Namespaces
	cuda
	common utils for cuda

	helper
	Helper functions.

	random

Classes
struct	csr_idx_check
	Indices should be non-negative, less than the number of columns and in ascending order per row. More...

struct	csr_indptr_check
	IndPtr should be non-negative, in non-decreasing order, start with 0 and end with value equal with size of indices. More...

class	LazyAllocArray

class	ObjectPool
	Object pool for fast allocation and deallocation. More...

struct	ObjectPoolAllocatable
	Helper trait class for easy allocation and deallocation. More...

struct	rsp_idx_check
	Indices of RSPNDArray should be non-negative, less than the size of first dimension and in ascending order. More...

struct	StaticArray
	Static array. This code is borrowed from struct Shape<ndim>, except that users can specify the type of the elements of the statically allocated array. The object instance of the struct is copyable between CPU and GPU. More...

Typedefs
typedef std::mt19937	RANDOM_ENGINE
	Random Engine. More...

Functions
bool	SetupDefaultBlobsIn (const std::vector< NDArray > &src, const std::vector< NDArray > bufs, std::vector< TBlob > blobs, std::vector< NDArray > temp_src, std::vector< NDArray > temp_dst, std::unordered_map< uint32_t, uint32_t > *idx_map)

bool	SetupDefaultBlobsOut (const std::vector< NDArray > &src, const std::vector< NDArray > bufs, std::vector< OpReqType > req, std::vector< TBlob > blobs, std::vector< NDArray > temp_src, std::vector< NDArray > *temp_dst)

void	SetupDefaultBlobsInOut (const std::vector< NDArray > &ndinputs, const std::vector< NDArray > &ndoutputs, const std::vector< NDArray > in_bufs, const std::vector< NDArray > out_bufs, std::vector< OpReqType > req, std::vector< TBlob > input_blobs, std::vector< TBlob > output_blobs, std::vector< NDArray > pre_temp_src, std::vector< NDArray > pre_temp_dst, std::vector< NDArray > post_temp_src, std::vector< NDArray > post_temp_dst, std::unordered_map< uint32_t, uint32_t > in_temp_idx_map, const std::vector< uint32_t > &mutate_idx)

void	CastNonDefaultStorage (const std::vector< NDArray > &src, const std::vector< NDArray > &dst, const OpContext &ctx, const bool is_gpu)

bool	SameType (const nnvm::NodeAttrs &attrs, std::vector< int > iattr, std::vector< int > oattr)
	The default type inference function, which assigns all undefined types to the same type of one of the inputs or outputs. More...

bool	DefaultStorageType (const nnvm::NodeAttrs &attrs, const int dev_mask, DispatchMode dispatch_mode, std::vector< int > iattr, std::vector< int > *oattr)
	The default storage type inference function, which assigns all undefined storage types to kDefaultStorage. If all of input and output storage types are kDefaultStorage, DispatchMode::kFCompute is assigned to dispatch_mode. Otherwise, DispatchMode::kFComputeFallback is assigned to dispatch_mode. More...

std::string	storage_str (int storage_id)

void	LogMemoryPlan (const nnvm::Graph &g)

void	LogInferStorage (const nnvm::Graph &g)

void	HandleInferShapeError (const size_t num_forward_inputs, const nnvm::IndexedGraph &idx, const mxnet::ShapeVector &inferred_shapes)

void	HandleInferTypeError (const size_t num_forward_inputs, const nnvm::IndexedGraph &idx, const nnvm::DTypeVector &inferred_dtypes)

void	HandleInferStorageTypeError (const size_t num_forward_inputs, const nnvm::IndexedGraph &idx, const StorageTypeVector &inferred_stypes)

NDArray	ReshapeOrCreate (const std::string &name, const mxnet::TShape &dest_arg_shape, const int dest_arg_dtype, const NDArrayStorageType dest_arg_stype, const Context &ctx, std::unordered_map< std::string, NDArray > *shared_buffer, bool enable_row_sparse_sharing)
	If the requested ndarray's shape size is less than the corresponding shared_data_array's shape size and the storage type is shareable, reuse the memory allocation in shared_buffer; otherwise, create a zero ndarray. Shareable storages include both default storage and row_sparse storage if enable_row_sparse_sharing is `True`, otherwise default storage only. More...

nnvm::Graph	AssignContext (nnvm::Graph g, const Context &default_ctx, const std::map< std::string, Context > &ctx_map, const std::vector< Context > &in_arg_ctxes, const std::vector< Context > &arg_grad_ctxes, const std::vector< Context > &aux_state_ctxes, const std::vector< OpReqType > &grad_req_types, size_t num_forward_inputs, size_t num_forward_outputs)
	Assign context to the graph. This is triggered by both simple_bind and bind flows. More...

template<typename xpu >
void	CheckFormatWrapper (const RunContext &rctx, const NDArray &input, const TBlob &err_cpu, const bool full_check)

template<typename xpu >
void	CheckFormatCSRImpl (const RunContext &rctx, const NDArray &input, const TBlob &err_cpu, const bool full_check)
	Check the validity of CSRNDArray. More...

template<typename xpu >
void	CheckFormatRSPImpl (const RunContext &rctx, const NDArray &input, const TBlob &err_cpu, const bool full_check)
	Check the validity of RowSparseNDArray. More...

template<typename xpu >
void	CheckFormatImpl (const RunContext &rctx, const NDArray &input, const TBlob &err_cpu, const bool full_check)

template<typename xpu >
void	SparseRetainOpForwardRspWrapper (mshadow::Stream< xpu > s, const NDArray &input_nd, const TBlob &idx_data, const OpReqType req, NDArray output_nd)
	Pick rows specified by user input index array from a row sparse ndarray and save them in the output sparse ndarray. More...

template<typename xpu >
void	CastStorageDispatch (const OpContext &ctx, const NDArray &input, const NDArray &output)

bool	ContainsOnlyStorage (const StorageTypeVector &vstorage, const NDArrayStorageType stype)
	returns true if all storage types in `vstorage` are the same as target `stype`. false is returned for empty inputs. More...

bool	ContainsOnlyStorage (const StorageTypeVector &vstorage, const NDArrayStorageType stype1, const NDArrayStorageType stype2, bool *has_both)
	returns true if all storage types in `vstorage` are the same as target `stype1` or `stype2'. Sets boolean if both found. false is returned for empty inputs. More...

bool	ContainsOnlyStorage (const std::vector< NDArray > &ndarrays, const NDArrayStorageType stype)
	returns true if the storage types of arrays in `ndarrays` are the same as target `stype`. false is returned for empty inputs. More...

bool	ContainsOnlyStorage (const std::vector< NDArray > &ndarrays, const NDArrayStorageType stype1, const NDArrayStorageType stype2, bool *has_both)
	returns true if the storage types of arrays in `ndarrays` are the same as targets `stype1` or `stype2`. false is returned for empty inputs. More...

bool	ContainsStorageType (const std::vector< NDArray > &ndarrays, const NDArrayStorageType stype)
	returns true if storage type of any array in `ndarrays` is the same as the target `stype`. false is returned for empty inputs. More...

bool	ContainsStorageType (const std::vector< int > &ndstypes, const NDArrayStorageType stype)
	returns true if any storage type `ndstype` in `ndstypes` is the same as the target `stype`. false is returned for empty inputs. More...

std::string	dispatch_mode_string (const DispatchMode x)
	get string representation of dispatch_mode More...

std::string	stype_string (const int x)
	get string representation of storage_type More...

std::string	dev_type_string (const int dev_type)
	get string representation of device type More...

std::string	operator_stype_string (const nnvm::NodeAttrs &attrs, const int dev_mask, const std::vector< int > &in_attrs, const std::vector< int > &out_attrs)
	get string representation of the operator stypes More...

std::string	operator_string (const nnvm::NodeAttrs &attrs, const OpContext &ctx, const std::vector< NDArray > &inputs, const std::vector< OpReqType > &req, const std::vector< NDArray > &outputs)
	get string representation of the operator More...

void	LogOnce (const std::string &message)
	log message once. Intended for storage fallback warning messages. More...

void	LogStorageFallback (const nnvm::NodeAttrs &attrs, const int dev_mask, const std::vector< int > in_attrs, const std::vector< int > out_attrs)
	log storage fallback event More...

int	GetNumThreadsPerGPU ()

int	GetExecNumMatchColor ()

template<typename T , typename V >
V	ParallelAccumulate (const T *a, const int n, V start)

template<typename RandomIt , typename Compare >
void	ParallelSortHelper (RandomIt first, size_t len, size_t grainsize, const Compare &comp)
	Helper function for ParallelSort. DO NOT call this function directly. Use the interface ParallelSort instead. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h. More...

template<typename RandomIt , typename Compare >
void	ParallelSort (RandomIt first, RandomIt last, size_t num_threads, Compare comp)
	Sort the elements in the range [first, last) into the ascending order defined by the comparator comp. If the length of the range [first, last) is greater than a certain threshold, the range will be recursively divided into two and assign two threads to sort each half range. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h. More...

template<typename RandomIt >
void	ParallelSort (RandomIt first, RandomIt last, size_t num_threads)
	Sort the elements in the range [first, last) into ascending order. The elements are compared using the default < operator. If the length of the range [first, last) is greater than a certain threshold, the range will be recursively divided into two and assign two threads to sort each half range. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h. More...

template<class T , class... Args>
helper::UniqueIf< T >::SingleObject	MakeUnique (Args &&...args)
	Constructs an object of type `T` and wraps it in a `std::unique_ptr`. More...

template<class T >
helper::UniqueIf< T >::UnknownBound	MakeUnique (size_t n)
	Constructs an object of type `T` and wraps it in a `std::unique_ptr`. More...

template<class T , class... Args>
helper::UniqueIf< T >::KnownBound	MakeUnique (Args &&...args)=delete
	Constructs an object of type `T` and wraps it in a `std::unique_ptr`. More...

template<typename FCompType >
FCompType	GetFCompute (const nnvm::Op *op, const std::string &name, const Context &ctx)

template<typename T >
constexpr size_t	MaxIntegerValue ()
	Return the max integer value representable in the type `T` without loss of precision. More...

template<>
constexpr size_t	MaxIntegerValue< mshadow::half::half_t > ()

MSHADOW_XINLINE int	ilog2ul (size_t a)

MSHADOW_XINLINE int	ilog2ui (unsigned int a)

NDArray	InitZeros (const NDArrayStorageType stype, const mxnet::TShape &shape, const Context &ctx, const int dtype)
	Return an NDArray of all zeros. More...

void	EmplaceBackZeros (const NDArrayStorageType stype, const mxnet::TShape &shape, const Context &ctx, const int dtype, std::vector< NDArray > *vec)
	Helper to add a NDArray of zeros to a std::vector. More...

template<typename DType >
void	ParallelCopy (DType dst, const DType src, index_t size)
	parallelize copy by OpenMP. More...

void	ConvertToNumpyShape (mxnet::TShape *shape)
	If numpy compatibility is turned off (default), the shapes passed in by users follow the legacy shape definition: More...

void	ConvertToNumpyShape (mxnet::ShapeVector *shapes)

void	ConvertToLegacyShape (mxnet::TShape *shape)
	This is function is used to convert shapes returned by the infer shape functions/pass to the legacy shape definition. More...

void	ConvertToLegacyShape (mxnet::ShapeVector *shapes)

Typedef Documentation

typedef std::mt19937 mxnet::common::RANDOM_ENGINE

Random Engine.

Function Documentation

nnvm::Graph mxnet::common::AssignContext	(	nnvm::Graph	g,
		const Context &	default_ctx,
		const std::map< std::string, Context > &	ctx_map,
		const std::vector< Context > &	in_arg_ctxes,
		const std::vector< Context > &	arg_grad_ctxes,
		const std::vector< Context > &	aux_state_ctxes,
		const std::vector< OpReqType > &	grad_req_types,
		size_t	num_forward_inputs,
		size_t	num_forward_outputs
	)

inline

Assign context to the graph. This is triggered by both simple_bind and bind flows.

void mxnet::common::CastNonDefaultStorage	(	const std::vector< NDArray > &	src,
		const std::vector< NDArray > &	dst,
		const OpContext &	ctx,
		const bool	is_gpu
	)

inline

template<typename xpu >

void mxnet::common::CastStorageDispatch	(	const OpContext &	ctx,
		const NDArray &	input,
		const NDArray &	output
	)

template<typename xpu >

void mxnet::common::CheckFormatCSRImpl	(	const RunContext &	rctx,
		const NDArray &	input,
		const TBlob &	err_cpu,
		const bool	full_check
	)

Check the validity of CSRNDArray.

Parameters

rctx	Execution context.
input	Input NDArray of CSRStorage.
err_cpu	Error number on cpu.
full_check	If true, rigorous check, O(N) operations, otherwise basic check, O(1) operations.

template<typename xpu >

void mxnet::common::CheckFormatImpl	(	const RunContext &	rctx,
		const NDArray &	input,
		const TBlob &	err_cpu,
		const bool	full_check
	)

template<typename xpu >

void mxnet::common::CheckFormatRSPImpl	(	const RunContext &	rctx,
		const NDArray &	input,
		const TBlob &	err_cpu,
		const bool	full_check
	)

Check the validity of RowSparseNDArray.

Parameters

rctx	Execution context.
input	Input NDArray of RowSparseStorage.
err_cpu	Error number on cpu.
full_check	If true, rigorous check, O(N) operations, otherwise basic check, O(1) operations.

template<typename xpu >

void mxnet::common::CheckFormatWrapper	(	const RunContext &	rctx,
		const NDArray &	input,
		const TBlob &	err_cpu,
		const bool	full_check
	)

bool mxnet::common::ContainsOnlyStorage	(	const StorageTypeVector &	vstorage,
		const NDArrayStorageType	stype
	)

inline

returns true if all storage types in vstorage are the same as target stype. false is returned for empty inputs.

bool mxnet::common::ContainsOnlyStorage	(	const StorageTypeVector &	vstorage,
		const NDArrayStorageType	stype1,
		const NDArrayStorageType	stype2,
		bool *	has_both
	)

inline

returns true if all storage types in vstorage are the same as target stype1 or `stype2'. Sets boolean if both found. false is returned for empty inputs.

bool mxnet::common::ContainsOnlyStorage	(	const std::vector< NDArray > &	ndarrays,
		const NDArrayStorageType	stype
	)

inline

returns true if the storage types of arrays in ndarrays are the same as target stype. false is returned for empty inputs.

bool mxnet::common::ContainsOnlyStorage	(	const std::vector< NDArray > &	ndarrays,
		const NDArrayStorageType	stype1,
		const NDArrayStorageType	stype2,
		bool *	has_both
	)

inline

returns true if the storage types of arrays in ndarrays are the same as targets stype1 or stype2. false is returned for empty inputs.

bool mxnet::common::ContainsStorageType	(	const std::vector< NDArray > &	ndarrays,
		const NDArrayStorageType	stype
	)

inline

returns true if storage type of any array in ndarrays is the same as the target stype. false is returned for empty inputs.

bool mxnet::common::ContainsStorageType	(	const std::vector< int > &	ndstypes,
		const NDArrayStorageType	stype
	)

inline

returns true if any storage type ndstype in ndstypes is the same as the target stype. false is returned for empty inputs.

void mxnet::common::ConvertToLegacyShape ( mxnet::TShape * shape )

inline

This is function is used to convert shapes returned by the infer shape functions/pass to the legacy shape definition.

void mxnet::common::ConvertToLegacyShape ( mxnet::ShapeVector * shapes )

inline

void mxnet::common::ConvertToNumpyShape ( mxnet::TShape * shape )

inline

If numpy compatibility is turned off (default), the shapes passed in by users follow the legacy shape definition:

0 ndim means the shape is completely unknown.
0 dim size means the dim size is unknown. We need to convert those shapes to use the numpy shape definition:

0 ndim means it's a scalar tensor.
-1 ndim means the shape is unknown.
0 dim size means no elements in that dimension.

-1 dim size means the dimension's size is unknown. so that operator's infer shape function can work in backend.

Parameters

shape to be converted. Note: It is possible that the shape to be converted is already numpy compatible. For example, when a subgraph operator's infer shape function is called from the infer shape pass of the whole graph, its input/output shapes have been converted to numpy compatible shapes.

void mxnet::common::ConvertToNumpyShape ( mxnet::ShapeVector * shapes )

inline

bool mxnet::common::DefaultStorageType	(	const nnvm::NodeAttrs &	attrs,
		const int	dev_mask,
		DispatchMode *	dispatch_mode,
		std::vector< int > *	iattr,
		std::vector< int > *	oattr
	)

inline

The default storage type inference function, which assigns all undefined storage types to kDefaultStorage. If all of input and output storage types are kDefaultStorage, DispatchMode::kFCompute is assigned to dispatch_mode. Otherwise, DispatchMode::kFComputeFallback is assigned to dispatch_mode.

std::string mxnet::common::dev_type_string ( const int dev_type )

inline

get string representation of device type

std::string mxnet::common::dispatch_mode_string ( const DispatchMode x )

inline

get string representation of dispatch_mode

void mxnet::common::EmplaceBackZeros	(	const NDArrayStorageType	stype,
		const mxnet::TShape &	shape,
		const Context &	ctx,
		const int	dtype,
		std::vector< NDArray > *	vec
	)

inline

Helper to add a NDArray of zeros to a std::vector.

int mxnet::common::GetExecNumMatchColor ( )

inline

template<typename FCompType >

FCompType mxnet::common::GetFCompute	(	const nnvm::Op *	op,
		const std::string &	name,
		const Context &	ctx
	)

int mxnet::common::GetNumThreadsPerGPU ( )

inline

void mxnet::common::HandleInferShapeError	(	const size_t	num_forward_inputs,
		const nnvm::IndexedGraph &	idx,
		const mxnet::ShapeVector &	inferred_shapes
	)

inline

void mxnet::common::HandleInferStorageTypeError	(	const size_t	num_forward_inputs,
		const nnvm::IndexedGraph &	idx,
		const StorageTypeVector &	inferred_stypes
	)

inline

void mxnet::common::HandleInferTypeError	(	const size_t	num_forward_inputs,
		const nnvm::IndexedGraph &	idx,
		const nnvm::DTypeVector &	inferred_dtypes
	)

inline

MSHADOW_XINLINE int mxnet::common::ilog2ui ( unsigned int a )

MSHADOW_XINLINE int mxnet::common::ilog2ul ( size_t a )

NDArray mxnet::common::InitZeros	(	const NDArrayStorageType	stype,
		const mxnet::TShape &	shape,
		const Context &	ctx,
		const int	dtype
	)

inline

Return an NDArray of all zeros.

void mxnet::common::LogInferStorage ( const nnvm::Graph & g )

inline

void mxnet::common::LogMemoryPlan ( const nnvm::Graph & g )

inline

void mxnet::common::LogOnce ( const std::string & message )

inline

log message once. Intended for storage fallback warning messages.

void mxnet::common::LogStorageFallback	(	const nnvm::NodeAttrs &	attrs,
		const int	dev_mask,
		const std::vector< int > *	in_attrs,
		const std::vector< int > *	out_attrs
	)

inline

log storage fallback event

template<class T , class... Args>

helper::UniqueIf<T>::SingleObject mxnet::common::MakeUnique ( Args &&... args )

delete

Constructs an object of type T and wraps it in a std::unique_ptr.

Parameters

args	List of arguments with which an instance of `T` will be constructed.

Returns: std::unique_ptr of an instance of type T.

Constructs a non-array type T. The arguments args are passed to the constructor of T. The function does not participate in the overload resolution if T is an array type.

template<class T >

helper::UniqueIf<T>::UnknownBound mxnet::common::MakeUnique ( size_t n )

Constructs an object of type T and wraps it in a std::unique_ptr.

Parameters

n	The size of the array to construct.

Returns: std::unique_ptr of an instance of type T.

Constructs an array of unknown bound T. The function does not participate in the overload resolution unless T is an array of unknown bound.

template<class T , class... Args>

helper::UniqueIf<T>::KnownBound mxnet::common::MakeUnique ( Args &&... args )

delete

Constructs an object of type T and wraps it in a std::unique_ptr.

Parameters

args	List of arguments with which an instance of `T` will be constructed.

Constructs an arrays of known bound is disallowed.

Parameters

args	List of arguments with which an instance of `T` will be constructed.

Returns: std::unique_ptr of an instance of type T.

Constructs a non-array type T. The arguments args are passed to the constructor of T. The function does not participate in the overload resolution if T is an array type.

template<typename T >

constexpr size_t mxnet::common::MaxIntegerValue ( )

Return the max integer value representable in the type T without loss of precision.

template<>

constexpr size_t mxnet::common::MaxIntegerValue< mshadow::half::half_t > ( )

std::string mxnet::common::operator_string	(	const nnvm::NodeAttrs &	attrs,
		const OpContext &	ctx,
		const std::vector< NDArray > &	inputs,
		const std::vector< OpReqType > &	req,
		const std::vector< NDArray > &	outputs
	)

inline

get string representation of the operator

std::string mxnet::common::operator_stype_string	(	const nnvm::NodeAttrs &	attrs,
		const int	dev_mask,
		const std::vector< int > &	in_attrs,
		const std::vector< int > &	out_attrs
	)

inline

get string representation of the operator stypes

template<typename T , typename V >

V mxnet::common::ParallelAccumulate	(	const T *	a,
		const int	n,
		V	start
	)

template<typename DType >

void mxnet::common::ParallelCopy	(	DType *	dst,
		const DType *	src,
		index_t	size
	)

inline

parallelize copy by OpenMP.

template<typename RandomIt , typename Compare >

void mxnet::common::ParallelSort	(	RandomIt	first,
		RandomIt	last,
		size_t	num_threads,
		Compare	comp
	)

Sort the elements in the range [first, last) into the ascending order defined by the comparator comp. If the length of the range [first, last) is greater than a certain threshold, the range will be recursively divided into two and assign two threads to sort each half range. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h.

template<typename RandomIt >

void mxnet::common::ParallelSort	(	RandomIt	first,
		RandomIt	last,
		size_t	num_threads
	)

Sort the elements in the range [first, last) into ascending order. The elements are compared using the default < operator. If the length of the range [first, last) is greater than a certain threshold, the range will be recursively divided into two and assign two threads to sort each half range. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h.

template<typename RandomIt , typename Compare >

void mxnet::common::ParallelSortHelper	(	RandomIt	first,
		size_t	len,
		size_t	grainsize,
		const Compare &	comp
	)

Helper function for ParallelSort. DO NOT call this function directly. Use the interface ParallelSort instead. Ref: https://github.com/dmlc/difacto/blob/master/src/common/parallel_sort.h.

NDArray mxnet::common::ReshapeOrCreate	(	const std::string &	name,
		const mxnet::TShape &	dest_arg_shape,
		const int	dest_arg_dtype,
		const NDArrayStorageType	dest_arg_stype,
		const Context &	ctx,
		std::unordered_map< std::string, NDArray > *	shared_buffer,
		bool	enable_row_sparse_sharing
	)

inline

If the requested ndarray's shape size is less than the corresponding shared_data_array's shape size and the storage type is shareable, reuse the memory allocation in shared_buffer; otherwise, create a zero ndarray. Shareable storages include both default storage and row_sparse storage if enable_row_sparse_sharing is True, otherwise default storage only.

bool mxnet::common::SameType	(	const nnvm::NodeAttrs &	attrs,
		std::vector< int > *	iattr,
		std::vector< int > *	oattr
	)

inline

The default type inference function, which assigns all undefined types to the same type of one of the inputs or outputs.

bool mxnet::common::SetupDefaultBlobsIn	(	const std::vector< NDArray > &	src,
		const std::vector< NDArray > *	bufs,
		std::vector< TBlob > *	blobs,
		std::vector< NDArray > *	temp_src,
		std::vector< NDArray > *	temp_dst,
		std::unordered_map< uint32_t, uint32_t > *	idx_map
	)

inline

void mxnet::common::SetupDefaultBlobsInOut	(	const std::vector< NDArray > &	ndinputs,
		const std::vector< NDArray > &	ndoutputs,
		const std::vector< NDArray > *	in_bufs,
		const std::vector< NDArray > *	out_bufs,
		std::vector< OpReqType > *	req,
		std::vector< TBlob > *	input_blobs,
		std::vector< TBlob > *	output_blobs,
		std::vector< NDArray > *	pre_temp_src,
		std::vector< NDArray > *	pre_temp_dst,
		std::vector< NDArray > *	post_temp_src,
		std::vector< NDArray > *	post_temp_dst,
		std::unordered_map< uint32_t, uint32_t > *	in_temp_idx_map,
		const std::vector< uint32_t > &	mutate_idx
	)

inline

bool mxnet::common::SetupDefaultBlobsOut	(	const std::vector< NDArray > &	src,
		const std::vector< NDArray > *	bufs,
		std::vector< OpReqType > *	req,
		std::vector< TBlob > *	blobs,
		std::vector< NDArray > *	temp_src,
		std::vector< NDArray > *	temp_dst
	)

inline

template<typename xpu >

void mxnet::common::SparseRetainOpForwardRspWrapper	(	mshadow::Stream< xpu > *	s,
		const NDArray &	input_nd,
		const TBlob &	idx_data,
		const OpReqType	req,
		NDArray *	output_nd
	)

Pick rows specified by user input index array from a row sparse ndarray and save them in the output sparse ndarray.

std::string mxnet::common::storage_str ( int storage_id )

inline

std::string mxnet::common::stype_string ( const int x )

inline

get string representation of storage_type

Namespaces

Classes

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