derived(), broadcast);
}
template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorConcatenationOp<Axis, const Derived, const OtherDerived>
concatenate(const OtherDerived& other, Axis axis) const {
return TensorConcatenationOp<Axis, const Derived, const OtherDerived>(derived(), other.derived(), axis);
}
template <typename PatchDims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorPatchOp<const PatchDims, const Derived>
extract_patches(const PatchDims& patch_dims) const {
return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
}
template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Rows, Cols, const Derived>
extract_image_patches() const {
return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, 1, 1, PADDING_SAME);
}
template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Rows, Cols, const Derived>
extract_image_patches(const PaddingType padding_type) const {
return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, 1, 1, padding_type);
}
template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Rows, Cols, const Derived>
extract_image_patches(const Index stride, const PaddingType padding_type) const {
return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, stride, stride, padding_type);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
extract_image_patches(const Index patch_rows, const Index patch_cols,
const Index row_stride = 1, const Index col_stride = 1) const {
return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
PADDING_SAME);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
extract_image_patches(const Index patch_rows, const Index patch_cols,
const Index row_stride, const Index col_stride,
const PaddingType padding_type) const {
return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
padding_type);
}
// Morphing operators.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorLayoutSwapOp<const Derived>
swap_layout() const {
return TensorLayoutSwapOp<const Derived>(derived());
}
template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReshapingOp<const NewDimensions, const Derived>
reshape(const NewDimensions& newDimensions) const {
return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
}
template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
slice(const StartIndices& startIndices, const Sizes& sizes) const {
return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
}
template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorChippingOp<DimId, const Derived>
chip(const Index offset) const {
return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorChippingOp<Dynamic, const Derived>
chip(const Index offset, const Index dim) const {
return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
}
template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReverseOp<const ReverseDimensions, const Derived>
reverse(const ReverseDimensions& rev) const {
return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
}
template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorPaddingOp<const PaddingDimensions, const Derived>
pad(const PaddingDimensions& padding) const {
return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding);
}
template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorShufflingOp<const Shuffle, const Derived>
shuffle(const Shuffle& shuffle) const {
return TensorShufflingOp<const Shuffle, const Derived>(derived(), shuffle);
}
template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorStridingOp<const Strides, const Derived>
stride(const Strides& strides) const {
return TensorStridingOp<const Strides, const Derived>(derived(), strides);
}
// Force the evaluation of the expression.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorForcedEvalOp<const Derived> eval() const {
return TensorForcedEvalOp<const Derived>(derived());
}
protected:
template <typename Scalar, std::size_t NumIndices, int Options> friend class Tensor;
template <typename Scalar, int Options> friend class TensorVarDim;
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
};
template<typename Derived>
class TensorBase<Derived, WriteAccessors> : public TensorBase<Derived, ReadOnlyAccessors> {
public:
typedef internal::traits<Derived> DerivedTraits;
typedef typename DerivedTraits::Scalar Scalar;
typedef typename DerivedTraits::Index Index;
typedef Scalar CoeffReturnType;
typedef typename internal::packet_traits<Scalar>::type PacketReturnType;
static const int NumDimensions = DerivedTraits::NumDimensions;
template <typename Scalar, std::size_t NumIndices, int Options> friend class Tensor;
template <typename Scalar, int Options> friend class TensorVarDim;
template <typename OtherDerived, int AccessLevel> friend class TensorBase;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& setZero() {
return setConstant(Scalar(0));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& setConstant(const Scalar& val) {
return derived() = this->constant(val);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& setRandom() {
return derived() = this->random();
}
template <typename RandomGenerator> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& setRandom() {
return derived() = this->template random<RandomGenerator>();
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& setValues(
const typename internal::Initializer<Derived, NumDimensions>::InitList& vals) {
TensorEvaluator<Derived, DefaultDevice> eval(derived(), DefaultDevice());
internal::initialize_tensor<Derived, NumDimensions>(eval, vals);
return derived();
}
#endif // EIGEN_HAS_VARIADIC_TEMPLATES
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator+=(const OtherDerived& other) {
return derived() = derived() + other.derived();
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const OtherDerived& other) {
return derived() = derived() - other.derived();
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator*=(const OtherDerived& other) {
return derived() = derived() * other.derived();
}
template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator/=(const OtherDerived& other) {
return derived() = derived() / other.derived();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorLayoutSwapOp<Derived>
swap_layout() const {
return TensorLayoutSwapOp<Derived>(derived());
}
template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorReshapingOp<const NewDimensions, Derived>
reshape(const NewDimensions& newDimensions) const {
return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
}
template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorSlicingOp<const StartIndices, const Sizes, Derived>
slice(const StartIndices& startIndices, const Sizes& sizes) const {
return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
}
template <DenseIndex DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorChippingOp<DimId, Derived>
chip(const Index offset) const {
return TensorChippingOp<DimId, Derived>(derived(), offset, DimId);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorChippingOp<Dynamic, Derived>
chip(const Index offset, const Index dim) const {
return TensorChippingOp<Dynamic, Derived>(derived(), offset, dim);
}
template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorShufflingOp<const Shuffle, Derived>
shuffle(const Shuffle& shuffle) const {
return TensorShufflingOp<const Shuffle, Derived>(derived(), shuffle);
}
template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorStridingOp<const Strides, Derived>
stride(const Strides& strides) const {
return TensorStridingOp<const Strides, Derived>(derived(), strides);
}
// Select the device on which to evaluate the expression.
template <typename DeviceType>
TensorDevice<Derived, DeviceType> device(const DeviceType& device) {
return TensorDevice<Derived, DeviceType>(device, derived());
}
protected:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Derived& derived() { return *static_cast<Derived*>(this); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_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>
//
// 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_BROADCASTING_H
#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
namespace Eigen {
/** \class TensorBroadcasting
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor broadcasting class.
*
*
*/
namespace internal {
template<typename Broadcast, typename XprType>
struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename Broadcast, typename XprType>
struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
{
typedef const TensorBroadcastingOp<Broadcast, XprType>& type;
};
template<typename Broadcast, typename XprType>
struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
{
typedef TensorBroadcastingOp<Broadcast, XprType> type;
};
} // end namespace internal
template<typename Broadcast, typename XprType>
class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
: m_xpr(expr), m_broadcast(broadcast) {}
EIGEN_DEVICE_FUNC
const Broadcast& broadcast() const { return m_broadcast; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const Broadcast m_broadcast;
};
// Eval as rvalue
template<typename Broadcast, typename ArgType, typename Device>
struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
{
typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const Broadcast& broadcast = op.broadcast();
for (int i = 0; i < NumDims; ++i) {
eigen_assert(input_dims[i] > 0);
m_dimensions[i] = input_dims[i] * broadcast[i];
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
} else {
m_inputStrides[NumDims-1] = 1;
m_outputStrides[NumDims-1] = 1;
for (int i = NumDims-2; i >= 0; --i) {
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
}
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
{
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return coeffColMajor(index);
} else {
return coeffRowMajor(index);
}
}
// TODO: attempt to speed this up. The integer divisions and modulo are slow
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
{
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>()(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
if (internal::index_statically_eq<Broadcast>()(0, 1)) {
eigen_assert(index < m_impl.dimensions()[0]);
inputIndex += index;
} else {
if (internal::index_statically_eq<InputDimensions>()(0, 1)) {
eigen_assert(index % m_impl.dimensions()[0] == 0);
} else {
inputIndex += (index % m_impl.dimensions()[0]);
}
}
return m_impl.coeff(inputIndex);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
{
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>()(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) {
eigen_assert(index < m_impl.dimensions()[NumDims-1]);
inputIndex += index;
} else {
if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) {
eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
} else {
inputIndex += (index % m_impl.dimensions()[NumDims-1]);
}
}
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
{
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return packetColMajor<LoadMode>(index);
} else {
return packetRowMajor<LoadMode>(index);
}
}
// Ignore the LoadMode and always use unaligned loads since we can't guarantee
// the alignment at compile time.
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
const Index originalIndex = index;
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>()(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
Index innermostLoc;
if (internal::index_statically_eq<Broadcast>()(0, 1)) {
eigen_assert(index < m_impl.dimensions()[0]);
innermostLoc = index;
} else {
if (internal::index_statically_eq<InputDimensions>()(0, 1)) {
eigen_assert(innermostLoc % m_impl.dimensions()[0] == 0);
innermostLoc = 0;
} else {
innermostLoc = index % m_impl.dimensions()[0];
}
}
inputIndex += innermostLoc;
// Todo: this could be extended to the second dimension if we're not
// broadcasting alongside the first dimension, and so on.
if (innermostLoc + packetSize <= m_impl.dimensions()[0]) {
return m_impl.template packet<Unaligned>(inputIndex);
} else {
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < packetSize; ++i) {
values[i] = coeffColMajor(originalIndex+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
const Index originalIndex = index;
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
if (internal::index_statically_eq<Broadcast>()(i, 1)) {
eigen_assert(idx < m_impl.dimensions()[i]);
inputIndex += idx * m_inputStrides[i];
} else {
if (internal::index_statically_eq<InputDimensions>()(i, 1)) {
eigen_assert(idx % m_impl.dimensions()[i] == 0);
} else {
inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
}
}
index -= idx * m_outputStrides[i];
}
Index innermostLoc;
if (internal::index_statically_eq<Broadcast>()(NumDims-1, 1)) {
eigen_assert(index < m_impl.dimensions()[NumDims-1]);
innermostLoc = index;
} else {
if (internal::index_statically_eq<InputDimensions>()(NumDims-1, 1)) {
eigen_assert(innermostLoc % m_impl.dimensions()[NumDims-1] == 0);
innermostLoc = 0;
} else {
innermostLoc = index % m_impl.dimensions()[NumDims-1];
}
}
inputIndex += innermostLoc;
// Todo: this could be extended to the second dimension if we're not
// broadcasting alongside the first dimension, and so on.
if (innermostLoc + packetSize <= m_impl.dimensions()[NumDims-1]) {
return m_impl.template packet<Unaligned>(inputIndex);
} else {
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
values[0] = m_impl.coeff(inputIndex);
for (int i = 1; i < packetSize; ++i) {
values[i] = coeffRowMajor(originalIndex+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_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>
//
// 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_CHIPPING_H
#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
namespace Eigen {
/** \class TensorKChippingReshaping
* \ingroup CXX11_Tensor_Module
*
* \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
*
*
*/
namespace internal {
template<DenseIndex DimId, typename XprType>
struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions - 1;
static const int Layout = XprTraits::Layout;
};
template<DenseIndex DimId, typename XprType>
struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
{
typedef const TensorChippingOp<DimId, XprType>& type;
};
template<DenseIndex DimId, typename XprType>
struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
{
typedef TensorChippingOp<DimId, XprType> type;
};
template <DenseIndex DimId>
struct DimensionId
{
DimensionId(DenseIndex dim) {
eigen_assert(dim == DimId);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
return DimId;
}
};
template <>
struct DimensionId<Dynamic>
{
DimensionId(DenseIndex dim) : actual_dim(dim) {
eigen_assert(dim >= 0);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
return actual_dim;
}
private:
const DenseIndex actual_dim;
};
} // end namespace internal
template<DenseIndex DimId, typename XprType>
class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorChippingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
: m_xpr(expr), m_offset(offset), m_dim(dim) {
}
EIGEN_DEVICE_FUNC
const Index offset() const { return m_offset; }
EIGEN_DEVICE_FUNC
const Index dim() const { return m_dim.actualDim(); }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorChippingOp& operator = (const TensorChippingOp& other)
{
typedef TensorAssignOp<TensorChippingOp, const TensorChippingOp> Assign;
Assign assign(*this, other);
static const bool Vectorize = TensorEvaluator<const Assign, DefaultDevice>::PacketAccess;
internal::TensorExecutor<const Assign, DefaultDevice, Vectorize>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
Assign assign(*this, other);
static const bool Vectorize = TensorEvaluator<const Assign, DefaultDevice>::PacketAccess;
internal::TensorExecutor<const Assign, DefaultDevice, Vectorize>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const Index m_offset;
const internal::DimensionId<DimId> m_dim;
};
// Eval as rvalue
template<DenseIndex DimId, typename ArgType, typename Device>
struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
typedef TensorChippingOp<DimId, ArgType> XprType;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
// Alignment can't be guaranteed at compile time since it depends on the
// slice offsets.
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_dim(op.dim()), m_device(device)
{
// We could also support the case where NumInputDims==1 if needed.
EIGEN_STATIC_ASSERT(NumInputDims >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(NumInputDims > m_dim.actualDim());
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
int j = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (i != m_dim.actualDim()) {
m_dimensions[j] = input_dims[i];
++j;
}
}
m_stride = 1;
m_inputStride = 1;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < m_dim.actualDim(); ++i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
} else {
for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
}
m_inputStride *= input_dims[m_dim.actualDim()];
m_inputOffset = m_stride * op.offset();
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(srcCoeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
Index inputIndex = index * m_inputStride + m_inputOffset;
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = m_impl.coeff(inputIndex);
inputIndex += m_inputStride;
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
return m_impl.template packet<LoadMode>(index + m_inputOffset);
} else {
const Index idx = index / m_stride;
const Index rem = index - idx * m_stride;
if (rem + packetSize <= m_stride) {
Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
return m_impl.template packet<LoadMode>(inputIndex);
} else {
// Cross the stride boundary. Fallback to slow path.
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index);
++index;
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const {
Scalar* result = m_impl.data();
if (m_dim.actualDim() == NumDims && result) {
return result + m_inputOffset;
} else {
return NULL;
}
}
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
{
Index inputIndex;
if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
inputIndex = index * m_inputStride + m_inputOffset;
} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims-1) ||
(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
inputIndex = index + m_inputOffset;
} else {
const Index idx = index / m_stride;
inputIndex = idx * m_inputStride + m_inputOffset;
index -= idx * m_stride;
inputIndex += index;
}
return inputIndex;
}
Dimensions m_dimensions;
Index m_stride;
Index m_inputOffset;
Index m_inputStride;
TensorEvaluator<ArgType, Device> m_impl;
const internal::DimensionId<DimId> m_dim;
const Device& m_device;
};
// Eval as lvalue
template<DenseIndex DimId, typename ArgType, typename Device>
struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
: public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
typedef TensorChippingOp<DimId, ArgType> XprType;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(this->srcCoeff(index));
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == 0) ||
(static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == NumInputDims-1)) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(this->m_stride == 1);
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
for (int i = 0; i < packetSize; ++i) {
this->m_impl.coeffRef(inputIndex) = values[i];
inputIndex += this->m_inputStride;
}
} else if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == NumInputDims-1) ||
(static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == 0)) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(this->m_stride > index);
this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
} else {
const Index idx = index / this->m_stride;
const Index rem = index - idx * this->m_stride;
if (rem + packetSize <= this->m_stride) {
const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
this->m_impl.template writePacket<StoreMode>(inputIndex, x);
} else {
// Cross stride boundary. Fallback to slow path.
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
for (int i = 0; i < packetSize; ++i) {
this->coeffRef(index) = values[i];
++index;
}
}
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_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>
//
// 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_CONCATENATION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
namespace Eigen {
/** \class TensorConcatenationOp
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor concatenation class.
*
*
*/
namespace internal {
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename promote_storage_type<typename LhsXprType::Scalar,
typename RhsXprType::Scalar>::ret Scalar;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
typename traits<RhsXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const int NumDimensions = traits<LhsXprType>::NumDimensions;
static const int Layout = traits<LhsXprType>::Layout;
enum { Flags = 0 };
};
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
};
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
{
typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
};
} // end namespace internal
template<typename Axis, typename LhsXprType, typename RhsXprType>
class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
{
public:
typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
typedef typename internal::traits<TensorConcatenationOp>::Packet Packet;
typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
typedef typename internal::traits<TensorConcatenationOp>::Index Index;
typedef typename internal::nested<TensorConcatenationOp>::type Nested;
typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename internal::promote_storage_type<typename LhsXprType::PacketReturnType,
typename RhsXprType::PacketReturnType>::ret PacketReturnType;
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return m_lhs_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
EIGEN_DEVICE_FUNC Axis axis() const { return m_axis; }
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const Axis m_axis;
};
// Eval as rvalue
template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(NumDims == RightNumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(0 <= m_axis && m_axis < NumDims);
const Dimensions& lhs_dims = m_leftImpl.dimensions();
const Dimensions& rhs_dims = m_rightImpl.dimensions();
int i = 0;
for (; i < m_axis; ++i) {
eigen_assert(lhs_dims[i] > 0);
eigen_assert(lhs_dims[i] == rhs_dims[i]);
m_dimensions[i] = lhs_dims[i];
}
eigen_assert(lhs_dims[i] > 0); // Now i == m_axis.
eigen_assert(rhs_dims[i] > 0);
m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
for (++i; i < NumDims; ++i) {
eigen_assert(lhs_dims[i] > 0);
eigen_assert(lhs_dims[i] == rhs_dims[i]);
m_dimensions[i] = lhs_dims[i];
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_leftStrides[0] = 1;
m_rightStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_leftStrides[i] = m_leftStrides[i-1] * lhs_dims[i-1];
m_rightStrides[i] = m_rightStrides[i-1] * rhs_dims[i-1];
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
} else {
m_leftStrides[NumDims - 1] = 1;
m_rightStrides[NumDims - 1] = 1;
m_outputStrides[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_leftStrides[i] = m_leftStrides[i+1] * lhs_dims[i+1];
m_rightStrides[i] = m_rightStrides[i+1] * rhs_dims[i+1];
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
// TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/)
{
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
{
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
// TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
// See CL/76180724 comments for more ideas.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
// Collect dimension-wise indices (subs).
array<Index, NumDims> subs;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
subs[i] = index / m_outputStrides[i];
index -= subs[i] * m_outputStrides[i];
}
subs[0] = index;
} else {
for (int i = 0; i < NumDims - 1; ++i) {
subs[i] = index / m_outputStrides[i];
index -= subs[i] * m_outputStrides[i];
}
subs[NumDims - 1] = index;
}
const Dimensions& left_dims = m_leftImpl.dimensions();
if (subs[m_axis] < left_dims[m_axis]) {
Index left_index;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
left_index = subs[0];
for (int i = 1; i < NumDims; ++i) {
left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
}
} else {
left_index = subs[NumDims - 1];
for (int i = NumDims - 2; i >= 0; --i) {
left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
}
}
return m_leftImpl.coeff(left_index);
} else {
subs[m_axis] -= left_dims[m_axis];
const Dimensions& right_dims = m_rightImpl.dimensions();
Index right_index;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
right_index = subs[0];
for (int i = 1; i < NumDims; ++i) {
right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
}
} else {
right_index = subs[NumDims - 1];
for (int i = NumDims - 2; i >= 0; --i) {
right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
}
}
return m_rightImpl.coeff(right_index);
}
}
// TODO(phli): Add a real vectorization.
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
EIGEN_ALIGN_DEFAULT CoeffReturnType values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_leftStrides;
array<Index, NumDims> m_rightStrides;
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
const Axis m_axis;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_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>
//
// 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_CONTRACTION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
namespace Eigen {
/** \class TensorContraction
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor contraction class.
*
*
*/
namespace internal {
enum {
Rhs = 0,
Lhs = 1,
};
/*
* Implementation of the Eigen blas_data_mapper class for tensors.
*/
template<typename Scalar, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
size_t packet_size, bool inner_dim_contiguous>
class BaseTensorContractionMapper {
public:
EIGEN_DEVICE_FUNC
BaseTensorContractionMapper(const Tensor& tensor,
const nocontract_t& nocontract_strides,
const nocontract_t& ij_strides,
const contract_t& contract_strides,
const contract_t& k_strides) :
m_tensor(tensor),
m_nocontract_strides(nocontract_strides),
m_ij_strides(ij_strides),
m_contract_strides(contract_strides),
m_k_strides(k_strides) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE void prefetch(Index /*i*/) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar operator()(Index row) const {
// column major assumption
return operator()(row, 0);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar operator()(Index row, Index col) const {
return m_tensor.coeff(computeIndex(row, col));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index computeIndex(Index row, Index col) const {
const bool left = (side == Lhs);
Index nocontract_val = left ? row : col;
Index linidx = 0;
for (int i = array_size<nocontract_t>::value - 1; i > 0; i--) {
const Index idx = nocontract_val / m_ij_strides[i];
linidx += idx * m_nocontract_strides[i];
nocontract_val -= idx * m_ij_strides[i];
}
if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
if (side == Lhs && inner_dim_contiguous) {
eigen_assert(m_nocontract_strides[0] == 1);
linidx += nocontract_val;
} else {
linidx += nocontract_val * m_nocontract_strides[0];
}
}
Index contract_val = left ? col : row;
for (int i = array_size<contract_t>::value - 1; i > 0; i--) {
const Index idx = contract_val / m_k_strides[i];
linidx += idx * m_contract_strides[i];
contract_val -= idx * m_k_strides[i];
}
EIGEN_STATIC_ASSERT(array_size<contract_t>::value > 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
if (side == Rhs && inner_dim_contiguous) {
eigen_assert(m_contract_strides[0] == 1);
linidx += contract_val;
} else {
linidx += contract_val * m_contract_strides[0];
}
return linidx;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE IndexPair<Index> computeIndexPair(Index row, Index col, const Index distance) const {
const bool left = (side == Lhs);
Index nocontract_val[2] = {left ? row : col, left ? row + distance : col};
Index linidx[2] = {0, 0};
for (int i = array_size<nocontract_t>::value - 1; i > 0; i--) {
const Index idx0 = nocontract_val[0] / m_ij_strides[i];
const Index idx1 = nocontract_val[1] / m_ij_strides[i];
linidx[0] += idx0 * m_nocontract_strides[i];
linidx[1] += idx1 * m_nocontract_strides[i];
nocontract_val[0] -= idx0 * m_ij_strides[i];
nocontract_val[1] -= idx1 * m_ij_strides[i];
}
if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
if (side == Lhs && inner_dim_contiguous) {
eigen_assert(m_nocontract_strides[0] == 1);
linidx[0] += nocontract_val[0];
linidx[1] += nocontract_val[1];
} else {
linidx[0] += nocontract_val[0] * m_nocontract_strides[0];
linidx[1] += nocontract_val[1] * m_nocontract_strides[0];
}
}
Index contract_val[2] = {left ? col : row, left ? col : row + distance};
for (int i = array_size<contract_t>::value - 1; i > 0; i--) {
const Index idx0 = contract_val[0] / m_k_strides[i];
const Index idx1 = contract_val[1] / m_k_strides[i];
linidx[0] += idx0 * m_contract_strides[i];
linidx[1] += idx1 * m_contract_strides[i];
contract_val[0] -= idx0 * m_k_strides[i];
contract_val[1] -= idx1 * m_k_strides[i];
}
EIGEN_STATIC_ASSERT(array_size<contract_t>::value > 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
if (side == Rhs && inner_dim_contiguous) {
eigen_assert(m_contract_strides[0] == 1);
linidx[0] += contract_val[0];
linidx[1] += contract_val[1];
} else {
linidx[0] += contract_val[0] * m_contract_strides[0];
linidx[1] += contract_val[1] * m_contract_strides[0];
}
return IndexPair<Index>(linidx[0], linidx[1]);
}
Index firstAligned(Index size) const {
return size;
}
Index stride() const {
return 1;
}
protected:
const Tensor m_tensor;
const nocontract_t m_nocontract_strides;
const nocontract_t m_ij_strides;
const contract_t m_contract_strides;
const contract_t m_k_strides;
};
template<typename Scalar, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
size_t packet_size,
bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment>
class TensorContractionInputMapper;
template<typename Scalar, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
size_t packet_size,
bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment>
class TensorContractionSubMapper {
public:
typedef typename packet_traits<Scalar>::type Packet;
typedef typename packet_traits<Scalar>::half HalfPacket;
typedef TensorContractionInputMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> ParentMapper;
typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self;
typedef Self LinearMapper;
EIGEN_DEVICE_FUNC TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset)
: m_base_mapper(base_mapper), m_vert_offset(vert_offset), m_horiz_offset(horiz_offset) { }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
return m_base_mapper(i + m_vert_offset, m_horiz_offset);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const {
return m_base_mapper(i + m_vert_offset, j + m_horiz_offset);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const {
return m_base_mapper.loadPacket(i + m_vert_offset, m_horiz_offset);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, Index j) const {
return m_base_mapper.loadPacket(i + m_vert_offset, j + m_horiz_offset);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HalfPacket loadHalfPacket(Index i) const {
return m_base_mapper.loadHalfPacket(i + m_vert_offset, m_horiz_offset);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Packet p) const {
m_base_mapper.storePacket(i + m_vert_offset, m_horiz_offset, p);
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
return LinearMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset);
}
template <typename PacketT, int AlignmentType>
EIGEN_ALWAYS_INLINE PacketT load(Index i) const {
EIGEN_STATIC_ASSERT((internal::is_same<PacketT, Packet>::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((AlignmentType == Aligned || Alignment == Unaligned), YOU_MADE_A_PROGRAMMING_MISTAKE);
return loadPacket(i);
}
template <typename Packet>
bool aligned(Index /*i*/) const {
return false;
}
private:
const ParentMapper& m_base_mapper;
const Index m_vert_offset;
const Index m_horiz_offset;
};
template<typename Scalar, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
size_t packet_size = (Tensor::PacketAccess ? packet_traits<Scalar>::size : 1),
bool inner_dim_contiguous = false, bool inner_dim_reordered = (side != Lhs), int Alignment=Unaligned>
class TensorContractionInputMapper
: public BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous> {
public:
typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous> Base;
typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper;
typedef SubMapper VectorMapper;
TensorContractionInputMapper(const Tensor& tensor,
const nocontract_t& nocontract_strides,
const nocontract_t& ij_strides,
const contract_t& contract_strides,
const contract_t& k_strides)
: Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
return SubMapper(*this, i, j);
}
EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
return VectorMapper(*this, i, j);
}
typedef typename packet_traits<Scalar>::type Packet;
typedef typename packet_traits<Scalar>::half HalfPacket;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Packet loadPacket(Index i, Index j) const {
// whole method makes column major assumption
// don't need to add offsets for now (because operator handles that)
// current code assumes packet size must be a multiple of 2
EIGEN_STATIC_ASSERT(packet_size % 2 == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
if (Tensor::PacketAccess && inner_dim_contiguous && !inner_dim_reordered) {
const Index index = this->computeIndex(i, j);
eigen_assert(this->computeIndex(i+packet_size-1, j) == index + packet_size-1);
return this->m_tensor.template packet<Alignment>(index);
}
const IndexPair<Index> indexPair = this->computeIndexPair(i, j, packet_size - 1);
const Index first = indexPair.first;
const Index last = indexPair.second;
// We can always do optimized packet reads from left hand side right now, because
// the vertical matrix dimension on the left hand side is never contracting.
// On the right hand side we need to check if the contracting dimensions may have
// been shuffled first.
if (Tensor::PacketAccess &&
(side == Lhs || internal::array_size<contract_t>::value <= 1 || !inner_dim_reordered) &&
(last - first) == (packet_size - 1)) {
return this->m_tensor.template packet<Alignment>(first);
}
EIGEN_ALIGN_DEFAULT Scalar data[packet_size];
data[0] = this->m_tensor.coeff(first);
for (Index k = 1; k < packet_size - 1; k += 2) {
const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
data[k] = this->m_tensor.coeff(internal_pair.first);
data[k + 1] = this->m_tensor.coeff(internal_pair.second);
}
data[packet_size - 1] = this->m_tensor.coeff(last);
return pload<Packet>(data);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE HalfPacket loadHalfPacket(Index i, Index j) const {
// whole method makes column major assumption
// don't need to add offsets for now (because operator handles that)
const Index half_packet_size = unpacket_traits<HalfPacket>::size;
if (half_packet_size == packet_size) {
return loadPacket(i, j);
}
EIGEN_ALIGN_DEFAULT Scalar data[half_packet_size];
for (Index k = 0; k < half_packet_size; k++) {
data[k] = operator()(i + k, j);
}
return pload<HalfPacket>(data);
}
};
template<typename Scalar, typename Index, int side,
typename Tensor,
typename nocontract_t, typename contract_t,
bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment>
class TensorContractionInputMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, Alignment>
: public BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous> {
public:
typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous> Base;
typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper;
typedef SubMapper VectorMapper;
TensorContractionInputMapper(const Tensor& tensor,
const nocontract_t& nocontract_strides,
const nocontract_t& ij_strides,
const contract_t& contract_strides,
const contract_t& k_strides)
: Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
return SubMapper(*this, i, j);
}
EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
return VectorMapper(*this, i, j);
}
typedef typename packet_traits<Scalar>::type Packet;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Packet loadPacket(Index i, Index j) const {
EIGEN_ALIGN_DEFAULT Scalar data[1];
data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
return pload<typename packet_traits<Scalar>::type>(data);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Packet loadHalfPacket(Index i, Index j) const {
return loadPacket(i, j);
}
};
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;
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename internal::promote_storage_type<typename LhsXprType::Scalar,
typename RhsXprType::Scalar>::ret Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
typename traits<RhsXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
// From NumDims below.
static const int NumDimensions = max_n_1<traits<RhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value>::size;
static const int Layout = traits<LhsXprType>::Layout;
enum {
Flags = 0,
};
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType>& type;
};
template<typename Dimensions, typename LhsXprType, typename RhsXprType>
struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >::type>
{
typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType> type;
};
template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename Device_>
struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_>, Device_> > {
typedef Indices_ Indices;
typedef LeftArgType_ LeftArgType;
typedef RightArgType_ RightArgType;
typedef Device_ Device;
// From NumDims below.
static const int NumDimensions = max_n_1<traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value>::size;
};
} // end namespace internal
template<typename Indices, typename LhsXprType, typename RhsXprType>
class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorContractionOp>::Packet Packet;
typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename internal::promote_storage_type<typename LhsXprType::PacketReturnType,
typename RhsXprType::PacketReturnType>::ret PacketReturnType;
typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested;
typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(
const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims) {}
EIGEN_DEVICE_FUNC
const Indices& indices() const { return m_indices; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return m_lhs_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const Indices m_indices;
};
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 Derived>
struct TensorContractionEvaluatorBase
{
typedef typename internal::traits<Derived>::Indices Indices;
typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
typedef typename internal::traits<Derived>::RightArgType RightArgType;
typedef typename internal::traits<Derived>::Device Device;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::Packet Packet;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
IsAligned = true,
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
// Most of the code is assuming that both input tensors are ColMajor. If the
// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
// If we want to compute A * B = C, where A is LHS and B is RHS, the code
// will pretend B is LHS and A is RHS.
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
static const int LDims =
internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
static const int RDims =
internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
static const int ContractDims = internal::array_size<Indices>::value;
static const int NumDims = internal::max_n_1<LDims + RDims - 2 * ContractDims>::size;
typedef array<Index, LDims> left_dim_mapper_t;
typedef array<Index, RDims> right_dim_mapper_t;
typedef array<Index, ContractDims> contract_t;
typedef array<Index, internal::max_n_1<LDims - ContractDims>::size> left_nocontract_t;
typedef array<Index, internal::max_n_1<RDims - ContractDims>::size> right_nocontract_t;
typedef DSizes<Index, NumDims> Dimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorContractionEvaluatorBase(const XprType& op, const Device& device)
: m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
op.lhsExpression(), op.rhsExpression()), device),
m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
op.rhsExpression(), op.lhsExpression()), device),
m_device(device),
m_result(NULL) {
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert((internal::array_size<contract_t>::value > 0) && "Must contract on some indices");
DSizes<Index, LDims> eval_left_dims;
DSizes<Index, RDims> eval_right_dims;
array<IndexPair<Index>, ContractDims> eval_op_indices;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
// For ColMajor, we keep using the existing dimensions
for (int i = 0; i < LDims; i++) {
eval_left_dims[i] = m_leftImpl.dimensions()[i];
}
for (int i = 0; i < RDims; i++) {
eval_right_dims[i] = m_rightImpl.dimensions()[i];
}
// We keep the pairs of contracting indices.
for (int i = 0; i < ContractDims; i++) {
eval_op_indices[i].first = op.indices()[i].first;
eval_op_indices[i].second = op.indices()[i].second;
}
} else {
// For RowMajor, we need to reverse the existing dimensions
for (int i = 0; i < LDims; i++) {
eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];
}
for (int i = 0; i < RDims; i++) {
eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];
}
// We need to flip all the pairs of contracting indices as well as
// reversing the dimensions.
for (int i = 0; i < ContractDims; i++) {
eval_op_indices[i].first = LDims - 1 - op.indices()[i].second;
eval_op_indices[i].second = RDims - 1 - op.indices()[i].first;
}
}
array<Index, LDims> lhs_strides;
lhs_strides[0] = 1;
for (int i = 0; i < LDims-1; ++i) {
lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i];
}
array<Index, RDims> rhs_strides;
rhs_strides[0] = 1;
for (int i = 0; i < RDims-1; ++i) {
rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i];
}
m_i_strides[0] = 1;
m_j_strides[0] = 1;
m_k_strides[0] = 1;
m_i_size = 1;
m_j_size = 1;
m_k_size = 1;
// To compute the dimension, we simply concatenate the non-contracting
// dimensions of the left and then the right tensor. Additionally, we also
// compute the strides corresponding to the left non-contracting
// dimensions and right non-contracting dimensions.
m_lhs_inner_dim_contiguous = true;
int dim_idx = 0;
int nocontract_idx = 0;
for (int i = 0; i < LDims; i++) {
// find if we are contracting on index i of left tensor
bool contracting = false;
for (int j = 0; j < ContractDims; j++) {
if (eval_op_indices[j].first == i) {
contracting = true;
break;
}
}
if (!contracting) {
// add dimension size to output dimensions
m_dimensions[dim_idx] = eval_left_dims[i];
m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];
if (dim_idx != i) {
m_lhs_inner_dim_contiguous = false;
}
if (nocontract_idx+1 < internal::array_size<left_nocontract_t>::value) {
m_i_strides[nocontract_idx+1] =
m_i_strides[nocontract_idx] * eval_left_dims[i];
} else {
m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];
}
dim_idx++;
nocontract_idx++;
}
}
nocontract_idx = 0;
for (int i = 0; i < RDims; i++) {
bool contracting = false;
// find if we are contracting on index i of right tensor
for (int j = 0; j < ContractDims; j++) {
if (eval_op_indices[j].second == i) {
contracting = true;
break;
}
}
if (!contracting) {
m_dimensions[dim_idx] = eval_right_dims[i];
if (nocontract_idx+1 < internal::array_size<right_nocontract_t>::value) {
m_j_strides[nocontract_idx+1] =
m_j_strides[nocontract_idx] * eval_right_dims[i];
} else {
m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];
}
m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];
dim_idx++;
nocontract_idx++;
}
}
// Now compute the strides corresponding to the contracting dimensions. We
// assumed above that non-contracting axes are represented in the same order
// in the matrix as they are in the tensor. This is not the case for
// contracting axes. As the contracting axes must be of the same size in
// each tensor, we'll only look at the first tensor here.
m_rhs_inner_dim_contiguous = true;
m_rhs_inner_dim_reordered = false;
for (int i = 0; i < ContractDims; i++) {
Index left = eval_op_indices[i].first;
Index right = eval_op_indices[i].second;
Index size = eval_left_dims[left];
eigen_assert(size == eval_right_dims[right] &&
"Contraction axes must be same size");
if (i+1 < internal::array_size<contract_t>::value) {
m_k_strides[i+1] = m_k_strides[i] * size;
} else {
m_k_size = m_k_strides[i] * size;
}
m_left_contracting_strides[i] = lhs_strides[left];
m_right_contracting_strides[i] = rhs_strides[right];
if (i > 0 && right < eval_op_indices[i-1].second) {
m_rhs_inner_dim_reordered = true;
}
if (right != i) {
m_rhs_inner_dim_contiguous = false;
}
}
// Scalar case. We represent the result as a 1d tensor of size 1.
if (LDims + RDims == 2 * ContractDims) {
m_dimensions[0] = 1;
}
// If the layout is RowMajor, we need to reverse the m_dimensions
if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) {
for (int i = 0, j = NumDims - 1; i < j; i++, j--) {
std::swap(m_dimensions[i], m_dimensions[j]);
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
if (data) {
evalTo(data);
return false;
} else {
m_result = static_cast<Scalar *>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
evalTo(m_result);
return true;
}
}
EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
if (this->m_lhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<true, true, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<true, true, false, Unaligned>(buffer);
}
}
else {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<true, false, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<true, false, false, Unaligned>(buffer);
}
}
}
else {
if (this->m_rhs_inner_dim_contiguous) {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<false, true, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<false, true, false, Unaligned>(buffer);
}
}
else {
if (this->m_rhs_inner_dim_reordered) {
static_cast<const Derived*>(this)->template evalProduct<false, false, true, Unaligned>(buffer);
}
else {
static_cast<const Derived*>(this)->template evalProduct<false, false, false, Unaligned>(buffer);
}
}
}
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalGemv(Scalar* buffer) const {
const Index rows = m_i_size;
const Index cols = m_k_size;
typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
const int lhs_packet_size = internal::packet_traits<LhsScalar>::size;
const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
LeftEvaluator, left_nocontract_t,
contract_t, lhs_packet_size,
lhs_inner_dim_contiguous,
false, Unaligned> LhsMapper;
typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
RightEvaluator, right_nocontract_t,
contract_t, rhs_packet_size,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Unaligned> RhsMapper;
LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides,
m_left_contracting_strides, m_k_strides);
RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides,
m_right_contracting_strides, m_k_strides);
const Scalar alpha(1);
const Index resIncr(1);
// zero out the result buffer (which must be of size at least rows * sizeof(Scalar)
m_device.memset(buffer, 0, rows * sizeof(Scalar));
internal::general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false>::run(
rows, cols, lhs, rhs,
buffer, resIncr, alpha);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
if (m_result != NULL) {
m_device.deallocate(m_result);
m_result = NULL;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
return m_result[index];
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
return internal::ploadt<Packet, LoadMode>(m_result + index);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
// Prevent assignment
TensorContractionEvaluatorBase& operator = (const TensorContractionEvaluatorBase&);
Dimensions m_dimensions;
contract_t m_k_strides;
contract_t m_left_contracting_strides;
contract_t m_right_contracting_strides;
bool m_lhs_inner_dim_contiguous;
bool m_rhs_inner_dim_contiguous;
bool m_rhs_inner_dim_reordered;
left_nocontract_t m_i_strides;
right_nocontract_t m_j_strides;
left_nocontract_t m_left_nocontract_strides;
right_nocontract_t m_right_nocontract_strides;
Index m_i_size;
Index m_j_size;
Index m_k_size;
TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
const Device& m_device;
Scalar* m_result;
};
// evaluator for default device
template<typename Indices, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> :
public TensorContractionEvaluatorBase<
TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> > {
typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
typedef TensorContractionEvaluatorBase<Self> Base;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::Packet Packet;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
};
// Most of the code is assuming that both input tensors are ColMajor. If the
// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
// If we want to compute A * B = C, where A is LHS and B is RHS, the code
// will pretend B is LHS and A is RHS.
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
static const int LDims =
internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
static const int RDims =
internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
static const int ContractDims = internal::array_size<Indices>::value;
typedef array<Index, LDims> left_dim_mapper_t;
typedef array<Index, RDims> right_dim_mapper_t;
typedef array<Index, ContractDims> contract_t;
typedef array<Index, internal::max_n_1<LDims - ContractDims>::size> left_nocontract_t;
typedef array<Index, internal::max_n_1<RDims - ContractDims>::size> right_nocontract_t;
static const int NumDims = internal::max_n_1<LDims + RDims - 2 * ContractDims>::size;
// Could we use NumDimensions here?
typedef DSizes<Index, NumDims> Dimensions;
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
Base(op, device) { }
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalProduct(Scalar* buffer) const {
if (this->m_j_size == 1) {
this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
return;
}
evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
EIGEN_DEVICE_FUNC void evalGemm(Scalar* buffer) const {
// columns in left side, rows in right side
const Index k = this->m_k_size;
// rows in left side
const Index m = this->m_i_size;
// columns in right side
const Index n = this->m_j_size;
// zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
// define mr, nr, and all of my data mapper types
typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
const Index nr = Traits::nr;
const Index mr = Traits::mr;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
const int lhs_packet_size = internal::packet_traits<LhsScalar>::size;
const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
LeftEvaluator, left_nocontract_t,
contract_t, lhs_packet_size,
lhs_inner_dim_contiguous,
false, Unaligned> LhsMapper;
typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
RightEvaluator, right_nocontract_t,
contract_t, rhs_packet_size,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Unaligned> RhsMapper;
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
// Declare GEBP packing and kernel structs
internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, mr, Traits::LhsProgress, ColMajor> pack_lhs;
internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, nr, ColMajor> pack_rhs;
internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper, mr, nr, false, false> gebp;
// initialize data mappers
LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
this->m_left_contracting_strides, this->m_k_strides);
RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
this->m_right_contracting_strides, this->m_k_strides);
OutputMapper output(buffer, m);
typedef typename internal::gemm_blocking_space<ColMajor, LhsScalar, RhsScalar, Dynamic, Dynamic, Dynamic> BlockingType;
// Sizes of the blocks to load in cache. See the Goto paper for details.
BlockingType blocking(m, n, k, 1, true);
const Index kc = blocking.kc();
const Index mc = (std::min)(m, blocking.mc());
const Index nc = (std::min)(n, blocking.nc());
const Index sizeA = mc * kc;
const Index sizeB = kc * nc;
LhsScalar* blockA = static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar)));
RhsScalar* blockB = static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar)));
for(Index i2=0; i2<m; i2+=mc)
{
const Index actual_mc = (std::min)(i2+mc,m)-i2;
for (Index k2 = 0; k2 < k; k2 += kc) {
// make sure we don't overshoot right edge of left matrix, then pack vertical panel
const Index actual_kc = (std::min)(k2 + kc, k) - k2;
pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc, 0, 0);
// series of horizontal blocks
for (Index j2 = 0; j2 < n; j2 += nc) {
// make sure we don't overshoot right edge of right matrix, then pack block
const Index actual_nc = (std::min)(j2 + nc, n) - j2;
pack_rhs(blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc, 0, 0);
// call gebp (matrix kernel)
// The parameters here are copied from Eigen's GEMM implementation
gebp(output.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, 1.0, -1, -1, 0, 0);
}
}
}
this->m_device.deallocate(blockA);
this->m_device.deallocate(blockB);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_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>
//
// 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_CONTRACTION_THREAD_POOL_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
// evaluator for thread pool device
#ifdef EIGEN_USE_THREADS
namespace Eigen {
namespace internal {
template<typename LhsScalar, typename LhsMapper, typename Index>
struct packLhsArg {
LhsScalar* blockA;
const LhsMapper& lhs;
const Index m_start;
const Index k_start;
const Index mc;
const Index kc;
};
template<typename LhsScalar, typename RhsScalar, typename RhsMapper, typename OutputMapper, typename Index>
struct packRhsAndKernelArg {
const std::vector<LhsScalar*>* blockAs;
RhsScalar* blockB;
const RhsMapper& rhs;
OutputMapper& output;
const Index m;
const Index k;
const Index n;
const Index mc;
const Index kc;
const Index nc;
const Index num_threads;
const Index num_blockAs;
const Index max_m;
const Index k_block_idx;
const Index m_block_idx;
const Index n_block_idx;
const Index m_blocks;
const Index n_blocks;
std::vector<Promise>* kernel_promises;
const std::vector<Future>* lhs_futures;
const bool need_to_pack;
};
} // end namespace internal
template<typename Indices, typename LeftArgType, typename RightArgType>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> :
public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> > {
typedef ThreadPoolDevice Device;
typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
typedef TensorContractionEvaluatorBase<Self> Base;
typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
typedef typename XprType::Packet Packet;
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
enum {
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
};
// Most of the code is assuming that both input tensors are ColMajor. If the
// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
// If we want to compute A * B = C, where A is LHS and B is RHS, the code
// will pretend B is LHS and A is RHS.
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
typedef typename internal::conditional<
static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
static const int LDims =
internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
static const int RDims =
internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
static const int ContractDims = internal::array_size<Indices>::value;
typedef array<Index, LDims> left_dim_mapper_t;
typedef array<Index, RDims> right_dim_mapper_t;
typedef array<Index, ContractDims> contract_t;
typedef array<Index, internal::max_n_1<LDims - ContractDims>::size> left_nocontract_t;
typedef array<Index, internal::max_n_1<RDims - ContractDims>::size> right_nocontract_t;
static const int NumDims = internal::max_n_1<LDims + RDims - 2 * ContractDims>::size;
typedef DSizes<Index, NumDims> Dimensions;
// typedefs needed in evalTo
typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
TensorEvaluator(const XprType& op, const Device& device) :
Base(op, device) {}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalProduct(Scalar* buffer) const {
if (this->m_j_size == 1) {
this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
return;
}
evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
}
template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
void evalGemm(Scalar* buffer) const {
// columns in left side, rows in right side
const Index k = this->m_k_size;
// rows in left side
const Index m = this->m_i_size;
// columns in right side
const Index n = this->m_j_size;
// zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
const int lhs_packet_size = internal::packet_traits<LhsScalar>::size;
const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
LeftEvaluator, left_nocontract_t,
contract_t, lhs_packet_size,
lhs_inner_dim_contiguous,
false, Unaligned> LhsMapper;
typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
RightEvaluator, right_nocontract_t,
contract_t, rhs_packet_size,
rhs_inner_dim_contiguous,
rhs_inner_dim_reordered, Unaligned> RhsMapper;
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
// TODO: packing could be faster sometimes if we supported row major tensor mappers
typedef internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, Traits::mr,
Traits::LhsProgress, ColMajor> LhsPacker;
typedef internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, Traits::nr, ColMajor> RhsPacker;
// TODO: replace false, false with conjugate values?
typedef internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper,
Traits::mr, Traits::nr, false, false> GebpKernel;
typedef internal::packLhsArg<LhsScalar, LhsMapper, Index> packLArg;
typedef internal::packRhsAndKernelArg<LhsScalar, RhsScalar, RhsMapper, OutputMapper, Index> packRKArg;
// initialize data mappers
LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
this->m_left_contracting_strides, this->m_k_strides);
RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
this->m_right_contracting_strides, this->m_k_strides);
OutputMapper output(buffer, m);
LhsPacker pack_lhs;
// compute block sizes (which depend on number of threads)
const Index num_threads = this->m_device.numThreads();
Index mc = m;
Index nc = n;
Index kc = k;
internal::computeProductBlockingSizes<LhsScalar,RhsScalar,1>(kc, mc, nc, num_threads);
eigen_assert(mc <= m);
eigen_assert(nc <= n);
eigen_assert(kc <= k);
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
const Index k_blocks = CEIL_DIV(k, kc);
const Index n_blocks = CEIL_DIV(n, nc);
const Index m_blocks = CEIL_DIV(m, mc);
const int sizeA = mc * kc;
const int sizeB = kc * nc;
/* cout << "m: " << m << " n: " << n << " k: " << k << endl;
cout << "mc: " << mc << " nc: " << nc << " kc: " << kc << endl;
cout << "m_blocks: " << m_blocks << " n_blocks: " << n_blocks << " k_blocks: " << k_blocks << endl;
cout << "num threads: " << num_threads << endl;
*/
// note: m_device.allocate should return 16 byte aligned pointers, but if blockA and blockB
// aren't 16 byte aligned segfaults will happen due to SIMD instructions
// note: You can get away with allocating just a single blockA and offsets and meet the
// the alignment requirements with the assumption that
// (Traits::mr * sizeof(ResScalar)) % 16 == 0
const Index numBlockAs = (std::min)(num_threads, m_blocks);
std::vector<LhsScalar *> blockAs;
blockAs.reserve(num_threads);
for (int i = 0; i < num_threads; i++) {
blockAs.push_back(static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar))));
}
// To circumvent alignment issues, I'm just going to separately allocate the memory for each thread
// TODO: is this too much memory to allocate? This simplifies coding a lot, but is wasteful.
// Other options: (1) reuse memory when a thread finishes. con: tricky
// (2) allocate block B memory in each thread. con: overhead
std::vector<RhsScalar *> blockBs;
blockBs.reserve(n_blocks);
for (int i = 0; i < n_blocks; i++) {
blockBs.push_back(static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar))));
}
// lhs_futures starts with all null futures
std::vector<Future> lhs_futures(num_threads);
// this should really be numBlockAs * n_blocks;
const Index num_kernel_promises = num_threads * n_blocks;
std::vector<Promise> kernel_promises(num_kernel_promises);
std::vector<Future> kernel_futures(num_kernel_promises);
for (int i = 0; i < kernel_promises.size(); ++i) {
kernel_promises[i].set_value();
kernel_futures[i] = kernel_promises[i].get_future();
}
for (Index k_block_idx = 0; k_block_idx < k_blocks; k_block_idx++) {
const Index k_start = k_block_idx * kc;
// make sure we don't overshoot right edge of left matrix
const Index actual_kc = (std::min)(k_start + kc, k) - k_start;
for (Index m_block_idx = 0; m_block_idx < m_blocks; m_block_idx += numBlockAs) {
const int num_blocks = (std::min)(m_blocks-m_block_idx, numBlockAs);
for (Index mt_block_idx = m_block_idx; mt_block_idx < m_block_idx+num_blocks; mt_block_idx++) {
const Index m_start = mt_block_idx * mc;
const Index actual_mc = (std::min)(m_start + mc, m) - m_start;
eigen_assert(actual_mc > 0);
int blockAId = (k_block_idx * m_blocks + mt_block_idx) % num_threads;
for (int i = 0; i < n_blocks; ++i) {
int future_id = (blockAId * n_blocks + i);
wait_until_ready(&kernel_futures[future_id]);
kernel_promises[future_id] = Promise();
kernel_futures[future_id] = kernel_promises[future_id].get_future();
}
const packLArg arg = {
blockAs[blockAId], // blockA
lhs, // lhs
m_start, // m
k_start, // k
actual_mc, // mc
actual_kc, // kc
};
lhs_futures[blockAId] =
this->m_device.enqueue(&Self::packLhs<packLArg, LhsPacker>, arg);
}
// now start kernels.
const Index m_base_start = m_block_idx * mc;
const bool need_to_pack = m_block_idx == 0;
for (Index n_block_idx = 0; n_block_idx < n_blocks; n_block_idx++) {
const Index n_start = n_block_idx * nc;
const Index actual_nc = (std::min)(n_start + nc, n) - n_start;
// first make sure the previous kernels are all done before overwriting rhs. Also wait if
// we're going to start new k. In both cases need_to_pack is true.
if (need_to_pack) {
for (int i = num_blocks; i < num_threads; ++i) {
int blockAId = (k_block_idx * m_blocks + i + m_block_idx) % num_threads;
int future_id = (blockAId * n_blocks + n_block_idx);
wait_until_ready(&kernel_futures[future_id]);
}
}
packRKArg arg = {
&blockAs, // blockA
blockBs[n_block_idx], // blockB
rhs, // rhs
output, // output
m_base_start, // m
k_start, // k
n_start, // n
mc, // mc
actual_kc, // kc
actual_nc, // nc
num_threads,
numBlockAs,
m,
k_block_idx,
m_block_idx,
n_block_idx, // n_block_idx
m_blocks, // m_blocks
n_blocks, // n_blocks
&kernel_promises, // kernel_promises
&lhs_futures, // lhs_futures
need_to_pack, // need_to_pack
};
this->m_device.enqueueNoFuture(&Self::packRhsAndKernel<packRKArg, RhsPacker, GebpKernel>, arg);
}
}
}
// Make sure all the kernels are done.
for (int i = 0; i < kernel_futures.size(); ++i) {
wait_until_ready(&kernel_futures[i]);
}
// deallocate all of the memory for both A and B's
for (int i = 0; i < blockAs.size(); i++) {
this->m_device.deallocate(blockAs[i]);
}
for (int i = 0; i < blockBs.size(); i++) {
this->m_device.deallocate(blockBs[i]);
}
#undef CEIL_DIV
}
/*
* Packs a LHS block of size (mt, kc) starting at lhs(m, k). Before packing
* the LHS block, check that all of the kernels that worked on the same
* mt_block_idx in the previous m_block are done.
*/
template <typename packLArg, typename LhsPacker>
static void packLhs(const packLArg arg) {
// perform actual packing
LhsPacker pack_lhs;
pack_lhs(arg.blockA, arg.lhs.getSubMapper(arg.m_start, arg.k_start), arg.kc, arg.mc);
}
/*
* Packs a RHS block of size (kc, nc) starting at (k, n) after checking that
* all kernels in the previous block are done.
* Then for each LHS future, we wait on the future and then call GEBP
* on the area packed by the future (which starts at
* blockA + future_idx * mt * kc) on the LHS and with the full packed
* RHS block.
* The output of this GEBP is written to output(m + i * mt, n).
*/
template <typename packRKArg, typename RhsPacker, typename GebpKernel>
static void packRhsAndKernel(packRKArg arg) {
if (arg.need_to_pack) {
RhsPacker pack_rhs;
pack_rhs(arg.blockB, arg.rhs.getSubMapper(arg.k, arg.n), arg.kc, arg.nc);
}
GebpKernel gebp;
for (Index mt_block_idx = 0; mt_block_idx < arg.num_blockAs; mt_block_idx++) {
const Index m_base_start = arg.m + arg.mc*mt_block_idx;
if (m_base_start < arg.max_m) {
int blockAId = (arg.k_block_idx * arg.m_blocks + mt_block_idx + arg.m_block_idx) % arg.num_threads;
wait_until_ready(&(*arg.lhs_futures)[blockAId]);
const Index actual_mc = (std::min)(m_base_start + arg.mc, arg.max_m) - m_base_start;
gebp(arg.output.getSubMapper(m_base_start, arg.n),
(*arg.blockAs)[blockAId], arg.blockB,
actual_mc, arg.kc, arg.nc, 1.0, -1, -1, 0, 0);
const Index set_idx = blockAId * arg.n_blocks + arg.n_block_idx;
(*arg.kernel_promises)[set_idx].set_value();
}
}
}
};
} // end namespace Eigen
#endif // EIGEN_USE_THREADS
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_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>
//
// 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_CONVOLUTION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
namespace Eigen {
/** \class TensorConvolution
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor convolution class.
*
*
*/
namespace internal {
template <typename Index, typename InputDims, size_t NumKernelDims> class IndexMapper {
public:
IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
const array<Index, NumKernelDims>& indices) {
array<Index, NumDims> dimensions = input_dims;
for (int i = 0; i < NumKernelDims; ++i) {
const Index index = indices[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
dimensions[index] = result_dim;
}
array<Index, NumDims> inputStrides;
array<Index, NumDims> outputStrides;
for (int i = 0; i < NumDims; ++i) {
if (i > 0) {
inputStrides[i] = inputStrides[i-1] * input_dims[i-1];
outputStrides[i] = outputStrides[i-1] * dimensions[i-1];
} else {
inputStrides[0] = 1;
outputStrides[0] = 1;
}
}
array<Index, NumDims> cudaInputDimensions;
array<Index, NumDims> cudaOutputDimensions;
array<Index, NumDims> tmp = dimensions;
array<Index, NumDims> ordering;
for (int i = 0; i < NumKernelDims; ++i) {
ordering[i] = indices[i];
tmp[indices[i]] = -1;
cudaInputDimensions[i] = input_dims[ordering[i]];
cudaOutputDimensions[i] = dimensions[ordering[i]];
}
int written = NumKernelDims;
for (int i = 0; i < NumDims; ++i) {
if (tmp[i] >= 0) {
ordering[written] = i;
cudaInputDimensions[written] = input_dims[i];
cudaOutputDimensions[written] = dimensions[i];
++written;
}
}
for (int i = 0; i < NumDims; ++i) {
m_inputStrides[i] = inputStrides[ordering[i]];
m_outputStrides[i] = outputStrides[ordering[i]];
}
for (int i = 0; i < NumDims; ++i) {
if (i > NumKernelDims) {
m_cudaInputStrides[i] = m_cudaInputStrides[i-1] * cudaInputDimensions[i-1];
m_cudaOutputStrides[i] = m_cudaOutputStrides[i-1] * cudaOutputDimensions[i-1];
} else {
m_cudaInputStrides[i] = 1;
m_cudaOutputStrides[i] = 1;
}
}
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputPlaneToTensorInputOffset(Index p) const {
Index inputIndex = 0;
for (int d = NumDims - 1; d > NumKernelDims; --d) {
const Index idx = p / m_cudaInputStrides[d];
inputIndex += idx * m_inputStrides[d];
p -= idx * m_cudaInputStrides[d];
}
inputIndex += p * m_inputStrides[NumKernelDims];
return inputIndex;
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputPlaneToTensorOutputOffset(Index p) const {
Index outputIndex = 0;
for (int d = NumDims - 1; d > NumKernelDims; --d) {
const Index idx = p / m_cudaOutputStrides[d];
outputIndex += idx * m_outputStrides[d];
p -= idx * m_cudaOutputStrides[d];
}
outputIndex += p * m_outputStrides[NumKernelDims];
return outputIndex;
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i) const {
return i * m_inputStrides[0];
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i) const {
return i * m_outputStrides[0];
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i, Index j) const {
return i * m_inputStrides[0] + j*m_inputStrides[1];
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i, Index j) const {
return i * m_outputStrides[0] + j * m_outputStrides[1];
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaInputKernelToTensorInputOffset(Index i, Index j, Index k) const {
return i * m_inputStrides[0] + j*m_inputStrides[1] + k*m_inputStrides[2];
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapCudaOutputKernelToTensorOutputOffset(Index i, Index j, Index k) const {
return i * m_outputStrides[0] + j*m_outputStrides[1] + k*m_outputStrides[2];
}
private:
static const size_t NumDims = internal::array_size<InputDims>::value;
array<Index, NumDims> m_inputStrides;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_cudaInputStrides;
array<Index, NumDims> m_cudaOutputStrides;
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename promote_storage_type<typename InputXprType::Scalar,
typename KernelXprType::Scalar>::ret Scalar;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
typename traits<KernelXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<InputXprType>::Index,
typename traits<KernelXprType>::Index>::type Index;
typedef typename InputXprType::Nested LhsNested;
typedef typename KernelXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const int NumDimensions = traits<InputXprType>::NumDimensions;
static const int Layout = traits<InputXprType>::Layout;
enum {
Flags = 0,
};
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
{
typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type>
{
typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
};
} // end namespace internal
template<typename Indices, typename InputXprType, typename KernelXprType>
class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename internal::promote_storage_type<typename InputXprType::PacketReturnType,
typename KernelXprType::PacketReturnType>::ret PacketReturnType;
typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
: m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Indices& indices() const { return m_indices; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<typename InputXprType::Nested>::type&
inputExpression() const { return m_input_xpr; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<typename KernelXprType::Nested>::type&
kernelExpression() const { return m_kernel_xpr; }
protected:
typename InputXprType::Nested m_input_xpr;
typename KernelXprType::Nested m_kernel_xpr;
const Indices m_indices;
};
template<typename Indices, typename InputArgType, typename KernelArgType, typename Device>
struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device>
{
typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
static const int NumKernelDims = internal::array_size<Indices>::value;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = TensorEvaluator<InputArgType, Device>::IsAligned & TensorEvaluator<KernelArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<InputArgType, Device>::PacketAccess & TensorEvaluator<KernelArgType, Device>::PacketAccess,
Layout = TensorEvaluator<InputArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
m_inputStride[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStride[i] = m_inputStride[i-1] * input_dims[i-1];
}
m_dimensions = m_inputImpl.dimensions();
for (int i = 0; i < NumKernelDims; ++i) {
const Index index = op.indices()[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
m_dimensions[index] = result_dim;
if (i > 0) {
m_kernelStride[i] = m_kernelStride[i-1] * kernel_dims[i-1];
} else {
m_kernelStride[0] = 1;
}
m_indexStride[i] = m_inputStride[index];
}
m_outputStride[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStride[i] = m_outputStride[i-1] * m_dimensions[i-1];
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
m_inputImpl.evalSubExprsIfNeeded(NULL);
preloadKernel();
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_inputImpl.cleanup();
if (m_local_kernel) {
m_device.deallocate((void*)m_kernel);
m_local_kernel = false;
}
m_kernel = NULL;
}
void evalTo(typename XprType::Scalar* buffer) {
evalSubExprsIfNeeded(NULL);
for (int i = 0; i < dimensions().TotalSize(); ++i) {
buffer[i] += coeff(i);
}
cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
CoeffReturnType result = CoeffReturnType(0);
convolve(firstInput(index), 0, NumKernelDims-1, result);
return result;
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(const Index index) const
{
const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
Index indices[2] = {index, index+PacketSize-1};
Index startInputs[2] = {0, 0};
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_outputStride[i];
const Index idx1 = indices[1] / m_outputStride[i];
startInputs[0] += idx0 * m_inputStride[i];
startInputs[1] += idx1 * m_inputStride[i];
indices[0] -= idx0 * m_outputStride[i];
indices[1] -= idx1 * m_outputStride[i];
}
startInputs[0] += indices[0];
startInputs[1] += indices[1];
if (startInputs[1]-startInputs[0] == PacketSize-1) {
PacketReturnType result = internal::pset1<PacketReturnType>(0);
convolvePacket(startInputs[0], 0, NumKernelDims-1, result);
return result;
} else {
EIGEN_ALIGN_DEFAULT Scalar data[PacketSize];
data[0] = Scalar(0);
convolve(startInputs[0], 0, NumKernelDims-1, data[0]);
for (int i = 1; i < PacketSize-1; ++i) {
data[i] = Scalar(0);
convolve(firstInput(index+i), 0, NumKernelDims-1, data[i]);
}
data[PacketSize-1] = Scalar(0);
convolve(startInputs[1], 0, NumKernelDims-1, data[PacketSize-1]);
return internal::pload<PacketReturnType>(data);
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
private:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
Index startInput = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStride[i];
startInput += idx * m_inputStride[i];
index -= idx * m_outputStride[i];
}
startInput += index;
return startInput;
}
EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
const Index input = firstIndex + j * m_indexStride[DimIndex];
const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
if (DimIndex > 0) {
convolve(input, kernel, DimIndex-1, accum);
} else {
accum += m_inputImpl.coeff(input) * m_kernel[kernel];
}
}
}
template <typename Packet>
EIGEN_DEVICE_FUNC void convolvePacket(Index firstIndex, Index firstKernel, int DimIndex, Packet& accum) const {
for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
const Index input = firstIndex + j * m_indexStride[DimIndex];
const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
if (DimIndex > 0) {
convolvePacket(input, kernel, DimIndex-1, accum);
} else {
accum = internal::pmadd<Packet>(m_inputImpl.template packet<Unaligned>(input), internal::pset1<Packet>(m_kernel[kernel]), accum);
}
}
}
EIGEN_STRONG_INLINE void preloadKernel() {
// Don't make a local copy of the kernel unless we have to (i.e. it's an
// expression that needs to be evaluated)
const Scalar* in_place = m_kernelImpl.data();
if (in_place) {
m_kernel = in_place;
m_local_kernel = false;
} else {
size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
typedef TensorEvalToOp<const KernelArgType> EvalTo;
EvalTo evalToTmp(local, m_kernelArg);
internal::TensorExecutor<const EvalTo, Device, TensorEvaluator<KernelArgType, Device>::PacketAccess>::run(evalToTmp, m_device);
m_kernel = local;
m_local_kernel = true;
}
}
array<Index, NumDims> m_inputStride;
array<Index, NumDims> m_outputStride;
array<Index, NumKernelDims> m_indexStride;
array<Index, NumKernelDims> m_kernelStride;
TensorEvaluator<InputArgType, Device> m_inputImpl;
TensorEvaluator<KernelArgType, Device> m_kernelImpl;
Dimensions m_dimensions;
KernelArgType m_kernelArg;
const Scalar* m_kernel;
bool m_local_kernel;
const Device& m_device;
};
// Use an optimized implementation of the evaluation code for GPUs whenever possible.
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
template <int StaticKernelSize>
struct GetKernelSize {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int /*kernelSize*/) const {
return StaticKernelSize;
}
};
template <>
struct GetKernelSize<Dynamic> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int kernelSize) const {
return kernelSize;
}
};
template <typename InputEvaluator, typename Index, typename InputDims, int StaticKernelSize>
__global__ void EigenConvolutionKernel1D(InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 1> indexMapper, const float* __restrict kernel, const int numPlanes, const int numX, const int maxX, const int kernelSize, float* buffer) {
extern __shared__ float s[];
const int first_x = blockIdx.x * maxX;
const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSize>()(kernelSize);
const int num_x_output = last_x - first_x + 1;
const int first_plane = blockIdx.y * blockDim.y;
const int plane_stride = blockDim.y * gridDim.y;
for (int p = first_plane + threadIdx.y; p < numPlanes; p += plane_stride) {
// Load inputs to shared memory
const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p);
const int plane_kernel_offset = threadIdx.y * num_x_input;
#pragma unroll
for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x);
s[i + plane_kernel_offset] = eval.coeff(tensor_index);
}
__syncthreads();
// Compute the convolution
const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p);
#pragma unroll
for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
const int kernel_offset = plane_kernel_offset + i;
float result = 0.0f;
#pragma unroll
for (int k = 0; k < GetKernelSize<StaticKernelSize>()(kernelSize); ++k) {
result += s[k + kernel_offset] * kernel[k];
}
const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x);
buffer[tensor_index] = result;
}
__syncthreads();
}
};
template <typename InputEvaluator, typename Index, typename InputDims, int StaticKernelSizeX, int StaticKernelSizeY>
__global__ void EigenConvolutionKernel2D(InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 2> indexMapper, const float* __restrict kernel, const int numPlanes, const int numX, const int maxX, const int numY, const int maxY, const int kernelSizeX, const int kernelSizeY, float* buffer) {
extern __shared__ float s[];
const int first_x = blockIdx.x * maxX;
const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSizeX>()(kernelSizeX);
const int num_x_output = last_x - first_x + 1;
const int first_y = blockIdx.y * maxY;
const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
const int num_y_input = last_y - first_y + GetKernelSize<StaticKernelSizeY>()(kernelSizeY);
const int num_y_output = last_y - first_y + 1;
const int first_plane = blockIdx.z * blockDim.z;
const int plane_stride = blockDim.z * gridDim.z;
for (int p = first_plane + threadIdx.z; p < numPlanes; p += plane_stride) {
const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p);
const int plane_kernel_offset = threadIdx.z * num_y_input;
// Load inputs to shared memory
#pragma unroll
for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
const int input_offset = num_x_input * (j + plane_kernel_offset);
#pragma unroll
for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x, j+first_y);
s[i + input_offset] = eval.coeff(tensor_index);
}
}
__syncthreads();
// Convolution
const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p);
#pragma unroll
for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
#pragma unroll
for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
float result = 0.0f;
#pragma unroll
for (int l = 0; l < GetKernelSize<StaticKernelSizeY>()(kernelSizeY); ++l) {
const int kernel_offset = kernelSizeX * l;
const int input_offset = i + num_x_input * (j + l + plane_kernel_offset);
#pragma unroll
for (int k = 0; k < GetKernelSize<StaticKernelSizeX>()(kernelSizeX); ++k) {
result += s[k + input_offset] * kernel[k + kernel_offset];
}
}
const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x, j+first_y);
buffer[tensor_index] = result;
}
}
__syncthreads();
}
};
template <typename InputEvaluator, typename Index, typename InputDims>
__global__ void EigenConvolutionKernel3D(InputEvaluator eval, const internal::IndexMapper<Index, InputDims, 3> indexMapper, const float* __restrict kernel, const size_t numPlanes, const size_t numX, const size_t maxX, const size_t numY, const size_t maxY, const size_t numZ, const size_t maxZ, const size_t kernelSizeX, const size_t kernelSizeY, const size_t kernelSizeZ, float* buffer) {
extern __shared__ float s[];
// Load inputs to shared memory
const int first_x = blockIdx.x * maxX;
const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
const int num_x_input = last_x - first_x + kernelSizeX;
const int first_y = blockIdx.y * maxY;
const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
const int num_y_input = last_y - first_y + kernelSizeY;
const int first_z = blockIdx.z * maxZ;
const int last_z = (first_z + maxZ < numZ ? first_z + maxZ : numZ) - 1;
const int num_z_input = last_z - first_z + kernelSizeZ;
for (int p = 0; p < numPlanes; ++p) {
const int plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p);
const int plane_kernel_offset = 0;
for (int k = threadIdx.z; k < num_z_input; k += blockDim.z) {
for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
const int tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i+first_x, j+first_y, k+first_z);
s[i + num_x_input * (j + num_y_input * (k + plane_kernel_offset))] = eval.coeff(tensor_index);
}
}
}
__syncthreads();
// Convolution
const int num_z_output = last_z - first_z + 1;
const int num_y_output = last_y - first_y + 1;
const int num_x_output = last_x - first_x + 1;
const int plane_output_offset = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p);
for (int k = threadIdx.z; k < num_z_output; k += blockDim.z) {
for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
float result = 0.0f;
for (int n = 0; n < kernelSizeZ; ++n) {
for (int m = 0; m < kernelSizeY; ++m) {
for (int l = 0; l < kernelSizeX; ++l) {
result += s[i + l + num_x_input * (j + m + num_y_input * (k + n + plane_kernel_offset))] * kernel[l + kernelSizeX * (m + kernelSizeY * n)];
}
}
}
const int tensor_index = plane_output_offset + indexMapper.mapCudaOutputKernelToTensorOutputOffset(i+first_x, j+first_y, k+first_z);
buffer[tensor_index] = result;
}
}
}
__syncthreads();
}
};
template<typename Indices, typename InputArgType, typename KernelArgType>
struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, GpuDevice>
{
typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions>::value;
static const int NumKernelDims = internal::array_size<Indices>::value;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions KernelDimensions;
enum {
IsAligned = TensorEvaluator<InputArgType, GpuDevice>::IsAligned & TensorEvaluator<KernelArgType, GpuDevice>::IsAligned,
PacketAccess = false,
Layout = TensorEvaluator<InputArgType, GpuDevice>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const GpuDevice& device)
: m_inputImpl(op.inputExpression(), device), m_kernelArg(op.kernelExpression()), m_kernelImpl(op.kernelExpression(), device), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
m_dimensions = m_inputImpl.dimensions();
for (int i = 0; i < NumKernelDims; ++i) {
const Index index = op.indices()[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
m_dimensions[index] = result_dim;
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename InputArgType::Scalar Scalar;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
preloadKernel();
m_inputImpl.evalSubExprsIfNeeded(NULL);
if (data) {
executeEval(data);
return false;
} else {
m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar));
executeEval(m_buf);
return true;
}
}
EIGEN_STRONG_INLINE void cleanup() {
m_inputImpl.cleanup();
if (m_buf) {
m_device.deallocate(m_buf);
m_buf = NULL;
}
if (m_local_kernel) {
m_device.deallocate((void*)m_kernel);
m_local_kernel = false;
}
m_kernel = NULL;
}
EIGEN_STRONG_INLINE void preloadKernel() {
// Don't make a local copy of the kernel unless we have to (i.e. it's an
// expression that needs to be evaluated)
const Scalar* in_place = m_kernelImpl.data();
if (in_place) {
m_kernel = in_place;
m_local_kernel = false;
} else {
size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
typedef TensorEvalToOp<const KernelArgType> EvalTo;
EvalTo evalToTmp(local, m_kernelArg);
internal::TensorExecutor<const EvalTo, GpuDevice, TensorEvaluator<KernelArgType, GpuDevice>::PacketAccess>::run(evalToTmp, m_device);
m_kernel = local;
m_local_kernel = true;
}
}
static unsigned int ceil(unsigned int num, unsigned int denom) {
const unsigned int rounded_toward_zero = num / denom;
if (num > rounded_toward_zero * denom) {
return rounded_toward_zero + 1;
}
return rounded_toward_zero;
}
void executeEval(Scalar* data) const {
typedef typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions InputDims;
const int maxSharedMem = sharedMemPerBlock();
const int maxThreadsPerBlock = maxCudaThreadsPerBlock();
const int maxBlocksPerProcessor = maxCudaThreadsPerMultiProcessor() / maxThreadsPerBlock;
const int numMultiProcessors = getNumCudaMultiProcessors();
const int warpSize = 32;
switch (NumKernelDims) {
case 1: {
const int kernel_size = m_kernelImpl.dimensions().TotalSize();
const int numX = dimensions()[m_indices[0]];
const int numP = dimensions().TotalSize() / numX;
int maxX;
dim3 block_size;
if (m_indices[0] == 0) {
// Maximum the reuse
const int inner_dim = ((maxSharedMem / (sizeof(Scalar)) - kernel_size + 1 + 31) / 32) * 32;
maxX = (std::min<int>)(inner_dim, numX);
const int maxP = (std::min<int>)(maxSharedMem / ((kernel_size - 1 + maxX) * sizeof(Scalar)), numP);
block_size.x = (std::min)(maxThreadsPerBlock, maxX);
block_size.y = (std::min<int>)(maxThreadsPerBlock / block_size.x, maxP);
}
else {
// Read as much as possible alongside the inner most dimension, that is the plane
const int inner_dim = maxSharedMem / ((warpSize + kernel_size) * sizeof(Scalar));
const int maxP = (std::min<int>)(inner_dim, numP);
maxX = (std::min<int>)(maxSharedMem / (inner_dim * sizeof(Scalar)) - kernel_size + 1, numX);
block_size.x = (std::min)(warpSize, maxX);
block_size.y = (std::min<int>)(maxThreadsPerBlock/block_size.x, maxP);
}
const int shared_mem = block_size.y * (maxX + kernel_size - 1) * sizeof(Scalar);
assert(shared_mem <= maxSharedMem);
const int num_x_blocks = ceil(numX, maxX);
const int blocksPerProcessor = (std::min)(maxBlocksPerProcessor, maxSharedMem / shared_mem);
const int num_y_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks);
dim3 num_blocks(num_x_blocks, min<int>(num_y_blocks, ceil(numP, block_size.y)));
//cout << "launching 1D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " maxX: " << maxX << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
const array<Index, 1> indices(m_indices[0]);
const array<Index, 1> kernel_dims(m_kernelImpl.dimensions()[0]);
internal::IndexMapper<Index, InputDims, 1> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
switch(kernel_size) {
case 4: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 4, data);
break;
}
case 7: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 7, data);
break;
}
default: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, kernel_size, data);
}
}
break;
}
case 2: {
const int kernel_size_x = m_kernelImpl.dimensions()[0];
const int kernel_size_y = m_kernelImpl.dimensions()[1];
const int numX = dimensions()[m_indices[0]];
const int numY = dimensions()[m_indices[1]];
const int numP = dimensions().TotalSize() / (numX*numY);
const float scaling_factor = sqrtf(static_cast<float>(maxSharedMem) / (sizeof(Scalar) * kernel_size_y * kernel_size_x));
// Snap maxX to warp size
int inner_dim = ((static_cast<int>(scaling_factor * kernel_size_x) - kernel_size_x + 1 + 32) / 32) * 32;
const int maxX = (std::min<int>)(inner_dim, numX);
const int maxY = (std::min<int>)(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1)) - kernel_size_y + 1, numY);
const int maxP = (std::min<int>)(maxSharedMem / ((kernel_size_x - 1 + maxX) * (kernel_size_y - 1 + maxY) * sizeof(Scalar)), numP);
dim3 block_size;
block_size.x = (std::min)(1024, maxX);
block_size.y = (std::min<int>)(1024/block_size.x, maxY);
block_size.z = (std::min<int>)(1024/(block_size.x*block_size.y), maxP);
const int shared_mem = block_size.z * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * sizeof(Scalar);
assert(shared_mem <= maxSharedMem);
const int num_x_blocks = ceil(numX, maxX);
const int num_y_blocks = ceil(numY, maxY);
const int blocksPerProcessor = (std::min)(maxBlocksPerProcessor, maxSharedMem / shared_mem);
const int num_z_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks * num_y_blocks);
dim3 num_blocks(num_x_blocks, num_y_blocks, min<int>(num_z_blocks, ceil(numP, block_size.z)));
//cout << "launching 2D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " maxX: " << maxX << " maxY: " << maxY << " maxP: " << maxP << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
const array<Index, 2> indices(m_indices[0], m_indices[1]);
const array<Index, 2> kernel_dims(m_kernelImpl.dimensions()[0], m_kernelImpl.dimensions()[1]);
internal::IndexMapper<Index, InputDims, 2> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
switch (kernel_size_x) {
case 4: {
switch (kernel_size_y) {
case 7: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, 7, data);
break;
}
default: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, kernel_size_y, data);
break;
}
}
break;
}
case 7: {
switch (kernel_size_y) {
case 4: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, 4, data);
break;
}
default: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, kernel_size_y, data);
break;
}
}
break;
}
default: {
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, kernel_size_x, kernel_size_y, data);
break;
}
}
break;
}
case 3: {
const int kernel_size_x = m_kernelImpl.dimensions()[0];
const int kernel_size_y = m_kernelImpl.dimensions()[1];
const int kernel_size_z = m_kernelImpl.dimensions()[2];
const int numX = dimensions()[m_indices[0]];
const int numY = dimensions()[m_indices[1]];
const int numZ = dimensions()[m_indices[2]];
const int numP = dimensions().TotalSize() / (numX*numY*numZ);
const int maxX = (std::min<int>)(128, (std::min<int>)(maxSharedMem / (sizeof(Scalar) * kernel_size_y * kernel_size_z) - kernel_size_x + 1, numX));
const int maxY = (std::min<int>)(128, (std::min<int>)(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * kernel_size_z) - kernel_size_y + 1, numY));
const int maxZ = (std::min<int>)(128, (std::min<int>)(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1)) - kernel_size_z + 1, numZ));
dim3 block_size;
block_size.x = (std::min)(32, maxX);
block_size.y = (std::min)(32, maxY);
block_size.z = (std::min<int>)(1024/(block_size.x*block_size.y), maxZ);
dim3 num_blocks(ceil(numX, maxX), ceil(numY, maxY), ceil(numZ, maxZ));
const int shared_mem = (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * (maxZ + kernel_size_z - 1) * sizeof(Scalar);
assert(shared_mem <= maxSharedMem);
//cout << "launching 3D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
const array<Index, 3> indices(m_indices[0], m_indices[1], m_indices[2]);
const array<Index, 3> kernel_dims(m_kernelImpl.dimensions()[0], m_kernelImpl.dimensions()[1], m_kernelImpl.dimensions()[2]);
internal::IndexMapper<Index, InputDims, 3> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
LAUNCH_CUDA_KERNEL((EigenConvolutionKernel3D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, numZ, maxZ, kernel_size_x, kernel_size_y, kernel_size_z, data);
break;
}
default: {
assert(false && "not supported yet");
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
eigen_assert(m_buf);
eigen_assert(index < m_dimensions.TotalSize());
return m_buf[index];
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const
{
eigen_assert(m_buf);
eigen_assert(index < m_dimensions.TotalSize());
return internal::ploadt<PacketReturnType, LoadMode>(m_buf+index);
}
private:
// No assignment (copies are needed by the kernels)
TensorEvaluator& operator = (const TensorEvaluator&);
TensorEvaluator<InputArgType, GpuDevice> m_inputImpl;
TensorEvaluator<KernelArgType, GpuDevice> m_kernelImpl;
KernelArgType m_kernelArg;
Indices m_indices;
Dimensions m_dimensions;
Scalar* m_buf;
const Scalar* m_kernel;
bool m_local_kernel;
const GpuDevice& m_device;
};
#endif
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_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>
//
// 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_DEVICE_H
#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
namespace Eigen {
/** \class TensorDevice
* \ingroup CXX11_Tensor_Module
*
* \brief Pseudo expression providing an operator = that will evaluate its argument
* on the specified computing 'device' (GPU, thread pool, ...)
*
* Example:
* C.device(EIGEN_GPU) = A + B;
*
* Todo: thread pools.
* Todo: operator +=, -=, *= and so on.
*/
template <typename ExpressionType, typename DeviceType> class TensorDevice {
public:
TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
Assign assign(m_expression, other);
static const bool Vectorize = TensorEvaluator<const Assign, DeviceType>::PacketAccess;
internal::TensorExecutor<const Assign, DeviceType, Vectorize>::run(assign, m_device);
return *this;
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
typedef typename OtherDerived::Scalar Scalar;
typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
Sum sum(m_expression, other);
typedef TensorAssignOp<ExpressionType, const Sum> Assign;
Assign assign(m_expression, sum);
static const bool Vectorize = TensorEvaluator<const Assign, DeviceType>::PacketAccess;
internal::TensorExecutor<const Assign, DeviceType, Vectorize>::run(assign, m_device);
return *this;
}
protected:
const DeviceType& m_device;
ExpressionType& m_expression;
};
#ifdef EIGEN_USE_THREADS
template <typename ExpressionType> class TensorDevice<ExpressionType, ThreadPoolDevice> {
public:
TensorDevice(const ThreadPoolDevice& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
Assign assign(m_expression, other);
static const bool Vectorize = TensorEvaluator<const Assign, ThreadPoolDevice>::PacketAccess;
internal::TensorExecutor<const Assign, ThreadPoolDevice, Vectorize>::run(assign, m_device);
return *this;
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
typedef typename OtherDerived::Scalar Scalar;
typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
Sum sum(m_expression, other);
typedef TensorAssignOp<ExpressionType, const Sum> Assign;
Assign assign(m_expression, sum);
static const bool Vectorize = TensorEvaluator<const Assign, ThreadPoolDevice>::PacketAccess;
internal::TensorExecutor<const Assign, ThreadPoolDevice, Vectorize>::run(assign, m_device);
return *this;
}
protected:
const ThreadPoolDevice& m_device;
ExpressionType& m_expression;
};
#endif
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
template <typename ExpressionType> class TensorDevice<ExpressionType, GpuDevice>
{
public:
TensorDevice(const GpuDevice& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
Assign assign(m_expression, other);
internal::TensorExecutor<const Assign, GpuDevice, false>::run(assign, m_device);
return *this;
}
template<typename OtherDerived>
EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
typedef typename OtherDerived::Scalar Scalar;
typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
Sum sum(m_expression, other);
typedef TensorAssignOp<ExpressionType, const Sum> Assign;
Assign assign(m_expression, sum);
internal::TensorExecutor<const Assign, GpuDevice, false>::run(assign, m_device);
return *this;
}
protected:
const GpuDevice& m_device;
ExpressionType m_expression;
};
#endif
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_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>
//
// 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_DEVICE_TYPE_H
#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_TYPE_H
namespace Eigen {
// Default device for the machine (typically a single cpu core)
struct DefaultDevice {
EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
return internal::aligned_malloc(num_bytes);
}
EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
internal::aligned_free(buffer);
}
EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
::memcpy(dst, src, n);
}
EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
::memset(buffer, c, n);
}
EIGEN_STRONG_INLINE size_t numThreads() const {
return 1;
}
};
// Multiple cpu cores
// We should really use a thread pool here but first we need to find a portable thread pool library.
#ifdef EIGEN_USE_THREADS
typedef std::future<void> Future;
typedef std::promise<void> Promise;
static EIGEN_STRONG_INLINE void wait_until_ready(const Future* f) {
f->wait();
}
static EIGEN_STRONG_INLINE void get_when_ready(Future* f) {
f->get();
}
struct ThreadPoolDevice {
ThreadPoolDevice(size_t num_cores) : num_threads_(num_cores) { }
EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
return internal::aligned_malloc(num_bytes);
}
EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
internal::aligned_free(buffer);
}
EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
::memcpy(dst, src, n);
}
EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
::memset(buffer, c, n);
}
EIGEN_STRONG_INLINE size_t numThreads() const {
return num_threads_;
}
template <class Function, class... Args>
EIGEN_STRONG_INLINE Future enqueue(Function&& f, Args&&... args) const {
return std::async(std::launch::async, f, args...);
}
template <class Function, class... Args>
EIGEN_STRONG_INLINE void enqueueNoFuture(Function&& f, Args&&... args) const {
std::async(std::launch::async, f, args...);
}
private:
size_t num_threads_;
};
#endif
// GPU offloading
#ifdef EIGEN_USE_GPU
static cudaDeviceProp m_deviceProperties;
static bool m_devicePropInitialized = false;
static void initializeDeviceProp() {
if (!m_devicePropInitialized) {
assert(cudaGetDeviceProperties(&m_deviceProperties, 0) == cudaSuccess);
m_devicePropInitialized = true;
}
}
static inline int getNumCudaMultiProcessors() {
initializeDeviceProp();
return m_deviceProperties.multiProcessorCount;
}
static inline int maxCudaThreadsPerBlock() {
initializeDeviceProp();
return m_deviceProperties.maxThreadsPerBlock;
}
static inline int maxCudaThreadsPerMultiProcessor() {
initializeDeviceProp();
return m_deviceProperties.maxThreadsPerMultiProcessor;
}
static inline int sharedMemPerBlock() {
initializeDeviceProp();
return m_deviceProperties.sharedMemPerBlock;
}
static inline void setCudaSharedMemConfig(cudaSharedMemConfig config) {
cudaError_t status = cudaDeviceSetSharedMemConfig(config);
assert(status == cudaSuccess);
}
struct GpuDevice {
// The cudastream is not owned: the caller is responsible for its initialization and eventual destruction.
GpuDevice(const cudaStream_t* stream) : stream_(stream) { eigen_assert(stream); }
EIGEN_STRONG_INLINE const cudaStream_t& stream() const { return *stream_; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
#ifndef __CUDA_ARCH__
void* result;
assert(cudaMalloc(&result, num_bytes) == cudaSuccess);
assert(result != NULL);
return result;
#else
assert(false && "The default device should be used instead to generate kernel code");
return NULL;
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
#ifndef __CUDA_ARCH__
assert(buffer != NULL);
assert(cudaFree(buffer) == cudaSuccess);
#else
assert(false && "The default device should be used instead to generate kernel code");
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
#ifndef __CUDA_ARCH__
assert(cudaMemcpyAsync(dst, src, n, cudaMemcpyDeviceToDevice, *stream_) == cudaSuccess);
#else
assert(false && "The default device should be used instead to generate kernel code");
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
#ifndef __CUDA_ARCH__
assert(cudaMemsetAsync(buffer, c, n, *stream_) == cudaSuccess);
#else
assert(false && "The default device should be used instead to generate kernel code");
#endif
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
// FIXME
return 32;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
cudaStreamSynchronize(*stream_);
}
private:
// TODO: multigpu.
const cudaStream_t* stream_;
};
#define LAUNCH_CUDA_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...) \
(kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__); \
assert(cudaGetLastError() == cudaSuccess);
#endif
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_TYPE_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>
//
// 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_DIMENSIONS_H
#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
namespace Eigen {
/** \internal
*
* \class TensorDimensions
* \ingroup CXX11_Tensor_Module
*
* \brief Set of classes used to encode and store the dimensions of a Tensor.
*
* The Sizes class encodes as part of the type the number of dimensions and the
* sizes corresponding to each dimension. It uses no storage space since it is
* entirely known at compile time.
* The DSizes class is its dynamic sibling: the number of dimensions is known
* at compile time but the sizes are set during execution.
*
* \sa Tensor
*/
// Can't use std::pair on cuda devices
template <typename Index> struct IndexPair {
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) { }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Index f, Index s) : first(f), second(s) { }
Index first;
Index second;
};
// Boilerplate code
namespace internal {
template<std::size_t n, typename Dimension> struct dget {
static const std::size_t value = get<n, Dimension>::value;
};
template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
struct fixed_size_tensor_index_linearization_helper
{
template <typename Dimensions> EIGEN_DEVICE_FUNC
static inline Index run(array<Index, NumIndices> const& indices,
const Dimensions& dimensions)
{
return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
dget<RowMajor ? n : (NumIndices - n - 1), Dimensions>::value *
fixed_size_tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
}
};
template<typename Index, std::size_t NumIndices, bool RowMajor>
struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
{
template <typename Dimensions> EIGEN_DEVICE_FUNC
static inline Index run(array<Index, NumIndices> const& indices,
const Dimensions&)
{
return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
}
};
} // end namespace internal
// Fixed size
#ifndef EIGEN_EMULATE_CXX11_META_H
template <typename std::size_t... Indices>
struct Sizes : internal::numeric_list<std::size_t, Indices...> {
typedef internal::numeric_list<std::size_t, Indices...> Base;
static const std::size_t total_size = internal::arg_prod(Indices...);
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
return Base::count;
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t TotalSize() {
return internal::arg_prod(Indices...);
}
Sizes() { }
template <typename DenseIndex>
explicit Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
// todo: add assertion
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template <typename... DenseIndex> Sizes(DenseIndex...) { }
explicit Sizes(std::initializer_list<std::size_t> /*l*/) {
// todo: add assertion
}
#endif
template <typename T> Sizes& operator = (const T& /*other*/) {
// add assertion failure if the size of other is different
return *this;
}
template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count - 1, false>::run(indices, *static_cast<const Base*>(this));
}
template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count - 1, true>::run(indices, *static_cast<const Base*>(this));
}
};
template <typename std::size_t... Indices>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_prod(const Sizes<Indices...>&) {
return Sizes<Indices...>::total_size;
}
#else
template <std::size_t n>
struct non_zero_size {
typedef internal::type2val<std::size_t, n> type;
};
template <>
struct non_zero_size<0> {
typedef internal::null_type type;
};
template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0, std::size_t V5=0> struct Sizes {
typedef typename internal::make_type_list<typename non_zero_size<V1>::type, typename non_zero_size<V2>::type, typename non_zero_size<V3>::type, typename non_zero_size<V4>::type, typename non_zero_size<V5>::type >::type Base;
static const size_t count = Base::count;
static const std::size_t total_size = internal::arg_prod<Base>::value;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
return count;
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t TotalSize() {
return internal::arg_prod<Base>::value;
}
Sizes() { }
template <typename DenseIndex>
explicit Sizes(const array<DenseIndex, Base::count>& indices) {
// todo: add assertion
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template <typename... DenseIndex> Sizes(DenseIndex... indices) { }
explicit Sizes(std::initializer_list<std::size_t> l) {
// todo: add assertion
}
#else
EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex i0) {
}
EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex i0, const DenseIndex i1) {
}
EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2) {
}
EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3) {
}
EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3, const DenseIndex i4) {
}
#endif
template <typename T> Sizes& operator = (const T& other) {
// to do: check the size of other
return *this;
}
template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
size_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count - 1, false>::run(indices, *static_cast<const Base*>(this);
}
template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
size_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count - 1, true>::run(indices, *static_cast<const Base*>(this);
}
};
template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) {
return Sizes<V1, V2, V3, V4, V5>::total_size;
};
#endif
// Boilerplate
namespace internal {
template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
struct tensor_index_linearization_helper
{
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const& dimensions)
{
return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
}
};
template<typename Index, std::size_t NumIndices, bool RowMajor>
struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
{
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const&)
{
return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
}
};
} // end namespace internal
// Dynamic size
template <typename DenseIndex, std::size_t NumDims>
struct DSizes : array<DenseIndex, NumDims> {
typedef array<DenseIndex, NumDims> Base;
static const std::size_t count = NumDims;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t rank() const {
return NumDims;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t TotalSize() const {
return internal::array_prod(*static_cast<const Base*>(this));
}
EIGEN_DEVICE_FUNC DSizes() {
for (int i = 0 ; i < NumDims; ++i) {
(*this)[i] = 0;
}
}
EIGEN_DEVICE_FUNC explicit DSizes(const array<DenseIndex, NumDims>& a) : Base(a) { }
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit DSizes(DenseIndex firstDimension, IndexTypes... otherDimensions) {
EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
(*this) = array<DenseIndex, NumDims>{{firstDimension, otherDimensions...}};
}
#else
EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0) {
eigen_assert(NumDims == 1);
(*this)[0] = i0;
}
EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0, const DenseIndex i1) {
eigen_assert(NumDims == 2);
(*this)[0] = i0;
(*this)[1] = i1;
}
EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2) {
eigen_assert(NumDims == 3);
(*this)[0] = i0;
(*this)[1] = i1;
(*this)[2] = i2;
}
EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3) {
eigen_assert(NumDims == 4);
(*this)[0] = i0;
(*this)[1] = i1;
(*this)[2] = i2;
(*this)[3] = i3;
}
EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3, const DenseIndex i4) {
eigen_assert(NumDims == 5);
(*this)[0] = i0;
(*this)[1] = i1;
(*this)[2] = i2;
(*this)[3] = i3;
(*this)[4] = i4;
}
#endif
EIGEN_DEVICE_FUNC DSizes& operator = (const array<DenseIndex, NumDims>& other) {
*static_cast<Base*>(this) = other;
return *this;
}
// A constexpr would be so much better here
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t IndexOfColMajor(const array<DenseIndex, NumDims>& indices) const {
return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, false>::run(indices, *static_cast<const Base*>(this));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t IndexOfRowMajor(const array<DenseIndex, NumDims>& indices) const {
return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, true>::run(indices, *static_cast<const Base*>(this));
}
};
// Boilerplate
namespace internal {
template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
struct tensor_vsize_index_linearization_helper
{
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const& dimensions)
{
return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
tensor_vsize_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
}
};
template<typename Index, std::size_t NumIndices, bool RowMajor>
struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor>
{
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const&)
{
return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
}
};
} // end namespace internal
namespace internal {
template <typename DenseIndex, std::size_t NumDims> struct array_size<const DSizes<DenseIndex, NumDims> > {
static const size_t value = NumDims;
};
template <typename DenseIndex, std::size_t NumDims> struct array_size<DSizes<DenseIndex, NumDims> > {
static const size_t value = NumDims;
};
#ifndef EIGEN_EMULATE_CXX11_META_H
template <typename std::size_t... Indices> struct array_size<const Sizes<Indices...> > {
static const size_t value = Sizes<Indices...>::count;
};
template <typename std::size_t... Indices> struct array_size<Sizes<Indices...> > {
static const size_t value = Sizes<Indices...>::count;
};
template <std::size_t n, typename std::size_t... Indices> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_get(const Sizes<Indices...>&) {
return get<n, internal::numeric_list<std::size_t, Indices...> >::value;
}
#else
template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > {
static const size_t value = Sizes<V1,V2,V3,V4,V5>::count;
};
template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > {
static const size_t value = Sizes<V1,V2,V3,V4,V5>::count;
};
template <std::size_t n, std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t array_get(const Sizes<V1,V2,V3,V4,V5>& a) {
return get<n, typename Sizes<V1,V2,V3,V4,V5>::Base>::value;
};
#endif
template <typename Dims1, typename Dims2, size_t n>
struct sizes_match_up_to_dim {
static inline bool run(Dims1& dims1, Dims2& dims2) {
return (array_get<n>(dims1) == array_get<n>(dims2)) &
sizes_match_up_to_dim<Dims1, Dims2, n-1>::run(dims1, dims2);
}
};
template <typename Dims1, typename Dims2>
struct sizes_match_up_to_dim<Dims1, Dims2, 0> {
static inline bool run(Dims1& dims1, Dims2& dims2) {
return (array_get<0>(dims1) == array_get<0>(dims2));
}
};
} // end namespace internal
template <typename Dims1, typename Dims2>
bool dimensions_match(Dims1& dims1, Dims2& dims2) {
if (internal::array_size<Dims1>::value != internal::array_size<Dims2>::value) {
return false;
}
return internal::sizes_match_up_to_dim<Dims1, Dims2, internal::array_size<Dims1>::value-1>::run(dims1, dims2);
}
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_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>
//
// 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_EVAL_TO_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
namespace Eigen {
/** \class TensorForcedEval
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reshaping class.
*
*
*/
namespace internal {
template<typename XprType>
struct traits<TensorEvalToOp<XprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
enum {
Flags = 0,
};
};
template<typename XprType>
struct eval<TensorEvalToOp<XprType>, Eigen::Dense>
{
typedef const TensorEvalToOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorEvalToOp<XprType>, 1, typename eval<TensorEvalToOp<XprType> >::type>
{
typedef TensorEvalToOp<XprType> type;
};
} // end namespace internal
template<typename XprType>
class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorEvalToOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(CoeffReturnType* buffer, const XprType& expr)
: m_xpr(expr), m_buffer(buffer) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC CoeffReturnType* buffer() const { return m_buffer; }
protected:
typename XprType::Nested m_xpr;
CoeffReturnType* m_buffer;
};
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorEvalToOp<ArgType>, Device>
{
typedef TensorEvalToOp<ArgType> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename ArgType::Packet Packet;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
enum {
IsAligned = true,
PacketAccess = true,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_device(device), m_buffer(op.buffer())
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~TensorEvaluator() {
}
typedef typename XprType::Index Index;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
m_buffer[i] = m_impl.coeff(i);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(m_buffer + i, m_impl.template packet<TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned>(i));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_buffer[index];
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return internal::ploadt<Packet, LoadMode>(m_buffer + index);
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
private:
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
CoeffReturnType* m_buffer;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_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>
//
// 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_EVALUATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
namespace Eigen {
/** \class TensorEvaluator
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor evaluator classes.
*
* These classes are responsible for the evaluation of the tensor expression.
*
* TODO: add support for more types of expressions, in particular expressions
* leading to lvalues (slicing, reshaping, etc...)
*/
// Generic evaluator
template<typename Derived, typename Device>
struct TensorEvaluator
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Packet Packet;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename Derived::Packet PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
// NumDimensions is -1 for variable dim tensors
static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
internal::traits<Derived>::NumDimensions : 0;
enum {
IsAligned = Derived::IsAligned,
PacketAccess = Derived::PacketAccess,
Layout = Derived::Layout,
CoordAccess = NumCoords > 0,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
: m_data(const_cast<Scalar*>(m.data())), m_dims(m.dimensions()), m_device(device)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* dest) {
if (dest) {
m_device.memcpy((void*)dest, m_data, sizeof(Scalar) * m_dims.TotalSize());
return false;
}
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
eigen_assert(m_data);
return m_data[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
eigen_assert(m_data);
return m_data[index];
}
template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketReturnType packet(Index index) const
{
return internal::ploadt<Packet, LoadMode>(m_data + index);
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const Packet& x)
{
return internal::pstoret<Scalar, Packet, StoreMode>(m_data + index, x);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
eigen_assert(m_data);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return m_data[m_dims.IndexOfColMajor(coords)];
} else {
return m_data[m_dims.IndexOfRowMajor(coords)];
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<DenseIndex, NumCoords>& coords) {
eigen_assert(m_data);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return m_data[m_dims.IndexOfColMajor(coords)];
} else {
return m_data[m_dims.IndexOfRowMajor(coords)];
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return m_data; }
protected:
Scalar* m_data;
Dimensions m_dims;
const Device& m_device;
};
// Default evaluator for rvalues
template<typename Derived, typename Device>
struct TensorEvaluator<const Derived, Device>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Packet Packet;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename Derived::Packet PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
// NumDimensions is -1 for variable dim tensors
static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
internal::traits<Derived>::NumDimensions : 0;
enum {
IsAligned = Derived::IsAligned,
PacketAccess = Derived::PacketAccess,
Layout = Derived::Layout,
CoordAccess = NumCoords > 0,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device&)
: m_data(m.data()), m_dims(m.dimensions())
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
eigen_assert(m_data);
#ifdef __CUDA_ARCH__
return __ldg(m_data+index);
#else
return m_data[index];
#endif
}
template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketReturnType packet(Index index) const
{
return internal::ploadt_ro<Packet, LoadMode>(m_data + index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
eigen_assert(m_data);
const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
: m_dims.IndexOfRowMajor(coords);
#ifdef __CUDA_ARCH__
return __ldg(m_data+index);
#else
return m_data[index];
#endif
}
EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
protected:
const Scalar* m_data;
Dimensions m_dims;
};
// -------------------- CwiseNullaryOp --------------------
template<typename NullaryOp, typename ArgType, typename Device>
struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
{
typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;
enum {
IsAligned = true,
PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC
TensorEvaluator(const XprType& op, const Device& device)
: m_functor(op.functor()), m_argImpl(op.nestedExpression(), device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename internal::traits<XprType>::Packet PacketReturnType;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(index);
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
private:
const NullaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
};
// -------------------- CwiseUnaryOp --------------------
template<typename UnaryOp, typename ArgType, typename Device>
struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
{
typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess & internal::functor_traits<UnaryOp>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
: m_functor(op.functor()),
m_argImpl(op.nestedExpression(), device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename internal::traits<XprType>::Packet PacketReturnType;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
m_argImpl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_argImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(m_argImpl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
private:
const UnaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
};
// -------------------- CwiseBinaryOp --------------------
template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
{
typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;
enum {
IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess &
internal::functor_traits<BinaryOp>::PacketAccess,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
: m_functor(op.functor()),
m_leftImpl(op.lhsExpression(), device),
m_rightImpl(op.rhsExpression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename internal::traits<XprType>::Packet PacketReturnType;
typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use right impl instead if right impl dimensions are known at compile time.
return m_leftImpl.dimensions();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
private:
const BinaryOp m_functor;
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
};
// -------------------- SelectOp --------------------
template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
{
typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
enum {
IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned & TensorEvaluator<ElseArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ThenArgType, Device>::PacketAccess & TensorEvaluator<ElseArgType, Device>::PacketAccess/* &
TensorEvaluator<IfArgType>::PacketAccess*/,
Layout = TensorEvaluator<IfArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
: m_condImpl(op.ifExpression(), device),
m_thenImpl(op.thenExpression(), device),
m_elseImpl(op.elseExpression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename internal::traits<XprType>::Packet PacketReturnType;
typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use then or else impl instead if they happen to be known at compile time.
return m_condImpl.dimensions();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
m_condImpl.evalSubExprsIfNeeded(NULL);
m_thenImpl.evalSubExprsIfNeeded(NULL);
m_elseImpl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_condImpl.cleanup();
m_thenImpl.cleanup();
m_elseImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
{
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
internal::Selector<PacketSize> select;
for (Index i = 0; i < PacketSize; ++i) {
select.select[i] = m_condImpl.coeff(index+i);
}
return internal::pblend(select,
m_thenImpl.template packet<LoadMode>(index),
m_elseImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
private:
TensorEvaluator<IfArgType, Device> m_condImpl;
TensorEvaluator<ThenArgType, Device> m_thenImpl;
TensorEvaluator<ElseArgType, Device> m_elseImpl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_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>
//
// 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_EXECUTOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
namespace Eigen {
/** \class TensorExecutor
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor executor class.
*
* This class is responsible for launch the evaluation of the expression on
* the specified computing device.
*/
namespace internal {
template <typename Device, typename Expression>
struct IsVectorizable {
static const bool value = TensorEvaluator<Expression, Device>::PacketAccess;
};
// Default strategy: the expression is evaluated with a single cpu thread.
template<typename Expression, typename Device = DefaultDevice, bool Vectorizable = IsVectorizable<Device, Expression>::value>
class TensorExecutor
{
public:
typedef typename Expression::Index Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const Device& device = Device())
{
TensorEvaluator<Expression, Device> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
for (Index i = 0; i < size; ++i) {
evaluator.evalScalar(i);
}
}
evaluator.cleanup();
}
};
template<typename Expression>
class TensorExecutor<Expression, DefaultDevice, true>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice())
{
TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
static const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size;
const Index VectorizedSize = (size / PacketSize) * PacketSize;
for (Index i = 0; i < VectorizedSize; i += PacketSize) {
evaluator.evalPacket(i);
}
for (Index i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i);
}
}
evaluator.cleanup();
}
};
// Multicore strategy: the index space is partitioned and each partition is executed on a single core
#ifdef EIGEN_USE_THREADS
template <typename Evaluator, typename Index, bool Vectorizable = Evaluator::PacketAccess>
struct EvalRange {
static void run(Evaluator evaluator, const Index first, const Index last) {
eigen_assert(last > first);
for (Index i = first; i < last; ++i) {
evaluator.evalScalar(i);
}
}
};
template <typename Evaluator, typename Index>
struct EvalRange<Evaluator, Index, true> {
static void run(Evaluator evaluator, const Index first, const Index last) {
eigen_assert(last > first);
Index i = first;
static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
if (last - first > PacketSize) {
eigen_assert(first % PacketSize == 0);
Index lastPacket = last - (last % PacketSize);
for (; i < lastPacket; i += PacketSize) {
evaluator.evalPacket(i);
}
}
for (; i < last; ++i) {
evaluator.evalScalar(i);
}
}
};
template<typename Expression, bool Vectorizable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const ThreadPoolDevice& device)
{
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
Evaluator evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
int blocksz = std::ceil<int>(static_cast<float>(size)/device.numThreads()) + PacketSize - 1;
const Index blocksize = std::max<Index>(PacketSize, (blocksz - (blocksz % PacketSize)));
const Index numblocks = size / blocksize;
Index i = 0;
std::vector<Future> results;
results.reserve(numblocks);
for (int i = 0; i < numblocks; ++i) {
results.push_back(device.enqueue(&EvalRange<Evaluator, Index>::run, evaluator, i*blocksize, (i+1)*blocksize));
}
if (numblocks * blocksize < size) {
EvalRange<Evaluator, Index>::run(evaluator, numblocks * blocksize, size);
}
for (int i = 0; i < numblocks; ++i) {
get_when_ready(&results[i]);
}
}
evaluator.cleanup();
}
};
#endif
// GPU: the evaluation of the expression is offloaded to a GPU.
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
template <typename Evaluator, typename Index>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel_NonVectorizable(Evaluator eval, Index size) {
const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
const Index step_size = blockDim.x * gridDim.x;
// Use the scalar path
for (Index i = first_index; i < size; i += step_size) {
eval.evalScalar(i);
}
}
template <typename Evaluator, typename Index>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel_Vectorizable(Evaluator eval, Index size) {
const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
const Index step_size = blockDim.x * gridDim.x;
// Use the vector path
const Index PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
const Index vectorized_step_size = step_size * PacketSize;
const Index vectorized_size = (size / PacketSize) * PacketSize;
for (Index i = first_index * PacketSize; i < vectorized_size;
i += vectorized_step_size) {
eval.evalPacket(i);
}
for (Index i = vectorized_size + first_index; i < size; i += step_size) {
eval.evalScalar(i);
}
}
template <typename Expression>
struct IsVectorizable<GpuDevice, Expression> {
static const bool value = TensorEvaluator<Expression, GpuDevice>::PacketAccess && TensorEvaluator<Expression, GpuDevice>::IsAligned;
};
template<typename Expression>
class TensorExecutor<Expression, GpuDevice, false>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const GpuDevice& device)
{
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock();
const int block_size = maxCudaThreadsPerBlock();
const Index size = array_prod(evaluator.dimensions());
LAUNCH_CUDA_KERNEL((EigenMetaKernel_NonVectorizable<TensorEvaluator<Expression, GpuDevice>, Index>), num_blocks, block_size, 0, device, evaluator, size);
}
evaluator.cleanup();
}
};
template<typename Expression>
class TensorExecutor<Expression, GpuDevice, true>
{
public:
typedef typename Expression::Index Index;
static inline void run(const Expression& expr, const GpuDevice& device)
{
TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const int num_blocks = getNumCudaMultiProcessors() * maxCudaThreadsPerMultiProcessor() / maxCudaThreadsPerBlock();
const int block_size = maxCudaThreadsPerBlock();
const Index size = array_prod(evaluator.dimensions());
LAUNCH_CUDA_KERNEL((EigenMetaKernel_Vectorizable<TensorEvaluator<Expression, GpuDevice>, Index>), num_blocks, block_size, 0, device, evaluator, size);
}
evaluator.cleanup();
}
};
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_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>
//
// 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_EXPR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
namespace Eigen {
/** \class TensorExpr
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor expression classes.
*
* The TensorCwiseNullaryOp class applies a nullary operators to an expression.
* This is typically used to generate constants.
*
* The TensorCwiseUnaryOp class represents an expression where a unary operator
* (e.g. cwiseSqrt) is applied to an expression.
*
* The TensorCwiseBinaryOp class represents an expression where a binary
* operator (e.g. addition) is applied to a lhs and a rhs expression.
*
*/
namespace internal {
template<typename NullaryOp, typename XprType>
struct traits<TensorCwiseNullaryOp<NullaryOp, XprType> >
: traits<XprType>
{
typedef typename XprType::Packet Packet;
typedef traits<XprType> XprTraits;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
enum {
Flags = 0,
};
};
} // end namespace internal
template<typename NullaryOp, typename XprType>
class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef TensorCwiseNullaryOp<NullaryOp, XprType> Nested;
typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const XprType& xpr, const NullaryOp& func = NullaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
const NullaryOp& functor() const { return m_functor; }
protected:
typename XprType::Nested m_xpr;
const NullaryOp m_functor;
};
namespace internal {
template<typename UnaryOp, typename XprType>
struct traits<TensorCwiseUnaryOp<UnaryOp, XprType> >
: traits<XprType>
{
// TODO(phli): Add InputScalar, InputPacket. Check references to
// current Scalar/Packet to see if the intent is Input or Output.
typedef typename result_of<UnaryOp(typename XprType::Scalar)>::type Scalar;
typedef traits<XprType> XprTraits;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename XprType::Nested XprTypeNested;
typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename UnaryOp, typename XprType>
struct eval<TensorCwiseUnaryOp<UnaryOp, XprType>, Eigen::Dense>
{
typedef const TensorCwiseUnaryOp<UnaryOp, XprType>& type;
};
template<typename UnaryOp, typename XprType>
struct nested<TensorCwiseUnaryOp<UnaryOp, XprType>, 1, typename eval<TensorCwiseUnaryOp<UnaryOp, XprType> >::type>
{
typedef TensorCwiseUnaryOp<UnaryOp, XprType> type;
};
} // end namespace internal
template<typename UnaryOp, typename XprType>
class TensorCwiseUnaryOp : public TensorBase<TensorCwiseUnaryOp<UnaryOp, XprType>, ReadOnlyAccessors>
{
public:
// TODO(phli): Add InputScalar, InputPacket. Check references to
// current Scalar/Packet to see if the intent is Input or Output.
typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef Scalar CoeffReturnType;
typedef typename internal::packet_traits<CoeffReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorCwiseUnaryOp>::type Nested;
typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
: m_xpr(xpr), m_functor(func) {}
EIGEN_DEVICE_FUNC
const UnaryOp& functor() const { return m_functor; }
/** \returns the nested expression */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
nestedExpression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const UnaryOp m_functor;
};
namespace internal {
template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
struct traits<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs
// are different.
// TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket. Check references to
// current Scalar/Packet to see if the intent is Inputs or Output.
typedef typename result_of<
BinaryOp(typename LhsXprType::Scalar,
typename RhsXprType::Scalar)>::type Scalar;
typedef traits<LhsXprType> XprTraits;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<
typename traits<LhsXprType>::StorageKind,
typename traits<RhsXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<
typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
enum {
Flags = 0,
};
};
template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
struct eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>& type;
};
template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
struct nested<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, 1, typename eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >::type>
{
typedef TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> type;
};
} // end namespace internal
template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
class TensorCwiseBinaryOp : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, ReadOnlyAccessors>
{
public:
// TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket. Check references to
// current Scalar/Packet to see if the intent is Inputs or Output.
typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef Scalar CoeffReturnType;
typedef typename internal::packet_traits<CoeffReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorCwiseBinaryOp>::type Nested;
typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const BinaryOp& func = BinaryOp())
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_functor(func) {}
EIGEN_DEVICE_FUNC
const BinaryOp& functor() const { return m_functor; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return m_lhs_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const BinaryOp m_functor;
};
namespace internal {
template<typename IfXprType, typename ThenXprType, typename ElseXprType>
struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
: traits<ThenXprType>
{
typedef typename traits<ThenXprType>::Scalar Scalar;
typedef traits<ThenXprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind,
typename traits<ElseXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<ElseXprType>::Index,
typename traits<ThenXprType>::Index>::type Index;
typedef typename IfXprType::Nested IfNested;
typedef typename ThenXprType::Nested ThenNested;
typedef typename ElseXprType::Nested ElseNested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename IfXprType, typename ThenXprType, typename ElseXprType>
struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense>
{
typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type;
};
template<typename IfXprType, typename ThenXprType, typename ElseXprType>
struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1, typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type>
{
typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type;
};
} // end namespace internal
template<typename IfXprType, typename ThenXprType, typename ElseXprType>
class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorSelectOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType,
typename ElseXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename internal::promote_storage_type<typename ThenXprType::PacketReturnType,
typename ElseXprType::PacketReturnType>::ret PacketReturnType;
typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested;
typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index;
TensorSelectOp(const IfXprType& a_condition,
const ThenXprType& a_then,
const ElseXprType& a_else)
: m_condition(a_condition), m_then(a_then), m_else(a_else)
{ }
const IfXprType& ifExpression() const { return m_condition; }
const ThenXprType& thenExpression() const { return m_then; }
const ElseXprType& elseExpression() const { return m_else; }
protected:
typename IfXprType::Nested m_condition;
typename ThenXprType::Nested m_then;
typename ElseXprType::Nested m_else;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_EXPR_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>
//
// 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_FIXED_SIZE_H
#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
namespace Eigen {
/** \class TensorFixedSize
* \ingroup CXX11_Tensor_Module
*
* \brief The fixed sized version of the tensor class.
*
* The fixed sized equivalent of
* Eigen::Tensor<float, 3> t(3, 5, 7);
* is
* Eigen::TensorFixedSize<float, Size<3,5,7>> t;
*/
template<typename Scalar_, typename Dimensions_, int Options_>
class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_> >
{
public:
typedef TensorFixedSize<Scalar_, Dimensions_, Options_> Self;
typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_> > Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<Self>::StorageKind StorageKind;
typedef typename internal::traits<Self>::Index Index;
typedef Scalar_ Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
static const int Options = Options_;
enum {
IsAligned = bool(EIGEN_ALIGN),
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = Options_ & RowMajor ? RowMajor : ColMajor,
CoordAccess = true,
};
typedef Dimensions_ Dimensions;
static const std::size_t NumIndices = Dimensions::count;
protected:
TensorStorage<Scalar, NumIndices, Dimensions::total_size, Options, Dimensions> m_storage;
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_storage.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); }
// This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
// work, because that uses base().coeffRef() - and we don't yet
// implement a similar class hierarchy
inline Self& base() { return *this; }
inline const Self& base() const { return *this; }
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
{
eigen_assert(checkIndexRange(indices));
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return coeff(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
{
// The bracket operator is only for vectors, use the parenthesis operator instead.
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
return coeff(index);
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
{
eigen_assert(checkIndexRange(indices));
return coeffRef(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return coeffRef(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator[](Index index)
{
// The bracket operator is only for vectors, use the parenthesis operator instead
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize()
: m_storage()
{
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const Self& other)
: m_storage(other.m_storage)
{
}
#ifdef EIGEN_HAVE_RVALUE_REFERENCES
inline TensorFixedSize(Self&& other)
: m_storage(other.m_storage)
{
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize& operator=(const TensorFixedSize& other)
{
// FIXME: check that the dimensions of other match the dimensions of *this.
// Unfortunately this isn't possible yet when the rhs is an expression.
typedef TensorAssignOp<Self, const TensorFixedSize> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize& operator=(const OtherDerived& other)
{
// FIXME: check that the dimensions of other match the dimensions of *this.
// Unfortunately this isn't possible yet when the rhs is an expression.
typedef TensorAssignOp<Self, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
protected:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const
{
using internal::array_apply_and_reduce;
using internal::array_zip_and_reduce;
using internal::greater_equal_zero_op;
using internal::logical_and_op;
using internal::lesser_op;
return true;
// check whether the indices are all >= 0
/* array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
// check whether the indices fit in the dimensions
array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());*/
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
{
if (Options&RowMajor) {
return m_storage.dimensions().IndexOfRowMajor(indices);
} else {
return m_storage.dimensions().IndexOfColMajor(indices);
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_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>
//
// 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_FORCED_EVAL_H
#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
namespace Eigen {
/** \class TensorForcedEval
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reshaping class.
*
*
*/
namespace internal {
template<typename XprType>
struct traits<TensorForcedEvalOp<XprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
enum {
Flags = 0,
};
};
template<typename XprType>
struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense>
{
typedef const TensorForcedEvalOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type>
{
typedef TensorForcedEvalOp<XprType> type;
};
} // end namespace internal
template<typename XprType>
class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested;
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr)
: m_xpr(expr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
};
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorForcedEvalOp<ArgType>, Device>
{
typedef TensorForcedEvalOp<ArgType> XprType;
typedef typename ArgType::Scalar Scalar;
typedef typename ArgType::Packet Packet;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
enum {
IsAligned = true,
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = TensorEvaluator<ArgType, Device>::Layout,
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_op(op.expression()), m_device(device), m_buffer(NULL)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
m_impl.evalSubExprsIfNeeded(NULL);
const Index numValues = m_impl.dimensions().TotalSize();
m_buffer = (CoeffReturnType*)m_device.allocate(numValues * sizeof(CoeffReturnType));
// Should initialize the memory in case we're dealing with non POD types.
if (!internal::is_arithmetic<CoeffReturnType>::value) {
for (Index i = 0; i < numValues; ++i) {
new(m_buffer+i) CoeffReturnType();
}
}
typedef TensorEvalToOp<const ArgType> EvalTo;
EvalTo evalToTmp(m_buffer, m_op);
internal::TensorExecutor<const EvalTo, Device, TensorEvaluator<ArgType, Device>::PacketAccess>::run(evalToTmp, m_device);
m_impl.cleanup();
return true;
}
EIGEN_STRONG_INLINE void cleanup() {
m_device.deallocate(m_buffer);
m_buffer = NULL;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_buffer[index];
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return internal::ploadt<Packet, LoadMode>(m_buffer + index);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return m_buffer; }
private:
TensorEvaluator<ArgType, Device> m_impl;
const ArgType m_op;
const Device& m_device;
CoeffReturnType* m_buffer;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_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>
//
// 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_FORWARD_DECLARATIONS_H
#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
namespace Eigen {
template<typename Scalar_, std::size_t NumIndices_, int Options_ = 0> class Tensor;
template<typename Scalar_, typename Dimensions, int Options_ = 0> class TensorFixedSize;
template<typename PlainObjectType, int Options_ = Unaligned> class TensorMap;
template<typename PlainObjectType> class TensorRef;
template<typename Derived, int AccessLevel = internal::accessors_level<Derived>::value> 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 IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
template<typename Op, typename Dims, typename XprType> class TensorReductionOp;
template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
template<typename PatchDim, typename XprType> class TensorPatchOp;
template<DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorImagePatchOp;
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 PaddingDimensions, typename XprType> class TensorPaddingOp;
template<typename Shuffle, typename XprType> class TensorShufflingOp;
template<typename Strides, typename XprType> class TensorStridingOp;
template<typename LeftXprType, typename RightXprType> class TensorAssignOp;
template<typename XprType> class TensorEvalToOp;
template<typename XprType> class TensorForcedEvalOp;
template<typename ExpressionType, typename DeviceType> class TensorDevice;
template<typename Derived, typename Device> struct TensorEvaluator;
namespace internal {
template<typename Expression, typename Device, bool Vectorizable> class TensorExecutor;
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_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>
//
// 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_FUNCTORS_H
#define EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
namespace Eigen {
namespace internal {
// Standard reduction functors
template <typename T> struct SumReducer
{
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
(*accum) += t;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = padd<Packet>(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return static_cast<T>(0);
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
return pset1<Packet>(0);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
return vaccum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return saccum + predux(vaccum);
}
};
template <typename T> struct MeanReducer
{
static const bool PacketAccess = true;
MeanReducer() : scalarCount_(0), packetCount_(0) { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) {
(*accum) += t;
scalarCount_++;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) {
(*accum) = padd<Packet>(*accum, p);
packetCount_++;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return static_cast<T>(0);
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
return pset1<Packet>(0);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum / scalarCount_;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
return pdiv(vaccum, pset1<Packet>(packetCount_));
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return (saccum + predux(vaccum)) / (scalarCount_ + packetCount_ * packet_traits<Packet>::size);
}
protected:
int scalarCount_;
int packetCount_;
};
template <typename T> struct MaxReducer
{
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
if (t > *accum) { *accum = t; }
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmax<Packet>(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return -(std::numeric_limits<T>::max)();
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
return pset1<Packet>(-(std::numeric_limits<T>::max)());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
return vaccum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return (std::max)(saccum, predux_max(vaccum));
}
};
template <typename T> struct MinReducer
{
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
if (t < *accum) { *accum = t; }
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmin<Packet>(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return (std::numeric_limits<T>::max)();
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
return pset1<Packet>((std::numeric_limits<T>::max)());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
return vaccum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return (std::min)(saccum, predux_min(vaccum));
}
};
template <typename T> struct ProdReducer
{
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
(*accum) *= t;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
(*accum) = pmul<Packet>(*accum, p);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
return static_cast<T>(1);
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
return pset1<Packet>(1);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
return accum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
return vaccum;
}
template <typename Packet>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
return saccum * predux_mul(vaccum);
}
};
#if !defined (EIGEN_USE_GPU) || !defined(__CUDACC__) || !defined(__CUDA_ARCH__)
// We're not compiling a cuda kernel
template <typename T> struct UniformRandomGenerator {
static const bool PacketAccess = true;
template<typename Index>
T operator()(Index, Index = 0) const {
return random<T>();
}
template<typename Index>
typename internal::packet_traits<T>::type packetOp(Index, Index = 0) const {
const int packetSize = internal::packet_traits<T>::size;
EIGEN_ALIGN_DEFAULT T values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = random<T>();
}
return internal::pload<typename internal::packet_traits<T>::type>(values);
}
};
#else
// We're compiling a cuda kernel
template <typename T> struct UniformRandomGenerator;
template <> struct UniformRandomGenerator<float> {
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC UniformRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
float operator()(Index, Index = 0) const {
return curand_uniform(&m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
float4 packetOp(Index, Index = 0) const {
return curand_uniform4(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
template <> struct UniformRandomGenerator<double> {
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC UniformRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
double operator()(Index, Index = 0) const {
return curand_uniform_double(&m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
double2 packetOp(Index, Index = 0) const {
return curand_uniform2_double(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
#endif
#if (!defined (EIGEN_USE_GPU) || !defined(__CUDACC__) || !defined(__CUDA_ARCH__)) && __cplusplus > 199711
// We're not compiling a cuda kernel
template <typename T> struct NormalRandomGenerator {
static const bool PacketAccess = true;
NormalRandomGenerator() : m_distribution(0, 1) {}
NormalRandomGenerator(const NormalRandomGenerator& other) : m_distribution(other.m_distribution) { }
template<typename Index>
T operator()(Index, Index = 0) const {
return m_distribution(m_generator);
}
template<typename Index>
typename internal::packet_traits<T>::type packetOp(Index, Index = 0) const {
const int packetSize = internal::packet_traits<T>::size;
EIGEN_ALIGN_DEFAULT T values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = m_distribution(m_generator);
}
return internal::pload<typename internal::packet_traits<T>::type>(values);
}
mutable std::normal_distribution<T> m_distribution;
mutable std::default_random_engine m_generator;
};
#elif defined (EIGEN_USE_GPU) && defined(__CUDACC__) && defined(__CUDA_ARCH__)
// We're compiling a cuda kernel
template <typename T> struct NormalRandomGenerator;
template <> struct NormalRandomGenerator<float> {
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC NormalRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
float operator()(Index, Index = 0) const {
return curand_normal(&m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
float4 packetOp(Index, Index = 0) const {
return curand_normal4(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
template <> struct NormalRandomGenerator<double> {
static const bool PacketAccess = true;
EIGEN_DEVICE_FUNC NormalRandomGenerator() {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(0, tid, 0, &m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
double operator()(Index, Index = 0) const {
return curand_normal_double(&m_state);
}
template<typename Index> EIGEN_DEVICE_FUNC
double2 packetOp(Index, Index = 0) const {
return curand_normal2_double(&m_state);
}
private:
mutable curandStatePhilox4_32_10_t m_state;
};
#endif
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_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>
//
// 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_IO_H
#define EIGEN_CXX11_TENSOR_TENSOR_IO_H
namespace Eigen {
namespace internal {
template<>
struct significant_decimals_impl<std::string>
: significant_decimals_default_impl<std::string, true>
{};
}
template <typename T>
std::ostream& operator << (std::ostream& os, const TensorBase<T, ReadOnlyAccessors>& expr) {
// Evaluate the expression if needed
TensorForcedEvalOp<const T> eval = expr.eval();
TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> tensor(eval, DefaultDevice());
tensor.evalSubExprsIfNeeded(NULL);
typedef typename internal::remove_const<typename T::Scalar>::type Scalar;
typedef typename T::Index Index;
typedef typename TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice>::Dimensions Dimensions;
const Index total_size = internal::array_prod(tensor.dimensions());
// Print the tensor as a 1d vector or a 2d matrix.
if (internal::array_size<Dimensions>::value == 1) {
Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size);
os << array;
} else {
const Index first_dim = tensor.dimensions()[0];
static const int layout = TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice>::Layout;
Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim);
os << matrix;
}
// Cleanup.
tensor.cleanup();
return os;
}
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_IO_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>
//
// 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_IMAGE_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
namespace Eigen {
/** \class TensorImagePatch
* \ingroup CXX11_Tensor_Module
*
* \brief Patch extraction specialized for image processing.
* This assumes that the input has a least 3 dimensions ordered as follow:
* 1st dimension: channels (of size d)
* 2nd dimension: rows (of size r)
* 3rd dimension: columns (of size c)
* There can be additional dimensions such as time (for video) or batch (for
* bulk processing after the first 3.
* Calling the image patch code with patch_rows and patch_cols is equivalent
* to calling the regular patch extraction code with parameters d, patch_rows,
* patch_cols, and 1 for all the additional dimensions.
*/
namespace internal {
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions + 1;
static const int Layout = XprTraits::Layout;
};
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
{
typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
};
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
{
typedef TensorImagePatchOp<Rows, Cols, XprType> type;
};
} // end namespace internal
template<DenseIndex Rows, DenseIndex Cols, typename XprType>
class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorImagePatchOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
DenseIndex row_strides, DenseIndex col_strides,
PaddingType padding_type)
: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_row_strides(row_strides), m_col_strides(col_strides),
m_padding_type(padding_type) {}
EIGEN_DEVICE_FUNC
DenseIndex patch_rows() const { return m_patch_rows; }
EIGEN_DEVICE_FUNC
DenseIndex patch_cols() const { return m_patch_cols; }
EIGEN_DEVICE_FUNC
DenseIndex row_strides() const { return m_row_strides; }
EIGEN_DEVICE_FUNC
DenseIndex col_strides() const { return m_col_strides; }
EIGEN_DEVICE_FUNC
PaddingType padding_type() const { return m_padding_type; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const DenseIndex m_patch_rows;
const DenseIndex m_patch_cols;
const DenseIndex m_row_strides;
const DenseIndex m_col_strides;
const PaddingType m_padding_type;
};
// Eval as rvalue
template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
{
typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = NumDims == 5,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(NumDims >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
// Caches a few variables.
m_inputRows = input_dims[1];
m_inputCols = input_dims[2];
m_row_strides = op.row_strides();
m_col_strides = op.col_strides();
// We only support same strides for both dimensions and square patches.
eigen_assert(m_row_strides == m_col_strides);
switch (op.padding_type()) {
case PADDING_VALID:
m_outputRows = ceil((m_inputRows - op.patch_rows() + 1.f) / static_cast<float>(m_row_strides));
m_outputCols = ceil((m_inputCols - op.patch_cols() + 1.f) / static_cast<float>(m_col_strides));
// Calculate the padding
m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
break;
case PADDING_SAME:
m_outputRows = ceil(m_inputRows / static_cast<float>(m_row_strides));
m_outputCols = ceil(m_inputCols / static_cast<float>(m_col_strides));
// Calculate the padding
m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
break;
default:
eigen_assert(false && "unexpected padding");
}
// Dimensions for result of extraction.
// 0: depth
// 1: patch_rows
// 2: patch_cols
// 3: number of patches
// 4 and beyond: anything else (such as batch).
m_dimensions[0] = input_dims[0];
m_dimensions[1] = op.patch_rows();
m_dimensions[2] = op.patch_cols();
m_dimensions[3] = m_outputRows * m_outputCols;
for (int i = 4; i < NumDims; ++i) {
m_dimensions[i] = input_dims[i-1];
}
// Strides for moving the patch in various dimensions.
m_colStride = m_dimensions[1];
m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
m_otherStride = m_patchStride * m_dimensions[3];
// Strides for navigating through the input tensor.
m_rowInputStride = input_dims[0];
m_colInputStride = input_dims[0] * input_dims[1];
m_patchInputStride = input_dims[0] * input_dims[1] * input_dims[2];
// Fast representations of different variables.
m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
// Number of patches in the width dimension.
m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[0]);
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
// Patch index corresponding to the passed in index.
const Index patchIndex = index / m_fastPatchStride;
// Find the offset of the element wrt the location of the first element.
const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastDimZero;
// Other ways to index this element.
const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
const Index colIndex = patch2DIndex / m_fastOutputRows;
const Index colOffset = patchOffset / m_fastColStride;
// Calculate col index in the input original tensor.
const Index inputCol = colIndex * m_col_strides + colOffset - m_colPaddingLeft;
if (inputCol < 0 || inputCol >= m_inputCols) {
return Scalar(0);
}
const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
const Index rowOffset = patchOffset - colOffset * m_colStride;
// Calculate row index in the original input tensor.
const Index inputRow = rowIndex * m_row_strides + rowOffset - m_rowPaddingTop;
if (inputRow < 0 || inputRow >= m_inputRows) {
return Scalar(0);
}
const Index depth = index - (index / m_fastDimZero) * m_dimensions[0];
const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex * m_patchInputStride;
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const Index packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
const Index indices[2] = {index, index + packetSize - 1};
const Index patchIndex = indices[0] / m_fastPatchStride;
if (patchIndex != indices[1] / m_fastPatchStride) {
return packetWithPossibleZero(index);
}
const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
// Find the offset of the element wrt the location of the first element.
const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastDimZero,
(indices[1] - patchIndex * m_patchStride) / m_fastDimZero};
const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
const Index colIndex = patch2DIndex / m_fastOutputRows;
const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
// Calculate col indices in the original input tensor.
const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
// all zeros
return internal::pset1<PacketReturnType>(Scalar(0));
}
if (inputCols[0] == inputCols[1]) {
const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
eigen_assert(rowOffsets[0] <= rowOffsets[1]);
// Calculate col indices in the original input tensor.
const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
// all zeros
return internal::pset1<PacketReturnType>(Scalar(0));
}
if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
// no padding
const Index depth = index - (index / m_fastDimZero) * m_dimensions[0];
const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
return m_impl.template packet<Unaligned>(inputIndex);
}
}
return packetWithPossibleZero(index);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
Index rowPaddingTop() const { return m_rowPaddingTop; }
Index colPaddingLeft() const { return m_colPaddingLeft; }
Index outputRows() const { return m_outputRows; }
Index outputCols() const { return m_outputCols; }
Index userRowStride() const { return m_row_strides; }
Index userColStride() const { return m_col_strides; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
// Location of the first element of the patch.
// 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches
const Index patchIndex = coords[3];
array<Index, NumDims-1> inputCoords;
inputCoords[0] = coords[0]; // depth
inputCoords[1] = patchIndex / m_inputCols + coords[1] - m_rowPaddingTop;
inputCoords[2] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft;
inputCoords[3] = coords[4]; // batch
// If the computed coordinates are outside the original image perimeter, return 0.
if (inputCoords[1] < 0 || inputCoords[1] >= m_inputRows ||
inputCoords[2] < 0 || inputCoords[2] >= m_inputCols) {
return Scalar(0);
}
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
return m_impl.coeff(inputCoords);
} else {
Index inputIndex =
inputCoords[3] * m_patchInputStride +
inputCoords[2] * m_colInputStride +
inputCoords[1] * m_rowInputStride +
inputCoords[0];
return m_impl.coeff(inputIndex);
}
}
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
Dimensions m_dimensions;
Index m_otherStride;
Index m_patchStride;
Index m_colStride;
Index m_row_strides;
Index m_col_strides;
internal::TensorIntDivisor<Index> m_fastOtherStride;
internal::TensorIntDivisor<Index> m_fastPatchStride;
internal::TensorIntDivisor<Index> m_fastColStride;
Index m_rowInputStride;
Index m_colInputStride;
Index m_patchInputStride;
Index m_inputRows;
Index m_inputCols;
Index m_outputRows;
Index m_outputCols;
Index m_rowPaddingTop;
Index m_colPaddingLeft;
internal::TensorIntDivisor<Index> m_fastOutputRows;
internal::TensorIntDivisor<Index> m_fastDimZero;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_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>
//
// 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_INDEX_LIST_H
#define EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
#ifdef EIGEN_HAS_CONSTEXPR
namespace Eigen {
/** \internal
*
* \class TensorIndexList
* \ingroup CXX11_Tensor_Module
*
* \brief Set of classes used to encode a set of Tensor dimensions/indices.
*
* The indices in the list can be known at compile time or at runtime. A mix
* of static and dynamic indices can also be provided if needed. The tensor
* code will attempt to take advantage of the indices that are known at
* compile time to optimize the code it generates.
*
* This functionality requires a c++11 compliant compiler. If your compiler
* is older you need to use arrays of indices instead.
*
* Several examples are provided in the cxx11_tensor_index_list.cpp file.
*
* \sa Tensor
*/
template <DenseIndex n>
struct type2index {
static const DenseIndex value = n;
constexpr operator DenseIndex() const { return n; }
void set(DenseIndex val) {
eigen_assert(val == n);
}
};
namespace internal {
template <typename T>
void update_value(T& val, DenseIndex new_val) {
val = new_val;
}
template <DenseIndex n>
void update_value(type2index<n>& val, DenseIndex new_val) {
val.set(new_val);
}
template <typename T>
struct is_compile_time_constant {
static constexpr bool value = false;
};
template <DenseIndex idx>
struct is_compile_time_constant<type2index<idx> > {
static constexpr bool value = true;
};
template <DenseIndex idx>
struct is_compile_time_constant<const type2index<idx> > {
static constexpr bool value = true;
};
template <DenseIndex idx>
struct is_compile_time_constant<type2index<idx>& > {
static constexpr bool value = true;
};
template <DenseIndex idx>
struct is_compile_time_constant<const type2index<idx>& > {
static constexpr bool value = true;
};
template <DenseIndex Idx>
struct tuple_coeff {
template <typename... T>
static constexpr DenseIndex get(const DenseIndex i, const std::tuple<T...>& t) {
return std::get<Idx>(t) * (i == Idx) + tuple_coeff<Idx-1>::get(i, t) * (i != Idx);
}
template <typename... T>
static void set(const DenseIndex i, std::tuple<T...>& t, const DenseIndex value) {
if (i == Idx) {
update_value(std::get<Idx>(t), value);
} else {
tuple_coeff<Idx-1>::set(i, t, value);
}
}
template <typename... T>
static constexpr bool value_known_statically(const DenseIndex i, const std::tuple<T...>& t) {
return ((i == Idx) & is_compile_time_constant<typename std::tuple_element<Idx, std::tuple<T...> >::type>::value) ||
tuple_coeff<Idx-1>::value_known_statically(i, t);
}
template <typename... T>
static constexpr bool values_up_to_known_statically(const std::tuple<T...>& t) {
return is_compile_time_constant<typename std::tuple_element<Idx, std::tuple<T...> >::type>::value &&
tuple_coeff<Idx-1>::values_up_to_known_statically(t);
}
template <typename... T>
static constexpr bool values_up_to_statically_known_to_increase(const std::tuple<T...>& t) {
return is_compile_time_constant<typename std::tuple_element<Idx, std::tuple<T...> >::type>::value &&
is_compile_time_constant<typename std::tuple_element<Idx-1, std::tuple<T...> >::type>::value &&
std::get<Idx>(t) > std::get<Idx-1>(t) &&
tuple_coeff<Idx-1>::values_up_to_statically_known_to_increase(t);
}
};
template <>
struct tuple_coeff<0> {
template <typename... T>
static constexpr DenseIndex get(const DenseIndex i, const std::tuple<T...>& t) {
// eigen_assert (i == 0); // gcc fails to compile assertions in constexpr
return std::get<0>(t) * (i == 0);
}
template <typename... T>
static void set(const DenseIndex i, std::tuple<T...>& t, const DenseIndex value) {
eigen_assert (i == 0);
update_value(std::get<0>(t), value);
}
template <typename... T>
static constexpr bool value_known_statically(const DenseIndex i, const std::tuple<T...>&) {
// eigen_assert (i == 0); // gcc fails to compile assertions in constexpr
return is_compile_time_constant<typename std::tuple_element<0, std::tuple<T...> >::type>::value & (i == 0);
}
template <typename... T>
static constexpr bool values_up_to_known_statically(const std::tuple<T...>&) {
return is_compile_time_constant<typename std::tuple_element<0, std::tuple<T...> >::type>::value;
}
template <typename... T>
static constexpr bool values_up_to_statically_known_to_increase(const std::tuple<T...>&) {
return true;
}
};
} // namespace internal
template<typename FirstType, typename... OtherTypes>
struct IndexList : std::tuple<FirstType, OtherTypes...> {
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr DenseIndex operator[] (const DenseIndex i) const {
return internal::tuple_coeff<std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value-1>::get(i, *this);
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const DenseIndex i, const DenseIndex value) {
return internal::tuple_coeff<std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value-1>::set(i, *this, value);
}
constexpr IndexList(const std::tuple<FirstType, OtherTypes...>& other) : std::tuple<FirstType, OtherTypes...>(other) { }
constexpr IndexList() : std::tuple<FirstType, OtherTypes...>() { }
constexpr bool value_known_statically(const DenseIndex i) const {
return internal::tuple_coeff<std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value-1>::value_known_statically(i, *this);
}
constexpr bool all_values_known_statically() const {
return internal::tuple_coeff<std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value-1>::values_up_to_known_statically(*this);
}
constexpr bool values_statically_known_to_increase() const {
return internal::tuple_coeff<std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value-1>::values_up_to_statically_known_to_increase(*this);
}
};
template<typename FirstType, typename... OtherTypes>
constexpr IndexList<FirstType, OtherTypes...> make_index_list(FirstType val1, OtherTypes... other_vals) {
return std::make_tuple(val1, other_vals...);
}
namespace internal {
template<typename FirstType, typename... OtherTypes> size_t array_prod(const IndexList<FirstType, OtherTypes...>& sizes) {
size_t result = 1;
for (int i = 0; i < array_size<IndexList<FirstType, OtherTypes...> >::value; ++i) {
result *= sizes[i];
}
return result;
};
template<typename FirstType, typename... OtherTypes> struct array_size<IndexList<FirstType, OtherTypes...> > {
static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
};
template<typename FirstType, typename... OtherTypes> struct array_size<const IndexList<FirstType, OtherTypes...> > {
static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
};
template<DenseIndex n, typename FirstType, typename... OtherTypes> constexpr DenseIndex array_get(IndexList<FirstType, OtherTypes...>& a) {
return std::get<n>(a);
}
template<DenseIndex n, typename FirstType, typename... OtherTypes> constexpr DenseIndex array_get(const IndexList<FirstType, OtherTypes...>& a) {
return std::get<n>(a);
}
template <typename T>
struct index_known_statically {
constexpr bool operator() (DenseIndex) const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct index_known_statically<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
}
};
template <typename FirstType, typename... OtherTypes>
struct index_known_statically<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
}
};
template <typename T>
struct all_indices_known_statically {
constexpr bool operator() () const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct all_indices_known_statically<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() () const {
return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
}
};
template <typename FirstType, typename... OtherTypes>
struct all_indices_known_statically<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() () const {
return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
}
};
template <typename T>
struct indices_statically_known_to_increase {
constexpr bool operator() () const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct indices_statically_known_to_increase<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() () const {
return IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
}
};
template <typename FirstType, typename... OtherTypes>
struct indices_statically_known_to_increase<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() () const {
return IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
}
};
template <typename Tx>
struct index_statically_eq {
constexpr bool operator() (DenseIndex, DenseIndex) const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_eq<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] == value);
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_eq<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] == value);
}
};
template <typename T>
struct index_statically_ne {
constexpr bool operator() (DenseIndex, DenseIndex) const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_ne<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] != value);
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_ne<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] != value);
}
};
template <typename T>
struct index_statically_gt {
constexpr bool operator() (DenseIndex, DenseIndex) const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_gt<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] > value);
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_gt<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] > value);
}
};
template <typename T>
struct index_statically_lt {
constexpr bool operator() (DenseIndex, DenseIndex) const {
return false;
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_lt<IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] < value);
}
};
template <typename FirstType, typename... OtherTypes>
struct index_statically_lt<const IndexList<FirstType, OtherTypes...> > {
constexpr bool operator() (const DenseIndex i, const DenseIndex value) const {
return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
(IndexList<FirstType, OtherTypes...>()[i] < value);
}
};
} // end namespace internal
} // end namespace Eigen
#else
namespace Eigen {
namespace internal {
// No C++11 support
template <typename T>
struct index_known_statically {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() (DenseIndex) const{
return false;
}
};
template <typename T>
struct all_indices_known_statically {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() () const {
return false;
}
};
template <typename T>
struct indices_statically_known_to_increase {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() () const {
return false;
}
};
template <typename T>
struct index_statically_eq {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() (DenseIndex, DenseIndex) const{
return false;
}
};
template <typename T>
struct index_statically_ne {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() (DenseIndex, DenseIndex) const{
return false;
}
};
template <typename T>
struct index_statically_gt {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() (DenseIndex, DenseIndex) const{
return false;
}
};
template <typename T>
struct index_statically_lt {
EIGEN_ALWAYS_INLINE EIGEN_DEVICE_FUNC bool operator() (DenseIndex, DenseIndex) const{
return false;
}
};
} // end namespace internal
} // end namespace Eigen
#endif
#endif // EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_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>
//
// 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_INITIALIZER_H
#define EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
#include <initializer_list>
namespace Eigen {
/** \class TensorInitializer
* \ingroup CXX11_Tensor_Module
*
* \brief Helper template to initialize Tensors from std::initializer_lists.
*/
namespace internal {
template <typename Derived, int N>
struct Initializer {
typedef std::initializer_list<
typename Initializer<Derived, N - 1>::InitList> InitList;
static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
const InitList& vals) {
int i = 0;
for (auto v : vals) {
(*indices)[traits<Derived>::NumDimensions - N] = i++;
Initializer<Derived, N - 1>::run(tensor, indices, v);
}
}
};
template <typename Derived>
struct Initializer<Derived, 1> {
typedef std::initializer_list<typename traits<Derived>::Scalar> InitList;
static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
const InitList& vals) {
int i = 0;
// There is likely a faster way to do that than iterating.
for (auto v : vals) {
(*indices)[traits<Derived>::NumDimensions - 1] = i++;
tensor.coeffRef(*indices) = v;
}
}
};
template <typename Derived, int N>
void initialize_tensor(TensorEvaluator<Derived, DefaultDevice>& tensor,
const typename Initializer<Derived, traits<Derived>::NumDimensions>::InitList& vals) {
Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions> indices;
Initializer<Derived, traits<Derived>::NumDimensions>::run(tensor, &indices, vals);
}
} // namespace internal
} // namespace Eigen
#endif // EIGEN_HAS_VARIADIC_TEMPLATES
#endif // EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_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>
//
// 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_INTDIV_H
#define EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
namespace Eigen {
/** \internal
*
* \class TensorIntDiv
* \ingroup CXX11_Tensor_Module
*
* \brief Fast integer division by a constant.
*
* See the paper from Granlund and Montgomery for explanation.
* (at http://dx.doi.org/10.1145/773473.178249)
*
* \sa Tensor
*/
namespace internal {
template <typename T>
struct TensorIntDivisor {
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
multiplier = 0;
shift1 = 0;
shift2 = 0;
}
// Must have 1 <= divider <= 2^31-1
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor(const T divider) {
const int N = 32;
eigen_assert(divider > 0);
eigen_assert(divider <= (1<<(N-1)) - 1);
// fast ln2
#ifndef __CUDA_ARCH__
const int leading_zeros = __builtin_clz(divider);
#else
const int leading_zeros = __clz(divider);
#endif
const int log_div = N - (leading_zeros+1);
multiplier = (static_cast<uint64_t>(1) << (N+log_div)) / divider - (static_cast<uint64_t>(1) << N) + 1;
shift1 = log_div > 1 ? 1 : log_div;
shift2 = log_div > 1 ? log_div-1 : 0;
}
// Must have 0 <= numerator <= 2^32-1
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T divide(const T numerator) const {
const int N = 32;
eigen_assert(numerator >= 0);
eigen_assert(numerator <= (1ull<<N) - 1);
uint32_t t1 = (multiplier * numerator) >> 32;
uint32_t t = (static_cast<uint32_t>(numerator) - t1) >> shift1;
return (t1 + t) >> shift2;
}
private:
uint64_t multiplier;
int32_t shift1;
int32_t shift2;
};
template <typename T>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator / (const T& numerator, const TensorIntDivisor<T>& divisor) {
return divisor.divide(numerator);
}
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_INTDIV_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>
//
// 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_LAYOUT_SWAP_H
#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
namespace Eigen {
/** \class TensorLayoutSwap
* \ingroup CXX11_Tensor_Module
*
* \brief Swap the layout from col-major to row-major, or row-major
* to col-major, and invert the order of the dimensions.
*
* Beware: the dimensions are reversed by this operation. If you want to
* preserve the ordering of the dimensions, you need to combine this
* operation with a shuffle.
*
* \example:
* Tensor<float, 2, ColMajor> input(2, 4);
* Tensor<float, 2, RowMajor> output = input.swap_layout();
* eigen_assert(output.dimension(0) == 4);
* eigen_assert(output.dimension(1) == 2);
*
* array<int, 2> shuffle(1, 0);
* output = input.swap_layout().shuffle(shuffle);
* eigen_assert(output.dimension(0) == 2);
* eigen_assert(output.dimension(1) == 4);
*
*/
namespace internal {
template<typename XprType>
struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = traits<XprType>::NumDimensions;
static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
};
template<typename XprType>
struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
{
typedef const TensorLayoutSwapOp<XprType>& type;
};
template<typename XprType>
struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type>
{
typedef TensorLayoutSwapOp<XprType> type;
};
} // end namespace internal
template<typename XprType>
class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr)
: m_xpr(expr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorLayoutSwapOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorLayoutSwapOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
};
// Eval as rvalue
template<typename ArgType, typename Device>
struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
typedef TensorLayoutSwapOp<ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
for(int i = 0; i < NumDims; ++i) {
m_dimensions[i] = m_impl.dimensions()[NumDims-1-i];
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
return m_impl.evalSubExprsIfNeeded(data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_impl.template packet<LoadMode>(index);
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_impl.data(); }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
protected:
TensorEvaluator<ArgType, Device> m_impl;
Dimensions m_dimensions;
};
// Eval as lvalue
template<typename ArgType, typename Device>
struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
: public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
{
typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
typedef TensorLayoutSwapOp<ArgType> XprType;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(index);
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
this->m_impl.template writePacket<StoreMode>(index, x);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_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>
//
// 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_MAP_H
#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
namespace Eigen {
/** \class TensorMap
* \ingroup CXX11_Tensor_Module
*
* \brief A tensor expression mapping an existing array of data.
*
*/
template<typename PlainObjectType, int Options_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_> >
{
public:
typedef TensorMap<PlainObjectType, Options_> Self;
typedef typename PlainObjectType::Base Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
typedef typename internal::traits<PlainObjectType>::Index Index;
typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
/* typedef typename internal::conditional<
bool(internal::is_lvalue<PlainObjectType>::value),
Scalar *,
const Scalar *>::type
PointerType;*/
typedef Scalar* PointerType;
typedef PointerType PointerArgType;
static const int Options = Options_;
static const Index NumIndices = PlainObjectType::NumIndices;
typedef typename PlainObjectType::Dimensions Dimensions;
enum {
IsAligned = ((int(Options_)&Aligned)==Aligned),
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = PlainObjectType::Layout,
CoordAccess = true,
};
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#endif
inline TensorMap(PointerArgType dataPtr, const array<Index, NumIndices>& dimensions)
: m_data(dataPtr), m_dimensions(dimensions)
{ }
template <typename Dimensions>
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, const Dimensions& dimensions)
: m_data(dataPtr), m_dimensions(dimensions)
{ }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar* data() { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
{
// eigen_assert(checkIndexRange(indices));
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(indices);
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(indices);
return m_data[index];
}
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
{
static_assert(sizeof...(otherIndices) + 1 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
return m_data[index];
}
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_data[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i1 + i0 * m_dimensions[0];
return m_data[index];
} else {
const Index index = i0 + i1 * m_dimensions[0];
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i2 + m_dimensions[1] * (i1 + m_dimensions[0] * i0);
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
return m_data[index];
}
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
{
// eigen_assert(checkIndexRange(indices));
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(indices);
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(indices);
return m_data[index];
}
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
{
static_assert(sizeof...(otherIndices) + 1 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
const std::size_t NumDims = sizeof...(otherIndices) + 1;
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, otherIndices...}});
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, otherIndices...}});
return m_data[index];
}
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_data[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i1 + i0 * m_dimensions[0];
return m_data[index];
} else {
const Index index = i0 + i1 * m_dimensions[0];
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i2 + m_dimensions[1] * (i1 + m_dimensions[0] * i0);
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
return m_data[index];
}
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Self& operator=(const Self& other)
{
typedef TensorAssignOp<Self, const Self> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Self& operator=(const OtherDerived& other)
{
typedef TensorAssignOp<Self, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
private:
Scalar* m_data;
Dimensions m_dimensions;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_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>
//
// 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_MORPHING_H
#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
namespace Eigen {
/** \class TensorReshaping
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reshaping class.
*
*
*/
namespace internal {
template<typename NewDimensions, typename XprType>
struct traits<TensorReshapingOp<NewDimensions, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = array_size<NewDimensions>::value;
static const int Layout = XprTraits::Layout;
};
template<typename NewDimensions, typename XprType>
struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense>
{
typedef const TensorReshapingOp<NewDimensions, XprType>& type;
};
template<typename NewDimensions, typename XprType>
struct nested<TensorReshapingOp<NewDimensions, XprType>, 1, typename eval<TensorReshapingOp<NewDimensions, XprType> >::type>
{
typedef TensorReshapingOp<NewDimensions, XprType> type;
};
} // end namespace internal
template<typename NewDimensions, typename XprType>
class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorReshapingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims)
: m_xpr(expr), m_dims(dims) {}
EIGEN_DEVICE_FUNC
const NewDimensions& dimensions() const { return m_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const TensorReshapingOp& other)
{
typedef TensorAssignOp<TensorReshapingOp, const TensorReshapingOp> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorReshapingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const NewDimensions m_dims;
};
// Eval as rvalue
template<typename NewDimensions, typename ArgType, typename Device>
struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
{
typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
typedef NewDimensions Dimensions;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_dimensions(op.dimensions())
{
// The total size of the reshaped tensor must be equal to the total size
// of the input tensor.
eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
return m_impl.evalSubExprsIfNeeded(data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_impl.template packet<LoadMode>(index);
}
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_impl.data(); }
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
protected:
TensorEvaluator<ArgType, Device> m_impl;
NewDimensions m_dimensions;
};
// Eval as lvalue
template<typename NewDimensions, typename ArgType, typename Device>
struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device>
: public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
{
typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base;
typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
typedef NewDimensions Dimensions;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(index);
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
this->m_impl.template writePacket<StoreMode>(index, x);
}
};
/** \class TensorSlicing
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor slicing class.
*
*
*/
namespace internal {
template<typename StartIndices, typename Sizes, typename XprType>
struct traits<TensorSlicingOp<StartIndices, Sizes, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = array_size<StartIndices>::value;
static const int Layout = XprTraits::Layout;
};
template<typename StartIndices, typename Sizes, typename XprType>
struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense>
{
typedef const TensorSlicingOp<StartIndices, Sizes, XprType>& type;
};
template<typename StartIndices, typename Sizes, typename XprType>
struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1, typename eval<TensorSlicingOp<StartIndices, Sizes, XprType> >::type>
{
typedef TensorSlicingOp<StartIndices, Sizes, XprType> type;
};
} // end namespace internal
template<typename StartIndices, typename Sizes, typename XprType>
class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorSlicingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices, const Sizes& sizes)
: m_xpr(expr), m_indices(indices), m_sizes(sizes) {}
EIGEN_DEVICE_FUNC
const StartIndices& startIndices() const { return m_indices; }
EIGEN_DEVICE_FUNC
const Sizes& sizes() const { return m_sizes; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorSlicingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const TensorSlicingOp& other)
{
typedef TensorAssignOp<TensorSlicingOp, const TensorSlicingOp> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const StartIndices m_indices;
const Sizes m_sizes;
};
// Eval as rvalue
template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
{
typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
static const int NumDims = internal::array_size<Sizes>::value;
enum {
// Alignment can't be guaranteed at compile time since it depends on the
// slice offsets and sizes.
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices())
{
for (int i = 0; i < internal::array_size<Dimensions>::value; ++i) {
eigen_assert(m_impl.dimensions()[i] >= op.sizes()[i] + op.startIndices()[i]);
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const Sizes& output_dims = op.sizes();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
}
m_outputStrides[0] = 1;
m_fastOutputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1];
m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
}
} else {
m_inputStrides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
}
m_outputStrides[NumDims-1] = 1;
m_fastOutputStrides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1];
m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
}
}
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef Sizes Dimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
m_impl.evalSubExprsIfNeeded(NULL);
if (internal::is_arithmetic<Scalar>::value && data && m_impl.data()) {
Index contiguous_values = 1;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < NumDims; ++i) {
contiguous_values *= dimensions()[i];
if (dimensions()[i] != m_impl.dimensions()[i]) {
break;
}
}
} else {
for (int i = NumDims-1; i >= 0; --i) {
contiguous_values *= dimensions()[i];
if (dimensions()[i] != m_impl.dimensions()[i]) {
break;
}
}
}
// Use memcpy if it's going to be faster than using the regular evaluation.
if (contiguous_values > 2 * m_device.numThreads()) {
Scalar* src = m_impl.data();
for (int i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) {
Index offset = srcCoeff(i);
m_device.memcpy((void*)(data+i), src+offset, contiguous_values * sizeof(Scalar));
}
return false;
}
}
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(srcCoeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_fastOutputStrides[i];
const Index idx1 = indices[1] / m_fastOutputStrides[i];
inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += (indices[0] + m_offsets[0]);
inputIndices[1] += (indices[1] + m_offsets[0]);
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx0 = indices[0] / m_fastOutputStrides[i];
const Index idx1 = indices[1] / m_fastOutputStrides[i];
inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += (indices[0] + m_offsets[NumDims-1]);
inputIndices[1] += (indices[1] + m_offsets[NumDims-1]);
}
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
typename internal::remove_const<CoeffReturnType>::type values[packetSize];
values[0] = m_impl.coeff(inputIndices[0]);
values[packetSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < packetSize-1; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords)
{
array<Index, NumDims> inputCoords;
for (int i = 0; i < NumDims; ++i) {
inputCoords = coords[i] + this->m_offsets[i];
}
return m_impl.coeff(inputCoords);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const {
Scalar* result = m_impl.data();
if (result) {
Index offset = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < NumDims; ++i) {
if (m_dimensions[i] != m_impl.dimensions()[i]) {
offset += m_offsets[i] * m_inputStrides[i];
for (int j = i+1; j < NumDims; ++j) {
if (m_dimensions[j] > 1) {
return NULL;
}
offset += m_offsets[j] * m_inputStrides[j];
}
break;
}
}
} else {
for (int i = NumDims - 1; i >= 0; --i) {
if (m_dimensions[i] != m_impl.dimensions()[i]) {
offset += m_offsets[i] * m_inputStrides[i];
for (int j = i-1; j >= 0; --j) {
if (m_dimensions[j] > 1) {
return NULL;
}
offset += m_offsets[j] * m_inputStrides[j];
}
break;
}
}
}
return result + offset;
}
return NULL;
}
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
{
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_fastOutputStrides[i];
inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
inputIndex += (index + m_offsets[0]);
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_fastOutputStrides[i];
inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
inputIndex += (index + m_offsets[NumDims-1]);
}
return inputIndex;
}
array<Index, NumDims> m_outputStrides;
array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
Dimensions m_dimensions;
const StartIndices m_offsets;
};
// Eval as lvalue
template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
: public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
{
typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base;
typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
static const int NumDims = internal::array_size<Sizes>::value;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef Sizes Dimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(this->srcCoeff(index));
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
indices[0] -= idx0 * this->m_outputStrides[i];
indices[1] -= idx1 * this->m_outputStrides[i];
}
inputIndices[0] += (indices[0] + this->m_offsets[0]);
inputIndices[1] += (indices[1] + this->m_offsets[0]);
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
indices[0] -= idx0 * this->m_outputStrides[i];
indices[1] -= idx1 * this->m_outputStrides[i];
}
inputIndices[0] += (indices[0] + this->m_offsets[NumDims-1]);
inputIndices[1] += (indices[1] + this->m_offsets[NumDims-1]);
}
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
this->m_impl.template writePacket<StoreMode>(inputIndices[0], x);
}
else {
EIGEN_ALIGN_DEFAULT CoeffReturnType values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
this->m_impl.coeffRef(inputIndices[0]) = values[0];
this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1];
for (int i = 1; i < packetSize-1; ++i) {
this->coeffRef(index+i) = values[i];
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(const array<Index, NumDims>& coords)
{
array<Index, NumDims> inputCoords;
for (int i = 0; i < NumDims; ++i) {
inputCoords = coords[i] + this->m_offsets[i];
}
return this->m_impl.coeffRef(inputCoords);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_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>
//
// 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_PADDING_H
#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
namespace Eigen {
/** \class TensorPadding
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor padding class.
* At the moment only 0-padding is supported.
*
*/
namespace internal {
template<typename PaddingDimensions, typename XprType>
struct traits<TensorPaddingOp<PaddingDimensions, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename PaddingDimensions, typename XprType>
struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense>
{
typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
};
template<typename PaddingDimensions, typename XprType>
struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type>
{
typedef TensorPaddingOp<PaddingDimensions, XprType> type;
};
} // end namespace internal
template<typename PaddingDimensions, typename XprType>
class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorPaddingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims)
: m_xpr(expr), m_padding_dims(padding_dims) {}
EIGEN_DEVICE_FUNC
const PaddingDimensions& padding() const { return m_padding_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const PaddingDimensions m_padding_dims;
};
// Eval as rvalue
template<typename PaddingDimensions, typename ArgType, typename Device>
struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device>
{
typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<PaddingDimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = true,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_padding(op.padding())
{
// Compute dimensions
m_dimensions = m_impl.dimensions();
for (int i = 0; i < NumDims; ++i) {
m_dimensions[i] += m_padding[i].first + m_padding[i].second;
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
m_outputStrides[NumDims] = m_outputStrides[NumDims-1] * m_dimensions[NumDims-1];
} else {
m_inputStrides[NumDims - 1] = 1;
m_outputStrides[NumDims] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
m_outputStrides[i+1] = m_outputStrides[i+2] * m_dimensions[i+1];
}
m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
eigen_assert(index < dimensions().TotalSize());
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0);
}
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
if (index < m_padding[0].first || index >= m_dimensions[0] - m_padding[0].second) {
return Scalar(0);
}
inputIndex += (index - m_padding[0].first);
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i+1];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0);
}
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i+1];
}
if (index < m_padding[NumDims-1].first ||
index >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
return Scalar(0);
}
inputIndex += (index - m_padding[NumDims-1].first);
}
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return packetColMajor(index);
}
return packetRowMajor(index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
Index inputIndex;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
const Index idx = coords[0];
if (idx < m_padding[0].first || idx >= m_dimensions[0] - m_padding[0].second) {
return Scalar(0);
}
inputIndex = idx - m_padding[0].first;
for (int i = 1; i < NumDims; ++i) {
const Index idx = coords[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0);
}
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
}
} else {
const Index idx = coords[NumDims-1];
if (idx < m_padding[NumDims-1].first || idx >= m_dimensions[NumDims-1] - m_padding[NumDims-1].second) {
return Scalar(0);
}
inputIndex = idx - m_padding[NumDims-1].first;
for (int i = NumDims - 2; i >= 0; --i) {
const Index idx = coords[i];
if (idx < m_padding[i].first || idx >= m_dimensions[i] - m_padding[i].second) {
return Scalar(0);
}
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
}
}
return m_impl.coeff(inputIndex);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
const Index initialIndex = index;
Index inputIndex = 0;
for (int i = NumDims - 1; i > 0; --i) {
const Index first = index;
const Index last = index + packetSize - 1;
const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
const Index lastPaddedRight = m_outputStrides[i+1];
if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones.
const Index idx = index / m_outputStrides[i];
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
else {
// Every other case
return packetWithPossibleZero(initialIndex);
}
}
const Index last = index + packetSize - 1;
const Index first = index;
const Index lastPaddedLeft = m_padding[0].first;
const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
const Index lastPaddedRight = m_outputStrides[1];
if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones.
inputIndex += (index - m_padding[0].first);
return m_impl.template packet<Unaligned>(inputIndex);
}
// Every other case
return packetWithPossibleZero(initialIndex);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
const Index initialIndex = index;
Index inputIndex = 0;
for (int i = 0; i < NumDims - 1; ++i) {
const Index first = index;
const Index last = index + packetSize - 1;
const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1];
const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1];
const Index lastPaddedRight = m_outputStrides[i];
if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones.
const Index idx = index / m_outputStrides[i+1];
inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
index -= idx * m_outputStrides[i+1];
}
else {
// Every other case
return packetWithPossibleZero(initialIndex);
}
}
const Index last = index + packetSize - 1;
const Index first = index;
const Index lastPaddedLeft = m_padding[NumDims-1].first;
const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second);
const Index lastPaddedRight = m_outputStrides[NumDims-1];
if (last < lastPaddedLeft) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= firstPaddedRight && last < lastPaddedRight) {
// all the coefficient are in the padding zone.
return internal::pset1<PacketReturnType>(Scalar(0));
}
else if (first >= lastPaddedLeft && last < firstPaddedRight) {
// all the coefficient are between the 2 padding zones.
inputIndex += (index - m_padding[NumDims-1].first);
return m_impl.template packet<Unaligned>(inputIndex);
}
// Every other case
return packetWithPossibleZero(initialIndex);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
Dimensions m_dimensions;
array<Index, NumDims+1> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
PaddingDimensions m_padding;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_PADDING_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>
//
// 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_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
namespace Eigen {
/** \class TensorPatch
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor patch class.
*
*
*/
namespace internal {
template<typename PatchDim, typename XprType>
struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions + 1;
static const int Layout = XprTraits::Layout;
};
template<typename PatchDim, typename XprType>
struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense>
{
typedef const TensorPatchOp<PatchDim, XprType>& type;
};
template<typename PatchDim, typename XprType>
struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type>
{
typedef TensorPatchOp<PatchDim, XprType> type;
};
} // end namespace internal
template<typename PatchDim, typename XprType>
class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorPatchOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
: m_xpr(expr), m_patch_dims(patch_dims) {}
EIGEN_DEVICE_FUNC
const PatchDim& patch_dims() const { return m_patch_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const PatchDim m_patch_dims;
};
// Eval as rvalue
template<typename PatchDim, typename ArgType, typename Device>
struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
{
typedef TensorPatchOp<PatchDim, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = true,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
// Only column major tensors are supported for now.
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
Index num_patches = 1;
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const PatchDim& patch_dims = op.patch_dims();
for (int i = 0; i < NumDims-1; ++i) {
m_dimensions[i] = patch_dims[i];
num_patches *= (input_dims[i] - patch_dims[i] + 1);
}
m_dimensions[NumDims-1] = num_patches;
m_inputStrides[0] = 1;
m_patchStrides[0] = 1;
for (int i = 1; i < NumDims-1; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1);
}
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
// Find the location of the first element of the patch.
Index patchIndex = index / m_outputStrides[NumDims - 1];
// Find the offset of the element wrt the location of the first element.
Index patchOffset = index - patchIndex * m_outputStrides[NumDims - 1];
Index inputIndex = 0;
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = patchOffset / m_outputStrides[i];
patchOffset -= offsetIdx * m_outputStrides[i];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
inputIndex += (patchIndex + patchOffset);
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
Index indices[2] = {index, index + packetSize - 1};
Index patchIndices[2] = {indices[0] / m_outputStrides[NumDims - 1],
indices[1] / m_outputStrides[NumDims - 1]};
Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[NumDims - 1],
indices[1] - patchIndices[1] * m_outputStrides[NumDims - 1]};
Index inputIndices[2] = {0, 0};
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
patchIndices[1] / m_patchStrides[i]};
patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i],
patchOffsets[1] / m_outputStrides[i]};
patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];
inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
}
inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
EIGEN_ALIGN_DEFAULT CoeffReturnType values[packetSize];
values[0] = m_impl.coeff(inputIndices[0]);
values[packetSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < packetSize-1; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
{
// Location of the first element of the patch.
const Index patchIndex = coords[NumDims - 1];
if (TensorEvaluator<ArgType, Device>::CoordAccess) {
array<Index, NumDims-1> inputCoords;
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i];
inputCoords[i] = coords[i] + patchIdx;
}
inputCoords[0] = (patchIndex + coords[0]);
return m_impl.coeff(inputCoords);
}
else {
Index inputIndex = 0;
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = coords[i];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
inputIndex += (patchIndex + coords[0]);
return m_impl.coeff(inputIndex);
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims-1> m_inputStrides;
array<Index, NumDims-1> m_patchStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H

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unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h Normal file
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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>
//
// 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
namespace Eigen {
/** \class TensorReduction
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reduction class.
*
*/
namespace internal {
template<typename Op, typename Dims, typename XprType>
struct traits<TensorReductionOp<Op, Dims, XprType> >
: traits<XprType>
{
typedef typename traits<XprType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
};
template<typename Op, typename Dims, typename XprType>
struct eval<TensorReductionOp<Op, Dims, XprType>, Eigen::Dense>
{
typedef const TensorReductionOp<Op, Dims, XprType>& type;
};
template<typename Op, typename Dims, typename XprType>
struct nested<TensorReductionOp<Op, Dims, XprType>, 1, typename eval<TensorReductionOp<Op, Dims, XprType> >::type>
{
typedef TensorReductionOp<Op, Dims, XprType> type;
};
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;
};
#ifdef EIGEN_HAS_CONSTEXPR
template <typename ReducedDims, int NumTensorDims>
struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
static const bool value = indices_statically_known_to_increase<ReducedDims>()() &&
index_statically_eq<ReducedDims>()(0, 0) &&
index_statically_eq<ReducedDims>()(array_size<ReducedDims>::value-1, array_size<ReducedDims>::value-1);
};
template <typename ReducedDims, int NumTensorDims>
struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
static const bool value = indices_statically_known_to_increase<ReducedDims>()() &&
index_statically_eq<ReducedDims>()(0, NumTensorDims - array_size<ReducedDims>::value) &&
index_statically_eq<ReducedDims>()(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
};
template <typename ReducedDims, int NumTensorDims>
struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
static const bool value = indices_statically_known_to_increase<ReducedDims>()() &&
index_statically_gt<ReducedDims>()(0, 0);
};
template <typename ReducedDims, int NumTensorDims>
struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
static const bool value = indices_statically_known_to_increase<ReducedDims>()() &&
index_statically_lt<ReducedDims>()(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
};
#endif
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, bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)>
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> {
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 int packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size;
const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize;
typename Self::PacketReturnType p = reducer.template initializePacket<typename Self::PacketReturnType>();
for (typename Self::Index j = 0; j < VectorizedSize; j += packetSize) {
reducer.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &p);
}
typename Self::CoeffReturnType accum = reducer.initialize();
for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; ++j) {
reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
}
return reducer.finalizeBoth(accum, p);
}
};
template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess & Op::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 (int 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& reducer, typename Self::PacketReturnType* accum) {
for (int j = 0; j < self.m_reducedDims[0]; ++j) {
const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
reducer.reducePacket(self.m_impl.template packet<Unaligned>(input), accum);
}
}
};
} // end namespace internal
template <typename Op, typename Dims, typename XprType>
class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>, ReadOnlyAccessors> {
public:
typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorReductionOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
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)
{ }
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; }
const Op& reducer() const { return m_reducer; }
protected:
typename XprType::Nested m_expr;
const Dims m_dims;
const Op m_reducer;
};
// Eval as rvalue
template<typename Op, typename Dims, typename ArgType, typename Device>
struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
{
typedef TensorReductionOp<Op, Dims, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumReducedDims = internal::array_size<Dims>::value;
static const int NumOutputDims = (NumInputDims==NumReducedDims) ? 1 : NumInputDims - NumReducedDims;
typedef DSizes<Index, NumOutputDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device> Self;
static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
enum {
IsAligned = false,
PacketAccess = Self::InputPacketAccess && Op::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
static const bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value;
static const bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_reducer(op.reducer())
{
EIGEN_STATIC_ASSERT(NumInputDims >= NumReducedDims, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
YOU_MADE_A_PROGRAMMING_MISTAKE);
// Bitmap indicating if an input dimension is reduced or not.
array<bool, NumInputDims> reduced;
for (int i = 0; i < NumInputDims; ++i) {
reduced[i] = false;
}
for (int i = 0; i < NumReducedDims; ++i) {
eigen_assert(op.dims()[i] >= 0);
eigen_assert(op.dims()[i] < NumInputDims);
reduced[op.dims()[i]] = true;
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
int outputIndex = 0;
int reduceIndex = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (reduced[i]) {
m_reducedDims[reduceIndex] = input_dims[i];
++reduceIndex;
} else {
m_dimensions[outputIndex] = input_dims[i];
++outputIndex;
}
}
// Precompute output strides.
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];
}
} else {
m_outputStrides[NumOutputDims - 1] = 1;
for (int i = NumOutputDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
}
}
// Precompute input strides.
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[NumInputDims - 1] = 1;
for (int i = NumInputDims - 2; i >= 0; --i) {
input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
}
}
outputIndex = 0;
reduceIndex = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (reduced[i]) {
m_reducedStrides[reduceIndex] = input_strides[i];
++reduceIndex;
} else {
m_preservedStrides[outputIndex] = input_strides[i];
++outputIndex;
}
}
// Special case for full reductions
if (NumInputDims == NumReducedDims) {
m_dimensions[0] = 1;
m_preservedStrides[0] = internal::array_prod(input_dims);
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
Op reducer(m_reducer);
if (ReducingInnerMostDims) {
const Index num_values_to_reduce =
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumOutputDims - 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
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
if (ReducingInnerMostDims) {
const Index num_values_to_reduce =
(static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumOutputDims - 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;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
private:
template <int, typename, typename> friend struct internal::GenericDimReducer;
template <typename, typename, bool> friend struct internal::InnerMostDimReducer;
template <int, typename, typename, bool> friend struct internal::InnerMostDimPreserver;
// 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[NumOutputDims - 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];
}
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];
}
startInput += index * m_preservedStrides[NumOutputDims - 1];
}
return startInput;
}
// Dimensions of the output of the operation.
Dimensions m_dimensions;
// Precomputed strides for the output tensor.
array<Index, NumOutputDims> m_outputStrides;
// Subset of strides of the input tensor for the non-reduced dimensions.
// Indexed by output dimensions.
array<Index, NumOutputDims> m_preservedStrides;
// 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;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_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>
//
// 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_REF_H
#define EIGEN_CXX11_TENSOR_TENSOR_REF_H
namespace Eigen {
namespace internal {
template <typename Dimensions, typename Scalar>
class TensorLazyBaseEvaluator {
public:
TensorLazyBaseEvaluator() : m_refcount(0) { }
virtual ~TensorLazyBaseEvaluator() { }
virtual const Dimensions& dimensions() const = 0;
virtual const Scalar* data() const = 0;
virtual const Scalar coeff(DenseIndex index) const = 0;
virtual Scalar& coeffRef(DenseIndex index) = 0;
void incrRefCount() { ++m_refcount; }
void decrRefCount() { --m_refcount; }
int refCount() const { return m_refcount; }
private:
// No copy, no assigment;
TensorLazyBaseEvaluator(const TensorLazyBaseEvaluator& other);
TensorLazyBaseEvaluator& operator = (const TensorLazyBaseEvaluator& other);
int m_refcount;
};
static char dummy[8];
template <typename Dimensions, typename Expr, typename Device>
class TensorLazyEvaluatorReadOnly : public TensorLazyBaseEvaluator<Dimensions, typename TensorEvaluator<Expr, Device>::Scalar> {
public:
// typedef typename TensorEvaluator<Expr, Device>::Dimensions Dimensions;
typedef typename TensorEvaluator<Expr, Device>::Scalar Scalar;
TensorLazyEvaluatorReadOnly(const Expr& expr, const Device& device) : m_impl(expr, device) {
m_dims = m_impl.dimensions();
m_impl.evalSubExprsIfNeeded(NULL);
}
virtual ~TensorLazyEvaluatorReadOnly() {
m_impl.cleanup();
}
virtual const Dimensions& dimensions() const {
return m_dims;
}
virtual const Scalar* data() const {
return m_impl.data();
}
virtual const Scalar coeff(DenseIndex index) const {
return m_impl.coeff(index);
}
virtual Scalar& coeffRef(DenseIndex /*index*/) {
eigen_assert(false && "can't reference the coefficient of a rvalue");
return *reinterpret_cast<Scalar*>(dummy);
};
protected:
TensorEvaluator<Expr, Device> m_impl;
Dimensions m_dims;
};
template <typename Dimensions, typename Expr, typename Device>
class TensorLazyEvaluatorWritable : public TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> {
public:
typedef TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> Base;
typedef typename Base::Scalar Scalar;
TensorLazyEvaluatorWritable(const Expr& expr, const Device& device) : Base(expr, device) {
}
virtual ~TensorLazyEvaluatorWritable() {
}
virtual Scalar& coeffRef(DenseIndex index) {
return this->m_impl.coeffRef(index);
}
};
template <typename Dimensions, typename Expr, typename Device>
class TensorLazyEvaluator : public internal::conditional<bool(internal::is_lvalue<Expr>::value),
TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type {
public:
typedef typename internal::conditional<bool(internal::is_lvalue<Expr>::value),
TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type Base;
typedef typename Base::Scalar Scalar;
TensorLazyEvaluator(const Expr& expr, const Device& device) : Base(expr, device) {
}
virtual ~TensorLazyEvaluator() {
}
};
} // namespace internal
/** \class TensorRef
* \ingroup CXX11_Tensor_Module
*
* \brief A reference to a tensor expression
* The expression will be evaluated lazily (as much as possible).
*
*/
template<typename PlainObjectType> class TensorRef : public TensorBase<TensorRef<PlainObjectType> >
{
public:
typedef TensorRef<PlainObjectType> Self;
typedef typename PlainObjectType::Base Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
typedef typename internal::traits<PlainObjectType>::Index Index;
typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
typedef Scalar* PointerType;
typedef PointerType PointerArgType;
static const Index NumIndices = PlainObjectType::NumIndices;
typedef typename PlainObjectType::Dimensions Dimensions;
enum {
IsAligned = false,
PacketAccess = false,
Layout = PlainObjectType::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_STRONG_INLINE TensorRef() : m_evaluator(NULL) {
}
template <typename Expression>
EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : m_evaluator(new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice())) {
m_evaluator->incrRefCount();
}
template <typename Expression>
EIGEN_STRONG_INLINE TensorRef& operator = (const Expression& expr) {
unrefEvaluator();
m_evaluator = new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice());
m_evaluator->incrRefCount();
return *this;
}
~TensorRef() {
unrefEvaluator();
}
TensorRef(const TensorRef& other) : m_evaluator(other.m_evaluator) {
eigen_assert(m_evaluator->refCount() > 0);
m_evaluator->incrRefCount();
}
TensorRef& operator = (const TensorRef& other) {
if (this != &other) {
unrefEvaluator();
m_evaluator = other.m_evaluator;
eigen_assert(m_evaluator->refCount() > 0);
m_evaluator->incrRefCount();
}
return *this;
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index rank() const { return m_evaluator->dimensions().size(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_evaluator->dimensions()[n]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_evaluator->dimensions(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index size() const { return m_evaluator->dimensions().TotalSize(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar* data() const { return m_evaluator->data(); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index index) const
{
return m_evaluator->coeff(index);
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const
{
const std::size_t NumIndices = (sizeof...(otherIndices) + 1);
const array<Index, NumIndices> indices{{firstIndex, otherIndices...}};
return coeff(indices);
}
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
{
const std::size_t NumIndices = (sizeof...(otherIndices) + 1);
const array<Index, NumIndices> indices{{firstIndex, otherIndices...}};
return coeffRef(indices);
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1) const
{
array<Index, 2> indices;
indices[0] = i0;
indices[1] = i1;
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2) const
{
array<Index, 3> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3) const
{
array<Index, 4> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
indices[3] = i3;
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
array<Index, 5> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
indices[3] = i3;
indices[4] = i4;
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1)
{
array<Index, 2> indices;
indices[0] = i0;
indices[1] = i1;
return coeffRef(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2)
{
array<Index, 3> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
return coeffRef(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
array<Index, 4> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
indices[3] = i3;
return coeffRef(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2, Index i3, Index i4)
{
array<Index, 5> indices;
indices[0] = i0;
indices[1] = i1;
indices[2] = i2;
indices[3] = i3;
indices[4] = i4;
return coeffRef(indices);
}
#endif
template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(const array<Index, NumIndices>& indices) const
{
const Dimensions& dims = this->dimensions();
Index index = 0;
if (PlainObjectType::Options & RowMajor) {
index += indices[0];
for (int i = 1; i < NumIndices; ++i) {
index = index * dims[i] + indices[i];
}
} else {
index += indices[NumIndices-1];
for (int i = NumIndices-2; i >= 0; --i) {
index = index * dims[i] + indices[i];
}
}
return m_evaluator->coeff(index);
}
template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
{
const Dimensions& dims = this->dimensions();
Index index = 0;
if (PlainObjectType::Options & RowMajor) {
index += indices[0];
for (int i = 1; i < NumIndices; ++i) {
index = index * dims[i] + indices[i];
}
} else {
index += indices[NumIndices-1];
for (int i = NumIndices-2; i >= 0; --i) {
index = index * dims[i] + indices[i];
}
}
return m_evaluator->coeffRef(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
{
return m_evaluator->coeff(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return m_evaluator->coeffRef(index);
}
private:
EIGEN_STRONG_INLINE void unrefEvaluator() {
if (m_evaluator) {
m_evaluator->decrRefCount();
if (m_evaluator->refCount() == 0) {
delete m_evaluator;
}
}
}
internal::TensorLazyBaseEvaluator<Dimensions, Scalar>* m_evaluator;
};
// evaluator for rvalues
template<typename Derived, typename Device>
struct TensorEvaluator<const TensorRef<Derived>, Device>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Packet Packet;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename Derived::Packet PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
enum {
IsAligned = false,
PacketAccess = false,
Layout = TensorRef<Derived>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const TensorRef<Derived>& m, const Device&)
: m_ref(m)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_ref.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
return m_ref.coeff(index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
return m_ref.coeffRef(index);
}
Scalar* data() const { return m_ref.data(); }
protected:
TensorRef<Derived> m_ref;
};
// evaluator for lvalues
template<typename Derived, typename Device>
struct TensorEvaluator<TensorRef<Derived>, Device> : public TensorEvaluator<const TensorRef<Derived>, Device>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Packet Packet;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename Derived::Packet PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
typedef TensorEvaluator<const TensorRef<Derived>, Device> Base;
enum {
IsAligned = false,
PacketAccess = false,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(TensorRef<Derived>& m, const Device& d) : Base(m, d)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
return this->m_ref.coeffRef(index);
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_REF_H

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
// 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_REVERSE_H
#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
namespace Eigen {
/** \class TensorReverse
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor reverse elements class.
*
*/
namespace internal {
template<typename ReverseDimensions, typename XprType>
struct traits<TensorReverseOp<ReverseDimensions,
XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename ReverseDimensions, typename XprType>
struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
{
typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
};
template<typename ReverseDimensions, typename XprType>
struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
{
typedef TensorReverseOp<ReverseDimensions, XprType> type;
};
} // end namespace internal
template<typename ReverseDimensions, typename XprType>
class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorReverseOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
StorageKind;
typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(const XprType& expr,
const ReverseDimensions& reverse_dims)
: m_xpr(expr), m_reverse_dims(reverse_dims) {}
EIGEN_DEVICE_FUNC
const ReverseDimensions& reverse() const { return m_reverse_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const ReverseDimensions m_reverse_dims;
};
// Eval as rvalue
template<typename ReverseDimensions, typename ArgType, typename Device>
struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
{
typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<ReverseDimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
const Device& device)
: m_impl(op.expression(), device), m_reverse(op.reverse())
{
// Compute strides
m_dimensions = m_impl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_strides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
}
} else {
m_strides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
}
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
eigen_assert(index < dimensions().TotalSize());
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
Index idx = index / m_strides[i];
index -= idx * m_strides[i];
if (m_reverse[i]) {
idx = m_dimensions[i] - idx - 1;
}
inputIndex += idx * m_strides[i] ;
}
if (m_reverse[0]) {
inputIndex += (m_dimensions[0] - index - 1);
} else {
inputIndex += index;
}
return m_impl.coeff(inputIndex);
} else {
for (int i = 0; i < NumDims - 1; ++i) {
Index idx = index / m_strides[i];
index -= idx * m_strides[i];
if (m_reverse[i]) {
idx = m_dimensions[i] - idx - 1;
}
inputIndex += idx * m_strides[i] ;
}
if (m_reverse[NumDims-1]) {
inputIndex += (m_dimensions[NumDims-1] - index - 1);
} else {
inputIndex += index;
}
return m_impl.coeff(inputIndex);
}
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
// TODO(ndjaitly): write a better packing routine that uses
// local structure.
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type
values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_strides;
TensorEvaluator<ArgType, Device> m_impl;
ReverseDimensions m_reverse;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_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>
//
// 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_SHUFFLING_H
#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
namespace Eigen {
/** \class TensorShuffling
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor shuffling class.
*
*
*/
namespace internal {
template<typename Shuffle, typename XprType>
struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename Shuffle, typename XprType>
struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
{
typedef const TensorShufflingOp<Shuffle, XprType>& type;
};
template<typename Shuffle, typename XprType>
struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
{
typedef TensorShufflingOp<Shuffle, XprType> type;
};
} // end namespace internal
template<typename Shuffle, typename XprType>
class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorShufflingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shuffle)
: m_xpr(expr), m_shuffle(shuffle) {}
EIGEN_DEVICE_FUNC
const Shuffle& shuffle() const { return m_shuffle; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other)
{
typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const Shuffle m_shuffle;
};
// Eval as rvalue
template<typename Shuffle, typename ArgType, typename Device>
struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
{
typedef TensorShufflingOp<Shuffle, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const Shuffle& shuffle = op.shuffle();
for (int i = 0; i < NumDims; ++i) {
m_dimensions[i] = input_dims[shuffle[i]];
}
array<Index, NumDims> inputStrides;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
inputStrides[0] = 1;
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1];
m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
}
} else {
inputStrides[NumDims - 1] = 1;
m_outputStrides[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
}
}
for (int i = 0; i < NumDims; ++i) {
m_inputStrides[i] = inputStrides[shuffle[i]];
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(srcCoeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
return inputIndex + index * m_inputStrides[0];
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
return inputIndex + index * m_inputStrides[NumDims - 1];
}
}
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
// Eval as lvalue
template<typename Shuffle, typename ArgType, typename Device>
struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
: public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
{
typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
typedef TensorShufflingOp<Shuffle, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
enum {
IsAligned = false,
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(this->srcCoeff(index));
}
template <int StoreMode> EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
for (int i = 0; i < packetSize; ++i) {
this->coeffRef(index+i) = values[i];
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H

103
unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
View File

@@ -30,39 +30,70 @@ namespace Eigen {
*
* \sa Tensor
*/
template<typename T, std::size_t NumIndices_, DenseIndex Size, int Options_, typename Dimensions = void> class TensorStorage;
template<typename T, DenseIndex NumIndices_, DenseIndex Size, int Options_, typename Dimensions = void> class TensorStorage;
// Pure fixed-size storage
template<typename T, DenseIndex NumIndices_, DenseIndex Size, int Options_, typename FixedDimensions>
class TensorStorage
{
private:
EIGEN_ALIGN_DEFAULT T m_data[Size];
FixedDimensions m_dimensions;
public:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorStorage() {
EIGEN_STATIC_ASSERT(Size == FixedDimensions::total_size, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE T *data() { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const FixedDimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE DenseIndex size() const { return m_dimensions.TotalSize(); }
};
// pure-dynamic, but without specification of all dimensions explicitly
template<typename T, std::size_t NumIndices_, int Options_>
template<typename T, DenseIndex NumIndices_, int Options_>
class TensorStorage<T, NumIndices_, Dynamic, Options_, void>
: public TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_numeric_list_repeated<DenseIndex, NumIndices_, Dynamic>::type>
{
typedef TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_numeric_list_repeated<DenseIndex, NumIndices_, Dynamic>::type> Base_;
typedef TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_numeric_list_repeated<DenseIndex, NumIndices_, Dynamic>::type> Base_;
public:
TensorStorage() = default;
TensorStorage(const TensorStorage<T, NumIndices_, Dynamic, Options_, void>&) = default;
TensorStorage(TensorStorage<T, NumIndices_, Dynamic, Options_, void>&&) = default;
TensorStorage() { }
TensorStorage(const TensorStorage<T, NumIndices_, Dynamic, Options_, void>& other) : Base_(other) { }
TensorStorage(internal::constructor_without_unaligned_array_assert) : Base_(internal::constructor_without_unaligned_array_assert()) {}
TensorStorage(DenseIndex size, const std::array<DenseIndex, NumIndices_>& dimensions) : Base_(size, dimensions) {}
TensorStorage<T, NumIndices_, Dynamic, Options_, void>& operator=(const TensorStorage<T, NumIndices_, Dynamic, Options_, void>&) = default;
TensorStorage(DenseIndex size, const array<DenseIndex, NumIndices_>& dimensions) : Base_(size, dimensions) {}
// TensorStorage<T, NumIndices_, Dynamic, Options_, void>& operator=(const TensorStorage<T, NumIndices_, Dynamic, Options_, void>&) = default;
};
// pure dynamic
template<typename T, std::size_t NumIndices_, int Options_>
template<typename T, DenseIndex NumIndices_, int Options_>
class TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_numeric_list_repeated<DenseIndex, NumIndices_, Dynamic>::type>
{
T *m_data;
std::array<DenseIndex, NumIndices_> m_dimensions;
DSizes<DenseIndex, NumIndices_> m_dimensions;
typedef TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_numeric_list_repeated<DenseIndex, NumIndices_, Dynamic>::type> Self_;
public:
TensorStorage() : m_data(0), m_dimensions(internal::template repeat<NumIndices_, DenseIndex>(0)) {}
TensorStorage() : m_data(0), m_dimensions() {}
TensorStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_dimensions(internal::template repeat<NumIndices_, DenseIndex>(0)) {}
TensorStorage(DenseIndex size, const std::array<DenseIndex, NumIndices_>& dimensions)
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size)), m_dimensions(dimensions)
{ EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN }
TensorStorage(const Self_& other)
TensorStorage(DenseIndex size, const array<DenseIndex, NumIndices_>& dimensions)
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size)), m_dimensions(dimensions)
{ EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN }
TensorStorage(const Self_& other)
: m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(other.m_dimensions)))
, m_dimensions(other.m_dimensions)
{
@@ -76,32 +107,19 @@ class TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_nu
}
return *this;
}
TensorStorage(Self_&& other)
: m_data(std::move(other.m_data)), m_dimensions(std::move(other.m_dimensions))
{
other.m_data = nullptr;
}
Self_& operator=(Self_&& other)
{
using std::swap;
swap(m_data, other.m_data);
swap(m_dimensions, other.m_dimensions);
return *this;
}
~TensorStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, internal::array_prod(m_dimensions)); }
void swap(Self_& other)
{ std::swap(m_data,other.m_data); std::swap(m_dimensions,other.m_dimensions); }
std::array<DenseIndex, NumIndices_> dimensions(void) const {return m_dimensions;}
void conservativeResize(DenseIndex size, const std::array<DenseIndex, NumIndices_>& nbDimensions)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<DenseIndex, NumIndices_>& dimensions() const {return m_dimensions;}
EIGEN_DEVICE_FUNC void resize(DenseIndex size, const array<DenseIndex, NumIndices_>& nbDimensions)
{
m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, internal::array_prod(m_dimensions));
m_dimensions = nbDimensions;
}
void resize(DenseIndex size, const std::array<DenseIndex, NumIndices_>& nbDimensions)
{
if(size != internal::array_prod(m_dimensions))
const DenseIndex currentSz = internal::array_prod(m_dimensions);
if(size != currentSz)
{
internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, internal::array_prod(m_dimensions));
internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, currentSz);
if (size)
m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
else
@@ -110,16 +128,13 @@ class TensorStorage<T, NumIndices_, Dynamic, Options_, typename internal::gen_nu
}
m_dimensions = nbDimensions;
}
const T *data() const { return m_data; }
T *data() { return m_data; }
};
// TODO: implement fixed-size stuff
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T *data() { return m_data; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T *data() const { return m_data; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex size() const { return m_dimensions.TotalSize(); }
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
/*
* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
*/

325
unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h Normal file
View File

@@ -0,0 +1,325 @@
// 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/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
namespace Eigen {
/** \class TensorStriding
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor striding class.
*
*
*/
namespace internal {
template<typename Strides, typename XprType>
struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename XprTraits::StorageKind StorageKind;
typedef typename XprTraits::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = XprTraits::NumDimensions;
static const int Layout = XprTraits::Layout;
};
template<typename Strides, typename XprType>
struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense>
{
typedef const TensorStridingOp<Strides, XprType>& type;
};
template<typename Strides, typename XprType>
struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
{
typedef TensorStridingOp<Strides, XprType> type;
};
} // end namespace internal
template<typename Strides, typename XprType>
class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorStridingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
: m_xpr(expr), m_dims(dims) {}
EIGEN_DEVICE_FUNC
const Strides& strides() const { return m_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorStridingOp& operator = (const TensorStridingOp& other)
{
typedef TensorAssignOp<TensorStridingOp, const TensorStridingOp> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorStridingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorStridingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const Strides m_dims;
};
// Eval as rvalue
template<typename Strides, typename ArgType, typename Device>
struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
{
typedef TensorStridingOp<Strides, ArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
m_dimensions = m_impl.dimensions();
for (int i = 0; i < NumDims; ++i) {
m_dimensions[i] = ceilf(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
}
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_outputStrides[0] = 1;
m_inputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_inputStrides[i-1] *= op.strides()[i-1];
}
m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
} else { // RowMajor
m_outputStrides[NumDims-1] = 1;
m_inputStrides[NumDims-1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
m_inputStrides[i+1] *= op.strides()[i+1];
}
m_inputStrides[0] *= op.strides()[0];
}
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(srcCoeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < dimensions().TotalSize());
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / m_outputStrides[i];
const Index idx1 = indices[1] / m_outputStrides[i];
inputIndices[0] += idx0 * m_inputStrides[i];
inputIndices[1] += idx1 * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += indices[0] * m_inputStrides[0];
inputIndices[1] += indices[1] * m_inputStrides[0];
} else { // RowMajor
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx0 = indices[0] / m_outputStrides[i];
const Index idx1 = indices[1] / m_outputStrides[i];
inputIndices[0] += idx0 * m_inputStrides[i];
inputIndices[1] += idx1 * m_inputStrides[i];
indices[0] -= idx0 * m_outputStrides[i];
indices[1] -= idx1 * m_outputStrides[i];
}
inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
}
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize];
values[0] = m_impl.coeff(inputIndices[0]);
values[packetSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < packetSize-1; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
{
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = index / m_outputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
inputIndex += index * m_inputStrides[0];
} else { // RowMajor
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / m_outputStrides[i];
inputIndex += idx * m_inputStrides[i];
index -= idx * m_outputStrides[i];
}
inputIndex += index * m_inputStrides[NumDims-1];
}
return inputIndex;
}
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_inputStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
// Eval as lvalue
template<typename Strides, typename ArgType, typename Device>
struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
: public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
{
typedef TensorStridingOp<Strides, ArgType> XprType;
typedef TensorEvaluator<const XprType, Device> Base;
// typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
// typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device) { }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
return this->m_impl.coeffRef(this->srcCoeff(index));
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+packetSize-1 < this->dimensions().TotalSize());
Index inputIndices[] = {0, 0};
Index indices[] = {index, index + packetSize - 1};
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx0 = indices[0] / this->m_outputStrides[i];
const Index idx1 = indices[1] / this->m_outputStrides[i];
inputIndices[0] += idx0 * this->m_inputStrides[i];
inputIndices[1] += idx1 * this->m_inputStrides[i];
indices[0] -= idx0 * this->m_outputStrides[i];
indices[1] -= idx1 * this->m_outputStrides[i];
}
inputIndices[0] += indices[0] * this->m_inputStrides[0];
inputIndices[1] += indices[1] * this->m_inputStrides[0];
} else { // RowMajor
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx0 = indices[0] / this->m_outputStrides[i];
const Index idx1 = indices[1] / this->m_outputStrides[i];
inputIndices[0] += idx0 * this->m_inputStrides[i];
inputIndices[1] += idx1 * this->m_inputStrides[i];
indices[0] -= idx0 * this->m_outputStrides[i];
indices[1] -= idx1 * this->m_outputStrides[i];
}
inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
}
if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
}
else {
EIGEN_ALIGN_DEFAULT Scalar values[packetSize];
internal::pstore<Scalar, PacketReturnType>(values, x);
this->m_impl.coeffRef(inputIndices[0]) = values[0];
this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1];
for (int i = 1; i < packetSize-1; ++i) {
this->coeffRef(index+i) = values[i];
}
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_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>
//
// 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_TRAITS_H
#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
namespace Eigen {
namespace internal {
template<typename Scalar, int Options>
class compute_tensor_flags
{
enum {
is_dynamic_size_storage = 1,
aligned_bit =
(
((Options&DontAlign)==0) && (
#if EIGEN_ALIGN_STATICALLY
(!is_dynamic_size_storage)
#else
0
#endif
||
#if EIGEN_ALIGN
is_dynamic_size_storage
#else
0
#endif
)
) ? AlignedBit : 0,
packet_access_bit = packet_traits<Scalar>::Vectorizable && aligned_bit ? PacketAccessBit : 0
};
public:
enum { ret = packet_access_bit | aligned_bit};
};
template<typename Scalar_, std::size_t NumIndices_, int Options_>
struct traits<Tensor<Scalar_, NumIndices_, Options_> >
{
typedef Scalar_ Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
static const int NumDimensions = NumIndices_;
static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
enum {
Options = Options_,
Flags = compute_tensor_flags<Scalar_, Options_>::ret | LvalueBit,
};
};
template<typename Scalar_, typename Dimensions, int Options_>
struct traits<TensorFixedSize<Scalar_, Dimensions, Options_> >
{
typedef Scalar_ Scalar;
typedef Dense StorageKind;
typedef DenseIndex Index;
static const int NumDimensions = array_size<Dimensions>::value;
static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
enum {
Options = Options_,
Flags = compute_tensor_flags<Scalar_, Options_>::ret | LvalueBit,
};
};
template<typename PlainObjectType, int Options_>
struct traits<TensorMap<PlainObjectType, Options_> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> BaseTraits;
typedef typename BaseTraits::Scalar Scalar;
typedef typename BaseTraits::StorageKind StorageKind;
typedef typename BaseTraits::Index Index;
static const int NumDimensions = BaseTraits::NumDimensions;
static const int Layout = BaseTraits::Layout;
enum {
Options = Options_,
Flags = ((BaseTraits::Flags | LvalueBit) & ~AlignedBit) | (Options&Aligned ? AlignedBit : 0),
};
};
template<typename PlainObjectType>
struct traits<TensorRef<PlainObjectType> >
: public traits<PlainObjectType>
{
typedef traits<PlainObjectType> BaseTraits;
typedef typename BaseTraits::Scalar Scalar;
typedef typename BaseTraits::StorageKind StorageKind;
typedef typename BaseTraits::Index Index;
static const int NumDimensions = BaseTraits::NumDimensions;
static const int Layout = BaseTraits::Layout;
enum {
Options = BaseTraits::Options,
Flags = ((BaseTraits::Flags | LvalueBit) & ~AlignedBit) | (Options&Aligned ? AlignedBit : 0),
};
};
template<typename _Scalar, std::size_t NumIndices_, int Options>
struct eval<Tensor<_Scalar, NumIndices_, Options>, Eigen::Dense>
{
typedef const Tensor<_Scalar, NumIndices_, Options>& type;
};
template<typename _Scalar, std::size_t NumIndices_, int Options>
struct eval<const Tensor<_Scalar, NumIndices_, Options>, Eigen::Dense>
{
typedef const Tensor<_Scalar, NumIndices_, Options>& type;
};
template<typename Scalar_, typename Dimensions, int Options>
struct eval<TensorFixedSize<Scalar_, Dimensions, Options>, Eigen::Dense>
{
typedef const TensorFixedSize<Scalar_, Dimensions, Options>& type;
};
template<typename Scalar_, typename Dimensions, int Options>
struct eval<const TensorFixedSize<Scalar_, Dimensions, Options>, Eigen::Dense>
{
typedef const TensorFixedSize<Scalar_, Dimensions, Options>& type;
};
template<typename PlainObjectType, int Options>
struct eval<TensorMap<PlainObjectType, Options>, Eigen::Dense>
{
typedef const TensorMap<PlainObjectType, Options>& type;
};
template<typename PlainObjectType, int Options>
struct eval<const TensorMap<PlainObjectType, Options>, Eigen::Dense>
{
typedef const TensorMap<PlainObjectType, Options>& type;
};
template<typename PlainObjectType>
struct eval<TensorRef<PlainObjectType>, Eigen::Dense>
{
typedef const TensorRef<PlainObjectType>& type;
};
template<typename PlainObjectType>
struct eval<const TensorRef<PlainObjectType>, Eigen::Dense>
{
typedef const TensorRef<PlainObjectType>& type;
};
template <typename Scalar_, std::size_t NumIndices_, int Options_>
struct nested<Tensor<Scalar_, NumIndices_, Options_> >
{
typedef const Tensor<Scalar_, NumIndices_, Options_>& type;
};
template <typename Scalar_, std::size_t NumIndices_, int Options_>
struct nested<const Tensor<Scalar_, NumIndices_, Options_> >
{
typedef const Tensor<Scalar_, NumIndices_, Options_>& type;
};
template <typename Scalar_, typename Dimensions, int Options>
struct nested<TensorFixedSize<Scalar_, Dimensions, Options> >
{
typedef const TensorFixedSize<Scalar_, Dimensions, Options>& type;
};
template <typename Scalar_, typename Dimensions, int Options>
struct nested<const TensorFixedSize<Scalar_, Dimensions, Options> >
{
typedef const TensorFixedSize<Scalar_, Dimensions, Options>& type;
};
template <typename PlainObjectType, int Options>
struct nested<TensorMap<PlainObjectType, Options> >
{
typedef const TensorMap<PlainObjectType, Options>& type;
};
template <typename PlainObjectType, int Options>
struct nested<const TensorMap<PlainObjectType, Options> >
{
typedef const TensorMap<PlainObjectType, Options>& type;
};
template <typename PlainObjectType>
struct nested<TensorRef<PlainObjectType> >
{
typedef const TensorRef<PlainObjectType>& type;
};
template <typename PlainObjectType>
struct nested<const TensorRef<PlainObjectType> >
{
typedef const TensorRef<PlainObjectType>& type;
};
} // end namespace internal
// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C,
// R, B), and convolve it with a set of filters, which can also be presented as
// a tensor (D, K, K, M), where M is the number of filters, K is the filter
// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For
// simplicity we assume that we always use square filters (which is usually the
// case in images), hence the two Ks in the tensor dimension. It also takes in
// a few additional parameters:
// Stride (S): The convolution stride is the offset between locations where we
// apply the filters. A larger stride means that the output will be
// spatially smaller.
// Padding (P): The padding we apply to the input tensor along the R and C
// dimensions. This is usually used to make sure that the spatial
// dimensions of the output matches our intention.
//
// Two types of padding are often used:
// SAME: The pad value is computed so that the output will have size
// R/S and C/S.
// VALID: no padding is carried out.
// When we do padding, the padded values at the padded locations are usually
// zero.
//
// The output dimensions for convolution, when given all the parameters above,
// are as follows:
// When Padding = SAME: the output size is (B, R', C', M), where
// R' = ceil(float(R) / float(S))
// C' = ceil(float(C) / float(S))
// where ceil is the ceiling function. The input tensor is padded with 0 as
// needed. The number of padded rows and columns are computed as:
// Pr = ((R' - 1) * S + K - R) / 2
// Pc = ((C' - 1) * S + K - C) / 2
// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2.
// This is where SAME comes from - the output has the same size as the input has.
// When Padding = VALID: the output size is computed as
// R' = ceil(float(R - K + 1) / float(S))
// C' = ceil(float(C - K + 1) / float(S))
// and the number of padded rows and columns are computed in the same way as in
// the SAME case.
// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0,
// Pc=0.
typedef enum {
PADDING_VALID = 1,
PADDING_SAME = 2,
} PaddingType;
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H