|
| 1 | +from __future__ import annotations |
| 2 | + |
| 3 | +import enum |
| 4 | +import os |
| 5 | +import pathlib |
| 6 | +from typing import Dict, List, Set, Tuple |
| 7 | + |
| 8 | +import libcst as cst |
| 9 | +from libcst import matchers |
| 10 | +from libcst._nodes.statement import FunctionDef |
| 11 | + |
| 12 | +from onnxscript.function_libs.torch_lib import registration |
| 13 | + |
| 14 | + |
| 15 | +class _StatusEnum(enum.Enum): |
| 16 | + SUCCESS = enum.auto() |
| 17 | + """Success.""" |
| 18 | + FAILURE_OVERLOAD_EXIST = enum.auto() |
| 19 | + """Failure: overload name already exists.""" |
| 20 | + FAILURE_OVERLOAD_INVALID = enum.auto() |
| 21 | + """Failure: overload name is invalid.""" |
| 22 | + FAILURE_OP_NOT_FOUND = enum.auto() |
| 23 | + """Failure: op not found.""" |
| 24 | + FAILURE_OP_MULTIPLE_IMPL = enum.auto() |
| 25 | + """Failure: op has multiple implementations. Cannot decide which to add new overload name to.""" |
| 26 | + |
| 27 | + |
| 28 | +def _cst_arg_to_overload_names(arg: cst.Arg) -> Tuple[str, ...]: |
| 29 | + if matchers.matches(arg, matchers.Arg(value=matchers.SimpleString())): |
| 30 | + overload_names = (cst.ensure_type(arg.value, cst.SimpleString).value,) |
| 31 | + else: |
| 32 | + overload_names = tuple( |
| 33 | + cst.ensure_type(element.value, cst.SimpleString).value |
| 34 | + for element in cst.ensure_type(arg.value, cst.Tuple).elements |
| 35 | + ) |
| 36 | + overload_names = tuple(name.replace('"', "") for name in overload_names) |
| 37 | + return overload_names |
| 38 | + |
| 39 | + |
| 40 | +def _overload_names_to_namespace_op(overload_names: Tuple[str, ...]) -> str: |
| 41 | + match = registration._QUALIFIED_OPERATOR_NAME_REGEX.fullmatch(overload_names[0]) |
| 42 | + assert match is not None |
| 43 | + namespace = match.group("namespace") |
| 44 | + name = match.group("name") |
| 45 | + return f"{namespace}::{name}" |
| 46 | + |
| 47 | + |
| 48 | +class _TorchlibOpOverloadCollector(cst.CSTVisitor): |
| 49 | + def __init__(self): |
| 50 | + self._op_overloads: Dict[str, List[Tuple[str, List[str]]]] = {} |
| 51 | + self._stack: List[str] = [] |
| 52 | + |
| 53 | + def visit_FunctionDef(self, node: FunctionDef) -> bool | None: |
| 54 | + self._stack.append(node.name.value) |
| 55 | + |
| 56 | + def leave_FunctionDef(self, node: FunctionDef) -> None: |
| 57 | + self._stack.pop() |
| 58 | + |
| 59 | + def visit_Call(self, node: cst.Call) -> None: |
| 60 | + if not matchers.matches(node.func, matchers.Name("torch_op")): |
| 61 | + return |
| 62 | + |
| 63 | + function_name = self._stack[-1] |
| 64 | + overload_names = _cst_arg_to_overload_names(node.args[0]) |
| 65 | + namespace_op_name = _overload_names_to_namespace_op(overload_names) |
| 66 | + |
| 67 | + self._op_overloads.setdefault(namespace_op_name, []) |
| 68 | + self._op_overloads[namespace_op_name].append((function_name, list(overload_names))) |
| 69 | + |
| 70 | + |
| 71 | +class _TorchlibOpOverloadAdder(cst.CSTTransformer): |
| 72 | + def __init__( |
| 73 | + self, |
| 74 | + overload_names: Dict[str, List[Tuple[str, List[str]]]], |
| 75 | + new_overload_names: Set[str], |
| 76 | + ): |
| 77 | + self._overload_names = overload_names |
| 78 | + self._results: Dict[str, _StatusEnum] = {} |
| 79 | + |
| 80 | + for new_overload_name in new_overload_names: |
| 81 | + match = registration._QUALIFIED_OPERATOR_NAME_REGEX.fullmatch(new_overload_name) |
| 82 | + if not match: |
| 83 | + self._results[new_overload_name] = _StatusEnum.FAILURE_OVERLOAD_INVALID |
| 84 | + continue |
| 85 | + overload = match.group("overload") or "" |
| 86 | + if overload == "default": |
| 87 | + overload = "" |
| 88 | + dot_overload = f".{overload}" if overload else "" |
| 89 | + op_name = match.group("name") |
| 90 | + namespace = match.group("namespace") |
| 91 | + namespace_op_name = f"{namespace}::{op_name}" |
| 92 | + qualified_name = f"{namespace_op_name}{dot_overload}" |
| 93 | + |
| 94 | + if namespace_op_name not in self._overload_names: |
| 95 | + self._results[new_overload_name] = _StatusEnum.FAILURE_OP_NOT_FOUND |
| 96 | + continue |
| 97 | + |
| 98 | + if len(self._overload_names[namespace_op_name]) > 1: |
| 99 | + self._results[new_overload_name] = _StatusEnum.FAILURE_OP_MULTIPLE_IMPL |
| 100 | + continue |
| 101 | + |
| 102 | + if qualified_name in self._overload_names[namespace_op_name][0][1]: |
| 103 | + self._results[new_overload_name] = _StatusEnum.FAILURE_OVERLOAD_EXIST |
| 104 | + continue |
| 105 | + |
| 106 | + self._overload_names[namespace_op_name][0][1].append(qualified_name) |
| 107 | + self._results[new_overload_name] = _StatusEnum.SUCCESS |
| 108 | + |
| 109 | + def leave_Call(self, original_node: cst.Call, updated_node: cst.Call) -> cst.Call: |
| 110 | + if not matchers.matches(original_node.func, matchers.Name("torch_op")): |
| 111 | + return original_node |
| 112 | + |
| 113 | + original_overload_names = _cst_arg_to_overload_names(original_node.args[0]) |
| 114 | + namespace_op_name = _overload_names_to_namespace_op(original_overload_names) |
| 115 | + overload_names = self._overload_names[namespace_op_name][0][1] |
| 116 | + if len(overload_names) == 1: |
| 117 | + return original_node |
| 118 | + return updated_node.with_changes( |
| 119 | + args=[ |
| 120 | + cst.Arg( |
| 121 | + value=cst.Tuple( |
| 122 | + elements=[ |
| 123 | + cst.Element(cst.SimpleString(value=f'"{name}"')) |
| 124 | + for name in overload_names |
| 125 | + ] |
| 126 | + ) |
| 127 | + ), |
| 128 | + *original_node.args[1:], |
| 129 | + ], |
| 130 | + ) |
| 131 | + |
| 132 | + |
| 133 | +def add_overload_names( |
| 134 | + module_path: pathlib.Path, overload_names: Set[str] |
| 135 | +) -> Dict[str, _StatusEnum]: |
| 136 | + """NOTE: This function assumes""" |
| 137 | + source_tree = cst.parse_module(module_path.read_text()) |
| 138 | + op_overload_collector = _TorchlibOpOverloadCollector() |
| 139 | + source_tree.visit(op_overload_collector) |
| 140 | + transformer = _TorchlibOpOverloadAdder(op_overload_collector._op_overloads, overload_names) |
| 141 | + modified_tree = source_tree.visit(transformer) |
| 142 | + module_path.write_text(modified_tree.code) |
| 143 | + return transformer._results |
| 144 | + |
| 145 | + |
| 146 | +def main(): |
| 147 | + new_overload_names = { |
| 148 | + "aten::add.Tensor", |
| 149 | + "aten::clamp.Tensor", |
| 150 | + "aten::div.Tensor", |
| 151 | + "aten::eq.Scalar", |
| 152 | + "aten::eq.Tensor", |
| 153 | + "aten::fill.Tensor", |
| 154 | + "aten::ge.Scalaraten::ge.Tensoraten::gt.Scalar", |
| 155 | + "aten::le.Tensor", |
| 156 | + "aten::lt.Scalar", |
| 157 | + "aten::mul.Tensor", |
| 158 | + "aten::ne.Scalar", |
| 159 | + "aten::roll.default", |
| 160 | + "aten::rsub.Scalar", |
| 161 | + "aten::select.int", |
| 162 | + "aten::slice.Tensor", |
| 163 | + "aten::split.Tensor", |
| 164 | + "aten::sub.Tensor", |
| 165 | + "aten::transpose.int", |
| 166 | + "aten::unbind.int", |
| 167 | + "aten::where.self", |
| 168 | + } |
| 169 | + file_paths = [ |
| 170 | + pathlib.Path(os.path.join(root, file)) |
| 171 | + for root, dirs, files in os.walk("onnxscript/function_libs/torch_lib/ops") |
| 172 | + for file in files |
| 173 | + ] |
| 174 | + for file_path in file_paths: |
| 175 | + print(add_overload_names(file_path, new_overload_names)) |
| 176 | + |
| 177 | + |
| 178 | +if __name__ == "__main__": |
| 179 | + main() |
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