27568a7ebed1a35f08ac0390f35b3de9b8dad0dd,fairseq/models/cmlm_transformer.py,CMLMNATransformerModel,forward_decoder,#CMLMNATransformerModel#Any#Any#Any#,56

Before Change

            output_tokens.masked_fill_(skeptical_mask, self.unk)
            output_scores.masked_fill_(skeptical_mask, 0.0)

        return {"output_tokens": output_tokens, "output_scores": output_scores}


@register_model_architecture("cmlm_transformer", "cmlm_transformer")
def base_architecture(args):

After Change

    def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):

        step = decoder_out.step
        max_step = decoder_out.max_step

        output_tokens = decoder_out.output_tokens
        output_scores = decoder_out.output_scores

        // execute the decoder
        output_masks = output_tokens.eq(self.unk)
        _scores, _tokens = self.decoder(
            output_tokens, encoder_out=encoder_out, decoding_format=decoding_format
        )
        output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
        output_scores.masked_scatter_(output_masks, _scores[output_masks])

        // skeptical decoding (depend on the maximum decoding steps.)
        if (step + 1) < max_step:
            skeptical_mask = _skeptical_unmasking(
                output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step
            )

            output_tokens.masked_fill_(skeptical_mask, self.unk)
            output_scores.masked_fill_(skeptical_mask, 0.0)

        return decoder_out._replace(
            output_tokens=output_tokens,
            output_scores=output_scores,
            attn=None,
        )


@register_model_architecture("cmlm_transformer", "cmlm_transformer")
def base_architecture(args):

Instances