bed4f2dd50ff54eb7629362c3d03a3b758e0745a,spotlight/layers.py,BloomEmbedding,forward,#BloomEmbedding#Any#,115

Before Change

        batch_size = indices.size()[0]

        compressed_indices = (indices
                              .unsqueeze(1)
                              .expand(batch_size, self.num_hash_functions))
        masks = self._mask_tensor.expand(batch_size, self.num_hash_functions)
        compressed_indices = compressed_indices.detach()
        compressed_indices = Variable((compressed_indices.data * masks.data) %
                                      self.compressed_num_embeddings)

        return self.embeddings(compressed_indices).mean(1)

After Change

        // where the embedding indices are already two-dimensional.
        embedding = self.embeddings(indices * self._masks[0] % self.compressed_num_embeddings)

        for mask in self._masks[1:]:
            embedding += self.embeddings(indices * mask % self.compressed_num_embeddings)

        embedding /= self.num_hash_functions

        return embedding

Instances