668c3ef362995c55633fde592354160fec1d1efd,onmt/encoders/transformer.py,TransformerEncoder,forward,#TransformerEncoder#Any#Any#,114

Before Change

        emb = self.embeddings(src)

        out = emb.transpose(0, 1).contiguous()
        words = src[:, :, 0].transpose(0, 1)
        w_batch, w_len = words.size()
        padding_idx = self.embeddings.word_padding_idx
        mask = words.data.eq(padding_idx).unsqueeze(1)  // [B, 1, T]

After Change

        emb = self.embeddings(src)

        out = emb.transpose(0, 1).contiguous()
        mask = ~sequence_mask(lengths).unsqueeze(1)
        // Run the forward pass of every layer of the tranformer.
        for layer in self.transformer:
            out = layer(out, mask)
        out = self.layer_norm(out)

Instances