Rasa: Load models in finetune mode based on command line parameters.

[wochinge]

Load models for incremental training.

Depends on https://github.com/RasaHQ/rasa/issues/7328.

• [ ] load NLU pipeline in fine-tune mode as shown here
• [ ] load Core policies in fine-tune mode as show here
• [ ] pass in new number of epochs

[dakshvar22]

@wochinge @joejuzl Do you folks know how the load method for each ML component will be called during finetuning? For example, would there be an extra parameter passed to load function of the components to load them in "finetune" mode and also pass other configurations parameters like number of epochs? Here's how I had done it in the working version branch but I am assuming it wouldn't be exactly the same. If we can plan this ahead, it would unblock me on some of the implementation due for my PR. 🙏

[joejuzl]

@dakshvar22 I haven' started looking at this area of the code yet (working on https://github.com/RasaHQ/rasa/issues/7330 first) so it's hard for me to say. @wochinge Any opinions/thoughts?

[dakshvar22]

@joejuzl @wochinge Making a proposal to see if we can reach to a consensus quickly on the above question:

1. New number of epochs get changed inside the meta parameter that is passed to the load method of all components.
2. Add a boolean parameter to load method - finetune_mode which is set to True if the component is loaded in finetune mode.

What do you folks think?

[joejuzl]

@dakshvar22 So the solution we have come up with (today!) is as follows:

For NLU:

• In Interpreter.load we pass in the new config along with the old model.
• Interpreter.load updates the old metadata with the new epochs and calls Interpreter.create with a flag should_finetune.
• Interpreter.create sets should_finetune in the context which gets passed to each component.
• Trainer.__init__ now optionally takes the old loaded model and uses that pipeline e.g. self.pipeline = old_model.pipeline

[dakshvar22]

@joejuzl Perfect! Small clarification:

Interpreter.create sets should_finetune in the context which gets passed to each component.

The context dictionary is what will be passed as part of the kwargs argument of load() methods of each component?

[joejuzl]

The context dictionary is what will be passed as part of the kwargs argument of load() methods of each component?

Yes exactly, via component_builder.load_component

[joejuzl]

For Core:

• The new config is passed from Agent.load -> PolicyEnsemble.load.
• Then the part of the new config for each policy is passed into its respective load which passes the new epochs into its constructor.
• Still not clear exactly how the should_finetune flag will be passed around. I guess through the same path @wochinge ?

[wochinge]

@dakshvar22 Our current approach would be to provide should_finetune through the constructor. The alternative would be to do so when calling train. Do you have preferences?

[dakshvar22]

@wochinge Do you mean for Core specifically or NLU components also?
Either ways, we don't need it when calling train. It definitely has to be passed to the load() method of the ML components(DIETClassifier, TEDPolicy) so that the model weights can be instantiated in "training" mode and then load() would pass this parameter to the constructor anyways. If you want to see an example of what I mean, here is how I do it for ML components inside NLU.