openvino/docs/IE_DG/Extensibility_DG/AddingNGraphOps.md

Custom nGraph Operation

The Inference Engine Extension API allows you to register operation sets (opsets) with custom nGraph operations to support models with operations that OpenVINO™ does not support out-of-the-box.

Operation Class

To add your custom nGraph operation, create a new class that extends ngraph::Op, which is in turn derived from ngraph::Node, the base class for all graph operations in nGraph. Follow the steps below to add a custom nGraph operation:

1. Add the NGRAPH_RTTI_DECLARATION and NGRAPH_RTTI_DEFINITION macros which define 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 nGraph operation currently consists of a string identifier and a version number, but this may change in the future.

2. Implement 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 ngraph::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 nGraph defined types.

6. Override evaluate, which is an optional method that enables 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 allow to get information about availability of evaluate method for the operation.

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

@snippet template_extension/old/op.hpp op:header

Class Fields

The provided implementation has several fields:

• add of type int64_t is an attribute of a custom operation.
• type_info of type ngraph::NodeTypeInfo defines the type and version of an operation.

Operation Constructors

nGraph 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

@snippet template_extension/old/op.cpp op:ctor

validate_and_infer_types()

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

@snippet template_extension/old/op.cpp op:validate

clone_with_new_inputs()

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

@snippet template_extension/old/op.cpp op:copy

visit_attributes()

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

@snippet template_extension/old/op.cpp op:visit_attributes

evaluate() and has_evaluate()

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

@snippet template_extension/old/op.cpp op:evaluate

Register Custom Operations in Extension Class

To add custom operations to the Extension class, create an operation set with custom operations and implement the InferenceEngine::IExtension::getOpSets method:

@snippet template_extension/old/extension.cpp extension:getOpSets

This method returns a map of opsets that exist in the extension library.

nGraph provides an opset mechanism to group operations into clusters. S. Different opsets distinguish between different versions of one operation.

When specifying opset names, follow the rules below:

• Use unique opset names.
• Do not use the following built-in opset names: extension, experimental, opset1, opset2, opset3, ... , opsetN.
• Make sure that the Model Optimizer and your extension use the same opset names.
• IR v10 operations have the mandatory version attribute specifying the opset. Operations from the default opset cannot be redefined.

Use a custom opset to create a new operation or extend functionality of an existing operation from another opset.