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@rbahumi rbahumi commented Mar 20, 2023

This notebook is an example of a (working) "Hacky" solution for wrapping the full 'generate' functionality inside a "forward" function. The perpose of this is to start a discussion and be a suggention on how to make the this functionality TorchScriptable.

To do so I:

  1. Inherited from T5Model:
  • extracted the decoding code from t5.forward() function to a standalone 'decode' function that returns a specific type.
  • added the GenerationUtils's 'generate' functionality as a class method (similar to HuggingFace).
  1. Added TorchScriptableT5, a module that implements the full generative logic in the forward method.
  2. Helper classes that build a jit (TorchScript) model from a predefined T5 Bundle

This notebook is an example of a (working) "Hacky" solution for wrapping the full 'generate' functionality inside a "forward" function.
The perpose of this is to start a discussion and be a suggention on how to make the this functionality TorchScriptable.

To do so, I:

1. T5TorchGenerative: inherited from T5Model:
- extracted the decoding code from t5.forward() function to a standalone 'decode' function that returns a specific type.
- added the GenerationUtils's 'generate' functionality as a class method (similar to HuggingFace).
2. Added TorchScriptableT5, a module that implements the full generative logic in the forward method.
3. Helper classes that build a jit (TorchScript) model from a predefined T5 Bundle
@joecummings joecummings merged commit dc94b32 into pytorch:main Jan 8, 2024
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4 participants