""" 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. """ import logging as log import numpy as np from extensions.middle.AddIsCyclicAttribute import AddIsCyclicAttribute from extensions.ops.TensorIterator_ops import TensorIteratorInput from mo.graph.graph import Graph from mo.middle.replacement import MiddleReplacementPattern class SmartInputMatcher(MiddleReplacementPattern): """ This pattern match partitioned inputs for TensorIterator in dynamic_rnn loops in TF. The structure of pattern without Data nodes between ops. Every node is named as op attribute of this node (data nodes is marked by (data)): TensorArray | | v v Condition (data) Flow(data) Handle(data)-------------- | | | | | v v v v Value (data) -> StridedSlice () -> Range(0;1) -> TensorArrayScatter -> Enter -> TensorArrayRead | ^ |__________________________________________________| """ enabled = True graph_condition = [lambda graph: graph.graph['is_cyclic']] def run_after(self): return [AddIsCyclicAttribute] def run_before(self): from extensions.middle.TensorIteratorMerge import TensorIteratorMerge return [TensorIteratorMerge] @staticmethod def pattern(): return dict( nodes=[ ('TensorArray', dict(kind='op', op='TensorArrayV3')), ('TensorArray_handle', dict(kind='data')), ('TensorArray_flow', dict(kind='data')), ('Enter', dict(kind='op', op='Enter')), ('Enter_data', dict(kind='data')), ('stack', dict(kind='op', op='Const')), ('stack_data', dict(kind='data')), ('stack_1', dict(kind='op', op='Const')), ('stack_1_data', dict(kind='data')), ('stack_2', dict(kind='op', op='Const')), ('stack_2_data', dict(kind='data')), ('start', dict(kind='op', op='Const')), ('start_data', dict(kind='data')), ('delta', dict(kind='op', op='Const')), ('delta_data', dict(kind='data')), ('StridedSlice', dict(kind='op', op='StridedSlice')), ('StridedSlice_data', dict(kind='data')), ('range', dict(kind='op', op='Range')), ('range_data', dict(kind='data')), ('TensorArrayScatter', dict(kind='op', op='TensorArrayScatterV3')), ('TensorArrayScatter_data', dict(kind='data')), ('Enter_1', dict(kind='op', op='Enter')), ('Enter_1_data', dict(kind='data')), ('TensorArrayRead', dict(kind='op', op='TensorArrayReadV3')), ('TensorArrayRead_data', dict(kind='data')), ('Condition_data', dict(kind='data')), ], edges=[ ('TensorArray', 'TensorArray_handle'), ('TensorArray', 'TensorArray_flow'), ('TensorArray_handle', 'Enter'), ('Enter', 'Enter_data'), ('stack', 'stack_data'), ('stack_1', 'stack_1_data'), ('stack_2', 'stack_2_data'), ('stack_data', 'StridedSlice', {'in': 1}), ('stack_1_data', 'StridedSlice', {'in': 2}), ('stack_2_data', 'StridedSlice', {'in': 3}), ('StridedSlice', 'StridedSlice_data'), ('StridedSlice_data', 'range', {'in': 1}), ('start', 'start_data'), ('delta', 'delta_data'), ('start_data', 'range', {'in': 0}), ('delta_data', 'range', {'in': 2}), ('range', 'range_data'), ('range_data', 'TensorArrayScatter'), ('TensorArray_handle', 'TensorArrayScatter'), ('TensorArray_flow', 'TensorArrayScatter'), ('TensorArrayScatter', 'TensorArrayScatter_data'), ('TensorArrayScatter_data', 'Enter_1'), ('Enter_1', 'Enter_1_data'), ('Enter_data', 'TensorArrayRead'), ('Enter_1_data', 'TensorArrayRead'), ('Condition_data', 'TensorArrayRead'), ('TensorArrayRead', 'TensorArrayRead_data'), ], ) @staticmethod def replace_pattern(graph: Graph, match: dict): log.debug('================== SmartInputFind ===============') assert match['Enter_data'].value is not None assert match['stack_data']['value'][0] == 0 and match['stack_1_data']['value'][0] == 1 and \ match['stack_2_data']['value'][0] == 1 assert match['start_data']['value'] == 0 and match['delta_data']['value'] == 1 ta_size_data = match['TensorArray'].in_node() ta_size = ta_size_data.in_node() value = match['TensorArrayScatter'].in_node(2) start, end = None, None if 0 in ta_size.in_nodes(): shape = match['StridedSlice'].in_node(0).in_node(0) # Case when value for Strided slice is Const, not Shape if shape['kind'] == 'op' and shape['op'] == 'Const': start = 0 end = shape.value[0] log.warning("You network cannot be reshaped since shapes of placeholders is a contants." "Please, provide non-constant shapes. ") # Create input node with params # axis == 0 because in TensorArray we ALWAYS iterate over 0 axis, other params will be fill later (with # condition) input_node = TensorIteratorInput(graph, dict(axis=0, start=start, stride=None, part_size=None, external_port_id=str(match['Enter_data'].value), internal_layer_id=match['TensorArrayRead_data'].id, name=match['TensorArrayRead'].name + '/TensorIteratorInput_' )) input_node.create_node_with_data(inputs=[ta_size_data, value, match['Condition_data']], data_nodes=[match['TensorArrayRead_data']]) # Delete useless nodes safe_nodes = ['TensorArrayRead_data', 'Condition', 'Condition_data'] nodes_for_remove = [] for node in match.keys(): if node not in safe_nodes: nodes_for_remove.append(match[node].id) graph.remove_nodes_from(nodes_for_remove) class SimpleInputMatcher(MiddleReplacementPattern): enabled = True graph_condition = [lambda graph: graph.graph['is_cyclic']] def run_after(self): from extensions.middle.DeleteNotExecutable import DeleteNotExecutable return [DeleteNotExecutable] def run_before(self): from extensions.middle.TensorIteratorMerge import TensorIteratorMerge return [TensorIteratorMerge] """ This pattern match simple inputs (without partitions) in while loops in TF (this inputs are set by Enter nodes). """ @staticmethod def pattern(): return dict( nodes=[ ('Enter', dict(kind='op', op='Enter')), ], edges=[ ], ) @staticmethod def replace_pattern(graph: Graph, match: dict): log.debug('================== SimpletInputFind ===============') input_node = TensorIteratorInput(graph, dict(external_port_id=None, internal_layer_id=None, name=match['Enter'].name + '/TensorIteratorInput_' )) input_node.create_node_with_data(inputs=[match['Enter'].in_node()], data_nodes=[match['Enter'].out_node()]) # Delete useless nodes graph.remove_nodes_from([match['Enter'].id]) class BackEdgeSimpleInputMatcher(MiddleReplacementPattern): enabled = True graph_condition = [lambda graph: graph.graph['is_cyclic']] def run_after(self): return [SimpleInputMatcher] def run_before(self): from extensions.middle.TensorIteratorMerge import TensorIteratorMerge return [TensorIteratorMerge] @staticmethod def pattern(): return dict( nodes=[ ('BackEdge', dict(kind='op', op='TensorIteratorBackEdge')), ], edges=[ ], ) @staticmethod def replace_pattern(graph: Graph, match: dict): log.debug('================== SimpleBackEdgeInputFind ===============') assert len(match['BackEdge'].in_nodes()) == 3 condition = match['BackEdge'].in_node(2) init_input = match['BackEdge'].in_node(0) cycle_input = match['BackEdge'].in_node(1) # We need to create new TensorItertorInput node only if this node doesn't exist already. if (len(init_input.in_nodes()) == 0 or \ (len(init_input.in_nodes()) == 1 and init_input.has_valid('value') and init_input.in_node(0).soft_get('op') != 'TensorIteratorInput')): input_node = TensorIteratorInput(graph, dict(external_port_id=None, internal_layer_id=None, name=match['BackEdge'].name + '/TensorIteratorInput_' )) # In case if data node has Constant producer if len(init_input.in_nodes()) == 1: graph.remove_edge(init_input.in_node(0).id, init_input.id) input_data_node = input_node.create_node_with_data(inputs=[init_input]) input_data_node.shape = np.array(init_input.shape, dtype=np.int64) graph.remove_edges_from([(init_input.id, match['BackEdge'].id)]) graph.add_edges_from([(input_data_node.id, match['BackEdge'].id, {'in': 0, 'out': 0})])