oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning

API Reference


The binary primitive computes the result of a binary elementwise operation between tensors source 0 and source 1 (the variable names follow the standard Naming Conventions):

\[ \dst(\overline{x}) = \src_0(\overline{x}) \mathbin{op} \src_1(\overline{x}), \]

where \(op\) is addition, subtraction, multiplication, division, get maximum value or get minimum value.

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

Execution Arguments

When executed, the inputs and outputs should be mapped to an execution argument index as specified by the following table.

Primitive input/output Execution argument index
\(\src_0\) DNNL_ARG_SRC_0
\(\src_1\) DNNL_ARG_SRC_1
\(\dst\) DNNL_ARG_DST
\(binary post-op\) DNNL_ARG_ATTR_MULTIPLE_POST_OP(binary_post_op_position) | DNNL_ARG_SRC_1

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 by 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. The \(\dst\) tensor dimensions must match the ones of the source 0 tensor.
  • 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. In-place mode requires the \(\dst\) and source 0 data types to be the same. Different data types will unavoidably lead to correctness issues.

Post-ops and Attributes

The following attributes are supported:

Type Operation Description Res
Attribute Scales Scales the corresponding input tensor by the given scale factor(s). The corresponding tensor has integer data type. Only one scale per tensor is supported. Input tensors only.
Post-op Sum Adds the operation result to the destination tensor instead of overwriting it.
Post-op Eltwise Applies an Eltwise operation to the result.
Post-op Binary Applies a Binary operation to the result General binary post-op restrictions

Data Types Support

The source and destination tensors may have f32, bf16, or int8 data types. The binary primitive supports the following combinations of data types:

Source 0 / 1 Des
f32 f32
bf16 bf16
f16 f16
s8, u8, f32 s8, u8
s8, u8, f32 s8, u8

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.


Engine Name Com
CPU/GPU Binary Primitive Example

This C++ API example demonstrates how to create and execute a Binary primitive.

Key optimizations included in this example:

  • In-place primitive execution;
  • Primitive attributes with fused post-ops.