76 lines
2.9 KiB
Python
76 lines
2.9 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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from typing import Dict
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from extensions.middle.MulFakeQuantizeFuse import resolve_shared_inputs
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from mo.graph.graph import Graph, Node
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from mo.middle.passes.conv import get_tensor_in_port, get_value_in_port
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from mo.middle.replacement import MiddleReplacementPattern
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class AddFakeQuantizeFuse(MiddleReplacementPattern):
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""" Fuses Add --> FakeQuantize sequence if possible
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"""
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enabled = False
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def run_after(self):
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return []
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def run_before(self):
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return []
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def pattern(self):
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return dict(
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nodes=[
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('preop', dict(op='Add', can_be_fused=True)),
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('preoped', dict()),
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('quantize', dict(op='FakeQuantize', keep_in_IR=True)),
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],
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edges=[
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('preop', 'preoped'),
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('preoped', 'quantize', {'in': 0}),
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]
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)
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def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
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quantize = match['quantize']
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preop = match['preop']
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for i in [0, 1]:
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if preop.in_port(i).get_source().node.soft_get('type') in ['Convolution', 'Deconvolution', 'MatMul']:
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return
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tensor_port, value_port = get_tensor_in_port(preop), get_value_in_port(preop)
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if value_port is None or value_port.data.get_value() is None:
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log.debug('AddQuantizeFuse: cannot fuse because Add op has dynamic inputs')
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return
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# Direct modifications to quantize 1-st and 2-nd port inputs are performed.
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# So the data nodes at those inputs shouldn't have more than 1 consumer maximum 2 consumers to the same
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# quantize op (consumed by 1st and 2nd ports). So we duplicate FakeQuantize in_port 1, 2, 3, 4 data
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resolve_shared_inputs(node=quantize, port_ids_to_duplicate=[1, 2])
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quantize.in_port(1).data.set_value(quantize.in_port(1).data.get_value() - value_port.data.get_value())
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if quantize.in_node(1).id != quantize.in_node(2).id:
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quantize.in_port(2).data.set_value(quantize.in_port(2).data.get_value() - value_port.data.get_value())
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in_add_connection = quantize.in_port(0).get_source().node.in_port(0).get_connection()
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quantize.in_port(0).disconnect()
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in_add_connection.add_destination(quantize.in_port(0))
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