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#!/usr/bin/env python3
""" Generates PyTorch ONNX Export Diagnostic rules for C++, Python and documentations.
The rules are defined in torch/onnx/_internal/diagnostics/rules.yaml.
Usage:
python -m tools.onnx.gen_diagnostics \
torch/onnx/_internal/diagnostics/rules.yaml \
torch/onnx/_internal/diagnostics \
torch/csrc/onnx/diagnostics/generated \
torch/docs/source
"""
import argparse
import os
import subprocess
import textwrap
from typing import Any, Mapping, Sequence
import yaml
from torchgen import utils as torchgen_utils
_RULES_GENERATED_COMMENT = """\
GENERATED CODE - DO NOT EDIT DIRECTLY
This file is generated by gen_diagnostics.py.
See tools/onnx/gen_diagnostics.py for more information.
Diagnostic rules for PyTorch ONNX export.
"""
_PY_RULE_TEMPLATE = """\
{0}: infra.Rule = dataclasses.field(
default=infra.Rule.from_sarif(**{1}),
init=False,
)
\"\"\"{2}\"\"\"
"""
_CPP_RULE_TEMPLATE = """\
/**
* @brief {1}
*/
{0},
"""
_RuleType = Mapping[str, Any]
def _kebab_case_to_snake_case(name: str) -> str:
return name.replace("-", "_")
def _kebab_case_to_pascal_case(name: str) -> str:
return "".join(word.capitalize() for word in name.split("-"))
def _format_rule_for_python(rule: _RuleType) -> str:
name = _kebab_case_to_snake_case(rule["name"])
short_description = rule["short_description"]["text"]
return _PY_RULE_TEMPLATE.format(name, rule, short_description)
def _format_rule_for_cpp(rule: _RuleType) -> str:
name = f"k{_kebab_case_to_pascal_case(rule['name'])}"
short_description = rule["short_description"]["text"]
return _CPP_RULE_TEMPLATE.format(name, short_description)
def gen_diagnostics_python(
rules: Sequence[_RuleType], out_py_dir: str, template_dir: str
) -> None:
rule_lines = [_format_rule_for_python(rule) for rule in rules]
fm = torchgen_utils.FileManager(
install_dir=out_py_dir, template_dir=template_dir, dry_run=False
)
fm.write_with_template(
"_rules.py",
"rules.py.in",
lambda: {
"generated_comment": _RULES_GENERATED_COMMENT,
"rules": textwrap.indent("\n".join(rule_lines), " " * 4),
},
)
_lint_file(os.path.join(out_py_dir, "_rules.py"))
def gen_diagnostics_cpp(
rules: Sequence[_RuleType], out_cpp_dir: str, template_dir: str
) -> None:
rule_lines = [_format_rule_for_cpp(rule) for rule in rules]
rule_names = [f'"{_kebab_case_to_snake_case(rule["name"])}",' for rule in rules]
fm = torchgen_utils.FileManager(
install_dir=out_cpp_dir, template_dir=template_dir, dry_run=False
)
fm.write_with_template(
"rules.h",
"rules.h.in",
lambda: {
"generated_comment": textwrap.indent(
_RULES_GENERATED_COMMENT,
" * ",
predicate=lambda x: True, # Don't ignore empty line
),
"rules": textwrap.indent("\n".join(rule_lines), " " * 2),
"py_rule_names": textwrap.indent("\n".join(rule_names), " " * 4),
},
)
_lint_file(os.path.join(out_cpp_dir, "rules.h"))
def gen_diagnostics_docs(
rules: Sequence[_RuleType], out_docs_dir: str, template_dir: str
) -> None:
# TODO: Add doc generation in a follow-up PR.
pass
def _lint_file(file_path: str) -> None:
p = subprocess.Popen(["lintrunner", "-a", file_path])
p.wait()
def gen_diagnostics(
rules_path: str,
out_py_dir: str,
out_cpp_dir: str,
out_docs_dir: str,
) -> None:
with open(rules_path, "r") as f:
rules = yaml.load(f, Loader=torchgen_utils.YamlLoader)
template_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "templates")
gen_diagnostics_python(
rules,
out_py_dir,
template_dir,
)
gen_diagnostics_cpp(
rules,
out_cpp_dir,
template_dir,
)
gen_diagnostics_docs(rules, out_docs_dir, template_dir)
def main() -> None:
parser = argparse.ArgumentParser(description="Generate ONNX diagnostics files")
parser.add_argument("rules_path", metavar="RULES", help="path to rules.yaml")
parser.add_argument(
"out_py_dir",
metavar="OUT_PY",
help="path to output directory for Python",
)
parser.add_argument(
"out_cpp_dir",
metavar="OUT_CPP",
help="path to output directory for C++",
)
parser.add_argument(
"out_docs_dir",
metavar="OUT_DOCS",
help="path to output directory for docs",
)
args = parser.parse_args()
gen_diagnostics(
args.rules_path,
args.out_py_dir,
args.out_cpp_dir,
args.out_docs_dir,
)
if __name__ == "__main__":
main()
|
