🔀 Rename get_batch_sample and add num_items_in_batch to compute_loss#2246
🔀 Rename get_batch_sample and add num_items_in_batch to compute_loss#2246qgallouedec merged 4 commits intomainfrom
get_batch_sample and add num_items_in_batch to compute_loss#2246Conversation
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qgallouedec
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get_batch_sample and add num_items_in_batch to compute_lossget_batch_sample and add num_items_in_batch to compute_loss
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Thanks for the fast fix! LGTM and I left a comment about some follow-up cleaning we can do with these generate methods in favour of extending the LogCompletions callback
| return SequentialSampler(self.train_dataset) | ||
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| def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]: | ||
| def generate_from_model_and_ref(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]: |
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Note for future: now that we have LogCompletions callback, it might be possible to enable the generative aspects of this method directly as a callback. We'd probably have to extend the LogCompletions callback to check if a reference model exists and generate for that too, but that seems better than having this code duplicated all over our preference trainers
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What does this PR do?
follows huggingface/transformers#34198
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