1f7f6d5b7481195b0214e835bb5d782db768d71c,parlai/agents/transformer/modules.py,TransformerEncoder,__init__,#TransformerEncoder#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,201

Before Change

                embedding_size is None or embedding_size == embedding.weight.shape[1]
            ), "Embedding dim must match the embedding size."

        if embedding is not None:
            self.embeddings = embedding
        else:
            assert False
            assert padding_idx is not None
            self.embeddings = nn.Embedding(
                vocabulary_size, embedding_size, padding_idx=padding_idx
            )
            nn.init.normal_(self.embeddings.weight, 0, embedding_size ** -0.5)

        // create the positional embeddings
        self.position_embeddings = nn.Embedding(n_positions, embedding_size)
        if not learn_positional_embeddings:
            create_position_codes(
                n_positions, embedding_size, out=self.position_embeddings.weight

After Change

        padding_idx=None,
        n_positions=1024,
    ):
        super().__init__()
        self.embedding_size = embedding_size
        self.ffn_size = ffn_size
        self.n_layers = n_layers
        self.n_heads = n_heads

Instances