openvino/model-optimizer/extensions/front/ATenToEmbeddingBag.py

"""
 Copyright (C) 2018-2020 Intel Corporation

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

from extensions.ops.embedding_bag import EmbeddingBagOffsetsSum, EmbeddingBagPackedSum
from extensions.ops.rank import Rank
from mo.front.common.partial_infer.utils import int64_array
from mo.front.common.replacement import FrontReplacementPattern
from mo.front.tf.graph_utils import create_op_with_const_inputs
from mo.graph.graph import Graph, rename_node
from mo.ops.broadcast import Broadcast
from mo.ops.concat import Concat
from mo.ops.shape import Shape
from mo.ops.unsqueeze import Unsqueeze
from mo.utils.shape import node_to_get_shape_value_of_indices, get_canonical_axis_index_node, \
    get_shape_values_by_indices_node


class AtenToEmbeddingBag(FrontReplacementPattern):
    """
    Converts the ATen layer to EmbeddingBag layer.
    """
    enabled = True

    def find_and_replace_pattern(self, graph: Graph):
        for node in graph.get_op_nodes(op='ATen', operator='embedding_bag'):
            assert node.soft_get('mode') == 0, 'ATen::embedding_bag has unsupported mode, only "sum" ' \
                                               'mode is supported for node {}.'.format(node.id)
            node_name = node.soft_get('name', node.id)
            rename_node(node, node_name + '/TBR')
            is_packed = False
            if len(node.in_ports()) < 3 or node.in_port(2).disconnected():
                is_packed = True
                embedding_bag = EmbeddingBagPackedSum(graph, {'name': node_name}).create_node()
            else:
                embedding_bag = EmbeddingBagOffsetsSum(graph, {'name': node_name}).create_node()
                node.in_port(2).get_connection().set_destination(embedding_bag.in_port(2))
            rename_node(embedding_bag, node_name)
            node.in_port(0).get_connection().set_destination(embedding_bag.in_port(0))
            node.in_port(1).get_connection().set_destination(embedding_bag.in_port(1))
            node.out_port(0).get_connection().set_source(embedding_bag.out_port(0))
            if len(node.in_ports()) == 4 and not node.in_port(3).disconnected():
                if is_packed:
                    node.in_port(3).get_connection().set_destination(embedding_bag.in_port(2))
                else:
                    # connect per_sample_weights
                    node.in_port(3).get_connection().set_destination(embedding_bag.in_port(4))

                    weights_shape_node = Shape(graph, {'name': node_name + '/WeightsShape'}).create_node()

                    weights_rank_node = Rank(graph, {'name': node_name + '/WeightsRank'}).create_node()
                    last_dim_node = get_canonical_axis_index_node(weights_rank_node, -1)
                    weights_last_dim = get_shape_values_by_indices_node(weights_shape_node, last_dim_node)

                    weights_first_dim = node_to_get_shape_value_of_indices(weights_shape_node, [0])

                    zero_col_node = create_op_with_const_inputs(graph, Broadcast, {0: int64_array([0])},
                                                                {'name': node_name + '/Broadcast'})
                    zero_col_node.in_port(1).connect(weights_last_dim.out_port(0))

                    default_embeddings_node = create_op_with_const_inputs(graph, Unsqueeze, {1: int64_array(0)},
                                                                          {'name': node_name + '/Unsqueeze'})
                    default_embeddings_node.in_port(0).connect(zero_col_node.out_port(0))

                    # expand embedding table with zeros
                    weights_concat = Concat(graph, {'axis': 0, 'in_ports_count': 2,
                                                    'name': node_name + '/Concat'}).create_node()
                    embedding_bag.in_port(0).get_connection().set_destination(weights_concat.in_port(0))
                    weights_concat.in_port(0).get_connection().add_destination(weights_shape_node.in_port(0))
                    weights_concat.in_port(0).get_connection().add_destination(weights_rank_node.in_port(0))
                    weights_concat.in_port(1).connect(default_embeddings_node.out_port(0))
                    weights_concat.out_port(0).connect(embedding_bag.in_port(0))

                    # point default index to expanded part of embedding table
                    weights_first_dim.out_port(0).connect(embedding_bag.in_port(3))