xgboost::linalg Namespace Reference

Namespaces
	detail

Classes
class	Tensor
	A tensor storage. To use it for other functionality like slicing one needs to obtain a view first. This way we can use it on both host and device. More...
class	TensorView
	A tensor view with static type and dimension. It implements indexing and slicing. More...

Typedefs
template<typename T >
using	VectorView = TensorView< T, 1 >
	A view over a vector, specialization of Tensor. More...
template<typename T >
using	MatrixView = TensorView< T, 2 >
	A view over a matrix, specialization of Tensor. More...

Functions
constexpr detail::AllTag	All ()
	Specify all elements in the axis for slicing. More...
template<typename I >
constexpr detail::RangeTag< I >	Range (I beg, I end)
	Specify a range of elements in the axis for slicing. More...
template<typename Container , typename I , int32_t D, std::enable_if_t<!common::detail::IsSpan< Container >::value > * = nullptr>
auto	MakeTensorView (Container &data, I const (&shape)[D], int32_t device)
	Constructor for automatic type deduction. More...
template<typename T , typename I , int32_t D>
LINALG_HD auto	MakeTensorView (common::Span< T > data, I const (&shape)[D], int32_t device)
template<size_t D>
LINALG_HD auto	UnravelIndex (size_t idx, common::Span< size_t const, D > shape)
	Turns linear index into multi-dimension index. Similar to numpy unravel. More...
template<typename T >
auto	MakeVec (T *ptr, size_t s, int32_t device=-1)
	Create a vector view from contigious memory. More...
template<typename T >
auto	MakeVec (HostDeviceVector< T > *data)
template<typename T >
auto	MakeVec (HostDeviceVector< T > const *data)
template<typename T , int32_t D>
Json	ArrayInterface (TensorView< T const, D > const &t)
	Array Interface defined by numpy. More...
template<typename T , int32_t D>
Json	ArrayInterface (TensorView< T, D > const &t)
	Same as const version, but returns non-readonly data pointer. More...
template<typename T , int32_t D>
auto	ArrayInterfaceStr (TensorView< T const, D > const &t)
	Return string representation of array interface. More...
template<typename T , int32_t D>
auto	ArrayInterfaceStr (TensorView< T, D > const &t)
template<typename T , int32_t D>
void	Stack (Tensor< T, D > *l, Tensor< T, D > const &r)
template<typename T , int32_t D, typename Fn >
void	ElementWiseTransformHost (linalg::TensorView< T, D > t, int32_t n_threads, Fn &&fn)
template<typename T , int32_t D, typename Fn >
void	ElementWiseKernelHost (linalg::TensorView< T, D > t, int32_t n_threads, Fn &&fn)
template<typename T , int32_t D, typename Fn >
void	ElementWiseKernelDevice (linalg::TensorView< T, D > t, Fn &&fn, void *s=nullptr)
template<typename T , int32_t D, typename Fn >
void	ElementWiseTransformDevice (linalg::TensorView< T, D > t, Fn &&fn, void *s=nullptr)
template<typename T , int32_t D, typename Fn >
void	ElementWiseKernel (GenericParameter const *ctx, linalg::TensorView< T, D > t, Fn &&fn)

Typedef Documentation

MatrixView

template<typename T >

using xgboost::linalg::MatrixView = typedef TensorView<T, 2>

A view over a matrix, specialization of Tensor.

Template Parameters

T	data type of matrix

VectorView

template<typename T >

using xgboost::linalg::VectorView = typedef TensorView<T, 1>

A view over a vector, specialization of Tensor.

Template Parameters

T	data type of vector

Function Documentation

All()

constexpr detail::AllTag xgboost::linalg::All ( )

constexpr

Specify all elements in the axis for slicing.

ArrayInterface() [1/2]

template<typename T , int32_t D>

Json xgboost::linalg::ArrayInterface

(

TensorView< T const, D > const &

t

)

Array Interface defined by numpy.

stream is optionally included when data is on CUDA device.

ArrayInterface() [2/2]

template<typename T , int32_t D>

Json xgboost::linalg::ArrayInterface

(

TensorView< T, D > const &

t

)

Same as const version, but returns non-readonly data pointer.

ArrayInterfaceStr() [1/2]

template<typename T , int32_t D>

auto xgboost::linalg::ArrayInterfaceStr

(

TensorView< T const, D > const &

t

)

Return string representation of array interface.

ArrayInterfaceStr() [2/2]

template<typename T , int32_t D>

auto xgboost::linalg::ArrayInterfaceStr

(

TensorView< T, D > const &

t

)

ElementWiseKernel()

template<typename T , int32_t D, typename Fn >

void xgboost::linalg::ElementWiseKernel	(	GenericParameter const *	ctx,
		linalg::TensorView< T, D >	t,
		Fn &&	fn
	)

ElementWiseKernelDevice()

template<typename T , int32_t D, typename Fn >

void xgboost::linalg::ElementWiseKernelDevice	(	linalg::TensorView< T, D >	t,
		Fn &&	fn,
		void *	s = nullptr
	)

ElementWiseKernelHost()

template<typename T , int32_t D, typename Fn >

void xgboost::linalg::ElementWiseKernelHost	(	linalg::TensorView< T, D >	t,
		int32_t	n_threads,
		Fn &&	fn
	)

ElementWiseTransformDevice()

template<typename T , int32_t D, typename Fn >

void xgboost::linalg::ElementWiseTransformDevice	(	linalg::TensorView< T, D >	t,
		Fn &&	fn,
		void *	s = nullptr
	)

ElementWiseTransformHost()

template<typename T , int32_t D, typename Fn >

void xgboost::linalg::ElementWiseTransformHost	(	linalg::TensorView< T, D >	t,
		int32_t	n_threads,
		Fn &&	fn
	)

MakeTensorView() [1/2]

template<typename T , typename I , int32_t D>

LINALG_HD auto xgboost::linalg::MakeTensorView	(	common::Span< T >	data,
		I const (&)	shape[D],
		int32_t	device
	)

MakeTensorView() [2/2]

template<typename Container , typename I , int32_t D, std::enable_if_t<!common::detail::IsSpan< Container >::value > * = nullptr>

auto xgboost::linalg::MakeTensorView	(	Container &	data,
		I const (&)	shape[D],
		int32_t	device
	)

Constructor for automatic type deduction.

MakeVec() [1/3]

template<typename T >

auto xgboost::linalg::MakeVec

(

HostDeviceVector< T > *

data

)

MakeVec() [2/3]

template<typename T >

auto xgboost::linalg::MakeVec

(

HostDeviceVector< T > const *

data

)

MakeVec() [3/3]

template<typename T >

auto xgboost::linalg::MakeVec	(	T *	ptr,
		size_t	s,
		int32_t	device = -1
	)

Create a vector view from contigious memory.

Parameters

ptr	Pointer to the contigious memory.
s	Size of the vector.
device	(optional) Device ordinal, default to be host.

Range()

template<typename I >

constexpr detail::RangeTag<I> xgboost::linalg::Range ( I  beg, I  end  )

constexpr

Specify a range of elements in the axis for slicing.

Stack()

template<typename T , int32_t D>

void xgboost::linalg::Stack	(	Tensor< T, D > *	l,
		Tensor< T, D > const &	r
	)

UnravelIndex()

template<size_t D>

LINALG_HD auto xgboost::linalg::UnravelIndex	(	size_t	idx,
		common::Span< size_t const, D >	shape
	)

Turns linear index into multi-dimension index. Similar to numpy unravel.