Passing custom attributes from TF op to TFL (MLIR)

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We are experimenting to have our own MLIR stack to import TFL models and compile them for a specific accelerator. We are also building our own runtime/simulator to run these imported models. Our current way of working is that we freeze the TF.keras model, convert to TFL and then use flatbuffer_translate to get MLIR tfl dialect.

Towards this goal, however, I need to pass some attributes with some operations special to our target architecture. I initially wanted to pass these attributes with an operation such as conv2d. However, I don't know a way (if at all possible) to extend such operations that are natively defined / supported by tfl.

I then tried to define and register a custom TF operation with its custom attributes. The semantics of the operation would be an identity function but I just intended to use it as a placeholder to pass my attributes. Once I tried this, I saw that the resulting TFL MLIR contains my custom op, however, the attributes are encoded into an opaque type with a byte stream as its value.

I could not find much documentation related to how I can decode these attributes. I'd appreciate any tips on decoding or any other suggestion to help achieving our goal.

Thanks!

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jpienaar On

How are you encoding it in the input? I'm guessing you are seeing it encoded as an AttrValue (https://github.com/tensorflow/tensorflow/blob/b1e813e2ec9634ec0e6562b836e372e393f3de43/tensorflow/core/framework/attr_value.proto#L18) and so you'd decode it as you would a protobuf normally.