Tensor object that is for Input and Output tensors from the user. More...

#include <UserTensor.h>

Collaboration diagram for onert::backend::builtin::UserTensor:

[legend]

Public Member Functions
	UserTensor (const ir::OperandInfo &info, ir::Layout layout, uint8_t *buffer, size_t size)

uint8_t *	buffer () const override

ir::Layout	layout () const

void	set_dynamic () override
	set this tensor dynamic

void	setShape (const ir::Shape &new_shape) override
	Set the shape of tenser to new_shape.

bool	applyShape (const ir::Shape &) override
	Set the shape to `shape` and possibly re-allocate the buffer.

Public Member Functions inherited from onert::backend::IPortableTensor
	IPortableTensor (const ir::OperandInfo &info)

virtual	~IPortableTensor ()

const ir::OperandInfo &	get_info () const

const ir::Sparsity *	sparsity () const

size_t	total_size () const override final

size_t	calcOffset (const ir::Coordinates &coords) const override final

ir::DataType	data_type () const override final

float	data_scale () const override final

int32_t	data_zero_point () const override final

const std::vector< float > &	data_scales () const override final

const std::vector< int32_t > &	data_zero_points () const override

bool	is_constant () const override final
	Return true if the tensor is constant.

bool	is_dynamic () const override final
	Return true if the tensor needs dynamic allocation, meaning that during compile-time the outpus shape cannot be known and the output shape is calculated during kernel execution-time.

ir::Shape	getShape () const override final
	Get ir::Shape of tensor.

bool	has_padding () const final

void	access (const std::function< void(ITensor &tensor)> &fn) final

Public Member Functions inherited from onert::backend::ITensor
virtual	~ITensor ()

virtual void	deallocBuffer ()
	Dealloc the buffer (only for dynamic tensors)

virtual bool	is_subtensor () const

virtual bool	needMemoryMap () const

virtual void	enqueueWriteBuffer (const void *, bool)

virtual void	enqueueReadBuffer (void *, bool)

Additional Inherited Members
Protected Attributes inherited from onert::backend::IPortableTensor
ir::OperandInfo	_info

Detailed Description

Tensor object that is for Input and Output tensors from the user.

This class is a wrapped buffer that is allocated by the user. So it does not have resposibility on allocation nor deallocation. All the model input/output tensors are wrapped with this class for execution.

Definition at line 38 of file UserTensor.h.

Constructor & Destructor Documentation

◆ UserTensor()

onert::backend::builtin::UserTensor::UserTensor	(	const ir::OperandInfo &	info,
		ir::Layout	layout,
		uint8_t *	buffer,
		size_t	size
	)

inline

Definition at line 41 of file UserTensor.h.

    : IPortableTensor{info}, _layout{layout}, _buffer{buffer}, _size{size}
  {
  }

Member Function Documentation

◆ applyShape()

bool onert::backend::builtin::UserTensor::applyShape ( const ir::Shape & )

overridevirtual

Set the shape to shape and possibly re-allocate the buffer.

If a tensor is dynamic tensor and previously allocated memory exists, it will be deallocated. If a tensor is static tensor (with previously allocated memory by StaticTensorManager), buffer() will be overwriten

Parameters

shape tensor's new shape. While allocating memory for this new_shape, tensor's shape is set to new_shape

Returns: true If applying shape is successful; false If not applying shape is not supported (it throws for other errors)

Reimplemented from onert::backend::ITensor.

Definition at line 29 of file UserTensor.cc.

{
  // User tensors cannot be reallocated.
  auto new_size = new_shape.num_elements() * ir::sizeOfDataType(data_type());
  if (_size < new_size)
    throw InsufficientBufferSizeException{"User given buffer size is too small."};
  setShape(new_shape);
  return true;
}