Parameter mapping from torch to paddle（broadcast_to） (#74449)

* add torch_to_paddle_decorator

* add DecoratorBase

Author: zhengshengning

python/paddle/tensor/manipulation.py

@@ -24,6 +24,7 @@
 import paddle
 from paddle import _C_ops
 from paddle.tensor import fill_constant
+from paddle.utils.decorator_utils import ParamAliasDecorator
 from paddle.utils.inplace_utils import inplace_apis_in_dygraph_only
 
 from ..base.data_feeder import (
@@ -4762,6 +4763,7 @@ def expand_as(x: Tensor, y: Tensor, name: str | None = None) -> Tensor:
         return out
 
 
+@ParamAliasDecorator({"x": ["input"], "shape": ["size"]})
 def broadcast_to(
     x: Tensor, shape: ShapeLike, name: str | None = None
 ) -> Tensor:

python/paddle/utils/__init__.py

@@ -15,6 +15,7 @@
 from ..base.framework import require_version
 from . import (  # noqa: F401
     cpp_extension,
+    decorator_utils,
     dlpack,
     download,
     image_util,

python/paddle/utils/decorator_utils.py

@@ -0,0 +1,107 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import functools
+import inspect
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    TypeVar,
+    cast,
+)
+
+from typing_extensions import ParamSpec
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+_DecoratedFunc = Callable[_P, _R]
+
+
+class DecoratorBase(Generic[_P, _R]):
+    """装饰器基类，提供通用装饰器框架
+
+    子类只需实现 `process` 方法定义核心逻辑
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        """初始化装饰器参数"""
+        self.args = args
+        self.kwargs = kwargs
+
+    def __call__(self, func: _DecoratedFunc[_P, _R]) -> _DecoratedFunc[_P, _R]:
+        """作为装饰器应用的入口点"""
+
+        @functools.wraps(func)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+            # 预处理参数
+            processed_args, processed_kwargs = self.process(args, kwargs)
+            # 调用原函数
+            return func(*processed_args, **processed_kwargs)
+
+        # 保留原始签名
+        wrapper.__signature__ = inspect.signature(func)
+        return cast("_DecoratedFunc[_P, _R]", wrapper)
+
+    def process(
+        self, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        """子类必须实现的核心处理方法
+
+        Args:
+            args: 位置参数
+            kwargs: 关键字参数
+
+        Returns:
+            处理后的 (args, kwargs) 元组
+        """
+        raise NotImplementedError("Subclasses must implement this method")
+
+
+# 示例实现：参数别名装饰器
+class ParamAliasDecorator(DecoratorBase[_P, _R]):
+    """参数别名处理的装饰器实现"""
+
+    def __init__(self, alias_mapping: dict[str, Iterable[str]]) -> None:
+        super().__init__()
+        if not isinstance(alias_mapping, dict):
+            raise TypeError("alias_mapping must be a dictionary")
+        for k, v in alias_mapping.items():
+            if not isinstance(v, (list, tuple, set)):
+                raise TypeError(f"Aliases for '{k}' must be iterable")
+        self.alias_mapping = alias_mapping
+
+    def process(
+        self, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        if not kwargs:
+            return args, kwargs
+        processed_kwargs = kwargs.copy()
+        for original, aliases in self.alias_mapping.items():
+            for alias in aliases:
+                if alias in processed_kwargs:
+                    if original not in processed_kwargs:
+                        processed_kwargs[original] = processed_kwargs.pop(alias)
+                    else:
+                        raise ValueError(
+                            f"Cannot specify both '{original}' and its alias '{alias}'"
+                        )
+        return args, processed_kwargs