Evgeny Lazarev
125a462400
* Initial version of ReduceL1, ReduceL2 and ReduceLp enabling in the MO * Added operations ReduceL1 and ReduceL2 to nGraph * Removed ReduceLp. Added ReduceL1 and ReduceL2 * Separated specification of ReduceLp into ReduceL1 and ReduceL2 * Updated ReduceL1 and ReduceL2 specification * Fixed ReduceL1 and ReduceL2 type prop tests * Implemented nGraph transformation to decompose ReduceL1 and ReduceL2. Disabled them for CPU and GPU plugins * Updated supported framework layers * Added unit tests for ReduceL1 and ReduceL2 reference implementation * Fixed ReduceXXX operations reference implementation by adding support for a new parameter 'keep_dims' * Fixed constant folding for v0::Any * Added ReduceL1 and ReduceL2 to Python API * Implemented ReduceL1 and ReduceL2 decomposition tests and fixed ReduceL2 decomposition * Added specific creator for ReduceXXX operations instead of NodeBuilders * Fixed conversion ReduceXXX to CNNLayer * Fixed parser for ReduceLogicalXXX operations
97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import logging as log
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import numpy as np
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from extensions.back.ScalarConstNormalize import ScalarNormalize
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from extensions.ops.ReduceOps import reduce_map
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from mo.back.replacement import BackReplacementPattern
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Graph
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from mo.ops.concat import Concat
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class ReduceMerge(BackReplacementPattern):
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"""
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Fuses sequence of Reduces of the same type into one Reduce layer of this particular type with updated axes input
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Limitations:
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- `keep_dims` attribute should be the same for all Reduces in the sequence
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- in case `keep_dims`=False: next Reduce axes should be strictly less than previous Reduce axes
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"""
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enabled = True
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force_clean_up = True
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def run_before(self):
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return [ScalarNormalize]
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@staticmethod
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def fuse_reduces(first_reduce, second_reduce):
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first_reduce_name = first_reduce.soft_get('name', first_reduce.id)
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second_reduce_name = second_reduce.soft_get('name', second_reduce.id)
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reduce_type = first_reduce.type
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assert first_reduce.type == second_reduce.type
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if len(first_reduce.out_port(0).get_destinations()) != 1:
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# data dependency
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return
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if first_reduce.keep_dims != second_reduce.keep_dims:
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return
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first_axes = first_reduce.in_port(1).data.get_value()
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second_axes = second_reduce.in_port(1).data.get_value()
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if first_axes is None or second_axes is None:
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# dynamic axes merging is not supported
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return
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if not first_reduce.keep_dims:
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if not np.all(first_axes > second_axes):
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# indexing of upper reduce input dimensions changed
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return
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graph = second_reduce.graph
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new_axes = Concat(graph, {'name': second_reduce_name + '/Axes', 'axis': int64_array(0), 'in_ports_count': 2,
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'override_output_shape': True}).create_node()
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new_axes.in_port(0).connect(first_reduce.in_port(1).get_source())
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new_axes.in_port(1).connect(second_reduce.in_port(1).get_source())
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first_reduce.in_port(0).get_source().node['need_shape_inference'] = True
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first_reduce.in_port(0).get_source().node['override_output_shape'] = True
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second_reduce.in_port(1).get_connection().set_source(new_axes.out_port(0))
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first_reduce.out_port(0).get_connection().set_source(first_reduce.in_port(0).get_connection().get_source())
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first_reduce.in_port(1).disconnect()
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graph.remove_node(first_reduce.id)
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log.debug('{0} nodes {1} and {2} were fused to a single {2} node with updated axes input'
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''.format(reduce_type, first_reduce_name, second_reduce_name))
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def find_and_replace_pattern(self, graph: Graph):
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rsorted_nodes = graph.pseudo_topological_sort(reverse=True)
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for reduce_type in reduce_map.keys():
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reduces_of_type = [n for n in rsorted_nodes if n.id in graph and n.soft_get('type') == reduce_type]
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for second_reduce_node in reduces_of_type:
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if second_reduce_node.id not in graph:
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continue
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first_reduce_node = second_reduce_node.in_port(0).get_source().node
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if first_reduce_node.soft_get('type', None) == reduce_type:
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ReduceMerge.fuse_reduces(first_reduce=first_reduce_node, second_reduce=second_reduce_node)
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