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fix RM accuracy

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hiyouga
2023-06-28 01:40:13 +08:00
parent 9cb1af71f3
commit 7826a8ca77
1 changed files with 6 additions and 3 deletions
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src/utils/pairwise.py
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@@ -13,8 +13,7 @@ logger = get_logger(__name__)
def compute_accuracy(eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]: def compute_accuracy(eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]:
preds, _ = eval_preds preds, _ = eval_preds
preds = np.array(preds) return {"accuracy": (preds[0] > preds[1]).sum() / len(preds[0])}
return {"accuracy": (preds[:, 0] > preds[:, 1]).sum() / len(preds)}
class PairwiseDataCollatorWithPadding(DynamicDataCollatorWithPadding): class PairwiseDataCollatorWithPadding(DynamicDataCollatorWithPadding):
@@ -49,9 +48,13 @@ class PairwisePeftTrainer(PeftTrainer):
We use score on the EOS token to represent reward of the whole sentence. We use score on the EOS token to represent reward of the whole sentence.
Subclass and override to inject custom behavior. It should not be directly used by external scripts. Subclass and override to inject custom behavior. It should not be directly used by external scripts.
Note that the first element will be removed from the output tuple.
See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
""" """
batch_size = inputs["input_ids"].size(0) // 2 batch_size = inputs["input_ids"].size(0) // 2
_, _, values = model(**inputs) _, _, values = model(**inputs)
r_accept, r_reject = values[:, -1].split(batch_size, dim=0) r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean() loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
return (loss, torch.stack((r_accept, r_reject), dim=-1)) if return_outputs else loss return (loss, [loss, r_accept, r_reject]) if return_outputs else loss