[Good First Issue][TF FE]: Support BatchMatrixInverse operation for TensorFlow

[rkazants]

Context

OpenVINO component responsible for support of TensorFlow models is called as TensorFlow Frontend (TF FE). TF FE converts a model represented in TensorFlow opset to a model in OpenVINO opset.

In order to infer TensorFlow models with BatchMatrixInverse operation by OpenVINO, TF FE needs to be extended with this operation support.

What needs to be done?

For BatchMatrixInverse operation support, you need to implement the corresponding loader into TF FE op directory and to register it into the dictionary of Loaders. One loader is responsible for conversion (or decomposition) of one type of TensorFlow operation.

Here is an example of loader implementation for TensorFlow Einsum operation:

OutputVector translate_einsum_op(const NodeContext& node) { 
     auto op_type = node.get_op_type(); 
     TENSORFLOW_OP_VALIDATION(node, op_type == "Einsum", "Internal error: incorrect usage of translate_einsum_op."); 
     auto equation = node.get_attribute<std::string>("equation"); 
  
     OutputVector inputs; 
     for (size_t input_ind = 0; input_ind < node.get_input_size(); ++input_ind) { 
         inputs.push_back(node.get_input(input_ind)); 
     } 
  
     auto einsum = make_shared<Einsum>(inputs, equation); 
     set_node_name(node.get_name(), einsum); 
     return {einsum}; 
 } 

In this example, translate_einsum_op converts TF Einsum into OV Einsum. NodeContext object passed into the loader packs all info about inputs and attributes of Einsum operation. The loader retrieves an attribute of the equation by using the NodeContext::get_attribute() method, prepares input vector, creates Einsum operation from OV opset and returns a vector of outputs.

Responsibility of a loader is to parse operation attributes, prepare inputs and express TF operation via OV operations sub-graph. Example for Einsum demonstrates the resulted sub-graph with one operation. In PR #19007 you can see operation decomposition into multiple node sub-graph.

Once you are done with implementation of the translator, you need to implement the corresponding layer tests test_tf_AdjustHue.py and put it into layer_tests/tensorflow_tests directory. Example how to run some layer test:

export TEST_DEVICE=CPU
cd openvino/tests/layer_tests/tensorflow_tests
pytest test_tf_Shape.py

Example Pull Requests

#23881

Hint

Check this out: #23881

Resources

• What is OpenVINO?
• How to Build OpenVINO
• Developer documentation for TensorFlow Frontend
• Contribution guide - start here!
• Intel DevHub Discord channel - engage in discussions, ask questions and talk to OpenVINO developers

Contact points

• @openvinotoolkit/openvino-tf-frontend-maintainers
• @rkazants in GitHub
• rkazants in Discord

Ticket

No response

[Vikrant-Khedkar]

Hey, I am interested to do this!

[rkazants]

Hi @Vikrant-Khedkar, the task is yours

[Vikrant-Khedkar]

Thank you will start working and get back here for any questions and updates

[Vikrant-Khedkar]

iam facing some issues while building on dev

[mlukasze]

it happens, show us an error message, please