Tool to modify torchlib overload names via libcst #920
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| Original file line number | Diff line number | Diff line change |
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| from __future__ import annotations | ||
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| import enum | ||
| import os | ||
| import pathlib | ||
| from typing import Dict, List, Set, Tuple | ||
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| import libcst as cst | ||
| from libcst import matchers | ||
| from libcst._nodes.statement import FunctionDef | ||
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| from onnxscript.function_libs.torch_lib import registration | ||
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| class _StatusEnum(enum.Enum): | ||
| SUCCESS = enum.auto() | ||
| """Success.""" | ||
| FAILURE_OVERLOAD_EXIST = enum.auto() | ||
| """Failure: overload name already exists.""" | ||
| FAILURE_OVERLOAD_INVALID = enum.auto() | ||
| """Failure: overload name is invalid.""" | ||
| FAILURE_OP_NOT_FOUND = enum.auto() | ||
| """Failure: op not found.""" | ||
| FAILURE_OP_MULTIPLE_IMPL = enum.auto() | ||
| """Failure: op has multiple implementations. Cannot decide which to add new overload name to.""" | ||
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| def _cst_arg_to_overload_names(arg: cst.Arg) -> Tuple[str, ...]: | ||
| if matchers.matches(arg, matchers.Arg(value=matchers.SimpleString())): | ||
| overload_names = (cst.ensure_type(arg.value, cst.SimpleString).value,) | ||
| else: | ||
| overload_names = tuple( | ||
| cst.ensure_type(element.value, cst.SimpleString).value | ||
| for element in cst.ensure_type(arg.value, cst.Tuple).elements | ||
| ) | ||
| overload_names = tuple(name.replace('"', "") for name in overload_names) | ||
| return overload_names | ||
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| def _overload_names_to_namespace_op(overload_names: Tuple[str, ...]) -> str: | ||
| match = registration._QUALIFIED_OPERATOR_NAME_REGEX.fullmatch(overload_names[0]) | ||
| assert match is not None | ||
| namespace = match.group("namespace") | ||
| name = match.group("name") | ||
| return f"{namespace}::{name}" | ||
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| class _TorchlibOpOverloadCollector(cst.CSTVisitor): | ||
| def __init__(self): | ||
| self._op_overloads: Dict[str, List[Tuple[str, List[str]]]] = {} | ||
| self._stack: List[str] = [] | ||
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| def visit_FunctionDef(self, node: FunctionDef) -> bool | None: | ||
| self._stack.append(node.name.value) | ||
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| def leave_FunctionDef(self, node: FunctionDef) -> None: | ||
| self._stack.pop() | ||
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| def visit_Call(self, node: cst.Call) -> None: | ||
| if not matchers.matches(node.func, matchers.Name("torch_op")): | ||
| return | ||
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| function_name = self._stack[-1] | ||
| overload_names = _cst_arg_to_overload_names(node.args[0]) | ||
| namespace_op_name = _overload_names_to_namespace_op(overload_names) | ||
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| self._op_overloads.setdefault(namespace_op_name, []) | ||
| self._op_overloads[namespace_op_name].append((function_name, list(overload_names))) | ||
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| class _TorchlibOpOverloadAdder(cst.CSTTransformer): | ||
| def __init__( | ||
| self, | ||
| overload_names: Dict[str, List[Tuple[str, List[str]]]], | ||
| new_overload_names: Set[str], | ||
| ): | ||
| self._overload_names = overload_names | ||
| self._results: Dict[str, _StatusEnum] = {} | ||
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| for new_overload_name in new_overload_names: | ||
| match = registration._QUALIFIED_OPERATOR_NAME_REGEX.fullmatch(new_overload_name) | ||
| if not match: | ||
| self._results[new_overload_name] = _StatusEnum.FAILURE_OVERLOAD_INVALID | ||
| continue | ||
| overload = match.group("overload") or "" | ||
| if overload == "default": | ||
| overload = "" | ||
| dot_overload = f".{overload}" if overload else "" | ||
| op_name = match.group("name") | ||
| namespace = match.group("namespace") | ||
| namespace_op_name = f"{namespace}::{op_name}" | ||
| qualified_name = f"{namespace_op_name}{dot_overload}" | ||
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| if namespace_op_name not in self._overload_names: | ||
| self._results[new_overload_name] = _StatusEnum.FAILURE_OP_NOT_FOUND | ||
| continue | ||
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| if len(self._overload_names[namespace_op_name]) > 1: | ||
| self._results[new_overload_name] = _StatusEnum.FAILURE_OP_MULTIPLE_IMPL | ||
| continue | ||
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| if qualified_name in self._overload_names[namespace_op_name][0][1]: | ||
| self._results[new_overload_name] = _StatusEnum.FAILURE_OVERLOAD_EXIST | ||
| continue | ||
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| self._overload_names[namespace_op_name][0][1].append(qualified_name) | ||
| self._results[new_overload_name] = _StatusEnum.SUCCESS | ||
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| def leave_Call(self, original_node: cst.Call, updated_node: cst.Call) -> cst.Call: | ||
| if not matchers.matches(original_node.func, matchers.Name("torch_op")): | ||
| return original_node | ||
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| original_overload_names = _cst_arg_to_overload_names(original_node.args[0]) | ||
| namespace_op_name = _overload_names_to_namespace_op(original_overload_names) | ||
| overload_names = self._overload_names[namespace_op_name][0][1] | ||
| if len(overload_names) == 1: | ||
| return original_node | ||
| return updated_node.with_changes( | ||
| args=[ | ||
| cst.Arg( | ||
| value=cst.Tuple( | ||
| elements=[ | ||
| cst.Element(cst.SimpleString(value=f'"{name}"')) | ||
| for name in overload_names | ||
| ] | ||
| ) | ||
| ), | ||
| *original_node.args[1:], | ||
| ], | ||
| ) | ||
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| def add_overload_names( | ||
| module_path: pathlib.Path, overload_names: Set[str] | ||
| ) -> Dict[str, _StatusEnum]: | ||
| """NOTE: This function assumes""" | ||
| source_tree = cst.parse_module(module_path.read_text()) | ||
| op_overload_collector = _TorchlibOpOverloadCollector() | ||
| source_tree.visit(op_overload_collector) | ||
| transformer = _TorchlibOpOverloadAdder(op_overload_collector._op_overloads, overload_names) | ||
| modified_tree = source_tree.visit(transformer) | ||
| module_path.write_text(modified_tree.code) | ||
| return transformer._results | ||
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| def main(): | ||
| new_overload_names = { | ||
| "aten::add.Tensor", | ||
|
There was a problem hiding this comment. Possible to add all overloads for these ops? I imagine There was a problem hiding this comment. Yes, the tool allows such usage. Frankly this PR is libcst exercise lol. I think the only useful scenario for this tool is when we have a large batch of missing overloads that we know would work, typically from model benchmark. |
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| "aten::clamp.Tensor", | ||
| "aten::div.Tensor", | ||
| "aten::eq.Scalar", | ||
| "aten::eq.Tensor", | ||
| "aten::fill.Tensor", | ||
| "aten::ge.Scalar", | ||
| "aten::ge.Tensor", | ||
| "aten::gt.Scalar", | ||
| "aten::le.Tensor", | ||
| "aten::lt.Scalar", | ||
| "aten::mul.Tensor", | ||
| "aten::ne.Scalar", | ||
| "aten::roll.default", | ||
| "aten::rsub.Scalar", | ||
| "aten::select.int", | ||
| "aten::slice.Tensor", | ||
| "aten::split.Tensor", | ||
| "aten::sub.Tensor", | ||
| "aten::transpose.int", | ||
| "aten::unbind.int", | ||
| "aten::where.self", | ||
| } | ||
| file_paths = [ | ||
| pathlib.Path(os.path.join(root, file)) | ||
| for root, dirs, files in os.walk("onnxscript/function_libs/torch_lib/ops") | ||
| for file in files | ||
| ] | ||
| for file_path in file_paths: | ||
| print(add_overload_names(file_path, new_overload_names)) | ||
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| if __name__ == "__main__": | ||
| main() | ||
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So the function def is visited before the decorator call?
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I think so, from cst tree level, decorators are a part of the function.