Deep Neural Network Library (DNNL)  1.2.0
Performance library for Deep Learning
Binary

API Reference

The binary primitive computes an operation between source 0 and source 1 element-wise:

$dst(\overline{x}) = src0(\overline{x}) \mathbin{op} src1(\overline{x}),$

where $$op$$ is addition or multiplication.

The binary primitive does not have a notion of forward or backward propagations.

## Implementation Details

### General Notes

• The binary primitive requires all source and destination tensors to have the same number of dimensions.
• The binary primitive supports implicit broadcast semantics for source 1. It means that if some dimension has value of one, this value will be used to compute an operation with each point of source 0 for this dimension.
• The $$dst$$ memory format can be either specified explicitly or be dnnl::memory::format_tag::any (recommended), in which case the primitive will derive the most appropriate memory format based on the format of the source 0 tensor.
• Destination memory descriptor should completely match source 0 memory descriptor.
• The binary primitive supports in-place operations, meaning that source 0 tensor may be used as the destination, in which case its data will be overwritten.

### Post-ops and Attributes

The following attributes are supported:

Type Operation Restrictions Description
Attribute Scales The corresponding tensor has integer data type. Only one scale per tensor is supported. Input tensors only. Scales the corresponding input tensor by the given scale factor(s).

### Data Types Support

The source and destination tensors may have f32, bf16, or int8 data types. See Data Types page for more details.

### Data Representation

#### Sources, Destination

The binary primitive works with arbitrary data tensors. There is no special meaning associated with any of tensors dimensions.

## Implementation Limitations

1. Refer to Data Types for limitations related to data types support.

## Performance Tips

1. Whenever possible, avoid specifying different memory formats for source tensors.