Data Model#

oneDNN Graph uses logical tensor to describe data type, shape, and layout. Besides 32-bit IEEE single-precision floating-point data type, oneDNN Graph can also support other data types. The shape contains multiple dimensions, and the total dimension and the size of the dimension could be set as unknown.

oneDNN Graph uses the following enumeration to refer to data types it supports. Different operation may support inputs and outputs with different data types, so it’s suggested to refer to the definition page of each operation.

enum dnnl::graph::logical_tensor::data_type


Undefined data type (used for empty logical tensor).


16-bit/half-precision floating point.


non-standard 16-bit floating point with 7-bit mantissa.


32-bit/single-precision floating point.


32-bit signed integer.


8-bit signed integer.


8-bit unsigned integer.

oneDNN Graph supports both public layout and opaque layout. When the layout_type of logical tensor is strided, it means that the tensor layout is public which the user can identify each tensor element in the physical memory.

For example, for \(dims[][][] = {x, y, z}\), \(strides[][][] = {s0, s1, s2}\), the physical memory location should be in \(s0*x+s1*y+s2*z\).

When the layout_type of logical tensor is opaque, users are not supposed to interpret the memory buffer directly. An opaque tensor can only be output from oneDNN Graph’s compiled partition and can only be fed to another compile partition as an input tensor.