[Good First Issue][TF FE]: Support complex tensors for Gather, GatherV2 operations

rkazants commented 6 months ago

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. Some audio models use tensors of complex type. Complex type tensor is a tensor that has elements of complex type. For example, 1D tensor with three elements x = [1+2j, 2, -2j].

For supporting GatherV2 operation on complex type tensor, you need to extend the corresponding loader for GatherV2.

What needs to be done?

The existing loader for GatherV2 needs to be extended by propagating ComplexTypeMark from input to output and to represent output complex type tensor as a floating-point type tensor with auxiliary dimension that concatenates real and imaginary parts of complex tensor. To validate the extension, the corresponding layer test needs to be updated with complex tensor cases.

Here is an example of how to extend Reshape loader to support complex type tensors:

OutputVector translate_reshape_op(const NodeContext& node) {
    default_op_checks(node, 2, {"Reshape"}, true);
    auto tensor = node.get_input(0);
    auto complex_type_mark = as_type_ptr<ComplexTypeMark>(tensor.get_node_shared_ptr());
    auto shape = node.get_input(1);
    if (complex_type_mark) {
        element::Type complex_part_type = complex_type_mark->get_complex_part_type();
        tensor = complex_type_mark->input_value(0);

        OutputVector concat_inputs;
        concat_inputs.push_back(shape);
        concat_inputs.push_back(make_shared<v0::Constant>(shape.get_element_type(), Shape{1}, 2));

        auto concat = make_shared<v0::Concat>(concat_inputs, 0);
        auto reshape = make_shared<v1::Reshape>(tensor, concat, false);
        set_node_name(node.get_name(), reshape);
        auto complex_reshape = make_shared<ComplexTypeMark>(reshape, complex_part_type);
        return {complex_reshape->output(0)};
    }

    auto reshape = make_shared<v1::Reshape>(tensor, shape, false);
    set_node_name(node.get_name(), reshape);
    return {reshape};
}

Since OpenVINO does not have native support of complex tensors, we handle complex type in intermediate layers by representing them as a floating-point type with additional dimension (specially created) to store real and imaginary parts of the original complex tensor so slicing by the last dimension will give either real or imaginary parts: x[...,0] - real and x[...,1] - imaginary parts.

On the first step, we update default_op_checks with true flag to indicate that loader for Reshape operation now handles complex tensors:

default_op_checks(node, 2, {"Reshape"}, true);

Secondly, we check if complex type mark exists by anticipated inputs. This mark indicates that input tensor of complex type:

auto complex_type_mark = as_type_ptr<ComplexTypeMark>(tensor.get_node_shared_ptr());

Thirdly, we retrieve a floating-point tensor (with additional dimension to store real and imaginary parts) simulating complex tensor:

tensor = complex_type_mark->input_value(0);

After that, we implement conversion for Reshape for this particular case. Since a floating-point tensor simulating complex tensor has additional dimension equal to 2, we update input target shape by appending 2 value and perform reshape on a floating-point tensor simulating complex tensor.

Finally, since Reshape should produce complex tensor by output we insert a new mark ComplexTypeMark into the output.

To validate support of complex tensors for Reshape, the new layer test TestComplexReshape was added.

Example how to run the layer test:

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

Example Pull Requests

Extensions for Shape, Mul, Reshape in https://github.com/openvinotoolkit/openvino/pull/21477
Extension for Roll operation in https://github.com/openvinotoolkit/openvino/pull/20860

Resources

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

Contact points

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

Ticket

No response

AishwaryaDekhane commented 6 months ago

.take

github-actions[bot] commented 6 months ago

Thank you for looking into this issue! Please let us know if you have any questions or require any help.

p-wysocki commented 6 months ago

Hello @AishwaryaDekhane, can we help you with anything?

AishwaryaDekhane commented 6 months ago

I'm getting multiple failures related to assertion error about the presence of certain parameters (params:0)

rkazants commented 6 months ago

Hi @AishwaryaDekhane,

we recently align tensor names for TF1 models so the test probably expect name with :0. Please check pytest with such update, for example, https://github.com/openvinotoolkit/openvino/blob/master/tests/layer_tests/tensorflow_tests/test_tf_ArgMinMax.py#L23 BTW, any update on this task?

Best regards, Roman

p-wysocki commented 5 months ago

I'm reopening the issue due to current assignee's inactivity.

MonalSD commented 5 months ago

.take

github-actions[bot] commented 5 months ago

Thank you for looking into this issue! Please let us know if you have any questions or require any help.

openvinotoolkit / openvino