bc Namespace Reference

The Evaluator determines if an expression needs to be greedily optimized. More...

Namespaces
	algorithms
	allocators
	blas
	caffe
	detail
	filesystem
	io
	nn
	oper
	random
	streams
	tensors
	traits
	utility

Classes
struct	device_tag
struct	Dim
struct	Dim< 0 >
struct	host_tag
struct	is_system_tag
struct	Shape
struct	Shape< 0 >
struct	Shape< 1 >
struct	Strided_Vector_Shape
struct	string
	Inherits from std::string. More...
class	system_tag_base
struct	system_tag_type

Typedefs
template<class ValueType >
using	Basic_Allocator = allocators::Allocator< ValueType, host_tag >
template<class ValueType >
using	Cuda_Allocator = allocators::Allocator< ValueType, device_tag >
template<class ValueType >
using	Cuda_Managed = allocators::Device_Managed< ValueType >
using	default_system_tag_t = host_tag
using	size_t = int
template<int dim, class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	Tensor = bc::tensors::Tensor_Base< bc::tensors::exprs::Array< bc::Shape< dim >, ValueType, Allocator > >
template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	Scalar = Tensor< 0, ValueType, Allocator >
template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	Vector = Tensor< 1, ValueType, Allocator >
template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	Matrix = Tensor< 2, ValueType, Allocator >
template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	Cube = Tensor< 3, ValueType, Allocator >
template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>
using	VecList = bc::tensors::Tensor_Base< bc::tensors::exprs::Vector< ValueType, Allocator > >

Functions
template<class RM_UNUSED_FUNCTION_WARNING = void>
void	set_print_stream (std::ostream *ostream)
template<class RM_UNUSED_FUNCTION_WARNING = void>
std::ostream *	get_print_stream ()
template<class RM_UNUSED_FUNCTION_WARNING = void>
void	set_error_stream (std::ostream *ostream)
template<class RM_UNUSED_FUNCTION_WARNING = void>
std::ostream *	get_error_stream ()
template<class... Ts>
void	print (const Ts &... args)
template<class... Ts>
void	printerr (const Ts &... args)
template<class str_type >
void	bc_assert (bool condition, str_type msg, const char *file, const char *function, int line)
void	BC_cuda_assert (cudaError_t code, const char *file, const char *function, int line)
void	BC_cuda_assert (cublasStatus_t code, const char *file, const char *function, int line)
template<class T >
const char *	bc_get_classname_of (const T &arg)
template<class Stream , class Indexes , class Image , class ImageOut >
void	max_pooling_forward (Stream stream, Image image, ImageOut out, Indexes mask, Dim< 2 > krnl_shape, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides={-1,-1})
template<class Stream , class Indexes , class Image , class ImageOut >
void	max_pooling_backward (Stream stream, Image image, ImageOut delta, Indexes mask, Dim< 2 > krnl_shape, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides=Dim< 2 >().fill(-1))
template<class Stream , class ColumnImage , class Image >
void	im2col (Stream stream, ColumnImage col_image, Image image, bc::Dim< 3 > krnl_shape, bc::Dim< 2 > padding=bc::Dim< 2 >().fill(0), bc::Dim< 2 > strides=bc::Dim< 2 >().fill(1), bc::Dim< 2 > dilation=bc::Dim< 2 >().fill(1), int numb_spatial_axis=2)
template<class Stream , class ColumnImage , class Image >
void	col2im (Stream stream, ColumnImage col_image, Image image, bc::Dim< 3 > krnl_shape, bc::Dim< 2 > padding=bc::Dim< 2 >(), bc::Dim< 2 > strides=bc::Dim< 2 >().fill(1), bc::Dim< 2 > dilation=bc::Dim< 2 >().fill(1))
template<class... Integers>
BCINLINE auto	dim (const Integers &... ints)
template<class... Integers, typename = std::enable_if_t< traits::sequence_of_v<size_t, Integers...>>>
BCINLINE auto	shape (Integers... ints)
template<class InnerShape , typename = std::enable_if_t<! traits::sequence_of_v<size_t, InnerShape>>>
BCINLINE auto	shape (InnerShape is)

Detailed Description

The Evaluator determines if an expression needs to be greedily optimized.

If it attempts to use the left-hand variable (the output) as cache to reduce temporaries as possible. Then, if functions still requiring evaluating will use temporaries to replace the functions and finally complete the evaluation of the expresssion with an elementwise (nd_evaluator).

Example: (assume matrices) y += a * b + c * d

Naively, this expression may generate 2 temporaries, one for each matrix multiplication. However, a more efficient way to evaluate this equation would be to make 2 gemm calls, gemm(y,a,b) and gemm(y, c, d).

This expression reordering works with more complex calls, such as.... y = abs(a * b + c * d).

Here we can apply... (the second gemm call updateing alpha to 1) gemm(y,a,b), gemm(y,c,d) followed by evaluating y := abs(y).

Typedef Documentation

Basic_Allocator

template<class ValueType >

using bc::Basic_Allocator = typedef allocators::Allocator<ValueType, host_tag>

Cube

template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::Cube = typedef Tensor<3, ValueType, Allocator>

Cuda_Allocator

template<class ValueType >

using bc::Cuda_Allocator = typedef allocators::Allocator<ValueType, device_tag>

Cuda_Managed

template<class ValueType >

using bc::Cuda_Managed = typedef allocators::Device_Managed<ValueType>

default_system_tag_t

using bc::default_system_tag_t = typedef host_tag

Matrix

template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::Matrix = typedef Tensor<2, ValueType, Allocator>

Scalar

template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::Scalar = typedef Tensor<0, ValueType, Allocator>

size_t

using bc::size_t = typedef int

Tensor

template<int dim, class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::Tensor = typedef bc::tensors::Tensor_Base< bc::tensors::exprs::Array< bc::Shape<dim>, ValueType, Allocator> >

VecList

template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::VecList = typedef bc::tensors::Tensor_Base<bc::tensors::exprs::Vector<ValueType, Allocator> >

Vector

template<class ValueType , class Allocator = tensors::detail::default_allocator<ValueType>>

using bc::Vector = typedef Tensor<1, ValueType, Allocator>

Function Documentation

bc_assert()

template<class str_type >

void bc::bc_assert ( bool  condition, str_type  msg, const char *  file, const char *  function, int  line  )

inline

BC_cuda_assert() [1/2]

void bc::BC_cuda_assert ( cudaError_t  code, const char *  file, const char *  function, int  line  )

inline

BC_cuda_assert() [2/2]

void bc::BC_cuda_assert ( cublasStatus_t  code, const char *  file, const char *  function, int  line  )

inline

bc_get_classname_of()

template<class T >

const char* bc::bc_get_classname_of ( const T &  arg )

inline

col2im()

template<class Stream , class ColumnImage , class Image >

void bc::col2im	(	Stream	stream,
		ColumnImage	col_image,
		Image	image,
		bc::Dim< 3 >	krnl_shape,
		bc::Dim< 2 >	padding = bc::Dim<2>(),
		bc::Dim< 2 >	strides = bc::Dim<2>().fill(1),
		bc::Dim< 2 >	dilation = bc::Dim<2>().fill(1)
	)

dim()

template<class... Integers>

BCINLINE auto bc::dim

(

const Integers &...

ints

)

get_error_stream()

template<class RM_UNUSED_FUNCTION_WARNING = void>

std::ostream* bc::get_error_stream ( )

inline

get_print_stream()

template<class RM_UNUSED_FUNCTION_WARNING = void>

std::ostream* bc::get_print_stream ( )

inline

im2col()

template<class Stream , class ColumnImage , class Image >

void bc::im2col	(	Stream	stream,
		ColumnImage	col_image,
		Image	image,
		bc::Dim< 3 >	krnl_shape,
		bc::Dim< 2 >	padding = bc::Dim<2>().fill(0),
		bc::Dim< 2 >	strides = bc::Dim<2>().fill(1),
		bc::Dim< 2 >	dilation = bc::Dim<2>().fill(1),
		int	numb_spatial_axis = 2
	)

max_pooling_backward()

template<class Stream , class Indexes , class Image , class ImageOut >

void bc::max_pooling_backward	(	Stream	stream,
		Image	image,
		ImageOut	delta,
		Indexes	mask,
		Dim< 2 >	krnl_shape,
		Dim< 2 >	padding = Dim<2>().fill(0 ),
		Dim< 2 >	strides = Dim<2>().fill(-1)
	)

max_pooling_forward()

template<class Stream , class Indexes , class Image , class ImageOut >

void bc::max_pooling_forward	(	Stream	stream,
		Image	image,
		ImageOut	out,
		Indexes	mask,
		Dim< 2 >	krnl_shape,
		Dim< 2 >	padding = Dim<2>().fill(0),
		Dim< 2 >	strides = {-1,-1}
	)

print()

template<class... Ts>

void bc::print

(

const Ts &...

args

)

printerr()

template<class... Ts>

void bc::printerr

(

const Ts &...

args

)

set_error_stream()

template<class RM_UNUSED_FUNCTION_WARNING = void>

void bc::set_error_stream ( std::ostream *  ostream )

inline

set_print_stream()

template<class RM_UNUSED_FUNCTION_WARNING = void>

void bc::set_print_stream ( std::ostream *  ostream )

inline

shape() [1/2]

template<class... Integers, typename = std::enable_if_t< traits::sequence_of_v<size_t, Integers...>>>

BCINLINE auto bc::shape

(

Integers...

ints

)

shape() [2/2]

template<class InnerShape , typename = std::enable_if_t<! traits::sequence_of_v<size_t, InnerShape>>>

BCINLINE auto bc::shape

(

InnerShape

is

)