[MO] fix MarkNodesWithShapeValues (#5808)
* fix marking of nodes with shape values: corrected 'leaky' starting condition
* cleaned up code
* fixed t2t type collision error: removed strict assert from Pow; now type_infer is same as for other eltwise ops; added warnings about type alignment
* type cast for Pow according to ONNX spec
* fix highlight in dump_graph_for_graphviz
* soft_get('op')
* Revert "soft_get('op')"
This reverts commit cadfe18f
* Revert "type cast for Pow according to ONNX spec"
This reverts commit bf85ebf8
* applied review comments
* comment for ShapeOf bfs, removed Error from elementwise op
* reverted back dump_graph_for_graphviz: fill_color -> node_attrs
* applied last review comments: returned back Error, added type_infer into unit-test utils
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Pavel Esir authored and 
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@@ -2,12 +2,30 @@
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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from typing import List
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import numpy as np
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from extensions.middle.MarkSubgraphsWithCorrectLayout import MarkSubGraphsWithCorrectLayout
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from mo.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph
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from mo.graph.graph import Graph, Node
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shape_accepting_ops = {
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'Interpolate': [1, 2], # sizes, scales inputs
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'Reshape': [1], # shape
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'Broadcast': [1], # target_shape
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'ConvBackPropData ': [2], # output_shape
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'GroupConvolutionBackpropData ': [2], # output_shape
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'BatchToSpace': [1, 2, 3], # block_shape, crops_begin, crops_end
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'SpaceToBatch': [1, 2, 3], # block_shape, pads_begin, pads_end
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'StridedSlice': [1, 2, 3], # begin, end, strides
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'VariadicSplit': [2], # split_lengths
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'Tile': [1], # repeats input
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'TopK': [1], # K input
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'Pad': [1, 2], # pads_begin, pads_end
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'Range': [0, 1, 2], # start, stop, step inputs
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'OneHot': [1], # depth input
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}
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class MarkNodesWithShapeValues(BackReplacementPattern):
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@@ -34,45 +52,44 @@ class MarkNodesWithShapeValues(BackReplacementPattern):
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return []
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@staticmethod
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def get_operations_with_shape_inputs():
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return {
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'Interpolate': [1, 2], # sizes, scales inputs
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'Reshape': [1], # shape
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'Broadcast': [1], # target_shape
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'ConvBackPropData ': [2], # output_shape
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'GroupConvolutionBackpropData ': [2], # output_shape
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'BatchToSpace': [1, 2, 3], # block_shape, crops_begin, crops_end
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'SpaceToBatch': [1, 2, 3], # block_shape, pads_begin, pads_end
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'StridedSlice': [1, 2, 3], # begin, end, strides
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'VariadicSplit': [2], # split_lengths
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'Tile': [1], # repeats input
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'TopK': [1], # K input
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'Pad': [1, 2], # pads_begin, pads_end
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'Range': [0, 1, 2], # start, stop, step inputs
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'OneHot': [1], # depth input
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}
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def find_and_replace_pattern(self, graph: Graph):
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shape_input_ops_map = self.get_operations_with_shape_inputs()
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nodes_with_shape_inputs = []
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def get_nodes_with_shape_inputs(graph: Graph) -> List[Node]:
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shape_accepting_nodes = []
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for node in graph.get_op_nodes():
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if node.soft_get('type') in shape_input_ops_map:
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nodes_with_shape_inputs.append(node)
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if node.soft_get('type') in shape_accepting_ops:
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shape_accepting_nodes.append(node)
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return shape_accepting_nodes
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start_nodes = []
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@staticmethod
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def get_shape_returning_start_nodes(nodes_with_shape_inputs: List[Node]) -> List[Node]:
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sources = []
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for node in nodes_with_shape_inputs:
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start_nodes.extend(
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[node.in_port(port_idx).get_source().node for port_idx in shape_input_ops_map[node.type] if
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node.is_in_port_connected(port_idx)])
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assert node.soft_get('type') in shape_accepting_ops
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for port_idx in shape_accepting_ops[node.soft_get('type')]:
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if not node.is_in_port_connected(port_idx):
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continue
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source_node = node.in_port(port_idx).get_source().node
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# no need to start BFS for ShapeOf nodes, indeed if there is a ShapeOf it's an end of BFS
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if source_node.soft_get('type') != 'ShapeOf':
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sources.append(source_node)
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return sources
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condition = lambda node: node.soft_get('type') != 'ShapeOf'
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nodes_with_shape_values = MarkSubGraphsWithCorrectLayout.bfs(start_nodes, set(), condition, forward=False)
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for node in nodes_with_shape_values:
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@staticmethod
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def mark_nodes(shape_returning_nodes: List[Node]):
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for node in shape_returning_nodes:
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node['returns_shape_value'] = True
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if node.soft_get('type') == 'Const':
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if node.value.dtype == np.float32:
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node.out_node(0)['correct_data_type'] = True
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elif node.value.dtype in [np.float16, np.float64]:
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log.debug('Const nodes {} with shape values have {} type'.format(node.soft_get('name', node.id),
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node.value.dtype))
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log.error("Const node '{}' returns shape values of '{}' type but it must be integer or float32. "
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"During Elementwise type inference will attempt to cast to float32".
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format(node.soft_get('name', node.id), node.value.dtype), extra={'is_warning': True})
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def find_and_replace_pattern(self, graph: Graph):
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shape_accepting_nodes = self.get_nodes_with_shape_inputs(graph)
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condition = lambda node: node.soft_get('type') != 'ShapeOf'
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shape_returning_start_nodes = self.get_shape_returning_start_nodes(shape_accepting_nodes)
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shape_returning_nodes = MarkSubGraphsWithCorrectLayout.bfs(shape_returning_start_nodes, set(),
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condition, forward=False)
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self.mark_nodes(shape_returning_nodes)
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@@ -10,6 +10,7 @@ from mo.graph.graph import Graph, Node
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from mo.middle.passes.infer import copy_type_infer
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from mo.ops.op import Op
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from mo.pipeline.common import convert_const_node_value_type
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from mo.utils.error import Error
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def override_data_type_of_constant(node: Node):
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@@ -22,13 +23,19 @@ def override_data_type_of_constant(node: Node):
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# power value 2, or when replacing Neg operation with Mul with -1 as second input.
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in_node_0 = node.in_port(0).get_source().node
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in_node_1 = node.in_port(1).get_source().node
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if in_node_0.op != 'Const' and in_node_1.op != 'Const':
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raise Error("Elementwise operation '{}' has inputs of different data types: '{}' and '{}' "
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"that cannot be aligned".format(node.soft_get('name'), in_type_0, in_type_1))
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if in_node_0.op == 'Const':
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convert_const_node_value_type(in_node_0, in_type_1)
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elif in_node_1.op == 'Const':
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convert_const_node_value_type(in_node_1, in_type_0)
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node_to_convert, src_type, dst_type = in_node_0, in_type_0, in_type_1
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else:
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log.error('Elementwise operation {} has inputs of different data types: {} and {}'.format(
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node.soft_get('name'), in_type_0, in_type_1))
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node_to_convert, src_type, dst_type = in_node_1, in_type_1, in_type_0
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log.error("Changing Const node '{}' data type from {} to {} for Elementwise operation".format(
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node_to_convert.soft_get('name', node_to_convert.id), src_type, dst_type),
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extra={'is_warning': True})
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convert_const_node_value_type(node_to_convert, dst_type)
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class Elementwise(Op):
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@@ -129,15 +136,6 @@ class Pow(Elementwise):
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return np.array(a.astype(np.float32) ** b, dtype=np.float32)
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return a ** b
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@staticmethod
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def type_infer(node):
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in_type_0 = node.in_port(0).get_data_type()
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in_type_1 = node.in_port(1).get_data_type()
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assert in_type_0 == in_type_1, \
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'Power operation {} has inputs of different data types: {} and {}'.format(
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node.soft_get('name'), in_type_0, in_type_1)
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node.out_port(0).set_data_type(in_type_0)
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class LogicalElementwise(Elementwise):
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@staticmethod
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