forked from microsoft/onnxruntime
-
Notifications
You must be signed in to change notification settings - Fork 0
/
create_reduced_build_config.py
159 lines (119 loc) · 5.91 KB
/
create_reduced_build_config.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import argparse
import pathlib
import sys
import typing
import onnx
from util.file_utils import files_from_file_or_dir, path_match_suffix_ignore_case
def _get_suffix_match_predicate(suffix: str):
def predicate(file_path: pathlib.Path):
return path_match_suffix_ignore_case(file_path, suffix)
return predicate
def _extract_ops_from_onnx_graph(graph, operators, domain_opset_map):
"""Extract ops from an ONNX graph and all subgraphs"""
for operator in graph.node:
# empty domain is used as an alias for 'ai.onnx'
domain = operator.domain if operator.domain else "ai.onnx"
if domain not in operators or domain not in domain_opset_map:
continue
operators[domain][domain_opset_map[domain]].add(operator.op_type)
for attr in operator.attribute:
if attr.type == onnx.AttributeProto.GRAPH: # process subgraph
_extract_ops_from_onnx_graph(attr.g, operators, domain_opset_map)
elif attr.type == onnx.AttributeProto.GRAPHS:
# Currently no ONNX operators use GRAPHS.
# Fail noisily if we encounter this so we can implement support
raise RuntimeError("Unexpected attribute proto of GRAPHS")
def _process_onnx_model(model_path, required_ops):
model = onnx.load(model_path)
# create map of domain to opset for the model
domain_opset_map = {}
for opset in model.opset_import:
# empty domain == ai.onnx
domain = opset.domain if opset.domain else "ai.onnx"
domain_opset_map[domain] = opset.version
if domain not in required_ops:
required_ops[domain] = {opset.version: set()}
elif opset.version not in required_ops[domain]:
required_ops[domain][opset.version] = set()
# check the model imports at least one opset. if it does not it's an unexpected edge case that we have to ignore
# as we don't know what opset nodes in the graph belong to.
if domain_opset_map:
_extract_ops_from_onnx_graph(model.graph, required_ops, domain_opset_map)
def _extract_ops_from_onnx_model(model_files: typing.Iterable[pathlib.Path]):
"""Extract ops from ONNX models"""
required_ops = {}
for model_file in model_files:
if not model_file.is_file():
raise ValueError(f"Path is not a file: '{model_file}'")
_process_onnx_model(model_file, required_ops)
return required_ops
def create_config_from_onnx_models(model_files: typing.Iterable[pathlib.Path], output_file: pathlib.Path):
required_ops = _extract_ops_from_onnx_model(model_files)
output_file.parent.mkdir(parents=True, exist_ok=True)
with open(output_file, "w") as out:
out.write("# Generated from ONNX model/s:\n")
for model_file in sorted(model_files):
out.write(f"# - {model_file}\n")
for domain in sorted(required_ops.keys()):
for opset in sorted(required_ops[domain].keys()):
ops = required_ops[domain][opset]
if ops:
out.write("{};{};{}\n".format(domain, opset, ",".join(sorted(ops))))
def main():
argparser = argparse.ArgumentParser(
"Script to create a reduced build config file from either ONNX or ORT format model/s. "
"See /docs/Reduced_Operator_Kernel_build.md for more information on the configuration file format.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
argparser.add_argument(
"-f", "--format", choices=["ONNX", "ORT"], default="ONNX", help="Format of model/s to process."
)
argparser.add_argument(
"-t",
"--enable_type_reduction",
action="store_true",
help="Enable tracking of the specific types that individual operators require. "
"Operator implementations MAY support limiting the type support included in the build "
"to these types. Only possible with ORT format models.",
)
argparser.add_argument(
"model_path_or_dir",
type=pathlib.Path,
help="Path to a single model, or a directory that will be recursively searched " "for models to process.",
)
argparser.add_argument(
"config_path",
nargs="?",
type=pathlib.Path,
default=None,
help="Path to write configuration file to. Default is to write to required_operators.config "
"or required_operators_and_types.config in the same directory as the models.",
)
args = argparser.parse_args()
if args.enable_type_reduction and args.format == "ONNX":
print("Type reduction requires model format to be ORT.", file=sys.stderr)
sys.exit(-1)
model_path_or_dir = args.model_path_or_dir.resolve()
if args.config_path:
config_path = args.config_path.resolve()
else:
config_path = model_path_or_dir if model_path_or_dir.is_dir() else model_path_or_dir.parent
if config_path.is_dir():
filename = "required_operators_and_types.config" if args.enable_type_reduction else "required_operators.config"
config_path = config_path.joinpath(filename)
if args.format == "ONNX":
model_files = files_from_file_or_dir(model_path_or_dir, _get_suffix_match_predicate(".onnx"))
create_config_from_onnx_models(model_files, config_path)
else:
from util.ort_format_model import create_config_from_models as create_config_from_ort_models
model_files = files_from_file_or_dir(model_path_or_dir, _get_suffix_match_predicate(".ort"))
create_config_from_ort_models(model_files, config_path, args.enable_type_reduction)
# Debug code to validate that the config parsing matches
# from util import parse_config
# required_ops, op_type_usage_processor, _ = parse_config(args.config_path, True)
# op_type_usage_processor.debug_dump()
if __name__ == "__main__":
main()