Add ShapeOfConstFolding transform (#5858)
* Add ShapeOfConstFolding transform * Add unit tests * Update bom file * Update transform file * Hot fix * Fix midle replaser * Update unit tests * Fix get value * Refactoring Const Folding transformation. Move to back * Update bom file * Remove unuse code * Add some unit tests * Refactoring unit test * Hot fix
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@@ -57,6 +57,7 @@ extensions/back/ReverseInputChannels.py
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extensions/back/RNNSequenceTypeRename.py
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extensions/back/ScalarConstNormalize.py
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extensions/back/SelectBroadcast.py
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extensions/back/ShapeOfConstFolding.py
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extensions/back/ShuffleChannelPatternOptimization.py
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extensions/back/ShufflenetReLUReorder.py
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extensions/back/SpecialNodesFinalization.py
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@@ -0,0 +1,29 @@
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# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from mo.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph, rename_nodes
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from mo.ops.const import Const
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class ShapeOfConstFolding(BackReplacementPattern):
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"""
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The transformation folds ShapeOf(Const) -> Const
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"""
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enabled = True
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def run_after(self):
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from extensions.back.MatMulNormalizer import SmartReshape_HC_Reshape_MatMul
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return [SmartReshape_HC_Reshape_MatMul]
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def find_and_replace_pattern(self, graph: Graph):
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for shapeof_node in graph.get_op_nodes(op='ShapeOf'):
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in_node = shapeof_node.in_port(0).get_source().node
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if in_node.op == 'Const':
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shapeof_node.in_port(0).disconnect()
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shape_name = shapeof_node.soft_get('name', shapeof_node.id)
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shape_value = shapeof_node.out_port(0).data.get_value()
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shape_const_node = Const(graph, {'name': shape_name + '/ExecutionConstValue',
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'value': shape_value}).create_node()
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shapeof_node.out_port(0).get_connection().set_source(shape_const_node.out_port(0))
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rename_nodes([(shapeof_node, shape_name + '/TBD'), (shape_const_node, shape_name)])
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@@ -0,0 +1,170 @@
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# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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import numpy as np
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from extensions.back.ShapeOfConstFolding import ShapeOfConstFolding
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from mo.front.common.partial_infer.eltwise import eltwise_infer
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from mo.front.common.partial_infer.utils import int64_array
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph
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const_value = np.random.rand(1, 3, 30, 30)
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nodes_attributes = {'input': {'shape': int64_array([1, 3, 30, 30]), 'type': 'Parameter', 'kind': 'op',
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'op': 'Parameter'},
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'input_data': {'value': None, 'shape': int64_array([1, 3, 30, 30]), 'kind': 'data'},
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'const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': const_value},
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'const_data': {'kind': 'data', 'value': const_value},
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'shapeof_input': {'kind': 'op', 'op': 'ShapeOf', 'value': int64_array([1, 3, 30, 30])},
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'shapeof_input_data': {'value': None, 'shape': None, 'kind': 'data',
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'value': int64_array([1, 3, 30, 30])},
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'shapeof_const': {'kind': 'op', 'op': 'ShapeOf', 'value': int64_array([1, 3, 30, 30])},
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'shapeof_const_data': {'value': None, 'shape': None, 'kind': 'data',
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'value': int64_array([1, 3, 30, 30])},
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'mul': {'kind': 'op', 'op': 'Mul', 'infer': lambda node: eltwise_infer(node, lambda a, b: a * b)},
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'mul_data': {'kind': 'data', 'value': np.array([1, 9, 900, 900])},
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'last': {'kind': 'op', 'op': 'Result'},
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# new nodes
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'new_const_shapeof': {'type': 'Const', 'kind': 'op', 'op': 'Const',
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'value': int64_array([1, 3, 30, 30])}
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}
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const_value2 = np.random.rand(30, 30)
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nodes_attributes2 = {'input': {'shape': int64_array([1, 3, 30, 30]), 'type': 'Parameter', 'kind': 'op',
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'op': 'Parameter'},
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'input_data': {'value': None, 'shape': int64_array([1, 3, 30, 30]), 'kind': 'data'},
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'const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': const_value2},
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'const_data': {'kind': 'data', 'value': const_value2},
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'shapeof_const': {'kind': 'op', 'op': 'ShapeOf', 'value': int64_array([2700, 30])},
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'shapeof_const_data': {'value': int64_array([2700, 30]), 'shape': None, 'kind': 'data'},
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'gather': {'kind': 'op', 'op': 'Gather', 'batch_dims': 0},
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'gather_data': {'kind': 'data'},
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'const_concat': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': [1]},
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'const_concat_data': {'kind': 'data', 'value': [1]},
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'concat': {'kind': 'op', 'op': 'Concat'},
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'concat_data': {'kind': 'data'},
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'reshape': {'kind': 'op', 'op': 'Reshape'},
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'reshape_data': {'kind': 'data'},
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'matmul': {'kind': 'op', 'op': 'MatMul'},
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'matmul_data': {'kind': 'data'},
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'last': {'kind': 'op', 'op': 'Result'},
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# new nodes
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'new_const_shapeof': {'type': 'Const', 'kind': 'op', 'op': 'Const',
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'value': int64_array([2700, 30])},
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}
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class ShapeOfConstFoldingTests(unittest.TestCase):
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def test_const_with_one_output(self):
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graph = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_data', 'shapeof_input'),
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('shapeof_input', 'shapeof_input_data'),
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('shapeof_input_data', 'mul'),
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('const', 'const_data'),
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('const_data', 'shapeof_const'),
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('shapeof_const', 'shapeof_const_data'),
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('shapeof_const_data', 'mul'),
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('mul', 'mul_data'),
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('mul_data', 'last')],
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{
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'input': {'shape': int64_array([1, 3, 30, 30])},
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'input_data': {'shape': int64_array([1, 3, 30, 30])},
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'shapeof_input': {'value': int64_array([1, 3, 30, 30])},
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'shapeof_input_data': {'value': int64_array([1, 3, 30, 30])},
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'const': {'value': const_value},
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'const_data': {'value': const_value},
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'shapeof_const': {'value': int64_array([1, 3, 30, 30])},
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'shapeof_const_data': {'value': int64_array([1, 3, 30, 30])},
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'mul_data': {'value': int64_array([1, 9, 900, 900])},
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},
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nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_data', 'shapeof_input'),
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('shapeof_input', 'shapeof_input_data'),
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('shapeof_input_data', 'mul'),
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('new_const_shapeof', 'shapeof_const_data'),
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('shapeof_const_data', 'mul'),
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('mul', 'mul_data'),
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('mul_data', 'last')],
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{
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'input': {'shape': int64_array([1, 3, 30, 30])},
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'input_data': {'shape': int64_array([1, 3, 30, 30])},
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'shapeof_input': {'value': int64_array([1, 3, 30, 30])},
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'shapeof_input_data': {'value': int64_array([1, 3, 30, 30])},
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'new_const_shapeof': {'value': int64_array([1, 3, 30, 30])},
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'shapeof_const_data': {'value': int64_array([1, 3, 30, 30])},
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'mul_data': {'value': int64_array([1, 9, 900, 900])},
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},
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nodes_with_edges_only=True)
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ShapeOfConstFolding().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'last')
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self.assertTrue(flag, resp)
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def test_const_with_two_outputs(self):
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graph = build_graph(nodes_attributes2,
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[('input', 'input_data'),
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('input_data', 'reshape'),
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('const', 'const_data'),
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('const_data', 'shapeof_const'),
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('shapeof_const', 'shapeof_const_data'),
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('shapeof_const_data', 'gather'),
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('gather', 'gather_data'),
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('const_concat', 'const_concat_data'),
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('const_concat_data', 'concat'),
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('gather_data', 'concat'),
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('concat', 'reshape'),
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('reshape', 'reshape_data'),
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('reshape_data', 'matmul'),
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('const_data', 'matmul'),
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('matmul', 'matmul_data'),
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('matmul_data', 'last')
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],
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{
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'input': {'shape': int64_array([1, 3, 30, 30])},
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'input_data': {'shape': int64_array([1, 3, 30, 30])},
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'shapeof_const': {'value': int64_array([2700, 30])},
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'shapeof_const_data': {'value': int64_array([2700, 30])},
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},
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nodes_with_edges_only=True)
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graph_ref = build_graph(nodes_attributes2,
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[('input', 'input_data'),
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('input_data', 'reshape'),
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('new_const_shapeof', 'shapeof_const_data'),
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('shapeof_const_data', 'gather'),
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('gather', 'gather_data'),
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('const_concat', 'const_concat_data'),
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('const_concat_data', 'concat'),
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('gather_data', 'concat'),
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('concat', 'reshape'),
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('reshape', 'reshape_data'),
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('reshape_data', 'matmul'),
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('const', 'const_data'),
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('const_data', 'matmul'),
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('matmul', 'matmul_data'),
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('matmul_data', 'last')],
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{
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'input': {'shape': int64_array([1, 3, 30, 30])},
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'input_data': {'shape': int64_array([1, 3, 30, 30])},
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'new_const_shapeof': {'value': int64_array([2700, 30])},
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'shapeof_const_data': {'value': int64_array([2700, 30])},
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},
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nodes_with_edges_only=True)
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ShapeOfConstFolding().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'last')
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self.assertTrue(flag, resp)
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