1c6679294848f303a361cba7b306b760e299bd9c,fairseq/sequence_scorer.py,SequenceScorer,generate,#SequenceScorer#Any#Any#,21

Before Change

            score_i = avg_probs_i.sum() / tgt_len
            if avg_attn is not None:
                avg_attn_i = avg_attn[i, start_idxs[i]:]
                _, alignment = avg_attn_i.max(dim=0)
            else:
                avg_attn_i = alignment = None
            hypos.append([{
                "tokens": ref,

After Change

            model.eval()
            decoder_out = model.forward(**net_input)
            attn = decoder_out[1]
            if type(attn) is dict:
                attn = attn.get("attn", None)

            batched = batch_for_softmax(decoder_out, orig_target)
            probs, idx = None, 0
            for bd, tgt, is_single in batched:
                sample["target"] = tgt

Instances