bc51dbc0c56f68ed30857755026633f78eef1ae8,spotlight/layers.py,BloomEmbedding,forward,#BloomEmbedding#Any#,171

Before Change

        if masked_indices.dim() == 2:
            embedding = self.embeddings(masked_indices).mean(1)
        else:
            embedding = self.embeddings(masked_indices[:, :, 0])

            for idx in range(1, len(self._masks)):
                embedding += self.embeddings(masked_indices[:, :, idx])

            embedding /= len(self._masks)

        return embedding

After Change

            batch_size, seq_size = indices.size(0), 1

        if not indices.is_contiguous():
            indices = indices.contiguous()
        indices = indices.data.view(batch_size * seq_size, 1)

        torch.mul(
            indices.expand_as(masks),
            masks,

Instances