EmbeddingSegmentsSum¶

Versioned name : EmbeddingSegmentsSum-3

Category : Sparse

Short description : Computes sums of segments of embeddings, without instantiating the intermediate embeddings.

Detailed description : This is sparse.segment_sum operation from Tensorflow. For each index in indices this operator gets values from data embedding table and sums all values belonging to each segment. Values in segment_ids define which segment index in indices tensor belong to, e.g. segments_ids with value [0,0,0,1,1,3,5,5] define 4 non empty segments other segments are empty, the number of segments is defined by num_segments input.

Attributes : EmbeddingSegmentsSum operation has no attributes.

Inputs :

1 : emb_table tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, ...] and of type T. Required.
2 : indices tensor of shape [num_indices] and of type T_IND. Required.
3 : segment_ids tensor of shape [num_indices] and of type T_IND with indices into the output Tensor. Values should be sorted and can be repeated. Required.
4 : num_segments scalar of type T_IND indicating the number of segments. Required.
5 : default_index scalar of type T_IND containing default index in embedding table to fill empty segments. If not provided empty segments are filled with zeros. Optional.
6 : per_sample_weights tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones.

Outputs :

1 : tensor of shape [num_segments, emb_dim1, emb_dim2, ...] and of type T containing embeddings for each bag.

Types

T : any numeric type.
T_IND : int32 or int64.

Example

<layer ... type="EmbeddingSegmentsSum" ... >
    <input>
        <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
            <dim>5</dim>
            <dim>2</dim>
        </port>
        <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
            <dim>4</dim>
        </port>
        <port id="2"/>    <!-- segment_ids value is: [0, 0, 2, 2] - second segment is empty -->
            <dim>4</dim>
        </port>
        <port id="3"/>    <!-- num_segments value is: 3 -->
        <port id="4"/>    <!-- default_index value is: 0 -->
        <port id="5"/>    <!-- per_sample_weigths value is: [0.5, 0.5, 0.5, 0.5] -->
            <dim>4</dim>
        </port>
    </input>
    <output>
        <port id="6">     <!-- output value is: [[-1.05, -1.2], [-0.2, -0.6], [-0.1, 0.4]] -->
            <dim>3</dim>
            <dim>2</dim>
        </port>
    </output>
</layer>