Custom OpenVINO Operations#

OpenVINO™ Extension API allows you to register custom operations to support models with operations which OpenVINO™ does not support out-of-the-box. This capability requires writing code in C++, so if you are using Python to develop your application you need to build a separate shared library implemented in C++ first and load it in Python using add_extension API. Please refer to Create library with extensions for more details on library creation and usage. The remaining part of this document describes how to implement an operation class.

Operation Class#

To add your custom operation, create a new class that extends ov::Op, which is in turn derived from ov::Node, the base class for all graph operations in OpenVINO™. To add ov::Op, include the next file:

#include <openvino/op/op.hpp>

Follow the steps below to add a custom operation:

  1. Add the OPENVINO_OP macro which defines a NodeTypeInfo object that identifies the type of the operation to the graph users and helps with dynamic type resolution. The type info of an operation currently consists of a string operation identifier and a string for operation version.

  2. Implement default constructor and constructors that optionally take the operation inputs and attributes as parameters.

  3. Override the shape inference method validate_and_infer_types. This method is called multiple times during graph manipulations to determine the shapes and element types of the operations outputs. To access the input shapes and input element types, use the get_input_partial_shape() and get_input_element_type() methods of ov::Node. Set the inferred shape and element type of the output using set_output_type.

  4. Override the clone_with_new_inputs method, which enables graph manipulation routines to create copies of this operation and connect it to different nodes during optimization.

  5. Override the visit_attributes method, which enables serialization and deserialization of operation attributes. An AttributeVisitor is passed to the method, and the implementation is expected to walk over all the attributes in the op using the type-aware on_attribute helper. Helpers are already implemented for standard C++ types like int64_t, float, bool, vector, and for existing OpenVINO defined types.

  6. Override evaluate, which is an optional method that enables fallback of some devices to this implementation and the application of constant folding if there is a custom operation on the constant branch. If your operation contains evaluate method you also need to override the has_evaluate method, this method allows to get information about availability of evaluate method for the operation.

Based on that, declaration of an operation class can look as follows:

Operation Constructors#

OpenVINO™ operation contains two constructors:

  • Default constructor, which enables you to create an operation without attributes

  • Constructor that creates and validates an operation with specified inputs and attributes

Identity::Identity(const ov::Output<ov::Node>& arg) : Op({arg}) {


ov::Node::validate_and_infer_types method validates operation attributes and calculates output shapes using attributes of the operation.

void Identity::validate_and_infer_types() {
    // Operation doesn't change shapes end element type
    set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));


ov::Node::clone_with_new_inputs method creates a copy of the operation with new inputs.

std::shared_ptr<ov::Node> Identity::clone_with_new_inputs(const ov::OutputVector& new_args) const {
    OPENVINO_ASSERT(new_args.size() == 1, "Incorrect number of new arguments");

    return std::make_shared<Identity>(;


ov::Node::visit_attributes method enables you to visit all operation attributes.

bool Identity::visit_attributes(ov::AttributeVisitor& visitor) {
    return true;

evaluate() and has_evaluate()#

ov::Node::evaluate method enables you to apply constant folding to an operation.

bool Identity::evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const {
    const auto& in = inputs[0];
    auto& out = outputs[0];
    if ( ==  // Nothing to do
        return true;
    memcpy(,, in.get_byte_size());
    return true;

bool Identity::has_evaluate() const {
    return true;