Evgeny Lazarev
970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
143 lines
7.8 KiB
Python
143 lines
7.8 KiB
Python
"""
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Copyright (c) 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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from typing import Dict
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import numpy as np
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from extensions.ops.Cast import Cast
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from mo.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph, Node
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from mo.middle.passes.convert_data_type import data_type_str_to_np
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from mo.ops.const import Const
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class CompressQuantizeWeights(BackReplacementPattern):
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"""
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Allows to store constant weights as uint8 data type instead fp32.
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The structure of pattern without Data nodes.
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Detects pattern:
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------------------------------------------
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| fp32_weights ---> Initial_FakeQuantize |
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------------------------------------------
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But actually it looks like:
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---------------------------------------------------------------------------
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| initial_input_low initial_input_high |
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| \ / |
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| \ / |
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| (in: 1) (in: 2) |
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| V V |
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| fp32_weights ----> Initial_FakeQuantize |
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| ^ ^ |
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| (in: 3) (in: 4) |
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| / \ |
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| / \ |
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| initial_output_low initial_output_high |
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---------------------------------------------------------------------------
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And transforms it to:
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-------------------------------------------------------------------------------------------------------------
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| initial_input_low initial_input_high initial_output_low initial_output_high |
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| \ / | / |
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| \ / | / |
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| (in: 1) (in: 2) (in: 3) (in: 4) |
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| V V V V |
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| fp32_weights ----> FakeQuantize ----> Convert (to fp32) ----> Initial_FakeQuantize |
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| (with int8 output type) ^ ^ |
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| ^ ^ (in: 1) (in: 2) |
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| (in: 3) (in: 4) | | |
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| | \ ------------------ | |
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| | \ / | |
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| output_low output_high | |
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| (0) (levels - 1) | |
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| | | |
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| ------------------------------------------------------------- |
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-------------------------------------------------------------------------------------------------------------
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Initial_FakeQuantize will restore original fp32 values during inference.
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After value propagation the sub-graph will look like:
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uint8_weights ---> Convert (to fp32) ---> Initial_FakeQuantize
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"""
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enabled = True
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graph_condition = [lambda graph: not graph.graph['cmd_params'].disable_weights_compression]
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force_clean_up = True
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def pattern(self):
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return dict(
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nodes=[
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('weights_const', dict(type='Const')),
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('weights_d', dict(kind='data')),
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('quantize', dict(type='FakeQuantize', levels=lambda x: x is not None and 2 < x <= 256)),
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('quantize_d', dict(kind='data')),
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('convolution', dict())
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],
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edges=[
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('weights_const', 'weights_d'),
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('weights_d', 'quantize', {'in': 0}),
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('quantize', 'quantize_d'),
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('quantize_d', 'convolution', {'in': 1})
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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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initial_fake_quantize = match['quantize']
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new_fake_quantize = initial_fake_quantize.copy_node(dict(name=initial_fake_quantize.name + '/Copy',
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stop_value_propagation=False), graph)
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initial_fake_quantize.in_port(1).get_connection().set_destination(new_fake_quantize.in_port(1))
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initial_fake_quantize.in_port(2).get_connection().set_destination(new_fake_quantize.in_port(2))
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dst_type = data_type_str_to_np(graph.graph['cmd_params'].data_type)
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i_min = np.array([0.], dtype=dst_type)
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i_max = np.array([initial_fake_quantize.levels - 1.], dtype=dst_type)
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new_out_low_node = Const(graph, dict(name=initial_fake_quantize.name + '/Copy/out_low',
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value=i_min)).create_node()
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new_out_high_node = Const(graph, dict(name=initial_fake_quantize.name + '/Copy/out_high',
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value=i_max)).create_node()
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new_out_low_node.out_port(0).connect(new_fake_quantize.in_port(3))
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new_out_high_node.out_port(0).connect(new_fake_quantize.in_port(4))
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new_out_low_node.out_port(0).connect(initial_fake_quantize.in_port(1))
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new_out_high_node.out_port(0).connect(initial_fake_quantize.in_port(2))
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cast_node = Cast(graph, dict(name=initial_fake_quantize.name + "/Convert_to_float", dst_type=dst_type,
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stop_value_propagation=True)).create_node()
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new_fake_quantize.out_port(0).connect(cast_node.in_port(0))
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initial_fake_quantize.in_port(0).get_connection().set_destination(new_fake_quantize.in_port(0))
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cast_node.out_port(0).connect(initial_fake_quantize.in_port(0))
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cast_node['force_precision_in_ports'] = {0: 'uint8'}
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