68fbfd1876c367323acf830736bae1af499cc0fe,onmt/modules/Transformer.py,TransformerDecoder,forward,#TransformerDecoder#Any#Any#Any#Any#,262

Before Change

        outputs = output.transpose(0, 1).contiguous()
        if state.previous_input is not None:
            outputs = outputs[state.previous_input.size(0):]
            attn = attn[:, state.previous_input.size(0):].squeeze()
            attn = torch.stack([attn])
        attns["std"] = attn
        if self._copy:
            attns["copy"] = attn

After Change

        tgt_pad_mask = tgt_words.data.eq(padding_idx).unsqueeze(1) \
            .expand(tgt_batch, tgt_len, tgt_len)

        saved_inputs = []
        for i in range(self.num_layers):
            prev_layer_input = None
            if state.previous_input is not None:
                prev_layer_input = state.previous_layer_inputs[i]

Instances