86 lines
3.7 KiB
Python
86 lines
3.7 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from extensions.ops.gather import Gather
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from extensions.ops.range import Range
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
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from mo.graph.graph import Graph, rename_nodes, Node
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from mo.ops.unsqueeze import Unsqueeze
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class ExpandRangeConstant(FrontReplacementSubgraph):
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"""
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Searches for Constant operations filled with range values starting from 0 and replaces it with Range operation
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Faced in ONNX BERT -- replacing it makes model reshape-able by sequence length
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WARNING: true BIDIRECTIONAL mode of Broadcast could cause issues
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(the probability is small, so we decided to keep the optimization)
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value_input[1, X] (value=range(0,X)) shape_input[Y, 1]
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\ /
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Broadcast(mode='bidirectional') [Y, X]
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"""
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enabled = True
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def find_and_replace_pattern(self, graph: Graph):
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for node in graph.get_op_nodes(type='Broadcast'):
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value = node.in_port(0).get_source().node
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if value.soft_get('type') == 'Const':
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self.replace(node, value)
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@staticmethod
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def replace(node: Node, const: Node):
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graph = node.graph
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shape = const.shape
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const_name = const.soft_get('name', const.id)
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non_one_dims = np.argwhere(shape != 1).flatten()
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one_dims = np.argwhere(shape == 1).flatten()
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if not (non_one_dims.size == 1 and 5 < np.prod(shape) < 500):
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# (5;500) range is deduced to affect less models
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return
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value = const.value
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if not np.array_equal(np.arange(0, np.prod(shape), 1).reshape(shape), value):
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return
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positive_idx = non_one_dims.item(0)
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negative_idx = positive_idx - len(shape)
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gather = create_op_with_const_inputs(graph, Gather, {1: int64_array(negative_idx), 2: int64_array(0)},
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{'name': node.soft_get('name', node.id) + '/BroadcastingDim'})
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range_node = create_op_with_const_inputs(graph, Range,
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{0: np.array(0, dtype=value.dtype),
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2: np.array(1, dtype=value.dtype)},
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{'name': const_name + '/Range', 'dtype': value.dtype})
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node.in_port(1).get_connection().add_destination(gather.in_port(0))
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gather.out_port(0).connect(range_node.in_port(1))
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node.in_port(0).get_connection().set_source(range_node.out_port(0))
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if one_dims.size:
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unsqueeze = create_op_node_with_second_input(graph, Unsqueeze, one_dims,
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{'name': const_name + '/KeepShape'})
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range_node.out_port(0).get_connection().insert_node(unsqueeze)
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rename_nodes([(const, const_name + '/ToBeDeleted'), (unsqueeze, const_name)])
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else:
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rename_nodes([(const, const_name + '/ToBeDeleted'), (range_node, const_name)])
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