Tensor

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 6/20/2024

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Tensor

Overview

Tensor is an abstraction for multi-dimensional input and output data needed in the execution of a compiled partition. More…

// typedefs

typedef struct dnnl_graph_tensor* dnnl_graph_tensor_t;
typedef const struct dnnl_graph_tensor* const_dnnl_graph_tensor_t;

// classes

class dnnl::graph::tensor;

// global functions

dnnl_status_t DNNL_API dnnl_graph_tensor_create(
    dnnl_graph_tensor_t* tensor,
    const dnnl_graph_logical_tensor_t* logical_tensor,
    dnnl_engine_t engine,
    void* handle
    );

dnnl_status_t DNNL_API dnnl_graph_tensor_destroy(dnnl_graph_tensor_t tensor);

dnnl_status_t DNNL_API dnnl_graph_tensor_get_data_handle(
    const_dnnl_graph_tensor_t tensor,
    void** handle
    );

dnnl_status_t DNNL_API dnnl_graph_tensor_set_data_handle(
    dnnl_graph_tensor_t tensor,
    void* handle
    );

dnnl_status_t DNNL_API dnnl_graph_tensor_get_engine(
    const_dnnl_graph_tensor_t tensor,
    dnnl_engine_t* engine
    );

Detailed Documentation

Tensor is an abstraction for multi-dimensional input and output data needed in the execution of a compiled partition.

A tensor object encapsulates a handle to a memory buffer allocated on a specific engine and a logical tensor which describes the dimensions, elements data type, and memory layout.

Typedefs

typedef struct dnnl_graph_tensor* dnnl_graph_tensor_t

A tensor handle.

typedef const struct dnnl_graph_tensor* const_dnnl_graph_tensor_t

A constant tensor handle.

Global Functions

dnnl_status_t DNNL_API dnnl_graph_tensor_create(
    dnnl_graph_tensor_t* tensor,
    const dnnl_graph_logical_tensor_t* logical_tensor,
    dnnl_engine_t engine,
    void* handle
    )

Creates a tensor with logical tensor, engine, and data handle.

Parameters:

tensor	Output tensor.
logical_tensor	Description for this tensor.
engine	Engine to use.
handle	Handle of the memory buffer to use as an underlying storage. A pointer to the user-allocated buffer. In this case the library doesn’t own the buffer. The DNNL_MEMORY_ALLOCATE special value. Instructs the library to allocate the buffer for the tensor. In this case the library owns the buffer. DNNL_MEMORY_NONE to create tensor without an underlying buffer.

Returns:

dnnl_success on success or a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_graph_tensor_destroy(dnnl_graph_tensor_t tensor)

Destroys a tensor.

Parameters:

tensor

The tensor to be destroyed.

Returns:

dnnl_success on success or a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_graph_tensor_get_data_handle(
    const_dnnl_graph_tensor_t tensor,
    void** handle
    )

Gets the data handle of a tensor.

Parameters:

tensor	The input tensor.
handle	Pointer to the data of input tensor.

Returns:

dnnl_success on success or a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_graph_tensor_set_data_handle(
    dnnl_graph_tensor_t tensor,
    void* handle
    )

Set data handle for a tensor.

Parameters:

tensor	The input tensor.
handle	New data handle for tensor.

Returns:

dnnl_success on success or a status describing the error otherwise.

dnnl_status_t DNNL_API dnnl_graph_tensor_get_engine(
    const_dnnl_graph_tensor_t tensor,
    dnnl_engine_t* engine
    )

Returns the engine of a tensor object.

Parameters:

tensor	The input tensor.
engine	Output engine on which the tensor is located.

Returns:

dnnl_success on success or a status describing the error otherwise.

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

Tensor

Overview

Detailed Documentation