Adding a new model to Transformers with additional dependencies

🤗Transformers
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Hi,

currently I am implementing a PyTorch version of the Tapas algorithm proposed by Google AI. Since the algorithm is based on BERT, I am using parts of modeling_bert.py to implement it. However, the original implementation of the Tapas algorithm (which is written in Tensorflow 1) contains operations on segmented tensors, such as:

  • tf.math.unsorted_segment_mean
  • tf.math.unsorted_segment_sum
  • tf.math.unsorted_segment_max
  • tf.math.unsorted_segment_min

These operations do not exist in PyTorch. However, there’s a small extension library called pytorch_scatter which implements these operations. So I would use this library in my implementation.

Is there any chance my implementation will be added to the Transformers library if it relies on libraries other than Pytorch? I read in the templates README of the Transformers repository that “the package is also designed to be as self-consistent and with a small and reliable set of packages dependencies. In consequence, additional dependencies are usually not allowed when adding a model but can be allowed for the inclusion of a new tokenizer (recent examples of dependencies added for tokenizer specificities include sentencepiece and sacremoses).”

Thanks!

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