06a07d00d11fb103a079a9adb0de4c3865a7f69e,spotlight/layers.py,BloomEmbedding,forward,#BloomEmbedding#Any#,138

Before Change

        // The iterative solution is necessitated by making
        // the implementation compatible with sequence-based models,
        // 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)

After Change

        masked_indices.remainder_(self.compressed_num_embeddings)
        masked_indices = Variable(masked_indices)

        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

Instances