5a2f76ede044b4904af9461e18253f2929cfc5a4,fairseq/models/levenshtein_transformer.py,LevenshteinTransformerModel,initialize_output_tokens,#LevenshteinTransformerModel#Any#Any#,435

Before Change

            *initial_output_tokens.size()
        ).type_as(encoder_out["encoder_out"])

        initial_attn = None
        if getattr(self.decoder.layers[-1], "need_attn", False):
            initial_attn = initial_output_tokens.new_zeros(
                src_tokens.size(0), 2, src_tokens.size(1)
            )
        return {
            "output_tokens": initial_output_tokens,
            "output_scores": initial_output_scores,
            "attn": initial_attn,
        }


class LevenshteinTransformerDecoder(TransformerDecoder):
    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):

After Change

        return [output_tokens, output_scores, attn, 0, 0]

    def initialize_output_tokens(self, encoder_out, src_tokens):
        initial_output_tokens = torch.cat(
            [
                torch.zeros(src_tokens.size(0), 1).fill_(self.bos),
                torch.zeros(src_tokens.size(0), 1).fill_(self.eos),
            ],
            1,
        )

        initial_output_scores = torch.zeros_like(initial_output_tokens).to(
            encoder_out[0]
        )

        initial_attn = torch.empty([0])
        if getattr(self.decoder.layers[-1], "need_attn", True):
            initial_attn = torch.zeros([src_tokens.size(0), 2, src_tokens.size(1)]).to(
                initial_output_tokens
            )

        return [initial_output_tokens, initial_output_scores, initial_attn, 0, 0]


class LevenshteinTransformerDecoder(TracingTransformerDecoder):
    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):

Instances