Frontend Extensions#

The goal of this chapter is to explain how to use Frontend extension classes to facilitate mapping of custom operations from framework model representation to OpenVINO representation. Refer to Introduction to OpenVINO Extension to understand the entire flow.

This API is applicable to new frontends only, which exist for ONNX, TensorFlow Lite, PaddlePaddle, and TensorFlow. If a different model format is used, follow legacy Model Optimizer Extensions guide.

Note

This documentation is written based on the Template extension, which demonstrates extension development details based on minimalistic Identity operation that is a placeholder for your real custom operation. You can review the complete code, which is fully compilable, to see how it works.

Note

You can find more examples of extensions in openvino_contrib repository.

Single Operation Mapping with OpExtension#

This section covers the case when a single operation in framework representation is mapped to a single operation in OpenVINO representation. This is called one-to-one mapping. There is OpExtension class that works well if all the following conditions are satisfied:

  1. Number of inputs to operation in the Framework representation is the same as in the OpenVINO representation.

  2. Number of outputs is also the same in both representations.

  3. Inputs can be indexed and are mapped in order correspondingly, e.g. input with index 0 in framework representation maps to input with index 0 in OpenVINO representation and so on.

  4. The same for outputs.

  5. Each attribute in OpenVINO operation can be initialized from one of the attributes of original operation or by some predefined constant value. Value of copied attributes cannot contain expressions, value is accepted as-is, so type of a value should be compatible.

Note

OpExtension class is currently available for ONNX and TensorFlow frontends. PaddlePaddle frontend has named inputs and outputs for operation (not indexed) therefore OpExtension mapping is not applicable for this case.

The following example maps ONNX operation with the type of Identity to OpenVINO template extension Identity class.

#include <openvino/frontend/extension.hpp>

auto extension1 = ov::frontend::OpExtension<TemplateExtension::Identity>("Identity");

// or even simpler if original FW type and OV type of operations match, that is "Identity"
auto extension2 = ov::frontend::OpExtension<TemplateExtension::Identity>();

The mapping doesn’t involve any attributes, as operation Identity doesn’t have them.

Extension objects, like just constructed extension can be used to add to the OpenVINO runtime just before loading a model that contains custom operations:

ov::Core core;
// Add arbitrary number of extensions before calling read_model method
core.add_extension(ov::frontend::OpExtension<TemplateExtension::Identity>());
core.read_model("/path/to/model.onnx");

However, extensions can also be constructed in a separately compiled shared library, that is suitable for loading models with custom operations in a Python application or tools like benchmark_app. For details on how to build and load such library, check the following guide.

If operation have multiple inputs and/or outputs they will be mapped in order. The type of elements in input/output tensors should match expected types in the surrounding operations. For example, if a custom operation produces the f32 data type, the operation that consumes this output should also support f32. Otherwise, model conversion fails with an error, as no automatic type conversion is performed.

Converting to Standard OpenVINO Operation#

OpExtension class can be used when mapping to one of the operations from standard OpenVINO operation set is what you need and there is no class like TemplateExtension::Identity implemented.

Here is an example of a custom framework operation ‘MyRelu’. Assume it is mathematically equivalent to standard Relu that exists in the OpenVINO operation set, but for some reason has the type name of ‘MyRelu’. In this case, you can directly say that ‘MyRelu’ -> Relu mapping should be used:

from openvino.frontend import OpExtension
core.add_extension(OpExtension("Relu", "MyRelu"))

core.add_extension(ov::frontend::OpExtension<>("Relu", "MyRelu"));

In the resulting converted OpenVINO model, “MyRelu” operation will be replaced by the standard operation Relu from the latest available OpenVINO operation set. Notice that when standard operation is used, it can be specified using just a type string (“Relu”) instead of using a ov::opset8::Relu class name as a template parameter for OpExtension. This method is available for operations from the standard operation set only. For a user custom OpenVINO operation the corresponding class should be always specified as a template parameter as it was demonstrated with TemplateExtension::Identity.

Attribute Mapping#

As described above, OpExtension is useful when attributes can be mapped one by one or initialized by a constant. Attributes in OpenVINO operators are identified by their names, so for frameworks that also have named attributes (like TensorFlow, PaddlePaddle, ONNX), you can specify name to name mapping. For frameworks where OpenVINO operator’s attributes can be mapped to one of the framework operator inputs (like PyTorch), there’s a name to input index mapping.

Named attributes mapping#

If the set of attributes in framework representation and OpenVINO representation completely match by their names and types, no attribute mapping has to be specified in OpExtension constructor parameters. The attributes are discovered and mapped automatically based on visit_attributes method that should be defined for any OpenVINO operation.

Imagine you have CustomOperation class implementation that has two attributes with names: attr1 and attr2.

class CustomOperation : public ov::op::Op {

    std::string attr1;
    int attr2;

public:

    OPENVINO_OP("CustomOperation");

    bool visit_attributes(ov::AttributeVisitor& visitor) override {
        visitor.on_attribute("attr1", attr1);
        visitor.on_attribute("attr2", attr2);
        return true;
    }

    // ... implement other required methods

And original model in framework representation also has operation with name CustomOperation with the same attr1 and attr2 attributes. Then with the following code:

core.add_extension(ov::frontend::OpExtension<CustomOperation>());

Both attr1 and attr2 are copied from framework representation to OpenVINO representation automatically.

If for some reason names of attributes are different but values still can be copied “as-is” you can pass attribute names mapping in OpExtension constructor:

core.add_extension(ov::frontend::OpExtension<CustomOperation>(
    std::map<std::string, std::string>{ {"attr1", "fw_attr1"}, {"attr2", "fw_attr2"} },
    {}
));

Where fw_attr1 and fw_attr2 are names for corresponding attributes in framework operation representation.

If copying of an attribute is not what you need, OpExtension also can set attribute to predefined constant value. For the same CustomOperation, imagine you want to set attr2 to value 5 instead of copying from fw_attr2, to achieve that do the following:

core.add_extension(ov::frontend::OpExtension<CustomOperation>(
    std::map<std::string, std::string>{ {"attr1", "fw_attr1"} },
    { {"attr2", 5} }
));

So the conclusion is that each attribute of target OpenVINO operation should be initialized either by

  1. Setting automatically due to name matching

  2. Mapped by attribute name

  3. Set to a constant value

This is achieved by specifying maps as arguments for OpExtension constructor.

Attribute mapping with named inputs and outputs#

Mappings in previous examples assume that inputs and outputs of an operator in framework model representation come with a particular order so you can directly map framework operation input 0 to OpenVINO operation input 0 and so on. That’s not always the case, for frameworks like PaddlePaddle, operation inputs and outputs are identified by their names and may be defined in any order. So to map it to OpenVINO operation inputs and outputs, you have to specify that order yourself. This can be done by creating two vector of strings, one for input and one for output, where framework operation input name at position i maps to OpenVINO operation input at position i (and similarly for outputs).

Let’s see the following example. Like previously, we’d like to map CustomOperation in the original model, to OpenVINO CustomOperation as is (so their name and attributes names match). This time, that framework operation inputs and outputs are not strictly ordered and can be identified by their names A, B, C for inputs and X, Y for outputs. Those inputs and outputs can be mapped to OpenVINO operation, such that inputs A, B, C map to OpenVINO CustomOperation first, second and third input and X and Y outputs map to OpenVINO CustomOperation first and second output respectively.

Given that, such custom operation can be registered by the following:

core.add_extension(ov::frontend::OpExtension<CustomOperation>({"A", "B", "C"}, {"X", "Y"}));

Second example shows how to map the operation with named inputs and outputs, but when names of attributes are different:

core.add_extension(ov::frontend::OpExtension<CustomOperation>(
    {"A", "B", "C"},
    {"X", "Y"},
    std::map<std::string, std::string>{ {"attr1", "fw_attr1"}, {"attr2", "fw_attr2"} },
    {}
));

and the last one shows how to map the operation with named inputs and outputs, but when (in order to correctly map framework operation to OpenVINO operation) one of the attributes has to be set to predefined value:

core.add_extension(ov::frontend::OpExtension<CustomOperation>(
    {"A", "B", "C"},
    {"X", "Y"},
    std::map<std::string, std::string>{ {"attr1", "fw_attr1"} },
    { {"attr2", 5} }
));

Mapping attributes from operation inputs#

For models (like PyTorch models), where operations have attributes on the input list, you can specify name to input index mapping. For example, imagine you have created a custom OpenVINO operation that implements a variant of ELU activation function with two attributes alpha and beta:

\[CustomElu=\left\lbrace \begin{array}{ll} beta * x & \textrm{if x > 0} \newline alpha * (exp(x) - 1) & \textrm{otherwise} \end{array} \right.\]

Below is a snippet of CustomElu class showing how to define its attributes:

class CustomElu : public ov::op::Op {
private:
    float m_alpha;
    float m_beta;

public:
    OPENVINO_OP("CustomElu");

    CustomElu() = default;

    CustomElu(const ov::Output<ov::Node>& input, float alpha, float beta) : Op({input}), m_alpha(alpha), m_beta(beta) {
        constructor_validate_and_infer_types();
    }

    void validate_and_infer_types() override {
        set_output_size(1);
        set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));
    }

    bool visit_attributes(ov::AttributeVisitor& visitor) override {
        visitor.on_attribute("alpha", m_alpha);
        visitor.on_attribute("beta", m_beta);
        return true;
    }

    std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector& inputs) const override {
        return std::make_shared<CustomElu>(inputs[0], m_alpha, m_beta);
    }
};

Let’s see an example of how you can map CustomElu to PyTorch aten::elu (note that if beta is equal to 1, CustomElu works the same as aten::elu). aten::elu has alpha attribute second on the input list, but it doesn’t have beta. So in order to map it to CustomElu you can use the following:

auto extension = std::make_shared<ov::frontend::OpExtension<CustomElu>>("aten::elu",
                                                                        std::map<std::string, size_t>{{"alpha", 1}},
                                                                        std::map<std::string, ov::Any>{{"beta", 1.0f}});

This will map alpha to the second input and map beta attribute to constant value 1.0f.

Such created extension can be used, e.g. in dynamic library, please refer to Create a library with extensions.

Mapping custom operations to frontends with OPENVINO_FRAMEWORK_MAP macro#

OPENVINO_FRAMEWORK_MAP is a macro that should be used inside OpenVINO operation’s class definition and that lets you specify the mapping between this operation to a frontend operation.

Let’s consider the following example. Imagine you have an ONNX model with CustomOp operation (and this operation has mode attribute), a TensorFlow model with CustomOpV3 operation (this operation has axis attribute) and a PaddlePaddle model with CustomOp (with mode attribute) that has input named “X” and output named “Out” and all of them can be implemented with a single OpenVINO operation CustomOp like follows:

#include <openvino/frontend/extension/op.hpp>
#include <openvino/frontend/onnx/extension/op.hpp>
#include <openvino/frontend/tensorflow/extension/op.hpp>
#include <openvino/frontend/paddle/extension/op.hpp>

class CustomOp : public ov::op::Op {
    std::string m_mode;
    int m_axis;

public:
    OPENVINO_OP("CustomOp");
    OPENVINO_FRAMEWORK_MAP(onnx, "CustomOp", { {"mode", "mode"} }, { {"axis", -1} });
    OPENVINO_FRAMEWORK_MAP(tensorflow, "CustomOpV3", { {"axis", "axis"} }, { {"mode", "linear"} });
    OPENVINO_FRAMEWORK_MAP(paddle, {"X"}, {"Out"}, "CustomOp", { {"mode", "mode"} }, { {"axis", -1} });

    bool visit_attributes(ov::AttributeVisitor& visitor) override {
        visitor.on_attribute("mode", m_mode);
        visitor.on_attribute("axis", m_axis);
        return true;
    }

    // ... implement other required methods

Let’s take a closer look at the parameters this macro takes (note that there are two flavors - the second one is to map for PaddlePaddle operations where input and output names have to be specified).

OPENVINO_FRAMEWORK_MAP(framework, name, attributes_map, attributes_values)
OPENVINO_FRAMEWORK_MAP(framework, input_names, output_names, name, attributes_map, attributes_values)

  • framework - framework name.

  • name - the framework operation name. It’s optional if the OpenVINO custom operation name (that is the name that is passed as the first parameter to OPENVINO_OP macro) is the same as the framework operation name and both attributes_map and attributes_values are not provided.

  • input_names - vector of strings that specify the names of inputs (needed to map PaddlePaddle to OpenVINO operations),

  • output_names - vector of strings that specify the names of outputs (needed to map PaddlePaddle to OpenVINO operations),

  • attributes_map - used to provide a mapping between OpenVINO operation attribute and framework operation attribute. Contains key-value pairs, where key is an OpenVINO operation attribute name and value is its corresponding framework operation attribute name. This parameter is optional if the number of OpenVINO operation attributes and their names match one-to-one with framework operation attributes.

  • attributes_values - used to provide default values for OpenVINO operation attributes that are not specified in attributes_map. Contains key-value pairs, where key is an OpenVINO operation attribute name and the value is this attribute value. This parameter cannot be provided if attributes_map contains all of OpenVINO operation attributes or if attributes_map is not provided.

In the example above, OPENVINO_FRAMEWORK_MAP is used three times. First, OpenVINO CustomOp is mapped to ONNX CustomOp operation, m_mode attribute is mapped to mode attribute, while m_axis attribute gets the default value -1. Secondly, OpenVINO CustomOp is mapped to TensorFlow CustomOpV3 operation, m_axis attribute is mapped to axis attribute, while m_mode attribute gets the default value "linear". Thirdly, OpenVINO CustomOp is mapped to PaddlePaddle CustomOp operation, m_mode attribute is mapped to mode attribute, while m_axis attribute gets the default value -1. This mapping also specifies the input name “X” and output name “Out”.

The last step is to register this custom operation by following:

ov::Core core;
core.add_extension(ov::OpExtension<CustomOp>());

Important

To map an operation on a specific framework, you have to link to a respective frontend (openvino::frontend::onnx, openvino::frontend::tensorflow, openvino::frontend::paddle) in the CMakeLists.txt file:

target_link_libraries(${TARGET_NAME} PRIVATE openvino::frontend::onnx)

Mapping to Multiple Operations with ConversionExtension#

Previous sections cover the case when a single operation is mapped to a single operation with optional adjustment in names and attribute values. That is likely enough for your own custom operation with existing C++ kernel implementation. In this case your framework representation and OpenVINO representation for the operation are under your control and inputs/outpus/attributes can be aligned to make OpExtension usable.

In case if one-to-one mapping is not possible, decomposition to multiple operations should be considered. It is achieved by using more verbose and less automated ConversionExtension class. It enables writing arbitrary code to replace a single framework operation by multiple connected OpenVINO operations constructing dependency graph of any complexity.

ConversionExtension maps a single operation to a function which builds a graph using OpenVINO operation classes. Follow chapter Build a Model in OpenVINO Runtime to learn how to use OpenVINO operation classes to build a fragment of model for replacement.

Below example illustrates using ConversionExtension for conversion of “ThresholdedRelu” from ONNX according to the formula: ThresholdedRelu(x, alpha) -> Multiply(x, Convert(Greater(x, alpha), type=float)).

Note

ThresholdedRelu is one of the standard ONNX operators which is supported by ONNX frontend natively out-of-the-box. Here we are re-implementing it to illustrate how you can add a similar support for your custom operation instead of ThresholdedRelu.

import openvino.runtime.opset12 as ops
from openvino.frontend import ConversionExtension

#include <openvino/opsets/opset11.hpp>

def conversion(node):
    input_node = node.get_input(0)
    input_type = input_node.get_element_type()
    greater = ops.greater(input_node, ops.constant([node.get_attribute("alpha")], input_type))
    casted = ops.convert(greater, input_type.get_type_name())
    return ops.multiply(input_node, casted).outputs()

core.add_extension(ConversionExtension("ThresholdedRelu", conversion))

core.add_extension(ov::frontend::ConversionExtension(
    "ThresholdedRelu",
    [](const ov::frontend::NodeContext& node) {
        auto greater = std::make_shared<ov::opset11::Greater>(
            node.get_input(0),
            ov::opset11::Constant::create(ov::element::f32, {}, {node.get_attribute<float>("alpha")}));
        auto casted = std::make_shared<ov::opset11::Convert>(greater, ov::element::f32);
        return ov::OutputVector{ std::make_shared<ov::opset11::Multiply>(node.get_input(0), casted) };
    }));

The next example shows how to use ConversionExtension to convert PyTorch aten::hardtanh to demonstrate how to use get_values_from_const_input function to fetch an attribute value from input:

import torch
from openvino.frontend import ConversionExtension, NodeContext
from openvino import convert_model


class HardTanh(torch.nn.Module):
    def __init__(self, min_val, max_val):
        super(HardTanh, self).__init__()
        self.min_val = min_val
        self.max_val = max_val

    def forward(self, inp):
        return torch.nn.functional.hardtanh(inp, self.min_val, self.max_val)


def convert_hardtanh(node: NodeContext):
    inp = node.get_input(0)
    min_value = node.get_values_from_const_input(1)
    max_value = node.get_values_from_const_input(2)
    return ops.clamp(inp, min_value, max_value).outputs()


model = HardTanh(min_val=0.1, max_val=2.0)
hardtanh_ext = ConversionExtension("aten::hardtanh", convert_hardtanh)
ov_model = convert_model(input_model=model, extension=[hardtanh_ext])

To access original framework operation attribute value and connect to inputs, node object of type NodeContext is used. It has three main methods:

  • NodeContext::get_input to get input with a given index,

  • NodeContext::get_attribute to get attribute value with a given name,

  • NodeContext::get_values_from_const_input to get an attribute with a given input index.

The conversion function should return a vector of node outputs that are mapped to corresponding outputs of the original framework operation in the same order.

Some frameworks require output names of the operation to be provided during conversion. For PaddlePaddle operations, it is generally necessary to provide names for all outputs using the NamedOutputs container. Usually those names can be found in source code of the individual operation in PaddlePaddle code. The following example shows such conversion for the top_k_v2 operation.

core.add_extension(ov::frontend::ConversionExtension("top_k_v2", [](const ov::frontend::NodeContext& node) {
    auto x = node.get_input("X");
    const auto k_expected = node.get_attribute<int>("k", 1);
    auto k_expected_node = ov::opset11::Constant::create(ov::element::i32, {}, {k_expected});

    auto axis = node.get_attribute<int32_t>("axis", -1);
    bool sorted = node.get_attribute<bool>("sorted", true);
    bool largest = node.get_attribute<bool>("largest", true);

    std::string sort_type = sorted ? "value" : "none";
    std::string mode = largest ? "max" : "min";

    auto node_topk = std::make_shared<ov::opset11::TopK>(x, k_expected_node, axis, mode, sort_type);

    ov::frontend::paddle::NamedOutputs named_outputs;
    named_outputs["Out"] = ov::OutputVector{node_topk->output(0)};
    named_outputs["Indices"] = ov::OutputVector{node_topk->output(1)};

    return named_outputs;
}));

For TensorFlow framework, if an operation has more than one output, it is recommended to assign names to those outputs using the NamedOutputVector structure which allows both indexed and named output access. For a description of TensorFlow operations, including the names of their outputs, refer to the tf.raw_ops documentation page. The next example shows such conversion for the TopKV2 operation.

core.add_extension(ov::frontend::ConversionExtension("TopKV2", [](const ov::frontend::NodeContext& node) {
    auto input = node.get_input(0);
    auto k_input = node.get_input(1);
    bool sorted = node.get_attribute<bool>("sorted", true);    
    auto mode = ov::opset11::TopK::Mode::MAX;
    auto sort_type = sorted ? ov::opset11::TopK::SortType::SORT_VALUES : ov::opset11::TopK::SortType::SORT_INDICES;
    auto top_k = std::make_shared<ov::opset11::TopK>(input, k_input, -1, mode, sort_type, ov::element::i32, true);
    return ov::frontend::NamedOutputVector{{"values", top_k->output(0)}, {"indices", top_k->output(1)}};
}));