c473c620d141968c2ed3a62f60bf466d5790393e,reagent/models/seq2slate.py,Seq2SlateTransformerModel,encoder_output_to_scores,#Seq2SlateTransformerModel#Any#Any#Any#Any#,658

Before Change

        // order encoder_output by tgt_out_idx
        // slate_encoder_output shape: batch_size, tgt_seq_len, dim_model
        slate_encoder_output = encoder_output[
            torch.arange(batch_size, device=device).repeat_interleave(tgt_seq_len),
            (tgt_out_idx - 2).flatten(),
        ].reshape(batch_size, tgt_seq_len, -1)
        // encoder_scores shape: batch_size, tgt_seq_len

After Change

        // order encoder_output by tgt_out_idx
        // slate_encoder_output shape: batch_size, tgt_seq_len, dim_model
        slate_encoder_output = gather(encoder_output, tgt_out_idx - 2)
        // encoder_scores shape: batch_size, tgt_seq_len
        return self.encoder_scorer(slate_encoder_output).squeeze()

    def encode(self, state, src_seq, src_mask):

Instances