170 lines
8.1 KiB
Python
170 lines
8.1 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 networkx as nx
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from extensions.ops.gather import Gather
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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from mo.ops.const import Const
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def get_node_with_permutation(node: Node, port_info: str):
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node_type, port = port_info.split(':')
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port = int(port)
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return node.in_node(port) if node_type == 'input' else node.out_node(port)
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def axis(op_node: Node, port_info: str, input_port: int):
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"""
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Performs layout change related transformation of the data on the in_port_idx port of op_node.
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Translates shape indexes from one layout to another according to inverse permutation
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Transformation inserts Gather operation with
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permutation as 0-port input data and
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actual data to translate as 1-port input indexes of Gather
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For example:
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NHWC Reduce operation has 0-port input with data of shape [1, 2, 3, 4] and
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1-port input with axis indices [0, 1].
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After translating such operation to NCHW layout:
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0-port input shape = [1, 4, 2, 3]
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1-port input axis indices = [0, 2]
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"""
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graph = op_node.graph
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permutation_data_node = get_node_with_permutation(op_node, port_info)
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assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
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'port_info "{}".'.format(permutation_data_node.id,
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op_node.id, port_info)
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permutation = permutation_data_node.permutation
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if len(permutation.perm) == 0:
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return
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data_node = op_node.in_node(input_port)
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const = Const(graph, {'value': permutation.inv, 'need_shape_inference': True}).create_node_with_data()
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axis_const = Const(graph, {'value': int64_array(0)}).create_node_with_data()
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gather = Gather(graph, {'name': op_node.name + '/AxisGather', 'need_shape_inference': True}).create_node_with_data(
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[const, data_node, axis_const])
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attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
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graph.add_edge(gather.id, op_node.id, **attrs)
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graph.remove_edge(data_node.id, op_node.id)
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op_node['need_shape_inference'] = True
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def order(op_node: Node, port_info: str, input_port: int):
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"""
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Performs layout change related transformation of the data on the in_port_idx port of op_node.
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Translates ordered shape indexes from one layout to another according to permutation
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Transformation inserts two Gather operations
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1 Gather reorders data to new layout according to direct permutation:
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actual data to translate as 1-port input indexes of Gather and
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permutation as 0-port input data
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2 Gather translates shape indexes from one layout to another according to inverse permutation
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permutation as 0-port input data and
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actual data to translate as 1-port input indexes of Gather
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For example:
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NHWC Transpose operation has 0-port input with data of shape [1, 2, 3, 4] and
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1-port input with new order indices [0, 1, 3, 2].
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After translating such operation to NCHW layout:
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0-port input shape = [1, 4, 2, 3]
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1 phase (after first Gather insertion):
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1-port input order indices = [0, 2, 1, 3]
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2 phase (after second Gather insertion):
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1-port input order indices = [0, 3, 2, 1]
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"""
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graph = op_node.graph
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permutation_data_node = get_node_with_permutation(op_node, port_info)
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assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
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'port_info "{}".'.format(permutation_data_node.id,
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op_node.id, port_info)
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permutation = permutation_data_node.permutation
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if len(permutation.perm) == 0:
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return
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data_node = op_node.in_node(input_port)
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const = Const(graph, {'value': permutation.perm, 'need_shape_inference': True}).create_node_with_data()
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axis_const = Const(graph, {'value': int64_array(0)}).create_node_with_data()
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gather = Gather(graph, {'name': op_node.name + '/OrderGather_1',
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'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])
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const_1 = Const(graph, {'value': permutation.inv, 'need_shape_inference': True}).create_node_with_data()
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axis_const_1 = Const(graph, {'value': int64_array(0)}).create_node_with_data()
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gather_1 = Gather(graph, {'name': op_node.name + '/OrderGather_2',
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'need_shape_inference': True}).create_node_with_data([const_1, gather, axis_const_1])
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attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
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graph.add_edge(gather_1.id, op_node.id, **attrs)
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graph.remove_edge(data_node.id, op_node.id)
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op_node['need_shape_inference'] = True
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def shape(op_node: Node, port_info: str, input_port: int):
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graph = op_node.graph
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permutation_data_node = get_node_with_permutation(op_node, port_info)
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assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
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'port_info "{}".'.format(permutation_data_node.id,
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op_node.id, port_info)
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permutation = permutation_data_node.permutation
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if len(permutation.perm) == 0:
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return
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data_node = op_node.in_node(input_port)
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const = Const(graph, {'value': permutation.perm, 'need_shape_inference': True}).create_node_with_data()
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axis_const = Const(graph, {'value': int64_array(0)}).create_node_with_data()
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gather = Gather(graph, {'name': op_node.name + '/ShapeGather',
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'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])
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attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
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graph.add_edge(gather.id, op_node.id, **attrs)
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graph.remove_edge(data_node.id, op_node.id)
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# need to run manually to override output shape value to resolve shape collision for nodes with
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# 'correct_data_layout' output port attrs
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op_node.infer(op_node)
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class PermuteInputs:
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common_inv_permutation = lambda node, port_info, input_port: axis(node, port_info, input_port)
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input_permutes = {
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'axis': common_inv_permutation,
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'order': lambda node, port_info, input_port: order(node, port_info, input_port),
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'shape': lambda node, port_info, input_port: shape(node, port_info, input_port),
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}
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def set_input_permutation(self, node1: Node, node2: Node, port_info: str, permutation_rule: str):
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"""
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Sets input permutation attribute on the edge between node1 and node2.
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Input permutation consists of function that perform input permutation and
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input port info 'input' or 'output' + <port_number> that points on the input with PermuteAttr.Permutation which
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current input depends on
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"""
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assert permutation_rule in self.input_permutes, 'No `{}` permutation rule in {}'.format(permutation_rule,
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__class__.__name__)
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nx.set_edge_attributes(G=node1.graph,
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values={(node1.id, node2.id, 0): (self.input_permutes[permutation_rule],
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port_info)},
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name='input_permutation')
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