Fix issue with mxnet rnn model (#14225)
* Fix issue with mxnet rnn model * Fix code style * Add test
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Maxim Vafin
GitHub
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commit
0377acfdfe
@ -198,16 +198,14 @@ class TransposeTensorIteratorLSTM(MiddleReplacementPattern):
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isomorphism['input_unsqueezed_i'].shape = isomorphism['input_unsqueezed_i'].shape[[1, 0, 2]]
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isomorphism['input_unsqueezed_i'].shape = isomorphism['input_unsqueezed_i'].shape[[1, 0, 2]]
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isomorphism['input_unsqueezed_i'].infer(isomorphism['input_unsqueezed_i'])
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isomorphism['input_unsqueezed_i'].infer(isomorphism['input_unsqueezed_i'])
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squeeze = isomorphism['squeeze']
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squeeze_dim = Const(squeeze.graph, {'value': [ti.input_port_map[data_input_port]['axis']],
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squeeze_dim = Const(squeeze.graph, {'value': ti.input_port_map[data_input_port]['axis'],
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'need_shape_inference': True,
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'need_shape_inference': True,
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'override_output_shape': True}).create_node()
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'override_output_shape': True}).create_node()
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squeeze.in_port(1).get_connection().set_source(squeeze_dim.out_port(0))
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squeeze.in_port(1).get_connection().set_source(squeeze_dim.out_port(0))
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squeeze['need_shape_inference'] = True
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squeeze['need_shape_inference'] = True
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unsqueeze = isomorphism['unsqueeze']
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unsqueeze_dim = Const(unsqueeze.graph, {'value': [ti.output_port_map[data_output_port]['axis']],
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unsqueeze_dim = Const(unsqueeze.graph, {'value': ti.output_port_map[data_output_port]['axis'],
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'need_shape_inference': True,
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'need_shape_inference': True,
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'override_output_shape': True}).create_node()
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'override_output_shape': True}).create_node()
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unsqueeze.in_port(1).get_connection().set_source(unsqueeze_dim.out_port(0))
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unsqueeze.in_port(1).get_connection().set_source(unsqueeze_dim.out_port(0))
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unsqueeze['need_shape_inference'] = True
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unsqueeze.infer(unsqueeze)
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@ -118,7 +118,7 @@ def compare_graphs(graph: Graph, graph_ref: Graph, last_node: str, last_node_ref
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if attr == 'value':
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if attr == 'value':
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if not values_are_equal(node.value, node_ref.value):
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if not values_are_equal(node.value, node_ref.value):
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stderr.append('Current node "{}" with type {} and reference node "{}" with type have '
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stderr.append('Current node "{}" with type {} and reference node "{}" with type "{}" have '
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'different values \n{} \nand \n{}'.format(
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'different values \n{} \nand \n{}'.format(
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node.id, cur_node_type, node_ref.id, ref_node_type, node.value, node_ref.value))
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node.id, cur_node_type, node_ref.id, ref_node_type, node.value, node_ref.value))
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continue
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continue
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137
tools/mo/unit_tests/mo/middle/permute_tensor_iterator_test.py
Normal file
137
tools/mo/unit_tests/mo/middle/permute_tensor_iterator_test.py
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@ -0,0 +1,137 @@
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# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array, shape_array
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from openvino.tools.mo.graph.graph import Node
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from openvino.tools.mo.middle.permute_tensor_iterator import TransposeTensorIteratorLSTM
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from openvino.tools.mo.ops.parameter import Parameter
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from openvino.tools.mo.ops.squeeze import Squeeze
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from openvino.tools.mo.ops.unsqueeze import Unsqueeze
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from openvino.tools.mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph, connect, connect_data, regular_op_with_shaped_data, \
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valued_const_with_data, regular_op_with_empty_data, result, shaped_const_with_data, empty_data
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class PermuteTensorIteratorTest(unittest.TestCase):
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def test_permute_ti(self):
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nodes = {
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**regular_op_with_shaped_data('input', [3, 1, 10],
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{'type': 'Parameter', 'op': 'Parameter'}),
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**valued_const_with_data('direct_permute_order', int64_array([1, 0, 2])),
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**regular_op_with_empty_data('direct_permute', {'type': 'Transpose', 'op': 'Transpose'}),
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**regular_op_with_empty_data('init_hidden', {'op': 'SomeOp'}),
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**regular_op_with_empty_data('init_cell', {'op': 'SomeOp'}),
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**regular_op_with_shaped_data('ti', [3, 1, 10],
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{'type': 'TensorIterator', 'op': 'TensorIterator', 'body': None,
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'output_port_map': [{'axis': 1, 'start': -1, 'end': 0, 'stride': -1,
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'external_port_id': 0}],
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'input_port_map': [{'axis': 1, 'start': 0, 'end': -1, 'stride': 1,
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'external_port_id': 0}]}),
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**valued_const_with_data('inverse_permute_order', int64_array([1, 0, 2])),
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**regular_op_with_shaped_data('inverse_permute', [3, 1, 10],
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{'type': 'Transpose', 'op': 'Transpose', 'shape': [3, 1, 10]}),
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**result()
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}
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graph = build_graph(nodes,
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[*connect('input', '0:direct_permute'),
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*connect('direct_permute_order', '1:direct_permute'),
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('direct_permute', 'direct_permute_d'),
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('direct_permute_d', 'ti', {'in': 0, 'external_port_id': 0}),
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*connect('init_hidden', '1:ti'),
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*connect('init_cell', '2:ti'),
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('ti', 'ti_d', {'external_port_id': 0}),
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('ti_d', 'inverse_permute', {'in': 0}),
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*connect('inverse_permute_order', '1:inverse_permute'),
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*connect('inverse_permute', 'output')],
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nodes_with_edges_only=True)
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nodes_internal = {
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**regular_op_with_shaped_data('input_unsqueezed', [3, 1, 10],
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{'type': 'Parameter', 'op': 'Parameter', 'shape': shape_array([3, 1, 10]),
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'infer': Parameter.infer}),
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**regular_op_with_empty_data('input_hidden', {'type': 'Parameter', 'op': 'Parameter'}),
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**regular_op_with_empty_data('input_cell', {'type': 'Parameter', 'op': 'Parameter'}),
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**valued_const_with_data("squeeze_dim", int64_array([1])),
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**valued_const_with_data("unsqueeze_dim", int64_array([1])),
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**regular_op_with_shaped_data('squeeze', [3, 10], {'type': 'Squeeze', 'op': 'Squeeze',
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'infer': Squeeze.infer}),
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**regular_op_with_shaped_data('unsqueeze', [3, 1, 10],
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{'type': 'Unsqueeze', 'op': 'Unsqueeze',
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'infer': Unsqueeze.infer}),
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**shaped_const_with_data("weights", [1, 2, 3]),
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**shaped_const_with_data("biases", [1, 2, 3]),
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**regular_op_with_shaped_data('lstm', [3, 10], {'type': 'LSTMCell', 'op': 'LSTMCell', 'shape': [3, 10]}),
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**empty_data('lstm_d2'),
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**result('op_output'),
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**result('op_output_1'),
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**result('op_output_2')
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}
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sub_graph = build_graph(nodes_internal,
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[*connect('input_unsqueezed', '0:squeeze'),
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*connect('squeeze_dim', '1:squeeze'),
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*connect('squeeze', '0:lstm'),
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*connect('input_hidden', '1:lstm'),
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*connect('input_cell', '2:lstm'),
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*connect('weights', '3:lstm'),
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*connect('biases', '4:lstm'),
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*connect('lstm:0', 'unsqueeze'),
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('lstm', 'lstm_d2', {'out': 1}),
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*connect_data('lstm:0', 'op_output_1'),
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('lstm_d2', 'op_output_2'),
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*connect('unsqueeze_dim', '1:unsqueeze'),
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*connect('unsqueeze', 'op_output')],
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nodes_with_edges_only=True)
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ti_node = Node(graph, 'ti')
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ti_node.body = sub_graph
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TransposeTensorIteratorLSTM().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref = build_graph(nodes,
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[('input', 'input_d'),
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('input_d', 'ti', {'in': 0, 'external_port_id': 0}),
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*connect('init_hidden', '1:ti'),
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*connect('init_cell', '2:ti'),
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('ti', 'ti_d', {'external_port_id': 0}),
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('ti_d', 'output', {'in': 0}), ],
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{'input_d': {'shape': [3, 1, 10]},
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'ti': {'output_port_map': [{'axis': 0, 'start': -1, 'end': 0, 'stride': -1,
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'external_port_id': 0}],
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'input_port_map': [{'axis': 0, 'start': 0, 'end': -1, 'stride': 1,
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'external_port_id': 0}]},
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'ti_d': {'shape': [1, 3, 10]}}, nodes_with_edges_only=True)
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sub_graph_ref = build_graph(nodes_internal,
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[*connect('input_unsqueezed', '0:squeeze'),
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*connect('squeeze_dim', '1:squeeze'),
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*connect('squeeze', '0:lstm'),
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*connect('input_hidden', '1:lstm'),
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*connect('input_cell', '2:lstm'),
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*connect('weights', '3:lstm'),
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*connect('biases', '4:lstm'),
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*connect('lstm:0', 'unsqueeze'),
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('lstm', 'lstm_d2', {'out': 1}),
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*connect_data('lstm:0', 'op_output_1'),
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('lstm_d2', 'op_output_2'),
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*connect('unsqueeze_dim', '1:unsqueeze'),
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*connect('unsqueeze', 'op_output')],
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{'input_unsqueezed': {'shape': shape_array([1, 3, 10])},
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'input_unsqueezed_d': {'shape': [1, 3, 10]},
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'squeeze_dim': {'value': int64_array([0])},
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'unsqueeze_dim': {'value': int64_array([1])},
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}, nodes_with_edges_only=True)
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flag, resp = compare_graphs(
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sub_graph, sub_graph_ref, 'op_output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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ti_node = Node(graph, 'ti')
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ti_node.body = None # compare main graph without body
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flag, resp = compare_graphs(
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graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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