Pavel Esir
6477e8ec01
* Initial working solution * moved bfs_search_apply_on_shapeof_subgraph_nodes from utils/graph.py to MarkShapeOfSubgraphDataType.py * Reused bfs from MarkSubgraphsWithCorrectLayout.py * fixed e2e precomit issues: specified correct const data_types, fixed BFS search staring point to avoid nodeless shapeof subgraphs * fixed mxnet_rnnt: added converting all Const nodes in ShapeOf subgraph in MarkAndChangeDataTypeInShapeOfSubgraphs.py, revised Const values in transformations that affect ShapeOf subgraph nodes * reverter ReverseV2ToReverseSequence.py and DecomposeBidirectionalRNNSequence.py * in MarkSubgraphsWithCorrectLayout BFS search beauty applied * apply review comments, returned back 'in_shape_subgraph' attribute * graph condition added * MO IR reader fix for mixed FP16 models, added replacer order placement comment * moved to back phase * new solution with marking nodes from bottom to top (WIP) * successfully tested on back phase * corrected unittest * removed check for start nodes size in bfs * fix transformations that insert f64 to f32 in shape subgraph * corrected log.warning -> log.debug * revised list if shape input operations added unittest for Const shape inputs * applied @lazarevevgeny's comments * licence head corrections
79 lines
3.5 KiB
Python
79 lines
3.5 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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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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class MarkNodesWithShapeValues(BackReplacementPattern):
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"""
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This transformation marks op nodes in ShapeOf subgraphs with 'returns_shape_value' bool attribute and
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data nodes of float32 constants with 'correct_data_type' attribute.
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So that float Consts and Cast float will be kept in FP32 even if argument --data_type=FP16 is specified.
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This is needed to enable conversion to FP16 even if values in ShapeOf subgraphs exceed max(float16)
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or because of FP16 lower precession shape inference is incorrect on some nodes (e.g. if Interpolate in scales mode
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accepts values from ShapeOf subgraph).
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This transformation should be executed after shape inference and after all transformations which insert/modify
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Cast nodes in ShapeOf subgraphs therefore it's placed at the end of the back phase.
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"""
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enabled = True
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graph_condition = [lambda graph: graph.graph['cmd_params'].data_type == 'FP16']
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def run_after(self):
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from extensions.back.pass_separator import BackFinish
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return [BackFinish]
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def run_before(self):
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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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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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start_nodes = []
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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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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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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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