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[Good First Issue][TF FE]: Support complex tensors for Prod operations #23233
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category: TF FE
OpenVINO TensorFlow FrontEnd
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good first issue
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category: TF FE
OpenVINO TensorFlow FrontEnd
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Mar 4, 2024
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### Details: - Fixed fifth case of `atan2` implementation (it returned `pi/2` instead of `-pi/2`). - Moved `atan2` to `utils`. - Create helper functions for converting complex number from rectangular to polar form and the other way around. - Support complex tensors for `Prod` operations + unit tests. ### Tickets: - [None](#23233) ### Resources used: - https://math.stackexchange.com/questions/1938894/imaginary-part-of-a-product-of-n-complex-numbers - https://en.m.wikipedia.org/wiki/Euler%27s_formula --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
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Labels
category: TF FE
OpenVINO TensorFlow FrontEnd
good first issue
Good for newcomers
no_stale
Do not mark as stale
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 Prod operation on complex type tensor, you need to extend the corresponding loader for Prod.
What needs to be done?
The existing loader for Prod 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: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 andx[...,1]
- imaginary parts.On the first step, we update
default_op_checks
withtrue
flag to indicate that loader forReshape
operation now handles complex tensors:Secondly, we check if complex type mark exists by anticipated inputs. This mark indicates that input tensor of complex type:
Thirdly, we retrieve a floating-point tensor (with additional dimension to store real and imaginary parts) simulating complex tensor:
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 markComplexTypeMark
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:
Example Pull Requests
Shape
,Mul
,Reshape
in [TF FE] Complex type support extended for Separate Bass model. #21477Roll
operation in [TF FE] Support Complex Tensors #20860Resources
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