Draft: Initial draft implementation of CFG for LLM #996
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Based on the paper
Sanchez, Guillaume, et al.
"Stay on topic with Classifier-Free Guidance."
arXiv preprint arXiv:2306.17806 (2023).
a draft implementation of classifier free guidance.
This is simply for sharing internally and might very well be
completely wrong. It is debatable if we should expose such
a feature as a flag to the network or make it a separate
classifier instance (or a mixin). In the past we were
very much against special (potentially short-lived) feature
flags and it was much nicer to have this implemented as
an addon/callback. We might need to do something similar
here as well.
Collaborator
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The paper in question is this one: https://arxiv.org/abs/2306.17806 Note that this method should have a greater effect the longer the labels are. Some random comments:
If this method works really well, I can see it being added explicitly. Alternatively, we could have a |
- Makes it possible to set gamma parameter - Setting it to `None` disabled functionality completely
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- `label_id` was misleading since it is actually a list of token ids related to a label and not a scalar value. Also the general process of generating logits it not related to labels at all but rather just to tokens - `kwargs` was named to be similar to transformers `generate` convention but is meant to be passed to `generate` and is therefore, in the context of `generate_logits` a model input. This should help the reader distinguish between expected input (`token_ids`) and model input (`model_input`)
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Based on the paper
a draft implementation of classifier free guidance.
This is simply for sharing internally and might very well be completely wrong. It is debatable if we should expose such a feature as a flag to the network or make it a separate classifier instance (or a mixin). In the past we were very much against special (potentially short-lived) feature flags and it was much nicer to have this implemented as an addon/callback. We might need to do something similar here as well.
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